DroidCat: Effective Android Malware Detection and Categorization via App-Level Profiling

Most existing Android malware detection and categorization techniques are static approaches, which suffer from evasion attacks, such as obfuscation. By analyzing program behaviors, dynamic approaches are potentially more resilient against these attacks. Yet existing dynamic approaches mostly rely on characterizing system calls which are subject to system-call obfuscation. This paper presents DroidCat, a novel dynamic app classification technique, to complement existing approaches. By using a diverse set of dynamic features based on method calls and inter-component communication (ICC) Intents without involving permission, app resources, or system calls while fully handling reflection, DroidCat achieves superior robustness than static approaches as well as dynamic approaches relying on system calls. The features were distilled from a behavioral characterization study of benign versus malicious apps. Through three complementary evaluation studies with 34 343 apps from various sources and spanning the past nine years, we demonstrated the stability of DroidCat in achieving high classification performance and superior accuracy compared with the two state-of-the-art peer techniques that represent both static and dynamic approaches. Overall, DroidCat achieved 97% F1-measure accuracy consistently for classifying apps evolving over the nine years, detecting or categorizing malware, 16%–27% higher than any of the two baselines compared. Furthermore, our experiments with obfuscated benchmarks confirmed higher robustness of DroidCat over these baseline techniques. We also investigated the effects of various design decisions on DroidCat’s effectiveness and the most important features for our dynamic classification. We found that features capturing app execution structure such as the distribution of method calls over user code and libraries are much more important than typical security features such as sensitive flows.


How We Transmit Memories to Other Brains: Constructing Shared Neural Representations Via Communication

Humans are able to mentally construct an episode when listening to another person's recollection, even though they themselves did not experience the events. However, it is unknown how strongly the neural patterns elicited by mental construction resemble those found in the brain of the individual who experienced the original events. Using fMRI and a verbal communication task, we traced how neural patterns associated with viewing specific scenes in a movie are encoded, recalled, and then transferred to a group of naïve listeners. By comparing neural patterns across the 3 conditions, we report, for the first time, that event-specific neural patterns observed in the default mode network are shared across the encoding, recall, and construction of the same real-life episode. This study uncovers the intimate correspondences between memory encoding and event construction, and highlights the essential role our common language plays in the process of transmitting one's memories to other brains.


Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance

Prediction of tunnel boring machine (TBM) performance parameters can be caused to reduce the risks associated with tunneling projects. This study is aimed to introduce a new hybrid model namely Firefly algorithm (FA) combined by artificial neural network (ANN) for solving problems in the field of geotechnical engineering particularly for estimation of penetration rate (PR) of TBM. For this purpose, the results obtained from the field observations and laboratory tests were considered as model inputs to estimate PR of TBMs operated in a water transfer tunnel in Malaysia. Five rock mass and material properties (rock strength, tensile strength of rock, rock quality designation, rock mass rating and weathering zone) and two machine factors (trust force and revolution per minute) were used in the new model for predicting PA. FA algorithm was used to optimize weight and bias of ANN to obtain a higher level of accuracy. A series of hybrid FA-ANN models using the most influential parameters on FA were constructed to estimate PR. For comparison, a simple ANN model was built to predict PR of TBM. This ANN model was improved on the basis of new ways. By doing this, the best ANN model was chosen for comparison purposes. After implementing the best models for two methods, the data were divided into five separate categories. This will minimize the chance of randomness. Then the best models were applied for these new categories. The results demonstrated that new hybrid intelligent model is able to provide higher performance capacity for predicting. Based on the coefficient of determination 0.948 and 0.936 and 0.885 and 0.889 for training and testing datasets of FA-ANN and ANN models, respectively, it was found that the new hybrid model can be introduced as a superior model for solving geotechnical engineering problems.


A whole-body control framework for humanoids operating in human environments

Tomorrow's humanoids will operate in human environments, where efficient manipulation and locomotion skills, and safe contact interactions are critical design factors. We report here our recent efforts into these issues, materialized into a whole-body control framework. This framework integrates task-oriented dynamic control and control prioritization allowing to control multiple task primitives while complying with physical and movement-related constraints. Prioritization establishes a hierarchy between control spaces, assigning top priority to constraint-handling tasks, while projecting operational tasks in the null space of the constraints, and controlling the posture within the residual redundancy. This hierarchy is directly integrated at the kinematic level, allowing the program to monitor behavior feasibility at runtime. In addition, prioritization allows us to characterize the dynamic behavior of the individual control primitives subject to the constraints, and to synthesize operational space controllers at multiple levels. To complete this framework, we have developed free-floating models of the humanoid and incorporate the associated dynamics and the effects of the resulting support contacts into the control hierarchy. As part of a long term collaboration with Honda, we are currently implementing this framework into the humanoid robot Asimo


Path diversification for future internet end-to-end resilience and survivability

Path Diversification is a new mechanism that can be used to select multiple paths between a given ingress and egress node pair using a quantified diversity measure to achieve maximum flow reliability. The path diversification mechanism is targeted at the end-to-end layer, but can be applied at any level for which a path discovery service is available. Path diversification also takes into account service requirements for low-latency or maximal reliability in selecting appropriate paths. Using this mechanism will allow future internetworking architectures to exploit naturally rich physical topologies to a far greater extent than is possible with shortest-path routing or equal-cost load balancing. We describe the path diversity metric and its application at various aggregation levels, and apply the path diversification process to 13 real-world network graphs as well as 4 synthetic topologies to asses the gain in flow reliability. Based on the analysis of flow reliability across a range of networks, we then extend our path diversity metric to create a composJ. P. Rohrer* E-mail: jprohrer@nps.edu Tel.: +1 831 656 3196 Computer Science Department, Naval Postgraduate School Monterey, California, USA A. Jabbar* E-mail: jabbar@ge.com Advanced Communication Systems Lab, GE Global Research Niskayuna, New York, USA J. P. G. Sterbenz E-mail: jpgs@{ittc.ku.edu|comp.lancs.ac.uk} Tel.: +1 785 864 7890 Information and Telecommunication Technology Center, The University of Kansas Lawrence, Kansas, USA and Lancaster University, Lancaster, UK * Work performed while at The University of Kansas ite compensated total graph diversity metric that is representative of a particular topology’s survivability with respect to distributed simultaneous link and node failures. We tune the accuracy of this metric having simulated the performance of each topology under a range of failure severities, and present the results. The topologies used are from nationalscale backbone networks with a variety of characteristics, which we characterize using standard graph-theoretic metrics. The end result is a compensated total graph diversity metric that accurately predicts the survivability of a given network topology.


Hyperbolic Graph Generator

Networks representing many complex systems in nature and society share some common structural properties like heterogeneous degree distributions and strong clustering. Recent research on network geometry has shown that those real networks can be adequately modeled as random geometric graphs in hyperbolic spaces. In this paper, we present a computer program to generate such graphs. Besides real-world-like networks, the program can generate random graphs from other well-known graph ensembles, such as the soft configuration model, random geometric graphs on a circle, or Erdős-Rényi random graphs. The simulations show a good match between the expected values of different network structural properties and the corresponding empirical values measured in generated graphs, confirming the accurate behavior of the program.


Sentiment analysis on Twitter posts: An analysis of positive or negative opinion on GoJek

Online transportation, such as GoJek, is preferred by many users especially in areas where public transport is difficult to access or when there is a traffic jam. Twitter is a popular social networking site in Indonesia that can generate information from users' tweets. In this study, we proposed a system that detect public sentiments based on Twitter post about online transportation services especially GoJek. The system will collect tweets, analyze the tweets sentiments using SVM, and group them into positive and negative sentiment.


Click Through Rate Prediction for Contextual Advertisment Using Linear Regression

This research presents an innovative and unique way of solving the advertisement prediction problem which is considered as a learning problem over the past several years. Online advertising is a multi-billion-dollar industry and is growing every year with a rapid pace. The goal of this research is to enhance click through rate of the contextual advertisements using Linear Regression. In order to address this problem, a new technique propose in this paper to predict the CTR which will increase the overall revenue of the system by serving the advertisements more suitable to the viewers with the help of feature extraction and displaying the advertisements based on context of the publishers. The important steps include the data collection, feature extraction, CTR prediction and advertisement serving. The statistical results obtained from the dynamically used technique show an efficient outcome by fitting the data close to perfection for the LR technique using optimized feature selection. Keywords-Click Through Rate(CTR), Contextual Advertisements, Machine Learning, Web advertisements, Regression Problem.


ICrafter: A Service Framework for Ubiquitous Computing Environments

In this paper, we propose ICrafter, a framework for services and their user interfaces in a class of ubiquitous computing environments. The chief objective of ICrafter is to let users flexibly interact with the services in their environment using a variety of modalities and input devices. We extend existing service frameworks in three ways. First, to offload services and user input devices, ICrafter provides infrastructure support for UI selection, generation, and adaptation. Second, ICrafter allows UIs to be associated with service patterns for on-the-fly aggregation of services. Finally, ICrafter facilitates the design of service UIs that are portable but still reflect the context of the local environment. In addition, we also focus on the system properties such as incremental deployability and robustness that are critical for ubiquitous computing environments. We describe the goals and architecture of ICrafter, a prototype implementation that validates its design, and the key lessons learnt from our


A signal detection method for temporal variation of adverse effect with vaccine adverse event reporting system data

BACKGROUND
To identify safety signals by manual review of individual report in large surveillance databases is time consuming; such an approach is very unlikely to reveal complex relationships between medications and adverse events. Since the late 1990s, efforts have been made to develop data mining tools to systematically and automatically search for safety signals in surveillance databases. Influenza vaccines present special challenges to safety surveillance because the vaccine changes every year in response to the influenza strains predicted to be prevalent that year. Therefore, it may be expected that reporting rates of adverse events following flu vaccines (number of reports for a specific vaccine-event combination/number of reports for all vaccine-event combinations) may vary substantially across reporting years. Current surveillance methods seldom consider these variations in signal detection, and reports from different years are typically collapsed together to conduct safety analyses. However, merging reports from different years ignores the potential heterogeneity of reporting rates across years and may miss important safety signals.

METHOD
Reports of adverse events between years 1990 to 2013 were extracted from the Vaccine Adverse Event Reporting System (VAERS) database and formatted into a three-dimensional data array with types of vaccine, groups of adverse events and reporting time as the three dimensions. We propose a random effects model to test the heterogeneity of reporting rates for a given vaccine-event combination across reporting years. The proposed method provides a rigorous statistical procedure to detect differences of reporting rates among years. We also introduce a new visualization tool to summarize the result of the proposed method when applied to multiple vaccine-adverse event combinations.

RESULT
We applied the proposed method to detect safety signals of FLU3, an influenza vaccine containing three flu strains, in the VAERS database. We showed that it had high statistical power to detect the variation in reporting rates across years. The identified vaccine-event combinations with significant different reporting rates over years suggested potential safety issues due to changes in vaccines which require further investigation.

CONCLUSION
We developed a statistical model to detect safety signals arising from heterogeneity of reporting rates of a given vaccine-event combinations across reporting years. This method detects variation in reporting rates over years with high power. The temporal trend of reporting rate across years may reveal the impact of vaccine update on occurrence of adverse events and provide evidence for further investigations.


Measuring effects of music, noise, and healing energy using a seed germination bioassay.

OBJECTIVE
To measure biologic effects of music, noise, and healing energy without human preferences or placebo effects using seed germination as an objective biomarker.

METHODS
A series of five experiments were performed utilizing okra and zucchini seeds germinated in acoustically shielded, thermally insulated, dark, humid growth chambers. Conditions compared were an untreated control, musical sound, pink noise, and healing energy. Healing energy was administered for 15-20 minutes every 12 hours with the intention that the treated seeds would germinate faster than the untreated seeds. The objective marker was the number of seeds sprouted out of groups of 25 seeds counted at 12-hour intervals over a 72-hour growing period. Temperature and relative humidity were monitored every 15 minutes inside the seed germination containers. A total of 14 trials were run testing a total of 4600 seeds.

RESULTS
Musical sound had a highly statistically significant effect on the number of seeds sprouted compared to the untreated control over all five experiments for the main condition (p < 0.002) and over time (p < 0.000002). This effect was independent of temperature, seed type, position in room, specific petri dish, and person doing the scoring. Musical sound had a significant effect compared to noise and an untreated control as a function of time (p < 0.03) while there was no significant difference between seeds exposed to noise and an untreated control. Healing energy also had a significant effect compared to an untreated control (main condition, p < 0.0006) and over time (p < 0.0001) with a magnitude of effect comparable to that of musical sound.

CONCLUSION
This study suggests that sound vibrations (music and noise) as well as biofields (bioelectromagnetic and healing intention) both directly affect living biologic systems, and that a seed germination bioassay has the sensitivity to enable detection of effects caused by various applied energetic conditions.


Indoor Localization Algorithm based on Fingerprint Using a Single Fifth Generation Wi-Fi Access Point

This paper proposes an indoor positioning system (IPS) based on WLAN using a single fifth-generation (5G) Wi-Fi access point. The proposed method uses fingerprint and classification models based on KNN (K-nearest neighbor) and Bayes rule. The fingerprint is formed by beam RSS (Received Signal Strength) samples, collected in some 2D locations of the indoor environment. Numerical simulations shown that using the best beam samples, it is possible to locate the stationary user's mobile device with average error less than 2.5 m.


Decision Trees for Mining Data Streams Based on the Gaussian Approximation

Since the Hoeffding tree algorithm was proposed in the literature, decision trees became one of the most popular tools for mining data streams. The key point of constructing the decision tree is to determine the best attribute to split the considered node. Several methods to solve this problem were presented so far. However, they are either wrongly mathematically justified (e.g., in the Hoeffding tree algorithm) or time-consuming (e.g., in the McDiarmid tree algorithm). In this paper, we propose a new method which significantly outperforms the McDiarmid tree algorithm and has a solid mathematical basis. Our method ensures, with a high probability set by the user, that the best attribute chosen in the considered node using a finite data sample is the same as it would be in the case of the whole data stream.


SCAN: Learning Hierarchical Compositional Visual Concepts

The seemingly infinite diversity of the natural world arises from a relatively small set of coherent rules, such as the laws of physics or chemistry. We conjecture that these rules give rise to regularities that can be discovered through primarily unsupervised experiences and represented as abstract concepts. If such representations are compositional and hierarchical, they can be recombined into an exponentially large set of new concepts. This paper describes SCAN (Symbol-Concept Association Network), a new framework for learning such abstractions in the visual domain. SCAN learns concepts through fast symbol association, grounding them in disentangled visual primitives that are discovered in an unsupervised manner. Unlike state of the art multimodal generative model baselines, our approach requires very few pairings between symbols and images and makes no assumptions about the form of symbol representations. Once trained, SCAN is capable of multimodal bi-directional inference, generating a diverse set of image samples from symbolic descriptions and vice versa. It also allows for traversal and manipulation of the implicit hierarchy of visual concepts through symbolic instructions and learnt logical recombination operations. Such manipulations enable SCAN to break away from its training data distribution and imagine novel visual concepts through symbolically instructed recombination of previously learnt concepts.


Data fusion schemes for cooperative spectrum sensing in cognitive radio networks

Cooperative spectrum sensing has proven its efficiency to detect spectrum holes in cognitive radio network (CRN) by combining sensing information of multiple cognitive radio users. In this paper, we study different fusion schemes that can be implemented in fusion center. Simulation comparison between these schemes based on hard, quantized and soft fusion rules are conducted. It is shown through computer simulation that the soft combination scheme outperforms the hard one at the cost of more complexity; the quantized combination scheme provides a good tradeoff between detection performance and complexity. In the paper, we also analyze a quantized combination scheme based on a tree-bit quantization and compare its performance with some hard and soft combination schemes.


The Meteor metric for automatic evaluation of machine translation

The Meteor Automatic Metric for Machine Translation evaluation, originally developed and released in 2004, was designed with the explicit goal of producing sentence-level scores which correlate well with human judgments of translation quality. Several key design decisions were incorporated into Meteor in support of this goal. In contrast with IBM’s Bleu, which uses only precision-based features, Meteor uses and emphasizes recall in addition to precision, a property that has been confirmed by several metrics as being critical for high correlation with human judgments. Meteor also addresses the problem of reference translation variability by utilizing flexible word matching, allowing for morphological variants and synonyms to be taken into account as legitimate correspondences. Furthermore, the feature ingredients within Meteor are parameterized, allowing for the tuning of the metric’s free parameters in search of values that result in optimal correlation with human judgments. Optimal parameters can be separately tuned for different types of human judgments and for different languages. We discuss the initial design of the Meteor metric, subsequent improvements, and performance in several independent evaluations in recent years.


A Stochastic Approximation Method

Let M(x) denote the expected value at level x of the response to a certain experiment. M(x) is assumed to be a monotone function of x but is unknown tot he experiment, and it is desire to find the solution x=0 of the equation M(x) = a, where x is a given constant. we give a method for making successive experiments at levels x1, x2,... in such a way that x, will tend to 0 in probability.


A Study of Translation Edit Rate with Targeted Human Annotation

We examine a new, intuitive measure for evaluating machine-translation output that avoids the knowledge intensiveness of more meaning-based approaches, and the labor-intensiveness of human judgments. Translation Edit Rate (TER) measures the amount of editing that a human would have to perform to change a system output so it exactly matches a reference translation. We show that the single-reference variant of TER correlates as well with human judgments of MT quality as the four-reference variant of BLEU. We also define a human-targeted TER (or HTER) and show that it yields higher correlations with human judgments than BLEU—even when BLEU is given human-targeted references. Our results indicate that HTER correlates with human judgments better than HMETEOR and that the four-reference variants of TER and HTER correlate with human judgments as well as—or better than—a second human judgment does.


A process study of computer-aided translation

We investigate novel types of assistance for human translators, based on statistical machine translation methods. We developed the computer-aided tool Caitra that makes suggestions for sentence completion, shows word and phrase translation options, and allows postediting of machine translation output. We carried out a study of the translation process that involved non-professional translators that were native in either French or English and recorded their interaction with the tool. Users translated 192 sentences from French news stories into English. Most translators were faster and better when assisted by our tool. A detailed examination of the logs also provides insight into the human translation process, such as time spent on different activities and length of pauses.


Miniaturized UWB Log-Periodic Square Fractal Antenna

In this letter, the log-periodic square fractal geometry is presented for the design of a miniaturized patch antenna for the ultra-wideband (UWB) services (3.1-10.6 GHz). A miniaturization factor of 23% is achieved with a constant and stable gain in the desired band. The radiation pattern is broadside, which finds suitable applications in the UWB radars and medical imaging. Furthermore, the time-domain performance of the proposed antenna is investigated. A prototype model of the proposed antenna is fabricated and measured as a proof of concept.


Analysis, design, and performance evaluation of asymmetrical half-bridge flyback converter for universal-line-voltage-range applications

The asymmetrical half-bridge (AHB) flyback converter is an attractive topology for operation at higher switching frequencies because it can operate with zero-voltage switching of the primary-side switches and zero-current switching of the secondary-side rectifier. In this paper, a detailed analysis and design procedure of the AHB flyback converter for the universal-line-voltage-range applications is presented. The performance of the AHB flyback converter is evaluated by loss analysis based on the simulation waveforms obtained in Simplis and experimentally verified on a laboratory prototype of a 65-W (19.5-V, 3.33-A) universal-line-voltage-range adapter.


Resistance to Change : The Moderating Effects of Leader-Member Exchange and Role Breadth Self-Efficacy

The prevalence of resistance during change initiatives is well recognized in the change management literature. The implementation of the lean production system is no exception. It often requires substantial changes to processes and the way people work. As such, understanding how to manage this resistance is important. One view argues that the extent of resistance during change depends on the characteristics of the change process. This view posits that resistance can be reduced if organizations manage information flow, create room for participation and develop trust in management. In addition, this paper also proposes that is Leader-Member Exchange (LMX) and Role Breadth Self-Efficacy (RBSE) moderate the effect on the employees’ resistance to change. 


Real-time wireless vibration monitoring system using LabVIEW

Vibration analysis provides relevant information about abnormal working condition of machine parts. Vibration measurement is prerequisite for vibration analysis which is used for condition monitoring of machinery. Also, wireless vibration monitoring has many advantages over wired monitoring. This Paper presents, implementation of a reliable and low cost wireless vibration monitoring system. Vibration measurement has been done using 3-Axis digital output MEMS Accelerometer sensor. This sensor can sense vibrations in the range 0.0156g to 8g where, 1g is 9.81m/s2. Accelerometer Sensor is interfaced with Arduino-derived microcontroller board having Atmel's AT-mega328p microcontroller. The implemented system uses ZigBee communication protocol i.e. standard IEEE 802.15.4, for wireless communication between Sensor Unit and Vibration Monitoring Unit. The wireless communication has been done using XBee RF modules. National Instruments's LabVIEW software has been used for development of graphical user interface, data-logging and alarm indication on the PC. Experimental results show continuous real-time monitoring of machine's vibrations on charts. These results, along with data-log file have been used for vibration analysis. This analysis is used to ensure safe working condition of machinery and used in predictive maintenance.


Big Data Cognitive Storage: An Optimum Way of handling the Educational Data in Indian Scenario

The concept of big data has been incorporated in majority of areas. The educational sector has plethora of data especially in online education which plays a vital in modern education. Moreover digital learning which comprises of data and analytics contributes significantly to enhance teaching and learning. The key challenge for handling such data can be a costly affair. IBM has introduced the technology "Cognitive Storage" which ensures that the most relevant information is always on hand. This technology governs the incoming data, stores the data in definite media, application of levels of data protection, policies for the lifecycle and retention of different classes of data. This technology can be very beneficial for online learning in Indian scenario. This technology will be very beneficial in Indian society so as to store more information for the upliftment of the students’ knowledge.


Development of an Assessment Model for Industry 4.0: Industry 4.0-MM

The application of new technologies in the manufacturing environment is ushering a new era referred to as the 4th industrial revolution, and this digital transformation appeals to companies due to various competitive advantages it provides. Accordingly, there is a fundamental need for assisting companies in the transition to Industry 4.0 technologies/practices, and guiding them for improving their capabilities in a standardized, objective, and repeatable way. Maturity Models (MMs) aim to assist organizations by providing comprehensive guidance. Therefore, the literature is reviewed systematically with the aim of identifying existing studies related to MMs proposed in the context of Industry 4.0. Seven identified MMs are analyzed by comparing their characteristics of scope, purpose, completeness, clearness, and objectivity. It is concluded that none of them satisfies all expected criteria. In order to satisfy the need for a structured Industry 4.0 assessment/maturity model, SPICE-based Industry 4.0-MM is proposed in this study. Industry 4.0-MM has a holistic approach consisting of the assessment of process transformation, application management, data governance, asset management, and organizational alignment areas. The aim is to create a common base for performing an assessment of the establishment of Industry 4.0 technologies, and to guide companies towards achieving a higher maturity stage in order to maximize the economic benefits of Industry 4.0. Hence, Industry 4.0-MM provides standardization in continuous benchmarking and improvement of businesses in the manufacturing industry.


Exosomes in tumor microenvironment: novel transporters and biomarkers

Tumor microenvironment (TME) plays an integral part in the biology of cancer, participating in tumor initiation, progression, and response to therapy. Exosome is an important part of TME. Exosomes are small vesicles formed in vesicular bodies with a diameter of 30–100 nm and a classic “cup” or “dish” morphology. They can contain microRNAs, mRNAs, DNA fragments and proteins, which are shuttled from a donor cell to recipient cells. Exosomes secreted from tumor cells are called tumor-derived (TD) exosomes. There is emerging evidence that TD exosomes can construct a fertile environment to support tumor proliferation, angiogenesis, invasion and premetastatic niche preparation. TD exosomes also may facilitate tumor growth and metastasis by inhibiting immune surveillance and by increasing chemoresistance via removal of chemotherapeutic drugs. Therefore, TD-exosomes might be potential targets for therapeutic interventions via their modification or removal. For example, exosomes can serve as specific delivery vehicles to tumors of drugs, small molecules, or agents of prevention and gene therapy. Furthermore, the biomarkers detected in exosomes of biological fluids imply a potential for exosomes in the early detection and diagnosis, prediction of therapeutic efficacy, and determining prognosis of cancer. Although exosomes may serve as cancer biomarkers and aid in the treatment of cancer, we have a long way to go before we can further enhance the anti-tumor therapy of exosomes and develop exosome-based cancer diagnostic and therapeutic strategies.


Weakly supervised named entity classification

In this paper, we describe a new method for the problem of named entity classification for specialized or technical domains, using distant supervision. Our approach relies on a simple observation: in some specialized domains, named entities are almost unambiguous. Thus, given a seed list of names of entities, it is cheap and easy to obtain positive examples from unlabeled texts using a simple string match. Those positive examples can then be used to train a named entity classifier, by using the PU learning paradigm, which is learning from positive and unlabeled examples. We introduce a new convex formulation to solve this problem, and apply our technique in order to extract named entities from financial reports corresponding to healthcare companies.


An Efficient Method of Number Plate Extraction from Indian Vehicles Image

Automatic Number Plate Recognition (ANPR) is an image-processing technology that identifies vehicles by their number plates without direct human intervention. It is an application of computer vision and important area of research due to its many applications. The main process of ANPR is divided into four stages. This paper presents a simple and efficient method for the extraction of number plate from the vehicle image based on morphological operations, thresholding and sobel edge detection, and the connected component analysis.


Information fusion for wireless sensor networks: Methods, models, and classifications

Wireless sensor networks produce a large amount of data that needs to be processed, delivered, and assessed according to the application objectives. The way these data are manipulated by the sensor nodes is a fundamental issue. Information fusion arises as a response to process data gathered by sensor nodes and benefits from their processing capability. By exploiting the synergy among the available data, information fusion techniques can reduce the amount of data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. In this work, we survey the current state-of-the-art of information fusion by presenting the known methods, algorithms, architectures, and models of information fusion, and discuss their applicability in the context of wireless sensor networks.


Leadership style and patient safety: implications for nurse managers.

OBJECTIVE
The purpose of this study was to explore the relationship between nurse manager (NM) leadership style and safety climate.

BACKGROUND
Nursing leaders are needed who will change the environment and increase patient safety. Hospital NMs are positioned to impact day-to-day operations. Therefore, it is essential to inform nurse executives regarding the impact of leadership style on patient safety.

METHODS
A descriptive correlational study was conducted in 41 nursing departments across 9 hospitals. The hospital unit safety climate survey and multifactorial leadership questionnaire were completed by 466 staff nurses. Bivariate and regression analyses were conducted to determine how well leadership style predicted safety climate.

RESULTS
Transformational leadership style was demonstrated as a positive contributor to safety climate, whereas laissez-faire leadership style was shown to negatively contribute to unit socialization and a culture of blame.

CONCLUSIONS
Nursing leaders must concentrate on developing transformational leadership skills while also diminishing negative leadership styles.


Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays

Given image labels as the only supervisory signal, we focus on harvesting, or mining, thoracic disease localizations from chest X-ray images. Harvesting such localizations from existing datasets allows for the creation of improved data sources for computer-aided diagnosis and retrospective analyses. We train a convolutional neural network (CNN) for image classification and propose an attention mining (AM) strategy to improve the model’s sensitivity or saliency to disease patterns. The intuition of AM is that once the most salient disease area is blocked or hidden from the CNN model, it will pay attention to alternative image regions, while still attempting to make correct predictions. However, the model requires to be properly constrained during AM, otherwise, it may overfit to uncorrelated image parts and forget the valuable knowledge that it has learned from the original image classification task. To alleviate such side effects, we then design a knowledge preservation (KP) loss, which minimizes the discrepancy between responses for X-ray images from the original and the updated networks. Furthermore, we modify the CNN model to include multi-scale aggregation (MSA), improving its localization ability on small-scale disease findings, e.g., lung nodules. We experimentally validate our method on the publicly-available ChestXray14 dataset, outperforming a class activation map (CAM)-based approach, and demonstrating the value of our novel framework for mining disease locations.


Content-Based Image Retrieval Using Multiple Features

Algorithms of Content-Based Image Retrieval (CBIR) have been well developed along with the explosion of information. These algorithms are mainly distinguished based on feature used to describe the image content. In this paper, the algorithms that are based on color feature and texture feature for image retrieval will be presented. Color Coherence Vector based image retrieval algorithm is also attempted during the implementation process, but the best result is generated from the algorithms that weights color and texture. 80% satisfying rate is achieved.


DIFusion: Fast Skip-Scan with Zero Space Overhead

Scan is a crucial operation in main-memory column-stores. It scans a column and returns a result bit vector indicating which records satisfy a filter predicate. ByteSlice is an in-memory data layout that chops data into multiple bytes and exploits early-stop capability by high-order bytes comparisons. As column widths are usually not multiples of byte, the last-byte of ByteSlice is padded with 0's, wasting memory bandwidth and computation power. To fully leverage the resources, we propose to weave a secondary index into the vacant bits (i.e., bits originally padded with 0's), forming our new layout coined DIFusion (Data Index Fusion). DIFusion enables skip-scan, a new fast scan that inherits the early-stopping capability from ByteSlice and at the same time possesses the data-skipping ability of index with zero space overhead. Empirical results show that skip-scan on DIFusion outperforms scan on ByteSlice.


Monocyte emigration from bone marrow during bacterial infection requires signals mediated by chemokine receptor CCR2

Monocytes recruited to tissues mediate defense against microbes or contribute to inflammatory diseases. Regulation of the number of circulating monocytes thus has implications for disease pathogenesis. However, the mechanisms controlling monocyte emigration from the bone marrow niche where they are generated remain undefined. We demonstrate here that the chemokine receptor CCR2 was required for emigration of Ly6Chi monocytes from bone marrow. Ccr2−/− mice had fewer circulating Ly6Chi monocytes and, after infection with Listeria monocytogenes, accumulated activated monocytes in bone marrow. In blood, Ccr2−/− monocytes could traffic to sites of infection, demonstrating that CCR2 is not required for migration from the circulation into tissues. Thus, CCR2-mediated signals in bone marrow determine the frequency of Ly6Chi monocytes in the circulation.


Incremental learning for ν-Support Vector Regression

The ν-Support Vector Regression (ν-SVR) is an effective regression learning algorithm, which has the advantage of using a parameter ν on controlling the number of support vectors and adjusting the width of the tube automatically. However, compared to ν-Support Vector Classification (ν-SVC) (Schölkopf et al., 2000), ν-SVR introduces an additional linear term into its objective function. Thus, directly applying the accurate on-line ν-SVC algorithm (AONSVM) to ν-SVR will not generate an effective initial solution. It is the main challenge to design an incremental ν-SVR learning algorithm. To overcome this challenge, we propose a special procedure called initial adjustments in this paper. This procedure adjusts the weights of ν-SVC based on the Karush-Kuhn-Tucker (KKT) conditions to prepare an initial solution for the incremental learning. Combining the initial adjustments with the two steps of AONSVM produces an exact and effective incremental ν-SVR learning algorithm (INSVR). Theoretical analysis has proven the existence of the three key inverse matrices, which are the cornerstones of the three steps of INSVR (including the initial adjustments), respectively. The experiments on benchmark datasets demonstrate that INSVR can avoid the infeasible updating paths as far as possible, and successfully converges to the optimal solution. The results also show that INSVR is faster than batch ν-SVR algorithms with both cold and warm starts.


Feature combination strategies for saliency-based visual attention systems

acin ral eais a000 Abstract. Bottom-up or saliency-based visual attention allows primates to detect nonspecific conspicuous targets in cluttered scenes. A classical metaphor, derived from electrophysiological and psychophysical studies, describes attention as a rapidly shiftable ‘‘spotlight.’’ We use a model that reproduces the attentional scan paths of this spotlight. Simple multi-scale ‘‘feature maps’’ detect local spatial discontinuities in intensity, color, and orientation, and are combined into a unique ‘‘master’’ or ‘‘saliency’’ map. The saliency map is sequentially scanned, in order of decreasing saliency, by the focus of attention. We here study the problem of combining feature maps, from different visual modalities (such as color and orientation), into a unique saliency map. Four combination strategies are compared using three databases of natural color images: (1) Simple normalized summation, (2) linear combination with learned weights, (3) global nonlinear normalization followed by summation, and (4) local nonlinear competition between salient locations followed by summation. Performance was measured as the number of false detections before the most salient target was found. Strategy (1) always yielded poorest performance and (2) best performance, with a threefold to eightfold improvement in time to find a salient target. However, (2) yielded specialized systems with poor generalization. Interestingly, strategy (4) and its simplified, computationally efficient approximation (3) yielded significantly better performance than (1), with up to fourfold improvement, while preserving generality. © 2001 SPIE and IS&T. [DOI: 10.1117/1.1333677]


Counter-Forensics: Attacking Image Forensics

This chapter discusses counter-forensics, the art and science of impeding or misleading forensic analyses of digital images. Research on counter-forensics is motivated by the need to assess and improve the reliability of forensic methods in situations where intelligent adversaries make efforts to induce a certain outcome of forensic analyses. Counter-forensics is first defined in a formal decision-theoretic framework. This framework is then interpreted and extended to encompass the requirements to forensic analyses in practice, including a discussion of the notion of authenticity in the presence of legitimate processing, and the role of image models with regard to the epistemic underpinning of the forensic decision problem. A terminology is developed that distinguishes security from robustness properties, integrated from post-processing attacks, and targeted from universal attacks. This terminology is directly applied in a self-contained technical survey of counter-forensics against image forensics, notably techniques that suppress traces of image processing and techniques that synthesize traces of authenticity, including examples and brief evaluations. A discussion of relations to other domains of multimedia security and an overview of open research questions concludes the chapter. 1 Definition of Counter-Forensics This final chapter changes the perspective. It is devoted to digital image counterforensics, the art and science of impeding and misleading forensic analyses of digital images. Rainer Böhme Westfälische Wilhelms-Universität Münster, Dept. of Information Systems, Leonardo-Campus 3, 48149 Münster, Germany e-mail: rainer.boehme@wi.uni-muenster.de Matthias Kirchner International Computer Science Institute, 1947 Center Street, Berkeley, CA 94704 e-mail: kirchner@icsi.berkeley.edu


Band-pass shielding enclosure for 2.4/5 GHz WLAN applications

This paper presents a design of band-pass shielding enclosure for two frequency bands of WLAN. The proposed design employs a compact frequency selective surface to highlight the advantages of these miniaturized structures for the selective shielding of such enclosures that are electrically small. The results indicate that the proposed shielding enclosure has high transmittance in both the pass bands for horizontal and vertical polarizations, while offers a good amount of electromagnetic shielding outside these frequency bands.


Big Data For Development: Applications and Techniques

With the explosion of social media sites and proliferation of digital computing devices and Internet access, massive amounts of public data is being generated on a daily basis. Efficient techniques/ algorithms to analyze this massive amount of data can provide near real-time information about emerging trends and provide early warning in case of an imminent emergency (such as the outbreak of a viral disease). In addition, careful mining of these data can reveal many useful indicators of socioeconomic and political events, which can help in establishing effective public policies. The focus of this study is to review the application of big data analytics for the purpose of human development. The emerging ability to use big data techniques for development (BD4D) promises to revolutionalize healthcare, education, and agriculture; facilitate the alleviation of poverty; and help to deal with humanitarian crises and violent conflicts. Besides all the benefits, the large-scale deployment of BD4D is beset with several challenges due to the massive size, fast-changing and diverse nature of big data. The most pressing concerns relate to efficient data acquisition and sharing, establishing of context (e.g., geolocation and time) and veracity of a dataset, and ensuring appropriate privacy. In this study, we provide a review of existing BD4D work to study the impact of big data on the development of society. In addition to reviewing the important works, we also highlight important challenges and open issues.


On-Chip High-Voltage Generation in Integrated Circuits Using an Improved Multiplier Technique

An improved oped for generating +40 voltage multiplier technique has been develV internally in p-channel MNOS integrated circuits to enrtble them to be operated from standard +5and : 12-V supply rails. With this technique, the multiplication efficiency and current driving capabtilty are both independent of the number of multiplier stages. A mathematical model and simple equivalent circuit have been developed for the multiplier and the predicted performance agrees well with measured results. A multiplier has already been incorporated into a TTL compatible nonvolatile quad-latch, in which it occupies a chip area of 600 ~m X 240 pm. It is operated with a clock frequency of 1 MHz and can supManuscript received December 9, 1975; revised February 18, 1976. This paper is based on part of a presentation entitled “A non-volatile MNOS quad-latch,” which was presented at the First European SolidState Circuits Conference, Canterbury, England, September 2-5, 1975. The author is with the Allen Clark Research Centre, The Plessey Company Ltd., CasweJl, Towcester, Northants., England. MNOS Voltage ply a maximum load current of about 10 NA. The output impedance is 3.2 Mfl. INTRODUCTION ALTHOUGH MNOS technology is now well established for fabricating nonvolatile memory circuits, the relatively high potentials necessary to write or erase information, typically 30-40 V, are an obvious disadvantage. In many applications, the need to generate these voltages has prevented the use of MNOS devices being economically viable, especially when only a few bits of nonvolatile data are required. To overcome this problem, a method of on-chip high-voltage generation using a new voltage multiplier technique has been developed, enabling MNOS circuits to be operated with standard DICKSON: ON-CHIP HIGH-VOLTAGEGENERATION 375


Using Fully Homomorphic Encryption for Statistical Analysis of Categorical, Ordinal and Numerical Data

In recent years, there has been a growing trend towards outsourcing of computational tasks with the development of cloud services. The Gentry’s pioneering work of fully homomorphic encryption (FHE) and successive works have opened a new vista for secure and practical cloud computing. In this paper, we consider performing statistical analysis on encrypted data. To improve the efficiency of the computations, we take advantage of the batched computation based on the ChineseRemainder-Theorem. We propose two building blocks that work with FHE: a novel batch greater-than primitive, and matrix primitive for encrypted matrices. With these building blocks, we construct secure procedures and protocols for different types of statistics including the histogram (count), contingency table (with cell suppression) for categorical data; k-percentile for ordinal data; and principal component analysis and linear regression for numerical data. To demonstrate the effectiveness of our methods, we ran experiments in five real datasets. For instance, we can compute a contingency table with more than 50 cells from 4000 of data in just 5 minutes, and we can train a linear regression model with more than 40k of data and dimension as high as 6 within 15 minutes. We show that the FHE is not as slow as commonly believed and it becomes feasible to perform a broad range of statistical analysis on thousands of encrypted data.


Anomalous Magnetic Field Activity During a Bioenergy Healing Experiment

A few studies have reported magnetic fi eld changes during bioenergy healing. In a pilot experiment, we examined magnetic fi eld activity during hands-on healing and distant healing of mice with experimentally induced tumors. During healing sessions, we observed distinct magnetic fi eld oscillations adjacent to the mice cages, which were similar in appearance to those reported by Zimmerman (1985). The magnetic fi eld oscillations began as 20–30 Hz oscillations, slowing to 8–9 Hz, and then to less than 1 Hz, at which point the oscillations reversed and increased in frequency, with an overall symmetrical appearance resembling a “chirp wave.” The waves ranged from 1–8 milliGauss peak-to-peak in strength and 60–120 sec in duration. Evidence to date suggests that bioenergy healing may be detectable with DC gaussmeters, although we have not ruled out an artifactual basis for the oscillations reported here.


Algorithms implementing distributed shared memory

Four basic algorithms for implementing distributed shared memory are compared. Conceptually, these algorithms extend local virtual address spaces to span multiple hosts connected by a local area network, and some of them can easily be integrated with the hosts' virtual memory systems. The merits of distributed shared memory and the assumptions made with respect to the environment in which the shared memory algorithms are executed are described. The algorithms are then described, and a comparative analysis of their performance in relation to application-level access behavior is presented. It is shown that the correct choice of algorithm is determined largely by the memory access behavior of the applications. Two particularly interesting extensions of the basic algorithms are described, and some limitations of distributed shared memory are noted.<<ETX>>


Adapting the Tesseract open source OCR engine for multilingual OCR

We describe efforts to adapt the Tesseract open source OCR engine for multiple scripts and languages. Effort has been concentrated on enabling generic multi-lingual operation such that negligible customization is required for a new language beyond providing a corpus of text. Although change was required to various modules, including physical layout analysis, and linguistic post-processing, no change was required to the character classifier beyond changing a few limits. The Tesseract classifier has adapted easily to Simplified Chinese. Test results on English, a mixture of European languages, and Russian, taken from a random sample of books, show a reasonably consistent word error rate between 3.72% and 5.78%, and Simplified Chinese has a character error rate of only 3.77%.


Multilingual OCR (MOCR): An Approach to Classify Words to Languages

There are immense efforts to design a complete OCR for most of the world’s leading languages, however, multilingual documents either of handwritten or of printed form. As a united attempt, Unicode based OCRs were studied mostly with some positive outcomes, despite the fact that a large character set slows down the recognition significantly. In this paper, we come out with a method to classify words to a language as the word segmentation is complete. For the purpose, we identified the characteristics of writings of several languages and utilized projecting method combined with some other feature extraction methods. In addition, this paper intends a modified statistical approach to correct the skewness before processing a segmented document. The proposed procedure, evaluated for a collection of both handwritten and printed documents, came with excellent outcomes in assigning words to languages. General Terms Pattern Recognition, Document Processing, Optical Character Recognition.


The OCRopus open source OCR system

OCRopus is a new, open source OCR system emphasizing modularity, easy extensibility, and reuse, aimed at both the research community and large scale commercial document conversions. This paper describes the current status of the system, its general architecture, as well as the major algorithms currently being used for layout analysis and text line recognition.


Automatic code generation of convolutional neural networks in FPGA implementation

Convolutional neural networks (CNNs) have gained great success in various computer vision applications. However, state-of-the-art CNN models are computation-intensive and hence are mainly processed on high performance processors like server CPUs and GPUs. Owing to the advantages of high performance, energy efficiency and reconfigurability, Field-Programmable Gate Arrays (FPGAs) have been widely explored as CNN accelerators. In this paper, we propose parallel structures to exploit the inherent parallelism and efficient computation units to perform operations in convolutional and fully-connected layers. Further, an automatic generator is proposed to generate Verilog HDL source code automatically according to high-level hardware description language. Execution time, DSP consumption and performance are analytically modeled based on some critical design variables. We demonstrate the automatic methodology by implementing two representative CNNs (LeNet and AlexNet) and evaluate the execution time models by comparing estimated and measured values. Our results show that the proposed automatic methodology yields hardware design with good performance and saves much developing round time.


Extending drift-diffusion paradigm into the era of FinFETs and nanowires

This paper presents a feasibility study that the drift-diffusion model can capture the ballistic transport of FinFETs and nanowires with a simple model extension. For FinFETs, Monte Carlo simulation is performed and the ballistic mobility is calibrated to linear & saturation currents. It is validated that the calibrated model works over a wide range of channel length and channel stress. The ballistic mobility model is then applied to a nanowire with 5nm design rules. Finally, the technology scaling trend of the ballistic ratio is explored.


Secure Communication Method Based on Encryption and Steganography

Encryption is a traditional method of scrambling data beyond recognition. For years, encryption has been widely used in various areas and domains where secrecy and confidentiality were required. However, there are certain encryption techniques that are not allowed in some countries. In these cases, steganography may come as a solution for hiding data using an apparently inoffensive and innocent carrier. The purpose of steganography is to deliver secret information from a sender to a receiver by scrambling the communication channel not the information itself. This way, it is very unlikely for someone to identify and extract the secret message without knowing exactly how it was embedded into the carrier. Moreover, this task is more difficult since most steganographic methods rely on file or packet header redundancy, lossy multimedia algorithms or insignificant content alteration. All of these are very hard to detect. In this paper, a combination of encryption with image based steganography is presented in order to offer similar robustness as offered by advanced encryption algorithms such as RSA, AES or DES but with lower resource needs. The most original contribution of this paper consists in how a secret message is embedded into a image carrier generating 3 different but very similar images. These images are sent to the destination using different channels and then used to recover the original (secret) message.


Jigsaw: indoor floor plan reconstruction via mobile crowdsensing

The lack of floor plans is a critical reason behind the current sporadic availability of indoor localization service. Service providers have to go through effort-intensive and time-consuming business negotiations with building operators, or hire dedicated personnel to gather such data. In this paper, we propose Jigsaw, a floor plan reconstruction system that leverages crowdsensed data from mobile users. It extracts the position, size and orientation information of individual landmark objects from images taken by users. It also obtains the spatial relation between adjacent landmark objects from inertial sensor data, then computes the coordinates and orientations of these objects on an initial floor plan. By combining user mobility traces and locations where images are taken, it produces complete floor plans with hallway connectivity, room sizes and shapes. Our experiments on 3 stories of 2 large shopping malls show that the 90-percentile errors of positions and orientations of landmark objects are about 1~2m and 5~9°, while the hallway connectivity is 100% correct.


Dielectric properties of human normal, malignant and cirrhotic liver tissue: in vivo and ex vivo measurements from 0.5 to 20 GHz using a precision open-ended coaxial probe.

Hepatic malignancies have historically been treated with surgical resection. Due to the shortcomings of this technique, there is interest in other, less invasive, treatment modalities, such as microwave hepatic ablation. Crucial to the development of this technique is the accurate knowledge of the dielectric properties of human liver tissue at microwave frequencies. To this end, we characterized the dielectric properties of in vivo and ex vivo normal, malignant and cirrhotic human liver tissues from 0.5 to 20 GHz. Analysis of our data at 915 MHz and 2.45 GHz indicates that the dielectric properties of ex vivo malignant liver tissue are 19 to 30% higher than normal tissue. The differences in the dielectric properties of in vivo malignant and normal liver tissue are not statistically significant (with the exception of effective conductivity at 915 MHz, where malignant tissue properties are 16% higher than normal). Also, the dielectric properties of in vivo normal liver tissue at 915 MHz and 2.45 GHz are 16 to 43% higher than ex vivo. No statistically significant differences were found between the dielectric properties of in vivo and ex vivo malignant tissue (with the exception of effective conductivity at 915 MHz, where malignant tissue properties are 28% higher than normal). We report the one-pole Cole-Cole parameters for ex vivo normal, malignant and cirrhotic liver tissue in this frequency range. We observe that wideband dielectric properties of in vivo liver tissue are different from the wideband dielectric properties of ex vivo liver tissue, and that the in vivo data cannot be represented in terms of a Cole-Cole model. Further work is needed to uncover the mechanisms responsible for the observed wideband trends in the in vivo liver data.


Camera parameters auto-adjusting technique for robust robot vision

How to make vision system work robustly under dynamic light conditions is still a challenging research focus in computer/robot vision community. In this paper, a novel camera parameters auto-adjusting technique based on image entropy is proposed. Firstly image entropy is defined and its relationship with camera parameters is verified by experiments. Then how to optimize the camera parameters based on image entropy is proposed to make robot vision adaptive to the different light conditions. The algorithm is tested by using the omnidirectional vision in indoor RoboCup Middle Size League environment and the perspective camera in outdoor ordinary environment, and the results show that the method is effective and color constancy to some extent can be achieved.


Image Processing And Pattern Recognition Fundamentals And Techniques

It's coming again, the new collection that this site has. To complete your curiosity, we offer the favorite image processing and pattern recognition fundamentals and techniques book as the choice today. This is a book that will show you even new to old thing. Forget it; it will be right for you. Well, when you are really dying of image processing and pattern recognition fundamentals and techniques, just pick it. You know, this book is always making the fans to be dizzy if not to find.


Rules for an Ontology-based Approach to Adaptation

Adaptation addresses the complexity and overload caused by an increasing amount of available resources by the identification and presentation of relevant resources. Drawbacks of existing approaches to this end include the limited degree of customization, difficulties in the acquisition of model information and the lack of control and transparency of the system's adaptive behavior. This paper proposes an approach which addresses these drawbacks to perform meaningful and effective adaptation by the application of semantic Web technologies. This comprises the use of an ontology and adaptation rules for knowledge representation and inference engines for reasoning. The focus of this paper lies in the presentation of adaptation rules


RTL implementation and analysis of fixed priority, round robin, and matrix arbiters for the NoC's routers

Networks-on-Chip (NoC) is an emerging on-chip interconnection centric platform that influences modern high speed communication infrastructure to improve the performance of many-core System-on-Chip (SoCs) designs. The core of each NoCs router involves arbiter and multiplier pairs that need to be carefully co-optimized in order to achieve an overall efficient implementation. Low transmission latency design is one of the most important parameters of NoC design. This paper uses parametric Verilog HDL to implement the designs and compares the performance in terms of power, area, and delay of different types of arbiters using for NoCs routers. The RTL implementation is performed using parametric Verilog HDL and analysis in term of power, area and delay is performed using Xilinx ISE 14.7 and Xpower Analyzer (XPA) with Xpower Estimator (XPE). The target device uses for these implementation is Vertex 6.


[Translation and validation for Brazil of the body image scale for adolescents--Offer Self-Image Questionnaire (OSIQ)].

OBJECTIVE
To evaluate the semantic and measure equivalence of the body image sub-scale of the Offer Self Image Questionnaire (OSIQ).

METHODS
Participants were 386 teenagers, 10 to 18 years old, both sexes, enrolled in a private school (junior and high school age). Translation, back-translation, technique revision and evaluation were conducted. The Portuguese instrument was evaluated for internal consistency, discriminate and concurrent validity.

RESULTS
Internal consistency showed values from 0.43 to 0.54 and was able to discriminate all groups studied--the whole population, boys and girls, and boys in early adolescence, by nutritional status (p<0.001; p<0.009; p=0.030; p=0.043, respectively). Concurrent analyses showed significant correlation with anthropometric measures only for girls (r=-0.16 and p=0.021; r=-0.19 and p=0.007), early adolescence (r=-0.23 and p=0.008; r=-0.26 and p=0.003) and intermediate adolescence (r=-0.29 and p=0.010) and the retest confirmed reliability by the coefficient of interclass correlation. Although the instrument has proven its ability to discriminate between the groups studied by nutritional state, other results were less satisfactory. More studies are necessary for full transcultural adaptation, including the application of other comparative scales.

CONCLUSION
The body image sub-scale of the OSIQ was translated, but the results are not promising and require more studies.


Inflammatory reaction to hyaluronic acid: A newly described complication in vocal fold augmentation.

OBJECTIVES/HYPOTHESIS
To establish the rate of inflammatory reaction to hyaluronic acid (HA) in vocal fold injection augmentation, determine the most common presenting signs and symptoms, and propose an etiology.

STUDY DESIGN
Retrospective chart review.

METHODS
Patients injected with HA over a 5-year period were reviewed to identify those who had a postoperative inflammatory reaction. Medical records were reviewed for patient demographic information, subjective complaints, Voice Handicap Index-10 (VHI-10) scores, medical intervention, and resolution time. Videolaryngostroboscopy examinations were also evaluated.

RESULTS
A total of 186 patients (245 vocal folds) were injected with HA over a 5-year period, with a postoperative inflammatory reaction rate of 3.8%. The most common complaints in these patients were odynophagia, dysphonia, and dyspnea with vocal fold erythema, edema, and loss of pliability on videolaryngostroboscopy. All patients were treated with corticosteroids. Return of vocal fold vibration ranged from 3 weeks to 26 months, with VHI-10 scores normalizing in 50% of patients.

CONCLUSIONS
This reaction may be a form of hypersensitivity related to small amounts of protein linked to HA. Alternatively, extravascular compression from the HA could lead to venous congestion of the vocal fold. The possibility of equipment contamination is also being investigated. Further studies are needed to determine the etiology and best treatment.

LEVEL OF EVIDENCE
4 Laryngoscope, 2016 127:445-449, 2017.


Global effects of smoking, of quitting, and of taxing tobacco.

From the Center for Global Health Research, St. Michael’s Hospital and Dalla Lana School of Public Health, University of Toronto, Toronto (P.J.); and the Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, Richard Doll Building, University of Oxford, Oxford, United Kingdom (R.P.). Address reprint requests to Dr. Jha at prabhat.jha@utoronto.ca.


User-centered security: stepping up to the grand challenge

User-centered security has been identified as a grand challenge in information security and assurance. It is on the brink of becoming an established subdomain of both security and human/computer interface (HCI) research, and an influence on the product development lifecycle. Both security and HCI rely on the reality of interactions with users to prove the utility and validity of their work. As practitioners and researchers in those areas, we still face major issues when applying even the most foundational tools used in either of these fields across both of them. This essay discusses the systemic roadblocks at the social, technical, and pragmatic levels that user-centered security must overcome to make substantial breakthroughs. Expert evaluation and user testing are producing effective usable security today. Principles such as safe staging, enumerating usability failure risks, integrated security, transparent security and reliance on trustworthy authorities can also form the basis of improved systems


Static and Moving Object Detection Using Flux Tensor with Split Gaussian Models

In this paper, we present a moving object detection system named Flux Tensor with Split Gaussian models (FTSG) that exploits the benefits of fusing a motion computation method based on spatio-temporal tensor formulation, a novel foreground and background modeling scheme, and a multi-cue appearance comparison. This hybrid system can handle challenges such as shadows, illumination changes, dynamic background, stopped and removed objects. Extensive testing performed on the CVPR 2014 Change Detection benchmark dataset shows that FTSG outperforms state-of-the-art methods.


A Quick Tour of BabelNet 1.1

In this paper we present BabelNet 1.1, a brand-new release of the largest “encyclopedic dictionary”, obtained from the automatic integration of the most popular computational lexicon of English, i.e. WordNet, and the largest multilingual Web encyclopedia, i.e. Wikipedia. BabelNet 1.1 covers 6 languages and comes with a renewed Web interface, graph explorer and programmatic API. BabelNet is available online at http://www.babelnet.org.


Cloud Computing: The impact on digital forensic investigations

Cloud Computing (CC) as a concept and business opportunity is likely to see many organisations experiencing the 'credit crunch', embrace the relatively low cost option of CC to ensure continued business viability and sustainability. The pay-as-you-go structure of the CC business model is typically suited to SME's who do not have the resources to completely fulfil their IT requirements. Private end users will also look to utilise the colossal pool of resources that CC offers in an attempt to provide mobile freedom of information. However, as with many opportunities that offer legitimate users enormous benefits, unscrupulous and criminal users will also look to use CC to exploit the loopholes that may exist within this new concept, design and business model. This paper will outline the tasks that the authors undertook for the CLOIDIFIN project and highlight where the Impact of CC will diversely effect digital forensic investigations.


An Improvement to Feature Selection of Random Forests on Spark

The Random Forests algorithm belongs to the class of ensemble learning methods, which are common used in classification problem. In this paper, we studied the problem of adopting the Random Forests algorithm to learn raw data from real usage scenario. An improvement, which is stable, strict, high efficient, data-driven, problem independent and has no impact on algorithm performance, is proposed to investigate 2 actual issues of feature selection of the Random Forests algorithm. The first one is to eliminate noisy features, which are irrelevant to the classification. And the second one is to eliminate redundant features, which are highly relevant with other features, but useless. We implemented our improvement approach on Spark. Experiments are performed to evaluate our improvement and the results show that our approach has an ideal performance.


Yield Trends Are Insufficient to Double Global Crop Production by 2050

Several studies have shown that global crop production needs to double by 2050 to meet the projected demands from rising population, diet shifts, and increasing biofuels consumption. Boosting crop yields to meet these rising demands, rather than clearing more land for agriculture has been highlighted as a preferred solution to meet this goal. However, we first need to understand how crop yields are changing globally, and whether we are on track to double production by 2050. Using ∼2.5 million agricultural statistics, collected for ∼13,500 political units across the world, we track four key global crops-maize, rice, wheat, and soybean-that currently produce nearly two-thirds of global agricultural calories. We find that yields in these top four crops are increasing at 1.6%, 1.0%, 0.9%, and 1.3% per year, non-compounding rates, respectively, which is less than the 2.4% per year rate required to double global production by 2050. At these rates global production in these crops would increase by ∼67%, ∼42%, ∼38%, and ∼55%, respectively, which is far below what is needed to meet projected demands in 2050. We present detailed maps to identify where rates must be increased to boost crop production and meet rising demands.


Characterizing affective instability in borderline personality disorder.

OBJECTIVE
This study sought to understand affective instability among patients with borderline personality disorder by examining the degree of instability in six affective domains. The authors also examined the subjective intensity with which moods are experienced and the association between instability and intensity of affect.

METHOD
In a group of 152 patients with personality disorders, subjective affective intensity and six dimensions of affective instability were measured. The mean scores for lability and intensity for each affective domain for patients with borderline personality disorder were compared with those of patients with other personality disorders through analyses that controlled for other axis I affective disorders, age, and sex.

RESULTS
Greater lability in terms of anger and anxiety and oscillation between depression and anxiety, but not in terms of oscillation between depression and elation, was associated with borderline personality disorder. Contrary to expectation, the experience of an increase in subjective affective intensity was not more prominent in patients with borderline personality disorder than in those with other personality disorders.

CONCLUSIONS
By applying a finer-grained perspective on affective instability than those of previous personality disorder studies, this study points to patterns of affective experience characteristic of patients with borderline personality disorder.


Proximal Algorithms in Statistics and Machine Learning

In this paper we develop proximal methods for statistical learning. Proximal point algorithms are useful in statistics and machine learning for obtaining optimization solutions for composite functions. Our approach exploits closedform solutions of proximal operators and envelope representations based on the Moreau, Forward-Backward, Douglas-Rachford and Half-Quadratic envelopes. Envelope representations lead to novel proximal algorithms for statistical optimisation of composite objective functions which include both non-smooth and non-convex objectives. We illustrate our methodology with regularized Logistic and Poisson regression and non-convex bridge penalties with a fused lasso norm. We provide a discussion of convergence of non-descent algorithms with acceleration and for non-convex functions. Finally, we provide directions for future research.


Chaotic bat algorithm

Bat algorithm (BA) is a recent metaheuristic optimization algorithm proposed by Yang. In the present study, we have introduced chaos into BA so as to increase its global search mobility for robust global optimization. Detailed studies have been carried out on benchmark problems with different chaotic maps. Here, four different variants of chaotic BA are introduced and thirteen different chaotic maps are vailable online xxx eywords: at algorithm haos etaheuristic lobal optimization utilized for validating each of these four variants. The results show that some variants of chaotic BAs can clearly outperform the standard BA for these benchmarks. © 2013 Elsevier B.V. All rights reserved. . Introduction Many design optimization problems are often highly nonlinar, which can typically have multiple modal optima, and it is thus ery challenging to solve such multimodal problems. To cope with his issue, global optimization algorithms are widely attempted, owever, traditional algorithms may not produce good results, and atest trends are to use new metaheuristic algorithms [1]. Metaeuristic techniques are well-known global optimization methods hat have been successfully applied in many real-world and comlex optimization problems [2,3]. These techniques attempt to imic natural phenomena or social behavior so as to generate etter solutions for optimization problem by using iterations and tochasticity [4]. They also try to use both intensification and iversification to achieve better search performance. Intensificaion typically searches around the current best solutions and selects he best candidate designs, while the diversification process allows he optimizer to explore the search space more efficiently, mostly y randomization [1]. In recent years, several novel metaheuristic algorithms have een proposed for global search. Such algorithms can increase he computational efficiency, solve larger problems, and implePlease cite this article in press as: A.H. Gandomi, X.-S. http://dx.doi.org/10.1016/j.jocs.2013.10.002 ent robust optimization codes [5]. For example, Xin-She Yang [6] ecently developed a promising metaheuristic algorithm, called bat lgorithm (BA). Preliminary studies suggest that the BA can have ∗ Corresponding author. Tel.: +44 2084112351. E-mail addresses: a.h.gandomi@gmail.com, ag72@uakron.edu (A.H. Gandomi), .yang@mdx.ac.uk (X.-S. Yang). 877-7503/$ – see front matter © 2013 Elsevier B.V. All rights reserved. ttp://dx.doi.org/10.1016/j.jocs.2013.10.002 superior performance over genetic algorithms and particle swarm optimization [6], and it can solve real world and engineering optimization problems [7–10]. On the other hand, recent advances in theories and applications of nonlinear dynamics, especially chaos, have drawn more attention in many fields [10]. One of these fields is the applications of chaos in optimization algorithms to replace certain algorithm-dependent parameters [11]. Previously, chaotic sequences have been used to tune parameters in metaheuristic optimization algorithms such as genetic algorithms [12], particle swarm optimization [13], harmony search [14], ant and bee colony optimization [15,16], imperialist competitive algorithm [17], firefly algorithm [18], and simulated annealing [19]. Such a combination of chaos with metaheuristics has shown some promise once the right set of chaotic maps are used. It is still not clear why the use of chaos in an algorithm to replace certain parameters may change the performance, however, empirical studies indeed indicate that chaos can have high-level of mixing capability, and thus it can be expected that when a fixed parameter is replaced by a chaotic map, the solutions generated may have higher mobility and diversity. For this reason, it may be useful to carry out more studies by introducing chaos to other, especially newer, metaheuristic algorithms. Therefore, one of the aims of this paper is to introduce chaos into the standard bat algorithm, and as a result, we propose a chaosbased bat algorithm (CBA). As different chaotic maps may lead to different behavior of the algorithm, we then have a set of chaosYang, Chaotic bat algorithm, J. Comput. Sci. (2013), based bat algorithms. In these algorithms, we use different chaotic systems to replace the parameters in BA. Thus different methods that use chaotic maps as potentially efficient alternatives to pseudorandom sequences have been proposed. In order to evaluate the


A novel bat algorithm with habitat selection and Doppler effect in echoes for optimization

A novel bat algorithm (NBA) is proposed for optimization in this paper, which focuses on further mimicking the bats’ behaviors and improving bat algorithm (BA) in view of biology. The proposed algorithm incorporates the bats’ habitat selection and their self-adaptive compensation for Doppler effect in echoes into the basic BA. The bats’ habitat selection is modeled as the selection between their quantum behaviors and mechanical behaviors. Having considered the bats’ self-adaptive compensation for Doppler effect in echoes and the individual’s difference in the compensation rate, the echolocation characteristics of bats can be further simulated in NBA. A self-adaptive local search strategy is also embedded into NBA. Simulations and comparisons based on twenty benchmark problems and four real-world engineering designs demonstrate the effectiveness, efficiency and stability of NBA compared with the basic BA and some well-known algorithms, and suggest that to improve algorithm based on biological basis should be very efficient. Further research topics are also discussed. 2015 Elsevier Ltd. All rights reserved.


Use of genetic algorithms to solve production and operations management problems: A review

Operations managers and scholars in their search for fast and good solutions to real-world problems have applied genetic algorithms to many problems. While genetic algorithms are promising tools for problem solving, future research will benefit from a review of the problems that have been solved and the designs of the genetic algorithms used to solve them. This paper provides a review of the use of genetic algorithms to solve operations problems. Reviewed papers are classified according to the problems they solve. The basic design of each genetic algorithm is described, the shortcomings of the current research are discussed and directions for future research are suggested.


A swarm optimization algorithm inspired in the behavior of the social-spider

Swarm intelligence is a research field that models the collective behavior in swarms of insects or animals. Several algorithms arising from such models have been proposed to solve a wide range of complex optimization problems. In this paper, a novel swarm algorithm called the Social Spider Optimization (SSO) is proposed for solving optimization tasks. The SSO algorithm is based on the simulation of cooperative behavior of social-spiders. In the proposed algorithm, individuals emulate a group of spiders which interact to each other based on the biological laws of the cooperative colony. The algorithm considers two different search agents (spiders): males and females. Depending on gender, each individual is conducted by a set of different evolutionary operators which mimic different cooperative behaviors that are typically found in the colony. In order to illustrate the proficiency and robustness of the proposed approach, it is compared to other well-known evolutionary methods. The comparison examines several standard benchmark functions that are commonly considered within the literature of evolutionary algorithms. The outcome shows a high performance of the proposed method for searching a global optimum with several benchmark functions.


Bat-Inspired Optimization Approach for the Brushless DC Wheel Motor Problem

This paper presents a metaheuristic algorithm inspired in evolutionary computation and swarm intelligence concepts and fundamentals of echolocation of micro bats. The aim is to optimize the mono and multiobjective optimization problems related to the brushless DC wheel motor problems, which has 5 design parameters and 6 constraints for the mono-objective problem and 2 objectives, 5 design parameters, and 5 constraints for multiobjective version. Furthermore, results are compared with other optimization approaches proposed in the recent literature, showing the feasibility of this newly introduced technique to high nonlinear problems in electromagnetics.


On-the-Job Learning with Bayesian Decision Theory

Our goal is to deploy a high-accuracy system starting with zero training examples. We consider an on-the-job setting, where as inputs arrive, we use real-time crowdsourcing to resolve uncertainty where needed and output our prediction when confident. As the model improves over time, the reliance on crowdsourcing queries decreases. We cast our setting as a stochastic game based on Bayesian decision theory, which allows us to balance latency, cost, and accuracy objectives in a principled way. Computing the optimal policy is intractable, so we develop an approximation based on Monte Carlo Tree Search. We tested our approach on three datasets—named-entity recognition, sentiment classification, and image classification. On the NER task we obtained more than an order of magnitude reduction in cost compared to full human annotation, while boosting performance relative to the expert provided labels. We also achieve a 8% F1 improvement over having a single human label the whole set, and a 28% F1 improvement over online learning. “Poor is the pupil who does not surpass his master.” – Leonardo da Vinci


Rethinking serializable multiversion concurrency control

Multi-versioned database systems have the potential to significantly increase the amount of concurrency in transaction processing because they can avoid read-write conflicts. Unfortunately, the increase in concurrency usually comes at the cost of transaction serializability. If a database user requests full serializability, modern multi-versioned systems significantly constrain read-write concurrency among conflicting transactions and employ expensive synchronization patterns in their design. In main-memory multi-core settings, these additional constraints are so burdensome that multiversioned systems are often significantly outperformed by singleversion systems. We propose BOHM, a new concurrency control protocol for mainmemory multi-versioned database systems. BOHM guarantees serializable execution while ensuring that reads never block writes. In addition, BOHM does not require reads to perform any bookkeeping whatsoever, thereby avoiding the overhead of tracking reads via contended writes to shared memory. This leads to excellent scalability and performance in multi-core settings. BOHM has all the above characteristics without performing validation based concurrency control. Instead, it is pessimistic, and is therefore not prone to excessive aborts in the presence of contention. An experimental evaluation shows that BOHM performs well in both high contention and low contention settings, and is able to dramatically outperform state-of-the-art multi-versioned systems despite maintaining the full set of serializability guarantees.


Overview of rotary steerable system and its control methods

Rotary steerable system (RSS) is a new technique of drilling technology adopted in directional drilling. Compared with conventional directional drilling, RSS has the ability to improve the drilling efficiency, reduce the drilling risk and decrease the drilling cost at the same time. This paper summaries the fundamental structure, classification, directional principle and development process of RSS; and gives detail analysis of the control methods in RSS. According to these summaries and analyses, the advantages and disadvantages of RSS at present are given, and some key suggestions about this technique and the related control methods are also proposed in this paper.


Commonsense Reasoning Based on Betweenness and Direction in Distributional Models

Several recent approaches use distributional similarity for making symbolic reasoning more flexible. While an important step in the right direction, the use of similarity has a number of inherent limitations. We argue that similarity-based reasoning should be complemented with commonsense reasoning patterns such as interpolation and a fortiori inference. We show how the required background knowledge for these inference patterns can be obtained from distributional models.


A Survey on Mobile Banking Applications and the Adopted Models

Many banks around the world are starting to offer banking services through mobile phones. Therefore, many studies have been proposed to help bankers to increase the number of customers. Despite the effort that has been founded in the state of the art, the adoption rate of mobile banking application has not reached the expected amount yet. Therefore, the aim of this study is to analyse the most well-known and accepted models to provide a comprehensive understanding of their impacts toward the adoption of mobile banking applications. Furthermore, this study aims also at exploring the most effected factors that have been used to influence the adoption behaviour intention of mobile banking applications. As a result of this survey study, some critical recommendations were stated


A review of Augmented Reality and its application in context aware library system

Augmented Reality has revolutionized the way of looking at actuality. The colossal developments in this area have led to visualizations beyond desktop. Unreal objects can be augmented on real surfaces. Augmented Reality has converged the knowledge of computer vision, virtual reality, image-processing, human-to-computer interaction and more such areas. It has found applications in various expanses like Games, Entertainment, Military, Navigation and many more. One such application could be in Library Management System. Earlier systems for book tracking involved immobile computers that only had the shelf numbers nested with book id to locate a book. A borrower needed to ask the Librarian/support staff to look up the shelf number from the system so as to find a book or simply do it manually by himself by searching each shelf. The application of Augmented Reality in Library Administration is useful in automating the proximity location tracking of a book and providing alternatives to the borrower in the books context. The borrower is navigated through the library till he/she reaches the shelf where the book is kept. The spine of the book serves as the index of the required book and contextual information is augmented once the book is located. Thus Augmented Reality can prove immensely helpful for Library Administration.


A new force-feedback arm exoskeleton for haptic interaction in virtual environments

The paper presents the mechanical design of the L-EXOS, a new exoskeleton for the human arm. The exoskeleton is a tendon driven wearable haptic interface with 5 dof 4 actuated ones, and is characterized by a workspace very close to the one of the human arm. The design has been optimized to obtain a solution with reduced mass and high stiffness, by employing special mechanical components and carbon fiber structural parts. The devised exoskeleton is very effective for simulating the touch by hand of large objects or the manipulation within the whole workspace of the arm. The main features of the first prototype that has been developed at PERCRO are presented, together with an indication of the achieved and tested performance.


Issues and Challenges in Scrum Implementation

The aim of this research paper is to bring the challenges and issues in Scrum implementation to light and proposing solutions for these. For this, a survey is conducted in two companies named Digital Prodigy Limited (DPL) and Bentley Systems Pakistan. Participants include experienced and inexperienced scrum personals from both companies. The analysis of the survey results exposed several issues that affect scrum implementation directly or indirectly and resulting in violation of Scrum rules. Quality items pileup, module integration issues, code quality, disruption in team work, mature vs. immature scrum, sprint duration, lack of Scrum training , release process, backlog management, no technical practices, multiple teams, metrics, risk management, documentation and over idealistic are some examples of these issues. During this study, it has been observed that proper training of Scrum can eliminate half of the issues such as, disruption in team work, immature Scrum, sprint duration and backlog management. At the end, suggestions to address these issues are also provided.


Investigating the global trend of RF power amplifiers with the arrival of 5G

To satisfy the continuously increasing demand for high data rates and mobility required by new wireless applications, the 5G has gained much attention recently. Radio frequency power amplifiers (RF-PAs), as one of critical components of 5G transmitter system, are becoming a hot issue. In this paper, the statistical analysis on RF PA papers shows the research on RF-PAs in Asia-Pacific and cooperation between different affiliations and countries are gradually becoming more prominent, showing the globalization trend of RF PA research and the 5G technologies. The decreased research cycle of RF PA shows the processes of research on PA and 5G technologies are speeding up. Some promising RF-PA technologies for 5G wireless communication system are also been discussed.


Grade Prediction with Temporal Course-wise Influence

There is a critical need to develop new educational technology applications that analyze the data collected by universities to ensure that students graduate in a timely fashion (4 to 6 years); and they are well prepared for jobs in their respective fields of study. In this paper, we present a novel approach for analyzing historical educational records from a large, public university to perform next-term grade prediction; i.e., to estimate the grades that a student will get in a course that he/she will enroll in the next term. Accurate next-term grade prediction holds the promise for better student degree planning, personalized advising and automated interventions to ensure that students stay on track in their chosen degree program and graduate on time. We present a factorization-based approach called Matrix Factorization with Temporal Course-wise Influence that incorporates course-wise influence effects and temporal effects for grade prediction. In this model, students and courses are represented in a latent “knowledge” space. The grade of a student on a course is modeled as the similarity of their latent representation in the “knowledge” space. Course-wise influence is considered as an additional factor in the grade prediction. Our experimental results show that the proposed method outperforms several baseline approaches and infer meaningful patterns between pairs of courses within academic programs.


The fragment assembly string graph

We present a concept and formalism, the string graph, which represents all that is inferable about a DNA sequence from a collection of shotgun sequencing reads collected from it. We give time and space efficient algorithms for constructing a string graph given the collection of overlaps between the reads and, in particular, present a novel linear expected time algorithm for transitive reduction in this context. The result demonstrates that the decomposition of reads into kmers employed in the de Bruijn graph approach described earlier is not essential, and exposes its close connection to the unitig approach we developed at Celera. This paper is a preliminary piece giving the basic algorithm and results that demonstrate the efficiency and scalability of the method. These ideas are being used to build a next-generation whole genome assembler called BOA (Berkeley Open Assembler) that will easily scale to mammalian genomes.


Fusion of laser and monocular camera data in object grid maps for vehicle environment perception

Occupancy grid maps provide a reliable vehicle environmental model and usually process data from range finding sensors. Object grid maps additionally contain information about the classes of objects, which is crucial for applications like autonomous driving. Unfortunately, they lack the precision of occupancy grid maps, since they mostly process classification results from camera data by projecting the corresponding images onto the ground plane. This paper proposes a modular framework to create precise object grid maps. The presented algorithm creates classical occupancy grid maps and object grid maps. In a combination step, it transforms both maps into the same frame of discernment based on the Dempster-Shafer theory of evidence. This allows fusing the maps to one object grid map, which contains valuable object information and at the same time benefits from the precision of the occupancy grid map.


Semantic Representation

In recent years, there has been renewed interest in the NLP community in genuine language understanding and dialogue. Thus the long-standing issue of how the semantic content of language should be represented is reentering the communal discussion. This paper provides a brief “opinionated survey” of broadcoverage semantic representation (SR). It suggests multiple desiderata for such representations, and then outlines more than a dozen approaches to SR–some longstanding, and some more recent, providing quick characterizations, pros, cons, and some comments on imple-


A 0.002-mm$^{2}$  6.4-mW 10-Gb/s Full-Rate Direct DFE Receiver With 59.6% Horizontal Eye Opening Under 23.3-dB Channel Loss at Nyquist Frequency

This paper reports a full-rate direct decision-feedback-equalization (DFE) receiver with circuit techniques to widen the data eye opening with competitive power and area efficiencies. Specifically, a current-reuse active-inductor (AI) linear equalizer is merged into a clocked-one-tap DFE core for joint-elimination of pre-cursor and long-tail post-cursors. Unlike the passive-inductor designs that are bulky and untunable, the AI linear equalizer offers orthogonally tunable low- and high-frequency de-emphasis. The clocked-one-tap DFE resolves the first post-cursor via return-to-zero feedback data patterns for sharper data transition (i.e., horizontal eye opening), and is followed by a D-flip-flop slicer to maximize the data height (i.e., vertical eye opening). A 10-Gb/s DFE receiver was fabricated in 65-nm CMOS. Measured over an 84-cm printed circuit board differential trace with 23.3-dB channel loss at Nyquist frequency (5 GHz), the achieved figure-of-merit is 0.027 pJ/bit/dB (power consumption/date rate/channel loss). At 10-12 bit error rate under 27-1 pseudorandom binary sequence, the horizontal and vertical eye opening are 59.6% and 189.3 mV, respectively. The die size is 0.002 mm2.