Automatic Keyword Extraction for Text Summarization in Multi-document e-Newspapers Articles

Summarization is the way towards lessening the content of a text file to make it brief that holds all the critical purposes in the content of original text file. In the process of extractive summarization, one extracts only those sentences which are the most relevant sentences in the text document and that conveys the moral of the content. The extractive summarization techniques usually revolve around the idea of discovering most relevant and frequent keywords and then extract the sentences based on those keywords. Manual extraction or explanation of relevant keywords are a dreary procedure overflowing with errors including loads of manual exertion and time. In this paper, we proposed a hybrid approach to extract keyword automatically for multi-document text summarization in enewspaper articles. The performance of the proposed approach is compared with three additional keyword extraction techniques namely, term frequency-inverse document frequency (TF-IDF), term frequency-adaptive inverse document frequency (TF-AIDF), and a number of false alarm (NFA) for automatic keyword extraction and summarization in e-newspapers articles for better analysis. Finally, we showed that our proposed techniques had been outperformed over other techniques for automatic keyword extraction and summarization.


Software-Defined “Hardware” Infrastructures: A Survey on Enabling Technologies and Open Research Directions

This paper provides an overview of software-defined “hardware” infrastructures (SDHI). SDHI builds upon the concept of hardware (HW) resource disaggregation. HW resource disaggregation breaks today’s physical server-oriented model where the use of a physical resource (e.g., processor or memory) is constrained to a physical server’s chassis. SDHI extends the definition of of software-defined infrastructures (SDI) and brings greater modularity, flexibility, and extensibility to cloud infrastructures, thus allowing cloud operators to employ resources more efficiently and allowing applications not to be bounded by the physical infrastructure’s layout. This paper aims to be an initial introduction to SDHI and its associated technological advancements. This paper starts with an overview of the cloud domain and puts into perspective some of the most prominent efforts in the area. Then, it presents a set of differentiating use-cases that SDHI enables. Next, we state the fundamentals behind SDI and SDHI, and elaborate why SDHI is of great interest today. Moreover, it provides an overview of the functional architecture of a cloud built on SDHI, exploring how the impact of this transformation goes far beyond the cloud infrastructure level in its impact on platforms, execution environments, and applications. Finally, an in-depth assessment is made of the technologies behind SDHI, the impact of these technologies, and the associated challenges and potential future directions of SDHI.


A Study On Unmanned Vehicles and Cyber Security

During the past years Unmanned Aerial Vehicle (UAV) usage has grown in military and civilian fields. Every year, emergency response operations are more and more dependent of these unmanned vehicles. Lacking direct human interference, the correction of onboard errors and security breaches is a growing concern in unmanned vehicles. One of the concerns raised by first responders using unmanned vehicles is the security and privacy of the victims they are rescuing. Video channels used for the control of unmanned vehicles are of particular importance, having great susceptibility to hijack, jamming and spoofing attacks. It is assumed the video feeds are not protected by any type of encryption and are therefore vulnerable to hacking and/or spoofing. This summer we have conducted a preliminary survey of the security vulnerabilities and their implications on the operation of unmanned vehicles from a network security perspective.


A stochastic data discrimination based autoencoder approach for network anomaly detection

Machine learning based network anomaly detection methods, which are already effective defense mechanisms against known network intrusion attacks, have also proven themselves to be more successful on the detection of zero-day attacks compared to other types of detection methods. Therefore, research on network anomaly detection using deep learning is getting more attention constantly. In this study we created an anomaly detection model based on a deterministic autoencoder, which discriminates normal and abnormal data by using our proposed stochastic threshold determination approach. We tested our proposed anomaly detection model on the NSL-KDD's test dataset KDDTest+ and obtained an accuracy of 88.28%. The experimental results show that our proposed anomaly detection model can perform almost the same as the most successful and up-to-date machine learning based anomaly detection models in the literature.


Electrophysiology in the age of light

Electrophysiology, the 'gold standard' for investigating neuronal signalling, is being challenged by a new generation of optical probes. Together with new forms of microscopy, these probes allow us to measure and control neuronal signals with spatial resolution and genetic specificity that already greatly surpass those of electrophysiology. We predict that the photon will progressively replace the electron for probing neuronal function, particularly for targeted stimulation and silencing of neuronal populations. Although electrophysiological characterization of channels, cells and neural circuits will remain necessary, new combinations of electrophysiology and imaging should lead to transformational discoveries in neuroscience.


Tagging Ingush - Language Technology For Low-Resource Languages Using Resources From Linguistic Field Work

This paper presents on-going work on creating NLP tools for under-resourced languages from very sparse training data coming from linguistic field work. In this work, we focus on Ingush, a Nakh-Daghestanian language spoken by about 300,000 people in the Russian republics Ingushetia and Chechnya. We present work on morphosyntactic taggers trained on transcribed and linguistically analyzed recordings and dependency parsers using English glosses to project annotation for creating synthetic treebanks. Our preliminary results are promising, supporting the goal of bootstrapping efficient NLP tools with limited or no task-specific annotated data resources available.


Vertex-Centric Graph Processing: The Good, the Bad, and the Ugly

We study distributed graph algorithms that adopt an iterati ve vertexcentric framework for graph processing, popularized by Goo gle’s Pregel system. Since then, there are several attempts to imp lement many graph algorithms in a vertex-centric framework, a s well as efforts to design optimization techniques for improving the efficiency. However, to the best of our knowledge, there has not been any systematic study to compare these vertex-centric i mplementations with their sequential counterparts. Our paper a dd esses this gap in two ways. (1) We analyze the computational complexity of such implementations with the notion of time-pro cessor product, and benchmark several vertex-centric graph algor ithms whether they perform more work with respect to their best-kn ow sequential solutions. (2) Employing the concept of balance d practical Pregel algorithms, we study if these implementations suffer from imbalanced workload and large number of iterations. Ou r findings illustrate that with the exception of Euler tour tre e algorithm, all other algorithms either perform asymptotically more work than their best-known sequential approach, or suffer from i mbalanced workload/ large number of iterations, or even both. We also emphasize on graph algorithms that are fundamentally diffic ult to be expressed in vertex-centric frameworks, and conclude by discussing the road ahead for distributed graph processing.


Vision-Based Classification of Skin Cancer using Deep Learning

This study proposes the use of deep learning algorithms to detect the presence of skin cancer, specifically melanoma, from images of skin lesions taken by a standard camera. Skin cancer is the most prevalent form of cancer in the US where 3.3 million people get treated each year. The 5-year survival rate of melanoma is 98% when detected and treated early yet over 10,000 people are lost each year due mostly to late-stage diagnoses [2]. Thus, there is a need to make melanoma screening and diagnoses methods cheaper, quicker, simpler, and more accessible. This study aims to produce an inexpensive and fast computer-vision based machine learning tool that can be used by doctors and patients to track and classify suspicious skin lesions as benign or malignant with adequate accuracy using only a cell phone camera. The data set was trained on 3 separate learning models with increasingly improved classification accuracy. The 3 models included logistic regression, a deep neural network, and a fine-tuned, pre-trained, VGG-16 Convolutional Neural Network (CNN) [7]. Preliminary results show the developed algorithm’s ability to segment moles from images with 70% accuracy and classify skin lesions as melanoma with 78% balanced accuracy using a fine-tuned VGG-16 CNN.


Ceiling continuum arm with extensible pneumatic actuators for desktop workspace

We propose an extensible pneumatic continuum arm that elongates to perform reaching movements and object grasping, and is suspended on the ceiling to prevent interference with human workers in a desktop workspace. The selected actuators with bellows aim to enhance the arm motion capabilities. A single actuator can provide a maximum tension force of 150 N, and the proposed arm has a three-segment structure with a bundle of three actuators per segment. We measured the three-dimensional motion at the arm tip by using an optical motion-capture system. The corresponding results show that the arm can grasp objects with approximate radius of 80 mm and reach any point on the desktop. Furthermore, the maximum elongation ratio is 180%, with length varying between 0.75 m and 2.1 m. Experiments verified that the arm can grasp objects of various sizes and shapes. Moreover, we demonstrate the vertical transportation of objects taking advantage of the arm extensibility. We expect to apply the proposed arm for tasks such as grasping objects, illuminating desktops, and physically interacting with users.


Eventually Returning to Strong Consistency

Eventually and weakly consistent distributed systems have emerged in the past decade as an answer to scalability and availability issues associated with strong consistency semantics, such as linearizability. However, systems offering strong consistency semantics have an advantage over systems based on weaker consistency models, as they are typically much simpler to reason about and are more intuitive to developers, exhibiting more predictable behavior. Therefore, a lot of research and development effort is being invested lately into the re-engineering of strongly consistent distributed systems, as well as into boosting their scalability and performance. This paper overviews and discusses several novel directions in the design and implementation of strongly consistent systems in industries and research domains such as cloud computing, data center networking and blockchain. It also discusses a general trend of returning to strong consistency in distributed systems, when system requirements permit so.


Food Recognition using Fusion of Classifiers based on CNNs

With the arrival of convolutional neural networks, the complex problem of food recognition has experienced an important improvement in recent years. The best results have been obtained using methods based on very deep convolutional ceural cetworks, which show that the deeper the model,the better the classification accuracy will be obtain. However, very deep neural networks may suffer from the overfitting problem. In this paper, we propose a combination of multiple classifiers based on different convolutional models that complement each other and thus, achieve an improvement in performance. The evaluation of our approach is done on two public datasets: Food-101 as a dataset with a wide variety of fine-grained dishes, and Food-11 as a dataset of high-level food categories, where our approach outperforms the independent CNN models.


Implementation of PID controller and pre-filter to control non-linear ball and plate system

In this paper, the authors try to make PID controller with Pre-filter that is implemented at ball and plate system. Ball and plate system will control the position of ball's axis in pixels value by using servo motor as its actuator and webcam as its sensor of position. PID controller with Pre-filter will have a better response than conventional PID controller. Eventhough the response of PID with Pre-filter is slower than conventional PID, the effect of Pre-filter in the system will give the less overshoot response.


Comparing a Scalable SDN Simulation Framework Built on ns-3 and DCE with Existing SDN Simulators and Emulators

As software-defined networking (SDN) grows beyond its original aim to simply separate the control and data network planes, it becomes useful both financially and analytically to provide adequate mechanisms for simulating this new paradigm. A number of simulation/emulation tools for modeling SDN, such as Mininet, are already available. A new, novel framework for providing SDN simulation has been provided in this work using the network simulator ns-3. The ns-3 module Direct Code Execution (DCE) allows real-world network applications to be run within a simulated network topology. This work employs DCE for running the SDN controller library POX and its applications on nodes in a simulated network topology. In this way, real-world controller applications can be completely portable between simulation and actual deployment. This work also describes a user-defined ns-3 application mimicking an SDN switch supporting OpenFlow 1.0 that can interact with real-world controllers. To evaluate its performance, this ns-3 DCE SDN framework is compared against Mininet as well as some other readily available SDN simulation/emulation tools. Metrics such as realtime performance, memory usage, and reliability in terms of packet loss are analyzed across the multiple simulation/emulation tools to gauge how they compare.


Pose optimization in edge distance field for textureless 3D object tracking

This paper presents a monocular model-based 3D tracking approach for textureless objects. Instead of explicitly searching for 3D-2D correspondences as previous methods, which unavoidably generates individual outlier matches, we aim to minimize the holistic distance between the predicted object contour and the query image edges. We propose a method that can directly solve 3D pose parameters in unsegmented edge distance field. We derive the differentials of edge matching distance with respect to the pose parameters, and search the optimal 3D pose parameters using standard gradient-based non-linear optimization techniques. To avoid being trapped in local minima and to deal with potential large inter-frame motions, a particle filtering process with a first order autoregressive state dynamics is exploited. Occlusions are handled by a robust estimator. The effectiveness of our approach is demonstrated using comparative experiments on real image sequences with occlusions, large motions and cluttered backgrounds.


Designing games with a purpose

Data generated as a side effect of game play also solves computational problems and trains AI algorithms.


A web-based IDE for IDP

IDP is a knowledge base system based on first order logic. It is finding its way to a larger public but is still facing practical challenges. Adoption of new languages requires a newcomer-friendly way for users to interact with it. Both an online presence to try to convince potential users to download the system and offline availability to develop larger applications are essential. We developed an IDE which can serve both purposes through the use of web technology. It enables us to provide the user with a modern IDE with relatively little effort.


The Stanford CoreNLP Natural Language Processing Toolkit

We describe the design and use of the Stanford CoreNLP toolkit, an extensible pipeline that provides core natural language analysis. This toolkit is quite widely used, both in the research NLP community and also among commercial and government users of open source NLP technology. We suggest that this follows from a simple, approachable design, straightforward interfaces, the inclusion of robust and good quality analysis components, and not requiring use of a large amount of associated baggage.


Marijuana and Coronary Heart Disease

Marijuana is the most commonly abused drug in the United States.1 It mainly acts on cannabinoid receptors. There are two types of cannabinoid receptors in humans: cannabinoid receptor type 1 (CB1) and cannabinoid receptor type 2 (CB2). CB1 receptor activation is pro-atherogenic, and CB2 receptor activation is largely anti-atherogenic. Marijuana use is also implicated as a trigger for myocardial infarction (MI) in patients with stable coronary artery disease (CAD).


Health of Things Algorithms for Malignancy Level Classification of Lung Nodules

Lung cancer is one of the leading causes of death world wide. Several computer-aided diagnosis systems have been developed to help reduce lung cancer mortality rates. This paper presents a novel structural co-occurrence matrix (SCM)-based approach to classify nodules into malignant or benign nodules and also into their malignancy levels. The SCM technique was applied to extract features from images of nodules and classifying them into malignant or benign nodules and also into their malignancy levels. The computed tomography exams from the lung image database consortium and image database resource initiative datasets provide information concerning nodule positions and their malignancy levels. The SCM was applied on both grayscale and Hounsfield unit images with four filters, to wit, mean, Laplace, Gaussian, and Sobel filters creating eight different configurations. The classification stage used three well-known classifiers: multilayer perceptron, support vector machine, and <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbors algorithm and applied them to two tasks: (<inline-formula> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula>) to classify the nodule images into malignant or benign nodules and (<inline-formula> <tex-math notation="LaTeX">$ii$ </tex-math></inline-formula>) to classify the lung nodules into malignancy levels (1 to 5). The results of this approach were compared to four other feature extraction methods: gray-level co-occurrence matrix, local binary patterns, central moments, and statistical moments. Moreover, the results here were also compared to the results reported in the literature. Our approach outperformed the other methods in both tasks; it achieved 96.7% for both accuracy and F-Score metrics in the first task, and 74.5% accuracy and 53.2% F-Score in the second. These experimental results reveal that the SCM successfully extracted features of the nodules from the images and, therefore may be considered as a promising tool to support medical specialist to make a more precise diagnosis concerning the malignancy of lung nodules.


Maximum Margin Clustering

We propose a new method for clustering based on finding maximum margin hyperplanes through data. By reformulating the problem in terms of the implied equivalence relation matrix, we can pose the problem as a convex integer program. Although this still yields a difficult computational problem, the hard-clustering constraints can be relaxed to a soft-clustering formulation which can be feasibly solved with a semidefinite program. Since our clustering technique only depends on the data through the kernel matrix, we can easily achieve nonlinear clusterings in the same manner as spectral clustering. Experimental results show that our maximum margin clustering technique often obtains more accurate results than conventional clustering methods. The real benefit of our approach, however, is that it leads naturally to a semi-supervised training method for support vector machines. By maximizing the margin simultaneously on labeled and unlabeled training data, we achieve state of the art performance by using a single, integrated learning principle.


A MapReduce Implementation of C 4 . 5 Decision Tree Algorithm

Recent years have witness the development of cloud computing and the big data era, which brings up challenges to traditional decision tree algorithms. First, as the size of dataset becomes extremely big, the process of building a decision tree can be quite time consuming. Second, because the data cannot fit in memory any more, some computation must be moved to the external storage and therefore increases the I/O cost. To this end, we propose to implement a typical decision tree algorithm, C4.5, using MapReduce programming model. Specifically, we transform the traditional algorithm into a series of Map and Reduce procedures. Besides, we design some data structures to minimize the communication cost. We also conduct extensive experiments on a massive dataset. The results indicate that our algorithm exhibits both time efficiency and scalability.


Despite deep reinforcement learning has recently achieved great successes, however in multiagent environments, a number of challenges still remain. Multiagent reinforcement learning (MARL) is commonly considered to suffer from the problem of non-stationary environments and exponentially increasing policy space. It would be even more challenging to learn effective policies in circumstances where the rewards are sparse and delayed over long trajectories. In this paper, we study Hierarchical Deep Multiagent Reinforcement Learning (hierarchical deep MARL) in cooperative multiagent problems with sparse and delayed rewards, where efficient multiagent learning methods are desperately needed. We decompose the original MARL problem into hierarchies and investigate how effective policies can be learned hierarchically in synchronous/asynchronous hierarchical MARL frameworks. Several hierarchical deep MARL architectures, i.e., Ind-hDQN, hCom and hQmix, are introduced for different learning paradigms. Moreover, to alleviate the issues of sparse experiences in high-level learning and non-stationarity in multiagent settings, we propose a new experience replay mechanism, named as Augmented Concurrent Experience Replay (ACER). We empirically demonstrate the effects and efficiency of our approaches in several classic Multiagent Trash Collection tasks, as well as in an extremely challenging team sports game, i.e., Fever Basketball Defense.


The McGill pain questionnaire: from description to measurement.

On the language of pain. By Ronald Melzack, Warren S. Torgerson. Anesthesiology 1971; 34:50-9. Reprinted with permission. The purpose of this study was to develop new approaches to the problem of describing and measuring pain in human subjects. Words used to describe pain were brought together and categorized, and an attempt was made to scale them on a common intensity dimension. The data show that: 1) there are many words in the English language to describe the varieties of pain experience; 2) there is a high level of agreement that the words fall into classes and subclasses that represent particular dimensions or properties of pain experience; 3) substantial portions of the words have approximately the same relative positions on a common intensity scale for people who have widely divergent backgrounds. The word lists provide a basis for a questionnaire to study the effects of anesthetic and analgesic agents on the experience of pain.


Low-Power Near-Threshold 10T SRAM Bit Cells With Enhanced Data-Independent Read Port Leakage for Array Augmentation in 32-nm CMOS

The conventional six-transistor static random access memory (SRAM) cell allows high density and fast differential sensing but suffers from half-select and read-disturb issues. Although the conventional eight-transistor SRAM cell solves the read-disturb issue, it still suffers from low array efficiency due to deterioration of read bit-line (RBL) swing and  $\text{I}_{\mathbf {on}}/\text{I}_{\mathbf {off}}$  ratio with increase in the number of cells per column. Previous approaches to solve these issues have been afflicted by low performance, data-dependent leakage, large area, and high energy per access. Therefore, in this paper, we present three iterations of SRAM bit cells with nMOS-only based read ports aimed to greatly reduce data-dependent read port leakage to enable 1k cells/RBL, improve read performance, and reduce area and power over conventional and 10T cell-based works. We compare the proposed work with other works by recording metrics from the simulation of a 128-kb SRAM constructed with divided-wordline-decoding architecture and a 32-bit word size. Apart from large improvements observed over conventional cells, up to 100-mV improvement in read-access performance, up to 19.8% saving in energy per access, and up to 19.5% saving in the area are also observed over other 10T cells, thereby enlarging the design and application gamut for memory designers in low-power sensors and battery-enabled devices.


Trainable Sentence Planning for Complex Information Presentations in Spoken Dialog Systems

A challenging problem for spoken dialog systems is the design of utterance generation modules that are fast, flexible and general, yet produce high quality output in particular domains. A promising approach is trainable generation, which uses general-purpose linguistic knowledge automatically adapted to the application domain. This paper presents a trainable sentence planner for the MATCH dialog system. We show that trainable sentence planning can produce output comparable to that of MATCH’s template-based generator even for quite complex information presentations.


Complications associated with injectable soft-tissue fillers: a 5-year retrospective review.

IMPORTANCE
Even when administered by experienced hands, injectable soft-tissue fillers can cause various unintended reactions, ranging from minor and self-limited responses to severe complications requiring prompt treatment and close follow-up.

OBJECTIVES
To review the complications associated with injectable soft-tissue filler treatments administered in the Williams Rejuva Center during a 5-year period and to discuss their management.

DESIGN AND SETTING
Retrospective medical record review in a private practice setting.

PARTICIPANTS
Patients receiving injectable soft-tissue fillers and having a treatment-related complication.

INTERVENTIONS
Injectable soft-tissue filler treatments.

MAIN OUTCOME MEASURES
A retrospective medical record review was conducted of patients undergoing treatment with injectable soft-tissue fillers between January 1, 2007, and December 31, 2011, and identified as having a treatment-related complication.

RESULTS
A total of 2089 injectable soft-tissue filler treatments were performed during the study period, including 1047 with hyaluronic acid, 811 with poly-L-lactic acid, and 231 with calcium hydroxylapatite. Fourteen complications were identified. The most common complication was nodule or granuloma formation. Treatment with calcium hydroxylapatite had the highest complication rate.

CONCLUSIONS AND RELEVANCE
Complications are rare following treatment with injectable soft-tissue fillers. Nevertheless, it is important to be aware of the spectrum of potential adverse sequelae and to be comfortable with their proper management.

LEVEL OF EVIDENCE
4.


On Data Clustering Analysis: Scalability, Constraints, and Validation

Clustering is the problem of grouping data based on similarity. While this problem has attracted the attention of many researchers for many years, we are witnessing a resurgence of interest in new clustering techniques. In this paper we discuss some very recent clustering approaches and recount our experience with some of these algorithms. We also present the problem of clustering in the presence of constraints and discuss the issue of clustering validation.


Better managed than memorized? Studying the Impact of Managers on Password Strength and Reuse

Despite their well-known security problems, passwords are still the incumbent authentication method for virtually all online services. To remedy the situation, users are very often referred to password managers as a solution to the password reuse and weakness problems. However, to date the actual impact of password managers on password strength and reuse has not been studied systematically. We provide the first large-scale study of the password managers’ influence on users’ real-life passwords. By combining qualitative data on users’ password creation and management strategies, collected from 476 participants of an online survey, with quantitative data (incl. password metrics and entry methods) collected in situ with a browser plugin from 170 users, we were able to gain a more complete picture of the factors that influence our participants’ password strength and reuse. Our approach allows us to quantify for the first time that password managers indeed influence the password security, however, whether this influence is beneficial or aggravating existing problems depends on the users’ strategies and how well the manager supports the users’ password management right from the time of password creation. Given our results, we think research should further investigate how managers can better support users’ password strategies in order to improve password security as well as stop aggravating the existing problems.


Advanced Beamformers for Cochlear Implant Users: Acute Measurement of Speech Perception in Challenging Listening Conditions

OBJECTIVE
To investigate the performance of monaural and binaural beamforming technology with an additional noise reduction algorithm, in cochlear implant recipients.

METHOD
This experimental study was conducted as a single subject repeated measures design within a large German cochlear implant centre. Twelve experienced users of an Advanced Bionics HiRes90K or CII implant with a Harmony speech processor were enrolled. The cochlear implant processor of each subject was connected to one of two bilaterally placed state-of-the-art hearing aids (Phonak Ambra) providing three alternative directional processing options: an omnidirectional setting, an adaptive monaural beamformer, and a binaural beamformer. A further noise reduction algorithm (ClearVoice) was applied to the signal on the cochlear implant processor itself. The speech signal was presented from 0° and speech shaped noise presented from loudspeakers placed at ±70°, ±135° and 180°. The Oldenburg sentence test was used to determine the signal-to-noise ratio at which subjects scored 50% correct.

RESULTS
Both the adaptive and binaural beamformer were significantly better than the omnidirectional condition (5.3 dB±1.2 dB and 7.1 dB±1.6 dB (p<0.001) respectively). The best score was achieved with the binaural beamformer in combination with the ClearVoice noise reduction algorithm, with a significant improvement in SRT of 7.9 dB±2.4 dB (p<0.001) over the omnidirectional alone condition.

CONCLUSIONS
The study showed that the binaural beamformer implemented in the Phonak Ambra hearing aid could be used in conjunction with a Harmony speech processor to produce substantial average improvements in SRT of 7.1 dB. The monaural, adaptive beamformer provided an averaged SRT improvement of 5.3 dB.


Robust ground plane tracking in cluttered environments from egocentric stereo vision

Estimating the ground plane is often one of the first steps in geometric reasoning processes as it offers easily accessible context knowledge. Especially unconstrained platforms that capture video from egocentric viewpoints can benefit from such knowledge in various ways. A key requirement here is keeping orientation, which can be greatly achieved by keeping track of the ground. We present an approach to keep track of the ground plane in cluttered inner-urban environments using stereo vision in real-time. We fuse a planar model fit in low-resolution disparity data with the direction of the vertical vanishing point. Our experiments show how this effectively decreases the error of plane attitude estimation compared to classic least-squares fitting and allows to track the plane with camera configurations in which the ground is not visible. We evaluate the approach using ground-truth from an inertial measurement unit and demonstrate long-term stability on a dataset of challenging inner city scenes.


Global refinement of random forest

Random forest is well known as one of the best learning methods. In spite of its great success, it also has certain drawbacks: the heuristic learning rule does not effectively minimize the global training loss; the model size is usually too large for many real applications. To address the issues, we propose two techniques, global refinement and global pruning, to improve a pre-trained random forest. The proposed global refinement jointly relearns the leaf nodes of all trees under a global objective function so that the complementary information between multiple trees is well exploited. In this way, the fitting power of the forest is significantly enhanced. The global pruning is developed to reduce the model size as well as the over-fitting risk. The refined model has better performance and smaller storage cost, as verified in extensive experiments.


3D geo-database research: Retrospective and future directions

3D geo-database research is a promising field to support challenging applications such as 3D urban planning, environmental monitoring, infrastructure management, and early warning or disaster management and response. In these fields, interdisciplinary research in GIScience and related fields is needed to support the modelling, analysis, management, and integration of large geo-referenced data sets, which describe human activities and geophysical phenomena. Geo-databases may serve as platforms to integrate 2D maps, 3D geo-scientific models, and other geo-referenced data. However, current geo-databases do not provide sufficient 3D data modelling and data handling techniques. New 3D geo-databases are needed to handle surface and volume models. This article first presents a 25-year retrospective of geo-database research. Data modelling, standards, and indexing of geo-data are discussed in detail. New directions for the development of 3D geo-databases to open new fields for interdisciplinary research are addressed. Two scenarios in the fields of early warning and emergency response demonstrate the combined management of human and geophysical phenomena. The article concludes with a critical outlook on open research problems. & 2011 Elsevier Ltd. All rights reserved.


How Online Basic Psychological Need Satisfaction Influences Self-Disclosure Online among Chinese Adolescents: Moderated Mediation Effect of Exhibitionism and Narcissism

Under the basic framework of self-determination theory, the present study examined a moderated mediation model in which exhibitionism mediated the relationship between online basic psychological need satisfaction and self-disclosure on the mobile Internet, and this mediation effect was moderated by narcissism. A total of 296 Chinese middle school students participated in this research. The results revealed that exhibitionism fully mediated the association between online competence need satisfaction and self-disclosure on the mobile net, and partly mediated the association between online relatedness need satisfaction and self-disclosure on the mobile net. The mediating path from online basic psychological need satisfaction (competence and relatedness) to exhibitionism was moderated by narcissism. Compared to the low level of narcissism, online competence need satisfaction had a stronger predictive power on exhibitionism under the high level of narcissism condition. In contrast, online relatedness need satisfaction had a weaker predictive power on exhibitionism.


Small-footprint Keyword Spotting Using Deep Neural Network and Connectionist Temporal Classifier

Mainly for the sake of solving the lack of keyword-specific data, we propose one Keyword Spotting (KWS) system using Deep Neural Network (DNN) and Connectionist Temporal Classifier (CTC) on power-constrained small-footprint mobile devices, taking full advantage of general corpus from continuous speech recognition which is of great amount. DNN is to directly predict the posterior of phoneme units of any personally customized key-phrase, and CTC to produce a confidence score of the given phoneme sequence as responsive decision-making mechanism. The CTC-KWS has competitive performance in comparison with purely DNN based keyword specific KWS, but not increasing any computational complexity.


Attacking state space explosion problem in model checking embedded TV software

The features of current TV sets is increasing rapidly resulting in more complicated embedded TV software that is also harder to test and verify. Using model checking in verification of embedded software is a widely accepted practice even though it is seriously affected by the exponential increase in the number of states being produced during the verification process. Using fully non-deterministic user agent models is one of the reasons that can result in a drastic increase in the number of states being produced. In order to shrink the state space being observed during the model checking process of TV software, a method is proposed that rely on using previous test logs to generate partly nondeterministic user agent model. Results show that by using partly non-deterministic user agents, the verification time of certain safety and liveness properties can be significantly decreased.


Bridging the Gap between Business Strategy and Software Development

In software-intensive organizations, an organizational management system will not guarantee organizational success unless the business strategy can be translated into a set of operational software goals. The Goal Question Metric (GQM) approach has proven itself useful in a variety of industrial settings to support quantitative software project management. However, it does not address linking software measurement goals to higher-level goals of the organization in which the software is being developed. This linkage is important, as it helps to justify software measurement efforts and allows measurement data to contribute to higher-level decisions. In this paper, we propose a GQMStrategies® measurement approach that builds on the GQM approach to plan and implement software measurement. GQMStrategies provides mechanisms for explicitly linking software measurement goals to higher-level goals for the software organization, and further to goals and strategies at the level of the entire business. An example application of the proposed method is illustrated in the context of an example measurement initiative.


Increased social fear and decreased fear of objects in monkeys with neonatal amygdala lesions

The amygdala has been implicated in the mediation of emotional and species-specific social behavior (Kling et al., 1970; Kling and Brothers, 1992; Kluver and Bucy, 1939; Rosvold et al., 1954). Humans with bilateral amygdala damage are impaired in judging negative emotion in facial expressions and making accurate judgements of trustworthiness (Adolphs et al., 1998, 1994). Amygdala dysfunction has also been implicated in human disorders ranging from social anxiety (Birbaumer et al., 1998) to depression (Drevets, 2000) to autism (Bachevalier, 1994; Baron-Cohen et al., 2000; Bauman and Kemper, 1993). We produced selective amygdala lesions in 2-week-old macaque monkeys who were returned to their mothers for rearing. At 6-8 months of age, the lesioned animals demonstrated less fear of novel objects such as rubber snakes than age-matched controls. However, they displayed substantially more fear behavior than controls during dyadic social interactions. These results suggest that neonatal amygdala lesions dissociate a system that mediates social fear from one that mediates fear of inanimate objects. Furthermore, much of the age-appropriate repertoire of social behavior was present in amygdala-lesioned infants indicating that these lesions do not produce autistic-like behavior in monkeys. Finally, amygdala lesions early in development have different effects on social behavior than lesions produced in adulthood.


Who Do I Look Like? Determining Parent-Offspring Resemblance via Gated Autoencoders

Recent years have seen a major push for face recognition technology due to the large expansion of image sharing on social networks. In this paper, we consider the difficult task of determining parent-offspring resemblance using deep learning to answer the question "Who do I look like?" Although humans can perform this job at a rate higher than chance, it is not clear how they do it [2]. However, recent studies in anthropology [24] have determined which features tend to be the most discriminative. In this study, we aim to not only create an accurate system for resemblance detection, but bridge the gap between studies in anthropology with computer vision techniques. Further, we aim to answer two key questions: 1) Do offspring resemble their parents? and 2) Do offspring resemble one parent more than the other? We propose an algorithm that fuses the features and metrics discovered via gated autoencoders with a discriminative neural network layer that learns the optimal, or what we call genetic, features to delineate parent-offspring relationships. We further analyze the correlation between our automatically detected features and those found in anthropological studies. Meanwhile, our method outperforms the state-of-the-art in kinship verification by 3-10% depending on the relationship using specific (father-son, mother-daughter, etc.) and generic models.


Performance of H.264, H.265, VP8 and VP9 Compression Standards for High Resolutions

Recently multimedia services increase, especially in video domain, leads to requirements of quality assessment. The main factors effected that quality are compression technology and transmission link. This paper presents a coding efficiency comparison of the well-known video compression standards Advanced Video Coding (H.264/AVC), High-Efficiency Video Coding (H.265/HEVC), VP8 and VP9 using objective metrics. An extensive range of bitrates from low to high bitrates was selected and encoded. The evaluation was done for four types of sequences in high resolutions depending on content. All four video sequences were encoded by using same encoding configurations for all the examined video codecs. The results showed that the quality of all compression standards rises logarithmically with increasing bitrate - in low bitrates the quality grows faster than in high bitrates. The efficiency of the new compression standards outperforms the older ones. The efficiency of VP8 compression standard outperforms the H.264/AVC compression standard. The efficiency of H.265/HEVC and VP9 compression standards is almost the same. The results also showed that VP8 codec cannot allow encoding by low bitrates. According to the results can be also said that the efficiency of the new compression standards decreases with the resolution. The results showed that the effectiveness of compression depends on the type of test sequence.


Detection of Ellipses by a Modified Hough Transformation

The Hough transformation can detect straight lines in an edge-enhanced picture, however its extension to recover ellipses requires too long a computing time. This correspondence proposes a modified method which utilizes two properties of an ellipse in such a way that it iteratively searches for clusters in two different parameter spaces to find almost complete ellipses, then evaluates their parameters by the least mean squares method.


Is dark silicon useful? Harnessing the four horsemen of the coming dark silicon apocalypse

Due to the breakdown of Dennardian scaling, the percentage of a silicon chip that can switch at full frequency is dropping exponentially with each process generation. This utilization wall forces designers to ensure that, at any point in time, large fractions of their chips are effectively dark or dim silicon, i.e., either idle or significantly underclocked.
 As exponentially larger fractions of a chip's transistors become dark, silicon area becomes an exponentially cheaper resource relative to power and energy consumption. This shift is driving a new class of architectural techniques that "spend" area to "buy" energy efficiency. All of these techniques seek to introduce new forms of heterogeneity into the computational stack. We envision that ultimately we will see widespread use of specialized architectures that leverage these techniques in order to attain orders-of-magnitude improvements in energy efficiency.
 However, many of these approaches also suffer from massive increases in complexity. As a result, we will need to look towards developing pervasively specialized architectures that insulate the hardware designer and the programmer from the underlying complexity of such systems. In this paper, I discuss four key approaches--the four horsemen--that have emerged as top contenders for thriving in the dark silicon age. Each class carries with its virtues deep-seated restrictions that requires a careful understanding of the underlying tradeoffs and benefits.


Distributed Multi-Robot Localization

This paper presents a new approach to the cooperative localization problem, namely distributed multi-robot localization. A group of M robots is viewed as a single system composed of robots that carry, in general, di erent sensors and have di erent positioning capabilities. A single Kalman lter is formulated to estimate the position and orientation of all the members of the group. This centralized schema is capable of fusing information provided by the sensors distributed on the individual robots while accommodating independencies and interdependencies among the collected data. In order to allow for distributed processing, the equations of the centralized Kalman lter are treated so that this lter can be decomposed into M modi ed Kalman lters each running on a separate robot. The distributed localization algorithm is applied to a group of 3 robots and the improvement in localization accuracy is presented.


Human model evaluation in interactive supervised learning

Model evaluation plays a special role in interactive machine learning (IML) systems in which users rely on their assessment of a model's performance in order to determine how to improve it. A better understanding of what model criteria are important to users can therefore inform the design of user interfaces for model evaluation as well as the choice and design of learning algorithms. We present work studying the evaluation practices of end users interactively building supervised learning systems for real-world gesture analysis problems. We examine users' model evaluation criteria, which span conventionally relevant criteria such as accuracy and cost, as well as novel criteria such as unexpectedness. We observed that users employed evaluation techniques---including cross-validation and direct, real-time evaluation---not only to make relevant judgments of algorithms' performance and interactively improve the trained models, but also to learn to provide more effective training data. Furthermore, we observed that evaluation taught users about what types of models were easy or possible to build, and users sometimes used this information to modify the learning problem definition or their plans for using the trained models in practice. We discuss the implications of these findings with regard to the role of generalization accuracy in IML, the design of new algorithms and interfaces, and the scope of potential benefits of incorporating human interaction in the design of supervised learning systems.


Teachable robots: Understanding human teaching behavior to build more effective robot learners

While Reinforcement Learning (RL) is not traditionally designed for interactive supervisory input from a human teacher, several works in both robot and software agents have adapted it for human input by letting a human trainer control the reward signal. In this work, we experimentally examine the assumption underlying these works, namely that the human-given reward is compatible with the traditional RL reward signal. We describe an experimental platform with a simulated RL robot and present an analysis of real-time human teaching behavior found in a study in which untrained subjects taught the robot to perform a new task. We report three main observations on how people administer feedback when teaching a robot a task through Reinforcement Learning: (a) they use the reward channel not only for feedback, but also for future-directed guidance; (b) they have a positive bias to their feedback — possibly using the signal as a motivational channel; and (c) they change their behavior as they develop a mental model of the robotic learner. Given this, we made specific modifications to the simulated RL robot, and analyzed and evaluated its learning behavior in four additional experiments with human trainers. We report significant improvements on several learning measures. This work demonstrates the importance of understanding the human-teacher/robot-learner partnership in order to design algorithms that support how people want to teach while simultaneously improving the robot’s learning behavior.


Natural methods for robot task learning: instructive demonstrations, generalization and practice

Among humans, teaching various tasks is a complex process which relies on multiple means for interaction and learning, both on the part of the teacher and of the learner. Used together, these modalities lead to effective teaching and learning approaches, respectively. In the robotics domain, task teaching has been mostly addressed by using only one or very few of these interactions. In this paper we present an approach for teaching robots that relies on the key features and the general approach people use when teaching each other: first give a demonstration, then allow the learner to refine the acquired capabilities by practicing under the teacher's supervision, involving a small number of trials. Depending on the quality of the learned task, the teacher may either demonstrate it again or provide specific feedback during the learner's practice trial for further refinement. Also, as people do during demonstrations, the teacher can provide simple instructions and informative cues, increasing the performance of learning. Thus, instructive demonstrations, generalization over multiple demonstrations and practice trials are essential features for a successful human-robot teaching approach. We implemented a system that enables all these capabilities and validated these concepts with a Pioneer 2DX mobile robot learning tasks from multiple demonstrations and teacher feedback.


XWand: UI for intelligent spaces

The XWand is a novel wireless sensor package that enables styles of natural interaction with intelligent environments. For example, a user may point the wand at a device and control it using simple gestures. The XWand system leverages the intelligence of the environment to best determine the user's intention. We detail the hardware device, signal processing algorithms to recover position and orientation, gesture recognition techniques, a multimodal (wand and speech) computational architecture and a preliminary user study examining pointing performance under conditions of tracking availability and audio feedback.


GloveTalkII: An Adaptive Gesture-to-Formant Interface

Glove-TaikII is a system which translates hand gestures-· to speech through an adaptive interface. Hand gestures are mapped continuously to 10 control parameters of a parallel formant speech synthesizer. The mapping allows the hand to act as an artificial vocal tract that produces speech in real time. This gives an unlimited vocabulary, multiple languages in addition to direct control of fundamental frequency and volume. Currently, the best version of Glove-TaikII uses several input devices (including a Cyberglove, a ContactGlove, a polhemus sensor, and a foot-pedal), a parallel formant speech synthesizer and 3 neural networks. The gestureto-speech task is divided into vowel and consonant production by using a gating network to weight the outputs of a vowel and a consonant neural network. The gating network and the consonant network are trained with examples from the user. The vowel network implements a fixed, user-defined relationship between hand-position and vowel sound and does not require any training examples from the user. Volume, fundamental frequency and stop consonants are produced with a fixed mapping from the input devices. One subject has trained for about 100 hours to speak intelligibly with Glove-TalkII. He passed through eight distinct stages while learning to speak. He speaks slowly with speech quality similar to a text-to-speech synthesizer but with far more natural-sounding pitch variations.


Collecting and analyzing qualitative data for system dynamics: methods and models

System dynamics depends heavily upon quantitative data to generate feedback models. Qualitative data and their analysis also have a central role to play at all levels of the modeling process. Although the classic literature on system dynamics strongly supports this argument, the protocols to incorporate this information during the modeling process are not detailed by the most influential authors. Data gathering techniques such as interviews and focus groups, and qualitative data analysis techniques such as grounded theory methodology and ethnographic decision models could have a strong, critical role in rigorous system dynamics efforts. This paper describes some of the main qualitative, social science techniques and explores their suitability in the different stages of the modeling process. Additionally, the authors argue that the techniques described in the paper could contribute to the understanding of the modeling process, facilitate


Skip graphs

Skip graphs are a novel distributed data structure, based on skip lists, that provide the full functionality of a balanced tree in a distributed system where elements are stored in separate nodes that may fail at any time. They are designed for use in searching peer-to-peer networks, and by providing the ability to perform queries based on key ordering, they improve on existing search tools that provide only hash table functionality. Unlike skip lists or other tree data structures, skip graphs are highly resilient, tolerating a large fraction of failed nodes without losing connectivity. In addition, constructing, inserting new elements into, searching a skip graph and detecting and repairing errors in the data structure introduced by node failures can be done using simple and straight-forward algorithms.


Sensors for 3D Imaging: Metric Evaluation and Calibration of a CCD/CMOS Time-of-Flight Camera

3D imaging with Time-of-Flight (ToF) cameras is a promising recent technique which allows 3D point clouds to be acquired at video frame rates. However, the distance measurements of these devices are often affected by some systematic errors which decrease the quality of the acquired data. In order to evaluate these errors, some experimental tests on a CCD/CMOS ToF camera sensor, the SwissRanger (SR)-4000 camera, were performed and reported in this paper. In particular, two main aspects are treated: the calibration of the distance measurements of the SR-4000 camera, which deals with evaluation of the camera warm up time period, the distance measurement error evaluation and a study of the influence on distance measurements of the camera orientation with respect to the observed object; the second aspect concerns the photogrammetric calibration of the amplitude images delivered by the camera using a purpose-built multi-resolution field made of high contrast targets.


Traceable Air Baggage Handling System Based on RFID Tags in the Airport

The RFID is not only a feasible, novel, and cost-effective candidate for daily object identification but it is also considered as a significant tool to provide traceable visibility along different stages of the aviation supply chain. In the air baggage handing application, the RFID tags are used to enhance the ability for baggage tracking, dispatching and conveyance so as to improve the management efficiency and the users’ satisfaction. We surveyed current related work and introduce the IATA RP1740c protocol used for the standard to recognize the baggage tags. One distributed aviation baggage traceable application is designed based on the RFID networks. We describe the RFID-based baggage tracking experiment in the BCIA (Beijing Capital International Airport). In this experiment the tags are sealed in the printed baggage label and the RFID readers are fixed in the certain interested positions of the BHS in the Terminal 2. We measure the accurate recognition rate and monitor the baggage’s real-time situation on the monitor’s screen. Through the analysis of the measured results within two months we emphasize the advantage of the adoption of RFID tags in this high noisy BHS environment. The economical benefits achieved by the extensive deployment of RFID in the baggage handing system are also outlined.


Learning continuous grasp stability for a humanoid robot hand based on tactile sensing

Grasp stability estimation with complex robots in environments with uncertainty is a major research challenge. Analytical measures such as force closure based grasp quality metrics are often impractical because tactile sensors are unable to measure contacts accurately enough especially in soft contact cases. Recently, an alternative approach of learning the stability based on examples has been proposed. Current approaches of stability learning analyze the tactile sensor readings only at the end of the grasp attempt, which makes them somewhat time consuming, because the grasp can be stable already earlier. In this paper, we propose an approach for grasp stability learning, which estimates the stability continuously during the grasp attempt. The approach is based on temporal filtering of a support vector machine classifier output. Experimental evaluation is performed on an anthropomorphic ARMAR-IIIb. The results demonstrate that the continuous estimation provides equal performance to the earlier approaches while reducing the time to reach a stable grasp significantly. Moreover, the results demonstrate for the first time that the learning based stability estimation can be used with a flexible, pneumatically actuated hand, in contrast to the rigid hands used in earlier works.


Authorship attribution based on Life-Like Network Automata

The authorship attribution is a problem of considerable practical and technical interest. Several methods have been designed to infer the authorship of disputed documents in multiple contexts. While traditional statistical methods based solely on word counts and related measurements have provided a simple, yet effective solution in particular cases; they are prone to manipulation. Recently, texts have been successfully modeled as networks, where words are represented by nodes linked according to textual similarity measurements. Such models are useful to identify informative topological patterns for the authorship recognition task. However, there is no consensus on which measurements should be used. Thus, we proposed a novel method to characterize text networks, by considering both topological and dynamical aspects of networks. Using concepts and methods from cellular automata theory, we devised a strategy to grasp informative spatio-temporal patterns from this model. Our experiments revealed an outperformance over structural analysis relying only on topological measurements, such as clustering coefficient, betweenness and shortest paths. The optimized results obtained here pave the way for a better characterization of textual networks.


High-Sensitivity Software-Configurable 5.8-GHz Radar Sensor Receiver Chip in 0.13-$\mu$ m CMOS for Noncontact Vital Sign Detection

In this paper, analyses on sensitivity and link budget have been presented to guide the design of high-sensitivity noncontact vital sign detector. Important design issues such as flicker noise, baseband bandwidth, and gain budget have been discussed with practical considerations of analog-to-digital interface and signal processing methods in noncontact vital sign detection. Based on the analyses, a direct-conversion 5.8-GHz radar sensor chip with 1-GHz bandwidth was designed and fabricated. This radar sensor chip is software configurable to set the operation point and detection range for optimal performance. It integrates all the analog functions on-chip so that the output can be directly sampled for digital signal processing. Measurement results show that the fabricated chip has a sensitivity of better than -101 dBm for ideal detection in the absence of random body movement. Experiments have been performed successfully in laboratory environment to detect the vital signs of human subjects.


The social impact of a systematic floor cleaner

Mint is an automatic cleaning robot that sweeps and mops hard-surface floors using dusting and mopping cloths. Thanks to the Northstar navigation technology it systematically cleans and navigates in people's homes. Since it first became commercially available in mid 2010, hundreds of thousands of Mint cleaners are nowadays in use at home. In this paper we investigate the product's social impact with respect to the attitude of customers towards a systematic floor cleaner and how such a robot influences their lifestyle. We first report feedback from users owning the product, and demonstrate how Mint changed their everyday life at home. We then evaluate the results of a survey launched in 2012 that addresses the technical understanding of the product and what impact it has on the social life of their users. Our findings suggest that Mint, on average, saves more than one hour of people's time per week, floors are cleaner leading to healthier homes and lives, systematic cleaning is seen as an important feature, and modifications to the environment to support the navigation of the robot are largely accepted.


Audit and Analysis of Impostors: An experimental approach to detect fake profile in online social network

In the present generation, the social life of every person has become associated with online social networks (OSN). These sites have made drastic changes in the way we socialize. Making friends and keeping in contact with them as well as being updated of their activities, has become easier. But with their rapid growth, problems like fake profiles, online impersonation have also increased. The risk lies in the fact that anybody can create a profile to impersonate a real person on the OSN. The fake profile could be exploited to build online relationship with a targeted person purely through online interactions with the friends of victim.
 In present work, we have proposed experimental framework with which detection of fake profile is feasible within the friend list, however this framework is restricted to a specific online social networking site namely Facebook. This framework extracts data from the friend list and uses it to classify them as real or fake by using unsupervised and supervised machine learning.


Blind PSF estimation and methods of deconvolution optimization

We have shown that the left side null space of the autoregression (AR) matrix operator is the lexicographical presentation of the point spread function (PSF) on condition the AR parameters are common for original and blurred images. The method of inverse PSF evaluation with regularization functional as the function of surface area is offered. The inverse PSF was used for primary image estimation. Two methods of original image estimate optimization were designed basing on maximum entropy generalization of sought and blurred images conditional probability density and regularization. The first method uses balanced variations of convolution and deconvolution transforms to obtaining iterative schema of image optimization. The variations balance was defined by dynamic regularization basing on condition of iteration process convergence. The regularization has dynamic character because depends on current and previous image estimate variations. The second method implements the regularization of deconvolution optimization in curved space with metric defined on image estimate surface. It is basing on target functional invariance to fluctuations of optimal argument value. The given iterative schemas have faster convergence in comparison with known ones, so they can be used for reconstruction of high resolution images series in real time.


The FEELING of WHAT HAPPENS : BODY AND EMOTION IN THE MAKING OF CONSCIOUSNESS

Antonio Damasio is a Portuguese-born American behavioral neurologist and neuroscientist. For a person without any knowledge of neuroscience, biology, or psychology, The Feeling of What Happens would presumably be a fascinating read. How often does one typically contemplate their own contemplation? The concepts presented by Damasio are abstract and uncommon, and undoubtedly are a new phenomenon for the average reader.


MELOGRAPH: Multi-Engine WorkfLOw Graph Processing

This paper introduces MELOGRAPH, a new system that exposes in the front-end a domain specific language(DSL) for graph processing tasks and in the back-end identifies, ranks and generates source code for the top-N ranked engines. This approach lets the specialized MELOGRAPH be part of a more general multi-engine workflow optimizer. The candidate execution engines are chosen from the contemporaneous Big Data ecosystem: graph databases (e.g. Neo4j, TitanDB, OrientDB, Sparksee/DEX) and robust graph processing frameworks with Java API or packaged libraries of algorithms (e.g. Giraph, Okapi, Flink Gelly, Hama, Gremlin). As MELOGRAPH is work in progress, our current paper stresses upon the state of the art in this field, provides a general architecture and some early implementation insights.


Trusted Click: Overcoming Security issues of NFV in the Cloud

Network Function Virtualization has received a large amount of research and recent efforts have been made to further leverage the cloud to enhance NFV. However, since there are privacy and security issues with using cloud computing, work has been done to allow for operating on encrypted data, which introduces a large amount of overhead in both computation and data, while only providing a limited set of operations, since these encryption schemes are not fully homomorphic. We propose using trusted computing to circumvent these limitations by having hardware enforce data privacy and provide guaranteed computation. Prior work has shown that Intel's Software Guard Extensions can be used to protect the state of network functions, but there are still questions about the usability of SGX in arbitrary NFV applications and the performance of SGX in these applications. We extend prior work to show how SGX can be used in network deployments by extending the Click modular router to perform secure packet processing with SGX. We also present a performance evaluation of SGX on real hardware to show that processing inside of SGX has a negligible performance impact, compared to performing the same processing outside of SGX.


Parathyroid Hormone PTH(1-34) Formulation that Enables Uniform Coating on a Novel Transdermal Microprojection Delivery System

Assess formulation parameters to enable >24-h continuous accurate and uniform coating of PTH(1-34) on a novel transdermal microprojection array delivery system. Surface activity and rheology of the liquid formulation was determined by contact angle measurement and cone-plate viscometry. The formulation’s delivery performance was assessed in vivo using the hairless guinea pig model. Peptide gelation was investigated by rheological and viscoelastic behavior changes. Accurate and uniform coating was achieved by formulating the liquid formulation to a preferred contact angle range of 30–60° with a surfactant and by establishing a Newtonian fluid (defined as a fluid maintaining a constant viscosity with shear rate and time) with a viscosity of ≥20 cps via adjusting the peptide concentration and using an appropriate acidic counterion. A non-volatile acidic counterion was found critical to compensate for the loss of the volatile acetate counterion to maintain the peptide formulation’s solubility upon rehydration in the skin. Finally, the 15.5% w/w PTH(1-34) concentration was found to be the most physically stable formulation (delayed gelation) in the roll-coating reservoir. With a properly designed coating reservoir for shear force reduction, the liquid formulation could last for more than 24 h without gelation. The study successfully offered scientific rationales for developing an optimal liquid formulation for a novel titanium microprojection array coating process. The resultant formulation has an enduring physical stability (>24 h) in the coating reservoir and maintained good in vivo dissolution performance.


An Ethics Evaluation Tool for Automating Ethical Decision-Making in Robots and Self-Driving Cars

As we march down the road of automation in robotics and artificial intelligence, we will need to automate an increasing amount of ethical decision-making in order for our devices to operate independently from us. But automating ethical decision-making raises novel questions for engineers and designers, who will have to make decisions about how to accomplish that task. For example, some ethical decisionmaking involves hard moral cases, which in turn requires user input if we are to respect established norms surrounding autonomy and informed consent. The author considers this and other ethical considerations that accompany the automation of ethical decision-making. He proposes some general ethical requirements that should be taken into account in the design room, and sketches a design tool that can be integrated into the design process to help engineers, designers, ethicists, and policymakers decide how best to automate certain forms of ethical decision-making.


Peppermint oil for treatment of irritable bowel syndrome.

22 AM J HEALTH-SYST PHARM | VOLUME 73 | NUMBER 2 | JANUARY 15, 2016 Peppermint oil for treatment of irritable bowel syndrome Irritable bowel syndrome (IBS) is a chronic gastrointestinal disorder that affects 5–15% of people worldwide. Typically, patients with IBS experience abdominal pain or discomfort and constipation or diarrhea with other gastrointestinal symptoms, such as abdominal distention and bloating; these symptoms reduce quality of life and can be difficult to control with currently available prescription agents. For these reasons, patients are often compelled to try nonprescription therapies, including natural products. In studies conducted in Australia and the United Kingdom, about 20–50% of patients with IBS reported using complementary and alternative medicines. Thus, it is important that healthcare professionals are knowledgeable of these therapies, including dietary supplements such as peppermint oil. Historically, the ingestion of peppermint oil has been associated with effects on the gastrointestinal tract. It is a classic essential oil known to have carminative properties (i.e., it is a naturally occurring remedy thought to help decrease bloating and gas by allowing passage of flatus) and may even be beneficial as an antiemetic. Peppermint oil comes from a perennial herb (Mentha × piperita), a plant found across North America and Europe. Mentha × piperita is a sterile hybrid of two herbs, spearmint (Menthaspicata) and water mint (Menthaaquatica). The main constituents of peppermint oil include menthol (35–55%), menthone (20–31%), menthyl acetate (3–10%), isomenthone, 1,8-cineole, limonene, b-myrcene, and carvone. Peppermint oil may be obtained through steam distillation of flowering parts that grow aboveground. It is a volatile oil, with menthol accounting for a majority of its potency. Due to the various constituents of peppermint oil, it has a variety of uses, including topical application as an antiseptic and for aches and pains, inhalation as aromatherapy, and oral formulations for flavoring or for use as digestive aids. Relaxation of intestinal muscle, both in vivo and in vitro, and relaxation of the lower esophageal sphincter have been reported with the use of peppermint oil, which is thought of as an antispasmodic that may confer benefits in conditions such as IBS. Several clinical studies of the use of peppermint oil for the treatment of


Human motion and emotion parameterization

This paper describes the methods and data structures used for generating new human motions out of existing motion data. These existing animation data are derived from live motion capture or produced through the traditional animation tools. We present a survey of techniques for generating new animations by physical and emotional parameterizations. Discussed new animations are generated by Fourier transformations, transformations of velocity and amplitude and scattered data interpolation in this report.


An Algorithm for Drawing General Undirected Graphs Tomihisa Kamada and Satoru Kawai

Graphs (networks) are very common data structures which are handled in computers. Diagrams are widely used to represent the graph structures visually in many information systems. In order to automatically draw the diagrams which are, for example, state graphs, data-flow graphs, Petri nets, and entity-relationship diagrams, basic graph drawing algorithms are required. The state of the art in automatic drawing is surveyed comprehensively in [7,19]. There have been only a few algorithms for general undirected graphs. This paper presents a simple but successful algorithm for drawing undirected graphs and weighted graphs. The basic idea of our algorithm is as follows. We regard the desirable "geometric" (Euclidean) distance between two vertices in the drawing as the "graph theoretic" distance between them in the corresponding graph. We introduce a virtual dynamic system in which every two vertices are connected by a "spring" of such desirable length. Then, we regard the optimal layout of vertices as the state in which the total spring energy of the system is minimal. The "spring" idea for drawing general graphs was introduced in [6], and similar methods were used for drawing planar graphs with fixed boundary [2,20]. This paper brings a new significant result in graph drawing based on the spring model.


Topic Model for Identifying Suicidal Ideation in Chinese Microblog

Suicide is one of major public health problems worldwide. Traditionally, suicidal ideation is assessed by surveys or interviews, which lacks of a real-time assessment of personal mental state. Online social networks, with large amount of user-generated data, offer opportunities to gain insights of suicide assessment and prevention. In this paper, we explore potentiality to identify and monitor suicide expressed in microblog on social networks. First, we identify users who have committed suicide and collect millions of microblogs from social networks. Second, we build suicide psychological lexicon by psychological standards and word embedding technique. Third, by leveraging both language styles and online behaviors, we employ Topic Model and other machine learning algorithms to identify suicidal ideation. Our approach achieves the best results on topic-500, yielding F1 − measure of 80.0%, Precision of 87.1%, Recall of 73.9%, and Accuracy of 93.2%. Furthermore, a prototype system for monitoring suicidal ideation on several social networks is deployed.


Unusual coexistence of caudal duplication and caudal regression syndromes.

Caudal duplication syndrome includes anomalies of the genitourinary system, gastrointestinal tract, and the distal neural tube. Caudal regression syndrome presents with lumbosacral hypogenesis, anomalies of the lower gastrointestinal tract, genitourinary system, and limb anomalies. Both happen as a result of insult to the caudal cell mass. We present a child having features consistent with both entities.


Biodynamic Excisional Skin Tension Lines for Cutaneous Surgery


Qualitative research methods in mental health.

As the evidence base for the study of mental health problems develops, there is a need for increasingly rigorous and systematic research methodologies. Complex questions require complex methodological approaches. Recognising this, the MRC guidelines for developing and testing complex interventions place qualitative methods as integral to each stage of intervention development and implementation. However, mental health research has lagged behind many other healthcare specialities in using qualitative methods within its evidence base. Rigour in qualitative research raises many similar issues to quantitative research and also some additional challenges. This article examines the role of qualitative methods within mental heath research, describes key methodological and analytical approaches and offers guidance on how to differentiate between poor and good quality qualitative research.


Reliability in the utility computing era: Towards reliable Fog computing

This paper considers current paradigms in computing and outlines the most important aspects concerning their reliability. The Fog computing paradigm as a non-trivial extension of the Cloud is considered and the reliability of the networks of smart devices are discussed. Combining the reliability requirements of grid and cloud paradigms with the reliability requirements of networks of sensor and actuators it follows that designing a reliable Fog computing platform is feasible.


Detection and analysis of drive-by-download attacks and malicious JavaScript code

JavaScript is a browser scripting language that allows developers to create sophisticated client-side interfaces for web applications. However, JavaScript code is also used to carry out attacks against the user's browser and its extensions. These attacks usually result in the download of additional malware that takes complete control of the victim's platform, and are, therefore, called "drive-by downloads." Unfortunately, the dynamic nature of the JavaScript language and its tight integration with the browser make it difficult to detect and block malicious JavaScript code.
 This paper presents a novel approach to the detection and analysis of malicious JavaScript code. Our approach combines anomaly detection with emulation to automatically identify malicious JavaScript code and to support its analysis. We developed a system that uses a number of features and machine-learning techniques to establish the characteristics of normal JavaScript code. Then, during detection, the system is able to identify anomalous JavaScript code by emulating its behavior and comparing it to the established profiles. In addition to identifying malicious code, the system is able to support the analysis of obfuscated code and to generate detection signatures for signature-based systems. The system has been made publicly available and has been used by thousands of analysts.


Beyond blacklists: learning to detect malicious web sites from suspicious URLs

Malicious Web sites are a cornerstone of Internet criminal activities. As a result, there has been broad interest in developing systems to prevent the end user from visiting such sites. In this paper, we describe an approach to this problem based on automated URL classification, using statistical methods to discover the tell-tale lexical and host-based properties of malicious Web site URLs. These methods are able to learn highly predictive models by extracting and automatically analyzing tens of thousands of features potentially indicative of suspicious URLs. The resulting classifiers obtain 95-99% accuracy, detecting large numbers of malicious Web sites from their URLs, with only modest false positives.


Anomaly Based Web Phishing Page Detection

Many anti-phishing schemes have recently been proposed in literature. Despite all those efforts, the threat of phishing attacks is not mitigated. One of the main reasons is that phishing attackers have the adaptability to change their tactics with little cost. In this paper, we propose a novel approach, which is independent of any specific phishing implementation. Our idea is to examine the anomalies in Web pages, in particular, the discrepancy between a Web site's identity and its structural features and HTTP transactions. It demands neither user expertise nor prior knowledge of the Web site. The evasion of our phishing detection entails high cost to the adversary. As shown by the experiments, our phishing detector functions with low miss rate and low false-positive rate


Effective analysis, characterization, and detection of malicious web pages

The steady evolution of the Web has paved the way for miscreants to take advantage of vulnerabilities to embed malicious content into web pages. Up on a visit, malicious web pages steal sensitive data, redirect victims to other malicious targets, or cease control of victim's system to mount future attacks. Approaches to detect malicious web pages have been reactively effective at special classes of attacks like drive-by-downloads. However, the prevalence and complexity of attacks by malicious web pages is still worrisome. The main challenges in this problem domain are (1) fine-grained capturing and characterization of attack payloads (2) evolution of web page artifacts and (3) exibility and scalability of detection techniques with a fast-changing threat landscape. To this end, we proposed a holistic approach that leverages static analysis, dynamic analysis, machine learning, and evolutionary searching and optimization to effectively analyze and detect malicious web pages. We do so by: introducing novel features to capture fine-grained snapshot of malicious web pages, holistic characterization of malicious web pages, and application of evolutionary techniques to fine-tune learning-based detection models pertinent to evolution of attack payloads. In this paper, we present key intuition and details of our approach, results obtained so far, and future work.


ADAM: Detecting Intrusions by Data Mining

Intrusion detection systems have traditionally been based on the characterization of an attack and the tracking of the activity on the system to see if it matches that characterization. Recently, new intrusion detection systems based on data mining are making their appearance in the field. This paper describes the design and experiences with the ADAM ( Audit Data Analysis and Mining) system, which we use as a testbed to study how useful data mining techniques can be in intrusion detection. Keywords—Intrusion Detection, Data Mining, Association Rules, Classifiers.


Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders

Computer-aided diagnosis (CAD) is a promising tool for accurate and consistent diagnosis and prognosis. Cell detection and segmentation are essential steps for CAD. These tasks are challenging due to variations in cell shapes, touching cells, and cluttered background. In this paper, we present a cell detection and segmentation algorithm using the sparse reconstruction with trivial templates and a stacked denoising autoencoder (sDAE). The sparse reconstruction handles the shape variations by representing a testing patch as a linear combination of shapes in the learned dictionary. Trivial templates are used to model the touching parts. The sDAE, trained with the original data and their structured labels, is used for cell segmentation. To the best of our knowledge, this is the first study to apply sparse reconstruction and sDAE with structured labels for cell detection and segmentation. The proposed method is extensively tested on two data sets containing more than 3000 cells obtained from brain tumor and lung cancer images. Our algorithm achieves the best performance compared with other state of the arts.


A shared " passengers & goods " city logistics system

Many strategic planning models have been developed to help decision making in city logistics. Such models do not take into account, or very few, the flow of passengers because the considered unit does not have the same nature (a person is active and a good is passive). However, it seems fundamental to gather the goods and the passengers in one model when their respective transports interact with each other. In this context, we suggest assessing a shared passengers & goods city logistics system where the spare capacity of public transport is used to distribute goods toward the city core. We model the problem as a vehicle routing problem with transfers and give a mathematical formulation. Then we propose an Adaptive Large Neighborhood Search (ALNS) to solve it. This approach is evaluated on data sets generated following a field study in the city of La Rochelle in France.


Speaker Identification using Mel Frequency Cepstral Coefficient and BPNN

Speech processing is emerged as one of the important application area of digital signal processing. Various fields for research in speech processing are speech recognition, speaker recognition, speech synthesis, speech coding etc. The objective of automatic speaker recognition is to extract, characterize and recognize the information about speaker identity. Feature extraction is the first step for speaker recognition. Many algorithms are suggested/developed by the researchers for feature extraction. In this work, the Mel Frequency Cepstrum Coefficient (MFCC) feature has been used for designing a text dependent speaker identification system. BPNN is used for identification of speaker after training the feature set from MFCC. Some modifications to the existing technique of MFCC for feature extraction are also suggested to improve the speaker recognition efficiency. Information from speech recognition can be used in various ways in state-of-the-art speaker recognition systems. This includes the obvious use of recognized words to enable the use of text-dependent speaker modeling techniques when the words spoken are not given. Furthermore, it has been shown that the choice of words and phones itself can be a useful indicator of speaker identity. Also, recognizer output enables higher-level features, in particular those related to prosodic properties of speech. Keywords— Speaker identification, BPNN, MFCC, speech processing, feature extraction, speech signal


Language support for fast and reliable message-based communication in singularity OS

Message-based communication offers the potential benefits of providing stronger specification and cleaner separation between components. Compared with shared-memory interactions, message passing has the potential disadvantages of more expensive data exchange (no direct sharing) and more complicated programming.In this paper we report on the language, verification, and run-time system features that make messages practical as the sole means of communication between processes in the Singularity operating system. We show that using advanced programming language and verification techniques, it is possible to provide and enforce strong system-wide invariants that enable efficient communication and low-overhead software-based process isolation. Furthermore, specifications on communication channels help in detecting programmer mistakes early---namely at compile-time---thereby reducing the difficulty of the message-based programming model.The paper describes our communication invariants, the language and verification features that support them, as well as implementation details of the infrastructure. A number of benchmarks show the competitiveness of this approach.


Timeline generation with social attention

Timeline generation is an important research task which can help users to have a quick understanding of the overall evolution of any given topic. It thus attracts much attention from research communities in recent years. Nevertheless, existing work on timeline generation often ignores an important factor, the attention attracted to topics of interest (hereafter termed "social attention"). Without taking into consideration social attention, the generated timelines may not reflect users' collective interests. In this paper, we study how to incorporate social attention in the generation of timeline summaries. In particular, for a given topic, we capture social attention by learning users' collective interests in the form of word distributions from Twitter, which are subsequently incorporated into a unified framework for timeline summary generation. We construct four evaluation sets over six diverse topics. We demonstrate that our proposed approach is able to generate both informative and interesting timelines. Our work sheds light on the feasibility of incorporating social attention into traditional text mining tasks.


Analyzing the State of Static Analysis: A Large-Scale Evaluation in Open Source Software

The use of automatic static analysis has been a software engineering best practice for decades. However, we still do not know a lot about its use in real-world software projects: How prevalent is the use of Automated Static Analysis Tools (ASATs) such as FindBugs and JSHint? How do developers use these tools, and how does their use evolve over time? We research these questions in two studies on nine different ASATs for Java, JavaScript, Ruby, and Python with a population of 122 and 168,214 open-source projects. To compare warnings across the ASATs, we introduce the General Defect Classification (GDC) and provide a grounded-theory-derived mapping of 1,825 ASAT-specific warnings to 16 top-level GDC classes. Our results show that ASAT use is widespread, but not ubiquitous, and that projects typically do not enforce a strict policy on ASAT use. Most ASAT configurations deviate slightly from the default, but hardly any introduce new custom analyses. Only a very small set of default ASAT analyses is widely changed. Finally, most ASAT configurations, once introduced, never change. If they do, the changes are small and have a tendency to occur within one day of the configuration's initial introduction.


Microrobot Design Using Fiber Reinforced Composites

Mobile microrobots with characteristic dimensions on the order of 1cm are difficult to design using either MEMS (microelectromechanical systems) technology or precision machining. This is due to the challenges associated with constructing the high strength links and high-speed, low-loss joints with micron scale features required for such systems. Here we present an entirely new framework for creating microrobots which makes novel use of composite materials. This framework includes a new fabrication process termed Smart Composite Microstructures (SCM) for integrating rigid links and large angle flexure joints through a laser micromachining and lamination process. We also present solutions to actuation and integrated wiring issues at this scale using SCM. Along with simple design rules that are customized for this process, our new complete microrobotic framework is a cheaper, quicker, and altogether superior method for creating microrobots that we hope will become the paradigm for robots at this scale.


Mr. DLib's Living Lab for Scholarly Recommendations

We introduce the first living lab for scholarly recommender systems. This lab allows recommender-system researchers to conduct online evaluations of their novel algorithms for scholarly recommendations, i.e., research papers, citations, conferences, research grants etc. Recommendations are delivered through the living lab ́s API in platforms such as reference management software and digital libraries. The living lab is built on top of the recommender system as-a-service Mr. DLib. Current partners are the reference management software JabRef and the CORE research team. We present the architecture of Mr. DLib’s living lab as well as usage statistics on the first ten months of operating it. During this time, 970,517 recommendations were delivered with a mean click-through rate of 0.22%.


A Genetic Algorithm Approach to Dynamic Job Shop Scheduling Problems

This paper describes a genetic algorithm approach to the dynamic job shop scheduling problem with jobs arriving continually. Both deterministic and stochastic models of the dynamic problem were investigated. The objective functions examined were weighted flow time, maximum tardiness, weighted tardiness, weighted lateness, weighted number of tardy jobs, and weighted earliness plus weighted tardi-ness. In the stochastic model, we further tested the approach under various manufacturing environments with respect to the machine workload, imbalance of machine workload, and due date tightness. The results indicate that the approach performs well and is robust with regard to the objective function and the manufacturing environment in comparison with priority rule approaches.


Recurrent Neural Aligner: An Encoder-Decoder Neural Network Model for Sequence to Sequence Mapping

We introduce an encoder-decoder recurrent neural network model called Recurrent Neural Aligner (RNA) that can be used for sequence to sequence mapping tasks. Like connectionist temporal classification (CTC) models, RNA defines a probability distribution over target label sequences including blank labels corresponding to each time step in input. The probability of a label sequence is calculated by marginalizing over all possible blank label positions. Unlike CTC, RNA does not make a conditional independence assumption for label predictions; it uses the predicted label at time t−1 as an additional input to the recurrent model when predicting the label at time t. We apply this model to end-to-end speech recognition. RNA is capable of streaming recognition since the decoder does not employ attention mechanism. The model is trained on transcribed acoustic data to predict graphemes and no external language and pronunciation models are used for decoding. We employ an approximate dynamic programming method to optimize negative log likelihood, and a sampling-based sequence discriminative training technique to fine-tune the model to minimize expected word error rate. We show that the model achieves competitive accuracy without using an external language model nor doing beam search decoding.


Effective Bug Triage Based on Historical Bug-Fix Information

For complex and popular software, project teams could receive a large number of bug reports. It is often tedious and costly to manually assign these bug reports to developers who have the expertise to fix the bugs. Many bug triage techniques have been proposed to automate this process. In this paper, we describe our study on applying conventional bug triage techniques to projects of different sizes. We find that the effectiveness of a bug triage technique largely depends on the size of a project team (measured in terms of the number of developers). The conventional bug triage methods become less effective when the number of developers increases. To further improve the effectiveness of bug triage for large projects, we propose a novel recommendation method called Bug Fixer, which recommends developers for a new bug report based on historical bug-fix information. Bug Fixer constructs a Developer-Component-Bug (DCB) network, which models the relationship between developers and source code components, as well as the relationship between the components and their associated bugs. A DCB network captures the knowledge of "who fixed what, where". For a new bug report, Bug Fixer uses a DCB network to recommend to triager a list of suitable developers who could fix this bug. We evaluate Bug Fixer on three large-scale open source projects and two smaller industrial projects. The experimental results show that the proposed method outperforms the existing methods for large projects and achieves comparable performance for small projects.


How Important is Weight Symmetry in Backpropagation?

Gradient backpropagation (BP) requires symmetric feedforward and feedback connections—the same weights must be used for forward and backward passes. This “weight transport problem” [1] is thought to be one of the main reasons of BP’s biological implausibility. Using 15 different classification datasets, we systematically study to what extent BP really depends on weight symmetry. In a study that turned out to be surprisingly similar in spirit to Lillicrap et al.’s demonstration [2] but orthogonal in its results, our experiments indicate that: (1) the magnitudes of feedback weights do not matter to performance (2) the signs of feedback weights do matter—the more concordant signs between feedforward and their corresponding feedback connections, the better (3) with feedback weights having random magnitudes and 100% concordant signs, we were able to achieve the same or even better performance than SGD. (4) some normalizations/stabilizations are indispensable for such asymmetric BP to work, namely Batch Normalization (BN) [3] and/or a “Batch Manhattan” (BM) update rule. This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF 1231216. ar X iv :1 51 0. 05 06 7v 3 [ cs .L G ] 2 D ec 2 01 5