Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.

Importance
A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases.

Objective
To evaluate the performance of a DLS in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes.

Design, Setting, and Participants
Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494 661 retinal images. A DLS was trained for detecting diabetic retinopathy (using 76 370 images), possible glaucoma (125 189 images), and AMD (72 610 images), and performance of DLS was evaluated for detecting diabetic retinopathy (using 112 648 images), possible glaucoma (71 896 images), and AMD (35 948 images). Training of the DLS was completed in May 2016, and validation of the DLS was completed in May 2017 for detection of referable diabetic retinopathy (moderate nonproliferative diabetic retinopathy or worse) and vision-threatening diabetic retinopathy (severe nonproliferative diabetic retinopathy or worse) using a primary validation data set in the Singapore National Diabetic Retinopathy Screening Program and 10 multiethnic cohorts with diabetes.

Exposures
Use of a deep learning system.

Main Outcomes and Measures
Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity of the DLS with professional graders (retinal specialists, general ophthalmologists, trained graders, or optometrists) as the reference standard.

Results
In the primary validation dataset (n = 14 880 patients; 71 896 images; mean [SD] age, 60.2 [2.2] years; 54.6% men), the prevalence of referable diabetic retinopathy was 3.0%; vision-threatening diabetic retinopathy, 0.6%; possible glaucoma, 0.1%; and AMD, 2.5%. The AUC of the DLS for referable diabetic retinopathy was 0.936 (95% CI, 0.925-0.943), sensitivity was 90.5% (95% CI, 87.3%-93.0%), and specificity was 91.6% (95% CI, 91.0%-92.2%). For vision-threatening diabetic retinopathy, AUC was 0.958 (95% CI, 0.956-0.961), sensitivity was 100% (95% CI, 94.1%-100.0%), and specificity was 91.1% (95% CI, 90.7%-91.4%). For possible glaucoma, AUC was 0.942 (95% CI, 0.929-0.954), sensitivity was 96.4% (95% CI, 81.7%-99.9%), and specificity was 87.2% (95% CI, 86.8%-87.5%). For AMD, AUC was 0.931 (95% CI, 0.928-0.935), sensitivity was 93.2% (95% CI, 91.1%-99.8%), and specificity was 88.7% (95% CI, 88.3%-89.0%). For referable diabetic retinopathy in the 10 additional datasets, AUC range was 0.889 to 0.983 (n = 40 752 images).

Conclusions and Relevance
In this evaluation of retinal images from multiethnic cohorts of patients with diabetes, the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases. Further research is necessary to evaluate the applicability of the DLS in health care settings and the utility of the DLS to improve vision outcomes.


Suicide and Suicide Risk in Lesbian, Gay, Bisexual, and Transgender Populations: Review and Recommendations

Despite strong indications of elevated risk of suicidal behavior in lesbian, gay, bisexual, and transgender people, limited attention has been given to research, interventions or suicide prevention programs targeting these populations. This article is a culmination of a three-year effort by an expert panel to address the need for better understanding of suicidal behavior and suicide risk in sexual minority populations, and stimulate the development of needed prevention strategies, interventions and policy changes. This article summarizes existing research findings, and makes recommendations for addressing knowledge gaps and applying current knowledge to relevant areas of suicide prevention practice.


Watch What You Wear: Preliminary Forensic Analysis of Smart Watches

This work presents preliminary forensic analysis of two popular smart watches, the Samsung Gear 2 Neo and LG G. These wearable computing devices have the form factor of watches and sync with smart phones to display notifications, track footsteps and record voice messages. We posit that as smart watches are adopted by more users, the potential for them becoming a haven for digital evidence will increase thus providing utility for this preliminary work. In our work, we examined the forensic artifacts that are left on a Samsung Galaxy S4 Active phone that was used to sync with the Samsung Gear 2 Neo watch and the LG G watch. We further outline a methodology for physically acquiring data from the watches after gaining root access to them. Our results show that we can recover a swath of digital evidence directly form the watches when compared to the data on the phone that is synced with the watches. Furthermore, to root the LG G watch, the watch has to be reset to its factory settings which is alarming because the process may delete data of forensic relevance. Although this method is forensically intrusive, it may be used for acquiring data from already rooted LG watches. It is our observation that the data at the core of the functionality of at least the two tested smart watches, messages, health and fitness data, e-mails, contacts, events and notifications are accessible directly from the acquired images of the watches, which affirms our claim that the forensic value of evidence from smart watches is worthy of further study and should be investigated both at a high level and with greater specificity and granularity.


Design of Broadband Constant-Beamwidth Conical Corrugated-Horn Antennas [Antenna Designer's Notebook]

In this paper, a new design procedure is proposed for the design of wideband constant-beamwidth conical corrugated-horn antennas, with minimum design and construction complexity. The inputs to the procedure are the operating frequency band, the required minimum beamwidth in the entire frequency band, and the frequency in which the maximum gain is desired to occur. Based on these values, the procedure gives a relatively good design with a relative bandwidth of up to 2.5:1. Based on the proposed procedure, a corrugated-horn antenna with a constant beamwidth over the frequencies of 8 to 18 GHz was designed and simulated using commercial software. The designed antenna was also constructed, and its electromagnetic performance was measured. The measured results of the constructed prototype antenna confirmed the simulation results and satisfied the design requirements, validating the proposed design procedure.


I See What You Say (ISWYS): Arabic lip reading system

The ability of communicating easily with everyone is a blessing people with hearing impairment do not have. They completely rely on their vision around healthy individuals to difficultly read lips. This paper proposes a solution for this problem, ISWYS (I See What You Say) is a research-oriented speech recognition system for the Arabic language that interprets lips movements into readable text. It is accomplished by analyzing a video of lips movements that resemble utterances, and then converting it to readable characters using video analysis and motion estimation. Our algorithm involves dividing the video into n number of frames to generate n-1 image frame which is produced by taking the difference between consecutive frames. Then, video features are extracted to be used by our error function which provided recognition of approximately 70%.


A Novel BiQuad Antenna for 2 . 4 GHz Wireless Link Application : A Proposed Design

An ISM Band (2.4GHz) Design for a Biquad Antenna with Reflector base is presented for satisfying the ISM Band point-to-point link Applications in this paper. This proposed design can be used for Reception as well as Transmission in Wi-Fi's, WLAN, Bluetooth and even Zigbee links. The proposed antenna consists of two squares of the same size of 1⁄4 wavelength as a radiating element and a metallic plate or grid as reflector. This antenna has a beam width of about 70 degrees and a gain in the order of 10-12 dBi. A Prototype of this Antenna is designed and constructed. Parametric study is performed to understand the characterstics of the proposed antenna. Almost good antenna performances such as radiation patterns and antenna gains over the operating bands have been observed and simulated peak gain of the antenna is 10.7 dBi at 2439MHz. The simulated return loss is-35dB, whereas simulated SWR is 1.036 over the operating bands. The Biquad antenna is simple to build and offers good directivity and gain for Point-to-Point communications [1]. It consists of two squares of the same size of 1⁄4 wavelength as a radiating element and a metallic plate or grid as reflector. This antenna has a beam width of about 70 degrees and a gain in the order of 10-12 dBi. It can be used as stand-alone antenna or as feeder for a Parabolic Dish [2]. The polarization is such that looking at the antenna from the front, if the squares are placed side by side the polarization is vertical. The element is made from a length of 2mm thick copper wire, bent into the appropriate shape. Note that the length of each " side " should be close to 31 mm as possible, when measured from center to center of the wire [4-5]. The Details of the proposed antenna design are described in the paper, and simulated results are presented and discussed in the following sections.


Regulation of OsSPL14 by OsmiR156 defines ideal plant architecture in rice

Increasing crop yield is a major challenge for modern agriculture. The development of new plant types, which is known as ideal plant architecture (IPA), has been proposed as a means to enhance rice yield potential over that of existing high-yield varieties. Here, we report the cloning and characterization of a semidominant quantitative trait locus, IPA1 (Ideal Plant Architecture 1), which profoundly changes rice plant architecture and substantially enhances rice grain yield. The IPA1 quantitative trait locus encodes OsSPL14 (SOUAMOSA PROMOTER BINDING PROTEIN-LIKE 14) and is regulated by microRNA (miRNA) OsmiR156 in vivo. We demonstrate that a point mutation in OsSPL14 perturbs OsmiR156-directed regulation of OsSPL14, generating an 'ideal' rice plant with a reduced tiller number, increased lodging resistance and enhanced grain yield. Our study suggests that OsSPL14 may help improve rice grain yield by facilitating the breeding of new elite rice varieties.


Introduction to smart learning analytics: foundations and developments in video-based learning

Smart learning has become a new term to describe technological and social developments (e.g., Big and Open Data, Internet of Things, RFID, and NFC) enable effective, efficient, engaging and personalized learning. Collecting and combining learning analytics coming from different channels can clearly provide valuable information in designing and developing smart learning. Although, the potential of learning analytics to enable smart learning is very promising area, it remains non-investigated and even ill-defined concept. The paper defines the subset of learning analytics that focuses on supporting the features and the processes of smart learning, under the term Smart Learning Analytics. This is followed by a brief discussion on the prospects and drawbacks of Smart Learning Analytics and their recent foundations and developments in the area of Video-Based Learning. Drawing from our experience with the recent international workshops in Smart Environments and Analytics in Video-Based Learning, we present the state-of-the-art developments as well as the four selected contributions. The paper further draws attention to the great potential and need for research in the area of Smart Learning Analytics.


Machine Learning for Semantic Parsing in Review

Spoken Language Understanding (SLU) and more specifically, semantic parsing is an indispensable task in each speech-enabled application. In this survey, we review the current research on SLU and semantic parsing with emphasis on machine learning techniques used for these tasks. Observing the current trends in semantic parsing, we conclude our discussion by suggesting some of the most promising future research trends.


Is a picture worth a thousand words? A Deep Multi-Modal Fusion Architecture for Product Classification in e-commerce

Classifying products precisely and efficiently is a major challenge in modern e-commerce. The high traffic of new products uploaded daily and the dynamic nature of the categories raise the need for machine learning models that can reduce the cost and time of human editors. In this paper, we propose a decision level fusion approach for multi-modal product classification based on text and image neural network classifiers. We train input specific state-of-the-art deep neural networks for each input source, show the potential of forging them together into a multi-modal architecture and train a novel policy network that learns to choose between them. Finally, we demonstrate that our multi-modal network improves classification accuracy over both networks on a real-world largescale product classification dataset that we collected from Walmart.com. While we focus on image-text fusion that characterizes e-commerce businesses, our algorithms can be easily applied to other modalities such as audio, video, physical sensors, etc.


Balancing Performance, Energy, and Quality in Pervasive Computing

We describe Spectra, a remote execution system for batterypowered clients used in pervasive computing. Spectra enables applications to combine the mobility of small devices with the greater processing power of static compute servers. Spectra is self-tuning: it monitors both application resource usage and the availability of resources in the environment, and dynamically determines how and where to execute application components. In making this determination, Spectra balances the competing goals of performance, energy conservation, and application quality. We have validated Spectra’s approach on the Compaq Itsy v2.2 and IBM ThinkPad 560X using a speech recognizer, a document preparation system, and a natural language translator. Our results confirm that Spectra almost always selects the best execution plan, and that its few suboptimal choices are very close to optimal.


Implicit and explicit ethnocentrism: revisiting the ideologies of prejudice.

Two studies investigated relationships among individual differences in implicit and explicit prejudice, right-wing ideology, and rigidity in thinking. The first study examined these relationships focusing on White Americans' prejudice toward Black Americans. The second study provided the first test of implicit ethnocentrism and its relationship to explicit ethnocentrism by studying the relationship between attitudes toward five social groups. Factor analyses found support for both implicit and explicit ethnocentrism. In both studies, mean explicit attitudes toward out groups were positive, whereas implicit attitudes were negative, suggesting that implicit and explicit prejudices are distinct; however, in both studies, implicit and explicit attitudes were related (r = .37, .47). Latent variable modeling indicates a simple structure within this ethnocentric system, with variables organized in order of specificity. These results lead to the conclusion that (a) implicit ethnocentrism exists and (b) it is related to and distinct from explicit ethnocentrism.


Harlequin fetus.

We report a case of harlequin fetus born to the consanguineous parents. She had the typical skin manifestations of thick armour like scales with fissures, complete ectropion and eclabium, atrophic and crumpled ears and swollen extremities with gangrenous digits. Supportive treatment was given but the neonate died on the 4th day.


Efficient Clustering-Based Outlier Detection Algorithm for Dynamic Data Stream

Anomaly detection is currently an important and active research problem in many fields and involved in numerous applications. Most of the existing methods are based on distance measure. But in case of data stream these methods are not very efficient as computational point of view. Most of the exiting work on outlier detection in data stream declare a point as an outlier/inlier as soon as it arrive due to limited memory resources as compared to the huge data stream, to declare an outlier as it arrive often can lead us to a wrong decision, because of dynamic nature of the incoming data. In this paper we introduced a clustering based approach, which divide the stream in chunks and cluster each chunk using k-mean in fixed number of clusters. Instead of keeping only the summary information, which often used in case of clustering data stream, we keep the candidate outliers and mean value of every cluster for the next fixed number of steam chunks, to make sure that the detected candidate outliers are the real outliers. By employing the mean value of the clusters of previous chunk with mean values of the current chunk of stream, we decide better outlierness for data stream objects. Several experiments on different dataset confirm that our technique can find better outliers with low computational cost than the other exiting distance based approaches of outlier detection in data stream.


Performance modeling for systematic performance tuning

The performance of parallel scientific applications depends on many factors which are determined by the execution environment and the parallel application. Especially on large parallel systems, it is too expensive to explore the solution space with series of experiments. Deriving analytical models for applications and platforms allow estimating and extrapolating their execution performance, bottlenecks, and the potential impact of optimization options. We propose to use such "performance modeling" techniques beginning from the application design process throughout the whole software development cycle and also during the lifetime of supercomputer systems. Such models help to guide supercomputer system design and re-engineering efforts to adopt applications to changing platforms and allow users to estimate costs to solve a particular problem. Models can often be built with the help of well-known performance profiling tools. We discuss how we successfully used modeling throughout the proposal, initial testing, and beginning deployment phase of the Blue Waters supercomputer system.


Programming by Feedback

This paper advocates a new ML-based programming framework, called Programming by Feedback (PF), which involves a sequence of interactions between the active computer and the user. The latter only provides preference judgments on pairs of solutions supplied by the active computer. The active computer involves two components: the learning component estimates the user’s utility function and accounts for the user’s (possibly limited) competence; the optimization component explores the search space and returns the most appropriate candidate solution. A proof of principle of the approach is proposed, showing that PF requires a handful of interactions in order to solve some discrete and continuous benchmark problems.


Forwarding in a content-based network

This paper presents an algorithm for content-based forwarding, an essential function in content-based networking. Unlike in traditional address-based unicast or multicast networks, where messages are given explicit destination addresses, the movement of messages through a content-based network is driven by predicates applied to the content of the messages. Forwarding in such a network amounts to evaluating the predicates stored in a router's forwarding table in order to decide to which neighbor routers the message should be sent. We are interested in finding a forwarding algorithm that can make this decision as quickly as possible in situations where there are numerous, complex predicates and high volumes of messages. We present such an algorithm and give the results of studies evaluating its performance.


Probabilistic in-network caching for information-centric networks

In-network caching necessitates the transformation of centralised operations of traditional, overlay caching techniques to a decentralised and uncoordinated environment. Given that caching capacity in routers is relatively small in comparison to the amount of forwarded content, a key aspect is balanced distribution of content among the available caches. In this paper, we are concerned with decentralised, real-time distribution of content in router caches. Our goal is to reduce caching redundancy and in turn, make more efficient utilisation of available cache resources along a delivery path.
 Our in-network caching scheme, called ProbCache, approximates the caching capability of a path and caches contents probabilistically in order to: i) leave caching space for other flows sharing (part of) the same path, and ii) fairly multiplex contents of different flows among caches of a shared path.
 We compare our algorithm against universal caching and against schemes proposed in the past for Web-Caching architectures, such as Leave Copy Down (LCD). Our results show reduction of up to 20% in server hits, and up to 10% in the number of hops required to hit cached contents, but, most importantly, reduction of cache-evictions by an order of magnitude in comparison to universal caching.


A survey on content-centric technologies for the current Internet: CDN and P2P solutions

0140-3664/$ see front matter 2011 Elsevier B.V. A doi:10.1016/j.comcom.2011.10.005 q This work was partially funded by the European AWARE RECOGNITION (257756), FIRE-SCAMPI (25841 ⇑ Tel.: +39 0503153269. E-mail address: a.passarella@iit.cnr.it One of the most striking properties of the Internet is its flexibility to accommodate features it was not conceived for. Among the most significant examples, in this survey we consider the transition of the Internet from a reliable fault-tolerant network for host-to-host communication to a content-centric network, i.e. a network mostly devoted to support efficient generation, sharing and access to content. We survey this research area according to a top-down approach. We present a conceptual framework that encompasses the key building blocks required to support content-centric networking in the Internet. Then we describe in detail the two most important types of content-centric Internet technologies, i.e., Content-Delivery Networks (CDNs) and P2P systems. For each of them, we show how they cover the key building blocks. We then identify the functional components of CDN and P2P content management solutions, and discuss the main solutions proposed in the literature for each of them. We consider different types of content (both real time and non real time), and different networking environments (fixed, mobile, . . .). Finally, we also discuss the main recent research trends focused on how to design the Future Internet as a native content-centric network. 2011 Elsevier B.V. All rights reserved.


A data-oriented (and beyond) network architecture

The Internet has evolved greatly from its original incarnation. For instance, the vast majority of current Internet usage is data retrieval and service access, whereas the architecture was designed around host-to-host applications such as telnet and ftp. Moreover, the original Internet was a purely transparent carrier of packets, but now the various network stakeholders use middleboxes to improve security and accelerate applications. To adapt to these changes, we propose the Data-Oriented Network Architecture (DONA), which involves a clean-slate redesign of Internet naming and name resolution.


Computational intelligence and tower defence games

The aim of this paper is to introduce the use of Tower Defence (TD) games in Computational Intelligence (CI) research. We show how TD games can provide an important test-bed for the often under-represented casual games research area. Additionally, the use of CI in the TD games has the potential to create a more interesting, interactive and ongoing game experience for casual gamers. We present a definition of the current state and development of TD games, and include a classification of TD game components. We then describe some potential ways CI can be used to augment the TD experience. Finally, a prototype TD game based on experience-driven procedural content generation is presented.


The Validity of Presence as a Reliable Human Performance Metric in Immersive Environments

Advances in interactive media technologies have reached the stage where users need no longer to act as passive observers. As the technology evolves further we will be able to engage in computer based synthetic performances that mimic events in the real or natural world. Performances in this context refers to plays, films, computer based simulations, engineering mock-ups, etc. At a more fundamental level, we are dealing with complex interactions of the human sensory and perceptual systems with a stimulus environment (covering visual, auditory and other components). Authors or rather directors of these new media environments will exploit computer generated perceptual illusions to convince the person (participant) who is interacting with the system that they are present in the environment. Taken to the limit the participant would not be able to distinguish between being in a real or natural environment compared with being in a purely synthetic environment. Unfortunately, the performance of current technology falls considerably short of even getting even close to providing a faithful perceptual illusion of the real world. Eventually, VR authoring tools will become more sophisticated and it will be possible to produce realistic virtual environments that match more closely with the real world. Some VR practitioners have attempted to collect data that they purport to represent the degree of presence a user experiences. Unfortunately, we do not yet have an agreed definition of presence and seem undecided whether it is a meaningful metric in its own right. This paper will examine the dangers of attempting to measure the 'presence' of a VR system as a one-dimensional parameter. The question of whether presence is a valid measure of a VR system will also be addressed. An important differentiating characteristic of VR systems compared with other human-computer interfaces are their ability to create a sense of 'being-in' the computer generated environment. Other forms of media such as film and TV are also known to induce a sense of 'being-in' the environment. Some VR practitioners have tended to use the term presence to describe this effect This means that people who are engaged in the virtual environment feel as though they are actually part of the virtual environment. In order to be perceptually present in a virtual environment it is first important to understand what it means to be present in the real-world. A good example of real world experience is that of a roller coaster ride. The sensory …


Using the opinion leaders in social networks to improve the cold start challenge in recommender systems

The increasing volume of information about goods and services has been growing confusion for online buyers in cyberspace and this problem still continues. One of the most important ways to deal with the information overload is using a system called recommender system. The task of a recommender system is to offer the most appropriate and the nearest product to the user's demands and needs. In this system, one of the main problems is the cold start challenge. This problem occurs when a new user logs on and because there is no sufficient information available in the system from the user, the system won't be able to provide appropriate recommendation and the system error will rise. In this paper, we propose to use a new measurement called opinion leaders to alleviate this problem. Opinion leader is a person that his opinion has an impact on the target user. As a result, in the case of a new user logging in and the user — item's matrix sparseness, we can use the opinion of opinion leaders to offer the appropriate recommendation for new users and thereby increase the accuracy of the recommender system. The results of several conducted tests showed that opinion leaders combined with recommender systems will effectively reduce the recommendation errors.


Anomaly detection using DBSCAN clustering technique for traffic video surveillance

Detecting anomalies such as rule violations, accidents, unusual driving and other suspicious action increase the need for automatic analysis in Traffic Video Surveillance (TVS). Most of the works in Traffic rule violation systems are based on probabilistic methods of classification for detecting the events as normal and abnormal. This paper proposes an un-supervised clustering technique namely Novel Anomaly Detection-Density Based Spatial Clustering of Applications with Noise (NAD-DBSCAN) which clusters the trajectories of moving objects of varying sizes and shapes. A trajectory is said to be abnormal if the event that never fit with the trained model. Epsilon (Eps) and Minimum Points (MinPts) are essential parameters for dynamically calculating the sum of clusters for a data point. The proposed system is validated using benchmark traffic dataset and found to perform accurately in detecting anomalies.


Security Attacks and Solutions in Clouds Kazi Zunnurhain

Cloud computing offers great potential to improve productivity and reduce costs, but at the same time it possesses many new security risks. In this paper we identify the possible security attacks on clouds including: Wrapping attacks, Malware-Injection attacks, Flooding attacks, Browser attacks, and also Accountability checking problems. We identify the root causes of these attacks and propose specific


What you look at is what you get: eye movement-based interaction techniques

In seeking hitherto-unused methods by which users and computers can communicate, we investigate the usefulness of eye movements as a fast and convenient auxiliary user-to-computer communication mode. The barrier to exploiting this medium has not been eye-tracking technology but the study of interaction techniques that incorporate eye movements into the user-computer dialogue in a natural and unobtrusive way. This paper discusses some of the human factors and technical considerations that arise in trying to use eye movements as an input medium, describes our approach and the first eye movement-based interaction techniques that we have devised and implemented in our laboratory, and reports our experiences and observations on them.


Depression Scale Recognition from Audio, Visual and Text Analysis

Depression is a major mental health disorder that is rapidly affecting lives worldwide. Depression not only impacts emotional but also physical and psychological state of the person. Its symptoms include lack of interest in daily activities, feeling low, anxiety, frustration, loss of weight and even feeling of selfhatred. This report describes work done by us for Audio Visual Emotion Challenge (AVEC) 2017 during our second year BTech summer internship. With the increase in demand to detect depression automatically with the help of machine learning algorithms, we present our multimodal feature extraction and decision level fusion approach for the same. Features are extracted by processing on the provided Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) database. Gaussian Mixture Model (GMM) clustering and Fisher vector approach were applied on the visual data; statistical descriptors on gaze, pose; low level audio features and head pose and text features were also extracted. Classification is done on fused as well as independent features using Support Vector Machine (SVM) and neural networks. The results obtained were able to cross the provided baseline on validation data set by 17% on audio features and 24.5% on video features. Keywords—AVEC 2017, SVM, Depression, Neural network, RMSE, MAE, fusion, speech processing.


Attacking DDoS at the Source

Distributed denial-of-service (DDoS) attacks present an Internet-wide threat. We propose D-WARD, a DDoS defense system deployed at source-end networks that autonomously detects and stops attacks originating from these networks. Attacks are detected by the constant monitoring of two-way traffic flows between the network and the rest of the Internet and periodic comparison with normal flow models. Mismatching flows are rate-limited in proportion to their aggressiveness. D-WARD offers good service to legitimate traffic even during an attack, while effectively reducing DDoS traffic to a negligible level. A prototype of the system has been built in a Linux router. We show its effectiveness in various attack scenarios, discuss motivations for deployment, and describe associated costs.


PAGE: A Partition Aware Engine for Parallel Graph Computation

Graph partition quality affects the overall performance of parallel graph computation systems. The quality of a graph partition is measured by the balance factor and edge cut ratio. A balanced graph partition with small edge cut ratio is generally preferred since it reduces the expensive network communication cost. However, according to an empirical study on Giraph, the performance over well partitioned graph might be even two times worse than simple random partitions. This is because these systems only optimize for the simple partition strategies and cannot efficiently handle the increasing workload of local message processing when a high quality graph partition is used. In this paper, we propose a novel partition aware graph computation engine named PAGE, which equips a new message processor and a dynamic concurrency control model. The new message processor concurrently processes local and remote messages in a unified way. The dynamic model adaptively adjusts the concurrency of the processor based on the online statistics. The experimental evaluation demonstrates the superiority of PAGE over the graph partitions with various qualities.


Making flexible magnetic aerogels and stiff magnetic nanopaper using cellulose nanofibrils as templates.

Nanostructured biological materials inspire the creation of materials with tunable mechanical properties. Strong cellulose nanofibrils derived from bacteria or wood can form ductile or tough networks that are suitable as functional materials. Here, we show that freeze-dried bacterial cellulose nanofibril aerogels can be used as templates for making lightweight porous magnetic aerogels, which can be compacted into a stiff magnetic nanopaper. The 20-70-nm-thick cellulose nanofibrils act as templates for the non-agglomerated growth of ferromagnetic cobalt ferrite nanoparticles (diameter, 40-120 nm). Unlike solvent-swollen gels and ferrogels, our magnetic aerogel is dry, lightweight, porous (98%), flexible, and can be actuated by a small household magnet. Moreover, it can absorb water and release it upon compression. Owing to their flexibility, high porosity and surface area, these aerogels are expected to be useful in microfluidics devices and as electronic actuators.


When the face reveals what words do not: facial expressions of emotion, smiling, and the willingness to disclose childhood sexual abuse.

For survivors of childhood sexual abuse (CSA), verbal disclosure is often complex and painful. The authors examined the voluntary disclosure-nondisclosure of CSA in relation to nonverbal expressions of emotion in the face. Consistent with hypotheses derived from recent theorizing about the moral nature of emotion, CSA survivors who did not voluntarily disclose CSA showed greater facial expressions of shame, whereas CSA survivors who voluntarily disclosed CSA expressed greater disgust. Expressions of disgust also signaled sexual abuse accompanied by violence. Consistent with recent theorizing about smiling behavior, CSA nondisclosers made more polite smiles, whereas nonabused participants expressed greater genuine positive emotion. Discussion addressed the implications of these findings for the study of disclosure of traumatic events, facial expression, and the links between morality and emotion.


OpinionFinder: A System for Subjectivity Analysis

Vancouver, October 2005. OpinionFinder: A system for subjectivity analysis Theresa Wilson‡, Paul Hoffmann‡, Swapna Somasundaran†, Jason Kessler†, Janyce Wiebe†‡, Yejin Choi§, Claire Cardie§, Ellen Riloff∗, Siddharth Patwardhan∗ ‡Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15260 †Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260 §Department of Computer Science, Cornell University, Ithaca, NY 14853 ∗School of Computing, University of Utah, Salt Lake City, UT 84112


Model-Based Quantitative Network Security Metrics: A Survey

Network security metrics (NSMs) based on models allow to quantitatively evaluate the overall resilience of networked systems against attacks. For that reason, such metrics are of great importance to the security-related decision-making process of organizations. Considering that over the past two decades several model-based quantitative NSMs have been proposed, this paper presents a deep survey of the state-of-the-art of these proposals. First, to distinguish the security metrics described in this survey from other types of security metrics, an overview of security metrics, in general, and their classifications is presented. Then, a detailed review of the main existing model-based quantitative NSMs is provided, along with their advantages and disadvantages. Finally, this survey is concluded with an in-depth discussion on relevant characteristics of the surveyed proposals and open research issues of the topic.


Study of wheel slip and traction forces in differential drive robots and slip avoidance control strategy

The effect of wheel slip in differential drive robots is investigated in this paper. We consider differential drive robots with two driven wheels and ball-type caster wheels that are used to provide balance and support to the mobile robot. The limiting values of traction forces for slip and no slip conditions are dependent on wheel-ground kinetic and static friction coefficients. The traction forces are used to determine the fraction of input torque that provides robot motion and this is used to calculate the actual position of the robot under slip conditions. The traction forces under no slip conditions are used to determine the limiting value of the wheel torque above which the wheel slips. This limiting torque value is used to set a saturation limit for the input torque to avoid slip. Simulations are conducted to evaluate the behavior of the robot during slip and no slip conditions. Experiments are conducted under similar slip and no slip conditions using a custom built differential drive mobile robot with one caster wheel to validate the simulations. Experiments are also conducted with the torque limiting strategy. Results from model simulations and experiments are presented and discussed.


Design and fabrication of W-band SIW horn antenna using PCB process

A W-band SIW horn antenna is designed and fabricated using PCB process. Measured S11 reaches -15 dB at 84 GHz with bandwidth of 1 GHz and the maximum simulated gain is 9 dBi. The antenna is loaded with dielectric to increase gain and the effect of different loading lengths on gain is studied. The antenna is fed using a WR10 standard waveguide through a coupling slot in the SIW. The effect of changing the slot width on the return loss is studied. Good agreement is achieved between measured and simulated results. The proposed antenna is suitable for medical imaging and air traffic radar.


Aligning 3D models to RGB-D images of cluttered scenes

The goal of this work is to represent objects in an RGB-D scene with corresponding 3D models from a library. We approach this problem by first detecting and segmenting object instances in the scene and then using a convolutional neural network (CNN) to predict the pose of the object. This CNN is trained using pixel surface normals in images containing renderings of synthetic objects. When tested on real data, our method outperforms alternative algorithms trained on real data. We then use this coarse pose estimate along with the inferred pixel support to align a small number of prototypical models to the data, and place into the scene the model that fits best. We observe a 48% relative improvement in performance at the task of 3D detection over the current state-of-the-art [34], while being an order of magnitude faster.


Adaptive Scale Selection for Multiscale Segmentation of Satellite Images

With dramatically increasing of the spatial resolution of satellite imaging sensors, object-based image analysis (OBIA) has been gaining prominence in remote sensing applications. Multiscale image segmentation is a prerequisite step that splits an image into hierarchical homogeneous segmented objects for OBIA. However, scale selection remains a challenge in multiscale segmentation. In this study, we presented an adaptive approach for defining and estimating the optimal scale in the multiscale segmentation process. Central to our method is the combined use of image features from segmented objects and prior knowledge from historical thematic maps in a top-down segmentation procedure. Specifically, the whole image was first split into segmented objects, with the largest scale in a presupposition segmentation scale sequence. Second, based on segmented object features and prior knowledge in the local region of thematic maps, we calculated complexity values for each segmented object. Third, if the complexity values of an object were large enough, this object would be further split into multiple segmented objects with a smaller scale in the scale sequence. Then, in the similar manner, complex segmented objects were split into the simplest objects iteratively. Finally, the final segmentation result was obtained and evaluated. We have applied this method on a GF-1 multispectral satellite image and a ZY-3 multispectral satellite image to produce multiscale segmentation maps and further classification maps, compared with the state-of-the-art and the traditional mean shift algorithm. The experimental results illustrate that the proposed method is practically helpful and efficient to produce the appropriate segmented image objects with optimal scales.


Moral dilemmas in cognitive neuroscience of moral decision-making: A principled review

Moral dilemma tasks have been a much appreciated experimental paradigm in empirical studies on moral cognition for decades and have, more recently, also become a preferred paradigm in the field of cognitive neuroscience of moral decision-making. Yet, studies using moral dilemmas suffer from two main shortcomings: they lack methodological homogeneity which impedes reliable comparisons of results across studies, thus making a metaanalysis manifestly impossible; and second, they overlook control of relevant design parameters. In this paper, we review from a principled standpoint the studies that use moral dilemmas to approach the psychology of moral judgment and its neural underpinnings. We present a systematic review of 19 experimental design parameters that can be identified in moral dilemmas. Accordingly, our analysis establishes a methodological basis for the required homogeneity between studies and suggests the consideration of experimental aspects that have not yet received much attention despite their relevance.


Interlingua based Sanskrit-English machine translation

This paper is based on the work towards developing a machine translation system for Sanskrit to English. An interlingua based machine translation system in Paninian framework was developed in this work. As lexical functional grammar can handle semantic information along with syntactic analysis, the proposed system uses lexical functional grammar in different levels of analysis phase. In this system the given Sanskrit text is first converted to an intermediate notation called interlingua. This notation is then used for mapping to the target language, English, and generates the translated output. The proposed system works in the translation of simple and complex sentences in the given corpus. The karaka analysis system of Panini is used for the semantic analysis.


Development and field test of novel two-wheeled UAV for bridge inspections

This paper presents the development and field test of a novel unmanned aerial vehicle (UAV) for bridge inspections. The proposed UAV, which consists of a quadrotor, a cylindrical cage installed in it, and two spokeless wheels freely rotating around the cage, can climb and run on the bridge surface. Its structure improves durability and reduces air resistance compared with conventional UAVs with a cage or wheels. These advantages expand the uses of the UAV in various fields. This paper evaluates the effectiveness of the proposed UAV in real-world bridge inspection scenarios. We tested basic locomotion and measured air resistances. Experimental results on bridges demonstrated the ability to inspect various locations on bridges that are difficult for human inspectors to access.


An 87 fJ/conversion-step 12 b 10 MS/s SAR ADC using a minimum number of unit capacitors

This work proposes a 12 b 10 MS/s 0.11 lm CMOS successive-approximation register ADC based on a C-R hybrid DAC for low-power sensor applications. The proposed C-R DAC employs a 2-step split-capacitor array of upper seven bits and lower five bits to optimize power consumption and chip area at the target speed and resolution. A VCM-based switching method for the most significant bit and reference voltage segments from an insensitive R-string for the last two least significant bits minimize the number of unit capacitors required in the C-R hybrid DAC. The comparator accuracy is improved by an open-loop offset cancellation technique in the first-stage pre-amp. The prototype ADC in a 0.11 lm CMOS process demonstrates the measured differential nonlinearity and integral nonlinearity within 1.18 LSB and 1.42 LSB, respectively. The ADC shows a maximum signal-to-noise-and-distortion ratio of 63.9 dB and a maximum spurious-free dynamic range of 77.6 dB at 10 MS/s. The ADC with an active die area of 0.34 mm consumes 1.1 mW at 1.0 V and 10 MS/s, corresponding to a figure-of-merit of 87 fJ/conversion-step.


Assessment of degree of risk from sources of microbial contamination in cleanrooms ; 3 : Overall application

The European Commission and the Food and Drug Administration in the USA suggest that risk management and assessment methods should be used to identify and control sources of microbial contamination1,2. A risk management method has been described by Whyte3,4 that is based on the Hazard Analysis and Critical Control Point (HACCP) system but reinterpreted for use in cleanrooms, and called Risk Management of Contamination (RMC). This method is also described in the PHSS Technical Monograph No 145 and has the following steps.


Mindfulness and Meditation Practice as Moderators of the Relationship between Age and Subjective Wellbeing among Working Adults

Promoting the health and wellbeing of an aging and age-diverse workforce is a timely and growing concern to organizations and to society. To help address this issue, we investigated the relationship between age and subjective wellbeing by examining the moderating role of mindfulness in two independent studies. In study 1, trait mindfulness was examined as a moderator of the relationship between age and vitality and between age and work-family balance in a sample of 240 participants. In study 2, data from the second phase of the Midlife Development in the USA (MIDUS II) project was used to investigate mindful-practice (i.e., meditation) as a moderator of the relationships between age and multiple measures of subjective wellbeing (life satisfaction, psychological health, physical health) in a sample of 2477 adults. Results revealed that mindfulness moderates the relationship between age and multiple indicators of subjective wellbeing. In addition, study 2 results indicated that individuals who reported that they mediated often combined with those who reported they meditated a lot reported better physical health than those who reported that they never meditate. The findings suggest that cultivating mindfulness can be a proactive tool for fostering health and subjective wellbeing in an aging and agediverse workforce.


A Survey of Intrusion Detection Techniques

Intrusion detection is an alternative to the situation of the security violation.Security mechanism of the network is necessary against the threat to the system. There are two types of intruders: external intruders, who are unauthorized users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. This paper describes a brief overview of various intrusion detection techniques such as fuzzy logic, neural network, pattern recognition methods, genetic algorithms and related techniques is presented. Among the several soft computing paradigms, fuzzy rule-based classifiers, decision trees, support vector machines, linear genetic programming is model fast and efficient intrusion detection systems. KeywordsIntroduction, intrusion detection methods, misuse detection techniques, anomaly detection techniques, genetic algorithms.


Face Recognition with Local Binary Patterns, Spatial Pyramid Histograms and Naive Bayes Nearest Neighbor Classification

Face recognition algorithms commonly assume that face images are well aligned and have a similar pose -- yet in many practical applications it is impossible to meet these conditions. Therefore extending face recognition to unconstrained face images has become an active area of research. To this end, histograms of Local Binary Patterns (LBP) have proven to be highly discriminative descriptors for face recognition. Nonetheless, most LBP-based algorithms use a rigid descriptor matching strategy that is not robust against pose variation and misalignment. We propose two algorithms for face recognition that are designed to deal with pose variations and misalignment. We also incorporate an illumination normalization step that increases robustness against lighting variations. The proposed algorithms use descriptors based on histograms of LBP and perform descriptor matching with spatial pyramid matching (SPM) and Naive Bayes Nearest Neighbor (NBNN), respectively. Our contribution is the inclusion of flexible spatial matching schemes that use an image-to-class relation to provide an improved robustness with respect to intra-class variations. We compare the accuracy of the proposed algorithms against Ahonen's original LBP-based face recognition system and two baseline holistic classifiers on four standard datasets. Our results indicate that the algorithm based on NBNN outperforms the other solutions, and does so more markedly in presence of pose variations.


Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading

Mobile-edge computation offloading (MECO) off-loads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we study resource allocation for a multiuser MECO system based on time-division multiple access (TDMA) and orthogonal frequency-division multiple access (OFDMA). First, for the TDMA MECO system with infinite or finite cloud computation capacity, the optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under the constraint on computation latency. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function, which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Moreover, for the cloud with finite capacity, a sub-optimal resource-allocation algorithm is proposed to reduce the computation complexity for computing the threshold. Next, we consider the OFDMA MECO system, for which the optimal resource allocation is formulated as a mixed-integer problem. To solve this challenging problem and characterize its policy structure, a low-complexity sub-optimal algorithm is proposed by transforming the OFDMA problem to its TDMA counterpart. The corresponding resource allocation is derived by defining an average offloading priority function and shown to have close-to-optimal performance in simulation.


Multi-Label Classification: An Overview

Multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization, and semantic scene classification. This article introduces the task of multi-label classification, organizes the sparse related literature into a structured presentation and performs comparative experimental results of certain multi-label classification methods. It also contributes the definition of concepts for the quantification of the multi-label nature of a data set.


Offloading in Mobile Cloudlet Systems with Intermittent Connectivity

The emergence of mobile cloud computing enables mobile users to offload applications to nearby mobile resource-rich devices (i.e., cloudlets) to reduce energy consumption and improve performance. However, due to mobility and cloudlet capacity, the connections between a mobile user and mobile cloudlets can be intermittent. As a result, offloading actions taken by the mobile user may fail (e.g., the user moves out of communication range of cloudlets). In this paper, we develop an optimal offloading algorithm for the mobile user in such an intermittently connected cloudlet system, considering the users' local load and availability of cloudlets. We examine users' mobility patterns and cloudlets' admission control, and derive the probability of successful offloading actions analytically. We formulate and solve a Markov decision process (MDP) model to obtain an optimal policy for the mobile user with the objective to minimize the computation and offloading costs. Furthermore, we prove that the optimal policy of the MDP has a threshold structure. Subsequently, we introduce a fast algorithm for energy-constrained users to make offloading decisions. The numerical results show that the analytical form of the successful offloading probability is a good estimation in various mobility cases. Furthermore, the proposed MDP offloading algorithm for mobile users outperforms conventional baseline schemes.


Above the Clouds: A Berkeley View of Cloud Computing

Cloud Computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service and shaping the way IT hardware is designed and purchased. Developers with innovative ideas for new Internet services no longer require the large capital outlays in hardware to deploy their service or the human expense to operate it. They need not be concerned about overprovisioning for a service whose popularity does not meet their predictions, thus wasting costly resources, or underprovisioning for one that becomes wildly popular, thus missing potential customers and revenue. Moreover, companies with large batch-oriented tasks can get results as quickly as their programs can scale, since using 1000 servers for one hour costs no more than using one server for 1000 hours. This elasticity of resources, without paying a premium for large scale, is unprecedented in the history of IT. Cloud Computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the datacenters that provide those services. The services themselves have long been referred to as Software as a Service (SaaS). The datacenter hardware and software is what we will call a Cloud. When a Cloud is made available in a pay-as-you-go manner to the general public, we call it a Public Cloud; the service being sold is Utility Computing. We use the term Private Cloud to refer to internal datacenters of a business or other organization, not made available to the general public. Thus, Cloud Computing is the sum of SaaS and Utility Computing, but does not include Private Clouds. People can be users or providers of SaaS, or users or providers of Utility Computing. We focus on SaaS Providers (Cloud Users) and Cloud Providers, which have received less attention than SaaS Users. From a hardware point of view, three aspects are new in Cloud Computing.


2.4 to 61 GHz Multiband Double-Directional Propagation Measurements in Indoor Office Environments

This paper presents the details and results of double-directional propagation measurements carried out in two indoor office environments: a semi-open, cubicle-type office space and a more traditional work environment with closed offices. The measurements cover seven frequency bands from 2.4 to 61 GHz, permitting the propagation characteristics to be compared over a wide range of candidate radio frequencies for next-generation mobile wireless systems, including ultra high frequency and millimeter-wave bands. A novel processing algorithm is introduced for the expansion of multiband measurement data into sets of discrete multipath components. Based on the resulting multipath parameter estimates, models are presented for frequency-dependent path loss, shadow fading, copolarization ratio, delay spread, and angular spreads, along with their interfrequency correlations. Our results indicate a remarkably strong consistency in multipath structure over the entire frequency range considered.


A review of content-based image retrieval systems in medical applications - clinical benefits and future directions

Content-based visual information retrieval (CBVIR) or content-based image retrieval (CBIR) has been one on the most vivid research areas in the field of computer vision over the last 10 years. The availability of large and steadily growing amounts of visual and multimedia data, and the development of the Internet underline the need to create thematic access methods that offer more than simple text-based queries or requests based on matching exact database fields. Many programs and tools have been developed to formulate and execute queries based on the visual or audio content and to help browsing large multimedia repositories. Still, no general breakthrough has been achieved with respect to large varied databases with documents of differing sorts and with varying characteristics. Answers to many questions with respect to speed, semantic descriptors or objective image interpretations are still unanswered. In the medical field, images, and especially digital images, are produced in ever-increasing quantities and used for diagnostics and therapy. The Radiology Department of the University Hospital of Geneva alone produced more than 12,000 images a day in 2002. The cardiology is currently the second largest producer of digital images, especially with videos of cardiac catheterization ( approximately 1800 exams per year containing almost 2000 images each). The total amount of cardiologic image data produced in the Geneva University Hospital was around 1 TB in 2002. Endoscopic videos can equally produce enormous amounts of data. With digital imaging and communications in medicine (DICOM), a standard for image communication has been set and patient information can be stored with the actual image(s), although still a few problems prevail with respect to the standardization. In several articles, content-based access to medical images for supporting clinical decision-making has been proposed that would ease the management of clinical data and scenarios for the integration of content-based access methods into picture archiving and communication systems (PACS) have been created. This article gives an overview of available literature in the field of content-based access to medical image data and on the technologies used in the field. Section 1 gives an introduction into generic content-based image retrieval and the technologies used. Section 2 explains the propositions for the use of image retrieval in medical practice and the various approaches. Example systems and application areas are described. Section 3 describes the techniques used in the implemented systems, their datasets and evaluations. Section 4 identifies possible clinical benefits of image retrieval systems in clinical practice as well as in research and education. New research directions are being defined that can prove to be useful. This article also identifies explanations to some of the outlined problems in the field as it looks like many propositions for systems are made from the medical domain and research prototypes are developed in computer science departments using medical datasets. Still, there are very few systems that seem to be used in clinical practice. It needs to be stated as well that the goal is not, in general, to replace text-based retrieval methods as they exist at the moment but to complement them with visual search tools.


Wavelet based real-time smoke detection in video

A method for smoke detection in video is proposed. It is assumed the camera monitoring the scene is stationary. Since the smoke is semi-transparent, edges of image frames start loosing their sharpness and this leads to a decrease in the high frequency content of the image. To determine the smoke in the field of view of the camera, the background of the scene is estimated and decrease of high frequency energy of the scene is monitored using the spatial wavelet transforms of the current and the background images. Edges of the scene are especially important because they produce local extrema in the wavelet domain. A decrease in values of local extrema is also an indicator of smoke. In addition, scene becomes grayish when there is smoke and this leads to a decrease in chrominance values of pixels. Periodic behavior in smoke boundaries and convexity of smoke regions are also analyzed. All of these clues are combined to reach a final decision.


Short-Circuit Current of Wind Turbines With Doubly Fed Induction Generator

The short-circuit current contribution of wind turbines has not received much attention so far. This paper considers the short-circuit behavior, especially the short-circuit current of wind turbines with a doubly fed induction generator. Mostly, these wind turbines have a crowbar to protect the power electronic converter that is connected to the rotor windings of the induction generator. First, the maximum value of the short-circuit current of a conventional induction machine is determined. The differences between a crowbar-protected doubly fed induction generator and a conventional induction generator are highlighted and approximate equations for the maximum short-circuit current of a doubly fed induction generator are determined. The values obtained in this way are compared to the values obtained from time domain simulations. The differences are less then 15%


Game Theory in Wireless Networks: A Tutorial

The behavior of a given wireless device may affect the communication capabilities of a neighboring device, notably because the radio communication channel is usually shared in wireless networks. In this tutorial, we carefully explain how situations of this kind can be modelled by making use of game theory. By leveraging on four simple running examples, we introduce the most fundamental concepts of non-cooperative game theory. This approach should help students and scholars to quickly master this fascinating analytical tool without having to read the existing lengthy, economics-oriented books. It should also assist them in modelling problems of their own.


Cryptography and cryptanalysis for embedded systems

A growing number of devices of daily use are equipped with computing capabilities. Today, already more than 98 % of all manufactured microprocessors are employed in embedded applications , leaving less than 2 % to traditional computers. Many of these embedded devices are enabled to communicate amongst each other and form networks. A side effect of the rising interconnectedness is a possible vulnerability of these embedded systems. Attacks that have formerly been restricted to PCs can suddenly be launched against cars, tickets, ID cards or even pacemakers. At the same time the security awareness of users and manufacturers of such systems is much lower than in classical PC environments. This renders security one key aspect of embedded systems design and for most pervasive computing applications. As embedded systems are usually deployed in large numbers, costs are a main concern of system developers. Hence embedded security solutions have to be cheap and efficient. Many security services such as digital signatures can only be realized by public key cryptography. Yet, public key schemes are in terms of computation orders of magnitude more expensive than private key cryptosystems. At the same time the prevailing schemes rely on very similar security assumptions. If one scheme gets broken, almost all cryptosystems employing asymmetric cryptography become useless. The first part of this work explores alternatives to the prevailing public key cryptosystems. Two alternative signature schemes and one public key encryption scheme from the family of post quantum cryptosystems are explored. Their security relies on different assumptions so that a break of one of the prevailing schemes does not affect the security of the studied alternatives. The main focus lies on the implementational aspects of these schemes for embedded systems. One actual outcome is that, contrary to common belief, the presented schemes provide similar and in some cases even better performance than the prevailing schemes. The presented solutions include a highly scalable software implementation of the Merkle signature scheme aimed at low-cost microprocessors. For signatures in hardware an FPGA framework for implementing a family of signature schemes based on multivariate quadratic equations is presented. Depending on the chosen scheme, multivariate quadratic signatures show better performance than elliptic curves in terms of area consumption and performance. The McEliece cryptosystem is an alternative public key encryption scheme which was believed to be infeasible on embedded platforms due to its large key size. This work shows that by applying certain …


Recall of childhood trauma: a prospective study of women's memories of child sexual abuse.

One hundred twenty-nine women with previously documented histories of sexual victimization in childhood were interviewed and asked detailed questions about their abuse histories to answer the question "Do people actually forget traumatic events such as child sexual abuse, and if so, how common is such forgetting?" A large proportion of the women (38%) did not recall the abuse that had been reported 17 years earlier. Women who were younger at the time of the abuse and those who were molested by someone they knew were more likely to have no recall of the abuse. The implications for research and practice are discussed. Long periods with no memory of abuse should not be regarded as evidence that the abuse did not occur.


Blind Identification of Underdetermined Mixtures by Simultaneous Matrix Diagonalization

In this paper, we study simultaneous matrix diagonalization-based techniques for the estimation of the mixing matrix in underdetermined independent component analysis (ICA). This includes a generalization to underdetermined mixtures of the well-known SOBI algorithm. The problem is reformulated in terms of the parallel factor decomposition (PARAFAC) of a higher-order tensor. We present conditions under which the mixing matrix is unique and discuss several algorithms for its computation.


The Internet of Things: A Review of Enabled Technologies and Future Challenges

The Internet of Things (IoT) is an emerging classical model, envisioned as a system of billions of small interconnected devices for posing the state-of-the-art findings to real-world glitches. Over the last decade, there has been an increasing research concentration in the IoT as an essential design of the constant convergence between human behaviors and their images on Information Technology. With the development of technologies, the IoT drives the deployment of across-the-board and self-organizing wireless networks. The IoT model is progressing toward the notion of a cyber-physical world, where things can be originated, driven, intermixed, and modernized to facilitate the emergence of any feasible association. This paper provides a summary of the existing IoT research that underlines enabling technologies, such as fog computing, wireless sensor networks, data mining, context awareness, real-time analytics, virtual reality, and cellular communications. Also, we present the lessons learned after acquiring a thorough representation of the subject. Thus, by identifying numerous open research challenges, it is presumed to drag more consideration into this novel paradigm.


THE RESTORATIVE BENEFITS OF NATURE : TOWARD AN INTEGRATIVE

Directed attention plays an important role in human information processing; its fatigue, in turn, has far reaching consequences. Attention Restoration Theory provides an analysis of the kinds of experiences that lead to recovery from such fatigue. Natural environments turn out to be particularly rich in the character istics necessary for restorative experiences. An integrative framework is proposed that places both directed attention and stress in the larger context of human-environment relationships. © 1995 Academic Press Limited


Intracranial EEG and human brain mapping

This review is an attempt to highlight the value of human intracranial recordings (intracranial electro-encephalography, iEEG) for human brain mapping, based on their technical characteristics and based on the corpus of results they have already yielded. The advantages and limitations of iEEG recordings are introduced in detail, with an estimation of their spatial and temporal resolution for both monopolar and bipolar recordings. The contribution of iEEG studies to the general field of human brain mapping is discussed through a review of the effects observed in the iEEG while patients perform cognitive tasks. Those effects range from the generation of well-localized evoked potentials to the formation of large-scale interactions between distributed brain structures, via long-range synchrony in particular. A framework is introduced to organize those iEEG studies according to the level of complexity of the spatio-temporal patterns of neural activity found to correlate with cognition. This review emphasizes the value of iEEG for the study of large-scale interactions, and describes in detail the few studies that have already addressed this point.


Survey on Hadoop and Introduction to YARN Amogh

Big Data, the analysis of large quantities of data to gain new insight has become a ubiquitous phrase in recent years. Day by day the data is growing at a staggering rate. One of the efficient technologies that deal with the Big Data is Hadoop, which will be discussed in this paper. Hadoop, for processing large data volume jobs uses MapReduce programming model. Hadoop makes use of different schedulers for executing the jobs in parallel. The default scheduler is FIFO (First In First Out) Scheduler. Other schedulers with priority, pre-emption and non-pre-emption options have also been developed. As the time has passed the MapReduce has reached few of its limitations. So in order to overcome the limitations of MapReduce, the next generation of MapReduce has been developed called as YARN (Yet Another Resource Negotiator). So, this paper provides a survey on Hadoop, few scheduling methods it uses and a brief introduction to YARN. Keywords—Hadoop, HDFS, MapReduce, Schedulers, YARN.


Face recognition in unconstrained videos with matched background similarity

Recognizing faces in unconstrained videos is a task of mounting importance. While obviously related to face recognition in still images, it has its own unique characteristics and algorithmic requirements. Over the years several methods have been suggested for this problem, and a few benchmark data sets have been assembled to facilitate its study. However, there is a sizable gap between the actual application needs and the current state of the art. In this paper we make the following contributions. (a) We present a comprehensive database of labeled videos of faces in challenging, uncontrolled conditions (i.e., ‘in the wild’), the ‘YouTube Faces’ database, along with benchmark, pair-matching tests1. (b) We employ our benchmark to survey and compare the performance of a large variety of existing video face recognition techniques. Finally, (c) we describe a novel set-to-set similarity measure, the Matched Background Similarity (MBGS). This similarity is shown to considerably improve performance on the benchmark tests.


Implementation of high speed radix-10 parallel multiplier using Verilog

The emerging computational complexities arises the need of fast multiplication unit. The importance of multiplication in various applications necessitates improvement in its design so as to obtain the multiplication result efficiently. Multiplication operation can be improved by reducing the number of partial products to be added and by enhancing the adder unit for obtaining sum. The number of partial products can be reduced by using higher radix multiplication. For better speed applications a radix-10 multiplier is proposed which uses recoded multiplier digits as in conventional parallel multiplier design. The multiplier digits are encoded using Signed Digit (SD) radix-10 method which converts the digit set to {-5 to 5} from {0 to 9} and also generate a sign bit. This recoding leads to minimized calculations as only five multiples are required to be calculated and the negative multiples are obtained using 2's complement approach. The negative multiples are used when sign bit is high. A different approach is availed in the multiples generation of the multiplicand digit and during accumulation of partial product obtained during multiplication procedure. A modified BCD adder is used which eliminates the post correction while calculating the sum of two BCD digits. The modified architecture eliminates the extra recoding logic thereby reducing the area of overall architecture. This paper delivers the design and implementation of 16-Bit multiplication unit. The design entry is done in Verilog Hardware Description Language (HDL) and simulated using ISIM Simulator. It is synthesized and implemented using Xilinx ISE 12.2. Synthesis results have shown that 20.3% reduction in 4-Input LUTs and 20.4% reduction in the number of slices is observed in the modified methodology. Further 11.5% reduction of maximum combinational path delay is also observed in the modified architecture, thereby leading to high speed multiplication for VLSI applications.


Scenario-Based Performance Analysis of Routing Protocols for Mobile ad-hoc Networks

This study is a comparison of three routing protocols proposed for wireless mobile ad-hoc networks. The protocols are: Destination Sequenced Distance Vector (DSDV), Ad-hoc On demand Distance Vector (AODV) and Dynamic Source Routing (DSR). Extensive simulations are made on a scenario where nodes moves randomly. Results are presented as a function of a novel mobility metric designed to reflect the relative speeds of the nodes in a scenario. Furthermore, three realistic scenarios are introduced to test the protocols in more specialized contexts. In most simulations the reactive protocols (AODV and DSR) performed significantly better than DSDV. At moderate traffic load DSR performed better than AODV for all tested mobility values, while AODV performed better than DSR at higher traffic loads. The latter is caused by the source routes in DSR data packets, which increase the load on the network. routers and hosts, thus a node may forward packets between other nodes as well as run user applications. Mobile ad-hoc networks have been the focus of many recent research and development efforts. Ad-hoc packet radio networks have so far mainly concerned military applications, where a decentralized network configuration is an operative advantage or even a necessity. Networks using ad-hoc configuration concepts can be used in many military applications, ranging from interconnected wireless access points to networks of wireless devices carried by individuals, e.g., digital maps, sensors attached to the body, voice communication, etc. Combinations of wide range and short range ad-hoc networks seek to provide robust, global coverage, even during adverse operating conditions.


Language, thought and reference.

How should we best analyse the meaning of proper names, indexicals, demonstratives, both simple and complex, and definite descriptions? In what relation do such expressions stand to the objects they designate? In what relation do they stand to mental representations of those objects? Do these expressions form a semantic class, or must we distinguish between those that arc referential and those that are quantificational? Such questions have constituted one of the core research areas in the philosophy of language for much of the last century, yet consensus remains elusive: the field is still divided, for instance, between those who hold that all such expressions are semantically descriptive and those who would analyse most as the natural language counterparts of logical individual constants. The aim of this thesis is to cast new light on such questions by approaching them from within the cognitive framework of Sperber and Wilson's Relevance Theory. Relevance Theory offers not just an articulated pragmatics but also a broad conception of the functioning of natural language which differs radically from that presupposed within (most of) the philosophy of language. The function of linguistic expressions, on this conception, is not to determine propositional content, but rather to provide inferential premises which, in parallel with context and general pragmatic principles, will enable a bearer to reach the speaker's intended interpretation. Working within this framework, I shall argue that the semantics of the expressions discussed should best be analysed not in terms of their relation to those objects which, on occasions of use, they may designate, but rather in terms of the indications they offer a hearer concerning the mental representation which constitutes the content of a speaker's informative intention. Such an analysis can, I shall claim, capture certain key data on reference which have proved notoriously problematic, while respecting a broad range of apparently conflicting intuitions.


Modeling Multibody Dynamic Systems With Uncertainties . Part II : Numerical Applications

This study applies generalized polynomial chaos theory to model complex nonlinear multibody dynamic systems operating in the presence of parametric and external uncertainty. Theoretical and computational aspects of this methodology are discussed in the companion paper “Modeling Multibody Dynamic Systems With Uncertainties. Part I: Theoretical and Computational Aspects”. In this paper we illustrate the methodology on selected test cases. The combined effects of parametric and forcing uncertainties are studied for a quarter car model. The uncertainty distributions in the system response in both time and frequency domains are validated against Monte-Carlo simulations. Results indicate that polynomial chaos is more efficient than Monte Carlo and more accurate than statistical linearization. The results of the direct collocation approach are similar to the ones obtained with the Galerkin approach. A stochastic terrain model is constructed using a truncated Karhunen-Loeve expansion. The application of polynomial chaos to differential-algebraic systems is illustrated using the constrained pendulum problem. Limitations of the polynomial chaos approach are studied on two different test problems, one with multiple attractor points, and the second with a chaotic evolution and a nonlinear attractor set. The overall conclusion is that, despite its limitations, generalized polynomial chaos is a powerful approach for the simulation of multibody dynamic systems with uncertainties.


Normative data for the Rey-Osterrieth and the Taylor complex figure tests in Quebec-French people.

The Rey-Osterrieth (ROCF) and Taylor (TCF) complex figure tests are widely used to assess visuospatial and constructional abilities as well as visual/non-verbal memory. Normative data adjusted to the cultural and linguistic reality of older Quebec-French individuals is still nonexistent for these tests. In this article, we report the results of two studies that aimed to establish normative data for Quebec-French people (aged at least 50 years) for the copy, immediate recall, and delayed recall trials of the ROCF (Study 1) and the TCF (Study 2). For both studies, the impact of age, education, and sex on test performance was examined. Moreover, the impact of copy time on test performance, the impact of copy score on immediate and delayed recall score, and the impact of immediate recall score on delayed recall performance were examined. Based on regression models, equations to calculate Z scores for copy and recall scores are provided for both tests.


LIBSVX: A Supervoxel Library and Benchmark for Early Video Processing

Supervoxel segmentation has strong potential to be incorporated into early video analysis as superpixel segmentation has in image analysis. However, there are many plausible supervoxel methods and little understanding as to when and where each is most appropriate. Indeed, we are not aware of a single comparative study on supervoxel segmentation. To that end, we study seven supervoxel algorithms, including both off-line and streaming methods, in the context of what we consider to be a good supervoxel: namely, spatiotemporal uniformity, object/region boundary detection, region compression and parsimony. For the evaluation we propose a comprehensive suite of seven quality metrics to measure these desirable supervoxel characteristics. In addition, we evaluate the methods in a supervoxel classification task as a proxy for subsequent high-level uses of the supervoxels in video analysis. We use six existing benchmark video datasets with a variety of content-types and dense human annotations. Our findings have led us to conclusive evidence that the hierarchical graph-based (GBH), segmentation by weighted aggregation (SWA) and temporal superpixels (TSP) methods are the top-performers among the seven methods. They all perform well in terms of segmentation accuracy, but vary in regard to the other desiderata: GBH captures object boundaries best; SWA has the best potential for region compression; and TSP achieves the best undersegmentation error.


A tutorial on simulation modeling in six dimensions

Simulation involves modeling and analysis of real-world systems. This tutorial will provide a broad overview of the modeling practice within simulation by introducing the reader to modeling choices found using six dimensions: abstraction, complexity, culture, engineering, environment, and process. Modeling can be a daunting task even for the seasoned modeling and simulation professional, and so my goal is to introduce modeling in two ways: 1) to use one specific type of model (Petri Net) as an anchor for cross-dimensional discussion, and 2) to provide a follow up discussion, with additional non Petri Net examples, to clarify the extent of each dimension. For example, in the abstraction dimension, one must think about scale, refinement, and hierarchy when modeling regardless of the type of modeling language. The reader will come away with a broad framework within which to understand the possibilities of models and of modeling within the practice of simulation.


A Case Study of the New York City 2012-2013 Influenza Season With Daily Geocoded Twitter Data From Temporal and Spatiotemporal Perspectives

BACKGROUND
Twitter has shown some usefulness in predicting influenza cases on a weekly basis in multiple countries and on different geographic scales. Recently, Broniatowski and colleagues suggested Twitter's relevance at the city-level for New York City. Here, we look to dive deeper into the case of New York City by analyzing daily Twitter data from temporal and spatiotemporal perspectives. Also, through manual coding of all tweets, we look to gain qualitative insights that can help direct future automated searches.

OBJECTIVE
The intent of the study was first to validate the temporal predictive strength of daily Twitter data for influenza-like illness emergency department (ILI-ED) visits during the New York City 2012-2013 influenza season against other available and established datasets (Google search query, or GSQ), and second, to examine the spatial distribution and the spread of geocoded tweets as proxies for potential cases.

METHODS
From the Twitter Streaming API, 2972 tweets were collected in the New York City region matching the keywords "flu", "influenza", "gripe", and "high fever". The tweets were categorized according to the scheme developed by Lamb et al. A new fourth category was added as an evaluator guess for the probability of the subject(s) being sick to account for strength of confidence in the validity of the statement. Temporal correlations were made for tweets against daily ILI-ED visits and daily GSQ volume. The best models were used for linear regression for forecasting ILI visits. A weighted, retrospective Poisson model with SaTScan software (n=1484), and vector map were used for spatiotemporal analysis.

RESULTS
Infection-related tweets (R=.763) correlated better than GSQ time series (R=.683) for the same keywords and had a lower mean average percent error (8.4 vs 11.8) for ILI-ED visit prediction in January, the most volatile month of flu. SaTScan identified primary outbreak cluster of high-probability infection tweets with a 2.74 relative risk ratio compared to medium-probability infection tweets at P=.001 in Northern Brooklyn, in a radius that includes Barclay's Center and the Atlantic Avenue Terminal.

CONCLUSIONS
While others have looked at weekly regional tweets, this study is the first to stress test Twitter for daily city-level data for New York City. Extraction of personal testimonies of infection-related tweets suggests Twitter's strength both qualitatively and quantitatively for ILI-ED prediction compared to alternative daily datasets mixed with awareness-based data such as GSQ. Additionally, granular Twitter data provide important spatiotemporal insights. A tweet vector-map may be useful for visualization of city-level spread when local gold standard data are otherwise unavailable.


CatBoost: unbiased boosting with categorical features

This paper presents the key algorithmic techniques behind CatBoost, a new gradient boosting toolkit. Their combination leads to CatBoost outperforming other publicly available boosting implementations in terms of quality on a variety of datasets. Two critical algorithmic advances introduced in CatBoost are the implementation of ordered boosting, a permutation-driven alternative to the classic algorithm, and an innovative algorithm for processing categorical features. Both techniques were created to fight a prediction shift caused by a special kind of target leakage present in all currently existing implementations of gradient boosting algorithms. In this paper, we provide a detailed analysis of this problem and demonstrate that proposed algorithms solve it effectively, leading to excellent empirical results.


Strabismus Recognition Using Eye-Tracking Data and Convolutional Neural Networks

Strabismus is one of the most common vision diseases that would cause amblyopia and even permanent vision loss. Timely diagnosis is crucial for well treating strabismus. In contrast to manual diagnosis, automatic recognition can significantly reduce labor cost and increase diagnosis efficiency. In this paper, we propose to recognize strabismus using eye-tracking data and convolutional neural networks. In particular, an eye tracker is first exploited to record a subject's eye movements. A gaze deviation (GaDe) image is then proposed to characterize the subject's eye-tracking data according to the accuracies of gaze points. The GaDe image is fed to a convolutional neural network (CNN) that has been trained on a large image database called ImageNet. The outputs of the full connection layers of the CNN are used as the GaDe image's features for strabismus recognition. A dataset containing eye-tracking data of both strabismic subjects and normal subjects is established for experiments. Experimental results demonstrate that the natural image features can be well transferred to represent eye-tracking data, and strabismus can be effectively recognized by our proposed method.


Assessing forecast model performance in an ERP environment

Purpose – The paper aims to describe and apply a commercially oriented method of forecast performance measurement (cost of forecast error – CFE) and to compare the results with commonly adopted statistical measures of forecast accuracy in an enterprise resource planning (ERP) environment. Design/methodology/approach – The study adopts a quantitative methodology to evaluate the nine forecasting models (two moving average and seven exponential smoothing) of SAP’s ERP system. Event management adjustment and fitted smoothing parameters are also assessed. SAP is the largest European software enterprise and the third largest in the world, with headquarters in Walldorf, Germany. Findings – The findings of the study support the adoption of CFE as a more relevant commercial decision-making measure than commonly applied statistical forecast measures. Practical implications – The findings of the study provide forecast model selection guidance to SAP’s 12 þ million worldwide users. However, the CFE metric can be adopted in any commercial forecasting situation. Originality/value – This study is the first published cost assessment of SAP’s forecasting models.


Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks

Sentiment analysis of online user generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using images and videos to express their opinions and share their experiences. Sentiment analysis of such large scale visual content can help better extract user sentiments toward events or topics, such as those in image tweets, so that prediction of sentiment from visual content is complementary to textual sentiment analysis. Motivated by the needs in leveraging large scale yet noisy training data to solve the extremely challenging problem of image sentiment analysis, we employ Convolutional Neural Networks (CNN). We first design a suitable CNN architecture for image sentiment analysis. We obtain half a million training samples by using a baseline sentiment algorithm to label Flickr images. To make use of such noisy machine labeled data, we employ a progressive strategy to fine-tune the deep network. Furthermore, we improve the performance on Twitter images by inducing domain transfer with a small number of manually labeled Twitter images. We have conducted extensive experiments on manually labeled Twitter images. The results show that the proposed CNN can achieve better performance in image sentiment analysis than competing algorithms.


Universals and cultural differences in the judgments of facial expressions of emotion.

We present here new evidence of cross-cultural agreement in the judgement of facial expression. Subjects in 10 cultures performed a more complex judgment task than has been used in previous cross-cultural studies. Instead of limiting the subjects to selecting only one emotion term for each expression, this task allowed them to indicate that multiple emotions were evident and the intensity of each emotion. Agreement was very high across cultures about which emotion was the most intense. The 10 cultures also agreed about the second most intense emotion signaled by an expression and about the relative intensity among expressions of the same emotion. However, cultural differences were found in judgments of the absolute level of emotional intensity.


The Influence of Background Music on the Behavior of Restaurant Patrons Author ( s ) :

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Music Emotion Identification from Lyrics

Very large online music databases have recently been created by vendors, but they generally lack content-based retrieval methods. One exception is Allmusic.com which offers browsing by musical emotion, using human experts to classify several thousand songs into 183 moods. In this paper, machine learning techniques are used instead of human experts to extract emotions in Music. The classification is based on a psychological model of emotion that is extended to 23 specific emotion categories. Our results for mining the lyrical text of songs for specific emotion are promising, generate classification models that are human-comprehensible, and generate results that correspond to commonsense intuitions about specific emotions. Mining lyrics focused in this paper is one aspect of research which combines different classifiers of musical emotion such as acoustics and lyrical text.


Fusing audio, visual and textual clues for sentiment analysis from multimodal content

A huge number of videos are posted every day on social media platforms such as Facebook and YouTube. This makes the Internet an unlimited source of information. In the coming decades, coping with such information and mining useful knowledge from it will be an increasingly difficult task. In this paper, we propose a novel methodology for multimodal sentiment analysis, which consists in harvesting sentiments from Web videos by demonstrating a model that uses audio, visual and textual modalities as sources of information. We used both featureand decision-level fusion methods to merge affective information extracted from multiple modalities. A thorough comparison with existing works in this area is carried out throughout the paper, which demonstrates the novelty of our approach. Preliminary comparative experiments with the YouTube dataset show that the proposed multimodal system achieves an accuracy of nearly 80%, outperforming all state-of-the-art systems by more than 20%. & 2015 Elsevier B.V. All rights reserved.


The DEXMART hand: Mechatronic design and experimental evaluation of synergy-based control for human-like grasping

This paper summarizes recent activities carried out for the development of an innovative anthropomorphic robotic hand called the DEXMART Hand. The main goal of this research is to face the problems that affect current robotic hands by introducing suitable design solutions aimed at achieving simplification and cost reduction while possibly enhancing robustness and performance. While certain aspects of the DEXMART Hand development have been presented in previous papers, this paper is the first to give a comprehensive description of the final hand version and its use to replicate humanlike grasping. In this paper, particular emphasis is placed on the kinematics of the fingers and of the thumb, the wrist architecture, the dimensioning of the actuation system, and the final implementation of the position, force and tactile sensors. The paper focuses also on how these solutions have been integrated into the mechanical structure of this innovative robotic hand to enable precise force and displacement control of the whole system. Another important aspect is the lack of suitable control tools that severely limits the development of robotic hand applications. To address this issue, a new method for the observation of human hand behavior during interaction with common day-to-day objects by means of a 3D computer vision system is presented in this work together with a strategy for mapping human hand postures to the robotic hand. A simple control strategy based on postural synergies has been used to reduce the complexity of the grasp planning problem. As a preliminary evaluation of the DEXMART Hand’s capabilities, this approach has been adopted in this paper to simplify and speed up the transfer of human actions to the robotic hand, showing its effectiveness in reproducing human-like grasping.


Coping with autism: a journey toward adaptation.

As the number of individuals with autism grows, it is critical for nurses in all settings to understand how autism influences the family unit, as they will likely interact with these children, the adults, and their families. The intent of this descriptive narrative study was to explore the experiences of families of individuals with autism as perceived by the mother. Through personal interviews, 16 mothers' perceptions of the impact of autism on the family unit during different stages of the life cycle were revealed through a constructivist lens. Pediatric nurses employed in acute care settings, community, and schools are poised to assess and support these families following diagnosis and throughout the child's life.