title: User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

abstract: Active contour segmentation and its robust implementation using level set methods are well-established theoretical approaches that have been studied thoroughly in the image analysis literature. Despite the existence of these powerful segmentation methods, the needs of clinical research continue to be fulfilled, to a large extent, using slice-by-slice manual tracing. To bridge the gap between methodological advances and clinical routine, we developed an open source application called ITK-SNAP, which is intended to make level set segmentation easily accessible to a wide range of users, including those with little or no mathematical expertise. This paper describes the methods and software engineering philosophy behind this new tool and provides the results of validation experiments performed in the context of an ongoing child autism neuroimaging study. The validation establishes SNAP intrarater and interrater reliability and overlap error statistics for the caudate nucleus and finds that SNAP is a highly reliable and efficient alternative to manual tracing. Analogous results for lateral ventricle


title: Combining hierarchical and goal-directed speed-up techniques for dijkstra's algorithm

abstract: In recent years, highly effective hierarchical and goal-directed speed-up techniques for routing in large road networks have been developed. This article makes a systematic study of combinations of such techniques. These combinations turn out to give the best results in many scenarios, including graphs for unit disk graphs, grid networks, and time-expanded timetables. Besides these quantitative results, we obtain general insights for successful combinations.


Least Squares Temporal Difference Learning and Galerkin ’ s Method


title: Universal approximation using feedforward networks with non-sigmoid hidden layer activation functions

abstract: K.M. Hornik, M. Stinchcombe, and H. White (Univ. of California at San Diego, Dept. of Economics Discussion Paper, June 1988; to appear in Neural Networks) showed that multilayer feedforward networks with as few as one hidden layer, no squashing at the output layer, and arbitrary sigmoid activation function at the hidden layer are universal approximators: they are capable of arbitrarily accurate approximation to arbitrary mappings, provided sufficiently many hidden units are available. The present authors obtain identical conclusions but do not require the hidden-unit activation to be sigmoid. Instead, it can be a rather general nonlinear function. Thus, multilayer feedforward networks possess universal approximation capabilities by virtue of the presence of intermediate layers with sufficiently many parallel processors; the properties of the intermediate-layer activation function are not so crucial. In particular, sigmoid activation functions are not necessary for universal approximation. >


TITLE Buses and Crossbars BYLINE


Enhancing Memory Error Detection for Large-Scale Applications and Fuzz Testing


title: Alpha-band oscillations, attention, and controlled access to stored information

abstract: Alpha-band oscillations are the dominant oscillations in the human brain and recent evidence suggests that they have an inhibitory function. Nonetheless, there is little doubt that alpha-band oscillations also play an active role in information processing. In this article, I suggest that alpha-band oscillations have two roles (inhibition and timing) that are closely linked to two fundamental functions of attention (suppression and selection), which enable controlled knowledge access and semantic orientation (the ability to be consciously oriented in time, space, and context). As such, alpha-band oscillations reflect one of the most basic cognitive processes and can also be shown to play a key role in the coalescence of brain activity in different frequencies.


title: The Internet of Things: Challenges & security issues

abstract: Propelled by large-scale advances in wireless technologies, sensing technologies and communication technologies, the transformation of the Internet into an integrated network of things termed as Internet of Things is rapidly unfolding. The Internet of Things enabled by Wireless Sensor Networks (WSN) and RFID sensors finds a plethora of applications in almost all the fields such as health, education, transportation and agriculture. This paper briefs the idea of Internet of Things (IoT) and the challenges to its future growth. Also, this paper describes the general layered architecture of IoT along with its constituent elements. Further, the paper provides for a secure construction of the IoT architecture, by tackling security issues at each layer of the architecture. The paper concludes by mentioning the potential applications of the IoT technologies in fields ranging from intelligent transportation to smart home to e-health care and green agriculture.


title: Identifying the influential bloggers in a community

abstract: Blogging becomes a popular way for a Web user to publish information on the Web. Bloggers write blog posts, share their likes and dislikes, voice their opinions, provide suggestions, report news, and form groups in Blogosphere. Bloggers form their virtual communities of similar interests. Activities happened in Blogosphere affect the external world. One way to understand the development on Blogosphere is to find influential blog sites. There are many non-influential blog sites which form the "the long tail". Regardless of a blog site being influential or not, there are influential bloggers. Inspired by the high impact of the influentials in a physical community, we study a novel problem of identifying influential bloggers at a blog site. Active bloggers are not necessarily influential. Influential bloggers can impact fellow bloggers in various ways. In this paper, we discuss the challenges of identifying influential bloggers, investigate what constitutes influential bloggers, present a preliminary model attempting to quantify an influential blogger, and pave the way for building a robust model that allows for finding various types of the influentials. To illustrate these issues, we conduct experiments with data from a real-world blog site, evaluate multi-facets of the problem of identifying influential bloggers, and discuss unique challenges. We conclude with interesting findings and future work


title: Global physical activity levels: surveillance progress, pitfalls, and prospects

abstract: To implement eff ective non-communicable disease prevention programmes, policy makers need data for physical activity levels and trends. In this report, we describe physical activity levels worldwide with data for adults (15 years or older) from 122 countries and for adolescents (13–15-years-old) from 105 countries. Worldwide, 31·1% (95% CI 30·9–31·2) of adults are physically inactive, with proportions ranging from 17·0% (16·8–17·2) in southeast Asia to about 43% in the Americas and the eastern Mediterranean. Inactivity rises with age, is higher in women than in men, and is increased in high-income countries. The proportion of 13–15-year-olds doing fewer than 60 min of physical activity of moderate to vigorous intensity per day is 80·3% (80·1–80·5); boys are more active than are girls. Continued improvement in monitoring of physical activity would help to guide development of policies and programmes to increase activity levels and to reduce the burden of non-communicable diseases.


title: Jointly Learning Sentence Embeddings and Syntax with Unsupervised Tree-LSTMs

abstract: We introduce a neural network that represents sentences by composing their words according to induced binary parse trees. We use Tree-LSTM as our composition function, applied along a tree structure found by a fully differentiable natural language chart parser. Our model simultaneously optimises both the composition function and the parser, thus eliminating the need for externally-provided parse trees which are normally required for Tree-LSTM. It can therefore be seen as a tree-based RNN that is unsupervised with respect to the parse trees. As it is fully differentiable, our model is easily trained with an off-the-shelf gradient descent method and backpropagation. We demonstrate that it achieves better performance compared to various supervised Tree-LSTM architectures on a textual entailment task and a reverse dictionary task.


title: Notice of Violation of IEEE Publication PrinciplesHybrid recommender systems: Survey and experiments

abstract: Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, system that combines content-based recommendation and collaborative filtering to recommend restaurants.


title: A low noise, high power supply rejection low dropout regulator for wireless system-on-chip applications

abstract: This paper presents a novel two-stage low dropout regulator (LDO) that minimizes output noise via a pre-regulator stage and achieves high power supply rejection via a simple subtractor circuit in the power driver stage. The LDO is fabricated with a standard 0.35mum CMOS process and occupies 0.26 mm2 and 0.39mm2 for single and dual output respectively. Measurement showed PSR is 60dB at 10kHz and integrated noise is 21.2uVrms ranging from 1kHz to 100kHz


title: Privacy models for big data: a survey

abstract: Big data is the next big thing in computing. As this data cannot be processed using traditional systems, it poses numerous challenges to the research community. Privacy is one of the important concerns with data, be it traditional data or big data. This paper gives an overview of big data, the challenges with big data and the privacy preserving data sharing and publishing scenario. We focus on the various privacy models that can be extended to big data domain. A description of each privacy model with its benefits and drawbacks is discussed in the review. This survey will contribute much to the benefit of researchers and industry players in uncovering the critical areas of big data privacy.


title: Getting plugged in: an overview of internet addiction.

abstract: Internet addiction is not formally recognised as a clinical disorder by the WHO despite increasing evidence that excessive internet use can interfere with daily life and work. There is increasing pressure from Psychologists for Internet addiction to be recognised. This article explores the prevalence, symptoms and management of Internet addiction and the consequences of ignoring the ever growing concerns from public figures and institutions.


title: Three Phase Auto-tuned Shunt Hybrid Filter for Harmonic and Reactive Power Compensation

abstract: Abstract Hybrid filters are highly recommended for harmonic and reactive compensation in existing installations. Since current harmonic compensation can limit voltage harmonics also to a great extent, shunt hybrid filters are preferred. The traditional shunt hybrid filter consists of shunt passive and shunt active filter. Here, shunt passive filter provides fixed compensation at all load conditions. Therefore, probably at low load conditions, shunt passive filter acts as major consumer. This embarrassing situation can be avoided by replacing traditional shunt passive filter with the shunt auto-tuned passive filter. The shunt auto-tuned passive filter uses an ANN based controller to select passive filter components to provide adequate harmonic and reactive compensation under all load conditions. Remaining harmonic and reactive power compensation are provided by ANN based active filter. The performance of the proposed hybrid filter was tested by simulation and laboratory experiments under various source/load conditions and the results show that the proposed shunt hybrid filter is adaptive to varying source/load conditions.


title: From Movement to Thought: Executive Function, Embodied Cognition, and the Cerebellum

abstract: This paper posits that the brain evolved for the control of action rather than for the development of cognition per se. We note that the terms commonly used to describe brain–behavior relationships define, and in many ways limit, how we conceptualize and investigate them and may therefore constrain the questions we ask and the utility of the “answers” we generate. Many constructs are so nonspecific and over-inclusive as to be scientifically meaningless. “Executive function” is one such term in common usage. As the construct is increasingly focal in neuroscience research, defining it clearly is critical. We propose a definition that places executive function within a model of continuous sensorimotor interaction with the environment. We posit that control of behavior is the essence of “executive function,” and we explore the evolutionary advantage conferred by being able to anticipate and control behavior with both implicit and explicit mechanisms. We focus on the cerebellum's critical role in these control processes. We then hypothesize about the ways in which procedural (skill) learning contributes to the acquisition of declarative (semantic) knowledge. We hypothesize how these systems might interact in the process of grounding knowledge in sensorimotor anticipation, thereby directly linking movement to thought and “embodied cognition.” We close with a discussion of ways in which the cerebellum instructs frontal systems how to think ahead by providing anticipatory control mechanisms, and we briefly review this model's potential applications.


The Industry 4.0 Journey: Start the Learning Journey with the Reference Architecture Model Industry 4.0


title: Advantages and Disadvantages of PowerPoint in Lectures to Science Students

abstract: PowerPoint is now widely used in lectures to science students in most colleges of China. We summarize its advantages as producing better visual effects, high efficiency in information transfer, precise and systemic knowledge structure. Disadvantages of PowerPoint may be induced by irrelevant information in slides, neglect of interaction with students, uncontrolled speed in presenting or too strict order of slides. Strategies to avoid these disadvantages are proposed.


Public Financing of Election Campaigns: Constitutional Constraints on Steps toward Equality of Political Influence of Citizens


title: Automatic road accident detection techniques: A brief survey

abstract: A large number of precious lives are lost due to road traffic accidents every day. The common reasons are driver's mistake and late response from emergency services. There is a need to have an effective road accident detection and information communication system in place to save injured persons. A system that sends information messages to nearby emergency services about the accident location for timely response is absolutely in need. In research literature, a number of automatic accident detection systems are proposed by numerous researchers. These include accident detection using smartphones, GSM and GPS technologies, vehicular ad-hoc networks and mobile applications. The implementation of an automatic road accident detection and information communication system in every vehicle is very crucial. This paper presents a brief review on automatic road accident detection techniques used to save affected persons. An automatic road accident detection technique based on low cost ultrasonic sensors is also proposed.


title: Research on the Mechanism of Employee Work Reshaping's Impact on Innovative Behavior

abstract: Based on 204 managers and employees of large enterprises in Shanghai, this paper conducts an empirical study on the impact mechanism of employee job remodeling and innovation behavior. The results show that there is a significant positive relationship between them, in which interpersonal love plays a mediating role, and inclusive leadership regulates the relationship between job remodeling and innovative behavior.


title: Siamese Network for Underwater Multiple Object Tracking

abstract: For underwater videos, the performance of object tracking is greatly affected by illumination changes, background disturbances and occlusion. Hence, there is a need to have a robust function that computes image similarity, to accurately track the moving object. In this work, a hybrid model that incorporates the Kalman Filter, a Siamese neural network and a miniature neural network has been developed for object tracking. It was observed that the usage of the Siamese network to compute image similarity significantly improved the robustness of the tracker. Although the model was developed for underwater videos, it was found that it performs well for both underwater and human surveillance videos. A metric has been defined for analyzing detections-to-tracks mapping accuracy. Tracking results have been analyzed using Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP)metrics.


title: Machine Learning Approach for IP-Flow Record Anomaly Detection

abstract: Faced to continuous arising new threats, the detection of anomalies in current operational networks has become essential. Network operators have to deal with huge data volumes for analysis purpose. To counter this main issue, dealing with IP flow (also known as Netflow) records is common in network management. However, still in modern networks, Netflow records represent high volume of data. In this paper, we present an approach for evaluating Netflow records by referring to a method of temporal aggregation applied to Machine Learning techniques. We present an approach that leverages support vector machines in order to analyze large volumes of Netflow records. Our approach is using a special kernel function, that takes into account both the contextual and the quantitative information of Netflow records. We assess the viability of our method by practical experimentation on data volumes provided by a major internet service provider in Luxembourg.


title: Bundle recommendation in ecommerce

abstract: Recommender system has become an important component in modern eCommerce. Recent research on recommender systems has been mainly concentrating on improving the relevance or profitability of individual recommended items. But in reality, users are usually exposed to a set of items and they may buy multiple items in one single order. Thus, the relevance or profitability of one item may actually depend on the other items in the set. In other words, the set of recommendations is a bundle with items interacting with each other. In this paper, we introduce a novel problem called the Bundle Recommendation Problem (BRP). By solving the BRP, we are able to find the optimal bundle of items to recommend with respect to preferred business objective. However, BRP is a large-scale NP-hard problem. We then show that it may be sufficient to solve a significantly smaller version of BRP depending on properties of input data. This allows us to solve BRP in real-world applications with millions of users and items. Both offline and online experimental results on a Walmart.com demonstrate the incremental value of solving BRP across multiple baseline models.


Latent Dirichlet Allocation: Towards a Deeper Understanding


title: Promoting Positive Post-Click Experience for In-Stream Yahoo Gemini Users

abstract: Click-through rate (CTR) is the most common metric used to assess the performance of an online advert; another performance of an online advert is the user post-click experience. In this paper, we describe the method we have implemented in Yahoo Gemini to measure the post-click experience on Yahoo mobile news streams via an automatic analysis of advert landing pages. We measure the post-click experience by means of two well-known metrics, dwell time and bounce rate. We show that these metrics can be used as proxy of an advert post-click experience, and that a negative post-click experience has a negative effect on user engagement and future ad clicks. We then put forward an approach that analyses advert landing pages, and show how these can affect dwell time and bounce rate. Finally, we develop a prediction model for advert quality based on dwell time, which was deployed on Yahoo mobile news stream app running on iOS. The results show that, using dwell time as a proxy of post-click experience, we can prioritise higher quality ads. We demonstrate the impact of this on users via A/B testing.


title: Enhanced CNN for image denoising

abstract: Owing to the flexible architectures of deep convolutional neural networks (CNNs) are successfully used for image denoising. However, they suffer from the following drawbacks: (i) deep network architecture is very difficult to train. (ii) Deeper networks face the challenge of performance saturation. In this study, the authors propose a novel method called enhanced convolutional neural denoising network (ECNDNet). Specifically, they use residual learning and batch normalisation techniques to address the problem of training difficulties and accelerate the convergence of the network. In addition, dilated convolutions are used in the proposed network to enlarge the context information and reduce the computational cost. Extensive experiments demonstrate that the ECNDNet outperforms the state-of-the-art methods for image denoising.


title: An Investigation on the Additional Cost of Green Products: The Willingness to Pay of University Students

abstract: Nowadays, it is inevitable for companies to be human oriented as well as being customer oriented. Because one of the factors that provide long-term profitability is to be socially responsible. Companies perform this with the help of green marketing applications. The aim of this study is to determine the willingness to pay of university students for the additional cost of green product, and reveal consequences for companies in terms of price adjustments. In this study used convenience sampling, 10 different durable and non-durable products have been determined. Different price levels selected in order to expose price sensitivity. While determining the price categories increases in the cost of 5 % have been taken into consideration. The survey implemented to 481 participants at Sakarya University. It’s concluded that consumers are willing to pay an additional price for green products. Price sensitivity is found to be relative. Product itself is also found important as well as price. Consequently for a better environment companies keep prices at acceptable levels and consumers purchase more green products.


title: Persuasive Games: The Expressive Power of Videogames

abstract: Videogames are both an expressive medium and a persuasive medium; they represent how real and imagined systems work, and they invite players to interact with those systems and form judgments about them. In this innovative analysis, Ian Bogost examines the way videogames mount arguments and influence players. Drawing on the 2,500-year history of rhetoric, the study of persuasive expression, Bogost analyzes rhetoric's unique function in software in general and videogames in particular. The field of media studies already analyzes visual rhetoric, the art of using imagery and visual representation persuasively. Bogost argues that videogames, thanks to their basic representational mode of procedurality (rule-based representations and interactions), open a new domain for persuasion; they realize a new form of rhetoric. Bogost calls this new form "procedural rhetoric," a type of rhetoric tied to the core affordances of computers: running processes and executing rule-based symbolic manipulation. He argues further that videogames have a unique persuasive power that goes beyond other forms of computational persuasion. Not only can videogames support existing social and cultural positions, but they can also disrupt and change those positions, leading to potentially significant long-term social change. Bogost looks at three areas in which videogame persuasion has already taken form and shows considerable potential: politics, advertising, and education. Bogost is both an academic researcher and a videogame designer, and Persuasive Games reflects both theoretical and game-design goals.


title: Plant Phenolics: Extraction, Analysis and Their Antioxidant and Anticancer Properties

abstract: Phenolics are broadly distributed in the plant kingdom and are the most abundant secondary metabolites of plants. Plant polyphenols have drawn increasing attention due to their potent antioxidant properties and their marked effects in the prevention of various oxidative stress associated diseases such as cancer. In the last few years, the identification and development of phenolic compounds or extracts from different plants has become a major area of health- and medical-related research. This review provides an updated and comprehensive overview on phenolic extraction, purification, analysis and quantification as well as their antioxidant properties. Furthermore, the anticancer effects of phenolics in-vitro and in-vivo animal models are viewed, including recent human intervention studies. Finally, possible mechanisms of action involving antioxidant and pro-oxidant activity as well as interference with cellular functions are discussed.


title: A tutorial on the flexible optical networking paradigm: State of the art, trends, and research challenges

abstract: Rigid fixed-grid wavelength division multiplexing (WDM) optical networks can no longer keep up with the emerging bandwidth-hungry and highly dynamic services in an efficient manner. As the available spectrum in optical fibers becomes occupied and is approaching fundamental limits, the research community has focused on seeking more advanced optical transmission and networking solutions that utilize the available bandwidth more effectively. To this end, the flexible/elastic optical networking paradigm has emerged as a way to offer efficient use of the available optical resources. In this work, we provide a comprehensive view of the different pieces composing the “flexible networking puzzle” with special attention given to capturing the occurring interactions between different research fields. Only when these interrelations are clearly defined, an optimal network-wide solution can be offered. Physical layer technological aspects, network optimization for flexible networks, and control plane aspects are examined. Furthermore, future research directions and open issues are discussed.


title: DEVELOPMENT PHENOMENON : AN ANALYSIS BASED ON SOCIAL NETWORK THEORY

abstract: The OSS movement is a phenomenon that challenges many traditional theories in economics, software engineering, business strategy, and IT management. Thousands of software programmers are spending tremendous amounts of time and effort writing and debugging software, most often with no direct monetary compensation. The programs, some of which are extremely large and complex, are written without the benefit of traditional project management, change tracking, or error checking techniques. Since the programmers are working outside of a traditional organizational reward structure, accountability is an issue as well. A significant portion of internet e-commerce runs on OSS, and thus many firms have little choice but to trust mission-critical e-commerce systems to run on such software, requiring IT management to deal with new types of socio-technical problems. A better understanding of how the OSS community functions may help IT planners make more informed decisions and develop more effective strategies for using OSS software. We hypothesize that open source software development can be modeled as self-organizing, collaboration, social networks. We analyze structural data on over 39,000 open source projects hosted at SourceForge.net involving over 33,000 developers. We define two software developers to be connected — part of a collaboration social network — if they are members of the same project, or are connected by a chain of connected developers. Project sizes, developer project participation, and clusters of connected developers are analyzed. We find evidence to support our hypothesis, primarily in the presence of power-law relationships on project sizes (number of developers per project), project membership (number of projects joined by a developer), and cluster sizes. Potential implications for IT researchers, IT managers, and governmental policy makers are discussed.


Securing Bitcoin-like Backbone Protocols against a Malicious Majority of Computing Power.


title: A Foundational Architecture for Artificial General Intelligence

abstract: Implementing and fleshing out a number of psychological and neuroscience theories of cognition, the LIDA conceptual model aims at being a cognitive “theory of everything.” With modules or processes for perception, working memory, episodic memories, “consciousness,” procedural memory, action selection, perceptual learning, episodic learning, deliberation, volition, and non-routine problem solving, the LIDA model is ideally suited to provide a working ontology that would allow for the discussion, design, and comparison of AGI systems. The LIDA architecture is based on the LIDA cognitive cycle, a sort of “cognitive atom.” The more elementary cognitive modules and processes play a role in each cognitive cycle. Higher-level processes are performed over multiple cycles. In addition to giving a quick overview of the LIDA conceptual model, and its underlying computational technology, we argue for the LIDA architecture's role as a foundational architecture for an AGI. Finally, lessons For AGI researchers drawn from the model and its architecture are discussed.


title: Major Impacts of Key Reinstallation Attack on Internet of Things System

abstract: The Internet of Things (IoT) is one of the emerging technologies that has seized the attention of researchers. The reason behind that was the fact that the IoT was expected to be applied in our daily life in the near future and humans will be wholly dependent on this technology for comfort and an easy life style. The standard Internet protocols raise significant challenges that could stand in the way of realising potential IoT benefits. In this paper, we introduced and discussed the major impacts of existing internet protocols vulnerably on the IoT system especially the venerability of the WPA2 protocol (cryptographic protocol) as one of the most crucial aspects of security and privacy in the IoT. Ways to mitigate the impact of the attack are then proposed.


Psychodynamics and the Insanity Defense: "Ordinary Common Sense" and Heuristic Reasoning


title: Comparison of smart grid architectures for monitoring and analyzing power grid data via Modbus and REST

abstract: Smart grid, smart metering, electromobility, and the regulation of the power network are keywords of the transition in energy politics. In the future, the power grid will be smart. Based on different works, this article presents a data collection, analyzing, and monitoring software for a reference smart grid. We discuss two possible architectures for collecting data from energy analyzers and analyze their performance with respect to real-time monitoring, load peak analysis, and automated regulation of the power grid. In the first architecture, we analyze the latency, needed bandwidth, and scalability for collecting data over the Modbus TCP/IP protocol and in the second one over a RESTful web service. The analysis results show that the solution with Modbus is more scalable as the one with RESTful web service. However, the performance and scalability of both architectures are sufficient for our reference smart grid and use cases.


title: POI2Vec: Geographical Latent Representation for Predicting Future Visitors

abstract: With the increasing popularity of location-aware social media applications, Point-of-Interest (POI) recommendation has recently been extensively studied. However, most of the existing studies explore from the users' perspective, namely recommending POIs for users. In contrast, we consider a new research problem of predicting users who will visit a given POI in a given future period. The challenge of the problem lies in the difficulty to effectively learn POI sequential transition and user preference, and integrate them for prediction. In this work, we propose a new latent representation model POI2Vec that is able to incorporate the geographical influence, which has been shown to be very important in modeling user mobility behavior. Note that existing representation models fail to incorporate the geographical influence. We further propose a method to jointly model the user preference and POI sequential transition influence for predicting potential visitors for a given POI. We conduct experiments on 2 real-world datasets to demonstrate the superiority of our proposed approach over the state-of-the-art algorithms for both next POI prediction and future user prediction.


title: Ego-lane estimation for downtown lane-level navigation

abstract: We present an ego-lane estimation algorithm for downtown lane-level navigation. It is capable of determining the currently used lane reliably, using sensors available in a modern production vehicle, such as odometry, GPS, visual lane-marking detection, and radar-based object detection. The method employs a particle filter with a novel step that combines the importance weight update and sampling. This step avoids performance deterioration in case of sparse particle sets even when the likelihood is very tight compared to the predicted particle set. Preprocessed odometry data allow for a further performance increase. In an extensive test in downtown scenarios on real roads with up to seven lanes, it achieves error probabilities below 1% in the 95th percentile at availabilities above 95%.


title: Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly

abstract: Due to the importance of zero-shot learning, i.e. classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadily. We argue that it is time to take a step back and to analyze the status quo of the area. The purpose of this paper is three-fold. First, given the fact that there is no agreed upon zero-shot learning benchmark, we first define a new benchmark by unifying both the evaluation protocols and data splits of publicly available datasets used for this task. This is an important contribution as published results are often not comparable and sometimes even flawed due to, e.g. pre-training on zero-shot test classes. Moreover, we propose a new zero-shot learning dataset, the Animals with Attributes 2 (AWA2) dataset which we make publicly available both in terms of image features and the images themselves. Second, we compare and analyze a significant number of the state-of-the-art methods in depth, both in the classic zero-shot setting but also in the more realistic generalized zero-shot setting. Finally, we discuss in detail the limitations of the current status of the area which can be taken as a basis for advancing it.


title: Pneumonectomy for Non-Small Cell Lung Cancer.

abstract: Lung cancer is the leading cause of cancer deaths and its incidence continues to increase. Emerging therapies as part of a multimodal approach are making more patients eligible for surgical resection. As more surgeons are treating locally advanced non-small cell lung cancer they find themselves recommending pneumonectomy as the surgical component of the multidisciplinary plan. Performing a pneumonectomy is technically demanding and is associated with many potential perioperative comorbidities. With the proper preparation, experience, and attention to perioperative care, pneumonectomy can be carried out safely with excellent outcomes and a good quality of life.


title: Force histograms computed in O(NlogN)

abstract: The relative position between two objects in a 2D raster image is often represented quantitatively by a force histogram. In the general case, force histograms are computed in O(KN?N) time: N is the number of pixels in the image and K is the number of directions in which forces are considered. When the objects are defined as fuzzy sets, this complexity also depends quadratically on the number M of possible membership degrees. In the present paper, an algorithm that runs in O(NlogN) is introduced. Computation times are basically independent of K and M. All objects (convex, concave, crisp, fuzzy) are handled in an equally fast manner. Experiments validate the theoretical analysis.


title: Effects of stress on sleep: the moderating role of coping style.

abstract: The aim of this prospective quasi-experimental study was to assess the role of coping style as a factor moderating the relationship between stress and sleep. Sleep of 36 students was assessed by means of actigraphy and daily logs during low-stress and high-stress periods. The high-stress period was the week that the students were evaluated for acceptance to graduate programs in clinical psychology. The low-stress period was a regular academic week. The students’ ways of coping were assessed during the baseline low-stress period using the COPE inventory. Data analysis revealed that a high emotion-focused coping score was significantly predictive of reduction in sleep time from the low- to the high-stress period. These results suggest that coping style is a key factor in assessing the relationship between stress and sleep.


title: Food Packaging?Roles, Materials, and Environmental Issues

abstract: The Institute of Food Technologists has issued this Scientific Status Summary to update readers on food packaging and its impact on the environment.


title: Original Article A Comparison of the Recognition of Overworkrelated Cardiovascular Disease in Japan, Korea,

abstract: In Japan, Korea, and Taiwan, cerebrovascular and cardiovascular diseases (CVDs) caused by overwork are recognized by government as work-related. These three countries are the only countries in the world that officially recognize CVDs caused by psychosocial factors (e.g., over - work) as work-related cerebrovascular and cardiovascular diseases (WR-CVDs), and compensate employees accordingly. The present study compared the similarities and differences among the rec- ognition of overwork-related CVDs in Japan, Korea, and Taiwan. The criteria by which WR-CVDs are identified are very similar in the three countries. However, in the interval surveyed (1996-2009), Korea had a remarkably larger number of recognized WR-CVD patients than did Japan or Tai- wan. Recognition of occupational diseases is influenced by various factors, including socio-cultural values, the nature of occupational health care schemes, the extent of the social security umbrella, national health insurance policy, and scientific evidence. Our results show that social factors may be very different among the three countries studied, although the recognition criteria for WR-CVDs are quite similar.


Novelty Goes Deep. A Deep Neural Solution To Document Level Novelty Detection


Dialogue-act-driven Conversation Model : An Experimental Study


title: Emotion Recognition in Speech using Cross-Modal Transfer in the Wild

abstract: Obtaining large, human labelled speech datasets to train models for emotion recognition is a notoriously challenging task, hindered by annotation cost and label ambiguity. In this work, we consider the task of learning embeddings for speech classification without access to any form of labelled audio. We base our approach on a simple hypothesis: that the emotional content of speech correlates with the facial expression of the speaker. By exploiting this relationship, we show that annotations of expression can be transferred from the visual domain (faces) to the speech domain (voices) through cross-modal distillation. We make the following contributions: (i) we develop a strong teacher network for facial emotion recognition that achieves the state of the art on a standard benchmark; (ii) we use the teacher to train a student, tabula rasa, to learn representations (embeddings) for speech emotion recognition without access to labelled audio data; and (iii) we show that the speech emotion embedding can be used for speech emotion recognition on external benchmark datasets. Code, models and data are available.


title: High boost ratio hybrid transformer DC-DC converter for photovoltaic module applications

abstract: This paper presents a non-isolated, high boost ratio hybrid transformer dc-dc converter with applications for low voltage renewable energy sources. The proposed converter utilizes a hybrid transformer to transfer the inductive and capacitive energy simultaneously, achieving a high boost ratio with a smaller size magnetic component. As a result of incorporating the resonant operation mode into the traditional high boost ratio PWM converter, the turn off loss of the switch is reduced, increasing the efficiency of the converter under all load conditions. The input current ripple is also reduced because of the linear-sinusoidal hybrid waveforms. The voltage stresses on the active switch and diodes are maintained at a low level and are independent of the changing input voltage over a wide range as a result of the resonant capacitor transferring energy to the output. The effectiveness of the proposed converter was experimentally verified using a 220 W prototype circuit. Utilizing an input voltage ranging from 20V to 45V and a load range of 30W to 220W, the experimental results show system of efficiencies greater than 96% with a peak efficiency of 97.4% at 35V input, 160W output. Because of high efficiency over wide output power range and the ability to operate with a wide variable input voltage, the proposed converter is an attractive design for alternative low dc voltage energy sources, such as solar photovoltaic (PV) modules.


The Digitization of Word-of-Mouth: Promise and Challenges of Online Reputation Systems


title: Ferroptosis as a p53-mediated activity during tumour suppression

abstract: Although p53-mediated cell-cycle arrest, senescence and apoptosis serve as critical barriers to cancer development, emerging evidence suggests that the metabolic activities of p53 are also important. Here we show that p53 inhibits cystine uptake and sensitizes cells to ferroptosis, a non-apoptotic form of cell death, by repressing expression of SLC7A11, a key component of the cystine/glutamate antiporter. Notably, p53(3KR), an acetylation-defective mutant that fails to induce cell-cycle arrest, senescence and apoptosis, fully retains the ability to regulate SLC7A11 expression and induce ferroptosis upon reactive oxygen species (ROS)-induced stress. Analysis of mutant mice shows that these non-canonical p53 activities contribute to embryonic development and the lethality associated with loss of Mdm2. Moreover, SLC7A11 is highly expressed in human tumours, and its overexpression inhibits ROS-induced ferroptosis and abrogates p53(3KR)-mediated tumour growth suppression in xenograft models. Our findings uncover a new mode of tumour suppression based on p53 regulation of cystine metabolism, ROS responses and ferroptosis.


Sentence-Ranking-Enhanced Keywords Extraction from Chinese Patents.


title: Sensitivity-optimized rigging for example-based real-time clothing synthesis

abstract: We present a real-time solution for generating detailed clothing deformations from pre-computed clothing shape examples. Given an input pose, it synthesizes a clothing deformation by blending skinned clothing deformations of nearby examples controlled by the body skeleton. Observing that cloth deformation can be well modeled with sensitivity analysis driven by the underlying skeleton, we introduce a sensitivity based method to construct a pose-dependent rigging solution from sparse examples. We also develop a sensitivity based blending scheme to find nearby examples for the input pose and evaluate their contributions to the result. Finally, we propose a stochastic optimization based greedy scheme for sampling the pose space and generating example clothing shapes. Our solution is fast, compact and can generate realistic clothing animation results for various kinds of clothes in real time.


title: Computational Models of Human Visual Attention and Their Implementations: A Survey

abstract: SUMMARY We humans are easily able to instantaneously detect the regions in a visual scene that are most likely to contain something of interest. Exploiting this pre-selection mechanism called visual attention for image and video processing systems would make them more sophisticated and therefore more useful. This paper briefly describes various computational models of human visual attention and their development, as well as related psychophysical findings. In particular, our objective is to carefully distinguish several types of studies related to human visual attention and saliency as a measure of attentiveness, and to provide a taxonomy from several viewpoints such as the main objective, the use of additional cues and mathematical principles. This survey finally discusses possible future directions for research into human visual attention and saliency computation.


title: A diffusion-neural-network for learning from small samples

abstract: Abstract Neural information processing models largely assume that the patterns for training a neural network are sufficient. Otherwise, there must exist a non-negligible error between the real function and the estimated function from a trained network. To reduce the error, in this paper, we suggest a diffusion-neural-network (DNN) to learn from a small sample consisting of only a few patterns. A DNN with more nodes in the input and layers is trained by using the deriving patterns instead of original patterns. In this paper, we give an example to show how to construct a DNN for recognizing a non-linear function. In our case, the DNN’s error is less than the error of the conventional BP network, about 48%. To substantiate the special case arguments, we also study other two non-linear functions with simulation technology. The results show that the DNN model is very effective in the case where the target function has a strong non-linearity or a given sample is very small.


title: Horror Image Recognition Based on Context-Aware Multi-Instance Learning

abstract: Horror content sharing on the Web is a growing phenomenon that can interfere with our daily life and affect the mental health of those involved. As an important form of expression, horror images have their own characteristics that can evoke extreme emotions. In this paper, we present a novel context-aware multi-instance learning (CMIL) algorithm for horror image recognition. The CMIL algorithm identifies horror images and picks out the regions that cause the sensation of horror in these horror images. It obtains contextual cues among adjacent regions in an image using a random walk on a contextual graph. Borrowing the strength of the fuzzy support vector machine (FSVM), we define a heuristic optimization procedure based on the FSVM to search for the optimal classifier for the CMIL. To improve the initialization of the CMIL, we propose a novel visual saliency model based on the tensor analysis. The average saliency value of each segmented region is set as its initial fuzzy membership in the CMIL. The advantage of the tensor-based visual saliency model is that it not only adaptively selects features, but also dynamically determines fusion weights for saliency value combination from different feature subspaces. The effectiveness of the proposed CMIL model is demonstrated by its use in horror image recognition on two large-scale image sets collected from the Internet.


title: A delta-sigma fractional-N frequency divider for a Phase Lock Loop in 60GHz transceiver

abstract: A design and optimization flow for digital delta-sigma fractional-N frequency divider of Phase Lock Loop (PLL) is introduced in this paper. The low power design is used for the 60 GHz RF transceiver using 0.18um SiGe BiCMOS technology. With full consideration of the low complexity and unconditional stability, MASH111 delta-sigma modulator is chosen as control module for the PLL. The input frequency is 6.48Ghz, and the integer division rate is from 36 to 64 with step of 2.


title: Intelligence and security informatics for homeland security: information, communication, and transportation

abstract: Intelligence and security informatics (ISI) is an emerging field of study aimed at developing advanced information technologies, systems, algorithms, and databases for national- and homeland-security-related applications, through an integrated technological, organizational, and policy-based approach. This paper summarizes the broad application and policy context for this emerging field. Three detailed case studies are presented to illustrate several key ISI research areas, including cross-jurisdiction information sharing; terrorism information collection, analysis, and visualization; and "smart-border" and bioterrorism applications. A specific emphasis of this paper is to note various homeland-security-related applications that have direct relevance to transportation researchers and to advocate security informatics studies that tightly integrate transportation research and information technologies.


Soft-to-Hard Vector Quantization for End-to-End Learned Compression of Images and Neural Networks


Distance Covariance Analysis


A Foundation for the Study of IT Effects: A New Look at DeSanctis and Poole’s Concepts of Structural Features and Spirit


Reconstruction of deformed, partially edentulous ridges, using full thickness onlay grafts. Part I. Technique and wound healing.


title: Mitigation of switching overvoltages in transmission lines via controlled switching

abstract: The goal of this work is to present a controlled switching strategy to reduce overvoltages due to transmission line closing and re-closing. Controlled closing and re-closing of transmission lines eliminates the need of pre-insertion resistors, reducing in this way the costs of transmission line circuit breakers. The switching strategy consists on finding a suitable circuit breaker making instant, i. e., the time in which the voltage across the circuit breaker contacts is zero and the time span between the closing instant of the first and the last pole is as small as possible, minimizing in this way, the effect of the electromagnetic coupling between transmission line phases. The method is based on a very simple zero crossing algorithm with constraints and is implemented in the Alternative Transients Program (ATP) using the MODELS language. Data from the North-Northeast Brazilian Power System Grid, planned to be in service by 2009, are used to produce a few case studies. Some line switching events are analyzed: line closing and re-closing taking into account trapped charges and shunt compensation effects. The controlled switching performance is compared to the pre-insertion resistorpsilas method. The simulations attest the efficiency of the method, limiting switching overvoltages to nearly 1.55 per unit for shunt compensated transmission lines. In addition, controlled switching provides an increase of power apparatus life time, improvement on power quality and possibly the elimination of impositions that makes prohibitive some switching operations.


title: Gabor noise by example

abstract: Procedural noise is a fundamental tool in Computer Graphics. However, designing noise patterns is hard. In this paper, we present Gabor noise by example, a method to estimate the parameters of bandwidth-quantized Gabor noise, a procedural noise function that can generate noise with an arbitrary power spectrum, from exemplar Gaussian textures, a class of textures that is completely characterized by their power spectrum. More specifically, we introduce (i) bandwidth-quantized Gabor noise, a generalization of Gabor noise to arbitrary power spectra that enables robust parameter estimation and efficient procedural evaluation; (ii) a robust parameter estimation technique for quantized-bandwidth Gabor noise, that automatically decomposes the noisy power spectrum estimate of an exemplar into a sparse sum of Gaussians using non-negative basis pursuit denoising; and (iii) an efficient procedural evaluation scheme for bandwidth-quantized Gabor noise, that uses multi-grid evaluation and importance sampling of the kernel parameters. Gabor noise by example preserves the traditional advantages of procedural noise, including a compact representation and a fast on-the-fly evaluation, and is mathematically well-founded.


title: Optimization Problems for Fast AAM Fitting in-the-Wild

abstract: We describe a very simple framework for deriving the most-well known optimization problems in Active Appearance Models (AAMs), and most importantly for providing efficient solutions. Our formulation results in two optimization problems for fast and exact AAM fitting, and one new algorithm which has the important advantage of being applicable to 3D. We show that the dominant cost for both forward and inverse algorithms is a few times mN which is the cost of projecting an image onto the appearance subspace. This makes both algorithms not only computationally realizable but also very attractive speed-wise for most current systems. Because exact AAM fitting is no longer computationally prohibitive, we trained AAMs in-the-wild with the goal of investigating whether AAMs benefit from such a training process. Our results show that although we did not use sophisticated shape priors, robust features or robust norms for improving performance, AAMs perform notably well and in some cases comparably with current state-of-the-art methods. We provide Matlab source code for training, fitting and reproducing the results presented in this paper at http://ibug.doc.ic.ac.uk/resources.


title: A Method of Resolver-to-Digital Conversion Based on Square Wave Excitation

abstract: A novel method of resolver-to-digital conversion based on square wave excitation is proposed in this paper. First, the outputs of the resolver under square wave excitation are theoretically analyzed in comparison with those under cosine excitation. Analysis results indicate that the former outputs are proportional to the desired demodulated waves, whereas the latter are coupled with the excitation signal. On this basis, demodulation can be achieved by multiplying the square wave excitation with the outputs, respectively, yielding the sine and cosine demodulated waves. Smooth and independent of the excitation, these demodulated waves can be sampled at any time with the sampling frequency no longer restricted to the excitation frequency, thus solving the problems of fixed-point and fixed-frequency sampling under the traditional cosine excitation. Finally, a simple design of the peripheral circuits is presented to realize the proposed method, and the effectiveness and feasibility of the method is verified by experimental results.


title: Cloud Data Auditing Techniques with a Focus on Privacy and Security

abstract: Nowadays, large amounts of data are stored with cloud service providers. Third-party auditors (TPAs), with the help of cryptography, are often used to verify this data. However, most auditing schemes don't protect cloud user data from TPAs. A review of the state of the art and research in cloud data auditing techniques highlights integrity and privacy challenges, current solutions, and future research directions.


title: A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms

abstract: Measurement of visual quality is of fundamental importance for numerous image and video processing applications, where the goal of quality assessment (QA) algorithms is to automatically assess the quality of images or videos in agreement with human quality judgments. Over the years, many researchers have taken different approaches to the problem and have contributed significant research in this area and claim to have made progress in their respective domains. It is important to evaluate the performance of these algorithms in a comparative setting and analyze the strengths and weaknesses of these methods. In this paper, we present results of an extensive subjective quality assessment study in which a total of 779 distorted images were evaluated by about two dozen human subjects. The "ground truth" image quality data obtained from about 25 000 individual human quality judgments is used to evaluate the performance of several prominent full-reference image quality assessment algorithms. To the best of our knowledge, apart from video quality studies conducted by the Video Quality Experts Group, the study presented in this paper is the largest subjective image quality study in the literature in terms of number of images, distortion types, and number of human judgments per image. Moreover, we have made the data from the study freely available to the research community . This would allow other researchers to easily report comparative results in the future


title: Stacking Graphic Elements to Avoid Over-Plotting

abstract: An ongoing challenge for information visualization is how to deal with over-plotting forced by ties or the relatively limited visual field of display devices. A popular solution is to represent local data density with area (bubble plots, treemaps), color(heatmaps), or aggregation (histograms, kernel densities, pixel displays). All of these methods have at least one of three deficiencies:1) magnitude judgments are biased because area and color have convex downward perceptual functions, 2) area, hue, and brightnesshave relatively restricted ranges of perceptual intensity compared to length representations, and/or 3) it is difficult to brush or link toindividual cases when viewing aggregations. In this paper, we introduce a new technique for visualizing and interacting with datasets that preserves density information by stacking overlapping cases. The overlapping data can be points or lines or other geometric elements, depending on the type of plot. We show real-dataset applications of this stacking paradigm and compare them to other techniques that deal with over-plotting in high-dimensional displays.


title: A First Look at Android Malware Traffic in First Few Minutes

abstract: With the advent of mobile era, mobile terminals are going through a trend of surpassing PC to become the most popular computing device. Meanwhile, the hackers and viruswriters are paying close attention to the mobile terminals, especially the Android platform. The growing of malwares on the Android system has drawn attentions from both the academia and security industry. Recently, mobile network traffic analysis has been used to identify the malware. But due to the lack of a large-scale malware repository and a systematic analysis of network traffic features, the existing research mostly remain in theory. In this paper, we design an Android malware traffic behavior monitoring scheme to capture traffic data generated by malware samples in a real Internet environment. We capture the network traffic from 5560 malware samples in the first 5 minutes, and analyze the major compositions of the traffic data. We discover that HTTP and DNS traffic are accounted for more than 99% on the application layer traffic. We then present an analysis of related network features: DNS query, HTTP packet length, ratio of downlink to uplink traffic amount, HTTP request and Ad traffic feature. Our statistical results illustrate that: (1) more than 70% malwares generate malicious traffic in the first 5 minutes, (2) DNS query and HTTP request can be used to identify the malware, and the detection rate reaches 69.55% and 40.89% respectively, (3) Ad traffic can greatly affect the malware detection. We believe our research provides an in-depth analysis into mobile malwares' network behaviors.


Boosting Unsupervised Grammar Induction by Splitting Complex Sentences on Function Words


title: Feature selection and intrusion classification in NSL-KDD cup 99 dataset employing SVMs

abstract: Intrusion is the violation of information security policy by malicious activities. Intrusion detection (ID) is a series of actions for detecting and recognising suspicious actions that make the expedient acceptance of standards of confidentiality, quality, consistency, and availability of a computer based network system. In this paper, we present a new approach consists with merging of feature selection and classification for multiple class NSL-KDD cup 99 intrusion detection dataset employing support vector machine (SVM). The objective is to improve the competence of intrusion classification with a significantly reduced set of input features from the training data. In supervised learning, feature selection is the process of selecting the important input training features and removing the irrelevant input training features, with the objective of obtaining a feature subset that produces higher classification accuracy. In the experiment, we have applied SVM classifier on several input feature subsets of training dataset of NSL-KDD cup 99 dataset. The experimental results obtained showed the proposed method successfully bring 91% classification accuracy using only three features and 99% classification accuracy using 36 features, while all 41 training features achieved 99% classification accuracy.


title: Significant Location Detection & Prediction in Cellular Networks Using Artificial Neural Networks

abstract: Location services and applications, based on network data or global positioning systems, are greatly influencing and changing the way people use mobile phone networks by improving not only user-applications but also the network management part. These applications and services can be further developed by introducing location prediction. We design a system that logs cell id and timestamp data from the users' mobile device, detects the significance of the location to the user, such as home and workplace, and predicts future locations over a chosen time period using artificial neural networks. A novel method is designed for location detection that automatically determines the significance of the location to the user, by spatial and temporal analysis. In our approach, the neural network is automatically adapted, with the help of the location detection algorithm, to the period of the week for which a prediction is desired, achieving accurate weekday and weekend location prediction.


title: Antenna Miniaturization Techniques: A Review of Topology- and Material-Based Methods

abstract: Antenna miniaturization has been the subject of numerous studies for almost 70 years [1]-[4]. Early studies showed that a decrease in the size of an antenna results in a direct reduction in its bandwidth and efficiency (hr) [1], [2]. The size limitation translates into a lower boundary on the achievable radiation quality factor (Q factor) and consequently on the maximum achievable impedance bandwidth. Recently, many new investigations have been conducted to reduce the form factor (or the overall size) of different types of antennas while trying to maintain acceptable matching properties and operating bandwidth. These miniaturization techniques are generally related to changing the electrical and physical properties of an antenna.


title: Article 1 A Survey on UWB Medium Access Control Schemes A Survey on Ultra Wide Band Medium Access Control Schemes Abstract

abstract: Ultra wideband (UWB) technology based primarily on the impulse radio paradigm has a huge potential for revolutionizing the world of digital communications especially wireless communications. UWB provides the integrated capabilities of data communications, advanced radar and precision tracking, location, imperceptibility and low power operation. It is therefore ideally suited for the development of robust and rapid wireless networks in complex and hostile environments. The distinct physical layer properties of the UWB technology warrants efficient design of medium access control (MAC) protocols. This paper introduces the unique UWB physical characteristics compared to the existing wireless technologies and discusses current research on MAC protocols for UWB. This report surveys most of the MAC protocols proposed so far for UWB, and may instigate further activities on this important and evolving technology.


title: Two algorithms for the Student-Project allocation problem

abstract: We study the Student-Project Allocation problem (SPA), a generalisation of the classical Hospitals/Residents problem (HR). An instance of SPA involves a set of students, projects and lecturers. Each project is offered by a unique lecturer, and both projects and lecturers have capacity constraints. Students have preferences over projects, whilst lecturers have preferences over students. We present two optimal linear-time algorithms for allocating students to projects, subject to the preference and capacity constraints. In particular, each algorithm finds a stable matching of students to projects. Here, the concept of stability generalises the stability definition in the HR context. The stable matching produced by the first algorithm is simultaneously best-possible for all students, whilst the one produced by the second algorithm is simultaneously best-possible for all lecturers. We also prove some structural results concerning the set of stable matchings in a given instance of SPA. The SPA problem model that we consider is very general and has applications to a range of different contexts besides student-project allocation.


title: An analysis of the dynamic behavior of JavaScript programs

abstract: The JavaScript programming language is widely used for web programming and, increasingly, for general purpose computing. As such, improving the correctness, security and performance of JavaScript applications has been the driving force for research in type systems, static analysis and compiler techniques for this language. Many of these techniques aim to reign in some of the most dynamic features of the language, yet little seems to be known about how programmers actually utilize the language or these features. In this paper we perform an empirical study of the dynamic behavior of a corpus of widely-used JavaScript programs, and analyze how and why the dynamic features are used. We report on the degree of dynamism that is exhibited by these JavaScript programs and compare that with assumptions commonly made in the literature and accepted industry benchmark suites.


Confronting Ageism: A MUST for Mental Health


title: Compact InP-Based DFB-EAM Enabling PAM-4 112 Gb/s Transmission Over 2 km

abstract: We demonstrate a 56-GBd pulse-amplitude modulation-4 compact InP transmitter module integrating a distributed feedback laser and an electro-absorption modulator, which exhibits 50-GHz bandwidth, >13-dB extinction ratio and up to 1.5-mW output power. Successful amplifier-free 112-Gb/s transmission is performed over 2 km using low complexity three-taps equalization. We also study the tradeoff between receiver bandwidth, equalizer length and performance, and show successful signal recovery with bandwidths as low as 18 GHz when increasing the equalizer length.


title: Deep Active Learning over the Long Tail

abstract: This paper is concerned with pool-based active learning for deep neural networks. Motivated by coreset dataset compression ideas, we present a novel active learning algorithm that queries consecutive points from the pool using farthest-first traversals in the space of neural activation over a representation layer. We show consistent and overwhelming improvement in sample complexity over passive learning (random sampling) for three datasets: MNIST, CIFAR-10, and CIFAR-100. In addition, our algorithm outperforms the traditional uncertainty sampling technique (obtained using softmax activations), and we identify cases where uncertainty sampling is only slightly better than random sampling.


title: mDARAL: A Multi-Radio Version for the DARAL Routing Algorithm

abstract: Smart Cities are called to change the daily life of human beings. This concept permits improving the efficiency of our cities in several areas such as the use of water, energy consumption, waste treatment, and mobility both for people as well as vehicles throughout the city. This represents an interconnected scenario in which thousands of embedded devices need to work in a collaborative way both for sensing and modifying the environment properly. Under this scenario, the majority of devices will use wireless protocols for communicating among them, representing a challenge for optimizing the use of the electromagnetic spectrum. When the density of deployed nodes increases, the competition for using the physical medium becomes harder and, in consequence, traffic collisions will be higher, affecting data-rates in the communication process. This work presents mDARAL, a multi-radio routing algorithm based on the Dynamic and Adaptive Radio Algorithm (DARAL), which has the capability of isolating groups of nodes into sub-networks. The nodes of each sub-network will communicate among them using a dedicated radio frequency, thus isolating the use of the radio channel to a reduced number of nodes. Each sub-network will have a master node with two physical radios, one for communicating with its neighbours and the other for being the contact point among its group and other sub-networks. The communication among sub-networks is done through master nodes in a dedicated radio frequency. The algorithm works to maximize the overall performance of the network through the distribution of the traffic messages into unoccupied frequencies. The obtained results show that mDARAL achieves great improvement in terms of the number of control messages necessary to connect a node to the network, convergence time and energy consumption during the connection phase compared to DARAL.


title: Stochastic Backpropagation and Approximate Inference in Deep Generative Models

abstract: We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent approximate posterior distributions, and that acts as a stochastic encoder of the data. We develop stochastic back-propagation -- rules for back-propagation through stochastic variables -- and use this to develop an algorithm that allows for joint optimisation of the parameters of both the generative and recognition model. We demonstrate on several real-world data sets that the model generates realistic samples, provides accurate imputations of missing data and is a useful tool for high-dimensional data visualisation.


title: A tunable dual-band square slot antenna with stub for DCS, ISM, and WiMAX applications

abstract: This paper presents the design and parametric study of a tunable dual band square slot antenna with stub for DSC, ISM, and WiMAX applications. This dual band antenna has an ability to be tuned to any of the mentioned bands, according to the required applications, depending on the length of square side and the stub. The overall size of the designed antenna is 50mm × 50mm. The antenna has been etched to an epoxy FR4 substrate with a relative permittivity of 4.4 and a thickness of 1.6 mm. A parametric study has been presented for the stub. The design equation for the lower band and the design curve for the upper band are obtained. The resulted radiation patterns have been shown for different frequencies which give the antenna a good characteristic to be candidate as a dual-band antenna for the aforementioned applications. A commercial High Frequency Structure Simulator (HFSS), ver. 15, has been used to analyze the design parameters.


Biofunctional change in yeast cell surface on treatment with triton X-100.


title: Difference-based Conformance Checking for Ecore Metamodels.

abstract: During modern model-driven development processes, generators and higherorder transformations are used to create metamodels with short life cycles. Since these metamodels often differ from each other only in small parts, instances as well as metamodels may be re-used if the difference between them does not lead to a violation of instance conformance. Existing co-evolution approaches describe this conformance based on change operators to a metamodel. Thus, they require that changes to the metamodels are carried out using special editors. To use this conformance for arbitrarily generated metamodels, we present a conformance validator for Ecore metamodels that is based on difference-based analysis. The validator has been implemented as a plug-in for the Eclipse framework. We demonstrate the completeness of our approach by covering state-of-the-art co-evolution change operators.


title: Reading of electronic documents: the usability of linear, fisheye, and overview+detail interfaces

abstract: Reading of electronic documents is becoming increasingly important as more information is disseminated electronically. We present an experiment that compares the usability of a linear, a fisheye, and an overview+detail interface for electronic documents. Using these interfaces, 20 subjects wrote essays and answered questions about scientific documents. Essays written using the overview+detail interface received higher grades, while subjects using the fisheye interface read documents faster. However, subjects used more time to answer questions with the overview+detail interface. All but one subject preferred the overview+detail interface. The most common interface in practical use, the linear interface, is found to be inferior to the fisheye and overview+detail interfaces regarding most aspects of usability. We recommend using overview+detail interfaces for electronic documents, while fisheye interfaces mainly should be considered for time-critical tasks.


title: Transfer Learning for Low-Resource Neural Machine Translation

abstract: The encoder-decoder framework for neural machine translation (NMT) has been shown effective in large data scenarios, but is much less effective for low-resource languages. We present a transfer learning method that significantly improves Bleu scores across a range of low-resource languages. Our key idea is to first train a high-resource language pair (the parent model), then transfer some of the learned parameters to the low-resource pair (the child model) to initialize and constrain training. Using our transfer learning method we improve baseline NMT models by an average of 5.6 Bleu on four low-resource language pairs. Ensembling and unknown word replacement add another 2 Bleu which brings the NMT performance on low-resource machine translation close to a strong syntax based machine translation (SBMT) system, exceeding its performance on one language pair. Additionally, using the transfer learning model for re-scoring, we can improve the SBMT system by an average of 1.3 Bleu, improving the state-of-the-art on low-resource machine translation.


title: An Optimized Classification of Human Cancer Disease for Gene Expression Data

abstract: It is everybody’s right to receive safe food irrespective of the industries. Foreign objects in food are a never ending issue. This article emphasizes on the Strategies to Eliminate Foreign Objects in Hospital Kitchen Food. Patients trust and believe that the food served to them will be safe. It is the responsibility of the food service provider to ensure that safe and hygienic food is provided to the patients and foreign objects should never find a place in the patient food. +


title: WebSHArk 1.0: A Benchmark Collection for Malicious Web Shell Detection

abstract: Web shells are programs that are written for a specific purpose in Web scripting languages, such as PHP, ASP, ASP.NET, JSP, PERL-CGI, etc. Web shells provide a means to communicate with the server's operating system via the interpreter of the web scripting languages. Hence, web shells can execute OS specific commands over HTTP. Usually, web attacks by malicious users are made by uploading one of these web shells to compromise the target web servers. Though there have been several approaches to detect such malicious web shells, no standard dataset has been built to compare various web shell detection techniques. In this paper, we present a collection of web shell files, WebSHArk 1.0, as a standard dataset for current and future studies in malicious web shell detection. To provide baseline results for future studies and for the improvement of current tools, we also present some benchmark results by scanning the WebSHArk dataset directory with three web shell scanning tools that are publicly available on the Internet. The WebSHArk 1.0 dataset is only available upon request via email to one of the authors, due to security and legal issues.


title: An Efficient Visual Hull Computation Algorithm

abstract: In this paper we describe an efficient algorithm for computing the visual hull of an object. This problem is equivalent to computing the intersection of generalized cones. The naive visual hull computation algorithm requires intersecting 3D polyhedra. We exploit the special structure of generalized cone polyhedra and show how to reduce this computation to a set of intersections in 2D. Moreover, we describe how the 2D intersections can be carried out efficiently.


title: CoMoFoD — New database for copy-move forgery detection

abstract: Due to the availability of many sophisticated image processing tools, a digital image forgery is nowadays very often used. One of the common forgery method is a copy-move forgery, where part of an image is copied to another location in the same image with the aim of hiding or adding some image content. Numerous algorithms have been proposed for a copy-move forgery detection (CMFD), but there exist only few benchmarking databases for algorithms evaluation. We developed new database for a CMFD that consist of 260 forged image sets. Every image set includes forged image, two masks and original image. Images are grouped in 5 categories according to applied manipulation: translation, rotation, scaling, combination and distortion. Also, postprocessing methods, such as JPEG compression, blurring, noise adding, color reduction etc., are applied at all forged and original images. In this paper we present database organization and content, creation of forged images, postprocessing methods, and database testing. CoMoFoD database is available at http://www.vcl.fer.hr/comofod.


title: Soft switching interleaved active clamp flyback inverter for a photovoltaic AC module system

abstract: A soft switching interleaved active clamp flyback inverter for a photovoltaic AC module system is proposed. The proposed system includes an interleaved active clamp flyback converter and an unfolding bridge inverter. The interleaved active clamp flyback converter has the advantages of zero voltage switching (ZVS) performance for the primary switches, reverse-recovery problem alleviation for the secondary output diodes and small input current ripple. Furthermore, each switch in the flyback converter operates with a high efficiency point along with an output power using a new optimal control method. Therefore, high efficiency and high power density conversion can be achieved in a with input voltage range by employing the proposed system. In order to verify these, theoretical analysis and simulation are performed.


title: Mediterranean Diet, Traditional Risk Factors, and the Rate of Cardiovascular Complications after 1 Myocardial Infarction. Final report of the Lyon Diet Heart Study

abstract: BACKGROUND ::: The Lyon Diet Heart Study is a randomized secondary prevention trial aimed at testing whether a Mediterranean-type diet may reduce the rate of recurrence after a first myocardial infarction. An intermediate analysis showed a striking protective effect after 27 months of follow-up. This report presents results of an extended follow-up (with a mean of 46 months per patient) and deals with the relationships of dietary patterns and traditional risk factors with recurrence. ::: ::: ::: METHODS AND RESULTS ::: Three composite outcomes (COs) combining either cardiac death and nonfatal myocardial infarction (CO 1), or the preceding plus major secondary end points (unstable angina, stroke, heart failure, pulmonary or peripheral embolism) (CO 2), or the preceding plus minor events requiring hospital admission (CO 3) were studied. In the Mediterranean diet group, CO 1 was reduced (14 events versus 44 in the prudent Western-type diet group, P=0.0001), as were CO 2 (27 events versus 90, P=0.0001) and CO 3 (95 events versus 180, P=0. 0002). Adjusted risk ratios ranged from 0.28 to 0.53. Among the traditional risk factors, total cholesterol (1 mmol/L being associated with an increased risk of 18% to 28%), systolic blood pressure (1 mm Hg being associated with an increased risk of 1% to 2%), leukocyte count (adjusted risk ratios ranging from 1.64 to 2.86 with count >9x10(9)/L), female sex (adjusted risk ratios, 0.27 to 0. 46), and aspirin use (adjusted risk ratios, 0.59 to 0.82) were each significantly and independently associated with recurrence. ::: ::: ::: CONCLUSIONS ::: The protective effect of the Mediterranean dietary pattern was maintained up to 4 years after the first infarction, confirming previous intermediate analyses. Major traditional risk factors, such as high blood cholesterol and blood pressure, were shown to be independent and joint predictors of recurrence, indicating that the Mediterranean dietary pattern did not alter, at least qualitatively, the usual relationships between major risk factors and recurrence. Thus, a comprehensive strategy to decrease cardiovascular morbidity and mortality should include primarily a cardioprotective diet. It should be associated with other (pharmacological?) means aimed at reducing modifiable risk factors. Further trials combining the 2 approaches are warranted.


title: Fast Localization in Large-Scale Environments Using Supervised Indexing of Binary Features

abstract: The essence of image-based localization lies in matching 2D key points in the query image and 3D points in the database. State-of-the-art methods mostly employ sophisticated key point detectors and feature descriptors, e.g., Difference of Gaussian (DoG) and Scale Invariant Feature Transform (SIFT), to ensure robust matching. While a high registration rate is attained, the registration speed is impeded by the expensive key point detection and the descriptor extraction. In this paper, we propose to use efficient key point detectors along with binary feature descriptors, since the extraction of such binary features is extremely fast. The naive usage of binary features, however, does not lend itself to significant speedup of localization, since existing indexing approaches, such as hierarchical clustering trees and locality sensitive hashing, are not efficient enough in indexing binary features and matching binary features turns out to be much slower than matching SIFT features. To overcome this, we propose a much more efficient indexing approach for approximate nearest neighbor search of binary features. This approach resorts to randomized trees that are constructed in a supervised training process by exploiting the label information derived from that multiple features correspond to a common 3D point. In the tree construction process, node tests are selected in a way such that trees have uniform leaf sizes and low error rates, which are two desired properties for efficient approximate nearest neighbor search. To further improve the search efficiency, a probabilistic priority search strategy is adopted. Apart from the label information, this strategy also uses non-binary pixel intensity differences available in descriptor extraction. By using the proposed indexing approach, matching binary features is no longer much slower but slightly faster than matching SIFT features. Consequently, the overall localization speed is significantly improved due to the much faster key point detection and descriptor extraction. It is empirically demonstrated that the localization speed is improved by an order of magnitude as compared with state-of-the-art methods, while comparable registration rate and localization accuracy are still maintained.


title: An Empirical Study of Bug Fixing Rate

abstract: Bug fixing is one of the most important activities in software development and maintenance. A software project often employs an issue tracking system such as Bugzilla to store and manage their bugs. In the issue tracking system, many bugs are invalid but take unnecessary efforts to identify them. In this paper, we mainly focus on bug fixing rate, i.e., The proportion of the fixed bugs in the reported closed bugs. In particular, we study the characteristics of bug fixing rate and investigate the impact of a reporter's different contribution behaviors to the bug fixing rate. We perform an empirical study on all reported bugs of two large open source software communities Eclipse and Mozilla. We find (1) the bug fixing rates of both projects are not high, (2) there exhibits a negative correlation between a reporter's bug fixing rate and the average time cost to close the bugs he/she reports, (3) the amount of bugs a reporter ever fixed has a strong positive impact on his/her bug fixing rate, (4) reporters' bug fixing rates have no big difference, whether their contribution behaviors concentrate on a few products or across many products, (5) reporters' bug fixing rates tend to increase as time goes on, i.e., Developers become more experienced at reporting bugs.


title: A novel miniaturized loaded line phase shifter

abstract: This study proposes a novel design of phase shifter for WLAN applications, by using Koch fractal curves to reduce size of conventional loaded line phase shifter (PS). Proposed PS provides an area reduction (41.88%) in comparison with conventional PS.


title: An insight-based methodology for evaluating bioinformatics visualizations

abstract: High-throughput experiments, such as gene expression microarrays in the life sciences, result in very large data sets. In response, a wide variety of visualization tools have been created to facilitate data analysis. A primary purpose of these tools is to provide biologically relevant insight into the data. Typically, visualizations are evaluated in controlled studies that measure user performance on predetermined tasks or using heuristics and expert reviews. To evaluate and rank bioinformatics visualizations based on real-world data analysis scenarios, we developed a more relevant evaluation method that focuses on data insight. This paper presents several characteristics of insight that enabled us to recognize and quantify it in open-ended user tests. Using these characteristics, we evaluated five microarray visualization tools on the amount and types of insight they provide and the time it takes to acquire it. The results of the study guide biologists in selecting a visualization tool based on the type of their microarray data, visualization designers on the key role of user interaction techniques, and evaluators on a new approach for evaluating the effectiveness of visualizations for providing insight. Though we used the method to analyze bioinformatics visualizations, it can be applied to other domains.


title: A DCT-domain system for robust image watermarking

abstract: Abstract Digital watermarking has been proposed as a solution to the problem of copyright protection of multimedia data in a networked environment. It makes possible to tightly associate to a digital document a code allowing the identification of the data creator, owner, authorized consumer, and so on. In this paper a new watermarking algorithm for digital images is presented: the method, which operates in the frequency domain, embeds a pseudo-random sequence of real numbers in a selected set of DCT coefficients. After embedding, the watermark is adapted to the image by exploiting the masking characteristics of the human visual system, thus ensuring the watermark invisibility. By exploiting the statistical properties of the embedded sequence, the mark can be reliably extracted without resorting to the original uncorrupted image. Experimental results demonstrate that the watermark is robust to several signal processing techniques, including JPEG compression, low pass and median filtering, histogram equalization and stretching, dithering, addition of Gaussian noise, resizing, and multiple watermarking.


title: Energy-Efficient Mobile Charging for Wireless Power Transfer in Internet of Things Networks

abstract: The Internet of Things (IoT) is expected to play an important role in the construction of next generation mobile communication services, and is currently used in various services. However, the power-hungry battery significantly limits the lifetime of IoT devices. Among the various lifetime extension techniques, this paper discusses mobile charging, which enables wireless power transfer based on radio frequency with mobile chargers (MCs). MCs function as traveling target IoT networks that provide energy to battery-operated IoT devices. However, MCs with an energy-constrained battery result in limitation of travel-time. This paper formulates a problem to minimize energy consumption for charging IoT devices by determining the path of motion of an MC and efficient charging points, and proves that the problem is NP-hard. An efficient algorithm, named best charging efficiency (BCE), is proposed to solve the problem and the upper bound of the BCE algorithm is guaranteed using the duality of linear programming. In addition, an improved BCE algorithm called branching second best efficiency algorithm with additional searching techniques is introduced. Finally, this paper analyzes the difference in performance among the proposed algorithms, optimal solutions, and the existing algorithm and concludes that the performance of the proposed algorithm is near optimal, within 1% of difference ratio in terms of charging efficiency and delay.


title: General Perspectives on Knowledge Management: Fostering a Research Agenda

abstract: We trace in pragmatic terms some of what we know about knowledge, information technology, knowledge management practice and research, and provide two complementary frameworks that highlight potential opportunities for building a research agenda in this area. The papers in this special issue are then discussed.


title: Bag of textons for image segmentation via soft clustering and convex shift

abstract: We propose an unsupervised image segmentation method based on texton similarity and mode seeking. The input image is first convolved with a filter-bank, followed by soft clustering on its filter response to generate textons. The input image is then superpixelized where each belonging pixel is regarded as a voter and a soft voting histogram is constructed for each superpixel by averaging its voters' posterior texton probabilities. We further propose a modified mode seeking method — called convex shift — to group superpixels and generate segments. The distribution of superpixel histograms is modeled nonparametrically in the histogram space, using Kullback-Leibler divergence (K-L divergence) and kernel density estimation. We show that each kernel shift step can be formulated as a convex optimization problem with linear constraints. Experiment on image segmentation shows that convex shift performs mode seeking effectively on an enforced histogram structure, grouping visually similar superpixels. With the incorporation of texton and soft voting, our method generates reasonably good segmentation results on natural images with relatively complex contents, showing significant superiority over traditional mode seeking based segmentation methods, while outperforming or being comparable to state of the art methods.


title: A 34 MB/s MLC Write Throughput 16 Gb NAND With All Bit Line Architecture on 56 nm Technology

abstract: -A 16 Gb 4-state MLC NAND flash memory augments the sustained program throughput to 34 MB/s by fully exercising all the available cells along a selected word line and by using additional performance enhancement modes. The same chip operating as an 8 Gb SLC device guarantees over 60 MB/s programming throughput. The newly introduced All Bit Line (ABL) architecture has multiple advantages when higher performance is targeted and it was made possible by adopting the "current sensing" (as opposed to the mainstream "voltage sensing") technique. The general chip architecture is presented in contrast to a state of the art conventional circuit and a double size data buffer is found to be necessary for the maximum parallelism attained. Further conceptual changes designed to counterbalance the area increase are presented, hierarchical column architecture being of foremost importance. Optimization of other circuits, such as the charge pump, is another example. Fast data access rate is essential, and ways of boosting it are described, including a new redundancy scheme. ABL contribution to energy saving is also acknowledged.


title: PMU-Based Monitoring of Rotor Angle Dynamics

abstract: Online monitoring of rotor angle stability in wide area power systems is an important task to avoid out-of-step instability conditions. In recent years, the installation of phasor measurement units (PMUs) on the power grids has increased significantly and, therefore, a large amount of real-time data is available for online monitoring of system dynamics. This paper proposes a PMU-based application for online monitoring of rotor angle stability. A technique based on Lyapunov exponents is used to determine if a power swing leads to system instability. The relationship between rotor angle stability and maximal Lyapunov exponent (MLE) is established. A computational algorithm is developed for the calculation of MLE in an operational environment. The effectiveness of the monitoring scheme is illustrated with a three-machine system and a 200-bus system model.


title: A secure two-party password-authenticated key exchange protocol

abstract: PAKE protocols which stands for Password Authenticated Key Exchange are of great importance for providing secure communications over the Internet. They permit two entities to share a session key in an authentic manner based on a human-memorable password. In 2006, Kolesnikov and Rackoff proposed an improvement to the Halevi and Krawczyk's PAKE protocols. In 2010, they revised their preceding protocol and introduced an improvement to it. In this paper, it is shown that not only are Halevi and Krawczyk's PAKE protocols vulnerable to ephemeral key compromise impersonation and malicious server attacks, but they also fail to provide key confirmation property simultaneously, one of which does not even satisfy Forward Secrecy attribute. It is also shown that Kolesnikov and Rackoffs protocols are susceptible to ephemeral key compromise impersonation and to Denial-of-Service (DoS) attacks, nor do they satisfy Forward Secrecy, mutual authentication, and key confirmation attributes. Additionally, they are prone to the disclosure of pre-shared secret key. Two improved protocols, called S2PAKEvl and S2PAKEv2, are also proposed providing several security attributes, both of which are still efficient. S2PAKEvl has two rounds with mutual authentication and not providing key confirmation, while S2PAKEv2 is of three rounds holding mutual authentication and key confirmation.


title: All-optical packet switching for metropolitan area networks: opportunities and challenges

abstract: The fast evolution of networks has been continuously driven by new advances in enabling technologies, as well as the growth of Internet traffic. All-optical packet switching provides high throughput, rich routing functionalities, and excellent flexibility. These characteristics make it an excellent candidate for next-generation metropolitan area networks, which will be much more dynamic and demanding than today's networks. In this article we not only discuss some of the architectural challenges involved in the design of all-optical packet switched networks, but also present the reader a high-level picture of how such future networks could be integrated with other network segments, to provide users end-to-end connectivity with performance and simplicity.


title: Geomagnetism aided inertial navigation system

abstract: Geomagnetic field is one of the geophysical fields of the Earth and can be used to limit the error accumulation of the inertial navigation system (INS). A geomagnetic aided navigation system is proposed in the paper. Two alternate processes are designed in the system - the match and the filter. In the former part, a contour constraint based correlation method is used to eliminate the initial position error of the system, greatly reducing the computational burden of the whole system. Heading error can be effectively estimated by an iterative closest point (ICP) algorithm. In the filter part, a linear Kalman filter is designed to update INS continuously. Position measurements are given by a ldquonearest pointrdquo principle and the position error caused by this principle does not accumulate with time, which leads to a nearest point based Kalman filter (NPKF). Geomagnetic anomaly maps are used to test the proposed system, and good performance is observed.