title: Hierarchical Gaussian Descriptors with Application to Person Re-Identification

text: Describing the color and textural information of a person image is one of the most crucial aspects of person re-identification (re-id). In this paper, we present novel meta-descriptors based on a hierarchical distribution of pixel features. Although hierarchical covariance descriptors have been successfully applied to image classification, the mean information of pixel features, which is absent from the covariance, tends to be the major discriminative information for person re-id. To solve this problem, we describe a local region in an image via hierarchical Gaussian distribution in which both means and covariances are included in their parameters. More specifically, the region is modeled as a set of multiple Gaussian distributions in which each Gaussian represents the appearance of a local patch. The characteristics of the set of Gaussians are again described by another Gaussian distribution. In both steps, we embed the parameters of the Gaussian into a point of Symmetric Positive Definite (SPD) matrix manifold. By changing the way to handle mean information in this embedding, we develop two hierarchical Gaussian descriptors. Additionally, we develop feature norm normalization methods with the ability to alleviate the biased trends that exist on the descriptors. The experimental results conducted on five public datasets indicate that the proposed descriptors achieve remarkably high performance on person re-id.


title: On Covert Acoustical Mesh Networks in Air

text: Covert channels can be used to circumvent system and network policies by establishing communications that have not been considered in the design of the computing system. We construct a covert channel between different computing systems that utilizes audio modulation/demodulation to exchange data between the computing systems over the air medium. The underlying network stack is based on a communication system that was originally designed for robust underwater communication. We adapt the communication system to implement covert and stealthy communications by utilizing the ultrasonic frequency range. We further demonstrate how the scenario of covert acoustical communication over the air medium can be extended to multi-hop communications and even to wireless mesh networks. A covert acoustical mesh network can be conceived as a meshed botnet or malnet that is accessible via inaudible audio transmissions. Different applications of covert acoustical mesh networks are presented, including the use for remote keylogging over multiple hops. It is shown that the concept of a covert acoustical mesh network renders many conventional security concepts useless, as acoustical communications are usually not considered. Finally, countermeasures against covert acoustical mesh networks are discussed, including the use of lowpass filtering in computing systems and a host-based intrusion detection system for analyzing audio input and output in order to detect any irregularities.


title: A developmental, mentalization-based approach to the understanding and treatment of borderline personality disorder.

text: The precise nature and etiopathogenesis of borderline personality disorder (BPD) continues to elude researchers and clinicians. Yet, increasing evidence from various strands of research converges to suggest that affect dysregulation, impulsivity, and unstable relationships constitute the core features of BPD. Over the last two decades, the mentalization-based approach to BPD has attempted to provide a theoretically consistent way of conceptualizing the interrelationship between these core features of BPD, with the aim of providing clinicians with a conceptually sound and empirically supported approach to BPD and its treatment. This paper presents an extended version of this approach to BPD based on recently accumulated data. In particular, we suggest that the core features of BPD reflect impairments in different facets of mentalization, each related to impairments in relatively distinct neural circuits underlying these facets. Hence, we provide a comprehensive account of BPD by showing how its core features are related to each other in theoretically meaningful ways. More specifically, we argue that BPD is primarily associated with a low threshold for the activation of the attachment system and deactivation of controlled mentalization, linked to impairments in the ability to differentiate mental states of self and other, which lead to hypersensitivity and increased susceptibility to contagion by other people's mental states, and poor integration of cognitive and affective aspects of mentalization. The combination of these impairments may explain BPD patients' propensity for vicious interpersonal cycles, and their high levels of affect dysregulation and impulsivity. Finally, the implications of this expanded mentalization-based approach to BPD for mentalization-based treatment and treatment of BPD more generally are discussed.


Statistical Methods for Fighting Financial Crimes


title: Design and Measurement of Reconfigurable Millimeter Wave Reflectarray Cells With Nematic Liquid Crystal

text: Numerical simulations are used to study the electromagnetic scattering from phase agile microstrip reflectarray cells which exploit the voltage controlled dielectric anisotropy property of nematic state liquid crystals (LCs). In the computer model two arrays of equal size elements constructed on a 15 mum thick tuneable LC layer were designed to operate at center frequencies of 102 GHz and 130 GHz. Micromachining processes based on the metallization of quartz/silicon wafers and an industry compatible LCD packaging technique were employed to fabricate the grounded periodic structures. The loss and the phase of the reflected signals were measured using a quasi-optical test bench with the reflectarray inserted at the beam waist of the imaged Gaussian beam, thus eliminating some of the major problems associated with traditional free-space characterization at these frequencies. By applying a low frequency AC bias voltage of 10 V, a 165deg phase shift with a loss 4.5-6.4 dB at 102 GHz and 130deg phase shift with a loss variation between 4.3-7 dB at 130 GHz was obtained. The experimental results are shown to be in close agreement with the computer model.


title: Reasoning about Categories in Conceptual Spaces

text: Understanding the process of categorization is a primary research goal in artificial intelligence. The conceptual space framework provides a flexible approach to modeling context-sensitive categorization via a geometrical representation designed for modeling and managing concepts. In this paper we show how algorithms developed in computational geometry, and the Region Connection Calculus can be used to model important aspects of categorization in conceptual spaces. In particular, we demonstrate the feasibility of using existing geometric algorithms to build and manage categories in conceptual spaces, and we show how the Region Connection Calculus can be used to reason about categories and other conceptual regions.


title: Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset

text: The proliferation of IoT systems, has seen them targeted by malicious third parties. To address this, realistic protection and investigation countermeasures need to be developed. Such countermeasures include network intrusion detection and network forensic systems. For that purpose, a well-structured and representative dataset is paramount for training and validating the credibility of the systems. Although there are several network, in most cases, not much information is given about the Botnet scenarios that were used. This paper, proposes a new dataset, Bot-IoT, which incorporates legitimate and simulated IoT network traffic, along with various types of attacks. We also present a realistic testbed environment for addressing the existing dataset drawbacks of capturing complete network information, accurate labeling, as well as recent and complex attack diversity. Finally, we evaluate the reliability of the BoT-IoT dataset using different statistical and machine learning methods for forensics purposes compared with the existing datasets. This work provides the baseline for allowing botnet identificaiton across IoT-specific networks. The Bot-IoT dataset can be accessed at [1].


title: Binary Input Layer: Training of CNN models with binary input data

text: For the efficient execution of deep convolutional neural networks (CNN) on edge devices, various approaches have been presented which reduce the bit width of the network parameters down to 1 bit. Binarization of the first layer was always excluded, as it leads to a significant error increase. Here, we present the novel concept of binary input layer (BIL), which allows the usage of binary input data by learning bit specific binary weights. The concept is evaluated on three datasets (PAMAP2, SVHN, CIFAR-10). Our results show that this approach is in particular beneficial for multimodal datasets (PAMAP2) where it outperforms networks using full precision weights in the first layer by 1.92 percentage points (pp) while consuming only 2% of the chip area.


title: Leveraging Legacy Code to Deploy Desktop Applications on the Web

text: Xax is a browser plugin model that enables developers to leverage existing tools, libraries, and entire programs to deliver feature-rich applications on the web. Xax employs a novel combination of mechanisms that collectively provide security, OS-independence, performance, and support for legacy code. These mechanisms include memory-isolated native code execution behind a narrow syscall interface, an abstraction layer that provides a consistent binary interface across operating systems, system services via hooks to existing browser mechanisms, and lightweight modifications to existing tool chains and code bases. We demonstrate a variety of applications and libraries from existing code bases, in several languages, produced with various tool chains, running in multiple browsers on multiple operating systems. With roughly two person-weeks of effort, we ported 3.3 million lines of code to Xax, including a PDF viewer, a Python interpreter, a speech synthesizer, and an OpenGL pipeline.


title: Stacked Convolutional and Recurrent Neural Networks for Music Emotion Recognition

text: This paper studies the emotion recognition from musical tracks in the 2-dimensional valence-arousal (V-A) emotional space. We propose a method based on convolutional (CNN) and recurrent neural networks (RNN), having significantly fewer parameters compared with state-of-theart method for the same task. We utilize one CNN layer followed by two branches of RNNs trained separately for arousal and valence. The method was evaluated using the “MediaEval2015 emotion in music” dataset. We achieved an RMSE of 0.202 for arousal and 0.268 for valence, which is the best result reported on this dataset.


title: Anchoring : accessibility as a cause of judgmental assimilation

text: Anchoring denotes assimilation of judgment toward a previously considered value — an anchor. The selective accessibility model argues that anchoring is a result of selective accessibility of information compatible with an anchor. The present review shows the similarities between anchoring and knowledge accessibility effects. Both effects depend on the applicability of the accessible information, which is also used similarly. Furthermore, both knowledge accessibility and anchoring influence the time needed for the judgment and both display temporal robustness. Finally, we offer recent evidence for the selective accessibility model and demonstrate how the model can be applied to reducing the anchoring effect.


title: POSTER: Watch Out Your Smart Watch When Paired

text: We coin a new term called \textit{data transfusion} as a phenomenon that a user experiences when pairing a wearable device with the host device. A large amount of data stored in the host device (e.g., a smartphone) is forcibly copied to the wearable device (e.g., a smart watch) due to pairing while the wearable device is usually less attended. To the best of knowledge, there is no previous work that manipulates how sensitive data is transfused even without user's consent and how users perceive and behave regarding such a phenomenon for smart watches. We tackle this problem by conducting an experimental study of data extraction from commodity devices, such as in Android Wear, watchOS, and Tizen platforms, and a following survey study with 205 smart watch users, in two folds. The experimental studies have shown that a large amount of sensitive data was transfused, but there was not enough user notification. The survey results have shown that users have lower perception on smart watches for security and privacy than smartphones, but they tend to set the same passcode on both devices when needed. Based on the results, we perform risk assessment and discuss possible mitigation that involves volatile transfusion.


title: ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems

text: We present a novel multi-robot simulator named ARGoS. ARGoS is designed to simulate complex experiments involving large swarms of robots of different types. ARGoS is the first multi-robot simulator that is at the same time both efficient (fast performance with many robots) and flexible (highly customizable for specific experiments). Novel design choices in ARGoS have enabled this breakthrough. First, in ARGoS, it is possible to partition the simulated space into multiple sub-spaces, managed by different physics engines running in parallel. Second, ARGoS’ architecture is multi-threaded, thus designed to optimize the usage of modern multi-core CPUs. Finally, the architecture of ARGoS is highly modular, enabling easy addition of custom features and appropriate allocation of computational resources. We assess the efficiency of ARGoS and showcase its flexibility with targeted experiments. Experimental results demonstrate that simulation run-time increases linearly with the number of robots. A 2D-dynamics simulation of 10,000 e-puck robots can be performed in 60 % of the time taken by the corresponding real-world experiment. We show how ARGoS can be extended to suit the needs of an experiment in which custom functionality is necessary to achieve sufficient simulation accuracy. ARGoS is open source software licensed under GPL3 and is downloadable free of charge.


title: Storytelling in Information Visualizations: Does it Engage Users to Explore Data?

text: We present the results of three web-based field experiments, in which we evaluate the impact of using initial narrative visualization techniques and storytelling on user-engagement with exploratory information visualizations. We conducted these experiments on a popular news and opinion outlet, and on a popular visualization gallery website. While data-journalism exposes visualizations to a large public, we do not know how effectively this public makes sense of interactive graphics, and in particular if people explore them to gain additional insight to that provided by the journalists. In contrast to our hypotheses, our results indicate that augmenting exploratory visualizations with introductory 'stories' does not seem to increase user-engagement in exploration.


title: We can learn your #hashtags: Connecting tweets to explicit topics

text: In Twitter, users can annotate tweets with hashtags to indicate the ongoing topics. Hashtags provide users a convenient way to categorize tweets. From the system's perspective, hashtags play an important role in tweet retrieval, event detection, topic tracking, and advertising, etc. Annotating tweets with the right hashtags can lead to a better user experience. However, two problems remain unsolved during an annotation: (1) Before the user decides to create a new hashtag, is there any way to help her/him find out whether some related hashtags have already been created and widely used? (2) Different users may have different preferences for categorizing tweets. However, few work has been done to study the personalization issue in hashtag recommendation. To address the above problems, we propose a statistical model for personalized hashtag recommendation in this paper. With millions of <;tweet, hashtag> pairs being published everyday, we are able to learn the complex mappings from tweets to hashtags with the wisdom of the crowd. Two questions are answered in the model: (1) Different from traditional item recommendation data, users and tweets in Twitter have rich auxiliary information like URLs, mentions, locations, social relations, etc. How can we incorporate these features for hashtag recommendation? (2) Different hashtags have different temporal characteristics. Hashtags related to breaking events in the physical world have strong rise-and-fall temporal pattern while some other hashtags remain stable in the system. How can we incorporate hashtag related features to serve for hashtag recommendation? With all the above factors considered, we show that our model successfully outperforms existing methods on real datasets crawled from Twitter.


title: Environment-Driven Lexicon Induction for High-Level Instructions

text: As described in the main paper, we collected a dataset D = (x(n), e(n), a(n), π(n))500 n=1. Environment Complexity. Our environments are 3D scenarios consisting of complex objects such as fridge, microwave and television with many states. These objects can be in different spatial relations with respect to other objects. For example, “bag of chips” can be found behind the television. Figure 1 shows some sample environments from our dataset. For example, an object of category television consists of 6 channels, volume level and power status. An object can have different values of states in different environment and different environment consists of different set of objects and their placement. For example, television might be powered on in one environment and closed in another, microwave might have an object inside it or not in different environment, etc. Moreover, there are often more than one object of the same category. For example, our environment typically have two books, five couches, four pillows etc. Objects of the same category can have different appearance. For example, a book can have the cover of a math book or a Guinness book of world record; resulting in complex object descriptions such as in “throw the sticky stuff in the bowl”. They can also have the same appearance making people use spatial relations or other means while describing them such as in “get me the cup next to the microwave”. This dataset is significantly more challenging compared to the 2D navigation dataset or GUI actions in windows dataset considered earlier.


title: Early Breast Cancer Detection using Mammogram Images: A Review of Image Processing Techniques

text: Breast cancer is one of the most common cancers worldwide among women so that one in eight women is affected by this disease during their lifetime. Mammography is the most effective imaging modality for early detection of breast cancer in early stages. Because of poor contrast and low visibility in the mammographic images, early detection of breast cancer is a significant step to efficient treatment of the disease. Different computeraided detection algorithms have been developed to help radiologists provide an accurate diagnosis. This paper reviews the most common image processing approaches developed for detection of masses and calcifications. The main focus of this review is on image segmentation methods and the variables used for early breast cancer detection. Texture analysis is the crucial step in any image segmentation techniques which are based on a local spatial variation of intensity or color. Therefore, various methods of texture analysis for mass and micro-calcification detection in mammography are discussed in details.


title: Is beautiful really usable? Toward understanding the relation between usability, aesthetics, and affect in HCI

text: This paper analyzes the relation between usability and aesthetics. In a laboratory study, 80 participants used one of four different versions of the same online shop, differing in interface-aesthetics (low vs. high) and interface-usability (low vs. high). Participants had to find specific items and rate the shop before and after usage on perceived aesthetics and perceived usability, which were assessed using four validated instruments. Results show that aesthetics does not affect perceived usability. In contrast, usability has an effect on post-use perceived aesthetics. Our findings show that the “what is beautiful is usable” notion, which assumes that aesthetics enhances the perception of usability can be reversed under certain conditions (here: strong usability manipulation combined with a medium to large aesthetics manipulation). Furthermore, our results indicate that the user’s affective experience with the usability of the shop might serve as a mediator variable within the aestheticsusability relation: The frustration of poor usability lowers ratings on perceived aesthetics. The significance of the results is discussed in context of the existing research on the relation between aesthetics and usability.


title: Trust-Aware Collaborative Filtering for Recommender Systems

text: Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours. Costly annotations by experts are replaced by a distributed process where the users take the initiative. While the collaborative approach enables the collection of a vast amount of data, a new issue arises: the quality assessment. The elicitation of trust values among users, termed “web of trust”, allows a twofold enhancement of Recommender Systems. Firstly, the filtering process can be informed by the reputation of users which can be computed by propagating trust. Secondly, the trust metrics can help to solve a problem associated with the usual method of similarity assessment, its reduced computability. An empirical evaluation on Epinions.com dataset shows that trust propagation allows to increase the coverage of Recommender Systems while preserving the quality of predictions. The greatest improuvements are achieved for new users, who provided few ratings.


title: Fake Review Detection : Classification and Analysis of Real and Pseudo Reviews

text: In recent years, fake review detection has attracted significant attention from both businesses and the research community. For reviews to reflect genuine user experiences and opinions, detecting fake reviews is an important problem. Supervised learning has been one of the main approaches for solving the problem. However, obtaining labeled fake reviews for training is difficult because it is very hard if not impossible to reliably label fake reviews manually. Existing research has used several types of pseudo fake reviews for training. Perhaps, the most interesting type is the pseudo fake reviews generated using the Amazon Mechanical Turk (AMT) crowdsourcing tool. Using AMT crafted fake reviews, [36] reported an accuracy of 89.6% using only word n-gram features. This high accuracy is quite surprising and very encouraging. However, although fake, the AMT generated reviews are not real fake reviews on a commercial website. The Turkers (AMT authors) are not likely to have the same psychological state of mind while writing such reviews as that of the authors of real fake reviews who have real businesses to promote or to demote. Our experiments attest this hypothesis. Next, it is naturally interesting to compare fake review detection accuracies on pseudo AMT data and real-life data to see whether different states of mind can result in different writings and consequently different classification accuracies. For real review data, we use filtered (fake) and unfiltered (non-fake) reviews from Yelp.com (which are closest to ground truth labels) to perform a comprehensive set of classification experiments also employing only n-gram features. We find that fake review detection on Yelp’s real-life data only gives 67.8% accuracy, but this accuracy still indicates that n-gram features are indeed useful. We then propose a novel and principled method to discover the precise difference between the two types of review data using the information theoretic measure KL-divergence and its asymmetric property. This reveals some very interesting psycholinguistic phenomena about forced and natural fake reviewers. To improve classification on the real Yelp review data, we propose an additional set of behavioral features about reviewers and their reviews for learning, which dramatically improves the classification result on real-life opinion spam data.


title: Towards More Confident Recommendations : Improving Recommender Systems Using Filtering Approach Based on Rating Variance

text: In the present age of information overload, it is becoming increasingly harder to find relevant content. Recommender systems have been introduced to help people deal with these vast amounts of information and have been widely used in research as well as e-commerce applications. In this paper, we propose several new approaches to improve the accuracy of recommender systems by using rating variance to gauge the confidence of recommendations. We then empirically demonstrate how these approaches work with various recommendation techniques. We also show how these approaches can generate more personalized recommendations, as measured by the coverage metric. As a result, users can be given a better control to choose whether to receive recommendations with higher coverage or higher accuracy.


title: Evaluating collaborative filtering recommender systems

text: Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.


title: Identify Online Store Review Spammers via Social Review Graph

text:
Online shopping reviews provide valuable information for customers to compare the quality of products, store services, and many other aspects of future purchases. However, spammers are joining this community trying to mislead consumers by writing fake or unfair reviews to confuse the consumers. Previous attempts have used reviewers’ behaviors such as text similarity and rating patterns, to detect spammers. These studies are able to identify certain types of spammers, for instance, those who post many similar reviews about one target. However, in reality, there are other kinds of spammers who can manipulate their behaviors to act just like normal reviewers, and thus cannot be detected by the available techniques.
 In this article, we propose a novel concept of review graph to capture the relationships among all reviewers, reviews and stores that the reviewers have reviewed as a heterogeneous graph. We explore how interactions between nodes in this graph could reveal the cause of spam and propose an iterative computation model to identify suspicious reviewers. In the review graph, we have three kinds of nodes, namely, reviewer, review, and store. We capture their relationships by introducing three fundamental concepts, the trustiness of reviewers, the honesty of reviews, and the reliability of stores, and identifying their interrelationships: a reviewer is more trustworthy if the person has written more honesty reviews; a store is more reliable if it has more positive reviews from trustworthy reviewers; and a review is more honest if many other honest reviews support it. This is the first time such intricate relationships have been identified for spam detection and captured in a graph model. We further develop an effective computation method based on the proposed graph model. Different from any existing approaches, we do not use an review text information. Our model is thus complementary to existing approaches and able to find more difficult and subtle spamming activities, which are agreed upon by human judges after they evaluate our results.


title: A Survey of Text Classification Algorithms

text: The problem of classification has been widely studied in the data mining, machine learning, database, and information retrieval communities with applications in a number of diverse domains, such as target marketing, medical diagnosis, news group filtering, and document organization. In this paper we will provide a survey of a wide variety of text classification


title: RMiner: a tool set for role mining

text: Recently, there are many approaches proposed for mining roles using automated technologies. However, it lacks a tool set that can be used to aid the application of role mining approaches and update role states. In this demonstration, we introduce a tool set, RMiner, which is based on the core of WEKA, an open source data mining tool. RMiner implements most of the classic and latest role mining algorithms and provides interactive tools for administrator to update role states. The running examples of RMiner are presented to demonstrate the effectiveness of the tool set.


title: Leading us not unto temptation: momentary allurements elicit overriding goal activation.

text: The present research explored the nature of automatic associations formed between short-term motives (temptations) and the overriding goals with which they interfere. Five experimental studies, encompassing several self-regulatory domains, found that temptations tend to activate such higher priority goals, whereas the latter tend to inhibit the temptations. These activation patterns occurred outside of participants' conscious awareness and did not appear to tax their mental resources. Moreover, they varied as a function of subjective goal importance and were more pronounced for successful versus unsuccessful self-regulators in a given domain. Finally, priming by temptation stimuli was found not only to influence the activation of overriding goals but also to affect goal-congruent behavioral choices.


title: Hedonism , Achievement , and Power : Universal values that characterize the Dark Triad

text: Using a sample of Swedes and Americans (N = 385), we attempted to understand the Dark Triad traits (i.e., Machiavellianism, narcissism, and psychopathy) in terms of universal social values. The Dark Triad traits correlated significantly with all 10 value types, forming a sinusoid pattern corresponding to the value model circumplex. In regression analyses, Machiavellianism and narcissism were positively associated with the values Achievement and Power, while psychopathy was positively associated with the values Hedonism, and Power. In addition, the Dark Triad traits explained significant variance over the Big Five traits in accounting for individual differences in social values. Differences between the Swedish and the US sample in the social value Achievement was mediated by the Dark Triad traits, as well as age. Given the unique complex of values accounted for by the Dark Triad traits compared to the Big Five traits, we argue that the former account for a system of self-enhancing ‘‘dark values’’, often hidden but constantly contributing in evaluations of others. 2015 Elsevier Ltd. All rights reserved.


title: Parallel Training of Neural Networks for Speech Recognition

text: In this paper we describe parallel implementation of ANN training procedure based on block mode back-propagation learning algorithm. Two different approaches to parallelization were implemented. The first is data parallelization using POSIX threads, it is suitable for multi-core computers. The second is node parallelization using high performance SIMD architecture of GPU with CUDA, suitable for CUDA enabled computers. We compare the speed-up of both approaches by learning typically-sized network on the real-world phoneme-state classification task, showing nearly 10 times reduction when using CUDA version, while the 8-core server with multi-thread version gives only 4 times reduction. In both cases we compared to an already BLAS optimized implementation. The training tool will be released as Open-Source software under project name TNet.


title: Neurologic foundations of spinal cord fusion (GEMINI).

text: Cephalosomatic anastomosis has been carried out in both monkeys and mice with preservation of brain function. Nonetheless the spinal cord was not reconstructed, leaving the animals unable to move voluntarily. Here we review the details of the GEMINI spinal cord fusion protocol, which aims at restoring electrophysiologic conduction across an acutely transected spinal cord. The existence of the cortico-truncoreticulo-propriospinal pathway, a little-known anatomic entity, is described, and its importance concerning spinal cord fusion emphasized. The use of fusogens and electrical stimulation as adjuvants for nerve fusion is addressed. The possibility of achieving cephalosomatic anastomosis in humans has become reality in principle.


title: High speed true random number generator based on open loop structures in FPGAs

text: Most hardware ‘‘True’’ Random Number Generators (TRNG) take advantage of the thermal agitation around a flip-flop metastable state. In Field Programmable Gate Arrays (FPGA), the classical TRNG structure uses at least two oscillators, build either from PLL or ring oscillators. This creates good TRNG albeit limited in frequency by the interference rate which cannot exceed a few Mbit/s. This article presents an architecture allowing higher bit rates while maintaining provable unconditional security. This speed requirement becomes stringent for secure communication applications such as the cryptographic quantum key distribution protocols. The proposed architecture is very simple and generic as it is based on an open loop structure with no specific component such as PLL. & 2009 Elsevier Ltd. All rights reserved.


title: Sources of Customer Satisfaction and Dissatisfaction with Information Technology Help Desks

text: As the use, develophient, and control of information systems continues to become diffused throughout organizations and society, the information technology (IT) help desk function plays an increasingly important role in effective management and utilization of information resources. Variously referred to as information centers, software support centers, software hotlines, and PC help desks, such centers have been established tohelp end users resolve problems and obtain information about new functions in the information systems they use. This study investigates the determinants of customer satisfaction and dissatisfaction with service encounters involving information technologyhelp desks. The IS satisfaction research that has been done to datehas viewed satisfaction as an attitudinal, bipolar evaluative construct (Melone 1990; Heckman 1993). Satisfaction as viewed in this way is a relatively enduring, stable cognitive state. In the marketing literature, however, satisfaction has also been conceptualized as a less enduring post-consumption response. In this study, a conceptualization of the satisfaction construct based on the service encounter and consumer product satisfaction literatures (e.g; Bitner, Boon}s and Tetreault 1990) is adopted as a starting point. After responses to help desk service encounters have been analyzed from this perspective, an attempt is made to integrate these findings with attitudinal satisfaction constructs. The study employs the Critical Incident Technique (CIT), an inductive, qualitative methodology. It consists of a set of specifically defined procedures for collecting observations of human behavior and classifying them in such a way as to make them useful in addressing practical problems (Flanagan 1954). It is a I thod that is comparable to other inductive grouping procedures such as factor analysis, cluster analysis, and multidimensional scaling. Unlike other such procedures, however, CIT uses content analysis of stories rather than quantitative solutions in the data analysis stage of the procedure. The study addressed four research questions: 1. What specific behaviors and events lead to user/customer satisfaction and dissatisfaction with IT help desk service encounters? 2. Are the underlying events and behaviors that lead to satisfactory and dissatisfactory encounters similar? That is, are these events and behaviors opposites or mirror images of each other? 3. Are the underlying events and behaviors in help desk service encounters similar to those found in other contexts? 4. Can an understanding ofuser/customer responses to help desk service encounters shed light on the development and modification ofa tudinal satisfaction constructs such as UIS (Ives, Olson and Baroudi 1983), EUIS (Doll and Torkzadeh 1988), and VMS Satisfaction (Heckman 1993)? Descriptions of approximately500 incidents have been obtained to dateand analyzed. A tentative classification scheme was developed from the preliminary anal>sis. It was modeled after the incident classification scheme developed by Bitner, Booms and Tetreault and uses the same three major categories: core service failure, special customer request, and extraordinary provider behavior. As in the Bitner, Booms and Tetreault analysis, results suggest that a core service failure does not inevitably lead to dissatisfaction. Initial analysis also suggests that while the scheme is applicable in some ways, the knowledge-based nature of the ]T help desk service encounter requires several additional constructs to account for various customer responses.


title: An evolutionary optimization framework for neural networks and neuromorphic architectures

text: As new neural network and neuromorphic architectures are being developed, new training methods that operate within the constraints of the new architectures are required. Evolutionary optimization (EO) is a convenient training method for new architectures. In this work, we review a spiking neural network architecture and a neuromorphic architecture, and we describe an EO training framework for these architectures. We present the results of this training framework on four classification data sets and compare those results to other neural network and neuromorphic implementations. We also discuss how this EO framework may be extended to other architectures.


THE DOMAIN AND CONCEPTUAL FOUNDATIONS OF RELATIONSHIP MARKETING


title: Development and design of a robotic manta ray featuring flexible pectoral fins

text: This paper showed our preliminary work in probing into fish robot with pectoral fin propulsion. The aim of this project is to develop a fish robot mimicking manta rays that swim with flexible pectoral fins in oscillation motion. Based on the analysis of motion feature and skeletal structure of pectoral fin of manta rays, a kind of flexible pectoral fin made of silicone rubber was designed. Experiments on the propulsion of fin prototype were carried out in tank. Experiment results showed that flexible pectoral fin in oscillation motion could generate great thrust and the thrust would increase with fin-beat frequency and amplitude increasing. In the end, a robot fish with flexible pectoral fins was built. The speed of robot fish could reach the maximum of 1.4 times BL (body length) per second. Experiments proved that it was feasible to develop a robot fish featuring flexible pectoral fins in oscillation motion and the method to mimic manta rays was a great idea to improve performance of underwater vehicles.


title: Video-Based Human Walking Estimation Using Joint Gait and Pose Manifolds

text: We study two fundamental issues about video-based human walking estimation, where the goal is to estimate 3D gait kinematics (i.e., joint positions) from 2D gait appearances (i.e., silhouettes). One is how to model the gait kinematics from different walking styles, and the other is how to represent the gait appearances captured under different views and from individuals of distinct walking styles and body shapes. Our research is conducted in three steps. First, we propose the idea of joint gait-pose manifold (JGPM), which represents gait kinematics by coupling two nonlinear variables, pose (a specific walking stage) and gait (a particular walking style) in a unified latent space. We extend the Gaussian process latent variable model (GPLVM) for JGPM learning, where two heuristic topological priors, a torus and a cylinder, are considered and several JGPMs of different degrees of freedom (DoFs) are introduced for comparative analysis. Second, we develop a validation technique and a series of benchmark tests to evaluate multiple JGPMs and recent GPLVMs in terms of their performance for gait motion modeling. It is shown that the toroidal prior is slightly better than the cylindrical one, and the JGPM of 4 DoFs that balances the toroidal prior with the intrinsic data structure achieves the best performance. Third, a JGPM-based visual gait generative model (JGPM-VGGM) is developed, where JGPM plays a central role to bridge the gap between the gait appearances and the gait kinematics. Our proposed JGPM-VGGM is learned from Carnegie Mellon University MoCap data and tested on the HumanEva-I and HumanEva-II data sets. Our experimental results demonstrate the effectiveness and competitiveness of our algorithms compared with existing algorithms.


title: Semantic Sentiment Analysis Challenge at ESWC2017

text: Sentiment Analysis is a widely studied research field in both research and industry, and there are different approaches for addressing sentiment analysis related tasks. Sentiment Analysis engines implement approaches spanning from lexicon-based techniques, to machine learning, or involving syntactical rules analysis. Such systems are already evaluated in international research challenges. However, Semantic Sentiment Analysis approaches, which take into account or rely also on large semantic knowledge bases and implement Semantic Web best practices, are not under specific experimental evaluation and comparison by other international challenges. Such approaches may potentially deliver higher performance, since they are also able to analyze the implicit, semantics features associated with natural language concepts. In this paper, we present the fourth edition of the Semantic Sentiment Analysis Challenge, in which systems implementing or relying on semantic features are evaluated in a competition involving large test sets, and on different sentiment tasks. Systems merely based on syntax/word-count or just lexicon-based approaches have been excluded by the evaluation. Then, we present the results of the evaluation for each task and show the winner of the most innovative approach award, that combines several knowledge bases for addressing the sentiment analysis task.


title: Beidou combined-FFT acquisition algorithm for resource-constrained embedded platform

text: The length of Beidou CB2I code is twice that of the Global Position System (GPS) C/A code, and the resource consumption will be doubled on embedded hardware with a finite resource if it is processed with the traditional algorithm. Hence, this paper proposes an acquisition algorithm based on combined Fast Fourier Transform (FFT), which separates a signal into odd and even sequences and processes them in serial. By doing so, FFT length is halved and memory usage is decreased. The paper then goes on to analyze the space complexity and time complexity of the proposed algorithm and the traditional algorithm on the embedded platform. The results show that the memory usage of the proposed algorithm is reduced by 23.3% and the amount of multiplication by 12% while the acquisition accuracy remains stable as the PTP values of these two algorithms are close. In other words, the proposed algorithm consumes less resource with no acquisition accuracy losses.


title: Speeding up distributed request-response workflows

text: We found that interactive services at Bing have highly variable datacenter-side processing latencies because their processing consists of many sequential stages, parallelization across 10s-1000s of servers and aggregation of responses across the network. To improve the tail latency of such services, we use a few building blocks: reissuing laggards elsewhere in the cluster, new policies to return incomplete results and speeding up laggards by giving them more resources. Combining these building blocks to reduce the overall latency is non-trivial because for the same amount of resource (e.g., number of reissues), different stages improve their latency by different amounts. We present Kwiken, a framework that takes an end-to-end view of latency improvements and costs. It decomposes the problem of minimizing latency over a general processing DAG into a manageable optimization over individual stages. Through simulations with production traces, we show sizable gains; the 99th percentile of latency improves by over 50% when just 0.1% of the responses are allowed to have partial results and by over 40% for 25% of the services when just 5% extra resources are used for reissues.


title: Beyond Wise et al . : Neuroleptic-induced “ anhedonia ” in rats : Pimozide blocks reward quality of food

text: O all the neurotransmitters in the brain the one that is best known to the lay public, by far, is dopamine (DA). To the public, and much of the media, DA is the brain’s “pleasure transmitter.” By this popular view, it is a burst of DA that produces the pleasure associated with the receipt or anticipation of natural rewards – a chocolate cake, sex, even social rewards – as well as the euphoria produced by addictive drugs, such as amphetamine, cocaine or heroin. In the popular media it is often suggested that it is pursuit of this DA “rush” that leads to a variety of impulse control disorders, such as over-eating, gambling and addiction. Of all the ideas, in all of neuroscience, this is one of the most widely known, and its origin can be traced to the 1978 paper by Roy Wise, Joan Spindler, Harriet deWit and Gary Gerber, that is the topic of this chapter. This paper is an excellent example of how the interpretation of a seemingly simple experimental finding can profoundly influence the thinking and trajectory of an entire field, and the public imagination, for decades – even if it is wrong. But before getting to that conclusion, we should step back and consider the time leading up to the publication of this extremely influential paper. It was only a little over a decade earlier that DA was first recognized as a neurotransmitter in its own right (rather than just being a precursor of norepinephrine). This came about from a series of important studies by researchers in Sweden who developed a histofluorescence method to identify and map monoamine neurotransmitters in the brain (including DA). They thus effectively created the field of chemical neuroanatomy, as well as vividly mapping the location of dopamine in the brain. They also produced useful methods to selectively lesion monoamine-containing cells with neurotoxins, such as 6-hydroxydopamine (6-OHDA), which would help reveal dopamine’s psychological functions via behavioral consequences. As a result of these studies it became clear that DA-producing neurons located in the midbrain (the substantia nigra and ventral tegmental area) send dense projections to the dorsal and ventral striatum (the caudate-putamen and nucleus accumbens) in the


title: Benchmarking distributed data warehouse solutions for storing genomic variant information

text:
Database URL
https://github.com/ZSI-Bio/variantsdwh.


title: Towards Automated Driving: Unmanned Protective Vehicle for Highway Hard Shoulder Road Works

text: Mobile road works on the hard shoulder of German highways bear an increased accident risk for the crew of the protective vehicle which safeguards road works against moving traffic. The project “Automated Unmanned Protective Vehicle for Highway Hard Shoulder Road Works” aims at the unmanned operation of the protective vehicle in order to reduce this risk. Simultaneously, this means the very first unmanned operation of a vehicle on German roads in public traffic. This contribution introduces the project by pointing out the main objectives and demonstrates the current state of the system design regarding functionality, modes of operation, as well as the functional system architecture. Pivotal for the project, the scientific challenges raised by the unmanned operation - strongly related to the general challenges in the field of automated driving - are presented as well. The results of the project shall serve as a basis to stimulate an advanced discussion about ensuring safety for fully automated vehicles in public traffic operating at higher speeds and in less defined environments. Thus, this contribution aims at contacting the scientific community in an early state of the project.


title: Overconfidence as a cause of diagnostic error in medicine.

text: The great majority of medical diagnoses are made using automatic, efficient cognitive processes, and these diagnoses are correct most of the time. This analytic review concerns the exceptions: the times when these cognitive processes fail and the final diagnosis is missed or wrong. We argue that physicians in general underappreciate the likelihood that their diagnoses are wrong and that this tendency to overconfidence is related to both intrinsic and systemically reinforced factors. We present a comprehensive review of the available literature and current thinking related to these issues. The review covers the incidence and impact of diagnostic error, data on physician overconfidence as a contributing cause of errors, strategies to improve the accuracy of diagnostic decision making, and recommendations for future research.


title: Forecasting the weather of Nevada: A deep learning approach

text: This paper compares two approaches for predicting air temperature from historical pressure, humidity, and temperature data gathered from meteorological sensors in Northwestern Nevada. We describe our data and our representation and compare a standard neural network against a deep learning network. Our empirical results indicate that a deep neural network with Stacked Denoising Auto-Encoders (SDAE) outperforms a standard multilayer feed forward network on this noisy time series prediction task. In addition, predicting air temperature from historical air temperature data alone can be improved by employing related weather variables like barometric pressure, humidity and wind speed data in the training process.


title: Equity price direction prediction for day trading: Ensemble classification using technical analysis indicators with interaction effects

text: We investigate the performance of complex trading rules in equity price direction prediction, over and above continuous-valued indicators and simple technical trading rules. Ten of the most popular technical analysis indicators are included in this research. We use Random Forest ensemble classifiers using minute-by-minute stock market data. Results show that our models have predictive power and yield better returns than the buy-and-hold strategy when disregarding transaction costs both in terms of number of stocks with profitable trades as well as overall returns. Moreover, our findings show that two-way and three-way combinations, i.e., complex trading rules, are important to “beat” the buy-and-hold strategy.


title: 10 Watts UHF broadband GaN based power amplifier for multi-band applications

text: The demand of broadband amplifier has been boosted to meet the requirements of multi-mode and multi-band wireless applications. GaN HEMT is the next generation of RF power transistor technology. In this paper, we have designed a 10W UHF broadband class-AB Power Amplifier (PA) based on GaN HEMT. The proposed amplifier has been designed and developed in the frequency range from 200-500 MHz. A maximum drain efficiency of 71% is achieved.


title: Experimental evaluation of the schunk 5-Finger gripping hand for grasping tasks

text: In order to perform useful tasks, a service robot needs to manipulate objects in its environment. In this paper, we propose a method for experimental evaluation of the suitability of a robotic hand for grasping tasks in service robotics. The method is applied to the Schunk 5-Finger Gripping Hand, which is a mechatronic gripper designed for service robots. During evaluation, it is shown, that it is able to grasp various common household objects and execute the grasps from the well known Cutkosky grasp taxonomy [1]. The result is, that it is a suitable hand for service robot tasks.


title: Keyphrase Extraction Using Knowledge Graphs

text: Extracting keyphrases from documents automatically is an important and interesting task since keyphrases provide a quick summarization for documents. Although lots of efforts have been made on keyphrase extraction, most of the existing methods (the co-occurrence-based methods and the statistic-based methods) do not take semantics into full consideration. The co-occurrence-based methods heavily depend on the co-occurrence relations between two words in the input document, which may ignore many semantic relations. The statistic-based methods exploit the external text corpus to enrich the document, which introduce more unrelated relations inevitably. In this paper, we propose a novel approach to extract keyphrases using knowledge graphs, based on which we could detect the latent relations of two keyterms (i.e., noun words and named entities) without introducing many noises. Extensive experiments over real data show that our method outperforms the state-of-the-art methods including the graph-based co-occurrence methods and statistic-based clustering methods.


title: The Situated Function - Behaviour - Structure Framework

text: This paper extends the Function-Behaviour-Structure (FBS) framework, which proposed eight fundamental processes involved in designing. This framework did not explicitly account for the dynamic character of the context in which designing takes place, described by the notion of situatedness. This paper describes this concept as a recursive interrelationship between different environments, which, together with a model of constructive memory, provides the foundation of a situated FBS framework. The eight fundamental processes are then reconstructed within this new framework to represent designing in a dynamic world.


title: Parametric Exponential Linear Unit for Deep Convolutional Neural Networks

text: Object recognition is an important task for improving the ability of visual systems to perform complex scene understanding. Recently, the Exponential Linear Unit (ELU) has been proposed as a key component for managing bias shift in Convolutional Neural Networks (CNNs), but defines a parameter that must be set by hand. In this paper, we propose learning a parameterization of ELU in order to learn the proper activation shape at each layer in the CNNs. Our results on the MNIST, CIFAR-10/100 and ImageNet datasets using the NiN, Overfeat, All-CNN and ResNet networks indicate that our proposed Parametric ELU (PELU) has better performances than the non-parametric ELU. We have observed as much as a 7.28% relative error improvement on ImageNet with the NiN network, with only 0.0003% parameter increase. Our visual examination of the non-linear behaviors adopted by Vgg using PELU shows that the network took advantage of the added flexibility by learning different activations at different layers.


title: Unsupervised feature learning for audio classification using convolutional deep belief networks

text: In recent years, deep learning approaches have gained significant interest as a way of building hierarchical representations from unlabeled data. However, to our knowledge, these deep learning approaches have not been extensively studied for auditory data. In this paper, we apply convolutional deep belief networks to audio data and empirically evaluate them on various audio classification tasks. In the case of speech data, we show that the learned features correspond to phones/phonemes. In addition, our feature representations learned from unlabeled audio data show very good performance for multiple audio classification tasks. We hope that this paper will inspire more research on deep learning approaches applied to a wide range of audio recognition tasks.


title: Bottom-Up Segmentation for Top-Down Detection

text: In this paper we are interested in how semantic segmentation can help object detection. Towards this goal, we propose a novel deformable part-based model which exploits region-based segmentation algorithms that compute candidate object regions by bottom-up clustering followed by ranking of those regions. Our approach allows every detection hypothesis to select a segment (including void), and scores each box in the image using both the traditional HOG filters as well as a set of novel segmentation features. Thus our model ``blends'' between the detector and segmentation models. Since our features can be computed very efficiently given the segments, we maintain the same complexity as the original DPM. We demonstrate the effectiveness of our approach in PASCAL VOC 2010, and show that when employing only a root filter our approach outperforms Dalal & Triggs detector on all classes, achieving 13% higher average AP. When employing the parts, we outperform the original DPM in $19$ out of $20$ classes, achieving an improvement of 8% AP. Furthermore, we outperform the previous state-of-the-art on VOC 2010 test by 4%.


title: Design of planar substrate integrated waveguide (SIW) phase shifter using air holes

text: In this paper, a design of Substrate Intergrated Waveguide (SIW) Phase Shifter is designed by placing multiple rows of air holes in a single substrate which modifies effective dielectric constant of the material and as a result, the phase of the propagating wave is modified while maintaining insertion loss less than 0.8 dB. The proposed design operates in X band (8-12 GHz) having reflection coefficient better than -15 dB within a band of 3 GHz (31.5%) and a phase shift of 24.8°±4.7° throughout the operating band.


title: Predictive coding under the free-energy principle.

text: This paper considers prediction and perceptual categorization as an inference problem that is solved by the brain. We assume that the brain models the world as a hierarchy or cascade of dynamical systems that encode causal structure in the sensorium. Perception is equated with the optimization or inversion of these internal models, to explain sensory data. Given a model of how sensory data are generated, we can invoke a generic approach to model inversion, based on a free energy bound on the model's evidence. The ensuing free-energy formulation furnishes equations that prescribe the process of recognition, i.e. the dynamics of neuronal activity that represent the causes of sensory input. Here, we focus on a very general model, whose hierarchical and dynamical structure enables simulated brains to recognize and predict trajectories or sequences of sensory states. We first review hierarchical dynamical models and their inversion. We then show that the brain has the necessary infrastructure to implement this inversion and illustrate this point using synthetic birds that can recognize and categorize birdsongs.


title: Self-inflicted explosive death by intra-oral detonation of a firecracker: a case report.

text: Self-inflicted explosive deaths due to detonation of fireworks are rare. In this case report, a peculiar case of an elderly male who discharged a firecracker inside his mouth, resulting in fatal blast induced craniofacial injuries, is described. There is paucity of published data describing fireworks-related suicidal and/or non-suicidal deaths. Even scantier data is present specifically describing fireworks-related blast induced neurotrauma and the mechanism(s) of injury involved in such cases. This case report emphasizes the severe damage that a commercially available explosive, the so-called "Gorilla Bomb", can cause, and raises questions about the relative ease of its acquisition.


title: COMPARISON OF DIFFERENT METHODS OF ANTIOXIDANT ACTIVITY EVALUATION OF GREEN AND ROAST C . ARABICA AND C . ROBUSTA COFFEE BEANS

text: aDepartment of Hygiene and Technology of Vegetable Foodstuff, University of Veterinary and Pharmaceutical Sciences Brno, Palackého tř. 1/3, 612 42 Brno Královo Pole. Czech Republic bDepartment of Biochemistry and Biophysics, University of Veterinary and Pharmaceutical Sciences Brno, Palackého tř. 1/3, 612 42 Brno Královo Pole. Czech Republic cDepartment of Food Science and Technology, Kaunas University of Technology, Radvilėnų pl. 19, LT-50254 Kaunas. Lithuania


title: Clustering algorithm for internet of vehicles (IoV) based on dragonfly optimizer (CAVDO)

text: Internet of vehicles (IoV) is a branch of the internet of things (IoT) which is used for communication among vehicles. As vehicular nodes are considered always in motion, hence it causes the frequent changes in the topology. These changes cause major issues in IoV like scalability, dynamic topology changes, and shortest path for routing. Clustering is among one of the solutions for such type of issues. In this paper, the stability of IoV topology in a dynamic environment is focused. The proposed metaheuristic dragonfly-based clustering algorithm CAVDO is used for cluster-based packet route optimization to make stable topology, and mobility aware dynamic transmission range algorithm (MA-DTR) is used with CAVDO for transmission range adaptation on the basis of traffic density. The proposed CAVDO with MA-DTR is compared with the progressive baseline techniques ant colony optimization (ACO) and comprehensive learning particle swarm optimization (CLPSO). Numerous experiments were performed keeping in view the transmission dynamics for stable topology. CAVDO performed better in many cases providing minimum number of clusters according to current channel condition. Considerable important parameters involved in clustering process are: number of un-clustered nodes as a re-clustering criterion, clustering time, re-clustering delay, dynamic transmission range, direction, and speed. According to these parameters, results indicate that CAVDO outperformed ACO-based clustering and CLPSO in various network settings. Additionally, to improve the network availability and to incorporate the functionalities of next-generation network infrastructure, 5G-enabled architecture is also utilized.


title: Influence of PEEK Coating on Hip Implant Stress Shielding: A Finite Element Analysis

text: Stress shielding is a well-known failure factor in hip implants. This work proposes a design concept for hip implants, using a combination of metallic stem with a polymer coating (polyether ether ketone (PEEK)). The proposed design concept is simulated using titanium alloy stems and PEEK coatings with thicknesses varying from 100 to 400 μm. The Finite Element analysis of the cancellous bone surrounding the implant shows promising results. The effective von Mises stress increases between 81 and 92% for the complete volume of cancellous bone. When focusing on the proximal zone of the implant, the increased stress transmission to the cancellous bone reaches between 47 and 60%. This increment in load transferred to the bone can influence mineral bone loss due to stress shielding, minimizing such effect, and thus prolonging implant lifespan.


title: A Multi-aspect Analysis of Automatic Essay Scoring for Brazilian Portuguese

text: While several methods for automatic essay scoring (AES) for the English language have been proposed, systems for other languages are unusual. To this end, we propose in this paper a multi-aspect AES system for Brazilian Portuguese which we apply to a collection of essays, which human experts evaluated according to the five aspects defined by the Brazilian Government for the National High School Exam (ENEM). These aspects are skills that student must master and every skill is assessed separately from one another. In addition to prediction, we also performed feature analysis for each aspect. The proposed AES system employs several features already used by AES systems for the English language. Our results show that predictions for some aspects performed well with the employed features, while predictions for other aspects performed poorly. Furthermore, the detailed feature analysis we performed made it possible to note their independent impacts on each of the five aspects. Finally, aside from these contributions, our work reveals some challenges and directions for future research, related, for instance, to the fact that the ENEM has over eight million yearly enrollments.


title: Physician well-being: A powerful way to improve the patient experience.

text: Improving the patient experience—or the patientcenteredness of care—is a key focus of health care organizations. With a shift to reimbursement models that reward higher patient experience scores, increased competition for market share, and cost constraints emphasizing the importance of patient loyalty, many health care organizations consider optimizing the patient experience to be a key strategy for sustaining financial viability. A survey conducted by The Beryl Institute, which supports research on the patient experience, found that hospital executives ranked patient experience as a clear top priority, falling a close second behind quality and safety.1 According to the institute, engagement with employees, including physicians, is the most effective way to improve the patient experience, as more engaged, satisfied staffers provide better service and care to patients.2 Data on physician satisfaction support this supposition: Research has shown that physician career satisfaction closely correlates with patient satisfaction within a geographic region.3 Given the importance of physician satisfaction to the patient experience, it is concerning that dissatisfaction and burnout are on the rise among physicians. In a 2012 online poll of more than 24,000 physicians across the country, only 54 percent would choose medicine again as a career, down from 69 percent in 2011.4 Almost half of the more than 7,000 physicians responding to a recent online survey reported at least one symptom of burnout.5 Physician dissatisfaction and burnout have a profound negative effect on the patient experience of care. Physician leaders can take steps within their organizations to foster physician well-being, improving the care experience for physicians and patients while strengthening the sustainability of their organizations.


title: Amodal Detection of 3D Objects: Inferring 3D Bounding Boxes from 2D Ones in RGB-Depth Images

text: This paper addresses the problem of amodal perception of 3D object detection. The task is to not only find object localizations in the 3D world, but also estimate their physical sizes and poses, even if only parts of them are visible in the RGB-D image. Recent approaches have attempted to harness point cloud from depth channel to exploit 3D features directly in the 3D space and demonstrated the superiority over traditional 2.5D representation approaches. We revisit the amodal 3D detection problem by sticking to the 2.5D representation framework, and directly relate 2.5D visual appearance to 3D objects. We propose a novel 3D object detection system that simultaneously predicts objects 3D locations, physical sizes, and orientations in indoor scenes. Experiments on the NYUV2 dataset show our algorithm significantly outperforms the state-of-the-art and indicates 2.5D representation is capable of encoding features for 3D amodal object detection. All source code and data is on https://github.com/phoenixnn/Amodal3Det.


title: An architecture of agent-based multi-layer interactive e-learning and e-testing platform

text: E-learning is the synthesis of multimedia and social media platforms powered by Internet and mobile technologies. Great popularity of e-learning is encouraging governments and educational institutions to adopt and develop e-learning cultures in societies in general and universities in particular. In traditional e-learning systems, all components (agents and services) are tightly coupled into a single system. In this study, we propose a new architecture for e-learning with two subsystems, namely, e-learning and e-testing. The motivation of the research is to improve the effectiveness of the learning process by extracting relevant features for elastic learning and testing process. We employ a multi-agent system because it contains five-layer architecture, including agents at various levels. We also propose a novel method for updating content through question and answer between e-learners and intelligent agents. To achieve optimization, we design a system that applies various technologies, which M. Arif (B)· A. Karim · K. A. Alam · S. Farid · S. Iqbal Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia e-mail: arifmuhammad36@siswa.um.edu.my M. Arif · M. Illahi Departmnet of Computer Science, COMSATS Institute of Information Technology Islamabad, Islamabad, Pakistan S. Shamshirband Faculty of Computer Science and Information Technology, Department of Computer System and Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia e-mail: shamshirband1396@gmail.com S. Shamshirband Department of Computer Science, Chalous Branch, Islamic Azad University (IAU), Chalous, 46615-397 Mazandaran, Iran Z. Buang Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia V. E. Balas Department of Automation and Applied Informatics, University of Arad, 310130 Arad, Romania


title: A Dynamic Bayesian Network Model for Autonomous 3D Reconstruction from a Single Indoor Image

text: When we look at a picture, our prior knowledge about the world allows us to resolve some of the ambiguities that are inherent to monocular vision, and thereby infer 3d information about the scene. We also recognize different objects, decide on their orientations, and identify how they are connected to their environment. Focusing on the problem of autonomous 3d reconstruction of indoor scenes, in this paper we present a dynamic Bayesian network model capable of resolving some of these ambiguities and recovering 3d information for many images. Our model assumes a "floorwall" geometry on the scene and is trained to recognize the floor-wall boundary in each column of the image. When the image is produced under perspective geometry, we show that this model can be used for 3d reconstruction from a single image. To our knowledge, this was the first monocular approach to automatically recover 3d reconstructions from single indoor images.


title: Forecasting exchange rate with deep belief networks

text: Forecasting exchange rates is an important financial problem which has received much attention. Nowadays, neural network has become one of the effective tools in this research field. In this paper, we propose the use of a deep belief network (DBN) to tackle the exchange rate forecasting problem. A DBN is applied to predict both British Pound/US dollar and Indian rupee/US dollar exchange rates in our experiments. We use six evaluation criteria to evaluate its performance. We also compare our method to a feedforward neural network (FFNN), which is the state-of-the-art method for forecasting exchange rate with neural networks. Experiments indicate that deep belief networks (DBNs) are applicable to the prediction of foreign exchange rate, since they achieve better performance than feedforward neural networks (FFNNs).


title: Applying the Clique Percolation Method to analyzing cross-market branch banking network structure: the case of Illinois

text: This study applies the Clique Percolation Method (CPM) to an investigation of the changing spatial organization of the Illinois cross-market branch banking network. Nonoverlapping community detection algorithms assign nodes into exclusive communities and, when results are mapped, these techniques may generate spatially disjointed geographical regions, an undesirable characteristic for geographical study. Alternative overlapping community detection algorithms allow overlapping membership where a node can be a member of different communities. Such a structure simultaneously accommodates spatial proximity and spatial separation which occur with respect to a node in relation to other nodes in the system. Applying such a structure in geographical analysis helps preserve well-established principles regarding spatial relationships within the geography discipline. The result can also be mapped for display and correct interpretation. The CPM is chosen in this study due to the complete connection within cliques which simulates the practice by banking institutions of forming highly connected networks through multi-location operations in order to diversify their business and hedge against risks. Applying the CPM helps reveal the spatial pattern of branch banking connections which would otherwise be difficult to see. However, the CPM has been shown to not be among the best performing overlapping community detection algorithms. Future research should explore other possible algorithms for detecting overlapping communities. Detecting communities in a network only reveals certain characteristics of the spatial organization of the network, rather than providing explanation of the spatial-network patterns revealed. Full interpretation of the pattern must rely on the attribute data and additional information. This may illustrate the value of an integrated approach in geographical analysis using both social network analysis and spatial analysis techniques.


title: Multi-platform molecular profiling of a large cohort of glioblastomas reveals potential therapeutic strategies

text: Glioblastomas (GBM) are the most aggressive and prevalent form of gliomas with abysmal prognosis and limited treatment options. We analyzed clinically relevant molecular aberrations suggestive of response to therapies in 1035 GBM tumors. Our analysis revealed mutations in 39 genes of 48 tested. IHC revealed expression of PD-L1 in 19% and PD-1 in 46%. MGMT-methylation was seen in 43%, EGFRvIII in 19% and 1p19q co-deletion in 2%. TP53 mutation was associated with concurrent mutations, while IDH1 mutation was associated with MGMT-methylation and TP53 mutation and was mutually exclusive of EGFRvIII mutation. Distinct biomarker profiles were seen in GBM compared with WHO grade III astrocytoma, suggesting different biology and potentially different treatment approaches. Analysis of 17 metachronous paired tumors showed frequent biomarker changes, including MGMT-methylation and EGFR aberrations, indicating the need for a re-biopsy for tumor profiling to direct subsequent therapy. MGMT-methylation, PR and TOPO1 appeared as significant prognostic markers in sub-cohorts of GBM defined by age. The current study represents the largest biomarker study on clinical GBM tumors using multiple technologies to detect gene mutation, amplification, protein expression and promoter methylation. These data will inform planning for future personalized biomarker-based clinical trials and identifying effective treatments based on tumor biomarkers.


title: Soft Pneumatic Actuators for Rehabilitation

text: Pneumatic artificial muscles are pneumatic devices with practical and various applications as common actuators. They, as human muscles, work in agonistic-antagonistic way, giving a traction force only when supplied by compressed air. The state of the art of soft pneumatic actuators is here analyzed: different models of pneumatic muscles are considered and evolution lines are presented. Then, the use of Pneumatic Muscles (PAM) in rehabilitation apparatus is described and the general characteristics required in different applications are considered, analyzing the use of proper soft actuators with various technical properties. Therefore, research activity carried out in the Department of Mechanical and Aerospace Engineering in the field of soft and textile actuators is presented here. In particular, pneumatic textile muscles useful for active suits design are described. These components are made of a tubular structure, with an inner layer of latex coated with a deformable outer fabric sewn along the edge. In order to increase pneumatic muscles forces and contractions Braided Pneumatic Muscles are studied. In this paper, new prototypes are presented, based on a fabric construction and various kinds of geometry. Pressure-force-deformation tests results are carried out and analyzed. These actuators are useful for rehabilitation applications. In order to reproduce the whole upper limb movements, new kind of soft actuators are studied, based on the same principle of planar membranes deformation. As an example, the bellows muscle model and worm muscle model are developed and described. In both cases, wide deformations are expected. Another issue for soft actuators is the pressure therapy. Some textile sleeve prototypes developed for massage therapy on patients suffering of lymph edema are analyzed. Different types of fabric and assembly techniques have been tested. In general, these OPEN ACCESS Actuators 2014, 3 85 Pressure Soft Actuators are useful for upper/lower limbs treatments, according to medical requirements. In particular devices useful for arms massage treatments are considered. Finally some applications are considered.


title: Scalable Content-Aware Collaborative Filtering for Location Recommendation

text: Location recommendation plays an essential role in helping people find attractive places. Though recent research has studied how to recommend locations with social and geographical information, few of them addressed the cold-start problem of new users. Because mobility records are often shared on social networks, semantic information can be leveraged to tackle this challenge. A typical method is to feed them into explicit-feedback-based content-aware collaborative filtering, but they require drawing negative samples for better learning performance, as users’ negative preference is not observable in human mobility. However, prior studies have empirically shown sampling-based methods do not perform well. To this end, we propose a scalable Implicit-feedback-based Content-aware Collaborative Filtering (ICCF) framework to incorporate semantic content and to steer clear of negative sampling. We then develop an efficient optimization algorithm, scaling linearly with data size and feature size, and quadratically with the dimension of latent space. We further establish its relationship with graph Laplacian regularized matrix factorization. Finally, we evaluate ICCF with a large-scale LBSN dataset in which users have profiles and textual content. The results show that ICCF outperforms several competing baselines, and that user information is not only effective for improving recommendations but also coping with cold-start scenarios.


title: E-commerce web page classification based on automatic content extraction

text: Currently, There are many E-commerce websites around the internet world. These E-commerce websites can be categorized into many types which one of them is C2C (Customer to Customer) websites such as eBay and Amazon. The main objective of C2C websites is an online market place that everyone can buy or sell anything at any time. Since, there are a lot of products in the E-commerce websites and each product are classified into its category by human. It is very hard to define their categories in automatic manner when the data is very large. In this paper, we propose the method for classifying E-commerce web pages based on their product types. Firstly, we apply the proposed automatic content extraction to extract the contents of E-commerce web pages. Then, we apply the automatic key word extraction to select words from these extracted contents for generating the feature vectors that represent the E-commerce web pages. Finally, we apply the machine learning technique for classifying the E-commerce web pages based on their product types. The experimental results signify that our proposed method can classify the E-commerce web pages in automatic fashion.


title: MIMO for DVB-NGH, the next generation mobile TV broadcasting [Accepted From Open Call]

text: DVB-NGH (Digital Video Broadcasting - Next Generation Handheld) is the next generation technology for mobile TV broadcasting, which has been developed by the DVB project with the most advanced transmission technologies. DVB-NGH is the first broadcasting standard to incorporate multiple-input multiple-output (MIMO) as the key technology to overcome the Shannon limit of single antenna communications. MIMO techniques can be used to improve the robustness of the transmitted signal by exploiting the spatial diversity of the MIMO channel, but also to achieve increased data rates through spatial multiplexing. This article describes the benefits of MIMO that motivated its incorporation in DVB-NGH, reviews the MIMO schemes adopted, and discusses some aspects related to the deployment of MIMO networks in DVB-NGH. The article also provides a feature comparison with the multi-antenna techniques for 3GGP's LTE/LTE-Advanced for cellular networks. Finally, physical layer simulation results calibrated within the DVB-NGH standardization process are provided to illustrate the gain of MIMO for the next generation of mobile TV broadcasting.


title: Early sensitivity to arguments: how preschoolers weight circular arguments.

text: Observational studies suggest that children as young as 2 years can evaluate some of the arguments people offer them. However, experimental studies of sensitivity to different arguments have not yet targeted children younger than 5 years. The current study aimed at bridging this gap by testing the ability of preschoolers (3-, 4-, and 5-year-olds) to weight arguments. To do so, it focused on a common type of fallacy-circularity-to which 5-year-olds are sensitive. The current experiment asked children-and, as a group control, adults-to choose between the contradictory opinions of two speakers. In the first task, participants of all age groups favored an opinion supported by a strong argument over an opinion supported by a circular argument. In the second task, 4- and 5-year-olds, but not 3-year-olds or adults, favored the opinion supported by a circular argument over an unsupported opinion. We suggest that the results of these tasks in 3- to 5-year-olds are best interpreted as resulting from the combination of two mechanisms: (a) basic skills of argument evaluations that process the content of arguments, allowing children as young as 3 years to favor non-circular arguments over circular arguments, and (b) a heuristic that leads older children (4- and 5-year-olds) to give some weight to circular arguments, possibly by interpreting these arguments as a cue to speaker dominance.


title: StackGuard: Simple Stack Smash Protection for GCC

text: Since 1998, StackGuard patches to GCC have been used to protect entire distributions from stack smashing buffer overflows. Performance overhead and software compatibility issues have been minimal. In its history, the parts of GCC that StackGuard has operated in have twice changed enough to require complete overhauls of the StackGuard patch. Since StackGuard is a mature technology, even seeing re-implementations in other compilers, we propose that GCC adopt StackGuard as a standard feature. This paper describes our recent work to bring StackGuard fully up to date with current GCC, introduce architecture independence, and extend the protection of stack data structures, while keeping the StackGuard patch as small, simple, and modular as possible.


title: Barriers to Physicians’ Adoption of Healthcare Information Technology: An Empirical Study on Multiple Hospitals

text: Prior research on technology usage had largely overlooked the issue of user resistance or barriers to technology acceptance. Prior research on the Electronic Medical Records had largely focused on technical issues but rarely on managerial issues. Such oversight prevented a better understanding of users’ resistance to new technologies and the antecedents of technology rejection. Incorporating the enablers and the inhibitors of technology usage intention, this study explores physicians’ reactions towards the electronic medical record. The main focus is on the barriers, perceived threat and perceived inequity. 115 physicians from 6 hospitals participated in the questionnaire survey. Structural Equation Modeling was employed to verify the measurement scale and research hypotheses. According to the results, perceived threat shows a direct and negative effect on perceived usefulness and behavioral intentions, as well as an indirect effect on behavioral intentions via perceived usefulness. Perceived inequity reveals a direct and positive effect on perceived threat, and it also shows a direct and negative effect on perceived usefulness. Besides, perceived inequity reveals an indirect effect on behavioral intentions via perceived usefulness with perceived threat as the inhibitor. The research finding presents a better insight into physicians’ rejection and the antecedents of such outcome. For the healthcare industry understanding the factors contributing to physicians’ technology acceptance is important as to ensure a smooth implementation of any new technology. The results of this study can also provide change managers reference to a smooth IT introduction into an organization. In addition, our proposed measurement scale can be applied as a diagnostic tool for them to better understand the status quo within their organizations and users’ reactions to technology acceptance. By doing so, barriers to physicians’ acceptance can be identified earlier and more effectively before leading to technology rejection.


title: Learning Low-Dimensional Representations of Medical Concepts

text: We show how to learn low-dimensional representations (embeddings) of a wide range of concepts in medicine, including diseases (e.g., ICD9 codes), medications, procedures, and laboratory tests. We expect that these embeddings will be useful across medical informatics for tasks such as cohort selection and patient summarization. These embeddings are learned using a technique called neural language modeling from the natural language processing community. However, rather than learning the embeddings solely from text, we show how to learn the embeddings from claims data, which is widely available both to providers and to payers. We also show that with a simple algorithmic adjustment, it is possible to learn medical concept embeddings in a privacy preserving manner from co-occurrence counts derived from clinical narratives. Finally, we establish a methodological framework, arising from standard medical ontologies such as UMLS, NDF-RT, and CCS, to further investigate the embeddings and precisely characterize their quantitative properties.


title: Nanowire dye-sensitized solar cells.

text: Excitonic solar cells-including organic, hybrid organic-inorganic and dye-sensitized cells (DSCs)-are promising devices for inexpensive, large-scale solar energy conversion. The DSC is currently the most efficient and stable excitonic photocell. Central to this device is a thick nanoparticle film that provides a large surface area for the adsorption of light-harvesting molecules. However, nanoparticle DSCs rely on trap-limited diffusion for electron transport, a slow mechanism that can limit device efficiency, especially at longer wavelengths. Here we introduce a version of the dye-sensitized cell in which the traditional nanoparticle film is replaced by a dense array of oriented, crystalline ZnO nanowires. The nanowire anode is synthesized by mild aqueous chemistry and features a surface area up to one-fifth as large as a nanoparticle cell. The direct electrical pathways provided by the nanowires ensure the rapid collection of carriers generated throughout the device, and a full Sun efficiency of 1.5% is demonstrated, limited primarily by the surface area of the nanowire array.


SECURE COMPUTER SYSTEMS : MATHEMATICAL FOUNDATIONS


title: Daily and compulsive internet use and well-being in adolescence: a diathesis-stress model based on big five personality traits.

text: This study examined the associations between adolescents' daily Internet use and low well-being (i.e., loneliness, low self-esteem, and depressive moods). We hypothesized that (a) linkages between high levels of daily Internet use and low well-being would be mediated by compulsive Internet use (CIU), and (b) that adolescents with low levels of agreeableness and emotional stability, and high levels of introversion would be more likely to develop CIU and lower well-being. Data were used from a sample of 7888 Dutch adolescents (11-21 years). Results from structural equation modeling analyses showed that daily Internet use was indirectly related to low well-being through CIU. In addition, daily Internet use was found to be more strongly related to CIU in introverted, low-agreeable, and emotionally less-stable adolescents. In turn, again, CIU was more strongly linked to loneliness in introverted, emotionally less-stable, and less agreeable adolescents.


title: Word Alignment Modeling with Context Dependent Deep Neural Network

text: In this paper, we explore a novel bilingual word alignment approach based on DNN (Deep Neural Network), which has been proven to be very effective in various machine learning tasks (Collobert et al., 2011). We describe in detail how we adapt and extend the CD-DNNHMM (Dahl et al., 2012) method introduced in speech recognition to the HMMbased word alignment model, in which bilingual word embedding is discriminatively learnt to capture lexical translation information, and surrounding words are leveraged to model context information in bilingual sentences. While being capable to model the rich bilingual correspondence, our method generates a very compact model with much fewer parameters. Experiments on a large scale EnglishChinese word alignment task show that the proposed method outperforms the HMM and IBM model 4 baselines by 2 points in F-score.


title: Contrastive Estimation: Training Log-Linear Models on Unlabeled Data

text: Conditional random fields (Lafferty et al., 2001) are quite effective at sequence labeling tasks like shallow parsing (Sha and Pereira, 2003) and namedentity extraction (McCallum and Li, 2003). CRFs are log-linear, allowing the incorporation of arbitrary features into the model. To train on u labeled data, we requireunsupervisedestimation methods for log-linear models; few exist. We describe a novel approach,contrastive estimation . We show that the new technique can be intuitively understood as exploiting implicit negative evidence and is computationally efficient. Applied to a sequence labeling problem—POS tagging given a tagging dictionary and unlabeled text—contrastive estimation outperforms EM (with the same feature set), is more robust to degradations of the dictionary, and can largely recover by modeling additional features.


title: Statistical Phrase-Based Translation

text: We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrase-based translation models. Within our framework, we carry out a large number of experiments to understand better and explain why phrase-based models outperform word-based models. Our empirical results, which hold for all examined language pairs, suggest that the highest levels of performance can be obtained through relatively simple means: heuristic learning of phrase translations from word-based alignments and lexical weighting of phrase translations. Surprisingly, learning phrases longer than three words and learning phrases from high-accuracy wordlevel alignment models does not have a strong impact on performance. Learning only syntactically motivated phrases degrades the performance of our systems.


title: HMM-Based Word Alignment in Statistical Translation

text: In this paper, we describe a new model for word alignment in statistical translation and present experimental results. The idea of the model is to make the alignment probabilities dependent on the differences in the alignment positions rather than on the absolute positions. To achieve this goal, the approach uses a first-order Hidden Markov model (HMM) for the word alignment problem as they are used successfully in speech recognition for the time alignment problem. The difference to the time alignment HMM is that there is no monotony constraint for the possible word orderings. We describe the details of the model and test the model on several bilingual corpora.


title: Background Removal in Image Indexing and Retrieval

text: This paper presents our research in image content based indexing and retrieval, a key technology in digital image libraries. In most of the existing image content-based techniques, image features used for indexing and retrieval are global, features are computed over the entire image. The major problem with the global image feature based retrieval methods is that background features can be easily mistakenly taken as object features. When a user attempts to retrieve images using color features, he/she usually means the color feature of an object or objects of interests contained in the image. The approach we describe in this paper utilizes color clusters for image background analysis. Once the background regions are identified, they are removed from the image indexing procedure; therefore, no longer interfering with the meaningful image content during the retrieval process. The algorithm consists of three major steps of computation, fuzzy clustering, color image segmentation, and background analysis.


title: Induction of fuzzy rules and membership functions from training examples

text: Most fuzzy controllers and fuzzy expert systems must predefine membership functions and fuzzy inference rules to map numeric data into linguistic variable terms and to make fuzzy reasoning work. In this paper, we propose a general learning method as a framework for automatically deriving membership functions and fuzzy if-then rules from a set of given training examples to rapidly build a prototype fuzzy expert system. Based on the membership functions and the fuzzy rules derived, a corresponding fuzzy inference procedure to process inputs is also developed.


title: Routing Attacks and Countermeasures in the RPL-Based Internet of Things

text: The Routing Protocol for Low-Power and Lossy Networks (RPL) is a novel routing protocol standardized for constrained environments such as 6LoWPAN networks. Providing security in IPv6/RPL connected 6LoWPANs is challenging because the devices are connected to the untrusted Internet and are resource constrained, the communication links are lossy, and the devices use a set of novel IoT technologies such as RPL, 6LoWPAN, and CoAP/CoAPs. In this paper we provide a comprehensive analysis of IoT technologies and their new security capabilities that can be exploited by attackers or IDSs. One of the major contributions in this paper is our implementation and demonstration of well-known routing attacks against 6LoWPAN networks running RPL as a routing protocol. We implement these attacks in the RPL implementation in the Contiki operating system and demonstrate these attacks in the Cooja simulator. Furthermore, we highlight novel security features in the IPv6 protocol and exemplify the use of these features for intrusion detection in the IoT by implementing a lightweight heartbeat protocol.


title: Speech emotion classification using tree-structured sparse logistic regression

text: The extraction and selection of acoustic features are crucial steps in the development of a system for classifying emotions in speech. Most works in the field use some kind of prosodic features, often in combination with spectral and glottal features, and select appropriate features in classifying emotions. In the methods, feature choices are mostly made regardless of existing relationships and structures between features. However, considering them can be beneficial, potentially both for interpretability and to improve classification performance. To this end, a structured sparse logistic regression model incorporated with the hierarchical structure of features derived from prosody, spectral envelope, and glottal information is proposed in this paper. The proposed model simultaneously addresses tree-structured sparse feature selection and emotion classification. Evaluation of the proposed model on Berlin emotional database showed substantial improvement over the conventional sparse logistic regression model.


title: Quantum clustering algorithms

text: By the term "quantization", we refer to the process of using quantum mechanics in order to improve a classical algorithm, usually by making it go faster. In this paper, we initiate the idea of quantizing clustering algorithms by using variations on a celebrated quantum algorithm due to Grover. After having introduced this novel approach to unsupervised learning, we illustrate it with a quantized version of three standard algorithms: divisive clustering, k-medians and an algorithm for the construction of a neighbourhood graph. We obtain a significant speedup compared to the classical approach.