title: Predicting Intent Using Activity Logs: How Goal Specificity and Temporal Range Affect User Behavior

abstract: People have different intents in using online platforms. They may be trying to accomplish specific, short-term goals, or less well-defined, longer-term goals. While understanding user intent is fundamental to the design and personalization of online platforms, little is known about how intent varies across individuals, or how it relates to their behavior. Here, we develop a framework for understanding intent in terms of goal specificity and temporal range. Our methodology combines survey-based methodology with an observational analysis of user activity. Applying this framework to Pinterest, we surveyed nearly 6000 users to quantify their intent, and then studied their subsequent behavior on the web site. We find that goal specificity is bimodal - users tend to be either strongly goal-specific or goal-nonspecific. Goal-specific users search more and consume less content in greater detail than goal-nonspecific users: they spend more time using Pinterest, but are less likely to return in the near future. Users with short-term goals are also more focused and more likely to refer to past saved content than users with long-term goals, but less likely to save content for the future. Further, intent can vary by demographic, and with the topic of interest. Last, we show that user's intent and activity are intimately related by building a model that can predict a user's intent for using Pinterest after observing their activity for only two minutes. Altogether, this work shows how intent can be predicted from user behavior.


title: Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation

abstract: We propose a novel segmentation approach based on deep 3D convolutional encoder networks with shortcut connections and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that consists of two interconnected pathways, a convolutional pathway, which learns increasingly more abstract and higher-level image features, and a deconvolutional pathway, which predicts the final segmentation at the voxel level. The joint training of the feature extraction and prediction pathways allows for the automatic learning of features at different scales that are optimized for accuracy for any given combination of image types and segmentation task. In addition, shortcut connections between the two pathways allow high- and low-level features to be integrated, which enables the segmentation of lesions across a wide range of sizes. We have evaluated our method on two publicly available data sets (MICCAI 2008 and ISBI 2015 challenges) with the results showing that our method performs comparably to the top-ranked state-of-the-art methods, even when only relatively small data sets are available for training. In addition, we have compared our method with five freely available and widely used MS lesion segmentation methods (EMS, LST-LPA, LST-LGA, Lesion-TOADS, and SLS) on a large data set from an MS clinical trial. The results show that our method consistently outperforms these other methods across a wide range of lesion sizes.


Chapter 1 A SURVEY OF MOBILITY MODELS in Wireless Adhoc Networks


title: Pedestrian Detection by Feature Selected Self-Similarity Features

abstract: This paper is concerned with the pedestrian detection problem. In this area, boosted decision tree (BDT) methods are highly successful and very efficient. However, to achieve the best performance, most BDT methods require a large number of input features, which make the algorithm scale poorly to large-scale data. Inspired by the effectiveness of self-similarity (SS) features, we use linear discriminant analysis to select features in the SS features according to their generalized Rayleigh quotient, leading to a small number but most discriminative features. These features are called feature selected self-similarity (FSSS) features. The FSSS features are only used for the late stages of the BDT cascade, making the training and detecting much more efficient. Extensive experiments on four well-known data sets demonstrate that the FSSS features are highly effective and the trained pedestrian detector achieves state-of-the-art performance among all existing non-deep-learning methods on several benchmarks. We also compare our method with deep learning methods and show its superiority in high-quality localization and will be a good complement to deep learning methods.


title: Schizophrenia Bulletin doi:10.1093/schbul/sbn062 Schizophrenia Bulletin Advance Access published June 17, 2008 The Role of Oscillations and Synchrony in Cortical Networks and Their Putative Relevance for the Pathophysiology of Schizophrenia

abstract: Neural oscillations and their synchronization may represent a versatile signal to realize flexible communication within and between cortical areas. By now, there is extensive evidence to suggest that cognitive functions depending on coordination of distributed neural responses, such as perceptual grouping, attention-dependent stimulus selection, subsystem integration, working memory, and consciousness, are associated with synchronized oscillatory activity in the theta-, alpha-, beta-, and gamma-band, suggesting a functional mechanism of neural oscillations in cortical networks. In addition to their role in normal brain functioning, there is increasing evidence that altered oscillatory activity may be associated with certain neuropsychiatric disorders, such as schizophrenia, that involve dysfunctional cognition and behavior. In the following article, we aim to summarize the evidence on the role of neural oscillations during normal brain functioning and their relationship to cognitive processes. In the second part, we review research that has examined oscillatory activity during cognitive and behavioral tasks in schizophrenia. These studies suggest that schizophrenia involves abnormal oscillations and synchrony that are related to cognitive dysfunctions and some of the symptoms of the disorder. Perspectives for future research will be discussed in relationship to methodological issues, the utility of neural oscillations as a biomarker, and the neurodevelopmental hypothesis of schizophrenia.


title: OSF Healthcareʼs Journey in Patient Safety

abstract: This article describes OSF Healthcare's recent journey in patient safety. It discusses the involvement of its 6 hospitals in collaboration with each other and the Institute for Healthcare Improvement. OSF focused on a strategy for decreasing adverse drug events (ADEs). They worked on impacting 4 major areas: safety culture, high-risk medication, dispensing of medication, and medication reconciliation. By doing this, OSF decreased its rate of ADEs per 1000 units of medication administered from 3.84 to 1.39 over the course of 20 months.


title: Design of wide input voltage range high step-up DC-DC converter based on secondary-side resonant tank full bridge LLC

abstract: Battery are widely utilized in uninterruptible power supply (UPS) to provide the power temporarily during power failure. In applications up to a few kilo watts, low voltage battery stack are typically used since it is not economically feasible to use high voltage battery stack in such application. To interface the battery to the dc-link of the UPS, power electronic converter are widely used. Due to the wide voltage variation at battery terminal that are mainly caused by battery state of charge and battery stack current, power converter for battery application are required to be capable to interface with wide battery voltage range. In this paper, a wide input range high step up converter was designed based on LLC converter. To significantly reduce the resonant capacitor current, secondary side resonant tank LLC converter was proposed. Finally, a 2 kW prototype was designed for 36 V∼ 72 V input voltage and 410 V output voltage, built, and from experimental test 93.59% peak efficiency was measured.


title: Sharing Residual Units Through Collective Tensor Factorization in Deep Neural Networks

abstract: Residual units are wildly used for alleviating optimization difficulties when building deep neural networks. However, the performance gain does not well compensate the model size increase, indicating low parameter efficiency in these residual units. In this work, we first revisit the residual function in several variations of residual units and demonstrate that these residual functions can actually be explained with a unified framework based on generalized block term decomposition. Then, based on the new explanation, we propose a new architecture, Collective Residual Unit (CRU), which enhances the parameter efficiency of deep neural networks through collective tensor factorization. CRU enables knowledge sharing across different residual units using shared factors. Experimental results show that our proposed CRU Network demonstrates outstanding parameter efficiency, achieving comparable classification performance to ResNet-200 with the model size of ResNet-50. By building a deeper network using CRU, we can achieve state-of-the-art single model classification accuracy on ImageNet-1k and Places365-Standard benchmark datasets. (Code and trained models are available on GitHub)


title: Augmented reality technologies, systems and applications

abstract: This paper surveys the current state-of-the-art of technology, systems and applications in Augmented Reality. It describes work performed by many different research groups, the purpose behind each new Augmented Reality system, and the difficulties and problems encountered when building some Augmented Reality applications. It surveys mobile augmented reality systems challenges and requirements for successful mobile systems. This paper summarizes the current applications of Augmented Reality and speculates on future applications and where current research will lead Augmented Reality's development. Challenges augmented reality is facing in each of these applications to go from the laboratories to the industry, as well as the future challenges we can forecast are also discussed in this paper. Section 1 gives an introduction to what Augmented Reality is and the motivations for developing this technology. Section 2 discusses Augmented Reality Technologies with computer vision methods, AR devices, interfaces and systems, and visualization tools. The mobile and wireless systems for Augmented Reality are discussed in Section 3. Four classes of current applications that have been explored are described in Section 4. These applications were chosen as they are the most famous type of applications encountered when researching AR apps. The future of augmented reality and the challenges they will be facing are discussed in Section 5.


title: Sustainable interaction design: invention & disposal, renewal & reuse

abstract: This paper presents the perspective that sustainability can and should be a central focus of interaction design-a perspective that is termed Sustainable Interaction Design (SID). As a starting point for a perspective of sustainability, design is defined as an act of choosing among or informing choices of future ways of being. This perspective of sustainability is presented in terms of design values, methods, and reasoning. The paper proposes (i) a rubric for understanding the material effects of particular interaction design cases in terms of forms of use, reuse, and disposal, and (ii) several principles to guide SID. The paper illustrates--with particular examples of design critique for interactive products and appeals to secondary research--how two of these principles may be applied to move the effects of designs from less preferred forms of use to more preferred ones. Finally, a vision for incorporating sustainability into the research and practice of interaction design is described.


title: Domain Specific Learning for Newborn Face Recognition

abstract: Biometric recognition of newborn babies is an opportunity for the realization of several useful applications, such as improved security against swapping and abduction, accurate census, and effective drug delivery. This paper explores the possibility of using face recognition toward an affordable and friendly biometric modality for newborns. The paper proposes an autoencoder-based feature representation followed by problem specific distance metric learning via one-shot similarity with one class-online support vector machine. The largest publicly available database of newborns collected from various sources to study face recognition is introduced. Several existing face recognition approaches and commercial systems are also evaluated on a common benchmark protocol. The efficacy of the proposed algorithm is evaluated under both verification and identification settings. With multiple galleries, rank-1 identification accuracy of 78.5% and verification accuracy of 63.4% at 0.1% false accept rate are achieved.


title: A Survey of Privacy Preserving Data Publishing using Generalization and Suppression

abstract: Nowadays, information sharing as an indispensable part appears in our vision, bringing about a mass of discussions about methods and techniques of privacy preserving data publishing which are regarded as strong guarantee to avoid information disclosure and protect individuals' privacy. Recent work focuses on propos ing different anonymity algorithms for varying data publishing scenarios to satisfy privacy requirements, and keep data utility at the same time. K-anonymity has been proposed for privacy preserving data publishing, which can prevent linkage attacks by the means of anonymity operation, such as generalization and suppression. Numerous anonymity algorithms have been utilized for achieving k-anonymity. This paper provides an overview of the development of privacy preserving data publishing, which is restricted to the scope of anonymity alg orithms using generalization and suppression. The privacy preserving models for attack is introduced at first. An overview of seve ral anonymity operations follow behind. The most important part is the coverage of anonymity algorithms and information metric which is essential ingredient of algorithms. The conclusion and perspective are proposed finally.


title: Do Stereotypic Images in Video Games Affect Attitudes and Behavior? Adolescents' Perspectives.

abstract: This study examined adolescents’ attitudes about video games along with their self-reported play frequency. Ninth and eleventh grade students (N = 361), approximately evenly divided by grade and gender, were surveyed about whether video games have stereotypic images, involve harmful consequences or affect one’s attitudes, whether game playing should be regulated by parents or the government, and whether game playing is a personal choice. Adolescents who played video games frequently showed decreased concern about the effects that games with negatively stereotyped images may have on the players’ attitudes compared to adolescents who played games infrequently or not at all. With age, adolescents were more likely to view images as negative, but were also less likely to recognize stereotypic images of females as harmful and more likely to judge video-game playing as a personal choice. The paper discusses other findings in relation to research on adolescents’ social cognitive judgments.


title: Its Data Fusion

abstract: This project has accomplished three significant tasks. First, a state-of-the-art literature review has provided an organizational framework for categorizing the various data fusion projects that have been conducted to date. A popular typology was discussed which situates data fusion technologies in one of three levels, depending on the degree to which sensor data is correlated to provide users with meaningful transit recommendations. The trade-offs that accompany higher-level data fusion efforts--in terms of computing power and memory requirements--were noted. The advantages of multiple-sensor data fusion projects in terms of cost, accuracy, and reliability were also discussed and contrasts were drawn with the traditional deployment of highly accurate, single sensors. Specific techniques of data fusion were described and their possible applications to Intelligent Transportation System (ITS) projects were explored. In fact, this report is one of the first to consider how data fusion technology might be productively applied to the needs of transportation management. A second major component of this report is the description provided of a local data fusion application. This project employs data fusion techniques to correlate input from multiple highway sensors and generate reliable traffic predictions. The resulting information can be displayed for use by commuters as they choose from among various transit options. The architecture of this data fusion system is described in detail. The third component of the project was to create a statistically based algorithm to estimate speed from volume and occupancy measurements. The algorithm presented explicitly accounts for the statistics of the problem and provides a robustness test for the speed estimate.


title: An Evolutionary Artificial Neural Network Approach for Breast Cancer Diagnosis

abstract: In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with an evolutionary artificial neural network approach based on adaptive genetic algorithm with strong macro-search capability and global optimization, which was used to optimize initial weights and thresholds of the network. Experimental results had better precision and much lower computational cost compared to the literature related to this concept in the Wisions breast cancer data set.


title: Multiview RGB-D Dataset for Object Instance Detection

abstract: This paper presents a new multi-view RGB-D dataset of nine kitchen scenes, each containing several objects in realistic cluttered environments including a subset of objects from the BigBird dataset. The viewpoints of the scenes are densely sampled and objects in the scenes are annotated with bounding boxes and in the 3D point cloud. Also, an approach for detection and recognition is presented, which is comprised of two parts: i) a new multi-view 3D proposal generation method and ii) the development of several recognition baselines using AlexNet to score our proposals, which is trained either on crops of the dataset or on synthetically composited training images. Finally, we compare the performance of the object proposals and a detection baseline to the Washington RGB-D Scenes (WRGB-D) dataset and demonstrate that our Kitchen scenes dataset is more challenging for object detection and recognition. The dataset is available at: this http URL


title: GPS-Data Analysis of Munich's Free-Floating Bike Sharing System and Application of an Operator-based Relocation Strategy

abstract: Public Bike Sharing Systems provide a progressive option for urban mobility, not only for commuters but also for spontaneous users and tourists. Such systems are only reasonable, if the bikes are available where the users need them at a certain time though. In so-called free-floating systems as it's implemented in Munich, the user is allowed to rent and return a bike within a clearly defined operating area. However, on one hand there are zones, where a shortage of returned bikes occurs. No bikes are available but needed there. On the other hand there are zones, where too many bikes were returned but the demand for renting a bike there is too low. Based on a detailed GPS-Data Analysis for the bike sharing system, mobility patterns of the usage were identified. Depending on different factors like weather conditions, time of the day and holidays/weekends, a demand model was created in order to obtain an optimal distribution of bikes within the operating area. At the end of this paper an application of an operater-based relocation strategy is given. By relocating at least some part of the fleet, it's ensured that the demand for bikes is optimally satisfied in time and space.


title: Text Mining of Tweet for Sentiment Classification and Association with Stock Prices

abstract: In present days, the social media and networking act as one of the key platforms for sharing information and opinions. Many people share ideas, express their view points and opinions on various topic of their interest. Social media text has rich information about the companies, their products and various services offered by them. In this research we focus exploring the association of sentiments of social media text and stock prices of a company. The tweets of several company has been extracted and performed sentiment classification using Naive Bayes classifier and SVM classifier. To perform the classification, N-gram based feature vectors are constructed using important words of tweets. Further, the pattern of association between number of tweets which are positive or negative and stock prices has been explored. Motivated by such an association, the features related to tweets such as number of positive, negative, neutral tweets and total number of tweets are used to predict the stock market status using Support Vector Machine classifier.


title: The cat is out of the bag: cortical simulations with 109 neurons, 1013 synapses

abstract: In the quest for cognitive computing, we have built a massively parallel cortical simulator, C2, that incorporates a number of innovations in computation, memory, and communication. Using C2 on LLNL's Dawn Blue Gene/P supercomputer with 147, 456 CPUs and 144 TB of main memory, we report two cortical simulations -- at unprecedented scale -- that effectively saturate the entire memory capacity and refresh it at least every simulated second. The first simulation consists of 1.6 billion neurons and 8.87 trillion synapses with experimentally-measured gray matter thalamocortical connectivity. The second simulation has 900 million neurons and 9 trillion synapses with probabilistic connectivity. We demonstrate nearly perfect weak scaling and attractive strong scaling. The simulations, which incorporate phenomenological spiking neurons, individual learning synapses, axonal delays, and dynamic synaptic channels, exceed the scale of the cat cortex, marking the dawn of a new era in the scale of cortical simulations.


title: Staffing remote rural areas in middle- and low-income countries: A literature review of attraction and retention

abstract: BackgroundMany countries in middle- and low-income countries today suffer from severe staff shortages and/or maldistribution of health personnel which has been aggravated more recently by the disintegration of health systems in low-income countries and by the global policy environment. One of the most damaging effects of severely weakened and under-resourced health systems is the difficulty they face in producing, recruiting, and retaining health professionals, particularly in remote areas. Low wages, poor working conditions, lack of supervision, lack of equipment and infrastructure as well as HIV and AIDS, all contribute to the flight of health care personnel from remote areas. In this global context of accelerating inequities health service policy makers and managers are searching for ways to improve the attraction and retention of staff in remote areas. But the development of appropriate strategies first requires an understanding of the factors which influence decisions to accept and/or stay in a remote post, particularly in the context of mid and low income countries (MLICS), and which strategies to improve attraction and retention are therefore likely to be successful. It is the aim of this review article to explore the links between attraction and retention factors and strategies, with a particular focus on the organisational diversity and location of decision-making.MethodsThis is a narrative literature review which took an iterative approach to finding relevant literature. It focused on English-language material published between 1997 and 2007. The authors conducted Pubmed searches using a range of different search terms relating to attraction and retention of staff in remote areas. Furthermore, a number of relevant journals as well as unpublished literature were systematically searched. While the initial search included articles from high- middle- and low-income countries, the review focuses on middle- and low-income countries. About 600 papers were initially assessed and 55 eventually included in the review.ResultsThe authors argue that, although factors are multi-facetted and complex, strategies are usually not comprehensive and often limited to addressing a single or limited number of factors. They suggest that because of the complex interaction of factors impacting on attraction and retention, there is a strong argument to be made for bundles of interventions which include attention to living environments, working conditions and environments and development opportunities. They further explore the organisational location of decision-making related to retention issues and suggest that because promising strategies often lie beyond the scope of human resource directorates or ministries of health, planning and decision-making to improve retention requires multi-sectoral collaboration within and beyond government. The paper provides a simple framework for bringing the key decision-makers together to identify factors and develop multi-facetted comprehensive strategies.ConclusionThere are no set answers to the problem of attraction and retention. It is only through learning about what works in terms of fit between problem analysis and strategy and effective navigation through the politics of implementation that any headway will be made against the almost universal challenge of staffing health service in remote rural areas.


title: Dual-polarized microstrip array antenna with orthogonal feed circuit

abstract: In this paper, a dual-polarized microstrip array antenna with orthogonal feed circuit is proposed. The proposed microstrip array antenna consists of a single substrate layer. The proposed array antenna has microstrip antenna elements, microstrip lines, air-bridges and cross slot lines. For dual polarization, an orthogonal feed circuit uses the Both-Sided MIC Technology including air-bridges. The Both-Sided MIC Technology is one of the useful MIC technologies for realizing a simple feed circuit. The air-bridges are often used for MMICs because it is possible to reduce the circuit complexity. The characteristics of proposed array antenna are investigated by both the simulation and the experiment. Consequently, it is confirmed that the proposed array antenna with the orthogonal feed circuit has dual polarization performance with very simple structure. The proposed array antenna will be a basic technology to realize high performance and attractive multifunction antennas.


title: Demonstration of L-Shaped Tunnel Field-Effect Transistors

abstract: An L-shaped tunnel FET (TFET), which features band-to-band tunneling (BTBT) perpendicular to the channel direction, is experimentally demonstrated for the first time. It is more scalable than other vertical-BTBT-based TFET designs and provides more than $1000\times $ higher ON-current ( $I_{{\mathrm{\scriptscriptstyle ON}}}$ ) than a conventional planar TFET with the same gate overdrive ( $V_{\mathrm{ov}}$ ) of 0.8 V, due to improved subthreshold swing ( $S$ ) and larger tunnel junction area. Its temperature dependence, constant $S$ , and nonlinear output characteristics are discussed.


title: Exploring the brain network: A review on resting-state fMRI functional connectivity

abstract: Our brain is a network. It consists of spatially distributed, but functionally linked regions that continuously share information with each other. Interestingly, recent advances in the acquisition and analysis of functional neuroimaging data have catalyzed the exploration of functional connectivity in the human brain. Functional connectivity is defined as the temporal dependency of neuronal activation patterns of anatomically separated brain regions and in the past years an increasing body of neuroimaging studies has started to explore functional connectivity by measuring the level of co-activation of resting-state fMRI time-series between brain regions. These studies have revealed interesting new findings about the functional connections of specific brain regions and local networks, as well as important new insights in the overall organization of functional communication in the brain network. Here we present an overview of these new methods and discuss how they have led to new insights in core aspects of the human brain, providing an overview of these novel imaging techniques and their implication to neuroscience. We discuss the use of spontaneous resting-state fMRI in determining functional connectivity, discuss suggested origins of these signals, how functional connections tend to be related to structural connections in the brain network and how functional brain communication may form a key role in cognitive performance. Furthermore, we will discuss the upcoming field of examining functional connectivity patterns using graph theory, focusing on the overall organization of the functional brain network. Specifically, we will discuss the value of these new functional connectivity tools in examining believed connectivity diseases, like Alzheimer's disease, dementia, schizophrenia and multiple sclerosis.


title: Q-Probabilistic Routing in Wireless Sensor Networks

abstract: Unpredictable topology changes, energy constraints and link unreliability make the information transmission a challenging problem in wireless sensor networks (WSN). Taking some ideas from machine learning methods, we propose a novel geographic routing algorithm for WSN, named Q-probabilistic routing (Q-PR), that makes intelligent routing decisions from the delayed reward of previous actions and the local interaction among neighbor nodes, by using reinforcement learning and a Bayesian decision model. Moreover, by considering the message importance embedded in the message itself routing decisions can be adapted to traffic importance. Experimental results show that Q-PR becomes a routing policy that, as a function of the message importance, achieves a trade-off among the expected number of retransmissions (ETX), the successful delivery rate and the network lifetime.


title: Attacks on biometric systems: a case study in fingerprints

abstract: In spite of numerous advantages of biometrics-based personal authentication systems over traditional security systems based on token or knowledge, they are vulnerable to attacks that can decrease their security considerably. In this paper, we analyze these attacks in the realm of a fingerprint biometric system. We propose an attack system that uses a hill climbing procedure to synthesize the target minutia templates and evaluate its feasibility with extensive experimental results conducted on a large fingerprint database. Several measures that can be utilized to decrease the probability of such attacks and their ramifications are also presented.


title: A Privacy-Preserving Pay-by-Phone Parking System

abstract: Most cities around the world require drivers to pay for the time they occupy a parking spot. In this way, drivers are encouraged to shorten the parking time so that other drivers are given a reasonable chance of finding parking. The traditional way, based on moving to a pay station and placing the issued parking ticket on the dashboard of the car, presents several drawbacks, such as having to predict in advance the duration of parking or the need to move to the car in case the parking time has to be extended. Over the past few years, several applications permitting payment through mobile phones have appeared. Such applications manage detailed information about parking operations so that accurate profiles of parking habits of car owners can be created. In this paper, we propose a system to pay for parking by phone that preserves the privacy of drivers. The information managed by the system is proven not to provide any help to the creation of profiles of parking habits. An attacker with full access to all this information can not do better than he or she would do by patrolling the city for collecting information about parked cars.


title: 3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversary Network

abstract: We introduce a data-driven approach to aid the repairing and conservation of archaeological objects: ORGAN, an object reconstruction generative adversarial network (GAN). By using an encoder-decoder 3D deep neural network on a GAN architecture, and combining two loss objectives: a completion loss and an Improved Wasserstein GAN loss, we can train a network to effectively predict the missing geometry of damaged objects. As archaeological objects can greatly differ between them, the network is conditioned on a variable, which can be a culture, a region or any metadata of the object. In our results, we show that our method can recover most of the information from damaged objects, even in cases where more than half of the voxels are missing, without producing many errors.


Data Classification using Support Vector Machines


title: Correlation Clustering in General Weighted Graphs

abstract: We consider the following general correlation-clustering problem [N. Bansal, A. Blum, S. Chawla, Correlation clustering, in: Proc. 43rd Annu. IEEE Symp. on Foundations of Computer Science, Vancouver, Canada, November 2002, pp. 238-250]: given a graph with real nonnegative edge weights and a 〈+〉/〈-〉 edge labelling, partition the vertices into clusters to minimize the total weight of cut 〈+〉 edges and uncut 〈-〉 edges. Thus, 〈+〉 edges with large weights (representing strong correlations between endpoints) encourage those endpoints to belong to a common cluster while 〈-〉 edges with large weights encourage the endpoints to belong to different clusters. In contrast to most clustering problems, correlation clustering specifies neither the desired number of clusters nor a distance threshold for clustering; both of these parameters are effectively chosen to be best possible by the problem definition.Correlation clustering was introduced by Bansal et al. [Correlation clustering, in: Proc. 43rd Annu. IEEE Syrup. on Foundations of Computer Science, Vancouver, Canada, November 2002, pp. 238-250], motivated by both document clustering and agnostic learning. They proved NP-hardness and gave constant-factor approximation algorithms for the special case in which the graph is complete (full information) and every edge has the same weight. We give an O(log n)-approximation algorithm for the general case based on a linear-programming rounding and the "region-growing" technique. We also prove that this linear program has a gap of Ω(log n), and therefore our approximation is tight under this approach. We also give an O(r3)-approximation algorithm for Kr, r-minor-free graphs. On the other hand, we show that the problem is equivalent to minimum multicut, and therefore APX-hard and difficult to approximate better than Θ(log n).


title: E-Commerce Recommendation Applications

abstract: i>Recommender systems are being used by an ever-increasing number of E-commerce sites to help consumers find products to purchase. What started as a novelty has turned into a serious business tool. Recommender systems use product knowledge—either hand-coded knowledge provided by experts or “mined” knowledge learned from the behavior of consumers—to guide consumers through the often-overwhelming task of locating products they will like. In this article we present an explanation of how recommender systems are related to some traditional database analysis techniques. We examine how recommender systems help E-commerce sites increase sales and analyze the recommender systems at six market-leading sites. Based on these examples, we create a taxonomy of recommender systems, including the inputs required from the consumers, the additional knowledge required from the database, the ways the recommendations are presented to consumers, the technologies used to create the recommendations, and the level of personalization of the recommendations. We identify five commonly used E-commerce recommender application models, describe several open research problems in the field of recommender systems, and examine privacy implications of recommender systems technology.


Joint Learning of Local and Global Features for Entity Linking via Neural Networks


A Wideband Compact Parallel-Strip 180 $^circ$ Wilkinson Power Divider for Push– Pull Circuitries


title: Policy Gradient Coagent Networks

abstract: We present a novel class of actor-critic algorithms for actors consisting of sets of interacting modules. We present, analyze theoretically, and empirically evaluate an update rule for each module, which requires only local information: the module's input, output, and the TD error broadcast by a critic. Such updates are necessary when computation of compatible features becomes prohibitively difficult and are also desirable to increase the biological plausibility of reinforcement learning methods.


title: Detection and Prevention against ARP Poisoning Attack Using Modified ICMP and Voting

abstract: Address Resolution Protocol (ARP) poisoning is the leading point for refined LAN attacks like denial-of-service (DOS) and Man-In-The-Middle (MITM). Weak point of ARP that is being Stateless, directly affects security standards of Network and specially Ethernet. In proposed mechanism of detection, initially traffic over the network is sniffed by Central Server (CS). Then, CS sends trap ICMP ping packet, analyze the response in terms of ICMP reply and successfully detects attacker. In order to prevent ARP poisoning over centralized system, voting process is used to elect legitimate CS. Validating and Correcting pair entries residing in hosts cache tables, CS successfully prevents ARP poisoning while maintaining performance of the system. Our technique is based on ICMP and Voting such mechanism with Backward Compatibility, Less Cost, Minimal Traffic and Easily Deployable is proposed to detect and prevent MITM based ARP poisoning which is effectual version overcoming weaknesses of ARP.


title: RGB-D SLAM in Dynamic Environments Using Points Correlations

abstract: This paper proposed a novel RGB-D SLAM method for dynamic environments. It follows traditional feature-based SLAM methods and utilizes a feature groups segmentation method to resist the disturbance caused by the dynamic objects using points correlations. The correlations between map points represented with a sparse graph are created by Delaunay triangulation. After removing non-consistency connections, the dynamic objects are separated from static background. The features only in the static map are used for motion estimation and bundle adjustment which improves the accuracy and robustness of SLAM in dynamic environments. The effectiveness of the proposed SLAM are evaluated using TUM RGB-D benchmark. The experiments demonstrate that the dynamic features are successfully removed and the system work perfectly in both low and high dynamic environments. The comparisons between proposed method and state-of-the-art visual systems clearly show that the comparable accurate results are achieved in low dynamic environments and the performance is improved significantly in high dynamic environments.


Community structure of modules in the Apache project


title: A variable DC link based novel multilevel inverter topology for low voltage applications

abstract: In this paper; a variable dc link based novel multilevel inverter (MLI) topology is proposed. This proposed topology comprises two variable dc links and a modified H-bridge unit. This new single-phase topology offers advantages such as reduction in count of switches; gate drivers and dc sources while simultaneously improving the power quality. The proposed topology has been analyzed for both symmetric and asymmetric modes of operation. A comparative study of the proposed topology with some recent MLI topologies has been presented. The comparative study indicates that the proposed topology requires less number of dc sources; switches and driver circuits as compared to other topologies. The proposed topology has been simulated for 31-level asymmetric configuration in MATLAB/SIMULINK environment to verify the proper operation of proposed topology. Harmonic analysis was also performed for 31-level inverter which showed significant reduction in the total harmonic distortion (THD) for phase voltage and current waveforms. The proposed topology is suitable for low voltage applications such as standalone photovoltaic (PV) systems and hybrid electric vehicles (HEVs).


title: Efficient Mining of Association Rules Using Closed Itemset Lattices

abstract: Abstract Discovering association rules is one of the most important task in data mining. Many efficient algorithms have been proposed in the literature. The most noticeable are Apriori, Mannila's algorithm, Partition, Sampling and DIC, that are all based on the Apriori mining method: pruning the subset lattice (itemset lattice). In this paper we propose an efficient algorithm, called Close, based on a new mining method: pruning the closed set lattice (closed itemset lattice). This lattice, which is a sub-order of the subset lattice, is closely related to Wille's concept lattice in formal concept analysis. Experiments comparing Close to an optimized version of Apriori showed that Close is very efficient for mining dense and/or correlated data such as census style data, and performs reasonably well for market basket style data.


A new approach to attribute reduction of consistent and inconsistent covering decision systems with covering rough sets


title: Electronic intermediaries: trust building and market differentiation

abstract: As electronic commerce becomes increasingly popular, new intermediaries are emerging and transforming marketing and distribution channels. Intermediaries in electronic marketplaces provide the IT and business infrastructure to facilitate the completion of commercial transactions over interorganizational computer networks. If electronic intermediary services are introduced to wholesale markets where qualities vary, the provision of IT alone cannot create reliable electronic marketplaces for traders who have no pre-established relationships. To build trust among market participants, electronic intermediaries should establish policies and processes that regulate responsibilities and duties of market participants and legitimate transactions. Institutional policies and processes reduce risks and help establish trust among market participants. This paper provides empirical evidence that trust building processes by electronic intermediaries can lend to concentration of electronic transactions on high quality products, thus differentiating electronic and traditional markets.


title: Gamification in MOOC: challenges, opportunities and proposals for advancing MOOC model

abstract: The phenomenon of MOOC (Massive Online Open Courses) is increasingly experienced and is challenged by very low completion rates. There are a large number of students who drop out of the course, so it is necessary to find new ways to motivate these students and get them to complete the course. This paper proposes a model to motivate MOOC's students based on gamification using the most attractive and addictive elements of games in environments that are not for play.


title: Evaluation of Computerized Physician Order Entry System—A Satisfaction Survey in Taiwan

abstract: In the rapidly developing world of information technology, computers have been used in various settings for clinical medicine application. Studies have focused on computerized physician order entry (CPOE) system interface design and functional development to achieve a successful technology adoption process. Therefore, the purpose of this study was to evaluate physician satisfaction with the CPOE system. This survey included user attitude toward interface design, operation functions/usage effectiveness, interface usability, and user satisfaction. We used questionnaires for data collection from June to August 2008, and 225 valid questionnaires were returned with a response rate of 84.5 %. Canonical correlation was applied to explore the relationship of personal attributes and usability with user satisfaction. The results of the data analysis revealed that certain demographic groups showed higher acceptance and satisfaction levels, especially residents, those with less pressure when using computers or those with less experience with the CPOE systems. Additionally, computer use pressure and usability were the best predictors of user satisfaction. Based on the study results, it is suggested that future CPOE development should focus on interface design and content links, as well as providing educational training programs for the new users; since a learning curve period should be considered as an indespensible factor for CPOE adoption.


title: MAGNETO: Covert Channel between Air-Gapped Systems and Nearby Smartphones via CPU-Generated Magnetic Fields

abstract: In this paper, we show that attackers can leak data from isolated, air-gapped computers to nearby smartphones via covert magnetic signals. The proposed covert channel works even if a smartphone is kept inside a Faraday shielding case, which aims to block any type of inbound and outbound wireless communication (Wi-Fi, cellular, Bluetooth, etc.). The channel also works if the smartphone is set in airplane mode in order to block any communication with the device. We implement a malware that controls the magnetic fields emanating from the computer by regulating workloads on the CPU cores. Sensitive data such as encryption keys, passwords, or keylogging data is encoded and transmitted over the magnetic signals. A smartphone located near the computer receives the covert signals with its magnetic sensor. We present technical background, and discuss signal generation, data encoding, and signal reception. We show that the proposed covert channel works from a user-level process, without requiring special privileges, and can successfully operate from within an isolated virtual machine (VM).


title: Copine-I represses NF-κB transcription by endoproteolysis of p65

abstract: Nuclear factor-kappaB (NF-kappaB) is a dynamic transcription factor that regulates important biological processes involved in cancer initiation and progression. Identifying regulators that control the half-life of NF-kappaB is important to understanding molecular processes that control the duration of transcriptional responses. In this study we identify copine-I, a calcium phospholipid-binding protein, as a novel repressor that physically interacts with p65 to inhibit NF-kappaB transcription. Knockdown of copine-I by siRNA increases tumor necrosis factor alpha-stimulated NF-kappaB transcription, while copine-I expression blocks endogenous transcription. Copine-I abolishes NF-kappaB transcription by inducing endoprotease processing of the N-terminus of p65, a process antagonized by IkappaB alpha. Copine-I stimulates endoproteolysis of p65 within a conserved region that is required for base-specific contact with DNA. p65 proteins lacking the N-terminus fail to bind to DNA and act as dominant-negative molecules that inhibit NF-kappaB transcription. Our work provides evidence that copine-I regulates the half-life of NF-kappaB transcriptional responses through a novel mechanism that involves endoproteolysis of the p65 protein.


title: Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Tasks

abstract: Conventional collaborative filtering techniques treat a top-n recommendations problem as a task of generating a list of the most relevant items. This formulation, however, disregards an opposite -- avoiding recommendations with completely irrelevant items. Due to that bias, standard algorithms, as well as commonly used evaluation metrics, become insensitive to negative feedback. In order to resolve this problem we propose to treat user feedback as a categorical variable and model it with users and items in a ternary way. We employ a third-order tensor factorization technique and implement a higher order folding-in method to support online recommendations. The method is equally sensitive to entire spectrum of user ratings and is able to accurately predict relevant items even from a negative only feedback. Our method may partially eliminate the need for complicated rating elicitation process as it provides means for personalized recommendations from the very beginning of an interaction with a recommender system. We also propose a modification of standard metrics which helps to reveal unwanted biases and account for sensitivity to a negative feedback. Our model achieves state-of-the-art quality in standard recommendation tasks while significantly outperforming other methods in the cold-start "no-positive-feedback" scenarios.


title: Image encryption and decryption using chaotic key sequence generated by sequence of logistic map and sequence of states of Linear Feedback Shift Register

abstract: In this paper image encryption and decryption using key sequence {K i } generated by sequence {K 1, i } of logistic map and sequence of states {K 2, i } of Linear Feedback Shift Register (LFSR) is proposed. Initially one dimensional sequence is generated in the range of 0 to 1 using logistic map for the bifurcation parameter r and initial value X 0 . The generated sequences are multiplied with 255 and bit by bit XOR operation employed on states of 8 bit LFSR to obtain final key sequence {K i }. So obtained key sequence {K i } is XORed with 8-bit grayscale image pixels {P i } to obtain the encrypted image. Performance of the proposed scheme is analyzed by computing Histogram, Entropy, Correlation and MSE (Mean Square Error) between the original and encrypted image. This scheme has been tested for two different 8-bit grayscale images of size 256×256 pixels. Proposed scheme is compared with encryption/decryption using sequence {K 1, i } generated using logistic map. Result of histogram plot of encrypted image using proposed scheme is flat and uniformly distributed compared to encryption scheme using generated sequence of logistic map. MSE measured between original and encrypted image found to be high. Also correlation between adjacent pixels of encrypted image found to be low.


title: ALGORITHMS FOR DISTRIBUTED DATA STREAM MINING

abstract: The field of Distributed Data Mining (DDM) deals with the problem of analyzing data by paying careful attention to the distributed computing, storage, communication, and human-factor related resources. Unlike the traditional centralized systems, DDM offers a fundamentally distributed solution to analyze data without necessarily demanding collection of the data to a single central site. This chapter presents an introduction to distributed data mining for continuous streams. It focuses on the situations where the data observed at different locations change with time. The chapter provides an exposure to the literature and illustrates the behavior of this class of algorithms by exploring two very different types of techniques—one for the peer-to-peer and another for the hierarchical distributed environment. The chapter also briefly discusses several different applications of these algorithms.


title: Effects of color on emotions.

abstract: Emotional reactions to color hue, saturation, and brightness (Munsell color system and color chips) were investigated using the Pleasure-Aiousal-Dominance emotion model. Saturation (S) and brightness (B) evidenced strong and consistent effects on emotions. Regression equations for standardized variables were: Pleasure = .69B + .228, Arousal = —.31B + .60S, Dominance = — .76B + .328. Brightness effects were nearly the same for chromatic and achromatic colors. Blue, blue-green, green, red-purple, purple, and purple-blue were the most pleasant hues, whereas yellow and green-yellow were the least pleasant. Green-yellow, blue-green, and green were the most arousing, whereas purple-blue and yellow-red were the least arousing. Green-yellow induced greater dominance than red—purple. There is a large body of literature on the psychology of color. The research spans more than a century, covers a wide range of interests, and exhibits varying degrees of methodological rigor. The topics of investigation include: (a) color reactions as functions of personality and psychopathology, (b) physiological reactions to color, (c) color preferences, (d) color effects on emotions, (e) color effects on behavior, and (f) reactions to color concepts. Methodological and Conceptual Issues Color stimuli are characterized completely in terms of hue (i.e., wavelength), brightness or value (i.e., black-to-white quality) and saturation or chroma (i.e., purity or vividness, with lower saturation colors containing more grey). The following, more precise, definition of saturation is helpful: "Munsell chroma is often considered to be the approximate counterpart of perceived saturation. The Munsell chroma of a color sample is defined as the difference from a grey of the same lightness" (Agoston, 1979, p. 87). As Gelineau (1981) noted, much of the research on color and affect is weak on several grounds. The methodological problems can be grouped in two broad categories. The first group includes studies that have failed to provide adequate specifications or controls of color stimuli (e.g., absence of controls for saturation and brightness while investigating effects of hue) and use of nonstandard or unspecified lighting conditions. The second group of studies failed to use sufficiently reliable, valid, or comprehensive measures of emotional responses to color stimuli. Thus, despite the substantial body of experimental work in this area, results have failed to provide a thorough and general characterization of relationships between color and affect.


title: Introduction to Spark 2.0 for Database Researchers

abstract: Originally started as an academic research project at UC Berkeley, Apache Spark is one of the most popular open source projects for big data analytics. Over 1000 volunteers have contributed code to the project; it is supported by virtually every commercial vendor; many universities are now offering courses on Spark. Spark has evolved significantly since the 2010 research paper: its foundational APIs are becoming more relational and structural with the introduction of the Catalyst relational optimizer, and its execution engine is developing quickly to adopt the latest research advances in database systems such as whole-stage code generation. This tutorial is designed for database researchers (graduate students, faculty members, and industrial researchers) interested in a brief hands-on overview of Spark. This tutorial covers the core APIs for using Spark 2.0, including DataFrames, Datasets, SQL, streaming and machine learning pipelines. Each topic includes slide and lecture content along with hands-on use of a Spark cluster through a web-based notebook environment. In addition, we will dive into the engine internals to discuss architectural design choices and their implications in practice. We will guide the audience to "hack" Spark by extending its query optimizer to speed up distributed join execution.


title: An Idea of Using Reinforcement Learning in Adaptive Control Systems

abstract: This paper concerns the concept of using a Reinforcement Learning approach for PID adaptive auto tuning, which may greatly increase possible application of the controller. The proposed idea can be utilised for off-line and on-line control systems. Though it becomes really powerful in the practical on-line applications, where the environment is unknown and all information is gained only by interactions. In the paper the algorithm based on Q-learning is proposed. The properties of the algorithm are illustrated on a simple examples.


title: Compact UWB and Low-RCS Vivaldi Antenna Using Ultrathin Microwave-Absorbing Materials

abstract: In this letter, we present an improved Vivaldi antenna by using ultrathin microwave-absorbing materials (MAMs). The reverse currents at the outer side edges of the Vivaldi antenna always distort its radiation patterns and worsen its VSWR in some frequencies. To solve this problem, the ultrathin MAMs are loaded at the side edges of the antenna to absorb the energy of the current, and also to reduce its radar cross section (RCS). Simulated and measured results show that the proposed antenna operates from 0.8 to 15.5 GHz with VSWR $\theta \in [- 30^{\circ}, + 30^{\circ}],\; \phi = [- 45, + 45^{\circ}]$ angle range are less than 0.001 m2, which make it very attractive for a variety of applications.


title: A Review on EEG-Based Automatic Sleepiness Detection Systems for Driver

abstract: Electroencephalography-based sleepiness detection system (ESDS) is a brain-computer interface that evaluates a driver’s sleepiness level directly from cerebral activity. The goals of ESDS research are to estimate and produce a timely warning to prevent declines in performance efficiency and to inhibit sleepiness-related accidents. We first, review different types of measures used in sleepiness detection systems (SDSs) and presents their advantages and drawbacks. Second, the review includes several techniques proposed in ESDSs to optimize the number of EEG electrodes, increasing the sleepiness level resolution and incorporation of circadian information. Finally, the review discusses future direction that can be considered in the development of ESDS.


title: BizPro: Extracting and categorizing business intelligence factors from textual news articles

abstract: Abstract Company movements and market changes often are headlines of the news, providing managers with important business intelligence (BI). While existing corporate analyses are often based on numerical financial figures, relatively little work has been done to reveal from textual news articles factors that represent BI. In this research, we developed BizPro, an intelligent system for extracting and categorizing BI factors from news articles. BizPro consists of novel text mining procedures and BI factor modeling and categorization. Expert guidance and human knowledge (with high inter-rater reliability) were used to inform system development and profiling of BI factors. We conducted a case study of using the system to profile BI factors of four major IT companies based on 6859 sentences extracted from 231 news articles published in major news sources. The results show that the chosen techniques used in BizPro – Naive Bayes (NB) and Logistic Regression (LR) – significantly outperformed a benchmark technique. NB was found to outperform LR in terms of precision, recall, F-measure, and area under ROC curve. This research contributes to developing a new system for profiling company BI factors from news articles, to providing new empirical findings to enhance understanding in BI factor extraction and categorization, and to addressing an important yet under-explored concern of BI analysis.


title: Efficient dynamic analysis for Node.js

abstract: Due to its popularity, there is an urgent need for dynamic program-analysis tools for Node.js, helping developers find bugs, performance bottlenecks, and bad coding practices. Frameworks based on code-level instrumentation enable dynamic analyses close to program semantics and are more flexible than Node.js built-in profiling tools. However, existing code-level instrumentation frameworks for JavaScript suffer from enormous overheads and difficulties in instrumenting the built-in module library of Node.js. In this paper, we introduce a new dynamic analysis framework for JavaScript and Node.js called NodeProf. While offering similar flexibility as code-level instrumentation frameworks, NodeProf significantly improves analysis performance while ensuring comprehensive code coverage. NodeProf supports runtime (de)activation of analyses and incurs zero overhead when no analysis is active. NodeProf is based on dynamic instrumentation of the JavaScript runtime and leverages automatic partial evaluation to generate efficient machine code. In addition, NodeProf makes use of the language interoperability provided by the runtime and thus allows dynamic analyses to be written in Java and JavaScript with compatibility to Jalangi, a state-of-the-art code-level JavaScript instrumentation framework. Our experiments show that the peak performance of running the same dynamic analyses using NodeProf can be up to three orders of magnitude faster than Jalangi.


title: Fog Computing: A Platform for Internet of Things and Analytics

abstract: Internet of Things (IoT) brings more than an explosive proliferation of endpoints. It is disruptive in several ways. In this chapter we examine those disruptions, and propose a hierarchical distributed architecture that extends from the edge of the network to the core nicknamed Fog Computing. In particular, we pay attention to a new dimension that IoT adds to Big Data and Analytics: a massively distributed number of sources at the edge.


title: A review on metrics for ontology evaluation

abstract: Ontology helps semantic systems to establish a model of the world in order to derive machine processed knowledge. It represents formally the concepts, objects, properties and establishes the relationship between them. By providing the structure and semantics for data representation ontology has opened an avenue for appropriate representation of the world for specific and explicit sharing of knowledge without ambiguities. The use of ontology in various applications and the availability of ontology on the web have increased. Ontology should be evaluated from various perspectives to determine the quality of ontology before use or reuse. Ontology evaluation is one of the key phases in ontology development. The ontology should be accurate and provide the accurate results to a user. Unless the quality of the ontology is assured the accuracy of the results cannot be guaranteed. Metrics is used to measure the quality of ontology from different dimensions. This paper explores various metrics used for ontology evaluation and investigates the underlying mechanisms.


title: Inaudible Voice Commands

abstract: Voice assistants like Siri enable us to control IoT devices conveniently with voice commands, however, they also provide new attack opportunities for adversaries. Previous papers attack voice assistants with obfuscated voice commands by leveraging the gap between speech recognition system and human voice perception. The limitation is that these obfuscated commands are audible and thus conspicuous to device owners. In this paper, we propose a novel mechanism to directly attack the microphone used for sensing voice data with inaudible voice commands. We show that the adversary can exploit the microphone's non-linearity and play well-designed inaudible ultrasounds to cause the microphone to record normal voice commands, and thus control the victim device inconspicuously. We demonstrate via end-to-end real-world experiments that our inaudible voice commands can attack an Android phone and an Amazon Echo device with high success rates at a range of 2-3 meters.


title: Designing for or designing with? Informant design for interactive learning environments

abstract: The value of involving people as ‘users’ or ‘participants’ in the design process is increasingly becoming a point of debate. In this paper we describe a new framework, called ‘informant design’, which advocates efficiency of input from different people: maximizing the value of contributions tlom various informants and design team members at different stages of the design process. To illustrate how this can be achieved we describe a project that uses children and teachers as informants at difTerent stages to help us design an interactive learning environment for teaching ecology.


title: Substructure and boundary modeling for continuous action recognition

abstract: This paper introduces a probabilistic graphical model for continuous action recognition with two novel components: substructure transition model and discriminative boundary model. The first component encodes the sparse and global temporal transition prior between action primitives in state-space model to handle the large spatial-temporal variations within an action class. The second component enforces the action duration constraint in a discriminative way to locate the transition boundaries between actions more accurately. The two components are integrated into a unified graphical structure to enable effective training and inference. Our comprehensive experimental results on both public and in-house datasets show that, with the capability to incorporate additional information that had not been explicitly or efficiently modeled by previous methods, our proposed algorithm achieved significantly improved performance for continuous action recognition.


title: Survey on Network Virtualization Hypervisors for Software Defined Networking

abstract: Software defined networking (SDN) has emerged as a promising paradigm for making the control of communication networks flexible. SDN separates the data packet forwarding plane, i.e., the data plane, from the control plane and employs a central controller. Network virtualization allows the flexible sharing of physical networking resources by multiple users (tenants). Each tenant runs its own applications over its virtual network, i.e., its slice of the actual physical network. The virtualization of SDN networks promises to allow networks to leverage the combined benefits of SDN networking and network virtualization and has therefore attracted significant research attention in recent years. A critical component for virtualizing SDN networks is an SDN hypervisor that abstracts the underlying physical SDN network into multiple logically isolated virtual SDN networks (vSDNs), each with its own controller. We comprehensively survey hypervisors for SDN networks in this paper. We categorize the SDN hypervisors according to their architecture into centralized and distributed hypervisors. We furthermore sub-classify the hypervisors according to their execution platform into hypervisors running exclusively on general-purpose compute platforms, or on a combination of general-purpose compute platforms with general- or special-purpose network elements. We exhaustively compare the network attribute abstraction and isolation features of the existing SDN hypervisors. As part of the future research agenda, we outline the development of a performance evaluation framework for SDN hypervisors.


title: Modeling for drivability and drivability improving control of HEV

abstract: Abstract A hybrid electric vehicle has more than one energy source and switching between the two sources leads to significant drivability problem. Drivability is used to describe the comfort of driving a vehicle under a wide variety of operating conditions like tip-in, tip-out, and gear shifting. Vehicle drivability is one of the keystones of product quality and is refined aggressively to achieve product differentiation and market position. Specific aspects of drivability problems can be improved using modern control techniques. In this paper, a generic model of the driveline of a plug-in hybrid vehicle is developed. Drivability evaluation indices have been developed and were used for evaluating the drivability performance. Analysis and measurements of the actual components of this vehicle followed by parameter identification based on experimental data are used to obtain a validated version of the driveline model. A comparison of the driveline under consideration with changes in critical parameters is presented to demonstrate the effect of these parameters on drivability. The paper then focuses on improving the tip-in and tip-out drivability problem by proposing a control architecture consisting of an input shaping feedforward control filter and a feedback controller around an inner disturbance observer loop. The proposed controller is tested in both simulations and in experimental road tests and is shown to improve the drive quality of the vehicle significantly.


title: Phylogeny and cryptic diversification in Southeast Asian flying geckos

abstract: The closed-canopy forests of Southeast Asia are home to an impressive number of vertebrates that have independently evolved morphologies that enhance directed aerial descent (gliding, parachuting). These assemblages include numerous mammal, frog, snake, and lizard clades. Several genera of gekkonid lizards, in particular, have evolved specialized structures such as cutaneous expansions, flaps, and midbody patagia, that enhance lift generation in the context of unique gliding and parachuting locomotion. The genus Ptychozoon represents arguably the most morphologically extreme, highly specialized clade of gliding geckos. Despite their notoriety and celebrated locomotor ability, members of the genus Ptychozoon have never been the subject of a species-level molecular phylogenetic analysis. In this paper, we utilize molecular sequence data from mitochondrial and nuclear gene fragments to estimate the evolutionary relationships of this unique group of flying geckos. Capitalizing on the recent availability of genetic samples for even the rarest of known species, we include the majority of known taxa and use model-based phylogenetic methods to reconstruct their evolutionary history. Because one species, P. kuhli, exhibits an unusually wide distribution coupled with an impressive range of morphological variation, we additionally use intensive phylogeographic/population genetic sampling, phylogenetic network analyses, and Bayesian species delimitation procedures to evaluate this taxon for the possible presence of cryptic evolutionary lineages. Our results suggest that P. kuhli may consist of between five and nine unrecognized, distinct species. Although we do not elevate these lineages to species status here, our findings suggest that lineage diversity in Ptychozoon is likely dramatically underestimated.


title: Lack of empathy in patients with narcissistic personality disorder

abstract: Abstract The study's objective was to empirically assess cognitive and emotional empathy in patients with narcissistic personality disorder (NPD). To date, “lack of empathy” is a core feature of NPD solely based on clinical observation. The study's method was that forty-seven patients with NPD, 53 healthy controls, and 27 clinical controls with borderline personality disorder (BPD) were included in the study. Emotional and cognitive empathy were assessed with traditional questionnaire measures, the newly developed Multifaceted Empathy Test (MET), and the Movie for the Assessment of Social Cognition (MASC). The study's results were that individuals with NPD displayed significant impairments in emotional empathy on the MET. Furthermore, relative to BPD patients and healthy controls, NPD patients did not show deficits in cognitive empathy on the MET or MASC. Crucially, this empathic profile of NPD is not captured by the Structured Clinical Interview for DSM-IV for Axis II Disorders (SCID-II). The study's conclusions were that while NPD involves deficits in emotional empathy, cognitive empathy seems grossly unaffected.


title: The web centipede: understanding how web communities influence each other through the lens of mainstream and alternative news sources

abstract: As the number and the diversity of news outlets on the Web grows, so does the opportunity for "alternative" sources of information to emerge. Using large social networks like Twitter and Facebook, misleading, false, or agenda-driven information can quickly and seamlessly spread online, deceiving people or influencing their opinions. Also, the increased engagement of tightly knit communities, such as Reddit and 4chan, further compounds the problem, as their users initiate and propagate alternative information, not only within their own communities, but also to different ones as well as various social media. In fact, these platforms have become an important piece of the modern information ecosystem, which, thus far, has not been studied as a whole. In this paper, we begin to fill this gap by studying mainstream and alternative news shared on Twitter, Reddit, and 4chan. By analyzing millions of posts around several axes, we measure how mainstream and alternative news flows between these platforms. Our results indicate that alt-right communities within 4chan and Reddit can have a surprising level of influence on Twitter, providing evidence that "fringe" communities often succeed in spreading alternative news to mainstream social networks and the greater Web.


title: Adapting Deep Visuomotor Representations with Weak Pairwise Constraints

abstract: Real-world robotics problems often occur in domains that differ significantly from the robot's prior training environment. For many robotic control tasks, real world experience is expensive to obtain, but data is easy to collect in either an instrumented environment or in simulation. We propose a novel domain adaptation approach for robot perception that adapts visual representations learned on a large easy-to-obtain source dataset (e.g. synthetic images) to a target real-world domain, without requiring expensive manual data annotation of real world data before policy search. Supervised domain adaptation methods minimize cross-domain differences using pairs of aligned images that contain the same object or scene in both the source and target domains, thus learning a domain-invariant representation. However, they require manual alignment of such image pairs. Fully unsupervised adaptation methods rely on minimizing the discrepancy between the feature distributions across domains. We propose a novel, more powerful combination of both distribution and pairwise image alignment, and remove the requirement for expensive annotation by using weakly aligned pairs of images in the source and target domains. Focusing on adapting from simulation to real world data using a PR2 robot, we evaluate our approach on a manipulation task and show that by using weakly paired images, our method compensates for domain shift more effectively than previous techniques, enabling better robot performance in the real world.


title: Rosbridge: ROS for Non-ROS Users

abstract: We present rosbridge, a middleware abstraction layer which provides robotics technology with a standard, minimalist applications development framework accessible to applications programmers who are not themselves roboticists. Rosbridge provides a simple, socket-based programmatic access to robot interfaces and algorithms provided (for now) by ROS, the open-source “Robot Operating System”, the current state-of-the-art in robot middleware. In particular, it facilitates the use of web technologies such as Javascript for the purpose of broadening the use and usefulness of robotic technology. We demonstrate potential applications in the interface design, education, human-robot interaction and remote laboratory environments.


Feature Selection for Support Vector Machine in the Study of Financial Early Warning System


title: Analysis of dual-reflector antennas with a reflectarray as subreflector

abstract: In this paper, a modular technique is described for the analysis of dual-reflector antennas using a reflectarray as a subreflector. An antenna configuration based on a sub-reflectarray and a parabolic main reflector provides better bandwidth than a single reflectarray, and has a number of advantages compared with a conventional dual-reflector antenna. Examples include the possibility of beam shaping by adjusting the phase on the sub-reflectarray, and potential capabilities to scan or reconfigure the beam. The modular technique implemented for the antenna analysis combines different methods for the analysis of each part of the antenna. First, the real field generated by the horn is considered as the incident field on each reflectarray element. Second, the reflectarray is analyzed with the same technique as for a single reflectarray, i.e., considering local periodicity and the real angle of incidence of the wave coming from the feed for each periodic cell. Third, the main reflector is analyzed using the Physical Optics (PO) technique, where the current on the reflector surface is calculated by summing the radiation from all the reflectarray elements. Finally, the field is calculated on a rectangular periodic mesh at a projected aperture, and then a time-efficient fast Fourier transform (FFT) algorithm is used to compute the radiation pattern of the antenna. The last step significantly improves the computational efficiency. However, it introduces a phase error, which reduces the accuracy of the radiation patterns for radiation angles far away from the antenna's axis. The phase errors have been evaluated for two integration apertures. It has been demonstrated that accurate patterns are obtained in an angular range of plusmn6deg, which is sufficient for large reflectors. The method of analysis has been validated by comparing the results with simulations obtained from GRASP8. Finally, the theoretical beam-scanning performance of the antenna is analyzed.


title: Improving Abstraction in Text Summarization

abstract: Abstractive text summarization aims to shorten long text documents into a human readable form that contains the most important facts from the original document. However, the level of actual abstraction as measured by novel phrases that do not appear in the source document remains low in existing approaches. We propose two techniques to improve the level of abstraction of generated summaries. First, we decompose the decoder into a contextual network that retrieves relevant parts of the source document, and a pretrained language model that incorporates prior knowledge about language generation. Second, we propose a novelty metric that is optimized directly through policy learning to encourage the generation of novel phrases. Our model achieves results comparable to state-of-the-art models, as determined by ROUGE scores and human evaluations, while achieving a significantly higher level of abstraction as measured by n-gram overlap with the source document.


title: Amobee at IEST 2018: Transfer Learning from Language Models

abstract: This paper describes the system developed at Amobee for the WASSA 2018 implicit emotions shared task (IEST). The goal of this task was to predict the emotion expressed by missing words in tweets without an explicit mention of those words. We developed an ensemble system consisting of language models together with LSTM-based networks containing a CNN attention mechanism. Our approach represents a novel use of language models (specifically trained on a large Twitter dataset) to predict and classify emotions. Our system reached 1st place with a macro $\text{F}_1$ score of 0.7145.


title: Data modeling versus simulation modeling in the big data era: case study of a greenhouse control system

abstract: Recently, big data has received greater attention in diverse research fields, including medicine, science, engineering, management, defense, politics, and others. Such research uses big data to predict target systems, thereby constructing a model of the system in two ways: data modeling and simulation modeling. Data modeling is a method in which a model represents correlation relationships between one set of data and the other set of data. On the other hand, physics-based simulation modeling (or simply simulation modeling) is a more classical, but more powerful, method in which a model represents causal relationships between a set of controlled inputs and corresponding outputs. This paper (i) clarifies the difference between the two modeling approaches, (ii) explains their advantages and limitations and compares each characteristic, and (iii) presents a complementary cooperation modeling approach. Then, we apply the proposed modeling to develop a greenhouse control system in the real world. Finally, we ex...


title: An Ultra-Low-Power Power Management IC for Wireless Sensor Nodes

abstract: A power interface IC is designed and demonstrated to convert and manage power for a wireless tire pressure sensor node. Power conversion is performed using on-chip switched-capacitor converters with size-optimized devices and level-shifting gate drivers. A synchronous rectifier efficiently harvests energy from an electromagnetic shaker and control circuitry regulates the output voltage while minimizing power consumption. The converters achieve efficiencies approaching 80%.


title: Security & Privacy Challenges in IoT-Based Health Cloud

abstract: Electronic Patient Health Information (EPHI) is considered confidential for patients and healthcare providers. Technology acceleration in several domains including embedded sensors, cloud computing and IoT facilitate the medical services and provide fast and real time communication. However, Several Health information security and privacy challenges arise. In this paper we discuss and survey security and privacy challenges in the context of healthcare. We also identify some compliance requirements and possible violations and their impact on the IoT health cloud.


title: The multisensory perception of flavor

abstract: Following on from ecological theories of perception, such as the one proposed by [Gibson, J. J. (1966). The senses considered as perceptual systems. Boston: Houghton Mifflin] this paper reviews the literature on the multisensory interactions underlying the perception of flavor in order to determine the extent to which it is really appropriate to consider flavor perception as a distinct perceptual system. We propose that the multisensory perception of flavor may be indicative of the fact that the taxonomy currently used to define our senses is simply not appropriate. According to the view outlined here, the act of eating allows the different qualities of foodstuffs to be combined into unified percepts; and flavor can be used as a term to describe the combination of tastes, smells, trigeminal, and tactile sensations as well as the visual and auditory cues, that we perceive when tasting food.


title: A New Writing Experience: Finger Writing in the Air Using a Kinect Sensor

abstract: With the introduction of Microsoft Kinect, there has been considerable interest in creating various attractive and feasible applications in related research fields. Kinect simultaneously captures the depth and color information and provides real-time reliable 3D full-body human-pose reconstruction that essentially turns the human body into a controller. This article presents a finger-writing system that recognizes characters written in the air without the need for an extra handheld device. This application adaptively merges depth, skin, and background models for the hand segmentation to overcome the limitations of the individual models, such as hand-face overlapping problems and the depth-color nonsynchronization. The writing fingertip is detected by a new real-time dual-mode switching method. The recognition accuracy rate is greater than 90 percent for the first five candidates of Chinese characters, English characters, and numbers.


title: SPEA2+: Improving the Performance of the Strength Pareto Evolutionary Algorithm 2

abstract: Multi-objective optimization methods are essential to resolve real-world problems as most involve several types of objects. Several multi-objective genetic algorithms have been proposed. Among them, SPEA2 and NSGA-II are the most successful. In the present study, two new mechanisms were added to SPEA2 to improve its searching ability a more effective crossover mechanism and an archive mechanism to maintain diversity of the solutions in the objective and variable spaces. The new SPEA2 with these two mechanisms was named SPEA2+. To clarify the characteristics and effectiveness of the proposed method, SPEA2+ was applied to several test functions. In the comparison of SPEA2+ with SPEA2 and NSGA-II, SPEA2+ showed good results and the effects of the new mechanism were clarified. From these results, it was concluded that SPEA2+ is a good algorithm for multi-objective optimization problems.


title: Interactive affordance map building for a robotic task

abstract: We describe a technique to build an affordance map interactively for robotic tasks. Affordances are predicted by a trained classifier using geometric features extracted from objects. Based on 2D occupancy grid, a Markov Random Field (MRF) model builds an affordance map with relational affordance with neighboring cells. The quality of the affordance map is refined by sequences of interactive manipulations selected from the model to yield the highest reduction in uncertainty.


title: LoRa-based localization systems for noisy outdoor environment

abstract: LoRa-based (Long Range Communication based) localization systems make use of RSSI (Received Signal Strength Indicator) to locate an object properly in an outdoor environment for IoT (Internet of Things) applications in smart environment and urban networking. The accuracy of localization is highly degraded, however, by noisy environments (e.g., electronic interference, blocking). To address this issue, this paper proposes two new localization algorithms to improve localization accuracy. Furthermore, the paper develops a winner selection strategy to select a winner from the traditional localization algorithm and the two new algorithms to minimize localization errors. Our simulations and real experiments found that the two new algorithms can significantly improve localization accuracy. Additionally, the winner selection strategy effectively selects a winner from the three algorithms to improve localization accuracy further. Using our proposed algorithms in some real experiments, the localization error is only a few meters in a real measurement field longer than 100 m.


title: Mouse models in oncoimmunology

abstract: Fundamental cancer research and the development of efficacious antineoplastic treatments both rely on experimental systems in which the relationship between malignant cells and immune cells can be studied. Mouse models of transplantable, carcinogen-induced or genetically engineered malignancies - each with their specific advantages and difficulties - have laid the foundations of oncoimmunology. These models have guided the immunosurveillance theory that postulates that evasion from immune control is an essential feature of cancer, the concept that the long-term effects of conventional cancer treatments mostly rely on the reinstatement of anticancer immune responses and the preclinical development of immunotherapies, including currently approved immune checkpoint blockers. Specific aspects of pharmacological development, as well as attempts to personalize cancer treatments using patient-derived xenografts, require the development of mouse models in which murine genes and cells are replaced with their human equivalents. Such 'humanized' mouse models are being progressively refined to characterize the leukocyte subpopulations that belong to the innate and acquired arms of the immune system as they infiltrate human cancers that are subjected to experimental therapies. We surmise that the ever-advancing refinement of murine preclinical models will accelerate the pace of therapeutic optimization in patients.


title: SVM for edge-preserving filtering

abstract: In this paper, we propose a new method to construct an edge-preserving filter which has very similar response to the bilateral filter. The bilateral filter is a normalized convolution in which the weighting for each pixel is determined by the spatial distance from the center pixel and its relative difference in intensity range. The spatial and range weighting functions are typically Gaussian in the literature. In this paper, we cast the filtering problem as a vector-mapping approximation and solve it using a support vector machine (SVM). Each pixel will be represented as a feature vector comprising of the exponentiation of the pixel intensity, the corresponding spatial filtered response, and their products. The mapping function is learned via ∊-SVM regression using the feature vectors and the corresponding bilateral filtered values from the training image. The major computation involved is the computation of the spatial filtered responses of the exponentiation of the original image which is invariant to the filter size given that an IIR O(1) solution is available for the spatial filtering kernel. To our knowledge, this is the first learning-based O(1) bilateral filtering method. Unlike previous O(1) methods, our method is valid for both low and high range variance Gaussian and the computational complexity is independent of the range variance value. Our method is also the fastest O(1) bilateral filtering yet developed. Besides, our method allows varying range variance values, based on which we propose a new bilateral filtering method avoiding the over-smoothing or under-smoothing artifacts in traditional bilateral filter.


title: Urinary Neuropilin-1: A Predictive Biomarker for Renal Outcome in Lupus Nephritis

abstract: At present, Lupus Nephritis (LN) is still awaiting a biomarker to better monitor disease activity, guide clinical treatment, and predict a patient’s long-term outcome. In the last decade, novel biomarkers have been identified to monitor the disease, but none have been incorporated into clinical practice. The transmembrane receptor neuropilin-1 (NRP-1) is highly expressed by mesangial cells and its genetic deletion results in proteinuric disease and glomerulosclerosis. NRP-1 is increased in kidney biopsies of LN. In this work we were interested in determining whether urinary NRP-1 levels could be a biomarker of clinical response in LN. Our results show that patients with active LN have increased levels of urinary NRP-1. When patients were divided according to clinical response, responders displayed higher urinary and tissue NRP-1 levels at the time of renal biopsy. Areas under the receiver operating characteristic curve, comparing baseline creatinine, proteinuria, urinary NRP-1, and VEGFA protein levels, showed NRP-1 to be an independent predictor for clinical response. In addition, in vitro studies suggest that NRP-1could promote renal recovery through endothelial proliferation and migration, mesangial migration and local T cell cytotoxicity. Based on these results, NRP-1 may be used as an early prognostic biomarker in LN.


title: Mobile fog: a programming model for large-scale applications on the internet of things

abstract: The ubiquitous deployment of mobile and sensor devices is creating a new environment, namely the Internet of Things(IoT), that enables a wide range of future Internet applications. In this work, we present Mobile Fog, a high level programming model for the future Internet applications that are geospatially distributed, large-scale, and latency-sensitive. We analyze use cases for the programming model with camera network and connected vehicle applications to show the efficacy of Mobile Fog. We also evaluate application performance through simulation.


title: Adaptive Blending Units: Trainable Activation Functions for Deep Neural Networks

abstract: The most widely used activation functions in current deep feed-forward neural networks are rectified linear units (ReLU), and many alternatives have been successfully applied, as well. However, none of the alternatives have managed to consistently outperform the rest and there is no unified theory connecting properties of the task and network with properties of activation functions for most efficient training. A possible solution is to have the network learn its preferred activation functions. In this work, we introduce Adaptive Blending Units (ABUs), a trainable linear combination of a set of activation functions. Since ABUs learn the shape, as well as the overall scaling of the activation function, we also analyze the effects of adaptive scaling in common activation functions. We experimentally demonstrate advantages of both adaptive scaling and ABUs over common activation functions across a set of systematically varied network specifications. We further show that adaptive scaling works by mitigating covariate shifts during training, and that the observed advantages in performance of ABUs likewise rely largely on the activation function's ability to adapt over the course of training.


An Optimization Algorithm for the Capacitated Vehicle Routing Problem Based on Ant Colony System 1


SlangNet: A WordNet like resource for English Slang.


title: Single Power-Conversion AC--DC Converter With High Power Factor and High Efficiency

abstract: This paper proposes a single power-conversion ac-dc converter with high power factor and high efficiency. The proposed converter is derived by integrating a full-bridge diode rectifier and a series-resonant active-clamp dc-dc converter. To obtain a high power factor without a power factor correction circuit, this paper proposes a novel control algorithm. The proposed converter provides single power-conversion by using the novel control algorithm for both power factor correction and output control. Also, the active-clamp circuit clamps the surge voltage of switches and recycles the energy stored in the leakage inductance of the transformer. Moreover, it provides zero-voltage turn-on switching of the switches. Also, a series-resonant circuit of the output-voltage doubler removes the reverse-recovery problem of the output diodes. The proposed converter provides maximum power factor 0.995 and maximum efficiency of 95.1% at the full load. The operation principle of the converter is analyzed and verified. Experimental results for a 400 W ac-dc converter at a constant switching frequency of 50 kHz are obtained to show the performance of the proposed converter.


title: Web Page Recommendation Based on Bitwise Frequent Pattern Mining

abstract: In many applications, web surfers would like to get recommendation on which collections of web pages that would be interested to them or that they should follow. In order to discover this information and make recommendation, data mining in general—or frequent pattern mining in specific—can be applicable. Since its introduction, frequent pattern mining has drawn attention from many researchers. Consequently, many frequent pattern mining algorithms have been proposed, which include levelwise Apriori-based algorithms, tree-based algorithms, hyperlinked array structure based algorithms, as well as vertical mining algorithms. While these algorithms are popular, they also suffer from some drawbacks. To avoid these drawbacks, we propose an alternative frequent pattern mining algorithm called BW-mine in this paper. Evaluation results show that our proposed algorithm is both space-and time-efficient. Furthermore, to show the practicality of BW-mine in real-life applications, we apply BW-mine to discover popular pages on the web, which in turn gives the web surfers recommendation of web pages that might be interested to them.


A Design of Fail-safe Gateway-embedded System for In-vehicle Networks


title: Reconstruction of data from the aquatic environment

abstract: This thesis describes, applies and compares statistical methods for reconstruction of concentrations of Dissolved Inorganic Nitrogen (DIN) and Dissolved Inorganic Phosphorus (DIP) in Kattegat. The measurements are taken in the period from 1993 to 1997 within the monitoring program, which was implemented by the adoption of the Action Plan on the Aquatic Environment in 1987. ::: The aim of the reconstruction methods is to estimate the concentration of the two species for each week in the five year period, and at any location in Kattegat. The methods are general and could be applied to other parameters. ::: The spatial distribution of DIN and DIP have been computed by three different variants of kriging, i.e. ordinary kriging, universal kriging and cokriging. In order to have a sufficient number of observations per week, methods for temporal reconstruction of data have to be applied prior to the computation of spatial predictions. Two methods have been used for this purpose, these are the General Linear Model and locally weighted regression. The methods are compared from a statistical and a physical point of view. ::: Furthermore, the thesis applies different 3 dimensional approaches. These are 3 dimensional kriging, locally weighted regression and an ARIMA model. The first two methods are applied to raw data, i.e. measurements, while the ARIMA model is applied to different stations, where the time series are temporally reconstructed by the General Linear Model. Kriging is applied to the parameters of the model, in order identify these at any location in Kattegat. Such 3 dimensional methods are much less developed and described in literature compared to methods for analysis of strictly temporal or spatial data.


title: Detecting energy-greedy anomalies and mobile malware variants

abstract: Mobile users of computation and communication services have been rapidly adopting battery-powered mobile handhelds, such as PocketPCs and SmartPhones, for their work. However, the limited battery-lifetime of these devices restricts their portability and applicability, and this weakness can be exacerbated by mobile malware targeting depletion of battery energy. Such malware are usually difficult to detect and prevent, and frequent outbreaks of new malware variants also reduce the effectiveness of commonly-seen signature-based detection. To alleviate these problems, we propose a power-aware malware-detection framework that monitors, detects, and analyzes previously unknown energy-depletion threats. The framework is composed of (1) a power monitor which collects power samples and builds a power consumption history from the collected samples, and (2) a data analyzer which generates a power signature from the constructed history. To generate a power signature, simple and effective noise-filtering and data-compression are applied, thus reducing the detection overhead. Similarities between power signatures are measured by the χ2-distance, reducing both false-positive and false-negative detection rates. According to our experimental results on an HP iPAQ running a Windows Mobile OS, the proposed framework achieves significant (up to 95%) storage-savings without losing the detection accuracy, and a 99% true-positive rate in classifying mobile malware.