title: A Network Based Approach for the Visualization and Analysis of Collaboratively Edited Texts

abstract: This paper describes an approach for network text analysis and visualization for collaboratively edited documents. It incorporates network extraction from texts where nodes represent concepts identified from the words in the text and the edges represent relations between the concepts. The visualization of the concept networks depicts the general structure of the underlying text in a compact way. In addition to that, latent relations between concepts become visible, which are not explicit in the text. This work concentrates on evolving texts such as wiki articles. This introduces additional complexity since dynamic texts lead to dynamic concept networks. The presented method retains the user information of each revision of a text and makes them visible in the network visualization. In a case study it is demonstrated how the proposed method can be used to characterize the contributors in collaborative writing scenarios regarding the nature of concept relations they introduce to the text.


title: A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data

abstract: Detecting outliers which are grossly different from or inconsistent with the remaining dataset is a major challenge in real-world KDD applications. Existing outlier detection methods are ineffective on scattered real-world datasets due to implicit data patterns and parameter setting issues. We define a novel Local Distance-based Outlier Factor (LDOF) to measure the outlier-ness of objects in scattered datasets which addresses these issues. LDOF uses the relative location of an object to its neighbours to determine the degree to which the object deviates from its neighbourhood. We present theoretical bounds on LDOF's false-detection probability. Experimentally, LDOF compares favorably to classical KNN and LOF based outlier detection. In particular it is less sensitive to parameter values.


title: Word image matching using dynamic time warping

abstract: Libraries and other institutions are interested in providing access to scanned versions of their large collections of handwritten historical manuscripts on electronic media. Convenient access to a collection requires an index, which is manually created at great labor and expense. Since current handwriting recognizers do not perform well on historical documents, a technique called word spotting has been developed: clusters with occurrences of the same word in a collection are established using image matching. By annotating "interesting" clusters, an index can be built automatically. We present an algorithm for matching handwritten words in noisy historical documents. The segmented word images are preprocessed to create sets of 1-dimensional features, which are then compared using dynamic time warping. We present experimental results on two different data sets from the George Washington collection. Our experiments show that this algorithm performs better and is faster than competing matching techniques.


title: Sentiment classification of Internet restaurant reviews written in Cantonese

abstract: Research highlights? Naive Bayes and SVM are used for Cantonese sentiment classification. ? Accuracy is influenced by interaction between classification models and features. ? Naive Bayes classifier achieves as well as or better accuracy than SVM. ? Character-based bigrams are better features than unigrams and trigrams in capturing Cantonese sentiment. Cantonese is an important dialect in some regions of Southern China. Local online users often represent their opinions and experiences on the web with written Cantonese. Although the information in those reviews is valuable to potential consumers and sellers, the huge amount of web reviews make it difficult to give an unbiased evaluation to a product and the Cantonese reviews are unintelligible for Mandarin Chinese speakers.In this paper, standard machine learning techniques naive Bayes and SVM are incorporated into the domain of online Cantonese-written restaurant reviews to automatically classify user reviews as positive or negative. The effects of feature presentations and feature sizes on classification performance are discussed. We find that accuracy is influenced by interaction between the classification models and the feature options. The naive Bayes classifier achieves as well as or better accuracy than SVM. Character-based bigrams are proved better features than unigrams and trigrams in capturing Cantonese sentiment orientation.


title: Automatic detection of seizures and spikes.

abstract: The recording of seizures and spikes is of primary importance in the evaluation of epileptic patients. This is not always an easy process because these events can be rare and are usually unpredictable. Since the earliest days of computer analysis of the EEG, researchers have developed methods for the automatic detection of spikes and, more recently, of seizures. The problems are complex because spikes and seizures are not clearly defined and have extremely varied morphologies. Nevertheless, it has been possible to develop automatic detection methods that can be of great assistance during long-term monitoring of epileptic patients. No method is absolutely fail-safe and all require human validation, but they save a considerable amount of time in the interpretation of long recordings. Recent developments include detection of the patterns specific to newborns, and the possibility of warning a patient or observer that a seizure is starting.


title: Cloud versus in-house cluster: evaluating Amazon cluster compute instances for running MPI applications

abstract: The emergence of cloud services brings new possibilities for constructing and using HPC platforms. However, while cloud services provide the flexibility and convenience of customized, pay-as-you-go parallel computing, multiple previous studies in the past three years have indicated that cloud-based clusters need a significant performance boost to become a competitive choice, especially for tightly coupled parallel applications. In this work, we examine the feasibility of running HPC applications in clouds. This study distinguishes itself from existing investigations in several ways: 1) We carry out a comprehensive examination of issues relevant to the HPC community, including performance, cost, user experience, and range of user activities. 2) We compare an Amazon EC2-based platform built upon its newly available HPC-oriented virtual machines with typical local cluster and supercomputer options, using benchmarks and applications with scale and problem size unprecedented in previous cloud HPC studies. 3) We perform detailed performance and scalability analysis to locate the chief limiting factors of the state-of-the-art cloud based clusters. 4) We present a case study on the impact of per-application parallel I/O system configuration uniquely enabled by cloud services. Our results reveal that though the scalability of EC2-based virtual clusters still lags behind traditional HPC alternatives, they are rapidly gaining in overall performance and cost-effectiveness, making them feasible candidates for performing tightly coupled scientific computing. In addition, our detailed benchmarking and profiling discloses and analyzes several problems regarding the performance and performance stability on EC2.


title: Optimistic linear programming gives logarithmic regret for irreducible MDPs

abstract: We present an algorithm called Optimistic Linear Programming (OLP) for learning to optimize average reward in an irreducible but otherwise unknown Markov decision process (MDP). OLP uses its experience so far to estimate the MDP. It chooses actions by optimistically maximizing estimated future rewards over a set of next-state transition probabilities that are close to the estimates, a computation that corresponds to solving linear programs. We show that the total expected reward obtained by OLP up to time T is within C(P) log T of the reward obtained by the optimal policy, where C(P) is an explicit, MDP-dependent constant. OLP is closely related to an algorithm proposed by Burnetas and Katehakis with four key differences: OLP is simpler, it does not require knowledge of the supports of transition probabilities, the proof of the regret bound is simpler, but our regret bound is a constant factor larger than the regret of their algorithm. OLP is also similar in flavor to an algorithm recently proposed by Auer and Ortner. But OLP is simpler and its regret bound has a better dependence on the size of the MDP.


title: A comprehensive analysis of Matrix Converters: Bidirectional switch, direct topology, modeling and control

abstract: Matrix Converters (MCs) are popular due to direct AC/AC conversion applications without the need of bulky, lifetime limited DC links capacitors. In this paper the mostly used MC topologies are explained. Each topology is also modeled and described as well. Moreover, various types of bidirectional switches required for building a MC are studied and compared to show their advantages and drawbacks. As well, some controlling methods associated to MCs are also discussed.


title: Serving Deep Learning Models in a Serverless Platform

abstract: Serverless computing has emerged as a compelling paradigm for the development and deployment of a wide range of event based cloud applications. At the same time, cloud providers and enterprise companies are heavily adopting machine learning and Artificial Intelligence to either differentiate themselves, or provide their customers with value added services. In this work we evaluate the suitability of a serverless computing environment for the inferencing of large neural network models. Our experimental evaluations are executed on the AWS Lambda environment using the MxNet deep learning framework. Our experimental results show that while the inferencing latency can be within an acceptable range, longer delays due to cold starts can skew the latency distribution and hence risk violating more stringent SLAs.


title: A sensitive Twitter earthquake detector

abstract: This paper describes early work at developing an earthquake detector for Australia and New Zealand using Twitter. The system is based on the Emergency Situation Awareness (ESA) platform which provides all-hazard information captured, filtered and analysed from Twitter. The detector sends email notifications of evidence of earthquakes from Tweets to the Joint Australian Tsunami Warning Centre. The earthquake detector uses the ESA platform to monitor Tweets and checks for specific earthquake related alerts. The Tweets that contribute to an alert are then examined to determine their locations: when the Tweets are identified as being geographically close and the retweet percentage is low an email notification is generated. The earthquake detector has been in operation since December 2012 with 31 notifications generated where 17 corresponded with real, although minor, earthquake events. The remaining 14 were a result of discussions about earthquakes but not prompted by an event. A simple modification to our algorithm results in 20 notifications identifying the same 17 real events and reducing the false positives to 3. Our detector is sensitive in that it can generate alerts from only a few Tweets when they are determined to be geographically close.


title: SSL/TLS Session-Aware User Authentication — Or How to Effectively Thwart the Man-in-the-Middle

abstract: Man-in-the-middle attacks pose a serious threat to SSL/TLS-based electronic commerce applications, such as Internet banking. In this paper, we argue that most deployed user authentication mechanisms fail to provide protection against this type of attack, even when they run on top of SSL/TLS. As a possible countermeasure, we introduce the notion of SSL/TLS session-aware user authentication, and present different possibilities for implementing it. We start with a basic implementation that employs impersonal authentication tokens. Afterwards, we address extensions and enhancements and discuss possibilities for implementing SSL/TLS session-aware user authentication in software.


Sarcasm Detection Using Grice ’ s Maxims


title: Open-source, anthropomorphic, underactuated robot hands with a selectively lockable differential mechanism: Towards affordable prostheses

abstract: In this paper we present an open-source design for the development of low-complexity, anthropomorphic, underactuated robot hands with a selectively lockable differential mechanism. The differential mechanism used is a variation of the whiffletree (or seesaw) mechanism, which introduces a set of locking buttons that can block the motion of each finger. The proposed design is unique since with a single motor and the proposed differential mechanism the user is able to control each finger independently and switch between different grasping postures in an intuitive manner. Anthropomorphism of robot structure and motion is achieved by employing in the design process an index of anthropomorphism. The proposed robot hands can be easily fabricated using low-cost, off-the-shelf materials and rapid prototyping techniques. The efficacy of the proposed design is validated through different experimental paradigms involving grasping of everyday life objects and execution of daily life activities. The proposed hands can be used as affordable prostheses, helping amputees regain their lost dexterity.


title: Fundamentals of Wireless Communication

abstract: 1. Introduction 2. The wireless channel 3. Point-to-point communication: detection, diversity and channel uncertainty 4. Cellular systems: multiple access and interference management 5. Capacity of wireless channels 6. Multiuser capacity and opportunistic communication 7. MIMO I: spatial multiplexing and channel modeling 8. MIMO II: capacity and multiplexing architectures 9. MIMO III: diversity-multiplexing tradeoff and universal space-time codes 10. MIMO IV: multiuser communication A. Detection and estimation in additive Gaussian noise B. Information theory background.


title: Parsing-based Sarcasm Sentiment Recognition in Twitter Data

abstract: Sentiment Analysis is a technique to identify people's opinion, attitude, sentiment, and emotion towards any specific target such as individuals, events, topics, product, organizations, services etc. Sarcasm is a special kind of sentiment that comprise of words which mean the opposite of what you really want to say (especially in order to insult or wit someone, to show irritation, or to be funny). People often expressed it verbally through the use of heavy tonal stress and certain gestural clues like rolling of the eyes. These tonal and gestural clues are obviously not available for expressing sarcasm in text, making its detection reliant upon other factors. In this paper, two approaches to detect sarcasm in the text of Twitter data were proposed. The first is a parsing-based lexicon generation algorithm (PBLGA) and the second was to detect sarcasm based on the occurrence of the interjection word. The combination of two approaches is also shown and compared with the existing state-of-the-art approach to detect sarcasm. First approach attains a 0.89, 0.81 and 0.84 precision, recall and f -- score respectively. Second approach attains 0.85, 0.96 and 0.90 precision, recall and f -- score respectively in tweets with sarcastic hashtag.


title: Research on the 3D Game Scene Optimization of Mobile Phone Based on the Unity 3D Engine

abstract: With the development of Computer technology, it promotes the growth of a range of related industries. Mobile phone is very popular in our society, and the trend of more people will play 3d game in mobile phone is more and more obivous. This paper discusses the scene optimiaztion problems of mobile phone based on the unity 3d, and describes some skills of mobile optimization, and concludes important problems during the processing of optimization.


title: SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation

abstract: In this paper, we study the problem of semantic annotation on 3D models that are represented as shape graphs. A functional view is taken to represent localized information on graphs, so that annotations such as part segment or keypoint are nothing but 0-1 indicator vertex functions. Compared with images that are 2D grids, shape graphs are irregular and non-isomorphic data structures. To enable the prediction of vertex functions on them by convolutional neural networks, we resort to spectral CNN method that enables weight sharing by parameterizing kernels in the spectral domain spanned by graph laplacian eigenbases. Under this setting, our network, named SyncSpecCNN, strive to overcome two key challenges: how to share coefficients and conduct multi-scale analysis in different parts of the graph for a single shape, and how to share information across related but different shapes that may be represented by very different graphs. Towards these goals, we introduce a spectral parameterization of dilated convolutional kernels and a spectral transformer network. Experimentally we tested our SyncSpecCNN on various tasks, including 3D shape part segmentation and 3D keypoint prediction. State-of-the-art performance has been achieved on all benchmark datasets.


title: Future directions for behavioral information security research

abstract: Information Security (InfoSec) research is far reaching and includes many approaches to deal with protecting and mitigating threats to the information assets and technical resources available within computer based systems. Although a predominant weakness in properly securing information assets is the individual user within an organization, much of the focus of extant security research is on technical issues. The purpose of this paper is to highlight future directions for Behavioral InfoSec research, which is a newer, growing area of research. The ensuing paper presents information about challenges currently faced and future directions that Behavioral InfoSec researchers should explore. These areas include separating insider deviant behavior from insider misbehavior, approaches to understanding hackers, improving information security compliance, cross-cultural Behavioral InfoSec research, and data collection and measurement issues in Behavioral InfoSec research.


title: Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs

abstract: We present a transition-based parser that jointly produces syntactic and semantic dependencies. It learns a representation of the entire algorithm state, using stack long short-term memories. Our greedy inference algorithm has linear time, including feature extraction. On the CoNLL 2008--9 English shared tasks, we obtain the best published parsing performance among models that jointly learn syntax and semantics.


An NFR-Based Framework for Aligning Software Architectures with System Architectures Abstract


title: Simultaneous generation-classification using LSTM

abstract: The idea that a concept is properly learned by an agent when the agent is able to generate examples and non-examples of the concept, has motivated research on generative models. Generative models are trained with the aim of improving performance of tasks such as classification. In this paper, a Long Short Term Memory (LSTM) architecture for simultaneous generation-classification is presented. The architecture is designed with the purpose of serving as a model which can generate sequence samples, while simultaneously classifying a given sequence. The presented generation-classification methodology was implemented on a sentiment analysis task. However, it can be applied to any sequence modelling or classification task. The experimental results suggest that this approach can be particularly useful as a regularization methodology which acts similarly to pre-training through Restricted Boltzmann Machines or auto-encoders.


title: Rover control based on an optimal torque distribution - Application to 6 motorized wheels passive rover

abstract: The capability to overcome terrain irregularities or obstacles, named terrainability, is mostly dependant on the suspension mechanism of the rover and its control. For a given wheeled robot, the terrainability can be improved by using a sophisticated control, and is somewhat related to minimizing wheel slip. The proposed control method, named torque control, improves the rover terrainability by taking into account the whole mechanical structure. The rover model is based on the Newton-Euler equations and knowing the complete state of the mechanical structures allows us to compute the force distribution in the structure, and especially between the wheels and the ground. Thus, a set of torques maximizing the traction can be used to drive the rover. The torque control algorithm is presented in this paper, as well as tests showing its impact and improvement in terms of terrainability. Using the CRAB rover platform, we show that the torque control not only increases the climbing performance but also limits odometric errors and reduces the overall power consumption.


title: Entity discovery and assignment for opinion mining applications

abstract: Opinion mining became an important topic of study in recent years due to its wide range of applications. There are also many companies offering opinion mining services. One problem that has not been studied so far is the assignment of entities that have been talked about in each sentence. Let us use forum discussions about products as an example to make the problem concrete. In a typical discussion post, the author may give opinions on multiple products and also compare them. The issue is how to detect what products have been talked about in each sentence. If the sentence contains the product names, they need to be identified. We call this problem entity discovery. If the product names are not explicitly mentioned in the sentence but are implied due to the use of pronouns and language conventions, we need to infer the products. We call this problem entity assignment. These problems are important because without knowing what products each sentence talks about the opinion mined from the sentence is of little use. In this paper, we study these problems and propose two effective methods to solve the problems. Entity discovery is based on pattern discovery and entity assignment is based on mining of comparative sentences. Experimental results using a large number of forum posts demonstrate the effectiveness of the technique. Our system has also been successfully tested in a commercial setting.


title: The wake-sleep algorithm for unsupervised neural networks

abstract: An unsupervised learning algorithm for a multilayer network of stochastic neurons is described. Bottom-up "recognition" connections convert the input into representations in successive hidden layers, and top-down "generative" connections reconstruct the representation in one layer from the representation in the layer above. In the "wake" phase, neurons are driven by recognition connections, and generative connections are adapted to increase the probability that they would reconstruct the correct activity vector in the layer below. In the "sleep" phase, neurons are driven by generative connections, and recognition connections are adapted to increase the probability that they would produce the correct activity vector in the layer above.


title: An i-vector Extractor Suitable for Speaker Recognition with both Microphone and Telephone Speech

abstract: It is widely believed that speaker verification systems perform better when there is sufficient background training data to deal with nuisance effects of transmission channels. It is also known that these systems perform at their best when the sound environment of the training data is similar to that of the context of use (test context). For some applications however, training data from the same type of sound environment is scarce, whereas a considerable amount of data from a different type of environment is available. In this paper, we propose a new architecture for text-independent speaker verification systems that are satisfactorily trained by virtue of a limited amount of application-specific data, supplemented with a sufficient amount of training data from some other context. This architecture is based on the extraction of parameters (i-vectors) from a low-dimensional space (total variability space) proposed by Dehak [1]. Our aim is to extend Dehak’s work to speaker recognition on sparse data, namely microphone speech. The main challenge is to overcome the fact that insufficient application-specific data is available to accurately estimate the total variability covariance matrix. We propose a method based on Joint Factor Analysis (JFA) to estimate microphone eigenchannels (sparse data) with telephone eigenchannels (sufficient data). For classification, we experimented with the following two approaches: Support Vector Machines (SVM) and Cosine Distance Scoring (CDS) classifier, based on cosine distances. We present recognition results for the part of female voices in the interview data of the NIST 2008 SRE. The best performance is obtained when our system is fused with the state-of-the-art JFA. We achieve 13% relative improvement on equal error rate and the minimum value of detection cost function decreases from 0.0219 to 0.0164.


title: Virtual Team Networks: How Group Social Capital Affects Team Success in a Massively Multiplayer Online Game

abstract: Virtual teams have become a ubiquitous form of organizing, but the impact of social structures within and between teams on group performance remains understudied. This paper uses the case study of a massively multiplayer online game and server log data from over 10,000 players to examine the connection between group social capital (operationalized through guild network structure measures) and team effectiveness, given a variety of in-game social networks. Three different networks, social, task, and exchange networks, are compared and contrasted while controlling for group size, group age, and player experience. Team effectiveness is maximized at a roughly moderate level of closure across the networks, suggesting that this is the optimal level of the group's network density. Guilds with high brokerage, meaning they have diverse connections with other groups, were more effective in achievement-oriented networks. In addition, guilds with central leaders were more effective when they teamed up with other guild leaders.


title: Survey on network-based botnet detection methods

abstract: Botnets are an important security problem on the Internet. They continuously evolve their structure, protocols and attacks. This survey analyzes and compares the most important efforts carried out in a network-based detection area. It accomplishes four tasks: first, the comparison of previous surveys and the proposal of four new dimensions to analyze their classification schemes; second, a new classification and comparison of network-based botnet detection proposals, which includes the definition of 20 desired properties of every botnet detection paper; third, an extensive comparison between the most representative detection proposals; and fourth, the description of the most important problems and highlights in the area. We conclude that the area has achieved great advances so far, but there are still many open problems. Copyright © 2013 John Wiley & Sons, Ltd.


title: Scalable Successive-Cancellation Hardware Decoder for Polar Codes

abstract: Polar codes are the first error-correcting codes to provably achieve channel capacity, asymptotically in code length, with an explicit construction. However, under successive-cancellation decoding, polar codes require very long code lengths to compete with existing modern codes. Nonetheless, the successive cancellation algorithm enables very-low-complexity implementations in hardware, due to the regular structure exhibited by polar codes. In this paper, we present an improved architecture for successive-cancellation decoding of polar codes, making use of a novel semi-parallel, encoder-based partial-sum computation module. We also provide quantization results for realistic code length N=2 ::: 15 ::: , and explore various optimization techniques such as a chained processing element and a variable quantization scheme. This design is shown to scale to code lengths of up to N=2 ::: 21 ::: , enabled by its low logic use, low register use and simple datapaths, limited almost exclusively by the amount of available SRAM. It also supports an overlapped loading of frames, allowing full-throughput decoding with a single set of input buffers.


title: Large Scale Graph Matching(LSGM): Techniques, Tools, Applications and Challenges

abstract: Large Scale Graph Matching (LSGM) is one of the fundamental problems in Graph theory and it has applications in many areas such as Computer Vision, Machine Learning, Pattern Recognition and Big Data Analytics (Data Science). Matching belongs to the combinatorial class of problems which refers to finding correspondence between the nodes of a graph or among set of graphs (subgraphs) either precisely or approximately. Precise Matching is also known as Exact Matching such as (sub)Graph Isomorphism and Approximate Matching is called Inexact Matching in which matching activity concerns with conceptual/semantic matching rather than focusing on structural details of graphs. In this article, a review of matching problem is presented i.e. Semantic Matching (conceptual), Syntactic Match-ing (structural) and Schematic Matching (Schema based). The aim is to present the current state of the art in Large Scale Graph Matching (LSGM), a systematic review of algorithms, tools and techniques along with the existing challenges of LSGM. Moreover, the potential application domains and related research activities are provided.


title: Implementing Design Diversity Using Portfolio Thinking to Dynamically and Adaptively Manage the Allocation of Web Services in the Cloud

abstract: We view the cloud as a marketplace for trading instances of web services, which can be “leased” by web applications. We argue that applications can “buy” diversity by selecting instances of web services from multiple cloud sellers in this market. By diversifying the selection and allocation of web service instances, an application can potentially improve its dependability and reduce risks associated with service level agreement (SLA) violations. We propose a novel, dynamic and adaptive approach for implementing design diversity in the cloud market. The approach uses portfolio theory to construct a diversified portfolio of web service instances, which are traded from multiple cloud providers. We illustrate the applicability of the approach. Controlled experiments are also used to (i) test the approach effectiveness in minimizing the risk of SLA violation; (ii) simulate the dynamic and adaptive behaviour of the approach in responding to changes in the market conditions and risk; (iii) evaluate the sensitivity of the allocation decisions to risk and its correlation with other candidates and (iv) evaluate the scalability of the approach and its ramifications on risk reduction under extreme scenarios.


title: On the security of CTR + CBC-MAC

abstract: We analyze the security of the CTR + CBC-MAC (CCM) encryption mode. This mode, proposed by Doug Whiting, Russ Housley, and Niels Ferguson, combines the CTR (counter) encryption mode with CBC-MAC message authentication and is based on a block cipher such as AES. We present concrete lower bounds for the security of CCM in terms of the security of the underlying block cipher. The conclusion is that CCM provides a level of privacy and authenticity that is in line with other proposed modes such as OCB.


title: Association between body mass index and health outcomes among adolescents: the mediating role of traditional and cyber bullying victimization

abstract: It is well-documented that obese children and adolescents tend to experience a variety of negative physical and psychological health consequences. Despite the association between obesity and physical and psychological well-being, few studies have examined the role of off-line and on-line forms of bullying victimization in this link. The main objective of the current study is to investigate the direct and mediating effects of traditional and cyber bullying victimization in explaining the relationship between the body mass index (BMI) and physical/psychological distress. A nationally representative sample of 10,160 school children (mean age = 12.95 ± 1.75) were collected from the 2009 Health Behavior in School-aged Children (HBSC) study. Data were collected on body mass index, physical and psychological health, bullying victimization experience, and demographic information. A seemingly unrelated regression (SUR) was employed to assess and compare the indirect effects in multiple mediation models. While a significant direct association was found between BMI and both physical and psychological health, the indirect effect of BMI on physical distress was significant only via traditional bullying victimization. Both forms of bullying victimization had a mediating impact between BMI and psychological distress. However, the indirect effect on psychological distress was manifested through a negative mediating role of cyberbullying victimization. The negative relation between cyberbullying victimization and psychological distress warrants further exploration. Obesity represents a serious risk to adolescent health and well-being, both physically and psychologically. If becoming a victim of traditional bullying mediates (specifically exacerbates) the level of physical and psychological distress among obese and overweight adolescents, health professionals need to focus on raising awareness of the importance of weight-based victimization for children and adolescents with obesity. School administrators and teachers could increase the efforts to identify school-age children who are stigmatized for their weight and recommend coping strategies for distressed victims of traditional and cyberbullying.


Named Graphs for Semantic Representation


title: A self-tuning Firefly algorithm to tune the parameters of Ant Colony System (ACSFA)

abstract: Ant colony system (ACS) is a promising approach which has been widely used in problems such as Travelling Salesman Problems (TSP), Job shop scheduling problems (JSP) and Quadratic Assignment problems (QAP). In its original implementation, parameters of the algorithm were selected by trial and error approach. Over the last few years, novel approaches have been proposed on adapting the parameters of ACS in improving its performance. The aim of this paper is to use a framework introduced for self-tuning optimization algorithms combined with the firefly algorithm (FA) to tune the parameters of the ACS solving symmetric TSP problems. The FA optimizes the problem specific parameters of ACS while the parameters of the FA are tuned by the selected framework itself. With this approach, the user neither has to work with the parameters of ACS nor the parameters of FA. Using common symmetric TSP problems we demonstrate that the framework fits well for the ACS. A detailed statistical analysis further verifies the goodness of the new ACS over the existing ACS and also of the other techniques used to tune the parameters of ACS.


title: Dual Learning for Cross-domain Image Captioning

abstract: Recent AI research has witnessed increasing interests in automatically generating image descriptions in text, which is coined as theimage captioning problem. Significant progresses have been made in domains where plenty of labeled training data (i.e. image-text pairs) are readily available or collected. However, obtaining rich annotated data is a time-consuming and expensive process, creating a substantial barrier for applying image captioning methods to a new domain. In this paper, we propose a cross-domain image captioning approach that uses a novel dual learning mechanism to overcome this barrier. First, we model the alignment between the neural representations of images and that of natural languages in the source domain where one can access sufficient labeled data. Second, we adjust the pre-trained model based on examining limited data (or unpaired data) in the target domain. In particular, we introduce a dual learning mechanism with a policy gradient method that generates highly rewarded captions. The mechanism simultaneously optimizes two coupled objectives: generating image descriptions in text and generating plausible images from text descriptions, with the hope that by explicitly exploiting their coupled relation, one can safeguard the performance of image captioning in the target domain. To verify the effectiveness of our model, we use MSCOCO dataset as the source domain and two other datasets (Oxford-102 and Flickr30k) as the target domains. The experimental results show that our model consistently outperforms previous methods for cross-domain image captioning.


title: Markedly enhanced up-conversion luminescence by combining IR-808 dye sensitization and core–shell–shell structures

abstract: Rare-earth-doped up-conversion nanoparticles (UCNPs), which are capable of converting infrared light to shorter-wavelength photons, have attracted worldwide attention due to their unique characteristics. However, the emission brightness of UCNPs is greatly limited by the unsatisfactory absorptivity of lanthanide ions. Herein, we adopted a novel strategy to enhance the up-conversion intensity using NIR dye IR-808 as an antenna to sensitize the core–shell–shell structured NaGdF4:Yb,Er@NaGdF4:Yb@NaNdF4:Yb UCNPs. When excited with 808 nm light, the IR-808 emitted a broadband peak, which perfectly overlapped with the absorption of Nd3+ and Yb3+ ions. Thus, the active shell of NaNdF4:Yb can efficiently capture the emitted NIR photons and transfer them to the transition layer of NaGdF4:Yb. The transition layer acted as an energy bridge to connect the active shell and up-converting zone, avoiding the energy back-transfer from the activators to Nd3+ ions. The optimized dye sensitization combined with the well-designed core–shell–shell structure tremendously enhances the NIR photon absorptivity of UCNPs and eliminates the deleterious cross-relaxation between the activators and sensitizers, eventually leading to dramatic enhancement of the up-conversion intensity. This study provides a new insight into the dye-sensitized up-conversion luminescence of rare earth-based nanoparticles and facilitates their practical applications.


title: Overlapping community detection at scale: a nonnegative matrix factorization approach

abstract: Network communities represent basic structures for understanding the organization of real-world networks. A community (also referred to as a module or a cluster) is typically thought of as a group of nodes with more connections amongst its members than between its members and the remainder of the network. Communities in networks also overlap as nodes belong to multiple clusters at once. Due to the difficulties in evaluating the detected communities and the lack of scalable algorithms, the task of overlapping community detection in large networks largely remains an open problem. In this paper we present BIGCLAM (Cluster Affiliation Model for Big Networks), an overlapping community detection method that scales to large networks of millions of nodes and edges. We build on a novel observation that overlaps between communities are densely connected. This is in sharp contrast with present community detection methods which implicitly assume that overlaps between communities are sparsely connected and thus cannot properly extract overlapping communities in networks. In this paper, we develop a model-based community detection algorithm that can detect densely overlapping, hierarchically nested as well as non-overlapping communities in massive networks. We evaluate our algorithm on 6 large social, collaboration and information networks with ground-truth community information. Experiments show state of the art performance both in terms of the quality of detected communities as well as in speed and scalability of our algorithm.


title: Using maximum entropy for text classification

abstract: This paper proposes the use of maximum entropy techniques for text classification. Maximum entropy is a probability distribution estimation technique widely used for a variety of natural language tasks, such as language modeling, part-of-speech tagging, and text segmentation. The underlying principle of maximum entropy is that without external knowledge, one should prefer distributions that are uniform. Constraints on the distribution, derived from labeled training data, inform the technique where to be minimally non-uniform. The maximum entropy formulation has a unique solution which can be found by the improved iterative scaling algorithm. In this paper, maximum entropy is used for text classification by estimating the conditional distribution of the class variable given the document. In experiments on several text datasets we compare accuracy to naive Bayes and show that maximum entropy is sometimes significantly better, but also sometimes worse. Much future work remains, but the results indicate that maximum entropy is a promising technique for text classification.


title: Fast virus detection by using high speed time delay neural networks

abstract: Fast packet detection is very important to overcome intrusion attack. In this paper, a new approach for fast packet detection in serial data sequence is presented. Such algorithm uses fast time delay neural networks (FTDNNs). The operation of these networks relies on performing cross correlation in the frequency domain between the input data and the input weights of neural networks. It is proved mathematically and practically that the number of computation steps required for the presented FTDNNs is less than that needed by conventional time delay neural networks (CTDNNs). Simulation results using MATLAB confirm the theoretical computations.


title: Deep Reinforcement Learning with Double Q-learning

abstract: The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common, whether they harm performance, and whether they can generally be prevented. In this paper, we answer all these questions affirmatively. In particular, we first show that the recent DQN algorithm, which combines Q-learning with a deep neural network, suffers from substantial overestimations in some games in the Atari 2600 domain. We then show that the idea behind the Double Q-learning algorithm, which was introduced in a tabular setting, can be generalized to work with large-scale function approximation. We propose a specific adaptation to the DQN algorithm and show that the resulting algorithm not only reduces the observed overestimations, as hypothesized, but that this also leads to much better performance on several games.


title: Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning

abstract: Learning policies for complex tasks that require multiple different skills is a major challenge in reinforcement learning (RL). It is also a requirement for its deployment in real-world scenarios. This paper proposes a novel framework for efficient multi-task reinforcement learning. Our framework trains agents to employ hierarchical policies that decide when to use a previously learned policy and when to learn a new skill. This enables agents to continually acquire new skills during different stages of training. Each learned task corresponds to a human language description. Because agents can only access previously learned skills through these descriptions, the agent can always provide a human-interpretable description of its choices. In order to help the agent learn the complex temporal dependencies necessary for the hierarchical policy, we provide it with a stochastic temporal grammar that modulates when to rely on previously learned skills and when to execute new skills. We validate our approach on Minecraft games designed to explicitly test the ability to reuse previously learned skills while simultaneously learning new skills.


Data Cleansing: Beyond Integrity Analysis


title: MANAGING UNCERTAINTY ON EWOM: A COMPARISON STUDY BETWEEN COMMERCIAL AND THIRD PARTY WEBSITES

abstract: This study utilizes elaboration likelihood model (ELM) as the theoretical foundation, to explore the effects of information comprehensiveness, argument strength, consistency and quantity on eWOM (electronic word-of-mouth) readers’ information adoption intention in two types of websites (commercial and third party), we collect 278 data from one commercial and one third party website, the results find, in general, all of these four determinant factors can significant affect information adoption intention; however, their effects will be modified in different websites, this finding indicates that people do perceive different level of uncertainties in different websites, and they will utilize different signals to alleviate their information uncertainty concerns. The theoretical and practical implications of this study are also introduced.


Tutorial : Data-Driven Approaches towards Malicious Behavior Modeling Tutorial


title: Community detection using multilayer edge mixture model

abstract: A wide range of complex systems can be modeled as networks with corresponding constraints on the edges and nodes, which have been extensively studied in recent years. Nowadays, with the progress of information technology, systems that contain the information collected from multiple perspectives have been generated. The conventional models designed for single perspective networks fail to depict the diverse topological properties of such systems, so multilayer network models aiming at describing the structure of these networks emerge. As a major concern in network science, decomposing the networks into communities, which usually refers to closely interconnected node groups, extracts valuable information about the structure and interactions of the network. Unlike the contention of dozens of models and methods in conventional single-layer networks, methods aiming at discovering the communities in the multilayer networks are still limited. In order to help explore the community structure in multilayer networks, we propose the multilayer edge mixture model, which explores a relatively general form of a community structure evaluator from an edge combination view. As an example, we demonstrate that the multilayer modularity and stochastic blockmodels can be derived from the proposed model. We also explore the decomposition of community structure evaluators with specific forms to the multilayer edge mixture model representation, which turns out to reveal some new interpretation of the evaluators. The flexibility and performance on different networks of the proposed model are illustrated with applications on a series of benchmark networks.


title: Diversified ranking on large graphs: an optimization viewpoint

abstract: Diversified ranking on graphs is a fundamental mining task and has a variety of high-impact applications. There are two important open questions here. The first challenge is the measure - how to quantify the goodness of a given top-k ranking list that captures both the relevance and the diversity? The second challenge lies in the algorithmic aspect - how to find an optimal, or near-optimal, top-k ranking list that maximizes the measure we defined in a scalable way? In this paper, we address these challenges from an optimization point of view. Firstly, we propose a goodness measure for a given top-k ranking list. The proposed goodness measure intuitively captures both (a) the relevance between each individual node in the ranking list and the query; and (b) the diversity among different nodes in the ranking list. Moreover, we propose a scalable algorithm (linear wrt the size of the graph) that generates a provably near-optimal solution. The experimental evaluations on real graphs demonstrate its effectiveness and efficiency.


title: Parallelization of DC Analysis through Multiport Decomposition

abstract: Physical problems offer scope for macro level parallelization of solution by their essential structure. For parallelization of electrical network simulation, the most natural structure based method is that of multiport decomposition. In this paper this method is used for the simulation of electrical networks consisting of resistances, voltage and current sources using a distributed cluster of weakly coupled processors. At the two levels in which equations are solved in this method the authors have used sparse LU for both levels in the first scheme and sparse LU in the inner level and conjugate gradient in the outer level in the second scheme. Results are presented for planar networks, for the cases where the numbers of slave processors are 1 and 2, and for circuit sizes up to 8.2 million nodes and 16.4 million edges using 8 slave processors. The authors use a cluster of Pentium IV processors linked through a 10/100MBPS Ethernet switch


title: Family interaction and the development of borderline personality disorder: A transactional model

abstract: Although no prospective epidemiological studies have evaluated the relationship between family interactions and the development of borderline personality disorder ~BPD!, there is considerable evidence for the central role of family interactions in the development of BPD. This paper describes the role of family interactions or processes, especially those that might be regarded as invalidating or conflictual, negative or critical, and the absence of more validating, positive, supportive, empathic interactions, in the development of BPD. Perhaps more importantly, the proposed model considers how these parental and family behaviors transact with the child’s own behaviors and emotional vulnerabilities, resulting in a developmental model of BPD that is neither blaming of the family member with BPD nor of her or his parents and caregivers, and has important and specific implications for both prevention and intervention.


title: A floating sleeve antenna yields localized hepatic microwave ablation

abstract: We report a novel coaxial antenna for hepatic microwave ablation. This device uses a floating sleeve, that is, a metal conductor electrically isolated from the outer connector of the antenna coaxial body, to achieve a highly localized specific absorption rate pattern that is independent of insertion depth. This floating sleeve coaxial dipole antenna has low power reflection in the 2.4-GHz IMS band. Ex vivo experiments confirm our numerical simulation results.


title: Goubau-Line Leaky-Wave Antenna for Wide-Angle Beam Scanning From Backfire to Endfire

abstract: A Goubau-line leaky-wave antenna (LWA) with a large scanning angle is presented in this letter. In contrast to the conventional Goubau-line leaky wave with a small scanning angle range, this letter employed a periodically bending Goubau line, which not only brings in a periodic perturbation for leaky-wave radiation, but also enhances the scanning range due to the increased delay for each line element. The simulation and experimental results show that the proposed LWA provides $90\%$ radiation efficiency and 7–10 dBi radiation gain from backfire to endfire through broadside as frequency changes. The proposed antenna features good radiation performance and has a compact and low-profile configuration.


title: MEMS 3-D Scan Mirror With SU-8 Membrane and Flexures for High NA Microscopy

abstract: We demonstrate a micro-electro-mechanical systems (MEMS) beam scanner capable of biaxial scanning with simultaneous focus control, for integration into a handheld confocal microscope for skin imaging. The device is based on a dual-axis gimbal structure with an integrated large-stroke deformable mirror. SU-8 polymer is used to construct both the deformable membrane as well as the torsional hinges for biaxial scanning. The 4-mm-diameter mirror can perform raster pattern scanning with a range of +/−1.5° and Lissajous scanning with a range of +/−3° (mechanical scan angle), and has a maximum deflection of $9~\mu \text{m}$ for focus control. The design, fabrication, and characterization of the opto-mechanical performance of the MEMS device are presented in this paper. [2018-0074]


title: 10.1177/1046878105282160 ARTICLE Contexts, gaming pleasures, and gendered preferences

abstract: In this article, the author explores computer gaming preferences of girls through observations of a games club at an all-girl state school in the United Kingdom. The author argues that gaming tastes are alterable and site specific. Gaming preferences certainly relate to the attributes of particular games, but they will also depend on the player’s recognition and knowledge of these attributes. Players accumulate these competencies according to the patterns of access and peer culture they encounter. The constituents of preference, such as access, are shaped by gender, and as a result, gaming preferences may manifest along gendered lines. It is not difficult to generate data, indicating that gendered tastes exist, but it is shortsighted to separate such outcomes from the various practices that contribute to their formation.


title: IntelliArm: An exoskeleton for diagnosis and treatment of patients with neurological impairments

abstract: This paper presents a novel 7 (active) +2 (passive) degrees of freedom (DOF) exoskeleton to achieve effective rehabilitation of upper limbs with neurological impairment. The 7+2 DOF robot was designed for allowing anatomically correct motions in the upper extremity, and for measuring 18 axis forces/torques and 9 DOF positions. A novel four-step integrated rehabilitation approach was developed and tested including (1) accurate and quantitative diagnosis which is not practical to do during manual clinical examinations or with existing robotic devices; (2) strenuous stretching of spastic joints based on the diagnosis and with intelligent control to adjust the stretching velocity constantly according to the joint conditions; (3) voluntary movement exercise to perform realistic functional tasks based on the patient-specific diagnosis, and (4) outcome evaluations which assessed the treatment outcome quantitatively with multiple physiological measures.


title: A tutorial on energy-based learning

abstract: Energy-Based Models (EBMs) capture dependencies between variables by associating a scalar energy to each configuration of the variab les. Inference consists in clamping the value of observed variables and finding config urations of the remaining variables that minimize the energy. Learning consists in finding an energy function in which observed configurations of the variables a re given lower energies than unobserved ones. The EBM approach provides a common theoretical framework for many learning models, including traditional discr iminative and generative approaches, as well as graph-transformer networks, co nditional random fields, maximum margin Markov networks, and several manifold learning methods. Probabilistic models must be properly normalized, which sometimes requires evaluating intractable integrals over the space of all poss ible variable configurations. Since EBMs have no requirement for proper normalization, this problem is naturally circumvented. EBMs can be viewed as a form of non-probabilistic factor graphs, and they provide considerably more flexibility in th e design of architectures and training criteria than probabilistic approaches .


title: Inflammation and cancer : How hot is the link ?

abstract: Although inflammation has long been known as a localized protective reaction of tissue to irritation, injury, or infection, characterized by pain, redness, swelling, and sometimes loss of function, there has been a new realization about its role in a wide variety of diseases, including cancer. While acute inflammation is a part of the defense response, chronic inflammation can lead to cancer, diabetes, cardiovascular, pulmonary, and neurological diseases. Several pro-inflammatory gene products have been identified that mediate a critical role in suppression of apoptosis, proliferation, angiogenesis, invasion, and metastasis. Among these gene products are TNF and members of its superfamily, IL-1alpha, IL-1beta, IL-6, IL-8, IL-18, chemokines, MMP-9, VEGF, COX-2, and 5-LOX. The expression of all these genes are mainly regulated by the transcription factor NF-kappaB, which is constitutively active in most tumors and is induced by carcinogens (such as cigarette smoke), tumor promoters, carcinogenic viral proteins (HIV-tat, HIV-nef, HIV-vpr, KHSV, EBV-LMP1, HTLV1-tax, HPV, HCV, and HBV), chemotherapeutic agents, and gamma-irradiation. These observations imply that anti-inflammatory agents that suppress NF-kappaB or NF-kappaB-regulated products should have a potential in both the prevention and treatment of cancer. The current review describes in detail the critical link between inflammation and cancer.


title: Towards Mobile Recognition and Verification of Holograms Using Orthogonal Sampling

abstract: Although holograms provide a reasonable level of security, for lay-people, the inspection is difficult due to the inherent lack of knowledge. Mobile AR setups using off-the-shelf smartphones can provide this information and support the verification process. However, current approaches lack in accuracy or are difficult to instruct to the user. We propose a series of designs for holograms suitable for mobile verification. They support the orthogonal sampling of elements within a constrained space along with an assessment of the capture conditions. We evaluated these designs regarding their verification performance and task completion time. The associated study revealed encouraging results for robust and efficient hologram verification on mobiles using a specially designed security element.


WASABI: Affect Simulation for Agents with Believable Interactivity


title: A Study of Web Print: What People Print in the Digital Era

abstract: This article analyzes a proprietary log of printed web pages and aims at answering questions regarding the content people print (what), the reasons they print (why), as well as attributes of their print profile (who). We present a classification of pages printed based on their print intent and we describe our methodology for processing the print dataset used in this study. In our analysis, we study the web sites, topics, and print intent of the pages printed along the following aspects: popularity, trends, activity, user diversity, and consistency. We present several findings that reveal interesting insights into printing. We analyze our findings and discuss their impact and directions for future work.


title: An analysis of online match-based games

abstract: Online match-based games, such as the online versions of the board game of chess, have already captured a global audience of tens of millions of players. Through a unique combination of characteristics, a relatively short duration—often “coffee-break” minutes instead of hours of continuous gameplay—, weak correlation between matches, and clear emphasis on winning, match-based games may serve a unique segment of the global player population. Although this segment of the gaming population may later become consumers of more sophisticated Massively Multiuser Virtual Environment (MMVE) systems, few previous studies have focused on the characteristics of online match-based games. Complementing them, in this work we collect and analyze information corresponding to 5 online match-based game datasets, totaling over 170 million matches played by a population of 1.3 million unique gamers over 14 years. Our analysis focuses on workload characteristics, win ratio, and player evolution. Studies such as ours may guide the multi-disciplinary field of MMVE design by providing new understanding of player lifetime and behavior. For example, we find a correlation between the interactivity of match-based games and the retention of players over both long and short term, that friendship does not always help to perform better in games, and that in match-based games each player explores tens of different play strategies over time.


title: Personality Matters: Balancing for Personality Types Leads to Better Outcomes for Crowd Teams

abstract: When personalities clash, teams operate less effectively. Personality differences affect face-to-face collaboration and may lower trust in virtual teams. For relatively short-lived assignments, like those of online crowdsourcing, personality matching could provide a simple, scalable strategy for effective team formation. However, it is not clear how (or if) personality differences affect teamwork in this novel context where the workforce is more transient and diverse. This study examines how personality compatibility in crowd teams affects performance and individual perceptions. Using the DISC personality test, we composed 14 five-person teams (N=70) with either a harmonious coverage of personalities (balanced) or a surplus of leader-type personalities (imbalanced). Results show that balancing for personality leads to significantly better performance on a collaborative task. Balanced teams exhibited less conflict and their members reported higher levels of satisfaction and acceptance. This work demonstrates a simple personality matching strategy for forming more effective teams in crowdsourcing contexts.


title: Reinforcement learning approach to learning human experience in tuning cavity filters

abstract: Owing to the rapid development of the communication industry, various kinds of radio frequency components are in great demand and put into mass production. Among them, passive devices such as microwave cavity filters, duplexers and combiners have experienced fast and unexpected upgrades. However, the tuning process of these products, which is always manually operated, still seems hard to be automatically replaced or improved because of the difficulties in extracting human experience. In this study, we make deep investigations into some previous automatic cavity filter tuning solutions, especially the ones using intelligent algorithms. In addition, we propose the method of intelligent tuning based on the reinforcement learning algorithm which dynamically extracts the human strategies during the tuning process. The experimental results prove the powerful performance of reinforcement learning in mastering human skills.


title: Quantum Cryptography and Quantum Key Distribution Protocols: A Survey

abstract: Quantum cryptography renders a cryptographic solution which is imperishable as it fortifies prime secrecy that is applied to quantum public key distribution. It is a prominent technology wherein two entities can communicate securely with the sights of quantum physics. In classical cryptography, bits are used to encode information where as quantum cryptography i.e. quantum computer uses photons or quantum particles and photon's polarization which is their quantized properties to encode the information. This is represented in qubits which is the unit for quantum cryptography. The transmissions are secure as it is depended on the inalienable quantum mechanics laws. The emphasis of this paper is to mark the rise of quantum cryptography, its elements, quantum key distribution protocols and quantum networks.


title: Modeling the Driving Behavior of Electric Vehicles Using Smartphones and Neural Networks

abstract: The modeling of eco-driving behaviors is a key issue in the research of Intelligent Transportation Systems. Most efforts have been made regarding internal combustion vehicles, and few works have been reported in the field of electric vehicles. On the other hand, these behavior analyses are usually conducted through naturalistic driving researches that involve the use of instrumented vehicles, available in a small number, which reduces the impact of the results. This paper presents a system for estimating the remaining charge of an electric vehicle by considering the driving behavior measured using a smartphone. For this purpose, first of all, data measured by the smartphone and by the onboard instrumentation were compared in order to demonstrate that both sources are equivalent and that the former is sufficiently accurate. The driving profiles obtained were then used to estimate the expected battery consumption of the electric vehicle using a Neural Network to represent the model that uses the information provided from the smartphone as input, such as speed, acceleration and jerk. The system has been tested with 10 drivers with a prediction capability of the expected battery consumption higher than 95%. These results show that a smartphone is a tool with a sufficient degree of fidelity to capture data from drivers, and so avoid expensive, complex systems like instrumented vehicles, and it can also be used for estimating energy consumption and predicting the remaining battery charge.


title: Generating and designing DNA with deep generative models

abstract: We propose generative neural network methods to generate DNA sequences and tune them to have desired properties. We present three approaches: creating synthetic DNA sequences using a generative adversarial network; a DNA-based variant of the activation maximization ("deep dream") design method; and a joint procedure which combines these two approaches together. We show that these tools capture important structures of the data and, when applied to designing probes for protein binding microarrays, allow us to generate new sequences whose properties are estimated to be superior to those found in the training data. We believe that these results open the door for applying deep generative models to advance genomics research.


title: The Paradox of Received Social Support The Importance of Responsiveness

abstract: Although the perception of available support is associated with positive outcomes, the receipt of actual support from close others is often associated with negative outcomes. In fact, support that is “invisible” (not perceived by the support recipient) is associated with better outcomes than “visible” support. To investigate this paradox, we proposed that received support (both visible and invisible) would be beneficial when it was responsive to the recipient's needs. Sixty-seven cohabiting couples participated in a daily-experience study in which they reported on the support they provided and received each day. Results indicated that both visible and invisible support were beneficial (i.e., associated with less sadness and anxiety and with greater relationship quality) only when the support was responsive. These findings suggest that the nature of support is an important determinant of when received support will be beneficial.


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

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


title: The Internet of Things for smart homes: An example

abstract: The internet of things (IoT) has a variety of application domains, including smart homes. This paper analyzes distinct IoT security and privacy features, including security requirements, threat models, and attacks from the smart home perspective. Further, this paper proposes an intelligent collaborative security management model to minimize security risk. The security challenges of the IoT for a smart home scenario are encountered, and a comprehensive IoT security management for smart homes has been proposed.


title: Metric Regression Forests for Correspondence Estimation

abstract: We present a new method for inferring dense data to model correspondences, focusing on the application of human pose estimation from depth images. Recent work proposed the use of regression forests to quickly predict correspondences between depth pixels and points on a 3D human mesh model. That work, however, used a proxy forest training objective based on the classification of depth pixels to body parts. In contrast, we introduce Metric Space Information Gain (MSIG), a new decision forest training objective designed to directly minimize the entropy of distributions in a metric space. When applied to a model surface, viewed as a metric space defined by geodesic distances, MSIG aims to minimize image-to-model correspondence uncertainty. A naive implementation of MSIG would scale quadratically with the number of training examples. As this is intractable for large datasets, we propose a method to compute MSIG in linear time. Our method is a principled generalization of the proxy classification objective, and does not require an extrinsic isometric embedding of the model surface in Euclidean space. Our experiments demonstrate that this leads to correspondences that are considerably more accurate than state of the art, using far fewer training images.


title: Efficient processing of distance queries in large graphs: a vertex cover approach

abstract: We propose a novel disk-based index for processing single-source shortest path or distance queries. The index is useful in a wide range of important applications (e.g., network analysis, routing planning, etc.). Our index is a tree-structured index constructed based on the concept of vertex cover. We propose an I/O-efficient algorithm to construct the index when the input graph is too large to fit in main memory. We give detailed analysis of I/O and CPU complexity for both index construction and query processing, and verify the efficiency of our index for query processing in massive real-world graphs.


title: A novel three-phase high-power soft-switched DC/DC converter for low-voltage fuel cell applications

abstract: An efficient DC/DC converter is needed as the interface between a low-voltage fuel cell source and a high-voltage bus for inverter operation. In this paper, a three-phase transformer-isolated DC/DC converter utilizing phase-shift modulation is proposed. The converter must be able to boost the voltage significantly and operate at current levels above 240 amps on the source side. Key features of the proposed converter include: reduced transformer turns-ratio by a factor of two while maintaining the same output voltage, reduced the size of passive components including output filter and input DC bus capacitor using three-phase interleaving, eliminated inductor current ripple at phase-shift angles above 120/spl deg/, and achieved soft switching over a wide load range without auxiliary circuitry. The proposed converter has been analyzed, simulated and implemented in hardware. An efficiency of above 96% was achieved using the prototype unit. Experimental results were used to verify all design and analysis.


title: A Column Generation Approach to Delivery Planning over Time with Inhomogeneous Service Providers and Service Interval Constraints

abstract: We consider a problem of delivery planning over multiple time periods. Deliveries must be made to customers having nominated demand in each time period. Demand must be met in each time period by use of some combination of inhomogeneous service providers. Each service provider has a different delivery capacity, different cost of delivery to each customer, a different utilisation requirement, and different rules governing the spread of deliveries in time. The problem is to plan deliveries so as to minimise overall costs, subject to demand being met and service rules obeyed. A natural integer programming model was found to be intractable, except on problems with loose demand constraints, with gaps between best lower bound and best feasible solution of up to 35.1%, with an average of 15.4% over the test data set. In all but the problem with loosest demand constraints, Cplex 6.5 applied to this formulation failed to find the optimal solution before running out of memory. However a column generation approach improved the lower bound by between 0.6% and 21.9%, with an average of 9.9%, and in all cases found the optimal solution at the root node, without requiring branching.


title: Interacting with spatially augmented reality

abstract: We present the notion of projector-based spatially augmented reality (SAR), and explore how it can be used as an effective user interface to enable users to easily and naturally interact with their real physical environment and the virtual environment. In SAR, the user's physical environment is illuminated with images projected from the projectors. We then describe a framework that can easily incorporate different types of interactions on a continuum of display surfaces and input devices. More specifically, in the framework, we first establish an understanding of the 3D relationship between the user, the projectors and the display surfaces. Then, with the help of some new calibration and rendering techniques, only a simple procedure is needed to effectively illuminate the display surfaces. We describe various underlying techniques and discuss the results in the context of three different applications.


Rethinking Access Control and Authentication for the Home Internet of Things (IoT).


title: PARAMETRIZED DEEP Q-NETWORKS LEARNING: PLAYING ONLINE BATTLE ARENA WITH DISCRETE-CONTINUOUS HYBRID ACTION SPACE

abstract: Most existing deep reinforcement learning (DRL) frameworks consider action spaces that are either discrete or continuous space. Motivated by the project of design Game AI for King of Glory (KOG), one the world’s most popular mobile game, we consider the scenario with the discrete-continuous hybrid action space. To directly apply existing DLR frameworks, existing approaches either approximate the hybrid space by a discrete set or relaxing it into a continuous set, which is usually less efficient and robust. In this paper, we propose a parametrized deep Q-network (P-DQN) for the hybrid action space without approximation or relaxation. Our algorithm combines DQN and DDPG and can be viewed as an extension of the DQN to hybrid actions. The empirical study on the game KOG validates the efficiency and effectiveness of our method.


title: Cross-label Suppression: A Discriminative and Fast Dictionary Learning with Group Regularization

abstract: This paper addresses image classification through learning a compact and discriminative dictionary efficiently. Given a structured dictionary with each atom (columns in the dictionary matrix) related to some label, we propose cross-label suppression constraint to enlarge the difference among representations for different classes. Meanwhile, we introduce group regularization to enforce representations to preserve label properties of original samples, meaning the representations for the same class are encouraged to be similar. Upon the cross-label suppression, we donot resort to frequently-used $\ell _{0}$ -norm or $\ell _{1}$ -norm for coding, and obtain computational efficiency without losing the discriminative power for categorization. Moreover, two simple classification schemes are also developed to take full advantage of the learnt dictionary. Extensive experiments on six data sets, including face recognition, object categorization, scene classification, texture recognition, and sport action categorization are conducted, and the results show that the proposed approach can outperform lots of recently presented dictionary algorithms on both recognition accuracy and computational efficiency.


title: A hybrid approach to enhance data security in cloud storage

abstract: Cloud storage is evolving into a reliable storage but there are substantial security issues which prevail in cloud storage as well as in cloud computing such as ensuring confidentiality, integrity and privacy. Even though cloud providers use different authentication policies, the sensitive data is still prone to attacks. In order to provide confidentiality, integrity and privacy, cloud users employ encryption schemes, there are various encryption technique used. Each of these encryption techniques has its own hindrance such as AES is a time consuming encryption algorithm. Hence, whichever encryption used, should satisfy data confidentiality and integrity. Homomorphic encryption is a reliable encryption technique which ensures data confidentiality and integrity. I propose a novel encryption and decryption technique which satisfies data confidentiality and integrity. This paper uses Homomorphic encryption and Location-based decryption as two different techniques to provide data security in cloud storage. The encryption process is based on Homomorphic encryption and decryption is based on user location. The location-based decryption enhances the data security in this framework. I also propose data classification model for cloud users as well as for cloud providers. The proposed framework provides a secure protection for sensitive data in cloud storage.


title: A Standing-Wave Architecture for Scalable and Wideband Millimeter-Wave and Terahertz Coherent Radiator Arrays

abstract: In this paper, we present a new architecture for implementation of millimeter-wave (mm-wave) and terahertz (THz) radiator arrays based on standing-wave properties. This structure is a continuous distributed coherent array that avoids lossy and parasitic coupling networks. Moreover, it can be scaled simply by extending the size of the structure and replicating the unit cell. The absence of coupling parasitics in addition to the unique characteristics of standing waves allows us to extend the tuning range without using varactors. The 0.34-THz four-element radiator array is designed and fabricated in a 130-nm SiGe BiCMOS process using microstrip transmission lines as the standing-wave mediums and on-chip patch antennas to radiate the desired fourth harmonic of the oscillation. The circuit was measured with no post processing or silicon lens and has 5.9% frequency tuning range (332.5–352.8 GHz) with less than 6-dB output power variation across the band. It consumes 425-mW power from 1.8-V supply and the radiated power is −10.5 dBm at center frequency with −98.2 dBc/Hz phase noise at 10-MHz offset frequency.


title: Trunk-Branch Ensemble Convolutional Neural Networks for Video-based Face Recognition

abstract: Human faces in surveillance videos often suffer from severe image blur, dramatic pose variations, and occlusion. In this paper, we propose a comprehensive framework based on Convolutional Neural Networks (CNN) to overcome challenges in video-based face recognition (VFR). First, to learn blur-robust face representations, we artificially blur training data composed of clear still images to account for a shortfall in real-world video training data. Using training data composed of both still images and artificially blurred data, CNN is encouraged to learn blur-insensitive features automatically. Second, to enhance robustness of CNN features to pose variations and occlusion, we propose a Trunk-Branch Ensemble CNN model (TBE-CNN), which extracts complementary information from holistic face images and patches cropped around facial components. TBE-CNN is an end-to-end model that extracts features efficiently by sharing the low- and middle-level convolutional layers between the trunk and branch networks. Third, to further promote the discriminative power of the representations learnt by TBE-CNN, we propose an improved triplet loss function. Systematic experiments justify the effectiveness of the proposed techniques. Most impressively, TBE-CNN achieves state-of-the-art performance on three popular video face databases: PaSC, COX Face, and YouTube Faces. With the proposed techniques, we also obtain the first place in the BTAS 2016 Video Person Recognition Evaluation.


title: A conceptual framework for an IoT-based health assistant and its authorization model

abstract: This paper proposes a framework for an IoT-based health prescription assistant (HPA), which helps each patient to follow the doctors recommendations properly. The paper also designs a security system that ensures user authentication and protected access to resources and services. Based on the OpenID standard, an access control mechanism is implemented to prevent unauthorized access to medical devices. Once the authentication is successful, the user is issued an authorization ticket, which this paper calls a security access token (SAT). The SAT contains a set of privileges that grants the user access to medical IoT devices and their services and/or resources. The SAT is encrypted to guard against forgery. A medical IoT device verifies the SAT prior to serving a request, and thus, ensures protected access


title: Mesh-Based Broadband Home Network Solution: Setup and Experiments

abstract: In this paper, we investigate architectural and practical issues related to the setup of a broadband home network solution. Our experience led us to the consideration of a hybrid, wireless and wired, Mesh-Network to enable high data rate service delivery everywhere in the home. We demonstrate the effectiveness of our proposal using a real experimental testbed. This latter consists of a multi-hop mesh network composed of a home gateway and "extenders" supporting several types of physical connectivity including PLC, WiFi, and Ethernet. The solution also includes a layer 2 implementation of the OLSR protocol for path selection. We developed an extension of this protocol for QoS assurance and to enable the proper execution of existing services. We have also implemented a fast WiFi handover algorithm to ensure service continuity in case of user mobility among the extenders inside the home.


title: Comparative Performance Analysis of XOR-XNOR Function Based High-Speed CMOS Full Adder Circuits For Low Voltage VLSI Design

abstract: This paper presents comparative study of high-speed, low-power and low voltage full adder circuits. Our approach is based on XOR-XNOR design full adder circuits in a single unit. A low power and high performance 9T full adder cell using a design style called “XOR (3T)” is discussed. The designed circuit commands a high degree of regularity and symmetric higher density than the conventional CMOS design style as well as it lowers power consumption by using XOR (3T) logic circuits. Gate Diffusion Input (GDI) technique of low-power digital combinatorial circuit design is also described. This technique helps in reducing the power consumption and the area of digital circuits while maintaining low complexity of logic design. This paper analyses, evaluates and compares the performance of various adder circuits. Several simulations conducted using different voltage supplies, load capacitors and temperature variation demonstrate the superiority of the XOR (3T) based full adder designs in term of delay, power and power delay product (PDP) compared to the other full adder circuits. Simulation results illustrate the superiority of the designed adder circuits against the conventional CMOS, TG and Hybrid full adder circuits in terms of power, delay and power delay product (PDP).


title: How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks

abstract: Variational autoencoders are a powerful framework for unsupervised learning. However, previous work has been restricted to shallow models with one or two layers of fully factorized stochastic latent variables, limiting the flexibility of the latent representation. We propose three advances in training algorithms of variational autoencoders, for the first time allowing to train deep models of up to five stochastic layers, (1) using a structure similar to the Ladder network as the inference model, (2) warm-up period to support stochastic units staying active in early training, and (3) use of batch normalization. Using these improvements we show state-of-the-art log-likelihood results for generative modeling on several benchmark datasets.


title: Fingerprint recognition using translation invariant scattering network

abstract: Fingerprint recognition has drawn a lot of attention during last decades. Different features and algorithms have been used for fingerprint recognition in the past. In this paper, a powerful image representation called scattering transform/network, is used for recognition. Scattering network is a convolutional network where its architecture and filters are predefined wavelet transforms. The first layer of scattering representation is similar to sift descriptors and the higher layers capture higher frequency content of the signal. After extraction of scattering features, their dimensionality is reduced by applying principal component analysis (PCA). At the end, multi-class SVM is used to perform template matching for the recognition task. The proposed scheme is tested on a well-known fingerprint database and has shown promising results with the best accuracy rate of 98\%.


title: Industrial Cyberphysical Systems: A Backbone of the Fourth Industrial Revolution

abstract: Cyberphysical systems (CPSs) are perceived as the pivotal enabler for a new era of real-time Internetbased communication and collaboration among value-chain participants, e.g., devices, systems, organizations, and humans. The CPS utilization in industrial settings is expected to revolutionize the way enterprises conduct their business from a holistic viewpoint, i.e., from shop-floor to business interactions, from suppliers to customers, and from design to support across the whole product and service lifecycle. Industrial CPS (ICPSs) blur the fabric of cyber (including business) and physical worlds and kickstart an era of systemwide collaboration and information-driven interactions among all stakeholders of the value chain. Therefore, ICPSs are expected to empower the transformation of industry and business at large to a digital, adaptive, networked, and knowledge-based industry with significant long-term impact on the economy, society, environment, and citizens.


title: On Challenges in Evaluating Malware Clustering

abstract: Malware clustering and classification are important tools that enable analysts to prioritize their malware analysis efforts. The recent emergence of fully automated methods for malware clustering and classification that report high accuracy suggests that this problem may largely be solved. In this paper, we report the results of our attempt to confirm our conjecture that the method of selecting ground-truth data in prior evaluations biases their results toward high accuracy. To examine this conjecture, we apply clustering algorithms from a different domain (plagiarism detection), first to the dataset used in a prior work's evaluation and then to a wholly new malware dataset, to see if clustering algorithms developed without attention to subtleties of malware obfuscation are nevertheless successful. While these studies provide conflicting signals as to the correctness of our conjecture, our investigation of possible reasons uncovers, we believe, a cautionary note regarding the significance of highly accurate clustering results, as can be impacted by testing on a dataset with a biased cluster-size distribution.


title: Time-frequency feature representation using energy concentration: An overview of recent advances

abstract: Signal processing can be found in many applications and its primary goal is to provide underlying information on specific problems for the purpose of decision making. Traditional signal processing approaches assume the stationarity of signals, which in practice is not often satisfied. Hence, time or frequency descriptions alone are insufficient to provide comprehensive information about such signals. On the contrary, time-frequency analysis is more suitable for nonstationary signals. Therefore, this paper provides a status report of feature based signal processing in the time-frequency domain through an overview of recent contributions. The feature considered here is energy concentration. The paper provides an analysis of several classes of feature extractors, i.e., time-frequency representations, and feature classifiers. The results of the literature review indicate that time-frequency domain signal processing using energy concentration as a feature is a very powerful tool and has been utilized in numerous applications. The expectation is that further research and applications of these algorithms will flourish in the near future.


title: Cultural Differences in Brand Extension Evaluation: The Influence of Analytic versus Holistic Thinking

abstract: Consumers evaluate brand extensions by judging how well they fit with the parent brand. We examine this process across cultures. We predict that consumers from Eastern cultures, characterized by holistic thinking, perceive higher brand extension fit and evaluate brand extensions more favorably than do Western consumers, characterized by analytic thinking. Study 1 supports the existence of these cultural differences, with study 2 providing support for styles of thinking (analytic vs. holistic) as the drivers of cultural differences in brand extension evaluations.


title: Do LoRa Low-Power Wide-Area Networks Scale?

abstract: New Internet of Things (IoT) technologies such as Long Range (LoRa) are emerging which enable power efficient wireless communication over very long distances. Devices typically communicate directly to a sink node which removes the need of constructing and maintaining a complex multi-hop network. Given the fact that a wide area is covered and that all devices communicate directly to a few sink nodes a large number of nodes have to share the communication medium. LoRa provides for this reason a range of communication options (centre frequency, spreading factor, bandwidth, coding rates) from which a transmitter can choose. Many combination settings are orthogonal and provide simultaneous collision free communications. Nevertheless, there is a limit regarding the number of transmitters a LoRa system can support. In this paper we investigate the capacity limits of LoRa networks. Using experiments we develop models describing LoRa communication behaviour. We use these models to parameterise a LoRa simulation to study scalability. Our experiments show that a typical smart city deployment can support 120 nodes per 3.8 ha, which is not sufficient for future IoT deployments. LoRa networks can scale quite well, however, if they use dynamic communication parameter selection and/or multiple sinks.


title: Educational Activities in the E-learning Environment

abstract: The article considers the various aspects of the potential capacities of school and university E-learning environment for the organization of educational activities with students. The environment of the e-learning opens a number of new opportunities which are not only for the organization of a learning activity, but also for creation of the conditions for self-realization of the personality and socialization of the student.


title: Model Predictive HVAC Control with Online Occupancy Model

abstract: This paper presents an occupancy-predicting control algorithm for heating, ventilation, and air conditioning (HVAC) systems in buildings. It incorporates the building's thermal properties, local weather predictions, and a self-tuning stochastic occupancy model to reduce energy consumption while maintaining occupant comfort. Contrasting with existing approaches, the occupancy model requires no manual training and adapts to changes in occupancy patterns during operation. A prediction-weighted cost function provides conditioning of thermal zones before occupancy begins and reduces system output before occupancy ends. Simulation results with real-world occupancy data demonstrate the algorithm's effectiveness.


title: Clique Percolation in Random Networks

abstract: The notion of k-clique percolation in random graphs is introduced, where k is the size of the complete subgraphs whose large scale organizations are analytically and numerically investigated. For the Erdos-Rényi graph of N vertices we obtain that the percolation transition of k-cliques takes place when the probability of two vertices being connected by an edge reaches the threshold p(c) (k) = [(k - 1)N](-1/(k - 1)). At the transition point the scaling of the giant component with N is highly nontrivial and depends on k. We discuss why clique percolation is a novel and efficient approach to the identification of overlapping communities in large real networks.


title: A Scalable, Lexicon Based Technique for Sentiment Analysis

abstract: Rapid increase in the volume of sentiment rich social media on the web has resulted in an increased interest among researchers regarding Sentimental Analysis and opinion mining. However, with so much social media available on the web, sentiment analysis is now considered as a big data task. Hence the conventional sentiment analysis approaches fails to efficiently handle the vast amount of sentiment data available now a days. The main focus of the research was to find such a technique that can efficiently perform sentiment analysis on big data sets. A technique that can categorize the text as positive, negative and neutral in a fast and accurate manner. In the research, sentiment analysis was performed on a large data set of tweets using Hadoop and the performance of the technique was measured in form of speed and accuracy. The experimental results shows that the technique exhibits very good efficiency in handling big sentiment data sets.


title: A Minimum Relative Entropy Principle for Learning and Acting

abstract: This paper proposes a method to construct an adaptive agent that is universal with respect to a given class of experts, where each expert is an agent that has been designed specifically for a particular environment. This adaptive control problem is formalized as the problem of minimizing the relative entropy of the adaptive agent from the expert that is most suitable for the unknown environment. If the agent is a passive observer, then the optimal solution is the well-known Bayesian predictor. However, if the agent is active, then its past actions need to be treated as causal interventions on the I/O stream rather than normal probability conditions. Here it is shown that the solution to this new variational problem is given by a stochastic controller called the Bayesian control rule, which implements adaptive behavior as a mixture of experts. Furthermore, it is shown that under mild assumptions, the Bayesian control rule converges to the control law of the most suitable expert.


title: Tree Detection in Aerial Lidar and Image Data

abstract: In this paper, we present an approach to detecting trees in registered aerial image and range data obtained via LiDAR. The motivation for this problem comes from automated 3D city modeling, in which such data is used to generate the models. Representing the trees in these models is problematic because the data is usually too sparsely sampled in tree regions to create an accurate 3-D model of the trees. Furthermore, including the tree data points interferes with the polygonization step of the building roof top models. Therefore, it is advantageous to detect and remove points that represent trees in both LiDAR and aerial imagery. In this paper we propose a two-step method for tree detection consisting of segmentation followed by classification. The segmentation is done using a simple region-growing algorithm using weighted features from aerial image and LiDAR, such as height, texture map, height variation, and normal vector estimates. The weights for the features are determined using a learning method on random walks. The classification is done using weighted support vector machines (SVM), allowing us to control the mis-classification rate. The overall problem is formulated as a binary detection problem, and receiver operating characteristic curves are shown to validate our approach.


Device-to-Device Communications for 5G Internet of Things


title: Conceptual Model for Smart Cities: Irrigation and Highway Lamps using IoT

abstract: Keeping in mind the need to preserve energy as well as utilize the available at its best the need was felt to develop a module that would be able to sort out the problem where resources such as water and electricity were wasted, in urban as well as rural area. Resource (electricity) was wasted as beside the point operation of Highway & High Mast Lamp; while wastage of water followed by improper trends and methodologies imparted for watering of city park, road side plantation and highway plantation. Thus as per Energy survey statistics of a City (Lucknow, India) it was found that major portion of resources (water and electricity) were being wasted due to negligent activities of officials who were in charge of resource management. So to facilitate energy saving trends and to completely modernize it to autonomous system, module below is proposed which incorporates modern technological peripheral and has its base ingrained in IoT (Internet of Things) which when put into consideration would result in large scale resource and energy saving.This developed module incorporates the peripherals such as Arduino, Texas Instruments ultra low power kits etc. in accordance with software technology including Lab View which help to monitor as well as control the various operation from the base station, located far away from the site. Lab View Interface interacts with all the module located at various city parks, subways and highway lighting modules. Later below in several section a detailed pattern and application frame has been put up.


title: Citation Resolution: A method for evaluating context-based citation recommendation systems

abstract: Wouldn’t it be helpful if your text editor automatically suggested papers that are relevant to your research? Wouldn’t it be even better if those suggestions were contextually relevant? In this paper we name a system that would accomplish this a context-based citation recommendation (CBCR) system. We specifically present Citation Resolution, a method for the evaluation of CBCR systems which exclusively uses readily-available scientific articles. Exploiting the human judgements that are already implicit in available resources, we avoid purpose-specific annotation. We apply this evaluation to three sets of methods for representing a document, based on a) the contents of the document, b) the surrounding contexts of citations to the document found in other documents, and c) a mixture of the two.