title: Collaborative Recommendation with Multi-Criteria Ratings

abstract: Abstract. Recommendation systems utilize data analysis techniques to the problem of helping users find the items they would like. Example applications include the recommendation systems for movies, books, CDs and many others. As recommendation systems emerge as an independent research area, the rating structure plays a critical role in recent studies. Among many alternatives, the collaborative filtering algorithms are gen-erally accepted to be successful to estimate user ratings of unseen items and then to derive proper recommen-dations. In this paper, we extend the concept of single criterion ratings to multi-criteria ones, i.e., an item can be evaluated in many different aspects. For example, the goodness of a restaurant can be evaluated in terms of its food, decor, service and cost. Since there are usually conflicts among different criteria, the recommen-dation problem cannot be formulated as an optimization problem any more. Instead, we propose in this paper to use data query techniques to solve this multi-criteria recommendation problem. Empirical studies show that our approach is of both theoretical and practical values.


abstract: Now established as an effective tool in the instructional process, multimedia has penetrated educational systems at almost every level of study. In their quest to maximize educational outcomes and identify best practices, multimedia researchers are now expanding their examinations to extend towards the cognitive functionality of multimedia.Cognitive Effects of Multimedia Learning identifies the role and function of multimedia in learning through a collection of research studies focusing on cognitive functionality. An advanced collection of critical theories and practices, this much needed contribution to the research is an essential holding for academic libraries, and will benefit researchers, practitioners and students in basic and applied fields ranging from education to cognitive sciences.


title: Abnormal Uterine Bleeding in Reproductive-aged Women

abstract: Abnormal uterine bleeding is a common medical condition with several causes. The International Federation of Gynecology and Obstetrics published guidelines in 2011 to develop universally accepted nomenclature and a classification system. In addition, the American College of Obstetrics and Gynecology recently updated recommendations on evaluation of abnormal uterine bleeding and indications for endometrial biopsies. This article reviews both medical and surgical treatments, including meta-analysis reviews of the most effective treatment options.


title: Strengthen Cloud Computing Security with Federal Identity Management Using Hierarchical Identity-Based Cryptography

abstract: More and more companies begin to provide different kinds of cloud computing services for Internet users at the same time these services also bring some security problems. Currently the majority of cloud computing systems provide digital identity for users to access their services, this will bring some inconvenience for a hybrid cloud that includes multiple private clouds and/or public clouds. Today most cloud computing system use asymmetric and traditional public key cryptography to provide data security and mutual authentication. Identity-based cryptography has some attraction characteristics that seem to fit well the requirements of cloud computing. In this paper, by adopting federated identity management together with hierarchical identity-based cryptography (HIBC), not only the key distribution but also the mutual authentication can be simplified in the cloud.


title: Public-Key Cryptography Standards (PKCS) #1: RSA Cryptography Specification Version 2.1

abstract: This memo represents a republication of PKCS #1 v2.1 from RSA Laboratories' Public-Key Cryptography Standards (PKCS) series, and change control is retained within the PKCS process. The body of this document is taken directly from the PKCS #1 v2.1 document, with certain corrections made during the publication process.


title: Multiple kinds of paper currency recognition using neural network and application for Euro currency

abstract: Up until now, we have developed banking machines for various kinds of paper currency using neural networks. In this paper, we report an enhanced neuro-recognition system to increase the number of recognition patterns using axis-symmetrical mask and two image sensors. One sensor's purpose is discrimination for a known image and another one is exclusion for an unknown image. Concretely, we implement the proposed method to an experimental system, which has two sensors, arranged one above and one below the moving banknote. Finally, we apply this proposed method to Euro currency, which will be issued in 2002, using dummy notes. The effectiveness of the proposed method is shown, numerically.


A short proof of the unpredictability of cipher block chaining


title: Locally adaptive metrics for clustering high dimensional data

abstract: Clustering suffers from the curse of dimensionality, and similarity functions that use all input features with equal relevance may not be effective. We introduce an algorithm that discovers clusters in subspaces spanned by different combinations of dimensions via local weightings of features. This approach avoids the risk of loss of information encountered in global dimensionality reduction techniques, and does not assume any data distribution model. Our method associates to each cluster a weight vector, whose values capture the relevance of features within the corresponding cluster. We experimentally demonstrate the gain in perfomance our method achieves with respect to competitive methods, using both synthetic and real datasets. In particular, our results show the feasibility of the proposed technique to perform simultaneous clustering of genes and conditions in gene expression data, and clustering of very high-dimensional data such as text data.


title: Urine Testing for Cocaine Abuse: Metabolic and Excretion Patterns following Different Routes of Administration and Methods for Detection of False-Negative Results

abstract: Although cocaine is typically the second-most identified drug of abuse in drug-testing programs, there is surprisingly little quantitative information on excretion patterns following different routes of administration. This report details the urinary excretion and terminal elimination kinetics for cocaine and eight metabolites [benzoylecgonine (BZE), ecgonine methylester (EME), norcocaine (NCOC), benzoylnorecgonine (BNE), m-hydroxy-BZE (m-HO-BZE), p-hydroxy-BZE (p-HO-BZE), m-hydroxy-COC (m-HO-COC), and p-hydroxy-COC (p-HO-COC)]. Six healthy males were administered approximately equipotent doses of cocaine by the intravenous (IV), smoking (SM), and inhalation (IN) routes of administration. Urine specimens were collected for a minimum of three days after drug administration, screened by immunoassay (EMIT and TDX, 300 ng/mL), and analyzed by GC-MS. Mean Cmax values were generally as follows: BZE > EME > COC > BNE p-HO-BZE > m-HO-BZE > m-HO-COC > NCOC > p-HO-COC. Elimination half-lives for cocaine and metabolites were generally shorter following SM, intermediate after IV, and longest following IN administration. m-HO-BZE demonstrated the longest half-life (mean range 7.0-8.9 h), and cocaine displayed the shortest (2.4-4.0 h). Mean detection times were extended progressively by lowering cutoff concentrations. The maximum increases were approximately 55% at 50 ng/mL for the TDx assay (e.g., the detection time for the last consecutive positive changed from 32.8 h to 50.6 h for IV cocaine) and up to 39% for GC-MS at a cutoff concentration of 40 ng/mL (e.g., the detection time for the last consecutive positive changed from 34.8 h to 48.1 h for IV cocaine). Sensitivity, specificity, and predictive values for EMIT and TDx were comparable at the 300-ng/mL cutoff concentration; but at lower cutoff concentrations, predictive values of positive results for TDx were diminished indicating a higher risk of false-positive results, that is, positive results that failed to meet administrative cutoff criteria. Detection of positive results was significantly enhanced through the use of the Zero Threshold Criteria Method, a method developed by the authors to differentiate false-negatives from true-negatives. The method was based on establishing mean immunoassay response (MIR) baselines and variance (SD) in assays of drug-free specimens. Arbitrary thresholds (MIR + 0.5 SD, MIR + 1 SD, MIR + 2 SD) were utilized to evaluate all negative specimens. Apparent true positives were identified by the presence of BZE at or above 40% GC-MS cutoff concentrations. With these criteria, up to 111 false-negative specimens were confirmed as true-positive specimens; this was in addition to the 208 true positives detected at recommended cutoff concentrations. This represents a 50% increase in positive detection rates through the use of this methodology. Such methodology is recommended for further evaluation by drug-testing programs for enhancement of positive detection rates and as an alternative to creatinine testing for dealing with dilute specimens that test negative by initial tests, but contain quantifiable concentrations of drugs of abuse.


title: Wind turbine condition monitoring and reliability analysis by SCADA information

abstract: As the deployment of onshore wind turbines becomes established and with an increasing deployment of offshore wind turbines, to ensure a high reliability and availability of these machines has become the focus of interest within the academic and industrial communities. One of the approaches to meet this requirement is to develop a Reliability Centered Maintenance (RCM) strategy with the aid of an advanced condition monitoring and reliability prediction system. The aim of this paper is to describe the basic idea of how wind farm Supervisory Control and Data Acquisition (SCADA) system contributes to establishing such a strategy. Examples have been given in the paper for providing a clear explanation of the opinions.


title: Localization in the Internet of Things Network: A Low-Rank Matrix Completion Approach

abstract: Location awareness, providing ability to identify the location of sensor, machine, vehicle, and wearable device, is a rapidly growing trend of hyper-connected society and one of key ingredients for internet of things (IoT). In order to make a proper reaction to the collected information from devices, location information of things should be available at the data center. One challenge for the massive IoT networks is to identify the location map of whole sensor nodes from partially observed distance information. This is especially important for massive sensor networks, relay-based and hierarchical networks, and vehicular to everything (V2X) networks. The primary goal of this paper is to propose an algorithm to reconstruct the Euclidean distance matrix (and eventually the location map) from partially observed distance information. By casting the low-rank matrix completion problem into the unconstrained minimization problem in Riemannian manifold in which a notion of differentiability can be defined, we are able to solve the low-rank matrix completion problem efficiently using a modified conjugate gradient algorithm. From the analysis and numerical experiments, we show that the proposed method, termed localization in Riemannian manifold using conjugate gradient (LRM-CG), is effective in recovering the Euclidean distance matrix for both noiseless and noisy environments.


title: Computing Matrix Functions

abstract: The need to evaluate a function $f(A)\in\mathbb{C}^{n \times n}$ of a matrix ::: $A\in\mathbb{C}^{n \times n}$ arises in a wide and growing number of ::: applications, ranging from the numerical solution of differential equations to ::: measures of the complexity of networks. ::: We give a survey of numerical methods for evaluating matrix functions, ::: along with a brief treatment of the underlying theory ::: and a description of two recent applications. ::: The survey is organized by classes of methods, ::: which are broadly those based on similarity transformations, ::: those employing approximation by polynomial or rational functions, ::: and matrix iterations. ::: Computation of the Fr\'echet derivative, ::: which is important for condition number estimation, is also treated, ::: along with the problem of computing $f(A)b$ without computing $f(A)$. ::: A summary of available software completes the survey.


title: Causality, Feedback And Directed Information

abstract: It is shown that the "usual definition" of a discrete memoryless channel (DMC) in fact prohibits the use of feedback. The difficulty stems from the confusion of causality and statistical dependence. An adequate definition of a DMC is given, as well as a definition of using a channel without feedback. A definition, closely based on an old idea of Marko, is given for the directed information flowing from one sequence to another. This directed information is used to give a simple proof of the well-known fact that the use of feedback cannot increase the capacity of a DMC. It is shown that, when feedback is present, directed information is a more useful quantity than the traditional mutual information.


title: Behavioural analysis of crime against women using a graph based clustering approach

abstract: Crime against women is increasing at an alarming rate in almost all parts of India. Women in the Indian society have been victims of humiliation, torture and exploitation. It has even existed in the past but only in the recent years the issues have been brought to the open for concern. According to the latest data released by the National Crime Records Bureau (NCRB), crime against women have increased more than doubled over the past ten years. While a number of analyses have been done in the field of crime pattern detection, none have done an extensive study on the crime against women in India. The present paper describes a behavioural analysis of crime against women in India from the year 2001 to 2014. The study evaluates the efficacy of Infomap clustering algorithm for detecting communities of states and union territories in India based on crimes. As it is a graph based clustering approach, all the states of India along with the union territories have been considered as nodes of the graph and similarity among the nodes have been measured based on different types of crimes. Each community is a group of states and / or union territories which are similar based on crime trends. Initially, the method finds the communities based on current year crime data, subsequently at the end of a year when new crime data for the next year is available, the graph is modified and new communities are formed. The process is repeated year wise that helps to predict how crime against women has significantly increased in various states of India over the past years. It also helps in rapid visualisation and identification of states which are densely affected with crimes. This approach proves to be quite effective and can also be used for analysing the global crime scenario.


title: Combining Reward Shaping and Curriculum Learning for Training Agents with High Dimensional Continuous Action Spaces

abstract: The needs for training agent with high dimensional continuous action spaces will increase as the robot hardware such as robotic arms and humanoid robots are becoming more and more sophisticated. However, it is difficult and time-consuming task. To tackle the problem, we combine reward shaping and curriculum learning. More specifically, the rewards are provided to the agent for every step it takes and the difficulty of the problem gradually increases depending on the agent learning. Both reward function and curriculum are designed to make the agent achieve its objective. The simulation results demonstrate that the proposed scheme outperforms the comparisons.


title: Breaking Ad-hoc Runtime Integrity Protection Mechanisms in Android Financial Apps

abstract: To protect customers' sensitive information, many mobile financial applications include steps to probe the runtime environment and abort their execution if the environment is deemed to have been tampered with. This paper investigates the security of such self-defense mechanisms used in 76 popular financial Android apps in the Republic of Korea. Our investigation found that existing tools fail to analyze these Android apps effectively because of their highly obfuscated code and complex, non-traditional control flows. We overcome this challenge by extracting a call graph with a self-defense mechanism, from a detailed runtime trace record of a target app's execution. To generate the call graph, we identify the causality between the system APIs (Android APIs and system calls) used to check device rooting and app integrity, and those used to stop an app's execution. Our analysis of 76 apps shows that we can pinpoint methods to bypass a self-defense mechanism using a causality graph in most cases. We successfully bypassed self-defense mechanisms in 67 out of 73 apps that check device rooting and 39 out of 44 apps that check app integrity. While analyzing the self-defense mechanisms, we found that many apps rely on third-party security libraries for their self-defense mechanisms. Thus we present in-depth studies of the top five security libraries. Our results demonstrate the necessity of a platform-level solution for integrity checks.


title: Design and Optimization of an XYZ Parallel Micromanipulator with Flexure Hinges

abstract: In this paper, a nearly decoupled XYZ translational compliant parallel micromanipulator (CPM) is designed for micro/nano scale manipulation with features of piezo-driven actuators and flexure hinges. The CPM structure improvement is made to enlarge the workspace and eliminate or reduce the stress stiffening, buckling phenomenon, and parasitic motions of the original XYZ CPM, which leads to a new CPM with a more compact structure. The CPM kinematics, parasitic motions, and workspace are determined analytically, and the mathematical models describing statics and dynamics of the CPM are established to evaluate its related performances, which are verified by the finite element analysis (FEA) undertaken in ANSYS environment. Based on the analytic models, the CPM dimensions have been optimized by resorting to the particle swarm optimization (PSO) approach, which produces a CPM having minimum parasitic motions and satisfying other performance specifications as validated by the FEA simulations.


title: Emotion recognition using brain activity

abstract: Our project focused on recognizing emotion from human brain activity, measured by EEG signals. We have proposed a system to analyze EEG signals and classify them into 5 classes on two emotional dimensions, valence and arousal. This system was designed using prior knowledge from other research, and is meant to assess the quality of emotion recognition using EEG signals in practice. In order to perform this assessment, we have gathered a dataset with EEG signals. This was done by measuring EEG signals from people that were emotionally stimulated by pictures. This method enabled us to teach our system the relationship between the characteristics of the brain activity and the emotion. We found that the EEG signals contained enough information to separate five different classes on both the valence and arousal dimension. However, using a 3-fold cross validation method for training and testing, we reached classification rates of 32% for recognizing the valence dimension from EEG signals and 37% for the arousal dimension. Much better classification rates were achieved when using only the extreme values on both dimensions, the rates were 71% and 81%.


title: Threat Modeling for Services in Cloud

abstract: Cloud computing offers various services ranging from infrastructure to computation, software and application. These services have gained popularity in both public and enterprise domains, and they process large amount of user data with varying privacy levels. However, due to the dynamic nature of cloud services, many enterprise level security policies, standards and practices cannot be implemented in cloud which leads to different security threats. These threats can be exploited by various attackers to compromise the cloud services. In this paper threat modeling for cloud services has been done by considering various attackers such as hackers, malicious administrators, malicious users and service providers. After describing possible threats to cloud services from these attackers, methodologies to exploit those threats have been presented. Moreover, the generalization of threat model has been done to determine the threats related to a specific service functionality for various attackers in cloud.


title: High Performance Methods for Linked Open Data Connectivity Analytics

abstract: The main objective of Linked Data is linking and integration, and a major step for evaluating whether this target has been reached, is to find all the connections among the Linked Open Data (LOD) Cloud datasets. Connectivity among two or more datasets can be achieved through common Entities, Triples, Literals, and Schema Elements, while more connections can occur due to equivalence relationships between URIs, such as owl:sameAs, owl:equivalentProperty and owl:equivalentClass, since many publishers use such equivalence relationships, for declaring that their URIs are equivalent with URIs of other datasets. However, there are not available connectivity measurements (and indexes) involving more than two datasets, that cover the whole content (e.g., entities, schema, triples) or “slices” (e.g., triples for a specific entity) of datasets, although they can be of primary importance for several real world tasks, such as Information Enrichment, Dataset Discovery and others. Generally, it is not an easy task to find the connections among the datasets, since there exists a big number of LOD datasets and the transitive and symmetric closure of equivalence relationships should be computed for not missing connections. For this reason, we introduce scalable methods and algorithms, (a) for performing the computation of transitive and symmetric closure for equivalence relationships (since they can produce more connections between the datasets); (b) for constructing dedicated global semantics-aware indexes that cover the whole content of datasets; and (c) for measuring the connectivity among two or more datasets. Finally, we evaluate the speedup of the proposed approach, while we report comparative results for over two billion triples.


An Investigation of the Essential Factors on Customer Loyalty in Banking Sector : A Case of Albanian Retail Bank Yrd


title: Rise and fall patterns of information diffusion: model and implications

abstract: The recent explosion in the adoption of search engines and new media such as blogs and Twitter have facilitated faster propagation of news and rumors. How quickly does a piece of news spread over these media? How does its popularity diminish over time? Does the rising and falling pattern follow a simple universal law? In this paper, we propose SpikeM, a concise yet flexible analytical model for the rise and fall patterns of influence propagation. Our model has the following advantages: (a) unification power: it generalizes and explains earlier theoretical models and empirical observations; (b) practicality: it matches the observed behavior of diverse sets of real data; (c) parsimony: it requires only a handful of parameters; and (d) usefulness: it enables further analytics tasks such as fore- casting, spotting anomalies, and interpretation by reverse- engineering the system parameters of interest (e.g. quality of news, count of interested bloggers, etc.). Using SpikeM, we analyzed 7.2GB of real data, most of which were collected from the public domain. We have shown that our SpikeM model accurately and succinctly describes all the patterns of the rise-and-fall spikes in these real datasets.


title: Multilabel dimensionality reduction via dependence maximization

abstract: Multilabel learning deals with data associated with multiple labels simultaneously. Like other data mining and machine learning tasks, multilabel learning also suffers from the curse of dimensionality. Dimensionality reduction has been studied for many years, however, multilabel dimensionality reduction remains almost untouched. In this article, we propose a multilabel dimensionality reduction method, MDDM, with two kinds of projection strategies, attempting to project the original data into a lower-dimensional feature space maximizing the dependence between the original feature description and the associated class labels. Based on the Hilbert-Schmidt Independence Criterion, we derive a eigen-decomposition problem which enables the dimensionality reduction process to be efficient. Experiments validate the performance of MDDM.


title: Small-Size Wideband Monopole With Distributed Inductive Strip for Seven-Band WWAN/LTE Mobile Phone

abstract: An on-board coupled-fed printed monopole with a distributed inductive shorted strip for seven-band operation covering the GSM850/900/1800/1900/UMTS2100/LTE2300/2500 bands in the mobile phone is presented. The presented antenna consists of a feeding strip and a distributed inductive shorted strip (formed by a printed distributed inductor and a simple shorted strip). By the coupled-fed excitation, the proposed antenna can generate multiple resonant modes at lower and upper bands for seven-band WWAN/LTE operation covering 824–960/1710–2690-MHz bands. The proposed antenna only occupies a size of 15 $\,\times\,$ 29 mm $^{2}$ and is easily printed on the top of an FR4 substrate, which makes it suitable for practical mobile applications. The proposed antenna was fabricated and tested. Measured and simulated results are presented.


title: MOS Capacitor Deep Trench Isolation for CMOS image sensors

abstract: This paper proposes the integration of MOS Capacitor Deep Trench Isolation (CDTI) as a solution to boost image sensors' pixels performances. We have investigated CDTI and compared it to oxide-filled Deep Trench Isolation (DTI) configurations, on silicon samples, with a fabrication based on TCAD simulations. The experiment measurements evaluated on CDTI without Sidewall Implantation (SWI) exhibit very low dark current (∼1aA at 60°C for a 1.4µm pixel), high full-well capacity (∼12000e-), and it shows quantum efficiency improvement compared to DTI configuration. Pixels with optimized CDTI gate oxide thickness have demonstrated comparable angular response to oxide-filled DTI counterparts.


title: Hacking Capitalism: The Free and Open Source Software Movement

abstract: The Free and Open Source Software (FOSS) movement demonstrates how labour can self-organise production, and, as is shown by the free operating system GNU/Linux, even compete with some of the worlds largest firms. The book examines the hopes of such thinkers as Friedrich Schiller, Karl Marx, Herbert Marcuse and Antonio Negri, in the light of the recent achievements of the hacker movement. This book is the first to examine a different kind of political activism that consists in the development of technology from below.


title: Future Work and Enterprise Systems

abstract: From its earliest days, research in business and information systems engineering (BISE) has been dedicated to envisioning how information technology will change the way we work and live. Today, technological innovation happens at a faster pace and reaches users more quickly than ever before. For example, while it took 75 years for the telephone to reach 100 million users, it was 16 years for mobile phones, 7 years for the World Wide Web, four and a half years for Facebook (Dreischmeier et al. 2015), and only a few weeks for Pokemon GO (Moon 2016). ::: ::: The rapid acceleration of technological diffusion confronts BISE researchers, who usually study technological innovations from the perspective of socio-technical systems (Bostrom and Heinen 1977). Work systems are conceptualized as an interplay of tasks, technologies, and people (vom Brocke and Rosemann 2014), systems “in which human participants and/or machines perform work (processes and activities) using information, technology, and other resources to produce specific products/services for specific internal and/or external customers” (Alter 2013, p. 75).


title: A tutorial on planning graph based reachability heuristics

abstract: The primary revolution in automated planning in the last decade has been the very impressive scale-up in planner performance. A large part of the credit for this can be attributed squarely to the invention and deployment of powerful reachability heuristics. Most, if not all, modern reachability heuristics are based on a remarkably extensible data structure called the planning graph, which made its debut as a bit player in the success of GraphPlan, but quickly grew in prominence to occupy the center stage. Planning graphs are a cheap means to obtain informative look-ahead heuristics for search and have become ubiquitous in state-of-the-art heuristic search planners. We present the foundations of planning graph heuristics in classical planning and explain how their flexibility lets them adapt to more expressive scenarios that consider action costs, goal utility, numeric resources, time, and uncertainty.


title: OpenNF: enabling innovation in network function control

abstract: Network functions virtualization (NFV) together with software-defined networking (SDN) has the potential to help operators satisfy tight service level agreements, accurately monitor and manipulate network traffic, and minimize operating expenses. However, in scenarios that require packet processing to be redistributed across a collection of network function (NF) instances, simultaneously achieving all three goals requires a framework that provides efficient, coordinated control of both internal NF state and network forwarding state. To this end, we design a control plane called OpenNF. We use carefully designed APIs and a clever combination of events and forwarding updates to address race conditions, bound overhead, and accommodate a variety of NFs. Our evaluation shows that OpenNF offers efficient state control without compromising flexibility, and requires modest additions to NFs.


title: Web Server Logs Preprocessing for Web Intrusion Detection

abstract: Securing e-commerce sites has become a necessity as they process critical and sensitive data to customers and organizations. When a customer navigates through an e-commerce site his/her clicks are recorded in web log file. Analyzing these log files using data mining reveal many interesting patterns. These results are used in many different applications and recently in detecting attacks on web. In order to improve quality of data and consequently the mining results data in log files need first to be preprocessed. In this paper, we will discuss how different web log files with different formats will be combined together in one unified format using XML in order to track and extract more attacks. And because log files usually contain noisy and ambiguous data this paper will show how data will be preprocessed before applying mining process in order to detect attacks. We will also discuss the difference between log preprocessing for web intrusion and for web usage mining


title: Natural Language Models for Predicting Programming Comments

abstract: Statistical language models have successfully been used to describe and analyze natural language documents. Recent work applying language models to programming languages is focused on the task of predicting code, while mainly ignoring the prediction of programmer comments. In this work, we predict comments from JAVA source files of open source projects, using topic models and n-grams, and we analyze the performance of the models given varying amounts of background data on the project being predicted. We evaluate models on their comment-completion capability in a setting similar to codecompletion tools built into standard code editors, and show that using a comment completion tool can save up to 47% of the comment typing.


Digital Image Restoration — Classical


title: Increasing trust in mobile commerce through design aesthetics

abstract: The growth of mobile commerce (m-commerce) has motivated a better understanding of how trust can be built on a mobile device. Researchers have previously examined design aesthetics (or visual aesthetics) of mobile website and incorporated a hedonic component of enjoyment in m-commerce domain, but the relationship between design aesthetics of mobile website design and customer trust in m-commerce has been rarely investigated. In this study, design aesthetics was enhanced to include a website characteristics component as important to trust development on the mobile Internet. This model was examined through an empirical study involving 200 subjects using structural equation modeling techniques. Our research found that design aesthetics did significantly impact website characteristics component, especially customization, perceived usefulness and ease of use, all of which were ultimately shown to have significant explanatory power in affecting customer trust.


title: CDK-regulated phase separation seeded by histone genes ensures precise growth and function of Histone Locus Bodies

abstract: Many membrane-less organelles form through liquid-liquid phase separation, but how their size is controlled and whether size is linked to function remain poorly understood. The Histone Locus Body (HLB) is an evolutionarily conserved nuclear body that regulates the transcription and processing of histone mRNAs. Here, we show that Drosophila HLBs form through phase separation of the scaffold protein multi-sex combs (Mxc). The size of HLBs is controlled in a precise and dynamic manner that is dependent on the cell cycle and zygotic gene activation. Control of HLB growth is achieved by a mechanism integrating nascent mRNAs at the histone locus, which catalyzes phase separation, and the nuclear concentration of Mxc, which is controlled by the activity of cyclin-dependent kinases. Reduced Cdk2 activity results in smaller HLBs and the appearance of nascent, misprocessed histone mRNAs. Our experiments thus identify a mechanism linking nuclear body growth and size with gene expression.


title: Swap Bribery

abstract: In voting theory, bribery is a form of manipulative behavior in which an external actor (the briber) offers to pay the voters to change their votes in order to get her preferred candidate elected. We investigate a model of bribery where the price of each vote depends on the amount of change that the voter is asked to implement. Specifically, in our model the briber can change a voter's preference list by paying for a sequence of swaps of consecutive candidates. Each swap may have a different price; the price of a bribery is the sum of the prices of all swaps that it involves. We prove complexity results for this model, which we call swap bribery , for a broad class of voting rules, including variants of approval and k -approval, Borda, Copeland, and maximin.


title: Understanding Human Mobility from Twitter

abstract: Understanding human mobility is crucial for a broad range of applications from disease prediction to communication networks. Most efforts on studying human mobility have so far used private and low resolution data, such as call data records. Here, we propose Twitter as a proxy for human mobility, as it relies on publicly available data and provides high resolution positioning when users opt to geotag their tweets with their current location. We analyse a Twitter dataset with more than six million geotagged tweets posted in Australia, and we demonstrate that Twitter can be a reliable source for studying human mobility patterns. Our analysis shows that geotagged tweets can capture rich features of human mobility, such as the diversity of movement orbits among individuals and of movements within and between cities. We also find that short- and long-distance movers both spend most of their time in large metropolitan areas, in contrast with intermediate-distance movers’ movements, reflecting the impact of different modes of travel. Our study provides solid evidence that Twitter can indeed be a useful proxy for tracking and predicting human movement.


title: HordeQBF: A Modular and Massively Parallel QBF Solver

abstract: The recently developed massively parallel satisfiability (SAT) solver HordeSAT was designed in a modular way to allow the integration of any sequential CDCL-based SAT solver in its core. We integrated the QCDCL-based quantified Boolean formula (QBF) solver DepQBF in HordeSAT to obtain a massively parallel QBF solver—HordeQBF. In this paper we describe the details of this integration and report on results of the experimental evaluation of HordeQBF’s performance. HordeQBF achieves superlinear average and median speedup on the hard application instances of the 2014 QBF Gallery.


title: Chinese midwifery: the history and modernity.

abstract: Abstract Aim to investigate how and why Chinese midwife numbers are dwindling, and to help understand the role of midwives in society in general. Method to critically examine Chinese midwifery in three stages: (1) historical literature overview; (2) identification and reinterpretation of Chinese midwifery and its development; (3) placing issues that have arisen within a sociological context (i.e. the modernisation of obstetric technologies and the meaning of modernity). Findings no books on the history of Chinese midwifery were found. History was classified into three stages: (1) before 1929, a period of an indigenous model; (2) 1929–1996, the highs and lows of the bio-medical model; (3) after 1996, the demise of Chinese midwives. The issues identified were the legitimacy and professionalisation of Chinese midwives, the meaning of modernity and the reasons for the decline of Chinese midwifery. Conclusion no sufficient evidence-based research was conducted to support the recent changes made to Chinese midwifery. The modernisation of maternity care in China took place amid dramatic social and cultural changes within society. As a consequence, midwifery as a profession in China has been marginalised. The modernisation of maternity care has failed to deliver on personal choice, quality of service and professional diversity. Implications for practice evidence-based research and the state's responsibility are essential to ensure the quality of maternity care and to protect the interests of women. The state's responsibilities include legislation regarding the role of midwives, code of practice, professional standards, responsibility and accountability in order to make midwifery care a true choice for women.


title: The Therapeutic Potential of Psychedelic Drugs: Past, Present, and Future

abstract: Plant-based psychedelics, such as psilocybin, have an ancient history of medicinal use. After the first English language report on LSD in 1950, psychedelics enjoyed a short-lived relationship with psychology and psychiatry. Used most notably as aids to psychotherapy for the treatment of mood disorders and alcohol dependence, drugs such as LSD showed initial therapeutic promise before prohibitive legislature in the mid-1960s effectively ended all major psychedelic research programs. Since the early 1990s, there has been a steady revival of human psychedelic research: last year saw reports on the first modern brain imaging study with LSD and three separate clinical trials of psilocybin for depressive symptoms. In this circumspective piece, RLC-H and GMG share their opinions on the promises and pitfalls of renewed psychedelic research, with a focus on the development of psilocybin as a treatment for depression.


title: Design and Implementation of a GaN-Based, 100-kHz, 102-W/in3 Single-Phase Inverter

abstract: High power density is a desirable feature of power electronics design, which prompts economic incentives for industrial applications. In this paper, a gallium nitride (GaN)-based 2-kVA single-phase inverter design was developed for the Google Little Box Challenge, which achieves a 102-W/in3 power density. First, the static and dynamic temperature-dependent characteristics of multiple SiC and enhancement-mode GaN FETs are investigated and compared. Based on the device testing results, several topologies of the inverter stage and different power decoupling solutions are compared with respect to the device volume, efficiency, and thermal requirements. Moreover, some design approaches for magnetic devices and the implementation of gate drives for GaN devices are discussed in this paper, which enable a compact and robust system. Finally, a dc notch filter and a hard switching full-bridge converter are combined as the proposed design for the prototype. A 2-kVA prototype is demonstrated, which meets the volume, efficiency, and thermal requirements. The performance of the prototype is verified by the experimental results.


title: SIDES: a cooperative tabletop computer game for social skills development

abstract: This paper presents a design case study of SIDES: Shared Interfaces to Develop Effective Social Skills. SIDES is a tool designed to help adolescents with Asperger's Syndrome practice effective group work skills using a four-player cooperative computer game that runs on tabletop technology. We present the design process and evaluation of SIDES conducted over six months with a middle school social group therapy class. Our findings indicate that cooperative tabletop computer games are a motivating and supportive tool for facilitating effective group work among our target population and reveal several design lessons to inform the development of similar systems.


Robust Text Classification for Sparsely Labelled Data Using Multi-level Embeddings


Automatic Discovery of Functional Dependencies and Conditional Functional Dependencies : A Comparative Study


Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy


title: Modeling interacting dynamic networks: II. Systematic study of the statistical properties of cross-links between two networks with preferred degrees

abstract: In a recent work \cite{LiuJoladSchZia13}, we introduced dynamic networks with preferred degrees and presented simulation and analytic studies of a single, homogeneous system as well as two interacting networks. Here, we extend these studies to a wider range of parameter space, in a more systematic fashion. Though the interaction we introduced seems simple and intuitive, it produced dramatically different behavior in the single- and two-network systems. Specifically, partitioning the single network into two identical sectors, we find the cross-link distribution to be a sharply peaked Gaussian. In stark contrast, we find a very broad and flat plateau in the case of two interacting identical networks. A sound understanding of this phenomenon remains elusive. Exploring more asymmetric interacting networks, we discover a kind of `universal behavior' for systems in which the `introverts' (nodes with smaller preferred degree) are far outnumbered. Remarkably, an approximation scheme for their degree distribution can be formulated, leading to very successful predictions.


title: The impact of web page text-background colour combinations on readability, retention, aesthetics and behavioural intention : HCI studies in management information systems

abstract: The purpose of this experiment was to examine the effect of web page text/background colour combination on readability, retention, aesthetics, and behavioural intention. One hundred and thirty-six participants studied two Web pages, one with educational content and one with commercial content, in one of four colour-combination conditions. Major findings were: (a) Colours with greater contrast ratio generally lead to greater readability; (b) colour combination did not significantly affect retention; (c) preferred colours (i.e., blues and chromatic colours) led to higher ratings of aesthetic quality and intention to purchase; and (d) ratings of aesthetic quality were significantly related to intention to purchase.


title: Online Communication, Compulsive Internet Use, and Psychosocial Well- Being Among Adolescents: A Longitudinal Study

abstract: The present study investigated the relationships between adolescents' online communication and compulsive Internet use, depression, and loneliness. The study had a 2-wave longitudinal design with an interval of 6 months. The sample consisted of 663 students, 318 male and 345 female, ages 12 to 15 years. Questionnaires were administered in a classroom setting. The results showed that instant messenger use and chatting in chat rooms were positively related to compulsive Internet use 6 months later. Moreover, in agreement with the well-known HomeNet study (R. Kraut et al., 1998), instant messenger use was positively associated with depression 6 months later. Finally, loneliness was negatively related to instant messenger use 6 months later.


title: A comprehensive and conservative approach for the restoration of abrasion and erosion. Part I: concepts and clinical rationale for early intervention using adhesive techniques.

abstract: Abstract Tooth wear represents a frequent pathology with multifactorial origins. Behavioral changes, unbalanced diet, various medical conditions and medications inducing acid regurgitation or influencing saliva composition and flow rate, trigger tooth erosion. Awake and sleep bruxism, which are widespread nowadays with functional disorders, induce attrition. It has become increasingly important to diagnose early signs of tooth wear so that proper preventive, and if needed, restorative measures are taken. Such disorders have biological, functional, and also esthetic consequences. Following a comprehensive clinical evaluation, treatment objectives, such as a proper occlusal and anatomical scheme as well as a pleasing smile line, are usually set on models with an anterior teeth full-mouth waxup, depending on the severity of tissue loss. Based on the new vertical dimension of occlusion (VDO), combinations of direct and indirect restorations can then help to reestablish anatomy and function. The use of adhesive techniques and resin composites has demonstrated its potential, in particular for the treatment of moderate tooth wear. Part I of this article reviews recent knowledge and clinical concepts dealing with the various forms of early restorative interventions and their potential to restrict ongoing tissue destruction.


title: Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition

abstract: We propose a method (TT-GP) for approximate inference in Gaussian Process (GP) models. We build on previous scalable GP research including stochastic variational inference based on inducing inputs, kernel interpolation, and structure exploiting algebra. The key idea of our method is to use Tensor Train decomposition for variational parameters, which allows us to train GPs with billions of inducing inputs and achieve state-of-the-art results on several benchmarks. Further, our approach allows for training kernels based on deep neural networks without any modifications to the underlying GP model. A neural network learns a multidimensional embedding for the data, which is used by the GP to make the final prediction. We train GP and neural network parameters end-to-end without pretraining, through maximization of GP marginal likelihood. We show the efficiency of the proposed approach on several regression and classification benchmark datasets including MNIST, CIFAR-10, and Airline.


A Distributed Architecture for Interactive


title: A Probabilistic Generative Grammar for Semantic Parsing

abstract: We present a framework that couples the syntax and semantics of natural language sentences in a generative model, in order to develop a semantic parser that jointly infers the syntactic, morphological, and semantic representations of a given sentence under the guidance of background knowledge. To generate a sentence in our framework, a semantic statement is first sampled from a prior, such as from a set of beliefs in a knowledge base. Given this semantic statement, a grammar probabilistically generates the output sentence. A joint semantic-syntactic parser is derived that returns the $k$-best semantic and syntactic parses for a given sentence. The semantic prior is flexible, and can be used to incorporate background knowledge during parsing, in ways unlike previous semantic parsing approaches. For example, semantic statements corresponding to beliefs in a knowledge base can be given higher prior probability, type-correct statements can be given somewhat lower probability, and beliefs outside the knowledge base can be given lower probability. The construction of our grammar invokes a novel application of hierarchical Dirichlet processes (HDPs), which in turn, requires a novel and efficient inference approach. We present experimental results showing, for a simple grammar, that our parser outperforms a state-of-the-art CCG semantic parser and scales to knowledge bases with millions of beliefs.


title: Improving relevance judgment of web search results with image excerpts

abstract: Current web search engines return result pages containing mostly text summary even though the matched web pages may contain informative pictures. A text excerpt (i.e. snippet) is generated by selecting keywords around the matched query terms for each returned page to provide context for user's relevance judgment. However, in many scenarios, we found that the pictures in web pages, if selected properly, could be added into search result pages and provide richer contextual description because a picture is worth a thousand words. Such new summary is named as image excerpts. By well designed user study, we demonstrate image excerpts can help users make much quicker relevance judgment of search results for a wide range of query types. To implement this idea, we propose a practicable approach to automatically generate image excerpts in the result pages by considering the dominance of each picture in each web page and the relevance of the picture to the query. We also outline an efficient way to incorporate image excerpts in web search engines. Web search engines can adopt our approach by slightly modifying their index and inserting a few low cost operations in their workflow. Our experiments on a large web dataset indicate the performance of the proposed approach is very promising.


MDig : Multi-digit Recognition using Convolutional Nerual Network on Mobile


title: Machine Learning Based Source Code Classification Using Syntax Oriented Features

abstract: As of today the programming language of the vast majority of the published source code is manually specified or programmatically assigned based on the sole file extension. In this paper we show that the source code programming language identification task can be fully automated using machine learning techniques. We first define the criteria that a production-level automatic programming language identification solution should meet. Our criteria include accuracy, programming language coverage, extensibility and performance. We then describe our approach: How training files are preprocessed for extracting features that mimic grammar productions, and then how these extracted grammar productions are used for the training and testing of our classifier. We achieve a 99 percent accuracy rate while classifying 29 of the most popular programming languages with a Maximum Entropy classifier.


title: Generalized Deep Transfer Networks for Knowledge Propagation in Heterogeneous Domains

abstract: In recent years, deep neural networks have been successfully applied to model visual concepts and have achieved competitive performance on many tasks. Despite their impressive performance, traditional deep networks are subjected to the decayed performance under the condition of lacking sufficient training data. This problem becomes extremely severe for deep networks trained on a very small dataset, making them overfitting by capturing nonessential or noisy information in the training set. Toward this end, we propose a novel generalized deep transfer networks (DTNs), capable of transferring label information across heterogeneous domains, textual domain to visual domain. The proposed framework has the ability to adequately mitigate the problem of insufficient training images by bringing in rich labels from the textual domain. Specifically, to share the labels between two domains, we build parameter- and representation-shared layers. They are able to generate domain-specific and shared interdomain features, making this architecture flexible and powerful in capturing complex information from different domains jointly. To evaluate the proposed method, we release a new dataset extended from NUS-WIDE at http://imag.njust.edu.cn/NUS-WIDE-128.html. Experimental results on this dataset show the superior performance of the proposed DTNs compared to existing state-of-the-art methods.


Methodology for 5 G-Multi-tier Wireless Networks Capacity Optimization


title: Governance as theory: five propositions

abstract: Science in the Department of Government, University of Strathclyde, Glasgow, G1 1XQ, UK, email: G.stokerK strathclyde.ac.uk His main research interests are in local government, urban politics, and cross-national policy transfer. Between 1992 and 1997 he was Director of the ESRC Local Governance Research Programme. He has authored or edited over a dozen books. His two most recent publications are: Rethinking Local Democracy, 1996 (edited with D. King) and The Privatisation of Urban Services in Europe, 1997 (edited with D. Lorrain). Governance as theory: five propositions


title: Progressively Parsing Interactional Objects for Fine Grained Action Detection

abstract: Fine grained video action analysis often requires reliable detection and tracking of various interacting objects and human body parts, denoted as Interactional Object Parsing. However, most of the previous methods based on either independent or joint object detection might suffer from high model complexity and challenging image content, e.g., illumination/pose/appearance/scale variation, motion, and occlusion etc. In this work, we propose an end-to-end system based on recurrent neural network to perform frame by frame interactional object parsing, which can alleviate the difficulty through an incremental/progressive manner. Our key innovation is that: instead of jointly outputting all object detections at once, for each frame we use a set of long-short term memory (LSTM) nodes to incrementally refine the detections. After passing through each LSTM node, more object detections are consolidated and thus more contextual information could be utilized to localize more difficult objects. The object parsing results are further utilized to form object specific action representation for fine grained action detection. Extensive experiments on two benchmark fine grained activity datasets demonstrate that our proposed algorithm achieves better interacting object detection performance, which in turn boosts the action recognition performance over the state-of-the-art.


title: Local Feature Based Multiple Object Instance Identification Using Scale and Rotation Invariant Implicit Shape Model

abstract: In this paper, we propose a Scale and Rotation Invariant Implicit Shape Model (SRIISM), and develop a local feature matching based system using the model to accurately locate and identify large numbers of object instances in an image. Due to repeated instances and cluttered background, conventional methods for multiple object instance identification suffer from poor identification results. In the proposed SRIISM, we model the joint distribution of object centers, scale, and orientation computed from local feature matches in Hough voting, which is not only invariant to scale changes and rotation of objects, but also robust to false feature matches. In the multiple object instance identification system using SRIISM, we apply a fast 4D bin search method in Hough space with complexity \(O(n)\), where \(n\) is the number of feature matches, in order to segment and locate each instance. Furthermore, we apply maximum likelihood estimation (MLE) for accurate object pose detection. In the evaluation, we created datasets simulating various industrial applications such as pick-and-place and inventory management. Experiment results on the datasets show that our method outperforms conventional methods in both accuracy (5 %–30 % gain) and speed (2x speed up).


title: Sixth-Sense: Context Reasoning for Potential Objects Detection in Smart Sensor Rich Environment

abstract: A new system named Sixth-Sense is proposed for obtaining physical world information in smart space. Our view is object-centered and sensors are attached to several objects in the space. What we focus is to use the smart sensor attached objects to obtain information from no sensor attached objects, i.e., the so called potential objects detection problem. The Sixth-Sense resolves this problem by Sixth-Sense Skeleton and inference rules. A series of inference rules are presented and illustrated.


title: Roll-to-roll production of 30-inch graphene films for transparent electrodes

abstract: Graphene films with electrical and optical characteristics superior to indium tin oxide are produced in a roll-to-roll process and used to construct devices with flexible touch-screen panels.


title: Superpixel Segmentation Using Gaussian Mixture Model

abstract: Superpixel segmentation partitions an image into perceptually coherent segments of similar size, namely, superpixels. It is becoming a fundamental preprocessing step for various computer vision tasks because superpixels significantly reduce the number of inputs and provide a meaningful representation for feature extraction. We present a pixel-related Gaussian mixture model (GMM) to segment images into superpixels. GMM is a weighted sum of Gaussian functions, each one corresponding to a superpixel, to describe the density of each pixel represented by a random variable. Different from previously proposed GMMs, our weights are constant, and Gaussian functions in the sums are subsets of all the Gaussian functions, resulting in segments of similar size and an algorithm of linear complexity with respect to the number of pixels. In addition to the linear complexity, our algorithm is inherently parallel and allows fast execution on multicore systems. During the expectation-maximization iterations of estimating the unknown parameters in the Gaussian functions, we impose two lower bounds to truncate the eigenvalues of the covariance matrices, which enables the proposed algorithm to control the regularity of superpixels. Experiments on a wellknown segmentation dataset show that our method can efficiently produce superpixels that adhere to object boundaries better than the current state-of-the-art methods.


title: Application of soft computing technique for optical add/drop multiplexer in terms of OSNR and optical noise power

abstract: The demand for transmission capacity is increasing day by day which forces the service providers to enhance their network performance for increasing data rates or number of wavelengths in Dense Wavelength Division Multiplexing systems. The most important element which decides the signal to be transmitted through the DWDM system is the Optical Add or Drop Multiplexer (OADM) which can drop or add the signal from or to the DWDM network based on the network requirements. In this paper a performance improved OADM is proposed with artificial intelligence. The work proposed contains three phases: Initially the DWDM network is modeled in the OptSystem which is an optical simulation network tool and the parameters such as Optical Signal to Noise Ratio(OSNR), Jitter, Chromatic Dispersion and Bit Error Rate(BER) and are obtained, which are used for training the Feed Forward Artificial Neural Network (FF-ANN) in second phase. Finally in the third phase, testing of the signal is carried out for classification of signals. MATLAB is used for training and testing of the FF-NN by interfacing the results from Optisystem and the classification accuracy of the proposed method is given. At last, the performance of the proposed method in terms of OSNR & Optical Noise Power is analyzed and compared with the existing methods.


title: Weighted consensus multi-document summarization

abstract: Multi-document summarization is a fundamental tool for document understanding and has received much attention recently. Given a collection of documents, a variety of summarization methods based on different strategies have been proposed to extract the most important sentences from the original documents. However, very few studies have been reported on aggregating different summarization methods to possibly generate better summary results. In this paper, we propose a weighted consensus summarization method to combine the results from single summarization systems. We evaluate and compare our proposed weighted consensus method with various baseline combination methods. Experimental results on DUC2002 and DUC2004 data sets demonstrate the performance improvement by aggregating multiple summarization systems, and our proposed weighted consensus summarization method outperforms other combination methods.


title: Design of an assistive wrist orthosis using conductive nylon actuators

abstract: This project outlines the design of an innovative wearable wrist orthosis which potentially can be used for rehabilitation therapy and for assisting with daily tasks. The orthosis uses recently discovered nylon artificial muscles as a means to actuate the device. Constructed from silver coated nylon sewing thread, these newly developed actuators have been shown to exhibit power to weight ratios that exceed that of human skeletal muscle. In this paper, we develop an electromechanical model for the force generated by the actuators and propose a simple closed loop controller to control the actuators. This system is then implemented in a wrist exoskeleton prototype to demonstrate the potential use of nylon actuators in biomedical applications.


title: A compact and wideband SMA connector to empty substrate integrated waveguide (ESIW) transition

abstract: In this paper, a novel excitation technique by a commercially available SMA connector is presented for the empty substrate integrated waveguide (ESIW). The SMA connector is directly connected to one end of the ESIW. A prototype back-to-back transition is designed and fabricated for X-band operation. Measurement results show that the insertion loss of the back-to-back transition is below 1 dB over 7.15 GHz to 12.5 GHz. Losses from the transition are studied in details. The transition has a compact dimension of 22 mm∗3.5 mm.


title: Knowledge-worker productivity: the biggest challenge

abstract: The most important contribution of management in the 20th century was to increase manual worker productivity fifty-fold. The most important contribution of management in the 21st century will be to increase knowledge worker productivity—hopefully by the same percentage. So far it is abysmally low and in many areas (hospital nurses, for instance, or design engineers in the automobile industry) actually lower than it was 70 years ago. So far, almost no one has addressed it. Yet we know how to increase—and rapidly—the productivity of knowledge workers. The methods, however, are totally different from those that increased the productivity of manual workers.


title: Constrained centroidal Voronoi tessellations for surfaces

abstract: Centroidal Voronoi tessellations are useful for subdividing a region in Euclidean space into Voronoi regions whose generators are also the centers of mass, with respect to a prescribed density function, of the regions. Their extensions to general spaces and sets are also available; for example, tessellations of surfaces in a Euclidean space may be considered. In this paper, a precise definition of such constrained centroidal Voronoi tessellations (CCVTs) is given and a number of their properties are derived, including their characterization as minimizers of an "energy." Deterministic and probabilistic algorithms for the construction of CCVTs are presented and some analytical results for one of the algorithms are given. Computational examples are provided which serve to illustrate the high quality of CCVT point sets. Finally, CCVT point sets are applied to polynomial interpolation and numerical integration on the sphere.


title: Personalized News Recommendation: A Review and an Experimental Investigation

abstract: Online news articles, as a new format of press releases, have sprung up on the Internet. With its convenience and recency, more and more people prefer to read news online instead of reading the paper-format press releases. However, a gigantic amount of news events might be released at a rate of hundreds, even thousands per hour. A challenging problem is how to efficiently select specific news articles from a large corpus of newly-published press releases to recommend to individual readers, where the selected news items should match the reader's reading preference as much as possible. This issue refers to personalized news recommendation. Recently, personalized news recommendation has become a promising research direction as the Internet provides fast access to real-time information from multiple sources around the world. Existing personalized news recommendation systems strive to adapt their services to individual users by virtue of both user and news content information. A variety of techniques have been proposed to tackle personalized news recommendation, including content-based, collaborative filtering systems and hybrid versions of these two. In this paper, we provide a comprehensive investigation of existing personalized news recommenders. We discuss several essential issues underlying the problem of personalized news recommendation, and explore possible solutions for performance improvement. Further, we provide an empirical study on a collection of news articles obtained from various news websites, and evaluate the effect of different factors for personalized news recommendation. We hope our discussion and exploration would provide insights for researchers who are interested in personalized news recommendation.


title: Author Disambiguation using Error-driven Machine Learning with a Ranking Loss Function

abstract: Author disambiguation is the problem of determining whether records in a publications database refer to the same person. A common supervised machine learning approach is to build a classifier to predict whether a pair of records is coreferent, followed by a clustering step to enforce transitivity. However, this approach ignores powerful evidence obtainable by examining sets (rather than pairs) of records, such as the number of publications or co-authors an author has. In this paper we propose a representation that enables these first-order features over sets of records. We then propose a training algorithm well-suited to this representation that is (1) error-driven in that training examples are generated from incorrect predictions on the training data, and (2) rankbased in that the classifier induces a ranking over candidate predictions. We evaluate our algorithms on three author disambiguation datasets and demonstrate error reductions of up to 60% over the standard binary classification approach. Introduction Record deduplication is the problem of deciding whether two records in a database refer to the same object. This problem is widespread in any large-scale database, and is particularly acute when records are constructed automatically from text mining. Author disambiguation, the problem of deduplicating author records, is a critical concern for digital publication libraries such as Citeseer, DBLP, Rexa, and Google Scholar. Author disambiguation is difficult in these domains because of abbreviations (e.g., Y. Smith) misspellings (e.g., Y. Smiht), and extraction errors (e.g., Smith Statistical). Many supervised machine learning approaches to author disambiguation have been proposed. Most of these are variants of the following recipe: (1) train a binary classifier to predict whether a pair of authors are duplicates, (2) apply the classifier to each pair of ambiguous authors, (3) combine the classification predictions to cluster the records into duplicate sets. Copyright c © 2007, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. This approach can be quite accurate, and is attractive because it builds upon existing machine learning technology (e.g., classification and clustering). However, because the core of this approach is a classifier over record pairs, nowhere are aggregate features of an author modeled explicitly. That is, by restricting the model representation to evidence over pairs of authors, we cannot leverage evidence available from examining more than two records. For example, we would like to model the fact that authors are generally affiliated with only a few institutions, have only one or two different email addresses, and are unlikely to publish more than thirty publications in one year. None of these constraints can be captured with a pairwise classifier, which, for example, can only consider whether pairs of institutions or emails match. In this paper we propose a representation for author disambiguation that enables these aggregate constraints. The representation can be understood as a scoring function over a set of authors, indicating how likely it is that all members of the set are duplicates. While flexible, this new representation can make it difficult to estimate the model parameters from training data. We therefore propose a class of training algorithms to estimate the parameters of models adopting this representation. The method has two main characteristics essential to its performance. First, it is errordriven in that training examples are generated based on mistakes made by the prediction algorithm. This approach focuses training effort on the types of examples expected during prediction. Second, it is rank-based in that the loss function induces a ranking over candidate predictions. By representing the difference between predictions, preferences can be expressed over partially-correct or incomplete solutions; additionally, intractable normalization constants can be avoided because the loss function is a ratio of terms. In the following sections, we describe the representation in more detail, then present our proposed training methods. We evaluate our proposals on three real-world author deduplication datasets and demonstrate errorreductions of up to 60%. Author Title Institution Year Y. Li Understanding Social Networks Stanford 2003 Y. Li Understanding Network Protocols Carnegie Mellon 2002 Y. Li Virtual Network Protocols Peking Univ. 2001 Table 1: Author disambiguation example with multiple institutions. Author Co-authors Title P. Cohen A. Howe How evaluation guides AI research P. Cohen M. Greenberg, A. Howe, ... Trial by Fire: Understanding the design requirements ... in complex environments P. Cohen M. Greenberg MU: a development environment for prospective reasoning systems Table 2: Author disambiguation example with overlapping co-authors.


title: Urdu optical character recognition technique using point feature matching; a generic approach

abstract: The complexity associated with Urdu fonts regarding OCR in newspapers is being dealt with active research. When creating an Urdu OCR you are limited to a certain font size i.e. if working with a font size of 12, you will have to create a database covering all characters/words of font size 12. In order to work with another font size of same Urdu font you'll have to cover all the characters/words of that respective font size. The OCR technique should be generic where the font size should not matter. The objective was to create a technique that could be applied to any Urdu script font size, without worrying about the variation of characters/words caused by the disposal of ink in Urdu newspaper clippings. In this paper the authors have developed a technique using point feature matching on cropped Urdu newspaper clippings with font Jameel Noori Nastaleeq and converted them into editable textual Unicodes.


title: Building information modeling: the Web3D application for AEC

abstract: There is currently a dramatic shift in the Architecture, Engineering, and Construction (AEC) industry to embrace Building Information Modeling (BIM) as a tool that can assist in integrating the fragmented industry by eliminating inefficiencies and redundancies, improving collaboration and communication, and enhancing overall productivity. In the context of this revolution, the intent of this paper is three-fold: 1) To introduce and define BIM to the Web3D community as an application of Web3D to the AEC industry. 2) To describe and illustrate the various ways innovative designers and contractors are applying BIM and Web3D tools in the AEC industry. 3) To challenge the Web3D community to collaborate with BIM and AEC-specific open standards organizations like the International Alliance for Interoperability and to discover ways to integrate X3D with the IFC file formats.


title: Packing Convolutional Neural Networks in the Frequency Domain

abstract: Deep convolutional neural networks (CNNs) are successfully used in a number of applications. However, their storage and computational requirements have largely prevented their widespread use on mobile devices. Here we present an effective CNN compression approach in the frequency domain, which focuses not only on smaller weights but on all the weights and their underlying connections. By treating convolutional filters as images, we decompose their representations in the frequency domain as common parts (i.e., cluster centers) shared by other similar filters and their individual private parts (i.e., individual residuals). A large number of low-energy frequency coefficients in both parts can be discarded to produce high compression without significantly compromising accuracy. We relax the computational burden of convolution operations in CNNs by linearly combining the convolution responses of discrete cosine transform (DCT) bases. The compression and speed-up ratios of the proposed algorithm are thoroughly analyzed and evaluated on benchmark image datasets to demonstrate its superiority over state-of-the-art methods.


title: Real-time measurement of the three-axis contact force distribution using a flexible capacitive polymer tactile sensor

abstract: In this paper, we report real-time measurement results of various contact forces exerted on a new flexible capacitive three-axis tactile sensor array based on polydimethylsiloxane (PDMS). A unit sensor consists of two thick PDMS layers with embedded copper electrodes, a spacer layer, an insulation layer and a bump layer. There are four capacitors in a unit sensor to decompose a contact force into its normal and shear components. They are separated by a wall-type spacer to improve the mechanical response time. Four capacitors are arranged in a square form. The whole sensor is an 8 × 8 array of unit sensors and each unit sensor responds to forces in all three axes. Measurement results show that the full-scale range of detectable force is around 0‐20 mN (250 kPa) for all three axes. The estimated sensitivities of a unit sensor with the current setup are 1.3, 1.2 and 1.2%/mN for the x-, y- and z-axes, respectively. A simple mechanical model has been established to calculate each axial force component from the measured capacitance value. Normal and shear force distribution images are captured from the fabricated sensor using a real-time measurement system. The mechanical response time of a unit sensor has been estimated to be less than 160 ms. The flexibility of the sensor has also been demonstrated by operating the sensor on a curved surface of 4 mm radius of curvature. (Some figures in this article are in colour only in the electronic version)


title: Functional MRI-based lie detection: scientific and societal challenges

abstract: and societal issues raised by this technol- ogy. Beginning with the science, we address three questions about the current state of the art in fMRI-based lie detection. First, do current findings on lie detection, from different laboratories and using different experimental tasks, identify a consistent set of brain regions and, if so, which areas are they? Second, how confidently can we inter- pret the results of these studies with respect to the neural substrates of deception per se, and what alternative interpretations have yet to be ruled out? Third, what additional chal- lenges do we face in the effort to use fMRI for the detection of deception in real-world contexts? We then raise a series of issues con- cerning the ethical, legal and societal impact of attempting to detect lies with fMRI. Abstract | Functional MRI (fMRI)-based lie detection has been marketed as a tool for enhancing personnel selection, strengthening national security and protecting personal reputations, and at least three US courts have been asked to admit the results of lie detection scans as evidence during trials. How well does fMRI-based lie detection perform, and how should the courts, and society more generally, respond? Here, we address various questions — some of which are based on a meta-analysis of published studies — concerning the scientific state of the art in fMRI-based lie detection and its legal status, and discuss broader ethical and societal implications. We close with three general policy recommendations.


title: LogMaster: Mining Event Correlations in Logs of Large-Scale Cluster Systems

abstract: This paper presents a set of innovative algorithms and a system, named Log Master, for mining correlations of events that have multiple attributions, i.e., node ID, application ID, event type, and event severity, in logs of large-scale cloud and HPC systems. Different from traditional transactional data, e.g., supermarket purchases, system logs have their unique characteristics, and hence we propose several innovative approaches to mining their correlations. We parse logs into an n-ary sequence where each event is identified by an informative nine-tuple. We propose a set of enhanced apriori-like algorithms for improving sequence mining efficiency, we propose an innovative abstractionevent correlation graphs (ECGs) to represent event correlations, and present an ECGs-based algorithm for fast predicting events. The experimental results on three logs of production cloud and HPC systems, varying from 433490 entries to 4747963 entries, show that our method can predict failures with a high precision and an acceptable recall rates.


title: Dermoscopic evaluation of skin in healthy cats

abstract: BACKGROUND: Dermoscopy is a diagnostic tool that can reveal morphological structures not visible upon clinical examination. ::: HYPOTHESIS/OBJECTIVES: To assess the usefulness and applicability of dermoscopy for the examination of healthy cat skin. ::: ANIMALS: Twenty-one domestic short-haired cats from a feline rescue association. ::: METHODS: Four regions (head, dorsal neck, sacral and abdominal regions) were examined with both a contact hand-held nonpolarized light dermoscope at 10-fold magnification and a videodermoscope at 70-fold magnification. Findings were assessed using histological analysis of skin samples cut both longitudinally and transversely, set as the gold standard. ::: RESULTS: With a hand-held dermoscope at 10-fold magnification, thick, straight primary hairs surrounded by multiple secondary hairs were observed. With a videodermoscope at 70-fold magnification, hair shaft thickness was measured and the follicular openings and arrangement of vessels were clearly observed. Correspondence was observed between dermoscopic and histological results. ::: CONCLUSIONS AND CLINICAL IMPORTANCE: Dermoscopy represents a valid noninvasive and reproducible technique that could be helpful in clinical examination.


title: Texture synthesis through convolutional neural networks and spectrum constraints

abstract: This paper presents a significant improvement for the synthesis of texture images using convolutional neural networks (CNNs), making use of constraints on the Fourier spectrum of the results. More precisely, the texture synthesis is regarded as a constrained optimization problem, with constraints conditioning both the Fourier spectrum and statistical features learned by CNNs. In contrast with existing methods, the presented method inherits from previous CNN approaches the ability to depict local structures and fine scale details, and at the same time yields coherent large scale structures, even in the case of quasi-periodic images. This is done at no extra computational cost. Synthesis experiments on various images show a clear improvement compared to a recent state-of-the art method relying on CNN constraints only.


title: 3D Convolutional Neural Networks for landing zone detection from LiDAR

abstract: We present a system for the detection of small and potentially obscured obstacles in vegetated terrain. The key novelty of this system is the coupling of a volumetric occupancy map with a 3D Convolutional Neural Network (CNN), which to the best of our knowledge has not been previously done. This architecture allows us to train an extremely efficient and highly accurate system for detection tasks from raw occupancy data. We apply this method to the problem of detecting safe landing zones for autonomous helicopters from LiDAR point clouds. Current methods for this problem rely on heuristic rules and use simple geometric features. These heuristics break down in the presence of low vegetation, as they do not distinguish between vegetation that may be landed on and solid objects that should be avoided. We evaluate the system with a combination of real and synthetic range data. We show our system outperforms various benchmarks, including a system integrating various hand-crafted point cloud features from the literature.


title: Continuance usage intention and intention to recommend on information based mobile application: A technological and user experience perspective

abstract: The purpose of this research is to understand the antecedents of continuance usage intention and intention to recommend of information based mobile application. The underlying research model was developed from technological as well as user experience perspectives, intertwined with the expectation-confirmation model. A total of 176 respondents participated in this study, whom were avid users of a respected information based mobile application in Indonesia. Data was analyzed using Structural Equational Modeling, aided by WarpPLS 4.0. The results revealed a number of factors that influence intention to reuse and intention to recommend, which include system quality, information quality, perceived usefulness, perceived enjoyment, confirmation, and satisfaction. Perceived usefulness and satisfaction were factors that directly affect continuance usage intention. Intention to recommend, on the other hand, was only directly influenced by satisfaction. Confirmation affected both user experience factors of perceived usefulness and perceived enjoyment, as well as satisfaction. Finally, perceived usefulness was affected by information quality and perceived enjoyment was affected by system quality.


THUHCSI in MediaEval 2017 Emotional Impact of Movies Task


title: Radar target detection and Doppler ambiguity resolution

abstract: A Continuous Wave (CW) radar with a linear Frequency Modulation (FM) and a very short chirp duration is considered in this paper. A single target range and Doppler frequency measurement consits of a sequence of L chirp signals with the same chirp duration. The maximum unambiguously measurable radial velocity in this case is limited by a minimum chirp duration. Therefore fast moving targets will cause ambiguous measurement results, that have to be resolved. The technique of resolving ambiguities on the basis of several subsequent measurements with different ambiguity interval lengths is well known as Chinese Remainder Theorem (CRT). It has been extensively described in literature [2]. Two different signal processing approaches to apply this technique for the resolution of ambiguities are compared in this paper. These do not differ in terms of the ambiguity resolution itself, because they are based on the same principle. The detection performance however is different.


title: Palatal and facial veneers to treat severe dental erosion: a case report following the three-step technique and the sandwich approach.

abstract: Minimally invasive principles should be the driving force behind rehabilitating young individuals affected by severe dental erosion. The maxillary anterior teeth of a patient, class ACE IV, has been treated following the most conservatory approach, the Sandwich Approach. These teeth, if restored by conventional dentistry (eg, crowns) would have required elective endodontic therapy and crown lengthening. To preserve the pulp vitality, six palatal resin composite veneers and four facial ceramic veneers were delivered instead with minimal, if any, removal of tooth structure. In this article, the details about the treatment are described.


title: A new stacked step-up converter

abstract: A new step-up converter with one active switch and two diodes is proposed. Static properties are derived after a dc analysis and then components stresses and ripples are evaluated and design procedures are provided. The theoretical concepts are confirmed by simulation. Finally, a prototype is built and the experimental results confirmed the feasibility of the new topology. The new proposed converter exhibits higher step up conversion compared to the classical boost. Moreover, in the same application, semiconductor voltage stresses are lower for the new converter compared to the classical one, while semiconductor current stresses are the same.


title: Contributing Knowledge to Electronic Knowledge Repositories: An Empirical Investigation

abstract: Organizations are attempting to leverage their knowledge resources by employing knowledge management (KM) systems, a key form of which are electronic knowledge repositories (EKRs). A large number of KM initiatives fail due to the reluctance of employees to share knowledge through these systems. Motivated by such concerns, this study formulates and tests a theoretical model to explain EKR usage by knowledge contributors. The model employs social exchange theory to identify cost and benefit factors affecting EKR usage, and social capital theory to account for the moderating influence of contextual factors. The model is validated through a large-scale survey of public sector organizations. The results reveal that knowledge self-efficacy and enjoyment in helping others significantly impact EKR usage by knowledge contributors. Contextual factors (generalized trust, pro-sharing norms, and identification) moderate the impact of codification effort, reciprocity, and organizational reward on EKR usage, respectively. It can be seen that extrinsic benefits (reciprocity and organizational reward) impact EKR usage contingent on particular contextual factors whereas the effects of intrinsic benefits (knowledge self-efficacy and enjoyment in helping others) on EKR usage are not moderated by contextual factors. The loss of knowledge power and image do not appear to impact EKR usage by knowledge contributors. Besides contributing to theory building in KM, the results of this study inform KM practice.


title: Distribution System Topology Detection Using Consumer Load and Line Flow Measurements

abstract: An outage detection framework for power distribution networks is proposed. Given the tree structure of the distribution system, a method is developed combining the use of real-time power flow measurements on edges of the tree with load forecasts at the nodes of the tree. A maximum a posteriori detector {\color{black} (MAP)} is formulated for arbitrary number and location of outages on trees which is shown to have an efficient detector. A framework relying on the maximum missed detection probability is used for optimal sensor placement and is solved for tree networks. Finally, a set of case studies is considered using feeder data from the Pacific Northwest National Laboratories. We show that a 10\% loss in mean detection reliability network wide reduces the required sensor density by 60 \% for a typical feeder if efficient use of measurements is performed.


title: LegalRuleML: XML-based rules and norms

abstract: Legal texts are the foundational resource where to discover rules and norms that feed into different concrete (often XML-based) Web applications. Legislative documents provide general norms and specific procedural rules for eGovernment and eCommerce environments, while contracts specify the conditions of services and business rules (e.g. service level agreements for cloud computing), and judgments provide information about the legal argumentation and interpretation of norms to concrete case-law. Such legal knowledge is an important source that should be detected, properly modeled and expressively represented in order to capture all the domain particularities. This paper provides an extension of RuleML called LegalRuleML for fostering the characteristics of legal knowledge and to permit its full usage in legal reasoning and in the business rule domain. LegalRuleML encourages the effective exchange and sharing of such semantic information between legal documents, business rules, and software applications.


title: How Knowledge Transfer Impacts Performance: A Multilevel Model of Benefits and Liabilities

abstract: When does knowledge transfer benefit performance? Combining field data from a global consulting firm with an agent-based model, we examine how efforts to supplement one's knowledge from coworkers interact with individual, organizational, and environmental characteristics to impact organizational performance. We find that once cost and interpersonal exchange are included in the analysis, the impact of knowledge transfer is highly contingent. Depending on specific characteristics and circumstances, knowledge transfer can better, matter little to, or even harm performance. Three illustrative studies clarify puzzling past results and offer specific boundary conditions: 1 At the individual level, better organizational support for employee learning diminishes the benefit of knowledge transfer for organizational performance. 2 At the organization level, broader access to organizational memory makes global knowledge transfer less beneficial to performance. 3 When the organizational environment becomes more turbulent, the organizational performance benefits of knowledge transfer decrease. The findings imply that organizations may forgo investments in both organizational memory and knowledge exchange, that wide-ranging knowledge exchange may be unimportant or even harmful for performance, and that organizations operating in turbulent environments may find that investment in knowledge exchange undermines performance rather than enhances it. At a time when practitioners are urged to make investments in facilitating knowledge transfer and collaboration, appreciation of the complex relationship between knowledge transfer and performance will help in reaping benefits while avoiding liabilities.


title: Augmented reality navigation systems

abstract: The augmented reality (AR) research community has been developing a manifold of ideas and concepts to improve the depiction of virtual objects in a real scene. In contrast, current AR applications require the use of unwieldy equipment which discourages their use. In order to essentially ease the perception of digital information and to naturally interact with the pervasive computing landscape, the required AR equipment has to be seamlessly integrated into the user’s natural environment. Considering this basic principle, this paper proposes the car as an AR apparatus and presents an innovative visualization paradigm for navigation systems that is anticipated to enhance user interaction.


title: Substrateless Amplifier Module Realized by Ridge Gap Waveguide Technology for Millimeter-Wave Applications

abstract: A novel substrateless power amplifier module realized by ridge gap waveguide (RGW) technology is proposed in Ka-band. Up to our knowledge, this is the first time that an active device is directly integrated with an RGW structure. No substrate is used in the RF section of the implemented power amplifier and the whole structure is made by machining a piece of metal. The amplifier was made of two pieces and was easily assembled without needing a precise contact between the two sections. The cavity resonance of the amplifier is suppressed using a pining structure. The fabricated amplifier module is self-packaged and does not need any packaging. It is shown that the simulated and measured results are in good agreement. This paper shows the capability of RGW technology for implementing the whole RF frontend in microwave- and millimeter-wave systems.


title: Osteoimmunology: the expanding role of immunoreceptors in osteoclasts and bone

abstract: The study of bone and immunology (termed osteoimmunology) has led to the discovery of many important similarities between the two systems including shared niches, mechanisms, cytokines and receptors. The bone marrow provides a niche for hematopoietic cells including those of the lymphoid and myeloid lineage. Osteoclasts, specialized polykarons arising from myeloid precursors, bind to bone and resorb the organic and inorganic components through secretion of acid and proteases. Osteoclasts are differentiated and activated by cytokines that can be produced by immune cells and osteoclast activity can be dysregulated in states of autoimmunity or high inflammation. Similar to B and T cells, osteoclasts require coordinated co-stimulation of signaling pathways provided in the form of receptorassociated immunoreceptor tyrosine-based activation motif adaptor proteins, DAP12 and FcR , to drive differentiation and activation. In this review, we will cover the differentiation process of osteoclasts from the earliest precursors shown to have differentiation potential and the signals needed to drive these cells into osteoclast commitment and activation.


title: 3D Convolutional Neural Networks for Cross Audio-Visual Matching Recognition

abstract: Audio–visual recognition (AVR) has been considered as a solution for speech recognition tasks when the audio is corrupted, as well as a visual recognition method used for speaker verification in multi-speaker scenarios. The approach of AVR systems is to leverage the extracted information from one modality to improve the recognition ability of the other modality by complementing the missing information. The essential problem is to find the correspondence between the audio and visual streams, which is the goal of this paper. We propose the use of a coupled 3D convolutional neural network (3D CNN) architecture that can map both modalities into a representation space to evaluate the correspondence of audio–visual streams using the learned multimodal features. The proposed architecture will incorporate both spatial and temporal information jointly to effectively find the correlation between temporal information for different modalities. By using a relatively small network architecture and much smaller data set for training, our proposed method surpasses the performance of the existing similar methods for audio–visual matching, which use 3D CNNs for feature representation. We also demonstrate that an effective pair selection method can significantly increase the performance. The proposed method achieves relative improvements over 20% on the equal error rate and over 7% on the average precision in comparison to the state-of-the-art method.


title: Metalearning: Applications to Data Mining

abstract: Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence.


title: When 2.5D is not enough: Simultaneous reconstruction, segmentation and recognition on dense SLAM

abstract: While the main trend of 3D object recognition has been to infer object detection from single views of the scene — i.e., 2.5D data — this work explores the direction on performing object recognition on 3D data that is reconstructed from multiple viewpoints, under the conjecture that such data can improve the robustness of an object recognition system. To achieve this goal, we propose a framework which is able (i) to carry out incremental real-time segmentation of a 3D scene while being reconstructed via Simultaneous Localization And Mapping (SLAM), and (ii) to simultaneously and incrementally carry out 3D object recognition and pose estimation on the reconstructed and segmented 3D representations. Experimental results demonstrate the advantages of our approach with respect to traditional single view-based object recognition and pose estimation approaches, as well as its usefulness in robotic perception and augmented reality applications.