title: Adaptive Image Registration via Hierarchical Voronoi Subdivision

abstract: Advances in image acquisition systems have made it possible to capture high-resolution images of a scene, recording considerable scene details. With increased resolution comes increased image size and geometric difference between multiview images, complicating image registration. Through Voronoi subdivision, we subdivide large images into small corresponding regions, and by registering small regions, we register the images in a piecewise manner. Image subdivision reduces the geometric difference between regions that are registered and simplifies the correspondence process. The proposed method is a hierarchical one. While previous methods use the same block size and shape at a hierarchy, the proposed method adapts the block size and shape to the local image details and geometric difference between the images. This adaptation makes it possible to keep geometric difference between corresponding regions small and simplifies the correspondence process. Implementational details of the proposed image registration method are provided, and experimental results on various types of images are presented and analyzed.


I. Scholarly Editors in Different Socio-Cultural Contexts


title: Quantitative evaluation of the major determinants of human gait.

abstract: Accurate knowledge of the isolated contributions of joint movements to the three-dimensional displacement of the center of mass (COM) is fundamental for understanding the kinematics of normal walking and for improving the treatment of gait disabilities. Saunders et al. (1953) identified six kinematic mechanisms to explain the efficient progression of the whole-body COM in the sagittal, transverse, and coronal planes. These mechanisms, referred to as the major determinants of gait, were pelvic rotation, pelvic list, stance knee flexion, foot and knee mechanisms, and hip adduction. The aim of the present study was to quantitatively assess the contribution of each major gait determinant to the anteroposterior, vertical, and mediolateral displacements of the COM over one gait cycle. The contribution of each gait determinant was found by applying the concept of an 'influence coefficient', wherein the partial derivative of the COM displacement with respect to a prescribed determinant was calculated. The analysis was based on three-dimensional measurements of joint angular displacements obtained from 23 healthy young adults walking at slow, normal and fast speeds. We found that hip flexion, stance knee flexion, and ankle-foot interaction (comprised of ankle plantarflexion, toe flexion and the displacement of the center of pressure) are the major determinants of the displacements of the COM in the sagittal plane, while hip adduction and pelvic list contribute most significantly to the mediolateral displacement of the COM in the coronal plane. Pelvic rotation and pelvic list contribute little to the vertical displacement of the COM at all walking speeds. Pelvic tilt, hip rotation, subtalar inversion, and back extension, abduction and rotation make negligible contributions to the displacements of the COM in all three anatomical planes.


title: The Acceptance of Online Grocery Shopping

abstract: The Internet has been increasingly used to facilitate online business transactions between business entities and consumers for various products and services. One of the applications that has received much attention in the last few years is Online Grocery Shopping (OGS). There is, however, no concrete evidence that OGS has been widely adopted as initially predicted. To enrich the existing studies in this context, which are currently limited, this paper explores the Australian consumer’s perception of Online Grocery Shopping in order to identify some factors that may foster or hinder its acceptance. The Technology Acceptance Model (TAM) is employed to provide the theoretical foundation for this study. Based on the empirical data collected from a survey, the findings demonstrate the strength of the TAM in exploring the acceptance of OGS in Australia. In addition, the visibility of OGS was also found to be an important factor for its acceptance by the community.


title: Phased Array Antenna Handbook

abstract: Phased Arrays in Radar and Communication Systems. Pattern Characteristics and Synthesis of Linear and Planar Arrays. Patterns of Nonplanar Arrays. Elements, Transmission Lines, and Feed Architectures for Phased Arrays. Summary of Element Pattern and Mutual Impedance Effects. Array Error Effects. Special Array Feeds for Limited Field of View and Wideband Arrays.


title: Provenance-Driven Data Curation Workflow Analysis

abstract: Manually designed workflows can be error-prone and inefficient. Workflow provenance contains fine-grained data processing information that can be used to detect workflow design problems. In this paper, we propose a provenance-driven workflow analysis framework that exploits both prospective and retrospective provenance. We show how provenance information can help the user gain a deeper understanding of a workflow and provide the user with insights into how to improve workflow design.


title: A Review of Facebook Research in the Social Sciences

abstract: With over 800 million active users, Facebook is changing the way hundreds of millions of people relate to one another and share information. A rapidly growing body of research has accompanied the meteoric rise of Facebook as social scientists assess the impact of Facebook on social life. In addition, researchers have recognized the utility of Facebook as a novel tool to observe behavior in a naturalistic setting, test hypotheses, and recruit participants. However, research on Facebook emanates from a wide variety of disciplines, with results being published in a broad range of journals and conference proceedings, making it difficult to keep track of various findings. And because Facebook is a relatively recent phenomenon, uncertainty still exists about the most effective ways to do Facebook research. To address these issues, the authors conducted a comprehensive literature search, identifying 412 relevant articles, which were sorted into 5 categories: descriptive analysis of users, motivations for using Facebook, identity presentation, the role of Facebook in social interactions, and privacy and information disclosure. The literature review serves as the foundation from which to assess current findings and offer recommendations to the field for future research on Facebook and online social networks more broadly.


Observation of certain plants used in skin diseases by Baiga tribes of Mandla district.


title: On the Susceptibility of the Deferred Acceptance Algorithm

abstract: The Deferred Acceptance Algorithm (DAA) is the most widely accepted and used algorithm to match students, workers, or residents to colleges, firms or hospitals respectively. In this paper, we consider for the first time, the complexity of manipulating DAA by agents such as colleges that have capacity more than one. For such agents, truncation is not an exhaustive strategy. We present efficient algorithms to compute a manipulation for the colleges when the colleges are proposing or being proposed to. We then conduct detailed experiments on the frequency of manipulable instances in order to get better insight into strategic aspects of two-sided matching markets. Our results bear somewhat negative news: assuming that agents have information other agents' preference, they not only often have an incentive to misreport but there exist efficient algorithms to find such a misreport.


title: Predicting intended to use of web portal using extended technology acceptance model (TAM): Some perspective on information management students

abstract: Extensive use of technology acceptance model (TAM) in measuring information system related stimulate other researchers to take part in contributing to the knowledge. Using information management student as a respondent to measure perceived attractiveness, physic cost perceptions, perceived usefulness and perceived playfulness toward intended to use of web portal. University student in Malaysia especially UiTM should make use of web portal effectively especially for their learning tools. Result shows ranging student from undergraduate to postgraduate in Faculty of Information Management in Puncak Perdana will use the student portal if the portal is useful to them and enjoyable to use.


title: VirtualSoC: A Full-System Simulation Environment for Massively Parallel Heterogeneous System-on-Chip

abstract: Driven by flexibility, performance and cost constraints of demanding modern applications, heterogeneous System-on-Chip (SoC) is the dominant design paradigm in the embedded system computing domain. SoC architecture and heterogeneity clearly provide a wider power/performance scaling, combining high performance and power efficient general-purpose cores along with massively parallel many-core-based accelerators. Besides the complex hardware, generally these kinds of platforms host also an advanced software ecosystem, composed by an operating system, several communication protocol stacks, and various computational demanding user applications. The necessity to efficiently cope with the hugeHW/SW design space provided by this scenario makes clearly full-system simulator one of the most important design tools. We present in this paper a new emulation framework, called Virtual SoC, targeting the full-system simulation of massively parallel heterogeneous SoCs.


Computational Methods in Authorship Attribution


title: Techniques for conic splines

abstract: A number of techniques are presented for making conic splines more effective for 2D computer graphics. We give a brief account of the theory of conic splines oriented to computer graphics. We make Pitteway's algorithm exact, and repair an "aliasing" problem that has plagued the algorithm since its introduction in 1967. The curvature-matching problem for conics is solved by way of a simple formula for curvature at an endpoint which permits curvature to be matched exactly at non-inflectior points and more closely than was previously realized possible at points of inflection. A formula for minimum-curvature-variation of conic splines is given. These techniques provide additional support for Pavlidis' position [6] that conics can often be very effective as splines.The work was motivated by, and provides much of the foundation for, an implementation of conic splines at Sun Microsystems as part of Sun's Pixrect graphics package, the lowest layer of Sun's graphics support.


title: TVD: A Reproducible and Multiply Aligned TV Series Dataset

abstract: We introduce a new dataset built around two TV series from different genres, The Big Bang Theory, a situation comedy and Game of Thrones, a fantasy drama. The dataset has multiple tracks extracted from diverse sources, including dialogue (manual and automatic transcripts, multilingual subtitles), crowd-sourced textual descriptions (brief episode summaries, longer episode outlines) and various metadata (speakers, shots, scenes). The paper describes the dataset and provide tools to reproduce it for research purposes provided one has legally acquired the DVD set of the series. Tools are also provided to temporally align a major subset of dialogue and description tracks, in order to combine complementary information present in these tracks for enhanced accessibility. For alignment, we consider tracks as comparable corpora and first apply an existing algorithm for aligning such corpora based on dynamic time warping and TFIDF-based similarity scores. We improve this baseline algorithm using contextual information, WordNet-based word similarity and scene location information. We report the performance of these algorithms on a manually aligned subset of the data. To highlight the interest of the database, we report a use case involving rich speech retrieval and propose other uses.


title: Internet Addiction and Psychiatric Symptoms Among Korean Adolescents

abstract: Background: The aims of this study were to identify the independent factors associated with intermittent addiction and addiction to the Internet and to examine the psychiatric symptoms in Korean adolescents when the demographic and Internet-related factors were controlled. ::: ::: ::: ::: Methods: Male and female students (N = 912) in the 7th-12th grades were recruited from 2 junior high schools and 2 academic senior high schools located in Seoul, South Korea. Data were collected from November to December 2004 using the Internet-Related Addiction Scale and the Symptom Checklist-90-Revision. A total of 851 subjects were analyzed after excluding the subjects who provided incomplete data. ::: ::: ::: ::: Results: Approximately 30% (n = 258) and 4.3% (n = 37) of subjects showed intermittent Internet addiction and Internet addiction, respectively. Multivariate logistic regression analysis showed that junior high school students and students having a longer period of Internet use were significantly associated with intermittent addiction. In addition, male gender, chatting, and longer Internet use per day were significantly associated with Internet addiction. When the demographic and Internet-related factors were controlled, obsessive-compulsive and depressive symptoms were found to be independently associated factors for intermittent addiction and addiction to the Internet, respectively. ::: ::: ::: ::: Conclusions: Staff working in junior or senior high schools should pay closer attention to those students who have the risk factors for intermittent addiction and addiction to the Internet. Early preventive intervention programs are needed that consider the individual severity level of Internet addiction.


title: A universal sequence of period-doubling bifurcations of the forced oscillations of a pendulum☆

abstract: Abstract One of the most typical modes of chaotization in deterministic systems occurs when variation of the parameter characterizing the intensity of a disturbance takes a dynamical system through a sequence of period-doubling bifurcations from a regular to a stochastic mode of behaviour. The transition occurs in regions of phase space characterized by a strong local instability and obeys the law of universality recently discovered by Feigenbaum /1/.


title: When social networks cross boundaries: a case study of workplace use of facebook and linkedin

abstract: The use of social networking software by professionals is increasing dramatically. How it is used, whether it enhances or reduces productivity, and how enterprise-friendly design and use might evolve are open questions. We examine attitudes and behaviors in a large, technologically-savvy organization through a broad survey and thirty focused interviews. We find extensive social and work uses, with complex patterns that differ with software system and networker age. Tensions arise when use spans social groups and the organization's firewall. Although use is predominantly to support weak ties whose contribution to productivity can be difficult to prove, we anticipate rapid uptake of social networking technology by organizations.


Development of Image Processing for Crack Detection on Concrete Structures through Terrestrial Laser Scanning Associated with the Octree Structure


title: Eye tracking the user experience - An evaluation of ontology visualization

abstract: Various ontology visualization techniques have been developed over the years, offering essential interfaces to users for browsing and interacting with ontologies, in an effort to assist with ontology understanding. Yet few studies have focused on evaluating the usability of existing ontology visualization techniques. This paper presents an eye-tracking user study that evaluates two commonly used ontology visualization techniques, namely, indented list and graph. The eye-tracking experiment and analysis presented in this paper complements the set of existing evaluation protocols for ontology visualization. In addition, the results found from this study contribute to a greater understanding of the strengths and weaknesses of the two visualization techniques, and in particular, how and why one is more effective than the other. Based on approximately 500 MB of eye movement data containing around 30 million rows of gaze data generated by a Tobii eye tracker, we found evidence suggesting indented lists are more efficient at supporting information searches and graphs are more efficient at supporting information processing.


COMPARISON OF DIFFERENT CLASSIFICATION TECHNIQUES ON PIMA INDIAN DIABETES DATA


title: An Empirical Comparison of Graph Databases

abstract: In recent years, more and more companies provide services that can not be anymore achieved efficiently using relational databases. As such, these companies are forced to use alternative database models such as XML databases, object-oriented databases, document-oriented databases and, more recently graph databases. Graph databases only exist for a few years. Although there have been some comparison attempts, they are mostly focused on certain aspects only. In this paper, we present a distributed graph database comparison framework and the results we obtained by comparing four important players in the graph databases market: Neo4j, Orient DB, Titan and DEX.


title: Towards variable stiffness control of antagonistic twisted string actuators

abstract: This paper shows the possibility of using two antagonistic twisted strings actuators as a new type of variable stiffness actuator. Variable stiffness model of the twisted string actuator is identified empirically, and the control strategy is proposed for simultaneous position and stiffness control of the actuator. A variable stiffness linear joint actuated by antagonistic twisted string actuators is proposed as a target system. The proposed model and control strategy make it possible to control the position and the stiffness of the joint without position and force sensor at the load side. The developed variable stiffness linear joint can be effectively used in applications where weight distribution is vital, such as exoskeleton systems and low weight manipulators.


title: Creating an ambient-intelligence environment using embedded agents

abstract: The Essex intelligent dormitory, iDorm, uses embedded agents to create an ambient-intelligence environment. In a five-and-a-half-day experiment, a user occupied the iDorm, testing its ability to learn user behavior and adapt to user needs. The embedded agent discreetly controls the iDorm according to user preferences. Our work focuses on developing learning and adaptation techniques for embedded agents. We seek to provide online, lifelong, personalized learning of anticipatory adaptive control to realize the ambient-intelligence vision in ubiquitous-computing environments. We developed the Essex intelligent dormitory, or iDorm, as a test bed for this work and an exemplar of this approach.


title: Generalized Higher-Order Tensor Decomposition via Parallel ADMM

abstract: Higher-order tensors are becoming prevalent in many scientific areas such as computer vision, social network analysis, data mining and neuroscience. Traditional tensor decomposition approaches face three major challenges: model selecting, gross corruptions and computational efficiency. To address these problems, we first propose a parallel trace norm regularized tensor decomposition method, and formulate it as a convex optimization problem. This method does not require the rank of each mode to be specified beforehand, and can automatically determine the number of factors in each mode through our optimization scheme. By considering the low-rank structure of the observed tensor, we analyze the equivalent relationship of the trace norm between a low-rank tensor and its core tensor. Then, we cast a non-convex tensor decomposition model into a weighted combination of multiple much smaller-scale matrix trace norm minimization. Finally, we develop two parallel alternating direction methods of multipliers (ADMM) to solve our problems. Experimental results verify that our regularized formulation is effective, and our methods are robust to noise or outliers.


title: Data confidentiality in cloud-based pervasive system

abstract: Data confidentiality and privacy is a serious concern in pervasive systems where cloud computing is used to process huge amount of data such as matrix multiplications typically used in HPC. Due to limited processing capabilities, smart devices need to rely on cloud servers for heavy-duty computations such as matrix multiplication. Conventional security mechanisms such as public key encryption is not an option to safeguard data from cloud servers to see them. Ensuring client data confidentiality in cloud computing can be achieved using data obfuscating techniques instead of encryption. In a matrix multiplication application, clients can protect their data from dishonest or curious cloud servers which perform multiplication operations on matrices without `knowing or seeing' actual values of input matrices. In our approach, we introduce random noise to the data, and generate several matrices randomly from each matrix in order to cloak data from cloud servers. The main idea is to mask the data as well as confuse the cloud server so it is unable to derive or guess the actual values of matrices as well as computer results.


Improving Data Augmentation for Medical Image Segmentation


title: An ubiquitous miniaturized android based ECG monitoring system

abstract: Miniaturization of devices has brought a lot of improvements in the healthcare delivery as a result of advancenments in Integrated circuit technologies. As a result, proper and effective way of analyzing various body conditions and diseases can be achieved. When the heart muscles repolarize and depolarize, it generates electrical impulses which could help the doctor or the health professional to diagnose various heart abnormalities. The aim of this project is to design and develop a miniaturized ECG system displayed on both PC and Tablet for preliminary diagnoses of the heart. This system utilizes a programmable single-chip microcontroller for analysing the biosignals to indicate the condition of the heart. The system is incomporated with alarm systems which prompt the doctor if there is any abnormality from a patient's heart. These abnormalities could be bradycardia or tachycardia. This system is built with a provision of enabling wireless transmission of ECG signals to a PC or TABLET through BLUETOOTH and ANDROID PLATFORM. The android application can be installed and tested on any smart phone or tablet and screen resolution can be adjusted later as per requirement. This helps the doctor to have visual description of the patient ECG without the need of mounted monitors. The experimental results show that device is clinically useful, compact, cost effective and user friendly.


title: An extractive text summarization technique for Bengali document(s) using K-means clustering algorithm

abstract: Text summarization, a field of data mining, is very important for developing various real-life applications. Many techniques have been developed for summarizing English text(s). But, a few attempts have been made for Bengali text because of its some multifaceted structure. This paper presents a method for text summarization which extracts important sentences from a single or multiple Bengali documents. The input document(s) should be pre-processed by tokenization, stemming operation etc. Then, word score is calculated by Term-Frequency/Inverse Document Frequency (TF/IDF) and sentence score is determined by summing up its constituent words' scores with its position. Cue and skeleton words have also been considered to calculate the sentence score. For single or multiple documents, K-means clustering algorithm has been applied to produce the final summary. The experimental result shows satisfactory outputs in comparison to the existing approaches possessing linear run time complexity.


title: A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research

abstract: In recent years, substantial progress has been made in the field of reverberant speech signal processing, including both single- and multichannel dereverberation techniques and automatic speech recognition (ASR) techniques that are robust to reverberation. In this paper, we describe the REVERB challenge, which is an evaluation campaign that was designed to evaluate such speech enhancement (SE) and ASR techniques to reveal the state-of-the-art techniques and obtain new insights regarding potential future research directions. Even though most existing benchmark tasks and challenges for distant speech processing focus on the noise robustness issue and sometimes only on a single-channel scenario, a particular novelty of the REVERB challenge is that it is carefully designed to test robustness against reverberation, based on both real, single-channel, and multichannel recordings. This challenge attracted 27 papers, which represent 25 systems specifically designed for SE purposes and 49 systems specifically designed for ASR purposes. This paper describes the problems dealt within the challenge, provides an overview of the submitted systems, and scrutinizes them to clarify what current processing strategies appear effective in reverberant speech processing.


title: RCS Reduction of Waveguide Slot Antenna With Metamaterial Absorber

abstract: This communication investigates the application of metamaterial absorber (MA) to waveguide slot antenna to reduce its radar cross section (RCS). A novel ultra-thin MA is presented, and its absorbing characteristics and mechanism are analyzed. The PEC ground plane of waveguide slot antenna is covered by this MA. As compared with the slot antenna with a PEC ground plane, the simulation and experiment results demonstrate that the monostatic and bistatic RCS of waveguide slot antenna are reduced significantly, and the performance of antenna is preserved simultaneously.


title: A novel approach for ontology-based dimensionality reduction for web text document classification

abstract: Dimensionality reduction of feature vector size plays a vital role in enhancing the text processing capabilities; it aims in reducing the size of the feature vector used in the mining tasks (classification, clustering… etc.). This paper proposes an efficient approach to be used in reducing the size of the feature vector for web text document classification process. This approach is based on using WordNet ontology, utilizing the benefit of its hierarchal structure, to eliminate words from the generated feature vector that has no relation with any of WordNet lexical categories; this leads to the reduction of the feature vector size without losing information on the text. For mining tasks, the Vector Space Model (VSM) is used to represent text documents and the Term Frequency Inverse Document Frequency (TFIDF) is used as a term weighting method. The proposed ontology based approach was evaluated against the Principal component analysis (PCA) approach using several experiments. The experimental results reveal the effectiveness of our proposed approach against other traditional approaches to achieve a better classification accuracy, F-measure, precision, and recall


title: Terahertz Dielectric Resonator Antenna Coupled to Graphene Plasmonic Dipole

abstract: This paper presents an efficient approach for exciting a dielectric resonator antenna (DRA) in the terahertz frequencies by means of a graphene plasmonic dipole. Design and analysis are performed in two steps. First, the propagation properties of hybrid plasmonic one-dimensional and two-dimensional structures are obtained by using transfer matrix theory and the finite-element method. The coupling amount between the plasmonic graphene mode and the dielectric wave mode is explored based on different parameters. These results, together with DRA and plasmonic antenna theory, are then used to design a DRA antenna that supports the TEy 112 mode at 2.4 THz and achieves a gain (IEEE) of up to 7 dBi and a radiation efficiency of up 70%. This gain is 6.5 dB higher than that of the graphene dipole alone and achieved with a moderate area overhead, demonstrating the value of the proposed structure.


title: Friendly competition: evidence for a dilution effect among competitors in a planktonic host–parasite system

abstract: The "dilution effect" concept in disease ecology offers the intriguing possibility that clever manipulation of less competent hosts could reduce disease prevalence in populations of more competent hosts. The basic concept is straightforward: host species vary in suitability (competence) for parasites, and disease transmission decreases when there are more incompetent hosts interacting with vectors or removing free-living stages of a parasite. However, host species also often interact with each other in other ecological ways, e.g., as competitors for resources. The net result of these simultaneous, multiple interactions (disease dilution and resource competition) is challenging to predict. Nonetheless, we see the signature of both roles operating concurrently in a planktonic host-parasite system. We document pronounced spatiotemporal variation in the size of epidemics of a virulent fungus (Metschnikowia bicuspidata) in Midwestern U.S. lake populations of a dominant crustacean grazer (Daphnia dentifera). We show that some of this variation is captured by changes in structure of Daphnia assemblages. Lake-years with smaller epidemics were characterized by assemblages dominated by less suitable hosts ("diluters," D. pulicaria and D. retrocurva, whose suitabilties were determined in lab experiments and field surveys) at the start of epidemics. Furthermore, within a season, less suitable hosts increased as epidemics declined. These observations are consistent with a dilution effect. However, more detailed time series analysis (using multivariate autoregressive models) of three intensively sampled epidemics show the signature of a likely interaction between dilution and resource competition between these Daphnia species. The net outcome of this interaction likely promoted termination of these fungal outbreaks. Should this outcome always arise in "friendly competition" systems where diluting hosts compete with more competent hosts? The answers to this question lie at a frontier of disease ecology.


title: Social engineering: the "Dark Art"

abstract: The key to maintaining the confidentiality, integrity, and availability of an organizations information and information systems is controlling who accesses what information. This is accomplished by being able to identify the requestor, verifying the requestor is not an impostor, and ensuring that the requestor has the proper level of clearance to access a given resource. There have always been those that attempt to by-pass this security mechanism through brute force or guile. In the past, those who use guile have been called confidence men and con artists. Today, these people are called social engineers, but the tactics remain the same even if the objectives have changed.


title: Extended Object Tracking: Introduction, Overview and Applications

abstract: This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.


title: Estimation of the use of voip server resources by neural networks

abstract: No researches on VoIP servers have highlighted their analytical models to study their behavior depending on the number of users. This is complex because the scheduling algorithms to solve it have limitations. The aim of our paper is to present a VoIP server modeling by neural network, having as input the number of calls and output as the use of hardware resources of the server. A generalization of this model is given at the end of our research.


title: Physical Human-Robot Interaction: Mutual Learning and Adaptation

abstract: Close physical interaction between robots and humans is a particularly challenging aspect of robot development. For successful interaction and cooperation, the robot must have the ability to adapt its behavior to the human counterpart. Based on our earlier work, we present and evaluate a computationally efficient machine learning algorithm that is well suited for such close-contact interaction scenarios. We show that this algorithm helps to improve the quality of the interaction between a robot and a human caregiver. To this end, we present two human-in-the-loop learning scenarios that are inspired by human parenting behavior, namely, an assisted standing-up task and an assisted walking task.


title: Learning and Transfer of Modulated Locomotor Controllers

abstract: We study a novel architecture and training procedure for locomotion tasks. A high-frequency, low-level "spinal" network with access to proprioceptive sensors learns sensorimotor primitives by training on simple tasks. This pre-trained module is fixed and connected to a low-frequency, high-level "cortical" network, with access to all sensors, which drives behavior by modulating the inputs to the spinal network. Where a monolithic end-to-end architecture fails completely, learning with a pre-trained spinal module succeeds at multiple high-level tasks, and enables the effective exploration required to learn from sparse rewards. We test our proposed architecture on three simulated bodies: a 16-dimensional swimming snake, a 20-dimensional quadruped, and a 54-dimensional humanoid. Our results are illustrated in the accompanying video at this https URL


title: K-Pop Genres: A Cross-Cultural Exploration

abstract: Current music genre research tends to focus heavily on classical and popular music from Western cultures. Few studies discuss the particular challenges and issues related to non-Western music. The objective of this study is to improve our understanding of how genres are used and perceived in different cultures. In particular, this study attempts to fill gaps in our understanding by examining K-pop music genres used in Korea and comparing them with genres used in North America. We provide background information on K-pop genres by analyzing 602 genre-related labels collected from eight major music distribution websites in Korea. In addition, we report upon a user study in which American and Korean users annotated genre information for 1894 K-pop songs in order to understand how their perceptions might differ or agree. The results show higher consistency among Korean users than American users demonstrated by the difference in Fleiss’ Kappa values and proportion of agreed genre labels. Asymmetric disagreements between Americans and Koreans on specific genres reveal some interesting differences in the perception of genres. Our findings provide some insights into challenges developers may face in creating global music services.


title: Integrated chargers for EV's and PHEV's: examples and new solutions

abstract: The battery is an important component in an electric vehicle (EV) or a plug-in hybrid electric vehicle (PHEV) and it should be charged from the grid in a cost efficient, preferably fast and definitely safe way. The charger could be an on board or an off board charger. For an on board charger it is possible to use available hardware of the traction system, mainly the inverter and the electric motor, in the charger circuit. This is called an integrated charger. In this paper, different examples of integrated chargers are reviewed and explained. Additionally, other possible solutions of integrated chargers are described.


title: Adding chemoprophylaxis to sequential compression might not reduce risk of venous thromboembolism in bariatric surgery patients.

abstract: BACKGROUND ::: Anticoagulation, the use of sequential compression devices on the lower extremities perioperatively, and early ambulation are thought to reduce the incidence of venous thromboembolism (VTE) postoperatively and are recommended to reduce VTE risk. However, the evidence on which this recommendation has been based is not particularly strong. We have demonstrated that even a large, multicenter cohort with carefully collected prospective data is inadequate to provide sufficient evidence to support, or refute, this recommendation. ::: ::: ::: METHODS ::: Longitudinal Assessment of Bariatric Surgery participants from 10 centers in the United States who underwent their first bariatric surgery between March 2005 and December 2007 constituted the study group. We examined the ability to address the question of whether anticoagulation therapy, in addition to sequential compression, reduces the 30-day incidence of VTE or death sufficiently to recommend the use of prophylactic anticoagulation, a therapy that is not without risk. ::: ::: ::: RESULTS ::: Of 4416 patients, 396 (9.0%) received sequential compression alone, and 4020 also received anticoagulation therapy. The incidence of VTE within 30 days of surgery was low (.25% among those receiving sequential compression alone and .47% when anticoagulation therapy was added), and the 30-day incidence of death was also low (.25% versus .34%, respectively, P = .76, for sequential compression alone versus sequential compression plus anticoagulation therapy). Estimates of the number of cases required to address the question of whether a difference exists in the outcome related to VTE chemoprophylaxis or whether the outcome rates are equivalent have ranged from 13,680 to ≥ 35,760 patients, depending on whether superiority or equivalence was being analyzed. ::: ::: ::: CONCLUSION ::: Sufficient evidence from a clinical trial study to determine whether prophylactic anticoagulation added to compression devices further prevents VTE is not available, and such a trial is likely to be impractical. The data presented are insufficient to make a final recommendation concerning prophylactic treatment to prevent VTE in the 30 days after bariatric surgery.


title: Evaluation and improvement of global pose estimation with multiple AprilTags for industrial manipulators

abstract: Given the advancing importance for light-weight production materials an increase in automation is crucial. This paper presents a prototypical setup to obtain a precise pose estimation for an industrial manipulator in a realistic production environment. We show the achievable precision using only a standard fiducial marker system (AprilTag) and a state-of-the art camera attached to the robot. The results obtained in a typical working space of a robot cell of about 4.5m × 4.5m are in the range of 15mm to 35mm compared to ground truth provided by a laser tracker. We then show several methods of reducing this error by applying state-of-the-art optimization techniques, which reduce the error significantly to less than 10mm compared to the laser tracker ground truth data and at the same time remove e×isting outliers.


title: Blockchain: the Emperors New PKI?

abstract: I would like to jump on the blockchain bandwagon. I would like to be able to say that blockchain is the solution to the longstanding problem of secure identity on the Internet. I would like to say that everyone in the world will soon have a digital identity. Put yourself on the blockchain and never again ask yourself, Who am I? - you are your blockchain address.


title: An Overview of Microsoft Academic Service (MAS) and Applications

abstract: In this paper we describe a new release of a Web scale entity graph that serves as the backbone of Microsoft Academic Service (MAS), a major production effort with a broadened scope to the namesake vertical search engine that has been publicly available since 2008 as a research prototype. At the core of MAS is a heterogeneous entity graph comprised of six types of entities that model the scholarly activities: field of study, author, institution, paper, venue, and event. In addition to obtaining these entities from the publisher feeds as in the previous effort, we in this version include data mining results from the Web index and an in-house knowledge base from Bing, a major commercial search engine. As a result of the Bing integration, the new MAS graph sees significant increase in size, with fresh information streaming in automatically following their discoveries by the search engine. In addition, the rich entity relations included in the knowledge base provide additional signals to disambiguate and enrich the entities within and beyond the academic domain. The number of papers indexed by MAS, for instance, has grown from low tens of millions to 83 million while maintaining an above 95% accuracy based on test data sets derived from academic activities at Microsoft Research. Based on the data set, we demonstrate two scenarios in this work: a knowledge driven, highly interactive dialog that seamlessly combines reactive search and proactive suggestion experience, and a proactive heterogeneous entity recommendation.


title: Experimentation in Software Engineering

abstract: Like other sciences and engineering disciplines, software engineering requires a cycle of model building, experimentation, and learning. Experiments are valuable tools for all software engineers who are involved in evaluating and choosing between different methods, techniques, languages and tools. The purpose of Experimentation in Software Engineering is to introduce students, teachers, researchers, and practitioners to empirical studies in software engineering, using controlled experiments. The introduction to experimentation is provided through a process perspective, and the focus is on the steps that we have to go through to perform an experiment. The book is divided into three parts. The first part provides a background of theories and methods used in experimentation. Part II then devotes one chapter to each of the five experiment steps: scoping, planning, execution, analysis, and result presentation. Part III completes the presentation with two examples. Assignments and statistical material are provided in appendixes. Overall the book provides indispensable information regarding empirical studies in particular for experiments, but also for case studies, systematic literature reviews, and surveys. It is a revision of the authors book, which was published in 2000. In addition, substantial new material, e.g. concerning systematic literature reviews and case study research, is introduced. The book is self-contained and it is suitable as a course book in undergraduate or graduate studies where the need for empirical studies in software engineering is stressed. Exercises and assignments are included to combine the more theoretical material with practical aspects. Researchers will also benefit from the book, learning more about how to conduct empirical studies, and likewise practitioners may use it as a cookbook when evaluating new methods or techniques before implementing them in their organization.


title: Media and Attention, Cognition, and School Achievement

abstract: Summary Marie Evans Schmidt and Elizabeth Vandewater review research on links between various types of electronic media and the cognitive skills of school-aged children and adolescents. One central finding of studies to date, they say, is that the content delivered by electronic media is far more influential than the media themselves. Most studies, they point out, find a small negative link between the total hours a child spends viewing TV and that child's academic achievement. But when researchers take into account characteristics of the child, such as IQ or socioeconomic status, this link typically disappears. Content appears to be crucial. Viewing educational TV is linked positively with academic achievement; viewing entertainment TV is linked negatively with achievement. When it comes to particular cognitive skills, say the authors, researchers have found that electronic media, particularly video games, can enhance visual spatial skills, such as visual tracking, mental rotation, and target localization. Gaming may also improve problem-solving skills. Researchers have yet to understand fully the issue of transfer of learning from electronic media. Studies suggest that, under some circumstances, young people are able to transfer what they learn from electronic media to other applications, but analysts are uncertain how such transfer occurs. In response to growing public concern about possible links between electronic media use and attention problems in children and adolescents, say the authors, researchers have found evidence for small positive links between heavy electronic media use and mild attention problems among young people but have found only inconsistent evidence so far for a link between attention deficit hyperactivity disorder and media use. The authors point out that although video games, interactive websites, and multimedia software programs appear to offer a variety of possible benefits for learning, there is as yet little empirical evidence to suggest that such media are more effective than other forms of instruction. Like their elders, America's youth have an almost dizzying assortment of entertainment technology from which to choose. (1) Children and adolescents, however, are a special media audience, in part because they are developmentally vulnerable and in part because they are among the earliest adopters and heaviest users of entertainment technology. (2) Adolescents in particular have widely adopted the use of digital media for daily life activities. Indeed, the stereotypical view of many Americans is that teenagers spend their lives immersed in electronic media. While adolescents are doing homework on the computer, with a word-processing program open for text, they are surfing the Internet. Simultaneously they are instant messaging with friends about events at school, about who likes whom, who "dissed" whom, or what a pain the homework assignment is. Meanwhile, television is on in the background, and they are listening to music on their iPods. At least some evidence confirms this picture, as Donald Roberts and Ulla Foehr describe in their article in this volume. Though concerns about the influence of media and technology on American youth are many and varied, especially prominent are fears that they impair cognitive development and academic achievement. Critics of television have long blamed the medium for various ills, including declines in standardized test scores, mental inactivity, and reduced attention and concentration. (3) Video games, computers, and the Internet have drawn similar charges. (4) In this article, we examine empirical evidence regarding the links between television and other electronic media, on the one hand, and learning and cognitive development in children and adolescents, on the other. We review research findings, in turn, on achievement, language and symbol systems, visual and spatial skills, problem-solving skills, attention, and, finally, hypertext. …


Image Analysis for Face Recognition


title: Adaptation Based on Generalized Discrepancy

abstract: We present a new algorithm for domain adaptation improving upon a discrepancy minimization algorithm, (DM), previously shown to outperform a number of algorithms for this problem. Unlike many previously proposed solutions for domain adaptation, our algorithm does not consist of a fixed reweighting of the losses over the training sample. Instead, the reweighting depends on the hypothesis sought. The algorithm is derived from a less conservative notion of discrepancy than the DM algorithm called generalized discrepancy. We present a detailed description of our algorithm and show that it can be formulated as a convex optimization problem. We also give a detailed theoretical analysis of its learning guarantees which helps us select its parameters. Finally, we report the results of experiments demonstrating that it improves upon discrepancy minimization in several tasks.


title: Seeing 3D Chairs: Exemplar Part-Based 2D-3D Alignment Using a Large Dataset of CAD Models

abstract: This paper poses object category detection in images as a type of 2D-to-3D alignment problem, utilizing the large quantities of 3D CAD models that have been made publicly available online. Using the "chair" class as a running example, we propose an exemplar-based 3D category representation, which can explicitly model chairs of different styles as well as the large variation in viewpoint. We develop an approach to establish part-based correspondences between 3D CAD models and real photographs. This is achieved by (i) representing each 3D model using a set of view-dependent mid-level visual elements learned from synthesized views in a discriminative fashion, (ii) carefully calibrating the individual element detectors on a common dataset of negative images, and (iii) matching visual elements to the test image allowing for small mutual deformations but preserving the viewpoint and style constraints. We demonstrate the ability of our system to align 3D models with 2D objects in the challenging PASCAL VOC images, which depict a wide variety of chairs in complex scenes.


title: Development of a cluster burst pulse generator based on a SOS diode switch for bioelectrics applications

abstract: Nanosecond and sub-nanosecond high voltage pulses can provide new biological applications. A cancer treatment by an ultra-short pulse high electric field is one of them. High power pulsed electromagnetic wave has been proposed to apply the high electric field for that treatment. This work focuses on the design of a compact high power pulsed electromagnetic wave generator using a nanosecond pulsed power generator for the cancer treatment. In this study, we have developed the pulsed power generator that outputs the multiple burst pulses in a cluster continuously. The generator uses a magnetic switch instead of the gap switch and uses a SOS diode with current cutoff time of nanoseconds. The cluster pulse has 7 burst pulses that are repeated at 2 Mpps. The frequency of the burst pulse is 50 MHz. The peak output voltage is 7.52 kV.


title: Deep Multilingual Correlation for Improved Word Embeddings

abstract: Word embeddings have been found useful for many NLP tasks, including part-of-speech tagging, named entity recognition, and parsing. Adding multilingual context when learning embeddings can improve their quality, for example via canonical correlation analysis (CCA) on embeddingsfromtwo languages. In this paper, we extend this idea to learn deep non-linear transformations of word embeddings of the two languages, using the recently proposed deep canonical correlation analysis. The resulting embeddings, when evaluated on multiple word and bigram similarity tasks, consistently improve over monolingual embeddings and over embeddings transformed with linear CCA.


title: Beyond co-existence: Exploiting WiFi white space for Zigbee performance assurance

abstract: Recent years have witnessed the increasing adoption of ZigBee technology for performance-sensitive applications such as wireless patient monitoring in hospitals. However, operating in unlicensed ISM bands, ZigBee devices often yield unpredictable throughput and packet delivery ratio due to the interference from ever increasing WiFi hotspots in 2.4 GHz band. Our empirical results show that, although WiFi traffic contains abundant white space, the existing coexistence mechanisms such as CSMA are surprisingly inadequate for exploiting it. In this paper, we propose a novel approach that enables ZigBee links to achieve assured performance in the presence of heavy WiFi interference. First, based on statistical analysis of real-life network traces, we present a Pareto model to accurately characterize the white space in WiFi traffic. Second, we analytically model the performance of a ZigBee link in the presence of WiFi interference. Third, based on the white space model and our analysis, we develop a new ZigBee frame control protocol called WISE, which can achieve desired trade-offs between link throughput and delivery ratio. Our extensive experiments on a testbed of 802.11 netbooks and 802.15.4 TelosB motes show that, in the presence of heavy WiFi interference, WISE achieves 4× and 2× performance gains over B-MAC and a recent reliable transmission protocol, respectively, while only incurring 10.9% and 39.5% of their overhead.


title: Differential Expression of Endophilin 1 and 2 Dimers at Central Nervous System Synapses

abstract: Endophilin 1 is proposed to participate in synaptic vesicle biogenesis through SH3 domain-mediated interactions with the polyphosphoinositide phosphatase synaptojanin and the GTPase dynamin. Endophilin family members have also been identified as binding partners for a number of diverse cellular proteins. We define here the endophilin 1-binding site within synaptojanin 1 and show that this sequence independently and selectively purifies from brain extracts endophilin 1 and a closely related protein, endophilin 2. Endophilin 2, like endophilin 1, is highly expressed in brain, concentrated in nerve terminals, and found in complexes with synaptojanin and dynamin. Although a fraction of endophilins 1 and 2 coexist in the same complex, the distribution of these endophilin isoforms among central synapses only partially overlaps. Endophilins 1 and 2 are found predominantly as stable dimers through a predicted coiled-coil domain in their conserved NH2-terminal moiety. Dimerization may allow endophilins to link a number of different cellular targets to the endocytic machinery.


title: Analytical Description and Design of Printed Dipole Arrays for Wideband Wide-Scan Applications

abstract: Wideband arrays of printed dipoles with inter-element loads are investigated. A closed-form expression for the active input impedance of the array element is derived with a spectral domain approach. The procedure is based on an extension of the Green's function formulation for arrays of connected dipoles to the case of dipoles coupled via loads. The accuracy of the formulation is investigated with the aid of parametric analyses. The model can be used in the preliminary design of wideband printed dipoles for the dimensioning of the radiating elements. The procedure remains accurate also for arrays of dipoles that are not tightly coupled, for which and the current along the element assumes a resonant-like sinusoidal distribution.


title: Repeatability and Reproducibility of Decisions by Latent Fingerprint Examiners

abstract: The interpretation of forensic fingerprint evidence relies on the expertise of latent print examiners. We tested latent print examiners on the extent to which they reached consistent decisions. This study assessed intra-examiner repeatability by retesting 72 examiners on comparisons of latent and exemplar fingerprints, after an interval of approximately seven months; each examiner was reassigned 25 image pairs for comparison, out of total pool of 744 image pairs. We compare these repeatability results with reproducibility (inter-examiner) results derived from our previous study. Examiners repeated 89.1% of their individualization decisions, and 90.1% of their exclusion decisions; most of the changed decisions resulted in inconclusive decisions. Repeatability of comparison decisions (individualization, exclusion, inconclusive) was 90.0% for mated pairs, and 85.9% for nonmated pairs. Repeatability and reproducibility were notably lower for comparisons assessed by the examiners as “difficult” than for “easy” or “moderate” comparisons, indicating that examiners' assessments of difficulty may be useful for quality assurance. No false positive errors were repeated (n = 4); 30% of false negative errors were repeated. One percent of latent value decisions were completely reversed (no value even for exclusion vs. of value for individualization). Most of the inter- and intra-examiner variability concerned whether the examiners considered the information available to be sufficient to reach a conclusion; this variability was concentrated on specific image pairs such that repeatability and reproducibility were very high on some comparisons and very low on others. Much of the variability appears to be due to making categorical decisions in borderline cases.


Neural Syntactic Generative Models with Exact Marginalization


title: An iterative learning approach to formation control of multi-agent systems

abstract: Abstract In this paper, an efficient framework is proposed to the formation control problem of multiple agents with unknown nonlinear dynamics, by means of the iterative learning approach. In particular, a distributed D-type iterative learning scheme is developed for the multi-agent system with switching topology, whose switching time and sequence are allowed to be varied at different iterations according to the actual trajectories of agents, and a sufficient condition is derived to ensure that the desired formation can be always preserved from the initial starting location to the final one after some iterations. Simulation results are provided to verify the effectiveness of the proposed approach.


title: Analysis of collaborative savings and cost allocation techniques for the cooperative carrier facility location problem

abstract: Transport companies may cooperate to increase their efficiency levels by, for example, the exchange of orders or vehicle capacity. In this paper a new approach to horizontal carrier collaboration is presented: the sharing of distribution centres (DCs) with partnering organisations. This problem can be classified as a cooperative facility location problem and formulated as an innovative mixed integer linear programme. To ensure cooperation sustainability, collaborative costs need to be allocated fairly to the different participants. To analyse the benefits of cooperative facility location and the effects of different cost allocation techniques, numerical experiments based on experimental design are carried out on a UK case study. Sharing DCs may lead to significant cost savings up to 21.6%. In contrast to the case of sharing orders or vehicles, there are diseconomies of scale in terms of the number of partners and more collaborative benefit can be expected when partners are unequal in size. Moreover, results indicate that horizontal collaboration at the level of DCs works well with a limited number of partners and can be based on intuitively appealing cost sharing techniques, which may reduce alliance complexity and enforce the strength of mutual partner relationships.


The PMHT for Passive Radar in a DAB/DVB Network


title: Extension and retraction of axonal projections by some developing neurons in the leech depends upon the existence of neighboring homologues. I. The HA cells.

abstract: The role of homologues in the establishment of the pattern of axonal projections of identified segmentally homologous neurons was investigated by means of selective cell ablation and dye injection. The cells studied were the bilateral pairs of heart accessory (HA) neurons found in the fifth and sixth segmental ganglia of the leech ventral nerve cord. Homologues start their morphological differentiation with identical axonal projections, and segmental differences are manifested later, when specific branches stop growing and disappear. The deletion of single HA cells at early stages, however, permits these branches to survive in their ipsilateral homologues and to grow and take over the projections of the deleted neurons. In addition, if both HA homologues on the same side of the nerve cord, or three of the four HA cells, are deleted in an animal, the remaining HA cells often extend novel projections. These observations suggest that either competition for targets, inputs or growth factors, or direct interactions among homologous cells may play a role in the differentiation of segment specific patterns of axonal projections.


title: In-lined reference monitoring in Prolog

abstract: A Prolog implementation of an In-lined Reference Monitoring system prototype for Adobe ActionScript Bytecode programs is presented. Prolog provides an elegant framework for implementing IRM’s. Its declarative and reversible nature facilitate the dual tasks of binary parsing and code generation, greatly simplifying many otherwise difficult IRM implementation challenges. The approach is demonstrated via the enforcement of several security policies on real-world Adobe Flash applets and AIR applications.


title: A transformerless high boost DC-DC converter for use in medium / high voltage applications

abstract: The proliferation of distributed energy resources has prompted interest in the expansion of DC power systems. One critical technological limitation that hinders this expansion is the absence of high step-down and high step-up DC converters for interconnecting DC systems. This work attempts to address the latter of these limitations. This paper presents a new transformerless high boost DC-DC converter intended for use as an interconnect between DC systems. With a conversion ratio of 1:10, the converter offers significantly higher boost ratio than the conventional non-isolated boost converter. It is designed to operate at medium to high voltage (> 1kV), and provides high voltage dc/dc gain (>5). Based on a current fed resonant topology, the design is well matched to available IGBT switch technology that enables use of relatively high switching frequencies yet accommodates the IGBTs inability to provide reverse blocking functionality. An advanced steady state model suitable for analysis of this converter is presented together with an experimental evaluation of the converter.


title: Improving the efficiency of dynamic malware analysis

abstract: Each day, security companies see themselves confronted with thousands of new malware programs. To cope with these large quantities, researchers and practitioners alike have developed dynamic malware analysis systems. These systems automatically execute a program in a controlled environment and produce a report describing the program's behavior. During the last three years, the number of malware programs appearing each day has increased by a factor of ten, and this number is expected to continue to grow. To keep pace with these developments without causing even more hardware costs for operating dynamic analysis systems, we have developed a technique that drastically reduces the overall analysis time. Our solution is based on the insight that the huge number of new malicious files is due to mutations of only a few malware programs. To save analysis time, we suggest a technique that avoids performing a full analysis of the same polymorphic file multiple times. In an experiment conducted on a set of 10,922 randomly chosen executable files, our prototype implementation was able to avoid a full dynamic analysis in 25.25 percent of the cases.


title: Novel amphoteric polyurethane dispersions with postpolymerization crosslinking function derived from hydroxylated tung oil: synthesis and properties

abstract: Novel hydroxylated tung oil-based amphoteric polyurethane polymer dispersions (HTO-AmPUDs) with postpolymerization crosslinking capability were synthesized and found to be reversibly stable in both acidic and basic media. Reactive HTO-AmPUDs showing surface activity and film properties adjustable with crosslinking are potentially new polymeric components and surfactants in polymer alloy synthesis.


title: Typhoon forecasting in the Philippines using an optimal Multilayer Feedforward Artificial Neural Network model trained in Resilient Propagation algorithm

abstract: Timely and accurate forecasting is indispensable during typhoons, and a rapid, efficient, and inexpensive forecasting method is a positive approach in reducing risks and fatalities in the population. This study developed 16 models for typhoon-forecasting in the Philippines using an artificial neural network (ANN). The models were based on three criteria: number of hidden neurons according to Kolmogorov's theorem; activation function (TANH or SIGMOID); and initial neuron weights (random or calculated). The optimal ANN model has 16 hidden neurons, calculated initial neuron weights, and used sigmoid activation function that obtained an error-rate of 0.070627 and mean prediction accuracy of 97.375 percent. The ANN model was trained using Resilient Propagation (RPROP) algorithm following the Multilayer Feedforward ANN (MLFANN) architecture capable of attaining ideal results with low Root-Mean-Square Errors. Neural network training used Join Typhoon Warning Center (JTWC) typhoon data from 1945 to 1995, and neural network evaluation used data from 1996 to 2013.


title: Manifold-ranking based image retrieval

abstract: In this paper, we propose a novel transductive learning framework named manifold-ranking based image retrieval (MRBIR). Given a query image, MRBIR first makes use of a manifold ranking algorithm to explore the relationship among all the data points in the feature space, and then measures relevance between the query and all the images in the database accordingly, which is different from traditional similarity metrics based on pair-wise distance. In relevance feedback, if only positive examples are available, they are added to the query set to improve the retrieval result; if examples of both labels can be obtained, MRBIR discriminately spreads the ranking scores of positive and negative examples, considering the asymmetry between these two types of images. Furthermore, three active learning methods are incorporated into MRBIR, which select images in each round of relevance feedback according to different principles, aiming to maximally improve the ranking result. Experimental results on a general-purpose image database show that MRBIR attains a significant improvement over existing systems from all aspects.


GronUP: Groningen User Profiling Notebook for PAN at CLEF 2016


title: Robust control and H∞-optimization - Tutorial paper

abstract: The paper presents a tutorial exposition of H∞-optimal regulation theory, emphasizing the relevance of the mixed sensitivity problem for robust control system design.


title: High availability in clouds: systematic review and research challenges

abstract: Cloud Computing has been used by different types of clients because it has many advantages, including the minimization of infrastructure resources costs, and its elasticity property, which allows services to be scaled up or down according to the current demand. From the Cloud provider point-of-view, there are many challenges to be overcome in order to deliver Cloud services that meet all requirements defined in Service Level Agreements (SLAs). High availability has been one of the biggest challenges for providers, and many services can be used to improve the availability of a service, such as checkpointing, load balancing, and redundancy. Beyond services, we can also find infrastructure and middleware solutions. This systematic review has as its main goal to present and discuss high available (HA) solutions for Cloud Computing, and to introduce some research challenges in this area. We hope this work can be used as a starting point to understanding and coping with HA problems in Cloud.


title: Learning Robot Activities from First-Person Human Videos Using Convolutional Future Regression

abstract: We design a new approach that allows robot learning of new activities from unlabeled human example videos. Given videos of humans executing an activity from their own viewpoint (i.e., first-person videos), our objective is to make the robot learn the temporal structure of the activity as its future regression network, and learn to transfer such model for its own motor execution. We present a new fully convolutional neural network architecture to regress the intermediate scene representation corresponding to the future frame, thereby enabling explicit forecasting of future hand locations given the current frame. The full version of the paper is available as [2].


title: Support Vector Machines Applied to Face Recognition

abstract: Face recognition is a K class problem. where K is the number of known individuals; and support vector machines (SVMs) are a binary classification method. By reformulating the face recognition problem and reinterpreting the output of the SVM classifier. we developed a SVM -based face recognition algorithm. The face recognition problem is formulated as a problem in difference space. which models dissimilarities between two facial images. In difference space we formulate face recognition as a two class problem. The classes are: dissimilarities between faces of the same person. and dissimilarities between faces of different people. By modifying the interpretation of the decision surface generated by SVM. we generated a similarity metric between faces that is learned from examples of differences between faces. The SVM-based algorithm is compared with a principal component analysis (PCA) based algorithm on a difficult set of images from the FERET database. Performance was measured for both verification and identification scenarios. The identification performance for SVM is 77-78% versus 54% for PCA. For verification. the equal error rate is 7% for SVM and 13% for PCA.


title: A 40 Gb/s 74.9 mW PAM4 receiver with novel clock and data recovery

abstract: A 40 Gb/s PAM4 receiver with novel digital clock and data recovery (CDR) and one tap decision feedback equalizer (DFE) has been presented. Without sophisticated transition detection and selection modules, the proposed CDR utilizes three bang-bang phase detectors sampling all the transitions to detect the phase error between the data and clock, achieving larger transition density and CDR bandwidth. Eight interleaved 5 GHz clocks with sense amplifiers are utilized to sample the data and edge, decreasing the power consumption of DFE. Two serial sense amplifiers are used to improve the gain, while two sets of phase interpolators are implemented to reduce the delay of the DFE loop. The 40 Gb/s PAM4 receiver is realized in 65nm CMOS technology. It provides as much as 16.7 dB equalization with linear equalizer and DFE. The overall power consumption is 74.9 mW at 1.2V supply, achieving a power efficiency of 1.87pJ/bit.


title: Optimal sizing and siting techniques for distributed generation in distribution systems: A review

abstract: To extract the maximum potential advantages in light of environmental, economical and technical aspects, the optimum installation and sizing of Distributed Generation (DG) in distribution network has always been challenging for utilities as well as customers. The installation of DG would be of maximum benefit where setting up of central power generating units are not practical, or in remote and small areas where the installation of transmission lines or availability of unused land is out of question. The objective of optimal installation of DG in distribution system is to achieve proper operation of distribution networks with minimization of the system losses, improvement of the voltage profile, enhanced system reliability, stability and loadability etc. In this respect analytical (classical) methods, although well-matched for small systems, perform adversely for large and complex objective functions. Unlike the analytical (classical) methods, the intelligent techniques for optimal sizing and siting of DGs are speedy, possess good convergence characteristics, and are well suited for large and complex systems. However, to find a global optimal solution of complex multi-objective problems, a hybrid of two or more meta-heuristic optimization techniques give more effective and reliable solution. This paper presents the fundamentals of DG and DG technologies review the classical and heuristic approaches for optimal sizing and placement of DG units in distribution networks and study their impacts on utilities and customers.


title: A Computational Architecture Based on RFID Sensors for Traceability in Smart Cities

abstract: Information Technology and Communications (ICT) is presented as the main element in order to achieve more efficient and sustainable city resource management, while making sure that the needs of the citizens to improve their quality of life are satisfied. A key element will be the creation of new systems that allow the acquisition of context information, automatically and transparently, in order to provide it to decision support systems. In this paper, we present a novel distributed system for obtaining, representing and providing the flow and movement of people in densely populated geographical areas. In order to accomplish these tasks, we propose the design of a smart sensor network based on RFID communication technologies, reliability patterns and integration techniques. Contrary to other proposals, this system represents a comprehensive solution that permits the acquisition of user information in a transparent and reliable way in a non-controlled and heterogeneous environment. This knowledge will be useful in moving towards the design of smart cities in which decision support on transport strategies, business evaluation or initiatives in the tourism sector will be supported by real relevant information. As a final result, a case study will be presented which will allow the validation of the proposal.


title: THE FEASIBILITY OF IDENTIFYING SPEEDING-RELATED AND FATIGUE-RELATED CRASHES IN POLICE-REPORTED MASS CRASH DATA

abstract: During 1999, the Transport Accident Commission suggested that Monash University Accident Research Centre (MUARC) undertake a project to examine the possibility of better defining crashes involving speeding and fatigue based on an analysis of Police-reported mass accident data in Victoria. It was proposed to obtain data from recent in-depth crash investigations in which the degree of speeding and/or fatigue was determined objectively. An in-depth crash study conducted by the NHMRC Road Accident Research Unit, RARU, of University of Adelaide, used accident reconstruction techniques to determine free-travelling vehicle speeds of crashed vehicles. This study was used in the development of a procedure to identify speed in Victorian Police reported mass crash data. Two types of models were developed; one that included only main effects and assumed no interactions between factors, and a second model that allowed for interactions and modelled them explicitly. The significant factors obtained from these models were used to develop indicator functions that estimated the travelling speed of a vehicle prior to a crash. (a)


title: Optimizing Daily Agent Scheduling in a Multiskill Call Center

abstract: We examine and compare simulation-based algorithms for solving the agent scheduling problem in a multiskill call center. This problem consists in minimizing the total costs of agents under constraints on the expected service level per call type, per period, and aggregated. We propose a solution approach that combines simulation with integer or linear programming, with cut generation. In our numerical experiments with realistic problem instances, this approach performs better than all other methods proposed previously for this problem. We also show that the two-step approach, which is the standard method for solving this problem, sometimes yield solutions that are highly suboptimal and inferior to those obtained by our proposed method.


title: Energy Aware Resource Management for MapReduce Jobs with Service Level Agreements in Cloud Data Centers

abstract: Clouds which continue to garner interest from practitioners in industry and academia require effective energy aware resource managers to leverage processing power of underlying resources while minimizing energy consumption in global data centers. We devise a novel Energy Aware MapReduce Resource Manager for an open system, called EAMR-RM, that can effectively perform matchmaking and scheduling of MapReduce jobs each of which is characterized by a Service Level Agreement (SLA) for performance that includes a client specified execution time and a deadline such that energy consumption is minimized. Results of the discrete event simulation-based performance analysis demonstrate that the proposed technique can effectively satisfy SLA requirements while achieving up to a 45% reduction in energy consumption compared to approaches which do not consider energy in resource management decisions.


title: Word-to-region attention network for visual question answering

abstract: Visual attention, which allows more concentration on the image regions that are relevant to a reference question, brings remarkable performance improvement in Visual Question Answering (VQA). Most VQA attention models employ the entire reference question representation to query relevant image regions. Nonetheless, only certain salient words of the question play an effective role in an attention operation. In this paper, we propose a novel Word-to-Region Attention Network (WRAN), which can 1) simultaneously locate pertinent object regions instead of a uniform grid of image regions of euqal size and identify the corresponding words of the reference question; as well as 2) enforce consistency between image object regions and core semantics in questions. We evaluate the proposed model on the VQA v1.0 and VQA v2.0 datasets. Experimental results demonstrate the superiority of the proposed model as compared to the state-of-the-arts.


title: Alterations in empathic responding among women with posttraumatic stress disorder associated with childhood trauma

abstract: Objective ::: ::: Although studies increasingly point toward problems with social cognition among individuals with posttraumatic stress disorder (PTSD), few studies have assessed empathic responding. The aim of the current study was to investigate empathic responding in women with PTSD related to childhood trauma, and the contribution of parental bonding to empathic abilities in this sample. ::: ::: ::: ::: Methods ::: ::: Participants with PTSD (n = 29) and sex- and age-matched healthy controls (n = 20) completed two self-report empathy measures, the Interpersonal Reactivity Index (IRI) and the Toronto Empathy Questionnaire (TEQ), and a self-report measure of attachment, the Parental Bonding Instrument (PBI). ::: ::: ::: ::: Results ::: ::: Women with PTSD, relative to controls, reported significantly lower levels of empathic concern (r = 0.29) and perspective taking (r = 0.30), yet significantly higher levels of personal distress (r = 0.45) on the IRI. Women with PTSD also reported elevated scores on the TEQ (η2 = 0.13). Levels of paternal care on the PBI, rather than childhood trauma severity or PTSD symptom severity best predicted perspective taking scores on the IRI in the PTSD sample (R2 = 0.20). ::: ::: ::: ::: Conclusion ::: ::: Women with PTSD associated with childhood trauma reported alterations among different domains of empathic functioning that may be related to low levels of paternal care.


title: The method of synonyms extraction from unannotated corpus

abstract: The structuring of large volumes of e-documents assumes the organization of text on several levels, namely paragraphs, sentences, phrases, words. Methods of lexical paradigms extraction using statistical analysis were developed long ago. In this paper we attempt to move from lexical correlatives to the list of synonyms on various levels of generalization on the basis of local and global contexts' statistics.


title: Defeating the Homogeneity Assumption

abstract: The statistical NLP and IR literatures tend to make a “homogeneity assumption” about the distribution of terms, either by adopting a “bag of words” model, or in their treatment of function words. In this paper we develop a notion of homogeneity detection to a level of statistical significance, and conduct a series of experiments on different datasets, to show that the homogeneity assumption does not generally hold. We show that it also does not hold for function words. Importantly, datasets and document collections are found not to be neutral with respect to the property of homogeneity, even for function words. The homogeneity assumption is defeated substantially even for collections known to contain similar documents, and more drastically for diverse collections. We conclude that it is statistically unreasonable to assume that word distribution within a corpus is homogeneous. Because homogeneity findings differ substantially between different collections, we argue for the use of homogeneity measures as a means of profiling datasets.


title: A novel hybrid feature selection via Symmetrical Uncertainty ranking based local memetic search algorithm

abstract: A novel correlation based memetic framework (MA-C) which is a combination of genetic algorithm (GA) and local search (LS) using correlation based filter ranking is proposed in this paper. The local filter method used here fine-tunes the population of GA solutions by adding or deleting features based on Symmetrical Uncertainty (SU) measure. The focus here is on filter methods that are able to assess the goodness or ranking of the individual features. Empirical study of MA-C on several commonly used datasets from the large-scale Gene expression datasets indicates that it outperforms recent existing methods in the literature in terms of classification accuracy, selected feature size and efficiency. Further, we also investigate the balance between local and genetic search to maximize the search quality and efficiency of MA-C.


title: Extension of technology acceptance model by using system usability scale to assess behavioral intention to use e-learning

abstract: This study examines the acceptance of technology and behavioral intention to use learning management systems (LMS). In specific, the aim of the research reported in this paper is to examine whether students ultimately accept LMSs such as eClass and the impact of behavioral intention on their decision to use them. An extended version of technology acceptance model has been proposed and used by employing one of the most reliable measures of perceived eased of use, the System Usability Scale. 345 university students participated in the study. The data analysis was based on partial least squares method. The majority of the research hypotheses were confirmed. In particular, social norm, system access and self-efficacy were found to significantly affect behavioral intention to use. As a result, it is suggested that e-learning developers and stakeholders should focus on these factors to increase acceptance and effectiveness of learning management systems.


title: Predictors of disruptive behavior, developmental delays, anxiety, and affective symptomatology among institutionally reared romanian children.

abstract: ABSTRACT Objective To examine the associations between duration of institutionalization, age at abandonment, and mental and physical health outcomes of young institutionalized children in Romania and to examine patterns of associations between placement history, physical growth variables, and psychiatric symptoms. Method Institutionalized children (ages 2–6 years) in a Romanian orphanage were studied through caregiver reports. Orphanage staff completed questionnaires on the children's psychiatric symptoms and physical growth measurements and placement histories on the children. Results Children showed high rates of developmental delays, anxiety/affective symptoms, and physical growth delays. Patterns of associations between history and growth variables (duration of institutionalization, age at abandonment, and physical stature) and psychiatric symptom clusters were examined using three sequential multiple regressions. Longer duration of institutionalization and shorter physical stature were significantly associated with anxiety/affective symptoms ( F 3,47 = 6.49, p F 3,47 = 5.603, p F 3,47 = 2.832, p Conclusions Institutionalized children demonstrate high rates of psychiatric symptoms. Duration of institutionalization, physical stature, and age at abandonment differentially relate to psychiatric symptoms. Findings are interpreted in light of implications for intervention and social policy.


title: Lexicon Knowledge Extraction with Sentiment Polarity Computation

abstract: Sentiment analysis is one of the most popular natural language processing techniques. It aims to identify the sentiment polarity (positive, negative, neutral or mixed) within a given text. The proper lexicon knowledge is very important for the lexicon-based sentiment analysis methods since they hinge on using the polarity of the lexical item to determine a text's sentiment polarity. However, it is quite common that some lexical items appear positive in the text of one domain but appear negative in another. In this paper, we propose an innovative knowledge building algorithm to extract sentiment lexicon knowledge through computing their polarity value based on their polarity distribution in text dataset, such as in a set of domain specific reviews. The proposed algorithm was tested by a set of domain microblogs. The results demonstrate the effectiveness of the proposed method. The proposed lexicon knowledge extraction method can enhance the performance of knowledge based sentiment analysis.


title: Effects of Amygdala Lesions on Reward-Value Coding in Orbital and Medial Prefrontal Cortex

abstract: Summary We examined the contribution of the amygdala to value signals within orbital prefrontal cortex (OFC) and medial prefrontal cortex (MFC). On each trial, monkeys chose between two stimuli that were associated with different quantities of reward. In intact monkeys, as expected, neurons in both OFC and MFC signaled the reward quantity associated with stimuli. Contrasted with MFC, OFC contained a larger proportion of neurons encoding reward quantity and did so with faster response latencies. Removing the amygdala eliminated these differences, mainly by decreasing value coding in OFC. Similar decreases occurred in OFC immediately before and after reward delivery. Although the amygdala projects to both OFC and MFC, we found that it has its greatest influence over reward-value coding in OFC. Notably, amygdala lesions did not abolish value coding in OFC, which shows that OFC's representations of the value of objects, choices, and outcomes depends, in large part, on other sources.


title: Lipid, adipokine and ghrelin levels in myocardial infarction patients with insulin resistance

abstract: Insulin resistance (IR) is a risk factor for ischaemic heart disease and myocardial infarction (MI). IR often manifests in MI and is regarded as an independent predictor of in-hospital mortality, which can provide early risk stratification for recurrent acute coronary events. The study enrolled 200 patients (130 males and 70 females aged 61.4 ± 1.12 years) diagnosed with ST elevation MI. At days 1 and 12 from the MI onset, IR levels and lipid profiles, as well as serum glucose, insulin, adipokine and ghrelin levels, were measured. Free fatty acid (FFA) levels had the most pronounced changes: IR patients had a 9-fold increase in FFA levels at day 1, and patients without IR had a 6-fold increase. Leptin levels at days 1 and 12, in IR patients were, on average, 1.5- and 2-fold higher compared to the controls and patients with no IR (р < 0.05). Leptin levels in IR patients were increased throughout the entire hospital stay. Resistin levels in IR patients were, on average, 1.4-fold higher throughout the entire hospital stay, while in non-IR patients, resistin levels were similar to the controls. Adiponectin levels in IR patients were decreased compared to the controls, while in patients with IR, they were similar to the controls. Both IR and non-IR MI patients had 3-fold and 3.7-fold lower ghrelin levels at day 1, respectively, compared to the controls. The correlation analysis showed a negative correlation between ghrelin and FFA (r = −0.48 р = 0.007), ghrelin and leptin (r = −0.4 р = 0.003), ghrelin and insulin (r = −0.54 р = 0.002), and ghrelin and glucose (r = −0.31 р = 0.002) in MI patients. Dyslipidaemia, along with insulinaemia and glycaemia, is one of the most significant IR risk factors in the acute and early recovery phases of MI. Dyslipidaemia is characterised by a high FFA level; an imbalance of leptin, resistin, and adiponectin; and a deficiency of ghrelin in the acute and early recovery periods of MI. FFA and ghrelin can be used as promising molecular markers to stratify the risk of recurrent acute coronary events and diabetes mellitus in MI patients.


title: Surface fitting and registration of point clouds using approximations of the unsigned distance function. Computer Aided Geometric Design 27

abstract: Many problems in computer aided geometric design and geometry processing are stated as least-squares optimizations. Least-squares problems are well studied and widely used but exhibit immanent drawbacks such as high sensitivity to outliers. For this reason, we consider techniques for the registration of point clouds and surface fitting to point sets based on the l"1-norm. We develop algorithms to solve l"1-registration and l"1-fitting problems and explore the emerging non-smooth minimization problems. We describe efficient ways to solve the optimization programs and present results for various applications.


title: Big Data Benchmark Compendium

abstract: The field of Big Data and related technologies is rapidly evolving. Consequently, many benchmarks are emerging, driven by academia and industry alike. As these benchmarks are emphasizing different aspects of Big Data and, in many cases, covering different technical platforms and uses cases, it is extremely difficult to keep up with the pace of benchmark creation. Also with the combinations of large volumes of data, heterogeneous data formats and the changing processing velocity, it becomes complex to specify an architecture which best suits all application requirements. This makes the investigation and standardization of such systems very difficult. Therefore, the traditional way of specifying a standardized benchmark with pre-defined workloads, which have been in use for years in the transaction and analytical processing systems, is not trivial to employ for Big Data systems. This document provides a summary of existing benchmarks and those that are in development, gives a side-by-side comparison of their characteristics and discusses their pros and cons. The goal is to understand the current state in Big Data benchmarking and guide practitioners in their approaches and use cases.


title: Holoportation: Virtual 3D Teleportation in Real-time

abstract: We present an end-to-end system for augmented and virtual reality telepresence, called Holoportation. Our system demonstrates high-quality, real-time 3D reconstructions of an entire space, including people, furniture and objects, using a set of new depth cameras. These 3D models can also be transmitted in real-time to remote users. This allows users wearing virtual or augmented reality displays to see, hear and interact with remote participants in 3D, almost as if they were present in the same physical space. From an audio-visual perspective, communicating and interacting with remote users edges closer to face-to-face communication. This paper describes the Holoportation technical system in full, its key interactive capabilities, the application scenarios it enables, and an initial qualitative study of using this new communication medium.


GERBIL – Benchmarking Named Entity Recognition and Linking consistently


title: of LaborBirth Order Matters: The Effect of Family Size and Birth Order on Educational Attainment

abstract: We use unique retrospective family background data from the 2003 British Household Panel Survey to explore the degree to which family size and birth order affect a child's subsequent educational attainment. Theory suggests a trade off between child quantity and 'quality'. Family size might adversely affect the production of child quality within a family. A number of arguments also suggest that siblings are unlikely to receive equal shares of the resources devoted by parents to their children's education. We construct a composite birth order index that effectively purges family size from birth order and use this to test if siblings are assigned equal shares in the family's educational resources. We find that they are not, and that the shares are decreasing with birth order. Controlling for parental family income, parental age at birth and family level attributes, we find that children from larger families have lower levels of education and that there is in addition a separate negative birth order effect. In contrast to Black, Devereux and Kelvanes (2005), the family size effect does not vanish once we control for birth order. Our findings are robust to a number of specification checks.


title: Bilingual Advantages in Inhibition or Selective Attention: More Challenges

abstract: A large sample (N=141) of college students participated in both a conjunctive visual search task and an ambiguous figures task that have been used as tests of selective attention. Tests for effects of bilingualism on selective attention were conducted by both partitioning the participants into bilinguals and monolinguals and by treating bilingualism as a continuous variable, but there were no effects of bilingualism in any of the tests. Bayes factor analyses confirmed that the evidence substantially favored the null hypothesis. These new findings mesh with failures to replicate language-group differences in congruency-sequence effects, inhibition-of-return, switch costs, and working memory capacity. The evidence that bilinguals are better than monolinguals at attentional control is equivocal at best.


title: A new ZVT snubber cell for PWM-CCM-PFC boost converters

abstract: In this study, a new Zero Voltage Transition (ZVT) snubber cell is proposed. Zero Voltage Transition (ZVT) turn on and zero voltage switching (ZVS) turn off are achieved for the main switch. Also, the auxiliary switch is turned on with zero current switching (ZCS) and turned off with ZVS. The main advantage of this snubber cell is that it reduces the current stresses of the auxiliary circuit components significantly. The proposed snubber cell, which has been used in Pulse Width Modulated (PWM) — Continues Current Modulation (CCM) — Power Factor Correction (PFC) boost converter, has been verified both by simulation software and experimentally.


title: A SVM-Based committee machine for prediction of Hong Kong horse racing

abstract: In Hong Kong and Macao, horse racing is the most famous gambling with a long history. This study proposes a novel approach to predict the horse racing results in Hong Kong. A three-years-long race records dataset obtained from Hong Kong Jockey Club was used for training a support-vector-machine-based committee machine. Bet suggestions could be made to gamblers by studying previous data though machine learning. In experiment, there are 2691 races and 33532 horse records obtained. Experiments focus on accuracy and return rate were conducted separately through constructing a committee machine. Experimental results showed that the accuracy and return rate achieve 70.86% and 800,000% respectively.


title: A 512-Gb 3-b/Cell 64-Stacked WL 3-D-NAND Flash Memory

abstract: A 64-word-line-stacked 512-Gb 3-b/cell 3-D NAND flash memory is presented. After briefly examining the challenges that occur to a stack, several technologies are suggested to resolve the issues. For performance enhancement, a novel program method hiding two-page data loading time is presented. This paper also discusses an electrical annealing improving reliability characteristic by removing holes in shallow traps. In addition, a valley tracking read for reducing timing overhead at a read retry is introduced by fast finding optimal read levels. Finally, a high-speed self-test mode for IO operation is presented. The chip, designed with the fourth generation of V-NAND technology, achieved an areal density of 3.98 Gb/mm2 and operated up to 1 Gb/s at 1.2 V.


title: DeepCredit: Exploiting User Cickstream for Loan Risk Prediction in P2P Lending

abstract: Peer-to-peer (P2P) lending or crowdlending,is a recent innovation allows a group of individual or institutional lenders tolend funds to individuals or businesses in return for interest payment on top of capital repayments.The rapid growth of P2P lending marketplaces has heightenedthe need to develop a support system to help lenders make sound lending decisions.But realizing such system is challenging in the absence of formal credit data used by the banking sector.In this paper, we attempt toexplore the possible connections between user credit riskand how users behave in the lendingsites.Wepresent the first analysis of user detailed clickstream datafrom a large P2P lending provider.Our analysisreveals that the users' sequences of repayment histories and financial activities in the lending site,have significant predictive value for their future loan repayments.In the light of this, we propose a deep architecture named DeepCredit, toautomatically acquire theknowledge of credit risk from the sequences of activities that users conduct on the site.Experiments on our large-scale real-world datasetshow that our model generates a high accuracy in predicting both loan delinquency and default, and significantly outperformsa numberof baselines and competitive alternatives.


Data Integration: The Current Status and the Way Forward