title: SpotCheck: designing a derivative IaaS cloud on the spot market

abstract: Infrastructure-as-a-Service (IaaS) cloud platforms rent resources, in the form of virtual machines (VMs), under a variety of contract terms that offer different levels of risk and cost. For example, users may acquire VMs in the spot market that are often cheap but entail significant risk, since their price varies over time based on market supply and demand and they may terminate at any time if the price rises too high. Currently, users must manage all the risks associated with using spot servers. As a result, conventional wisdom holds that spot servers are only appropriate for delay-tolerant batch applications. In this paper, we propose a derivative cloud platform, called SpotCheck, that transparently manages the risks associated with using spot servers for users. SpotCheck provides the illusion of an IaaS platform that offers always-available VMs on demand for a cost near that of spot servers, and supports all types of applications, including interactive ones. SpotCheck's design combines the use of nested VMs with live bounded-time migration and novel server pool management policies to maximize availability, while balancing risk and cost. We implement SpotCheck on Amazon's EC2 and show that it i) provides nested VMs to users that are 99.9989% available, ii) achieves nearly 5x cost savings compared to using equivalent types of on-demand VMs, and iii) eliminates any risk of losing VM state.


title: Research on the deficit irrigation scheduling to winter wheat at critical period based on crop modeling method

abstract: Less water at critical period has significant effect on crop yield. The deficit irrigation scheduling is important especially for the drought-prone area. The crop water production function frequently used for deficit irrigation analysis neither has the disadvantage in mechanism explanation. In this study, we use crop model instead of the crop water production function to study deficit irrigation scheduling issue for winter wheat in Jinan City, in North Plain of China. The crop model we employed is APSIM model. The model was calibrated and validated specifically for winter wheat in Jinan using data in 2000-2013 years. Three typical years were selected to represent the normal, light drought and drought conditions. There are 24 irrigation scheduling schemes obtained by combination of three deficit irrigation quotas, the 150 mm, 100 mm and 50 mm, and different irrigation dates from early April to mid-May. By simulating and comparing the ultimate yield of each irrigation scheduling for winter wheat in Jinan, we strongly suggest that: 1) if the antecedent soil moisture is low, perform irrigation at the beginning of critical period, 2) if it under drought stress, perform irrigation once in early May, and 3) if severe drought hits, perform irrigation twice, and allocating more quota for the second time.


title: A memory-efficient real-time non-copying garbage collector

abstract: Garbage collectors used in embedded systems such as Personal Java and Inferno or in operating systems such as SPIN must operate with limited resources and minimize their impact on application performance. Consequently, they must maintain short real-time pauses, low overhead, and a small memory footprint. Most garbage collectors, including the Treadmill algorithm, are inadequate because they sacrifice space for time. We have implemented a new Treadmill variant that provides good memory utilization by using real-time page-level management techniques that reduce fragmentation. A page-wise collection is used to locate pages of free memory, which are then dynamically reassigned between free lists as needed. Virtual memory is used to dynamically remap free pages into continuous ranges, and objects are allocated from under-utilized pages when needed. Finally, the use of header compaction and arbitrary free-list sizes reduces internal fragmentation. Our experiments demonstrate that we have substantially improved memory utilization without compromising latency or overhead, and that the new collector performs very well for SPIN's workloads and regular Modula-3 programs.


title: Combining Career Progression and Profile Matching in a Job Recommender System

abstract: In this paper we consider the problem of job recommendation, suggesting suitable jobs to users based on their profiles. We compare a baseline method treating users and jobs as documents, where suitability is measured using cosine similarity, with a model that incorporates job transitions trained on the career progressions of a set of users. We show that the job transition model outperforms cosine similarity. Furthermore, a cascaded system combining career transitions with cosine similarity generates more recommendations of a similar quality. The analysis is conducted by examining data from 2,400 LinkedIn users, and evaluated by determining how well the methods predict users’ current positions from their profiles and previous position history.


title: Performance analysis of encryption algorithms for Information Security

abstract: Information Security has become an important issue in data communication. Encryption algorithms have come up as a solution and play an important role in information security system. On other side, those algorithms consume a significant amount of computing resources such as CPU time, memory and battery power. Therefore it is essential to measure the performance of encryption algorithms. In this work, three encryption algorithms namely DES, AES and Blowfish are analyzed by considering certain performance metrics such as execution time, memory required for implementation and throughput. Based on the experiments, it has been concluded that the Blowfish is the best performing algorithm among the algorithms chosen for implementation.


title: Pancreas transplant alone.

abstract: PURPOSE OF REVIEW ::: The present article aims to review the current state of diabetes, including its treatment options, and highlight current issues in pancreas transplantation. ::: ::: ::: RECENT FINDINGS ::: Compared with other areas of transplantation, pancreas transplant/transplantation alone in the absence of kidney disease remains a relatively small field. As a consequence, reported new research articles are few in number, and often data regarding pancreas transplant/transplantation alone are mixed in with simultaneous kidney-pancreas and pancreas after kidney transplantation, which are covered separately. The prevalence of diabetes is reaching epidemic levels and continues to increase. New developments in diabetes care include the bionic pancreas and immunotherapy. Persistent efforts at gene and stem cell therapies are ongoing. Pancreas transplantation outcomes are improving, yet numbers are declining, even though pancreas transplantation still offers the most optimal glycemic control. ::: ::: ::: SUMMARY ::: Pancreas transplantation has improving outcomes but stands in competition with other diabetes management options.


title: Reduction of Rotor Losses in Multilayer Interior Permanent-Magnet Synchronous Motors by Introducing Novel Topology of Rotor Flux Barriers

abstract: A novel topology of flux barriers in multilayer interior permanent-magnet motors is proposed in order to reduce the rotor losses, including magnet eddy-current loss for the purpose of preventing thermal demagnetization of permanent magnets. First, the mechanism of rotor loss generation is investigated by using 3-D finite-element analysis that considers inverter carrier. The electromagnetic field is decomposed into time-harmonic components in order to understand the main factors that cause the rotor losses. From the observations, a novel topology of rotor flux barriers, which are jointed between poles, is invented. Finally, prototype motors are manufactured in order to confirm the validity of the calculation. From both results of the experiments and the calculations, it is revealed that the rotor loss of the proposed motor is significantly reduced, whereas the decrease in the peak torque is only a few percent, as compared with the conventional multilayer motors.


title: Toward Detecting Malicious Links in Online Social Networks through User Behavior

abstract: It is becoming increasingly difficult to ignore the importance of using online social networks (OSNs) for various purposes such as marketing, education, entertainment, and business. However, OSNs also open the door for harmful activities and behaviors. Committing financial fraud and propagating malware and spam advertisements are very common criminal actions that people engage in by accessing uniform resource locators (URLs). It has been reported that advanced attackers tend to exploit human flaws rather than system flaws, thus, users are targeted in social media threats by hour. This research aims to understand the state of literature on detecting malicious URLs in OSNs, with a focus on two major aspects: URL and OSN objects. Although the literature presents these two aspects in a different context, this paper will mainly focus on their features in relation to malicious URL detection using classification methods. We firstly review three features of URLs: lexical features, hosts, and domains, then we discuss their limitations. We then introduce social spam analytics and detection models using combined features from both URLs and OSNs, particularly the use of user profiles and posts together with URL features, to enhance the detection of malicious behavior. This combination can help in understanding the interests of the user either explicitly, by stating choices in the profile, or implicitly, by analyzing the post behavior, as the spammers do not maintain a regular interest and tend to exploit events or top trend topics.


title: Discrete Delaunay: Boundary extraction from voxel objects

abstract: We present a discrete approach for boundary extraction from 3D image data. The proposed technique is based on the duality between the Voronoi graph computed accross the digital boundary and the Delaunay triangulation. The originality of the approach is that algorithms perform only integer arithmetic and the method does not suffer from standard round problems and numerical instabilities in the case of floating point computations. This method has been applied both on segmented anatomical structures and on manufactured objects presenting corners and edges. The experimental results show that the method allows to produce a polygonal boundary representation which is guaranteed to be a 2-manifold. This representation is successfully transformed into a triangular quality mesh which meets all topological and geometrical requirements of applications such as augmented reality or simulation.


title: Green tracker: a tool for estimating the energy consumption of software

abstract: The energy consumption of computers has become an important environmental issue. This paper describes the development of Green Tracker, a tool that estimates the energy consumption of software in order to help concerned users make informed decisions about the software they use. We present preliminary results gathered from this system's initial usage. Ultimately the information gathered from this tool will be used to raise awareness and help make the energy consumption of software a more central concern among software developers.


title: Unconventional implant treatment: Primary teeth as functional provisional restoration

abstract: Replacement of hopeless retained primary teeth with dental implants offers various advantages, such as preservation of the crestal bone and elimination of the need to restore the adjacent teeth. Various methods of temporization have been advocated with the aim to support the peri-implant tissues and esthetics; however, longer chairside time is required to accurately reproduce the interproximal contacts and identify the location of the cervical margin. Recently, few case reports utilized a patient's natural teeth as provisional restorations supported by the immediately placed implants or splinted to the adjacent teeth. The case report presented here illustrates the implant placed through the crown of primary teeth in the maxillary and mandibular first molar and utilization of primary teeth as functional provisional restoration. These primary teeth acts as natural space maintainer, maintaining the peri-implant tissues, during the healing period of implant as well as minimize the chairside clinician's time for the fabrication of temporary prosthesis.


title: Research on key application issues of smart synchronous rectifier driver IC in LLC resonant converter

abstract: In this paper, several key application issues of smart synchronous rectifier (SR) driver IC which is applied in LLC resonant converter are studied and researched. With the smart SR diver IC applied to drive the secondary-side SRs, the LLC resonant converter can further improve the efficiency and be suitable for the application which requires high power density such as brick power modules. Many smart SR driver ICs have been produced and are widely used in this application. However, with the smart SR driver IC applied in the LLC resonant converter, the reverse current problem in secondary side may occur when the converter operates at the status of light load, start up and load transient. The reverse current will low down the efficiency of circuit or even break down the converter. This paper mainly studies and analyzes the reasons of the reverse current problem caused by the utilization of smart SR driver IC in LLC resonant converter in detail and proposes some simple solutions in theory as well. The experimental results are obtained from a 400W (12V/33A) power module prototype which verifies the theoretical analysis.


title: Multi-view ensemble learning: an optimal feature set partitioning for high-dimensional data classification

abstract: Multi-view ensemble learning has the potential to address issues related to the high dimensionality of data. It attempts to utilize all the relevant only discarding the irrelevant features. The view of a dataset is the sub-table of the training data with respect to a subset of the feature set. The problem of discarding the irrelevant features and obtaining subsets of the relevant features is useful for dimension reduction and dealing with the problem of having fewer training examples than even the reduced set of relevant features. A feature set partitioning resulting in the blocks of relevant features may not yield multiple-view-based classifiers with good classification performance. In this work the optimal feature set partition approach has been proposed. Further, the ensemble learning from views aims to maximize the performance of the classifier. The experiments study the performance of random feature set partitioning, attribute bagging, view generation using attribute clustering, view construction using genetic algorithm and OFSP proposed method. The blocks of relevant feature subsets are used to construct the multi-view classifier ensemble using K-nearest neighbor, Naive Bayesian and support vector machine algorithm applied to sixteen high-dimensional data sets from UCI machine learning repository. The performance parameters considered for comparison are classification accuracy, disagreement among the classifiers, execution time and percentage reduction of attributes.


title: Thread Scheduling for Multiprogrammed Multiprocessors

abstract: We present a user-level thread scheduler for shared-memory multiprocessors, and we analyze its performance under multiprogramming. We model multiprogramming with two scheduling levels: our scheduler runs at user-level and schedules threads onto a fixed collection of processes, while below this level, the operating system kernel schedules processes onto a fixed collection of processors. We consider the kernel to be an adversary, and our goal is to schedule threads onto processes such that we make efficient use of whatever processor resources are provided by the kernel. Our thread scheduler is a non-blocking implementation of the work-stealing algorithm. For any multithreaded computation with work T 1 and critical-path length T ∈ fty , and for any number P of processes, our scheduler executes the computation in expected time O(T 1 /P A + T ∈ fty P/P A ) , where P A is the average number of processors allocated to the computation by the kernel. This time bound is optimal to within a constant factor, and achieves linear speedup whenever P is small relative to the parallelism T 1 /T ∈ fty .


title: Fast and Accurate Time Series Classification with WEASEL

abstract: Time series (TS) occur in many scientific and commercial applications, ranging from earth surveillance to industry automation to the smart grids. An important type of TS analysis is classification, which can, for instance, improve energy load forecasting in smart grids by detecting the types of electronic devices based on their energy consumption profiles recorded by automatic sensors. Such sensor-driven applications are very often characterized by (a) very long TS and (b) very large TS datasets needing classification. However, current methods to time series classification (TSC) cannot cope with such data volumes at acceptable accuracy; they are either scalable but offer only inferior classification quality, or they achieve state-of-the-art classification quality but cannot scale to large data volumes. In this paper, we present WEASEL (Word ExtrAction for time SEries cLassification), a novel TSC method which is both scalable and accurate. Like other state-of-the-art TSC methods, WEASEL transforms time series into feature vectors, using a sliding-window approach, which are then analyzed through a machine learning classifier. The novelty of WEASEL lies in its specific method for deriving features, resulting in a much smaller yet much more discriminative feature set. On the popular UCR benchmark of 85 TS datasets, WEASEL is more accurate than the best current non-ensemble algorithms at orders-of-magnitude lower classification and training times, and it is almost as accurate as ensemble classifiers, whose computational complexity makes them inapplicable even for mid-size datasets. The outstanding robustness of WEASEL is also confirmed by experiments on two real smart grid datasets, where it out-of-the-box achieves almost the same accuracy as highly tuned, domain-specific methods.


title: Longitudinal Associations Among Relationship Satisfaction, Sexual Satisfaction, and Frequency of Sex in Early Marriage

abstract: The current research used two 8-wave longitudinal studies spanning the first 4–5 years of 207 marriages to examine the potential bidirectional associations among marital satisfaction, sexual satisfaction, and frequency of sex. All three variables declined over time, though the rate of decline in each variable became increasingly less steep. Controlling for these changes, own marital and sexual satisfaction were bidirectionally positively associated with one another; higher levels of marital satisfaction at one wave of assessment predicted more positive changes in sexual satisfaction from that assessment to the next and higher levels of sexual satisfaction at one wave of assessment predicted more positive changes in marital satisfaction from that assessment to the next. Likewise, own sexual satisfaction and frequency of sex were bidirectionally positively associated with one another. Additionally, partner sexual satisfaction positively predicted changes in frequency of sex and own sexual satisfaction among husbands, yet partner marital satisfaction negatively predicted changes in both frequency of sex and own sexual satisfaction. Controlling these associations, marital satisfaction did not directly predict changes in frequency of sex or vice versa. Only the association between partner sexual satisfaction and changes in own sexual satisfaction varied across men and women and none of the key effects varied across the studies. These findings suggest that sexual and relationship satisfaction are intricately intertwined and thus that interventions to treat and prevent marital distress may benefit by targeting the sexual relationship and interventions to treat and prevent sexual distress in marriage may benefit by targeting the marital relationship.


title: Modification of the motion characteristics of a one-mass linear vibratory conveyor

abstract: Linear vibratory conveyors are common equipment to convey goods. They are used in different industries such as for example the building, mining and food industry. The systems are often used for the supply of bulk goods into further processing operations. The goods to be conveyed, typically requesting a heavy-duty design of the conveyor and eccentric excitation drives with relatively high torque. Often the motion of these conveyors is not ideal with reference to the conveying process of the bulk goods. Motion speed and amplitude of the goods are not constant and blocking effects as well as separation effects can occur. The target of this article is to determine a dynamic model for the targeted displacement of the centre of elasticity of a one-mass conveyor, enabling an optimal motion of the conveyor by an optimized set of springs, connecting the conveying element to the frame.


title: Performance Evaluation of 3D Keypoint Detectors

abstract: In the past few years detection of repeatable and distinctive keypoints on 3D surfaces has been the focus of intense research activity, due on the one hand to the increasing diffusion of low-cost 3D sensors, on the other to the growing importance of applications such as 3D shape retrieval and 3D object recognition. This work aims at contributing to the maturity of this field by a thorough evaluation of several recent 3D keypoint detectors. A categorization of existing methods in two classes, that allows for highlighting their common traits, is proposed, so as to abstract all algorithms to two general structures. Moreover, a comprehensive experimental evaluation is carried out in terms of repeatability, distinctiveness and computational efficiency, based on a vast data corpus characterized by nuisances such as noise, clutter, occlusions and viewpoint changes.


title: Automatically discover vulnerability through similar functions

abstract: In this paper a new vulnerability discovery method based on symbolic execution and machine learning is proposed. Given a vulnerable function, similar function set can be located efficiently based on machine learning technique. Function call graphs of each function from the similar function set are obtained by dataflow analysis of the whole program. At last, the symbolic execution engine KLEE is used to travel certain paths of the tested program according to the function call graphs obtained. With the help of the function call graphs, KLEE has no need to travel all of the program paths that can absolutely increase the efficiency of symbolic execution. Upon the dual effect of machine learning and symbolic execution, the method proposed is more efficient than solely using symbolic execution and is more precise than solely using machine learning.


title: Ultrasound and the Pavlik harness in CDH

abstract: Sixty-nine hips in 62 patients were treated by the Pavlik harness for congenital dislocation. Ultrasonography showed three degrees of residual head displacement when the harness was first applied. In type A, the femoral head showed contact with the inner posterior wall of the acetabulum. In type B, it contacted the posterior margin of the socket, with its centre at or anterior to the acetabular rim. In type C, the femoral head was displaced outside the socket, with its centre posterior to the acetabular rim. All 51 hips with type A displacement remained reduced. Of nine hips of type B, five were reduced, but the other four were not. None of the nine hips with type C dislocation became reduced with continued use of the harness. The Pavlik harness is indicated for type A and some type B dislocations, but the latter need daily ultrasound monitoring, with a change in method of treatment if type C displacement appears or if the hip is not reduced within one or two weeks. Treatment by Pavlik harness is not indicated in hips with type C dislocations.


title: Development and experimental verification of counter-rotating dual rotor/dual generator wind turbine: Generating, yawing and furling

abstract: This paper deals with the control techniques for counter-rotating dual rotor/dual generator(CRDRDG) wind turbine and its experimental verifications of the performance in the field. The authors have already proposed the dynamic model and some control techniques in the previous researches, which exploit some features of the CRDRDG structures, and verified the controllers by numerical simulations and lab-scale experiments. In this paper, we present experimental results obtained using a full scale (15[kW]) CRDRDG in the field. To perform the field experiments, an integrated control algorithm is proposed. The experimental results show that the integrated control algorithm effectively maximizes the output power of the CRDRDG wind turbine while operating in the region below rated power, and limits the output power while operating in the region above rated power. Furthermore, the experimental data reveals an additional advantage of the counter-rotating dual rotor system. The data shows that the system can lower the tip speed ratio at which Cp curve attains its maximum, almost by half in comparison to the conventional three bladed single rotor system, possibly reducing the noise caused by rotor blades.


title: Short Group Signatures

abstract: We construct a short group signature scheme. Signatures in our scheme are approximately the size of a standard RSA signature with the same security. Security of our group signature is based on the Strong Diffie-Hellman assumption and a new assumption in bilinear groups called the Decision Linear assumption. We prove security of our system, in the random oracle model, using a variant of the security definition for group signatures recently given by Bellare, Micciancio, and Warinschi.


title: A Review on Big Data Analysis and Internet of Things

abstract: Billions of devices are currently connected to the Internet forming the Internet of Things (IoT). These devices are generating enormous amount of data. The transmission and processing of this data is a challenging task. This paper aims at reviewing the role of big data in IoT through discussion of its protocols and architectural structure. Methods for testing these protocols and security considerations are presented. Multiple IoT applications and future research directions are also discussed.


title: The Tower of Hanoi - Myths and Maths

abstract: This is the first comprehensive monograph on the mathematical theory of the solitaire game The Tower of Hanoi which was invented in the 19th century by the French number theorist douard Lucas. The book comprises a survey of the historical development from the games predecessors up to recent research in mathematics and applications in computer science and psychology. Apart from long-standing myths it contains a thorough, largely self-contained presentation of the essential mathematical facts with complete proofs, including also unpublished material. The main objects of research today are the so-called Hanoi graphs and the related Sierpiski graphs. Acknowledging the great popularity of the topic in computer science, algorithms and their correctness proofs form an essential part of the book. In view of the most important practical applications of the Tower of Hanoi and its variants, namely in physics, network theory, and cognitive (neuro)psychology, other related structures and puzzles like, e.g., the Tower of London, are addressed. Numerous captivating integer sequences arise along the way, but also many open questions impose themselves. Central among these is the famed Frame-Stewart conjecture. Despite many attempts to decide it and large-scale numerical experiments supporting its truth, it remains unsettled after more than 70 years and thus demonstrates the timeliness of the topic. Enriched with elaborate illustrations, connections to other puzzles and challenges for the reader in the form of (solved) exercises as well as problems for further exploration, this book is enjoyable reading for students, educators, game enthusiasts and researchers alike.


Social competence and emotional intelligence of future PE teachers and their participation in psychological workshops


title: Secure Information Sharing among Autonomous Vehicles in NDN

abstract: Autonomous vehicles must communicate with each other effectively and securely to make robust decisions. However, today's Internet falls short in supporting efficient data delivery and strong data security, especially in a mobile ad-hoc environment. Named Data Networking (NDN), a new data-centric Internet architecture, provides a better foundation for secure data sharing among autonomous vehicles. We examine two potential threats, false data dissemination and vehicle tracking, in an NDN-based autonomous vehicular network. To detect false data, we propose a four-level hierarchical trust model and the associated naming scheme for vehicular data authentication. Moreover, we address vehicle tracking concerns using a pseudonym scheme to anonymize vehicle names and certificate issuing proxies to further protect vehicle identity. Finally, we implemented and evaluated our AutoNDN application on Raspberry Pi-based mini cars in a wireless environment.


title: Friend-in-the-Middle Attacks: Exploiting Social Networking Sites for Spam

abstract: Friend-in-the-middle attacks on social networking sites can be used to harvest social data in an automated fashion. Attackers can then exploit this data for large-scale attacks using context-aware spam and social phishing. The authors prove the feasibility of such an attack and simulate the impact on Facebook. Alarmingly, all major social networking sites are vulnerable to this attack because they fail to appropriately secure the network layer.


title: Design, simulation and implementation of a self-oscillating control circuit to drive series resonant inverter feeding a brazing induction furnace

abstract: This research deals with the design and simulation of induction furnace power source (inverter) using MATLAB package. This source designed to lock on the resonant frequency of the load by using self-oscillating technique, also it has the capability to control the power supplied to the load using phase shift pulse width modulation (PSPWM) technique. These characteristics used to overcome the load nonlinear behavior during the brazing process and to achieve soft switching of the inverter elements. Also, the inverter has the capability to operate with or without load (workpiece). The implemented prototype operates at a frequency range (50–100)kHz and 10kW was successfully used for brazing two copper workpieces.


CAPTCHA Breaking with Deep Learning


title: A Curvature-Tensor-Based Perceptual Quality Metric for 3D Triangular Meshes

abstract: Perceptual quality assessment of 3D triangular meshes is crucial for a variety of applications. In this paper, we present a new objective metric for assessing the visual difference between a reference triangular mesh and its distorted version produced by lossy operations such as noise addition, simplification, compression and watermarking. The proposed metric is based on the measurement of a distance between curvature tensors of the two meshes under comparison. Our algorithm uses not only tensor eigenvalues (i.e., curvature amplitudes) but also tensor eigenvectors (i.e., principal curvature directions) to derive a perceptually-oriented tensor distance. The proposed metric also accounts for the visual masking effect of the human visual system, through a roughness-based weighting of the local tensor distance. A final score that reflects the visual difference between two meshes is obtained via a Minkowski pooling of the weighted local tensor distances over the mesh surface. We validate the performance of our algorithm on four subjectively-rated mesh visual quality databases, and compare the proposed method with state-of-the-art objective metrics. Experimental results show that our approach achieves high correlation between objective scores and subjective assessments.


Problem solving with reinforcement learning


title: A New Disability Rating Method according to the Job Using the Korean Academy of Medical Science Disability Guideline

abstract: The purpose of this study was to develop a disability rating scale according to job classification using the Korean Academy of Medical Society (KAMS) guidelines. All jobs were categorized based on their level of physical activity and professional skills. The KAMS guidelines were used for the impairment rating. We modified the California Schedule for rating permanent disabilities. The differences were plotted to compare between the impairment rate and the job-adjusted disability rate. The KAMS job-adjusted disability rates were then compared to the McBride and workers' compensation rates. A total of 1,206 occupations were classified into 44 groups. The occupational disability indexes were rated on a scale of 1 to 7. The differences in the McBride disability rates varied inconsistently from 0% to 35%, while the differences in the KAMS disability rates were between 0% and 18%. The KAMS disability rates were slightly higher than the McBride disability rates for the upper extremities, but were lower for the lower extremities and internal organs. This is the first Korean job-adjusted disability rating method. There are several limitations, but its impairment rating is more scientific and reflects the current Korean occupational environment.


title: Preprocessing CVS data for fine-grained analysis

abstract: All analyses of version archives have one phase in common: the preprocessing of data. Preprocessing has a direct impact on the quality of the results returned by an analysis. In this paper we discuss four essential preprocessing tasks necessary for a fine-grained analysis of CVS archives: (a) data extraction, (b) transaction recovery, (c) mapping of changes to fine-grained entities, and (d) data cleaning. We formalize the concept of sliding time windows and show how commit mails can relate revisions to transactions. We also present two approaches that map changes to the affected building blocks of a file, e.g. functions or sections.


title: Novel 3.3-kV advanced trench HiGT with low loss and low dv/dt noise

abstract: Novel 3.3-kV trench IGBT with low loss and low dvAK/dt noise was developed. The structural feature of the IGBTs is deep p-WELL layers separated from trench gates. This structure suppresses excess VGE overshoot and then reduces recovery dvAK/dt. Moreover, this effect is enhanced by reducing the resistance of the deep p-WELL layers (RFP). It was found that, for the first time, the trade-off characteristics between VCEsat and recovery dvAK/dt were drastically improved by separating p-WELL layers from trench gates and decreasing RFP. The recovery dvAK/dt could be reduced by 79% more than that for the conventional trench IGBT, maintaining a small VCEsat and Eon equal to the conventional one.


title: Data Mining Algorithms: An Overview

abstract: The research on data mining has successfully yielded numerous tools, algorithms, methods and approaches for handling large amounts of data for various purposeful use and problem solving. Data mining has become an integral part of many application domains such as data ware housing, predictive analytics, business intelligence, bio-informatics and decision support systems. Prime objective of data mining is to effectively handle large scale data, extract actionable patterns, and gain insightful knowledge. Data mining is part and parcel of knowledge discovery in databases (KDD) process. Success and improved decision making normally depends on how quickly one can discover insights from data. These insights could be used to drive better actions which can be used in operational processes and even predict future behaviour. This paper presents an overview of various algorithms necessary for handling large data sets. These algorithms define various structures and methods implemented to handle big data. The review also discusses the general strengths and limitations of these algorithms. This paper can quickly guide or an eye opener to the data mining researchers on which algorithm(s) to select and apply in solving the problems they will be investigating .


title: Crowd density estimation based on rich features and random projection forest

abstract: Current state of the art crowd density estimation methods are based on computationally expensive Gaussian process regression or Ridge regression models which can only handle a small number of features. In many computer vision applications, it has been empirically shown that a richer set of image features can lead to enhanced performances. In this paper, we reason that using more image features could potentially boost the performances of crowd detection and thus propose to employ much extensive and richer feature sets for crowd density estimation. To achieve computational efficiency and scalability, we use random forest as the regression model whose tree structure is intrinsically fast and scalable. Unlike traditional approaches to random forest construction, we embed random projection in the tree nodes to simultaneously combat the curse of dimensionality and to introduce randomness in the tree construction (we call this Random Projection Forest) thus making our new method very efficient and effective. Experimental results on two public pedestrian detection video databases show that our new method achieves state of the art performances that are superior to those of previously published regression techniques.


title: Mr. Emo: music retrieval in the emotion plane

abstract: This technical demo presents a novel emotion-based music retrieval platform, called Mr. Emo, for organizing and browsing music collections. Unlike conventional approaches which quantize emotions into classes, Mr. Emo defines emotions by two continuous variables arousal and valence and employs regression algorithms to predict them. Associated with arousal and valence values (AV values), each music sample becomes a point in the arousal-valence emotion plane, so a user can easily retrieve music samples of certain emotion(s) by specifying a point or a trajectory in the emotion plane. Being content centric and functionally powerful, such emotion-based retrieval complements traditional keyword- or artist-based retrieval. The demo shows the effectiveness and novelty of music retrieval in the emotion plane.


Zara The Supergirl: An Empathetic Personality Recognition System


title: The Offshore Wind Farm Array Cable Layout Problem: A Planar Open Vehicle Routing Problem

abstract: In an offshore wind farm (OWF), the turbines are connected to a transformer by cable routes that cannot cross each other. Finding the minimum cost array cable layout thus amounts to a vehicle routing problem with the additional constraints that the routes must be embedded in the plane. For this problem, both exact and heuristic methods are of interest. We optimize cable layouts for real-world OWFs by a hop-indexed integer programming formulation, and develop a heuristic for computing layouts based on the Clarke and Wright savings heuristic for vehicle routing. Our heuristic computes layouts on average only 2% more expensive than the optimal layout. Finally, we present two problem extensions arising from real-world OWF cable layouts, and adapt the integer programming formulation to one of them. The thus obtained optimal layouts are up to 13% cheaper than the actually installed layouts.


title: Neural networks for document image preprocessing: state of the art

abstract: Neural network are most popular in the research community due to its generalization abilities. Additionally, it has been successfully implemented in biometrics, features selection, object tracking, document image preprocessing and classification. This paper specifically, clusters, summarize, interpret and evaluate neural networks in document Image preprocessing. The importance of the learning algorithms in neural networks training and testing for preprocessing is also highlighted. Finally, a critical analysis on the reviewed approaches and the future research guidelines in the field are suggested.


title: Image specificity

abstract: For some images, descriptions written by multiple people are consistent with each other. But for other images, descriptions across people vary considerably. In other words, some images are specific - they elicit consistent descriptions from different people - while other images are ambiguous. Applications involving images and text can benefit from an understanding of which images are specific and which ones are ambiguous. For instance, consider text-based image retrieval. If a query description is moderately similar to the caption (or reference description) of an ambiguous image, that query may be considered a decent match to the image. But if the image is very specific, a moderate similarity between the query and the reference description may not be sufficient to retrieve the image. In this paper, we introduce the notion of image specificity. We present two mechanisms to measure specificity given multiple descriptions of an image: an automated measure and a measure that relies on human judgement. We analyze image specificity with respect to image content and properties to better understand what makes an image specific. We then train models to automatically predict the specificity of an image from image features alone without requiring textual descriptions of the image. Finally, we show that modeling image specificity leads to improvements in a text-based image retrieval application.


title: Writing good software engineering research papers: minitutorial

abstract: Software engineering researchers solve problems of several different kinds. To do so, they produce several different kinds of results, and they should develop appropriate evidence to validate these results. They often report their research in conference papers. I analyzed the abstracts of research papers submitted to ICSE 2002 in order to identify the types of research reported in the submitted and accepted papers, and I observed the program committee discussions about which papers to accept. This report presents the research paradigms of the papers, common concerns of the program committee, and statistics on success rates. This information should help researchers design better research projects and write papers that present their results to best advantage.


title: Reinforcement-Learning-Based Intelligent Maximum Power Point Tracking Control for Wind Energy Conversion Systems

abstract: This paper proposes an intelligent maximum power point tracking (MPPT) algorithm for variable-speed wind energy conversion systems (WECSs) based on the reinforcement learning (RL) method. The model-free Q-learning algorithm is used by the controller of the WECS to learn a map from states to optimal control actions online by updating the action values according to the received rewards. The experienced action values are stored in a Q-table, based on which the maximum power points (MPPs) are obtained after a certain period of online learning. The learned MPPs are then used to generate an optimum speed–power curve for fast MPPT control of the WECS. Since RL enables the WECS to learn by directly interacting with the environment, knowledge of wind turbine parameters or wind speed information is not required. The proposed MPPT control algorithm is validated by simulation studies for a 1.5-MW doubly-fed induction generator-based WECS and experimental results for a 200-W permanent-magnet synchronous generator-based WECS emulator.


title: An operational and performance overview of the IRIDIUM low earth orbit satellite system

abstract: Today, both the military and commercial sectors are placing an increased emphasis on global communications. This has prompted the development of several low earth orbit satellite systems that promise worldwide connectivity and real-time voice communications. This article provides a tutorial overview of the IRIDIUM low earth orbit satellite system and performance results obtained via simulation. First, it presents an overview of key IRIDIUM design parameters and features. Then, it examines the issues associated with routing in a dynamic network topology, focusing on network management and routing algorithm selection. Finally, it presents the results of the simulation and demonstrates that the IRIDIUM system is a robust system capable of meeting published specifications.


title: The Dilemma of Performance-Approach Goals: The Use of Multiple Goal Contexts to Promote Students' Motivation and Learning.

abstract: The study examines the effects of a quasi-experimental classroom goal condition (mastery, performanceapproach, combined mastery/performance-approach) and entering personal goal orientations on motivation, emotional well-being, help seeking, cognitive engagement, and achievement for 237 upper elementary students during a 5-week math unit emphasizing small groups. The classroom goal condition had a significant effect on help seeking and achievement, with the combined condition showing the most beneficial pattern. Personal mastery goals were beneficial for 11 of 12 outcomes including achievement; personal performance-approach goals were detrimental for achievement and test anxiety and unrelated to the remaining outcomes. The effect of the classroom goal condition did not vary on the basis of entering personal goal orientations. Implications for the current achievement goal theory debate regarding multiple goals are discussed.


title: Optimal Markdown Pricing and Inventory Allocation for Retail Chains with Inventory Dependent Demand

abstract: We analyze how inventory dependence of demand affects the optimal pricing and distribution of inventory across multiple retail locations. Our problem formulation corresponds to the typical markdown management decisions in a retail chain. The solution methodology determines the optimal combinations of three important operational levers—pricing, inventory allocation, and store consolidation. Inventory dependence causes the optimal price trajectory to decrease over time as the current on-hand inventory decreases. The optimal inventory allocation shifts more inventory to the larger stores as the inventory effect becomes stronger. Solution methodologies are also developed for the case of optimal constant prices, which leads to the same optimal inventory allocations. If all stores have the same seasonal variations and the inventory is allocated optimally, then all stores have identical price trajectories. Optimal consolidation of the inventory to a subset of the stores is also determined. Consolidation of inven...


Performance and Security of ECDSA


Automated Structural and Spatial Comprehension of Data Tables


title: One Million Posts: A Data Set of German Online Discussions

abstract: In this paper we introduce a new data set consisting of user comments posted to the website of a German-language Austrian newspaper. Professional forum moderators have annotated 11,773 posts according to seven categories they considered crucial for the efficient moderation of online discussions in the context of news articles. In addition to this taxonomy and annotated posts, the data set contains one million unlabeled posts. Our experimental results using six methods establish a first baseline for predicting these categories. The data and our code are available for research purposes from https://ofai.github.io/million-post-corpus.


title: Overweight - early stage of "adipokines related cardiovascular diseases": leptin and adiponectin relation to anthropometric parametars.

abstract: AIM: Adipose tissue posses endocrine functions that includes synthesis of various adipocytokines, which affect the metabolism of lipids and glucoses, development of atherosclerotic processes, and are involved in the development of hypertension and cardiovascular diseases. Body fat distribution is a significant risk factor for development of obesity related diseases. Leptin and adiponectin are adipokines whose concentrations vary with the level of obesity. The aim of this research is to determine the relation of changed leptin and adiponectin concentrations in overweight persons with anthropometric parameters of the subjects. METHODS: The study included 40 adult, hypertensive subjects - divided into two groups according to sex, with the body mass index 25-30 kg/m², with regular glycaemia and renal function. All subjects underwent biochemical (serum lipid, glucose and creatinine level) and anthropometric measurements. Leptin concentration was determined by radioimmunoassay, while adiponectin concentration was determined by enzyme immunoassay. RESULTS: Both leptin and adiponectin concentrations significantly correlated with the waist circumference in both groups of subjects. In male subjects, leptin concentration correlated positively with the body mass index and the hip circumference, while adiponectin concentration correlated negatively with the hip circumference. CONCLUSIONS: Being overweight is a condition marked by pathologically altered values of leptin concentration (increased level) and adiponectin (decreased level), that are changing as the obesity level and the waist circumference increase. It can be concluded that abdominal obesity and higher body mass index increase the risk for development of overweight-obesity-related diseases.


title: A qualitative study on adoption of biometrics technologies: Canadian banking industry

abstract: This study examines the adoption of biometrics technology in the Canadian banking industry. By comparing Canadian banks with the financial institutions in other countries in terms of their adoption to biometrics, it explores current status of adoption to biometrics technology by Canadian banks and the potential future consequences as a result of their delayed adoption. Through literature review, this study first provides various aspects of biometrics technologies; technical specifications, performance metrics, types, current applications, and some issues and concerns with this technology. Second, it discusses current and future potential applications of biometrics in the banking industry. Then, the study provides a context with which to analyze the extent of the popularity and practicality of the technology in Canadian banks. Finally, based on qualitative interviews with biometric researchers and professionals in Canada, this study reports the research findings with regards to the following four topics: 1) Integration and accessibility to biometrics, 2) Public opinion and concern, 3) Transition of technologies, and 4) Current accommodations for biometrics, followed by discussion of the contributions of this study.


title: Deep Recurrent Neural Networks for Acoustic Modelling

abstract: We present a novel deep Recurrent Neural Network (RNN) model for acoustic modelling in Automatic Speech Recognition (ASR). We term our contribution as a TC-DNN-BLSTM-DNN model, the model combines a Deep Neural Network (DNN) with Time Convolution (TC), followed by a Bidirectional Long Short-Term Memory (BLSTM), and a final DNN. The first DNN acts as a feature processor to our model, the BLSTM then generates a context from the sequence acoustic signal, and the final DNN takes the context and models the posterior probabilities of the acoustic states. We achieve a 3.47 WER on the Wall Street Journal (WSJ) eval92 task or more than 8% relative improvement over the baseline DNN models.


title: Workflow Mining: Current Status and Future Directions

abstract: Current workflow management systems require the explicit design of the workflows that express the business process of an organization. This process design is very time consuming and error prone. Considerable work has been done to develop heuristics to mine event-data logs to produce a process model that can support the workflow design process. However, all the existing heuristic-based mining algorithms have their limitations. To achieve more insight into these limitations the starting point in this paper is the α-algorithm [3] for which it is proved under which conditions and process constructs the algorithm works. After presentation of the α-algorithm, a classification is given of the process constructs that are difficult to handle for this type of algorithms. Then, for some constructs (i.e. short loops) it is illustrated in which way the α-algorithm can be extended so that it can correctly discover these constructs.


title: The side of chronic low back pain matters: evidence from the primary motor cortex excitability and the postural adjustments of multifidi muscles

abstract: Hemispheric lateralization of pain processing was reported with overactivation of the right frontal lobe. Specifically in chronic low back pain (CLBP), functional changes in the left primary motor cortex (M1) with impaired anticipatory postural activation (APA) of trunk muscles have been observed. Given the connections between frontal and M1 areas for motor planning, it is hypothesized that the pain side could differently influence M1 function and APA of paravertebral multifidus (MF) muscles. This study aimed at testing whether people with right- versus left-sided CLBP showed different M1 excitability and APA. Thirty-five individuals with lateralized CLBP (19 right-sided and 16 left-sided) and 13 pain-free subjects (normative values) were tested for the excitability of MF M1 area (active motor threshold—AMT) with transcranial magnetic stimulation and for the latency of MF APA during bilateral shoulder flexion and during unilateral hip extension in prone lying. In the right-sided CLBP group, the AMT of both M1 areas was lower than in the left-sided group and the pain-free subjects; the latency of MF APA was shorter in bilateral shoulder flexion and in the left hip extension tasks as compared to the left-sided group. In CLBP, an earlier MF APA was correlated with lower AMT in both tasks. People with right-sided CLBP presented with increased M1 excitability in both hemispheres and earlier MF APA. These results likely rely on cortical motor adaptation related to the tasks and axial muscles tested. Future studies should investigate whether CLBP side-related differences have a clinical impact, e.g. in diagnosis and intervention.


title: From Empathy to Apathy: The Bystander Effect Revisited

abstract: The bystander effect, the reduction in helping behavior in the presence of other people, has been explained predominantly by situational influences on decision making. Diverging from this view, we highlight recent evidence on the neural mechanisms and dispositional factors that determine apathy in bystanders. We put forward a new theoretical perspective that integrates emotional, motivational, and dispositional aspects. In the presence of other bystanders, personal distress is enhanced, and fixed action patterns of avoidance and freezing dominate. This new perspective suggests that bystander apathy results from a reflexive emotional reaction dependent on the personality of the bystander.


title: Neural Compositional Denotational Semantics for Question Answering

abstract: Answering compositional questions requiring multi-step reasoning is challenging. We introduce an end-to-end differentiable model for interpreting questions about a knowledge graph (KG), which is inspired by formal approaches to semantics. Each span of text is represented by a denotation in a KG and a vector that captures ungrounded aspects of meaning. Learned composition modules recursively combine constituent spans, culminating in a grounding for the complete sentence which answers the question. For example, to interpret "not green", the model represents "green" as a set of KG entities and "not" as a trainable ungrounded vector---and then uses this vector to parameterize a composition function that performs a complement operation. For each sentence, we build a parse chart subsuming all possible parses, allowing the model to jointly learn both the composition operators and output structure by gradient descent from end-task supervision. The model learns a variety of challenging semantic operators, such as quantifiers, disjunctions and composed relations, and infers latent syntactic structure. It also generalizes well to longer questions than seen in its training data, in contrast to RNN, its tree-based variants, and semantic parsing baselines.


title: A Game-like Application for Dance Learning Using a Natural Human Computer Interface

abstract: Game-based learning and gamification techniques are recently becoming a popular trend in the field of Technology Enhanced Learning. In this paper, we mainly focus on the use of game design elements for the transmission of Intangible Cultural Heritage (ICH) knowledge and, especially, for the learning of traditional dances. More specifically, we present a 3D game environment that employs an enjoyable natural human computer interface, which is based on the fusion of multiple depth sensors data in order to capture the body movements of the user/learner. In addition, the system automatically assesses the learner’s performance by utilizing a combination of Dynamic Time Warping (DTW) with Fuzzy Inference System (FIS) approach and provides feedback in a form of a score as well as instructions from a virtual tutor in order to promote self-learning. As a pilot use case, a Greek traditional dance, namely Tsamiko, has been selected. Preliminary small-scaled experiments with students of the Department of Physical Education and Sports Science at Aristotle University of Thessaloniki have shown the great potential of the proposed application.


title: Channel Feedback Codebook Design for Millimeter-Wave Massive MIMO Systems Relying on Lens Antenna Array

abstract: The recently proposed millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system relying on a lens antenna array (LAA) significantly reduces the number of radio frequency chains using beam selection. A high data rate can be achieved based on the reduced-dimensional equivalent channel after beam selection. In frequency division duplexing systems, the equivalent channel has to be fed back to the base station (BS) via a feedback channel based on a codebook. However, no dedicated codebook has been proposed for LAA-aided mmWave systems. To fill this gap, in this letter, we propose a reduced-dimensional subspace codebook (RDSC) for such systems. Specifically, under the recently proposed concept of angle coherence time , we first generate the large-dimensional vectors in the channel subspace , which is determined by the angles-of-departure of the dominant paths. Then, based on these vectors in the channel subspace, we create the RDSC by considering both the lens and the beam selector. Finally, the equivalent channel is quantized using the proposed RDSC and fed back to the BS. Finally, we carry out mathematical performance analysis of the proposed RDSC and show that its feedback overhead is rendered proportional to the relatively small number of dominant paths per user. The analytical results are verified by our simulations.


title: Resting-state networks distinguish locked-in from vegetative state patients

abstract: Purpose ::: Locked-in syndrome and vegetative state are distinct outcomes from coma. Despite their differences, they are clinically difficult to distinguish at the early stage and current diagnostic tools remain insufficient. Since some brain functions are preserved in locked-in syndrome, we postulated that networks of spontaneously co-activated brain areas might be present in locked-in patients, similar to healthy controls, but not in patients in a vegetative state.


title: Temporal analysis and geo-mapping of fire incidents in the City of Manila

abstract: Fire incidents are costly occurrences that may be preventable. This study aims to analyze fire data in the City of Manila from 2011 to 2014 based on the various causes of fires. Temporal analytical techniques complemented by geo-mapping are used to determine fire patterns based on time, day, month and year. A total of 2,316 fire incidents were included in the study and fires due to faulty electrical connections occurring from 6PM to 9PM emerged as the time with the most number of fire incidents. The daily pattern does not show much variation although the monthly pattern shows that the summer months have the more number of fire occurrences with faulty electrical connections as the main cause. The yearly pattern also do not offer much variation though the same cause of fire is noted to have the highest occurrence. Patterns identified may be useful inputs in formulating proactive fire preventive measures and in allocating fire resources. Future research directions in spatial and spatiotemporal analyses have been identified.


title: Complementary and alternative therapies for treating multiple sclerosis symptoms: a systematic review.

abstract: Multiple sclerosis (MS) is a chronic disease of the central nervous system without a known cure. Thus the role of complementary and alternative therapies (CATs) for the management of symptoms lies in palliative care and this is borne out by the popularity of these treatments amongst MS sufferers. This review is aimed at determining whether this use is supported by evidence of effectiveness from rigorous clinical trials. Database literature searches were performed and papers were extracted in a pre-defined manner. Twelve randomized controlled trials were located that investigated a CAT for MS: nutritional therapy (4), massage (1), Feldenkrais bodywork (1), reflexology (1), magnetic field therapy (2), neural therapy (1) and psychological counselling (2). The evidence is not compelling for any of these therapies, with many trials suffering from significant methodological flaws. There is evidence to suggest some benefit of nutritional therapy for the physical symptoms of MS. Magnetic field therapy and neural therapy appear to have a short-term beneficial effect on the physical symptoms of MS. Massage/bodywork and psychological counselling seem to improve depression, anxiety and self-esteem. The effectiveness for other CATs is unproven at this time. In all the CATs examined further investigations are needed in the form of rigorous large-scale trials.


title: Traits and states in mindfulness meditation

abstract: Personality is the collection of characteristic thoughts, attitudes, feelings, and behaviors that impact how we view ourselves and what we believe about others and the world around us. In general, personality traits are relatively stable characteristics, but states are transient. Research has shown that our beliefs and attitudes are learned and shaped by our own experience and expectation, and that these are not fixed and can be changed. Studies have shown that mindfulness induces state changes that influence cognition, feelings, and corresponding brain activity and connectivity after short-term training, and traits changes are also found following long-term practice. This chapter explores how traits and states contribute to mindfulness practice, and explain how this knowledge could improve our practice efficiency.


title: Wireless wearable pulse oximeter for health monitoring using ZigBee wireless sensor network

abstract: In this work, a portable real-time wireless health monitoring system is developed. The system is used for remote monitoring of patients' heart rate and oxygen saturation in blood. The system was designed and implemented using ZigBee wireless technologies. All pulse oximetry data are transferred within a group of wireless personal area network (WPAN) to database computer server. The sensor modules were designed for low power operation with a program that can adjust power management depending on scenarios of power source and current power operation. The sensor unit consists of (1) two types of LEDs and photodiode packed in Velcro strip that is facing to a patient's fingertip; (2) Microcontroller unit for interfacing with ZigBee module, processing pulse oximetry data and storing some data before sending to base PC; (3) ZigBee module for communicating the data of pulse oximetry, ZigBee module gets all commands from microcontroller unit and it has a complete ZigBee stack inside and (4) Base node for receiving and storing the data before sending to PC.


title: Millimeter-Wave Communication with Out-of-Band Information

abstract: Configuring the antenna arrays is the main source of overhead in millimeter wave (mmWave) communication systems. In high mobility scenarios, the problem is exacerbated, as achieving the highest rates requires frequent link reconfiguration. One solution is to exploit spatial congruence between signals at different frequency bands and extract mmWave channel parameters from side information obtained in another band. In this paper we propose the concept of out-of-band information aided mmWave communication. We analyze different strategies to leverage information derived from sensors or from other communication systems operating at sub-6 GHz bands to help configure the mmWave communication link. The overhead reductions that can be obtained when exploiting out-of-band information are characterized in a preliminary study. Finally, the challenges associated with using out-of-band signals as a source of side information at mmWave are analyzed in detail.


title: Marketplace and technology standards for B2B e-commerce: progress, challenges, and the state of the art

abstract: We have examined standards required for successful e-commerce (EC) architectures and evaluated the strengths and limitations of current systems that have been developed to support EC. We find that there is an unfilled need for systems that can reliably locate buyers and sellers in electronic marketplaces and also facilitate automated transactions. The notion of a ubiquitous network where loosely coupled buyers and sellers can reliably find each other in real time, evaluate products, negotiate prices, and conduct transactions is not adequately supported by current systems. These findings were based on an analysis of mainline EC architectures: EDI, company Websites, B2B hubs, e-Procurement systems, and Web Services. Limitations of each architecture were identified. Particular attention was given to the strengths and weaknesses of the Web Services architecture, since it may overcome some limitations of the other approaches.


title: Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities

abstract: Many complex networks display a mesoscopic structure with groups of nodes sharing many links with the other nodes in their group and comparatively few with nodes of different groups. This feature is known as community structure and encodes precious information about the organization and the function of the nodes. Many algorithms have been proposed but it is not yet clear how they should be tested. Recently we have proposed a general class of undirected and unweighted benchmark graphs, with heterogeneous distributions of node degree and community size. An increasing attention has been recently devoted to develop algorithms able to consider the direction and the weight of the links, which require suitable benchmark graphs for testing. In this paper we extend the basic ideas behind our previous benchmark to generate directed and weighted networks with built-in community structure. We also consider the possibility that nodes belong to more communities, a feature occurring in real systems, such as social networks. As a practical application, we show how modularity optimization performs on our benchmark.


title: HMM-based Vietnamese speech synthesis

abstract: In this paper, improving naturalness HMM-based speech synthesis for Vietnamese language is described. By this synthesis method, trajectories of speech parameters are generated from the trained Hidden Markov models. A final speech waveform is synthesized from those speech parameters. The main objective for the development is to achieve maximum naturalness in output speech through key points. Firstly, system uses a high quality recorded Vietnamese speech database appropriate for training, especially in statistical parametric model approach. Secondly, prosodic information such as tone, POS (part of speech) and features based on characteristics of Vietnamese language are added to ensure the quality of synthetic speech. Third, system uses STRAIGHT which showed its ability to produce high-quality voice manipulation and was successfully incorporated into HMM-based speech synthesis. The results collected show that the speech produced by our system has the best result when being compared with the other Vietnamese TTS systems trained from the same speech data.


title: Hierarchical Mixtures of Experts and the EM Algorithm

abstract: We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain.


title: Multivariate Data Visualization

abstract: Multivariate data visualization is an exciting area of current research by statisticians, engineers and those involved in data mining. This article first gives a brief overview of the most important current software available for data visualization and then discusses the general principles or philosophy involved in data visualization. This is followed by a discussion of some of the most interesting and useful multivariate graphical techniques.


A Novel Low power and Area Efficient Carry-Lookahead Adder Using MOD-GDI Technique


title: Visual Room-Awareness for Humanoid Robot Self-Localization

abstract: Humanoid robots without internal sensors such as a compass tend to lose their orientation after a fall. Furthermore, re-initialisation is often ambiguous due to symmetric man-made environments. The room- awareness module proposed here is inspired by the results of psychological experiments and improves existing self-localization strategies by mapping and matching the visual background with colour histograms. The match- ing algorithm uses a particle-filter to generate hypotheses of the view- ing directions independent of the self-localization algorithm and generates confidence values for various possible poses. The robot's behaviour con- troller uses those confidence values to control self-localization algorithm to converge to the most likely pose and prevents the algorithm from getting stuck in local minima. Experiments with a symmetric Standard Platform League RoboCup playing field with a simulated and a real humanoid NAO robot show the significant improvement of the system.


title: Robust Named Entity Recognition in Idiosyncratic Domains

abstract: Named entity recognition often fails in idiosyncratic domains. That causes a problem for depending tasks, such as entity linking and relation extraction. We propose a generic and robust approach for high-recall named entity recognition. Our approach is easy to train and offers strong generalization over diverse domain-specific language, such as news documents (e.g. Reuters) or biomedical text (e.g. Medline). Our approach is based on deep contextual sequence learning and utilizes stacked bidirectional LSTM networks. Our model is trained with only few hundred labeled sentences and does not rely on further external knowledge. We report from our results F1 scores in the range of 84-94% on standard datasets.


title: Embracing data abundance: BookTest Dataset for Reading Comprehension

abstract: There is a practically unlimited amount of natural language data available. Still, recent work in text comprehension has focused on datasets which are small relative to current computing possibilities. This article is making a case for the community to move to larger data and as a step in that direction it is proposing the BookTest, a new dataset similar to the popular Children's Book Test (CBT), however more than 60 times larger. We show that training on the new data improves the accuracy of our Attention-Sum Reader model on the original CBT test data by a much larger margin than many recent attempts to improve the model architecture. On one version of the dataset our ensemble even exceeds the human baseline provided by Facebook. We then show in our own human study that there is still space for further improvement.


title: Analysis of stator winding parallel-connected branches circulating current and its application in generator fault diagnosis

abstract: Through calculating the generator parameters of air-gap magnetomotive force, magnetic permeability, magnetic flux density, stator winding parallel-connected branches electromotive force distribution, the characteristics of the stator winding parallel-connected branches circulating current are analyzed. The results discover that the second harmonics circulating current will increase when the rotor winding inter-turn short circuit fault occurs, and the fundamental circulating current will increase when the stator winding inter-turn short circuit fault or the air-gap eccentricity fault occurs, neither being strongly affected by the imbalance fault Considering the differences of the circulating current characteristics caused by different generator faults, a method of generator fault diagnosis, based on the stator winding parallel-connected branches circulating current characteristics, is developed. Finally, the circulating current of a type SDF-9 generator is measured in the laboratory to verify the theoretical analysis presented above.


title: 60-GHz Circularly Polarized Antenna Array Made in Low-Cost Fabrication Process

abstract: Circularly polarized (CP) antenna operating in the unlicensed 60 GHz frequency range is useful for millimeter-wave radio and radar applications. In this letter, a 2 $\,\times\,$ 2 right-handed circularly polarized (RHCP) planar array is demonstrated by using four CP antenna elements. A single element has a gain of 7.5 dBiC, while the resulting 2 $\,\times\,$ 2 array yields a CP gain of 12.5 dBiC. Substrate integrated waveguide (SIW) cavity is used to back each element in order to reduce the mutual coupling between elements and also improve the overall gain. An experimental antenna prototype made through a conventional low-cost printed circuit board (PCB) technique operates in RHCP mode covering the frequency range of interest from 58 to 62 GHz. The antenna is compact, showing a good radiation performance.


title: State-Space Modeling of a Class ${\bf E}^{\bf 2}$ Converter for Inductive Links

abstract: This paper presents a state-space-based analysis of a Class $\hbox{E}^2$ converter for wireless power systems based on a two-coil inductive link. The Class $\hbox{E}^2$ converter consists of a 200-kHz Class E inverter as the primary coil driver and a voltage-driven Class E synchronous rectifier at the secondary coil of the inductive link. A piecewise linear seventh-order state-space model is used to calculate several parameters and values to achieve optimum switching operation of the Class E inverter and the Class E rectifier. Simulation results are presented to compare the accuracy of the state-space modeling approach with the established analytical approach. For validation of the state-space analysis, an investigation of the influence of variation of coil alignment and load for a 20-W Class $\hbox{E}^2$ converter prototype system is performed by means of a novel compensation method that maintains optimum switching conditions irrespective of variations. Experimental results are presented to confirm the accuracy of the state-space modeling approach over a wide range of operational conditions and the utility of the compensation method.


title: Feature Selection Based on High Dimensional Model Representation for Hyperspectral Images

abstract: In hyperspectral image analysis, the classification task has generally been addressed jointly with dimensionality reduction due to both the high correlation between the spectral features and the noise present in spectral bands, which might significantly degrade classification performance. In supervised classification, limited training instances in proportion with the number of spectral features have negative impacts on the classification accuracy, which is known as Hughes effects or curse of dimensionality in the literature. In this paper, we focus on dimensionality reduction problem, and propose a novel feature-selection algorithm, which is based on the method called high dimensional model representation. The proposed algorithm is tested on some toy examples and hyperspectral datasets in comparison with conventional feature-selection algorithms in terms of classification accuracy, stability of the selected features and computational time. The results show that the proposed approach provides both high classification accuracy and robust features with a satisfactory computational time.


title: Representation Benefits of Deep Feedforward Networks

abstract: This note provides a family of classification problems, indexed by a positive integer $k$, where all shallow networks with fewer than exponentially (in $k$) many nodes exhibit error at least $1/6$, whereas a deep network with 2 nodes in each of $2k$ layers achieves zero error, as does a recurrent network with 3 distinct nodes iterated $k$ times. The proof is elementary, and the networks are standard feedforward networks with ReLU (Rectified Linear Unit) nonlinearities.


title: A Guide to Heuristicbased Path Planning

abstract: We describe a family of recently developed heuristicbased algorithms used for path planning in the real world. We discuss the fundamental similarities between static algorithms (e.g. A*), replanning algorithms (e.g. D*), anytime algorithms (e.g. ARA*), and anytime replanning algorithms (e.g. AD*). We introduce the motivation behind each class of algorithms, discuss their use on real robotic systems, and highlight their practical benefits and disadvantages.


Absolute Pitch Psyche Loui The Oxford Handbook of Music Psychology , Second Edition ( Forthcoming )


title: Wize Mirror - a smart, multisensory cardio-metabolic risk monitoring system

abstract: A multi-sensor device for health self-monitoring and assessment is proposed.A real-time head pose estimation and tracking method is introduced.An inexpensive 3D scanner facilitating facial morphology analysis is described.Face 3D shape analysis facilitates tracking changes in weight and BMI index.The evaluation of stress and anxiety seems possible using dynamic facial features. In the recent years personal health monitoring systems have been gaining popularity, both as a result of the pull from the general population, keen to improve well-being and early detection of possibly serious health conditions and the push from the industry eager to translate the current significant progress in computer vision and machine learning into commercial products. One of such systems is the Wize Mirror, built as a result of the FP7 funded SEMEOTICONS (SEMEiotic Oriented Technology for Individuals CardiOmetabolic risk self-assessmeNt and Self-monitoring) project. The project aims to translate the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, multispectral images, and 3D scans of the face. The multisensory platform, being developed as the result of that project, in the form of a smart mirror, looks for signs related to cardio-metabolic risks. The goal is to enable users to self-monitor their well-being status over time and improve their life-style via tailored user guidance. This paper is focused on the description of the part of that system, utilising computer vision and machine learning techniques to perform 3D morphological analysis of the face and recognition of psycho-somatic status both linked with cardio-metabolic risks. The paper describes the concepts, methods and the developed implementations as well as reports on the results obtained on both real and synthetic datasets.


title: A Powerful Finite Control Set-Model Predictive Control Algorithm for Quasi Z-Source Inverter

abstract: Today, a predictive controller becomes one of the state of the art in power electronics control techniques. The performance of this powerful control approach will be pushed forward by simplifying the main control criterion and objective function, and decreasing the number of calculations per sampling time. Recently, predictive control has been incorporated in the Z-source inverter (ZSI) family. For example, in quasi ZSI, the inverter capacitor voltage, inductor current, and output load currents are controlled to their setting points through deciding the required state; active or shoot through. The proposed algorithm reduces the number of calculations, where it decides the shoot-through (ST) case without checking the other possible states. The ST case is roughly optimized every two sampling periods. Through the proposed strategy, about 50% improvement in the computational power has been achieved as compared with the previous algorithm. Also, the objective function for the proposed algorithm consists of one weighting factor for the capacitor voltage without involving the inductor current term in the main objective function. The proposed algorithm is investigated with the simulation results based on MATLAB/SIMULINK software. A prototype of qZSI is constructed in the laboratory to obtain the experimental results using the Digital Signal Processor F28335.


title: The association between socioeconomic status and autism diagnosis in the United Kingdom for children aged 5–8 years of age: Findings from the Born in Bradford cohort

abstract: There has been recent interest in the relationship between socioeconomic status and the diagnosis of autism in children. Studies in the United States have found lower rates of autism diagnosis associated with lower socioeconomic status, while studies in other countries report no association, or the opposite. This article aims to contribute to the understanding of this relationship in the United Kingdom. Using data from the Born in Bradford cohort, comprising 13,857 children born between 2007 and 2011, it was found that children of mothers educated to A-level or above had twice the rate of autism diagnosis, 1.5% of children (95% confidence interval: 1.1%, 1.9%) compared to children of mothers with lower levels of education status 0.7% (95% confidence interval: 0.5%, 0.9%). No statistically significant relationship between income status or neighbourhood material deprivation was found after controlling for mothers education status. The results suggest a substantial level of underdiagnosis for children of low...


title: PolyUnpack: Automating the Hidden-Code Extraction of Unpack-Executing Malware

abstract: Modern malware often hide the malicious portion of their program code by making it appear as data at compile-time and transforming it back into executable code at runtime. This obfuscation technique poses obstacles to researchers who want to understand the malicious behavior of new or unknown malware and to practitioners who want to create models of detection and methods of recovery. In this paper we propose a technique for automating the process of extracting the hidden-code bodies of this class of malware. Our approach is based on the observation that sequences of packed or hidden code in a malware instance can be made self-identifying when its runtime execution is checked against its static code model. In deriving our technique, we formally define the unpack-executing behavior that such malware exhibits and devise an algorithm for identifying and extracting its hidden-code. We also provide details of the implementation and evaluation of our extraction technique; the results from our experiments on several thousand malware binaries show our approach can be used to significantly reduce the time required to analyze such malware, and to improve the performance of malware detection tools.


title: Detecting Privileged Side-Channel Attacks in Shielded Execution with Déjà Vu

abstract: Intel Software Guard Extension (SGX) protects the confidentiality and integrity of an unprivileged program running inside a secure enclave from a privileged attacker who has full control of the entire operating system (OS). Program execution inside this enclave is therefore referred to as shielded. Unfortunately, shielded execution does not protect programs from side-channel attacks by a privileged attacker. For instance, it has been shown that by changing page table entries of memory pages used by shielded execution, a malicious OS kernel could observe memory page accesses from the execution and hence infer a wide range of sensitive information about it. In fact, this page-fault side channel is only an instance of a category of side-channel attacks, here called privileged side-channel attacks, in which privileged attackers frequently preempt the shielded execution to obtain fine-grained side-channel observations. In this paper, we present Deja Vu, a software framework that enables a shielded execution to detect such privileged side-channel attacks. Specifically, we build into shielded execution the ability to check program execution time at the granularity of paths in its control-flow graph. To provide a trustworthy source of time measurement, Deja Vu implements a novel software reference clock that is protected by Intel Transactional Synchronization Extensions (TSX), a hardware implementation of transactional memory. Evaluations show that Deja Vu effectively detects side-channel attacks against shielded execution and against the reference clock itself.


title: Sketch Retrieval via Dense Stroke Features.

abstract: Sketch retrieval aims at retrieving most similar sketches from a large database based on one hand-drawn query. Successful retrieval hinges on an effective representation of sketch images and an efficient search method. In this paper, we propose a representation scheme which takes sketch strokes into account with local features, thereby facilitating efficient retrieval with codebooks. Stroke features are detected via densely sampled points on stroke lines from which local gradients are further enhanced and described by a quantized histogram of gradients. A codebook is organized in a hierarchical vocabulary tree, which maintains structural information of visual words and enables efficient retrieval in sub-linear time. Experimental results on three data sets demonstrate the merits of the proposed algorithm for effective and efficient sketch retrieval.


title: Plant Disease Detection using Image Processing- A Review

abstract: This paper holds a survey on plant leaf diseases classification using image processing. Digital image processing has three basic steps: image processing, analysis and understanding. Image processing contains the preprocessing of the plant leaf as segmentation, color extraction, diseases specific data extraction and filtration of images. Image analysis generally deals with the classification of diseases. Plant leaf can be classified based on their morphological features with the help of various classification techniques such as PCA, SVM, and Neural Network. These classifications can be defined various properties of the plant leaf such as color, intensity, dimensions. Back propagation is most commonly used neural network. It has many learning, training, transfer functions which is used to construct various BP networks. Characteristics features are the performance parameter for image recognition. BP networks shows very good results in classification of the grapes leaf diseases. This paper provides an overview on different image processing techniques along with BP Networks used in leaf disease classification.


title: THF 0 – The core of the TPTP Language for Higher-Order Logic

abstract: One of the keys to the success of the Thousands of Problems for Theorem Provers (TPTP) problem library and related infrastructure is the consistent use of the TPTP language. This paper introduces the core of the TPTP language for higher-order logic --- THF0, based on Church's simple type theory. THF0 is a syntactically conservative extension of the untyped first-order TPTP language.


title: About Sleep's Role in Memory

abstract: Over more than a century of research has established the fact that sleep benefits the retention of memory. In this review we aim to comprehensively cover the field of “sleep and memory” research by providing a historical perspective on concepts and a discussion of more recent key findings. Whereas initial theories posed a passive role for sleep enhancing memories by protecting them from interfering stimuli, current theories highlight an active role for sleep in which memories undergo a process of system consolidation during sleep. Whereas older research concentrated on the role of rapid-eye-movement (REM) sleep, recent work has revealed the importance of slow-wave sleep (SWS) for memory consolidation and also enlightened some of the underlying electrophysiological, neurochemical, and genetic mechanisms, as well as developmental aspects in these processes. Specifically, newer findings characterize sleep as a brain state optimizing memory consolidation, in opposition to the waking brain being optimized for encoding of memories. Consolidation originates from reactivation of recently encoded neuronal memory representations, which occur during SWS and transform respective representations for integration into long-term memory. Ensuing REM sleep may stabilize transformed memories. While elaborated with respect to hippocampus-dependent memories, the concept of an active redistribution of memory representations from networks serving as temporary store into long-term stores might hold also for non-hippocampus-dependent memory, and even for nonneuronal, i.e., immunological memories, giving rise to the idea that the offline consolidation of memory during sleep represents a principle of long-term memory formation established in quite different physiological systems.


title: NengoDL: Combining Deep Learning and Neuromorphic Modelling Methods

abstract: NengoDL is a software framework designed to combine the strengths of neuromorphic modelling and deep learning. NengoDL allows users to construct biologically detailed neural models, intermix those models with deep learning elements (such as convolutional networks), and then efficiently simulate those models in an easy-to-use, unified framework. In addition, NengoDL allows users to apply deep learning training methods to optimize the parameters of biological neural models. In this paper we present basic usage examples, benchmarking, and details on the key implementation elements of NengoDL. More details can be found at https://www.nengo.ai/nengo-dl.


title: Visual and Predictive Analytics on Singapore News: Experiments on GDELT, Wikipedia, and ^STI

abstract: The open-source Global Database of Events, Language, and Tone (GDELT) is the most comprehensive and updated Big Data source of important terms extracted from international news articles . We focus only on GDELT's Singapore events to better understand the data quality of its news articles, accuracy of its term extraction, and potential for prediction. To test news completeness and validity, we visually compared GDELT (Singapore news articles' terms from 1979 to 2013) to Wikipedia's timeline of Singaporean history. To test term extraction accuracy, we visually compared GDELT (CAMEO codes and TABARI system of extraction from Singapore news articles' text from April to December 2013) to SAS Text Miner's term and topic extraction. To perform predictive analytics, we propose a novel feature engineering method to transform row-level GDELT from articles to a user-specified temporal resolution. For example, we apply a decision tree using daily counts of feature values from GDELT to predict Singapore stock market's Straits Times Index (^STI). Of practical interest from the above results is SAS Visual Analytics' ability to highlight the various impacts of June 2013 Southeast Asian haze and December 2013 Little India riot on Singapore. Although Singapore is unique as a sovereign city-state, a leading financial centre, has strong international influence, and consists of a highly multi-cultural population, the visual and predictive analytics reported here are highly applicable to another country's GDELT data.


title: Top-down and bottom-up cues for scene text recognition

abstract: Scene text recognition has gained significant attention from the computer vision community in recent years. Recognizing such text is a challenging problem, even more so than the recognition of scanned documents. In this work, we focus on the problem of recognizing text extracted from street images. We present a framework that exploits both bottom-up and top-down cues. The bottom-up cues are derived from individual character detections from the image. We build a Conditional Random Field model on these detections to jointly model the strength of the detections and the interactions between them. We impose top-down cues obtained from a lexicon-based prior, i.e. language statistics, on the model. The optimal word represented by the text image is obtained by minimizing the energy function corresponding to the random field model. We show significant improvements in accuracies on two challenging public datasets, namely Street View Text (over 15%) and ICDAR 2003 (nearly 10%).


title: Power semiconductors for hybrid and electric vehicles

abstract: The aim of this paper is to give an overview of the power semiconductor solutions for the hybrid electric vehicles, as based on the technologies existing today. First, the motivation for the electric vehicles is given, followed by the brief system overview. After that, the application specifics are discussed and mapped directly into power semiconductor requirements. Special attention is given to the IGBT based power modules. Based on the specific application a different module power ratings and technologies are proposed. A solution overview of both front-end (silicon chip) and back-end (packaging) technologies is given. Next point is the investigation of packaging and interconnection options, with quality and reliability of power modules in the focus. In the end, a future outlook, including ultra low module inductance, ultra thin wafer and SiC technology is given.


title: An Empirical Evaluation of Thompson Sampling

abstract: Thompson sampling is one of oldest heuristic to address the exploration / exploitation trade-off, but it is surprisingly unpopular in the literature. We present here some empirical results using Thompson sampling on simulated and real data, and show that it is highly competitive. And since this heuristic is very easy to implement, we argue that it should be part of the standard baselines to compare against.


title: ApproxRank: Estimating Rank for a Subgraph

abstract: Customized semantic query answering, personalized search, focused crawlers and localized search engines frequently focus on ranking the pages contained within a subgraph of the global Web graph. The challenge for these applications is to compute PageRank-style scores efficiently on the subgraph, i.e., the ranking must reflect the global link structure of the Web graph but it must do so without paying the high overhead associated with a global computation. We propose a framework of an exact solution and an approximate solution for computing ranking on a subgraph.The IdealRank algorithm is an exact solution with the assumption that the scores of external pages are known. We prove that the IdealRank scores for pages in the subgraph converge. Since the PageRank-style scores of external pages may not typically be available, we propose the ApproxRank algorithm to estimate scores for the subgraph. Both IdealRank and ApproxRank represent the set of external pages with an external node $\Lambda$ and extend the subgraph with links to $\Lambda$. They also modify the PageRank-style transition matrix with respect to $\Lambda$.We analyze the $L_1$ distance between IdealRank scores and ApproxRank scores of the subgraph and show that it is within a constant factor of the $L_1$ distance of the external pages (e.g., the true PageRank scores and uniform scores assumed by ApproxRank). We compare ApproxRank and a stochastic complementation approach (SC), a current best solution for this problem, on different types of subgraphs. ApproxRank has similar or superior performance to SC and typically improves on the runtime performance of SC by an order of magnitude or better. We demonstrate that ApproxRank provides a good approximation to PageRank for a variety of subgraphs.


title: Self-Harm and Suicidal Behaviors in Hong Kong Adolescents: Prevalence and Psychosocial Correlates

abstract: The present paper examined the prevalence and psychosocial correlates of adolescent deliberate self-harm (DSH) and suicidal behavior in a representative sample of 3,328 secondary school students in Hong Kong. With reference to the previous year, 32.7% of the students reported at least one form of DSH, 13.7% of the respondents had suicide thoughts, 4.9% devised specific suicidal plans, and 4.7% had actually attempted suicide. Adolescent girls had significantly higher rates of DSH and suicidal behavior than did adolescent boys. Having remarried parents was related to an increased likelihood of DSH and suicide. While high levels of family functioning, overall positive youth development, and academic and school performance predicted low rates of DSH and suicidal behavior, cognitive and behavioral competencies were unexpectedly found to be positively associated with DSH and suicidal behavior. Theoretical and practical implications of the findings are discussed.