title: Resonant DC/DC converter with class DE inverter and class E rectifier using thinned-out method (deleting some of the pulses to the rectifier)

abstract: The class DE inverter offers high-efficiency power conversion under high operating frequency (megahertz order) with low switching noises and low switch voltage stress. It has been applied to DC/DC converters as high-density power sources. Conventional converters with the class DE inverter are controlled by varying the operating frequency, the switch duty ratio or the phase difference with two inverters. However, in general, frequency-modulated control requires a complex control unit. Changing the switching duty ratio puts the switch-voltage waveforms out of shape, and the phase shift control needs the double space of inverters, which is not suitable for the low output power circuit. As a remedy to these problems, we propose to combine the class DE inverter with the class E rectifier using the thinned-out method (deleting some of the pulses to the rectifier). The DC/DC converter's output power is controlled in the rectifier by eliminating its diode-voltage pulse at a desirable rate. The operating frequency is fixed and the waveforms keep the characteristics of class DE. An experimental circuit was designed at 1 MHz operating frequency and in the optimum operation, the measured DC/DC power conversion efficiency was over 80% with 2.2 W output power at 1 MHz. Characteristics of the converter for the line and load variations are given by circuit experiments. It is shown that the proposed circuit has good characteristics with high power conversion efficiency in a wide range of these variations.


A SYSTEMATIC APPROACH FOR THE ANALYTICAL ANALYSIS AND PREDICTION OF THE YIELD FROM LIQUID PROPELLANT EXPLOSIONS


title: Sparse projections for high-dimensional binary codes

abstract: This paper addresses the problem of learning long binary codes from high-dimensional data. We observe that two key challenges arise while learning and using long binary codes: (1) lack of an effective regularizer for the learned high-dimensional mapping and (2) high computational cost for computing long codes. In this paper, we overcome both these problems by introducing a sparsity encouraging regularizer that reduces the effective number of parameters involved in the learned projection operator. This regularizer not only reduces overfitting but, due to the sparse nature of the projection matrix, also leads to a dramatic reduction in the computational cost. To evaluate the effectiveness of our method, we analyze its performance on the problems of nearest neighbour search, image retrieval and image classification. Experiments on a number of challenging datasets show that our method leads to better accuracy than dense projections (ITQ [11] and LSH [16]) with the same code lengths, and meanwhile is over an order of magnitude faster. Furthermore, our method is also more accurate and faster than other recently proposed methods for speeding up high-dimensional binary encoding.


title: Video Shot Boundary Detection: Seven Years of TRECVid Activity

abstract: Shot boundary detection (SBD) is the process of automatically detecting the boundaries between shots in video. It is a problem which has attracted much attention since video became available in digital form as it is an essential pre-processing step to almost all video analysis, indexing, summarisation, search, and other content-based operations. Automatic SBD was one of the tracks of activity within the annual TRECVid benchmarking exercise, each year from 2001 to 2007 inclusive. Over those seven years we have seen 57 different research groups from across the world work to determine the best approaches to SBD while using a common dataset and common scoring metrics. In this paper we present an overview of the TRECVid shot boundary detection task, a high-level overview of the most significant of the approaches taken, and a comparison of performances, focussing on one year (2005) as an example.


title: Analysis of an Adaptive Observer for Sensorless Control of Interior Permanent Magnet Synchronous Motors

abstract: This paper deals with a speed and position estimation method for the sensorless control of permanent magnet synchro- nous motors. The method is based on a speed-adaptive observer. The dynamics of the system are analyzed by linearizing both the motor model and the observer, and the observer gain is selected to give improved damping and noise suppression. At low speeds, the observer is augmented with a signal injection technique, providing stable operation down to zero speed. The experimental results, obtained using a 2.2-kW interior magnet motor, are in agreement with the results of the analysis.


title: Automatic Single Page-Based Algorithms for Medieval Manuscript Analysis

abstract: We propose three automatic algorithms for analyzing digitized medieval manuscripts, text block computation, text line segmentation, and special component extraction, by taking advantage of previous clustering algorithms and a template-matching technique. These three methods are completely automatic, so no user intervention or input is required to make them work. Moreover, they are all per-page based; that is, unlike some prior methods—that need a set of pages from the same manuscript for training purposes—they are able to analyze a single page without requiring any additional pages for input, eliminating the need for training on additional pages with similar layout. We extensively evaluated the algorithms on 1,771 images of pages of six different publicly available historical manuscripts, which differ significantly from each other in terms of layout structure, acquisition resolution, writing style, and so on. The experimental results indicate that they are able to achieve very satisfactory performance, that is, the average precision and recall values obtained by the text block computation method can reach as high as 98% and 99%, respectively.


title: Cybernetic Human HRP-4C: A Humanoid Robot with Human-Like Proportions

abstract: Cybernetic human HRP-4C is a humanoid robot whose body dimensions were designed to match the average Japanese young female. In this paper, we explain the aim of the development, realization of human-like shape and dimensions, research to realize human-like motion and interactions using speech recognition.


title: Disaster Management and Social Media Use for Decision Making by Humanitarian Organizations

abstract: In times of a crisis, disasters or extreme events, affected people use social media solutions to share information about the situation. Hence, the use of this information for supporting humanitarian operations is becoming a valuable source to develop a real time understanding about the situation even before they arrive at the ground. From a scholarly perspective, the use of social media management during and after a disaster has hit has been researched, but no special focus has been given to the needs of humanitarian organizations and how their management phases can be supported by social media analytics in real time. In this research, we will identify the few papers that have been concerned with this topic and will apply a theoretical lens from disaster management to investigate viable areas where social media research can support humanitarian organization in the different phases of disaster response.


title: A Pathway to Psychological Difficulty: Perceived Chronic Social Adversity and Its Symptomatic Reactions

abstract: In this paper, we attempt to predict and explain psychological maladjustment or difficulty. Specifically, we discuss the concept of perceived chronic social adversity, and we expect that such perceived chronic social adversity may potentially lead to chronic stress responses. Accordingly, we propose the symptomatic reactions of perceived chronic social adversity. We put forward a set of hypotheses regarding the relationships between perceived chronic social adversity and those chronic stress responses, and we further hypothesize a mediating role of individualized negative essentialism brought by perceived chronical social adversity. Resilience and individual differences in the ability to cope with perceived adversity are discussed. Future research and prevention need to pay more attention to effects of subjective personal experiences on psychological difficulty, focusing on the importance of exploring daily social experiences in improving cognitive construction processes and developing appropriate preventions.


Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network


title: Wizard of Wikipedia: Knowledge-Powered Conversational agents

abstract: In open-domain dialogue intelligent agents should exhibit the use of knowledge, however there are few convincing demonstrations of this to date. The most popular sequence to sequence models typically"generate and hope"generic utterances that can be memorized in the weights of the model when mapping from input utterance(s) to output, rather than employing recalled knowledge as context. Use of knowledge has so far proved difficult, in part because of the lack of a supervised learning benchmark task which exhibits knowledgeable open dialogue with clear grounding. To that end we collect and release a large dataset with conversations directly grounded with knowledge retrieved from Wikipedia. We then design architectures capable of retrieving knowledge, reading and conditioning on it, and finally generating natural responses. Our best performing dialogue models are able to conduct knowledgeable discussions on open-domain topics as evaluated by automatic metrics and human evaluations, while our new benchmark allows for measuring further improvements in this important research direction.


title: Structural, ferroelectric and piezoelectric properties of Mn-modified BiFeO3–BaTiO3 high-temperature ceramics

abstract: Mn modified BiFeO3–BaTiO3 (abbreviated as BFBT-Mnx%, x = 0.1, 0.3, 0.6, 0.9, 1.2) high-temperature lead-free ceramics were prepared by conventional oxide-mixed method and the effect of Mn doping on microstructure and electrical properties was investigated. The solid solutions show a single phase perovskite structure, and the content of Mn has a significant effect on the microstructure of ceramics. The addition of Mn can induce combinatory “hard” and “soft” piezoelectric characteristics due to aliovalent substitutions. In particular, x = 0.6 BFBT-Mnx% ceramic, with a Curie temperature, T c, of ~463 °C, shows optimum piezoelectric properties of d 33 = 131pC/N, k p = 0.298. The simultaneous existence of good piezoelectric properties and high T c makes these ceramics suitable for elevated temperature piezoelectric devices.


title: A proposal of LDMOS using Deep Trench poly field plate

abstract: In this paper, we developed Deep Trench structure of LDMOS instead of STI in conventional one, with 0.18um 60V BCD process. By forming vertical drift region using Deep Trench, we achieved lower Ron.sp from cell pitch shrink while BVdss does not change. As a result we achieved 76V BVdss and 49.3 mohm∗mm2 Ron.sp of low side 60V rated LDNMOS.


title: The influence of extro/introversion on the intention to pay for social networking sites

abstract: Although social networking sites (SNSs) have attracted increased attention and members in recent years, there has been little research on it: particularly on how a users' extroversion or introversion can affect their intention to pay for these services and what other factors might influence them. We therefore proposed and tested a model that measured the users' value and satisfaction perspectives by examining the influence of these factors in an empirical survey of 288 SNS members. At the same time, the differences due to their psychological state were explored. The causal model was validated using PLS-Graph 3.0; six out of eight study hypotheses were supported. The results indicated that perceived value significantly influenced the intention to pay SNS subscription fees while satisfaction did not. Moreover, extroverts thought more highly of the social value of the SNS, while introverts placed more importance on its emotional and price value. The implications of these findings are discussed.


title: Using Machine Learning to Detect Fake Identities: Bots vs Humans

abstract: There are a growing number of people who hold accounts on social media platforms (SMPs) but hide their identity for malicious purposes. Unfortunately, very little research has been done to date to detect fake identities created by humans, especially so on SMPs. In contrast, many examples exist of cases where fake accounts created by bots or computers have been detected successfully using machine learning models. In the case of bots these machine learning models were dependent on employing engineered features, such as the “friend-to-followers ratio.” These features were engineered from attributes, such as “friend-count” and “follower-count,” which are directly available in the account profiles on SMPs. The research discussed in this paper applies these same engineered features to a set of fake human accounts in the hope of advancing the successful detection of fake identities created by humans on SMPs.


title: A multilevel meta-analysis of studies reporting correlations between the h index and 37 different h index variants

abstract: This paper presents the first meta-analysis of studies that computed correlations between the h index and variants of the h index (such as the g index; in total 37 different variants) that have been proposed and discussed in the literature. A high correlation between the h index and its variants would indicate that the h index variants hardly provide added information to the h index. This meta-analysis included 135 correlation coefficients from 32 studies. The studies were based on a total sample size of N=9005; on average, each study had a sample size of n=257. The results of a three-level cross-classified mixed-effects meta-analysis show a high correlation between the h index and its variants: Depending on the model, the mean correlation coefficient varies between .8 and .9. This means that there is redundancy between most of the h index variants and the h index. There is a statistically significant study-to-study variation of the correlation coefficients in the information they yield. The lowest correlation coefficients with the h index are found for the h index variants MII and m index. Hence, these h index variants make a non-redundant contribution to the h index.


title: Automatic Hashtag Recommendation for Microblogs using Topic-Specific Translation Model

abstract: Microblogging services continue to grow in popularity, users publish massive instant messages every day through them. Many tweets are marked with hashtags, which usually represent groups or topics of tweets. Hashtags may provide valuable information for lots of applications, such as retrieval, opinion mining, classification, and so on. However, since hashtags should be manually annotated, only 14.6% tweets contain them (Wang et al., 2011). In this paper, we adopt topic-specific translation model(TSTM) to suggest hashtags for microblogs. It combines the advantages of both topic model and translation model. Experimental result on dataset crawled from real world microblogging service demonstrates that the proposed method can outperform some state-of-the-art methods.


title: Kernel-based associative memory

abstract: We propose a new approach to pseudo-inverse associative memories using kernel machine methodology. Basing on Hopfield-type pseudoinverse associative memories we developed a series of kernel-based hetero- and auto-associative algorithms. There are convergence processes possible during examination procedures even for continuous data. Kernel approach enables to overcome capacity limitations inherent to Hopfield-type networks. Memory capacity virtually does not depend on data dimension. We provide theoretical investigation for proposed methods and prove its attraction properties. Also we have experimentally tested them for tasks of classification and associative retrieval.


title: 3D Ultrasound image segmentation: A Survey

abstract: Three-dimensional Ultrasound image segmentation methods are surveyed in this paper. The focus of this report is to investigate applications of these techniques and a review of the original ideas and concepts. Although many two-dimensional image segmentation in the literature have been considered as a three-dimensional approach by mistake but we review them as a three-dimensional technique. We select the studies that have addressed the problem of medical three-dimensional Ultrasound image segmentation utilizing their proposed techniques. The evaluation methods and comparison between them are presented and tabulated in terms of evaluation techniques, interactivity, and robustness.


title: Supervised LogEuclidean Metric Learning for Symmetric Positive Definite Matrices

abstract: Metric learning has been shown to be highly effective to improve the performance of nearest neighbor classification. In this paper, we address the problem of metric learning for Symmetric Positive Definite (SPD) matrices such as covariance matrices, which arise in many real-world applications. Naively using standard Mahalanobis metric learning methods under the Euclidean geometry for SPD matrices is not appropriate, because the difference of SPD matrices can be a non-SPD matrix and thus the obtained solution can be uninterpretable. To cope with this problem, we propose to use a properly parameterized LogEuclidean distance and optimize the metric with respect to kernel-target alignment, which is a supervised criterion for kernel learning. Then the resulting non-trivial optimization problem is solved by utilizing the Riemannian geometry. Finally, we experimentally demonstrate the usefulness of our LogEuclidean metric learning algorithm on real-world classification tasks for EEG signals and texture patches.


title: Ten questions to ask when planning a course or curriculum.

abstract: This brief practical aid to course or curriculum development cannot replace educational qualifications or experience, but it does examine ten basic questions, any of which may be all too easily neglected. These are: What are the needs in relation to the product of the training programme? What are the aims and objectives? What content should be included? How should the content be organized? What educational strategies should be adopted? What teaching methods should be used? How should assessment be carried out? How should details of the curriculum be communicated? What educational environment or climate should be fostered? How should the process be managed? Each aspect is illustrated through the analogy of car manufacturing. The ten questions are relevant in all situations where a course or curriculum is being planned, including an undergraduate degree course, a short postgraduate course or a 1-hour lecture.


title: $HS^2$: Active Learning over Hypergraphs

abstract: We propose a hypergraph-based active learning scheme which we term $HS^2$, $HS^2$ generalizes the previously reported algorithm $S^2$ originally proposed for graph-based active learning with pointwise queries [Dasarathy et al., COLT 2015]. Our $HS^2$ method can accommodate hypergraph structures and allows one to ask both pointwise queries and pairwise queries. Based on a novel parametric system particularly designed for hypergraphs, we derive theoretical results on the query complexity of $HS^2$ for the above described generalized settings. Both the theoretical and empirical results show that $HS^2$ requires a significantly fewer number of queries than $S^2$ when one uses $S^2$ over a graph obtained from the corresponding hypergraph via clique expansion.


title: Varieties of capitalism: comparative institutional approaches to economic organization and innovation

abstract: Innovation and Institutions is an extensive elaboration on the make up of systems of innovation. It examines why some countries are more innovative than others, why national styles of innovation differ, and goes on to explore why some countries make radical innovations but fail to successfully market them, whilst others making incremental innovations have more commercial success.


title: Convective burn from use of hairdryer for heel warming prior to the heel prick test - a case report

abstract: BackgroundBlood sampling through heel lancing is the most common invasive painful procedure performed on newborn infants.Case PresentationWe report the case of a five day old infant who sustained burns to the left foot and leg after the mother's hairdryer was used by the midwife to warm the baby's heel prior to capillary blood sampling (CBS) with an automated device.ConclusionHeel warming is not recommended for routine CBS although it is often practiced. If pre-warming is to be practiced, standardised devices should be used rather than improvised techniques. This will reduce the risk of injury to these infants.


title: Adaptive stock trading with dynamic asset allocation using reinforcement learning

abstract: Stock trading is an important decision-making problem that involves both stock selection and asset management. Though many promising results have been reported for predicting prices, selecting stocks, and managing assets using machine-learning techniques, considering all of them is challenging because of their complexity. In this paper, we present a new stock trading method that incorporates dynamic asset allocation in a reinforcement-learning framework. The proposed asset allocation strategy, called meta policy (MP), is designed to utilize the temporal information from both stock recommendations and the ratio of the stock fund over the asset. Local traders are constructed with pattern-based multiple predictors, and used to decide the purchase money per recommendation. Formulating the MP in the reinforcement learning framework is achieved by a compact design of the environment and the learning agent. Experimental results using the Korean stock market show that the proposed MP method outperforms other fixed asset-allocation strategies, and reduces the risks inherent in local traders.


title: Music - an aid to productivity.

abstract: A series of experiments has investigated the relationship between the playing of background music during the performance of repetitive work and efficiency in performing such a task. The results give strong support to the contention that economic benefits can accure from the use of music in industry. The studies show that music is effective in raising efficiency in this type of work even when in competition with the unfavourable conditions produced by machine noise.


title: Evolutionary artificial neural networks: a review

abstract: This paper reviews the use of evolutionary algorithms (EAs) to optimize artificial neural networks (ANNs). First, we briefly introduce the basic principles of artificial neural networks and evolutionary algorithms and, by analyzing the advantages and disadvantages of EAs and ANNs, explain the advantages of using EAs to optimize ANNs. We then provide a brief survey on the basic theories and algorithms for optimizing the weights, optimizing the network architecture and optimizing the learning rules, and discuss recent research from these three aspects. Finally, we speculate on new trends in the development of this area.


title: Maximizing energy conservation in a centralized cognitive radio network architecture

abstract: This paper proposes two energy-aware algorithms exploited for optimal resources administration and maximum energy conservation of secondary communication nodes in a centralized cognitive radio network architecture. The proposed algorithms optimally enable efficient TV White Spaces exploitation, through a radio spectrum broker that administrates the resources trading process, based on real-time secondary spectrum market policy. In addition, both algorithms adopt a joint path lifetime and utility maximization scheme, which encompasses a capacity-aware policy for achieving minimum energy consumption and reliability during the resource sharing process. Validity of both algorithms is verified through several sets of extended experimental/simulation tests, carried out under controlled conditions.


title: Secure biometric template generation for multi-factor authentication

abstract: In the light of recent security incidents, leading to compromise of services using single factor authentication mechanisms, industry and academia researchers are actively investigating novel multi-factor authentication schemes. Moreover, exposure of unprotected authentication data is a high risk threat for organizations with online presence. The challenge is how to ensure security of multi-factor authentication data without deteriorating the performance of an identity verification system? To solve this problem, we present a novel framework that applies random projections to biometric data (inherence factor), using secure keys derived from passwords (knowledge factor), to generate inherently secure, efficient and revocable/renewable biometric templates for users' verification. We evaluate the security strength of the framework against possible attacks by adversaries. We also undertake a case study of deploying the proposed framework in a two-factor authentication setup that uses users' passwords and dynamic handwritten signatures. Our system preserves the important biometric information even when the user specific password is compromised - a highly desirable feature but not existent in the state-of-the-art transformation techniques. We have evaluated the performance of the framework on three publicly available signature datasets. The results prove that the proposed framework does not undermine the discriminating features of genuine and forged signatures and the verification performance is comparable to that of the state-of-the-art benchmark results. HighlightsA novel two-factor authentication framework involving user biometrics and passwords.A hybrid scheme for template security using subspace mapping and arithmetic hashing.Detailed security analysis of the proposed framework under different attack scenarios.Application of the proposed approach to protect dynamic signature templates.To show that proposed technique does not undermine the verification performance.


title: Multi-segment Green Light Optimal Speed Advisory

abstract: The problem of how to adjust speed of vehicles so that they can arrive at the intersection when the light is green can be solved by means of Green Light Optimal Speed Advisory (GLOSA). The existing GLOSA approaches are single segment, that is, they consider traffic lights independently by providing vehicles with the optimal speed for the segment ahead of the nearest traffic lights. In this article we introduce a new approach-a multi segment GLOSA-according to which several lights in sequence on a vehicle's route are taken into account. The speed optimisation process is performed using a genetic algorithm. We assume that a vehicle has access to all traffic light phase schedules that it will encounter on its route. The route is composed of segments divided by traffic lights. The proposed GLOSA provides a driver with speed advisory for each segment according to selected preferences like minimisation of total traveling time or fuel consumption. We demonstrate, that in free-flow conditions such multi-segment GLOSA results in much better results when compared with single-segment approach.


title: On ranking and choice models

abstract: In today's big data era, huge amounts of ranking and choice data are generated on a daily basis, and consequently, many powerful new computational tools for dealing with ranking and choice data have emerged in recent years. This paper highlights recent developments in two areas of ranking and choice modeling that cross traditional boundaries and are of multidisciplinary interest: ranking from pairwise comparisons, and automatic discovery of latent categories from choice survey data.


title: Broadband Compact 180 $^{\circ}$ Hybrid Derived From the Wilkinson Divider

abstract: A novel 180° hybrid is proposed. This hybrid is derived from the Wilkinson divider, where the resistor is simply replaced by a fourth port of equal characteristic impedance, which acts as the ? port of the hybrid, while the input port of the original Wilkinson divider acts as the ? port. Due to this structure, including only two quarter-wavelength transmission lines, this hybrid has a greatly reduced size compared with the conventional hybrid rat-race and miniaturized variants of it. Furthermore, it exhibits a significantly improved bandwidth. The measured 0.5-dB insertion loss bandwidth is over 200% for ? power division and 66% for ? power division (with 50-? load). Due to its perfectly symmetric configuration, the proposed hybrid features frequency-independent power-dividing balance performance. The 0.5-dB amplitude imbalance for both the ? and ? port divisions has essentially unlimited bandwidth, and the measured phase imbalance is within 3° over all frequencies. The proposed hybrid also shows excellent ? - ? port isolation with a measured result exceeding 30 dB over all frequencies.


title: Volterra models and three-layer perceptrons

abstract: This paper proposes the use of a class of feedforward artificial neural networks with polynomial activation functions (distinct for each hidden unit) for practical modeling of high-order Volterra systems. Discrete-time Volterra models (DVMs) are often used in the study of nonlinear physical and physiological systems using stimulus-response data. However, their practical use has been hindered by computational limitations that confine them to low-order nonlinearities (i.e., only estimation of low-order kernels is practically feasible). Since three-layer perceptrons (TLPs) can be used to represent input-output nonlinear mappings of arbitrary order, this paper explores the basic relations between DVMs and TLPs with tapped-delay inputs in the context of nonlinear system modeling. A variant of TLP with polynomial activation functions-termed "separable Volterra networks" (SVNs)-is found particularly useful in deriving explicit relations with DVM and in obtaining practicable models of highly nonlinear systems from stimulus-response data. The conditions under which the two approaches yield equivalent representations of the input-output relation are explored, and the feasibility of DVM estimation via equivalent SVN training using backpropagation is demonstrated by computer-simulated examples and compared with results from the Laguerre expansion technique (LET). The use of SVN models allows practicable modeling of high-order nonlinear systems, thus removing the main practical limitation of the DVM approach.


title: First version of a data flow procedure language

abstract: A language for representing computational procedures based on the concept of data flow is presented in terms of a semantic model that permits concurrent execution of noninterfering program parts. Procedures in the language operate on elementary and structured values, and always define functional transformations of values. The language is equivalent in expressive power to a block structured language with internal procedure variables and is a generalization of pure Lisp. The language is being used as a model for study of fundamental semantic constructs for programming, as a target language for evaluating trans-latability of programs expressed at the user-language level, and as a guide for research in advanced computer architecture.


title: When recommenders fail: predicting recommender failure for algorithm selection and combination

abstract: Hybrid recommender systems --- systems using multiple algorithms together to improve recommendation quality --- have been well-known for many years and have shown good performance in recent demonstrations such as the NetFlix Prize. Modern hybridization techniques, such as feature-weighted linear stacking, take advantage of the hypothesis that the relative performance of recommenders varies by circumstance and attempt to optimize each item score to maximize the strengths of the component recommenders. Less attention, however, has been paid to understanding what these strengths and failure modes are. Understanding what causes particular recommenders to fail will facilitate better selection of the component recommenders for future hybrid systems and a better understanding of how individual recommender personalities can be harnessed to improve the recommender user experience. We present an analysis of the predictions made by several well-known recommender algorithms on the MovieLens 10M data set, showing that for many cases in which one algorithm fails, there is another that will correctly predict the rating.


title: Phonotactic language recognition using i-vectors and phoneme posteriogram counts

abstract: This paper describes a novel approach to phonotactic LID, where instead of using soft-counts based on phoneme lattices, we use posteriogram to obtain n-gram counts. The high-dimensional vectors of counts are reduced to low-dimensional units for which we adapted the commonly used term i-vectors. The reduction is based on multinomial subspace modeling and is designed to work in the total-variability space. The proposed technique was tested on the NIST 2009 LRE set with better results to a system based on using soft-counts (Cavg on 30s: 3.15% vs 3.43%), and with very good results when fused with an acoustic i-vector LID system (Cavg on 30s acoustic 2.4% vs 1.25%). The proposed technique is also compared with another low dimensional projection system based on PCA. In comparison with the original soft-counts, the proposed technique provides better results, reduces the problems due to sparse counts, and avoids the process of using pruning techniques when creating the lattices.


title: Automated generation of geometric theorems from images of diagrams

abstract: We propose an approach to generate geometric theorems from electronic images of diagrams automatically. The approach makes use of techniques of Hough transform to recognize geometric objects and their labels and of numeric verification to mine basic geometric relations. Candidate propositions are generated from the retrieved information by using six strategies and geometric theorems are obtained from the candidates via algebraic computation. Experiments with a preliminary implementation illustrate the effectiveness and efficiency of the proposed approach for generating nontrivial theorems from images of diagrams. This work demonstrates the feasibility of automated discovery of profound geometric knowledge from simple image data and has potential applications in geometric knowledge management and education.


title: A portable high-frequency digitally controlled oscillator (DCO)

abstract: A novel digitally-controlled oscillator (DCO) is reported. Utilizing a new capacitive load, the new DCO is capable of producing much higher output frequencies than existing DCOs. All other components are fully digital and modular, allowing portability to any CMOS process and customization for different applications. At the heart of the DCO is a digital ring oscillator (DRO) that utilizes the new shunt-capacitive loads. Unprecedented higher frequencies are obtained through a novel idea of electrically removing the effect of un-enabled loads. Simple design conditions for achieving proper operation of the DRO are provided. Spice simulations verified the correct and superior operation of the DCO even with device mismatch. A custom layout of the DRO was generated using LFoundry's 150 nm technology. The total DRO area was found to be 418 μm2.


title: Link prediction in social networks using hierarchical community detection

abstract: Social network analysis is an approach to the study of social structures. One of the important fields in social networks analyses is link prediction. Link prediction tries to reach an appropriate answer to this question: what kinds of interaction among members of a network would possible form in future, given a snapshot of the network in current time. The main purpose of this paper is to boost the performance of similarity based link prediction methods by using community information. This information is derived from the structure of the graph, based on the number of community levels that two vertices have in common, in a hierarchical representation of communities. To evaluate the performance of the proposed method, four datasets are used as benchmark. The results suggest that the information of communities often increases the efficiency and accuracy of link prediction.


title: Distributed Event Routing in Publish/Subscribe Communication Systems: a Survey

abstract: Since the early nineties, anonymous and asynchronous dissemination of information has been a basic building block for typical distributed application such as stock exchanges, news tickers and air-traffic control. With the advent of ubiquitous computing and of the ambient intelligence, information dissemination solutions have to face challenges such as the exchange of huge amounts of information, large and dynamic number of participants possibly deployed over a large network (e.g. peerto-peer systems), mobility and scarcity of resources (e.g. mobile ad-hoc and sensor networks) [9]. Publish/Subscribe (pub/sub) systems are a key technology for information dissemination. Each participant in a pub/sub communication system can take on the role of a publisher or a subscriber of information. Publishers produce information in form of events, which is consumed by subscribers issuing subscriptions representing their interest only in specific events. The main semantical characterization of pub/sub is in the way events flow from senders to receivers: receivers are not directly targeted from publisher, but rather they are indirectly addressed according to the content of events. Thanks to this anonymity, publishers and subscribers exchange information without directly knowing each other, this enabling the possibility for the system to seamlessly expand to massive, Internet-scale size. Interaction between publishers and subscribers is actually mediated by the pub/sub system, that in general is constituted by a set of distributed nodes that coordinate among themselves in order to dispatch published events to all (and possibly only) interested subscribers. A distributed pub/sub system for scalable information dissemination can be decomposed in three functional layers: namely the overlay infrastructure, the event routing and the algorithm for matching events against subscriptions. The overlay infrastructure represents the organization of the various entities that compose the system, (e.g., overlay network of dedicated servers, peer-topeer structured overlay, etc.) while event routing is the mechanism for dispatching information from publishers to subscribers. Event routing has to effectively exploit


title: Directional Binary Wavelet Patterns for Biomedical Image Indexing and Retrieval

abstract: A new algorithm for medical image retrieval is presented in the paper. An 8-bit grayscale image is divided into eight binary bit-planes, and then binary wavelet transform (BWT) which is similar to the lifting scheme in real wavelet transform (RWT) is performed on each bitplane to extract the multi-resolution binary images. The local binary pattern (LBP) features are extracted from the resultant BWT sub-bands. Three experiments have been carried out for proving the effectiveness of the proposed algorithm. Out of which two are meant for medical image retrieval and one for face retrieval. It is further mentioned that the database considered for three experiments are OASIS magnetic resonance imaging (MRI) database, NEMA computer tomography (CT) database and PolyU-NIRFD face database. The results after investigation shows a significant improvement in terms of their evaluation measures as compared to LBP and LBP with Gabor transform.


title: Constraint Programming in OPL

abstract: OPL is a modeling language for mathematical programming and combinatorial optimization problems. It is the first modeling language to combine high-level algebraic and set notations from modeling languages with a rich constraint language and the ability to specify search procedures and strategies that is the essence of constraint programming. In addition, OPL models can be controlled and composed using OPLSCRIPT, a script language that simplifies the development of applications that solve sequences of models, several instances of the same model, or a combination of both as in column-generation applications. This paper illustrates some of the functionalities of OPL for constraint programming using frequency allocation, sport-scheduling, and job-shop scheduling applications. It also illustrates how OPL models can be composed using OPLSCRIPT on a simple configuration example.


title: A novel approach to cancer staging : application to esophageal cancer

abstract: A novel 3-step random forests methodology involving survival data (survival forests), ordinal data (multiclass forests), and continuous data (regression forests) is introduced for cancer staging. The methodology is illustrated for esophageal cancer using worldwide esophageal cancer collaboration data involving 4627 patients.


title: Deep Reinforcement Learning and the Deadly Triad

abstract: We know from reinforcement learning theory that temporal difference learning can fail in certain cases. Sutton and Barto (2018) identify a deadly triad of function approximation, bootstrapping, and off-policy learning. When these three properties are combined, learning can diverge with the value estimates becoming unbounded. However, several algorithms successfully combine these three properties, which indicates that there is at least a partial gap in our understanding. In this work, we investigate the impact of the deadly triad in practice, in the context of a family of popular deep reinforcement learning models - deep Q-networks trained with experience replay - analysing how the components of this system play a role in the emergence of the deadly triad, and in the agent's performance


title: Cross-Domain 3D Equivariant Image Embeddings

abstract: Spherical convolutional networks have been introduced recently as tools to learn powerful feature representations of 3D shapes. Spherical CNNs are equivariant to 3D rotations making them ideally suited for applications where 3D data may be observed in arbitrary orientations. In this paper we learn 2D image embeddings with a similar equivariant structure: embedding the image of a 3D object should commute with rotations of the object. We introduce a cross-domain embedding from 2D images into a spherical CNN latent space. Our model is supervised only by target embeddings obtained from a spherical CNN pretrained for 3D shape classification. The trained model learns to encode images with 3D shape properties and is equivariant to 3D rotations of the observed object. We show that learning only a rich embedding for images with appropriate geometric structure is in and of itself sufficient for tackling numerous applications. Evidence from two different applications, relative pose estimation and novel view synthesis, demonstrates that equivariant embeddings are sufficient for both applications without requiring any task-specific supervised training.


title: Adversarially Learned Anomaly Detection

abstract: Anomaly detection is a significant and hence well-studied problem. However, developing effective anomaly detection methods for complex and high-dimensional data remains a challenge. As Generative Adversarial Networks (GANs) are able to model the complex high-dimensional distributions of real-world data, they offer a promising approach to address this challenge. In this work, we propose an anomaly detection method, Adversarially Learned Anomaly Detection (ALAD) based on bi-directional GANs, that derives adversarially learned features for the anomaly detection task. ALAD then uses reconstruction errors based on these adversarially learned features to determine if a data sample is anomalous. ALAD builds on recent advances to ensure data-space and latent-space cycle-consistencies and stabilize GAN training, which results in significantly improved anomaly detection performance. ALAD achieves state-of-the-art performance on a range of image and tabular datasets while being several hundred-fold faster at test time than the only published GAN-based method.


title: Parasocial Interaction and Parasocial Relationship: Conceptual Clarification and a Critical Assessment of Measures

abstract: Parasocial interaction and parasocial relationship are often conflated conceptually and methodologically, leaving researchers unclear as to which concept is being tapped. This research clarifies these concepts and experimentally compares the most common measure of parasocial interaction, the Parasocial Interaction Scale (PSI-Scale), with a newer measure, the Experience of Parasocial Interaction Scale (EPSI-Scale). Participants (N = 383) viewed a brief videorecording of a woman who either bodily addressed the viewer or not, then completed a questionnaire. The EPSI-Scale was a better measure of parasocial interaction, understood as a within-viewing experience of mutual awareness, whereas the PSI-Scale may measure short- or long-term liking, or something else. To avoid conceptual and empirical confusion, researchers must choose measures with greater care.


title: LLC resonant converter using a planar transformer with new core shape

abstract: In this paper, a low profile LLC resonant converter with two transformers using a planar core is proposed for a slim switching mode power supply (SMPS). Design procedures, magnetic modeling and voltage gain characteristics on the proposed planar transformer and converter are described in detail. LLC resonant converter including two transformers using a planar core is connected in series at primary and in parallel by the center-tap winding at secondary. Based on the theoretical analysis and simulation results of the voltage gain characteristics, a 300W LLC resonant converter is designed and tested.


title: Multi-Attribute Robust Component Analysis for Facial UV Maps

abstract: Recently, due to the collection of large scale 3D face models, as well as the advent of deep learning, a significant progress has been made in the field of 3D face alignment"in-the-wild". That is, many methods have been proposed that establish sparse or dense 3D correspondences between a 2D facial image and a 3D face model. The utilization of 3D face alignment introduces new challenges and research directions, especially on the analysis of facial texture images. In particular, texture does not suffer any more from warping effects (that occurred when 2D face alignment methods were used). Nevertheless, since facial images are commonly captured in arbitrary recording conditions, a considerable amount of missing information and gross outliers is observed (e.g., due to self-occlusion, or subjects wearing eye-glasses). Given that many annotated databases have been developed for face analysis tasks, it is evident that component analysis techniques need to be developed in order to alleviate issues arising from the aforementioned challenges. In this paper, we propose a novel component analysis technique that is suitable for facial UV maps containing a considerable amount of missing information and outliers, while additionally, incorporates knowledge from various attributes (such as age and identity). We evaluate the proposed Multi-Attribute Robust Component Analysis (MA-RCA) on problems such as UV completion and age progression, where the proposed method outperforms compared techniques. Finally, we demonstrate that MA-RCA method is powerful enough to provide weak annotations for training deep learning systems for various applications, such as illumination transfer.


title: The Perpetual Music Track The Phenomenon of Constant Musical Imagery

abstract: The perpetual music track is a new concept that describes a condition of constant or near-constant musical imagery. This condi- tion appears to be very rare even among composers and musicians. I present here a detailed self-analysis of musical imagery for the pur- pose of defining the psychological features of a perpetual music track. I have music running through my head almost constantly during wak- ing hours, consisting of a combination of recently-heard pieces and distant pieces that spontaneously pop into the head. Imagery consists mainly of short musical fragments that get looped repeatedly upon themselves. Corporeal manifestations of imagery occur in the form of unconscious finger movements whose patterns correspond to the melodic contour of the imagined piece. Musical dreams occur every week or two, and contain a combination of familiar and originally- composed music. These results are discussed in light of theories of imagery, consciousness, hallucination, obsessive cognition, and most especially the notion that acoustic consciousness can be split into multiple parallel streams.


title: IITKGP-SESC: Speech Database for Emotion Analysis

abstract: In this paper, we are introducing the speech database for analyzing the emotions present in speech signals. The proposed database is recorded in Telugu language using the professional artists from All India Radio (AIR), Vijayawada, India. The speech corpus is collected by simulating eight different emotions using the neutral (emotion free) statements. The database is named as Indian Institute of Technology Kharagpur Simulated Emotion Speech Corpus (IITKGP-SESC). The proposed database will be useful for characterizing the emotions present in speech. Further, the emotion specific knowledge present in speech at different levels can be acquired by developing the emotion specific models using the features from vocal tract system, excitation source and prosody. This paper describes the design, acquisition, post processing and evaluation of the proposed speech database (IITKGP-SESC). The quality of the emotions present in the database is evaluated using subjective listening tests. Finally, statistical models are developed using prosodic features, and the discrimination of the emotions is carried out by performing the classification of emotions using the developed statistical models.


title: On Demystifying the Android Application Framework: Re-Visiting Android Permission Specification Analysis

abstract: In contrast to the Android application layer, Android’s application framework’s internals and their influence on the platform security and user privacy are still largely a black box for us. In this paper, we establish a static runtime model of the application framework in order to study its internals and provide the first high-level classification of the framework’s protected resources. We thereby uncover design patterns that differ highly from the runtime model at the application layer. We demonstrate the benefits of our insights for security-focused analysis of the framework by re-visiting the important use-case of mapping Android permissions to framework/SDK API methods. We, in particular, present a novel mapping based on our findings that significantly improves on prior results in this area that were established based on insufficient knowledge about the framework’s internals. Moreover, we introduce the concept of permission locality to show that although framework services follow the principle of separation of duty, the accompanying permission checks to guard sensitive operations violate it.


A Comparative Study between DTC, SVM-DTC and SVM-DTC with PI Controller of Induction Motor


title: A survey on measuring indirect discrimination in machine learning

abstract: Nowadays, many decisions are made using predictive models built on historical data.Predictive models may systematically discriminate groups of people even if the computing process is fair and well-intentioned. Discrimination-aware data mining studies how to make predictive models free from discrimination, when historical data, on which they are built, may be biased, incomplete, or even contain past discriminatory decisions. Discrimination refers to disadvantageous treatment of a person based on belonging to a category rather than on individual merit. In this survey we review and organize various discrimination measures that have been used for measuring discrimination in data, as well as in evaluating performance of discrimination-aware predictive models. We also discuss related measures from other disciplines, which have not been used for measuring discrimination, but potentially could be suitable for this purpose. We computationally analyze properties of selected measures. We also review and discuss measuring procedures, and present recommendations for practitioners. The primary target audience is data mining, machine learning, pattern recognition, statistical modeling researchers developing new methods for non-discriminatory predictive modeling. In addition, practitioners and policy makers would use the survey for diagnosing potential discrimination by predictive models.


title: THE USE OF RECURRENT NEURAL NETWORKS IN CONTINUOUS SPEECH RECOGNITION

abstract: This chapter describes a use of recurrent neural networks (i.e., feedback is incorporated in the computation) as an acoustic model for continuous speech recognition. The form of the recurrent neural network is described along with an appropriate parameter estimation procedure. For each frame of acoustic data, the recurrent network generates an estimate of the posterior probability of of the possible phones given the observed acoustic signal. The posteriors are then converted into scaled likelihoods and used as the observation probabilities within a conventional decoding paradigm (e.g., Viterbi decoding). The advantages of using recurrent networks are that they require a small number of parameters and provide a fast decoding capability (relative to conventional, large-vocabulary, HMM systems)3.


title: Boundary recognition in sensor networks by topological methods

abstract: Wireless sensor networks are tightly associated with the underlying environment in which the sensors are deployed. The global topology of the network is of great importance to both sensor network applications and the implementation of networking functionalities. In this paper we study the problem of topology discovery, in particular, identifying boundaries in a sensor network. Suppose a large number of sensor nodes are scattered in a geometric region, with nearby nodes communicating with each other directly. Our goal is to find the boundary nodes by using only connectivity information. We do not assume any knowledge of the node locations or inter-distances, nor do we enforce that the communication graph follows the unit disk graph model. We propose a simple, distributed algorithm that correctly detects nodes on the boundaries and connects them into meaningful boundary cycles. We obtain as a byproduct the medial axis of the sensor field, which has applications in creating virtual coordinates for routing. We show by extensive simulation that the algorithm gives good results even for networks with low density. We also prove rigorously the correctness of the algorithm for continuous geometric domains.


title: Partially occluded object detection and counting

abstract: In today's fast-moving life many a pathological labs still use probabilistic approach to count the number of microscopic cells in a sample slide, which is a tedious job and not accurate all the time. The problem of finding the position of objects in an image as well as to count the occurrence of that object in the image is a hitch for the researcher. We have restricted our problem only to completely circular disk shaped objects. Our aim is to accurately calculate the geometrical position of the objects and the object count. Boundaries of the object patches have been extracted using morphological operation and with the help of classical geometry we locate the exact boundary coordinate of the objects. In this paper we propose a new, simple framework for visual object detection and object counting for the image of circular objects, whether these are partially occluded to each other or not.


title: Individual Differences, Perceived Ease of Use, and Perceived Usefulness in the E-Library Usage

abstract: This study examines the usage of e-library among students in a public university in Malaysia using the Technology Acceptance Model. Data from a questionnaire survey of 201 students were analyzed using linear regression. The results show that individual differences (computer self-efficacy and knowledge of search domain) had a significant positive relationship with perceived ease of use. Perceived ease of use showed significant relationship with perceived usefulness but non significant with the actual usage of the e-library. For perceived usefulness, it showed a significant relationship with the actual usage of the e-library. Lessons and discussions for individual differences, perceived ease of use, perceived usefulness, and actual usage are presented.


title: Joint Downlink Cell Association and Bandwidth Allocation for Wireless Backhauling in Two-Tier HetNets with Large-Scale Antenna Arrays

abstract: The problem of joint downlink cell association (CA) and wireless backhaul bandwidth allocation (WBBA) in two-tier cellular heterogeneous networks (HetNets) is considered. Large-scale antenna array is implemented at the macro base station (BS), while the small cells within the macro cell range are single-antenna BSs and they rely on over-the-air links to the macro BS for backhauling. A sum logarithmic user rate maximization problem is investigated considering wireless backhauling constraints. A duplex and spectrum sharing scheme based on co-channel reverse time-division duplex (TDD) and dynamic soft frequency reuse (SFR) is proposed for interference management in two-tier HetNets with large-scale antenna arrays at the macro BS and wireless backhauling for small cells. Two in-band WBBA scenarios, namely, unified bandwidth allocation and per-small-cell bandwidth allocation scenarios, are investigated for joint CA-WBBA in the HetNet. A two-level hierarchical decomposition method for relaxed optimization is employed to solve the mixed-integer nonlinear program (MINLP). Solutions based on the General Algorithm Modeling System (GAMS) optimization solver and fast heuristics are also proposed for cell association in the per-small-cell WBBA scenario. It is shown that when all small cells have to use in-band wireless backhaul, the system load has more impact on both the sum log-rate and per-user rate performance than the number of small cells deployed within the macro cell range. The proposed joint CA-WBBA algorithms have an optimal load approximately equal to the size of the large-scale antenna array at the macro BS. The cell range expansion (CRE) strategy, which is an efficient cell association scheme for HetNets with perfect backhauling, is shown to be inefficient when in-band wireless backhauling for small cells comes into play.


title: A hardware framework for the fast generation of multiple long-period random number streams

abstract: Stochastic simulations and other scientific applications that depend on random numbers are increasingly implemented in a parallelized manner in programmable logic. High-quality pseudo-random number generators (PRNG), such as the Mersenne Twister, are often based on binary linear recurrences and have extremely long periods (more than 21024. Many software implementations of such PRNGs exist, but hardware implementations are rare. We have developed an optimized, resource-efficient parallel framework for this class of random number generators that exploits the underlying algorithm as well as FPGA-specific architectural features. The framework also incorporates fast "jump-ahead" capability for these PRNGs, allowing simultaneous, independent sub-streams to be generated in parallel by partitioning one long-period pseudo-random sequence We demonstrate parallelized implementations of three types of PRNGs -- the 32-, 64- and 128-bit SIMD Mersenne Twister -- on Xilinx Virtex-II Pro FPGAs. Their area/throughput performance is impressive: for example, compared clock-for-clock with a previous FPGA implementation, a "two-parallelized" 32-bit Mersenne Twister uses 41% fewer resources. It can also scale to 350 MHz for a throughput of 22.4 Gbps, which is 5.5x faster than the older FPGA implementation and 7.1x faster than a dedicated software implementation. The quality of generated random numbers is verified with the standard statistical test batteries Diehard and TestU01. We also present two real-world application studies with multiple RNG streams: the Ziggurat method for generating normal random variables and a Monte Carlo photon-transport simulation.The availability of fast long-period random number generators with multiple streams accelerates hardware-based scientific simulations and allows them to scale to greater complexities


An Efficient Recommender System using Collaborative Filtering Methods with K-separability Approach


title: Precision Position Control of a Voice Coil Motor Using Self-Tuning Fractional Order Proportional-Integral-Derivative Control

abstract: The object of this study is to develop a self-tuning fractional order proportional-integral-derivative (SFOPID) controller for controlling the mover position of a direct drive linear voice coil motor (VCM) accurately under different operational conditions. The fractional order proportional-integral-derivative (FOPID) controller can improve the control performances of the conventional integer order PID controller with respect to the additional fractional differential and integral orders; however, choosing five interdependent control parameters including proportional, integral, and derivative gains, as well as fractional differential and integral orders appropriately is arduous in practical applications. In this regard, the SFOPID controller is proposed in which the five control parameters are optimized dynamically and concurrently according to an adaptive differential evolution algorithm with a high efficiency adaptive selection mechanism. Experimental results reveal that the SFOPID controller outperforms PID and FOPID controllers with regard to the nonlinear VCM control system under both nominal and payload conditions.


title: A Flexible, Corpus-Driven Model of Regular and Inverse Selectional Preferences

abstract: We present a vector space-based model for selectional preferences that predicts plausibility scores for argument headwords. It does not require any lexical resources (such as WordNet). It can be trained either on one corpus with syntactic annotation, or on a combination of a small semantically annotated primary corpus and a large, syntactically analyzed generalization corpus. Our model is able to predict inverse selectional preferences, that is, plausibility scores for predicates given argument heads. ::: ::: We evaluate our model on one NLP task (pseudo-disambiguation) and one cognitive task (prediction of human plausibility judgments), gauging the influence of different parameters and comparing our model against other model classes. We obtain consistent benefits from using the disambiguation and semantic role information provided by a semantically tagged primary corpus. As for parameters, we identify settings that yield good performance across a range of experimental conditions. However, frequency remains a major influence of prediction quality, and we also identify more robust parameter settings suitable for applications with many infrequent items.


title: Communicating with cost-based implicature: A game-theoretic approach to ambiguity

abstract: A game-theoretic approach to linguistic communication predicts that speakers can meaningfully use ambiguous forms in a discourse context in which only one of several available referents has a costly unambiguous form and in which rational interlocutors share knowledge of production costs. If a speaker produces a low-cost ambiguous form to avoid using the high-cost unambiguous form, a rational listener will infer that the high-cost entity was the intended entity, or else the speaker would not have risked ambiguity. We report data from two studies in which pairs of speakers show alignment of their use of ambiguous forms based on this kind of shared knowledge. These results extend the analysis of cost-based pragmatic inferencing beyond that previously associated only with fixed lexical hosts.


title: Daily life behaviour monitoring for health assessment using machine learning: bridging the gap between domains

abstract: Analysis of human behaviour for deducing health and well-being information is one of the contemporary challenges given the ageing in place. To this end, existing and newly developed machine learning methods are needed to be evaluated using annotated real-world data sets. However, the metrics used in performance evaluation are directly taken from the machine learning domain, and they do not necessarily consider the specific needs of human behaviour analysis such as recognizing the duration, start time and frequency of the activities. Moreover, the commonly used metrics such as accuracy or F-measure can be misleading in the presence of skewed class distributions as in the case of human behaviour recognition. In this study, we evaluate the performance of two machine learning methods, hidden Markov model and time windowed neural network on five different real-world data sets through human behaviour understanding for health assessment perspective. According to the experimental results, standard metrics fail to reveal the actual performance of the two compared machine learning methods in terms of behaviour recognition. On the other hand, the proposed evaluation mechanism which considers three different activity categories leads to a more realistic evaluation of the overall performance.


title: “Shallow draughts intoxicate the brain”: Lessons from Cognitive Science for Cognitive Neuropsychology

abstract: This article presents a sobering view of the discipline of cognitive neuropsychology as practiced over the last three or four decades. Our judgment is that, although the study of abnormal cognition resulting from brain injury or disease in previously normal adults has produced a catalogue of fascinating and highly selective deficits, it has yielded relatively little advance in understanding how the brain accomplishes its cognitive business. We question the wisdom of the following three ‘‘choices’’ in mainstream cognitive neuropsychology: (a) single-case methodology, (b) dissociation between functions as the most important source of evidence, and (c) a central goal of diagramming the functional architecture of cognition rather than specifying how its components work. These choices may all stem from an excessive commitment to strict and fine-grained modularity. Although different brain regions are undoubtedly specialized for different functions, we argue that parallel and interactive processing is a better assumption about cognitive processing. The essential goal of specifying representations and processes can, we claim, be significantly assisted by computational modeling which, by its very nature, requires such specification.


title: Picante: R tools for integrating phylogenies and ecology

abstract: Summary: Picante is a software package that provides a comprehensive set of tools for analyzing the phylogenetic and trait diversity of ecological communities. The package calculates phylogenetic diversity metrics, performs trait comparative analyses, manipulates phenotypic and phylogenetic data, and performs tests for phylogenetic signal in trait distributions, community structure and species interactions. Availability: Picante is a package for the R statistical language and environment written in R and C, released under a GPL v2 open-source license, and freely available on the web (http://picante .r-forge.r-project.org) and from CRAN (http://cran.r-project.org).


title: High-Dimensional Bayesian Clustering with Variable Selection: The R Package bclust

abstract: The R package bclust is useful for clustering high-dimensional continuous data. The package uses a parametric spike-and-slab Bayesian model to downweight the effect of noise variables and to quantify the importance of each variable in agglomerative clustering. We take advantage of the existence of closed-form marginal distributions to estimate the model hyper-parameters using empirical Bayes, thereby yielding a fully automatic method. We discuss computational problems arising in implementation of the procedure and illustrate the usefulness of the package through examples.


title: Mobile Augmented Reality for Learning

abstract: Specht, M. (2012, April). Augmented Reality for Learning. Technical report for workshop at eLearning congress 2012. 's Hertogenbosch, The Netherlands.


title: Large Margin Deep Neural Networks: Theory and Algorithms

abstract: Deep neural networks (DNN) have achieved huge practical success in recent years. However, its theoretical properties (in particular generalization ability) are not yet very clear, since existing error bounds for neural networks cannot be directly used to explain the statistical behaviors of practically adopted DNN models (which are multi-class in their nature and may contain convolutional layers). To tackle the challenge, we derive a new margin bound for DNN in this paper, in which the expected 0-1 error of a DNN model is upper bounded by its empirical margin error plus a Rademacher Average based capacity term. This new bound is very general and is consistent with the empirical behaviors of DNN models observed in our experiments. According to the new bound, minimizing the empirical margin error can effectively improve the test performance of DNN. We therefore propose large margin DNN algorithms, which impose margin penalty terms to the cross entropy loss of DNN, so as to reduce the margin error during the training process. Experimental results show that the proposed algorithms can achieve significantly smaller empirical margin errors, as well as better test performances than the standard DNN algorithm.


title: Social media in higher education: A literature review of Facebook

abstract: The rapid adoption of social media technologies has resulted in a fundamental shift in the way communication and collaboration take place. As staff and students use social media technologies in their personal lives, it is important to explore how social media technologies are being used as an educational tool. The aim of this paper is to analyse the role of social media, in particular, Facebook, as an educational tool in higher education. Through a review of the literature, this paper explores the myriad ways in which Facebook is being used as an educational instrument for learning and teaching. Multiple benefits of Facebook usage for learning and teaching have been identified such as increased teacher-student and student-student interaction, improved performance, the convenience of learning and higher engagement. The paper also highlights the potential problems and limitations of Facebook usage ranging from educators’ dominance to privacy concerns. Finally, Facebook usage guidelines that can be adopted by educators to encourage social media adoption are proposed. As social media usage continues to grow in higher education, future empirical research is warranted.


title: Encoder-Based Optimization of CNFET-Based Ternary Logic Circuits

abstract: In existing CNFET-based design methodologies that are used to implement ternary logic circuits, ternary signals are first converted to binary signals, which are then passed through binary gates and an encoder to get the final ternary output. In a ternary circuit, encoder is used to convert intermediate binary signals to final ternary outputs. This paper presents improved encoder designs that are used in implementation of ternary logic circuits. A detailed analysis is carried out on encoders to understand the effect of using CNFETs with CNTs of different diameter on the overall propagation delay and power consumption of the encoder. Based on this analysis, algorithms, which choose appropriate encoders for different output stages of a ternary circuit while optimizing different design parameters, such as power consumption, propagation delay or power-delay product, are presented. These algorithms are used to map appropriate encoders for different outputs of a ternary adder resulting in adder designs that are optimized for delay, power or power-delay product. Simulation results indicate that the ternary adder designs, which use encoder mapping obtained from proposed algorithms, result in 54%–82% reduction in power consumption, 0%–75% in propagation delay and 54%–94% in power-delay product when compared with different existing ripple carry-based ternary adders.


title: A novel piezoelectric driven laparoscopic instrument with multiple degree of freedom parallel kinematic structure

abstract: The development of a novel actuator driven laparoscopic instrument with parallel kinematic instrument tip serves to overcome the workspace restrictions of classic laparoscopic instruments and provides the surgeon multiple degrees of freedom (DOF) inside the patients body.


title: An Algorithm For Open Text Semantic Parsing

abstract: This paper describes an algorithm for open text shallow semantic parsing. The algorithm relies on a frame dataset (FrameNet) and a semantic network (WordNet), to identify semantic relations between words in open text, as well as shallow semantic features associated with concepts in the text. Parsing semantic structures allows semantic units and constituents to be accessed and processed in a more meaningful way than syntactic parsing, moving the automation of understanding natural language text to a higher level.


title: Scientific Workflow Management and the Kepler System

abstract: Many scientific disciplines are now data and information driven, and new scientific knowledge is often gained by scientists putting together data analysis and knowledge discovery “pipelines”. A related trend is that more and more scientific communities realize the benefits of sharing their data and computational services, and are thus contributing to a distributed data and computational community infrastructure (a.k.a. “the Grid”). However, this infrastructure is only a means to an end and scientists ideally should be bothered little with its existence. The goal is for scientists to focus on development and use of what we call scientific workflows. These are networks of analytical steps that may involve, e.g., database access and querying steps, data analysis and mining steps, and many other steps including computationally intensive jobs on high performance cluster computers. In this paper we describe characteristics of and requirements for scientific workflows as identified in a number of our application projects. We then elaborate on Kepler, a particular scientific workflow system, currently under development across a number of scientific data management projects. We describe some key features of Kepler and its underlying Ptolemyii system, planned extensions, and areas of future research. Kepler is a communitydriven, open source project, and we always welcome related projects and new contributors to join.


title: Logistic regression with an auxiliary data source

abstract: To achieve good generalization in supervised learning, the training and testing examples are usually required to be drawn from the same source distribution. In this paper we propose a method to relax this requirement in the context of logistic regression. Assuming Dp and Da are two sets of examples drawn from two mismatched distributions, where Da are fully labeled and Dp partially labeled, our objective is to complete the labels of Dp. We introduce an auxiliary variable μ for each example in Da to reflect its mismatch with Dp. Under an appropriate constraint the μ's are estimated as a byproduct, along with the classifier. We also present an active learning approach for selecting the labeled examples in Dp. The proposed algorithm, called "Migratory-Logit" or M-Logit, is demonstrated successfully on simulated as well as real data sets.


Segmentation of Touching, Overlapping, Skewed and Short Handwritten Text Lines


title: Silicon CMOS devices beyond scaling

abstract: To a large extent, scaling was not seriously challenged in the past. However, a closer look reveals that early signs of scaling limits were seen in high-performance devices in recent technology nodes. To obtain the projected performance gain of 30% per generation, device designers have been forced to relax the device subthreshold leakage continuously from one to several nA/µm for the 250-nm node to hundreds of nA/µm for the 65-nm node. Consequently, passive power density is now a significant portion of the power budget of a high-speed microprocessor. In this paper we discuss device and material options to improve device performance when conventional scaling is power-constrained. These options can be separated into three categories: improved short-channel behavior, improved current drive, and improved switching behavior. In the first category fall advanced dielectrics and multi-gate devices. The second category comprises mobility-enhancing measures through stress and substrate material alternatives. The third category focuses mainly on scaling of SOI body thickness to reduce capacitance. We do not provide details of the fabrication of these different device options or the manufacturing challenges that must be met. Rather, we discuss the fundamental scaling issues related to the various device options. We conclude with a brief discussion of the ultimate FET close to the fundamental silicon device limit.


title: Study of Web Crawler and its Different Types

abstract: Due to the current size of the Web and its dynamic nature, building an efficient search mechanism is very important. A vast number of web pages are continually being added every day, and information is constantly changing. Search engines are used to extract valuable Information from the internet. Web crawlers are the principal part of search engine, is a computer program or software that browses the World Wide Web in a methodical, automated manner or in an orderly fashion. It is an essential method for collecting data on, and keeping in touch with the rapidly increasing Internet. This Paper briefly reviews the concepts of web crawler, its architecture and its various types. Keyword: Crawling techniques, Web Crawler, Search engine, WWW


title: Templating a Molecular Tug-of-War

abstract: The activated transport of organelles, vesicles, and many other subcellular commodities along cytoskeletal filaments is central to mechanisms that regulate the internal organization of eukaryotic cells ( 1 ). The motions of these cargos are driven by several classes of ATP–dependent enzymes called motor proteins that are capable of converting chemical energy into mechanical work. A variety of bulk biochemical and single-molecule techniques have been developed over the past decade to characterize the principles that allow these enzymes to function effectively as molecular machines ( 2 , 3 ). Yet most cargos are transported by multicomponent motor systems containing multiple copies of the same motor, or even by mixtures of different classes of motors that move with different velocities, in opposite directions, and along different types of cytoskeletal filaments ( 4 , 5 ). Understanding how motors cooperate productively and compete antagonistically has therefore become increasingly important for dissecting mechanisms that regulate intracellular transport as well as the impact of motor mutations in diseases. On page 662 of this issue, Derr et al. ( 6 ) demonstrate a new materials approach to these problems that allows the characterization of key relationships among the structural organization of multiple motor systems, the properties of motors within these complexes, and their collective dynamic behaviors.


title: Supervised Methods for Aspect-Based Sentiment Analysis

abstract: In this paper, we present our contribution in SemEval2014 ABSA task, some supervised methods for Aspect-Based Sentiment Analysis of restaurant and laptop reviews are proposed, implemented and evaluated. We focus on determining the aspect terms existing in each sentence, finding out their polarities, detecting the categories of the sentence and the polarity of each category. The evaluation results of our proposed methods exhibit a significant improvement in terms of accuracy and f-measure over all four subtasks regarding to the baseline proposed by SemEval organisers.


title: CPS: market analysis of attacks against demand response in the smart grid

abstract: Demand response systems assume an electricity retail-market with strategic electricity consuming agents. The goal in these systems is to design load shaping mechanisms to achieve efficiency of resources and customer satisfaction. Recent research efforts have studied the impact of integrity attacks in simplified versions of the demand response problem, where neither the load consuming agents nor the adversary are strategic. In this paper, we study the impact of integrity attacks considering strategic players (a social planner or a consumer) and a strategic attacker. We identify two types of attackers: (1) a malicious attacker who wants to damage the equipment in the power grid by producing sudden overloads, and (2) a selfish attacker that wants to defraud the system by compromising and then manipulating control (load shaping) signals. We then explore the resiliency of two different demand response systems to these fraudsters and malicious attackers. Our results provide guidelines for system operators deciding which type of demand-response system they want to implement, how to secure them, and directions for detecting these attacks.


title: Comparison of Deceptive and Truthful Travel Reviews

abstract: As the use of online reviews grows, so does the risk of providers trying to influence review postings through the submission of false reviews. It is difficult for users of online review platforms to detect deception as important cues are missing in online environments. Automatic screening technologies promise a reduction in the risk but need to be informed by research as to how to classify reviews as suspicious. Using findings from deception theory, a study was conducted to compare the language structure of deceptive and truthful hotel reviews. The results show that deceptive and truthful reviews are different in terms of lexical complexity, the use of first person pronouns, the inclusion of brand names, and their sentiment. However, the results suggest that it might be difficult to distinguish between deceptive and truthful reviews based on structural properties.


title: Advanced public transport and intelligent transport systems: new modelling challenges

abstract: ABSTRACTTransit system ‘big data’ collecting and processing, and bidirectional communication between transit travellers and information centres are emerging as two factors that enhance the tools supporting short-term forecasting of network status for transit operations control and for traveller information. However, the current methodologies applied in these tools do not seem to have reached the level of research in the field of transit network modelling. Therefore, several methodological issues connected to the development of such tools are analysed in this paper. These issues concern application and development of real-time on-board load short-term forecasting methods, real-time best path advice, real-time transit assignment modelling, individual path choice modelling, and real-time updating and upgrading of demand and supply model parameters.


title: Implants in children with ectodermal dysplasia: a case report and literature review

abstract: Abstract: The replacement of teeth by implants is usually restricted to patients with completed craniofacial growth. Implant insertions in children or adolescents are circumvented due to several unfavorable potential effects including trauma to tooth germs, tooth eruption disorders and multidimensional restrictions of skeletal craniofacial growth. Moreover, the functional and esthetic results of the oral rehabilitation are only temporary acceptable. However, to a small number of pediatric patients suffering congenitally from severe hypodontia caused by syndromes such as ectodermal dysplasia, conventional prosthodontic rehabilitations are insufficient. We report the case of a boy with ectodermal dysplasia who exhibited a severe hypodontia and who was treated with implants inserted into the anterior mandible at the age of 8 years. The implants were functionally loaded and resulted in a high patient satisfaction. We recommend the early insertion of dental implants in children with severe hypodontia. Reviewing the current literature, several aspects of syndromic hypodontia, patient selection and implant planning are discussed.


title: Influence of child abuse on adult depression: Moderation by the corticotropin-releasing hormone receptor gene

abstract: CONTEXT: Genetic inheritance and developmental life stress both contribute to major depressive disorder in adults. Child abuse and trauma alter the endogenous stress response, principally corticotropin-releasing hormone and its downstream effectors, suggesting that a gene x environment interaction at this locus may be important in depression. OBJECTIVE: To examine whether the effects of child abuse on adult depressive symptoms are moderated by genetic polymorphisms within the corticotropin-releasing hormone type 1 receptor (CRHR1) gene. DESIGN: Association study examining gene x environment interactions between genetic polymorphisms at the CRHR1 locus and measures of child abuse on adult depressive symptoms. SETTING: General medical clinics of a large, public, urban hospital and Emory University, Atlanta, Georgia. PARTICIPANTS: The primary participant population was 97.4% African American, of low socioeconomic status, and with high rates of lifetime trauma (n = 422). A supportive independent sample (n = 199) was distinct both ethnically (87.7% Caucasian) and socioeconomically (less impoverished). MAIN OUTCOME MEASURES: Beck Depression Inventory scores and history of major depressive disorder by the Structured Clinical Interview for DSM-IV Axis I Disorders. RESULTS: Fifteen single-nucleotide polymorphisms spanning 57 kilobases of the CRHR1 gene were examined. We found significant gene x environment interactions with multiple individual single-nucleotide polymorphisms (eg, rs110402, P = .008) as well as with a common haplotype spanning intron 1 (P Language: en


title: Probabilistic Latent Semantic Analysis

abstract: Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two-mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent Semantic Analysis which stems from linear algebra and performs a Singular Value Decomposition of co-occurrence tables, the proposed method is based on a mixture decomposition derived from a latent class model. This results in a more principled approach which has a solid foundation in statistics. In order to avoid overfitting, we propose a widely applicable generalization of maximum likelihood model fitting by tempered EM. Our approach yields substantial and consistent improvements over Latent Semantic Analysis in a number of experiments.


title: A Hindi-English Code-Switching Corpus

abstract: The aim of this paper is to investigate the rules and constraints of code-switching (CS) in Hindi-English mixed language data. In this paper, we’ll discuss how we collected the mixed language corpus. This corpus is primarily made up of student interview speech. The speech was manually transcribed and verified by bilingual speakers of Hindi and English. The code-switching cases in the corpus are discussed and the reasons for code-switching are explained.


title: DeLay: Robust Spatial Layout Estimation for Cluttered Indoor Scenes

abstract: We consider the problem of estimating the spatial layout of an indoor scene from a monocular RGB image, modeled as the projection of a 3D cuboid. Existing solutions to this problem often rely strongly on hand-engineered features and vanishing point detection, which are prone to failure in the presence of clutter. In this paper, we present a method that uses a fully convolutional neural network (FCNN) in conjunction with a novel optimization framework for generating layout estimates. We demonstrate that our method is robust in the presence of clutter and handles a wide range of highly challenging scenes. We evaluate our method on two standard benchmarks and show that it achieves state of the art results, outperforming previous methods by a wide margin.


title: Russian Stress Prediction using Maximum Entropy Ranking

abstract: We explore a model of stress prediction in Russian using a combination of local contextual features and linguisticallymotivated features associated with the word’s stem and sux. We frame this as a ranking problem, where the objective is to rank the pronunciation with the correct stress above those with incorrect stress. We train our models using a simple Maximum Entropy ranking framework allowing for ecient prediction. An empirical evaluation shows that a model combining the local contextual features and the linguistically-motivated non-local features performs best in identifying both primary and secondary stress.


title: Human Pose Estimation Using Deep Structure Guided Learning

abstract: In this paper, we propose a novel approach to incorporate structure knowledge into Convolutional Neural Networks (CNNs) for articulated human pose estimation from a single still image. Recent research on pose estimation adopt CNNs as base blocks to combine with other graphical models. Different from existing methods using features from CNNs to model the tree structure, we directly use the structure pose prior to guide the learning of CNN. First, we introduce a deep CNN with effective receptive fields which capture the holistic context of the whole image. Second, limb loss is used as intermediate supervision of CNN to learn the correlations of joints. Both parts and joints features are extracted in the middle of neural network and then are used to guide the following network learning. The proposed framework can exploit an implicit structure model of human body. Only using one stage and without any complex post processing, our method achieves state-of-art results on both FLIC and LSP benchmarks.


title: Polarization Reconfigurable U-Slot Patch Antenna

abstract: A compact U-slot microstrip patch antenna with reconfigurable polarization is proposed for wireless local area network (WLAN) applications. PIN diodes are appropriately positioned to change the length of the U-slot arms, which alters the antenna's polarization state. Two antenna prototypes with identical dimensions are designed, fabricated and measured. The first antenna prototype enables switching between linear and circular polarization by using a PIN diode and a capacitor located on the U-slot. The second antenna prototype uses two PIN diodes to switch between the two circular polarization senses. A good impedance match (S11 ≤-10 dB) for both linear and circular polarization is achieved from 5.725 to 5.85 GHz, a band typically used for WLAN applications, and the 3 dB axial ratio bandwidth is greater than 2.8%. Details of the simulated and measured reflection coefficient, axial ratio, gain and radiation patterns are presented.


title: Character Recognition Using Matlab's Neural Network Toolbox

abstract: Recognition of Handwritten text has been one of the active and challenging areas of research in the field of image processing and pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. In this paper we focus on recognition of English alphabet in a given scanned text document with the help of Neural Networks. Using Mat labNeural Network toolbox, we tried to recognize handwritten characters by projecting them on different sized grids. The first step is image acquisition which acquires the scanned image followed by noise filtering, smoothing and normalization of scanned image, rendering image suitable for segmentation where image is decomposed into sub images. Feature Extraction improves recognition rate and misclassification. We use character extraction and edge detection algorithm for training the neural network to classify and recognize the handwritten characters.


Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimal Bit Allocation and Path Planning


title: A Nano-power Switched-capacitor Voltage Reference Using MOS Body Effect for Applications in Subthreshold LSI

abstract: A nano-power CMOS voltage reference is proposed in this paper. Through a combination of switched-capacitor technology with the body effect in MOSFETs, the output voltage is defined as the difference between two gate-source voltages using only a single PMOS transistor operated in the subthreshold region, which has low sensitivity to the temperature and supply voltage. A low output, which breaks the threshold restriction, is produced without any subdivision of the components, and flexible trimming capability can be achieved with a composite transistor, such that the chip area is saved. The chip is implemented in 0.18 ?m standard CMOS technology. Measurements show that the output voltage is approximately 123.3 mV, the temperature coefficient is 17.6 ppm/oC, and the line sensitivity is 0.15 %/V. When the supply voltage is 1 V, the supply current is less than 90 nA at room temperature. The area occupation is approximately 0.03 mm2.


title: Software development governance: A meta-management perspective

abstract: Software development governance is a nascent field of research. Establishing how it is framed early, can significantly affect the progress of contributions. This position paper considers the nature and role of governance in organizations and in the software development domain in particular. In contrast to the dominant functional and structural perspectives, an integrated view of governance is proposed as managing the management of a particular domain (that is, a meta-management perspective). Principles are developed and applied to software development governance and illustrated by a case study.


title: Methanol extracts from Cystoseira tamariscifolia and Cystoseira nodicaulis are able to inhibit cholinesterases and protect a human dopaminergic cell line from hydrogen peroxide-induced cytotoxicity.

abstract: Context Marine macroalgae contain several bioactive molecules that may be developed as functional foods, but information about their neuroprotective potential is scarce. Objective The objective of this study is to determine the in vitro antioxidant and neuroprotective features of marine algae from the southern coast of Portugal and to assess the total content of different types of bioactives. Materials and methods Methanol extracts from 21 macroalgal species from the southern Portugal were evaluated for in vitro antioxidant and acetylcholinesterase (AChE) inhibition. Active extracts were further evaluated for inhibitory activity against butyrylcholinesterase (BuChE) and tyrosinase (TYRO), and for their ability to attenuate hydrogen peroxide (H2O2)-induced toxicity in SH-SY5Y cells. The total contents of different phenolic groups were determined for the most active extracts. Results Cystoseira tamariscifolia (Hudson) Papenfuss (Sargassaceae) had the highest antiradical activity (92%, 1 mg/mL). Cystoseira nodicaulis (Withering) M. Roberts (Sargassaceae) (75%) and Cystoseira humilis Schousboe ex Kützing (Sargassaceae) (70%) had the highest iron-chelating activity at 10 mg/mL. Cystoseira baccata (S.G. Gmelin) P.C. Silva (Sargassaceae) was more active towards copper (66%, 10 mg/mL). Cystoseira tamariscifolia had the highest AChE inhibitory capacity (85%, 10 mg/mL). Cystoseira tamariscifolia and C. nodicaulis were also active against BuChE and TYRO, and were able to protect SH-SY5Y cells against oxidative stress induced by H2O2. Cystoseira tamariscifolia had the highest content of all the groups of phenolics, and was particularly enriched in hydroxycinnamic acids (106 mg CAE/g DW). Discussion and conclusion Results indicate that C. tamariscifolia and C. nodicaulis are important sources of nutraceutical compounds and may be considered functional foods that could improve cognitive functions.


title: Low temperature aging tests for lithium-ion batteries

abstract: This paper presents the aging test results of a 100Ah prismatic LiFeMnPO 4 battery cell at low temperatures. Accelerated aging tests have been carried out on 4 cells to address the effect of cold temperatures on Lithium-ion (Li-ion) batteries. Three of them were aged through a normalized driving cycle at three temperature tests (−20°C, 0°C and 25°C). The calendar test was carried out on one single battery at −20°C and mid-range of SOC (50%). Their capacities were continuously measured every two or three days. From the analysis, this paper shows that it is mandatory to consider the impact of cold temperatures on a battery pack lifespan. Therefore, there is a need for having a proper thermal strategy in order to keep the battery cell in the most appropriate operating condition and thus reduce the cold effect on the battery performances.