title: Back EMF Sensorless-Control Algorithm for High-Dynamic Performance PMSM

abstract: In this paper, a low-time-consuming and low-cost sensorless-control algorithm for high-dynamic performance permanent-magnet synchronous motors, both surface and internal permanent-magnet mounted for position and speed estimation, is introduced, discussed, and experimentally validated. This control algorithm is based on the estimation of rotor speed and angular position starting from the back electromotive force space-vector determination without voltage sensors by using the reference voltages given by the current controllers instead of the actual ones. This choice obviously introduces some errors that must be vanished by means of a compensating function. The novelties of the proposed estimation algorithm are the position-estimation equation and the process of compensation of the inverter phase lag that also suggests the final mathematical form of the estimation. The mathematical structure of the estimation guarantees a high degree of robustness against parameter variation as shown by the sensitivity analysis reported in this paper. Experimental verifications of the proposed sensorless-control system have been made with the aid of a flexible test bench for brushless motor electrical drives. The test results presented in this paper show the validity of the proposed low-cost sensorless-control algorithm and, above all, underline the high dynamic performances of the sensorless-control system also with a reduced equipment.


title: Noise Limits of CMOS Current Interfaces for Biosensors: A Review

abstract: Current sensing readout is one of the most frequent techniques used in biosensing due to the charge-transfer phenomena occurring at solid-liquid interfaces. The development of novel nanodevices for biosensing determines new challenges for electronic interface design based on current sensing, especially when compact and efficient arrays need to be organized, such as in recent trends of rapid label-free electronic detection of DNA synthesis. This paper will review the basic noise limitations of current sensing interfaces with particular emphasis on integrated CMOS technology. Starting from the basic theory, the paper presents, investigates and compares charge-sensitive amplifier architectures used in both continuous-time and discrete-time approaches, along with their design trade-offs involving noise floor, sensitivity to stray capacitance and bandwidth. The ultimate goal of this review is providing analog designers with helpful design rules and analytical tools. Also, in order to present a comprehensive overview of the state-of-the-art, the most relevant papers recently appeared in the literature about this topic are discussed and compared.


title: Packing Steiner trees

abstract: The Steiner packing problem is to find the maximum number of edge-disjoint subgraphs of a given graph G that connect a given set of required points S. This problem is motivated by practical applications in VLSI- layout and broadcasting, as well as theoretical reasons. In this paper, we study this problem and present an algorithm with an asymptotic approximation factor of vSv/4. This gives a sufficient condition for the existence of k edge-disjoint Steiner trees in a graph in terms of the edge-connectivity of the graph. We will show that this condition is the best possible if the number of terminals is 3. At the end, we consider the fractional version of this problem, and observe that it can be reduced to the minimum Steiner tree problem via the ellipsoid algorithm.


title: Automatically Deriving Event Ontologies for a CommonSense Knowledge Base

abstract: We describe work aimed at building commonsense knowledge by reading word definitions using deep understanding techniques. The end result is a knowledge base allowing complex concepts to be reasoned about using OWL-DL reasoners. We show that we can use this system to automatically create a mid-level ontology for WordNet verbs that has good agreement with human intuition with respect to both the hypernym and causality relations. We present a detailed error analysis that reveals areas of future work needed to enable high-performance learning of conceptual knowledge by reading.


title: Steady state analysis of LCLC resonant converter with capacitive output filter

abstract: This paper presents the mathematical analysis and modeling of a 4th order LCLC resonant converter with capacitive output filter in steady-state condition. Due to the nonlinearity of the LCLC resonant circuit with capacitive output filter, the conventional modeling procedure cannot thoroughly describe the behavior of the converter. In this paper, a mathematical model is proposed that can accommodate the absence of the output inductor and predict the converter performance for a wide range of operating conditions. A 2.25KW prototype converter is provided to evaluate the accuracy of the proposed model. Experimental results show that the proposed model can precisely predict the behavior of the converter for a wide range of operating conditions.


title: The Neural Network Pushdown Automaton: Model, Stack and Learning Simulations

abstract: In order for neural networks to learn complex languages or grammars, they must have sufficient computational power or resources to recognize or generate such languages. Though many approaches have been discussed, one ob- vious approach to enhancing the processing power of a recurrent neural network is to couple it with an external stack memory - in effect creating a neural network pushdown automata (NNPDA). This paper discusses in detail this NNPDA - its construction, how it can be trained and how useful symbolic information can be extracted from the trained network. ::: In order to couple the external stack to the neural network, an optimization method is developed which uses an error function that connects the learning of the state automaton of the neural network to the learning of the operation of the external stack. To minimize the error function using gradient descent learning, an analog stack is designed such that the action and storage of information in the stack are continuous. One interpretation of a continuous stack is the probabilistic storage of and action on data. After training on sample strings of an unknown source grammar, a quantization procedure extracts from the analog stack and neural network a discrete pushdown automata (PDA). Simulations show that in learning deterministic context-free grammars - the balanced parenthesis language, 1*n0*n, and the deterministic Palindrome - the extracted PDA is correct in the sense that it can correctly recognize unseen strings of arbitrary length. In addition, the extracted PDAs can be shown to be identical or equivalent to the PDAs of the source grammars which were used to generate the training strings.


title: Attitudes towards User Experience (UX) Measurement

abstract: Abstract User experience (UX), as a recently established research area, is still haunted by the challenges of defining the scope of UX in general and operationalising experiential qualities in particular. To explore the basic question whether UX constructs are measurable, we conducted semi-structured interviews with 10 UX researchers from academia and one UX practitioner from the industry where a set of questions in relation to UX measurement were explored (Study 1). The interviewees expressed scepticism as well as ambivalence towards UX measures and shared anecdotes related to such measures in different contexts. Interestingly, the results suggested that design-oriented UX professionals tended to be sceptical about UX measurement. To examine whether such an attitude prevailed in the HCI community, we conducted a survey – UX Measurement Attitudes Survey (UXMAS) – with essentially the same set of 13 questions used in the interviews (Study 2). Specifically, participants were asked to rate a set of five statements to assess their attitude towards UX measurement, to identify (non)measurable experiential qualities with justifications, and to discuss the topic from the theoretical, methodological and practical perspectives. The survey was implemented in a paper-based and an online format. Altogether, 367 responses were received; 170 of them were valid and analysed. The survey provided empirical evidence on this issue as a baseline for progress in UX measurement. Overall, the survey results indicated that the attitude towards UX measurement was more positive than that identified in the interviews, and there were nuanced views on details of UX measurement. Implications for enhancing the acceptance of UX measures and the interplay between UX evaluation and system development are drawn: UX modelling grounded in theories to link experiential qualities with outcomes; the development of UX measurement tools with good measurement properties, and education within the HCI community to disseminate validated models, and measurement tools as well as their successful applications. Mutual recognition of the value of objective measures and subjective accounts of user experience can enhance the maturity of this area.


title: An Overview Of Character Recognition

abstract: Character recognition (CR) has been extensively studied in the last half century and has progressed to a level that is sufficient to produce technology-driven applications. Now, rapidly growing computational power is enabling the implementation of the present CR methodologies and is creating an increasing demand in many emerging application domains which require more advanced methodologies. This paper serves as a guide and update for readers working in the CR area. First, the historical evolution of CR systems is presented. Then, the available CR techniques, with their superiorities and weaknesses, are reviewed. Finally, the current status of CR is discussed and directions for future research are suggested. Special attention is given to off-line handwriting recognition, since this area requires more research in order to reach the ultimate goal of machine simulation of human reading.


title: Multi-cycle Test with Partial Observation on Scan-Based BIST Structure

abstract: Field test for reliability is usually performed with small amount of memory resource, and it requires a new technique which might be somewhat different from the conventional manufacturing tests. This paper proposes a novel technique that improves fault coverage or reduces the number of test vectors that is needed for achieving the given fault coverage on scan-based BIST structure. We evaluate a multi-cycle test method that observes the values of partial flip-flops on a chip during capture-mode. The experimental result shows that the partial observation achieves fault coverage improvement with small hardware overhead than the full observation.


title: Grid-connected photovoltaic systems with energy storage

abstract: There are different interesting ways that can be followed in order to reduce costs of grid-connected photovoltaic systems, i.e., by maximizing their energy production in every operating conditions, minimizing electrical losses on the plant, utilizing grid-connected photovoltaic systems not only to generate electrical energy to be put into the power system but also to implement some other important auxiliary services normally implemented by properly conceived apparatus (electrical power quality control, load demand peak control, …), making easy the integration of different kind of renewable electrical energy resources (wind plant, fuel-cells, …).


title: When to fix it and when to leave: Relationships among perceived control, self-efficacy, dissent, and exit.

abstract: Using a sample of hospital nurses, the author tested the hypothesis that both self-efficacy and perceived control over decision making contribute to individuals' willingness to engage in reformist dissent when faced with injustice and to their intentions to exit. Reformist dissent is defined as dissent that occurs within the confines of an organization's rules and norms. Perceived control over decision making was expected to be positively related to willingness to engage in reformist dissent and to be inversely related to exit. Because it was expected that both dissent and exit require confidence, self-efficacy was predicted to be positively related to both dissent and exit. Control was positively related to willingness to dissent and inversely related to exit


3D Exploitation of 2D Imagery


title: Internet Over-Users' Psychological Profiles: A Behavior Sampling Analysis on Internet Addiction

abstract: What kinds of psychological features do people have when they are overly involved in usage of the internet? Internet users in Korea were investigated in terms of internet over-use and related psychological profiles by the level of internet use. We used a modified Young's Internet Addiction Scale, and 13,588 users (7,878 males, 5,710 females), out of 20 million from a major portal site in Korea, participated in this study. Among the sample, 3.5% had been diagnosed as internet addicts (IA), while 18.4% of them were classified as possible internet addicts (PA). The Internet Addiction Scale showed a strong relationship with dysfunctional social behaviors. More IA tried to escape from reality than PA and Non-addicts (NA). When they got stressed out by work or were just depressed, IA showed a high tendency to access the internet. The IA group also reported the highest degree of loneliness, depressed mood, and compulsivity compared to the other groups. The IA group seemed to be more vulnerable to interpersonal d...


title: An efficient approach for parameter estimation of PV model using DE and fuzzy based MPPT controller

abstract: Photovoltaic (PV) panel can be represented by an equivalent electric circuit where the major problem with this PV model is the determination of the model parameters. In this paper, PV parameter estimation problem is converted to an optimization problem where Differential Evolution (DE) as an efficient optimizing technique is employed to estimate the model parameters at standard test condition (STC) (1000 W/m2 and 25°C) using the data provided by the manufacturer. A complete equivalent electric circuit model of the PV panel is developed in the MATLAB/Simulink and estimated parameters values are verified by comparing the determined I-V curves with the experimental data given in the data sheet. The developed PV model has been examined under different operating conditions and its accuracy has been verified. An efficient maximum power point tracking (MPPT) controller based on the Fuzzy Logic is also proposed. Analysis and comparison shows that the FLC based controller is more efficient and overcome the shortcomings of the conventional methods.


title: A Gaussian Firefly Algorithm

abstract: Firefly algorithm is one of the evolutionary optimization algorithms, and is inspired by fireflies behavior in nature. Each firefly movement is based on absorption of the other one. In this paper to stabilize firefly's movement, it is proposed a new behavior to direct fireflies movement to global best if there was no any better solution around them. In addition to increase convergence speed it is proposed to use Gaussian distribution to move all fireflies to global best in each iteration. Proposed algorithm was tested on five standard functions that have ever used for testing the static optimization algorithms. Experimental results show better performance and more accuracy than standard Firefly algorithm.


title: A semantic approach for question answering using DBpedia and WordNet

abstract: Semantic Question Answering (SQA) was concerned about the natural language processing. The purpose of this study was to help facilitate the users to access the information through the natural language and to obtain the concise and needed information. As considered the current studies, it was found that this processing still encountered the problems of flexibility and accuracy, particularly those of the question processing, which was a very important processing for developing question answering system. Thus, this study proposed a semantic approach for question answering using DBpedia and WordNet. For this paper, the techniques for solving the problems were proposed consisting of (1) extracting named entities from the question and solving the problems of similarities of named entities, (2) extracting properties from the question and solving the problems of similarities of properties, and (3) evaluating the accurate capability of the answer of question. This approach evaluated the test dataset from TREC question collections, DBpedia and achieved an F-measure score of 93.43%, an average precision of 92.73%, and an average recall of 94.15% over 500 questions.


title: ProSLAM: Graph SLAM from a Programmer's Perspective

abstract: In this paper we present ProSLAM, a lightweight open-source stereo visual SLAM system designed with simplicity in mind. This work stems from the experience gathered by the authors while teaching SLAM and aims at providing a highly modular system that can be easily implemented and understood. Rather than focusing on the well known mathematical aspects of stereo visual SLAM, we highlight the data structures and the algorithmic aspects required to realize such a system. We implemented ProSLAM using the C++ programming language in combination with a minimal set of standard libraries. The results of a thorough validation performed on several standard benchmark datasets show that ProSLAM achieves precision comparable to state-of-the-art approaches, while requiring substantially less computation.


title: Model-Based Policy Gradients with Parameter-Based Exploration by Least-Squares Conditional Density Estimation

abstract: The goal of reinforcement learning (RL) is to let an agent learn an optimal control policy in an unknown environment so that future expected rewards are maximized. The model-free RL approach directly learns the policy based on data samples. Although using many samples tends to improve the accuracy of policy learning, collecting a large number of samples is often expensive in practice. On the other hand, the model-based RL approach first estimates the transition model of the environment and then learns the policy based on the estimated transition model. Thus, if the transition model is accurately learned from a small amount of data, the model-based approach is a promising alternative to the model-free approach. In this paper, we propose a novel model-based RL method by combining a recently proposed model-free policy search method called policy gradients with parameter-based exploration and the state-of-the-art transition model estimator called least-squares conditional density estimation. Through experiments, we demonstrate the practical usefulness of the proposed method.


title: A review of inverse reinforcement learning theory and recent advances

abstract: A major challenge faced by machine learning community is the decision making problems under uncertainty. Reinforcement Learning (RL) techniques provide a powerful solution for it. An agent used by RL interacts with a dynamic environment and finds a policy through a reward function, without using target labels like Supervised Learning (SL). However, one fundamental assumption of existing RL algorithms is that reward function, the most succinct representation of the designer's intention, needs to be provided beforehand. In practice, the reward function can be very hard to specify and exhaustive to tune for large and complex problems, and this inspires the development of Inverse Reinforcement Learning (IRL), an extension of RL, which directly tackles this problem by learning the reward function through expert demonstrations. IRL introduces a new way of learning policies by deriving expert's intentions, in contrast to directly learning policies, which can be redundant and have poor generalization ability. In this paper, the original IRL algorithms and its close variants, as well as their recent advances are reviewed and compared.


title: Tools and applications in synthetic biology

abstract: Advances in synthetic biology have enabled the engineering of cells with genetic circuits in order to program cells with new biological behavior, dynamic gene expression, and logic control. This cellular engineering progression offers an array of living sensors that can discriminate between cell states, produce a regulated dose of therapeutic biomolecules, and function in various delivery platforms. In this review, we highlight and summarize the tools and applications in bacterial and mammalian synthetic biology. The examples detailed in this review provide insight to further understand genetic circuits, how they are used to program cells with novel functions, and current methods to reliably interface this technology in vivo; thus paving the way for the design of promising novel therapeutic applications.


title: Large-scale modular quantum-computer architecture with atomic memory and photonic interconnects

abstract: The practical construction of scalable quantum-computer hardware capable of executing nontrivial quantum algorithms will require the juxtaposition of different types of quantum systems. We analyze a modular ion trap quantum-computer architecture with a hierarchy of interactions that can scale to very large numbers of qubits. Local entangling quantum gates between qubit memories within a single register are accomplished using natural interactions between the qubits, and entanglement between separate registers is completed via a probabilistic photonic interface between qubits in different registers, even over large distances. We show that this architecture can be made fault tolerant, and demonstrate its viability for fault-tolerant execution of modest size quantum circuits.


title: Probabilistic Models for Learning a Semantic Parser Lexicon

abstract: We introduce several probabilistic models for learning the lexicon of a semantic parser. Lexicon learning is the first step of training a semantic parser for a new application domain and the quality of the learned lexicon significantly affects both the accuracy and efficiency of the final semantic parser. Existing work on lexicon learning has focused on heuristic methods that lack convergence guarantees and require significant human input in the form of lexicon templates or annotated logical forms. In contrast, our probabilistic models are trained directly from question/answer pairs using EM and our simplest model has a concave objective that guarantees convergence to a global optimum. An experimental evaluation on a set of 4th grade science questions demonstrates that our models improve semantic parser accuracy (35-70% error reduction) and efficiency (4-25x more sentences per second) relative to prior work despite using less human input. Our models also obtain competitive results on GEO880 without any datasetspecific engineering.


title: Wavelet Filter Evaluation for Image Compression

abstract: Choice of filter bank in wavelet compression is a critical issue that affects image quality as well as system design. Although regularity is sometimes used in filter evaluation, its success at predicting compression performance is only partial. A more reliable evaluation can be obtained by considering an L-level synthesis/analysis system as a single-input, single-output, linear shift-variant system with a response that varies according to the input location module (2(L),2(L)). By characterizing a filter bank according to its impulse response and step response in addition to regularity, we obtain reliable and relevant (for image coding) filter evaluation metrics. Using this approach, we have evaluated all possible reasonably short (less than 36 taps in the synthesis/analysis pair) minimum-order biorthogonal wavelet filter banks. Of this group of over 4300 candidate filter banks, we have selected and present here the filters best suited to image compression. While some of these filters have been published previously, others are new and have properties that make them attractive in system design.


title: Review of Natural Compounds for Potential Skin Cancer Treatment

abstract: Centre of Excellence for Pharmaceutical Sciences, Faculty of Health Sciences, North-West University, South Africa and the National Research Foundation of South-Africa (Grant # - CPRR13091742482).


title: Synchronization of Human Autonomic Nervous System Rhythms with Geomagnetic Activity in Human Subjects

abstract: A coupling between geomagnetic activity and the human nervous system's function was identified by virtue of continuous monitoring of heart rate variability (HRV) and the time-varying geomagnetic field over a 31-day period in a group of 10 individuals who went about their normal day-to-day lives. A time series correlation analysis identified a response of the group's autonomic nervous systems to various dynamic changes in the solar, cosmic ray, and ambient magnetic field. Correlation coefficients and p values were calculated between the HRV variables and environmental measures during three distinct time periods of environmental activity. There were significant correlations between the group's HRV and solar wind speed, Kp, Ap, solar radio flux, cosmic ray counts, Schumann resonance power, and the total variations in the magnetic field. In addition, the time series data were time synchronized and normalized, after which all circadian rhythms were removed. It was found that the participants' HRV rhythms synchronized across the 31-day period at a period of approximately 2.5 days, even though all participants were in separate locations. Overall, this suggests that daily autonomic nervous system activity not only responds to changes in solar and geomagnetic activity, but is synchronized with the time-varying magnetic fields associated with geomagnetic field-line resonances and Schumann resonances.


title: Restoring Natural Sensory Feedback in Real-Time Bidirectional Hand Prostheses

abstract: Hand loss is a highly disabling event that markedly affects the quality of life. To achieve a close to natural replacement for the lost hand, the user should be provided with the rich sensations that we naturally perceive when grasping or manipulating an object. Ideal bidirectional hand prostheses should involve both a reliable decoding of the user's intentions and the delivery of nearly "natural" sensory feedback through remnant afferent pathways, simultaneously and in real time. However, current hand prostheses fail to achieve these requirements, particularly because they lack any sensory feedback. We show that by stimulating the median and ulnar nerve fascicles using transversal multichannel intrafascicular electrodes, according to the information provided by the artificial sensors from a hand prosthesis, physiologically appropriate (near-natural) sensory information can be provided to an amputee during the real-time decoding of different grasping tasks to control a dexterous hand prosthesis. This feedback enabled the participant to effectively modulate the grasping force of the prosthesis with no visual or auditory feedback. Three different force levels were distinguished and consistently used by the subject. The results also demonstrate that a high complexity of perception can be obtained, allowing the subject to identify the stiffness and shape of three different objects by exploiting different characteristics of the elicited sensations. This approach could improve the efficacy and "life-like" quality of hand prostheses, resulting in a keystone strategy for the near-natural replacement of missing hands.


title: Optimized GSE packet scheduling over DVB-S2

abstract: The evolution of satellite communication towards packet-based communication and the adoption of modern physical layer techniques, such as Adaptive Coding and Modulation, raise new issues, brought by this variability, for scheduling algorithms. This paper addresses the problem of GSE packets scheduling over DVB-S2 with QoS support by adopting a utility-based scheduling strategy. Utility functions are considered as a mean to encompass parameters from several layers into a single scheduling process. By making use of a first-order approximation, a low complexity scheduling algorithm is derived from the utility functions optimization problem, adaptable to a wide range of functions. The algorithm determines the characteristics of the next BBFrame to be send, and which GSE packets it should contain. We focus particularly on delay as a joint metric to represent QoS requirements, and assess the performance of our algorithm and the relevance of our assumptions using comparative simulations.


title: Search Engine Guided Non-Parametric Neural Machine Translation

abstract: In this paper, we extend an attention-based neural machine translation (NMT) model by allowing it to access an entire training set of parallel sentence pairs even after training. The proposed approach consists of two stages. In the first stage--retrieval stage--, an off-the-shelf, black-box search engine is used to retrieve a small subset of sentence pairs from a training set given a source sentence. These pairs are further filtered based on a fuzzy matching score based on edit distance. In the second stage--translation stage--, a novel translation model, called translation memory enhanced NMT (TM-NMT), seamlessly uses both the source sentence and a set of retrieved sentence pairs to perform the translation. Empirical evaluation on three language pairs (En-Fr, En-De, and En-Es) shows that the proposed approach significantly outperforms the baseline approach and the improvement is more significant when more relevant sentence pairs were retrieved.


DAWNBench : An End-to-End Deep Learning Benchmark and Competition


title: Effect of Physical Education and Activity Levels on Academic Achievement in Children

abstract: Purpose: This study was conducted to determine the effect of physical education class enrollment and physical activity on academic achievement in middle school children. ::: ::: Methods: Participants were 214 sixth-grade students randomly assigned to physical education during either first or second semesters. Moderate and vigorous physical activity (MVPA) (number of 30-min time blocks) outside of school was assessed using the 3-d physical activity recall (3DPAR). The 3DPAR time blocks were converted to ordinal data with scores of 1 (no activity), 2 (some activity), or 3 (activity meeting Healthy People 2010 guidelines). Academic achievement was assessed using grades from four core academic classes and standardized test scores (Terra Nova percentiles). ::: ::: Results: Grades were similar regardless of whether students were enrolled in physical education during first or second semesters. Physical education classes averaged only 19 min of MVPA. Students who either performed some or met Healthy People 2010 guidelines for vigorous activity had significantly higher grades (P < 0.05) than students who performed no vigorous activity in both semesters. Moderate physical activity did not affect grades. Standardized test scores were not significantly related to physical education class enrollment or physical activity levels. ::: ::: Conclusion: Although academic achievement was not significantly related to physical education enrollment, higher grades were associated with vigorous physical activity, particularly activity meeting recommended Healthy People 2010 levels.


title: A new approach to query segmentation for relevance ranking in web search

abstract: In this paper, we try to answer the question of how to improve the state-of-the-art methods for relevance ranking in web search by query segmentation. Here, by query segmentation it is meant to segment the input query into segments, typically natural language phrases, so that the performance of relevance ranking in search is increased. We propose employing the re-ranking approach in query segmentation, which first employs a generative model to create top $k$ candidates and then employs a discriminative model to re-rank the candidates to obtain the final segmentation result. The method has been widely utilized for structure prediction in natural language processing, but has not been applied to query segmentation, as far as we know. Furthermore, we propose a new method for using the result of query segmentation in relevance ranking, which takes both the original query words and the segmented query phrases as units of query representation. We investigate whether our method can improve three relevance models, namely BM25, key n-gram model, and dependency model. Our experimental results on three large scale web search datasets show that our method can indeed significantly improve relevance ranking in all the three cases.


title: Optimal load balancing in cloud computing by efficient utilization of virtual machines

abstract: Load balancing is the major concern in the cloud computing environment. Cloud comprises of many hardware and software resources and managing these will play an important role in executing a client's request. Now a day's clients from different parts of the world are demanding for the various services in a rapid rate. In this present situation the load balancing algorithms built should be very efficient in allocating the request and also ensuring the usage of the resources in an intelligent way so that underutilization of the resources will not occur in the cloud environment. In the present work, a novel VM-assign load balance algorithm is proposed which allocates the incoming requests to the all available virtual machines in an efficient manner. Further, the performance is analyzed using Cloudsim simulator and compared with existing Active-VM load balance algorithm. Simulation results demonstrate that the proposed algorithm distributes the load on all available virtual machines without under/over utilization.


title: Reviewing CS1 exam question content

abstract: Many factors have been cited for poor performance of students in CS1. To investigate how assessment mechanisms may impact student performance, nine experienced CS1 instructors reviewed final examinations from a variety of North American institutions. The majority of the exams reviewed were composed predominantly of high-value, integrative code-writing questions, and the reviewers regularly underestimated the number of CS1 concepts required to answer these questions. An evaluation of the content and cognitive requirements of individual questions suggests that in order to succeed, students must internalize a large amount of CS1 content. This emphasizes the need for focused assessment techniques to provide students with the opportunity to demonstrate their knowledge.


The politics of ‘postcolonial’ memory (extract from My Father Was a Terrorist? A Kind of Memoir—work in progress)


title: Multilevel Cloud Detection in Remote Sensing Images Based on Deep Learning

abstract: Cloud detection is one of the important tasks for remote sensing image processing. In this paper, a novel multilevel cloud detection method based on deep learning is proposed for remote sensing images. First, the simple linear iterative clustering (SLIC) method is improved to segment the image into good quality superpixels. Then, a deep convolutional neural network (CNN) with two branches is designed to extract the multiscale features from each superpixel and predict the superpixel as one of three classes including thick cloud, thin cloud, and noncloud. Finally, the predictions of all the superpixels in the image yield the cloud detection result. In the proposed cloud detection framework, the improved SLIC method can obtain accurate cloud boundaries by optimizing initial cluster centers, designing dynamic distance measure, and expanding search space. Moreover, different from traditional cloud detection methods that cannot achieve multilevel detection of cloud, the designed deep CNN model can not only detect cloud but also distinguish thin cloud from thick cloud. Experimental results indicate that the proposed method can detect cloud with higher accuracy and robustness than compared methods.


title: A Model to Assess Open Government Data in Public Agencies

abstract: In this article a maturity model is proposed, named OD-MM (Open Data Maturity Model) to assess the commitment and capabilities of public agencies in pursuing the principles and practices of open data. The OD-MM model has a three level hierarchical structure, called domains, sub-domains and critical variables. Four capacity levels are defined for each of the 33 critical variables distributed in nine sub-domains in order to determine the organization maturity level. The model is a very valuable diagnosis tool for public services, given it shows all weaknesses and the way (a roadmap) to progress in the implementation of open data.


title: SVNE: Survivable Virtual Network Embedding Algorithms for Network Virtualization

abstract: Network virtualization can offer more flexibility and better manageability for the future Internet by allowing multiple heterogeneous virtual networks (VN) to coexist on a shared infrastructure provider (InP) network. A major challenge in this respect is the VN embedding problem that deals with the efficient mapping of virtual resources on InP network resources. Previous research focused on heuristic algorithms for the VN embedding problem assuming that the InP network remains operational at all times. In this paper, we remove this assumption by formulating the survivable virtual network embedding (SVNE) problem. We then develop a pro-active, and a hybrid policy heuristic to solve it, and a baseline policy heuristic to compare to. The hybrid policy is based on a fast re-routing strategy and utilizes a pre-reserved quota for backup on each physical link. Our evaluation results show that our proposed heuristics for SVNE outperform the baseline heuristic in terms of long term business profit for the InP, acceptance ratio, bandwidth efficiency, and response time.


title: Visualization of Server Log Data for Detecting Abnormal Behaviour

abstract: Modern large-scale IT operations are getting complex every day and generate thousands of log and event files, which contain essential information about the systems and their behavior. System errors are examined manually by human experts, which takes a considerable amount of time and efforts. The system log files, besides other attributes such as time and location, contain messages in textual form, which is essential for analyzing logs behavior. In this paper, we propose a method to extract meaningful information through topic modeling from log messages, combine them into a sequence of events and present them to the practitioners to visually identify abnormal behavioral patterns, which is used for predictive analysis.


title: Disk Failure Prediction in Data Centers via Online Learning

abstract: Disk failure has become a major concern with the rapid expansion of storage systems in data centers. Based on SMART (Self-Monitoring, Analysis and Reporting Technology) attributes, many researchers derive disk failure prediction models using machine learning techniques. Despite the significant developments, the majority of works rely on offline training and thereby hinder their adaption to the continuous update of forthcoming data, suffering from the 'model aging' problem. We are therefore motivated to uncover the root cause -- the dynamic SMART distribution for 'model aging', aiming to resolve the performance degradation as to pave a comprehensive study in practice. In this paper, we introduce a novel disk failure prediction model using Online Random Forests (ORFs). Our ORF-based model can automatically evolve with sequential arrival of data on-the-fly and thus is highly adaptive to the variance of SMART distribution over time. Moreover, it has favourable advantage against the offline counterparts in terms of superior prediction performance. Experiments on real-world datasets show that our ORF model converges rapidly to the offline random forests and achieves stable failure detection rates of 93-99% with low false alarm rates. Furthermore, we demonstrate the ability of our approach on maintaining stable prediction performance for the long-term usage in data centers.


title: Solar Cells: In Research and Applications—A Review

abstract: The light from the Sun is a non-vanishing renewable source of energy which is free from environmental pollution and noise. It can easily compensate the energy drawn from the non-renewable sources of energy such as fossil fuels and petroleum deposits inside the earth. The fabrication of solar cells has passed through a large number of improvement steps from one generation to another. Silicon based solar cells were the first generation solar cells grown on Si wafers, mainly single crystals. Further development to thin films, dye sensitized solar cells and organic solar cells enhanced the cell efficiency. The development is basically hindered by the cost and efficiency. In order to choose the right solar cell for a specific geographic location, we are required to understand fundamental mechanisms and functions of several solar technologies that are widely studied. In this article, we have reviewed a progressive development in the solar cell research from one generation to other, and discussed about their future trends and aspects. The article also tries to emphasize the various practices and methods to promote the benefits of solar energy.


title: Outlier Detection Techniques for Wireless Sensor Networks: A Survey

abstract: In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree.


title: A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data

abstract: Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal with multimodal data, such as in image annotation tasks. Another popular approach to model the multimodal data is through deep neural networks, such as the deep Boltzmann machine (DBM). Recently, a new type of topic model called the Document Neural Autoregressive Distribution Estimator (DocNADE) was proposed and demonstrated state-of-the-art performance for text document modeling. In this work, we show how to successfully apply and extend this model to multimodal data, such as simultaneous image classification and annotation. First, we propose SupDocNADE, a supervised extension of DocNADE, that increases the discriminative power of the learned hidden topic features and show how to employ it to learn a joint representation from image visual words, annotation words and class label information. We test our model on the LabelMe and UIUC-Sports data sets and show that it compares favorably to other topic models. Second, we propose a deep extension of our model and provide an efficient way of training the deep model. Experimental results show that our deep model outperforms its shallow version and reaches state-of-the-art performance on the Multimedia Information Retrieval (MIR) Flickr data set.


title: Molecular diagnostics of Lemon Myrtle (Backhousia citriodora versus Leptospermum citratum)

abstract: ‘Lemon Myrtle’ is becoming increasingly popular in Europe both for use in cuisine and phytotherapy. However, this common name covers two completely different species, Backhousia citriodora F. Muell. and Leptospermum citratum Challinor, Cheel & A.R.Penfold. These species differ with respect to secondary compounds and even can cause, if mixed up and applied in high dose, toxic effects. We describe how the two species can be discriminated microscopically making use of differences in the morphology of leaf pavement cells and the relative size of palisade parenchyma. Based on the large subunit of ribulose-1,5-bisphosphate carboxylase oxygenase (rbcL) as molecular marker, the phylogenetic position of the two species within the Myrtaceae could be clarified. This sequence information was used to develop a simple assay to discriminate the two species even in dried and highly fragmented mixtures as typically occurring in commercial samples. This assay utilises the occurrence of single-nucleotide exchanges between those species that produce different fragments when the rbcL amplificates are restricted with Sac II.


title: SMOTE: Synthetic Minority Over-sampling Technique

abstract: An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of"normal"examples with only a small percentage of"abnormal"or"interesting"examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.


title: Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification

abstract: We address the problem of multi-class classification in the case where the number of classes is very large. We propose a double sampling strategy on top of a multi-class to binary reduction strategy, which transforms the original multi-class problem into a binary classification problem over pairs of examples. The aim of the sampling strategy is to overcome the curse of long-tailed class distributions exhibited in majority of large-scale multi-class classification problems and to reduce the number of pairs of examples in the expanded data. We show that this strategy does not alter the consistency of the empirical risk minimization principle defined over the double sample reduction. Experiments are carried out on DMOZ and Wikipedia collections with 10,000 to 100,000 classes where we show the efficiency of the proposed approach in terms of training and prediction time, memory consumption, and predictive performance with respect to state-of-the-art approaches.


title: FORMS: Unifying reference model for formal specification of distributed self-adaptive systems

abstract: The challenges of pervasive and mobile computing environments, which are highly dynamic and unpredictable, have motivated the development of self-adaptive software systems. Although noteworthy successes have been achieved on many fronts, the construction of such systems remains significantly more challenging than traditional systems. We argue this is partially because researchers and practitioners have been struggling with the lack of a precise vocabulary for describing and reasoning about the key architectural characteristics of self-adaptive systems. Further exacerbating the situation is the fact that existing frameworks and guidelines do not provide an encompassing perspective of the different types of concerns in this setting. In this article, we present a comprehensive reference model, entitled FOrmal Reference Model for Self-adaptation (FORMS), that targets both issues. FORMS provides rigor in the manner such systems can be described and reasoned about. It consists of a small number of formally specified modeling elements that correspond to the key concerns in the design of self-adaptive software systems, and a set of relationships that guide their composition. We demonstrate FORMS's ability to precisely describe and reason about the architectural characteristics of distributed self-adaptive software systems through its application to several existing systems. FORMS's expressive power gives it a potential for documenting reusable architectural solutions (e.g., architectural patterns) to commonly encountered problems in this area.


title: A finite difference method for time-fractional telegraph equation

abstract: A finite difference scheme is presented for a kind of linear time-fractional telegraph equation. The equation is derived from the classical integer order telegraph equation by replacing the second order time derivative with a fractional derivative of order 1 < α < 2. The stability and convergence of the scheme are proved by using the energy method. A numerical example is provided to illustrate the results derived in this paper.


title: Phosphoproteomics-Based Profiling of Kinase Activities in Cancer Cells

abstract: Cellular signaling, predominantly mediated by phosphorylation through protein kinases, is found to be deregulated in most cancers. Accordingly, protein kinases have been subject to intense investigations in cancer research, to understand their role in oncogenesis and to discover new therapeutic targets. Despite great advances, an understanding of kinase dysfunction in cancer is far from complete.A powerful tool to investigate phosphorylation is mass-spectrometry (MS)-based phosphoproteomics, which enables the identification of thousands of phosphorylated peptides in a single experiment. Since every phosphorylation event results from the activity of a protein kinase, high-coverage phosphoproteomics data should indirectly contain comprehensive information about the activity of protein kinases.In this chapter, we discuss the use of computational methods to predict kinase activity scores from MS-based phosphoproteomics data. We start with a short explanation of the fundamental features of the phosphoproteomics data acquisition process from the perspective of the computational analysis. Next, we briefly review the existing databases with experimentally verified kinase-substrate relationships and present a set of bioinformatic tools to discover novel kinase targets. We then introduce different methods to infer kinase activities from phosphoproteomics data and these kinase-substrate relationships. We illustrate their application with a detailed protocol of one of the methods, KSEA (Kinase Substrate Enrichment Analysis). This method is implemented in Python within the framework of the open-source Kinase Activity Toolbox (kinact), which is freely available at http://github.com/saezlab/kinact/ .


title: MAdScope: Characterizing Mobile In-App Targeted Ads

abstract: Advertising is the primary source of revenue for many mobile apps. One important goal of the ad delivery process is targeting users, based on criteria like users' geolocation, context, demographics, long-term behavior, etc. In this paper we report an in-depth study that broadly characterizes what targeting information mobile apps send to ad networks and how effectively, if at all, ad networks utilize the information for targeting users. Our study is based on a novel tool, called MadScope, that can (1) quickly harvest ads from a large collection of apps, (2) systematically probe an ad network to characterize its targeting mechanism, and (3) emulate user profiles of specific preferences and interests to study behavioral targeting. Our analysis of 500K ad requests from 150K Android apps and 101 ad networks indicates that apps do not yet exploit the full potential of targeting: even though ad controls provide APIs to send a lot of information to ad networks, much key targeting information is optional and is often not provided by app developers. We also use MadScope to systematically probe top 10 in-app ad networks to harvest over 1 million ads and find that while targeting is used by many of the top networks, there remain many instances where targeting information or behavioral profile does not have a statistically significant impact on how ads are chosen. We also contrast our findings with a recent study of targeted in-browser ads.


title: A real time system for dynamic hand gesture recognition with a depth sensor

abstract: Recent advances in depth sensing provide exciting opportunities for the development of new methods for human activity understanding. Yet, little work has been done in the area of hand gesture recognition which has many practical applications. In this paper we propose a real-time system for dynamic hand gesture recognition. It is fully automatic and robust to variations in speed and style as well as in hand orientations. Our approach is based on action graph, which shares similar robust properties with standard HMM but requires less training data by allowing states shared among different gestures. To deal with hand orientations, we have developed a new technique for hand segmentation and orientation normalization. The proposed system is evaluated on a challenging dataset of twelve dynamic American Sign Language (ASL) gestures.


Cloud Forensics


title: Web table taxonomy and formalization

abstract: The Web is the largest repository of data available, with over 150 million high-quality tables. Several works have combined efforts to allow queries on these tables, but there are still challenges, like the various different types of structures found on the Web. In this paper, we propose a taxonomy for the tabular structures and formalize the ones used with relational data and show, through an experimental evaluation, that WTClassifier, our supervised framework, classifies Web tables with high accuracy. Additionally, we use WTClassifier to categorize more than 300 thousandWeb tables into our taxonomy and found that 82.25% are not formatted similarly to relational structure.


title: A Colpitts Oscillator Design for a GSM Base Station Synthesizer

abstract: This paper presents the design of a VCO using a Colpitts topology. The VCO system is fully integrated and suitable to operate in GSM base station synthesizers. The challenges and requirements for this application will be discussed and the possibility of achieving the performance of the oscillator will be demonstrated. The topology of the VCO core is a varactor tuned Colpitts oscillator which achieves a phase noise of -148 dBc/Hz at 1.8 MHz offset frequency. A 130 nm RF-CMOS process was used for the implementation. The process supports thick oxide devices for higher breakdown voltages. These transistors were chosen in order to obtain the highest possible voltage swing in the VCO core.


title: Concepts and methods in fault-tolerant control

abstract: Faults in automated processes will often cause undesired reactions and shut-down of a controlled plant, and the consequences could be damage to technical parts of the plant, to personnel or the environment. Fault-tolerant control combines diagnosis with control methods to handle faults in an intelligent way. The aim is to prevent that simple faults develop into serious failure and hence increase plant availability and reduce the risk of safety hazards. Fault-tolerant control merges several disciplines into a common framework to achieve these goals. The desired features are obtained through online fault diagnosis, automatic condition assessment and calculation of appropriate remedial actions to avoid certain consequences of a fault. The envelope of the possible remedial actions is very wide. Sometimes, simple re-tuning can suffice. In other cases, accommodation of the fault could be achieved by replacing a measurement from a faulty sensor by an estimate. In yet other situations, complex reconfiguration or online controller redesign is required. This paper gives an overview of recent tools to analyze and explore structure and other fundamental properties of an automated system such that any inherent redundancy in the controlled process can be fully utilized to maintain availability, even though faults may occur.


title: The infrared camera-based system to evaluate the human sleepiness

abstract: The eye's blinking is a significant indicator of the sleepiness. The existing systems of blink detection and sleepiness analysis require usually to fix camera on spectacle frame or a special helmet that is not convenient and can affect the obtained result. In this paper, the infrared camera-based contact-less system is proposed to evaluate the human's sleepiness. The infrared light switching is used to detect the pupil in each frame and, as a result, the blink event. The active pan-tilt unit is used to make possible free head movements. The algorithm is pointed out to process the camera frames in order to distinguish the involuntary blinks from voluntary ones. Preliminary experimental tests are shown with the intent to validate the proposed hardware and software system pointed out.


title: A review of wind power and wind speed forecasting methods with different time horizons

abstract: In recent years, environmental considerations have prompted the use of wind power as a renewable energy resource. However, the biggest challenge in integrating wind power into the electric grid is its intermittency. One approach to deal with wind intermittency is forecasting future values of wind power production. Thus, several wind power or wind speed forecasting methods have been reported in the literature over the past few years. This paper provides insight on the foremost forecasting techniques, associated with wind power and speed, based on numeric weather prediction (NWP), statistical approaches, artificial neural network (ANN) and hybrid techniques over different time-scales. An overview of comparative analysis of various available forecasting techniques is discussed as well. In addition, this paper further gives emphasis on the major challenges and problems associated with wind power prediction.


title: Classifying Developers into Core and Peripheral: An Empirical Study on Count and Network Metrics

abstract: Knowledge about the roles developers play in a software project is crucial to understanding the project's collaborative dynamics. In practice, developers are often classified according to the dichotomy of core and peripheral roles. Typically, count-based operationalizations, which rely on simple counts of individual developer activities (e.g., number of commits), are used for this purpose, but there is concern regarding their validity and ability to elicit meaningful insights. To shed light on this issue, we investigate whether count-based operationalizations of developer roles produce consistent results, and we validate them with respect to developers' perceptions by surveying 166 developers. Improving over the state of the art, we propose a relational perspective on developer roles, using fine-grained developer networks modeling the organizational structure, and by examining developer roles in terms of developers' positions and stability within the developer network. In a study of 10 substantial open-source projects, we found that the primary difference between the count-based and our proposed network-based core-peripheral operationalizations is that the network-based ones agree more with developer perception than count-based ones. Furthermore, we demonstrate that a relational perspective can reveal further meaningful insights, such as that core developers exhibit high positional stability, upper positions in the hierarchy, and high levels of coordination with other core developers, which confirms assumptions of previous work.


title: A multi-DoF anthropomorphic transradial prosthetic arm

abstract: An anthropomorphic transradial prosthetic arm is proposed in this paper. In order to generate the wrist flexion/extension and ulna/radial deviation, a novel wrist mechanism is proposed based on the parallel prismatic manipulators. It is expected to realize high speed operation, higher positional accuracy and anthropomorphic features using the proposed mechanism. The prosthetic arm consists of an under-actuated hand as the terminal device. The hand mechanism is capable of providing the grasping adaptation. With the intention of verifying the effectiveness of the mechanisms in motion generation, motion simulation and kinematic analysis are carried out.


title: Biomimetic design of musculoskeletal humanoid knee joint with patella and screw-home mechanism

abstract: We are trying to create a novel musculoskeletal humanoid robot which has a humanlike structure. In this paper, we present a new knee joint which is usually simplified in robotics for high controllability. The knee joint has three human mimetic points, patella, screw-home mechanism and four-linked linkage. Patella is for a longer moment arm. Screw-home mechanism is for locking knee joint. Four-linked linkage is for making humanlike motion. Furthermore, we confirmed those performances by three experiments.


title: A review of antenna design and development for Indian regional navigational satellite system

abstract: The Indian regional navigational satellite system (IRNSS) developed by the Indian Space Research Organization (ISRO) is an autonomous regional satellite navigation system which is under the complete control of Government of India. The requirement of indigenous regional navigational satellite system is driven by the fact that access to Global Navigation Satellite System, like GPS is not guaranteed in hostile situations. Design of IRNSS antenna at user segment is mandatory for Indian region. The IRNSS satellites will be placed at a higher geostationary orbit to have a larger signal footprint and minimum satellites for regional mapping. IRNSS signals will consist of a Special Positioning Service and a Precision Service. Both will be carried on L5 band (1176.45 MHz) and S band (2492.08 MHz). As it is be long range communication system needs high frequency signals and high gain receiving antennas. So, different antennas can be designed to enhance the gain and directivity. Based on this the rectangular Microstrip patch antenna, planar array of patch antennas and planar, wideband feed slot spiral antenna are designed by using various software simulations. Use of array of spiral antennas will increase the gain position. Spiral antennas are comparatively small size and these antennas with its windings making it an extremely small structure. The performance of the designed antennas was compared in terms of return loss, bandwidth, directivity, radiation pattern and gain. In this paper, Review results of all antennas designed for IRNSS have presented.


title: Excitation of coupled slotline mode in finite-ground CPW with unequal ground-plane widths

abstract: The coupling between the desired coplanar-waveguide (CPW) mode and the unwanted coupled slotline mode is presented for finite-ground CPWs with unequal ground-plane widths. Measurements, quasi-static conformal mapping, and finite-difference time-domain analysis are performed to determine the dependence of the slotline-mode excitation on the physical dimensions of the finite-ground coplanar line and on the frequency range of operation. It is shown that the ratio of the slotline mode to the CPW mode can be as high as 10 dB. Air-bridges are shown to reduce the slotline mode by 15 dB immediately after the air-bridge, but the slotline mode fully reestablishes itself within 2000 mum of the air-bridge. Furthermore, these results are independent of frequency


title: A Siamese inception architecture network for person re-identification

abstract: Person re-identification is an extremely challenging problem as person’s appearance often undergoes dramatic changes due to the large variations of viewpoints, illuminations, poses, image resolutions, and cluttered backgrounds. How to extract discriminative features is one of the most critical ways to address these challenges. In this paper, we mainly focus on learning high-level features and combine the low-level, mid-level, and high-level features together to re-identify a person across different cameras. Firstly, we design a Siamese inception architecture network to automatically learn effective semantic features for person re-identification in different camera views. Furthermore, we combine multi-level features in null space with the null Foley–Sammon transform metric learning approach. In this null space, images of the same person are projected to a single point, which minimizes the intra-class scatter to the extreme and maximizes the relative inter-class separation simultaneously. Finally, comprehensive evaluations demonstrate that our approach achieves better performance on four person re-identification benchmark datasets, including Market-1501, CUHK03, PRID2011, and VIPeR.


title: A * -based key tree structure generation for group key management in wireless sensor networks

abstract: The application of wireless sensor networks to areas such as combat field surveillance, terrorist tracking and highway traffic monitoring requires secure communication among the sensor nodes within the networks. Logical key hierarchy (LKH) is a tree-based key management model which provides secure group communication. When a sensor node is added or evicted from the communication group, LKH updates the group key in order to ensure the security of the communications. In order to efficiently update the group key, we propose an A^*-based key tree structure, an extension to LKH, that reduces the number of re-key messages by considering the addition and eviction ratios of the history data. For the generation of the proposed key tree structure the A^* algorithm is applied, in which the branching factor at each level can take on different value. The experimental results demonstrate the efficiency of the proposed key tree structure against the existing key tree structures of fixed branching factors.


title: From Usability to Playability: Introduction to Player-Centred Video Game Development Process

abstract: While video games have traditionally been considered simple entertainment devices, nowadays they occupy a privileged position in the leisure and entertainment market, representing the fastest-growing industry globally. We regard the video game as a special type of interactive system whose principal aim is to provide the player with fun and entertainment. In this paper we will analyse how, in Video Games context, Usability alone is not sufficient to achieve the optimum Player Experience. It needs broadening and deepening, to embrace further attributes and properties that identify and describe the Player Experience. We present our proposed means of defining Playability. We also introduce the notion of Facets of Playability. Each facet will allow us to characterize the Playability easily, and associate them with the different elements of a video game. To guarantee the optimal Player Experience, Playability needs to be assessed throughout the entire video game development process, taking a Player-Centred Video Game Design approach.


title: Towards Automating Inter-organizational Workflow Semantic Resolution

abstract: Interoperability in sharing work through services between organizations requires understanding the perspective of each partner. Available resources and requirements are widely distributed with heterogeneous descriptions and enactments. No centralized registration and discovery process exists, nor automated means to map the variety of formal organizations within various overlapping domains, some defined and many not. While drawing from a common language, the mission and culture of each organization shapes their particular collection and definition of symbols, vocabulary and signals. Mining execution logs of historical inter-organizational workflows and tapping into explicitly specified domain and organizational knowledge ontologies provides a corpus of information useful to identify emergent patterns. This paper proposes a novel mediator approach using feature alignment demonstrated in previous partnerships, identified through the employment of topic modeling and word sense disambiguation methods, to infer semantic matches between parties without a priori relationships. This model demonstrates an inter-organizational workflow middleware proof of concept coupling workflow management systems interoperating between a set of organizations to automatically resolve meaning, providing the right services for requirements.


title: Mathematical programming techniques in water network optimization

abstract: In this article we survey mathematical programming approaches to problems in the field of drinking water distribution network optimization. Among the predominant topics treated in the literature, we focus on two different, but related problem classes. One can be described by the notion of network design, while the other is more aptly termed by network operation. The basic underlying model in both cases is a nonlinear network flow model, and we give an overview on the more specific modeling aspects in each case. The overall mathematical model is a Mixed Integer Nonlinear Program having a common structure with respect to how water dynamics in pipes are described. Finally, we survey the algorithmic approaches to solve the proposed problems and we discuss computation on various types of water networks.


title: Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters

abstract: Near-infrared spectroscopy (NIRS) can be used to noninvasively measure changes in the concentrations of oxy- and deoxyhemoglobin in tissue. We have previously shown that while global changes can be reliably measured, focal changes can produce erroneous estimates of concentration changes (NeuroImage 13 (2001), 76). Here, we describe four separate sources for systematic error in the calculation of focal hemoglobin changes from NIRS data and use experimental methods and Monte Carlo simulations to examine the importance and mitigation methods of each. The sources of error are: (1) the absolute magnitudes and relative differences in pathlength factors as a function of wavelength, (2) the location and spatial extent of the absorption change with respect to the optical probe, (3) possible differences in the spatial distribution of hemoglobin species, and (4) the potential for simultaneous monitoring of multiple regions of activation. We found wavelength selection and optode placement to be important variables in minimizing such errors, and our findings indicate that appropriate experimental procedures could reduce each of these errors to a small fraction (10%) of the observed concentration changes.


title: Design of Multipassband Microstrip Branch-Line Couplers With Open Stubs

abstract: The microstrip branch-line coupler with multiple passbands is proposed; moreover, the design equations are provided in this paper. In order to obtain multiple passbands, open stubs are added to the conventional branch-line coupler's ports; in addition, these open stubs' impedance and electrical length need to be properly chosen. The proposed couplers can be easily fabricated on the printed circuit board without any lumped element. The agreement between measured results and the theoretical prediction validates the feasible configuration of the proposed coupler.


title: How Gesture Promotes Learning Throughout Childhood

abstract: The gestures children use when they talk often reveal knowledge that they do not express in speech. Gesture is particularly likely to reveal these unspoken thoughts when children are on the verge of learning a new task. It thus reflects knowledge in child learners. But gesture can also play a role in changing the child's knowl- edge, indirectly through its effects on the child's communi- cative environment and directly through its effects on the child's cognitive state. Because gesture reflects thought and is an early marker of change, it may be possible to use it diagnostically. Gesture (or its lack) may be the first sign of future developmental difficulty. And because gesture can change thought, it may prove to be useful in the home, the classroom, and the clinic as a way to alter the pace, and perhaps the course, of learning and development. KEYWORDS—cognitive load; gesture; learning; problem solving; communication; math


title: Sequence Generation with Guider Network

abstract: Sequence generation with reinforcement learning (RL) has received significant attention recently. However, a challenge with such methods is the sparse-reward problem in the RL training process, in which a scalar guiding signal is often only available after an entire sequence has been generated. This type of sparse reward tends to ignore the global structural information of a sequence, causing generation of sequences that are semantically inconsistent. In this paper, we present a model-based RL approach to overcome this issue. Specifically, we propose a novel guider network to model the sequence-generation environment, which can assist next-word prediction and provide intermediate rewards for generator optimization. Extensive experiments show that the proposed method leads to improved performance for both unconditional and conditional sequence-generation tasks.


title: A Celestial Analytic Positioning Method by Stellar Horizon Atmospheric Refraction

abstract: Abstract This article, in allusion to the limitation of conventional stellar horizon atmospheric refraction based on orbital dynamics model and nonlinear Kalman filter in practical applications, proposes a new celestial analytic positioning method by stellar horizon atmospheric refraction for high-altitude flight vehicles, such as spacecraft, airplanes and ballistic missiles. First, by setting up the geometric connexion among the flight vehicle, the Earth and the altitude of starlight refraction, an expression for the relationship of starlight refraction angle and atmospheric density is deduced. Second, there are produced a novel measurement model of starlight refraction in a continuous range of altitudes (CRA) from 20 km to 50 km on the basis of the standard atmospheric data in stratosphere, and an empirical formula of stellar horizon atmospheric refraction in the same altitudes against the tangent altitude. Third, there is introduced a celestial analytic positioning algorithm, which uses the least square differential correction instead of nonlinear Kalman filter. The information about positions of a flight vehicle can be obtained directly by solving a set of nonlinear measurement equations. The stellar positioning algorithm adopts the characteristics of stellar horizon atmospheric refraction thereby removing needs for orbit dynamics models and priori knowledge of flight vehicles. The simulation results evidence the validity of the proposed stellar positioning algorithm.


title: Epidermal mucus, a major determinant in fish health: a review.

abstract: Fish epidermal mucus contains innate immune components, secreted by globlet cells that provide the primary defence against different pathogenic microbes and act as a barrier between fish and its immediate niche. The major function of mucus includes entrapment and sloughing of microbes. The mucus also contains many factors such as antimicrobial peptides (AMPs), lysozymes, lectins, proteases, etc that provide innate immunity. The AMPs secreted by epidermal mucus cells displayed antimicrobial activity against a variety of pathogens. Besides, mucosal lysozyme was found to produce significant bacteriolytic action whereas different proteases found in skin mucus of fish can kill the pathogens by cleaving its protein or by activating immunological mechanisms. Lectins are also mucosal agglutinins that play a diverse role in innate immunity like opsonization, activation of complement, etc. Epidermal mucus in fish thus provides an innate and fast acting protection which is non-specific and is found to be relatively temperature independent. The aim of the present review is to provide a broad overview of the different components of epidermal mucus including AMPs, proteases, lysozymes as well as their mode of action on pathogens.


title: Real-Time Fire Detection for Video-Surveillance Applications Using a Combination of Experts Based on Color, Shape, and Motion

abstract: In this paper, we propose a method that is able to detect fires by analyzing videos acquired by surveillance cameras. Two main novelties have been introduced. First, complementary information, based on color, shape variation, and motion analysis, is combined by a multiexpert system. The main advantage deriving from this approach lies in the fact that the overall performance of the system significantly increases with a relatively small effort made by the designer. Second, a novel descriptor based on a bag-of-words approach has been proposed for representing motion. The proposed method has been tested on a very large dataset of fire videos acquired both in real environments and from the web. The obtained results confirm a consistent reduction in the number of false positives, without paying in terms of accuracy or renouncing the possibility to run the system on embedded platforms.


title: A Broadband Substrate Integrated Waveguide (SIW) Planar Balun

abstract: A broadband planar balun is presented in this work that makes use of a substrate integrated waveguide (SIW) technique using a printed circuit board process. The proposed balun structure is able to operate at millimeter wave frequencies and it does not require any tight line coupling sections as frequently used in monolithic microwave integrated circuit balun design. In addition, this balun can be integrated with other planar topologies including nonplanar circuits made of the same substrate for achieving high efficiency. This balun structure consists of a 3 dB SIW power divider and microstrip lines that are placed on both sides of the substrate at balanced ports to obtain an 180deg phase shift. The concept is validated by simulations and measurements. Our measured results suggest that a 10 dB return loss at unbalanced port can easily be achieved across a 42% bandwidth from 19 to 29 GHz. Measured amplitude and phase imbalance between two balanced ports are within 1 dB and plusmn5deg, respectively.


Numerical simulation of fluid-structure interaction with application to aneurysm hemodynamics


title: Systematic review of time trends in the prevalence of Helicobacter pylori infection in China and the USA

abstract: It has been suggested that the prevalence of Helicobacter pylori infection has stabilized in the USA and is decreasing in China. We conducted a systematic literature analysis to test this hypothesis. PubMed and Embase searches were conducted up to 19 January 2015. Trends in the prevalence of H. pylori infection over time were assessed by regression analysis using Microsoft Excel. Overall, 25 Chinese studies (contributing 28 datasets) and 11 US studies (contributing 11 datasets) were included. There was a significant decrease over time in the H. pylori infection prevalence for the Chinese studies overall (p = 0.00018) and when studies were limited to those that used serum immunoglobulin G (IgG) assays to detect H. pylori infection (p = 0.014; 20 datasets). The weighted mean prevalence of H. pylori infection was 66 % for rural Chinese populations and 47 % for urban Chinese populations. There was a significant trend towards a decreasing prevalence of H. pylori infection for studies that included only urban populations (p = 0.04; 9 datasets). This trend was no longer statistically significant when these studies were further restricted to those that used serum IgG assays to detect H. pylori infection, although this may have been because of low statistical power due to the small number of datasets available for this analysis (p = 0.28; 6 datasets). There were no significant trends in terms of changes in the prevalence of H. pylori infection over time for studies conducted in the USA. In conclusion, the prevalence of H. pylori infection is most likely decreasing in China, due to a combination of increasing urbanization, which we found to be associated with lower H. pylori infection rates, and possibly also decreasing rates of H. pylori infection within urban populations. This will probably result in a gradual decrease in peptic ulcer and gastric cancer rates in China over time.


title: Accelerating Convolutional Networks via Global & Dynamic Filter Pruning

abstract: Accelerating convolutional neural networks has recently received ever-increasing research focus. Among various approaches proposed in the literature, filter pruning has been regarded as a promising solution, which is due to its advantage in significant speedup and memory reduction of both network model and intermediate feature maps. To this end, most approaches tend to prune filters in a layerwise fixed manner, which is incapable to dynamically recover the previously removed filter, as well as jointly optimize the pruned network across layers. In this paper, we propose a novel global & dynamic pruning (GDP) scheme to prune redundant filters for CNN acceleration. In particular, GDP first globally prunes the unsalient filters across all layers by proposing a global discriminative function based on prior knowledge of each filter. Second, it dynamically updates the filter saliency all over the pruned sparse network, and then recovers the mistakenly pruned filter, followed by a retraining phase to improve the model accuracy. Specially, we effectively solve the corresponding nonconvex optimization problem of the proposed GDP via stochastic gradient descent with greedy alternative updating. Extensive experiments show that the proposed approach achieves superior performance to accelerate several cutting-edge CNNs on the ILSVRC 2012 benchmark, comparing to the state-of-the-art filter pruning methods.


title: Simultaneous Algebraic Reconstruction Technique (SART): A Superior Implementation of the Art Algorithm

abstract: In this paper we have discussed what appears to be a superior implementation of the Algebraic Reconstruction Technique (ART). The method is based on 1) simultaneous application of the error correction terms as computed by ART for all rays in a given projection; 2) longitudinal weighting of the correction terms back-distributed along the rays; and 3) using bilinear elements for discrete approximation to the ray integrals of a continuous image. Since this implementation generates a good reconstruction in only one iteration, it also appears to have a computational advantage over the more traditional implementation of ART. Potential applications of this implementation include image reconstruction in conjunction with ray tracing for ultrasound and microwave tomography in which the curved nature of the rays leads to a non-uniform ray density across the image.


title: Density Estimation for Geolocation via Convolutional Mixture Density Network

abstract: Nowadays, geographic information related to Twitter is crucially important for fine-grained applications. However, the amount of geographic information avail- able on Twitter is low, which makes the pursuit of many applications challenging. Under such circumstances, estimating the location of a tweet is an important goal of the study. Unlike most previous studies that estimate the pre-defined district as the classification task, this study employs a probability distribution to represent richer information of the tweet, not only the location but also its ambiguity. To realize this modeling, we propose the convolutional mixture density network (CMDN), which uses text data to estimate the mixture model parameters. Experimentally obtained results reveal that CMDN achieved the highest prediction performance among the method for predicting the exact coordinates. It also provides a quantitative representation of the location ambiguity for each tweet that properly works for extracting the reliable location estimations.


Anomaly Detection for Discrete Sequences: A Survey


title: Deep Gaussian Mixture-Hidden Markov Model for Classification of EEG Signals

abstract: Electroencephalography (EEG) signals are complex dynamic phenomena that exhibit nonlinear and nonstationary behaviors. These characteristics tend to undermine the reliability of existing hand-crafted EEG features that ignore time-varying information and impair the performances of classification models. In this paper, we propose a novel method that can automatically capture the nonstationary dynamics of EEG signals for diverse classification tasks. It consists of two components. The first component uses an autoregressive-deep variational autoencoder model for automatic feature extraction, and the second component uses a Gaussian mixture-hidden Markov model for EEG classification with the extracted features. We compare the performance of our proposed method and the state-of-the-art methods in two EEG classification tasks, subject, and event classification. Results show that our approach outperforms the others by averages of $\text{15}\%\pm \text{6.3}$ (p-value $ ) and $\text{22}\%\pm \text{5.7}$ (p-value $ ) for subject and event classifications, respectively.


Internet Argument Corpus 2.0: An SQL schema for Dialogic Social Media and the Corpora to go with it.


title: LTV-MPC approach for lateral vehicle guidance by front steering at the limits of vehicle dynamics

abstract: In this paper, a linear time-varying model-based predictive controller (LTV-MPC) for lateral vehicle guidance by front steering is proposed. Due to the fact that this controller is designed in the scope of a Collision Avoidance System, it has to fulfill the requirement of an appropiate control performance at the limits of vehicle dynamics. To achieve this objective, the introduced approach employs estimations of the applied steering angle as well as the state variable trajectory to be used for successive linearization of the nonlinear prediction model over the prediction horizon. To evaluate the control performance, the proposed controller is compared to a LTV-MPC controller that uses linearizations of the nonlinear prediction model that remain unchanged over the prediction horizon. Simulation results show that an improved control performance can be achieved by the estimation based approach.


title: Embedding Projector: Interactive Visualization and Interpretation of Embeddings

abstract: Embeddings are ubiquitous in machine learning, appearing in recommender systems, NLP, and many other applications. Researchers and developers often need to explore the properties of a specific embedding, and one way to analyze embeddings is to visualize them. We present the Embedding Projector, a tool for interactive visualization and interpretation of embeddings.


title: Complementary filter design on the special orthogonal group SO(3)

abstract: This paper considers the problem of obtaining high quality attitude extraction and gyros bias estimation from typical low cost intertial measurement units for applications in control of unmanned aerial vehiccles. Two different non-linear complementary filters are proposed: Direct complementary filter and Passive non-linear complementary filter. Both filters evolve explicity on the special orthogonal group SO(3) and can be expressed in quaternion form for easy implementation. An extension to the passive ocmplementary filter is proposed to provide adaptive gyro bias estimation.


title: ANALYSIS OF CROP YIELD PREDICTION USING DATA MINING TECHNIQUES

abstract: Agrarian sector in India is facing rigorous problem to maximize the crop productivity. More than 60 percent of the crop still depends on monsoon rainfall. Recent developments in Information Technology for agriculture field has become an interesting research area to predict the crop yield. The problem of yield prediction is a major problem that remains to be solved based on available data. Data Mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. This paper presents a brief analysis of crop yield prediction using Multiple Linear Regression (MLR) technique and Density based clustering technique for the selected region i.e. East Godavari district of Andhra Pradesh in India.


Streaming Music Market and the Chance of Adoption of a New Online Streaming Music Service


title: Alias analysis for optimization of dynamic languages

abstract: Dynamic languages such as Python allow programs to be written more easily using high-level constructs such as comprehensions for queries and using generic code. Efficient execution of programs then requires powerful optimizations - incrementalization of expensive queries and specialization of generic code. Effective incrementalization and specialization of dynamic languages require precise and scalable alias analysis. This paper describes the development and experimental evaluation of a may-alias analysis for a full dynamic object-oriented language, for program optimization by incrementalization and specialization. The analysis is flow-sensitive; we show that this is necessary for effective optimization of dynamic languages. It uses precise type analysis and a powerful form of context sensitivity, called trace sensitivity, to further improve analysis precision. It uses a compressed representation to significantly reduce the memory used by flow-sensitive analyses.We evaluate the effectiveness of this analysis and 17 variants of it for incrementalization and specialization of Python programs, and we evaluate the precision, memory usage, and running time of these analyses on programs of diverse sizes. The results show that our analysis has acceptable precision and efficiency and represents the best trade-off between them compared to the variants.


title: UAV Position Estimation and Collision Avoidance Using the Extended Kalman Filter

abstract: Unmanned aerial vehicles (UAVs) play an invaluable role in information collection and data fusion. Because of their mobility and the complexity of deployed environments, constant position awareness and collision avoidance are essential. UAVs may encounter and/or cause danger if their Global Positioning System (GPS) signal is weak or unavailable. This paper tackles the problem of constant positioning and collision avoidance on UAVs in outdoor (wildness) search scenarios by using received signal strength (RSS) from the onboard communication module. Colored noise is found in the RSS, which invalidates the unbiased assumptions in least squares (LS) algorithms that are widely used in RSS-based position estimation. A colored noise model is thus proposed and applied in the extended Kalman filter (EKF) for distance estimation. Furthermore, the constantly changing path-loss factor during UAV flight can also affect the accuracy of estimation. To overcome this challenge, we present an adaptive algorithm to estimate the path-loss factor. Given the position and velocity information, if a collision is detected, we further employ an orthogonal rule to adapt the UAV predefined trajectory. Theoretical results prove that such an algorithm can provide effective modification to satisfy the required performance. Experiments have confirmed the advantages of the proposed algorithms.


title: Boda-RTC: Productive generation of portable, efficient code for convolutional neural networks on mobile computing platforms

abstract: The popularity of neural networks (NNs) spans academia, industry, and popular culture. In particular, convolutional neural networks (CNNs) have been applied to many image based machine learning tasks and have yielded strong results. The availability of hardware/software systems for efficient training and deployment of large and/or deep CNN models has been, and continues to be, an important consideration for the field. Early systems for NN computation focused on leveraging existing dense linear algebra techniques and libraries. Current approaches use low-level machine specific programming and/or closed-source, purpose-built vendor libraries. In this work, we present an open source system that, compared to existing approaches, achieves competitive computational speed while achieving higher portability. We achieve this by targeting the vendor-neutral OpenCL platform using a code-generation approach. We argue that our approach allows for both: (1) the rapid development of new computational kernels for existing hardware targets, and (2) the rapid tuning of existing computational kernels for new hardware targets. Results are presented for a case study of targeting the Qualcomm Snapdragon 820 mobile computing platform for CNN deployment.


title: Life Stress, the "Kindling" Hypothesis, and the Recurrence of Depression: Considerations From a Life Stress Perspective

abstract: Major depression is frequently characterized by recurrent episodes over the life course. First lifetime episodes of depression, however, are typically more strongly associated with major life stress than are successive recurrences. A key theoretical issue involves how the role of major life stress changes from an initial episode over subsequent recurrences. The primary conceptual framework for research on life stress and recurrence of depression is the “kindling” hypothesis (R. M. Post, 1992). Despite the strengths of the kindling hypothesis, a review of the research literature reveals inconsistencies and confusion about life stress and its implications for the recurrence of depression. Adopting a life stress perspective, the authors introduce 3 major themes that resolve the inconsistencies in the current literature. They integrate these themes and extrapolate the ideas with available data to develop a preliminary framework for evaluating competing explanatory models and to guide research on life stress and the recurrence of depression.


Analyzing Linguistic Knowledge in Sequential Model of Sentence


title: Characterizing user navigation and interactions in online social networks

abstract: Understanding how users navigate and interact when they connect to social networking sites creates opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems. In this paper, we present an in-depth analysis of user workloads in online social networks. This study is based on detailed clickstream data, collected over a 12-day period, summarizing HTTP sessions of 37,024 users who accessed four popular social networks: Orkut, MySpace, Hi5, and LinkedIn. The data were collected from a social network aggregator website in Brazil, which enables users to connect to multiple social networks with a single authentication. Our analysis of the clickstream data reveals key features of the social network workloads, such as how frequently people connect to social networks and for how long, as well as the types and sequences of activities that users conduct on these sites. Additionally, we gather the social network topology of Orkut, so that we could analyze user interaction data in light of the social graph. Our data analysis suggests insights into how users interact with friends in Orkut, such as how frequently users visit their friends' and non-immediate friends' pages. Results show that browsing, which cannot be inferred from crawling publicly available data, accounts for 92% of all user activities. Consequently, compared to using only crawled data, silent interactions like browsing friends' pages increase the measured level of interaction among users. Additionally, we find that friends requesting content are often within close geographical proximity of the uploader. We also discuss a series of implications of our findings for efficient system and interface design as well as for advertisement placement in online social networks.


title: Sexualized Avatars Lead to Women’s Self-Objectification and Acceptance of Rape Myths

abstract: Research has indicated that many video games and virtual worlds are populated by unrealistic, hypersexualized representations of women, but the effects of using these representations remain understudied. Objectification theory suggests that women’s exposure to sexualized media representations leads to self-objectification. Further, we anticipated this process would lead to increases in rape myth acceptance (RMA). Two experiments (Study 1, N = 87; Study 2, N = 81) examined the effects of avatar features on women’s experiences of self-objectification. In both studies, college women exposed to sexualized avatars experienced higher levels of self-objectification after the virtual experience than those exposed to nonsexualized avatars. Furthermore, in Study 2, self-objectification mediated the relationship between controlling a sexualized avatar and subsequent levels of RMA. We discuss the implications of women using sexualized avatars in video games and virtual environments, which may lead to negative attitud...


Mixing cognitive science concepts with computer science algorithms and data structures: An integrative approach to strong AI


title: Physiological Effects of Deep Touch Pressure on Anxiety Alleviation: The Weighted Blanket Approach

abstract: The application of deep touch pressure (DTP) has been suggested to provide positive effects on anxiety modulation. However, empirical and theoretical evidence linked to the clinical effects of DTP are relatively rare. This study conducts a quantitative analysis of behavioral assessments and performs physiological measurements, including those of electrodermal activity and heart rate variability, to understand the modulation of the autonomic nervous system (ANS), and the orchestration of sympathetic (SNS) and parasympathetic nervous systems (PsNS). The results suggest that the activation of PsNS plays a critical role in ANS modulation. This study provides physiological evidence to support the positive clinical effects of DTP for reducing anxiety in dental environments.