title: Dynamics Assessment of Advanced Single-Phase PLL Structures

abstract: Recently, several advanced phase-locked loop (PLL) techniques have been proposed for single-phase applications. Among these, the Park-PLL and the second-order-generalized-integrator-based PLL are very attractive, owing to their simple digital implementation, low computational burden, and desired performance under frequency-varying and harmonically distorted grid conditions. Despite the wide acceptance and use of these two advanced PLLs, no comprehensive design guidelines to fine-tune their parameters have been reported yet. Through a detailed mathematical analysis, it is shown that these two PLL structures are equivalent to each other, from the control point of view. Then, a linearized model is developed which is valid for both PLLs. The derived model significantly simplifies the stability analysis and the parameter design. To fine-tune the PLL parameters, a systematic design approach is suggested afterward, which guarantees a fast dynamic response, a high disturbance rejection ability, and a robust performance. Finally, the simulation and experimental results are presented to support the theoretical analysis.


title: Sentence and Word Embedding Employed in Open Question-Answering

abstract: The Automatic Question Answering, or AQA, system is a representative of open domain QA systems, where the answer selection process leans on syntactic and semantic similarities between the question and the answering text snippets. Such approach is specifically oriented to languages with fine grained syntactic and morphologic features that help to guide the correct QA match. In this paper, we present the latest results of the AQA system with new word embedding criteria implementation. All AQA processing steps (question processing, answer selection and answer extraction) are syntax-based with advanced scoring obtained by a combination of several similarity criteria (TF-IDF, tree distance, ...). Adding the word embedding parameters helped to resolve the QA match in cases, where the answer is expressed by semantically near equivalents. We describe the design and implementation of the whole QA process and provide a new evaluation of the AQA system with the word embedding criteria measured with an expanded version of Simple Question-Answering Database, or SQAD, with more than 3000 question-answer pairs extracted from the Czech Wikipedia.


title: Enterprise Information Systems as Objects and Carriers of Institutional Forces: The New Iron Cage? ∗

abstract: This paper draws upon the institutional theory lens to examine enterprise information systems. We propose that these information systems engender a duality. On one hand, these systems are subject to institutional forces and institutional processes that set the rules of rationality. On the other hand, they are an important embodiment of institutional commitments and serve to preserve these rules by constraining the actions of human agents. The complexity inherent to enterprise technologies renders them an equivoque. This, when combined with the propensity toward lack of mindfulness in organizations, is likely to lead to acquiescence to institutional pressures. Enterprise information systems bind organizations to fundamental choices about how their activities should be organized; unquestioned choices that tend to appear natural. We suggest implications of this view and develop propositions examining: (1) enterprise information systems as objects of institutional forces in the “chartering” and “project” phases, (2) the resolution of institutional misalignments caused by the introduction of new systems, and (3) enterprise information systems as carriers of institutional logics in the “shakeout” and “onward & upward” phases.


title: Development of a Matlab software for analysis of heart rate variability

abstract: The analysis of heart rate variability (HRV) signals is an important tool for studying the autonomic nervous system, as it allows the evaluation of the balance between the sympathetic and parasympathetic influences on heart rhythm. This paper presents a tool for analysis of HRV called ECGLab, which was developed in Matlab language in order to help research on HRV by making the analysis process faster and easier. The software obtains the HRV signal by using an automatic QRS detection algorithm. The user can inspect the ECG and correct mistakes in the detection process, and also identify ectopic beats. Importing RR intervals from previously typed ASCII files is also possible. Some of the most popular HRV analysis techniques were implemented: statistical and time series analysis, spectral analysis (using FFT, autoregressive and Lomb methods), Poincare plot analysis and sequential trend analysis.


title: SLA-based task scheduling algorithms for heterogeneous multi-cloud environment

abstract: Service-level agreement (SLA) is a major issue in cloud computing because it defines important parameters such as quality of service, uptime, downtime, period of service, pricing, and security. However, the service may vary from one cloud service provider (CSP) to another. The collaboration of the CSPs in the heterogeneous multi-cloud environment is very challenging, and it is not well covered in the recent literatures. In this paper, we present two SLA-based task scheduling algorithms, namely SLA-MCT and SLA-Min-Min for heterogeneous multi-cloud environment. The former algorithm is a single-phase scheduling, whereas the latter one is a two-phase scheduling. The proposed algorithms support three levels of SLA determined by the customers. Furthermore, the algorithms incorporate the SLA gain cost for the successful completion of the service and SLA violation cost for the unsuccessful end of the service. We simulate the proposed algorithms using benchmark and synthetic datasets. The experimental results of the proposed SLA-MCT are compared with three single-phase task scheduling algorithms, namely CLS, Execution-MCT, and Profit-MCT, and the results of the proposed SLA-Min-Min are compared with two-phase scheduling algorithms, namely Execution-Min-Min and Profit-Min-Min in terms of four performance metrics, namely makespan, average cloud utilization, gain, and penalty cost of the services. The results clearly show that the proposed algorithms properly balance between makespan and gain cost of the services in comparison with other algorithms.


title: Lateralization of phonetic and pitch discrimination in speech processing

abstract: Cerebral activation was measured with positron emission tomography in ten human volunteers. The primary auditory cortex showed increased activity in response to noise bursts, whereas acoustically matched speech syllables activated secondary auditory cortices bilaterally. Instructions to make judgments about different attributes of the same speech signal resulted in activation of specific lateralized neural systems. Discrimination of phonetic structure led to increased activity in part of Broca9s area of the left hemisphere, suggesting a role for articulatory recoding in phonetic perception. Processing changes in pitch produced activation of the right prefrontal cortex, consistent with the importance of right-hemisphere mechanisms in pitch perception.


title: RESEARCH ARTICLE OPEN ACCESS A Zigbee Based Patient Health Monitoring System

abstract: This paper, presents a Wireless Sensor Network (WSN) for monitoring patient’s physiological conditions continuously using Zigbee. Here the physiological conditions of the patient's are monitored by sensors and the output of these sensors is transmitted via Zigbee and the same has to be sent to the remote wireless monitor for acquiring the observed patient’s physiological signal. The remote wireless monitor is constructed of Zigbee and Personal Computer (PC). The measured signal has to be sent to the PC, which can be data collection. Although Bluetooth is better than Zigbee for transmission rate, Zigbee has lower power consumption. The first procedure of the system is that the wireless sensors are used to measure Heart rate, temperature and fall monitoring from human body using Zigbee. Next procedure of the system is to measure saline level in bottle using zigbee. The measured signal is sent to the PC via the RS-232 serial port communication interface. In particular, when measured signals cross the standard value, the personal computer will send a message to the caretaker’s mobile phone.


title: A Novel Wavelet-Based Ensemble Method for Short-Term Load Forecasting with Hybrid Neural Networks and Feature Selection

abstract: In this paper, a new ensemble forecasting model for short-term load forecasting (STLF) is proposed based on extreme learning machine (ELM). Four important improvements are used to support the ELM for increased forecasting performance. First, a novel wavelet-based ensemble scheme is carried out to generate the individual ELM-based forecasters. Second, a hybrid learning algorithm blending ELM and the Levenberg–Marquardt method is proposed to improve the learning accuracy of neural networks. Third, a feature selection method based on the conditional mutual information is developed to select a compact set of input variables for the forecasting model. Fourth, to realize an accurate ensemble forecast, partial least squares regression is utilized as a combining approach to aggregate the individual forecasts. Numerical testing shows that proposed method can obtain better forecasting results in comparison with other standard and state-of-the-art methods.


Object Recognition in Multi-View Dual Energy X-ray Images.


title: Tajik-Farsi Persian Transliteration Using Statistical Machine Translation

abstract: Tajik Persian is a dialect of Persian spoken primarily in Tajikistan and written with a modified Cyrillic alphabet. Iranian Persian, or Farsi, as it is natively called, is the lingua franca of Iran and is written with the Persian alphabet, a modified Arabic script. Although the spoken versions of Tajik and Farsi are mutually intelligible to educated speakers of both languages, the difference between the writing systems constitutes a barrier to text compatibility between the two languages. This paper presents a system to transliterate text between these two different Persian dialects that use incompatible writing systems. The system also serves as a mechanism to facilitate sharing of computational linguistic resources between the two languages. This is relevant because of the disparity in resources for Tajik versus Farsi.


title: Distributed Training Large-Scale Deep Architectures

abstract: Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Taking both the algorithmic and system aspects into consideration, we develop a procedure for setting mini-batch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training.


title: Enhanced differential super class-AB OTA

abstract: A fully differential Super Class AB Operational Transconductance Amplifier (OTA) is presented. To provide additional dynamic current boosting and increased gain-bandwidth product (GBW), not only adaptive biasing techniques has been applied at the differential input stage, but also Local Common Mode Feedback (LCMFB). Additionally, Quasi Floating Gate (QFG) transistors are employed, enhancing the performance of the amplifier. The OTA has been fabricated in a standard 0.5-μm CMOS process. Simulation results yield a slew rate improvement by factor 86 and a GBW enhancement by factor 16 when compared to the class A OTA while driving the same 70-pF load. Supply voltages are ±1-V and the value of the quiescent current is 10 μA. The overhead in terms of quiescent power, silicon area, and noise level is small.


title: A Novel Transform for Secret Key Generation in Time-Varying TDD Channel under Hardware Fingerprint Deviation

abstract: Channel reciprocity can be used for providing sufficient key generation in time division duplex (TDD) system. However, in practice, its application is limited by the hardware fingerprint deviation (HFD) problem. In this paper, we propose a novel real-time transform that can cope with this problem in time- varying TDD channel without any calibration period or feedback loops. More specifically, a log-domain differential (LDD) transform is developed and the resulting performance is analyzed in terms of mean square error (MSE) between receptions at Alice and Bob and effective signal to error ratio (ESER). The analysis shows that the proposed transform can eliminate the impact of HFD, yet its performance is very sensitive to channel noise and moving speed. For this purpose, an enhanced version is proposed including an efficient noise reduction technique and the impact of mobility on parameter design is also analyzed. Numerical results show that the proposed LDD advanced transform provides performance comparable to the ideal case without HFD, and thus, can be used to form a simple, practical and flexible solution for secret key generation in time-varying TDD channel.


title: Secure Cloud Container: Runtime Behavior Monitoring Using Most Privileged Container (MPC)

abstract: Hypervisor-based virtualization rapidly becomes a commodity, and it turns valuable in many scenarios such as resource optimization, uptime maximization, and consolidation. Container-based application virtualization is an appropriate solution to develop a light weighted partitioning by providing application isolation with less overhead. Undoubtedly, container based virtualization delivers a lightweight and efficient environment, however raises some security concerns as it allows isolated processes to utilize an underlying host kernel. A new security layer with the Most Privileged Container (MPC) is proposed in this article. The proposed MPC layer exhibits three main functional blocks: Access policies, Black list database, and Runtime monitoring. The introduced MPC layer implements privilege based access control and assigns resource access permissions based on policies and the security profiles of containerized application user processes. Furthermore, the monitoring block examines the runtime behavior of containers and black list database is updated if the container violets its policies. The proposed MPC layer provides higher level of application container security against potential threats.


title: Direct Reduction and Exact Solutions for Generalized Variable Coefficients 2D KdV Equation under Some Integrability Conditions

abstract: Based on the closed connections among the homogeneous balance (HB) method and Clarkson—Kruskal (CK) method, we study the similarity reductions of the generalized variable coefficients 2D KdV equation. In the meantime it is shown that this leads to a direct reduction in the form of ordinary differential equation under some integrability conditions between the variable coefficients. Two different cases have been discussed, the search for solutions of those ordinary differential equations yielded many exact travelling and solitonic wave solutions in the form of hyperbolic and trigonometric functions under some constraints between the variable coefficients.


title: A NOVEL WIDEBAND CIRCULARLY POLARIZED ANTENNA FOR WORLDWIDE UHF BAND RFID READER APPLICATIONS

abstract: This paper presents the novel design of a wideband circularly polarized (CP) Radio Frequency Identiflcation (RFID) reader microstrip patch antenna for worldwide Ultra High Frequency (UHF) band which covers 840{960MHz. The proposed antenna, which consists of a microstrip patch with truncated corners and a cross slot, is placed on a foam substrate ("r = 1:06) above a ground plane and is fed through vias through ground plane holes that extend from the quadrature 3dB branch line hybrid coupler placed below the ground plane. This helps to separate feed network radiation, from the patch antenna and keeping the CP purity. The prototype antenna was fabricated with a total size of 225 £ 250 £ 12:8mm 3 which shows a measured impedance matching band of 840{1150MHz (31.2%) as well as measured rotating linear based circularly polarized radiation patterns. The simulated and measured 3dB Axial Ratio (AR) bandwidth is better than 23% from 840{1050MHz meeting and exceeding the target worldwide RFID UHF band.


title: FVQA: Fact-Based Visual Question Answering

abstract: Visual Question Answering (VQA) has attracted a lot of attention in both Computer Vision and Natural Language Processing communities, not least because it offers insight into the relationships between two important sources of information. Current datasets, and the models built upon them, have focused on questions which are answerable by direct analysis of the question and image alone. The set of such questions that require no external information to answer is interesting, but very limited. It excludes questions which require common sense, or basic factual knowledge to answer, for example. Here we introduce FVQA, a VQA dataset which requires, and supports, much deeper reasoning. FVQA only contains questions which require external information to answer. ::: We thus extend a conventional visual question answering dataset, which contains image-question-answerg triplets, through additional image-question-answer-supporting fact tuples. The supporting fact is represented as a structural triplet, such as . ::: We evaluate several baseline models on the FVQA dataset, and describe a novel model which is capable of reasoning about an image on the basis of supporting facts.


title: The positive false discovery rate: A Bayesian interpretation and the q-value

abstract: Multiple hypothesis testing is concerned with controlling the rate of false positives when testing several hypotheses simultaneously. One multiple hypothesis testing error measure is the false discovery rate (FDR), which is loosely defined to be the expected proportion of false positives among all significant hypotheses. The FDR is especially appropriate for exploratory analyses in which one is interested in finding several significant results among many tests. In this work, we introduce a modified version of the FDR called the “positive false discovery rate” (pFDR). We discuss the advantages and disadvantages of the pFDR and investigate its statistical properties. When assuming the test statistics follow a mixture distribution, we show that the pFDR can be written as a Bayesian posterior probability and can be connected to classification theory. These properties remain asymptotically true under fairly general conditions, even under certain forms of dependence. Also, a new quantity called the “q-value” is introduced and investigated, which is a natural “Bayesian posterior p-value,” or rather the pFDR analogue of the p-value. 1. Introduction. When testing a single hypothesis, one is usually concerned with controlling the false positive rate while maximizing the probability of detecting an effect when one really exists. In statistical terms, we maximize the power conditional on the Type I error rate being at or below some level. The field of multiple hypothesis testing tries to extend this basic paradigm to the situation where several hypotheses are tested simultaneously. One must define an appropriate compound error measure according to the rate of false positives one is willing to encounter. Then a procedure is developed that allows one to control the error rate at a desired level, while maintaining the power of each test as much as possible. The most commonly controlled quantity when testing multiple hypotheses is the family wise error rate (FWER), which is the probability of yielding one or more false positives out of all hypotheses tested. The most familiar example of this is the Bonferroni method. If there are m hypothesis tests, each test is controlled so that the probability of a false positive is less than or equal to α/m for some chosen value of α. It then follows that the overall FWER is less than or equal to α .M any


title: Patterns of trends in niveograph characteristics across the western United States from snow telemetry data

abstract: The snowpack is changing across the globe, as the climate warms and changes. We used daily snow water equivalent (SWE) niveograph (time series of SWE) data from 458 snow telemetry (SNOTEL) stations for the period 1982 through 2012. Nineteen indices based on amount, timing, time length, and rates were used to describe the annual temporal evolution in SWE accumulation and ablation. The trends in these annual indices were computed over the time period for each station using the Theil-Sen slope. These trends were then clustered into four groups to determine the spatial pattern of SWE trends. Temperature and precipitation data were extracted from the PRISM data set, due to the shorter time period of temperature measurement at the SNOTEL stations. Results show that SNOTEL stations can be clustered in four clusters according to the observed trends in snow indices. Cluster 1 stations are mostly located in the Eastern- and South-eastern most parts of the study area and they exhibit a generalized decrease in the indices related with peak SWE and snow accumulation. Those stations recorded a negative trend in precipitation and an increase in temperature. Cluster 4 that is mostly restricted to the North and North-west of the study area shows an almost opposite pattern to cluster 1, due to months with positive trends and a more moderate increase of temperature. Stations grouped in clusters 2 and 3 appear mixed with clusters 1 and 4, in general they show very little trends in the snow indices.


title: The Infamous Relationship Between Violent Video Game Use and Aggression: Uncharted Moderators and Small Effects Make It a Far Cry from Certain

abstract: There is ongoing scientific and public interest in whether playing violent video games (VVGs) increases aggression. Here, we review the extant evidence for the effects of VVGs on aggression across three different types of research – (1) experimental, lab-based studies, (2) cross-sectional and longitudinal studies, and (3) epidemiological studies of societal violence (e.g. violent crime rates). Although the idea that playing VVGs causes aggression is intuitively appealing, empirical evidence suggests that strong claims about a positive relationship are unjustified. In the laboratory, playing VVGs leads to only small increases in aggression, and, when issues such as publication bias are accounted for, these estimates decline further in size (Hilgard et al. Psychological Bulletin 143:757, 2017). In terms of societal violence, the evidence consistently shows no relationship between VVG use and violence, sometimes even showing decreases in violence associated with increased VVG use (e.g. Markey, Markey & French, 2015). Evidence further suggests that the relationship between VVGs and aggression is not well understood, with factors such as narrative context, game difficulty, and competitiveness potentially influencing or accounting for any relationship between gameplay and aggression. Some studies suggest that, under some circumstances, VVGs may even reduce hostility (e.g. when used for mood management; Ferguson & Rueda (European Psychologist 15:99, 2010). At present, we feel the evidence weighs against the notion that VVGs cause aggression, though more sophisticated theoretical and methodological approaches are necessary to more fully understand the boundary conditions of the relationship.


title: Active-feedback frequency-compensation technique for low-power multistage amplifiers

abstract: An active-feedback frequency-compensation (AFFC) technique for low-power operational amplifiers is presented in this paper. With an active-feedback mechanism, a high-speed block separates the low-frequency high-gain path and high-frequency signal path such that high gain and wide bandwidth can be achieved simultaneously in the AFFC amplifier. The gain stage in the active-feedback network also reduces the size of the compensation capacitors such that the overall chip area of the amplifier becomes smaller and the slew rate is improved. Furthermore, the presence of a left-half-plane zero in the proposed AFFC topology improves the stability and settling behavior of the amplifier. Three-stage amplifiers based on AFFC and nested-Miller compensation (NMC) techniques have been implemented by a commercial 0.8-/spl mu/m CMOS process. When driving a 120-pF capacitive load, the AFFC amplifier achieves over 100-dB dc gain, 4.5-MHz gain-bandwidth product (GBW) , 65/spl deg/ phase margin, and 1.5-V//spl mu/s average slew rate, while only dissipating 400-/spl mu/W power at a 2-V supply. Compared to a three-stage NMC amplifier, the proposed AFFC amplifier provides improvement in both the GBW and slew rate by 11 times and reduces the chip area by 2.3 times without significant increase in the power consumption.


title: How (Not) to Predict Elections

abstract: Using social media for political discourse is increasingly becoming common practice, especially around election time. Arguably, one of the most interesting aspects of this trend is the possibility of ''pulsing'' the public's opinion in near real-time and, thus, it has attracted the interest of many researchers as well as news organizations. Recently, it has been reported that predicting electoral outcomes from social media data is feasible, in fact it is quite simple to compute. Positive results have been reported in a few occasions, but without an analysis on what principle enables them. This, however, should be surprising given the significant differences in the demographics between likely voters and users of online social networks. This work aims to test the predictive power of social media metrics against several Senate races of the two recent US Congressional elections. We review the findings of other researchers and we try to duplicate their findings both in terms of data volume and sentiment analysis. Our research aim is to shed light on why predictions of electoral (or other social events) using social media might or might not be feasible. In this paper, we offer two conclusions and a proposal: First, we find that electoral predictions using the published research methods on Twitter data are not better than chance. Second, we reveal some major challenges that limit the predictability of election results through data from social media. We propose a set of standards that any theory aiming to predict elections (or other social events) using social media should follow.


title: Reverse engineering digital circuits using functional analysis

abstract: Integrated circuits (ICs) are now designed and fabricated in a globalized multi-vendor environment making them vulnerable to malicious design changes, the insertion of hardware trojans/malware and intellectual property (IP) theft. Algorithmic reverse engineering of digital circuits can mitigate these concerns by enabling analysts to detect malicious hardware, verify the integrity of ICs and detect IP violations. In this paper, we present a set of algorithms for the reverse engineering of digital circuits starting from an unstructured netlist and resulting in a high-level netlist with components such as register files, counters, adders and subtracters. Our techniques require no manual intervention and experiments show that they determine the functionality of more than 51% and up to 93% of the gates in each of the practical test circuits that we examine.


title: Using OPC-UA for the autoconfiguration of real-time Ethernet systems

abstract: In the future, production systems will consist of modular and flexible production components, being able to adapt to completely new manufacturing processes. This requirement arises from market turbulences caused by customer demands, i. e. highly customized goods in smaller production batches, or phenomenon like commercial crisis. In order to achieve adaptable production systems, one of the major challenges is to develop suitable autoconfiguration mechanisms for industrial automation systems. This paper presents a two-step architecture for the autoconfiguration of real-time Ethernet (RTE) systems. As a first step, an RTE-independent device discovery mechanism is introduced. Afterwards, it is shown how the parameters of an RTE can be configured automatically using Profinet IO as an exemplary RTE system. In contrast to the existing approaches, the proposed discovery mechanism is based on the OPC Unified Architecture (OPC-UA). In addition, a procedure to autoconfigure modular IO-Devices is introduced.


title: Visual pattern discovery for architecture image classification and product image search

abstract: Many objects have repetitive elements, and finding repetitive patterns facilitates object recognition and numerous applications. We devise a representation to describe configurations of repetitive elements. By modeling spatial configurations, visual patterns are more discriminative than local features, and are able to tackle with object scaling, rotation, and deformation. We transfer the pattern discovery problem into finding frequent subgraphs from a graph, and exploit a graph mining algorithm to solve this problem. Visual patterns are then exploited in architecture image classification and product image retrieval, based on the idea that visual pattern can describe elements conveying architecture styles and emblematic motifs of brands. Experimental results show that our pattern discovery approach has promising performance and is superior to the conventional bag-of-words approach.


An Application of Heuristic and Evolutionary Algorithms for Solving Some Practical Two-dimensional Bin Packing Problems


title: Intelligent forest fire monitoring system

abstract: This paper presents iForestFire, an Environmental Monitoring Information System for forest fire protection. The system is composed of several components, each having a particular function. Automatic fire detection is a crucial component of the system. It is based on various complex image processing algorithms. Complexity of the system also emerges from integration, based on multi agent technology, of different environment information. The presented system contributes to the environment protection and is in use in Croatia for several years.


title: Ion Milling of Compound Semiconductors for Transmission Electron Microscopy

abstract: Serious artefactual structures can be introduced into transmission electron microscope specimens of compound semiconductors when the latter are prepared by ion milling under non-optimum conditions. In the present investigations, the nature of these artefacts is characterised and results obtained using conventional argon ion milling are compared with those obtained using alternative ion species, including iodine ions. This work demonstrates the large improvement in specimen structural quality which can be achieved by combining a logical selection of ion species with a careful choice of milling conditions.


title: Epilepsy in Laos: Knowledge, attitudes, and practices in the community

abstract: Objective. We evaluated beliefs about, attitudes toward, and stigma associated with epilepsy in four districts of central Laos. Method. For this study, 83 people with epilepsy, 83 family members, and 166 matched villagers in Vientiane Province were interviewed. Results. From patients to families to villagers, there existed a significantly increasing gradient of misbeliefs. Dubbed locally as ‘‘mad pig disease,’’ epilepsy was viewed as having a supernatural origin by 25–42% of respondents, a life-threatening disease by 60%, a disease transmissible by consumption of pork meat by 10–21%, and a disease transmissible by contact with patients’ saliva by 14.5% of patients and 44% of villagers (P < 0.01). Stigma was high. People thought that they should avoid contact or sharing meals with patients (15% of patients, 62% of family members, P < 0.001), and that persons with epilepsy should not get a job, get married (29 and 42%, P < 0.016), or raise children (33–42%). Conclusion. Wrong beliefs may lead to stigma and hamper access to or compliance with modern epilepsy treatment. In traditional countries, education is the cornerstone of epilepsy management. 2007 Elsevier Inc. All rights reserved.


title: Unwillingness-to-communicate and college students ’ motives in SMS mobile messaging q

abstract: Results from a random sample of 532 college students show that students who made the heaviest use of SMS (Short Message Service) were motivated by its convenience, its low cost, and its utility for coordinating events. People who were socially anxious and were unwilling-to-communicate face-to-face and were put off by the confusing acronyms used in mobile messaging appeared to be those who spent less time, and not more, using SMS despite the fact that SMS could help overcome student's shyness about bringing up difficult topics with friends. In broad terms, SMS is a social technology and has become a popular communication utility for college students.


title: Online Learning: Beyond Regret

abstract: We study online learnability of a wide class of problems, extending the results of Rakhlin et al. (2010a) to general notions of performance measure well beyond external regret. Our framework simultaneously captures such well-known notions as internal and general regret, learning with non-additive global cost functions, Blackwell’s approachability, calibration of forecasters, and more. We show that learnability in all these situations is due to control of the same three quantities: a martingale convergence term, a term describing the ability to perform well if future is known, and a generalization of sequential Rademacher complexity, studied in Rakhlin et al. (2010a). Since we directly study complexity of the problem instead of focusing on ecient algorithms, we are able to improve and extend many known results which have been previously derived via an algorithmic construction.


title: Mobile application testing — Challenges and solution approach through automation

abstract: By the time this paper has been presented, the mobile app landscape will have changed. New OS versions will have been released. A bunch of new devices will have hit the market. And mobile application testing will have become that much more complex and challenging for all of us. There is no doubt that mobile applications need specific testing approaches. This paper wants to investigate new directions in research on the type of testing and skills required on mobile app testing by answering the following three research questions: (RQ1) How mobile applications testing are so different from traditional web applications, that require specialized testing skills and techniques?, (RQ2) What are the new challenges and future trends in mobile application testing, and (RQ3) How far automation effective in testing mobile application?. We answer those questions by analyzing the current trends in mobile application development and testing, and by proposing my view on the topic.


title: Robust chinese traffic sign detection and recognition with deep convolutional neural network

abstract: Detection and recognition of traffic sign, including various road signs and text, play an important role in autonomous driving, mapping/navigation and traffic safety. In this paper, we proposed a traffic sign detection and recognition system by applying deep convolutional neural network (CNN), which demonstrates high performance with regard to detection rate and recognition accuracy. Compared with other published methods which are usually limited to a predefined set of traffic signs, our proposed system is more comprehensive as our target includes traffic signs, digits, English letters and Chinese characters. The system is based on a multi-task CNN trained to acquire effective features for the localization and classification of different traffic signs and texts. In addition to the public benchmarking datasets, the proposed approach has also been successfully evaluated on a field-captured Chinese traffic sign dataset, with performance confirming its robustness and suitability to real-world applications.


title: FPGA based ASIC fast prototyping system for new generation piezo injectors control units

abstract: In automotive systems, piezoelectric injectors allow fine electronic control over the fuel injection time and quantity. They are typically driven by current controlled bidirectional converter. This power board is managed by an ASIC device which is part of the car ECU. This paper describes a new FPGA-based ASIC fast prototyping methodology for piezo injectors control units.


title: Circle Polarization Shift Keying With Direct Detection for Free-Space Optical Communication

abstract: A binary polarization shift-keying scheme with two circle polarization states, circle polarization shift keying (CPolSK), is proposed and discussed. The propagation of the circle-polarized Gaussian Schell model beam is studied based on the cross-spectral density matrix. The results show that the state of polarization remains unchanged and the degree of polarization slightly increases upon propagation in a turbulent atmosphere. The analysis of the bit-error-rate performance shows that the CPolSK system has about a 3 dB lower requirement in signal-to-noise ratio than the on-off keying system. The modulation scheme will be helpful for practical free-space optical communication systems in the future.


title: Automated Controversy Detection on the Web

abstract: Alerting users about controversial search results can encourage critical literacy, promote healthy civic discourse and counteract the “filter bubble” effect, and therefore would be a useful feature in a search engine or browser extension. In order to implement such a feature, however, the binary classification task of determining which topics or webpages are controversial must be solved. Earlier work described a proof of concept using a supervised nearest neighbor classifier with access to an oracle of manually annotated Wikipedia articles. This paper generalizes and extends that concept by taking the human out of the loop, leveraging the rich metadata available in Wikipedia articles in a weakly-supervised classification approach. The new technique we present allows the nearest neighbor approach to be extended on a much larger scale and to other datasets. The results improve substantially over naive baselines and are nearly identical to the oracle-reliant approach by standard measures of F 1, F 0.5, and accuracy. Finally, we discuss implications of solving this problem as part of a broader subject of interest to the IR community, and suggest several avenues for further exploration in this exciting new space.


title: A 10b linear interpolation DAC using body-transconductance control for AMLCD column driver

abstract: A 10-b interpolation DAC is implemented for AMLCD column driver using 4b control of body potential via a built-in body buffer for interpolation in the buffer amplifier. Measured INL and DNL are 0.36 LSB and 0.35LSB on 10b accuracy, respectively. Each channel of driver IC has a height of 295μm, a pitch of 14μm and a static current of ΙμΑ The fabricated chip is in 90n CMOS process.


title: Probabilistic Attribute Tree in Convolutional Neural Networks for Facial Expression Recognition

abstract: In this paper, we proposed a novel Probabilistic Attribute Tree-CNN (PAT-CNN) to explicitly deal with the large intra-class variations caused by identity-related attributes, e.g., age, race, and gender. Specifically, a novel PAT module with an associated PAT loss was proposed to learn features in a hierarchical tree structure organized according to attributes, where the final features are less affected by the attributes. Then, expression-related features are extracted from leaf nodes. Samples are probabilistically assigned to tree nodes at different levels such that expression-related features can be learned from all samples weighted by probabilities. We further proposed a semi-supervised strategy to learn the PAT-CNN from limited attribute-annotated samples to make the best use of available data. Experimental results on five facial expression datasets have demonstrated that the proposed PAT-CNN outperforms the baseline models by explicitly modeling attributes. More impressively, the PAT-CNN using a single model achieves the best performance for faces in the wild on the SFEW dataset, compared with the state-of-the-art methods using an ensemble of hundreds of CNNs.


oro.open.ac.uk A Framework for Security Requirements Engineering


title: Velocity Fluctuations of a Hard-Core Brownian Particle

abstract: The velocity autocorrelation function $〈v(t)v(0)〉$ of a hard-sphere Brownian particle moving in an incompressible viscous fluid is calculated on the basis of hydrodynamics. The usually assumed exponential decay at long times is shown to be inconsistent with fluid mechanics. A $\mathrm{slower}\mathrm{decay}\ensuremath{\propto}{t}^{\ensuremath{-}\frac{3}{2}}$ as $t\ensuremath{\rightarrow}\ensuremath{\infty}$ is derived. This decay has been observed in molecular dynamic computations carried out by Alder and Wainwright.


High precision natural language interfaces to databases: a graph theoretic approach


Survey of Real-time Processing Technologies of IoT Data Streams


title: Research advances in fault diagnosis and prognostic based on deep learning

abstract: Aiming to condition based maintenance for complex equipment, numerous intelligent fault diagnosis and prognostic methods based on machine learning have been researched. Compared with the traditional shallow models, which have problems of lacking expression capacity and existing the curse of dimensionality, using deep learning theory can effectively mine characteristics and accurately recognize the health condition. In consequence, fault diagnosis and prognostic based on deep learning have turned into an innovative and promising research field. This paper gives a review of fault diagnosis and prognostic based on deep learning. First of all, a brief introduction to deep learning architecture and the framework of fault diagnosis based on deep learning is described. Second, tracking describes the latest progress of fault diagnosis and prognostic based on deep learning in chronological order. In this section, the deep learning methods used in fault diagnosis and prognostic are discussed, including Deep Neural Network (DNN), Deep Belief Network (DBN) and Convolutional Neural Network (CNN). Then the engineering application fields are summarized, such as mechanical equipment diagnosis, electrical equipment diagnosis, etc. Finally, this paper indicates the potential future research issues in this field.


title: A meta-analysis of human-system interfaces in unmanned aerial vehicle (UAV) swarm management.

abstract: A meta-analysis was conducted to systematically evaluate the current state of research on human-system interfaces for users controlling semi-autonomous swarms composed of groups of drones or unmanned aerial vehicles (UAVs). UAV swarms pose several human factors challenges, such as high cognitive demands, non-intuitive behavior, and serious consequences for errors. This article presents findings from a meta-analysis of 27 UAV swarm management papers focused on the human-system interface and human factors concerns, providing an overview of the advantages, challenges, and limitations of current UAV management interfaces, as well as information on how these interfaces are currently evaluated. In general allowing user and mission-specific customization to user interfaces and raising the swarm's level of autonomy to reduce operator cognitive workload are beneficial and improve situation awareness (SA). It is clear more research is needed in this rapidly evolving field.


Personalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning


title: Friend Recommendation in Location-Based Social Networks via Deep Pairwise Learning

abstract: Generating friend recommendations in location-based social networks is a challenging task, as we have to learn how different contextual factors influence users' behavior to form social relationships. For example, the contextual information of users' check-in behavior at common locations and users' activities at close regions may impact users' relationships. In this paper we propose a deep pairwise learning model, namely FDPL. Our model first learns the low dimensional latent embeddings of users' social relationships by jointly factorizing them with the available contextual information based on a multi-view learning strategy. In addition, to account for the fact that the contextual information is non-linearly correlated with users' social relationships we design a deep pairwise learning architecture based on a Bayesian personalized ranking strategy. We learn the non-linear deep representations of the computed low dimensional latent embeddings by formulating the top- $k$ friend recommendation task at location-based social networks as a ranking task in our deep pairwise learning strategy. Our experiments on three real world location-based social networks from Brightkite, Gowalla and Foursquare show that the proposed FDPL model significantly outperforms other state-of-the-art methods. Finally, we evaluate the impact of contextual information on our model and we experimentally show that it is a key factor to boost the friend recommendation accuracy at location-based social networks.


title: A Paper Recommendation System Based on User's Research Interests

abstract: Researchers and scientists read articles to improve their studies. Researchers spend too much time and struggle to find the suitable article they are looking for. The purpose of article recommendation system is to reduce the time they spend and present to them the related articles they are not aware of. Classic article recommendation systems do not consider the user's information, they show the same results in the same sort for each researcher. In this study, an article recommendation system that takes into consideration the researcher's work field and the publisher's previous articles is presented. One of the most important innovations of this work is the use of TF-IDF and Cosinus similarity to make article recommendations taking user's past articles into consideration. As a result of the work, the users have been recommended articles, and the method we present has proved more successful results compared to equivalent methods according to f-Measure criterion.


title: Design of high power waveguide to microstrip directional coupler

abstract: A novel Rectangular Waveguide to microstripline multi-aperture directional coupler has been designed in this paper. Coupling between two different and cross-guide transmission lines is realized by slot in microstrip ground plane, the coupling is 35dB with the deviation of 0.8 dB, and the directivity is better than 20dB. This type of coupler is useful in applications where small size, flat coupling, high directivity and capability to carry high main-line power are required.


title: FastCDC: a fast and efficient content-defined chunking approach for data deduplication

abstract: Content-Defined Chunking (CDC) has been playing a key role in data deduplication systems in the past 15 years or so due to its high redundancy detection ability. However, existing CDC-based approaches introduce heavy CPU overhead because they declare the chunk cutpoints by computing and judging the rolling hashes of the data stream byte by byte. In this paper, we propose FastCDC, a Fast and efficient CDC approach, that builds and improves on the latest Gear-based CDC approach, one of the fastest CDC methods to our knowledge. The key idea behind FastCDC is the combined use of three key techniques, namely, simplifying and enhancing the hash judgment to address our observed challenges facing Gear-based CDC, skipping sub-minimum chunk cut-point to further speed up CDC, and normalizing the chunk-size distribution in a small specified region to address the problem of the decreased deduplication ratio stemming from the cut-point skipping. Our evaluation results show that, by using a combination of the three techniques, FastCDC is about 10× faster than the best of open-source Rabin-based CDC, and about 3× faster than the state-of-the-art Gear- and AE-based CDC, while achieving nearly the same deduplication ratio as the classic Rabin-based approach.


title: Eavesdropping attacks . . .

abstract: Electromagnetic information leakage from computer displays was first demonstrated to the general public by van Eck in 1985. Nearby eavesdroppers can pick up compromising emanations from computer hardware with directional antennas and wideband receivers. The basic phenomenon is easily demonstrated with modified TV sets. However, to separate practically readable text shown on modern high-resolution displays from interfering background noise, special digital wideband signal-processing systems are needed. Thanks to Moore’s law, access to such hardware is no longer restricted to well-funded spies, but has come within the reach of amateurs. The problem is not restricted to cathode-ray tubes; some contemporary flat-panel systems are at least as vulnerable.


title: Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task

abstract: End-to-end control for robot manipulation and grasping is emerging as an attractive alternative to traditional pipelined approaches. However, end-to-end methods tend to either be slow to train, exhibit little or no generalisability, or lack the ability to accomplish long-horizon or multi-stage tasks. In this paper, we show how two simple techniques can lead to end-to-end (image to velocity) execution of a multi-stage task, which is analogous to a simple tidying routine, without having seen a single real image. This involves locating, reaching for, and grasping a cube, then locating a basket and dropping the cube inside. To achieve this, robot trajectories are computed in a simulator, to collect a series of control velocities which accomplish the task. Then, a CNN is trained to map observed images to velocities, using domain randomisation to enable generalisation to real world images. Results show that we are able to successfully accomplish the task in the real world with the ability to generalise to novel environments, including those with dynamic lighting conditions, distractor objects, and moving objects, including the basket itself. We believe our approach to be simple, highly scalable, and capable of learning long-horizon tasks that have until now not been shown with the state-of-the-art in end-to-end robot control.


title: Extrinsic calibration between a multi-layer lidar and a camera

abstract: In this paper, we present a novel approach for solving the extrinsic calibration between a camera and a multi-layer laser range finder. Our approach is oriented for intelligent vehicle applications, where the separation distance between sensors frames are frequently very important. For this purpose, we use a circle-based calibration object because its geometry allows us to obtain not only an accurate estimation pose by taking advantage of the 3D multi-layer laser range finder perception but also a simultaneous estimation of the pose in the camera frame and the camera intrinsic parameters. These advantages simplify the calibration task in outdoor environments. The method determines the relative position of the sensors by estimating sets of corresponded features and by solving the classical absolute orientation problem. The proposed method is evaluated by using different synthetics environments and real data. An error propagation analysis is made in order to estimate the calibration accuracy and the confidence intervals. Finally, we present a laser data projection into images to validate the consistency of the results.


title: Amorphous silicon back-plane electronics for OLED displays

abstract: This paper reviews design considerations along with measurement results pertinent to amorphous silicon (a-Si:H) thin-film transistor (TFT) drive circuits for active matrix organic light-emitting diode displays, and follows from work presented earlier (A. Nathan et al., 2002), (A. Nathan et al., 2003). We describe both pixel architectures and TFT circuit topologies that are amenable for vertically integrated, high aperture ratio pixels. Here, the organic light-emitting diode layer is integrated directly above the TFT circuit layer to provide an active pixel area that is at least 90% of the total pixel area with an aperture ratio that remains virtually independent of scaling. Both voltage-programmed and current-programmed drive circuits are considered. The latter provides compensation for shifts in device characteristics due to metastable shifts in the threshold voltage of the TFT. Integration of on-panel gate drivers is also discussed, where we present the architecture of an a-Si:H-based gate demultiplexer that is threshold voltage shift invariant. In addition, a programmable current mirror with good linearity and stability is presented. Programmable current sources are an essential requirement in the design of source driver output stages.


title: Microsensors and microscale gradients in biofilms.

abstract: Understanding the limiting factors and mechanisms of biofilm processes requires the direct measurement of microscale gradients using the appropriate tools. Microscale measurements can provide mechanistic information that cannot be obtained from bulk-scale measurements. Among the most used and trusted tools in microscale biofilm research are microsensors. The goal of this chapter is to introduce microsensor technology along with several examples to illustrate microscale processes in biofilms that are usually absent in bulk. We define a microsensor for biofilm research as a needle-type sensor with tip diameter of a few microns and a length up to several hundred microns. Microsensors can be used noninvasively to monitor in situ biofilm processes. Both optical and electrochemical microsensors can be used for biofilm applications. Because of newly discovered biofilm processes, the design and use of microsensors require customization and carefully designed experiments. In this chapter we present several examples describing the use of microsensors (1) in environmental biofilms, (2) in medical biofilms, and (3) in biofilms for energy and bioproducts. Microsensors can be the most useful if the measured profiles are integrated into the study of overall biofilm processes.


title: Triclustering in gene expression data analysis: A selected survey

abstract: Mining microarray data sets is important in bioinformatics research and biomedical applications. Recently, mining triclusters or 3D clusters in a Gene Sample Time or 3D microarray data is an emerging area of research. Each tricluster contains a subset of genes and a subset of samples such that the genes are coherent on the samples along the time series. There is a scarcity of triclustering algorithms in the literature of microarray data analysis. We review some existing triclustering algorithms and discuss their merits and demerits. Finally we are trying to provide the researcher who are new to this field a base platform by exposing the issues which are still challenging in triclustering through our analysis of these algorithms.


title: Mind reading with regularized multinomial logistic regression

abstract: In this paper, we consider the problem of multinomial classification of magnetoencephalography (MEG) data. The proposed method participated in the MEG mind reading competition of ICANN’11 conference, where the goal was to train a classifier for predicting the movie the test person was shown. Our approach was the best among ten submissions, reaching accuracy of 68 % of correct classifications in this five category problem. The method is based on a regularized logistic regression model, whose efficient feature selection is critical for cases with more measurements than samples. Moreover, a special attention is paid to the estimation of the generalization error in order to avoid overfitting to the training data. Here, in addition to describing our competition entry in detail, we report selected additional experiments, which question the usefulness of complex feature extraction procedures and the basic frequency decomposition of MEG signal for this application.


title: Optimization Method for Weighting Explicit and Latent Concepts in Clinical Decision Support Queries

abstract: Accurately answering verbose queries that describe a clinical case and aim at finding articles in a collection of medical literature requires capturing many explicit and latent aspects of complex information needs underlying such queries. Proper representation of these aspects often requires query analysis to identify the most important query concepts as well as query transformation by adding new concepts to a query, which can be extracted from the top retrieved documents or medical knowledge bases. Traditionally, query analysis and expansion have been done separately. In this paper, we propose a method for representing verbose domain-specific queries based on weighted unigram, bigram, and multi-term concepts in the query itself, as well as extracted from the top retrieved documents and external knowledge bases. We also propose a graduated non-convexity optimization framework, which allows to unify query analysis and expansion by jointly determining the importance weights for the query and expansion concepts depending on their type and source. Experiments using a collection of PubMed articles and TREC Clinical Decision Support (CDS) track queries indicate that applying our proposed method results in significant improvement of retrieval accuracy over state-of-the-art methods for ad hoc and medical IR.


title: Nicotine and nonnicotine factors in cigarette addiction

abstract: RATIONALE ::: A great deal of research supports the role of nicotine in cigarette addiction. However, the effectiveness of nicotine replacement therapy (NRT) as a smoking cessation treatment has fallen short of initial hopes. A key reason may be that NRT does not address nonnicotine components of smoking reinforcement. These include constituents that provide reinforcing sensory stimulation, components that minimize excessive irritation from inhaled nicotine and other pharmacologically active compounds in cigarette smoke. ::: ::: ::: OBJECTIVE ::: Studies using various paradigms to dissociate nicotine from other components of smoking are summarized. ::: ::: ::: RESULTS ::: Nonnicotine components provide many rewarding effects, often surpassing the direct effects of nicotine. Substitutes for the sensory effects of smoking may be effective in relieving craving for cigarettes and in facilitating smoking cessation. Moreover, techniques for devaluing smoking-related cues may decrease craving and enhance subsequent abstinence. Promising approaches for devaluing smoke cues include extinction-based treatments employing denicotinized cigarettes and the use of nicotinic agonist and/or antagonist treatment during the weeks leading up to a quit attempt. Recent studies suggest that incorporating these approaches into a treatment program may significantly increase smoking abstinence rates. Preliminary findings also suggest that replacement of the effects of monoamine oxidase inhibitors contained in cigarette smoke may enhance quit rates. ::: ::: ::: CONCLUSIONS ::: While current NRT methods have been the mainstay of smoking cessation treatment and will likely continue to serve a useful role, the next stage of progress will likely entail the development of tools designed with recognition of the importance of nonnicotine components of cigarette smoking.


Energy Efficiency Mechanisms in Wireless Sensor Networks: A Survey


title: Construing Experience Through Meaning: A Language-Based Approach to Cognition

abstract: Part I: Introduction 1. Theoretical preliminaries Part II: The ideation base 2. Overview of the general ideational potential 3. Sequences 4. Figures 5. Elements 6. Grammatical metaphors 7. Comparison with Chinese Part III: The meaning base as a resource in language processing systems 8. Building an ideation base 9. Using the ideation base in text processing Part IV: Theoretical and descriptive alternatives 10. Alternative approaches to meaning. 11. Distortion and transformation 12. Figures and processes Part V: Language and the construal of experience 13. Language as a multifunctional system and process 14. Construing ideational models: consciousness in daily life and in cognitive science 15. Language and the making of meaning.


title: Development of remotely secure e-voting system

abstract: Due to the importance of the election process, it is developed to be performed electronically in a secure manner by using electronic voting (e-voting), this leads to find a way to do to grantee this requirements. Electronic voting should be developed to achieve requirements of election process with a relative high degree of security and accuracy. In this paper a secure remote e-voting system is developed trying to achieve all security requirements. The security strategy is based on the homomorphic encryption technique which can be considered the most suitable method for such application.


title: Iterated Support Vector Machines for Distance Metric Learning

abstract: Distance metric learning aims to learn from the given training data a valid distance metric, with which the similarity between data samples can be more effectively evaluated for classification. Metric learning is often formulated as a convex or nonconvex optimization problem, while many existing metric learning algorithms become inefficient for large scale problems. In this paper, we formulate metric learning as a kernel classification problem, and solve it by iterated training of support vector machines (SVM). The new formulation is easy to implement, efficient in training, and tractable for large-scale problems. Two novel metric learning models, namely Positive-semidefinite Constrained Metric Learning (PCML) and Nonnegative-coefficient Constrained Metric Learning (NCML), are developed. Both PCML and NCML can guarantee the global optimality of their solutions. Experimental results on UCI dataset classification, handwritten digit recognition, face verification and person re-identification demonstrate that the proposed metric learning methods achieve higher classification accuracy than state-of-the-art methods and they are significantly more efficient in training.


On Decentralizing Prediction Markets and Order Books


COMPOUND FOOT FOR INCREASED MILLIROBOT JUMPING ABILITY


Genetic Programming for Feature Discovery and Image Discrimination


title: Compliance-based dynamic steering for hexapods

abstract: This paper proposes a novel dynamic gait of locomotion for hexapedal robots which enables them to crawl forward, backward, and rotate using a single actuator. The gait exploits the compliance difference between the two sides of the tripods, to generate clockwise or counter clockwise rotation by controlling the acceleration of the robot. The direction of turning depends on the configuration of the legs -tripod left of right- and the direction of the acceleration. Alternating acceleration in successive steps allows for continuous rotation in the desired direction. An analysis of the locomotion is presented as a function of the mechanical properties of the robot and the contact with the surface. A numerical simulation was performed for various conditions of locomotion. The results of the simulation and analysis were compared and found to be in excellent match.


title: Adaptive user interface for mobile devices

abstract: Adapting a graphical user interface (GUI) to a variety of resources with different capabilities is one of the most interesting questions of today's mobile computation. The GUI constructed for one application should be usable on different interactive devices, e.g. WebTV terminals, WAP phones or Java-enabled devices. In this paper, we discuss existing solutions and present a solution based on mobile agents. Mobile agents construct their GUI using third-party eXtensible User interface Language (XUL), jXUL middleware and XSL transformations. Mobile agents move to host computers and then build their GUI, or act as a proxy to devices without sufficient processing capabilities (e.g., WAP devices). The result is an adaptable GUI platform that can be run on multiple devices without modifications, supporting different resources and architectures. We show the application of this approach by implementing a mobile currency converter and survey.


title: End-to-end optimization of goal-driven and visually grounded dialogue systems

abstract: End-to-end design of dialogue systems has recently become a popular research topic thanks to powerful tools such as encoder-decoder architectures for sequence-to-sequence learning. Yet, most current approaches cast human-machine dialogue management as a supervised learning problem, aiming at predicting the next utterance of a participant given the full history of the dialogue. This vision is too simplistic to render the intrinsic planning problem inherent to dialogue as well as its grounded nature , making the context of a dialogue larger than the sole history. This is why only chitchat and question answering tasks have been addressed so far using end-to-end architectures. In this paper, we introduce a Deep Reinforcement Learning method to optimize visually grounded task-oriented dialogues , based on the policy gradient algorithm. This approach is tested on a dataset of 120k dialogues collected through Mechanical Turk and provides encouraging results at solving both the problem of generating natural dialogues and the task of discovering a specific object in a complex picture.


title: Design Patterns and Extensibility of REST API for Networking Applications

abstract: REST architectural style has become a prevalent choice for distributed resources, such as the northbound API of software-defined networking (SDN). As services often undergo frequent changes and updates, the corresponding REST APIs need to change and update accordingly. To allow REST APIs to change and evolve without breaking its clients, a REST API can be designed to facilitate hypertext-driven navigation and its related mechanisms to deal with structure changes in the API. This paper addresses the issues in hypertext-driven navigation in REST APIs from three aspects. First, we present REST Chart, a Petri-Net-based REST service description framework and language to design extensible REST APIs, and it is applied to cope with the rapid evolution of SDN northbound APIs. Second, we describe some important design patterns, such as backtracking and generator, within the REST Chart framework to navigate through large scale APIs in the RESTful architecture. Third, we present a client side differential cache mechanism to reduce the overhead of hypertext-driven navigation, addressing a major issue that affects the application of REST API. The proposed approach is applied to applications in SDN, which is integrated with a generalized SDN controller, SOX. The benefits of the proposed approach are verified in different conditions. Experimental results on SDN applications show that on average, the proposed cache mechanism reduces the overhead of using the hypertext-driven REST API by 66%, while fully maintaining the desired flexibility and extensibility of the REST API.


title: Japanese Sentiment Classification with Stacked Denoising Auto-Encoder using Distributed Word Representation

abstract: Traditional sentiment classification methods often require polarity dictionaries or crafted features to utilize machine learning. However, those approaches incur high costs in the making of dictionaries and/or features, which hinder generalization of tasks. Examples of these approaches include an approach that uses a polarity dictionary that cannot handle unknown or newly invented words and another approach that uses a complex model with 13 types of feature templates. We propose a novel high performance sentiment classification method with stacked denoising auto-encoders that uses distributed word representation instead of building dictionaries or utilizing engineering features. The results of experiments conducted indicate that our model achieves state-of-the-art performance in Japanese sentiment classification tasks.


title: Listening while speaking: Speech chain by deep learning

abstract: Despite the close relationship between speech perception and production, research in automatic speech recognition (ASR) and text-to-speech synthesis (TTS) has progressed more or less independently without exerting much mutual influence on each other. In human communication, on the other hand, a closed-loop speech chain mechanism with auditory feedback from the speaker's mouth to her ear is crucial. In this paper, we take a step further and develop a closed-loop speech chain model based on deep learning. The sequence-to-sequence model in close-loop architecture allows us to train our model on the concatenation of both labeled and unlabeled data. While ASR transcribes the unlabeled speech features, TTS attempts to reconstruct the original speech waveform based on the text from ASR. In the opposite direction, ASR also attempts to reconstruct the original text transcription given the synthesized speech. To the best of our knowledge, this is the first deep learning model that integrates human speech perception and production behaviors. Our experimental results show that the proposed approach significantly improved the performance more than separate systems that were only trained with labeled data.


title: GUITest: a Java library for fully automated GUI robustness testing

abstract: Graphical User Interfaces (GUIs) are substantial parts of today's applications, no matter whether these run on tablets, smartphones or desktop platforms. Since the GUI is often the only component that humans interact with, it demands for thorough testing to ensure an efficient and satisfactory user experience. Being the glue between almost all of an application's components, GUIs also lend themselves for system level testing. However, GUI testing is inherently difficult and often involves great manual labor, even with modern tools which promise automation. This paper introduces a Java library called GUITest, which allows to generate fully automated GUI robustness tests for complex applications, without the need to manually generate models or input sequences. We will explain how it operates and present first results on its applicability and effectivity during a test involving Microsoft Word.


title: Encoding informal architectural descriptions with UML: An experience report

abstract: An architectural description records and presents different system characteristics through various architectural views. UML, which provides a set of general-purpose modeling notations, has been already used to model software architecture in industry. In practice, most of the existing architectural descriptions are informal documents. By only using these informal architecture documents it is difficult to analyze the architecture design and convey rigorous architectural concepts and principles to the development team. In this paper, we demonstrate our approach to convert those informal documents to formal UML-conformed documents to facilitate architecture analysis and verification.


title: Privacy-friendly spatial crowdsourcing in vehicular networks

abstract: With the evolution of conventional VANETs (Vehicle Ad-hoc Networks) into the IoV (Internet of Vehicles), vehicle-based spatial crowdsourcing has become a potential solution for crowdsourcing applications. In vehicular networks, a spatial-temporal task/question can be outsourced (i.e., task/question relating to a particular location and in a specific time period) to some suitable smart vehicles (also known as workers) and then these workers can help solve the task/question. However, an inevitable barrier to the widespread deployment of spatial crowdsourcing applications in vehicular networks is the concern of privacy. Hence, We propose a novel privacy-friendly spatial crowdsourcing scheme. Unlike the existing schemes, the proposed scheme considers the privacy issue from a new perspective according that the spatial-temporal tasks can be linked and analyzed to break the location privacy of workers. Specifically, to address the challenge, three privacy requirements (i.e. anonymity, untraceability, and unlinkability) are defined and the proposed scheme combines an efficient anonymous technique with a new composite privacy metric to protect against attackers. Detailed privacy analyses show that the proposed scheme is privacy-friendly. In addition, performance evaluations via extensive simulations are also conducted, and the results demonstrate the efficiency and effectiveness of the proposed scheme.


Intelligent Robotics: Past, Present and Future


title: Improving the Transformer Translation Model with Document-Level Context

abstract: Although the Transformer translation model (Vaswani et al., 2017) has achieved state-of-the-art performance in a variety of translation tasks, how to use document-level context to deal with discourse phenomena problematic for Transformer still remains a challenge. In this work, we extend the Transformer model with a new context encoder to represent document-level context, which is then incorporated into the original encoder and decoder. As large-scale document-level parallel corpora are usually not available, we introduce a two-step training method to take full advantage of abundant sentence-level parallel corpora and limited document-level parallel corpora. Experiments on the NIST Chinese-English datasets and the IWSLT French-English datasets show that our approach improves over Transformer significantly.


Measuring Graph Proximity with Blink Model


title: Anomaly detection with low-level processes in videos

abstract: A phonograph cartridge interface which enables any magnetic phonograph cartridge to be operated into its proper termination impedance. One embodiment of the device includes a capacitance test section which is used to determine the unknown capacitance of a tonearm cable. A termination network is then adjusted to provide a proper amount of capacitance in view of the determined capacitance and the value of total termination capacitance specified by the cartridge manufacturer. The network also is adjustable to provide the proper termination resistance in parallel with the capacitance. In a second embodiment capacitance matching circuitry is included which enables the total termination capacitance provided by the tonearm cable and termination network to be adjusted to a desired value. In this embodiment, actual measurement of the tonearm cable capacitance is not required. Either embodiment of the invention can be (a) configured as a stand-alone device for use with any phono preamplifier or (b) incorporates as an integral part of a phono preamplifier. An isolation amplifier is included in the stand-along version to eliminate effects of the input capacitance of the phono preamplifier to which the interface is connected.


title: An empirical study on dependence clusters for effort-aware fault-proneness prediction

abstract: A dependence cluster is a set of mutually inter-dependent program elements. Prior studies have found that large dependence clusters are prevalent in software systems. It has been suggested that dependence clusters have potentially harmful effects on software quality. However, little empirical evidence has been provided to support this claim. The study presented in this paper investigates the relationship between dependence clusters and software quality at the function-level with a focus on effort-aware fault-proneness prediction. The investigation first analyzes whether or not larger dependence clusters tend to be more fault-prone. Second, it investigates whether the proportion of faulty functions inside dependence clusters is significantly different from the proportion of faulty functions outside dependence clusters. Third, it examines whether or not functions inside dependence clusters playing a more important role than others are more fault-prone. Finally, based on two groups of functions (i.e., functions inside and outside dependence clusters), the investigation considers a segmented fault-proneness prediction model. Our experimental results, based on five well-known open-source systems, show that (1) larger dependence clusters tend to be more fault-prone; (2) the proportion of faulty functions inside dependence clusters is significantly larger than the proportion of faulty functions outside dependence clusters; (3) functions inside dependence clusters that play more important roles are more fault-prone; (4) our segmented prediction model can significantly improve the effectiveness of effort-aware fault-proneness prediction in both ranking and classification scenarios. These findings help us better understand how dependence clusters influence software quality.


title: Generalized Instrumental Variables

abstract: This paper concerns the assessment of direct causal effects from a combination of: (i) nonexperimental data, and (ii) qualitative domain knowledge. Domain knowledge is encoded in the form of a directed acyclic graph (DAG), in which all interactions are assumed linear, and some variables are presumed to be unobserved. We provide a generalization of the well-known method of Instrumental Variables, which allows its application to models with few conditional independeces.


title: Moving Vehicle Detection for Automatic Traffic Monitoring

abstract: A video-based traffic monitoring system must be capable of working in various weather and illumination conditions. In this paper, we will propose an example-based algorithm for moving vehicle detection. Different from previous works, this algorithm learns from examples and does not rely on any a priori model for vehicles. First, a novel scheme for adaptive background estimation is introduced. Then, the image is divided into many small nonoverlapped blocks. The candidates of the vehicle part can be found from the blocks if there is some change in gray level between the current image and the background. A low-dimensional feature is produced by applying principal component analysis to two histograms of each candidate, and a classifier based on a support vector machine is designed to classify it as a part of a real vehicle or not. Finally, all classified results are combined, and a parallelogram is built to represent the shape of each vehicle. Experimental results show that our algorithm has a satisfying performance under varied conditions, which can robustly and effectively eliminate the influence of casting shadows, headlights, or bad illumination


title: An investigation of multi-speaker training for wavenet vocoder

abstract: In this paper, we investigate the effectiveness of multi-speaker training for WaveNet vocoder. In our previous work, we have demonstrated that our proposed speaker-dependent (SD) WaveNet vocoder, which is trained with a single speaker's speech data, is capable of modeling temporal waveform structure, such as phase information, and makes it possible to generate more naturally sounding synthetic voices compared to conventional high-quality vocoder, STRAIGHT. However, it is still difficult to generate synthetic voices of various speakers using the SD-WaveNet due to its speaker-dependent property. Towards the development of speaker-independent WaveNet vocoder, we apply multi-speaker training techniques to the WaveNet vocoder and investigate its effectiveness. The experimental results demonstrate that 1) the multispeaker WaveNet vocoder still outperforms STRAIGHT in generating known speakers' voices but it is comparable to STRAIGHT in generating unknown speakers' voices, and 2) the multi-speaker training is effective for developing the WaveNet vocoder capable of speech modification.


title: Two decades of internet video streaming: A retrospective view

abstract: For over two decades, video streaming over the Internet has received a substantial amount of attention from both academia and industry. Starting from the design of transport protocols for streaming video, research interests have later shifted to the peer-to-peer paradigm of designing streaming protocols at the application layer. More recent research has focused on building more practical and scalable systems, using Dynamic Adaptive Streaming over HTTP. In this article, we provide a retrospective view of the research results over the past two decades, with a focus on peer-to-peer streaming protocols and the effects of cloud computing and social media.


title: Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm

abstract: The problem of maximizing a concave function f(x) in a simplex S can be solved approximately by a simple greedy algorithm. For given k, the algorithm can find a point x(k) on a k-dimensional face of S, such that f(x(k)) ≥ f(x*) - O(1/k). Here f(x*) is the maximum value of f in S. This algorithm and analysis were known before, and related to problems of statistics and machine learning, such as boosting, regression, and density mixture estimation. In other work, coming from computational geometry, the existence of e-coresets was shown for the minimum enclosing ball problem, by means of a simple greedy algorithm. Similar greedy algorithms, that are special cases of the Frank-Wolfe algorithm, were described for other enclosure problems. Here these results are tied together, stronger convergence results are reviewed, and several coreset bounds are generalized or strengthened.


title: Neural network construction using grammatical evolution

abstract: A method which is based on grammatical evolution is presented in this paper for the construction of artificial neural networks (ANNs). The method is capable to construct ANNs with an arbitrary number of hidden levels or even recurrent neural networks. The efficiency of the method is tested on a series of classification and regression problems and the results are compared against traditional neural networks


title: Addressing Big Data challenges for Scientific Data Infrastructure

abstract: This paper discusses the challenges that are imposed by Big Data Science on the modern and future Scientific Data Infrastructure (SDI). The paper refers to different scientific communities to define requirements on data management, access control and security. The paper introduces the Scientific Data Lifecycle Management (SDLM) model that includes all the major stages and reflects specifics in data management in modern e-Science. The paper proposes the SDI generic architecture model that provides a basis for building interoperable data or project centric SDI using modern technologies and best practices. The paper explains how the proposed models SDLM and SDI can be naturally implemented using modern cloud based infrastructure services provisioning model.


title: The framework of web 3.0-based enterprise knowledge management system

abstract: Internet has been running into the era of Web 3.0 equipped with the development of semantic technology. Therefore, how to manage the enterprise knowledge that suits Web 3.0 becomes an urgent problem. In this paper, we first summarize the evolution of Internet and describe the feature of Web 3.0. After analyzing the practice of linked data applied in Web 3.0, we propose a new framework of enterprise knowledge management system combining the theory of linked data and the close loop of knowledge management. Moreover, we analyze each layer’s function in the framework and discuss the implementation of the knowledge publish layer in detail. As a remarkable result, the proposed enterprise knowledge management system can extend the knowledge sharing to a new level, and reduce the cost of enterprise collaboration caused by heterogeneous data as well.


title: The Battle for Business Ethics: A Struggle Theory

abstract: textabstractTo be and to remain ethical requires struggle from organizations. Struggling is necessary due to the pressures and temptations management and employees encounter in and around organizations. As the relevance of struggle for business ethics has not yet been analyzed systematically in the scientific literature, this paper develops a theory of struggle that elaborates on the meaning and dimensions of struggle in organizations, why and when it is needed, and what its antecedents and consequences are. An important conclusion is that the greater the ethics gap and opposing forces, the greater the struggle required. Viewing business ethics as struggle has several implications for theory and practice.


title: Titan: a high-performance remote-sensing database

abstract: There are two major challenges for a high performance remote sensing database. First, it must provide low latency retrieval of very large volumes of spatio temporal data. This requires effective declustering and placement of a multidimensional dataset onto a large disk farm. Second, the order of magnitude reduction in data size due to post processing makes it imperative, from a performance perspective, that the post processing be done on the machine that holds the data. This requires careful coordination of computation and data retrieval. The paper describes the design, implementation and evaluation of Titan, a parallel shared nothing database designed for handling remote sensing data. The computational platform for Titan is a 16 processor IBM SP-2 with four fast disks attached to each processor. Titan is currently operational and contains about 24 GB of AVHRR data from the NOAA-7 satellite. The experimental results show that Titan provides good performance for global queries and interactive response times for local queries.


title: Optimal Multi-taxi Dispatch for Mobile Taxi-Hailing Systems

abstract: Traditional taxi-hailing systems through wireless networks in metropolitan areas allow taxis to compete for passengers chaotically and accidentally, which generally result in inefficiencies, long waiting time and low satisfaction of taxi-hailing passengers. In this paper, we propose a new Mobile Taxi-hailing System (called MTS) based on optimal multi-taxi dispatch, which can be used by taxi service companies (TSCs). Different from the competition modes used in traditional taxi-hailing systems, MTS assigns vacant taxis to taxi-hailing passengers proactively. For the taxi dispatch problem in MTS, we define a system utility function, which involves the total net profits of taxis and waiting time of passengers. Moreover, in the utility function, we take into consideration the various classes of taxis with different resource configurations, and the cost associated with taxis' empty travel distances. Our goal is to maximize the system utility function, restricted by the individual net profits of taxis and the passengers' requirements for specified classes of taxis. To solve this problem, we design an optimal algorithm based on the idea of Kuhn-Munkres (called KMBA), and prove the correctness and optimality of the proposed algorithm. Additionally, we demonstrate the significant performances of our algorithm through extensive simulations.


title: Program analysis for event-based distributed systems

abstract: Designing distributed applications around the idiom of events has several benefits including extensibility and scalability. To improve conciseness, safety, and efficiency of corresponding programs, several authors have recently proposed programming languages or language extensions with support for event-based programming. The presence of a dedicated programming language and compilation process offers avenues for program analyses to further improve simplicity, safety, and expressiveness of distributed event-based software. This paper presents three program analyses specifically designed for event-based programs: immutability analysis avoids costly cloning of events in the presence of co-located handlers for same events; guard analysis allows for simple yet expressive subscriptions which can be further simplified and handled efficiently; causality analysis determines causal dependencies among events which are related, allowing unrelated events to be transferred independently for efficiency. We convey the benefits of our approach by empirically evaluating their performance benefits.


title: Television violence and children's aggression: testing the priming, social script, and disinhibition predictions.

abstract: The effect of television violence on boys' aggression was investigated with consideration of teacher-rated characteristic aggressiveness, timing of frustration, and violence-related cues as moderators. Boys in Grades 2 and 3 (N = 396) watched violent or nonviolent TV in groups of 6, and half the groups were later exposed to a cue associated with the violent TV program. They were frustrated either before or after TV viewing. Aggression was measured by naturalistic observation during a game of floor hockey. Groups containing more characteristically high-aggressive boys showed higher aggression following violent TV plus the cue than following violent TV alone, which in turn produced more aggression than did the nonviolent TV condition. There was evidence that both the violent content and the cue may have suppressed aggression among groups composed primarily of boys low in characteristic aggressiveness. Results were interpreted in terms of current information-processing theories of media effects on aggression. Language: en


title: Epinephrine enhancement of human memory consolidation: Interaction with arousal at encoding

abstract: Abundant evidence indicates that endogenous stress hormones like epinephrine and cortisol modulate memory consolidation in animals. Despite this evidence, there has been no demonstration that endogenous stress hormones modulate memory consolidation in humans. In the present study, healthy subjects viewed a series of 21 slides, and immediately after received an intravenous infusion of either saline or epinephrine (40 or 80 ng/kg/min). Memory for the first three (primacy) and last three (recency) slides viewed was assessed with an incidental free recall test one week later. Epinephrine dose-dependently increased memory for the primacy slides, but did not affect memory of the recency slides. A subsequent experiment involving new subjects revealed significantly higher electrodermal responses to the primacy compared with recency slides. These findings support the view (Gold & McGaugh, 1975) that endogenous stress hormones modulate memory consolidation for experiences that induce their release. Additionally, they suggest that in humans these hormones may interact with the degree of arousal at initial encoding of information to modulate memory consolidation processes for that information.


title: Medicinal mushrooms in prevention and control of diabetes mellitus

abstract: Diabetes mellitus is a life-threatening chronic metabolic disease caused by lack of insulin and/or insulin dysfunction, characterized by high levels of glucose in the blood (hyperglycemia). Millions worldwide suffer from diabetes and its complications. Significantly, it has been recognized that type 2 diabetes is an important preventable disease and can be avoided or delayed by lifestyle intervention. Presently, there are many chemical and biochemical hypoglycemic agents (synthetic drugs), that are used in treating diabetes and are effective in controlling hyperglycemia. However, as they may have harmful side-effects and fail to significantly alter the course of diabetic complications, natural anti-diabetic drugs from medicinal plants have attracted a great deal of attention. Medicinal mushrooms have been valued as a traditional source of natural bioactive compounds over many centuries and have been targeted as potential hypoglycemic and anti-diabetic agents. Bioactive metabolites including polysaccharides, proteins, dietary fibres, and many other biomolecules isolated from medicinal mushrooms and their cultured mycelia have been shown to be successful in diabetes treatment as biological anti-hyperglycemic agents. In this review we discuss the biological nature of diabetes and, in particular, explore some promising mushrooms that have experimental anti-diabetic properties, preventing or reducing the development of diabetes mellitus. The importance of medicinal mushrooms as agents of medical nutrition therapy and how their metabolites can be used as supportive candidates for prevention and control of diabetes is explored. Future prospects for this field of study and the difficulties and constraints that might affect the development of rational drug products from medicinal mushrooms are discussed.


title: Discovery-driven graph summarization

abstract: Large graph datasets are ubiquitous in many domains, including social networking and biology. Graph summarization techniques are crucial in such domains as they can assist in uncovering useful insights about the patterns hidden in the underlying data. One important type of graph summarization is to produce small and informative summaries based on user-selected node attributes and relationships, and allowing users to interactively drill-down or roll-up to navigate through summaries with different resolutions. However, two key components are missing from the previous work in this area that limit the use of this method in practice. First, the previous work only deals with categorical node attributes. Consequently, users have to manually bucketize numerical attributes based on domain knowledge, which is not always possible. Moreover, users often have to manually iterate through many resolutions of summaries to identify the most interesting ones. This paper addresses both these key issues to make the interactive graph summarization approach more useful in practice. We first present a method to automatically categorize numerical attributes values by exploiting the domain knowledge hidden inside the node attributes values and graph link structures. Furthermore, we propose an interestingness measure for graph summaries to point users to the potentially most insightful summaries. Using two real datasets, we demonstrate the effectiveness and efficiency of our techniques.


title: Decreasing Impact of SLA Violations:A Proactive Resource Allocation Approachfor Cloud Computing Environments

abstract: User satisfaction as a significant antecedent to user loyalty has been highlighted by many researchers in market based literatures. SLA violation as an important factor can decrease users’ satisfaction level. The amount of this decrease depends on user's characteristics. Some of these characteristics are related to QoS requirements and announced to service provider through SLAs. But some of them are unknown for service provider and selfish users are not interested to reveal them truly. Most the works in literature ignore considering such characteristics and treat users just based on SLA parameters. So, two users with different characteristics but similar SLAs have equal importance for the service provider. In this paper, we use two user's hidden characteristics, named willingness to pay for service and willingness to pay for certainty, to present a new proactive resource allocation approach with aim of decreasing impact of SLA violations. New methods based on learning automaton for estimation of these characteristics are provided as well. To validate our approach we conducted some numerical simulations in critical situations. The results confirm that our approach has ability to improve users’ satisfaction level that cause to gain in profitability.


title: Evaluating topic-driven web crawlers

abstract: Due to limited bandwidth, storage, and computational resources, and to the dynamic nature of the Web, search engines cannot index every Web page, and even the covered portion of the Web cannot be monitored continuously for changes. Therefore it is essential to develop effective crawling strategies to prioritize the pages to be indexed. The issue is even more important for topic-specific search engines, where crawlers must make additional decisions based on the relevance of visited pages. However, it is difficult to evaluate alternative crawling strategies because relevant sets are unknown and the search space is changing. We propose three different methods to evaluate crawling strategies. We apply the proposed metrics to compare three topic-driven crawling algorithms based on similarity ranking, link analysis, and adaptive agents.


title: Writing Strengthens Orthography and Alphabetic-Coding Strengthens Phonology in Learning to Read Chinese

abstract: Learning to write words may strengthen orthographic representations and thus support word-specific recognition processes. This hypothesis applies especially to Chinese because its writing system encourages character-specific recognition that depends on accurate representation of orthographic form. We report 2 studies that test this hypothesis in adult learners of Chinese. In those studies, the researchers 1st compared the effects of an online writing tutor that included character handwriting with an instructional tutor that included reading only. The writing condition led to better performance on word recognition and on character-meaning links but not on the character-phonology link. In the 2nd experiment, we added an alphabetic (Pinyin) typing tutor to strengthen the phonology link and to control for manual motor activity during instruction. This experiment replicated the effects of writing on word recognition and character-meaning links, whereas alphabetic (Pinyin) typing supported only phonological representations and the character-phonology link. Theoretically, the studies suggest constituent-specific effects: writing on orthography and alphabetic coding on phonology. We suggest the mechanism for the writing effect is the refinement of visual-spatial information needed for character recognition and the addition of a sensory-motor memory that accompanies writing. The practical implication is that an integration of character handwriting and Pinyin typing promotes learning to read Chinese in a second language learning context.