title: Motion control of a spherical mobile robot by feedback linearization

abstract: BYQ-III is a spherical mobile robot. This paper deals with the problem of its simple motion control in a fixed motion mode. The robot BYQ-III is designed to be partially self-contained. A simplified dynamic model of the rotation motion in the median sagital plane is derived under some assumptions. A full-state linear feedback controller for the dynamic model and a PI controller are designed to implement the motion control in the median coronal plane and the sagital plane respectively. The model and the controllers were validated through a set of experiments. Experimental trajectories for a circle of the robot on the plane were found to agree to a reasonable accuracy. The overall design, actuator mechanism, control system, and initial results are presented in this paper.


title: Activation of VTA GABA Neurons Disrupts Reward Consumption

abstract: Summary The activity of ventral tegmental area (VTA) dopamine (DA) neurons promotes behavioral responses to rewards and environmental stimuli that predict them. VTA GABA inputs synapse directly onto DA neurons and may regulate DA neuronal activity to alter reward-related behaviors; however, the functional consequences of selective activation of VTA GABA neurons remains unknown. Here, we show that in vivo optogenetic activation of VTA GABA neurons disrupts reward consummatory behavior but not conditioned anticipatory behavior in response to reward-predictive cues. In addition, direct activation of VTA GABA projections to the nucleus accumbens (NAc) resulted in detectable GABA release but did not alter reward consumption. Furthermore, optogenetic stimulation of VTA GABA neurons directly suppressed the activity and excitability of neighboring DA neurons as well as the release of DA in the NAc, suggesting that the dynamic interplay between VTA DA and GABA neurons can control the initiation and termination of reward-related behaviors.


title: Multi-View Gait Recognition Based on a Spatial-Temporal Deep Neural Network

abstract: This paper proposes a novel spatial–temporal deep neural network (STDNN) that is applied to multi-view gait recognition. The STDNN comprises a temporal feature network (TFN) and a spatial feature network (SFN). In TFN, a feature sub-network is adopted to extract the low-level edge features of gait silhouettes. These features are input to the spatial–temporal gradient (STG) network that adopts a STG unit and a long short-term memory unit to extract the STG features. In SFN, the spatial features of gait sequences are extracted by multilayer convolutional neural networks from a gait energy image. The SFN is optimized by classification loss and verification loss jointly, which makes inter-class variations larger than intra-class variations. After training, the TFN and the SFN are employed to extract temporal and spatial features, respectively, which are applied to multi-view gait recognition. Finally, the combined predicted probability is adopted to identify individuals by the differences in their gaits. To evaluate the performance of the STDNN, extensive evaluations are carried out based on the CASIA-B, OU-ISIR, and CMU MoBo data sets. The best recognition scores achieved by STDNN are 95.67% under an identical view, 93.64% under a cross-view, and 92.54% under a multi-view. State-of-the-art approaches are compared with the STDNN in various situations. The results show that the STDNN outperforms the other methods and demonstrates the great potential of the STDNN for practical applications in the future.


title: Shakespeare and Kierkegaard: "Dread" in Macbeth

abstract: George Washington University Shakespeare and Kierkegaard: Dread in Macbeth Author(s): King-Kok Cheung Source: Shakespeare Quarterly, Vol. 35, No. 4 (Winter, 1984), pp. 430-439 Published by: Folger Shakespeare Library in association with George Washington University Stable URL: http://www.jstor.org/stable/2870162 . Accessed: 15/08/2013 19:10 Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at . http://www.jstor.org/page/info/about/policies/terms.jsp JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. Folger Shakespeare Library and George Washington University are collaborating with JSTOR to digitize, preserve and extend access to Shakespeare Quarterly. http://www.jstor.org This content downloaded from 128.97.7.131 on Thu, 15 Aug 2013 19:10:45 PM All use subject to JSTOR Terms and Conditions


title: Photometric stereo for general isotropic reflectances by spherical linear interpolation

abstract: We present a practical photometric stereo method that works with general isotropic reflectances. Unlike previous approaches that use special hardware setups or dense measurements, our method only requires dozens of random yet known lighting directions. By spherically interpolating the light source directions to approximate the scene appearance under desired lighting directions, our method separately computes azimuth and elevation angles of the surface normal for each pixel. The effectiveness and accuracy of the proposed method are validated using a measured reflectance database with 100 isotropic materials and various real-world objects.


title: Decentralized name-based security for content distribution using blockchains

abstract: User, content, and device names as a security primitive have been an attractive approach especially in the context of Information-Centric Networking (ICN) architectures. We leverage Hierarchical Identity Based Encryption (HIBE) to build (content) name-based security mechanisms used for securely distributing content. In contrast to similar approaches, in our system each user maintains his own Private Key Generator used for generating the master secret key and the public system parameters required by the HIBE algorithm. This way our system does not suffer from the key escrow problem, which is inherent in many similar solutions. In order to disseminate the system parameters of a content owner in a fully distributed way, we use blockchains, a distributed, community managed, global list of transactions.


title: Peristalic pump application for automated dilution during atomic absorption spectroscopy analysis

abstract: Abstract Analytical variability and labor cost increases when soil‐test procedures require dilution steps. This study was conducted to develop an automated diluting technique that reduces both analytical variability and labor costs. A peristalic pump was connected to an atomic absorption spectrophotometer to provide sample dilution during instrument analysis. One hundred soil samples were extracted with IN ammonium acetate. The extracts were diluted both manually and automatically, and were then analyzed for their Ca, Mg, K, and Na contents by atomic absorption spectrophotometry. Analytical results were unaffected by method of dilution. The automatic dilution system reduced analytical variability by 25% and labor costs by 15 min/100 samples. This study shows that extracts can be diluted with a peristalic pump during atomic absorption spectrometry analysis.


Role based access control on MLS systems without kernel changes


title: AHP-based approaches for supplier evaluation: Problems and perspectives

abstract: Supplier evaluation has assumed a strategic role in determining competitiveness of large manufacturing companies. An increasing number of researches have been devoted to the development of different kind of methodologies to cope with this problem. Nevertheless, while the number of applications is growing, there is little empirical evidence of the practical usefulness of such tools with a dichotomy between theoretical approaches and empirical applications. Considering this evidence, the goal of this paper is to contribute to understand the above dichotomy by implementing, in a corporate environment, a model for supplier evaluation based on the Analytical Hierarchical Process (AHP), one of the most prominent methodologies used to address the problem. The analysis of the implementation process of the methodology allows the identification of strengths and weaknesses of using formalized supplier selection models to tackle the supplier evaluation problem, also highlighting potential barriers preventing firms to adopt such methods. Relevant issues arising from the application and managerial implications for both customer and suppliers are discussed.


title: Computational Intelligence and AI in Games: A New IEEE Transactions

abstract: The author first provides an overview of computational intelligence and AI in games. Then he describes the new IEEE Transactions, which will publish archival quality original papers in all aspects of computational intelligence and AI related to all types of games. To name some examples, these include computer and video games, board games, card games, mathematical games, games that model economies or societies, serious games with educational and training applications, and games involving physical objects such as robot football and robotic car racing. Emphasis will also be placed on the use of these methods to improve performance in, and understanding of, the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It will also include using games as a platform for building intelligent embedded agents for real-world applications. The journal builds on a scientific community that has already been active in recent years with the development of new conference series such as the IEEE Symposium on Computational Intelligence in Games (CIG) and Artificial Intelligence and Interactive Digital Entertainment (AIIDE), as well as special issues on games in journals such as the IEEE Transactions on Evolutionary Computation. When setting up the journal, a decision was made to include both artificial intelligence (AI) and computational intelligence (CI) in the title. AI seeks to simulate intelligent behavior in any way that can be programmed effectively. Some see the field of AI as being all-inclusive, while others argue that there is nothing artificial about real intelligence as exhibited by higher mammals.


Approximation Schemes for Maximum Cardinality Matching


title: Rock-paper-scissors with Myo Armband pose detection

abstract: Many new devices come out with the idea of making more comfortable life. One of these ideas is an Myo Armband which is a wireless device for interacting with other devices such as smartphone or computer to use as a mouse, keyboard, or even game controller by using Bluetooth technology and electromyography (EMG) sensor. In order to communicate with computer, user has to pose his/her hand and arm to matched the specified patterns for sending a command to the computer. However, the pose detection from itself is failed to detect the real pose from user even if it is synced following the guideline from its developer. This may cause some error in the application that depended on accuracy of the pose detection. Concept of this paper presents another way to detect the poses by using decision tree to solve the problem by sending EMG data sets from all 8 EMG sensors around it for deciding the pose from user. Rock-paper-scissors game is created to test the concept of Myo Armband training. The experiment results show that the proposed technique can use as a pose detection algorithm in around 78% correction.


title: Impact of User Pairing on 5G Non-Orthogonal Multiple Access

abstract: Non-orthogonal multiple access (NOMA) represents a paradigm shift from conventional orthogonal multiple access (MA) concepts, and has been recognized as one of the key enabling technologies for 5G systems. In this paper, the impact of user pairing on the performance of two NOMA systems, NOMA with fixed power allocation (F-NOMA) and cognitive radio inspired NOMA (CR-NOMA), is characterized. For FNOMA, both analytical and numerical results are provided to demonstrate that F-NOMA can offer a larger sum rate than orthogonal MA, and the performance gain of F-NOMA over conventional MA can be further enlarged by selecting users whose channel conditions are more distinctive. For CR-NOMA, the quality of service (QoS) for users with the poorer channel condition can be guaranteed since the transmit power allocated to other users is constrained following the concept of cognitive radio networks. Because of this constraint, CR-NOMA has different behavior compared to F-NOMA. For example, for the user with the best channel condition, CR-NOMA prefers to pair it with the user with the second best channel condition, whereas the user with the worst channel condition is preferred by F-NOMA.


title: Public perceptions of non-governmental organisations in Serbia, Bosnia & Herzegovina, and Macedonia

abstract: Abstract The article examines the relationship between NGOs and the general public in Serbia, Bosnia & Herzegovina, and Macedonia. This relationship is often perceived as troubled. Focus group findings, however, suggest that ordinary people's attitudes towards local NGOs are rather more nuanced and that quite a few people would like to join an NGO themselves, if given an opportunity. NGOs should therefore make more effort to tap into this potential, by providing more and better information about their activities available to the general public and by being more inclusive, thus enhancing both NGO legitimacy and future sustainability in the region.


title: Customized logistics service and online shoppers’ satisfaction: an empirical study

abstract: – Fast-growing online retail market provides convenience for customers. But it makes competition among online retailers fiercer as well. Online retailers need to improve online shoppers’ satisfaction level for the purpose of keeping their loyalty. As an important component in online retail market, logistics service affects online shoppers’ satisfaction level. By applying expectation confirmation theory, the purpose of this paper is to explore how customized logistics services (CLS) influences online shoppers’ satisfaction level and whether the relationship between them is moderated by product type. , – Data were collected from Tmall.com, the biggest online retail market in China. Exploratory factor analysis and two-way ANOVA were conducted to analyze the data. , – The results indicate that CLS positively impacts online shoppers’ satisfaction level. But product type does not have moderate effect on the relationship between CLS and online shoppers’ satisfaction level. , – Online retail market grows fast. As the number of retailers keeps increasing, competition in this market becomes fiercer. In order to attract new shoppers and increase shoppers’ loyalty, online retailers must make shopper satisfied with their purchase. CLS positively impacts online shoppers’ satisfaction level, online retailers should offer shoppers CLS. , – This study examines how customized logistics influences customer satisfaction, and extends literatures related to online customer satisfaction by integrating the factor CLS and opens the way for other studies related to logistics service customization. Furthermore, this study discusses how IoT will improve online retailers’ abilities in providing CLS.


title: Health-related quality of life in early Parkinson's disease

abstract: Nonmotor symptoms (NMS) are common in patients with established Parkinson's disease (PD) and have a major impact upon quality of life. We investigated the significance of NMS in relation to health- related quality of life (HRQoL) in patients with newly diag- nosed PD. Patients and healthy controls were recruited as part of the Incidence of Cognitive Impairment in Cohorts with Longitudinal Evaluation in Parkinson's Disease Study. Prevalence of NMS was determined with the Non-Motor Symptom Questionnaire. HRQoL was recorded with the 39-item Parkinson's Disease Quality of Life Questionnaire (PDQ-39). Further assessments included measures of motor disability, depression, sleep, and cognition. One hundred and fifty-eight patients with newly diagnosed PD and 99 controls participated in this cross-sectional study. Patients reported greater numbers of NMS than controls (mean 8.3 6 4.3 versus 2.8 6 2.5 symptoms; P < 0.001). Patients reported lowest HRQoL in the domains assessing bodily discomfort, mobility, and activities of daily living. Motor and nonmotor symptoms impacted negatively upon HRQoL scores. Patients with the postural instability and gait difficulty motor subtype reported worse HRQoL, com- pared with those with tremor-dominant disease. Depres- sion (P < 0.001), incomplete bowel emptying (P < 0.001), anxiety (P < 0.001), impaired concentration (P < 0.001), memory complaints (P < 0.001), and insomnia (P 5 0.001) had the greatest negative impact upon HRQoL. NMS are common in patients with early PD and represent a signifi- cant cause of poorer health-related quality of life. Cogni- tive, neuropsychiatric, and sleep disturbances are particularly associated with reduced well-being. Screening and management of these symptoms should be prioritized at the time of diagnosis. V C 2013 International Parkinson and Movement Disorder Society


title: DeepEye: Towards Automatic Data Visualization

abstract: Data visualization is invaluable for explaining the significance of data to people who are visually oriented. The central task of automatic data visualization is, given a dataset, to visualize its compelling stories by transforming the data (e.g., selecting attributes, grouping and binning values) and deciding the right type of visualization (e.g., bar or line charts). We present DEEPEYE, a novel system for automatic data visualization that tackles three problems: (1) Visualization recognition: given a visualization, is it "good or "bad"? (2) Visualization ranking: given two visualizations, which one is "better"? And (3) Visualization selection: given a dataset, how to find top-k visualizations? DEEPEYE addresses (1) by training a binary classifier to decide whether a particular visualization is good or bad. It solves (2) from two perspectives: (i) Machine learning: it uses a supervised learning-to-rank model to rank visualizations; and (ii) Expert rules: it relies on experts' knowledge to specify partial orders as rules. Moreover, a "boring" dataset may become interesting after data transformations (e.g., binning and grouping), which forms a large search space. We also discuss optimizations to efficiently compute top-k visualizations, for approaching (3). Extensive experiments verify the effectiveness of DEEPEYE."


title: Design of a dual-mode substrate integrated waveguide filter with slot line perturbation

abstract: A single-cavity dual-mode substrate integrated waveguide (SIW) bandpass filter (BPF) for X-band application is presented in this paper. Coplanar waveguide (CPW) is used as SIW-microstrip transition in this design. Two slots of the CPW with unequal lengths are used to excite two degenerate modes, i.e. TE 102 and TE 201 . A slot line is etched on the ground plane of the SIW cavity for perturbation. Its size and position are related to the effect of mode-split, namely the coupling between the two degenerate modes. Due to the cancellation of the two modes, a transmission zero in the lower stopband of the BPF is achieved, which improves the selectivity of the proposed BPF. And the location of the transmission zero can be controlled by adjusting the position and the size of the slot line perturbation properly. By introducing source-load coupling, an additional transmission zero is produced in the upper stopband of the BPF, it enhances the stopband performance of the BPF. Influences of the slot line perturbation on the BPF have been studied. A dual-mode BPF for X-band application has been designed, fabricated and measured. A good agreement between simulation and measurement verifies the validity of this design methodology.


title: Genetic Epidemiology of Major Depression: Review and Meta-Analysis

abstract: OBJECTIVE: The authors conducted a meta-analysis of relevant data from primary studies of the genetic epidemiology of major depression.METHOD: The authors searched MEDLINE and the reference lists of previous review articles to identify relevant primary studies. On the basis of a review of family, adoption, and twin studies that met specific inclusion criteria, the authors derived quantitative summary statistics. RESULTS: Five family studies met the inclusion criteria. The odds ratios for proband (subjects with major depression or comparison subjects) versus first-degree relative status (affected or unaffected with major depression) were homogeneous across the five studies (Mantel-Haenszel odds ratio=2.84, 95% CI=2.31–3.49). No adoption study met the inclusion criteria, but the results of two of the three reports were consistent with genetic influences on liability to major depression. Five twin studies met the inclusion criteria, and their statistical summation suggested that familial aggregation was due ...


title: Effect of ZnO Seed Layer on the Electrical Characteristics of Pd/ZnO Thin-Film-Based Schottky Contacts Grown on n-Si Substrates

abstract: The electrical characteristics of Pd/ZnO thin-film Schottky contacts grown on n-Si substrates with and without using a ZnO seed layer by simple thermal evaporation method have been investigated. The structural, morphological, and optical properties of the ZnO thin films were investigated using X-ray diffraction (XRD), field-emission scanning electron microscopy (FESEM), atomic force microscopy (AFM), and photoluminescence (PL) measurements. The Schottky devices with ZnO seed layer exhibits superior diode characteristics (with excellent rectification ratio of ~2.5 × 103, barrier height = 0.81 eV, and ideality factor = 1.46 at room temperature to those without using ZnO seed layer. This is attributed to the reduction of native defects states from the ZnO thin film surface caused by ZnO seed layer as evidenced by the room-temperature PL measurement. Thus, the use of ZnO seed layer on the n-Si substrates maybe treated as an effective approach for fabricating Pd/ZnO thin-film-based Schottky diodes on n-Si substrates for electronic and optoelectronic applications.


title: Sentiment analysis leveraging emotions and word embeddings

abstract: Abstract Sentiment analysis and opinion mining are valuable for extraction of useful subjective information out of text documents. These tasks have become of great importance, especially for business and marketing professionals, since online posted products and services reviews impact markets and consumers shifts. This work is motivated by the fact that automating retrieval and detection of sentiments expressed for certain products and services embeds complex processes and pose research challenges, due to the textual phenomena and the language specific expression variations. This paper proposes a fast, flexible, generic methodology for sentiment detection out of textual snippets which express people’s opinions in different languages. The proposed methodology adopts a machine learning approach with which textual documents are represented by vectors and are used for training a polarity classification model. Several documents’ vector representation approaches have been studied, including lexicon-based, word embedding-based and hybrid vectorizations. The competence of these feature representations for the sentiment classification task is assessed through experiments on four datasets containing online user reviews in both Greek and English languages, in order to represent high and weak inflection language groups. The proposed methodology requires minimal computational resources, thus, it might have impact in real world scenarios where limited resources is the case.


A RapidMiner extension for Open Machine Learning


title: Optical Flow and Trajectory Estimation Methods

abstract: This brief focuses on two main problems in the domain of optical flow and trajectory estimation: (i) The problem of finding convex optimization methods to apply sparsity to optical flow; and (ii) The problem of how to extend sparsity to improve trajectories in a computationally tractable way. Beginning with a review of optical flow fundamentals, it discusses the commonly used flow estimation strategies and the advantages or shortcomings of each. The brief also introduces the concepts associated with sparsity including dictionaries and low rank matrices. Next, it provides context for optical flow and trajectory methods including algorithms, data sets, and performance measurement. The second half of the brief covers sparse regularization of total variation optical flow and robust low rank trajectories. The authors describe a new approach that uses partially-overlapping patches to accelerate the calculation and is implemented in a coarse-to-fine strategy. Experimental results show that combining total variation and a sparse constraint from a learned dictionary is more effective than employing total variation alone. The brief is targeted at researchers and practitioners in the fields of engineering and computer science. It caters particularly to new researchers looking for cutting edge topics in optical flow as well as veterans of optical flow wishing to learn of the latest advances in multi-frame methods.


title: A modified labeling theory approach to mental disorders: An empirical assessment.

abstract: Critics of labeling theory vigorously dispute Scheff's (1966) provocative etiological hypothesis and downplay the importance of factors such as stigma and stereotyping. We propose a modified labeling perspective which claims that even if labeling does not directly produce mental disorder, it can lead to negative outcomes. Our approach asserts that socialization leads individuals to develop a set of beliefs about how most people treat mental patients. When individuals enter treatment, these beliefs take on new meaning. The more patients believe that they will be devalued and discriminated against, the more they feel threatened by interacting with others. They may keep their treatment a secret, try to educate others about their situation, or withdraw from social contacts that they perceive as potentially rejecting. Such strategies can lead to negative consequences for social support networks, jobs, and self-esteem. We test this modified labeling perspective using samples of patients and untreated community residents, and find that both believe that "most people" will reject mental patients. Additionally, patients endorse strategies of secrecy, withdrawal, and education to cope with the threat they perceive. Finally, patients' social support networks are affected by the extent to which they fear rejection and by the coping responses they adopt to deal with their stigmatized status.


title: An Abstract State Machine (ASM) Representation of Learning Process in FLOSS Communities

abstract: Free/Libre Open Source Software (FLOSS) communities as collaborative environments enable the occurrence of learning between participants in these groups. With the increasing interest research on understanding the mechanisms and processes through which learning occurs in FLOSS, there is an imperative to describe these processes. One successful way of doing this is through specification methods. In this paper, we describe the adoption of Abstract States Machines (ASMs) as a specification methodology for the description of learning processes in FLOSS. The goal of this endeavor is to represent the many possible steps and/or activities FLOSS participants go through during interactions that can be categorized as learning processes. Through ASMs, we express learning phases as states while activities that take place before moving from one state to another are expressed as transitions.


title: Co-evolution of Fitness Maximizers and Fitness Predictors

abstract: an estimation of distribution algorithm (EDA) based on co-evolution of fitness maximizers and fitness predictors for improving the performance of evolutionary search when evaluations are prohibitively expensive. Fitness predictors are lightweight objects which, given an evolving individual, heuristically approximate the true fitness. The predictors are trained by their ability to correctly differentiate between good and bad solutions using reduced computation. We apply co-evolving fitness prediction to symbolic regression and measure its impact. Our results show that the additional computational investment in training the co-evolving fitness predictors can greatly enhance both speed and convergence of individual solutions while overall reducing the number of evaluations. In application to symbolic regression, the advantage of using fitness predictors grows as the complexity of models increases.


title: Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors

abstract: We propose several econometric measures of connectedness based on principal-components analysis and Granger-causality networks, and apply them to the monthly returns of hedge funds, banks, broker/dealers, and insurance companies. We find that all four sectors have become highly interrelated over the past decade, likely increasing the level of systemic risk in the finance and insurance industries through a complex and time-varying network of relationships. These measures can also identify and quantify financial crisis periods, and seem to contain predictive power in out-of-sample tests. Our results show an asymmetry in the degree of connectedness among the four sectors, with banks playing a much more important role in transmitting shocks than other financial institutions.


title: Learning Monolingual Compositional Representations via Bilingual Supervision

abstract: Bilingual models that capture the semantics of sentences are typically only evaluated on cross-lingual transfer tasks such as cross-lingual document categorization or machine translation. In this work, we evaluate the quality of the monolingual representations learned with a variant of the bilingual compositional model of Hermann and Blunsom (2014), when viewing translations in a second language as a semantic annotation as the original language text. We show that compositional objectives based on phrase translation pairs outperform compositional objectives based on bilingual sentences and on monolingual paraphrases.


title: SSI Prize Essay for Female Sexual Dysfunction—Research ‘The microvascular architecture of the rat vagina revealed by image analysis of vascular corrosion casts’

abstract: Background: Female sexual dysfunction is a common but poorly understood human condition. One of the aspects hindering progress in this area is the lack of appropriate animal models that can be used to study the complex factors involved in this sexual health problem. Recently, attention has focused on the probable role of vascular dynamics of the genital organs and their potential for impact on female sexuality. The objective of this study was to provide a better description of the vascular anatomy of the female rat vagina and external genital organs in an attempt to better develop this as an animal model to study female sexual dysfunction.Methods: Young female (nonestrous) virgin rats were anesthetized, the abdominal aorta was cannulated, and the distal vasculature was flushed and fixed in vivo for histological studies or for subsequent infusion with Mercox resin for vascular corrosion casting. Vascular corrosion casts of the external genitalia (vagina and vulva) were studied using a scanning electron microscope (SEM). Fixed tissue specimens were also embedded and sectioned for histochemical and immunohistochemical analysis.Results: Scanning electron microscopy imaging allowed a description of the vascular and microvascular system of the nonestrous female rat genitalia. Major feeding vessels were located laterally in the muscular and serosal layers of the vagina with a complex system of interanastomosing collaterals between these large lateral trunks. The sub-epithelial region of the vaginal wall contains a dense and rich network of capillaries that perfuse the epithelium. These data were corroborated by two- dimensional histochemistry and immunostaining for endothelial and smooth muscle cells on paraffin-embedded thin sections of the female vagina and vulva.Conclusion: This study provides the first detailed three-dimensional en bloc view of the macro- and microvascular anatomy of the female rat vagina and vulva. The findings suggest an active interaction between the microvasculature and the epithelial cells of the vaginal wall. This study will provide the basic anatomic groundwork for future experiments on perturbations of the vascular system of the rat female genitalia in response to hormonal stimuli and various disease states.


title: A randomized algorithm for the decomposition of matrices

abstract: Abstract Given an m × n matrix A and a positive integer k, we describe a randomized procedure for the approximation of A with a matrix Z of rank k. The procedure relies on applying A T to a collection of l random vectors, where l is an integer equal to or slightly greater than k; the scheme is efficient whenever A and A T can be applied rapidly to arbitrary vectors. The discrepancy between A and Z is of the same order as l m times the ( k + 1 ) st greatest singular value σ k + 1 of A, with negligible probability of even moderately large deviations. The actual estimates derived in the paper are fairly complicated, but are simpler when l − k is a fixed small nonnegative integer. For example, according to one of our estimates for l − k = 20 , the probability that the spectral norm ‖ A − Z ‖ is greater than 10 ( k + 20 ) m σ k + 1 is less than 10 − 17 . The paper contains a number of estimates for ‖ A − Z ‖ , including several that are stronger (but more detailed) than the preceding example; some of the estimates are effectively independent of m. Thus, given a matrix A of limited numerical rank, such that both A and A T can be applied rapidly to arbitrary vectors, the scheme provides a simple, efficient means for constructing an accurate approximation to a singular value decomposition of A. Furthermore, the algorithm presented here operates reliably independently of the structure of the matrix A. The results are illustrated via several numerical examples.


title: A survey of communication/networking in Smart Grids

abstract: Smart Grid is designed to integrate advanced communication/networking technologies into electrical power grids to make them ''smarter''. Current situation is that most of the blackouts and voltage sags could be prevented if we have better and faster communication devices and technologies for the electrical grid. In order to make the current electrical power grid a Smart Grid, the design and implementation of a new communication infrastructure for the grid are two important fields of research. However, Smart Grid projects have only been proposed in recent years and only a few proposals for forward-looking requirements and initial research work have been offered in this field. No any systematic reviews of communication/networking in Smart Grids have been conducted yet. Therefore, we conduct a systematic review of communication/networking technologies in Smart Grid in this paper, including communication/networking architecture, different communication technologies that would be employed into this architecture, quality of service (QoS), optimizing utilization of assets, control and management, etc.


title: Investigating the impact of video games on high school students' engagement and learning about genetics

abstract: The popularity of video games has transcended entertainment crossing into the world of education. While the literature base on educational gaming is growing, there is still a lack of systematic study of this emerging technology's efficacy. This quasi-experimental study evaluated a teacher created video game on genetics in terms of its affective and cognitive impact on student users. While statistical results indicated no differences (p>.05) in student learning as measured by our instrument, there were significant differences (p<.05) found in the participants' level of engagement while interfacing with the video game. Implications on this emerging line of inquiry are discussed.


title: Reference Injected Phase-Locked Loops (PLL-RIs)

abstract: In this paper, we use synchronization to reduce phase-locked loop (PLL) phase noise and improve its locking behavior with an attenuated reference signal injection (RI) into a voltage-controlled CMOS delay-line ring-type oscillator. The transient and steady-state behavior of the PLL-RI are described by a nonlinear differential equation, which is further studied by the phase-plane method. The nonlinear equation is linearized for the small-signal condition and the s-domain noise transfer functions and noise bandwidths for different noise sources are derived. The effect of the loop parameters and the injection strength on the output phase noise, loop settling time, and lock in range is analyzed. Finally, the analysis is verified by the SPICE simulation and measurement results from an 1-GHz PLL-RI with 130-nm standard RF CMOS technology. Simulation and measurement results show phase noise reduction and improved settling behavior of a PLL-RI compared with a conventional PLL.


title: Effects of prosocial video games on prosocial behavior

abstract: Previous research has documented that playing violent video games has various negative effects on social behavior in that it causes an increase in aggressive behavior and a decrease in prosocial behavior. In contrast, there has been much less evidence on the effects of prosocial video games. In the present research, 4 experiments examined the hypothesis that playing a prosocial (relative to a neutral) video game increases helping behavior. In fact, participants who had played a prosocial video game were more likely to help after a mishap, were more willing (and devoted more time) to assist in further experiments, and intervened more often in a harassment situation. Results further showed that exposure to prosocial video games activated the accessibility of prosocial thoughts, which in turn promoted prosocial behavior. Thus, depending on the content of the video game, playing video games not only has negative effects on social behavior but has positive effects as well. Language: en


title: OHSUMED: an interactive retrieval evaluation and new large test collection for research

abstract: A series of information retrieval experiments was carried out with a computer installed in a medical practice setting for relatively inexperienced physician end-users. Using a commercial MEDLINE product based on the vector space model, these physicians searched just as effectively as more experienced searchers using Boolean searching. The results of this experiment were subsequently used to create a new large medical test collection, which was used in experiments with the SMART retrieval system to obtain baseline performance data as well as compare SMART with the other searchers.


title: Image Caption Generation with Text-Conditional Semantic Attention

abstract: We propose a semantic attention mechanism for image caption generation, called text-conditional semantic attention, which allows the caption generator to automatically learn which parts of the image feature to focus on given previously generated text. To acquire text-related image features for our attention model, we also improve the guiding Long Short-Term Memory (gLSTM) structure by back-propagating the training loss though semantic guidance to fine-tune the CNN weights. In contrast to existing gLSTM methods, such as emb-gLSTM, our fine-tuned model enables guidance information to be more text-related. This also allows jointly learning of the image embedding, text embedding, semantic attention and language model with one simple network architecture in an end-to-end manner. We implement our model based on NeuralTalk2, an open-source image caption generator, and test it on MSCOCO dataset. We evaluate the proposed method with three metrics: BLEU, METEOR and CIDEr. The proposed methods outperform state-of-the-art methods.


title: Responsive supply chain: A competitive strategy in a networked economy

abstract: Supply chain management (SCM) has been considered as the most popular operations strategy for improving organizational competitiveness in the twenty-first century. In the early 1990s, agile manufacturing (AM) gained momentum and received due attention from both researchers and practitioners. In the mid-1990s, SCM began to attract interest. Both AM and SCM appear to differ in philosophical emphasis, but each complements the other in objectives for improving organizational competitiveness. For example, AM relies more on strategic alliances/partnerships (virtual enterprise environment) to achieve speed and flexibility. But the issues of cost and the integration of suppliers and customers have not been given due consideration in AM. By contrast, cost is given a great deal of attention in SCM, which focuses on the integration of suppliers and customers to achieve an integrated value chain with the help of information technologies and systems. Considering the significance of both AM and SCM for firms to improve their performance, an attempt has been made in this paper to analyze both AM and SCM with the objective of developing a framework for responsive supply chain (RSC). We compare their characteristics and objectives, review the selected literature, and analyze some case experiences on AM and SCM, and develop an integrated framework for a RSC. The proposed framework can be employed as a competitive strategy in a networked economy in which customized products/services are produced with virtual organizations and exchanged using e-commerce.


title: An Adaptive Traffic Signal Control in a Connected Vehicle Environment: A Systematic Review

abstract: In the last few years, traffic congestion has become a growing concern due to increasing vehicle ownerships in urban areas. Intersections are one of the major bottlenecks that contribute to urban traffic congestion. Traditional traffic signal control systems cannot adjust the timing pattern depending on road traffic demand. This results in excessive delays for road users. Adaptive traffic signal control in a connected vehicle environment has shown a powerful ability to effectively alleviate urban traffic congestions to achieve desirable objectives (e.g., delay minimization). Connected vehicle technology, as an emerging technology, is a mobile data platform that enables the real-time data exchange among vehicles and between vehicles and infrastructure. Although several reviews about traffic signal control or connected vehicles have been written, a systemic review of adaptive traffic signal control in a connected vehicle environment has not been made. Twenty-six eligible studies searched from six databases constitute the review. A quality evaluation was established based on previous research instruments and applied to the current review. The purpose of this paper is to critically review the existing methods of adaptive traffic signal control in a connected vehicle environment and to compare the advantages or disadvantages of those methods. Further, a systematic framework on connected vehicle based adaptive traffic signal control is summarized to support the future research. Future research is needed to develop more efficient and generic adaptive traffic signal control methods in a connected vehicle environment.


title: Modeling Users? Information Needs in a Document Recommender for Meetings

abstract: People are surrounded by an unprecedented wealth of information. Access to it depends on the availability of suitable search engines, but even when these are available, people often do not initiate a search, because their current activity does not allow them, or they are not aware of the existence of this information. Just-in-time retrieval brings a radical change to the process of query-based retrieval, by proactively retrieving documents relevant to users' current activities, in an easily accessible and non-intrusive manner. This thesis presents a novel set of methods intended to improve the relevance of a just-in-time retrieval system, specifically a document recommender system designed for conversations, in terms of precision and diversity of results. Additionally, we designed an evaluation protocol to compare the proposed methods in the thesis with other ones using crowdsourcing. In contrast to previous systems, which model users' information needs by extracting keywords from clean and well-structured texts, this system models them from the conversation transcripts, which contain noise from automatic speech recognition (ASR) and have a free structure, often switching between several topics. To deal with these issues, we first propose a novel keyword extraction method which preserves both the relevance and the diversity of topics of the conversation, to properly capture possible users' needs with minimum ASR noise. Implicit queries are then built from these keywords. However, the presence of multiple unrelated topics in one query introduces significant noise into the retrieval results. To reduce this effect, we separate users' needs by topically clustering keyword sets into several subsets or implicit queries. We introduce a merging method which combines the results of multiple queries which are prepared from users' conversation to generate a concise, diverse and relevant list of documents. This method ensures that the system does not distract its users from their current conversation by frequently recommending them a large number of documents. Moreover, we address the problem of explicit queries that may be asked by users during a conversation. We introduce a query refinement method which leverages the conversation context to answer the users' information needs without asking for additional clarifications and therefore, again, avoiding to distract users during their conversation. Finally, we implemented the end-to-end document recommender system by integrating the ideas proposed in this thesis and then proposed an evaluation scenario with human users in a brainstorming meeting.


title: Internet function and Internet addictive behavior

abstract: Abstract Ju [Research on personal characteristics, the behavior of using Internet and Internet addiction for Taiwanese college study. Master thesis. Taiwan, 2000] found the Internet function was related to the severity of Internet addiction, and this study explored the relationship between the Internet function and the Internet addictive behavior. Seventy-six college students were included in this study and had completed the Internet use function questionnaire (social function, informational function, leisure function and the virtual emotional function) and the Chinese Internet Addiction Scale (compulsive use, withdrawal, tolerance, time management problem and interpersonal and health problems). The study revealed not only the relationship between the social function and the index of the Internet dependence (compulsive use, withdrawal, tolerance and the problems in the interpersonal relationship and health), but also the correlation between the information function and the index of the Internet abuse (the problems in the interpersonal relationship and health). We found the social function played the core role in the Internet addictive behavior and the mechanism needs further discussion.


title: A robust, low power, high speed voltage level shifter with built-in short circuit current reduction

abstract: A novel topology for a high speed voltage level shifter (VLS) is presented. It features a built-in short circuit current reduction which increases the speed and reduces the power consumption. Unlike the conventional VLSs, the proposed VLS does not need complex digital timing signals. The simplicity of its operation results into robustness of operation, high speed and low power. The VLS was designed in CMOS 0.18um process. Simulation results, at the layout extraction level, are presented to validate the design concept. The speed and energy consumption of the VLS are compared with the state of the art VLSs. The proposed VLS proves to be better in both the speed and power aspects than the state of the art VLSs.


title: Enhancing Suricata intrusion detection system for cyber security in SCADA networks

abstract: Industrial Control and SCADA (Supervisory Control and Data Acquisition) networks control critical infrastructure such as power plants, nuclear facilities, and water supply systems. These systems are increasingly the target of cyber attacks by threat actors of different kinds, with successful attacks having the potential to cause damage, cost and injury/loss of life. As a result, there is a strong need for enhanced tools to detect cyber threats in SCADA networks. This paper makes a number of contributions to advance research in this area. First, we study the level of support for SCADA protocols in well-known open source intrusion detection systems (IDS). Second, we select a specific IDS, Suricata, and enhance it to include support for detecting threats against SCADA systems running the EtherNet/IP (ENIP) industrial control protocol. Finally, we conduct a traffic-based study to evaluate the performance of the new ENIP module in Suricata - analyzing its performance in low performance hardware systems.


title: A HITS-based POI recommendation algorithm for location-based social networks

abstract: Location-Based Social Networks (LBSNs), (also called as Geo-Social Networks), has been attracting more and more users by providing services that integrate social activities with location information. LBSN systems usually provide support for indicating various Points of Interest (POIs) but there is no straightforward rating mechanism for POIs in most LBSNs [1]. POI recommendations in LBSNs, thus, is an important and challenging research topic. In this paper, we first investigate the dataset crawled from Foursquare to explore the features that attract and influence users to check in at various POIs. Based on the analysis results, we propose a HITS (Hypertext Induced Topic Search)-based POI recommendation algorithm to recommend POIs to LBSN users that can also incorporate the impact of the social relationships on recommendations. We evaluate our proposed model on Foursquare dataset and compare our results with the latest POI recommendation algorithm. The experimental results show that our approach performs better.


title: A Card Game Description Language

abstract: We present initial research regarding a system capable of generating novel card games. We furthermore propose a method for computationally analysing existing games of the same genre. Ultimately, we present a formalisation of card game rules, and a context-free grammar Gcardgame capable of expressing the rules of a large variety of card games. Example derivations are given for the poker variant Texas hold 'em, Blackjack and UNO. Stochastic simulations are used both to verify the implementation of these well-known games, and to evaluate the results of new game rules derived from the grammar. In future work, this grammar will be used to evolve completely novel card games using a grammar-guided genetic program.


title: New Memristor-Based Crossbar Array Architecture with 50-% Area Reduction and 48-% Power Saving for Matrix-Vector Multiplication of Analog Neuromorphic Computing

abstract: In this paper, we propose a new memristorbased crossbar array architecture, where a single memristor array and constant-term circuit are used to represent both plus-polarity and minus-polarity matrices. This is different from the previous crossbar array architecture which has two memristor arrays to represent plus-polarity and minus-polarity connection matrices, respectively. The proposed crossbar architecture is tested and verified to have the same performance with the previous crossbar architecture for applications of character recognition. For areal density, however, the proposed crossbar architecture is twice better than the previous architecture, because only single memristor array is used instead of two crossbar arrays. Moreover, the power consumption of the proposed architecture can be smaller by 48% than the previous one because the number of memristors in the proposed crossbar architecture is reduced to half compared to the previous crossbar architecture. From the high areal density and high energy efficiency, we can know that this newly proposed crossbar array architecture is very suitable to various applications of analog neuromorphic computing that demand high areal density and low energy consumption.


Elementary methods in the theory of L-functions, VI. On the least prime quadratic residue (mod p)


title: Loneliness and Friendship in High-Functioning Children with Autism

abstract: Loneliness and friendship were examined in 22 high-functioning children with autism and 19 typically developing children equated with the autistic children for IQ, CA, gender, mother's education, and ethnicity. Children between the ages of 8 and 14 were asked to report on both their understanding and feelings of loneliness and the quality of their friendship. Compared to typically developing children, children with autism were both lonelier and had less complete understandings of loneliness. Although all children with autism reported having at least one friend, the quality of their friendships was poorer in terms of companionship, security, and help. Fewer associations were found between loneliness and friendship for the autistic than for the non-autistic children, suggesting less understanding of the relation between loneliness and friendship. Implications of these results are discussed for conceptualizing the social deficits in autism.


title: VANET topology characteristics under realistic mobility and channel models

abstract: Developing real-time safety and non-safety applications for vehicular ad hoc networks (VANET) requires understanding the dynamics of the network topology characteristics since these dynamics determine both the performance of routing protocols and the feasibility of an application over VANET. Using various key metrics of interest including node degree, number of clusters, link duration and link quality, we provide a realistic analysis of the VANET topology characteristics. In this analysis, we integrate real-world road topology and real-time data extracted from Freeway Performance Measurement System database into the microscopic mobility model in order to generate realistic traffic flows along the highway. Moreover, we use more realistic, recently proposed, obstacle-based channel model and compare the performance of this sophisticated model to the most commonly used more simplistic channel models including unit disc and log-normal shadowing model. Our investigation on the key system metrics reveal that largely used unit disc model fails to realistically model communication channel, while parameters of simplistic models like log normal can be adjusted to match the corresponding system metrics of more complex and hard to implement obstacle based model.


title: Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review

abstract: Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. The manufacturers and users of these drives are now keen to include diagnostic features in the software to improve salability and reliability. Apart from locating specific harmonic components in the line current (popularly known as motor current signature analysis), other signals, such as speed, torque, noise, vibration etc., are also explored for their frequency contents. Sometimes, altogether different techniques, such as thermal measurements, chemical analysis, etc., are also employed to find out the nature and the degree of the fault. In addition, human involvement in the actual fault detection decision making is slowly being replaced by automated tools, such as expert systems, neural networks, fuzzy-logic-based systems; to name a few. It is indeed evident that this area is vast in scope. Hence, keeping in mind the need for future research, a review paper describing different types of faults and the signatures they generate and their diagnostics' schemes will not be entirely out of place. In particular, such a review helps to avoid repetition of past work and gives a bird's eye view to a new researcher in this area.


title: Correcting robot mistakes in real time using EEG signals

abstract: Communication with a robot using brain activity from a human collaborator could provide a direct and fast feedback loop that is easy and natural for the human, thereby enabling a wide variety of intuitive interaction tasks. This paper explores the application of EEG-measured error-related potentials (ErrPs) to closed-loop robotic control. ErrP signals are particularly useful for robotics tasks because they are naturally occurring within the brain in response to an unexpected error. We decode ErrP signals from a human operator in real time to control a Rethink Robotics Baxter robot during a binary object selection task. We also show that utilizing a secondary interactive error-related potential signal generated during this closed-loop robot task can greatly improve classification performance, suggesting new ways in which robots can acquire human feedback. The design and implementation of the complete system is described, and results are presented for realtime closed-loop and open-loop experiments as well as offline analysis of both primary and secondary ErrP signals. These experiments are performed using general population subjects that have not been trained or screened. This work thereby demonstrates the potential for EEG-based feedback methods to facilitate seamless robotic control, and moves closer towards the goal of real-time intuitive interaction.


title: Big Batch SGD: Automated Inference using Adaptive Batch Sizes

abstract: Classical stochastic gradient methods for optimization rely on noisy gradient approximations that become progressively less accurate as iterates approach a solution. The large noise and small signal in the resulting gradients makes it difficult to use them for adaptive stepsize selection and automatic stopping. We propose alternative "big batch" SGD schemes that adaptively grow the batch size over time to maintain a nearly constant signal-to-noise ratio in the gradient approximation. The resulting methods have similar convergence rates to classical SGD, and do not require convexity of the objective. The high fidelity gradients enable automated learning rate selection and do not require stepsize decay. Big batch methods are thus easily automated and can run with little or no oversight.


title: A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions

abstract: Robots operating alongside humans in diverse, stochastic environments must be able to accurately interpret natural language commands. These instructions often fall into one of two categories: those that specify a goal condition or target state, and those that specify explicit actions, or how to perform a given task. Recent approaches have used reward functions as a semantic representation of goal-based commands, which allows for the use of a state-of-the-art planner to find a policy for the given task. However, these reward functions cannot be directly used to represent action-oriented commands. We introduce a new hybrid approach, the Deep Recurrent Action-Goal Grounding Network (DRAGGN), for task grounding and execution that handles natural language from either category as input, and generalizes to unseen environments. Our robot-simulation results demonstrate that a system successfully interpreting both goal-oriented and action-oriented task specifications brings us closer to robust natural language understanding for human-robot interaction.


Inverse Game Theory


title: Integrated Anomaly Detection for cyber security of the substations

abstract: Cyber intrusions to substations of a power grid are a source of vulnerability since most substations are unmanned and with limited protection of the physical security. In the worst case, simultaneous intrusions into multiple substations can lead to severe cascading events, causing catastrophic power outages. In this paper, an integrated Anomaly Detection System (ADS) is proposed which contains host- and network-based anomaly detection systems for the substations, and simultaneous anomaly detection for multiple substations. Potential scenarios of simultaneous intrusions into the substations have been simulated using a substation automation testbed. The host-based anomaly detection considers temporal anomalies in the substation facilities, e.g., user-interfaces, Intelligent Electronic Devices (IEDs) and circuit breakers. The malicious behaviors of substation automation based on multicast messages, e.g., Generic Object Oriented Substation Event (GOOSE) and Sampled Measured Value (SMV), are incorporated in the proposed network-based anomaly detection. The proposed simultaneous intrusion detection method is able to identify the same type of attacks at multiple substations and their locations. The result is a new integrated tool for detection and mitigation of cyber intrusions at a single substation or multiple substations of a power grid.


title: A 1-V 42.6-μW 1.5-bit continuous-time Delta-Sigma modulator for audio applications

abstract: A fourth-order continuous-time (CT) Delta-Sigma modulator with 1.5-bit quantizer is presented in this paper. This design is targeted for audio applications that demand high resolution, low supply voltage, and low power consumption. The input-feedforward topology, with optimized coefficients, is utilized to reduce internal signal swings as well as the power consumption. A 1.5-bit quantizer with simple dynamic element matching (DEM) is used to improve resolution and stability. A novel feedforward and summation structure is applied in the 1.5-bit modulator to reduce the power consumption and the chip area, and to simplify the circuit. The modulator, designed in a 0.13-μm CMOS technology, achieves 100.5-dB peak SQNR (Signal-to-Quantization Noise Ratio), and 97.5-dB peak SQNDR (Signal-to-Quantization Noise-and-Distortion Ratio) over a 20-kHz signal bandwidth with a 2.56-MHz clock. The power consumption of the modulator is 42.6 μW under a 1-V supply, and the chip core area is 0.13 mm2.


title: Large-scale mapping in complex field scenarios using an autonomous car

abstract: We present a mapping system for large-scale environments with changing features.We describe in a high level of detail a mapping algorithm for 3D-LiDAR.G-ICP was used for loop closure displacement calculation in GraphSLAM.Experiments were made with an autonomous vehicle in 3 real world environments. In this paper, we present an end-to-end framework for precise large-scale mapping with applications in autonomous driving. In special, the problem of mapping complex environments, with features changing from tree-lined streets to urban areas with dense traffic, is studied. The robotic car is equipped with an odometry sensor, a 3D LiDAR Velodyne HDL-32E, a IMU, and a low cost GPS, and the data generated by these sensors are integrated in a pose-based GraphSLAM estimator. A new strategy for identification and correction of odometry data using evolutionary algorithms is presented. This new strategy makes odometry data significantly more consistent with GPS. Loop closures are detected using GPS data, and GICP, a 3D point cloud registration algorithm, is used to estimate the displacement between the different travels over the same region. After path estimation, 3D LiDAR data is used to build an occupancy grid mapping of the environment. A detailed mathematical description of how occupancy evidence can be calculated from the point clouds is given, and a submapping strategy to handle memory limitations is presented as well. The proposed framework is tested in three real world environments with different sizes, and features: a parking lot, a university beltway, and a city neighborhood. In all cases, satisfactory maps were built, with precise loop closures even when the vehicle traveled long distances between them.


title: Traffic sign recognition application based on image processing techniques

abstract: Abstract This paper describes a software application for traffic sign recognition (TSR). The application works in four stages. First, an image preprocessing step and the detection of regions of interest (ROIs), which involves a series of steps that include transforming the image to grayscale and applying edge detection by the Laplacian of Gaussian (LOG) filter. Secondly, the potential traffic signs detection, where the ROIs are compared with each shape pattern. Thirdly, a recognition stage using a cross-correlation algorithm, where each potential traffic sign, if validated, is classified according to the data-base of traffic signs. Finally, the previous stages can be managed and controlled by a graphical user interface, which has been specially designed for this purpose. The results obtained show a good performance of the developed application, taking into account acceptable conditions of size and contrast of the input image.


Fake News in Financial Markets


title: MapReduce Design of K-Means Clustering Algorithm

abstract: Cluster is a collection of data members having similar characteristics. The process of establishing a relation or deriving information from raw data by performing some operations on the data set like clustering is known as data mining. Data collected in practical scenarios is more often than not completely random and unstructured. Hence, there is always a need for analysis of unstructured data sets to derive meaningful information. This is where unsupervised algorithms come in to picture to process unstructured or even semi structured data sets by resultant. K-Means Clustering is one such technique used to provide a structure to unstructured data so that valuable information can be extracted. This paper discusses the implementation of the K-Means Clustering Algorithm over a distributed environment using ApacheTM Hadoop. The key to the implementation of the K-Means Algorithm is the design of the Mapper and Reducer routines which has been discussed in the later part of the paper. The steps involved in the execution of the K-Means Algorithm has also been described in this paper based on a small scale implementation of the K-Means Clustering Algorithm on an experimental setup to serve as a guide for practical implementations.


title: The hybrid flow shop scheduling problem

abstract: The scheduling of flow shops with multiple parallel machines per stage, usually referred to as the hybrid flow shop (HFS), is a complex combinatorial problem encountered in many real world applications. Given its importance and complexity, the HFS problem has been intensively studied. This paper presents a literature review on exact, heuristic and metaheuristic methods that have been proposed for its solution. The paper briefly discusses and reviews several variants of the HFS problem, each in turn considering different assumptions, constraints and objective functions. Research opportunities in HFS are also discussed.


title: SAN: Self-Adaptive Navigation for Drone Battery Charging in Wireless Drone Networks

abstract: This paper introduces an optimal path finding problem for drone battery charging where their batteries should be charged for the travel from a source to a destination as needed. We present a practically reasonable heuristic to solve the problem by monitoring drones' battery status and traffic conditions in real time through a cloud-based service called traffic control center. This study will be the cornerstone of path finding problems for drone battery charging in drone networks.


title: CBE: Corpus-based of emotion for emotion detection in text document

abstract: Emotion Detection is a part of Natural Language Processing (NLP) that still evolve. Emotional Corpus that had been widely used are Wordnet Affect Emotion (WNA) and ANEW (Affective Norms for English Words). There are two ways to analyze the text based Emotion Detection: Categorical and Dimensional Model. Each model has different advantages and disadvantages. And each model has a different concept to predict emotion. The contribution of this research is forming automatic emotional corpus with merging two computational model. It called Corpus-Based of Emotion (CBE). CBE developed from ANEW and WNA with term similarity measure and distance of node approach. Latent Dirichlet Allocation (LDA) is used too for automatically expand CBE. The CBE attributes are a score of Valence (V), Arousal (A), Dominance (D) and categorical label emotion. Categorical label emotion based on six basic emotion of Ekman. Based on experiment results, it is known that CBE is able to improve the accuracy in detection of emotions. F-Measure using WNA+ANEW is 0.50 and F-Measure using CBE with expanding is 0.61.


title: Wide-band high-efficiency printed loop antenna design for wireless communication systems

abstract: A new wide-band high-efficiency coplanar waveguide-fed printed loop antenna is presented for wireless communication systems in this paper. By adjusting geometrical parameters, the proposed antenna can easily achieve a wide bandwidth. To optimize the antenna performances, a parametric study was conducted with the aid of a commercial software, and based on the optimized geometry, a prototype was designed, fabricated, and tested. The simulated and measured results confirmed that the proposed antenna can operate at (1.68-2.68 GHz) band and at (1.46-2.6 GHz) band with bandwidth of 1 and 1.14 GHz, respectively. Moreover, the antenna has a nearly omnidirectional radiation pattern with a reasonable gain and high efficiency. Due to the above characteristics, the proposed antenna is very suitable for applications in PCS and IMT2000 systems.


title: Neural network prediction of obstructive sleep apnea from clinical criteria.

abstract: STUDY OBJECTIVES ::: Clinical prediction models for the diagnosis of obstructive sleep apnea (OSA) have lacked the accuracy necessary to confidently replace polysomnography (PSG). Artificial neural networks are computer programs that can be trained to predict outcomes based on experience. This study was conducted to test the hypothesis that a generalized regression neural network (GRNN) could accurately classify patients with OSA from clinical data. ::: ::: ::: STUDY DESIGN ::: Retrospective review. ::: ::: ::: SETTING ::: Regional sleep referral center. ::: ::: ::: PATIENTS ::: Randomly selected records of patients referred for possible OSA. ::: ::: ::: MEASUREMENTS ::: The neural network was trained using 23 clinical variables from 255 patients, and the predictive performance was evaluated using 150 other patients. ::: ::: ::: RESULTS ::: The prevalence of OSA in this series of 405 patients (293 men and 112 women) was 69%. The trained GRNN had an accuracy of 91.3% (95% confidence interval [CI], 86.8 to 95.8). The sensitivity was 98.9% for having OSA (95% CI, 96.7 to 100), and the specificity was 80% (95% CI, 70 to 90). The positive predictive value that the patient would have OSA was 88.1% (95% CI, 81.8 to 94.4), whereas the negative predictive value that the patient would not have OSA (if so classified) was 98% (95% CI, 94 to 100). ::: ::: ::: CONCLUSIONS ::: Appropriately trained GRNN has the ability to accurately rule in OSA from clinical data, and GRNN did not misclassify patients with moderate to severe OSA. In this study, use of the neural network could have reduced the number of PSG studies performed. Prospective validation of the neural network for the diagnosis of OSA is now required.


title: An innovative cost-effective smart meter with embedded non intrusive load monitoring

abstract: Most of the time, domestic energy usage is invisible to the users. Households have only a vague idea about the amount of energy they are using for different purposes during the day, and the electric bill is usually the only monthly/bimonthly feedback. Making the energy consumption promptly visible can make the difference and foster a really efficient usage of the energy in a city. The emerging smart metering infrastructure has the potential to address some of these goals, by providing both households and electric utilities useful information about energy consumption, with a detailed breakdown to single appliance contribution. This paper presents a novel ultra-low-cost smart metering system capable of providing domestic AC load identification, by implementing both basic/typical smart meter measurement and a simple yet effective load disaggregation algorithm, which can detect and monitor domestic loads starting from a single point of measurement.


title: Gaussian Process Regression Networks

abstract: We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the nonparametric exibility of Gaussian processes. GPRN accommodates input (predictor) dependent signal and noise correlations between multiple output (response) variables, input dependent length-scales and amplitudes, and heavy-tailed predictive distributions. We derive both elliptical slice sampling and variational Bayes inference procedures for GPRN. We apply GPRN as a multiple output regression and multivariate volatility model, demonstrating substantially improved performance over eight popular multiple output (multi-task) Gaussian process models and three multivariate volatility models on real datasets, including a 1000 dimensional gene expression dataset.


title: A benchmark study of large-scale unconstrained face recognition

abstract: Many efforts have been made in recent years to tackle the unconstrained face recognition challenge. For the benchmark of this challenge, the Labeled Faces in the Wild (LFW) database has been widely used. However, the standard LFW protocol is very limited, with only 3,000 genuine and 3,000 impostor matches for classification. Today a 97% accuracy can be achieved with this benchmark, remaining a very limited room for algorithm development. However, we argue that this accuracy may be too optimistic because the underlying false accept rate may still be high (e.g. 3%). Furthermore, performance evaluation at low FARs is not statistically sound by the standard protocol due to the limited number of impostor matches. Thereby we develop a new benchmark protocol to fully exploit all the 13,233 LFW face images for large-scale unconstrained face recognition evaluation under both verification and open-set identification scenarios, with a focus at low FARs. Based on the new benchmark, we evaluate 21 face recognition approaches by combining 3 kinds of features and 7 learning algorithms. The benchmark results show that the best algorithm achieves 41.66% verification rates at FAR=0.1%, and 18.07% open-set identification rates at rank 1 and FAR=1%. Accordingly we conclude that the large-scale unconstrained face recognition problem is still largely unresolved, thus further attention and effort is needed in developing effective feature representations and learning algorithms. We thereby release a benchmark tool to advance research in this field.


title: Design and Implementation of Pedestrian Dead Reckoning System on a Mobile Phone

abstract: Pedestrian dead reckoning (PDR) based on human gait locomotion is a promising solution for indoor location services, which independently determine the relative position of the user using multiple sensors. Most existing PDR methods assume that all sensors are mounted in a fixed position on the user's body while walking. However, it is inconvenient for a user to mount his/her mobile phone or additional sensor modules in a specific position on his/her body such as the torso. In this paper, we propose a new PDR method and a prototype system suitable for indoor navigation systems on a mobile phone. Our method determines the user's relative position even if the sensors' orientation relative to the user is not given and changes from moment to moment. Therefore, the user does not have to mount the mobile phone containing sensors on the body and can carry it in a natural way while walking, e.g., while swinging the arms. Detailed algorithms, implementation and experimental evaluation results are presented.


title: Opportunities and challenges with autonomous micro aerial vehicles

abstract: We survey the recent work on micro unmanned aerial vehicles (UAVs), a fast-growing field in robotics, outlining the opportunities for research and applications, along with the scientific and technological challenges. Micro-UAVs can operate in three-dimensional environments, explore and map multi-story buildings, manipulate and transport objects, and even perform such tasks as assembly. While fixed-base industrial robots were the main focus in the first two decades of robotics, and mobile robots enabled most of the significant advances during the next two decades, it is likely that UAVs, and particularly micro-UAVs, will provide a major impetus for the next phase of education, research, and development.


title: Machine Learning in DNA Microarray Analysis for Cancer Classification

abstract: The development of microarray technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it expectedly helps us to exactly predict and diagnose cancer. To precisely classify cancer we have to select genes related to cancer because extracted genes from microarray have many noises. In this paper, we attempt to explore many features and classifiers using three benchmark datasets to systematically evaluate the performances of the feature selection methods and machine learning classifiers. Three benchmark datasets are Leukemia cancer dataset, Colon cancer dataset and Lymphoma cancer data set. Pearson's and Spearman's correlation coefficients, Euclidean distance, cosine coefficient, information gain, mutual information and signal to noise ratio have been used for feature selection. Multi-layer perceptron, k-nearest neighbour, support vector machine and structure adaptive self-organizing map have been used for classification. Also, we have combined the classifiers to improve the performance of classification. Experimental results show that the ensemble with several basis classifiers produces the best recognition rate on the benchmark dataset.


title: Bidirectional Learning for Robust Neural Networks

abstract: A multilayer perceptron can behave as a generative classifier by applying bidirectional learning (BL). It consists of training an undirected neural network to map input to output and vice-versa; therefore it can produce a classifier in one direction, and a generator in the opposite direction for the same data. The learning process of BL tries to reproduce the neuroplasticity stated in Hebbian theory using only backward propagation of errors. In this paper, two learning techniques are independently introduced which use BL for improving robustness to white noise static and adversarial examples. The first method is bidirectional propagation of errors, which the error propagation occurs in backward and forward directions. Motivated by the fact that its generative model receives as input a constant vector per class, we introduce as a second method the novel hybrid adversarial networks (HAN). Its generative model receives a random vector as input and its training is based on generative adversarial networks (GAN). To assess the performance of BL, we perform experiments using several architectures with fully and convolutional layers, with and without bias. Experimental results show that both methods improve robustness to white noise static and adversarial examples, and even increase accuracy, but have different behavior depending on the architecture and task, being more beneficial to use the one or the other. Nevertheless, HAN using a convolutional architecture with batch normalization presents outstanding robustness, reaching state-of-the-art accuracy on adversarial examples of hand-written digits.


title: Learning to Teach in Cooperative Multiagent Reinforcement Learning

abstract: Collective human knowledge has clearly benefited from the fact that innovations by individuals are taught to others through communication. Similar to human social groups, agents in distributed learning systems would likely benefit from communication to share knowledge and teach skills. The problem of teaching to improve agent learning has been investigated by prior works, but these approaches make assumptions that prevent application of teaching to general multiagent problems, or require domain expertise for problems they can apply to. This learning to teach problem has inherent complexities related to measuring long-term impacts of teaching that compound the standard multiagent coordination challenges. In contrast to existing works, this paper presents the first general framework and algorithm for intelligent agents to learn to teach in a multiagent environment. Our algorithm, Learning to Coordinate and Teach Reinforcement (LeCTR), addresses peer-to-peer teaching in cooperative multiagent reinforcement learning. Each agent in our approach learns both when and what to advise, then uses the received advice to improve local learning. Importantly, these roles are not fixed; these agents learn to assume the role of student and/or teacher at the appropriate moments, requesting and providing advice in order to improve teamwide performance and learning. Empirical comparisons against state-of-the-art teaching methods show that our teaching agents not only learn significantly faster, but also learn to coordinate in tasks where existing methods fail.


title: Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 2: Application examples

abstract: This paper is part two of a two part series. The originality of part one was the proposal of a novelty approach for wind turbine supervisory control and data acquisition (SCADA) data mining for condition monitoring purposes. The novelty concerned the usage of adaptive neuro-fuzzy interference system (ANFIS) models in this context and the application of a proposed procedure to a wide range of different SCADA signals. The applicability of the set up ANFIS models for anomaly detection was proven by the achieved performance of the models. In combination with the fuzzy interference system (FIS) proposed the prediction errors provide information about the condition of the monitored components. Part two presents application examples illustrating the efficiency of the proposed method. The work is based on continuously measured wind turbine SCADA data from 18 modern type pitch regulated wind turbines of the 2 MW class covering a period of 35 months. Several real life faults and issues in this data are analyzed and evaluated by the condition monitoring system (CMS) and the results presented. It is shown that SCADA data contain crucial information for wind turbine operators worth extracting. Using full signal reconstruction (FSRC) adaptive neuro-fuzzy interference system (ANFIS) normal behavior models (NBM) in combination with fuzzy logic (FL) a setup is developed for data mining of this information. A high degree of automation can be achieved. It is shown that FL rules established with a fault at one turbine can be applied to diagnose similar faults at other turbines automatically via the CMS proposed. A further focus in this paper lies in the process of rule optimization and adoption, allowing the expert to implement the gained knowledge in fault analysis. The fault types diagnosed here are: (1) a hydraulic oil leakage; (2) cooling system filter obstructions; (3) converter fan malfunctions; (4) anemometer offsets and (5) turbine controller malfunctions. Moreover, the graphical user interface (GUI) developed to access, analyze and visualize the data and results is presented.


title: Some metric properties of arithmetic quotients of symmetric spaces and an extension theorem

abstract: A thermally isolating structural support system to support a cryogenic refrigerator or cryogen tank and/or a refrigeration load within an insulated assembly in a manner to minimize heat transfer by conduction from the surroundings. The support system comprises a rigid support member, supporting pins affixed to the rigid member, and a bushing for each pin in which ball supports maintain a spacing between the pin and bushing and permit the pin to move freely parallel to its axis, but constrains its movement perpendicular to its axis. The support system may be used to support several loads within the same assembly.


title: Optical Character Recognition Techniques: A survey

abstract: This paper presents a literature review on English OCR techniques. English OCR system is compulsory to convert numerous published books of English into editable computer text files. Latest research in this area has been able to grown some new methodologies to overcome the complexity of English writing style. Still these algorithms have not been tested for complete characters of English Alphabet. Hence, a system is required which can handle all classes of English text and identify characters among these classes.


title: Universal Planning Networks

abstract: A key challenge in complex visuomotor control is learning abstract representations that are effective for specifying goals, planning, and generalization. To this end, we introduce universal planning networks (UPN). UPNs embed differentiable planning within a goal-directed policy. This planning computation unrolls a forward model in a latent space and infers an optimal action plan through gradient descent trajectory optimization. The plan-by-gradient-descent process and its underlying representations are learned end-to-end to directly optimize a supervised imitation learning objective. We find that the representations learned are not only effective for goal-directed visual imitation via gradient-based trajectory optimization, but can also provide a metric for specifying goals using images. The learned representations can be leveraged to specify distance-based rewards to reach new target states for model-free reinforcement learning, resulting in substantially more effective learning when solving new tasks described via image-based goals. We were able to achieve successful transfer of visuomotor planning strategies across robots with significantly different morphologies and actuation capabilities.


title: Search-Free DOD, DOA and Range Estimation for Bistatic FDA-MIMO Radar

abstract: Frequency diverse array (FDA) is an emerging technology, the hybrid of FDA and multiple-input-multiple-output (FDA-MIMO) under monostatic scenarios has received much attention in recent years. However, little work have been done for bistatic FDA-MIMO radar. In this paper, we investigate strategies on estimating direction-of-departure (DOD), direction-of-arrival and range for bistatic FDA-MIMO radar. Our strategies have two aspects. First, nonlinear frequency increments including both subarray and random modes are employed to overcome the problem that the DOD and range parameters of FDA transmitting steering vectors are coupled. Second, in order to reduce the computational complexity associated with the 3-D spectral peak searching algorithms, estimation of signal parameters via rotational invariance technique and parallel factor algorithms with their corresponding phase ambiguity resolving methods, are proposed for subarray and random modes, respectively. Both of the two algorithms perform well while the range parameter of targets satisfy a range constraint criterion. This criterion can also be used for designing frequency increments of bistatic FDA-MIMO radar. Additionally, the Cramer-Rao bound of bistatic FDA-MIMO radar and the algorithm performance analysis consist of identifiability and complexity are derived. All the proposed methods are verified by both theoretical analysis and numerical simulations. And satisfactory results are achieved.


title: Gaussian process uncertainty in age estimation as a measure of brain abnormality☆

abstract: Multivariate regression models for age estimation are a powerful tool for assessing abnormal brain morphology associated to neuropathology. Age prediction models are built on cohorts of healthy subjects and are built to reflect normal aging patterns. The application of these multivariate models to diseased subjects usually results in high prediction errors, under the hypothesis that neuropathology presents a similar degenerative pattern as that of accelerated aging. In this work, we propose an alternative to the idea that pathology follows a similar trajectory than normal aging. Instead, we propose the use of metrics which measure deviations from the mean aging trajectory. We propose to measure these deviations using two different metrics: uncertainty in a Gaussian process regression model and a newly proposed age weighted uncertainty measure. Consequently, our approach assumes that pathologic brain patterns are different to those of normal aging. We present results for subjects with autism, mild cognitive impairment and Alzheimer's disease to highlight the versatility of the approach to different diseases and age ranges. We evaluate volume, thickness, and VBM features for quantifying brain morphology. Our evaluations are performed on a large number of images obtained from a variety of publicly available neuroimaging databases. Across all features, our uncertainty based measurements yield a better separation between diseased subjects and healthy individuals than the prediction error. Finally, we illustrate differences in the disease pattern to normal aging, supporting the application of uncertainty as a measure of neuropathology.


title: A Fully Integrated Shark-Fin Antenna for MIMO-LTE, GPS, WLAN, and WAVE Applications

abstract: In this study, a three-dimensional compact antenna solution for the automotive industry is proposed. The antenna solution is designed to fit in a shark-fin case and is easily fabricated from a printed circuit board and a metal sheet with low-cost process and materials. The antenna solution covers Long Term Evolution (LTE), GPS, WLAN, and Wireless Access in the Vehicular Environment (WAVE) bands (850 MHz, 1575 MHz, 2.4 GHz, and 5.9 GHz, respectively). The planar inverted-F antennas are used as multiple-input–multiple-output antennas for the LTE band due to their low-profile structure. Modified planar monopoles are used to obtain omnidirectional radiation patterns for WLAN and WAVE bands. Antenna characteristics such as return loss, isolation, and radiation pattern have been simulated and measured to confirm the possibility for use in automotive applications.


title: Inf-convolution of g_\Gamma-solution and its applications

abstract: A risk-neutral method is always used to price and hedge contingent claims in complete market, but another method based on utility maximization or risk minimization is wildly used in more general case. One can find all kinds of special risk measure in literature. In this paper, instead of using market modified risk measure, we use a kind of risk measure induced by g_\Gamma-solution or the minimal solution of a Constrained Backward Stochastic Differential Equation (CBSDE) directly when constraints on wealth and portfolio process comes to our consideration. Such g_\Gamma-solution and the risk measure generated by it is well defined on appropriate space under suitable conditions. We adopt the inf-convolution of convex risk measures to solve some optimization problem. A dynamic version risk measures defined through g_\Gamma-solution and some similar results about optimal problem can be got in our new framework and by our new approach.


title: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.

abstract: Methods of evaluating and comparing the performance of diagnostic tests are of increasing importance as new tests are developed and marketed. When a test is based on an observed variable that lies on a continuous or graded scale, an assessment of the overall value of the test can be made through the use of a receiver operating characteristic (ROC) curve. The curve is constructed by varying the cutpoint used to determine which values of the observed variable will be considered abnormal and then plotting the resulting sensitivities against the corresponding false positive rates. When two or more empirical curves are constructed based on tests performed on the same individuals, statistical analysis on differences between curves must take into account the correlated nature of the data. This paper presents a nonparametric approach to the analysis of areas under correlated ROC curves, by using the theory on generalized U-statistics to generate an estimated covariance matrix.


title: Design of a under voltage lock out circuit with bandgap structure

abstract: According to the necessary function of Under Voltage Lock Out (UVLO) in DC-DC power management systems, an improved UVLO circuit is proposed. The circuit realizes stabilization of parameters such as threshold point voltage, hysteretic range of the comparator etc. without utilizing an extra bandgap reference voltage source for comparison. The UVLO circuit is implemented in CSMC 0.5µm BCD process. Hspice simulation results show that the UVLO circuit presents advantages such as simple topology, sensitive response, low temperature draft, low power consumption.


title: Towards Self-explanatory Ontology Visualization with Contextual Verbalization

abstract: Ontologies are one of the core foundations of the Semantic Web. To participate in Semantic Web projects, domain experts need to be able to understand the ontologies involved. Visual notations can provide an overview of the ontology and help users to understand the connections among entities. However, the users first need to learn the visual notation before they can interpret it correctly. Controlled natural language representation would be readable right away and might be preferred in case of complex axioms, however, the structure of the ontology would remain less apparent. We propose to combine ontology visualizations with contextual ontology verbalizations of selected ontology (diagram) elements, displaying controlled natural language (CNL) explanations of OWL axioms corresponding to the selected visual notation elements. Thus, the domain experts will benefit from both the high-level overview provided by the graphical notation and the detailed textual explanations of particular elements in the diagram.


title: Unsupervised Anomaly Detection in Online Game

abstract: Online game is one of the most successful business on the Internet. As online game business grows, cheating in game becomes popular and is the biggest challenge of online game systems. In this paper, we investigate the application of anomaly detection techniques to cheating detection in an online game (JX2) of VNG company. A method to evaluate the performance of unsupervised anomaly detection techniques was proposed. Six unsupervised anomaly detection algorithms were tested. The experimental results show that the kernel density based technique and ensemble techniques performed best on this game data. Our post analysis helped to identify and eliminate some cheating players in the game.


title: Opening the neural network black box: an algorithm for extracting rules from function approximating artificial neural networks

abstract: Artificial neural networks have been successfully applied to solve a variety of business applications involving classification and function approximation. In many such applications, it is desirable to extract knowledge from trained neural networks so that the users can gain a better understanding of the solution. Existing research works have focused primarily on extracting symbolic rules for classification problems with few methods devised for function approximation problems. In order to fill this gap, we propose an approach to extract rules from neural networks that have been trained to solve function approximation problems. The extracted rules divide the data samples into groups. For all samples within a group, a linear function of the relevant input attributes of the data approximates the network output. Experimental results show that the proposed approach generates rules that are more accurate than the existing methods based on decision trees and regression.


title: Efficient and Cost-Effective ZCS Direct AC–AC Resonant Converter for Induction Heating

abstract: Domestic induction-heating technology requires specific features such as high output power levels in a reduced enclosure, elevated operating temperature, large load variation, and reduced cost. To fulfill these requirements, classical solutions are based on the combination of a rectifier and a dc-link inverter. This is a well-balanced solution, but there is still room for efficiency and cost improvements. Unlike previous proposals, this paper proposes a direct ac-ac converter to reduce the component count, reduce cost, improve reliability, and increase efficiency. The proposed converter is a voltage-source series-resonant converter that achieves linear output power control, reducing control complexity. Moreover, the proposed converter achieves soft switching during both turn-on and turn-off transitions, further improving the efficiency and enabling the selection of low-speed devices with improved conduction properties. A 3.6-kW converter has been designed and implemented, verifying the feasibility of the proposal and the expected performance.