title: Identifying emotions in images from valence and arousal ratings

abstract: Experimental studies of emotion usually use datasets of normative emotional pictures to elicit specific emotional responses in human subjects. However, most of these datasets are not annotated with discriminating and reliable emotional tags, having only valence and arousal ratings for each image. Complementing this information with emotional tags would enrich the datasets, by increasing the number of annotated images available and consequently reducing the use of the same images in consecutive studies. This paper describes a multi-label recognizer that combines a Fuzzy approach with a Random Forest classifier to recognize both polarity and discrete emotions elicited by an image, using its valence and arousal ratings. Polarity indicates whether the emotional content of the image is negative, neutral, or positive, whereas emotions provide a more detailed description of the emotional content conveyed by the image. We evaluated our multi-label recognizer using pictures from four existing datasets containing images annotated with emotional content and valence and arousal ratings. Experimental results show that our recognizer is able to identify polarity with a precision of 84.8%, single emotions with 80.7%, and two emotions with 81.1%. Our recognizer can be useful to researchers who want to identify polarity and/or emotions from stimuli annotated with valence and arousal ratings. In particular, it can be used to automatically annotate with emotional tags already existent image datasets, avoiding the costs of manually annotating them with human subjects.


title: DWT Based Detection of Epileptic Seizure From EEG Signals Using Naive Bayes and k-NN Classifiers

abstract: Electroencephalogram (EEG) comprises valuable details related to the different physiological state of the brain. In this paper, a framework is offered for detecting the epileptic seizures from EEG data recorded from normal subjects and epileptic patients. This framework is based on a discrete wavelet transform (DWT) analysis of EEG signals using linear and nonlinear classifiers. The performance of the 14 different combinations of two-class epilepsy detection is studied using naive Bayes (NB) and k-nearest neighbor (k-NN) classifiers for the derived statistical features from DWT. It has been found that the NB classifier performs better and shows an accuracy of 100% for the individual and combined statistical features derived from the DWT values of normal eyes open and epileptic EEG data provided by the University of Bonn, Germany. It has been found that the computation time of NB classifier is lesser than k-NN to provide better accuracy. So, the detection of an epileptic seizure based on DWT statistical features using NB classifiers is more suitable in real time for a reliable, automatic epileptic seizure detection system to enhance the patient’s care and the quality of life.


title: The energetics of middle-distance running

abstract: In order to assess the relative contribution of aerobic processes to running velocity (v), 27 male athletes were selected on the basis of their middle-distance performances over 800, 1500, 3000 or 5000 m, during the 1987 track season. To be selected for study, the average running velocity (v) corresponding to their performances had to be superior to 90% of the best French v of the season. Maximum O2 consumption (VO2max) and energy cost of running (C) had been measured within the 2 months preceding the track season, which, together with oxygen consumption at rest (VO2rest) allowed us to calculate the maximal v that could be sustained under aerobic conditions: vamax = (VO2max - VO2rest) x C-1. The treadmill running v corresponding to a blood lactate of 4 mmol.l-1 (vla4), was also calculated. In the whole group, C was significantly related to height (r = -0.43; P less than 0.03). Neither C nor VO2max (with, in this case, the exception of the 3000 m athletes) were correlated to v. On the other hand, vamax was significantly correlated to v over distances longer than 800 m. These v were also correlated to vla4. However vla4 occurred at 87.5% SD 3.3% of vamax, this relationship was interpreted as being an expression of the correlation between vamax and v. Calculation of vamax provided a useful means of analysing the performances. At the level of achievement studied, v sustained over 3000 m corresponded to vamax.(ABSTRACT TRUNCATED AT 250 WORDS)


title: A randomized controlled trial of compassion cultivation training: Effects on mindfulness, affect, and emotion regulation

abstract: Compassion is a positive orientation towards suffering that may be enhanced through compassion training and is thought to influence psychological functioning. However, the effects of compassion training on mindfulness, affect, and emotion regulation are not known. We conducted a randomized controlled trial in which 100 adults from the community were randomly assigned to either a 9-week compassion cultivation training (CCT) or a waitlist (WL) control condition. Participants completed self-report inventories that measured mindfulness, positive and negative affect, and emotion regulation. Compared to WL, CCT resulted in increased mindfulness and happiness, as well as decreased worry and emotional suppression. Within CCT, the amount of formal meditation practiced was related to reductions in worry and emotional suppression. These findings suggest that compassion cultivation training effects cognitive and emotion factors that support psychological flexible and adaptive functioning.


Implementation of Improved Harmony Search Based Clustering Algorithm in Wireless Sensor Networks


title: Construction and analysis of a Vibrio cholerae delta-aminolevulinic acid auxotroph which confers protective immunity in a rabbit model.

abstract: Abstract ::: Vibrio cholerae CVD101 is a very effective live vaccine. Although this strain does not produce active cholera toxin because of a mutation in the gene for the cholera toxin A subunit, it still shows residual pathogenicity. To attenuate CVD101 further, we set out to isolate derivatives of CVD101 which were limited in their ability to proliferate in vivo. Two delta-aminolevulinic acid auxotrophs of CVD101, designated V286 and V287, were isolated by transposon mutagenesis and penicillin enrichment. Southern blotting revealed that the mutants differed with respect to the location of the transposon insertion. Under aerobic conditions, in the absence of delta-aminolevulinic acid, both mutants showed diminished growth compared with CVD101. The growth of V286 was most severely affected. Microaerophilic growth of both mutants was less affected. Competition experiments with a rabbit model showed that strain V286 was found in numbers 10(3)- to 10(4)-fold lower than its parental strain. This observation indicates that strain V286 is impaired in its ability to colonize the rabbit intestine. It also supports an important role for aerobic growth in the colonization of the intestine by V. cholerae. Vaccination of rabbits with a single dose of strain V286 resulted in full protection against challenge with a virulent strain. Strain V286 was not shed from rabbits in a cultivatable form. Our results suggest that delta-aminolevulinic acid auxotrophy can attenuate V. cholerae by limiting its ability to colonize without affecting its capacity to induce protective immunity. Furthermore, this type of mutation may prevent the spread of V. cholerae vaccine strains in the environment.


title: Epidermal Growth Factor Receptor Cell Proliferation Signaling Pathways

abstract: The epidermal growth factor receptor (EGFR) is a receptor tyrosine kinase that is commonly upregulated in cancers such as in non-small-cell lung cancer, metastatic colorectal cancer, glioblastoma, head and neck cancer, pancreatic cancer, and breast cancer. Various mechanisms mediate the upregulation of EGFR activity, including common mutations and truncations to its extracellular domain, such as in the EGFRvIII truncations, as well as to its kinase domain, such as the L858R and T790M mutations, or the exon 19 truncation. These EGFR aberrations over-activate downstream pro-oncogenic signaling pathways, including the RAS-RAF-MEK-ERK MAPK and AKT-PI3K-mTOR pathways. These pathways then activate many biological outputs that are beneficial to cancer cell proliferation, including their chronic initiation and progression through the cell cycle. Here, we review the molecular mechanisms that regulate EGFR signal transduction, including the EGFR structure and its mutations, ligand binding and EGFR dimerization, as well as the signaling pathways that lead to G1 cell cycle progression. We focus on the induction of CYCLIN D expression, CDK4/6 activation, and the repression of cyclin-dependent kinase inhibitor proteins (CDKi) by EGFR signaling pathways. We also discuss the successes and challenges of EGFR-targeted therapies, and the potential for their use in combination with CDK4/6 inhibitors.


UWB at SemEval-2016 Task 1: Semantic Textual Similarity using Lexical, Syntactic, and Semantic Information


title: Web Crawler: A Review

abstract: Information Retrieval deals with searching and retrieving information within the documents and it also searches the online databases and internet. Web crawler is defined as a program or software which traverses the Web and downloads web documents in a methodical, automated manner. Based on the type of knowledge, web crawler is usually divided in three types of crawling techniques: General Purpose Crawling, Focused crawling and Distributed Crawling. In this paper, the applicability of Web Crawler in the field of web search and a review on Web Crawler to different problem domains in web search is discussed.


title: A framework and a methodology for data quality assessment and monitoring

abstract: A method for producing triethylenediamines and piperazines, which comprises contacting an amine compound having a group of the formula (1):wherein each of R1 to R4 which are independent of one another, is a hydrogen atom or a C1-3 alkyl group which may have a substituent, with a catalyst consisting of a crystalline aluminosilicate calcinated at a temperature of from 500 to 950° C. and then contacted with an inorganic acid, and having a molar ratio of silica to alumina of at least 12.


title: Scaling laws in the macro-, micro- and nanoworlds

abstract: The microworld and the nanoworld are now of interest to the public, scientists, engineers and industrialists. Effects negligible at the macroscopic level become important at the micrometre scale, and vice versa. Moreover, at the nanoscale, quantum effects may become dominant. Scaling laws are useful for an understanding of the origin of such differences and/or to generalize results obtained at various scales. It is the aim of this paper to examine scaling laws relevant to mechanics, fluids, electromagnetism, thermodynamics, optics and quantum mechanics. Examples related to biology, micromachines and nanotechnologies are treated to show the usefulness of the scaling laws. In recent years, interest in the domains of the microworld and the nanoworld has increased rapidly. The advent of so-called mesoscopic physics, the development of micromachines or micro-electrical mechanical systems (MEMS), the synthesis of nanotubes, the development of nanotechnology, etc… are among the most cited subjects. From both the fundamental and applied points of view, there are interesting questions about these micro- and nanoworlds. In particular, effects negligible at the macroscopic level are important at the micrometre scale, and vice versa. Moreover, when one goes from the macroworld to the nanoworld, one passes through two diffuse limits. When the characteristic dimensions of the elements decrease from the macroscopic to the micrometre size, the effects of gravity become negligible as compared with adhesive and friction effects. Surface tension dominates gravity; other examples are given later. This implies that our reasoning, based on our experience at the macroscopic level, is no longer valid. We have to modify our rules of thinking. The boundary between the macroscopic and microscopic levels is not sharp; it depends on the effect to be considered. When the characteristic size decreases further to attain the nanometre range, another limit is encountered. While the macroscopic properties of matter remain generally valid at the micrometre size, surface effects become dominant at the nanometre scale. Moreover, when one reaches the interatomic distance range, quantum effects appear. 0143-0807/01/060601+11$30.00 © 2001 IOP Publishing Ltd Printed in the UK 601


title: Tridiagonal Commuting Matrices and Fractionalizations of DCT and DST Matrices of Types I, IV, V, and VIII

abstract: In this paper, we first establish new relationships in matrix forms among discrete Fourier transform (DFT), generalized DFT (GDFT), and various types of discrete cosine transform (DCT) and discrete sine transform (DST) matrices. Two new independent tridiagonal commuting matrices for each of DCT and DST matrices of types I, IV, V, and VIII are then derived from the existing commuting matrices of DFT and GDFT. With these new commuting matrices, the orthonormal sets of Hermite-like eigenvectors for DCT and DST matrices can be determined and the discrete fractional cosine transform (DFRCT) and the discrete fractional sine transform (DFRST) are defined. The relationships among the discrete fractional Fourier transform (DFRFT), fractional GDFT, and various types of DFRCT and DFRST are developed to reduce computations for DFRFT and fractional GDFT.


Efficient Construction of Dictionary using Directed Acyclic Word Graph


title: Finding Face Features

abstract: We describe a computer program which understands a greyscale image of a face well enough to locate individual face features such as eyes and mouth. The program has two distinct components: modules designed to locate particular face features, usually in a restricted area; and the overall control strategy which activates modules on the basis of the current solution state, and assesses and integrates the results of each module.


title: Applications and Trust Issues When Crowdsourcing a Crisis

abstract: The United States Department of Defense funded a research project at the University of Virginia to increase community resilience after a crisis (e.g., natural disaster, bioterrorism attack). The premise is that the public would be better served if it had access to reliable, near real-time information concerning the "ground truth" (actual observations and pictures) of unfolding events. One way (of many) to collect those reports is through crowdsourcing. By taking advantage of the army of people connected to the internet via both wired and wireless technologies, we can easily gather information that would help a citizen make an informed decision regarding his or her health and welfare (e.g., shelter-in-place vs. flight). The widespread use of smartphones allows people to post geolocated reports and pictures with ease. Disaster management teams would likewise benefit from the input of more observers. On the other hand, the trustworthiness of these reports may be uncertain. For instance, a terrorist group might use crowdsourcing to redirect the community's emergency responses to the wrong place. We first explore how a crowdsourcing system might be used to enhance disaster management, and then we investigate three ways to enhance the trust that the citizen places in such a system.


title: Asymmetrical V-Shape Rotor Configuration of an Interior Permanent Magnet Machine for Improving Torque Characteristics

abstract: In comparison to surface-mounted permanent magnet machines, IPM machines offer several advantages such as higher power density and wider speed range, since the PM is inserted into the rotor core. Such machines have been generally selected as drive motors widely applied in electrical vehicle, CNC machine tool and other variable speed applications. Nevertheless, due to this structural characteristic, IPM machines produce extremely higher cogging torque and more rectangularly distributed flux density in air gap, compared to SPM machines. In general, IPM machines are subjected to higher torque ripple due to cogging torque and harmonic torque, which causes the undesirable vibration and noise. The smooth electromagnetic torque is required for most applications. Thus, one of the most significant tasks for IPM machine design is to make torque ripple as small as possible. This study presents an asymmetry V-shape rotor configuration of IPM machines for improving torque characteristics. IPM machines with the asymmetry V-shape rotor configuration are better in torque characteristics.


"Psychology": Noun or adjective?


title: PADS: Passive detection of moving targets with dynamic speed using PHY layer information

abstract: Device-free passive detection is an emerging technology to detect whether there exists any moving entities in the area of interests without attaching any device to them. It is an essential primitive for a broad range of applications including intrusion detection for safety precautions, patient monitoring in hospitals, child and elder care at home, etc. Despite of the prevalent signal feature Received Signal Strength (RSS), most robust and reliable solutions resort to finer-grained channel descriptor at physical layer, e.g., the Channel State Information (CSI) in the 802.11n standard. Among a large body of emerging techniques, however, few of them have explored full potentials of CSI for human detection. Moreover, space diversity supported by nowadays popular multi-antenna systems are not investigated to the comparable extent as frequency diversity. In this paper, we propose a novel scheme for device-free PAssive Detection of moving humans with dynamic Speed (PADS). Both amplitude and phase information of CSI are extracted and shaped into sensitive metrics for target detection; and CSI across multi-antennas in MIMO systems are further exploited to improve the detection accuracy and robustness. We prototype PADS on commercial WiFi devices and experiment results in different scenarios demonstrate that PADS achieves great performance improvement in spite of dynamic human movements.


title: Learning the Semantics of Structured Data Sources

abstract: Information sources such as relational databases, spreadsheets, XML, JSON, and Web APIs contain a tremendous amount of structured data that can be leveraged to build and augment knowledge graphs. However, they rarely provide a semantic model to describe their contents. Semantic models of data sources represent the implicit meaning of the data by specifying the concepts and the relationships within the data. Such models are the key ingredients to automatically publish the data into knowledge graphs. Manually modeling the semantics of data sources requires significant effort and expertise, and although desirable, building these models automatically is a challenging problem. Most of the related work focuses on semantic annotation of the data fields (source attributes). However, constructing a semantic model that explicitly describes the relationships between the attributes in addition to their semantic types is critical.We present a novel approach that exploits the knowledge from a domain ontology and the semantic models of previously modeled sources to automatically learn a rich semantic model for a new source. This model represents the semantics of the new source in terms of the concepts and relationships defined by the domain ontology. Given some sample data from the new source, we leverage the knowledge in the domain ontology and the known semantic models to construct a weighted graph that represents the space of plausible semantic models for the new source. Then, we compute the top k candidate semantic models and suggest to the user a ranked list of the semantic models for the new source. The approach takes into account user corrections to learn more accurate semantic models on future data sources. Our evaluation shows that our method generates expressive semantic models for data sources and services with minimal user input. These precise models make it possible to automatically integrate the data across sources and provide rich support for source discovery and service composition. They also make it possible to automatically publish semantic data into knowledge graphs.


title: Computer-aided assessment of breast density: Comparison of supervised deep learning and feature based statistical learning

abstract: Breast density is one of the most significant factors that is associated with cancer risk. In this study, our purpose was to develop a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammograms (DMs). The input 'for processing' DMs was first log-transformed, enhanced by a multi-resolution preprocessing scheme, and subsampled to a pixel size of 800 µm × 800 µm from 100 µm × 100 µm. A deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD) by using a domain adaptation resampling method. The PD was estimated as the ratio of the dense area to the breast area based on the PMD. The DCNN approach was compared to a feature-based statistical learning approach. Gray level, texture and morphological features were extracted and a least absolute shrinkage and selection operator was used to combine the features into a feature-based PMD. With approval of the Institutional Review Board, we retrospectively collected a training set of 478 DMs and an independent test set of 183 DMs from patient files in our institution. Two experienced mammography quality standards act radiologists interactively segmented PD as the reference standard. Ten-fold cross-validation was used for model selection and evaluation with the training set. With cross-validation, DCNN obtained a Dice's coefficient (DC) of 0.79 ± 0.13 and Pearson's correlation (r) of 0.97, whereas feature-based learning obtained DC = 0.72 ± 0.18 and r = 0.85. For the independent test set, DCNN achieved DC = 0.76 ± 0.09 and r = 0.94, while feature-based learning achieved DC = 0.62 ± 0.21 and r = 0.75. Our DCNN approach was significantly better and more robust than the feature-based learning approach for automated PD estimation on DMs, demonstrating its potential use for automated density reporting as well as for model-based risk prediction.


title: Data-Dependent Label Distribution Learning for Age Estimation

abstract: As an important and challenging problem in computer vision, face age estimation is typically cast as a classification or regression problem over a set of face samples with respect to several ordinal age labels, which have intrinsically cross-age correlations across adjacent age dimensions. As a result, such correlations usually lead to the age label ambiguities of the face samples. Namely, each face sample is associated with a latent label distribution that encodes the cross-age correlation information on label ambiguities. Motivated by this observation, we propose a totally data-driven label distribution learning approach to adaptively learn the latent label distributions. The proposed approach is capable of effectively discovering the intrinsic age distribution patterns for cross-age correlation analysis on the basis of the local context structures of face samples. Without any prior assumptions on the forms of label distribution learning, our approach is able to flexibly model the sample-specific context aware label distribution properties by solving a multi-task problem, which jointly optimizes the tasks of age-label distribution learning and age prediction for individuals. Experimental results demonstrate the effectiveness of our approach.


title: Skeletal structure with artificial perspiration for cooling by latent heat for musculoskeletal humanoid Kengoro

abstract: In this paper we propose a novel method to utilize the skeletal structure not only for supporting force but for releasing heat by latent heat.


title: Database-guided segmentation of anatomical structures with complex appearance

abstract: The segmentation of anatomical structures has been traditionally formulated as a perceptual grouping task, and solved through clustering and variational approaches. However, such strategies require the a priori knowledge to be explicitly defined in the optimization criterion, e.g., "high-gradient border", "smoothness"', or "similar intensity or texture". This approach is limited by the validity of underlying assumptions and cannot capture complex structure appearance. This paper introduces database-guided segmentation as a new data-driven paradigm that directly exploits expert annotation of interest structures in large medical databases. Segmentation is formulated as a two-step learning problem. The first step is structure detection where we learn how to discriminate between the object of interest and background. The resulting classifier based on a boosted cascade of simple features also provides a global rigid transformation of the structure. The second step is shape inference where we use a sample-based representation of the joint distribution of appearance and shape annotations. To learn the association between the complex appearance and shape we propose a feature selection mechanism and the corresponding metric. We show that the selected features are better than using directly the appearance and illustrate the performance of the proposed method on a large set of ultrasound heart images.


title: A new approach to forecasting intermittent demand for service parts inventories

abstract: Abstract A fundamental aspect of supply chain management is accurate demand forecasting. We address the problem of forecasting intermittent (or irregular) demand, i.e. random demand with a large proportion of zero values. This pattern is characteristic of demand for service parts inventories and capital goods and is difficult to predict. We forecast the cumulative distribution of demand over a fixed lead time using a new type of time series bootstrap. To assess accuracy in forecasting an entire distribution, we adapt the probability integral transformation to intermittent demand. Using nine large industrial datasets, we show that the bootstrapping method produces more accurate forecasts of the distribution of demand over a fixed lead time than do exponential smoothing and Croston’s method.


title: Mobile application development process: A practical experience

abstract: Nowadays there is an increasing interest in mobile application development. However, developers often disregard, or at least significantly adapt, existing software development processes to suit their purpose, given the existing specific constraints. Such adjustments can introduce variations and new trends in existing processes that in many occasions are not shared with the scientific community since there is no official documentation, thus justifying further research. In this paper, we present a study and characterization of current mobile application development processes based on a practical experience. We consider a set of real case studies to investigate the current development processes for mobile applications used by software development companies, as well as by independent developers. The result of the present study is the identification of mobile software development processes, namely agile approaches, and also of shortcomings in current methodologies applied in industry and academy, namely the lack of informed and experienced resources to develop mobile apps.


title: An efficient message access quality model in vehicular communication networks

abstract: In vehicular ad hoc network (VANET), vehicles equipped with computing, sensing, and communication capabilities can exchange information within a geographical area to distribute emergency messages and achieve safety system. Then how to enforce fine grained control of these messages and ensure the receiving messages coming from the claimed source in such a highly dynamic environments remains a key challenge that affects the quality of service. In this paper, we propose a hierarchical access control with authentication scheme for transmitted messages with security assurance over VANET. By extending ciphertext-policy attribute-based encryption (CP-ABE) with a hierarchical structure of multiple authorities, the scheme not only achieves scalability due to its hierarchical structure, but also inherits fine-grained access control on the transmitted messages. Also by exploiting attribute-based signature (ABS), the scheme can authorize the vehicles that can most appropriately deal with the message efficiently. The results of efficiency analysis and comparison with the related works show that the proposed scheme is efficient and scalable in dealing with access control and message authentication for data dissemination in VANET. HighlightsWe propose a hierarchical message access quality model in VANET which is scalable and efficient in dynamic vehicular communication environment.Our scheme achieves fine-grained access control among various types of vehicles using well defined attributes.Our scheme enforces message authentication and maintain anonymity and privacy of the vehicles.


title: A case of neuropathic brachioradial pruritus caused by cervical disc herniation

abstract: This Case Study describes a 64-year-old woman who presented with a 2-year history of itch on her right dorsal forearm. The patient was diagnosed with brachioradial pruritus caused by cervical disc herniation, and the symptoms resolved after surgery to decompress the sixth cervical nerve root. Various alternative treatment options for brachioradial pruritus are discussed.


title: Annual research review: Prenatal stress and the origins of psychopathology: An evolutionary perspective.

abstract: If a mother is stressed or anxious while pregnant her child is more likely to show a range of symptoms such as those of attention deficit hyperactivity disorder, conduct disorder, aggression or anxiety. While there remains some debate about what proportion of these effects are due to the prenatal or the postnatal environment, and the role of genetics, there is good evidence that prenatal stress exposure can increase the risk for later psychopathology. Why should this be? In our evolutionary history it is possible that some increase in these characteristics in some individuals was adaptive in a stressful environment, and that this type of fetal programming prepared the child or group for the environment in which they were going to find themselves. Anxiety may have been associated with increased vigilance, distractible attention with more perception of danger, impulsivity with more exploration, conduct disorder with a willingness to break rules, and aggression with the ability to fight intruders or predators. This adaptation for a future dangerous environment may explain why stress and anxiety, rather than depression, seem to have these programming effects; why there is a dose‐response relationship with prenatal stress from moderate to severe and it is not only toxic stress that has consequences; why not all children are affected and why individual children are affected in different ways; and why the outcomes affected can depend on the sex of the offspring. An evolutionary perspective may give a different understanding of children in our society with these symptoms, and suggest new directions for research. For example, there is some evidence that the type of cognitive deficits observed after prenatal stress have specific characteristics; these may be those which were adaptive in a past environment. Keywords: Prenatal, stress, anxiety, evolution, child development, psychopathology. In this paper I will review some of the more recent papers relevant to the effects of prenatal stress on


title: Variational Shape from Light Field

abstract: In this paper we propose an efficient method to calculate a high-quality depth map from a single raw image captured by a light field or plenoptic camera. The proposed model combines the main idea of Active Wavefront Sampling AWS with the light field technique, i.e. we extract so-called sub-aperture images out of the raw image of a plenoptic camera, in such a way that the virtual view points are arranged on circles around a fixed center view. By tracking an imaged scene point over a sequence of sub-aperture images corresponding to a common circle, one can observe a virtual rotation of the scene point on the image plane. Our model is able to measure a dense field of these rotations, which are inversely related to the scene depth.


title: A First Look into Transnational Routing Detours

abstract: An increasing number of countries are passing laws that facilitate the mass surveillance of their citizens. In response, governments and citizens are increasingly paying attention to the countries that their Internet traffic traverses. In some cases, countries are taking extreme steps, such as building new IXPs and encouraging local interconnection to keep local traffic local. We find that although many of these efforts are extensive, they are often futile, due to the inherent lack of hosting and route diversity for many popular sites. We investigate how the use of overlay network relays and the DNS open resolver infrastructure can prevent traffic from traversing certain jurisdictions.


title: CMOS High Power SPDT Switch using Multigate Structure

abstract: A novel CMOS high power RF switch using the multi-gate structure in a 0.18-mum triple-well CMOS process is designed, implemented, and characterized. The receive switch incorporates the multi-gate structure in order to provide high power handling capability to the transmit switch side. In addition, the RF switch with the multi-gate structure reduces insertion loss more than the one with the stacked transistor by reducing parasitic capacitance to the substrate. For performance comparison purposes, a triple-gate, and dual-gate NMOS switch were fabricated and characterized. Experimental data show that the SPDT switch exhibits 26 dBm of P1dB with the triple gate structure, and 24 dBm of P1dB with the dual gate structure at 900 MHz and 1.9 GHz. The multi-gate switch demonstrates 0.2 dB lower insertion loss than the multi-stacked switch. The switch die is also minimized by employing the compact multi-gate layout structure. Compared to the multi-stacked structure using an RF NMOS device in standard CMOS process, the die size of the triple gate and the dual gate is reduced by 50 % in both cases.


title: The Yo-Yo Intermittent Recovery Test: A Useful Tool for Evaluation of Physical Performance in Intermittent Sports

abstract: The two Yo-Yo intermittent recovery (IR) tests evaluate an individual’s ability to repeatedly perform intense exercise. The Yo-Yo IR level 1 (Yo-Yo IR1) test focuses on the capacity to carry out intermittent exercise leading to a maximal activation of the aerobic system, whereas Yo-Yo IR level 2 (Yo-Yo IR2) determines an individual’s ability to recover from repeated exercise with a high contribution from the anaerobic system. Evaluations of elite athletes in various sports involving intermittent exercise showed that the higher the level of competition the better an athlete performs in the Yo-Yo IR tests. Performance in the Yo- Yo IR tests for young athletes increases with rising age. The Yo-Yo IR tests have shown to be a more sensitive measure of changes in performance than maximum oxygen uptake. The Yo-Yo IR tests provide a simple and valid way to obtain important information of an individual’s capacity to perform repeated intense exercise and to examine changes in performance.


title: Double-orientation code and nonlinear matching scheme for palmprint recognition

abstract: Many palmprint authentication approaches have been proposed in recent years. Among them, the orientation based coding approach, in which the dominant orientation features of palmprints are extracted and encoded into bitwise codes, is one of the most promising approaches. The distance between codes created from two palmprint images is calculated in the matching stage. Reliable orientation feature extraction and efficient matching are the two most crucial problems in orientation based coding approaches. However, conventional coding based approaches usually extract only one dominant orientation feature by adopting filters with discrete orientations, which is sensitive to the noise and rotation. This paper proposed a novel double-orientation code (DOC) scheme to represent the orientation feature of palmprint and designed an effective nonlinear angular matching score to evaluate the similarity between the DOC. Extensive experiments performed on three types of palmprint databases demonstrate that the proposed approach has excellent performance in comparison with previously proposed state-of-the-art approaches. Proposed a novel DOC based method for palmprint identification.A nonlinear matching scheme is used in the coding based method.Double orientations with top-two responses are more robust.DOC is reasonable and reliable for palmprint feature extraction.Three different types of databases are employed in experiments.


title: Artificial intelligence test: a case study of intelligent vehicles

abstract: To meet the urgent requirement of reliable artificial intelligence applications, we discuss the tight link between artificial intelligence and intelligence test in this paper. We highlight the role of tasks in intelligence test for all kinds of artificial intelligence. We explain the necessity and difficulty of describing tasks for intelligence test, checking all the tasks that may encounter in intelligence test, designing simulation-based test, and setting appropriate test performance evaluation indices. As an example, we present how to design reliable intelligence test for intelligent vehicles. Finally, we discuss the future research directions of intelligence test.


title: A review of consumer decision-making models and development of a new model for financial services

abstract: It is recognised that existing theories of Consumer Decision Making (CDM) are not well suited for financial services and there have been calls for development of a new conceptual model. This article reviews prominent models of CDM and identifies strengths and limitations. A new conceptual model that is applicable to financial services is developed. An important element of the model is the recognition that the components interact rather than a consumer following a linear progression through a series of stages. The new model better reflects the iterative decision-making process relevant to financial services and enhances marketers’ understanding of the process and thus their ability to influence it to increase the likelihood of positive outcomes for all. The model has three main components: inputs, processes and outcomes. Inputs include the purchase situation (contextual and environmental variables), consumer characteristics (psychological and social influences) and information sources (marketing mix and interpersonal). Processes include need arousal, information utility, criteria development and evaluation of alternatives. Outcomes include the decision (that may be to abort the purchase), the purchase itself and post-decision evaluation. Further research is required to test the relationships between the variables in different contexts, and thus enable refinement and/or validation of the model.


title: The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods

abstract: We present the Voice Conversion Challenge 2018, designed as a follow up to the 2016 edition with the aim of providing a common framework for evaluating and comparing different state-of-the-art voice conversion (VC) systems. The objective of the challenge was to perform speaker conversion (i.e. transform the vocal identity) of a source speaker to a target speaker while maintaining linguistic information. As an update to the previous challenge, we considered both parallel and non-parallel data to form the Hub and Spoke tasks, respectively. A total of 23 teams from around the world submitted their systems, 11 of them additionally participated in the optional Spoke task. A large-scale crowdsourced perceptual evaluation was then carried out to rate the submitted converted speech in terms of naturalness and similarity to the target speaker identity. In this paper, we present a brief summary of the state-of-the-art techniques for VC, followed by a detailed explanation of the challenge tasks and the results that were obtained.


title: Question-answering by predictive annotation

abstract: We present a new technique for question answering called Predictive Annotation. Predictive Annotation identifies potential answers to questions in text, annotates them accordingly and indexes them. This technique, along with a complementary analysis of questions, passage-level ranking and answer selection, produces a system effective at answering natural-language fact-seeking questions posed against large document collections. Experimental results show the effects of different parameter settings and lead to a number of general observations about the question-answering problem.


title: An improved local best searching in Particle Swarm Optimization using Differential Evolution

abstract: Particle Swarm Optimization (PSO) has achieved remarkable attentions for its capability to solve diverse global optimization problems. However, this method also shows several limitations. PSO easily trapped in the global optimum and often required vast computational cost when solving high dimensional problems. Therefore, we propose some modifications to overcome these issues. In this work, Differential Evolution (DE) mutation and crossover operations are implemented to improve local best particles searching in PSO. A numerical analysis is carried out using benchmark functions and is compared with standard PSO and DE method. Results presented suggest the prospective of our proposed method.


title: Peak-Load Reduction by Coordinated Response of Photovoltaics, Battery Storage, and Electric Vehicles

abstract: Peak-load management is an important process that allows energy providers to reshape load profiles, increase energy efficiency, and reduce overall operational costs and carbon emissions. This paper presents an improved decision-tree-based algorithm to reduce the peak load in residential distribution networks by coordinated control of electric vehicles (EVs), photovoltaic (PV) units, and battery energy-storage systems (BESSs). The peak-load reduction is achieved by reading the domestic load in real time through a smart meter and taking appropriate coordinated action by a controller using the proposed algorithm. The proposed control algorithm was tested on a real distribution network using real load patterns and load dynamics, and validated in a laboratory experiment. Two types of EVs with fast and flexible charging capability, a PV unit, and BESSs were used to test the performance of the proposed control algorithm, which is compared with that of an artificial-neural-network technique. The results show that using the proposed method, the peak demand on the distribution grid can be reduced significantly, thereby greatly improving the load factor.


title: Exploring Game Space of Minimal Action Games via Parameter Tuning and Survival Analysis

abstract: Game designers can use computer-aided game design methods to model how players may experience the perceived difficulty of a game. We present methods to generate and analyze the difficulty of a wide variety of minimal action game variants throughout game space, where each point in this abstract design space represents a unique game variant. Focusing on a parameterized version of Flappy Bird , we predict hazard rates and difficulty curves using automatic playtesting, Monte Carlo simulation, a player model based on human motor skills (precision and actions per second), and survival analysis of score histograms. We demonstrate our techniques using simulated game play and actual game data from over 106 million play sessions of a popular online Flappy Bird variant, showing quantitative reasons why balancing a game for a wide range of player skill can be difficult. Some applications of our techniques include searching for a specific difficulty, game space visualization, computational creativity to find unique variants, and tuning game balance to adjust the difficulty curve even when game parameters are time varying, score dependent, or changing based on game progress.


title: Image Segmentation Based on 2D Otsu Method with Histogram Analysis

abstract: Image segmentation plays an important role in image analysis and computer vision system. Among all segmentation techniques, the automatic thresholding methods are widely used because of their advantages of simple implement and time saving. Otsu method is one of thresholding methods and frequently used in various fields. Two-dimensional (2D) Otsu method behaves well in segmenting images of low signal-to-noise ratio than one-dimensional (1D). But it gives satisfactory results only when the numbers of pixels in each class are close to each other. Otherwise, it gives the improper results. In this paper, 2D histogram projection is used to correct the Otsu threshold. The 1D histograms are acquired by 2D histogram projection in x and y axes and a fast algorithm for searching the extrema of the projected histogram is proposed based on the wavelet transform in this paper. Experimental results show that the proposed method performs better than the traditional Otsu method for our renal biopsy samples.


title: Designing a health data management system based hadoop-agent

abstract: Nowadays the amount of data that is being generated every day is increasing in a high level for various sectors. In fact, this volume and diversity of data push us to think wisely for a better solution to store, process and analyze it in the right way. Taking into consideration the healthcare industry, there is a great benefit for using the concept of big data, due to the diversity of data that we are dealing with, the extant, and the velocity which lead us to think about providing the best care for the patients. In this paper, we aim to present a new architecture model for health data. The framework supports the storage and the management of unstructured medical data in a distributed environment based on multi-agent paradigm. The integration of the mobile agent model into hadoop ecosystem will give us the opportunity to enable instant communication process between multiple health repositories.


title: Adventures in Semantic Publishing: Exemplar Semantic Enhancements of a Research Article

abstract: Scientific innovation depends on finding, integrating, and re-using the products of previous research. Here we explore how recent developments in Web technology, particularly those related to the publication of data and metadata, might assist that process by providing semantic enhancements to journal articles within the mainstream process of scholarly journal publishing. We exemplify this by describing semantic enhancements we have made to a recent biomedical research article taken from PLoS Neglected Tropical Diseases, providing enrichment to its content and increased access to datasets within it. These semantic enhancements include provision of live DOIs and hyperlinks; semantic markup of textual terms, with links to relevant third-party information resources; interactive figures; a re-orderable reference list; a document summary containing a study summary, a tag cloud, and a citation analysis; and two novel types of semantic enrichment: the first, a Supporting Claims Tooltip to permit “Citations in Context”, and the second, Tag Trees that bring together semantically related terms. In addition, we have published downloadable spreadsheets containing data from within tables and figures, have enriched these with provenance information, and have demonstrated various types of data fusion (mashups) with results from other research articles and with Google Maps. We have also published machine-readable RDF metadata both about the article and about the references it cites, for which we developed a Citation Typing Ontology, CiTO (http://purl.org/net/cito/). The enhanced article, which is available at http://dx.doi.org/10.1371/journal.pntd.0000228.x001, presents a compelling existence proof of the possibilities of semantic publication. We hope the showcase of examples and ideas it contains, described in this paper, will excite the imaginations of researchers and publishers, stimulating them to explore the possibilities of semantic publishing for their own research articles, and thereby break down present barriers to the discovery and re-use of information within traditional modes of scholarly communication.


title: Mining 3D Key-Pose-Motifs for Action Recognition

abstract: Recognizing an action from a sequence of 3D skeletal poses is a challenging task. First, different actors may perform the same action in various styles. Second, the estimated poses are sometimes inaccurate. These challenges can cause large variations between instances of the same class. Third, the datasets are usually small, with only a few actors performing few repetitions of each action. Hence training complex classifiers risks over-fitting the data. We address this task by mining a set of key-pose-motifs for each action class. A key-pose-motif contains a set of ordered poses, which are required to be close but not necessarily adjacent in the action sequences. The representation is robust to style variations. The key-pose-motifs are represented in terms of a dictionary using soft-quantization to deal with inaccuracies caused by quantization. We propose an efficient algorithm to mine key-pose-motifs taking into account of these probabilities. We classify a sequence by matching it to the motifs of each class and selecting the class that maximizes the matching score. This simple classifier obtains state-of the-art performance on two benchmark datasets.


Probabilistic Topic Models


title: Optimizing N-dimensional, winograd-based convolution for manycore CPUs

abstract: Recent work on Winograd-based convolution allows for a great reduction of computational complexity, but existing implementations are limited to 2D data and a single kernel size of 3 by 3. They can achieve only slightly better, and often worse performance than better optimized, direct convolution implementations. We propose and implement an algorithm for N-dimensional Winograd-based convolution that allows arbitrary kernel sizes and is optimized for manycore CPUs. Our algorithm achieves high hardware utilization through a series of optimizations. Our experiments show that on modern ConvNets, our optimized implementation, is on average more than 3 x, and sometimes 8 x faster than other state-of-the-art CPU implementations on an Intel Xeon Phi manycore processors. Moreover, our implementation on the Xeon Phi achieves competitive performance for 2D ConvNets and superior performance for 3D ConvNets, compared with the best GPU implementations.


title: Perceiving others’ personalities: Examining the dimensionality, assumed similarity to the self, and stability of perceiver effects

abstract: In interpersonal perception, “perceiver effects” are tendencies of perceivers to see other people in a particular way. Two studies of naturalistic interactions examined perceiver effects for personality traits: seeing a typical other as sympathetic or quarrelsome, responsible or careless, and so forth. Several basic questions were addressed. First, are perceiver effects organized as a global evaluative halo, or do perceptions of different traits vary in distinct ways? Second, does assumed similarity (as evidenced by self-perceiver correlations) reflect broad evaluative consistency or trait-specific content? Third, are perceiver effects a manifestation of stable beliefs about the generalized other, or do they form in specific contexts as group-specific stereotypes? Findings indicated that perceiver effects were better described by a differentiated, multidimensional structure with both trait-specific content and a higher order global evaluation factor. Assumed similarity was at least partially attributable to trait-specific content, not just to broad evaluative similarity between self and others. Perceiver effects were correlated with gender and attachment style, but in newly formed groups, they became more stable over time, suggesting that they grew dynamically as group stereotypes. Implications for the interpretation of perceiver effects and for research on personality assessment and psychopathology are discussed.


title: The Design and Implementation of Smart Battery Management System Balance Technology

abstract: To reduce the imbalance of Smart Battery and prolong the life of Smart Battery, the smart battery management system equalization charging protection system of battery series is designed. The imbalance reason of Li-Ion battery series and common balance technology was firstly analyzed, and then the battery balance principle based on X3100 chip was analyzed, and smart battery balance control circuit and software implementation were designed. According to the test, the balance design project can implement effectively the balance function of battery, and is characterized by low cost and simple circuit. It can be used in low power consumption equipment such as laptop computer and can improve the performance of battery and prolong the life of battery series.


title: Bluetooth indoor localization with multiple neural networks

abstract: Over the last years, many different methods have been proposed for indoor localization and navigation services based on Radio frequency (RF) technology and Radio Signal Strength Indicator (RSSI). The accuracy achieved with such systems is typically low, mainly due to the variability of RSSI values, unsuitable for classic localization methods (e.g. triangulation). In this paper, we propose a novel approach based on multiple neural networks. We demonstrate with experimental results that by training and then activating different neural networks, tailored on the user orientation, high definition accuracy is achievable, allowing indoor navigation with a cost effective Bluetooth (BT) architecture.


title: Building a Remote Supervisory Control Network System for Smart Home Applications

abstract: Wireless sensor networks are often found in the fields of home security, industrial control and maintenance, medical assistance and traffic monitoring and the appearance of ZigBee/IEEE 802.15.4 indicates a network system which is highly reliable, cost-effective, low power consumption, programmable and fast establishing. Currently, many of the wireless sensor network systems are now using ZigBee to implement the designs. A smart sensor network is the infrastructure of home automation and supervisory control systems, as the proposed functions such as intelligence entrance guard's management, home security, environmental monitor and light control can be implemented by a smart network integrating with multimedia access service, image processing, security, and sensor and control technologies. Details of the development process of a smart home network in Taiwan based on ZigBee technology with the combination of smart home appliance communication protocol, SAANet, will be given in this article.


title: Timing of Spermarche and Menarche are Associated with Physical Activity and Sedentary Behavior Among Korean Adolescents

abstract: OBJECTIVES ::: This study examined the timing of menarche and spermarche and their associations with physical activity (PA) and sedentary behavior (SB) after controlling for body mass index (BMI). ::: ::: ::: METHODS ::: Multiple logistic regression analyses were conducted to determine whether the timing of menarche in girls and spermarche in boys is associated with PA and SB independent of BMI in a nationally representative sample of Korean adolescents (13-18 years; N = 74,186). ::: ::: ::: RESULTS ::: After controlling for age, family economic status, and BMI, early timing of spermarche among boys was associated with a higher likelihood of engaging in PA and a lower likelihood of engaging in SB for < 2 hours during weekdays. By contrast, boys with late timing of spermarche were less likely to engage in PA and more likely to engage in SB for < 2 hours. Among girls, early or late timing of menarche was associated with a higher likelihood of engaging in PA and a lower likelihood of engaging in SB. ::: ::: ::: CONCLUSION ::: Timing of menarche in girls and spermarche in boys could be a marker for PA and SB among Korean adolescents. To promote PA and discourage SB among Korean adolescents, school-based, grade-specific interventions can be tailored by the absence or presence of menarche/spermarche.


title: Discovering Regression Rules with Ant Colony Optimization

abstract: The majority of Ant Colony Optimization (ACO) algorithms for data mining have dealt with classification or clustering problems. Regression remains an unexplored research area to the best of our knowledge. This paper proposes a new ACO algorithm that generates regression rules for data mining applications. The new algorithm combines components from an existing deterministic (greedy) separate and conquer algorithm---employing the same quality metrics and continuous attribute processing techniques---allowing a comparison of the two. The new algorithm has been shown to decrease the relative root mean square error when compared to the greedy algorithm. Additionally a different approach to handling continuous attributes was investigated showing further improvements were possible.


title: Monitoring unauthorized internet accesses through a ‘honeypot’ system

abstract: The role of the Internet is continuously increasing and many technical, commercial, and business transactions are carried out by a multitude of users who exploit a set of specialized/sophisticated network applications. In this context, the task of network monitoring and surveillance is gaining great relevance and honeypots represent promising tools to get information, and understanding about the ‘areas of interests’ of attackers, as well as about the possible relations among ‘blackhat’ teams. The paper presents and discusses the results achieved by a group of honeypots deployed within the networks of the Department of Communication, Computer and System Science at the University of Genoa. The collected statistics, measured over 4-month long period, reveal that approximately 10 000 different attackers, coming from 130 different countries, have ‘contacted’ the honeypot system and that about 60 000 TCP distinct connections have logged in. Our high-interaction honeypot has counted more than 25 000 attempts to access a ssh server, thus permitting to trace many attempts to install rootkits. A comparison with results obtained by similar researches carried out in other laboratories is presented and commented. Copyright © 2010 John Wiley & Sons, Ltd.


title: The effects of violent video game habits on adolescent hostility , aggressive behaviors , and school performance

abstract: Video games have become one of the favorite activities of American children. A growing body of research is linking violent video game play to aggressive cognitions, attitudes, and behaviors. The first goal of this study was to document the video games habits of adolescents and the level of parental monitoring of adolescent video game use. The second goal was to examine associations among violent video game exposure, hostility, arguments with teachers, school grades, and physical fights. In addition, path analyses were conducted to test mediational pathways from video game habits to outcomes. Six hundred and seven 8th- and 9th-grade students from four schools participated. Adolescents who expose themselves to greater amounts of video game violence were more hostile, reported getting into arguments with teachers more frequently, were more likely to be involved in physical fights, and performed more poorly in school. Mediational pathways were found such that hostility mediated the relationship between violent video game exposure and outcomes. Results are interpreted within and support the framework of the General Aggression Model.


title: Parameter design and optimization for mobile mechanism of a transformable wheel-track robot

abstract: According to the terrain features of the irregular environment, we propose a novel mobile mechanism of the transformable wheel-track robot (TWTR). The TWTR can adapt autonomously to the irregular environment by transforming the track configuration or by changing the locomotion mode between track mode and wheel mode. The TWTR mechanism consists of a control system unit and two symmetric transformable wheel-track units. As the mobile mechanism of TWTR, the wheel-track unit is composed of a transformable track mechanism and a drive wheel mechanism. The drive system of the wheel-track unit provides two outputs in different form only by one servo motor, or rather enables the wheel-track unit to have the abilities to transform the configuration of the track and to make the robot move by wheel or by track. In this paper, we design the parameters of track mechanism by parametric optimization based on genetic algorithm, and design the parameters of the drive wheel mechanism according to the configuration features of the wheel-track unit. The validity of the parameters is demonstrated by simulation on the robot transforming the track configuration and changing the locomotion mode.


title: ME-VoLTE: Network functions for energy-efficient video transcoding at the mobile edge

abstract: Encoding multimedia streams of video calls is a very compute-intense task that significantly decreases battery lifetime of mobile phones. This paper introduces an approach to reduce power consumption of mobile phones by offloading video encoding efforts from mobile devices to external services. These services are hosted on servers co-located with cellular base stations. The paper describes how these services are integrated into the existing mobile network architecture and presents a communication protocol for negotiating offloading settings. First measurement results indicate that power consumption of mobile devices is reduced by approximately 13%.


title: Quantum-secured blockchain

abstract: Blockchain is a distributed database which is cryptographically protected against malicious modifications. While promising for a wide range of applications, current blockchain platforms rely on digital signatures, which are vulnerable to attacks by means of quantum computers. The same, albeit to a lesser extent, applies to cryptographic hash functions that are used in preparing new blocks, so parties with access to quantum computation would have unfair advantage in procuring mining rewards. Here we propose a possible solution to the quantum-era blockchain challenge and report an experimental realization of a quantum-safe blockchain platform that utilizes quantum key distribution across an urban fiber network for information-theoretically secure authentication. These results address important questions about realizability and scalability of quantum-safe blockchains for commercial and governmental applications.


title: Vehicle Detection and Tracking for Traffic Monitoring System

abstract: This paper presents a real-time video traffic monitoring application based on object detection and tracking, for determining traffic parameters such as vehicle velocity and number of vehicles. In detection step, background modeling approach based on edge information is proposed for separating moving foreground objects from the background. An advantage of edge is more robust to lighting changes in outdoor environments and requires significantly less computing resource. In tracking step, optical flow Lucas-Kanade (Pyramid) is applied to track each segmented object. The proposed system was evaluated on six video sequences recorded in various daytime environment.


title: Equivalent Circuit Derivation and Performance Analysis of a Single-Sided Linear Induction Motor Based on the Winding Function Theory

abstract: A linear metro that is propelled by a single-sided linear induction motor (SLIM) has recently attracted much attention. Compared with the rotating-induction-machine drive system, the SLIM drive has advantages such as direct thrust without needing friction between the wheel and the railway track, small cross-sectional area, lack of gear box, and flexible line choice on account of the greater climbing capability and smaller turning circle. However, due to its cut-open primary magnetic circuit, the SLIM has a longitudinal end effect and half-filled slots on the primary ends, which can reduce the air-gap average flux linkage and thrust. Based on the winding function method, the SLIM is supposed to have the following three groups of windings: 1) primary windings; 2) secondary fundamental windings; and 3) secondary end effect windings. The proposed method considers the actual winding distribution and structure dimensions. It can calculate the mutual, self, and leakage inductance to describe the influence of the longitudinal end effect and half-filled slots. Moreover, a new equivalent model is presented to analyze the different dynamic and steady-state performance. Comprehensive comparisons between simulation and experimental results that were obtained from both one arc induction machine and one linear metro indicate that the proposed model can be applied to predict the SLIM performance and control scheme evaluation.


title: DEMU: A DPDK-based network latency emulator

abstract: A network latency emulator allows IT architects to thoroughly investigate how network latencies impact workload performance. Software-based emulation tools have been widely used by researchers and engineers. It is possible to use commodity server computers for emulation and set up an emulation environment quickly without outstanding hardware cost. However, existing software-based tools built in the network stack of an operating system are not capable of supporting the bandwidth of today's standard interconnects (e.g., 10GbE) and emulating sub-milliseconds latencies likely caused by network virtualization in a datacenter. In this paper, we propose a network latency emulator (DEMU) supporting broad bandwidth traffic with sub-milliseconds accuracy, which is based on an emerging packet processing framework, DPDK. It avoids the overhead of the network stack by directly interacting with NIC hardware. Through experiments, we confirmed that DEMU can emulate latencies on the order of 10 µs for short-packet traffic at the line rate of 10GbE. The standard deviation of inserted delays was only 2–3 µs. This is a significant improvement from a network emulator built in the Linux Kernel (i.e., NetEm), which loses more than 50% of its packets for the same 10GbE traffic. For 1 Gbps traffic, the latency deviation of NetEm was approximately 20 µs, while that of our mechanism was 2 orders of magnitude smaller (i.e., only 0.3 µs).


title: Thai Connected Digit Speech Recognition Using Hidden Markov Models

abstract: A Connected digit speech recognition is important in many applications such as voice-dialing telephone, automated banking system, automatic data entry, PIN entry, etc. This research presents speech recognition system of speaker-independent Thai connected digit. The system employs mel frequency cepstrum coefficient (MFCC), delta MFCC, delta-delta MFCC, delta energy and delta-delta energy as features, and applies continuous density hidden Markov model (CDHMM) in the recognition process. The Viterbi beam search algorithm is used in decoding process. In training set, we use 100 speakers (50 females, 50 males) for 2000 utterances within the range of 20-28 years old. For the experiment, we used 50 speakers (25 females, 25 males) as testing set. The average recognition rate is 75.25 % for known length strings and 70.33 % for unknown length strings.


title: Spindle Net: Person Re-identification with Human Body Region Guided Feature Decomposition and Fusion

abstract: Person re-identification (ReID) is an important task in video surveillance and has various applications. It is non-trivial due to complex background clutters, varying illumination conditions, and uncontrollable camera settings. Moreover, the person body misalignment caused by detectors or pose variations is sometimes too severe for feature matching across images. In this study, we propose a novel Convolutional Neural Network (CNN), called Spindle Net, based on human body region guided multi-stage feature decomposition and tree-structured competitive feature fusion. It is the first time human body structure information is considered in a CNN framework to facilitate feature learning. The proposed Spindle Net brings unique advantages: 1) it separately captures semantic features from different body regions thus the macro-and micro-body features can be well aligned across images, 2) the learned region features from different semantic regions are merged with a competitive scheme and discriminative features can be well preserved. State of the art performance can be achieved on multiple datasets by large margins. We further demonstrate the robustness and effectiveness of the proposed Spindle Net on our proposed dataset SenseReID without fine-tuning.


title: Deep Brain Stimulation for Neurologic and Neuropsychiatric Disorders

abstract: In the 1960s, ablative stereotactic surgery was employed for a variety of movement disorders and psychiatric conditions. Although largely abandoned in the 1970s because of highly effective drugs, such as levodopa for Parkinson's disease (PD), and a reaction against psychosurgery, the field has undergone a virtual renaissance, guided by a better understanding of brain circuitry and the circuit abnormalities underlying movement disorders such as PD and neuropsychiatric conditions, such as obsessive compulsive disorder. High-frequency electrical deep brain stimulation (DBS) of specific targets, introduced in the early 1990s for tremor, has gained widespread acceptance because of its less invasive, reversible, and adjustable features and is now utilized for an increasing number of brain disorders. This review summarizes the rationale behind DBS and the use of this technique for a variety of movement disorders and neuropsychiatric diseases.


title: Integrated 2D modeling and interpretation of geophysical and geotechnical data to delineate quick clays at a landslide site in southwest Sweden

abstract: Radio magnetotellurics (RMT), electrical resistivity tomography (ERT), and high-resolution reflection seismic data were collected along four lines to characterize the geometry and physical properties of geologic structures at a quick-clay landslide site in southwest Sweden. The site is situated in the Gota River valley where the normally consolidated materials mainly consist of glacial and postglacial sediments. Geotechnical data suggest the presence of quick clays above coarse-grained layers. These layers play a key role in the formation of quick clays and landslide triggering. The RMT and ERT data were individually and jointly inverted in 2D to study the resolution of resulting models for each data set. The resistivity models from the joint inversions demonstrate superior resolution and accuracy compared with individual ones. The geometry and location of shallower structures resolved in the 2D resistivity models from joint RMT&ERT inversions correlated well with those imaged in the reflection seismic data and observed in the existing geotechnical boreholes. The models were poor in resolving deeper resistive bedrock at locations where the thickness of the conductive overburden exceeds a certain limit. However, information from the reflection seismic data could be used to estimate the depth to the top of the bedrock along all the four lines. Comparison between the geotechnical data and the resistivity models suggested that quick clays overlying the coarse-grained layer have higher electrical resistivity than the marine clays. We further validated and refined the obtained results by performing synthetic tests. We showed that integration of ERTand RMT data with reflection seismic data is ideal for quick-clay landslide studies especially when the clay materials are thick.


title: Performance Comparison Between Diesel-Methanol Injection and Diesel-Ethanol Injection with Dual Injection System in a DI Compression Ignition Engine

abstract: Performance, fuel consumption and exhaust emissions under constant speed and variable load conditions were experimentally investigated in a direct injection compression ignition (CI) engine with dual Injection systems, where methanol and/or ethanol being injected as main fuel and diesel being injected as the ignition fuel. The experimental results show that diesel ignited ethanol CI engine has better equivalent fuel consumption, higher NOx and smoke emissions and higher CO and THC emissions than those of diesel ignited methanol CI engine for the same power output condition.


title: What's in a word? How instructions, suggestions, and social information change pain and emotion

abstract: Abstract Instructions, suggestions, and other types of social information can have powerful effects on pain and emotion. Prominent examples include observational learning, social influence, placebo, and hypnosis. These different phenomena and their underlying brain mechanisms have been studied in partially separate literatures, which we discuss, compare, and integrate in this review. Converging findings from these literatures suggest that (1) instructions and social information affect brain systems associated with the generation of pain and emotion, and with reinforcement learning, and that (2) these changes are mediated by alterations in prefrontal systems responsible for top-down control and the generation of affective meaning. We argue that changes in expectation and appraisal, a process of assessing personal meaning and implications for wellbeing, are two potential key mediators of the effects of instructions and social information on affective experience. Finally, we propose a tentative model of how prefrontal regions, especially dorsolateral and ventromedial prefrontal cortex may regulate affective processing based on instructions and socially transmitted expectations more broadly.


title: Making Financial Trading by Recurrent Reinforcement Learning

abstract: In this paper we propose a financial trading system whose strategy is developed by means of an artificial neural network approach based on a recurrent reinforcement learning algorithm. In general terms, this kind of approach consists in specifying a trading policy based on some predetermined investor's measure of profitability, and in setting the financial trading system while using it. In particular, with respect to the prominent literature, in this contribution: first, we take into account as measure of profitability the reciprocal of the returns weighted direction symmetry index instead of the wide-spread Sharpe ratio; second, we obtain the differential version of this measure of profitability and obtain all the related learning relationships; third, we propose a procedure for the management of drawdown-like phenomena; finally, we apply our financial trading approach to some of the major world financial market indices.


title: Event Tactic Analysis Based on Broadcast Sports Video

abstract: Most existing approaches on sports video analysis have concentrated on semantic event detection. Sports professionals, however, are more interested in tactic analysis to help improve their performance. In this paper, we propose a novel approach to extract tactic information from the attack events in broadcast soccer video and present the events in a tactic mode to the coaches and sports professionals. We extract the attack events with far-view shots using the analysis and alignment of web-casting text and broadcast video. For a detected event, two tactic representations, aggregate trajectory and play region sequence, are constructed based on multi-object trajectories and field locations in the event shots. Based on the multi-object trajectories tracked in the shot, a weighted graph is constructed via the analysis of temporal-spatial interaction among the players and the ball. Using the Viterbi algorithm, the aggregate trajectory is computed based on the weighted graph. The play region sequence is obtained using the identification of the active field locations in the event based on line detection and competition network. The interactive relationship of aggregate trajectory with the information of play region and the hypothesis testing for trajectory temporal-spatial distribution are employed to discover the tactic patterns in a hierarchical coarse-to-fine framework. Extensive experiments on FIFA World Cup 2006 show that the proposed approach is highly effective.


title: Rigid-flexible coupling dynamics analysis of a spot-welding robot

abstract: This paper studied a dynamics simulation analysis method for a rigid-flexible coupling system of spot-welding robot. The 6R spot-welding robot multi-body dynamics model is created by FEA (finite element analysis) and MBD (multi-body dynamics) software. Based on rigid-flexible coupling analysis method, we analyze mechanics characteristic of upper arm and get the deformation of end measuring point, maximum stress position and stress curve, when spot-welding robot is moving under loads. The analysis method is intuitional and accurate, and can increase the accuracy of dynamic response analysis of parts under the dynamic loads. The simulation results are very important theoretical basis for structure design and optimization of the spot-welding robot.


Modeling Website Design across Cultures : Relationships to Trust , Satisfaction and E-loyalty ( Forthcoming Journal of Management Information Systems )


title: Slip flow fluid-structure-interaction

abstract: Abstract While many microscale systems are subject to both rarefaction and fluid-structure-interaction (FSI) effects, most commercial algorithms cannot model both, if either, of these for general applications. This study modifies the momentum and thermal energy exchange models of an existing, continuum based, multifield, compressible, unsteady, Eulerian-Lagrangian FSI algorithm, such that the equivalent of first-order slip velocity and temperature jump boundary conditions are achieved at fluid-solid surfaces, which may move with time. Following the development and implementation of the slip flow momentum and energy exchange models, several basic configurations are considered and compared to established data to verify the resulting algorithm's capabilities.


title: Massaging human feet by a redundant manipulator equipped with a tactile sensor

abstract: Reflexology is a kind of assisting physical care through massaging or applying pressure to parts of the feet where will reflect an image of the human body in order to improve general health of the human. Based on this principle, we propose a new application of 7-DOF(degree of freedom) redundant manipulator to do the massaging work for human feet with the tactile sensor equipped to the end-effector. To facilitate the flexible and dexterous manipulation of redundant manipulator, the hybrid impedance control is adopted and extended to include the null space motion, not only to generate a desired motion of the end-effector, but also manage the contact force between the end-effector and the human feet. In order to improve the safety and comfortability, the reactive forces of the tactile sensor cells are monitored and recorded. A real massaging experiment is carried out on the human feet and the results demonstrate the effectiveness of the proposed control system.


title: A Modified SRP-PHAT Functional for Robust Real-Time Sound Source Localization With Scalable Spatial Sampling

abstract: The Steered Response Power - Phase Transform (SRP-PHAT) algorithm has been shown to be one of the most robust sound source localization approaches operating in noisy and reverberant environments. However, its practical implementation is usually based on a costly fine grid-search procedure, making the computational cost of the method a real issue. In this letter, we introduce an effective strategy that extends the conventional SRP-PHAT functional with the aim of considering the volume surrounding the discrete locations of the spatial grid. As a result, the modified functional performs a full exploration of the sampled space rather than computing the SRP at discrete spatial positions, increasing its robustness and allowing for a coarser spatial grid. To this end, the Generalized Cross-Correlation (GCC) function corresponding to each microphone pair must be properly accumulated according to the defined microphone setup. Experiments carried out under different acoustic conditions confirm the validity of the proposed approach.


title: Interdisciplinarity of scientific fields and its evolution based on graph of project collaboration and co-authoring

abstract: The paper investigates interdisciplinarity of scientific fields based on graph of collaboration between the researchers. A new measure for interdisciplinarity is proposed that takes into account graph content and structure. Similarity between science categories is estimated based on text similarity between their descriptions. The proposed new measure is applied in exploratory analysis of research community in Slovenia. We found that Biotechnology and Natural sciences are the most interdisciplinary in their publications and collaborations on research projects. In addition evolution of interdisciplinarity of scientific fields in Slovenia is observed, showing that over the last decade interdisciplinarity increases the fastest in Medical sciences mainly due to collaborations with Natural and Technical sciences.


title: Automatically Creating Datasets For Measures Of Semantic Relatedness

abstract: Semantic relatedness is a special form of linguistic distance between words. Evaluating semantic relatedness measures is usually performed by comparison with human judgments. Previous test datasets had been created analytically and were limited in size. We propose a corpus-based system for automatically creating test datasets. Experiments with human subjects show that the resulting datasets cover all degrees of relatedness. As a result of the corpus-based approach, test datasets cover all types of lexical-semantic relations and contain domain-specific words naturally occurring in texts.


title: The neurobiology of pathological gambling and drug addiction: an overview and new findings

abstract: Gambling is a prevalent recreational behaviour. Approximately 5% of adults have been estimated to experience problems with gambling. The most severe form of gambling, pathological gambling (PG), is recognized as a mental health condition. Two alternate non-mutually exclusive conceptualizations of PG have considered it as an obsessive-compulsive spectrum disorder and a ‘behavioural’ addiction. The most appropriate conceptualization of PG has important theoretical and practical implications. Data suggest a closer relationship between PG and substance use disorders than exists between PG and obsessive-compulsive disorder. This paper will review data on the neurobiology of PG, consider its conceptualization as a behavioural addiction, discuss impulsivity as an underlying construct, and present new brain imaging findings investigating the neural correlates of craving states in PG as compared to those in cocaine dependence. Implications for prevention and treatment strategies will be discussed.


title: Accelerating K-12 computational thinking using scaffolding, staging, and abstraction

abstract: We describe a three-stage model of computing instruction beginning with a simple, highly scaffolded programming environment (Kodu) and progressing to more challenging frameworks (Alice and Lego NXT-G). In moving between frameworks, students explore the similarities and differences in how concepts such as variables, conditionals, and looping are realized. This can potentially lead to a deeper understanding of programming, bringing students closer to true computational thinking. Some novel strategies for teaching with Kodu are outlined. Finally, we briefly report on our methodology and select preliminary results from a pilot study using this curriculum with students ages 10-17, including several with disabilities.


title: A richly interactive exploratory data analysis and visualization tool using electronic medical records

abstract: Electronic medical records (EMRs) contain vast amounts of data that is of great interest to physicians, clinical researchers, and medial policy makers. As the size, complexity, and accessibility of EMRs grow, the ability to extract meaningful information from them has become an increasingly important problem to solve.


title: Detection and Tracking of Large Number of Targets in Wide Area Surveillance

abstract: In this paper, we tackle the problem of object detection and tracking in a new and challenging domain of wide area surveillance. This problem poses several challenges: large camera motion, strong parallax, large number of moving objects, small number of pixels on target, single channel data and low framerate of video. We propose a method that overcomes these challenges and evaluate it on CLIF dataset. We use median background modeling which requires few frames to obtain a workable model. We remove false detections due to parallax and registration errors using gradient information of the background image. In order to keep complexity of the tracking problem manageable, we divide the scene into grid cells, solve the tracking problem optimally within each cell using bipartite graph matching and then link tracks across cells. Besides tractability, grid cells allow us to define a set of local scene constraints such as road orientation and object context. We use these constraints as part of cost function to solve the tracking problem which allows us to track fast-moving objects in low framerate videos. In addition to that, we manually generated groundtruth for four sequences and performed quantitative evaluation of the proposed algorithm.


title: Automatic Detection and Classification of Road Lane Markings Using Onboard Vehicular Cameras

abstract: This paper presents a new approach for road lane classification using an onboard camera. Initially, lane boundaries are detected using a linear–parabolic lane model, and an automatic on-the-fly camera calibration procedure is applied. Then, an adaptive smoothing scheme is applied to reduce noise while keeping close edges separated, and pairs of local maxima–minima of the gradient are used as cues to identify lane markings. Finally, a Bayesian classifier based on mixtures of Gaussians is applied to classify the lane markings present at each frame of a video sequence as dashed, solid, dashed solid, solid dashed, or double solid. Experimental results indicate an overall accuracy of over 96% using a variety of video sequences acquired with different devices and resolutions.


title: Deep Q Learning with LSTM for Traffic Light Control

abstract: Most Conventional traffic light control (TLC) techniques do not provide enough efficiency to control dynamic traffic situations in real-time. Recently, DQN (Deep Q Network) algorithm is considered for TLC at the intersection because of its optimization technique for complex problems, where key features of the intersection traffic, such as vehicle positions and velocities, are obtained from the intersection by the camera installed at well above the ground. However, the general DQN-based TLC algorithms have failed to utilize the fact that vehicle trajectories are continuous, which can be very useful in sensing real-time traffic. To utilize the continuous vehicle motion for TLC improvement, we propose DRQN-TLC (Deep Recurrent Q Network for TLC) algorithm that is based on LSTM (Long-Short Term Memory) with DQN. The superior performance of the proposed algorithm is demonstrated with the simulation; the proposed algorithm reduces the average traveling time by 23% and the overall vehicle waiting time by 10% when compared with the general DQN-based TLC algorithm.


title: Abstractive Summarization of Spoken and Written Conversation

abstract: Nowadays, lots of information is available in form of dialogues. We propose a novel abstractive summarization system for conversations. We use sequence tagging of utterances for identifying the discourse relations of the dialogue. After aptly capturing these relations in a paragraph, we feed it into an Attention-based pointer network to produce abstractive summaries. We obtain ROUGE-1, 2 F-scores similar to those of extractive summaries of various previous works.


title: Deep visual-semantic alignments for generating image descriptions

abstract: We present a model that generates natural language descriptions of images and their regions. Our approach leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between language and visual data. Our alignment model is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Multimodal Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate that our alignment model produces state of the art results in retrieval experiments on Flickr8K, Flickr30K and MSCOCO datasets. We then show that the generated descriptions significantly outperform retrieval baselines on both full images and on a new dataset of region-level annotations.


title: Weakly Supervised Cyberbullying Detection Using Co-Trained Ensembles of Embedding Models

abstract: Social media has become an inevitable part of individuals personal and business lives. Its benefits come with various negative consequences. One major concern is the prevalence of detrimental online behavior on social media, such as online harassment and cyberbullying. In this study, we aim to address the computational challenges associated with harassment detection in social media by developing a machine learning framework with three distinguishing characteristics. (1) It uses minimal supervision in the form of expert-provided key phrases that are indicative of bullying or non-bullying. (2) It detects harassment with an ensemble of two learners that co-train one another; one learner examines the language content in the message, and the other learner considers the social structure. (3) It incorporates distributed word and graph-node representations by training nonlinear deep models. The model is trained by optimizing an objective function that balances a co-training loss with a weak-supervision loss. We evaluate the effectiveness of our approach using post-hoc, crowdsourced annotation of Twitter, Ask.fm, and Instagram data, finding that our deep ensembles outperform previous non-deep methods for weakly supervised harassment detection.


title: Application of Small-Sized SMES in an EV Charging Station With DC Bus and PV System

abstract: As small-sized superconducting magnetic energy storage (SMES) system is commercially available at present, the function and effect of a small-sized SMES in an EV charging station including photovoltaic (PV) generation system is studied in this paper, which provides a practical application of small-sized SMES. The comparison of three quick response energy storage systems including flywheel, capacitor (super-capacitor) and SMES is also presented to clarify the features of SMES. SMES, PV generation system, and EV battery are connected to a common dc bus with corresponding converters respectively. Voltage source converter (VSC) is used for grid-connection. With characteristic of quick power response, SMES is utilized to maintain the dc bus steady. During the long-term operation of EV charging station, an energy management strategy is designed to control the energy transfer among PV units, SMES, EV battery, and power grid. The EV charging station system is modeled in MATLAB/SIMULINK and simulation tests are carried out to verify the function and performance of SMES.


title: Thoracic paravertebral block versus pectoral nerve block for analgesia after breast surgery

abstract: Background: Pectoral nerve block (Pecs) is a novel interfascial plane block which can provide analgesia after breast surgery while paravertebral block (PVB) is widely used for this pur- pose. We evaluated the difference between the two techniques in regard to morphine consumption and analgesic efficacy after modified radical mastectomy (MRM). Methods: Sixty patients undergoing elective MRM were randomly allocated into either PVB with 15-20 ml of levobupivacaine 0.25% at the level of fourth thoracic vertebra or Pecs block with 10 ml of levobupivacaine 0.25% injected inbetween pectoralis major and pectoralis minor muscle and another 20 ml levobubivacaine 0.25% inbetween pectorlis minor and serratus anterior muscle. Primary outcome measure was morphine consumption in the first 24 h while secondary outcome measures included pain scores, intraoperative fentanyl consumption as well as postoperative nausea and vomiting (PONV). Results: Postoperative morphine consumed at 24 h was significantly lower in Pecs group (21 (20-25) mg) than in PVB group (28 (22-31) mg), (p = 0.002). Time for first request of morphine was longer in Pecs group (175 (155-220) min) than in PVB group (137.5 (115-165) min), (p < 0.001). Numerical rating score (NRS) at rest was lower in Pecs group compared with PVB group at 1 h, 6 h and 12 h (p < 0.001) but at 18 h and 24 h it was lower in PVB group compared with Pecs group (p = 0.008 and <0.001 respectively). During movement, NRS was significantly lower at 1st hour in Pecs group (p < 0.001) while at 18 h and 24 h it was significantly lower in PVB group (p < 0.001). PONV was comparable between both groups. Conclusion: Pecs block reduced postoperative morphine consumption in the first 24 and pain scores in the first 12 h in comparison with PVB after mastectomy. a 2013 Production and hosting by Elsevier B.V. on behalf of Egyptian Society of Anesthesiologists.


title: An X10-Based Distributed Streaming Graph Database Engine

abstract: Streaming graph data mining has become a significant issue in high performance graph mining due to the increasing appearance of graph data sets as streams. In this paper we propose Acacia-Stream which is a scalable distributed streaming graph database engine developed with X10 programming language. Graph streams are partitioned using a streaming graph partitioner algorithm in Acacia-Stream and streaming graph processing queries are run on the graph streams. The partitioned data sets are persisted on secondary storage across X10 places. We investigate on the use of three different streaming graph partitioner algorithms called hash, Linear Deterministic Greedy, and Fennel algorithms and report their performance. Furthermore, to demonstrate Acacia-Stream's streaming graph processing capabilities we implement streaming triangle counting with Acacia-Stream. We present performance results gathered from Acacia-Stream with different large scale streaming data sets in both horizontal and vertical scalability experiments. Furthermore, we compare streaming graph loading performance of Acacia-Stream with Neo4j and Oracle's PGX graph database servers. From these experiments we observed that Acacia-Stream's Fennel partitioner based graph uploader can upload a 948MB rmat22 graph in 1283.42 seconds which is 38% faster than PGX graph database server and 12.8 times faster than Neo4j database server. Acacia-Stream's Streaming Partitioner's batch size adjustments based optimizations reduced the time used by the network communications almost by half.


title: Skeleton Trees for the Efficient Decoding of Huffman Encoded Texts

abstract: A new data structure is investigated, which allows fast decoding of texts encoded by canonical Huffman codes. The storage requirements are much lower than for conventional Huffman trees, O(log2 n) for trees of depth O(log n), and decoding is faster, because a part of the bit-comparisons necessary for the decoding may be saved. Empirical results on large real-life distributions show a reduction of up to 50% and more in the number of bit operations. The basic idea is then generalized, yielding further savings.


title: AN EFFICIENT TEXT CLASSIFICATION USING KNN AND NAIVE BAYESIAN

abstract: The main objective is to propose a text classification based on the features selection and pre- processing thereby reducing the dimensionality of the Feature vector and increase the classification accuracy. Text classification is the process of assigning a document to one or more target categories, based on its contents. In the proposed method, machine learning methods for text classification is used to apply some text preprocessing methods in different dataset, and then to extract feature vectors for each new document by using various feature weighting methods for enhancing the text classification accuracy. Further training the classifier by Naive Bayesian (NB) and K-nearest neighbor (KNN) algorithms, the predication can be made according to the category distribution among this k nearest neighbors. Experimental results show that the methods are favorable in terms of their effectiveness and efficiency when compared with other classifier such as SVM.


Reference: A Dual Coding Theoretical Model of Reading


Privacy Preserving Payments in Credit Networks: Enabling trust with privacy in online marketplaces