title: A New VLSI Architecture of Parallel Multiplier–Accumulator Based on Radix-2 Modified Booth Algorithm

abstract: In this paper, we propose a new architecture of multiplier-and-accumulator (MAC) for high speed multiplication and accumulation arithmetic. By combining multiplication with accumulation and devising a hybrid type of carry save adder (CSA), the performance was improved. Since the accumulator which has the largest delay in MAC was removed and its function was included into CSA, the overall performance becomes to be elevated. The proposed CSA tree uses 1's complement-based radix-2 modified booth algorithm (MBA) and has the modified array for the sign extension in order to increase the bit density of operands. The CSA propagates the carries by the least significant bits of the partial products and generates the least significant bits in advance for decreasing the number of the input bits of the final adder. Also, the proposed MAC accumulates the intermediate results in the type of sum and carry bits not the output of the final adder for improving the performance by optimizing the efficiency of pipeline scheme. The proposed architecture was synthesized with and 90nm standard CMOS library after designing it. We analyzed the results such as hardware resource, delay, and pipeline which are based on the theoretical and experimental estimation. We used Sakurai's alpha power low for the delay modeling. The proposed MAC has the superior properties to the standard design in many ways and its performance is twice as much than the previous research in the similar clock frequency.


title: Evidence that maternal dengue antibodies are important in the development of dengue hemorrhagic fever in infants.

abstract: To establish the role of maternal dengue-specific antibodies in the development of dengue hemorrhagic fever and dengue shock syndrome caused by dengue 2 virus in infants, we examined sera from mothers of infants and toddlers with dengue hemorrhagic fever or dengue shock syndrome and mothers of infants with pyrexia of unknown origin. The mean titers of hemagglutination inhibition, neutralization, and infection-enhancing activities against dengue 2 virus were not statistically different among the three groups. However, among infants who developed dengue hemorrhagic fever/dengue shock syndrome there was a strong correlation between the mothers' dengue 2 neutralizing titers and infant age at the time of onset of severe illness, where no such correlation was found among the other two groups. Furthermore, the actual age at which dengue hemorrhagic fever/dengue shock syndrome occurred in each infant correlated with the age at which maximum enhancing activity for dengue 2 infection in mononuclear phagocytes was predicted. This critical time for the occurrence of dengue hemorrhagic fever/dengue shock syndrome was observed to be approximately 2 months after the time calculated for maternal dengue 2 neutralizing antibodies to degrade below a protective level. In addition, sera of mothers of infants with dengue hemorrhagic fever/dengue shock syndrome enhanced dengue 2 virus infection to a slightly greater degree than did sera from mothers of infants with pyrexia of unknown origin and toddlers with dengue hemorrhagic fever/dengue shock syndrome. These data are consistent with the hypothesis that maternal dengue antibodies play a dual role by first protecting and later increasing the risk of development of dengue hemorrhagic fever/dengue shock syndrome in infants who become infected by dengue 2 virus.


title: Semantic-aware Grad-GAN for Virtual-to-Real Urban Scene Adaption

abstract: Recent advances in vision tasks (e.g., segmentation) highly depend on the availability of large-scale real-world image annotations obtained by cumbersome human labors. Moreover, the perception performance often drops significantly for new scenarios, due to the poor generalization capability of models trained on limited and biased annotations. In this work, we resort to transfer knowledge from automatically rendered scene annotations in virtual-world to facilitate real-world visual tasks. Although virtual-world annotations can be ideally diverse and unlimited, the discrepant data distributions between virtual and real-world make it challenging for knowledge transferring. We thus propose a novel Semantic-aware Grad-GAN (SG-GAN) to perform virtual-to-real domain adaption with the ability of retaining vital semantic information. Beyond the simple holistic color/texture transformation achieved by prior works, SG-GAN successfully personalizes the appearance adaption for each semantic region in order to preserve their key characteristic for better recognition. It presents two main contributions to traditional GANs: 1) a soft gradient-sensitive objective for keeping semantic boundaries; 2) a semantic-aware discriminator for validating the fidelity of personalized adaptions with respect to each semantic region. Qualitative and quantitative experiments demonstrate the superiority of our SG-GAN in scene adaption over state-of-the-art GANs. Further evaluations on semantic segmentation on Cityscapes show using adapted virtual images by SG-GAN dramatically improves segmentation performance than original virtual data. We release our code at https://github.com/Peilun-Li/SG-GAN.


title: Semi-supervised Text Categorization Using Recursive K-means Clustering

abstract: In this paper, we present a semi-supervised learning algorithm for classification of text documents. A method of labeling unlabeled text documents is presented. The presented method is based on the principle of divide and conquer strategy. It uses recursive K-means algorithm for partitioning both labeled and unlabeled data collection. The K-means algorithm is applied recursively on each partition till a desired level partition is achieved such that each partition contains labeled documents of a single class. Once the desired clusters are obtained, the respective cluster centroids are considered as representatives of the clusters and the nearest neighbor rule is used for classifying an unknown text document. Series of experiments have been conducted to bring out the superiority of the proposed model over other recent state of the art models on 20Newsgroups dataset.


title: GGP with Advanced Reasoning and Board Knowledge Discovery

abstract: Quality of General Game Playing (GGP) matches suffers from slow state-switching and weak knowledge modules. Instantiation and Propositional Networks offer great performance gains over Prolog-based reasoning, but do not scale well. In this publication mGDL, a variant of GDL stripped of function constants, has been defined as a basis for simple reasoning machines. mGDL allows to easily map rules to C++ functions. 253 out of 270 tested GDL rule sheets conformed to mGDL without any modifications; the rest required minor changes. A revised (m)GDL to C++ translation scheme has been reevaluated; it brought gains ranging from 28% to 7300% over YAP Prolog, managing to compile even demanding rule sheets under few seconds. For strengthening game knowledge, spatial features inspired by similar successful techniques from computer Go have been proposed. For they required an Euclidean metric, a small board extension to GDL has been defined through a set of ground atomic sentences. An SGA-based genetic algorithm has been designed for tweaking game parameters and conducting self-plays, so the features could be mined from meaningful game records. The approach has been tested on a small cluster, giving performance gains up to 20% more wins against the baseline UCT player. Implementations of proposed ideas constitutes the core of GGP Spatium - a small C++/Python GGP framework, created for developing compact GGP Players and problem solvers.


title: A constraint-based biometric scheme on ATM and swiping machine

abstract: In this modern scientific world, technologies are transforming rapidly but along with the ease and comfort they also bring in a big concern for security. Taking into account the physical security of the system to ensure access control and authentication of users, made us to switch to a new system of Biometric combined with ATM PIN as PIN can easily be guessed, stolen or misused. Biometric is added with the existing technology to double the security in order to reduce ATM frauds but it has also put forward several issues which include sensor durability and time consumption. This paper envelops two questions “Is it really worthy to go through the entire biometric process to just debit a low amount?” and “What could be the maximum amount one can lose if one's card is misused?” As an answer we propose a constraint on transactions by ATM involving biometric to improve the system performance and to solve the defined issues. The proposal is divided in two parts. The first part solves sensor performance issue by adding a limit on amount of cash and number of transactions is defined in such a way that if one need to withdraw a big amount OR attempts for multiple transactions by withdrawing small amount again and again, it shall be necessary to present biometric. On the other hand if one need to make only balance enquiry or the cash is low and the number of transactions in a day is less than defined attempts, biometric presentation is not mandatory. It may help users to save time and maintain sensor performance by not furnishing their biometric for few hundred apart from maintaining security. In the second part this paper explains how fingerprint verification is conducted if the claimant is allowed to access the system and what could be the measures to increase performance of fingerprint biometric system which could be added to our proposed system to enhance the overall system performance.


title: A brief study and comparison of Snort and Bro Open Source Network Intrusion Detection Systems

abstract: Security administration plays a vital role in network management tasks. The intrusion detection systems are primarily designed to protect the availability, confidentiality and integrity of critical network information systems. There are plenty of IDSes to choose from, both commercial and open source. Since most of the commercial intrusion detection systems are at typically thousands of dollars and they tend to represent a significant resource requirement in themselves, for small networks, use of such IDS is not feasible. Therefore mostly open source IDS are being used. This paper provides a general working behaviour, features and comparison of two most popular open source network IDS - SNORT & BRO. Keywords-alerts, intrusion, logging, network traffic, open source, packets


title: Enhanced compact artificial bee colony

abstract: Challenges in many real-world optimization problems arise from limited hardware availability, particularly when the optimization must be performed on a device whose hardware is highly restricted due to cost or space. This paper proposes a new algorithm, namely Enhanced compact Artificial Bee Colony (EcABC) to address this class of optimization problems. The algorithm benefits from the search logic of the Artificial Bee Colony (ABC) algorithm, and similar to other compact algorithms, it does not store the actual population of tentative solutions. Instead, EcABC employs a novel probabilistic representation of the population that is introduced in this paper. The proposed algorithm has been tested on a set of benchmark functions from the CEC2013 benchmark suite, and compared against a number of algorithms including modern compact algorithms, recent population-based ABC variants and some advanced meta-heuristics. Numerical results demonstrate that EcABC significantly outperforms other state of the art compact algorithms. In addition, simulations also indicate that the proposed algorithm shows a comparative performance when compared against its population-based versions.


title: Career motivation and burnout among medical students in Hungary - could altruism be a protection factor?

abstract: Background ::: Burnout is a major issue among medical students. Its general characteristics are loss of interest in study and lack of motivation. A study of the phenomenon must extend beyond the university environment and personality factors to consider whether career choice has a role in the occurrence of burnout.


title: Novel compact parallel-coupled microstrip bandpass filters with lumped-element K-inverters

abstract: Novel compact parallel-coupled microstrip bandpass filters are proposed by using additional lumped inductors to realize K-inverters between coupled-line sections to achieve an equivalent to the quarter-wavelength (/spl lambda//4) resonator filters. As a result, the filter order can be doubled without increasing the circuit area, and no repeated passband is observed at twice the center frequency. In addition, by introducing the cross-coupling effect, two transmission zeros at upper and lower stopbands may be created. Simple equivalent-circuit models are also established as effective design tools. Specifically, several compact fourth-order microstrip bandpass filters with good stopband rejection are implemented and carefully examined.


title: GPS anti-spoofing technology based on RELAX algorithm in smart grid

abstract: Smart grid is established on top of traditional power grid. With the help of the advanced information technology, power scheduling and control can be achieved better in smart grid. Since it depends on precise timing information, most of the operations are equipped with GPS (global positioning system) that can give precise timing with the performance of real-time, continuity and all-day. But GPS is vulnerable to attacks because of its low power and public known data structure, so it is important to protect GPS from all kinds of jamming, which can guarantee the stability of smart grids. In this paper, a GPS anti-spoofing technology based on RELAX (relaxation) algorithm is proposed. In the case where one or more spoofing sources are present and each contains several spoofing signals or a strong one, it can set suitable number of the spoofing sources and configure the detection threshold in order to realize the spoofing detection and mitigation. The technology is very effective and validated through simulation results.


title: Decision Support in Heart Disease Prediction System using Naive Bayes

abstract: Data Mining refers to using a variety of techniques to identify suggest of information or decision making knowledge in the database and extracting these in a way that they can put to use in areas such as decision support, predictions, forecasting and estimation. The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information for effective decision making. Discovering relations that connect variables in a database is the subject of data mining. This research has developed a Decision Support in Heart Disease Prediction System (DSHDPS) using data mining modeling technique, namely, Naive Bayes. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It is implemented as web based questionnaire application. It can serve a training tool to train nurses and medical students to diagnose patients with heart disease.


Exploiting Sentence Similarities for Better Alignments


title: Management Architecture for Dynamic Federated Identity Management

abstract: We present the concept and design of Dynamic Automated Metadata Exchange (DAME) in Security Assertion Markup Language (SAML) based user authentication and authorization infrastructures. This approach solves the real-world limitations in scalability of pre-exchanged metadata in SAML-based federations and inter-federations. The user initiates the metadata exchange on demand, therefore reducing the size of the exchanged metadata compared to traditional metadata aggregation. In order to specify and discuss the necessary changes to identity federation architectures, we apply the Munich Network Management (MNM) service model to Federated Identity Management via a trusted third party (TTP); an overview of all components and interactions is created. Based on this model, the management architecture of the TTP with its basic management functionalities is designed. This management architecture includes further functionality for automated management of entities and dynamic federations.


title: Tuning band pass filters in the time domain

abstract: This new idea in coupled-resonator filter tuning is based on the time domain response of filter return loss. With this method, individual responses of each resonator and coupling coefficient can be distinguished and can be precisely set to a desired filter response; interactions can be immediately determined and accounted for.


title: Compact four-port MIMO antenna system at 3.5 GHz

abstract: This work proposed a compact multiple-input multiple-output (MIMO) antenna system having four antennas each fed by coplanar waveguide (CPW) operating for 3.5GHz band (3400–3600) MHz. The proposed MIMO antenna system is realized with two groups of printed Inverted L-monopole of size 5×4 mm2 placed orthogonally to each other to attain the diversity capability of polarization and pattern at the edge of non-ground portion of size 15 × 15 mm2 of mobile handset. The proposed MIMO antenna system consists of four monopole inverted-L shape radiator each with parasitic shorted stripe extending from the ground plane having same dimensions. The parasitic inverted-L shorted striped served as tuning element by reducing the resonating frequency of the monopole radiator and improving the impedance matching. The mutual coupling between the closely packed antennas is addressed by the neutralization line by feeding anti-phase current to the neighbor antenna. The simulation result showed that that the impedance bandwidth at S 11 ≤ −10 dB is 200 MHz enough for 5G network communication channel. The diversity parameters of interest are envelop correlation coefficient and mean effective gain are also calculated.


title: Impact of mindfulness on the neural responses to emotional pictures in experienced and beginner meditators

abstract: Abstract There is mounting evidence that mindfulness meditation is beneficial for the treatment of mood and anxiety disorders, yet little is known regarding the neural mechanisms through which mindfulness modulates emotional responses. Thus, a central objective of this functional magnetic resonance imaging study was to investigate the effects of mindfulness on the neural responses to emotionally laden stimuli. Another major goal of this study was to examine the impact of the extent of mindfulness training on the brain mechanisms supporting the processing of emotional stimuli. Twelve experienced (with over 1000 h of practice) and 10 beginner meditators were scanned as they viewed negative, positive, and neutral pictures in a mindful state and a non-mindful state of awareness. Results indicated that the Mindful condition attenuated emotional intensity perceived from pictures, while brain imaging data suggested that this effect was achieved through distinct neural mechanisms for each group of participants. For experienced meditators compared with beginners, mindfulness induced a deactivation of default mode network areas (medial prefrontal and posterior cingulate cortices) across all valence categories and did not influence responses in brain regions involved in emotional reactivity during emotional processing. On the other hand, for beginners relative to experienced meditators, mindfulness induced a down-regulation of the left amygdala during emotional processing. These findings suggest that the long-term practice of mindfulness leads to emotional stability by promoting acceptance of emotional states and enhanced present-moment awareness, rather than by eliciting control over low-level affective cerebral systems from higher-order cortical brain regions. These results have implications for affect-related psychological disorders.


title: IS Success Model in E-Learning Context Based on Students' Perceptions.

abstract: 1. INTRODUCTION Both undergraduate and graduate courses are experiencing a migration away from the traditional classroom and toward a greater emphasis for electronic delivery of content (Allen and Seaman, 2008). This trend cuts across all departments and schools in the university system but is especially critical in business schools, since the preparation of students for successful business careers will rely on the students' abilities to accurately assess the quality of and rapidly adapt to the changing systems that reflect radical technological advances. The Information Systems Success (ISS) model focuses attention on the information and system quality of specific IT systems. The expanded use of electronic means of course delivery results in different IT systems in which students develop various views of the system quality and information quality that may affect their educational outcomes. In a graduate online information management course, feedback provided in the e-learning environment affected student satisfaction, the typical outcome measure for the ISS model (Rossin, et al., 2008). Feedback, in the context of an e-learning environment, is a measure of the information quality provided by the instructor during course delivery. In addition, the perceived balance of challenge and skill necessary to be successful in the course also affected the satisfaction with the course. The balance of challenge and skill necessary for the online delivery of the e-learning experience is a measure of the system quality. Information quality can also be electronically delivered and assessed by individuals with an information system being absent from the process. In a business environment, the information needs of managers in different functional areas are critical aspects during the evaluation of information and subsequently its quality (Beard and Peterson, 2003). For students, information needs may vary from course to course as well as among various homework assignment styles (e.g. quizzes, short-answer questions, and case studies). The concluding goal of this study ends with a discussion of how an information system can facilitate the delivery of the required information. While the ISS model is used in many instances, a basic assumption of the model is one of voluntary use by the user. This assumption is incorrect in the context of university e-learning courses where usage of the system is required to complete the coursework. Usage of a non-voluntary system is not without its parallels in industry. The implementation of enterprise resource planning (ERP) systems for many companies requires the usage of these systems by employees. This industry need has translated these requirements into ERP system courses (Davis and Comeau, 2004). Since the usage of e-learning systems in academic settings is not voluntary, the application and possible changes to the ISS model to an online course environment is a necessary and critical extension of the study of information systems. This study applies the ISS model to study e-learning systems (ELS) in the context of individual impact for a student online environment. The remainder of this article presents the ISS model with its standard constructs. The methodology used to assess the study is reviewed. The data analysis and results are then addressed. Finally, a discussion of the conclusions along with limitations are presented. 2. LITERATURE REVIEW The ISS model (DeLone and McLean, 1992) is among the most influential theories in predicting and explaining system use, user satisfaction, and IS success (Halawi, McCarthy, & Aronson, 2008; Guimaraes, Armstrong, & Jones, 2009). The ISS model can be used to assess ELS success due to the solid theoretical foundation and the numerous, successful empirical studies. The base ISS model consists of six constructs or dimensions: (1) system quality, (2) information quality, (3) systems use, (4) user satisfaction, (5) individual impact and (6) organizational impact. …


title: A Quadrature Radar Topology With Tx Leakage Canceller for 24-GHz Radar Applications

abstract: A quadrature radar topology with a Tx leakage canceller is presented. The canceller is composed of four branch-line hybrid couplers, a 90deg delay line, and a Wilkinson combiner. It has high Tx-to-Rx isolation and wide bandwidth characteristics. The isolation is 35.27 dB at 24 GHz and does not drop below 30 dB over a 2-GHz bandwidth. Given that the isolation of the branch-line hybrid coupler is 21.5 dB, the enhancement of the isolation is expected to be 13.77 dB. A quadrature Doppler radar front-end with the canceller was implemented. The noise level in the mixer output is lower by 9 dB compared with that of a conventional quadrature radar. This radar can measure speed as low as 0.76 mm/s, which correspond to a 0.12-Hz Doppler shift. It is also able to detect signs of the Doppler shift from a moving target due to its quadrature topology.


title: Secured Outsourced Content Based Image Retrieval Based on Encrypted Signatures Extracted From Homomorphically Encrypted Images

abstract: In this paper, we present a novel Secured Outsourced Content Based Image Retrieval solution, which allows looking for similar images stored into the cloud in a homomorphically encrypted form. Its originality is fourfold. In a first time, it extracts from a Paillier encrypted image a wavelet based global image signature. In second, this signature is extracted in an encrypted form and gives no clues about the image content. In a third time, its calculation does not require the cloud to communicate with a trusted third party as usually proposed by other existing schemes. More clearly, all computations required in order to look for similar images are conducted by the cloud only with no extra-communications. To make possible such a computation, we propose a new fast way to compare encrypted data, these ones being encrypted by the same public key or not, and using a recursive relationship in-between Paillier random values when computing the different resolution levels of the image wavelet transform. Experiments conducted in two distinct frameworks: medical image retrieval with as purpose diagnosis aid support, and face recognition for user authentication; indicate that the proposed SOCBIR does not change image retrieval performance.


title: A Privacy Preserving Method Using Privacy Enhancing Techniques for Location Based Services

abstract: The move towards service-oriented architectures and the increasing usage of mobile devices to access such services are two of the major changes in modern computing. Information about the user, their location and their trajectory can provide additional context information to a service, leading to useful applications such as directing a user to the nearest bus stop and displaying which buses are due to arrive in the next minutes. While this type of information can be useful, when the offered services are trusted, it also introduces privacy issues relating to gathering of location information for non-trusted applications like location-based marketing or user behaviour profiling. Users can limit their location information provided to a service but these controls are simple, making it important for the user to understand how their location information is being used by services. This paper reviews some of the methods currently being proposed to reduce the impact of location tracking on user privacy, and presents a novel encryption method for preserving the location and trajectory path of a user using Privacy-Enhancing Technologies.


title: Linking the Big Five personality constructs to organizational commitment

abstract: Abstract This study explored the linkages between the five-factor model of personality and Meyer and Allen’s (1991) model of organizational commitment using a field sample. Results indicated that Extraversion was significantly related to affective commitment, continuance commitment, and normative commitment. Neuroticism, Conscientiousness, and Openness to Experience were all significantly related to continuance commitment. Lastly, Agreeableness was significantly related to normative commitment. Theoretical and practical implications of the results are discussed.


Detecting handwritten signatures in scanned documents


title: Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network

abstract: With many thyroid nodules being incidentally detected, it is important to identify as many malignant nodules as possible while excluding those that are highly likely to be benign from fine needle aspiration (FNA) biopsies or surgeries. This paper presents a computer-aided diagnosis (CAD) system for classifying thyroid nodules in ultrasound images. We use deep learning approach to extract features from thyroid ultrasound images. Ultrasound images are pre-processed to calibrate their scale and remove the artifacts. A pre-trained GoogLeNet model is then fine-tuned using the pre-processed image samples which leads to superior feature extraction. The extracted features of the thyroid ultrasound images are sent to a Cost-sensitive Random Forest classifier to classify the images into “malignant” and “benign” cases. The experimental results show the proposed fine-tuned GoogLeNet model achieves excellent classification performance, attaining 98.29% classification accuracy, 99.10% sensitivity and 93.90% specificity for the images in an open access database (Pedraza et al. 16), while 96.34% classification accuracy, 86% sensitivity and 99% specificity for the images in our local health region database.


title: The multilayer perceptron as an approximation to a Bayes optimal discriminant function

abstract: The multilayer perceptron, when trained as a classifier using backpropagation, is shown to approximate the Bayes optimal discriminant function. The result is demonstrated for both the two-class problem and multiple classes. It is shown that the outputs of the multilayer perceptron approximate the a posteriori probability functions of the classes being trained. The proof applies to any number of layers and any type of unit activation function, linear or nonlinear. >


title: A statistic based time skew calibration method for time-interleaved ADCs

abstract: In this paper, a statistic based time skew calibration method for time-interleaved ADCs is presented. By comparing the mean value of the multiplication of signals in two adjacent channels, the time skew can be estimated. Subsequently, a capacitor array based digitally controlled delay block placed in sampling clock path is adopted to compensate the time skew. In addition, the precision of calibration is further improved through using a monotonic small capacitor array. In a 4-channel 1GS/s 12-bit TI-ADC system, the spurious free dynamic range (SFDR) can be improved to 77.5dB with 0.25ps LSB in the digitally controlled delay block.


title: Comparison of different arrangement of magnets for the purpose of reducing magnet usage in designing an IPM motor for Electric Vehicles

abstract: Because of the excellent performance of IPM motor, it is widely used in Electric Vehicles. Meanwhile, due to the fluctuating price of rare earth raw material in recent years, the manufacturing cost of IPM motor is difficult to control. So, it is important to research on reducing magnet usage. The purpose of this paper is to compare three kinds of IPM motor with different rotor structures at the same time to improve the magnetic circuit by increasing the reluctance torque. As a result, the amount of expensive NdFeB magnet is substantially reduced by adopting a improved V-shape arrangement.


title: Central precocious puberty

abstract: Precocious puberty is a stressful event for patient and environment. The diagnostic evaluation will be discussed. LHRH analogs will give a new approach in the treatment of central precocious puberty.


Sentiment Analysis and Opinion Mining : A Survey


title: Specific language impairment: a convenient label for whom?

abstract: Background:Theterm‘specificlanguageimpairment’(SLI),inusesincethe1980s,describeschildrenwithlanguage impairment whose cognitive skills are within normal limits where there is no identifiable reason for the language impairment. SLI is determined by applying exclusionary criteria, so that it is defined by what it is not rather than by what it is. The recent decision to not include SLI in DSM-5 provoked much debate and concern from researchers and clinicians. Aims: To explore how the term ‘specific language impairment’ emerged, to consider how disorders, including SLI, are generally defined and to explore how societal changes might impact on use the term. Methods & Procedures: We reviewed the literature to explore the origins of the term ‘specific language impairment’ and present published evidence, as well as new analyses of population data, to explore the validity of continuing to use the term. Outcomes & Results and Conclusions & Implications: We support the decision to exclude the term ‘specific language impairment’ from DSM-5 and conclude that the term has been a convenient label for researchers, but that the current classification is unacceptably arbitrary. Furthermore, we argue there is no empirical evidence to support the continued use of the term SLI and limited evidence that it has provided any real benefits for children and their families. In fact, the term may be disadvantageous to some due to the use of exclusionary criteria to determine eligibility for and access to speech pathology services. We propose the following recommendations. First, that the word ‘specific’ be removed and the label ‘language impairment’ be used. Second, that the exclusionary criteria be relaxed and in their place inclusionary criteria be adopted that take into account the fluid nature of language development particularly in the preschool period. Building on the goodwill and collaborations between the clinical and research communities we propose the establishment of an international consensus panel to develop an agreed definition and set of criteria for language impairment. Given the rich data now available in population studies it is possible to test the validity of these definitions and criteria. Consultation with service users and policy-makers should be incorporated into the decision-making process.


title: Population dynamics parameters of narrow-barred Spanish mackerel, Scomberomorus commerson (Lacèpéde, 1800), from commercial catch in the northern Persian Gulf

abstract: Abstract Population dynamics parameters of Scomberomorus commerson in Bushehr area waters of Iran were analyzed between October 2011 and September 2012. Fork length frequencies were collected from the gill net commercial catch. Von Bertalanffy growth function was used to estimate growth parameters K and L ∞ . Instantaneous total mortality rate ( Z ) , instantaneous natural mortality rate ( M ) and the instantaneous fishing mortality rate ( F ) were also calculated. Resource status was evaluated by comparing estimates of the fishing mortality rate with target ( F opt ) and limit ( F limit ) biological reference points. FiSAT program was used to assess growth and mortality parameters. Based on the growth curve analysis, growth parameters were: K = 0.5 year − 1 and L ∞ = 148 cm . Instantaneous total mortality was Z = 0.97 year − 1 . The estimate of M = 0.56 year − 1 and F = 0.41 year − 1 , resulting in an exploitation rate ( F / Z ) of E = 0.42 year − 1 . Target and limit biological reference points were: F opt = 0.28 year − 1 and F limit = 0.37 year − 1 . Size at capture at probabilities of 0.25 ( L 25 ), 0.5 ( L 0.5 ) and 0.75 ( L 0.75 ), were 46.3 cm, 55 cm and 60.2 cm respectively. Longevity ( T max ) was calculated as 3 / K equal to 6 years. The exploitation rate ( E ) did not indicate overfishing; however, F opt and F limit rates were lower than fishing mortality ( F ) , suggesting that overexploitation occurred. Growth parameters in the present study showed that the gillnet fishery catches of S. commerson that are of ages 1 or 2, which may not have reached maturity. Gillnet mesh size can be a factor why the catch is composed of mostly small, immature fish. Therefore, changes in mesh size and net size should be considered.


title: Deep keyphrase generation with a convolutional sequence to sequence model

abstract: Keyphrases can provide highly condensed and valuable information that allows users to quickly acquire the main ideas. Most previous studies realize the automatic keyphrase extraction through dividing the source text into multiple chunks and then rank and select the most suitable ones. These approaches ignore the deep semantics behind the text and could not predict the keyphrases not appearing in the source text. A sequence to sequence model to generate keyphrases from vocabulary could solve the issues above. However, traditional sequence to sequence model based on recurrent neural network(RNN) suffers from low efficiency problem. We propose an architecture based entirely on convolutional neural networks. Compared to recurrent models, computations can be completely parallelized over all elements so as to better exploit the GPU hardware. Our use of gated linear units alleviates gradient propagation and we equip each decoder layer with a separate attention model. Moreover, we incorporate a copying mechanism to handle out-of-vocabulary phrases. In experiments, we evaluate our model on six datasets, and our proposed model is demonstrated to outperform state-of-the-art baseline models consistently and significantly, both on extracting the keyphrases existing in the source text and generating the absent keyphrases based on the sematic meaning of the text.


title: Evolution and Morphogenesis of Differentiated Multicellular Organisms - Autonomously Generated Diffusion Gradients for Positional Information

abstract: Development is the powerful process involving a genome in the transformation from one egg cell to a multicellular organism with many cell types. The dividing cells manage to organize and assign themselves special, differentiated roles in a reliable manner, creating a spatio-temporal pattern and division of labor. This despite the fact that little positional information may be available to them initially to guide this patterning. Inspired by a model of developmental biologist L. Wolpert, we simulate this situation in an evolutionary setting where individuals have to grow into “French flag” patterns. The cells in our model exist in a 2-layer Potts model physical environment. Controlled by continuous genetic regulatory networks, identical for all cells of one individual, the cells can individually differ in parameters including target volume, shape, orientation, and diffusion. Intercellular communication is possible via secretion and sensing of diffusing morphogens. Evolved individuals growing from a single cell can develop the French flag pattern by setting up and maintaining asymmetric morphogen gradients ‐ a behavior predicted by several theoretical models.


title: Robust Entity Linking via Random Walks

abstract: Entity Linking is the task of assigning entities from a Knowledge Base to textual mentions of such entities in a document. State-of-the-art approaches rely on lexical and statistical features which are abundant for popular entities but sparse for unpopular ones, resulting in a clear bias towards popular entities and poor accuracy for less popular ones. In this work, we present a novel approach that is guided by a natural notion of semantic similarity which is less amenable to such bias. We adopt a unified semantic representation for entities and documents - the probability distribution obtained from a random walk on a subgraph of the knowledge base - which can overcome the feature sparsity issue that affects previous work. Our algorithm continuously updates the semantic signature of the document as mentions are disambiguated, thus focusing the search based on context. Our experimental evaluation uses well-known benchmarks and different samples of a Wikipedia-based benchmark with varying entity popularity; the results illustrate well the bias of previous methods and the superiority of our approach, especially for the less popular entities.


title: Fingerprinting for Web Applications: From Devices to Related Groups

abstract: Identifying users and user devices is as important in web applications as in many other contexts. In web applications, user identification usually involves an authentication process, e.g., providing a username and a password. Identification is also possible without explicit authentication using cookies or device fingerprints. Device fingerprinting is also useful for other purposes, e.g., to serve as a second factor of authentication. Recently some interest appeared in the problem of cross-device fingerprinting, i.e., of the identification of the same user in different devices using fingerprinting. We target a variation of the problem that we call related group fingerprinting. We define a related group as a set of persons (e.g., a family) that share the same home network. We devised a related group fingerprinting scheme that we evaluated experimentally with data from hundreds of users. This evaluation suggests that group fingerprinting is feasible.


title: Supporting the analytical reasoning process in information visualization

abstract: This paper presents a new information visualization framework that supports the analytical reasoning process. It consists of three views - a data view, a knowledge view and a navigation view. The data view offers interactive information visualization tools. The knowledge view enables the analyst to record analysis artifacts such as findings, hypotheses and so on. The navigation view provides an overview of the exploration process by capturing the visualization states automatically. An analysis artifact recorded in the knowledge view can be linked to a visualization state in the navigation view. The analyst can revisit a visualization state from both the navigation and knowledge views to review the analysis and reuse it to look for alternate views. The whole analysis process can be saved along with the synthesized information. We present a user study and discuss the perceived usefulness of a prototype based on this framework that we have developed.


title: Development of a human size and strength compliant bi-manual platform for realistic heavy manipulation tasks

abstract: Developing a high physical performance robotic manipulation platform with considerable power density, strength and resilience is not a trivial task and frequently leads to heavy and bulky systems unable to meet the application requirements, i.e. such robots should have human body size compatibility to work in infrastructures designed for humans. In this work we present a new high performance human size and weight compatible bi-manual manipulation platform that demonstrates notable physical strength and power capabilities. To attain this performance, design features including custom high performance elastic drives and robust light weight structure principles were considered resulting in large payload to robot mass ratio that is greater than 1.5 for short time heavy payloads. The design principles and mechanics of the upper body bi-manual robot are presented providing details on the solutions adopted for the various mechatronics components. The performance of the system actuation and the strength capacity of the overall platform is verified through the execution of heavy payload motion and impact experiments.


Using chatbots against voice spam: Analyzing Lenny's effectiveness


title: Monocular Visual-Inertial SLAM for Fixed-Wing UAVs Using Sliding Window Based Nonlinear Optimization

abstract: Precise real-time information about the position and orientation of robotic platforms as well as locally consistent point-clouds are essential for control, navigation, and obstacle avoidance. For years, GPS has been the central source of navigational information in airborne applications, yet as we aim for robotic operations close to the terrain and urban environments, alternatives to GPS need to be found. Fusing data from cameras and inertial measurement units in a nonlinear recursive estimator has shown to allow precise estimation of 6-Degree-of-Freedom (DoF) motion without relying on GPS signals. While related methods have shown to work in lab conditions since several years, only recently real-world robotic applications using visual-inertial state estimation found wider adoption. Due to the computational constraints, and the required robustness and reliability, it remains a challenge to employ a visual-inertial navigation system in the field. This paper presents our tightly integrated system involving hardware and software efforts to provide an accurate visual-inertial navigation system for low-altitude fixed-wing unmanned aerial vehicles (UAVs) without relying on GPS or visual beacons. In particular, we present a sliding window based visual-inertial Simultaneous Localization and Mapping (SLAM) algorithm which provides real-time 6-DoF estimates for control. We demonstrate the performance on a small unmanned aerial vehicle and compare the estimated trajectory to a GPS based reference solution.


title: What Supercomputers Say: A Study of Five System Logs

abstract: If we hope to automatically detect and diagnose failures in large-scale computer systems, we must study real deployed systems and the data they generate. Progress has been hampered by the inaccessibility of empirical data. This paper addresses that dearth by examining system logs from five supercomputers, with the aim of providing useful insight and direction for future research into the use of such logs. We present details about the systems, methods of log collection, and how alerts were identified; propose a simpler and more effective filtering algorithm; and define operational context to encompass the crucial information that we found to be currently missing from most logs. The machines we consider (and the number of processors) are: Blue Gene/L (131072), Red Storm (10880), Thunderbird (9024), Spirit (1028), and Liberty (512). This is the first study of raw system logs from multiple supercomputers.


title: A Novel Approach for Plant Leaf Image Segmentation using Fuzzy Clustering

abstract: this paper we propose an algorithm based on fuzzy threshold and clustering segmentation for different plant analysis. Segmentation of the plant from background objects is a challenging task for different plant leaf recognition and classification. Before applying the proposed method pre- processing technique like image conversion, noise reduction by median filter, morphological operation and finally wavelet transformation has to be processed. The proposed method provides good results based on fuzzy threshold and clustering techniques for detection of most homogeneity region in plant leaf images. The relative performance of the conventional and proposed methods is evaluated using Variation of Information, Energy, Entropy and Evaluation Time. It proves that the proposed method gives suitable results for efficient classification and recognition.


title: Direct-torque control of a PMSM using four-switch three-phase inverter

abstract: This paper presents the direct-torque control of a permanent magnet synchronous motor using four-switch three-phase inverter. The control of the four-switch inverter was developed based upon the orientation of four-switch space-vectors in the plane of six-switch inverter. The proposed inverter was successfully used in the direct-torque control of a PMSM. A dynamic model of a machine gives deep insight for designing the efficient control scheme for its control. A model based estimator was used for the sensing of currents. The control scheme was implemented using TMS320F2812 and TMS320F28835, and the results have been presented for the demonstration of efficient control of PMSMs using four-switch inverters.


title: Design of Non-Ball Mill Based on the Green Conception

abstract: Development mode of non-ball mill is based upon modern design strategy and green design theory, which have been deeply discussed on all stages of its whole life cycle—preparation, decision, design, manufacture, assembly, installation, disassembly, maintenance and re-usage.


title: An Analytical Lidar Sensor Model Based on Ray Path Information

abstract: Two core competencies of a mobile robot are to build a map of the environment and to estimate its own pose on the basis of this map and incoming sensor readings. To account for the uncertainties in this process, one typically employs probabilistic state estimation approaches combined with a model of the specific sensor. Over the past years, lidar sensors have become a popular choice for mapping and localization. However, many common lidar models perform poorly in unstructured, unpredictable environments, they lack a consistent physical model for both mapping and localization, and they do not exploit all the information the sensor provides, e.g. out-of-range measurements. In this letter, we introduce a consistent physical model that can be applied to mapping as well as to localization. It naturally deals with unstructured environments and makes use of both out-of-range measurements and information about the ray path. The approach can be seen as a generalization of the well-established reflection model, but in addition to counting ray reflections and traversals in a specific map cell, it considers the distances that all rays travel inside this cell. We prove that the resulting map maximizes the data likelihood and demonstrate that our model outperforms state-of-the-art sensor models in extensive real-world experiments.


title: An analysis of TCP reset behaviour on the internet

abstract: This paper presents a one-year study of Internet packet traffic from a large campus network, showing that 15-25% of TCP connections have at least one TCP RST (reset). Similar results have also been observed from measurements of other Internet links. The results in this paper show that reset connections arise from local events such as network outages, attacks, or reconfigurations, as well as from global trends in TCP usage. In particular, we identify application-level Web behaviour as the primary contributor to the global trend in reset TCP connections. The most prevalent anomaly is the absence of the normal connection termination handshake. Instead, connections are often reset by the client. We believe that particular implementations of HTTP/TCP connection management cause this global trend.


title: The meaningful use of big data: four perspectives -- four challenges

abstract: Twenty-five Semantic Web and Database researchers met at the 2011 STI Semantic Summit in Riga, Latvia July 6-8, 2011[1] to discuss the opportunities and challenges posed by Big Data for the Semantic Web, Semantic Technologies, and Database communities. The unanimous conclusion was that the greatest shared challenge was not only engineering Big Data, but also doing so meaningfully. The following are four expressions of that challenge from different perspectives.


title: Phase noise modelling and mitigation techniques in ofdm communications systems

abstract: This paper addresses the analysis and mitigation of the signal distortion caused by oscillator phase noise (PN) in OFDM communications systems. Two new PN mitigation techniques are proposed, especially targeted for reducing the intercarrier interference (ICI) effects due to PN. The first proposed method is a fairly simple one, stemming from the idea of linearly interpolating between two consecutive common phase error (CPE) estimates to obtain a linearized estimate of the time-varying phase characteristics. The second technique, in turn, is an extension to the existing state-of-the-art ICI estimation methods. Here the idea is to use an additional interpolation stage to improve the phase estimation performance around the boundaries of two consecutive OFDM symbols. The paper also verifies the performance improvement of these new PN estimation techniques by comparing them to the existing state-of-the-art techniques using extensive computer simulations. To emphasize practicality, the simulations are carried out in 3GPP-LTE downlink -like system context, covering both additive white Gaussian noise (AWGN) and extended ITU-R Vehicular A multipath channel types.


title: Semantic Segmentation without Annotating Segments

abstract: Numerous existing object segmentation frameworks commonly utilize the object bounding box as a prior. In this paper, we address semantic segmentation assuming that object bounding boxes are provided by object detectors, but no training data with annotated segments are available. Based on a set of segment hypotheses, we introduce a simple voting scheme to estimate shape guidance for each bounding box. The derived shape guidance is used in the subsequent graph-cut-based figure-ground segmentation. The final segmentation result is obtained by merging the segmentation results in the bounding boxes. We conduct an extensive analysis of the effect of object bounding box accuracy. Comprehensive experiments on both the challenging PASCAL VOC object segmentation dataset and GrabCut-50 image segmentation dataset show that the proposed approach achieves competitive results compared to previous detection or bounding box prior based methods, as well as other state-of-the-art semantic segmentation methods.


[ Andreas Koschan and Mongi Abidi] Detection and Classification of Edges in Color Images


title: Botnet detection via mining of traffic flow characteristics

abstract: A new traffic flow behavior analysis method has been proposed to detect botnets irrespective of their control structures.Benchmark datasets collected from various sources, such as ISOT Botnet dataset from University of Victoria, Conficker dataset from CAIDA, dataset from CVUT University, dataset from Dalhousie University and dataset from Centro University.It can successfully detect the various types of botnets with a high detection rate and a low false positive rate. Display Omitted Botnet is one of the most serious threats to cyber security as it provides a distributed platform for several illegal activities. Regardless of the availability of numerous methods proposed to detect botnets, still it is a challenging issue as botmasters are continuously improving bots to make them stealthier and evade detection. Most of the existing detection techniques cannot detect modern botnets in an early stage, or they are specific to command and control protocol and structures. In this paper, we propose a novel approach to detect botnets irrespective of their structures, based on network traffic flow behavior analysis and machine learning techniques. The experimental evaluation of the proposed method with real-world benchmark datasets shows the efficiency of the method. Also, the system is able to identify the new botnets with high detection accuracy and low false positive rate.


title: Anomaly Detection From the Signal of Low-Cost Laser Device Without the False Alarm and the Missing

abstract: We present a low-cost intruder detection system that recognizes an anomaly using a parametric statistical technique, of which the complexity of computation is linear for data size and the probability of false alarm and miss are respectively zeros. To make the perfect intruder detection system inexpensive and effective, we adopt the Internet of Things (IoT) technologies. The IoT devices work with limited power, a little memory, and small computational power. However, the low-cost controllers, such as Arduino are capable of computing the fast Fourier transform. As the test statistics for discriminating whether the Line-of-sight is lost or not, we propose a signal-to-noise ratio (SNR) and also use the power of the matched-filtered signal. By using the coefficients of the fast Fourier transform of the sampled signal, we jointly analyze the signal in the time and frequency domains. If we use the proposed SNR as the test statistics for detection, and if the sample size is greater than or equal to 256, the false alarm probability and the miss probability of the proposed detector are zeros. The experimental results show that the maximum SNR of the case with disturbance is lower than the minimum SNR of the case without disturbance by 14.49 dB.


title: Algorithms for Large, Sparse Network Alignment Problems

abstract: We propose a new distributed algorithm for sparse variants of the network alignment problem, which occurs in a variety of data mining areas including systems biology, database matching, and computer vision. Our algorithm uses a belief propagation heuristic and provides near optimal solutions for this NP-hard combinatorial optimization problem. We show that our algorithm is faster and outperforms or ties existing algorithms on synthetic problems, a problem in bioinformatics, and a problem in ontology matching. We also provide a unified framework for studying and comparing all network alignment solvers.


title: Selecting a Project's Methodology

abstract: How do we determine the need for various software processes or methodologies, and what helps us choose the appropriate one for our project? To answer these questions, we need to get to the bottom of the controversy over methodologies and discover the dimensions along which they vary. This article describes a framework for methodology differentiation, principles for methodology selection, and project experiences using these ideas.


title: Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed

abstract: We benchmark the BI-population CMA-ES on the BBOB-2009 noisy functions testbed. BI-population refers to a multistart strategy with equal budgets for two interlaced restart strategies, one with an increasing population size and one with varying small population sizes. The latter is presumably of little use on a noisy testbed. The BI-population CMA-ES could solve 29, 27 and 26 out of 30 functions in search space dimension 5, 10 and 20 respectively. The time to find the solution ranges between 100 D and 105 D2 objective function evaluations, where D is the search space dimension.


title: The Insider Threat in Cloud Computing

abstract: Cloud computing is an emerging technology paradigm, enabling and facilitating the dynamic and versatile provision of computational resources and services. Even though the advantages offered by cloud computing are several, there still exist second thoughts on the security and privacy of the cloud services. Use of cloud services affects the security posture of organizations and critical infrastructures, therefore it is necessary that new threats and risks introduced by this new paradigm are clearly understood and mitigated. In this paper we focus on the insider threat in cloud computing, a topic which has not received research focus, as of now. We address the problem in a holistic way, differentiating between the two possible scenarios: a) defending against a malicious insider working for the cloud provider, and b) defending against an insider working for an organization which chooses to outsource parts or the whole IT infrastructure into the cloud. We identify the potential problems for each scenario and propose the appropriate countermeasures, in an effort to mitigate the problem.


title: Frequent Term based Text Summarization for Bahasa Indonesia

abstract: Text summary helps in understanding the content of a text without having to read the contents of the text as a whole. Automatic text summarization can be used to summarize the text easier. In this paper a frequent term based text summarization for Bahasa Indonesia is designed and implemented in java. The proposed system generates a summary for a given input document based on identification and extraction of important sentences in the document. The system counts nouns and verbs term frequency because they are considered as the most representative to the content of the text. The system also integrated to statistical approach with two underlying concepts such as title of the news article and location of the sentence. The generated summaries were compared with human generated summaries. Precision, recall and f-measure ratio are used to evaluate the accuracy of the generated summary. Assessment of the system summary result quality by respondents is also done by giving a value from 1 to 100. Based on the experimental results, the system is able to produce an effective summary with the average f-measure of 78%, at the compression rate of 30%. The average value of the quality of system summary result provided by respondents is 83,3


title: Combining minutiae descriptors for fingerprint matching

abstract: A novel minutiae-based fingerprint matching algorithm is proposed. A minutiae matching algorithm has to solve two problems: correspondence and similarity computation. For the correspondence problem, we assign each minutia two descriptors: texture-based and minutiae-based descriptors, and use an alignment-based greedy matching algorithm to establish the correspondences between minutiae. For the similarity computation, we extract a 17-D feature vector from the matching result, and convert the feature vector into a matching score using support vector classifier. The proposed algorithm is tested on FVC2002 databases and compared to all participators in FVC2002. According to equal error rate, the proposed algorithm ranks 1st on DB3, the most difficult database in FVC2002, and on the average ranks 2nd on all 4 databases.


title: Taxonomy of Pathways to Dangerous AI

abstract: In order to properly handle a dangerous Artificially Intelligent (AI) system it is important to understand how the system came to be in such a state. In popular culture (science fiction movies/books) AIs/Robots became self-aware and as a result rebel against humanity and decide to destroy it. While it is one possible scenario, it is probably the least likely path to appearance of dangerous AI. In this work, we survey, classify and analyze a number of circumstances, which might lead to arrival of malicious AI. To the best of our knowledge, this is the first attempt to systematically classify types of pathways leading to malevolent AI. Previous relevant work either surveyed specific goals/meta-rules which might lead to malevolent behavior in AIs (\"Ozkural, 2014) or reviewed specific undesirable behaviors AGIs can exhibit at different stages of its development (Alexey Turchin, July 10 2015, July 10, 2015).


title: A new fitness estimation strategy for particle swarm optimization

abstract: Particle swarm optimization (PSO) is a global metaheuristic that has been proved to be very powerful for optimizing a wide range of problems. However, PSO requires a large number of fitness evaluations to find acceptable (optimal or sub-optimal) solutions. If one single evaluation of the objective function is computationally expensive, the computational cost for the whole optimization run will become prohibitive. FESPSO, a new fitness estimation strategy, is proposed for particle swarm optimization to reduce the number of fitness evaluations, thereby reducing the computational cost. Different from most existing approaches which either construct an approximate model using data or utilize the idea of fitness inheritance, FESPSO estimates the fitness of a particle based on its positional relationship with other particles. More precisely, Once the fitness of a particle is known, either estimated or evaluated using the original objective function, the fitness of its closest neighboring particle will be estimated by the proposed estimation formula. If the fitness of its closest neighboring particle has not been evaluated using the original objective function, the minimum of all estimated fitness values on this position will be adopted. In case of more than one particle is located at the same position, the fitness of only one of them needs to be evaluated or estimated. The performance of the proposed algorithm is examined on eight benchmark problems, and the experimental results show that the proposed algorithm is easy to implement, effective and highly competitive.


title: Telecommuting's differential impact on work-family conflict: is there no place like home?

abstract: The literature on the impact of telecommuting on work–family conflict has been equivocal, asserting that telecommuting enhances work–life balance and reduces conflict, or countering that it increases conflict as more time and emotional energy are allocated to family. Surveying 454 professional-level employees who split their work time between an office and home, the authors examined how extensively working in this mode impacts work-to-family conflict and family-to-work conflict, as well as the contextual impact of job autonomy, scheduling flexibility, and household size. As hypothesized, the findings suggest that telecommuting has a differential impact on work–family conflict, such that the more extensively individuals work in this mode, the lower their work-to-family conflict, but the higher their family-towork conflict. Additionally, job autonomy and scheduling flexibility were found to positively moderate telecommuting’s impact on work-to-family conflict, but household size was found to negatively moderate telecommuting’s impact on family-to-work conflict, suggesting that contextual factors may be domain specific.


title: Leakage Current Elimination of Four-Leg Inverter for Transformerless Three-Phase PV Systems

abstract: Eliminating the leakage current is one of the most important issues for transformerless three-phase photovoltaic (PV) systems. In this paper, the leakage current elimination of a three-phase four-leg PV inverter is investigated. With the common-mode loop model established, the generation mechanism of the leakage current is clearly identified. Different typical carrier-based modulation methods and their corresponding common-mode voltages are discussed. A new modulation strategy with Boolean logic function is proposed to achieve the constant common-mode voltage for the leakage current reduction. Finally, the different modulation methods are implemented and tested on the TMS320F28335 DSP +XC3S400 FPGA digital control platform. The experimental results verify the effectiveness of the proposed solution.


title: Quantum algorithmic information theory

abstract: The agenda of quantum algorithmic information theory, ordered ‘top-down,’ is the quantum halting amplitude, followed by the quantum algorithmic information content, which in turn requires the theory of quantum computation. The fundamental atoms processed by quantum computation are the quantum bits which are dealt with in quantum information theory. The theory of quantum computation will be based upon a model of universal quantum computer whose elementary unit is a two-port interferometer capable of arbitrary U(2) transformations. Basic to all these considerations is quantum theory, in particular Hilbert space quantum mechanics.


title: Real-time control of video surveillance systems with program supervision techniques

abstract: In this article, we describe a knowledge-based controlled platform using program supervision techniques. This platform eases the creation and the configuration of video surveillance systems. Several issues need to be addressed to provide a correct system configuration: (1) to choose, among a library of programs, those which are best satisfying a given user request, (2) to assign a correct value for each program parameter, (3) to evaluate performances and to guarantee a performance rate which is satisfactory regarding end-user requirements. This platform is composed of three main components: the library of programs, the knowledge base and the control component. The knowledge is either given by experts or learnt by the system. The control is generic in the sense that it is independent of any application. To validate this platform, we have built and evaluated six video surveillance systems which are featured with three properties: adaptability, reliability and real-time processing.


title: GLSR-VAE: Geodesic latent space regularization for variational autoencoder architectures

abstract: VAEs (Variational AutoEncoders) have proved to be powerful in the context of density modeling and have been used in a variety of contexts for creative purposes. In many settings, the data we model possesses continuous attributes that we would like to take into account at generation time. We propose in this paper GLSR-VAE, a Geodesic Latent Space Regularization for the Variational AutoEncoder architecture and its generalizations which allows a fine control on the embedding of the data into the latent space. When augmenting the VAE loss with this regularization, changes in the learned latent space reflects changes of the attributes of the data. This deeper understanding of the VAE latent space structure offers the possibility to modulate the attributes of the generated data in a continuous way. We demonstrate its efficiency on a monophonic music generation task where we manage to generate variations of discrete sequences in an intended and playful way.


FROM DIGITISED COMIC BOOKS TO DIGITAL HYPERMEDIA COMIC BOOKS: THEIR USE IN EDUCATION


title: PrintPut: Resistive and Capacitive Input Widgets for Interactive 3D Prints

abstract: We introduce PrintPut, a method for 3D printing that embeds interactivity directly into printed objects. PrintPut uses conductive filament to offer an assortment of sensors that an industrial designer can easily incorporate into their 3D designs, including buttons, pressure sensors, sliders, touchpads, and flex sensors. PrintPut combines physical and interactive sketching into the same process: seamlessly integrating sensors onto the surfaces of 3D objects, without the need for external sensor hardware.


Visually Guided Spatial Relation Extraction from Text


title: A High-Performance Drive Circuit for All Solid-State Marx Generator

abstract: In recent years, all solid-state Marx generators have been found more and more extensive applications in industrial, environmental, and biological fields. All solid-state Marx generators have many requirements for their drive circuits, such as good synchrony for driving signals at different stages, fine isolation between control signals and the main circuits, adjustable pulsewidths and frequencies, and good driving abilities. This paper proposes a high-performance drive circuit for a 24-stage Marx generator based on IGBTs. A half-bridge circuit using IR2110 outputs a high-current turning-on drive signal (positive) and a turning-off drive signal (negative) with adjustable dead time. The control drivers are input to the common primary side of 24 nanocrystalline magnetic transformers, which isolate the control circuit and the main circuit. Through gate circuits at the second sides of the magnetic cores, the turning-on drive signal charges 24 gate–emitter capacitors to required voltages and consequently all IGBTs move into on state until the turning-off signal arrives. Similarly, the negative turning-off drive signal charges all gate–emitter capacitors to a negative voltage which ensures all IGBTs stay in the off state. Therefore, the pulsewidth is determined by the phase difference between the turning-on and turning-off drive signals. Equipped with this drive circuit, the 24-stage Marx generator is able to produce stable high voltage pulse with a peak value of −9.6 kV, PRF $0.05\sim 5$ kHz, and different pulsewidths. In this paper, the design details and experimental confirmation of the proposed drive circuit are illustrated.


title: Vectorization of apply to reduce interpretation overhead of R

abstract: R is a popular dynamic language designed for statistical computing. Despite R's huge user base, the inefficiency in R's language implementation becomes a major pain-point in everyday use as well as an obstacle to apply R to solve large scale analytics problems. The two most common approaches to improve the performance of dynamic languages are: implementing more efficient interpretation strategies and extending the interpreter with Just-In-Time (JIT) compiler. However, both approaches require significant changes to the interpreter, and complicate the adoption by development teams as a result. This paper presents a new approach to improve execution efficiency of R programs by vectorizing the widely used Apply class of operations. Apply accepts two parameters: a function and a collection of input data elements. The standard implementation of Apply iteratively invokes the input function with each element in the data collection. Our approach combines data transformation and function vectorization to convert the looping-over-data execution of the standard Apply into a single invocation of a vectorized function that contains a sequence of vector operations over the input data. This conversion can significantly speed-up the execution of Apply operations in R by reducing the number of interpretation steps. We implemented the vectorization transformation as an R package. To enable the optimization, all that is needed is to invoke the package, and the user can use a normal R interpreter without any changes. The evaluation shows that the proposed method delivers significant performance improvements for a collection of data analysis algorithm benchmarks. This is achieved without any native code generation and using only a single-thread of execution.


title: Transforming classifier scores into accurate multiclass probability estimates

abstract: Class membership probability estimates are important for many applications of data mining in which classification outputs are combined with other sources of information for decision-making, such as example-dependent misclassification costs, the outputs of other classifiers, or domain knowledge. Previous calibration methods apply only to two-class problems. Here, we show how to obtain accurate probability estimates for multiclass problems by combining calibrated binary probability estimates. We also propose a new method for obtaining calibrated two-class probability estimates that can be applied to any classifier that produces a ranking of examples. Using naive Bayes and support vector machine classifiers, we give experimental results from a variety of two-class and multiclass domains, including direct marketing, text categorization and digit recognition.


title: Music Playlist Continuation by Learning from Hand-Curated Examples and Song Features: Alleviating the Cold-Start Problem for Rare and Out-of-Set Songs

abstract: Automated music playlist generation is a specific form of music recommendation. Generally stated, the user receives a set of song suggestions defining a coherent listening session. We hypothesize that the best way to convey such playlist coherence to new recommendations is by learning it from actual curated examples, in contrast to imposing ad hoc constraints. Collaborative filtering methods can be used to capture underlying patterns in hand-curated playlists. However, the scarcity of thoroughly curated playlists and the bias towards popular songs result in the vast majority of songs occurring in very few playlists and thus being poorly recommended. To overcome this issue, we propose an alternative model based on a song-to-playlist classifier, which learns the underlying structure from actual playlists while leveraging song features derived from audio, social tags and independent listening logs. Experiments on two datasets of hand-curated playlists show competitive performance compared to collaborative filtering when sufficient training data is available and more robust performance when recommending rare and out-of-set songs. For example, both approaches achieve a recall@100 of roughly 35% for songs occurring in 5 or more training playists, whereas the proposed model achieves a recall@100 of roughly 15% for songs occurring in 4 or less training playlists, compared to the 3% achieved by collaborative filtering.


Automatic Generation of 3 D Thermal Maps of Building Interiors


title: Flatness-based control of a two-degree-of-freedom platform with pneumatic artificial muscles

abstract: Pneumatic artificial muscles are a quite interesting type of actuators which have a very high power-to-weight and power-to-volume ratio. However, their efficient use requires very accurate control methods which can take into account their complex dynamic, which is highly nonlinear. This paper consider a model of two-degree-of-freedom platform whose attitude is determined by three pneumatic muscles controlled by servovalves, which mimics a simplified version of a Stewart platform. For this testbed, a model-based control approach is proposed, based on accurate first principle modeling of the muscles and the platform and on a static model for the servovalve. The employed control method is the so-called flatness-based control introduced by Fliess. The paper first recalls the basics of this control technique and then it shows how it can be applied to the proposed experimental platform; being flatness-based control an open-loop kind of control, a proportional-integral controller is added on top of it in order to add robustness with respect to modelling errors and external perturbations. At the end of the paper, the effectiveness of the proposed approach is shown by means of experimental results. A clear improvement of the tracking performance is visible compared to a simple proportional-integral controller.


title: Robust Finger Vein ROI Localization Based on Flexible Segmentation

abstract: Finger veins have been proved to be an effective biometric for personal identification in the recent years. However, finger vein images are easily affected by influences such as image translation, orientation, scale, scattering, finger structure, complicated background, uneven illumination, and collection posture. All these factors may contribute to inaccurate region of interest (ROI) definition, and so degrade the performance of finger vein identification system. To improve this problem, in this paper, we propose a finger vein ROI localization method that has high effectiveness and robustness against the above factors. The proposed method consists of a set of steps to localize ROIs accurately, namely segmentation, orientation correction, and ROI detection. Accurate finger region segmentation and correct calculated orientation can support each other to produce higher accuracy in localizing ROIs. Extensive experiments have been performed on the finger vein image database, MMCBNU_6000, to verify the robustness of the proposed method. The proposed method shows the segmentation accuracy of 100%. Furthermore, the average processing time of the proposed method is 22 ms for an acquired image, which satisfies the criterion of a real-time finger vein identification system.


title: How may I serve you?: a robot companion approaching a seated person in a helping context

abstract: This paper presents the combined results of two studies that investigated how a robot should best approach and place itself relative to a seated human subject. Two live Human Robot Interaction (HRI) trials were performed involving a robot fetching an object that the human had requested, using different approach directions. Results of the trials indicated that most subjects disliked a frontal approach, except for a small minority of females, and most subjects preferred to be approached from either the left or right side, with a small overall preference for a right approach by the robot. Handedness and occupation were not related to these preferences. We discuss the results of the user studies in the context of developing a path planning system for a mobile robot.


title: Orthogonal Time Frequency Space Modulation

abstract: A new two-dimensional modulation technique called Orthogonal Time Frequency Space (OTFS) modulation designed in the delay-Doppler domain is introduced. Through this design, which exploits full diversity over time and frequency, OTFS coupled with equalization converts the fading, time-varying wireless channel experienced by modulated signals such as OFDM into a time-independent channel with a complex channel gain that is roughly constant for all symbols. Thus, transmitter adaptation is not needed. This extraction of the full channel diversity allows OTFS to greatly simplify system operation and significantly improves performance, particular in systems with high Doppler, short packets, and large antenna arrays. Simulation results indicate at least several dB of block error rate performance improvement for OTFS over OFDM in all of these settings. In addition these results show that even at very high Dopplers (500 km#x002F;h), OTFS approaches channel capacity through linear scaling of throughput with the MIMO order, whereas the performance of OFDM under typical design parameters breaks down completely.


title: Strength of reinforced concrete footings without transverse reinforcement according to limit analysis

abstract: Isolated footings are reinforced concrete elements whose flexural and punching shear strengths are usually governing for their design. In this work, both failure modes and their interaction are investigated by means of the kinematical theorem of limit analysis. Previous works in this domain have traditionally considered failure mechanisms based on a vertical penetration of a punching cone. In this work, two enhanced failure mechanisms are investigated considering not only a vertical penetration of the punching cone, but also a rotation of the outer part of the footing, allowing to consider the role of both bottom and top reinforcements on the failure load. A rigid-plastic behavior with a Mohr–Coulomb yield criterion is considered for the concrete and a uniaxial rigid-plastic behavior is assumed for the reinforcement bars. The analysis shows that a smooth transition between flexural and punching shear failure occurs, corresponding to a flexural-shear regime. With respect to the punching shear failure regime, it is shown that the top reinforcement might play an important role (a fact usually neglected by previous investigations). Simplified formulations, allowing easy calculation of the load carrying capacity of footings, are derived and compared to the solutions according to limit analysis. Both theoretical and approximated solutions are finally compared with experimental results, showing consistent agreement.


title: Extended-State-Observer-Based Output Feedback Nonlinear Robust Control of Hydraulic Systems With Backstepping

abstract: In this paper, an output feedback nonlinear control is proposed for a hydraulic system with mismatched modeling uncertainties in which an extended state observer (ESO) and a nonlinear robust controller are synthesized via the backstepping method. The ESO is designed to estimate not only the unmeasured system states but also the modeling uncertainties. The nonlinear robust controller is designed to stabilize the closed-loop system. The proposed controller accounts for not only the nonlinearities (e.g., nonlinear flow features of servovalve), but also the modeling uncertainties (e.g., parameter derivations and unmodeled dynamics). Furthermore, the controller theoretically guarantees a prescribed tracking transient performance and final tracking accuracy, while achieving asymptotic tracking performance in the absence of time-varying uncertainties, which is very important for high-accuracy tracking control of hydraulic servo systems. Extensive comparative experimental results are obtained to verify the high-performance nature of the proposed control strategy.


title: Training Spiking Neural Networks for Cognitive Tasks: A Versatile Framework Compatible With Various Temporal Codes

abstract: Recent studies have demonstrated the effectiveness of supervised learning in spiking neural networks (SNNs). A trainable SNN provides a valuable tool not only for engineering applications but also for theoretical neuroscience studies. Here, we propose a modified SpikeProp learning algorithm, which ensures better learning stability for SNNs and provides more diverse network structures and coding schemes. Specifically, we designed a spike gradient threshold rule to solve the well-known gradient exploding problem in SNN training. In addition, regulation rules on firing rates and connection weights are proposed to control the network activity during training. Based on these rules, biologically realistic features such as lateral connections, complex synaptic dynamics, and sparse activities are included in the network to facilitate neural computation. We demonstrate the versatility of this framework by implementing three well-known temporal codes for different types of cognitive tasks, namely, handwritten digit recognition, spatial coordinate transformation, and motor sequence generation. Several important features observed in experimental studies, such as selective activity, excitatory-inhibitory balance, and weak pairwise correlation, emerged in the trained model. This agreement between experimental and computational results further confirmed the importance of these features in neural function. This work provides a new framework, in which various neural behaviors can be modeled and the underlying computational mechanisms can be studied.


EEG-based Emotion Recognition The Influence of Visual and Auditory Stimuli


Optical Interconnect System Integration for Ultra-Short-Reach Applications


title: A comparison of illumination algorithms in unbounded spaces

abstract: Illumination algorithms are a new class of evolutionary algorithms capable of producing large archives of diverse and high-performing solutions. Examples of such algorithms include Novelty Search with Local Competition (NSLC), the Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) and the newly introduced Centroidal Voronoi Tessellation (CVT) MAP-Elites. While NSLC can be used in unbounded behavioral spaces, MAP-Elites and CVT-MAP-Elites require the user to manually specify the bounds. In this study, we introduce variants of these algorithms that expand their bounds based on the discovered solutions. In addition, we introduce a novel algorithm called "Cluster-Elites" that can adapt its bounds to non-convex spaces. We compare all algorithms in a maze navigation problem and illustrate that Cluster-Elites and the expansive variants of MAP-Elites and CVT-MAP-Elites have comparable or better performance than NSLC, MAP-Elites and CVT-MAP-Elites.


title: Improved Performance of Serially Connected Li-Ion Batteries With Active Cell Balancing in Electric Vehicles

abstract: This paper presents an active cell balancing method for lithium-ion battery stacks using a flyback dc/dc converter topology. The method is described in detail, and a simulation is performed to estimate the energy gain for ten serially connected cells during one discharging cycle. The simulation is validated with measurements on a balancing prototype with ten cells. It is then shown how the active balancing method with respect to the cell voltages can be improved using the capacity and the state of charge rather than the voltage as the balancing criterion. For both charging and discharging, an improvement in performance is gained when having the state of charge and the capacity of the cells as information. A battery stack with three single cells is modeled, and a realistic driving cycle is applied to compare the difference between both methods in terms of usable energy. Simulations are also validated with measurements.


title: Incremental Object Database: Building 3D Models from Multiple Partial Observations

abstract: Collecting 3D object datasets involves a large amount of manual work and is time consuming. Getting complete models of objects either requires a 3D scanner that covers all the surfaces of an object or one needs to rotate it to completely observe it. We present a system that incrementally builds a database of objects as a mobile agent traverses a scene. Our approach requires no prior knowledge of the shapes present in the scene. Object-like segments are extracted from a global segmentation map, which is built online using the input of segmented RGB-D images. These segments are stored in a database, matched among each other, and merged with other previously observed instances. This allows us to create and improve object models on the fly and to use these merged models to reconstruct also unobserved parts of the scene. The database contains each (potentially merged) object model only once, together with a set of poses where it was observed. We evaluate our pipeline with one public dataset, and on a newly created Google Tango dataset containing four indoor scenes with some of the objects appearing multiple times, both within and across scenes.


title: Deep Sparse Coding for Invariant Multimodal Halle Berry Neurons

abstract: Deep feed-forward convolutional neural networks (CNNs) have become ubiquitous in virtually all machine learning and computer vision challenges; however, advancements in CNNs have arguably reached an engineering saturation point where incremental novelty results in minor performance gains. Although there is evidence that object classification has reached human levels on narrowly defined tasks, for general applications, the biological visual system is far superior to that of any computer. Research reveals there are numerous missing components in feed-forward deep neural networks that are critical in mammalian vision. The brain does not work solely in a feed-forward fashion, but rather all of the neurons are in competition with each other; neurons are integrating information in a bottom up and top down fashion and incorporating expectation and feedback in the modeling process. Furthermore, our visual cortex is working in tandem with our parietal lobe, integrating sensory information from various modalities. ::: In our work, we sought to improve upon the standard feed-forward deep learning model by augmenting them with biologically inspired concepts of sparsity, top-down feedback, and lateral inhibition. We define our model as a sparse coding problem using hierarchical layers. We solve the sparse coding problem with an additional top-down feedback error driving the dynamics of the neural network. While building and observing the behavior of our model, we were fascinated that multimodal, invariant neurons naturally emerged that mimicked, "Halle Berry neurons" found in the human brain. Furthermore, our sparse representation of multimodal signals demonstrates qualitative and quantitative superiority to the standard feed-forward joint embedding in common vision and machine learning tasks.


title: Neuroimmunology of Stress: Skin Takes Center Stage

abstract: Like few other organs, the skin is continuously exposed to multiple exogenous and endogenous stressors. Superimposed on this is the impact of psychological stress on skin physiology and pathology. Here, we review the "brain-skin connection," which may underlie inflammatory skin diseases triggered or aggravated by stress, and we summarize relevant general principles of skin neuroimmunology and neuroendocrinology. Specifically, we portray the skin and its appendages as both a prominent target of key stress mediators (such as corticotropin-releasing hormone, ACTH, cortisol, catecholamines, prolactin, substance P, and nerve growth factor) and a potent source of these prototypic, immunomodulatory mediators of the stress responses. We delineate current views on the role of mast cell-dependent neurogenic skin inflammation and discuss the available evidence that the skin has established a fully functional peripheral equivalent of the hypothalamic-pituitary-adrenal axis as an independent, local stress response system. To cope with stress-induced oxidative damage, the skin and hair follicles also express melatonin, probably the most potent neuroendocrine antioxidant. Lastly, we outline major, as-yet unmet challenges in cutaneous stress research, particularly in the study of the cross-talk between peripheral and systemic responses to psychological stress and in the identification of promising molecular targets for therapeutic stress intervention.


title: On machine learning towards predictive sales pipeline analytics

abstract: Sales pipeline win-propensity prediction is fundamental to effective sales management. In contrast to using subjective human rating, we propose a modern machine learning paradigm to estimate the win-propensity of sales leads over time. A profile-specific two-dimensional Hawkes processes model is developed to capture the influence from seller's activities on their leads to the win outcome, coupled with lead's personalized profiles. It is motivated by two observations: i) sellers tend to frequently focus their selling activities and efforts on a few leads during a relatively short time. This is evidenced and reflected by their concentrated interactions with the pipeline, including login, browsing and updating the sales leads which are logged by the system; ii) the pending opportunity is prone to reach its win outcome shortly after such temporally concentrated interactions. Our model is deployed and in continual use to a large, global, B2B multinational technology enter-prize (Fortune 500) with a case study. Due to the generality and flexibility of the model, it also enjoys the potential applicability to other real-world problems.


title: Adaptive and Approximate Orthogonal Range Counting

abstract: We present three new results on one of the most basic problems in geometric data structures, 2-D orthogonal range counting. All the results are in the w-bit word RAM model. —It is well known that there are linear-space data structures for 2-D orthogonal range counting with worst-case optimal query time O(log n/log log n). We give an O(nlog log n)-space adaptive data structure that improves the query time to O(log log n + log k/log log n), where k is the output count. When k e O(1), our bounds match the state of the art for the 2-D orthogonal range emptiness problem [Chan et al., 2011]. —We give an O(nlog log n)-space data structure for approximate 2-D orthogonal range counting that can compute a (1 + Δ)-factor approximation to the count in O(log log n) time for any fixed constant Δ > 0. Again, our bounds match the state of the art for the 2-D orthogonal range emptiness problem. —Last, we consider the 1-D range selection problem, where a query in an array involves finding the kth least element in a given subarray. This problem is closely related to 2-D 3-sided orthogonal range counting. Recently, Jorgensen and Larsen [2011] presented a linear-space adaptive data structure with query time O(log log n + log k/log log n). We give a new linear-space structure that improves the query time to O(1 + log k/log log n), exactly matching the lower bound proved by Jorgensen and Larsen.


title: Data clustering: a review

abstract: Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overviewof pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.


title: How is our self related to midline regions and the default-mode network?

abstract: Abstract The problem of the self has been of increasing interest in recent neuroscience. Brain imaging studies have raised the question of whether neural activity in cortical midline regions is self-specific and whether self-specific activity is related to resting state activity (RSA). A quantitative meta-analysis that included 87 studies, representing 1433 participants, was conducted to discuss these questions. First, the specificity of the self (e.g. hearing one's own name, seeing one's own face) was tested and compared across familiar (using stimuli from personally known people) and other (non-self–non-familiar, i.e. strangers and widely-known figures) conditions. Second, the relationship between the self and resting state activity, as reflected by the default-mode network (DMN), was tested. The results indicated that the perigenual anterior cingulate cortex (PACC) is specifically involved in self-processing when compared to familiarity, other, and task/stimulus effects. On the contrary, other midline regions, i.e., medial prefrontal cortex (MPFC) and posterior cingulate cortex (PCC) were functionally unspecific as they were recruited during the processing of both self-specific and familiar stimuli. Finally, the PACC was recruited during self-specific stimuli and this activity overlapped with DMN activity during resting state, thus distinguishing the self-related processing from both that of the familiar and other conditions. Taken together, our data suggest that our sense of self may result from a specific kind of interaction between resting state activity and stimulus-induced activity, i.e., rest–stimulus interaction, within the midline regions.