title: Audio-visual speech modeling for continuous speech recognition

abstract: This paper describes a speech recognition system that uses both acoustic and visual speech information to improve recognition performance in noisy environments. The system consists of three components: a visual module; an acoustic module; and a sensor fusion module. The visual module locates and tracks the lip movements of a given speaker and extracts relevant speech features. This task is performed with an appearance-based lip model that is learned from example images. Visual speech features are represented by contour information of the lips and grey-level information of the mouth area. The acoustic module extracts noise-robust features from the audio signal. Finally the sensor fusion module is responsible for the joint temporal modeling of the acoustic and visual feature streams and is realized using multistream hidden Markov models (HMMs). The multistream method allows the definition of different temporal topologies and levels of stream integration and hence enables the modeling of temporal dependencies more accurately than traditional approaches. We present two different methods to learn the asynchrony between the two modalities and how to incorporate them in the multistream models. The superior performance for the proposed system is demonstrated on a large multispeaker database of continuously spoken digits. On a recognition task at 15 dB acoustic signal-to-noise ratio (SNR), acoustic perceptual linear prediction (PLP) features lead to 56% error rate, noise robust RASTA-PLP (relative spectra) acoustic features to 7.2% error rate and combined noise robust acoustic features and visual features to 2.5% error rate.


title: Micromachined high aspect ratio coplanar waveguide with high impedance and low loss on low resistivity silicon

abstract: A micromachining process has been developed to create high impedance and low loss high aspect ratio coplanar waveguide (HARC) on low resistivity silicon. The process uses silicon DRIE to create an array of tall mesas that are spaced with a precise pitch. The silicon mesa array is then merged into a single solid SiO2 mesa using thermal oxidation. The solid SiO2 mesa creates a wide dielectric for use in high impedance HARC. The complete fabrication process includes DRIE, thermal oxidation, electroplating, planarization, and substrate removal to create HARC on low resistivity silicon with a planar surface. A high impedance HARC has been fabricated on silicon using this method. Measurements show that silicon substrate removal increases the line impedance from ∼ 20 Ohms to 57 Ohms, reduces effective dielectric constant from ∼ 6 to 2, and reduces attenuation constant from ∼ 33 dB/cm to 4 dB/cm @ 30 GHz. Measurements are compared to an analytical model derived for HARC.


title: Modeling and predicting the user next input by Bayesian reasoning

abstract: We focus on enhancing the user experience by predicting entries when a form is filled, according to past interactions. The purpose of having a predictive model of form filling is to reduce the amount of time required to fill a form, and thus to reduce the fatigue and repetitiveness associated to this common task. Generally predictive models ignore the values entered by users in the other fields in the form, and just focus on the value getting entered at the current field. This is a limit to the model capabilities. Instead, we are aimed at predicting the sequence of entries in a form, instead of the value of single fields in isolation. This is done by means of inference over a Bayesian network, able to compute the a posteriori probability that remaining fields will assume certain values, given the set of values entered so far. The model structure and parameters can be learned from a dataset of past entries. The paper investigates computational and convergence issues under both the closed world assumption and the open world assumptions. As case study, we considered forms used for online payment of money order used at Poste Italiane, and we exploited this approach to prototype two different solutions for desktop and mobile applications. Results of experimentation with a user test group prove the proposed approach is able to provide an effective and appreciated support in filling a form.


title: Blog effects on brand attitude and purchase intention

abstract: This research attempts to investigate the effects of blog marketing on brand attitude and purchase intention. The elements of blog marketing are identified as community identification, interpersonal trust, message exchange, and two-way communication. The relationships among variables are pictured on the fundamental research framework provided by this study. Data were collected via an online questionnaire and 727 useable samples were collected and analyzed utilizing AMOS 5.0. The empirical findings show that the blog marketing elements can impact on brand attitude positively except for the element of community identification. Further, the analysis result also verifies the moderating effects on the relationship between blog marketing elements and brand attitude.


Interpretable Drug Target Prediction Using Deep Neural Representation


title: Neural Networks for Online Classification of Hand and Finger Movements Using Surface EMG signals

abstract: Myoelectric signals (MES) are the electrical manifestation of muscular contractions and they can be used to create myoelectric prosthesis which are able to function with the amputee's muscle movements. This signal recorded at the surface of the skin of the forearm has been exploited to provide recognition of the movement of the hand and finger Movements of healthy subject. The objective of the paper is to describe the identification procedure, based on EMG patterns of forearm activity using various Neural Networks methods and to make a comparison between different intelligent computational methods of identification, which are used in this work. Then an online algorithm for movement identification and classification that utilises the trained Neural Networks is presented.


title: Multi-lingual speech recognition with low-rank multi-task deep neural networks

abstract: Multi-task learning (MTL) for deep neural network (DNN) multilingual acoustic models has been shown to be effective for learning parameters that are common or shared between multiple languages[1, 2]. In the MTL paradigm, the number of parameters in the output layer is large and scales with the number of languages used in training. This output layer becomes a computational bottleneck. For mono-lingual DNNs, low-rank matrix factorization (LRMF) of weight matrices have yielded large computational savings[3, 4]. The LRMF proposed in this work for MTL, is for the original languagespecific block matrices to “share” a common matrix, with resulting low-rank language specific block matrices. The impact of LRMF is presented in two scenarios, namely : (a) improving performance in a target language when auxiliary languages are included during multi-lingual training; and (b) cross-language transfer to an unseen language with only 1 hour of transcribed training data. A 44% parameter reduction in the final layer, manifests itself in providing a lower memory footprint and faster training times. An experimental study shows that the LRMF multi-lingual DNN provides competitive performance compared to a full-rank multi-lingual DNN in both scenarios.


title: Human Activity Recognition using Deep Neural Network with Contextual Information

abstract: Human activity recognition is an important yet challenging research topic in the computer vision community. In this paper, we propose context features along with a deep model to recognize the individual subject activity in the videos of real-world scenes. Besides the motion features of the subject, we also utilize context information from multiple sources to improve the recognition performance. We introduce the scene context features that describe the environment of the subject at global and local levels. We design a deep neural network structure to obtain the high-level representation of human activity combining both motion features and context features. We demonstrate that the proposed context feature and deep model improve the activity recognition performance by comparing with baseline approaches. We also show that our approach outperforms state-of-the-art methods on 5-activities and 6-activities versions of the Collective Activities Dataset.


title: Learning 3-D Scene Structure from a Single Still Image

abstract: We consider the problem of estimating detailed 3D structure from a single still image of an unstructured environment. Our goal is to create 3D models that are both quantitatively accurate as well as visually pleasing. For each small homogeneous patch in the image, we use a Markov random field (MRF) to infer a set of "plane parametersrdquo that capture both the 3D location and 3D orientation of the patch. The MRF, trained via supervised learning, models both image depth cues as well as the relationships between different parts of the image. Other than assuming that the environment is made up of a number of small planes, our model makes no explicit assumptions about the structure of the scene; this enables the algorithm to capture much more detailed 3D structure than does prior art and also give a much richer experience in the 3D flythroughs created using image-based rendering, even for scenes with significant nonvertical structure. Using this approach, we have created qualitatively correct 3D models for 64.9 percent of 588 images downloaded from the Internet. We have also extended our model to produce large-scale 3D models from a few images.


title: Object tracking using SIFT and KLT tracker for UAV-based applications

abstract: In this paper, we propose a semi-automatic object tracking method based on a Scale Invariant Feature Transform (SIFT) and Kanade-Lucas-Tomasi (KLT) tracker. In our approach, the region of interest is specified by the user and then the interest points are detected. The tracker is then used to track the specified object in the consecutive frames. To overcome rapid changes of appearance, occlusion or disappearance from the camera view, we employ a forward-backward error compensation. Experimental results on VIVID dataset indicates that the proposed method has superior overall performance compared to more common methods in the field.


title: Flux-weakening in IPM motor drives: Comparison of state-of-art algorithms and a novel proposal for controller design

abstract: A review and comparison of some state-of-the-art flux-weakening algorithms for Interior Permanent Magnet Synchronous Motor (IPMSM) is presented in this paper, having voltage exploitation, dynamical performances and implementation efforts as key parameters of the comparison. Moreover an original proposal for the theoretical analysis of the overall dynamics of the voltage control loop is carried out, also taking into account non-linear effects and discrete-time implementation issues. All the considered algorithms have at least one feed-back path, thus providing steady-state voltage control even in presence of parameter errors, but different strategies are adopted leading to completely different behavioral characteristics. The approaches that will be analyzed are based on the control of the synchronous reference frame currents, the torque-flux characteristics and the voltage space vector angle. The effort for improving the voltage limitation performances, aiming at increasing dc bus voltage exploitation while still maintaining reasonable dynamical performances, is gaining importance due to both the efficiency demand (i.e. reducing the phase current magnitude for the required power) and some specific application issues, like the need for widening the speed-torque range given a relatively low voltage bus (e.g. in electrical or hybrid traction applications). A motor drive system for home appliances is considered in this paper as test bench for comparing the reviewed techniques. Simulation and experimental results are included.


title: Time Series Clustering: A Superior Alternative for Market Basket Analysis

abstract: Market Basket Analysis often involves applying the de facto association rule mining method on massive sales transaction data. In this paper, we argue that association rule mining is not always the most suitable method for analysing big market-basket data. This is because the data matrix to be used for association rule mining is usually large and sparse, resulting in sluggish generation of many trivial rules with little insight. To address this problem, we summarise a real-world sales transaction data set into time series format. We then use time series clustering to discover commonly purchased items that are useful for pricing or formulating cross-selling strategies. We show that this approach uses a data set that is substantially smaller than the data to be used for association analysis. In addition, it reveals significant patterns and insights that are otherwise hard to uncover when using association analysis.


title: Target value design: using collaboration and a lean approach to reduce construction cost

abstract: Target costing is an effective management technique that has been used in manufacturing for decades to achieve cost predictability during new product development. Adoption of this technique promises benefits for the construction industry as it struggles to raise the number of successful outcomes and certainty of project delivery in terms of cost, quality and time. Target value design is a management approach that takes the best features of target costing and adapts them to the peculiarities of construction. The concept of target value design is introduced based on the results of action research carried out on 12 construction projects in the USA. It has been shown that systemic application of target value design leads to significant improvement of project performance—the final cost of projects was on average 15% less than market cost. The construction industry already has approaches that have similarities with elements of the target value design process or use the same terminology, e.g. partnering and target cost contracts, cost planning, etc. Following an exploration of the similarities and differences target value design is positioned as a form of target costing for construction that offers a more reliable route to successful project outcomes.


title: A review of OSI-based charging standards and eMobility open protocols

abstract: According to the U.S. Department of Energy, a plug in Electrical Vehicle (PEV) can be defined as a vehicle that consumes the power from a battery with a capacity of no less than 4 kw/h and the vehicle can be recharged form an external energy motive power. Electrical mobility is a new emerging field that has drawn from electrical powertrain technologies and electrical vehicles charging infrastructures in the transition toward a green smart environment. There is a need to create a solid network communication platform, standards, best practices and protocols for E-mobility. As we see, in the academic domain, less attention has been paid in the area of reviewing the e-mobility protocols and the available requirements needed for a smooth transition to the smart gird. This article focuses on the most common standards and open protocols in the field of e-mobility, especially the communication means between the PEV, Electrical Vehicle Supply Equipment (EVSE), Central Back Office and possibly Roaming Center. Moreover, this paper provides an Open System Interconnection (OSI) network representation model for the given standards.


title: Centralized healthcare cyber-physical system's data analysis module development

abstract: Cyber-physical systems have a huge potential in healthcare for improving treatment quality and patients' assistance speed. Cyber-physical systems will help automatizing treatment process that can be realized by the continuous monitoring of each patient's health state, and if necessary, using multiple medical devices. That allows gathering information about patient's health and perform operations on patients, in order to reduce the progressions of a disease and improve patient's healing process. This paper provides data analysis module of overlooked centralized healthcare cyber-physical system's architecture development. Proposed concept will allow identifying causes of diseases, research new diseases, provide actual information about existing disease and treatment methods for clinics that are a part of centralized healthcare cyber-physical system, and, finally, automatize patients' health monitoring and remote treatment.


title: Good Language-Switchers are Good Task-Switchers: Evidence from Spanish-English and Mandarin-English Bilinguals

abstract: Bilingual advantages in executive control tasks are well documented, but it is not yet clear what degree or type of bilingualism leads to these advantages. To investigate this issue, we compared the performance of two bilingual groups and monolingual speakers in task-switching and language-switching paradigms. Spanish–English bilinguals, who reported switching between languages frequently in daily life, exhibited smaller task-switching costs than monolinguals after controlling for between-group differences in speed and parent education level. By contrast, Mandarin–English bilinguals, who reported switching languages less frequently than Spanish–English bilinguals, did not exhibit a task-switching advantage relative to monolinguals. Comparing the two bilingual groups in language-switching, Spanish–English bilinguals exhibited smaller costs than Mandarin–English bilinguals, even after matching for fluency in the non-dominant language. These results demonstrate an explicit link between language-switching and bilingual advantages in taskswitching, while also illustrating some limitations on bilingual advantages. (JINS, 2011, 17, 682–691)


title: Using Technology Acceptance Model to assess teachers' attitude towards use of technology as teaching tool: a SEM Approach

abstract: To supplement the traditional teaching , in the last few years a steady growth of ICT applications and IT courseware development has taken place in India. Only a few studies have evaluated acceptance of such new technologies among teachers. An attempt is made to understand the attitude of mathematics teachers towards IT as an instructional tool in the State Schools of Kerala (India) , using the Technology Acceptance Model (TAM) framework. Results suggested that TAM being a robust model can be effectively used in this context too and a good fit for the proposed model for the data was found. The sample consisted of only the high school mathematics teachers, who seem to agree about the usefulness of computers in teaching mathematics, but were found not very conversant in using these as teaching tools. This factor leads to the finding of the study showing Teachers’ Perceived Ease of Use having significant impact on their perception about Usefulness and Attitude towards use of IT in teaching.


title: Predicting Depression via Social Media

abstract: Major depression constitutes a serious challenge in personal and public health. Tens of millions of people each year suffer from depression and only a fraction receives adequate treatment. We explore the potential to use social media to detect and diagnose major depressive disorder in individuals. We first employ crowdsourcing to compile a set of Twitter users who report being diagnosed with clinical depression, based on a standard psychometric instrument. Through their social media postings over a year preceding the onset of depression, we measure behavioral attributes relating to social engagement, emotion, language and linguistic styles, ego network, and mentions of antidepressant medications. We leverage these behavioral cues, to build a statistical classifier that provides estimates of the risk of depression, before the reported onset. We find that social media contains useful signals for characterizing the onset of depression in individuals, as measured through decrease in social activity, raised negative affect, highly clustered egonetworks, heightened relational and medicinal concerns, and greater expression of religious involvement. We believe our findings and methods may be useful in developing tools for identifying the onset of major depression, for use by healthcare agencies; or on behalf of individuals, enabling those suffering from depression to be more proactive about their mental health.


A New Model for Software Risk Management


A Graph-Theoretic Method to Representing a Concept Lattice


title: Defining a NoSQL Document Database of Accessible Transport Routes

abstract: Transport is one the aspects that most critically affects the population's life conditions, particularly in the case of those people with various special mobility needs. In spite of the existence of numerous Websites that provide information about public transport services, there is a lack of open data regarding accessible public transportation systems. We are, therefore, working on the development of a technological framework for the processing, management and exploitation of open data, with the goal of promoting accessibility to city public transport, which is taking place within the framework of the Access@city project. In this paper we specifically focus on the problem of designing the NoSQL document database with which to manage the information concerning accessible routes obtained by means of crowdsourcing techniques.


title: Pseudo Random Pulse Driven Advanced In-Cell Touch Screen Panel for Spectrum Spread Electromagnetic Interference

abstract: For the first time, we analyze the mechanism of electromagnetic interference (EMI) in an advanced in-cell touch (AIT) panel, which is a state-of-the-art in-cell touch screen panel technology. The AIT panel has stronger EMI generation at a specific frequency band (0.15 ~ 30 MHz) due to a load free driving (LFD) method. The LFD is adopted to overcome the structural vulnerability of in-cell touch panel, which incurs large parasitic capacitance due to the touch electrodes within the display panel. In order to overcome the EMI problem, we present a novel driving method using pseudo random pulse (PRP). The power spectrum of PRP is well spread over the frequency band, resulting in a lower EMI. We measured the EMI level with a near-field probe, and the proposed driving method shows the EMI reduction of 6.5 dB. This EMI reduction is accomplished without structural reconfiguration, as well as the touch performance remains the same as that before PRP is applied. Therefore, the proposed driving method can be utilized in the field of automotive, military, and aviation industries as a user interface which requires high touch performance and low EMI generation due to the peculiarities.


title: Systematic and random variability analysis of two different 6T-SRAM layout topologies

abstract: This paper studies the device variability influence on 6T-SRAM cells in a function of the regularity level of their layout. Systematic and random variations have been analyzed when these memory circuits are implemented on a 45nm technology node. The NBTI aging relevance on these cells has been also studied for two layout topologies and SNM has been seen as the parameter that suffers the highest impact with respect to cell aging and variability.


title: Recent innovations in DRAM manufacturing

abstract: Recent generations of Dynamic Random Access Memory (DRAM) have seen remarkable changes in both processes and the materials used. In the past five years the industry has gone from the 9x-nm node through the 7x, 6x, and 5x nodes to the 4x node chips starting to come on the market. In retrospect it is little short of amazing that the competing companies have crammed the required ∼25 fF into an ever decreasing amount of floorspace; the cell size of the latest 4x-nm DRAM is ∼0.0137 μm2 – or maybe we should say ∼13,700 nm2. This has been achieved by the adoption of dual-layer capacitors, high-k dielectrics, and raised source/drains, among other techniques. Chipworks, as a supplier of competitive intelligence to the semiconductor and electronics industries, monitors the evolution of chip technologies as they come into commercial production. Chipworks has obtained parts from the leading edge manufacturers, and performed structural and compositional analyses to examine the features and manufacturing processes of the devices. This paper illustrates some of the different structures of DRAM cells seen in the last few years from some of the leading companies in the sector, ranging from the 9x-nm node to the latest 4x-nm product.


title: Fuzzy sets in machine learning and data mining

abstract: Machine learning, data mining, and several related research areas are concerned with methods for the automated induction of models and the extraction of interesting patterns from empirical data. Automated knowledge acquisition of that kind has been an essential aspect of artificial intelligence since a long time and has more recently also attracted considerable attention in the fuzzy sets community. This paper briefly reviews some typical applications and highlights potential contributions that fuzzy set theory can make to machine learning, data mining, and related fields. In this connection, some advantages of fuzzy methods for representing and mining vague patterns in data are especially emphasized.


title: A Novel Method for Improving Antipodal Vivaldi Antenna Performance

abstract: Herein, a new method for improving the directivity and bandwidth of the antipodal Vivaldi antenna structure is presented. The method is based on introducing a parasitic elliptical patch in the flare aperture to enhance the field coupling between the arms and produce stronger radiation in the endfire direction. This approach improves the directivity without compromising the low frequency performance and removes the need for electrically thin dielectric substrates. The proposed antenna structure including the feeding line and transition measures $140 \times 66 \times 1.5 \text{mm}^3$ and has a peak gain $> 0\;{\text{dBi}}$ over the 2–32 GHz frequency range and ${>} 10\;{\text{dBi}}$ over the 6–21 GHz range, which is an improvement to what has been reported for Vivaldi antennas with similar size.


title: Dopamine: A Research Framework for Deep Reinforcement Learning

abstract: Deep reinforcement learning (deep RL) research has grown significantly in recent years. A number of software offerings now exist that provide stable, comprehensive implementations for benchmarking. At the same time, recent deep RL research has become more diverse in its goals. In this paper we introduce Dopamine, a new research framework for deep RL that aims to support some of that diversity. Dopamine is open-source, TensorFlow-based, and provides compact and reliable implementations of some state-of-the-art deep RL agents. We complement this offering with a taxonomy of the different research objectives in deep RL research. While by no means exhaustive, our analysis highlights the heterogeneity of research in the field, and the value of frameworks such as ours.


title: A millimeter-wave ultra-compact broadband diode mixer using modified Marchand balun

abstract: In this paper, a broadband mixer has been developed using 0.15-/spl mu/m pHEMT technology. This singly balanced mixer was realized by using Schottky diodes. A modified Marchand balun was employed at the LO port and matched to the diodes directly to achieve wideband performance and compact size. This diode mixer achieves conversion loss of better than 10 dB from 46 to 78 GHz with LO power of 12.5 dBm, and the chip size is only 0.57 mm /spl times/ 0.52 mm. Compared with previously reported MMIC mixers in millimeter-wave frequencies, this circuit has the smallest chip size with competitive performance and wide bandwidth.


title: Cyber Security Threats Detection Using Ensemble Architecture

abstract: This paper describes an ensemble design for cyber security threats detection, which fuses the results from multiple classifiers together to make a final assessment decision. For promoting both speed and accuracy in the detection performance, only some of the features in traffic data are selected for each base classifier. In the kernel of each classifier, we combine Dempster-Shafer theory with k-nearest neighbor technique to solve the uncertainty problems caused by ambiguous and limited intrusion information. In addition, we apply data mining techniques to reduce the number of false alarms. The results indicate that our ensemble approach achieves higher detection rates than that of using a full feature set of classifiers.


title: Simpler is better? Lexicon-based ensemble sentiment classification beats supervised methods

abstract: It has been shown in this paper that simplistic Bag of Words (BoW) lexicon methods for sentiment polarity assignment with ensemble classifiers are much faster than a supervised approach to sentiment classification while yielding similar accuracy. BoW methods also proved to be efficient and fast across all examined datasets. Moreover, a new approach to lexicon extraction that can be successfully used for sentiment polarity assignment is presented in the paper. It has been shown that accuracy obtained from such lexicons outperforms other lexicon based approaches.


title: Performance of a High-Resolution Polarimetric SAR Automatic Target Recognition System

abstract: targets were used to study the performance of these algorithms. The prescreener required a low threshold to detect most of the targets in the data, which resulted in a high density of false alarms. The discriminator and classifier stages then reduced this false-alarm density by a factor of 100. We improved targetdetection performance by using fully polarimetric imagery processed by the polarimetric whitening filter (PWF), rather than by using single-channel imagery. In addition, the PWF-processed imagery improved the probability of correct classification in a four-class (tank, armored personnel carrier, howitzer, or clutter) classifier.


title: Brain-wide neuronal dynamics during motor adaptation in zebrafish

abstract: Neural activity is recorded at the cellular level, throughout the brain of larval zebrafish, while the animals interact with a virtual environment and adapt their motor output to changes in visual feedback; this is used to derive candidates of functional elements driving motor learning.


title: WikiRank: Improving Keyphrase Extraction Based on Background Knowledge

abstract: Keyphrase is an efficient representation of the main idea of documents. While background knowledge can provide valuable information about documents, they are rarely incorporated in keyphrase extraction methods. In this paper, we propose WikiRank, an unsupervised method for keyphrase extraction based on the background knowledge from Wikipedia. Firstly, we construct a semantic graph for the document. Then we transform the keyphrase extraction problem into an optimization problem on the graph. Finally, we get the optimal keyphrase set to be the output. Our method obtains improvements over other state-of-art models by more than 2% in F1-score.


title: Fast Predictive Image Registration

abstract: We present a method to predict image deformations based on patch-wise image appearance. Specifically, we design a patch-based deep encoder-decoder network which learns the pixel/voxel-wise mapping between image appearance and registration parameters. Our approach can predict general deformation parameterizations, however, we focus on the large deformation diffeomorphic metric mapping (LDDMM) registration model. By predicting the LDDMM momentum-parameterization we retain the desirable theoretical properties of LDDMM, while reducing computation time by orders of magnitude: combined with patch pruning, we achieve a \(1500\mathsf {x}\)/\(66\mathsf {x}\) speed-up compared to GPU-based optimization for 2D/3D image registration. Our approach has better prediction accuracy than predicting deformation or velocity fields and results in diffeomorphic transformations. Additionally, we create a Bayesian probabilistic version of our network, which allows evaluation of deformation field uncertainty through Monte Carlo sampling using dropout at test time. We show that deformation uncertainty highlights areas of ambiguous deformations. We test our method on the OASIS brain image dataset in 2D and 3D.


title: Integration of electrolysis to produce hydrogen and oxygen in a pulp mill process

abstract: The pulp and paper industry faces demand for totally carbon free production. Due to local conditions, there are challenges to sell produced additional electricity. A novel way, generating hydrogen and oxygen through the electrolysis of water can decarbonize the largest fossil carbon dioxide producing equipment, the lime kiln. The oxygen produced can be used for generation of bleaching chemicals, and also for effluent treatment. The operation and capacity of the recovery boiler can be improved by oxygen use. Using excess electricity of a typical, large South-American pulp mill, 73% of the heat demand of the lime kiln can be covered with hydrogen and the produced amount of oxygen exceeds the mill's oxygen requirement. The profitability of the integration depends on the other possible uses for electricity. When sales price for electricity is low or purchased oil price is high electrolysis can decrease the operating costs of a modern pulp mill.


title: Pseudogen: A Tool to Automatically Generate Pseudo-Code from Source Code

abstract: Understanding the behavior of source code written in an unfamiliar programming language is difficult. One way to aid understanding of difficult code is to add corresponding pseudo-code, which describes in detail the workings of the code in a natural language such as English. In spite of its usefulness, most source code does not have corresponding pseudo-code because it is tedious to create. This paper demonstrates a tool Pseudogen that makes it possible to automatically generate pseudo-code from source code using statistical machine translation (SMT).1 Pseudogen currently supports generation of English or Japanese pseudo-code from Python source code, and the SMT framework makes it easy for users to create new generators for their preferred source code/pseudo-code pairs.


title: Commentary: 'Smoking and lung cancer'--the embryogenesis of modern epidemiology

abstract: Conflict of interest: None declared. References 1 Cornfield J, Haenszel W, Hammond EC, Lilienfeld AM, Shimkin MB, Wynder EL. Smoking and lung cancer: recent evidence and a discussion of some principles. J Natl Cancer Inst 1959;22:173–204. Reprinted Int J Epidemiol 2009;38:1175–91. 2 Cochran WG. The planning of observational studies of human populations (with discussion). J R Stat Soc A 1965;128:234–66.


title: IRIS: Illustrative Rendering for Integral Surfaces

abstract: Integral surfaces are ideal tools to illustrate vector fields and fluid flow structures. However, these surfaces can be visually complex and exhibit difficult geometric properties, owing to strong stretching, shearing and folding of the flow from which they are derived. Many techniques for non-photorealistic rendering have been presented previously. It is, however, unclear how these techniques can be applied to integral surfaces. In this paper, we examine how transparency and texturing techniques can be used with integral surfaces to convey both shape and directional information. We present a rendering pipeline that combines these techniques aimed at faithfully and accurately representing integral surfaces while improving visualization insight. The presented pipeline is implemented directly on the GPU, providing real-time interaction for all rendering modes, and does not require expensive preprocessing of integral surfaces after computation.


title: Is Question Answering fit for the Semantic Web ? : a Survey

abstract: Abstract. With the recent rapid growth of the Semantic Web (SW), the processes of searching and querying content that is both massive in scale and heterogeneous have become increasingly challenging. User-friendly interfaces, which can support end users in querying and exploring this novel and diverse, structured information space, are needed to make the vision of the SW a reality. We present a survey on ontology-based Question Answering (QA), which has emerged in recent years to exploit the opportunities offered by structured semantic information on the Web. First, we provide a comprehensive perspective by analyzing the general background and history of the QA research field, from influential works from the artificial intelligence and database communities developed in the 70s and later decades, through open domain QA stimulated by the QA track in TREC since 1999, to the latest commercial semantic QA solutions, before tacking the current state of the art in open userfriendly interfaces for the SW. Second, we examine the potential of this technology to go beyond the current state of the art to support end-users in reusing and querying the SW content. We conclude our review with an outlook for this novel research area, focusing in particular on the R&D directions that need to be pursued to realize the goal of efficient and competent retrieval and integration of answers from large scale, heterogeneous, and continuously evolving semantic sources.


title: Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors

abstract: Human activity recognition involves classifying times series data, measured at inertial sensors such as accelerometers or gyroscopes, into one of pre-defined actions. Recently, convolutional neural network (CNN) has established itself as a powerful technique for human activity recognition, where convolution and pooling operations are applied along the temporal dimension of sensor signals. In most of existing work, 1D convolution operation is applied to individual univariate time series, while multi-sensors or multi-modality yield multivariate time series. 2D convolution and pooling operations are applied to multivariate time series, in order to capture local dependency along both temporal and spatial domains for uni-modal data, so that it achieves high performance with less number of parameters compared to 1D operation. However for multi-modal data existing CNNs with 2D operation handle different modalities in the same way, which cause interferences between characteristics from different modalities. In this paper, we present CNNs (CNN-pf and CNN-pff), especially CNN-pff, for multi-modal data. We employ both partial weight sharing and full weight sharing for our CNN models in such a way that modality-specific characteristics as well as common characteristics across modalities are learned from multi-modal (or multi-sensor) data and are eventually aggregated in upper layers. Experiments on benchmark datasets demonstrate the high performance of our CNN models, compared to state of the arts methods.


title: Volatility of Power Grids Under Real-Time Pricing

abstract: The paper proposes a framework for modeling and analysis of the dynamics of supply, demand, and clearing prices in power systems with real-time retail pricing and information asymmetry. Characterized by passing on the real-time wholesale electricity prices to the end consumers, real-time pricing creates a closed-loop feedback system between the physical layer and the market layer of the system. In the absence of a carefully designed control law, such direct feedback can increase sensitivity and lower the system's robustness to uncertainty in demand and generation. It is shown that price volatility can be characterized in terms of the system's maximal relative price elasticity, defined as the maximal ratio of the generalized price-elasticity of consumers to that of the producers. As this ratio increases, the system may become more volatile. Since new demand response technologies increase the price-elasticity of demand, and since increased penetration of distributed generation can also increase the uncertainty in price-based demand response, the theoretical findings suggest that the architecture under examination can potentially lead to increased volatility. This study highlights the need for assessing architecture systematically and in advance, in order to optimally strike the trade-offs between volatility/robustness and performance metrics such as economic efficiency and environmental efficiency.


title: Optimized Performance Evaluation of LTE Hard Handover Algorithm with Average RSRP Constraint

abstract: Hard handover mechanism is adopted to be used in 3GPP Long Term Evolution (3GPP LTE) in order to reduce the complexity of the LTE network architecture. This mechanism comes with degradation in system throughput as well as a higher system delay. This paper proposes a new handover algorithm known as LTE Hard Handover Algorithm with Average Received Signal Reference Power (RSRP) Constraint (LHHAARC) in order to minimize number of handovers and the system delay as well as maximize the system throughput. An optimized system performance of the LHHAARC is evaluated and compared with three well-known handover algorithms via computer simulation. The simulation results show that the LHHAARC outperforms three well-known handover algorithms by having less number of average handovers per UE per second, shorter total system delay whilst maintaining a higher total system throughput.


title: Embedding Learning with Events in Heterogeneous Information Networks

abstract: In real-world applications, objects of multiple types are interconnected, forming Heterogeneous Information Networks . In such heterogeneous information networks, we make the key observation that many interactions happen due to some event and the objects in each event form a complete semantic unit. By taking advantage of such a property, we propose a generic framework called H yper E dge- B ased E mbedding ( Hebe ) to learn object embeddings with events in heterogeneous information networks, where a hyperedge encompasses the objects participating in one event. The Hebe framework models the proximity among objects in each event with two methods: (1) predicting a target object given other participating objects in the event, and (2) predicting if the event can be observed given all the participating objects. Since each hyperedge encapsulates more information of a given event, Hebe is robust to data sparseness and noise. In addition, Hebe is scalable when the data size spirals. Extensive experiments on large-scale real-world datasets show the efficacy and robustness of the proposed framework.


Protecting Privacy when Disclosing Information: k-Anonymity and Its Enforcement through Generalization and Suppression


title: Design of ultra high-speed CMOS CML buffers and latches

abstract: A comprehensive study of ultra high-speed current-mode logic (CML) buffers and regenerative CML latches will be illustrated. A new design procedure to systematically design a chain of tapered CML buffers is proposed. Next, a new 20GHz regenerative latch circuit will be introduced. Experimental results show a higher performance for the new latch architecture compared to a conventional CML latch circuit at ultra high-frequencies. It is also shown, both through the experiments and by using efficient analytical models, why CML buffers are better than CMOS inverters in high-speed low-voltage applications.


title: Cloud-based deep learning of big EEG data for epileptic seizure prediction

abstract: Developing a Brain-Computer Interface (BCI) for seizure prediction can help epileptic patients have a better quality of life. However, there are many difficulties and challenges in developing such a system as a real-life support for patients. Because of the nonstationary nature of EEG signals, normal and seizure patterns vary across different patients. Thus, finding a group of manually extracted features for the prediction task is not practical. Moreover, when using implanted electrodes for brain recording massive amounts of data are produced. This big data calls for the need for safe storage and high computational resources for real-time processing. To address these challenges, a cloud-based BCI system for the analysis of this big EEG data is presented. First, a dimensionality-reduction technique is developed to increase classification accuracy as well as to decrease the communication bandwidth and computation time. Second, following a deep-learning approach, a stacked autoencoder is trained in two steps for unsupervised feature extraction and classification. Third, a cloud-computing solution is proposed for real-time analysis of big EEG data. The results on a benchmark clinical dataset illustrate the superiority of the proposed patient-specific BCI as an alternative method and its expected usefulness in real-life support of epilepsy patients.


title: Multi-level bootstrap analysis of stable clusters in resting-state fMRI

abstract: Abstract A variety of methods have been developed to identify brain networks with spontaneous, coherent activity in resting-state functional magnetic resonance imaging (fMRI). We propose here a generic statistical framework to quantify the stability of such resting-state networks (RSNs), which was implemented with k-means clustering. The core of the method consists in bootstrapping the available datasets to replicate the clustering process a large number of times and quantify the stable features across all replications. This bootstrap analysis of stable clusters (BASC) has several benefits: (1) it can be implemented in a multi-level fashion to investigate stable RSNs at the level of individual subjects and at the level of a group; (2) it provides a principled measure of RSN stability; and (3) the maximization of the stability measure can be used as a natural criterion to select the number of RSNs. A simulation study validated the good performance of the multi-level BASC on purely synthetic data. Stable networks were also derived from a real resting-state study for 43 subjects. At the group level, seven RSNs were identified which exhibited a good agreement with the previous findings from the literature. The comparison between the individual and group-level stability maps demonstrated the capacity of BASC to establish successful correspondences between these two levels of analysis and at the same time retain some interesting subject-specific characteristics, e.g. the specific involvement of subcortical regions in the visual and fronto-parietal networks for some subjects.


title: Cognitive Network Interference

abstract: Opportunistic spectrum access creates the opening of under-utilized portions of the licensed spectrum for reuse, provided that the transmissions of secondary radios do not cause harmful interference to primary users. Such a system would require secondary users to be cognitive-they must accurately detect and rapidly react to varying spectrum usage. Therefore, it is important to characterize the effect of cognitive network interference due to such secondary spectrum reuse. In this paper, we propose a new statistical model for aggregate interference of a cognitive network, which accounts for the sensing procedure, secondary spatial reuse protocol, and environment-dependent conditions such as path loss, shadowing, and channel fading. We first derive the characteristic function and cumulants of the cognitive network interference at a primary user. Using the theory of truncated-stable distributions, we then develop the statistical model for the cognitive network interference. We further extend this model to include the effect of power control and demonstrate the use of our model in evaluating the system performance of cognitive networks. Numerical results show the effectiveness of our model for capturing the statistical behavior of the cognitive network interference. This work provides essential understanding of interference for successful deployment of future cognitive networks.


title: Generate pareto optimal solutions of scheduling problems using normal boundary intersection technique

abstract: The problem of short-term scheduling under uncertainty is addressed in this paper through a multiobjective optimization framework that incorporates economic expectation, robustness, and flexibility in terms of demand satisfaction. In order to be able to identify Pareto optimal solutions, a new approach is applied which is based on normal boundary intersection (NBI) technique. The main advantage of this technique is that it avoids the selection of arbitrary parameters and generates a set of evenly distributed set of points independent of the scales of the objectives. Utilizing this idea, alternative schedules are generated that represent trade-off between the various objectives in the face of uncertainty. The approach is illustrated through three case studies and the special characteristics of the scheduling problems are discussed.


abstract: While physics conveys knowledge of nature built from an interplay between observations and theory, it has been considered less importantly in deep neural networks. Especially, there are few works leveraging physics behaviors when the knowledge is given less explicitly. In this work, we propose a novel architecture called Differentiable Physics-informed Graph Networks (DPGN) to incorporate implicit physics knowledge which is given from domain experts by informing it in latent space. Using the concept of DPGN, we demonstrate that climate prediction tasks are significantly improved. Besides the experiment results, we validate the effectiveness of the proposed module and provide further applications of DPGN, such as inductive learning and multistep predictions.


title: A Driving Right Leg Circuit (DgRL) for Improved Common Mode Rejection in Bio-Potential Acquisition Systems

abstract: The paper presents a novel Driving Right Leg (DgRL) circuit designed to mitigate the effect of common mode signals deriving, say, from power line interferences. The DgRL drives the isolated ground of the instrumentation towards a voltage which is fixed with respect to the common mode potential on the subject, therefore minimizing common mode voltage at the input of the front-end. The paper provides an analytical derivation of the common mode rejection performances of DgRL as compared to the usual grounding circuit or Driven Right Leg (DRL) loop. DgRL is integrated in a bio-potential acquisition system to show how it can reduce the common mode signal of more than 70 dB with respect to standard patient grounding. This value is at least 30 dB higher than the reduction achievable with DRL, making DgRL suitable for single-ended front-ends, like those based on active electrodes. EEG signal acquisition is performed to show how the system can successfully cancel power line interference without any need for differential acquisition, signal post-processing or filtering.


title: A Semi-supervised Learning Approach for Ontology Matching

abstract: Ontology matching is the task of finding correspondences between semantically related entities in different ontologies, which is a key solution to the semantic heterogeneity problem. Recently, several supervised learning approaches for ontology matching have been proposed, which outperform traditional unsupervised approaches. The existing learning based approaches treat the similarity values of matchers as normal numerical features, and need a lot of training examples. In this paper, we propose a semi-supervised learning approach for ontology matching. Our approach needs a small set of training examples, and exploit the dominant relation of similarity metrics to enrich the training examples. A label propagation algorithm is used to determine the matching results. Experimental results show that our approach can achieve good matching results with a few training examples.


Portfolio Optimization, Heuristics, and the "Butterfly Effect"


A Data Mining Framework for Forest Fire Mapping


title: Deep neural networks for ultra-short-term wind forecasting

abstract: The aim of this paper is to present input variable selection algorithm and deep neural networks application to ultra-short-term wind prediction. Shallow and deep neural networks coupled with input variable selection algorithm are compared on the ultra-short-term wind prediction task for a set of different locations. Results show that carefully selected deep neural networks outperform shallow ones. Input variable selection use reduces the neural network complexity and simplifies deep neural network training.


title: Polarization imaging reflectometry in the wild

abstract: We present a novel approach for on-site acquisition of surface reflectance for planar, spatially varying, isotropic samples in uncontrolled outdoor environments. Our method exploits the naturally occurring linear polarization of incident and reflected illumination for this purpose. By rotating a linear polarizing filter in front of a camera at three different orientations, we measure the polarization reflected off the sample and combine this information with multi-view analysis and inverse rendering in order to recover per-pixel, high resolution reflectance and surface normal maps. Specifically, we employ polarization imaging from two near orthogonal views close to the Brewster angle of incidence in order to maximize polarization cues for surface reflectance estimation. To the best of our knowledge, our method is the first to successfully extract a complete set of reflectance parameters with passive capture in completely uncontrolled outdoor settings. To this end, we analyze our approach under the general, but previously unstudied, case of incident partial linear polarization (due to the sky) in order to identify the strengths and weaknesses of the method under various outdoor conditions. We provide practical guidelines for on-site acquisition based on our analysis, and demonstrate high quality results with an entry level DSLR as well as a mobile phone.


title: An IoT electric business model based on the protocol of bitcoin

abstract: Nowadays, the development of traditional business models become more and more mature that people use them to guide various kinds of E-business activities. Internet of things(IoT), being an innovative revolution over the Internet, becomes a new platform for E-business. However, old business models could hardly fit for the E-business on the IoT. In this article, we 1) propose an IoT E-business model, which is specially designed for the IoT E-business; 2) redesign many elements in traditional E-business models; 3) realize the transaction of smart property and paid data on the IoT with the help of P2P trade based on the Blockchain and smart contract. We also experiment our design and make a comprehensive discuss.


title: Smart Cities in Europe

abstract: Urban performance currently depends not only on a city's endowment of hard infrastructure (physical capital), but also, and increasingly so, on the availability and quality of knowledge communication and social infrastructure (human and social capital). The latter form of capital is decisive for urban competitiveness. Against this background, the concept of the “smart city” has recently been introduced as a strategic device to encompass modern urban production factors in a common framework and, in particular, to highlight the importance of Information and Communication Technologies (ICTs) in the last 20 years for enhancing the competitive profile of a city. The present paper aims to shed light on the often elusive definition of the concept of the “smart city.” We provide a focused and operational definition of this construct and present consistent evidence on the geography of smart cities in the EU27. Our statistical and graphical analyses exploit in depth, for the first time to our knowledge, the most re...


title: Understanding personality of portrait by social embedding visual features

abstract: Image understanding achieves unprecedented performance in content recognition and emotion rating recently. However, previous research mainly focused on visual features. In this paper, inspired by human cognitive activities, we discuss how to measure portrait distance in respective of high level semantic: personality, by employing features from both visual contents and behavior contents. Firstly, a new image distance metric, named Social and Visual Portrait Distance, is designed by jointly considering visual features and human social media behavior features. In portrait images, visual features are defined globally and locally utilizing theoretical and empirical concepts from psychology theory. While in social media, behavior features are designed with considerations of demographic factors, identity claims and behavioral residue. And the new distance is estimated referred to feature reliability. Secondly, we modify the proposed distance calculation formula in order to apply it on only visual features but preserving both visual and social relations by a new Social Embedding Portrait Distance Learning method. In this manner, we could measure the social embedding visual distance of common portrait images in absence of social media information, such as web portrait, or daily photos. Comprehensive experiments are employed to investigate the effectiveness of the new portrait distance and the metric learning method in representing personality distance compared with several baselines. Moreover, the learned distance matrix reveals a reasonable explanation of social preferred visual features with contribute partitions.


Towards Compact Interpretable Models : Shrinking of Learned Probabilistic Sentential Decision Diagrams


title: Functional morphology of neck musculature in the Tyrannosauridae (Dinosauria, Theropoda) as determined via a hierarchical inferential approach

abstract: With multiple well-preserved specimens, tyrannosaurid dinosaurs are ideal subjects for exploring the role of the neck for feeding in an extinct amniote clade. Detailed exploration of the morphology of avian and crocodilian neck muscles grounds phylogenetic inference of musculature of tyrannosaurids. Introduced methods of kinematic and physiological inference explicate action and function of tyrannosaurid neck muscles, and the explicit method of extant behavioural interpolation (EBI) allows inference of generalized behaviours. Anteriorly originating craniocervical muscles of tyrannosaurids, and insertions of the neck dorsiflexor m. transversospinalis cervicis, were similar to those of birds, while the major respective head dorsiflexor and lateroflexor, m. transversospinalis capitis and m. longissimus capitis superficialis, resembled their homologues in crocodilians. Because the proposed inferential methods are cumulative and emphasize mutual falsification, behavioural effects of muscle function become immediately testable rather than assumed. Kinematic inference of muscle moment topology, and bracketed and unipolar inference of muscle activity, facilitate EBI of rapid gaze shifts, lateral and sagittal strikes, inertial feeding, and tearing of flesh by specific permutational actions of tyrannosaurid neck muscles. Several examples of EBI are tested further through biomechanical considerations. © 2007 The Linnean Society of London, Zoological Journal of the Linnean Society, 2007, 151, 759‐808. ADDITIONAL KEYWORDS: Aves ‐ behaviour ‐ Crocodylia ‐ electromyography ‐ feeding ‐ muscle.


title: Unsupervised 3D object recognition and reconstruction in unordered datasets

abstract: This paper presents a system for fully automatic recognition and reconstruction of 3D objects in image databases. We pose the object recognition problem as one of finding consistent matches between all images, subject to the constraint that the images were taken from a perspective camera. We assume that the objects or scenes are rigid. For each image, we associate a camera matrix, which is parameterised by rotation, translation and focal length. We use invariant local features to find matches between all images, and the RANSAC algorithm to find those that are consistent with the fundamental matrix. Objects are recognised as subsets of matching images. We then solve for the structure and motion of each object, using a sparse bundle adjustment algorithm. Our results demonstrate that it is possible to recognise and reconstruct 3D objects from an unordered image database with no user input at all.


title: Large-scale, AST-based API-usage analysis of open-source Java projects

abstract: Research on API migration and language conversion can be informed by empirical data about API usage. For instance, such data may help with designing and defending mapping rules for API migration in terms of relevance and applicability. We describe an approach to large-scale API-usage analysis of open-source Java projects, which we also instantiate for the Source-Forge open-source repository in a certain way. Our approach covers checkout, building, tagging with metadata, fact extraction, analysis, and synthesis with a large degree of automation. Fact extraction relies on resolved (type-checked) ASTs. We describe a few examples of API-usage analysis; they are motivated by API migration. These examples are concerned with analysing API footprint (such as the numbers of distinct APIs used in a project), API coverage (such as the percentage of methods of an API used in a corpus), and framework-like vs. class-library-like usage.


title: Leveraging Linguistic Structure For Open Domain Information Extraction

abstract: Relation triples produced by open domain information extraction (open IE) systems are useful for question answering, inference, and other IE tasks. Traditionally these are extracted using a large set of patterns; however, this approach is brittle on out-of-domain text and long-range dependencies, and gives no insight into the substructure of the arguments. We replace this large pattern set with a few patterns for canonically structured sentences, and shift the focus to a classifier which learns to extract self-contained clauses from longer sentences. We then run natural logic inference over these short clauses to determine the maximally specific arguments for each candidate triple. We show that our approach outperforms a state-of-the-art open IE system on the end-to-end TAC-KBP 2013 Slot Filling task.


title: Comparative Analysis of Torque Compensation Control Algorithms of Interior Permanent Magnet Machines for Automotive Applications Considering the Effects of Temperature Variation

abstract: For automotive applications, accurate torque production and high efficiency of the traction motor are very important. However, the performance of widely used interior permanent magnet (IPM) machines is influenced by temperature variation, and the temperature variation in automotive applications can be profound. In this paper, the state-of-the-art torque compensation control algorithms of IPM machines in the literature are evaluated and compared. The influence of temperature variation on the performance of IPM machines is identified. Based on the compensation principle, existing algorithms have been classified into two different types: torque open-loop and torque closed-loop method. The operating principles, advantages, and disadvantages of each compensation control algorithm have been discussed in detail. It is shown that the interpolation method using lookup tables and the torque closed-loop method with torque estimation and maximum torque per ampere (MTPA) online calculation are the best available candidates in the literature, since they are the only methods which can maintain both torque accuracy and MTPA operation simultaneously during temperature variation. Special opportunities of compensation control algorithms for traction applications are also identified in this paper. With the comprehensive overview provided in this paper, tradeoff and better decisions can be made during the selection of appropriate compensation control algorithms.


title: Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and x-ray photography.

abstract: Abstract We give a new method for direct reconstruction of three-dimensional objects from a few electron micrographs taken at angles which need not exceed a range of 60 degrees. The method works for totally asymmetric objects, and requires little computer time or storage. It is also applicable to X-ray photography, and may greatly reduce the exposure compared to current methods of body-section radiography.


title: Robust Hand Gestural Interaction for Smartphone Based AR/VR Applications

abstract: The future of user interfaces will be dominated by hand gestures. In this paper, we explore an intuitive hand gesture based interaction for smartphones having a limited computational capability. To this end, we present an efficient algorithm for gesture recognition with First Person View (FPV), which focuses on recognizing a four swipe model (Left, Right, Up and Down) for smartphones through single monocular camera vision. This can be used with frugal AR/VR devices such as Google Cardboard1 andWearality2 in building AR/VR based automation systems for large scale deployments, by providing a touch-less interface and real-time performance. We take into account multiple cues including palm color, hand contour segmentation, and motion tracking, which effectively deals with FPV constraints put forward by a wearable. We also provide comparisons of swipe detection with the existing methods under the same limitations. We demonstrate that our method outperforms both in terms of gesture recognition accuracy and computational time.


title: Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks

abstract: A long-term goal of AI is to produce agents that can learn a diversity of skills throughout their lifetimes and continuously improve those skills via experience. A longstanding obstacle towards that goal is catastrophic forgetting, which is when learning new information erases previously learned information. Catastrophic forgetting occurs in artificial neural networks (ANNs), which have fueled most recent advances in AI. A recent paper proposed that catastrophic forgetting in ANNs can be reduced by promoting modularity, which can limit forgetting by isolating task information to specific clusters of nodes and connections (functional modules). While the prior work did show that modular ANNs suffered less from catastrophic forgetting, it was not able to produce ANNs that possessed task-specific functional modules, thereby leaving the main theory regarding modularity and forgetting untested. We introduce diffusion-based neuromodulation, which simulates the release of diffusing, neuromodulatory chemicals within an ANN that can modulate (i.e. up or down regulate) learning in a spatial region. On the simple diagnostic problem from the prior work, diffusion-based neuromodulation 1) induces task-specific learning in groups of nodes and connections (task-specific localized learning), which 2) produces functional modules for each subtask, and 3) yields higher performance by eliminating catastrophic forgetting. Overall, our results suggest that diffusion-based neuromodulation promotes task-specific localized learning and functional modularity, which can help solve the challenging, but important problem of catastrophic forgetting.


title: Efficient Graph-Oriented Smart Transportation Using Internet of Things Generated Big Data

abstract: The rapid growth in the population density inurban cities and the advancement in technology demands real-time provision of services and infrastructure. Citizens, especially travelers, want to be reached within time to the destination. Consequently, they require to be facilitated with smart and real-time traffic information depending on the current traffic scenario. Therefore, in this paper, we proposed a graph-oriented mechanism to achieve the smarttransportation system in the city. We proposed to deploy road sensors to get the overall traffic information as well as the vehicular network to obtain location and speed information of the individual vehicle. These Internet of Things (IoT) based networks generate enormous volume of data, termed as Big Data, depicting the traffic information of the city. To process incoming Big Data from IoT devices, then generating big graphs from the data, and processing them, we proposed an efficient architecture that uses the Giraph tool with parallel processing servers to achieve real-time efficiency. Later, various graph algorithms are used to achieve smart transportation by making real-time intelligentdecisions to facilitate the citizens as well as the metropolitan authorities. Vehicular Datasets from various reliable resources representing the real city traffic are used for analysis and evaluation purpose. The system is implemented using Giraph and Spark tool at the top of the Hadoop parallel nodes to generate and process graphs with near real-time. Moreover, the system is evaluated in terms of efficiency by considering the system throughput and processing time. The results show that the proposed system is more scalable and efficient.


title: SPICE simulation method for BCI component tests

abstract: This paper presents a virtual immunity test method using SPICE. This method facilitates EMC-compliant eletronic design. As an example, it is used to virtualize the BCI test method. It can also be applied to e.g. the stripline method. The required SPICE models of the components of the measurement setup are generated just once. They are based on scattering parameter measurements. These measurements are made with real RF components of the BCI test setup. In a schematic the SPICE models and the test circuit are connected according to the real test setup. At first in an AC analysis all voltages and currents are simulated by modelling the BCI clamp as an AC source with a constant stimulus of one volt. During real measurements a control computer sets the frequency-dependent amplitude of the generator. For this process, the standard DIN ISO 11452:4 specifies two test conditions: “open loop” and “closed loop”. In the virtual immunity test, all simulated voltages and currents due to the one volt stimulus are linearly scaled in order to match the real conditions using a computer-controlled frequency-dependent RF test power. This virtual test method makes the development process faster and cheaper.


title: Augmenting Neural Response Generation with Context-Aware Topical Attention

abstract: Sequence-to-Sequence (Seq2Seq) models have witnessed a notable success in generating natural conversational exchanges. Notwithstanding the syntactically well-formed responses generated by these neural network models, they are prone to be acontextual, short and generic. In this work, we introduce a Topical Hierarchical Recurrent Encoder Decoder (THRED), a novel, fully data-driven, multi-turn response generation system intended to produce contextual and topic-aware responses. Our model is built upon the basic Seq2Seq model by augmenting it with a hierarchical joint attention mechanism that incorporates topical concepts and previous interactions into the response generation. To train our model, we provide a clean and high-quality conversational dataset mined from Reddit comments. We evaluate THRED on two novel automated metrics, dubbed Semantic Similarity and Response Echo Index, as well as with human evaluation. Our experiments demonstrate that the proposed model is able to generate more diverse and contextually relevant responses compared to the strong baselines.


title: Multimodal Music Mood Classification Using Audio and Lyrics

abstract: In this paper we present a study on music mood classification using audio and lyrics information. The mood of a song is expressed by means of musical features but a relevant part also seems to be conveyed by the lyrics. We evaluate each factor independently and explore the possibility to combine both, using natural language processing and music information retrieval techniques. We show that standard distance-based methods and latent semantic analysis are able to classify the lyrics significantly better than random, but the performance is still quite inferior to that of audio-based techniques. We then introduce a method based on differences between language models that gives performances closer to audio-based classifiers. Moreover, integrating this in a multimodal system (audio+text) allows an improvement in the overall performance. We demonstrate that lyrics and audio information are complementary, and can be combined to improve a classification system.


title: Relative phase information for detecting human speech and spoofed speech.

abstract: The detection of human and spoofed (synthetic/converted) speech has started to receive more attention. In this study, relative phase information extracted from a Fourier spectrum is used to detect human and spoofed speech. Because original/natural phase information is almost entirely lost in spoofed speech using current synthesis/conversion techniques, a modified group delay based feature, the frequency derivative of the phase spectrum, has been shown effective for detecting human speech and spoofed speech. The modified group delay based phase contains both the magnitude spectrum and phase information. Therefore, the relative phase information, which contains only phase information, is expected to achieve a better spoofing detection performance. In this study, the relative phase information is also combined with the Mel-Frequency Cepstral Coefficient (MFCC) and modified group delay. The proposed method was evaluated using the “ASVspoof 2015: Automatic Speaker Verification Spoofing and Countermeasures Challenge” dataset. The results show that the proposed relative phase information significantly outperforms the MFCC and modified group delay. The equal error rate (EER) was reduced from 1.74% of MFCC, 0.83% of modified group delay to 0.013% of relative phase. By combining the relative phase with MFCC and modified group delay, the EER was reduced to 0.002%. Index Terms: Spoofing detection, relative phase information, group delay, GMM, countermeasures


title: Congenital vesicovaginal fistula: spontaneous or forced? Two cases and literature review

abstract: Introduction and hypothesis ::: Congenital vesicovaginal fistula is an exceedingly rare entity. There is no consensus regarding the nature and origin of this condition. We report two cases with congenital vesicovaginal fistula and compile previously reported cases in the English literature. Theories behind the genesis of this anomaly will be briefly presented.


title: Array calibration methods for sensor position and pointing errors

abstract: In order to make an antenna array more robust to array uncertainties, this paper extends the gain/phase calibration technique proposed by Ng, Er, and Kot to the problems of bearing estimation and adaptive beamforming involving sensor position error and signal pointing error, respectively. The basic idea of this technique is that the true array steering vector is approximated in terms of the nominal one by applying the first-order Taylor series expansion, and then using the null spectrum characteristics of the MUSIC algorithm; the steering vector error containing the information on the array error can be obtained by solving a set of constrained minimization formulations. For sensor position error, a calibration signal is used to impinge upon an array at two different angles. As for pointing error, we impose the orthogonal property between the transformed steering vectors of the signal and jammers on the calibration method. © 2000 John Wiley & Sons, Inc. Microwave Opt Technol Lett 26: 132–137, 2000.


title: Item recommending by item co-purchasing network and user preference

abstract: A novel recommendation approach based on multiple-objectives optimization is introduced. It utilizes histories of item co-purchases as represented by directed graphs and histories of user preferences for items as represented by user-item rating matrices to create models of user behavior. Herein, multiple objectives for item recommendations are considering to maximize the ratings and rankings of each item as derived from user preferences and co-purchasing networks of items, respectively. Finally, optimal items, called Pareto optimal solutions, will be recommended.


Cyber-Security in Smart Grid: Survey and Challenges


title: Bridging the uncanny: an impossible traverse?

abstract: This paper proposes that increasing technological sophistication in the creation of realism for human-like virtual characters is matched by increasing technological discernment on the part of the viewer. One of the goals for achieving a realism that is believable for virtual characters is to overcome the Uncanny Valley where perceived eeriness or familiarity are rated against perceived human-likeness. Empirical evidence shows the uncanny can be applied to virtual characters, yet implies a more complex picture than the shape of a deep valley with a sharp gradient as depicted in Mori's original plot of the Uncanny Valley. Our results imply that: (1) perceived familiarity is dependent upon a wider range of variables other than appearance and behaviour; and (2) for realistic, human-like characters, the Uncanny Valley is better replaced with the notion of an Uncanny Wall because the Uncanny Valley, as a concept, is not fully supported by the empirical evidence but, more importantly as a standard for creating human-like realism, is an impossible traverse.


title: Fast and Accurate Neural Word Segmentation for Chinese

abstract: Neural models with minimal feature engineering have achieved competitive performance against traditional methods for the task of Chinese word segmentation. However, both training and working procedures of the current neural models are computationally inefficient. This paper presents a greedy neural word segmenter with balanced word and character embedding inputs to alleviate the existing drawbacks. Our segmenter is truly end-to-end, capable of performing segmentation much faster and even more accurate than state-of-the-art neural models on Chinese benchmark datasets.


title: Markov Determinantal Point Processes

abstract: A determinantal point process (DPP) is a random process useful for modeling the combinatorial problem of subset selection. In particular, DPPs encourage a random subset Y to contain a diverse set of items selected from a base set Y. For example, we might use a DPP to display a set of news headlines that are relevant to a user's interests while covering a variety of topics. Suppose, however, that we are asked to sequentially select multiple diverse sets of items, for example, displaying new headlines day-by-day. We might want these sets to be diverse not just individually but also through time, offering headlines today that are unlike the ones shown yesterday. In this paper, we construct a Markov DPP (M-DPP) that models a sequence of random sets {Yt}. The proposed M-DPP defines a stationary process that maintains DPP margins. Crucially, the induced union process Zt = Yt u Yt-1 is also marginally DPP-distributed. Jointly, these properties imply that the sequence of random sets are encouraged to be diverse both at a given time step as well as across time steps. We describe an exact, efficient sampling procedure, and a method for incrementally learning a quality measure over items in the base set Y based on external preferences. We apply the M-DPP to the task of sequentially displaying diverse and relevant news articles to a user with topic preferences.


title: Design, kinematics and dynamics modeling of a lower-limb walking assistant robot

abstract: This paper focuses on a lower limb walking assistant robot. This robot has been designed and modeled for two tasks of facilitating healthy people, and assisting patients for walking. First, basic concepts of the motion mechanism are presented. Next, its kinematics and dynamics equations have been developed. Kinematics analysis has been done using Denavit-Hartenberg scheme to obtain position, velocity and acceleration of center of mass (COM) of all links. Dynamics equations have been developed using recursive Lagrange method and finally inverse dynamics problem has been solved. Obtained simulation results will be discussed which validate the proposed model.


title: Formal Verification of Autonomous Vehicle Platooning

abstract: The coordination of multiple autonomous vehicles into convoys or platoons is expected on our highways in the near future. However, before such platoons can be deployed, the new autonomous behaviors of the vehicles in these platoons must be certified. An appropriate representation for vehicle platooning is as a multi-agent system in which each agent captures the "autonomous decisions" carried out by each vehicle. In order to ensure that these autonomous decision-making agents in vehicle platoons never violate safety requirements, we use formal verification. However, as the formal verification technique used to verify the agent code does not scale to the full system and as the global verification technique does not capture the essential verification of autonomous behavior, we use a combination of the two approaches. This mixed strategy allows us to verify safety requirements not only of a model of the system, but of the actual agent code used to program the autonomous vehicles.


title: Emergent models, frameworks, and hardware technologies for Big data analytics

abstract: Today’s state-of-the-art Big data analytics engines handle masses of data, but will reach to their limits, as the future Big data flood is predicted to still grow with an increasing speed. Hence we need to think about the next development phase and future features of Big data analytics engines. In this paper, we discuss possible future enhancements in the area of Big data analytics with focus on emergent models, frameworks, and hardware technologies. We point out a selection of new challenges and open research questions.


title: Aircraft Taxi Path Optimization Based on Ant Colony Algorithm

abstract: The rapid increase of flight flow and density has caused serious congestions and potential operation risks for busy airport, so how to avoid the aircraft taxiing conflicts of airport surface management system is a urgent problem to be solved. In this paper, the aircraft taxi path optimization model with three kinds of conflict constraints is presented to solve the aircraft safe taxiing problems by using the ant colony algorithm. Unlike the re-routing strategy of conflict resolution in our previous works, the waiting strategy is applied in the improved ant colony algorithm. The simulations are made by using the realistic data of Chengdu Shuangliu airport and an illustrative example is given to show the superiority of waiting strategy of conflict resolution.


title: Cold-start recommendations for audio news stories using matrix factorization

abstract: We investigate a suite of recommendation algorithms for audio news listening applications. This domain presents several challenges that distinguish it from more commonly studied applications such as movie recommendations: (1) we do not receive explicit rating feedback, instead only observing when a user skips a story; (2) new stories arrive continuously, increasing the importance of making recommendations for items with few observations (the cold start problem); (3) story attributes have high dimensionality, making it challenging to identify similar stories. To address the first challenge, we formulate the problem as predicting the percentage of a story a user will listen to; to address the remaining challenges, we propose several matrix factorization algorithms that cluster users, n-grams, and stories simultaneously, while optimizing prediction accuracy. We empirically evaluate our approach on a dataset of 50K users, 26K stories, and 975K interactions collected over a five month period. We find that while simple models work well for stories with many observations, our proposed approach performs best for stories with few ratings, which is critical for the real-world deployment of such an application.


title: A Frequency Domain Approach to Registration of Aliased Images with Application to Super-resolution

abstract: Super-resolution algorithms reconstruct a high-resolution image from a set of low-resolution images of a scene. Precise alignment of the input images is an essential part of such algorithms. If the low-resolution images are undersampled and have aliasing artifacts, the performance of standard registration algorithms decreases. We propose a frequency domain technique to precisely register a set of aliased images, based on their low-frequency, aliasing-free part. A high-resolution image is then reconstructed using cubic interpolation. Our algorithm is compared to other algorithms in simulations and practical experiments using real aliased images. Both show very good visual results and prove the attractivity of our approach in the case of aliased input images. A possible application is to digital cameras where a set of rapidly acquired images can be used to recover a higher-resolution final image.


N-Version Programming


title: Compulsive Smartphone Use: The Roles of Flow, Reinforcement Motives, and Convenience

abstract: Along with its rapid growth of penetration, smartphone has become highly prevalent in recent years. Meanwhile, compulsive smartphone use emerges as a rising concern. Given that research on compulsive smartphone use is scarce in the information systems literature, this paper aims to reveal its significant determinants to enrich the theoretical development in this area. In particular, we incorporate flow, reinforcement motives (i.e., instant gratification and mood regulation), and convenience in the research model to examine their influences on compulsive smartphone use. We conduct an empirical online survey with 384 valid responses to assess the model. The findings show that flow and reinforcement motives have direct and significant effects on compulsive use. Convenience affects compulsive use indirectly through flow, while flow further mediates the effects of reinforcement motives on compulsive use. Implications for both research and practice are offered.


The Big Idea : Creating Shared Value Rethinking Capitalism


title: Benefit of Direct Charge Measurement (DCM) on Interconnect Capacitance Measurement

abstract: This paper discusses application of direct charge measurement (DCM) on characterizing on-chip interconnect capacitance. Measurement equipment and techniques are leveraged from Flat Panel Display testing. On-chip active device is not an essential necessity for DCM test structure and it is easy to implement parallel measurements. Femto-Farad measurement sensitivity is achieved without having on-chip active device. Measurement results of silicon and glass substrates, including parallel measurements, are presented.


title: Midpalatal implants vs headgear for orthodontic anchorage--a randomized clinical trial: cephalometric results.

abstract: INTRODUCTION ::: The purpose of this study was to compare the clinical effectiveness of the midpalatal implant as a method of reinforcing anchorage during orthodontic treatment with that of conventional extraoral anchorage. This was a prospective, randomized, clinical trial at Chesterfield and North Derbyshire Royal Hospital NHS Trust and the Charles Clifford Dental Hospital, Sheffield, in the United Kingdom. ::: ::: ::: METHODS ::: Fifty-one orthodontic patients between the ages of 12 and 39, with Class II Division 1 malocclusion and absolute anchorage requirements, were randomly allocated to receive either a midpalatal implant or headgear to reinforce orthodontic anchorage. The main outcome was to compare the mesial movement of the molars and the incisors of the 2 treatment groups between the start and the end of anchorage reinforcement as measured from cephalometric radiographs. ::: ::: ::: RESULTS ::: The reproducibility of the measuring technique was acceptable. There were significant differences between T1 and T2 in the implant group for the positions of the maxillary central incisor (P <.001), the maxillary molar (P = .009), and the mandibular molar (P <.001). There were significant differences between T1 and T2 in the headgear group for the positions of the mandibular central incisor (P <.045), the maxillary molar (P <.001), and the mandibular molar (P <.001). All skeletal and dental points moved mesially more in the headgear group during treatment than in the implant group. These ranged from an average of 0.5 mm more mesially for the mandibular permanent molar to 1.5 mm more mesially for the maxillary molar and the mandibular base. No treatment changes between the groups were statistically significant. ::: ::: ::: CONCLUSIONS ::: Midpalatal implants are an acceptable technique for reinforcing anchorage in orthodontic patients.


Chapter 2 Data Mining Methods for Recommender Systems


title: Intra-class Variation Isolation in Conditional GANs

abstract: Current state-of-the-art conditional generative adversarial networks (C-GANs) require strong supervision via labeled datasets in order to generate images with continuously adjustable, disentangled semantics. In this paper we introduce a new formulation of the C-GAN that is able to learn realistic models with continuous, semantically meaningful input parameters and which has the advantage of requiring only the weak supervision of binary attribute labels. We coin the method intra-class variation isolation (IVI) and the resulting network the IVI-GAN. The method allows continuous control over the attributes in synthesised images where precise labels are not readily available. For example, given only labels found using a simple classifier of ambient / non-ambient lighting in images, IVI has enabled us to learn a generative face-image model with controllable lighting that is disentangled from other factors in the synthesised images, such as the identity. We evaluate IVI-GAN on the CelebA and CelebA-HQ datasets, learning to disentangle attributes such as lighting, pose, expression and age, and provide a quantitative comparison of IVI-GAN with a classical continuous C-GAN.


title: A ultra-low-energy convolution engine for fast brain-inspired vision in multicore clusters

abstract: State-of-art brain-inspired computer vision algorithms such as Convolutional Neural Networks (CNNs) are reaching accuracy and performance rivaling that of humans; however, the gap in terms of energy consumption is still many degrees of magnitude wide. Many-core architectures using shared-memory clusters of power-optimized RISC processors have been proposed as a possible solution to help close this gap. In this work, we propose to augment these clusters with Hardware Convolution Engines (HWCEs): ultra-low energy coprocessors for accelerating convolutions, the main building block of many brain-inspired computer vision algorithms. Our synthesis results in ST 28nm FD-SOI technology show that the HWCE is capable of performing a convolution in the lowest-energy state spending as little as 35 pJ/pixel on average, with an optimum case of 6.5 pJ/pixel. Furthermore, we show that augmenting a cluster with a HWCE can lead to an average boost of 40x or more in energy efficiency in convolutional workloads.


title: Yoga and Cognition: A Meta-Analysis of Chronic and Acute Effects

abstract: ABSTRACTObjectivesTo review and synthesize the existing literature on the effects of yoga on cognitive function by determining effect sizes that could serve as a platform to design, calculate statistical power, and implement future studies.MethodsThrough electronic databases, we identified acute stu


title: A support vector machine classifier with automatic confidence and its application to gender classification

abstract: In this paper, we propose a support vector machine with automatic confidence (SVMAC) for pattern classification. The main contributions of this work to learning machines are twofold. One is that we develop an algorithm for calculating the label confidence value of each training sample. Thus, the label confidence values of all of the training samples can be considered in training support vector machines. The other one is that we propose a method for incorporating the label confidence value of each training sample into learning and derive the corresponding quadratic programming problems. To demonstrate the effectiveness of the proposed SVMACs, a series of experiments are performed on three benchmarking pattern classification problems and a challenging gender classification problem. Experimental results show that the generalization performance of our SVMACs is superior to that of traditional SVMs.


title: NCBI BLAST: a better web interface

abstract: Basic Local Alignment Search Tool (BLAST) is a sequence similarity search program. The public interface of BLAST, http://www.ncbi.nlm.nih.gov/ blast, at the NCBI website has recently been reengineered to improve usability and performance. Key new features include simplified search forms, improved navigation, a list of recent BLAST results, saved search strategies and a documentation directory. Here, we describe the BLAST web application’s new features, explain design decisions and outline plans for future improvement.


title: Wall contact by octo-rotor UAV with one DoF manipulator for bridge inspection

abstract: This paper describes a octo-rotor unmanned aerial vehicle (UAV) with one degree of freedom (DoF) manipulator for bridge inspection. The UAV has an ability to contact bridge pier to conduct hammering test. To control contact force applied by the UAV to the bridge pier, one DoF is enough for the manipulator implemented on the UAV. A preliminary experiment shows that the force is proportional to the pitch angle of the UAV when it contacts the bridge pier. A bridge pier contact experiment shows that our developed UAV can control contact force between its end-effector and the bridge pier; mean error of the contact force is 4.27 ± 3.64 [N] against 20[N] desired force.


title: Can we explain natural language inference decisions taken with neural networks? Inference rules in distributed representations

abstract: Natural Language Inference (NLI) is a key, complex task where machine learning (ML) is playing an important role. However, ML has progressively obfuscated the role of linguistically-motivated inference rules, which should be the core of NLI systems. In this paper, we introduce distributed inference rules as a novel way to encode linguistically-motivated inference rules in learning interpretable NLI classifiers. We propose two encoders: the Distributed Partial Tree Encoder and the Distributed Smoothed Partial Tree Encoder. These encoders allow modeling syntactic and syntactic-semantic inference rules as distributed representations ready to be used in ML models over large datasets. Although far from the state-of-the-art of end-to-end deep learning systems on large datasets, our shallow networks positively exploit inference rules for NLI, improving over baseline systems. This is a first positive step towards interpretable and explainable end-to-end deep learning systems.


title: Autoscaling for Hadoop Clusters

abstract: Unforeseen events such as node failures and resource contention can have a severe impact on the performance of data processing frameworks, such as Hadoop, especially in cloud environments where such incidents are common. SLA compliance in the presence of such events requires the ability to quickly and dynamically resize infrastructure resources. Unfortunately, the distributed and stateful nature of data processing frameworks makes it challenging to accurately scale the system at run-time. In this paper, we present the design and implementation of a model-driven autoscaling solution for Hadoop clusters. We first develop novel gray-box performance models for Hadoop workloads that specifically relate job execution times to resource allocation and workload parameters. We then employ these models to dynamically determine the resources required to successfully complete the Hadoop jobs as per the user-specified SLA under various scenarios including node failures and multi-job executions. Our experimental results on three different Hadoop cloud clusters and across different workloads demonstrate the efficacy of our models and highlight their autoscaling capabilities.


A frame based spoken dialog system for home care.


title: The extension of TAM: The effects of social media and perceived risk in online purchase

abstract: This present study is designed to propose a conceptual framework extended from the previously advanced Theory of Acceptance Model (TAM). The framework makes it possible to examine the effects of social media, and perceived risk as the moderating effects between intention and actual purchase to be able to advance the Theory of Acceptance Model (TAM). 400 samples will be randomly selected among Saudi in Jeddah, Dammam and Riyadh. Data will be collected using questionnaire survey. As the research involves the analysis of numerical data, the assessment is carried out using Structural Equation Model (SEM). The hypothesis will be tested and the result is used to explain the proposed TAM. The findings from the present study will be beneficial for marketers to understand the intrinsic behavioral factors that influence consumers' selection hence avoid trial and errors in their advertising drives.