title: Efficient tree modeling from airborne LiDAR point clouds

abstract: Abstract Modeling real-world trees is important in many application areas, including computer graphics, botany and forestry. An example of a modeling method is reconstruction from light detection and ranging (LiDAR) scans. In contrast to terrestrial LiDAR systems, airborne LiDAR systems – even current high-resolution systems – capture only very few samples on tree branches, which makes the reconstruction of trees from airborne LiDAR a challenging task. In this paper, we present a new method to model plausible trees with fine details from airborne LiDAR point clouds. To reconstruct tree models, first, we use a normalized cut method to segment an individual tree point cloud. Then, trunk points are added to supplement the incomplete point cloud, and a connected graph is constructed by searching sufficient nearest neighbors for each point. Based on the observation of real-world trees, a direction field is created to restrict branch directions. Then, branch skeletons are constructed using a bottom-up greedy algorithm with a priority queue, and leaves are arranged according to phyllotaxis. We demonstrate our method on a variety of examples and show that it can generate a plausible tree model in less than one second, in addition to preserving features of the original point cloud.


title: Large-Scale Image Retrieval with Attentive Deep Local Features

abstract: We propose an attentive local feature descriptor suitable for large-scale image retrieval, referred to as DELF (DEep Local Feature). The new feature is based on convolutional neural networks, which are trained only with image-level annotations on a landmark image dataset. To identify semantically useful local features for image retrieval, we also propose an attention mechanism for keypoint selection, which shares most network layers with the descriptor. This framework can be used for image retrieval as a drop-in replacement for other keypoint detectors and descriptors, enabling more accurate feature matching and geometric verification. Our system produces reliable confidence scores to reject false positives---in particular, it is robust against queries that have no correct match in the database. To evaluate the proposed descriptor, we introduce a new large-scale dataset, referred to as Google-Landmarks dataset, which involves challenges in both database and query such as background clutter, partial occlusion, multiple landmarks, objects in variable scales, etc. We show that DELF outperforms the state-of-the-art global and local descriptors in the large-scale setting by significant margins. Code and dataset can be found at the project webpage: this https URL .


title: Multi-agent Process Coordination Based on Extended Xinpai

abstract: Xinpai has been used to coordinate activities consisting of business processes. However, the coordination between process agents cannot be described wholly only in terms of activities and their relationships. In this paper, a new kind of coordination mechanism between multi-agents is proposed using the extended-Xinpai. In order to make process agents collaborate in a more effective way, five basic operations are firstly given to describe the extended-Xinpai, and the relationship among process agents is further analyzed from the view of knowledge flow. Moreover, the multi-agent mechanism uses a distributed way to manage the extended Xinpai, which enhances the flexibility of workflow management to some extent. Finally, the paper review process is used to illustrate the Xinpai-driven multi-agent process coordination.


title: Shifts in isotopic signatures of animals with complex life-cycles can complicate conclusions on cross-boundary trophic links

abstract: Stable isotopes (13C and 15N) are widely applied in studies of trophic links. We used this method to investigate the contribution of aquatic and terrestrial prey to the diet of riparian predatory arthropods in two mountain headwater streams in Colorado, USA. Aquatic and terrestrial prey and riparian predators were collected during summer 2009. To evaluate the reliability of conclusions based on stable isotope ratios, we compared the isotopic signatures of aquatic larval and terrestrial adult stages of three abundant stream insect species and assessed variation in mixing model estimates for spider diet composition under varying assumptions for trophic fractionation. Adult isotopic signatures of some aquatic prey species were indistinguishable from those of prey species with exclusively terrestrial life histories (stoneflies: 13C and 15N, chironomids: 13C). Other prey had distinctly aquatic isotopic signatures as both larvae and adults (a mayfly and a caddisfly). There was no evidence that prey with aquatic isotopic signatures contributed to the diet of the spiders near one stream. For the other stream, mixing model analysis suggested that chironomids were included in the diets of lycosid, linyphiid and liocranid spiders. Reliable estimates of the contributions of prey sources were compromised by the sensitivity of mixing models to assumptions on trophic fractionation and the presence of “isotopically cryptic” prey. This study emphasizes the importance of supporting isotope-based studies on cross-boundary trophic links with data on isotopic shifts in prey with complex life cycles and assessment of fractionation rates specific to the study system.


title: A Learning Approach to Wireless Information and Power Transfer Signal and System Design

abstract: The end-to-end learning of Simultaneous Wireless Information and Power Transfer (SWIPT) over a noisy channel is studied. Adopting a nonlinear model for the energy harvester (EH) at the receiver, a joint optimization of the transmitter and the receiver is implemented using Deep Neural Network (DNN)-based autoencoders. Modulation constellations for different levels of "power" and "information rate" demand at the receiver are obtained. The numerically optimized signal constellations are inline with the previous theoretical results. In particular, it is observed that as the receiver energy demand increases, all but one of the modulation symbols are concentrated around the origin and the other symbol is shot away from the origin along either the real or imaginary subchannel.


title: Spotting Rumors via Novelty Detection

abstract: Rumour detection is hard because the most accurate systems operate retrospectively, only recognizing rumours once they have collected repeated signals. By then the rumours might have already spread and caused harm. We introduce a new category of features based on novelty, tailored to detect rumours early on. To compensate for the absence of repeated signals, we make use of news wire as an additional data source. Unconfirmed (novel) information with respect to the news articles is considered as an indication of rumours. Additionally we introduce pseudo feedback, which assumes that documents that are similar to previous rumours, are more likely to also be a rumour. Comparison with other real-time approaches shows that novelty based features in conjunction with pseudo feedback perform significantly better, when detecting rumours instantly after their publication.


title: A survey of circuit innovations in ferroelectric random-access memories

abstract: This paper surveys circuit innovations in ferroelectric memories at three circuit levels: memory cell, sensing and architecture. A ferroelectric memory cell consists of at least one ferroelectric capacitor, where binary data are stored, and one or two transistors that either allow access to the capacitor or amplify its contents for a read operation. Once a cell is accessed for a read operation, its data are presented in the form of an analog signal to a sense amplifier, where it is compared against a reference voltage to determine its logic level. The circuit techniques used to generate the reference voltage must be robust to semiconductor processing variations across the chip and the device imperfections of ferroelectric capacitors. We review six methods of generating a reference voltage, two being presented for the first time in this paper. These methods are discussed and evaluated in terms of their accuracy, area overhead and sensing complexity. Ferroelectric memories share architectural features such as addressing schemes and input/output circuitry with other types of random-access memories such as dynamic random-access memories. However, they have distinct features with respect to accessing the stored data, sensing, and overall circuit topology. We review nine different architectures for ferroelectric memories and discuss them in terms of speed, density and power consumption.


title: The effect of temperature on the efficiency parameters in adsorptive liquid chromatography

abstract: The effect of temperature on the efficiency of column systems in adsorptive liquid chromatography is reported. The influence of temperature changes on the values of the terms Cm2 and Ck in the HETP equation, which characterizessystems involving silica gel and benzene, ethylene chloride, butyl ether or cyclo- hexanone + heptane, is discussed for 9 substances. The results of the study have led to the following conclusions: ::: ::: 1 ::: Temperature increase will always improve the efficiency of typical chromatographic systems. ::: ::: 2 ::: As expected, an essential role is played by mass transfer resistance in the mobile phase occupying the pores of the packing material (parameter Cm2). ::: ::: 3 ::: Mass transfer resistance resulting from sorption-desorption kinetics (Ck) does not play as essential a role in the systems investigated as does Cm2, but its value is significantly higher than that reported in the literature.


title: Tactile and shear stress sensing array using capacitive mechanisms with floating electrodes

abstract: This work presents the development of a capacitive tactile and shear-stress sensing array which can be easily and reliably fabricated by using MEMS techniques and flexible printed circuit board (FPCB) technologies. The sensing array consists of two micromachined PDMS structures and an FPCB with sensing electrodes. Each shear sensing element comprises four capacitive sensing element arranged in 2×2 array, and each capacitive sensing element has two sensing electrodes and a common floating electrode. The proposed design can effectively reduce the complexity of the capacitor structure without compromise in sensitivity.


title: Genetic Architect: Discovering Genomic Structure with Learned Neural Architectures

abstract: Each human genome is a 3 billion base pair set of encoding instructions. Decoding the genome using deep learning fundamentally differs from most tasks, as we do not know the full structure of the data and therefore cannot design architectures to suit it. As such, architectures that fit the structure of genomics should be learned not prescribed. Here, we develop a novel search algorithm, applicable across domains, that discovers an optimal architecture which simultaneously learns general genomic patterns and identifies the most important sequence motifs in predicting functional genomic outcomes. The architectures we find using this algorithm succeed at using only RNA expression data to predict gene regulatory structure, learn human-interpretable visualizations of key sequence motifs, and surpass state-of-the-art results on benchmark genomics challenges.


title: Synthesis of Naphthalene Amino Esters by the Blaise Reaction ofo-Alkynylarenenitriles

abstract: The action of a Reformatsky reagent on o-alkynylarenenitriles provides a convenient access to naphthalene amino esters via tandem 6-endo-dig carbannulation of in situ generated Blaise reaction intermediates. The products are formed in moderate to good yields with high chemo- and regioselectivity.


title: Social Network Security: Issues, Challenges, Threats, and Solutions

abstract: Abstract Social networks are very popular in today's world. Millions of people use various forms of social networks as they allow individuals to connect with friends and family, and share private information. However, issues related to maintaining the privacy and security of a user's information can occur, especially when the user's uploaded content is multimedia, such as photos, videos, and audios. Uploaded multimedia content carries information that can be transmitted virally and almost instantaneously within a social networking site and beyond. In this paper, we present a comprehensive survey of different security and privacy threats that target every user of social networking sites. In addition, we separately focus on various threats that arise due to the sharing of multimedia content within a social networking site. We also discuss current state-of- the-art defense solutions that can protect social network users from these threats. We then present future direction and discuss some easy-to-apply response techniques to achieve the goal of a trustworthy and secure social network ecosystem.


title: Examining WeChat users' motivations, trust, attitudes, and positive word-of-mouth: Evidence from China

abstract: Entertainment, sociality, and information positively influence users' attitudes.Trust has a positive effect on WeChat users' attitudes.Increasing users' trust will increase their willingness in making positive WOM.Improving users' attitudes will enhance their willingness in making positive WOM. WeChat is a mobile instant text and voice messaging communication service and has become an important social media platform in China. The objectives of this article are to examine the effects of psychological motivations (entertainment, sociality, and information) and trust on WeChat users' attitudes and to assess the influence of users' attitudes and their trust on positive word-of-mouth. This study represents one of the few that empirically investigates WeChat users' motives, attitudes, trust, and their associated behavior. The research model was tested using data randomly collected from the database of Sojump. The numbers of valid observations were 264. Structure equation modeling was employed to verify and validate the research model. The outcomes confirm the path effects showing that entertainment, sociality, information, and trust positively influence WeChat users' attitudes and users' trust and their attitudes significantly affect positive WOM. The research results provide insight into how WeChat can motivate users and build their trust to improve their attitudes which in turn will increase WeChat users' willingness in making positive comments on products and services.


title: Securing the Cloud—Governance, Risk, and Compliance Issues Reign Supreme

abstract: While acknowledging the many benefits that cloud computing solutions bring to the world, it is important to note that recent research and studies of these technologies have identified a myriad of potential governance, risk, and compliance (GRC) issues. While industry clearly acknowledges their existence and seeks to them as much as possible, timing-wise it is still well before the legal framework has been put in place to adequately protect and adequately respond to these new and differing global challenges. This paper seeks to inform the potential cloud adopter, not only of the perceived great technological benefit, but to also bring to light the potential security, privacy, and related GRC issues which will need to be prioritized, managed, and mitigated before full implementation occurs.


title: Real-Time Image-Guided Telerobotic System Integrating 3D Slicer and the Da Vinci Research Kit

abstract: In image-guided surgery, preoperative images are registered to an intraoperative tracking system and thereby provide visual feedback of the position of one or more surgical instruments with respect to the patient anatomy in the preoperative image. Two significant limitations of this approach are that relying on a preoperative image cannot account for registration error or intraoperative changes to the anatomy, and that the navigation system cannot provide mechanical assistance to the surgeon. We propose a system architecture to overcomeboth limitations by incorporating real-time image feedback in a telerobotic system that can provide guidance via virtual fixtures. We describe the implementation of this system, which integrates 3D Slicer and the da Vinci Research Kit (dVRK), and present a photoacoustic image-guided telerobotic system developed for endonasal skull base surgery as an example application.


Wearable Technology Design for Autism Spectrum Disorders


title: Adversarial Ranking for Language Generation

abstract: Generative adversarial networks (GANs) have great successes on synthesizing data. However, the existing GANs restrict the discriminator to be a binary classifier, and thus limit their learning capacity for tasks that need to synthesize output with rich structures such as natural language descriptions. In this paper, we propose a novel generative adversarial network, RankGAN, for generating high-quality language descriptions. Rather than training the discriminator to learn and assign absolute binary predicate for individual data sample, the proposed RankGAN is able to analyze and rank a collection of human-written and machine-written sentences by giving a reference group. By viewing a set of data samples collectively and evaluating their quality through relative ranking scores, the discriminator is able to make better assessment which in turn helps to learn a better generator. The proposed RankGAN is optimized through the policy gradient technique. Experimental results on multiple public datasets clearly demonstrate the effectiveness of the proposed approach.


title: Constant power loads and negative impedance instability in automotive systems: definition, modeling, stability, and control of power electronic converters and motor drives

abstract: Power electronic converters and electric motor drives are being put into use at an increasingly rapid rate in advanced automobiles. However, the new advanced automotive electrical systems employ multivoltage level hybrid ac and dc as well as electromechanical systems that have unique characteristics, dynamics, and stability problems that are not well understood due to the nonlinearity and time dependency of converters and because of their constant power characteristics. The purpose of this paper is to present an assessment of the negative impedance instability concept of the constant power loads (CPLs) in automotive power systems. The main focus of this paper is to analyze and propose design criteria of controllers for automotive converters/systems operating with CPLs. The proposed method is to devise a new comprehensive approach to the applications of power electronic converters and motor drives in advanced automotive systems. Sliding-mode and feedback linearization techniques along with large-signal phase plane analysis are presented as methods to analyze, control, and stabilize automotive converters/systems with CPLs


title: Image analysis of blood microscopic images for acute leukemia detection

abstract: Acute lymphoblastic leukemia (ALL) is an serious hematological neoplasia of childhood which is characterized by abnormal growth and development of immature white blood cells (lymphoblasts). ALL makes around 80% of childhood leukemia and it mostly occur in the age group of 3–7. The nonspecific nature of the signs and symptoms of ALL often leads to wrong diagnosis. Diagnostic confusion is also posed due to imitation of similar signs by other disorders. Careful microscopic examination of stained blood smear or bone marrow aspirate is the only way to effective diagnosis of leukemia. Techniques such as fluorescence in situ hybridization (FISH), immunophenotyping, cytogenetic analysis and cytochemistry are also employed for specific leukemia detection. The need for automation of leukemia detection arises since the above specific tests are time consuming and costly. Morphological analysis of blood slides are influenced by factors such as hematologists experience and tiredness, resulting in non standardized reports. A low cost and efficient solution is to use image analysis for quantitative examination of stained blood microscopic images for leukemia detection. A fuzzy clustering based two stage color segmentation strategy is employed for segregating leukocytes or white blood cells (WBC) from other blood components. Discriminative features i.e. nucleus shape, texture are used for final detection of leukemia. In the present paper two novel shape features i.e., Hausdorff Dimension and contour signature is implemented for classifying a lymphocytic cell nucleus. Support Vector Machine (SVM) is employed for classification. A total of 108 blood smear images were considered for feature extraction and final performance evaluation is validated with the results of a hematologist.


Cell Line Techniques and Gene Editing Tools for Antibody Production: A Review


A Review on Deep Learning Techniques Applied to Answer Selection


Author Profiling for Abuse Detection


title: All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey

abstract: Abstract With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security-critical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.


title: Online Implicit Agent Modelling

abstract: The traditional view of agent modelling is to infer the explicit parameters of another agent's strategy (i.e., their probability of taking each action in each situation). Unfortunately, in complex domains with high dimensional strategy spaces, modelling every parameter often requires a prohibitive number of observations. Furthermore, given a model of such a strategy, computing a response strategy that is robust to modelling error may be impractical to compute online. Instead, we propose an implicit modelling framework where agents aim to estimate the utility of a fixed portfolio of pre-computed strategies. Using the domain of heads-up limit Texas hold'em poker, this work describes an end-to-end approach for building an implicit modelling agent. We compute robust response strategies, show how to select strategies for the portfolio, and apply existing variance reduction and online learning techniques to dynamically adapt the agent's strategy to its opponent. We validate the approach by showing that our implicit modelling agent would have won the heads-up limit opponent exploitation event in the 2011 Annual Computer Poker Competition.


title: The Recovery of Causal Poly-Trees from Statistical Data

abstract: Poly-trees are singly connected causal networks in which variables may arise from multiple causes. This paper develops a method of recovering ply-trees from empirically measured probability distributions of pairs of variables. The method guarantees that, if the measured distributions are generated by a causal process structured as a ply-tree then the topological structure of such tree can be recovered precisely and, in addition, the causal directionality of the branches can be determined up to the maximum extent possible. The method also pinpoints the minimum (if any) external semantics required to determine the causal relationships among the variables considered.


title: Occupational Therapy Incorporating Animals for Children With Autism: A Pilot Investigation

abstract: This investigation compared language use and social interaction in children with autism receiving two forms of occupational therapy: occupational therapy using standard techniques, and occupational therapy incorporating animals. Twenty-two children between the ages of 7 and 13 received both forms of therapy in a schoolbased occupational therapy program for children with autism. Results suggest that the children demonstrated significantly greater use of language and significantly greater social interaction in sessions incorporating animals when compared to sessions using exclusively standard occupational therapy techniques. Findings are discussed in the context of recent research that has highlighted the importance of enhancing the motivation of children with autism to engage actively in therapeutic and learning processes.


title: Ensemble with neural networks for bankruptcy prediction

abstract: In a bankruptcy prediction model, the accuracy is one of crucial performance measures due to its significant economic impact. Ensemble is one of widely used methods for improving the performance of classification and prediction models. Two popular ensemble methods, Bagging and Boosting, have been applied with great success to various machine learning problems using mostly decision trees as base classifiers. In this paper, we propose an ensemble with neural network for improving the performance of traditional neural networks on bankruptcy prediction tasks. Experimental results on Korean firms indicated that the bagged and the boosted neural networks showed the improved performance over traditional neural networks.


title: Part-of-Speech Tagging for Code-mixed Indian Social Media Text at ICON 2015

abstract: This paper discusses the experiments carried out by us at Jadavpur University as part of the participation in ICON 2015 task: POS Tagging for Code-mixed Indian Social Media Text. The tool that we have developed for the task is based on Trigram Hidden Markov Model that utilizes information from dictionary as well as some other word level features to enhance the observation probabilities of the known tokens as well as unknown tokens. We submitted runs for Bengali-English, Hindi-English and Tamil-English Language pairs. Our system has been trained and tested on the datasets released for ICON 2015 shared task: POS Tagging For Code-mixed Indian Social Media Text. In constrained mode, our system obtains average overall accuracy (averaged over all three language pairs) of 75.60% which is very close to other participating two systems (76.79% for IIITH and 75.79% for AMRITA_CEN) ranked higher than our system. In unconstrained mode, our system obtains average overall accuracy of 70.65% which is also close to the system (72.85% for AMRITA_CEN) which obtains the highest average overall accuracy.


abstract: Dunn, Robert B., Reynolds, Douglas A., and Quatieri, Thomas F., Approaches to Speaker Detection and Tracking in Conversational Speech, Digital Signal Processing10(2000), 93?112.Two approaches to detecting and tracking speakers in multispeaker audio are described. Both approaches use an adapted Gaussian mixture model, universal background model (GMM-UBM) speaker detection system as the core speaker recognition engine. In one approach, the individual log-likelihood ratio scores, which are produced on a frame-by-frame basis by the GMM-UBM system, are used to first partition the speech file into speaker homogenous regions and then to create scores for these regions. We refer to this approach as internal segmentation. Another approach uses an external segmentationalgorithm, based on blind clustering, to partition the speech file into speaker homogenous regions. The adapted GMM-UBM system then scores each of these regions as in the single-speaker recognition case. We show that the external segmentation system outperforms the internal segmentation system for both detection and tracking. In addition, we show how different components of the detection and tracking algorithms contribute to the overall system performance.


title: Multifocusing and Depth Estimation Using a Color Shift Model-Based Computational Camera

abstract: This paper presents a novel approach to depth estimation using a multiple color-filter aperture (MCA) camera and its application to multifocusing. An image acquired by the MCA camera contains spatially varying misalignment among RGB color channels, where the direction and length of the misalignment is a function of the distance of an object from the plane of focus. Therefore, if the misalignment is estimated from the MCA output image, multifocusing and depth estimation become possible using a set of image processing algorithms. We first segment the image into multiple clusters having approximately uniform misalignment using a color-based region classification method, and then find a rectangular region that encloses each cluster. For each of the rectangular regions in the RGB color channels, color shifting vectors are estimated using a phase correlation method. After the set of three clusters are aligned in the opposite direction of the estimated color shifting vectors, the aligned clusters are fused to produce an approximately in-focus image. Because of the finite size of the color-filter apertures, the fused image still contains a certain amount of spatially varying out-of-focus blur, which is removed by using a truncated constrained least-squares filter followed by a spatially adaptive artifacts removing filter. Experimental results show that the MCA-based multifocusing method significantly enhances the visual quality of an image containing multiple objects of different distances, and can be fully or partially incorporated into multifocusing or extended depth of field systems. The MCA camera also realizes single camera-based depth estimation, where the displacement between multiple apertures plays a role of the baseline of a stereo vision system. Experimental results show that the estimated depth is accurate enough to perform a variety of vision-based tasks, such as image understanding, description, and robot vision.


title: Riemannian coding and dictionary learning: Kernels to the rescue

abstract: While sparse coding on non-flat Riemannian manifolds has recently become increasingly popular, existing solutions either are dedicated to specific manifolds, or rely on optimization problems that are difficult to solve, especially when it comes to dictionary learning. In this paper, we propose to make use of kernels to perform coding and dictionary learning on Riemannian manifolds. To this end, we introduce a general Riemannian coding framework with its kernel-based counterpart. This lets us (i) generalize beyond the special case of sparse coding; (ii) introduce efficient solutions to two coding schemes; (iii) learn the kernel parameters; (iv) perform unsupervised and supervised dictionary learning in a much simpler manner than previous Riemannian coding methods. We demonstrate the effectiveness of our approach on three different types of non-flat manifolds, and illustrate its generality by applying it to Euclidean spaces, which also are Riemannian manifolds.


Byzantine Agreement , Made Trivial Silvio Micali


title: OPTIMAL POWER FLOW USING PARTICLE SWARM OPTIMIZATION

abstract: The Optimal Power Flow (OPF) is an important criterion in today’s power system operation and control due to scarcity of energy resources, increasing power generation cost and ever growing demand for electric energy. As the size of the power system increases, load may be varying. The generators should share the total demand plus losses among themselves. The sharing should be based on the fuel cost of the total generation with respect to some security constraints. Conventional optimization methods that make use of derivatives and gradients are, in general, not able to locate or identify the global optimum. Heuristic algorithms such as genetic algorithms (GA) and evolutionary programming have been recently proposed for solving the OPF problem. Unfortunately, recent research has identified some deficiencies in GA performance. Recently, a new evolutionary computation technique, called Particle Swarm Optimization (PSO), has been proposed and introduced. This technique combines social psychology principles in socio-cognition human agents and evolutionary computations. In this paper, a novel PSO based approach is presented to solve Optimal Power Flow problem.


title: Unified Neural Architecture for Drug, Disease and Clinical Entity Recognition

abstract: Abstract Most existing methods for biomedical entity recognition tasks rely on explicit feature engineering where many features are either specific to a particular task or depend on the output of other natural language processing tools. Neural architectures have shown across various domains that efforts for explicit feature design can be reduced. In this work, we propose a unified framework using a bi-directional long short-term memory network (BLSTM) for named entity recognition (NER) tasks in biomedical and clinical domains. Three important characteristics of the framework are as follows: (1) The model learns contextual as well as morphological features using two different BLSTMs in a hierarchy, (2) the model uses a first-order linear conditional random field (CRF) in its output layer in cascade of BLSTM to infer label or tag sequence, and (3) the model does not use any domain-specific features or dictionary, that is, in another words, the same set of features are used in the three NER tasks, namely, disease name recognition (Disease NER), drug name recognition (Drug NER), and clinical entity recognition (Clinical NER). We compare the performance of the proposed model with existing state-of-the-art models on the standard benchmark datasets of the three tasks. We show empirically that the proposed framework outperforms all existing models. We analyze the importance of CRF layer, different feature types, and word embedding obtained using character-based embedding. The error analysis of the model indicates that a major proportion of errors are due to difficulty in recognizing acronyms and nested forms of entity names.


title: Index Modulation Techniques for Next-Generation Wireless Networks

abstract: What is index modulation (IM)? This is an interesting question that we have started to hear more and more frequently over the past few years. The aim of this paper is to answer this question in a comprehensive manner by covering not only the basic principles and emerging variants of IM, but also reviewing the most recent as well as promising advances in this field toward the application scenarios foreseen in next-generation wireless networks. More specifically, we investigate three forms of IM: spatial modulation, channel modulation and orthogonal frequency division multiplexing (OFDM) with IM, which consider the transmit antennas of a multiple-input multiple-output system, the radio frequency mirrors (parasitic elements) mounted at a transmit antenna and the subcarriers of an OFDM system for IM techniques, respectively. We present the up-to-date advances in these three promising frontiers and discuss possible future research directions for IM-based schemes toward low-complexity, spectrum- and energy-efficient next-generation wireless networks.


title: Self-scaled conjugate gradient training algorithms

abstract: This article presents some efficient training algorithms, based on conjugate gradient optimization methods. In addition to the existing conjugate gradient training algorithms, we introduce Perry's conjugate gradient method as a training algorithm [A. Perry, A modified conjugate gradient algorithm, Operations Research 26 (1978) 26-43]. Perry's method has been proven to be a very efficient method in the context of unconstrained optimization, but it has never been used in MLP training. Furthermore, a new class of conjugate gradient (CG) methods is proposed, called self-scaled CG methods, which are derived from the principles of Hestenes-Stiefel, Fletcher-Reeves, Polak-Ribiere and Perry's method. This class is based on the spectral scaling parameter introduced in [J. Barzilai, J.M. Borwein, Two point step size gradient methods, IMA Journal of Numerical Analysis 8 (1988) 141-148]. The spectral scaling parameter contains second order information without estimating the Hessian matrix. Furthermore, we incorporate to the CG training algorithms an efficient line search technique based on the Wolfe conditions and on safeguarded cubic interpolation [D.F. Shanno, K.H. Phua, Minimization of unconstrained multivariate functions, ACM Transactions on Mathematical Software 2 (1976) 87-94]. In addition, the initial learning rate parameter, fed to the line search technique, was automatically adapted at each iteration by a closed formula proposed in [D.F. Shanno, K.H. Phua, Minimization of unconstrained multivariate functions, ACM Transactions on Mathematical Software 2 (1976) 87-94; D.G. Sotiropoulos, A.E. Kostopoulos, T.N. Grapsa, A spectral version of Perry's conjugate gradient method for neural network training, in: D.T. Tsahalis (Ed.), Fourth GRACM Congress on Computational Mechanics, vol. 1, 2002, pp. 172-179]. Finally, an efficient restarting procedure was employed in order to further improve the effectiveness of the CG training algorithms. Experimental results show that, in general, the new class of methods can perform better with a much lower computational cost and better success performance.


title: Evaluating the consistency of immediate aesthetic

abstract: Two experiments were designed to replicate and extend [Lindgaard et al.'s, 2006. Attention web designers: you have 50 ms to make a good first impression! Behaviour and Information Technology 25(2), 115-126] findings that users can form immediate aesthetic impression of web pages, and that these impressions are highly stable. Using explicit (subjective evaluations) and implicit (response latency) measures, the experiments demonstrated that, averaged over users, immediate aesthetic impressions of web pages are remarkably consistent. In Experiment 1, 40 participants evaluated 50 web pages in two phases. The average attractiveness ratings of web pages after a very short exposure of 500 ms were highly correlated with average attractiveness ratings after an exposure of 10 s. Extreme attractiveness evaluations (both positive and negative) were faster than moderate evaluations, landing convergent evidence to the hypothesis of immediate impression. The findings also suggest considerable individual differences in evaluations and in the consistency of those evaluations. In Experiment 2, 24 of the 50 web pages from Experiment 1 were evaluated again for their attractiveness after 500ms exposure. Subsequently, users evaluated the design of the web pages on the dimensions of classical and expressive aesthetics. The results showed high correlation between attractiveness ratings from Experiments 1 and 2. In addition, it appears that low attractiveness is associated mainly with very low ratings of expressive aesthetics. Overall, the results provide direct evidence in support of the premise that aesthetic impressions of web pages are formed quickly. Indirectly, these results also suggest that visual aesthetics plays an important role in users' evaluations of the IT artifact and in their attitudes toward interactive systems.


Dimension Reduction


Unobtrusive textography of a university building as an innovative research method


title: Adversarial Training for Disease Prediction from Electronic Health Records with Missing Data

abstract: Electronic health records (EHRs) have contributed to the computerization of patient records and can thus be used not only for efficient and systematic medical services, but also for research on biomedical data science. However, there are many missing values in EHRs when provided in matrix form, which is an important issue in many biomedical EHR applications. In this paper, we propose a two-stage framework that includes missing data imputation and disease prediction to address the missing data problem in EHRs. We compared the disease prediction performance of generative adversarial networks (GANs) and conventional learning algorithms in combination with missing data prediction methods. As a result, we obtained a level of accuracy of 0.9777, sensitivity of 0.9521, specificity of 0.9925, area under the receiver operating characteristic curve (AUC-ROC) of 0.9889, and F-score of 0.9688 with a stacked autoencoder as the missing data prediction method and an auxiliary classifier GAN (AC-GAN) as the disease prediction method. The comparison results show that a combination of a stacked autoencoder and an AC-GAN significantly outperforms other existing approaches. Our results suggest that the proposed framework is more robust for disease prediction from EHRs with missing data.


title: Stability of cyberbullying victimization among adolescents: Prevalence and association with bully-victim status and psychosocial adjustment

abstract: Adolescents show different trajectories of cyberbullying victimization.5.8% of adolescents were stable victims in a one-year follow-up.Stable victims were more likely to be "bully-victims."Overall, stable victims presented more depression and alcohol use. The aims of this study were as follows: (a) to examine the possible presence of an identifiable group of stable victims of cyberbullying; (b) to analyze whether the stability of cybervictimization is associated with the perpetration of cyberbullying and bully-victim status (i.e., being only a bully, only a victim, or being both a bully and a victim); and (c) to test whether stable victims report a greater number of psychosocial problems compared to non-stable victims and uninvolved peers. A sample of 680 Spanish adolescents (410 girls) completed self-report measures on cyberbullying perpetration and victimization, depressive symptoms, and problematic alcohol use at two time points that were separated by one year. The results of cluster analyses suggested the existence of four distinct victimization profiles: "Stable-Victims," who reported victimization at both Time 1 and Time 2 (5.8% of the sample), "Time 1-Victims," and "Time 2-Victims," who presented victimization only at one time (14.5% and 17.6%, respectively), and "Non-Victims," who presented minimal victimization at both times (61.9% of the sample). Stable victims were more likely to fall into the "bully-victim" category and presented more cyberbullying perpetration than the rest of the groups. Overall, the Stable Victims group displayed higher scores of depressive symptoms and problematic alcohol use over time than the other groups, whereas the Non-Victims displayed the lowest of these scores. These findings have major implications for prevention and intervention efforts aimed at reducing cyberbullying and its consequences.


title: Broken Hearts and Broken Bones A Neural Perspective on the Similarities Between Social and Physical Pain

abstract: Although it is common to describe experiences of social rejection or loss with words typically reserved for physical pain, the idea that these social experiences might actually be experienced as painful seems more far-fetched. However, accumulating evidence demonstrates that social pain—the painful feelings following social rejection or loss—may rely on pain-related neural circuitry. Here, I summarize a program of research that has explored whether social pain relies on pain-related neural regions, as well as some of the expected consequences of a physical–social pain overlap. I also discuss the implications of these findings for our understanding of social pain.


title: A review of Planar Ultrawideband Modular Antenna (PUMA) Arrays

abstract: Planar Ultrawideband Modular Antenna (PUMA) arrays are low-cost, wide-scan, and low-cross polarization dual-polarized UWB arrays that combine excellent electrical performance with convenient and practical feeding/fabrication processes. Each member of the PUMA array family consists of tightly coupled horizontal dipoles over a ground plane with novel feeding schemes that enable simple PCB fabrication. This feeding eliminates the need for baluns, “cable organizers,” and other external support mechanisms to produce stand-alone, high-efficiency radiators. Additionally, all PUMA arrays consist of dual-offset dual-polarized lattice arrangements for modular, tile-based assembly. This paper will review the basic operation principles of the PUMA arrays followed by the technological evolution of the PUMA array family. Fabricated PUMA arrays and full-wave simulations of structures that can be manufactured with standard fabrication technologies will be shown along with results.


title: The soybean GmSNAP18 gene underlies two types of resistance to soybean cyst nematode

abstract: Two types of resistant soybean (Glycine max (L.) Merr.) sources are widely used against soybean cyst nematode (SCN, Heterodera glycines Ichinohe). These include Peking-type soybean, whose resistance requires both the rhg1-a and Rhg4 alleles, and PI 88788-type soybean, whose resistance requires only the rhg1-b allele. Multiple copy number of PI 88788-type GmSNAP18, GmAAT, and GmWI12 in one genomic segment simultaneously contribute to rhg1-b resistance. Using an integrated set of genetic and genomic approaches, we demonstrate that the rhg1-a Peking-type GmSNAP18 is sufficient for resistance to SCN in combination with Rhg4. The two SNAPs (soluble NSF attachment proteins) differ by only five amino acids. Our findings suggest that Peking-type GmSNAP18 is performing a different role in SCN resistance than PI 88788-type GmSNAP18. As such, this is an example of a pathogen resistance gene that has evolved to underlie two types of resistance, yet ensure the same function within a single plant species. Peking-type and PI 88788-type soybeans are both used as sources of resistance against soybean cyst nematode. Here the authors show that in contrast to PI 88788-type resistance, whereGmSNAP18 acts in combination with two neighbouring genes, in Peking-type resistance GmSNAP18 acts with GmSHMT08to confer resistance.


title: Footprint evaluation for volume rendering

abstract: This paper presents a forward mapping rendering algorithm to display regular volumetric grids that may not have the same spacings in the three grid directions. It takes advantage of the fact that convolution can be thought of as distributing energy from input samples into space. The renderer calculates an image plane footprint for each data sample and uses the footprint to spread the sample's energy onto the image plane. A result of the technique is that the forward mapping algorithm can support perspective without excessive cost, and support adaptive resampling of the three-dimensional data set during image generation.


title: Crowdsourcing of Pollution Data using Smartphones

abstract: In this paper we present our research into participatory sensing based solutions for the collection of data on urban pollution and nuisance. In the past 2 years we have been involved in the NoiseTube project which explores a crowdsourcing approach to measuring and mapping urban noise pollution using smartphones. By involving the general public and using off-the-shelf smartphones as noise sensors, we seek to provide a low cost solution for citizens to measure their personal exposure to noise in their everyday environment and participate in the creation of collective noise maps by sharing their geo-localized and annotated measurements with the community. We believe our work represents an interesting example of the novel mobile crowdsourcing applications which are enabled by ubiquitous computing systems. Furthermore we believe the NoiseTube system, and the currently ongoing validation experiments, provide an illustrative context for some of the open challenges faced by creators of ubiquitous crowdsourcing applications and services in general. We will also take the opportunity to present the insights we gained into some of the challenges.


title: Neural Feature Learning From Relational Database

abstract: Feature engineering is one of the most important but most tedious tasks in data science. This work studies automation of feature learning from relational database. We first prove theoretically that finding the optimal features from relational data for predictive tasks is NP-hard. We propose an efficient rule-based approach based on heuristics and a deep neural network to automatically learn appropriate features from relational data. We benchmark our approaches in ensembles in past Kaggle competitions. Our new approach wins late medals and beats the state-of-the-art solutions with significant margins. To the best of our knowledge, this is the first time an automated data science system could win medals in Kaggle competitions with complex relational database.


title: Simplified neuron model as a principal component analyzer

abstract: A simple linear neuron model with constrained Hebbian-type synaptic modification is analyzed and a new class of unconstrained learning rules is derived. It is shown that the model neuron tends to extract the principal component from a stationary input vector sequence.


title: Understanding dopamine and reinforcement learning: The dopamine reward prediction error hypothesis

abstract: A number of recent advances have been achieved in the study of midbrain dopaminergic neurons. Understanding these advances and how they relate to one another requires a deep understanding of the computational models that serve as an explanatory framework and guide ongoing experimental inquiry. This intertwining of theory and experiment now suggests very clearly that the phasic activity of the midbrain dopamine neurons provides a global mechanism for synaptic modification. These synaptic modifications, in turn, provide the mechanistic underpinning for a specific class of reinforcement learning mechanisms that now seem to underlie much of human and animal behavior. This review describes both the critical empirical findings that are at the root of this conclusion and the fantastic theoretical advances from which this conclusion is drawn.


Corpus-Based Content Analysis : A Method for Investigating News Coverage on War and Intervention


title: Examining The Adoption Of Human Resource Information System In The Context Of Saudi Arabia

abstract: The aim of this paper is to examine the factors influencing the adoption of the human resource information system (HRIS) in the Saudi context. A few researches have looked at the adoption of HRIS, but none of these studies have examined the adoption of such a system from the users’ perspective in general and Saudi Arabia (SA) in particular. This study has developed a model that integrates service quality with perceived usefulness and perceived ease of use to investigate the factors affecting the users’ adoption of HRIS. The model was empirically tested using the structure equation modelling by employing data collected from a survey of HRIS users. The findings and discussion presented in this study will help the Saudi organisations to understand the current status of their HRIS to improve it and benefit from it. This study provides guidelines for its implementation for theory and practices, limitations and future directions.


title: Business Process Cockpit

abstract: workflow, business process, HPPM (HP Process Manager), data analysis, visualization of data Business Process Cockpit (BPC) is a tool that supports real-time monitoring, analysis, management, and optimization of business processes running on top of HP Process Manager, the Business Process Management System developed by HewlettPackard. The main goal of the Business Process Cockpit is to enable business users to perform business-level quality analysis, monitoring, and management of business processes. The BPC visualizes process execution data according to different focus points that identify the process entities that are the focus of the analysis, and different perspectives that define a way to look at the information. The BPC also allows users to define new concepts, such as “slow” and “fast” executions, and use those concepts to categorize the viewed data and make it much easier for users to interpret.


title: Contour Motion Estimation for Asynchronous Event-Driven Cameras Emulating the relevant transient retinal responses holds the key to efficient tracking in a visual motion camera

abstract: This paper compares image motion estimation with asynchronous event-based cameras to Computer Vision approaches using as input frame-based video sequences. Since dynamic events are triggered at significant intensity changes, which often are at the border of objects, we refer to the event- based image motion as ''contour motion.'' Algorithms are presented for the estimation of accurate contour motion from local spatio-temporal information for two camera models: the dynamic vision sensor (DVS), which asynchronously records temporal changes of the luminance, and a family of new sensors which combine DVS data with intensity signals. These algorithms take advantage of the high temporal resolution of the DVS and achieve robustness using a multiresolution scheme in time. It is shown that, because of the coupling of velocity and luminance information in the event distribution, the image motion estimation problem becomes much easier with the new sensors which provide both events and image intensity than with the DVS alone. Experiments on synthesized data from computer vision benchmarks show that our algorithm on combined data outperforms computer vision methods in accuracy and can achieve real-time performance, and experiments on real data confirm the feasibility of the approach. Given that current image motion (or so-called optic flow) methods cannot estimate well at object boundaries, the approach presented here could be used complementary to optic flow techniques, and can provide new avenues for computer vision motion research.


title: A General Account of Argumentation with Preferences

abstract: This paper builds on the recent ASPIC^+ formalism, to develop a general framework for argumentation with preferences. We motivate a revised definition of conflict free sets of arguments, adapt ASPIC^+ to accommodate a broader range of instantiating logics, and show that under some assumptions, the resulting framework satisfies key properties and rationality postulates. We then show that the generalised framework accommodates Tarskian logic instantiations extended with preferences, and then study instantiations of the framework by classical logic approaches to argumentation. We conclude by arguing that ASPIC^+@?s modelling of defeasible inference rules further testifies to the generality of the framework, and then examine and counter recent critiques of Dung@?s framework and its extensions to accommodate preferences.


title: On Defining Virtual Emotional Intelligence

abstract: In this paper virtual teams are defined as living systems and as such made up of people with different needs and characteristics. Groups generally perform better when they are able to establish a high level of group cohesion. According to Druskat and Wolff [2001] this status can be reached by establishing group emotional intelligence. Group emotional intelligence is reached via interactions among members and the interactions are allowed through the disposable linking factors. Virtual linking factors differ from traditional linking factors; therefore, the concept of Virtual Emotional Intelligence is here introduced in order to distinguish the group cohesion reaching process in virtual teams.


title: Topic Modeling for Classification of Clinical Reports

abstract: Electronic health records (EHRs) contain important clinical information about patients. Efficient and effective use of this information could supplement or even replace manual chart review as a means of studying and improving the quality and safety of healthcare delivery. However, some of these clinical data are in the form of free text and require pre-processing before use in automated systems. A common free text data source is radiology reports, typically dictated by radiologists to explain their interpretations. We sought to demonstrate machine learning classification of computed tomography (CT) imaging reports into binary outcomes, i.e. positive and negative for fracture, using regular text classification and classifiers based on topic modeling. Topic modeling provides interpretable themes (topic distributions) in reports, a representation that is more compact than the commonly used bag-of-words representation and can be processed faster than raw text in subsequent automated processes. We demonstrate new classifiers based on this topic modeling representation of the reports. Aggregate topic classifier (ATC) and confidence-based topic classifier (CTC) use a single topic that is determined from the training dataset based on different measures to classify the reports on the test dataset. Alternatively, similarity-based topic classifier (STC) measures the similarity between the reports' topic distributions to determine the predicted class. Our proposed topic modeling-based classifier systems are shown to be competitive with existing text classification techniques and provides an efficient and interpretable representation.


title: Explaining Consumer Behavior and Consumer Action: From Fragmentation to Unity

abstract: This article begins with an analysis of the state of the field of consumer research and scrutinizes three leading streams of research: the cognitive response paradigm, attitude theory, and the interpretive turn. After arguing that the field lacks direction and risks being overwhelmed by many fragmented additions to knowledge, I propose a unified approach to consumer research integrating consumer behavior with consumer action. The underlying theme is that consumers undergo a fundamental tension between selfhood and sociality, and agency in the form of self-regulation functions to reconcile the tension, thereby helping consumers achieve moral balance, integration of personal and social identity, and personal and social flourishing.


title: A survey of data warehouse architectures — Preliminary results

abstract: In this abridged version, which is a summary of the authors' study, we present some of our findings for consideration. The principal objective of the study was to investigate empirically the architectures of data warehouse (DW), or more specifically, the types of the architectures and a number of factors, which are believed to influence their selection, were explored. A questionnaire survey, which targeted the information systems managers, was used to collect data from 150 Polish companies about the respondents' firms, the architecture they use, and the factors which influence the selection of the architecture. The findings of the study give us practical insights into DW's field in Poland.


title: Optimization and Energy Management in Smart Home Considering Photovoltaic, Wind, and Battery Storage System With Integration of Electric Vehicles

abstract: With the emergence of smart grid, which presents the next generation of electrical power systems, residents have the opportunities to manage their home energy usage to reduce energy expenditure. This paper presents a mixed integer linear programming model to optimize the energy production and consumption systems in a smart home with the integration of renewable energy resources, battery storage systems, and gridable vehicles. Numerous case studies are presented by varying significant factors through the design of experiments with the Taguchi method. After that, a heuristic technique is proposed to solve the problem of residential energy management and to minimize the electricity cost of the consumer. Results find the global optimum solution for many consecutive days with important reduction of execution time and by achieving significant energy cost savings of the considered scenarios.


title: The evaluation of framework reusability

abstract: Since reuse costs are often higher than expected, several approaches to development with reuse have been proposed. Among the most interesting is the idea of frameworks: sets of single components collaborating according to predefined cooperation rules. This approach can have a great impact on the reusability of parts of a software system, but it is not an easy task to give a quantitative measure of this reusability. The existing reusability metrics can be used, but the results are not fully satisfactory, since they do not take into account the peculiar characteristics of a framework and the particular way it is reused (a mix between black box reuse and white box reuse). For these reasons, a set of criteria adapting the traditional reusability metrics is proposed. These criteria try to evaluate the four main factors affecting the reusability of a framework: portability, adaptability, understandability, and confidence.


title: Automatic Image Cropping: A Computational Complexity Study

abstract: Attention based automatic image cropping aims at preserving the most visually important region in an image. A common task in this kind of method is to search for the smallest rectangle inside which the summed attention is maximized. We demonstrate that under appropriate formulations, this task can be achieved using efficient algorithms with low computational complexity. In a practically useful scenario where the aspect ratio of the cropping rectangle is given, the problem can be solved with a computational complexity linear to the number of image pixels. We also study the possibility of multiple rectangle cropping and a new model facilitating fully automated image cropping.


title: No Regret Bound for Extreme Bandits

abstract: Algorithms for hyperparameter optimization abound, all of which work well under different and often unverifiable assumptions. Motivated by the general challenge of sequentially choosing which algorithm to use, we study the more specific task of choosing among distributions to use for random hyperparameter optimization. This work is naturally framed in the extreme bandit setting, which deals with sequentially choosing which distribution from a collection to sample in order to minimize (maximize) the single best cost (reward). Whereas the distributions in the standard bandit setting are primarily characterized by their means, a number of subtleties arise when we care about the minimal cost as opposed to the average cost. For example, there may not be a well-defined"best"distribution as there is in the standard bandit setting. The best distribution depends on the rewards that have been obtained and on the remaining time horizon. Whereas in the standard bandit setting, it is sensible to compare policies with an oracle which plays the single best arm, in the extreme bandit setting, there are multiple sensible oracle models. We define a sensible notion of"extreme regret"in the extreme bandit setting, which parallels the concept of regret in the standard bandit setting. We then prove that no policy can asymptotically achieve no extreme regret.


A Study on Change Detection Methods


title: A Systematic Mapping Study on the Multi-tenant Architecture of SaaS Systems

abstract: Background: SaaS (Software as a Service) is a services delivery model in Cloud Computing whose applications are remotely hosted by the service provider and available to customers on demand over the Internet. Multi-tenant Architecture (MTA) is an organizational pattern for SaaS that enables a single instance of an application to be hosted on the same hardware and accessed by multiple customers, so-called tenants, with the aim of lowering costs. Tenants are able to configure the system according to their particular needs. Objective: This research aims at the obtaining an overview of the challenges and research opportunities in MTA context for SaaS through a Systematic Mapping Study. Results: Eighty nine primary studies were selected for discussions on advances and opportunities for further investigations. The results showed the relevancy of MTA and pointed out the main research trends for next years in this topic. Cloud Computing; software as a service; multi-tenant architecture; systematic mapping study.


title: A survey of recent advances in visual feature detection

abstract: Feature detection is a fundamental and important problem in computer vision and image processing. It is a low-level processing step which serves as the essential part for computer vision based applications. The goal of this paper is to present a survey of recent progress and advances in visual feature detection. Firstly we describe the relations among edges, corners and blobs from the psychological view. Secondly we classify the algorithms in detecting edges, corners and blobs into different categories and provide detailed descriptions for representative recent algorithms in each category. Considering that machine learning becomes more involved in visual feature detection, we put more emphasis on machine learning based feature detection methods. Thirdly, evaluation standards and databases are also introduced. Through this survey we would like to present the recent progress in visual feature detection and identify future trends as well as challenges. We survey the recent progress and advances in visual feature detection.The relations among different kinds of features are covered.Representative feature detection algorithms are described.We categorize and discuss the pros/cons for different kinds of visual features.We put some emphasis on future challenges in feature design through this survey.


title: Siblings of individuals with autism spectrum disorders across the life course

abstract: In this article, we review the literature on siblings of individuals with autism spectrum disorders (ASD) from a lifespan developmental perspective, from infancy through adulthood, focusing on the sibling relationship and sibling well-being. We situate this review within the larger body of research on siblings of individuals with developmental disabilities (DD) across the lifespan. We then consider the genetic aspects of ASDs and their implications for siblings. We conclude that there is an evidence of atypical social and communication development in some siblings of children with an ASD during infancy. During childhood and adolescence, siblings describe both positive and negative aspects of their sibling relationship and there is some evidence that siblings of children with an ASD may be at heightened risk for social and behavioral adjustment problems. The limited research on adulthood suggests that lack of closeness in the sibling relationship and social and emotional difficulties may continue. We encourage more attention focused on developmental issues, specifically with respect to samples in narrower age groups and in longitudinal research. Finally, we note the variability in sibling outcomes, and suggest further examination of potential moderating and mediating factors, including genetic predispositions. © 2007 Wiley-Liss, Inc. MRDD Research Reviews 2007;13:313–320.


title: Multi-view Deep Network for Cross-View Classification

abstract: Cross-view recognition that intends to classify samples between different views is an important problem in computer vision. The large discrepancy between different even heterogenous views make this problem quite challenging. To eliminate the complex (maybe even highly nonlinear) view discrepancy for favorable cross-view recognition, we propose a multi-view deep network (MvDN), which seeks for a non-linear discriminant and view-invariant representation shared between multiple views. Specifically, our proposed MvDN network consists of two sub-networks, view-specific sub-network attempting to remove view-specific variations and the following common sub-network attempting to obtain common representation shared by all views. As the objective of MvDN network, the Fisher loss, i.e. the Rayleigh quotient objective, is calculated from the samples of all views so as to guide the learning of the whole network. As a result, the representation from the topmost layers of the MvDN network is robust to view discrepancy, and also discriminative. The experiments of face recognition across pose and face recognition across feature type on three datasets with 13 and 2 views respectively demonstrate the superiority of the proposed method, especially compared to the typical linear ones.


title: A novel approach to virtual networks embedding for SDN management and orchestration

abstract: The development of methodologies to manage and orchestrate virtualised resources and network functions is a fundamental enabler for optimally utilising physical ICT infrastructures. Algorithms for optimal location (embedding) of network functions, IT and CT resources, services and corresponding states, especially at the network edge, will enable new business models and provide a key competitive advantage to network administrators. This paper introduces a novel Mixed Integer Programming (MIP) formulation for a coordinated node and link mapping onto the underlying network infrastructure. Extensive simulation results show that the proposed algorithm outperforms prior art formulations: two digit gains were attained in terms of resources utilisation, embedding, revenues and, especially convergence time. The proposed methodology is applicable to a number of relevant use cases, as constraints and objective functions can be flexibly defined by network operators.


title: Blur insensitive texture classification using local phase quantization

abstract: In this paper, we propose a new descriptor for texture classification that is robust to image blurring. The descriptor utilizes phase information computed locally in a window for every image position. The phases of the four low-frequency coefficients are decorrelated and uniformly quantized in an eight-dimensional space. A histogram of the resulting code words is created and used as a feature in texture classification. Ideally, the low-frequency phase components are shown to be invariant to centrally symmetric blur. Although this ideal invariance is not completely achieved due to the finite window size, the method is still highly insensitive to blur. Because only phase information is used, the method is also invariant to uniform illumination changes. According to our experiments, the classification accuracy of blurred texture images is much higher with the new method than with the well-known LBP or Gabor filter bank methods. Interestingly, it is also slightly better for textures that are not blurred.


title: Intraspecific diversity in Sinningia speciosa (Gesneriaceae: Sinningieae), and possible origins of the cultivated florist's gloxinia

abstract: Background and aims The florist’s gloxinia is a familiar houseplant in the Gesneriaceae, the botanical family that includes the African violet (Saintpaulia) and other ornamental species. The gloxinia’s wild progenitor is Sinningia speciosa (Lodd.) Hiern, a Brazilian endemic. Although it has been cultivated for almost 200 years, little is known about the genetic diversity in S. speciosa, how the wild populations relate to one another or even where the cultivated forms originated. Using available wild collections, preliminary phenetic and phylogenetic investigations were conducted to elucidate the interspecific relationships within S. speciosa and to infer the origins of the cultivars. Methodology Amplified fragment length polymorphism (AFLP) analysis was applied to 24 accessions of S. speciosa (17 wild collections, seven cultivars) and one accession each of Sinningia guttata and Sinningia macrophylla. A maximum likelihood (ML) tree was also calculated from an alignment of the nuclear ribosomal internal transcribed spacer sequence from the same 26 accessions. Principal results Dice/UPGMA and principal coordinates analysis of the AFLP data partitioned S. speciosa into several distinct clusters, one of which included S. macrophylla. All cultivated ‘gloxinias’ grouped together in a major cluster with plants from Rio de Janeiro. The AFLP results were compared with a phylogenetic analysis of the ribosomal spacer region, which was informative in S. speciosa. The ML tree generally supported the AFLP results, although several clades lacked strong statistical support. Conclusions Independent analyses of two different data sets show that S. speciosa is a diverse species comprised of several lineages. Genetic distance estimates calculated from the AFLP data were positively correlated with geographic distances between populations, indicating that reproductive isolation could be driving speciation in this taxon. Molecular markers are under development for population genetic studies in S. speciosa, which will make it possible to define evolutionarily significant units for purposes of conservation.


title: Link Prediction Based on Graph Neural Networks

abstract: Traditional methods for link prediction can be categorized into three main types: graph structure feature-based, latent feature-based, and explicit feature-based. Graph structure feature methods leverage some handcrafted node proximity scores, e.g., common neighbors, to estimate the likelihood of links. Latent feature methods rely on factorizing networks' matrix representations to learn an embedding for each node. Explicit feature methods train a machine learning model on two nodes' explicit attributes. Each of the three types of methods has its unique merits. In this paper, we propose SEAL (learning from Subgraphs, Embeddings, and Attributes for Link prediction), a new framework for link prediction which combines the power of all the three types into a single graph neural network (GNN). GNN is a new type of neural network which directly accepts graphs as input and outputs their labels. In SEAL, the input to the GNN is a local subgraph around each target link. We prove theoretically that our local subgraphs also reserve a great deal of high-order graph structure features related to link existence. Another key feature is that our GNN can naturally incorporate latent features and explicit features. It is achieved by concatenating node embeddings (latent features) and node attributes (explicit features) in the node information matrix for each subgraph, thus combining the three types of features to enhance GNN learning. Through extensive experiments, SEAL shows unprecedentedly strong performance against a wide range of baseline methods, including various link prediction heuristics and network embedding methods.


title: Context-Aware Recommendation Using Role-Based Trust Network

abstract: Recommender systems have been studied comprehensively in both academic and industrial fields over the past decade. As user interests can be affected by context at any time and any place in mobile scenarios, rich context information becomes more and more important for personalized context-aware recommendations. Although existing context-aware recommender systems can make context-aware recommendations to some extent, they suffer several inherent weaknesses: (1) Users’ context-aware interests are not modeled realistically, which reduces the recommendation quality; (2) Current context-aware recommender systems ignore trust relations among users. Trust relations are actually context-aware and associated with certain aspects (i.e., categories of items) in mobile scenarios. In this article, we define a term role to model common context-aware interests among a group of users. We propose an efficient role mining algorithm to mine roles from a “user-context-behavior” matrix, and a role-based trust model to calculate context-aware trust value between two users. During online recommendation, given a user u in a context c, an efficient weighted set similarity query (WSSQ) algorithm is designed to build u’s role-based trust network in context c. Finally, we make recommendations to u based on u’s role-based trust network by considering both context-aware roles and trust relations. Extensive experiments demonstrate that our recommendation approach outperforms the state-of-the-art methods in both effectiveness and efficiency.


title: Advanced Control Architectures for Intelligent Microgrids—Part II: Power Quality, Energy Storage, and AC/DC Microgrids

abstract: This paper summarizes the main problems and solutions of power quality in microgrids, distributed-energy-storage systems, and ac/dc hybrid microgrids. First, the power quality enhancement of grid-interactive microgrids is presented. Then, the cooperative control for enhance voltage harmonics and unbalances in microgrids is reviewed. Afterward, the use of static synchronous compensator (STATCOM) in grid-connected microgrids is introduced in order to improve voltage sags/swells and unbalances. Finally, the coordinated control of distributed storage systems and ac/dc hybrid microgrids is explained.


title: Scalable business intelligence with graph collections

abstract: Abstract Using graph data models for business intelligence applications is a novel and promising approach. In contrast to traditional data warehouse models, graph models enable the mining of relationship patterns. In our prior work, we introduced an approach to graph-based data integration and analytics called BIIIG (Business Intelligence with Integrated Instance Graphs). In this work, we compare state-of-the-art systems for graph data management and analytics with regard to the support for our approach in Big Data scenarios. To exemplify the analytical value of graph models for business intelligence, we propose an analytical workflow to extract knowledge from graph-integrated business data. Finally, we show how we use Gradoop, a novel framework for distributed graph analytics, to implement our approach.


title: File Multicast Transport Protocol (FMTP)

abstract: This paper describes a new reliable transport protocol designed to run on top of a multicast network service for delivery of continuously generated files. The motivation for this work is to support scientific computing Grid applications that require file transfers between geographically distributed data enters. For example, atmospheric research scientists at various universities subscribe to real-time meteorology data that is being distributed by the University Corporation for Atmospheric Research (UCAR). UCAR delivers 30 different feed types, such as radar data and satellite imagery, to over 240 institutions. The current solution uses an application-layer (AL) multicast tree with uncast TCP connections between the AL servers. Recently, Internet2 and other research-and-education networks have deployed a Layer-2 service using OpenFlow/Software Defined Network (SDN) technologies. Our new transport protocol, FMTP, is designed to run on top of a multipoint Layer-2 topology. A key design aspect of FMTP is the tradeoffs between file delivery throughput of fast receivers and robustness (measure of successful reception) of slow receivers. A configurable parameter, called the retransmission timeout factor, is used to trade off these two metrics. In a multicast setting, it is difficult to achieve full reliability without sacrificing throughput under moderate-to-high loads, and throughput is important in scientific computing grids. A backup solution allows receivers to use uncast TCP connections to request files that were not received completely via multicast. For a given load and a multicast group of 30 receivers, robustness increased significantly from 81.4 to 97.5% when the retransmission timeout factor was increased from 10 to 50 with a small drop in average throughput from 85 to 82.8 Mbps.


title: Energy efficiency of Fog Computing and Networking services in 5G networks

abstract: Today many users with their smart mobile devices enjoy the benefits of broadband Internet services. This is primarily enabled by pushing computing, control, data storage and processing into the cloud. However, the cloud encounters growing limitations, such as reduced latency, high mobility, high scalability and real-time execution in order to meet the computing and intelligent networking demands for the next 5G mobile and wireless network. A new paradigm called Fog Computing and Networking, or briefly Fog has emerged to resolve these limits. Fog distributes computing, data processing, and networking services closer to the end users. It is an architecture where distributed edge and user devices collaborate with each other and with the clouds to carry out computing, control, networking, and data management tasks. Fog applied in 5G network can significantly improve network performance in terms of spectral and energy efficiency, enable direct device-to-device wireless communications, and support the growing trend of network function virtualization and separation of network control intelligence from radio network hardware. This paper evaluates the quality of cloud and fog computing and networking orchestrated services in 5G mobile and wireless network in terms of energy efficiency.


title: Parallel GPU Implementation of Hough Transform for Circles

abstract: Hough transform is one of the most widely used algorithms in image processing. The major problems of Hough’s transform are its time consuming and its abundant requirement of computational resources. In this paper, we try to solve this problem by paralleling this algorithm and implementing it on GPUs(Graphic Process unit) using CUDA(Compute Unified Device Architecture) . We have introduced two methods for parallelization, each of which has been implemented on four different graphic cards using CUDA. After executing the proposed methods on GPUs, we have compared our results with sequential algorithm execution on CPU and it is observable that we have about 65 times more speedup toward the sequential algorithm.


title: Semantics guided regression test cost reduction

abstract: Software maintainers are faced with the task of regression testing: retesting a modified program on an often large number of test cases. The cost of regression testing can be reduced if the size of the program is reduced and if old test cases and results can be reused. Two complimentary algorithms for reducing the cost of regression testing are presented. The first produces a program called Differences that captures the semantic change between Certified, a previously tested program, and Modified, a changed version of Certified. It is more efficient to test Differences, because it omits unchanged computations. The program Differences is computed using a combination of program slices. The second algorithm identifies test cases for which Certified and Modified produce the same output and existing test cases that test new components in Modified. The algorithm is based on the notion of common execution patterns. Program components with common execution patterns have the same execution pattern during some call to their procedure. They are computed using a calling context slice. Whereas an interprocedural slice includes the program components necessary to capture all possible executions of a statement, a calling context slice includes only those program components necessary to capture the execution of a statement in a particular calling context. Together with Differences, it is possible to test Modified by running Differences on a smaller number of test cases. This is more efficient than running Modified on a large number of test cases. A prototype implementation has been built to examine and illustrate these algorithms.


title: Adaptive feedforward control for disturbance torque rejection in seeker stabilizing loop

abstract: Important sources of disturbance torque inputs, which may severely degrade the seeker tracking accuracy of a guided missile, involve seeker mass imbalance, seeker gimbal friction, and seeker head restoring torque. The paper presents an approach to treat the disturbance rejection problem for the missile seeker. As the disturbance variables are measurable, we show that the quality of seeker tracking accuracy can be improved by the addition of feedforward control. A multilayer feedforward neural network is proposed to realize the nonlinear adaptive feedforward controller. Extensive simulation studies are performed to examine the effect of the proposed control scheme. The result shows that the neural controller affords possibilities for improving the miss distance performance.


title: Issues in Code-Switching: Competing Theories and Models

abstract: This paper provides a critical overview of the theoretical, analytical, and practical questions most prevalent in the study of the structural and the sociolinguistic dimensions of code-switching (CS). In doing so, it reviews a range of empirical studies from around the world. The paper first looks at the linguistic research on the structural features of CS focusing in particular on the code-switching versus borrowing distinction, and the syntactic constraints governing its operation. It then critically reviews sociological, anthropological, and linguistic perspectives dominating the sociolinguistic research on CS over the past three decades. Major empirical studies on the discourse functions of CS are discussed, noting the similarities and differences between socially motivated CS and style-shifting. Finally, directions for future research on CS are discussed, giving particular emphasis to the methodological issue of its applicability to the analysis of bilingual classroom interaction.


title: BIM Guidelines Inform Facilities Management Databases: A Case Study over Time

abstract: A building information model (BIM) contains data that can be accessed and exported for other uses during the lifetime of the building especially for facilities management (FM) and operations. Working under the guidance of well-designed BIM guidelines to insure completeness and compatibility with FM software, architects and contractors can deliver an information rich data model that is valuable to the client. Large owners such as universities often provide these detailed guidelines and deliverable requirements to their building teams. Investigation of the University of Southern California (USC) Facilities Management Service’s (FMS) website showed a detailed plan including standards, file names, parameter lists, and other requirements of BIM data, which were specifically designated for facilities management use, as deliverables on new construction projects. Three critical details were also unearthed in the reading of these documents: Revit was the default BIM software; COBie was adapted to help meet facilities management goals; and EcoDomus provided a display of the collected data viewed through Navisworks. Published accounts about the Cinema Arts Complex developed with and under these guidelines reported positive results. Further examination with new projects underway reveal the rapidly changing relational database landscape evident in the new USC “Project Record Revit Requirement Execution Plan (PRxP)”.


title: Towards a Shared Ledger Business Collaboration Language Based on Data-Aware Processes

abstract: Shared ledger technologies, as exemplified by Blockchain, provide a new framework for supporting business collaborations that is based on having a high-reliability, shared, trusted, privacy-preserving, nonrepudiable data repository that includes programmable logic in the form of “smart contracts”. The framework has the potential to dramatically transform business collaboration across numerous industry sectors, including finance, supply chain, food production, pharmaceuticals, and healthcare. Widespread adoption of this technology will be accelerated by the development of business-level languages for specifying smart contracts. This paper proposes that data-aware business processes, and in particular the Business Artifact paradigm, can provide a robust basis for a shared ledger Business Collaboration Language (BCL). The fundamental rationale for adopting data-aware processes is that shared ledgers focus on both data and process in equal measure. The paper examines potential advantages of the artifact-based approach from two perspectives: conceptual modeling, and opportunities for formal reasoning (verification). Broad research challenges for the development, understanding, and usage of a shared ledger BCL are highlighted.


title: Secure visual secret sharing scheme for color images using visual cryptography and digital watermarking

abstract: In today's world of digital communication, as technology progresses, there is more and more attention required on image security. Many visual cryptography algorithms have been suggested and digital watermarking in association with visual cryptography is also proposed for more image security. An image watermarking model based on progressive visual cryptography is proposed to decide optimal number of shares. A study on implementation of meaningful shares in combination with visual cryptography scheme for secret images is carried out for implementation of algorithm. A visual cryptography techniques are used to create meaningful shares. In this paper analysis of different algorithms are performed which generates meaningful shares. These shares are watermarked with cover images. After transmission of these watermarked images to receiving end, the receiving end will extract the shares from watermarked images and stacking of these extracted meaningful shares will generate the original secret image. Combination of Digital watermarking and visual cryptography adds enhanced security to secret images.


title: Modelling the control strategies against dengue in Singapore.

abstract: Notified cases of dengue infections in Singapore reached historical highs in 2004 (9459 cases) and 2005 (13,817 cases) and the reason for such an increase is still to be established. We apply a mathematical model for dengue infection that takes into account the seasonal variation in incidence, characteristic of dengue fever, and which mimics the 2004-2005 epidemics in Singapore. We simulated a set of possible control strategies and confirmed the intuitive belief that killing adult mosquitoes is the most effective strategy to control an ongoing epidemic. On the other hand, the control of immature forms was very efficient in preventing the resurgence of dengue epidemics. Since the control of immature forms allows the reduction of adulticide, it seems that the best strategy is to combine both adulticide and larvicide control measures during an outbreak, followed by the maintenance of larvicide methods after the epidemic has subsided. In addition, the model showed that the mixed strategy of adulticide and larvicide methods introduced by the government seems to be very effective in reducing the number of cases in the first weeks after the start of control.


Assessing the Usability of a Wearable Computer System For Outdoor Pedestrian Navigation


title: Artificial Immune Clonal Selection Classification Algorithms for Classifying Malware and Benign Processes Using API Call Sequences

abstract: Machine learning is an important field of artificial intelligence in which models are generated by extracting rules and functions from large datasets. Machine learning includes a diversity of methods and algorithms such as decision trees, lazy learning, knearest neighbors, Bayesian methods, Gaussian processes, artificial neural networks, support vector machines, kernel algorithms, and artificial immune systems (AIS). AIS are computation tools that emulate processes and mechanisms of the biological immune system. AIS use the learning, memory, and optimization capabilities of the immune system to develop computational algorithms for function optimization, pattern recognition, novelty detection, and process control, and classification. There are four main sub fields of research that have emerged in AIS cantered on prominent immunological theories; negative selection algorithms, immune network algorithms, danger theory algorithms, and clonal selection algorithms. In this paper, we will analyze API call sequence of a process to classify it as benign or malicious. We have collected API call traces of real malware and benign processes running on Windows operating system. We will employ eight commonly used clonal selection algorithms: AIRS1, AIRS2, AIRS2 Parallel, CLONALG, CSCA, IMMUNOS-1, IMMUNOS -81, and IMMUNOS -99. We evaluate the accuracy of these algorithms for classifying between malware and benign processes using API call sequences.


title: Face Detection and Recognition in Color Images under Matlab

abstract: In this paper we describe our implementation of algorithms for face detection and recognition in color images under Matlab. For face detection, we trained a feedforward neural network to perform skin segmentation, followed by the eyes detection, face alignment, lips detection and face delimitation. The eyes were detected by analyzing the chrominance and the angle between neighboring pixels and, then, the results were used to perform face alignment. The lips were detected based on the analysis of the Red color component intensity in the lower face region. Finally, the faces were delimited using the eyes and lips positions. The face recognition involved a classifier that used the standard deviation of the difference between color matrices of the faces to identify the input face. The algorithms were run on Faces 1999 dataset. The proposed method achieved 96.9%, 89% and 94% correct detection rate of face, eyes and lips, respectively. The correctness rate of the face recognition algorithm was 70.7%.


title: Social learning, sexual and physical abuse, and adult crime

abstract: This research examines the relationship between childhood physical and sexual abuse and the types of crimes committed by male adult offenders. We use the method of discriminant prediction to determine whether independent and dependent variables are related in ways that theories predict. Our analyses of data from the Survey of Inmates in State and Federal Correctional Facilities suggest that offenders model specific behaviors to which they have been exposed. Male offenders who were sexually abused as a child are more likely to commit sexual offenses, particularly sexual offenses against children, than nonsexual offenses. Offenders who were physically abused are more likely to engage in violent offenses than nonviolent offenses. Further analyses show that sexual offenders, and to a lesser extent violent offenders, are likely to specialize in those offenses. Our results are consistent with a social learning approach. They address a heretofore neglected issue: what exactly do children model when they are mistreated. Language: en


title: SURE: A Modeling and Simulation Integration Platform for Evaluation of Secure and Resilient Cyber–Physical Systems

abstract: The exponential growth of information and communication technologies have caused a profound shift in the way humans engineer systems leading to the emergence of closed-loop systems involving strong integration and coordination of physical and cyber components, often referred to as cyber–physical systems (CPSs). Because of these disruptive changes, physical systems can now be attacked through cyberspace and cyberspace can be attacked through physical means. The paper considers security and resilience as system properties emerging from the intersection of system dynamics and the computing architecture. A modeling and simulation integration platform for experimentation and evaluation of resilient CPSs is presented using smart transportation systems as the application domain. Evaluation of resilience is based on attacker–defender games using simulations of sufficient fidelity. The platform integrates 1) realistic models of cyber and physical components and their interactions; 2) cyber attack models that focus on the impact of attacks to CPS behavior and operation; and 3) operational scenarios that can be used for evaluation of cybersecurity risks. Three case studies are presented to demonstrate the advantages of the platform: 1) vulnerability analysis of transportation networks to traffic signal tampering; 2) resilient sensor selection for forecasting traffic flow; and 3) resilient traffic signal control in the presence of denial-of-service attacks.


title: Using ArchiMate and TOGAF to Understand the Enterprise Architecture and ITIL Relationship

abstract: Business/IT alignment has become one of the most relevant concerns on organizations. Enterprise Architecture (EA) and ITIL, two distinct governance approaches with different perspectives, have become recently dominant between practitioners. However, parallel EA and ITIL projects can lead to wasted resources and a duplication of costs and efforts. In this paper we propose an EA and ITIL integration using ArchiMate as a common frame of reference. We also want to point out that implementing ITIL is like implementing any other architecture change and demonstrate it by using TOGAF to perform an ITIL implementation on ArchiSurance, a fictitious organization from the well known ArchiMate case study.


title: Self-Delimiting Neural Networks

abstract: Self-delimiting (SLIM) programs are a central concept of theoretical computer science, particularly algorithmic information & probability theory, and asymptotically optimal program search (AOPS). To apply AOPS to (possibly recurrent) neural networks (NNs), I introduce SLIM NNs. Neurons of a typical SLIM NN have threshold activation functions. During a computational episode, activations are spreading from input neurons through the SLIM NN until the computation activates a special halt neuron. Weights of the NN's used connections define its program. Halting programs form a prefix code. The reset of the initial NN state does not cost more than the latest program execution. Since prefixes of SLIM programs influence their suffixes (weight changes occurring early in an episode influence which weights are considered later), SLIM NN learning algorithms (LAs) should execute weight changes online during activation spreading. This can be achieved by applying AOPS to growing SLIM NNs. To efficiently teach a SLIM NN to solve many tasks, such as correctly classifying many different patterns, or solving many different robot control tasks, each connection keeps a list of tasks it is used for. The lists may be efficiently updated during training. To evaluate the overall effect of currently tested weight changes, a SLIM NN LA needs to re-test performance only on the efficiently computable union of tasks potentially affected by the current weight changes. Future SLIM NNs will be implemented on 3-dimensional brain-like multi-processor hardware. Their LAs will minimize task-specific total wire length of used connections, to encourage efficient solutions of subtasks by subsets of neurons that are physically close. The novel class of SLIM NN LAs is currently being probed in ongoing experiments to be reported in separate papers.