title: Towards Sub-Word Level Compositions for Sentiment Analysis of Hindi-English Code Mixed Text

abstract: Sentiment analysis (SA) using code-mixed data from social media has several applications in opinion mining ranging from customer satisfaction to social campaign analysis in multilingual societies. Advances in this area are impeded by the lack of a suitable annotated dataset. We introduce a Hindi-English (Hi-En) code-mixed dataset for sentiment analysis and perform empirical analysis comparing the suitability and performance of various state-of-the-art SA methods in social media. ::: In this paper, we introduce learning sub-word level representations in LSTM (Subword-LSTM) architecture instead of character-level or word-level representations. This linguistic prior in our architecture enables us to learn the information about sentiment value of important morphemes. This also seems to work well in highly noisy text containing misspellings as shown in our experiments which is demonstrated in morpheme-level feature maps learned by our model. Also, we hypothesize that encoding this linguistic prior in the Subword-LSTM architecture leads to the superior performance. Our system attains accuracy 4-5% greater than traditional approaches on our dataset, and also outperforms the available system for sentiment analysis in Hi-En code-mixed text by 18%.


title: Facial expression recognition based on transfer learning from deep convolutional networks

abstract: It is well-known that deep models could extract robust and abstract features. We propose a efficient facial expression recognition model based on transfer features from deep convolutional networks (ConvNets). We train the deep ConvNets through the task of 1580-class face identification on the MSRA-CFW database and transfer high-level features from the trained deep model to recognize expression. To train and test the facial expression recognition model on a large scope, we built a facial expression database of seven basic emotion states and 2062 imbalanced samples depending on four facial expression databases (CK+, JAFFE, KDEF, Pain expressions form PICS). Compared with 50.65% recognition rate based on Gabor features with the seven-class SVM and 78.84% recognition rate based on distance features with the seven-class SVM, we achieve average 80.49% recognition rate with the seven-class SVM classifier on the self-built facial expression database. Considering occluded face in reality, we test our model in the occluded condition and demonstrate the model could keep its ability of classification in the small occlusion case. To increase the ability further, we improve the facial expression recognition model. The modified model merges high-level features transferred from two trained deep ConvNets of the same structure and the different training sets. The modified model obviously improves its ability of classification in the occluded condition and achieves average 81.50% accuracy on the self-built facial expression database.


title: Using Blockchain and smart contracts for secure data provenance management

abstract: Blockchain technology has evolved from being an immutable ledger of transactions for cryptocurrencies to a programmable interactive the environment for building distributed reliable applications. Although, blockchain technology has been used to address various challenges, to our knowledge none of the previous work focused on using blockchain to develop a secure and immutable scientific data provenance management framework that automatically verifies the provenance records. In this work, we leverage blockchain as a platform to facilitate trustworthy data provenance collection, verification, and management. The developed system utilizes smart contracts and open provenance model (OPM) to record immutable data trails. We show that our proposed framework can efficiently and securely capture and validate provenance data, and prevent any malicious modification to the captured data as long as the majority of the participants are honest.


title: Automating Cloud Services Life Cycle through Semantic Technologies

abstract: Managing virtualized services efficiently over the cloud is an open challenge. Traditional models of software development are not appropriate for the cloud computing domain, where software (and other) services are acquired on demand. In this paper, we describe a new integrated methodology for the life cycle of IT services delivered on the cloud and demonstrate how it can be used to represent and reason about services and service requirements and so automate service acquisition and consumption from the cloud. We have divided the IT service life cycle into five phases of requirements, discovery, negotiation, composition, and consumption. We detail each phase and describe the ontologies that we have developed to represent the concepts and relationships for each phase. To show how this life cycle can automate the usage of cloud services, we describe a cloud storage prototype that we have developed. This methodology complements previous work on ontologies for service descriptions in that it is focused on supporting negotiation for the particulars of a service and going beyond simple matchmaking.


title: On Budgeted Influence Maximization in Social Networks

abstract: Given a fixed budget and an arbitrary cost for selecting each node, the budgeted influence maximization (BIM) problem concerns selecting a set of seed nodes to disseminate some information that maximizes the total number of nodes influenced (termed as influence spread) in social networks at a total cost no more than the budget. Our proposed seed selection algorithm for the BIM problem guarantees an approximation ratio of (1-1/√e). The seed selection algorithm needs to calculate the influence spread of candidate seed sets, which is known to be #P-complex. Identifying the linkage between the computation of marginal probabilities in Bayesian networks and the influence spread, we devise efficient heuristic algorithms for the latter problem. Experiments using both large-scale social networks and synthetically generated networks demonstrate superior performance of the proposed algorithm with moderate computation costs. Moreover, synthetic datasets allow us to vary the network parameters and gain important insights on the impact of graph structures on the performance of different algorithms.


title: Cloudlet Softwarization for Pervasive Healthcare

abstract: Patient monitoring is emerging as an important application of embedded sensors network. Many wireless sensors are implanted in/on the patient body. These tiny wireless sensors make Wireless Body Area Networks (WBANs). WBANs can observe physiological conditions of patient under supervision, and can provide us real-time feedback. Through WBAN a patient is constantly monitored, and in case of some critical situation an immediate action should be required. In today's world most of the healthcare organizations need to modernize their IT infrastructure so that they can be able to provide safer, faster and more efficient healthcare delivery. This paper presents a new architecture based on cloudlet and software defined networking to smooth out devices and access networks heterogeneities and enable fast access for data collection and analysis through the cloud environments.


6-DOF Monocular Relocalization with Convolutional Neural Networks


title: Laryngeal Muscle Activity and Vocal Fold Adduction During Chest, Chestmix, Headmix, and Head Registers in Females

abstract: Summary Hypothesis Commercial singers produce chestmix register by maintaining or increasing adduction of the vocal processes (VPs) and by engaging the thyroarytenoid (TA) muscle to a greater degree than they would to produce head register. Study Design Prospective cohort study. Methods Simultaneous recordings of TA and cricothyroid (CT) muscle activity, videonasendoscopy, and audio were obtained from seven female singers during production of a variety of midrange pitches in chest, chestmix, headmix, and head registers. Fast Fourier transforms were performed to measure the energy in the fundamental frequency and in mid and upper frequency harmonics to determine if the productions that were judged as perceptually distinct registers also showed distinctive acoustic characteristics. Then, measures of TA and CT muscle activity and vocal fold adduction ratings were obtained to determine how these varied as a function of pitch and register. Results Spectral tilt increased as subjects shifted from chest to chestmix to headmix and finally into head register. For same pitch phonation, subjects increased TA muscle activity and vocal fold adduction as they shifted register from head to headmix to chestmix to chest, particularly during production of higher frequencies. CT activity appeared to be more related to pitch rather than register control. Conclusion Nonclassically trained singers were able to produce pitches at the high end of the midrange in chestmix register by increasing TA muscle activity and adduction of the VPs.


title: Detection of Repackaged Android Malware with Code-Heterogeneity Features

abstract: During repackaging, malware writers statically inject malcode and modify the control flow to ensure its execution. Repackaged malware is difficult to detect by existing classification techniques, partly because of their behavioral similarities to benign apps. By exploring the app's internal different behaviors, we propose a new Android repackaged malware detection technique based on code heterogeneity analysis. Our solution strategically partitions the code structure of an app into multiple dependence-based regions (subsets of the code). Each region is independently classified on its behavioral features. We point out the security challenges and design choices for partitioning code structures at the class and method level graphs, and present a solution based on multiple dependence relations. We have performed experimental evaluation with over 7,542 Android apps. For repackaged malware, our partition-based detection reduces false negatives (i.e., missed detection) by 30-fold, when compared to the non-partition-based approach. Overall, our approach achieves a false negative rate of 0.35 percent and a false positive rate of 2.97 percent.


title: THink: inferring cognitive status from subtle behaviors

abstract: The Digital Clock Drawing Test is a fielded application that provides a major advance over existing neuropsychological testing technology. It captures and analyzes high precision information about both outcome and process, opening up the possibility of detecting subtle cognitive impairment even when test results appear superficially normal. We describe the design and development of the test, document the role of AI in its capabilities, and report on its use over the past seven years. We outline its potential implications for earlier detection and treatment of neurological disorders. We also set the work in the larger context of the THink project, which is exploring multiple approaches to determining cognitive status through the detection and analysis of subtle behaviors.


title: Spatial and spatio-temporal models with R-INLA.

abstract: During the last three decades, Bayesian methods have developed greatly in the field of epidemiology. Their main challenge focusses around computation, but the advent of Markov Chain Monte Carlo methods (MCMC) and in particular of the WinBUGS software has opened the doors of Bayesian modelling to the wide research community. However model complexity and database dimension still remain a constraint. Recently the use of Gaussian random fields has become increasingly popular in epidemiology as very often epidemiological data are characterised by a spatial and/or temporal structure which needs to be taken into account in the inferential process. The Integrated Nested Laplace Approximation (INLA) approach has been developed as a computationally efficient alternative to MCMC and the availability of an R package (R-INLA) allows researchers to easily apply this method. In this paper we review the INLA approach and present some applications on spatial and spatio-temporal data.


title: Contraction moves for geometric model fitting

abstract: This paper presents a new class of moves, called α-expansion-contraction, which generalizes α-expansion graph cuts for multi-label energy minimization problems. The new moves are particularly useful for optimizing the assignments in model fitting frameworks whose energies include Label Cost (LC), as well as Markov Random Field (MRF) terms. These problems benefit from the contraction moves' greater scope for removing instances from the model, reducing label costs. We demonstrate this effect on the problem of fitting sets of geometric primitives to point cloud data, including real-world point clouds containing millions of points, obtained by multi-view reconstruction.


title: A graphical user interface toolkit approach to thin-client computing

abstract: Network and server-centric computing paradigms are quickly returning to being the dominant methods by which we use computers. Web applications are so prevalent that the role of a PC today has been largely reduced to a terminal for running a client or viewer such as a Web browser. Implementers of network-centric applications typically rely on the limited capabilities of HTML, employing proprietary "plug ins" or transmitting the binary image of an entire application that will be executed on the client. Alternatively, implementers can develop without regard for remote use, requiring users who wish to run such applications on a remote server to rely on a system that creates a virtual frame buffer on the server, and transmits a copy of its raster image to the local client.We review some of the problems that these current approaches pose, and show how they can be solved by developing a distributed user interface toolkit. A distributed user interface toolkit applies techniques to the high level components of a toolkit that are similar to those used at a low level in the X Window System. As an example of this approach, we present RemoteJFC, a working distributed user interface toolkit that makes it possible to develop thin-client applications using a distributed version of the Java Foundation Classes.


title: Understanding player perceptions of RegnaTales, a mobile game for teaching social problem solving skills

abstract: Research on the use of serious games to support child and adolescent mental health interventions is in its early stages. Work is needed to provide evidence of the applicability and effectiveness of using such games in teaching children skills needed to overcome their behavioral and emotional problems. The present study adds to the knowledge in this area through the development and evaluation of RegnaTales, a mobile game for teaching social problem solving skills among children. The study examined the playability and usability of the mobile game among 12 children (mean age = 9.58; SD = 1.78) from international schools in Basel. Results showed that 76% of participants found the game fun and 58% would play it again. Our findings further highlight the potential of serious games in teaching skills needed to address anger feelings and provide support for its use in child and adolescent mental health interventions.


title: Human heart disease prediction system using data mining techniques

abstract: Nowadays, health disease are increasing day by day due to life style, hereditary. Especially, heart disease has become more common these days, i.e. life of people is at risk. Each individual has different values for Blood pressure, cholesterol and pulse rate. But according to medically proven results the normal values of Blood pressure is 120/90, cholesterol is and pulse rate is 72. This paper gives the survey about different classification techniques used for predicting the risk level of each person based on age, gender, Blood pressure, cholesterol, pulse rate. The patient risk level is classified using datamining classification techniques such as Naive Bayes, KNN, Decision Tree Algorithm, Neural Network. etc., Accuracy of the risk level is high when using more number of attributes.


title: An improved λ-linear genetic programming evaluated in solving the Santa Fe ant trail problem

abstract: We propose in this paper a new approach called λ-LGP (λ-Linear Genetic Programming), a variation of the well-know LGP (Linear Genetic Programming) algorithm. Starting with an LGP based only on effective macro and micromutations, the λ-LGP proposed in this work consists in extending the way in which the individuals are chosen for reproduction. In this model, a constant number (λ) of a particular kind of mutation is applied to each individual, thus exploring its neighbouring fitness regions, and might be replaced by one of its children according to different criteria. Several configurations were tested in the benchmark problem known as Santa Fe Ant Trail. Results obtained show a very significant improvement by using this proposed variation. For the Ant Trail problem, λ-LGP outperformed not only LGP, but also several state-of-the-art methods.


title: Education for Sustainable Development: Enhancing Climate Change Adaptation Expertise in Developing Countries

abstract: This article presents the results of an innovative education capacity assessment and delivery project to promote sustainable development in large ocean states in the Western Indian Ocean (WIO) region. Science education can help prepare the present and coming generations for stability in an uncertain future. Limited financial, geographical and knowledge-based resources make large ocean states particularly vulnerable to future uncertainties, such as those associated with climate change. With island populations already feeling the impacts of a changing climate, improved adaptive capacity and disaster risk reduction have become increasingly essential. Thus, climate change adaptation forms the basis for a stakeholder-designed curriculum to meet the capacity-building needs of stakeholders. Partnerships between external experts and local stakeholders were formed to build upon human resource capacity and maximize delivery through a programme of â€˜train-the-trainerâ€™ activities. In this way, the mentoring of local stakeholders to undertake their own training can advance sustainable development goals beyond traditional non-collaborative educational approaches. This article shows how this aim can be accomplished through the design and delivery of a phased, coherent professional development programme.


title: Effective Online Knowledge Graph Fusion

abstract: Recently, Web search engines have empowered their search with knowledge graphs to satisfy increasing demands of complex information needs about entities. Each engine offers an online knowledge graph service to display highly relevant information about the query entity in form of a structured summary called knowledge card. The cards from different engines might be complementary. Therefore, it is necessary to fuse knowledge cards from these engines to get a comprehensive view. Such a problem can be considered as a new branch of ontology alignment, which is actually an on-the-fly online data fusion based on the users' needs. In this paper, we present the first effort to work on knowledge cards fusion. We propose a novel probabilistic scoring algorithm for card disambiguation to select the most likely entity a card should refer to. We then design a learning-based method to align properties from cards representing the same entity. Finally, we perform value deduplication to group equivalent values of the aligned properties as value clusters. The experimental results show that our approach outperforms the state of the art ontology alignment algorithms in terms of precision and recall.


title: Switchable Temporal Propagation Network

abstract: Videos contain highly redundant information between frames. Such redundancy has been extensively studied in video compression and encoding, but is less explored for more advanced video processing. In this paper, we propose a learnable unified framework for propagating a variety of visual properties of video images, including but not limited to color, high dynamic range (HDR), and segmentation information, where the properties are available for only a few key-frames. Our approach is based on a temporal propagation network (TPN), which models the transition-related affinity between a pair of frames in a purely data-driven manner. We theoretically prove two essential factors for TPN: (a) by regularizing the global transformation matrix as orthogonal, the"style energy"of the property can be well preserved during propagation; (b) such regularization can be achieved by the proposed switchable TPN with bi-directional training on pairs of frames. We apply the switchable TPN to three tasks: colorizing a gray-scale video based on a few color key-frames, generating an HDR video from a low dynamic range (LDR) video and a few HDR frames, and propagating a segmentation mask from the first frame in videos. Experimental results show that our approach is significantly more accurate and efficient than the state-of-the-art methods.


title: PatchMatch Based Joint View Selection and Depthmap Estimation

abstract: We propose a multi-view depthmap estimation approach aimed at adaptively ascertaining the pixel level data associations between a reference image and all the elements of a source image set. Namely, we address the question, what aggregation subset of the source image set should we use to estimate the depth of a particular pixel in the reference image? We pose the problem within a probabilistic framework that jointly models pixel-level view selection and depthmap estimation given the local pairwise image photoconsistency. The corresponding graphical model is solved by EM-based view selection probability inference and PatchMatch-like depth sampling and propagation. Experimental results on standard multi-view benchmarks convey the state-of-the art estimation accuracy afforded by mitigating spurious pixel level data associations. Additionally, experiments on large Internet crowd sourced data demonstrate the robustness of our approach against unstructured and heterogeneous image capture characteristics. Moreover, the linear computational and storage requirements of our formulation, as well as its inherent parallelism, enables an efficient and scalable GPU-based implementation.


title: Fast Kernel Classifiers With Online And Active Learning

abstract: Very high dimensional learning systems become theoretically possible when training examples are abundant. The computing cost then becomes the limiting factor. Any efficient learning algorithm should at least take a brief look at each example. But should all examples be given equal attention?This contribution proposes an empirical answer. We first present an online SVM algorithm based on this premise. LASVM yields competitive misclassification rates after a single pass over the training examples, outspeeding state-of-the-art SVM solvers. Then we show how active example selection can yield faster training, higher accuracies, and simpler models, using only a fraction of the training example labels.


title: Bridging the semantic gap in sports video retrieval and summarization

abstract: One of the major challenges facing current media management systems and related applications is the so-called ''semantic gap'' between the rich meaning that a user desires and the shallowness of the content descriptions that are automatically extracted from the media. In this paper, we address the problem of bridging this gap in the sports domain. We propose a general framework for indexing and summarizing sports broadcast programs, with a high-level model of sports broadcast video using the concept of an event, defined according to domain-specific knowledge for different types of sports. Within this general framework, we develop automatic event detection algorithms that are based on automatic analysis of the visual and aural signals in the media. We have successfully applied the event detection algorithms to different types of sports including American football, baseball, Japanese sumo wrestling, and soccer. Event modeling and detection contribute to the reduction of the semantic gap by providing rudimentary semantic information obtained through media analysis. We further propose a novel approach, which makes use of independently generated rich textual metadata, to fill the gap completely through synchronization of the information-laden textual data with the basic event segments. We implemented an MPEG-7 compliant browsing system for semantic retrieval and summarization of sports video using the proposed algorithms.


title: Delta parallel robot workspace and dynamic trajectory tracking of delta parallel robot

abstract: In a design process of a Delta or parallel robot, link parameters and actuators size must be selected to maximize appropriate workspace and payload capability. To derive a workspace of the Delta robot, the Cartesian reachable position of the Delta-robot end effector can be efficiently identified using an inverse kinematics. By varying upper-arm and fore-arm lengths, a maximize workspace for a desired reachable work area could be obtained. Furthermore, a trajectory tracking of the Delta robot for pick-and-place operation can be analyzed in terms of load-carrying ability using simplified dynamic equations such that proper actuators and gears could be correctly chosen in the design phase before constructing the Delta robot.


Outlier (Anomaly) Detection Modelling in PMML


title: The Bayesian Structural EM Algorithm

abstract: In recent years there has been a flurry of works on learning Bayesian networks from data. One of the hard problems in this area is how to effectively learn the structure of a belief network from incomplete data--that is, in the presence of missing values or hidden variables. In a recent paper, I introduced an algorithm called Structural EM that combines the standard Expectation Maximization (EM) algorithm, which optimizes parameters, with structure search for model selection. That algorithm learns networks based on penalized likelihood scores, which include the BIC/MDL score and various approximations to the Bayesian score. In this paper, I extend Structural EM to deal directly with Bayesian model selection. I prove the convergence of the resulting algorithm and show how to apply it for learning a large class of probabilistic models, including Bayesian networks and some variants thereof.


title: Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization

abstract: This paper studies the Tensor Robust Principal Component (TRPCA) problem which extends the known Robust PCA \cite{RPCA} to the tensor case. Our model is based on a new tensor Singular Value Decomposition (t-SVD) \cite{kilmer2011factorization} and its induced tensor tubal rank and tensor nuclear norm. Consider that we have a 3-way tensor $\bm{\mathcal{X}}\in\mathbb{R}^{n_1\times n_2\times n_3}$ such that $\bm{\mathcal{X}}=\bm{\mathcal{L}}_0+\bm{\mathcal{S}}_0$, where $\bm{\mathcal{L}}_0$ has low tubal rank and $\bm{\mathcal{S}}_0$ is sparse. Is that possible to recover both components? In this work, we prove that under certain suitable assumptions, we can recover both the low-rank and the sparse components exactly by simply solving a convex program whose objective is a weighted combination of the tensor nuclear norm and the $\ell_1$-norm, i.e., \begin{align*} \min_{\bm{\mathcal{L}},\bm{\mathcal{E}}} \ \|{\bm{\mathcal{L}}}\|_*+\lambda\|{\bm{\mathcal{E}}}\|_1, \ \text{s.t.} \ \bm{\mathcal{X}}=\bm{\mathcal{L}}+\bm{\mathcal{E}}, \end{align*} where $\lambda= {1}/{\sqrt{\max(n_1,n_2)n_3}}$. Interestingly, TRPCA involves RPCA as a special case when $n_3=1$ and thus it is a simple and elegant tensor extension of RPCA. Also numerical experiments verify our theory and the application for the image denoising demonstrates the effectiveness of our method.


title: Acquiring Axially-Symmetric Transparent Objects Using Single-View Transmission Imaging

abstract: We propose a novel, practical solution for high quality reconstruction of axially-symmetric transparent objects. While a special case, such transparent objects are ubiquitous in the real world. Common examples of these are glasses, goblets, tumblers, carafes, etc., that can have very unique and visually appealing forms making their reconstruction interesting for vision and graphics applications. Our acquisition setup involves imaging such objects from a single viewpoint while illuminating them from directly behind with a few patterns emitted by an LCD panel. Our reconstruction step is then based on optimization of the objects geometry and its refractive index to minimize the difference between observed and simulated transmission/refraction of rays passing through the object. We exploit the objects axial symmetry as a strong shape prior which allows us to achieve robust reconstruction from a single viewpoint using a simple, commodity acquisition setup. We demonstrate high quality reconstruction of several common rotationally symmetric as well as more complex n-fold symmetric transparent objects with our approach.


title: The Probabilistic Model Checking Landscape

abstract: Randomization is a key element in sequential and distributed computing. Reasoning about randomized algorithms is highly non-trivial. In the 1980s, this initiated first proof methods, logics, and model-checking algorithms. The field of probabilistic verification has developed considerably since then. This paper surveys the algorithmic verification of probabilistic models, in particular probabilistic model checking. We provide an informal account of the main models, the underlying algorithms, applications from reliability and dependability analysis---and beyond---and describe recent developments towards automated parameter synthesis.


title: Comparison and Evaluation of Segmentation Techniques for Subcortical Structures in Brain MRI

abstract: The automation of segmentation of medical images is an active research area. However, there has been criticism of the standard of evaluation of methods. We have comprehensively evaluated four novel methods of automatically segmenting subcortical structures using volumetric, spatial overlap and distance-based measures. Two of the methods are atlas-based --- classifier fusion and labelling (CFL) and expectation-maximisation segmentation using a dynamic brain atlas (EMS), and two model-based --- profile active appearance models (PAM) and Bayesian appearance models (BAM). Each method was applied to the segmentation of 18 subcortical structures in 270 subjects from a diverse pool varying in age, disease, sex and image acquisition parameters. Our results showed that all four methods perform on par with recently published methods. CFL performed significantly better than the other three methods according to all three classes of metrics.


title: Semi-supervised Spectral Clustering for Image Set Classification

abstract: We present an image set classification algorithm based on unsupervised clustering of labeled training and unlabeled test data where labels are only used in the stopping criterion. The probability distribution of each class over the set of clusters is used to define a true set based similarity measure. To this end, we propose an iterative sparse spectral clustering algorithm. In each iteration, a proximity matrix is efficiently recomputed to better represent the local subspace structure. Initial clusters capture the global data structure and finer clusters at the later stages capture the subtle class differences not visible at the global scale. Image sets are compactly represented with multiple Grassmannian manifolds which are subsequently embedded in Euclidean space with the proposed spectral clustering algorithm. We also propose an efficient eigenvector solver which not only reduces the computational cost of spectral clustering by many folds but also improves the clustering quality and final classification results. Experiments on five standard datasets and comparison with seven existing techniques show the efficacy of our algorithm.


title: The biology of human psychosexual differentiation

abstract: Abstract Most attempts to identify biological underpinnings of gender identity and sexual orientation in humans have investigated effects of sex steroids, so pivotal in the differentiation of the genitalia, showing strong parallels between animals and the human. The information on humans is derived from the so-called ‘experiments of nature’, clinical entities with a lesser-than-normal androgen exposure in XY subjects and a higher than normal androgen exposure in XX subjects. Prenatal androgenization appears to predispose to a male gender identity development, but apparently not decisively since 40–50% of 46,XY intersexed children with a history of prenatal androgen exposure do not develop a male gender identity. Obviously, male-to-female transsexuals, with a normal androgen exposure prenatally (there is no serious evidence to the contrary) develop a female gender identity, through unknown biological mechanisms apparently overriding the effects of prenatal androgens. The latest studies in 46, XX subjects exposed to prenatal androgens show that prenatal androgenization of 46,XX fetuses leads to marked masculinization of later gender-related behavior but does not lead to gender confusion/dysphoria. The example of female-to-male transsexuals, without evidence of prenatal androgen exposure, indicates that a male gender identity can develop without a significant androgen stimulus. So we are far away from any comprehensive understanding of hormonal imprinting on gender identity formation. Brain studies in homosexuals have not held up in replication studies or are in need of replication in transsexuals. Genetic studies and the fraternal birth order hypothesis provide indications of familial clustering of homosexuality but in many homosexuals these genetic patterns cannot be identified. The biological explanations advanced for the birth order hypothesis lack any experimental support.


title: Simplified Successive-Cancellation List decoding of polar codes

abstract: The Successive-Cancellation List (SCL) decoding algorithm is one of the most promising approaches towards practical polar code decoding. It is able to provide a good trade-off between error-correction performance and complexity, tunable through the size of the list. In this paper, we show that in the conventional formulation of SCL, there are redundant calculations which do not need to be performed in the course of the algorithm. We simplify SCL by removing these redundant calculations and prove that the proposed simplified SCL and the conventional SCL algorithms are equivalent. The simplified SCL algorithm is valid for any code and can reduce the time-complexity of SCL without affecting the space complexity.


title: A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species

abstract: Advances in next generation technologies have driven the costs of DNA sequencing down to the point that genotyping-by-sequencing (GBS) is now feasible for high diversity, large genome species. Here, we report a procedure for constructing GBS libraries based on reducing genome complexity with restriction enzymes (REs). This approach is simple, quick, extremely specific, highly reproducible, and may reach important regions of the genome that are inaccessible to sequence capture approaches. By using methylation-sensitive REs, repetitive regions of genomes can be avoided and lower copy regions targeted with two to three fold higher efficiency. This tremendously simplifies computationally challenging alignment problems in species with high levels of genetic diversity. The GBS procedure is demonstrated with maize (IBM) and barley (Oregon Wolfe Barley) recombinant inbred populations where roughly 200,000 and 25,000 sequence tags were mapped, respectively. An advantage in species like barley that lack a complete genome sequence is that a reference map need only be developed around the restriction sites, and this can be done in the process of sample genotyping. In such cases, the consensus of the read clusters across the sequence tagged sites becomes the reference. Alternatively, for kinship analyses in the absence of a reference genome, the sequence tags can simply be treated as dominant markers. Future application of GBS to breeding, conservation, and global species and population surveys may allow plant breeders to conduct genomic selection on a novel germplasm or species without first having to develop any prior molecular tools, or conservation biologists to determine population structure without prior knowledge of the genome or diversity in the species.


title: Towards a Model-Driven-Architecture Process for Smart Grid Projects

abstract: The complexity in electrical power systems is continuously increasing due to its advancing distribution. This affects the topology of the grid infrastructure as well as the IT-infrastructure, leading to various heterogeneous systems, data models, protocols, and interfaces. This in turn raises the need for appropriate processes and tools that facilitate the management of the systems architecture on different levels and from different stakeholders’ view points. In order to achieve this goal, a common understanding of architecture elements and means of classification shall be gained. The Smart Grid Architecture Model (SGAM) proposed in context of the European standardization mandate M/490 provides a promising basis for domainspecific architecture models. The idea of following a Model-Driven-Architecture (MDA)-approach to create such models, including requirements specification based on Smart Grid use cases, is detailed in this contribution. The SGAM-Toolbox is introduced as tool-support for the approach and its utility is demonstrated by two real-world case studies.


title: Oligopoly Supergames: Some Empirical Evidence on Prices and Margins

abstract: This paper is a panel data study on the behavior of prices and margins of oligopolies involved in repeated games. The authors examine two supergame models which generate very different predictions about the cyclical behavior of prices and margins. Evidence on the levels of price-cost margins indicates that oligopolies achieve equilibria that more closely resemble a one-shot Cournot-Nash outcome than monopoly. The authors find, however, that industries with "high" price-cost margins exhibit cyclical price behavior which is quite different from that of unconcentrated industries. Little evidence of price wars during either recessions or booms is found. Copyright 1987 by Blackwell Publishing Ltd.


title: Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning

abstract: Transferring the knowledge learned from large scale datasets (e.g., ImageNet) via fine-tuning offers an effective solution for domain-specific fine-grained visual categorization (FGVC) tasks (e.g., recognizing bird species or car make and model). In such scenarios, data annotation often calls for specialized domain knowledge and thus is difficult to scale. In this work, we first tackle a problem in large scale FGVC. Our method won first place in iNaturalist 2017 large scale species classification challenge. Central to the success of our approach is a training scheme that uses higher image resolution and deals with the long-tailed distribution of training data. Next, we study transfer learning via fine-tuning from large scale datasets to small scale, domain-specific FGVC datasets. We propose a measure to estimate domain similarity via Earth Mover's Distance and demonstrate that transfer learning benefits from pre-training on a source domain that is similar to the target domain by this measure. Our proposed transfer learning outperforms ImageNet pre-training and obtains state-of-the-art results on multiple commonly used FGVC datasets.


title: Diffraction field of a low frequency vibrator in soft tissues using transient elastography

abstract: For the last 10 years, interest has grown in low frequency shear waves that propagate in the human body. However, the generation of shear waves by acoustic vibrators is a relatively complex problem, and the directivity patterns of shear waves produced by the usual vibrators are more complicated than those obtained for longitudinal ultrasonic transducers. To extract shear modulus parameters from the shear wave propagation in soft tissues, it is important to understand and to optimize the directivity pattern of shear wave vibrators. This paper is devoted to a careful study of the theoretical and the experimental directivity pattern produced by a point source in soft tissues. Both theoretical and experimental measurements show that the directivity pattern of a point source vibrator presents two very strong lobes for an angle around 35/spl deg/. This paper also points out the impact of the near field in the problem of shear wave generation.


title: An autonomous scale ship model for towing tank testing

abstract: This paper presents the work done for developing a self-propelled scale ship model for towing tank testing, with the main characteristic of not having any material link to a towing device to carry out the tests. This model has been fully instrumented in order to acquire all the significant raw data, process them onboard and communicate with an inshore station, and all this for both seakeeping and resistance tests. In order to illustrate the applicability and advantages of the proposed model, some results obtained by its application on a towing tank test campaign, aimed at developing a parametric roll detection system, are also presented.


title: Querying Massive Trajectories by Path on the Cloud

abstract: A path query aims to find the trajectories that pass a given sequence of connected road segments within a time period. It is very useful in many urban applications, e.g., 1) traffic modeling, 2) frequent path mining, and 3) traffic anomaly detection. Existing solutions for path query are implemented based on single machines, which are not efficient for the following tasks: 1) indexing large-scale historical data; 2) handling real-time trajectory updates; and 3) processing concurrent path queries. In this paper, we design and implement a cloud-based path query processing framework based on Microsoft Azure. We modify the suffix tree structure to index the trajectories using Azure Table. The proposed system consists of two main parts: 1) backend processing, which performs the pre-processing and suffix index building with distributed computing platform (i.e., Storm) used to efficiently handle massive real-time trajectory updates; and 2) query processing, which answers path queries using Azure Storm to improve efficiency and overcome the I/O bottleneck. We evaluate the performance of our proposed system based on a real taxi dataset from Guiyang, China.


title: Evolving neural networks to focus minimax search

abstract: Neural networks were evolved through genetic algorithms to focus minimax search in the game of Othello. At each level of the search tree, the focus networks decide which moves are promising enough to be explored further. The networks effectively hide problem states from minimax based on the knowledge they have evolved about the limitations of minimax and the evaluation function. Focus networks were encoded in marker-based chromosomes and were evolved against a full-width minimax opponent that used the same evaluation function. The networks were able to guide the search away from poor information, resulting in stronger play while examining fewer states. When evolved with a highly sophisticated evaluation function of the Bill program, the system was able to match Bill's performance while only searching a subset of the moves.


title: Maximum Sum Rate of Slotted Aloha With Capture

abstract: The sum rate performance of random-access networks crucially depends on the access protocol and receiver structure. Despite extensive studies, how to characterize the maximum sum rate of the simplest version of random access, Aloha, remains an open question. In this paper, a comprehensive study of the sum rate performance of slotted Aloha networks is presented. By extending the unified analytical framework from the classical collision model to the capture model, the network steady-state point in saturated conditions is derived as a function of the signal-to-interference-plus-noise ratio (SINR) threshold, which determines a fundamental tradeoff between the information encoding rate and the network throughput. To maximize the sum rate, both the SINR threshold and backoff parameters of nodes should be properly selected. Explicit expressions of the maximum sum rate and the optimal setting are obtained, which show that similar to the sum capacity of the multiple access channel, the maximum sum rate of slotted Aloha also logarithmically increases with the mean received signal-to-noise ratio (SNR), but the high-SNR slope is only $e^{-1}$ . Effects of backoff and power control on the sum rate performance of slotted Aloha networks are further discussed, which shed important light on the practical network design.


title: Graph stream classification using labeled and unlabeled graphs

abstract: Graph classification is becoming increasingly popular due to the rapidly rising applications involving data with structural dependency. The wide spread of the graph applications and the inherent complex relationships between graph objects have made the labels of the graph data expensive and/or difficult to obtain, especially for applications involving dynamic changing graph records. While labeled graphs are limited, the copious amounts of unlabeled graphs are often easy to obtain with trivial efforts. In this paper, we propose a framework to build a stream based graph classification model by combining both labeled and unlabeled graphs. Our method, called gSLU, employs an ensemble based framework to partition graph streams into a number of graph chunks each containing some labeled and unlabeled graphs. For each individual chunk, we propose a minimum-redundancy subgraph feature selection module to select a set of informative subgraph features to build a classifier. To tackle the concept drifting in graph streams, an instance level weighting mechanism is used to dynamically adjust the instance weight, through which the subgraph feature selection can emphasize on difficult graph samples. The classifiers built from different graph chunks form an ensemble for graph stream classification. Experiments on real-world graph streams demonstrate clear benefits of using minimum-redundancy subgraph features to build accurate classifiers. By employing instance level weighting, our graph ensemble model can effectively adapt to the concept drifting in the graph stream for classification.


title: Interplay of Hippocampus and Prefrontal Cortex in Memory

abstract: Recent studies on the hippocampus and the prefrontal cortex have considerably advanced our understanding of the distinct roles of these brain areas in the encoding and retrieval of memories, and of how they interact in the prolonged process by which new memories are consolidated into our permanent storehouse of knowledge. These studies have led to a new model of how the hippocampus forms and replays memories and how the prefrontal cortex engages representations of the meaningful contexts in which related memories occur, as well as how these areas interact during memory retrieval. Furthermore, they have provided new insights into how interactions between the hippocampus and prefrontal cortex support the assimilation of new memories into pre-existing networks of knowledge, called schemas, and how schemas are modified in this process as the foundation of memory consolidation.


title: Trust assessment in cloud environment: Taxonomy and analysis

abstract: Cloud environment consists of many components playing different roles which need to interact with each other. Based on the context the nature of interaction may vary. Trust is an essential facet for confidence-based interactions among the various entities in cloud environment. This paper presents the taxonomy of trust models and classification of information sources for trust assessment in cloud paradigm. It analyzes further the existing approaches of trust assessment in cloud environment and portrays the potential for future research. The intent of the paper is to also identify different dimensions that are needed for effective assessment of trust in cloud environment.


title: Testing a Family-Centered Intervention to Promote Functional and Cognitive Recovery in Hospitalized Older Adults

abstract: A comparative trial using a repeated-measures design was designed to evaluate the feasibility and outcomes of the Family-Centered Function-Focused-Care (Fam-FFC) intervention, which is intended to promote functional recovery in hospitalized older adults. A family-centered resource nurse and a facility champion implemented a three-component intervention (environmental assessment and modification, staff education, individual and family education and partnership in care planning with follow-up after hospitalization for an acute illness). Control units were exposed to function-focused-care education only. Ninety-seven dyads of medical patients aged 65 and older and family caregivers (FCGs) were recruited from three medical units of a community teaching hospital. Fifty-three percent of patients were female, 89% were white, 51% were married, and 40% were widowed, and they had a mean age of 80.8 ± 7.5. Seventy-eight percent of FCGs were married, 34% were daughters, 31% were female spouses or partners, and 38% were aged 46 to 65. Patient outcomes included functional outcomes (activities of daily living (ADLs), walking performance, gait, balance) and delirium severity and duration. FCG outcomes included preparedness for caregiving, anxiety, depression, role strain, and mutuality. The intervention group demonstrated less severity and shorter duration of delirium and better ADL and walking performance but not better gait and balance performance than the control group. FCGs who participated in Fam-FFC showed a significant increase in preparedness for caregiving and a decrease in anxiety and depression from admission to 2 months after discharge but no significant differences in strain or quality of the relationship with the care recipient from FCGs in the control group. Fam-FFC is feasible and has the potential to improve outcomes for hospitalized older adults and their caregivers.


title: RenderGAN: Generating Realistic Labeled Data

abstract: Deep Convolutional Neuronal Networks (DCNNs) are showing remarkable performance on many computer vision tasks. Due to their large parameter space, they require many labeled samples when trained in a supervised setting. The costs of annotating data manually can render the use of DCNNs infeasible. We present a novel framework called RenderGAN that can generate large amounts of realistic, labeled images by combining a 3D model and the Generative Adversarial Network framework. In our approach, image augmentations (e.g. lighting, background, and detail) are learned from unlabeled data such that the generated images are strikingly realistic while preserving the labels known from the 3D model. We apply the RenderGAN framework to generate images of barcode-like markers that are attached to honeybees. Training a DCNN on data generated by the RenderGAN yields considerably better performance than training it on various baselines.


title: Proactive Spatiotemporal Resource Allocation and Predictive Visual Analytics for Community Policing and Law Enforcement

abstract: In this paper, we present a visual analytics approach that provides decision makers with a proactive and predictive environment in order to assist them in making effective resource allocation and deployment decisions. The challenges involved with such predictive analytics processes include end-users' understanding, and the application of the underlying statistical algorithms at the right spatiotemporal granularity levels so that good prediction estimates can be established. In our approach, we provide analysts with a suite of natural scale templates and methods that enable them to focus and drill down to appropriate geospatial and temporal resolution levels. Our forecasting technique is based on the Seasonal Trend decomposition based on Loess (STL) method, which we apply in a spatiotemporal visual analytics context to provide analysts with predicted levels of future activity. We also present a novel kernel density estimation technique we have developed, in which the prediction process is influenced by the spatial correlation of recent incidents at nearby locations. We demonstrate our techniques by applying our methodology to Criminal, Traffic and Civil (CTC) incident datasets.


title: Power electronics and future marine electrical systems

abstract: Tomorrow's marine electrical systems will be profoundly different from today's systems. Power electronics is making major impacts on virtually every marine system including propulsion, power distribution, auxiliaries, sonar, and radar. Newly emerging materials, components, and system concepts (such as wide band-gap materials, silicon-carbide-based power semiconductor devices, power electronics building blocks (PEBBs), and integrated power systems) are, and will continue, enabling future marine systems as different from today's systems as steam ships were to sailing ships. However, these enabling technologies and concepts are not well known and have been difficult to understand. This paper will introduce these new concepts and technologies, identify potential impacts, and explore new design methods to simplify marine electrical system development.


title: Spatial parallel soft robotic architectures

abstract: In this paper we present a class of soft-robotic systems with superior load bearing capacity and expanded degrees of freedom. Spatial parallel soft robotic systems utilize spatial arrangement of soft actuators in a manner similar to parallel kinematic machines. In this paper we demonstrate that such an arrangement of soft actuators enhances stiffness and yield dramatic motions. The current work utilizes tri-chamber actuators made from silicone rubber to demonstrate the viability of the concept.


title: A Presentation Support Service Using Presentation Semantics

abstract: The main topic addressed in this paper is to support researchers and students to make, share, and reuse their presentation documents. The presentation documents are well-organized information for their research. However, it is not so easy to share and reuse such documents since the semantic structure of the documents are not explicitly represented. In order to resolve this issue, we propose a presentation semantics model which represents roles and relations among the presentation objects in the presentation document. We also introduce a presentation support service for editing the presentation semantics and for designing a new presentation document as a Web 2.0 service.


title: Learning Hash Functions for Cross-View Similarity Search

abstract: Many applications in Multilingual and Multimodal Information Access involve searching large databases of high dimensional data objects with multiple (conditionally independent) views. In this work we consider the problem of learning hash functions for similarity search across the views for such applications. We propose a principled method for learning a hash function for each view given a set of multiview training data objects. The hash functions map similar objects to similar codes across the views thus enabling cross-view similarity search. We present results from an extensive empirical study of the proposed approach which demonstrate its effectiveness on Japanese language People Search and Multilingual People Search problems.


title: Arriving on time: estimating travel time distributions on large-scale road networks

abstract: Most optimal routing problems focus on minimizing travel time or distance traveled. Oftentimes, a more useful objective is to maximize the probability of on-time arrival, which requires statistical distributions of travel times, rather than just mean values. We propose a method to estimate travel time distributions on large-scale road networks, using probe vehicle data collected from GPS. We present a framework that works with large input of data, and scales linearly with the size of the network. Leveraging the planar topology of the graph, the method computes efficiently the time correlations between neighboring streets. First, raw probe vehicle traces are compressed into pairs of travel times and number of stops for each traversed road segment using a `stop-and-go' algorithm developed for this work. The compressed data is then used as input for training a path travel time model, which couples a Markov model along with a Gaussian Markov random field. Finally, scalable inference algorithms are developed for obtaining path travel time distributions from the composite MM-GMRF model. We illustrate the accuracy and scalability of our model on a 505,000 road link network spanning the San Francisco Bay Area.


title: Power Losses and Current Distribution Studies by Infrared Thermal Imaging in Soft- and Hard-Switched IGBTs Under Resonant Load

abstract: A test bench is proposed to study, at die-level, the power losses and current distribution in power devices. It is based on an infrared camera and a flexible half-bridge resonant inverter with a tunable resonance frequency fres. With this setup, the die surface temperature is acquired in steady state, while the device is under real operation. The power losses are derived from the temperature mean value averaged, first, over a few switching cycles and, then, across the die surface. By contrast, the current distribution is inferred from the spectral component of the surface thermal map at the switching frequency (fSw). As a proof of concept, two case studies are reported considering 650 V-40 A insulated gate bipolar transistors (IGBTs) soft- and hard-switched within and outside the zero voltage switching condition. First, the power losses are analyzed under switching conditions representative of domestic induction heating applications (fres = 29.6 kHz) at fSw = 40 kHz and fSw = 20 kHz. Second, the power losses and local current distribution are investigated when fres = 9.25 kHz at fSw ranged from 8.91 to 9.51 kHz. Such results are assessed with power losses electrical measurements and simulations, obtaining a satisfactory agreement. Moreover, hot spots are identified as current crowding points at fSw, whose location is fixed by the bonding wires attachment to the die and the device edge termination. As main benefits of this technique, a higher spatial resolution is achieved and problems related with noisy electrical measurements resulting from the insertion of the used probes or transducers, power circuit stray elements, or device packaging parasitics are avoided.


title: A Quantitative Comparison of the Subgraph Miners MoFa, gSpan, FFSM, and Gaston

abstract: Several new miners for frequent subgraphs have been published recently. Whereas new approaches are presented in detail, the quantitative evaluations are often of limited value: only the performance on a small set of graph databases is discussed and the new algorithm is often only compared to a single competitor based on an executable. It remains unclear, how the algorithms work on bigger/other graph databases and which of their distinctive features is best suited for which database. We have re-implemented the subgraph miners MoFa, gSpan, FFSM, and Gaston within a common code base and with the same level of programming expertise and optimization effort. This paper presents the results of a comparative benchmarking that ran the algorithms on a comprehensive set of graph databases.


title: EEG Activity during Relaxation and Food Imagery

abstract: EEG activity was recorded in nine volunteer subjects while they engaged in eight cognitive tasks. The tasks involved mental arithmetic, relaxation imagery, food imagery and imagery related to “neutral” stimuli (bicycle and automobile). Period analysis of the EEG indicated significant differences in EEG factor activity related to tension and anxiety for the subtraction, relaxation and food imagery trials. Imagery of heaviness and the subject's favorite dessert produced EEG factor activity most characteristic of relaxation. Results of this study are discussed in terms of the relation of odor to food imagery and the ecological validity of the use of food imagery in relaxation training.


title: A survey on service-oriented middleware for wireless sensor networks

abstract: Wireless sensor networks (WSN) are used for many applications such as environmental monitoring, infrastructure security, healthcare applications, and traffic control. The design and development of such applications must address many challenges dictated by WSN characteristics on one hand and the targeted applications on the other. One of the emerging approaches used for relaxing these challenges is using service-oriented middleware (SOM). Service-oriented computing, in general, aims to make services available and easily accessible through standardized models and protocols without having to worry about the underlying infrastructures, development models, or implementation details. SOM could play an important role in facilitating the design, development, and implementation of service-oriented systems. This will help achieve interoperability, loose coupling, and heterogeneity support. Furthermore, SOM approaches will provision non-functional requirements like scalability, reliability, flexibility, and Quality of Service (QoS) assurance. This paper surveys the current work in SOM and the trends and challenges to be addressed when designing and developing these solutions for WSN.


title: Prediction of Possible Business of a Newly Launched Film using Ordinal Values of Film-genres

abstract: Film industry is the most important component of entertainment industry. Both profit and loss are very high for this business. Like every other business, business prediction system plays a vital role for this industry. Before release of a particular movie, if the Production Houses or distributors get any type of prediction that how the film will do business, then it will be very useful to reduce the risk of the investors. In this paper we have proposed a method using back propagation neural network for prediction about a given movie's profitability. Initially the entire range of profit- loss has been divided into a number of groups. The proposed algorithm can assign a given movie to it's appropriate profit-loss group. Note that, a similar such method has been successfully applied in the field of Stock Market Prediction, Weather Prediction and Image Processing.


title: Mining Social Entrepreneurship Strategies Using Topic Modeling

abstract: Despite the burgeoning research on social entrepreneurship (SE), SE strategies remain poorly understood. Drawing on extant research on the social activism and social change, empowerment and SE models, we explore, classify and validate the strategies used by 2,334 social entrepreneurs affiliated with the world’s largest SE support organization, Ashoka. The results of the topic modeling of the social entrepreneurs’ strategy profiles reveal that they employed a total of 39 change-making strategies that vary across resources (material versus symbolic strategies), specificity (general versus specific strategies), and mode of participation (mass versus elite participation strategies); they also vary across fields of practice and time. Finally, we identify six meta-SE strategies―a reduction from the 39 strategies―and identify four new meta-SE strategies (i.e., system reform, physical capital development, evidence-based practices, and prototyping) that have been overlooked in prior SE research. Our findings extend and deepen the research into SE strategies and offer a comprehensive model of SE strategies that advances theory, practice and policy making.


title: Dark vs. Dim Silicon and Near-Threshold Computing

abstract: Due to limited scaling of supply voltage, power density is expected to grow in future technology nodes. This increasing power density potentially limits the number of transistors switching at full speed in the future. Near-threshold operation can increase the number of simultaneously active cores, at the expense of much lower operating frequency (“dim silicon”). Although promising to increase overall throughput, dim cores suffer from diminishing returns as the number of cores increases. At this point, hardware accelerators become more efficient alternatives. To explore such a broad design space, we have developed an analytical framework called Lumos to quantify the performance limits of many-core, heterogeneous systems operating at near-threshold voltage. Lumos augments Amdahl’s Law with detailed scaling of frequency and power, calibrated by circuit-level simulations using a modified Predictive Technology Model (PTM) and factors in effects of process variations. While our results show that dim cores do indeed boost throughput, even in the presence of process variations, significant benefits are only achieved in applications with very high parallelism or with novel architectures to mitigate variation. A more beneficial and scalable approach is to use accelerators. However, reconfigurable logic that supports a variety of accelerators is more beneficial than a dedicated, fixed logic accelerator, unless (1) the dedicated kernel has overwhelming coverage across applications (e.g., twice as large as the total of all others), or (2) the speedup of the dedicated accelerator over the reconfigurable equivalent is significant (e.g., 10×–50×).


title: Self-organizing maps for multi-objective pareto frontiers

abstract: Decision makers often need to take into account multiple conflicting objectives when selecting a solution for their problem. This can result in a potentially large number of candidate solutions to be considered. Visualizing a Pareto Frontier, the optimal set of solutions to a multi-objective problem, is considered a difficult task when the problem at hand spans more than three objective functions. We introduce a novel visual-interactive approach to facilitate coping with multi-objective problems. We propose a characterization of the Pareto Frontier data and the tasks decision makers face as they reach their decisions. Following a comprehensive analysis of the design alternatives, we show how a semantically-enhanced Self-Organizing Map, can be utilized to meet the identified tasks. We argue that our newly proposed design provides both consistent orientation of the 2D mapping as well as an appropriate visual representation of individual solutions. We then demonstrate its applicability with two real-world multi-objective case studies. We conclude with a preliminary empirical evaluation and a qualitative usefulness assessment.


title: Information retrieval support systems

abstract: The study of Information Retrieval Support Systems (IRSS) focuses on the next generation retrieval systems and Web search engines. It is an emerging research field driven by the demands, challenges, and opportunities of the fast growing Web. The philosophy of IRSS requires us to go beyond the two basic tasks of browsing and searching provided by current retrieval systems. We must provide the necessary new utilities, tools, and languages that help and support a user to perform multiple tasks in finding useful information and knowledge. In the Web context, IRSS may be viewed as an important type of Web-based systems supporting Web Intelligence (WI). The main objective of this entry is to provide an overview of the study of IRSS, covering the emergence of IRSS, the architecture of IRSS, and implementation-related issues


title: Neutrophil oxidants inactivate alpha-1-protease inhibitor and promote PMN-mediated detachment of cultured endothelium. Protection by free methionine

abstract: Activated granulocytes have been implicated in mediating pulmonary endothelial damage in the Adult Respiratory Distress Syndrome. In another lung disease, emphysema, pulmonary granulocytes (PMNs) are thought to be doubly responsible for lung dissolution: they release potent proteolytic enzymes including elastase, and they generate reactive oxygen species that oxidize a reactive site methionine group in alpha-1-protease inhibitor (alpha-1-PI) rendering it, in turn, impotent as an anti-elastase. This suggested an analogous scenario for pulmonary vascular damage: namely, undefended PMN elastase might also mediate endothelial injury. Our strategy to prove this notion used 51chromium-labeled human endothelial cells exposed to intact PMN or to enucleate "neutroplasts." The latter are elastase-free cytoplasmic blebs derived from PMN. When activated, both PMN and neutroplasts generate similar amounts of toxic oxygen species; yet neutroplasts caused insignificant endothelial damage, measured as 51Cr "lift-off"from anchoring matrix (PMN = 24.3% +/- 1.8% vs neutroplast = 1.2% +/- 0.4%; p less than 0.001). Adding pure elastase back to neutroplasts increased endothelial cell lift-off (7% +/- 0.2%). Although the prototypic serine protease inhibitor phenyl methylsulfonylfluoride (PMSF) protected endothelium from PMNs, pure alpha-1-PI (also a potent anti-elastase) when added in physiologic amounts did not protect endothelial cells from PMN assault, suggesting that PMN oxidants might inactivate it. By adding exogenous myeloperoxidase (MPO) to MPO-deficient neutroplasts, we demonstrated that MPO-dependent oxidants, probably N-chloramines, are critical inactivators of alpha-1-PI. This was further confirmed since added free methionine, a scavenger of chloramine, protected alpha-1-PI from inactivation by reagent chloramine or that produced by rearmed neutroplasts or PMN.(ABSTRACT TRUNCATED AT 250 WORDS)


title: The dynamics of innovation: from national systems and "Mode" 2 to a triple helix of university-industry-government relations.

abstract: Abstract The Triple Helix of university–industry–government relations is compared with alternative models for explaining the current research system in its social contexts. Communications and negotiations between institutional partners generate an overlay that increasingly reorganizes the underlying arrangements. The institutional layer can be considered as the retention mechanism of a developing system. For example, the national organization of the system of innovation has historically been important in determining competition. Reorganizations across industrial sectors and nation states, however, are induced by new technologies (biotechnology, ICT). The consequent transformations can be analyzed in terms of (neo-)evolutionary mechanisms. University research may function increasingly as a locus in the “laboratory” of such knowledge-intensive network transitions.


title: Zero moment point-measurements from a human walker wearing robot feet as shoes

abstract: The anthropomorphic biped robot Bip is equipped with sensors for measuring the ground/feet forces in order to localize the center of pressure (CoP) and zero moment point (ZMP). This paper focuses on experimental results regarding the evolution of the ground contact forces, obtained from a human walker wearing the robot feet as shoes. First, one determines the influence of heavy and rigid metallic shoes on the gait of the subject, during experiments carried out on flat ground. Second, the evolution of the contact forces is studied while walking on parallel planes with different elevations (stairs), then one addresses the case where the feet are supported by two nonparallel planes (uneven terrain). The corresponding analysis is founded on the concepts of virtual supporting surface and pseudo-CoP-ZMP introduced in a companion paper, discussing the theoretical aspects of CoP and ZMP (Sardain and Bessonnet, 2004). Beyond the academic contribution, the data analyzed in this paper could constitute an incitement to design truly anthropomorphic feet, for Bip as well as for other biped robots, because all the current robot feet are functionally rather poor.


title: Electrical Power Load Forecasting using Hybrid Self-Organizing Maps and Support Vector Machines

abstract: Forecasting of future electricity demand is very important for decision making in power system operation and planning. In recent years, due to the privatization and deregulation of the power industry, accurate forecasting of future electricity demand has become an important research area for secure operation, management of modern power systems and electricity production in the power generation sector. This paper presents a novel approach for mid-term electricity load forecasting. It uses a hybrid artificial intelligence scheme based on self-organizing maps (SOMs) and support vector machines (SVMs). According to the similarity degree of time series input samples, the SOM is used as a filtering scheme to cluster historical electricity load data into two subsets using the Kohonen rule in an unsupervised manner. As a novel learning machine, the SVM based on statistical learning theory is used for prediction, using support vector regression (SVR). Two epsilon-SVRs are employed to fit the training data of each SOM clustered subset individually in a supervised manner for load prediction. The proposed hybrid SOM-SVR model is evaluated in MATLAB on the electricity load dataset used in the European Network on Intelligent Technologies (EUNITE) competition, arranged by the Eastern Slovakian Electricity Corporation. This proposed model is robust with different data types and can deal well with non- stationarity of load series. Practical application results show that this hybrid technique gives far better prediction accuracy for mid-term electricity load forecasting compared to previous research findings.


N.: 4store: The Design and Implementation of a Clustered RDF Store


title: Reconstructing Core Dumps

abstract: When a software failure occurs in the field, it is often difficult to reproduce. Guided by a memory dump at the moment of failure (a “core dump”), our RECORE test case generator searches for a series of events that precisely reconstruct the failure from primitive data. Applied on seven non-trivial Java bugs, RECORE reconstructs the exact failure in five cases without any runtime overhead in production code.


title: What Do We Need To Know To Practice Occupational Therapy in the Community?

abstract: One of the main challenges to occupational therapists in community practice is applying our existing knowledge base to support a different kind of practice in the community. This article explores the theory base in occupational therapy that informs three models of community practice: client-centered, community-based rehabilitation, and independent living. It defines these three preferred models of service delivery for community practice and looks at the knowledge requirements for each. Using a taxonomy developed to classify occupational therapy theory, the article examines existing theory and gaps in the theory base for occupational therapy in community practice. Finally, the article challenges practicing therapists, educators, and researchers to examine their use of theory and their underlying assumptions for meeting the challenge of community practice.


title: An information theoretic approach for analyzing temporal patterns of gene expression

abstract: Motivation: Arrays allow measurements of the expression levels of thousands of mRNAs to be made simultaneously. The resulting data sets are information rich but require extensive mining to enhance their usefulness. Information theoretic methods are capable of assessing similarities and dissimilarities between data distributions and may be suited to the analysis of gene expression experiments. The purpose of this study was to investigate information theoretic data mining approaches to discover temporal patterns of gene expression from array-derived gene expression data. Results: The Kullback–Leibler divergence, an informationtheoretic distance that measures the relative dissimilarity between two data distribution profiles, was used in conjunction with an unsupervised self-organizing map algorithm. Two published, array-derived gene expression data sets were analyzed. The patterns obtained with the KL clustering method were found to be superior to those obtained with the hierarchical clustering algorithm using the Pearson correlation distance measure. The biological significance of the results was also examined. Availability: Software code is available by request from the authors. All programs were written in ANSI C and Matlab (Mathworks Inc., Natick, MA).


Robust named entity disambiguation with random walks


title: On Learning Vector-Valued Functions

abstract: In this letter, we provide a study of learning in a Hilbert space of vectorvalued functions. We motivate the need for extending learning theory of scalar-valued functions by practical considerations and establish some basic results for learning vector-valued functions that should prove useful in applications. Specifically, we allow an output space Y to be a Hilbert space, and we consider a reproducing kernel Hilbert space of functions whose values lie in Y. In this setting, we derive the form of the minimal norm interpolant to a finite set of data and apply it to study some regularization functionals that are important in learning theory. We consider specific examples of such functionals corresponding to multiple-output regularization networks and support vector machines, for both regression and classification. Finally, we provide classes of operator-valued kernels of the dot product and translation-invariant type.


title: When morality opposes justice: Conservatives have moral intuitions that liberals may not recognize

abstract: Researchers in moral psychology and social justice have agreed that morality is about matters of harm, rights, and justice. On this definition of morality, conservative opposition to social justice programs appears to be immoral, and has been explained as a product of various non-moral processes such as system justification or social dominance orientation. In this article we argue that, from an anthropological perspective, the moral domain is usually much broader, encompassing many more aspects of social life and valuing institutions as much or more than individuals. We present theoretical and empirical reasons for believing that there are five psychological systems that provide the foundations for the worlds many moralities. The five foundations are psychological preparations for detecting and reacting emotionally to issues related to harm/care, fairness/reciprocity, ingroup/loyalty, authority/respect, and purity/sanctity. Political liberals have moral intuitions primarily based upon the first two foundations, and therefore misunderstand the moral motivations of political conservatives, who generally rely upon all five foundations.


title: Episodic Memory and Beyond: The Hippocampus and Neocortex in Transformation

abstract: The last decade has seen dramatic technological and conceptual changes in research on episodic memory and the brain. New technologies, and increased use of more naturalistic observations, have enabled investigators to delve deeply into the structures that mediate episodic memory, particularly the hippocampus, and to track functional and structural interactions among brain regions that support it. Conceptually, episodic memory is increasingly being viewed as subject to lifelong transformations that are reflected in the neural substrates that mediate it. In keeping with this dynamic perspective, research on episodic memory (and the hippocampus) has infiltrated domains, from perception to language and from empathy to problem solving, that were once considered outside its boundaries. Using the component process model as a framework, and focusing on the hippocampus, its subfields, and specialization along its longitudinal axis, along with its interaction with other brain regions, we consider these new developm...


title: Multipacket Hot-Potato Routing on Processor Arrays

abstract: In this paper, we consider the problems of multipacket batch and balanced routing on d-dimensional (constant d≥2) torus and mesh-connected processor arrays. We present new “hot-potato” routing algorithms which achieve the best known average-case and worst-case time bounds for both problems on all such networks. In particular, our results include the following:


title: Citizens and institutions as information prosumers. The case study of italian municipalities on Twitter

abstract: The aim of this paper is to address changes in public communication following the advent of Internet social networking tools and the emerging web 2.0 technologies which are providing new ways of sharing information and knowledge. In particular public administrations are called upon to reinvent the governance of public affairs and to update the means for interacting with their communities. The paper develops an analysis of the distribution, diffusion and performance of the official profiles on Twitter adopted by the Italian municipalities ( comuni ) up to November 2013. It aims to identify the patterns of spatial distribution and the drivers of the diffusion of Twitter profiles; the performance of the profiles through an aggregated index, called the Twitter performance index ( Twiperindex ), which evaluates the profiles’ activity with reference to the gravitational areas of the municipalities in order to enable comparisons of the activity of municipalities with different demographic sizes and functional roles. The results show that only a small portion of innovative municipalities have adopted Twitter to enhance e-participation and e-governance and that the drivers of the diffusion seem to be related either to past experiences and existing conditions (i.e. civic networks, digital infrastructures) developed over time or to strong local community awareness. The better performances are achieved mainly by small and medium-sized municipalities. Of course, the phenomenon is very new and fluid, therefore this analysis should be considered as a first step in ongoing research which aims to grasp the dynamics of these new means of public communication.


title: Dissecting Robotics - historical overview and future perspectives

abstract: Robotics is called to be the next technological revolution and estimations indicate that it will trigger the fourth industrial revolution. This article presents a review of some of the most relevant milestones that occurred in robotics over the last few decades and future perspectives. Despite the fact that, nowadays, robotics is an emerging field, the challenges in many technological aspects and more importantly bringing innovative solutions to the market still remain open. The need of reducing the integration time, costs and a common hardware infrastructure are discussed and further analysed in this work. We conclude with a discussion of the future perspectives of robotics as an engineering discipline and with suggestions for future research directions.


title: Arabic sign language fingerspelling recognition from depth and intensity images

abstract: Automatic Arabic sign language recognition (ArSL) and fingerspelling considered to be the preferred communication method among deaf people. In this paper, we propose a system for alphabetic Arabic sign language recognition using depth and intensity images which acquired from SOFTKINECT™ sensor. The proposed method does not require any extra gloves or any visual marks. Local features from depth and intensity images are learned using unsupervised deep learning method called PCANet. The extracted features are then recognized using linear support vector machine classifier. The performance of the proposed method is evaluated on dataset of real images captured from multi-users. Experiments using a combination of depth and intensity images and also using depth and intensity images separately are performed. The obtained results show that the performance of the proposed system improved by combining both depth and intensity information which give an average accuracy of 99:5%.


title: Introduction to the Special Issue on Meta-Learning

abstract: Recent advances in meta-learning are providing the foundations to construct meta-learning assistants and task-adaptive learners. The goal of this special issue is to foster an interest in meta-learning by compiling representative work in the field. The contributions to this special issue provide strong insights into the construction of future meta-learning tools. In this introduction we present a common frame of reference to address work in meta-learning through the concept of meta-knowledge. We show how meta-learning can be simply defined as the process of exploiting knowledge about learning that enables us to understand and improve the performance of learning algorithms.


title: EgoCoder: Intelligent Program Synthesis with Hierarchical Sequential Neural Network Model

abstract: Programming has been an important skill for researchers and practitioners in computer science and other related areas. To learn basic programing skills, a long-time systematic training is usually required for beginners. According to a recent market report, the computer software market is expected to continue expanding at an accelerating speed, but the market supply of qualified software developers can hardly meet such a huge demand. In recent years, the surge of text generation research works provides the opportunities to address such a dilemma through automatic program synthesis. In this paper, we propose to make our try to solve the program synthesis problem from a data mining perspective. To address the problem, a novel generative model, namely EgoCoder, will be introduced in this paper. EgoCoder effectively parses program code into abstract syntax trees (ASTs), where the tree nodes will contain the program code/comment content and the tree structure can capture the program logic flows. Based on a new unit model called Hsu, EgoCoder can effectively capture both the hierarchical and sequential patterns in the program ASTs. Extensive experiments will be done to compare EgoCoder with the state-of-the-art text generation methods, and the experimental results have demonstrated the effectiveness of EgoCoder in addressing the program synthesis problem.


title: POS Tagging of English-Hindi Code-Mixed Social Media Content

abstract: Code-mixing is frequently observed in user generated content on social media, especially from multilingual users. The linguistic complexity of such content is compounded by presence of spelling variations, transliteration and non-adherance to formal grammar. We describe our initial efforts to create a multi-level annotated corpus of Hindi-English codemixed text collated from Facebook forums, and explore language identification, back-transliteration, normalization and POS tagging of this data. Our results show that language identification and transliteration for Hindi are two major challenges that impact POS tagging accuracy.


title: Efficiency and Security of Data with Symmetric Encryption Algorithms

abstract: In this paper, we proposed the encryption algorithm to encrypt plaintext to cipher text and the decryption algorithm to do the reverse. We applied the basic computing operations to design these algorithms in this study. We used the inserting dummy symbols, rotating, transposition, shifting, complement and inserting control byte to build the data and tables in the encryption algorithm. These operations are simple and easily to implement. Without knowing the data and tables of encryption, it is difficult to do cryptanalysis. In the decryption algorithm, we used these data and tables to decrypt cipher text to plaintext. We can easily apply these algorithms to transmit data in network and the data transmission is secure.


title: The Use of Summation to Aggregate Software Metrics Hinders the Performance of Defect Prediction Models

abstract: Defect prediction models help software organizations to anticipate where defects will appear in the future. When training a defect prediction model, historical defect data is often mined from a Version Control System (VCS, e.g., Subversion), which records software changes at the file-level. Software metrics, on the other hand, are often calculated at the class- or method-level (e.g., McCabe's Cyclomatic Complexity). To address the disagreement in granularity, the class- and method-level software metrics are aggregated to file-level, often using summation (i.e., McCabe of a file is the sum of the McCabe of all methods within the file). A recent study shows that summation significantly inflates the correlation between lines of code ( Sloc ) and cyclomatic complexity ( Cc ) in Java projects. While there are many other aggregation schemes (e.g., central tendency, dispersion), they have remained unexplored in the scope of defect prediction. In this study, we set out to investigate how different aggregation schemes impact defect prediction models. Through an analysis of 11 aggregation schemes using data collected from 255 open source projects, we find that: (1) aggregation schemes can significantly alter correlations among metrics, as well as the correlations between metrics and the defect count; (2) when constructing models to predict defect proneness, applying only the summation scheme (i.e., the most commonly used aggregation scheme in the literature) only achieves the best performance (the best among the 12 studied configurations) in 11 percent of the studied projects, while applying all of the studied aggregation schemes achieves the best performance in 40 percent of the studied projects; (3) when constructing models to predict defect rank or count, either applying only the summation or applying all of the studied aggregation schemes achieves similar performance, with both achieving the closest to the best performance more often than the other studied aggregation schemes; and (4) when constructing models for effort-aware defect prediction, the mean or median aggregation schemes yield performance values that are significantly closer to the best performance than any of the other studied aggregation schemes. Broadly speaking, the performance of defect prediction models are often underestimated due to our community's tendency to only use the summation aggregation scheme. Given the potential benefit of applying additional aggregation schemes, we advise that future defect prediction models should explore a variety of aggregation schemes.


title: SMS spam filtering techniqu based on artificial immune system

abstract: The Short Message Service (SMS) have an important economic impact for end users and service providers. Spam is a serious universal problem that causes problems for almost all users. Several studies have been presented, including implementations of spam filters that prevent spam from reaching their destination. Naive Bayesian algorithm is one of the most effective approaches used in filtering techniques. The computational power of smart phones are increasing, making increasingly possible to perform spam filtering at these devices as a mobile agent application, leading to better personalization and effectiveness. The challenge of filtering SMS spam is that the short messages often consist of few words composed of abbreviations and idioms. In this paper, we propose an anti-spam technique based on Artificial Immune System (AIS) for filtering SMS spam messages. The proposed technique utilizes a set of some features that can be used as inputs to spam detection model. The idea is to classify message using trained dataset that contains Phone Numbers, Spam Words, and Detectors. Our proposed technique utilizes a double collection of bulk SMS messages Spam and Ham in the training process. We state a set of stages that help us to build dataset such as tokenizer, stop word filter, and training process. Experimental results presented in this paper are based on iPhone Operating System (iOS). The results applied to the testing messages show that the proposed system can classify the SMS spam and ham with accurate compared with Naive Bayesian algorithm.


title: Harnessing the Digital Lens to Measure and Manage Information Work

abstract: Revolutions in measurement inevitably revolutionize science and practice. Over 300 years ago, Anton van Leeuwenhoek developed a better microscope to verify thread counts on imported carpets. Using this new tool, he discovered microorganisms he called “animalcules” in drops of water and individual blood corpuscles in drops of blood. Biology and medicine have never been the same.Just as the microscope gave rise to the germ theory of disease and the remarkable advances that followed, our new-found ability to observe fine-grained information flows in electronic data portends a revolution in our understanding of information work. Managers who harness this new Digital Lens will uncover a wealth of insights into performance improvement for individuals, teams and organizations - insights that will ultimately affect the economy as a whole.In this article, we draw on our own research, and that of others, to describe tools and techniques for tracking information work in unprecedented detail. The result is a step change in our ability to manage information work and information workers. During the course of our research, we used these tools to uncover lessons for individuals on information novelty, workload and transformation, as well as for organizations on automation, decentralization, and incentives. Perhaps more importantly, this fine-grained view of work provides a toolkit for deriving new lessons, many of which will vary from company to company, but each of which will be grounded in detailed, actionable data.


title: A PROXIMAL POINT ALGORITHM FOR LOG-DETERMINANT OPTIMIZATION WITH GROUP LASSO REGULARIZATION

abstract: We consider the covariance selection problem where variables are clustered into groups and the inverse covariance matrix is expected to have a blockwise sparse structure. This problem is realized via penalizing the maximum likelihood estimation of the inverse covariance ma- trix by group Lasso regularization. We propose to solve the resulting log-determinant optimization problem with the classical proximal point algorithm (PPA). At each iteration, as it is difficult to update the primal variables directly, we first solve the dual subproblem by an inexact semismooth Newton-CG method and then update the primal variables by explicit formulas based on the com- puted dual variables. We also propose to accelerate the PPA by an inexact generalized Newton's method when the iterate is close to the solution. Theoretically, we prove that at the optimal solution, the nonsingularity of the generalized Hessian matrices of the dual subproblem is equivalent to the constraint nondegeneracy condition for the primal problem. Global and local convergence results are also presented for the proposed PPA. Moreover, based on the augmented Lagrangian function of the dual problem we derive an alternating direction method (ADM), which is easily implementable and is demonstrated to be efficient for random problems. Numerical results, including comparisons with the ADM on both synthetic and real data, are presented to demonstrate that the proposed Newton-CG based PPA is stable and efficient and, in particular, outperforms the ADM when high accuracy is required.


title: Low-Profile PIFA Array Antennas for UHF Band RFID Tags Mountable on Metallic Objects

abstract: Two PIFA array antennas, designed for UHF band RFID tags mountable on metallic objects, are presented in this paper. The proposed array antennas are fabricated on a very thin substrate with a thickness of 0.8 mm. The first array that has shorting pins placed at the outer edges of PIFAs is fed by two quarter-wavelength microstrip lines. This array has a simple feeding structure, which is its main advantage. Meanwhile, the second array has shorting pins located at the inner edges of PIFAs, and incorporating additional matching stubs into the quarter-wavelength microstrip lines is needed to achieve complex impedance matching, which can offer a broad bandwidth. By properly selecting the spacing between the PIFAs, an enhanced antenna gain and a good reading-range performance can be obtained for both array antennas. In addition, the two array antennas proposed are studied mounted on differently sized metallic plates. It is found that there is a negligible influence of the metal-plate size on the reading range is found for the second array antenna, which is superior to the first one.


title: The design of smart educational environments

abstract: This paper discusses the key characteristics of smart learning and the main challenges to be overcome when designing smart educational environments to support personalisation. In order to integrate smart learning environments into the learning ecosystem and educational contexts, innovative uses and new pedagogical approaches need to be implemented to orchestrate formal and informal learning. This contribution describes the main characteristics of smart learning and smart learning environments and sustains the relevance of taking the participation of future users into account during the design process to increase knowledge of the design and the implementation of new pedagogical approaches in smart learning environments.


title: HDR image construction from multi-exposed stereo LDR images

abstract: In this paper, we present an algorithm that generates high dynamic range (HDR) images from multi-exposed low dynamic range (LDR) stereo images. The vast majority of cameras in the market only capture a limited dynamic range of a scene. Our algorithm first computes the disparity map between the stereo images. The disparity map is used to compute the camera response function which in turn results in the scene radiance maps. A refinement step for the disparity map is then applied to eliminate edge artifacts in the final HDR image. Existing methods generate HDR images of good quality for still or slow motion scenes, but give defects when the motion is fast. Our algorithm can deal with images taken during fast motion scenes and tolerate saturation and radiometric changes better than other stereo matching algorithms.


Online Attendance Management System Using RFID with Object Counter