title: Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories

abstract: In many forecasting applications, it is valuable to predict not only the value of a signal at a certain time point in the future, but also the values leading up to that point. This is especially true in clinical applications, where the future state of the patient can be less important than the patient's overall trajectory. This requires multi-step forecasting, a forecasting variant where one aims to predict multiple values in the future simultaneously. Standard methods to accomplish this can propagate error from prediction to prediction, reducing quality over the long term. In light of these challenges, we propose multi-output deep architectures for multi-step forecasting in which we explicitly model the distribution of future values of the signal over a prediction horizon. We apply these techniques to the challenging and clinically relevant task of blood glucose forecasting. Through a series of experiments on a real-world dataset consisting of 550K blood glucose measurements, we demonstrate the effectiveness of our proposed approaches in capturing the underlying signal dynamics. Compared to existing shallow and deep methods, we find that our proposed approaches improve performance individually and capture complementary information, leading to a large improvement over the baseline when combined (4.87 vs. 5.31 absolute percentage error (APE)). Overall, the results suggest the efficacy of our proposed approach in predicting blood glucose level and multi-step forecasting more generally.


Iterative design of seamless collaboration media


title: EUROGRAPHICS 2005 / J. Dingliana and F. Ganovelli Short Presentations Predicting Natural Hair Shapes by Solving the Statics of Flexible Rods

abstract: This paper presents a new physically-based method for predicting natural hairstyles in the presence of gravity and collisions. The method is based upon a mechanically accurate model for static elastic rods (Kirchhoff model), which accounts for the natural curliness of hair, as well as for hair ellipticity. The equilibrium shape is computed in a stable and easy way by energy minimization. This yields various typical hair configurations that can be observed in the real world, such as ringlets. As our results show, the method can generate different hair types with a very few input parameters, and perform virtual hairdressing operations such as wetting, cutting and drying hair.


Genomic and Functional Characterization of Poultry Escherichia coli From India Revealed Diverse Extended-Spectrum β-Lactamase-Producing Lineages With Shared Virulence Profiles


title: Manifold Learning of Brain MRIs by Deep Learning

abstract: Manifold learning of medical images plays a potentially important role for modeling anatomical variability within a population with applications that include segmentation, registration, and prediction of clinical parameters. This paper describes a novel method for learning the manifold of 3D brain images that, unlike most existing manifold learning methods, does not require the manifold space to be locally linear, and does not require a predefined similarity measure or a prebuilt proximity graph. Our manifold learning method is based on deep learning, a machine learning approach that uses layered networks (called deep belief networks, or DBNs) and has received much attention recently in the computer vision field due to their success in object recognition tasks. DBNs have traditionally been too computationally expensive for application to 3D images due to the large number of trainable parameters. Our primary contributions are 1) a much more computationally efficient training method for DBNs that makes training on 3D medical images with a resolution of up to 128 × 128 × 128 practical, and 2) the demonstration that DBNs can learn a low-dimensional manifold of brain volumes that detects modes of variations that correlate to demographic and disease parameters.


title: Putting together phylogenetic and ontogenetic perspectives on empathy

abstract: a b s t r a c t The ontogeny of human empathy is better understood with reference to the evolutionary history of the social brain. Empathy has deep evolutionary, biochemical, and neurological underpinnings. Even the most advanced forms of empathy in humans are built on more basic forms and remain connected to core mechanisms associated with affective commu- nication, social attachment, and parental care. In this paper, we argue that it is essential to consider empathy within a neurodevelopmental framework that recognizes both the continuities and changes in socioemotional understanding from infancy to adulthood. We bring together neuroevolutionary and developmental perspectives on the information pro- cessing and neural mechanisms underlying empathy and caring, and show that they are grounded in multiple interacting systems and processes. Moreover, empathy in humans is assisted by other abstract and domain-general high-level cognitive abilities such as exec- utive functions, mentalizing and language, as well as the ability to differentiate another's mental states from one's own, which expand the range of behaviors that can be driven by empathy.


title: Semantic Mapping of Large-Scale Outdoor Scenes for Autonomous Off-Road Driving

abstract: Semantic mapping is a very active and growing research area, with important applications in indoor and outdoor robotic applications. However, most of the research on semantic mapping has focused on indoor mapping and there is a need for developing semantic mapping methodologies for large-scale outdoor scenarios. In this work, a novel semantic mapping methodology for large-scale outdoor scenes in autonomous off-road driving applications is proposed. The semantic map representation consists of a large-scale topological map built using semantic image information. Thus, the proposed representation aims to solve the large-scale outdoors semantic mapping problem by using a graph based topological map, where relevant information for autonomous driving is added using semantic information from the image description. As a proof of concept, the proposed methodology is applied to the semantic map building of a real outdoor scenario.


title: Knowledge Discovery and Data Mining in Medicine

abstract: Medical databases store diagnostic information based on patients’ medical records. Because of deficits in patients’ medical records, medical databases do not provide all the required information for learning algorithms. Moreover, we may meet some contradictory cases, in which the pattern of input signals is the same, but the pattern of output signals is different. Learning algorithms cannot correctly classify such cases. Even medical doctors require more information to make the final diagnosis. In this chapter, we describe three methods of classifying medical databases based on neural networks and genetic programming (GP). To verify the effectiveness of our proposed methods, we apply them to real medical databases and prove their high classification capability. We also introduce techniques for extracting If-Then rules from the trained networks.


title: A Generic Approach for Extracting Aspects and Opinions of Arabic Reviews

abstract: New opportunities and challenges arise with the growing availability of online Arabic reviews. Sentiment analysis of these reviews can help the beneficiary by summarizing the opinions of others about entities or events. Also, for opinions to be comprehensive, analysis should be provided for each aspect or feature of the entity. In this paper, we propose a generic approach that extracts the entity aspects and their attitudes for reviews written in modern standard Arabic. The proposed approach does not exploit predefined sets of features, nor domain ontology hierarchy. Instead we add sentiment tags on the patterns and roots of an Arabic lexicon and used these tags to extract the opinion bearing words and their polarities. The proposed system is evaluated on the entity-level using two datasets of 500 movie reviews with accuracy 96% and 1000 restaurant reviews with accuracy 86.7%. Then the system is evaluated on the aspect-level using 500 Arabic reviews in different domains (Novels, Products, Movies, Football game events and Hotels). It extracted aspects, at 80.8% recall and 77.5% precision with respect to the aspects defined by domain experts.


title: Patch Mosaic for Fast Motion Deblurring

abstract: This paper proposes using a mosaic image patches composed of the most informative edges found in the original blurry image for the purpose of estimating a motion blur kernel with minimum computational cost. To select these patches we develop a new image analysis tool to efficiently locate informative patches we call the informative-edge map. The combination of patch mosaic and informative patch selection enables a new motion blur kernel estimation algorithm to recover blur kernels far more quickly and accurately than existing state-of-the-art methods. We also show that patch mosaic can form a framework for reducing the computation time of other motion deblurring algorithms with minimal modification. Experimental results with various test images show that our algorithm to be 5-100 times faster than previously published blind motion deblurring algorithms while achieving equal or better estimation accuracy.


title: How to Boost Product Line Engineering with MBSE - A Case Study of a Rolling Stock Product Line

abstract: This paper presents the first results of current product line engineering efforts at Alstom Transport. It describes the background that has led us to these efforts as well as the approach that we have adopted on Model-Based Systems Engineering (MBSE) and Product Line Engineering (PLE). We present a real-life application of MBSE and PLE that “goes beyond” SysML along with a quick overview of the first results stemming from this application to Rolling Stock systems.


title: Integrated position estimation using aerial image sequences

abstract: Presents an integrated system for navigation parameter estimation using sequential aerial images, where the navigation parameters represent the positional and velocity information of an aircraft for autonomous navigation. The proposed integrated system is composed of two parts: relative position estimation and absolute position estimation. Relative position estimation recursively computes the current position of an aircraft by accumulating relative displacement estimates extracted from two successive aerial images. Simple accumulation of parameter values reduces the reliability of the extracted parameter estimates as an aircraft goes on navigating, resulting in a large positional error. Therefore, absolute position estimation is required to compensate for the positional error generated by the relative position estimation. Absolute position estimation algorithms using image matching and digital elevation model (DEM) matching are presented. In the image matching, a robust-oriented Hausdorff measure (ROHM) is employed, whereas in the DEM matching, an algorithm using multiple image pairs is used. Experiments with four real aerial image sequences show the effectiveness of the proposed integrated position estimation algorithm.


title: Electromyography sensor based control for a hand exoskeleton

abstract: This paper presents a electromyography (EMG) control for a hand exoskeleton. The device was developed with focus on support of the rehabilitation process after hand injuries or strokes. As the device is designed for the later use on patients, which have limited hand mobility, fast undesired movements have to be averted. Safety precautions in the hardware and software design of the system must be taken to ensure this. The construction allows controlling the motion of finger joints. However, due to friction in gears and mechanical construction it is not possible to move finger joints within the construction without help of actuators. Therefore force sensors are integrated into the construction to measure force exchanged between human and exoskeleton. These allow the human to control the movements of the hand exoskeleton which is useful to teach new trajectories, for muscle training, or for diagnostic purposes. The control method using electromyography (EMG) sensor presented in this paper uses the EMG sensor values to generate a trajectory, which is executed by a position control loop based on sliding mode control.


title: Concurrent Action Detection with Structural Prediction

abstract: Action recognition has often been posed as a classification problem, which assumes that a video sequence only have one action class label and different actions are independent. However, a single human body can perform multiple concurrent actions at the same time, and different actions interact with each other. This paper proposes a concurrent action detection model where the action detection is formulated as a structural prediction problem. In this model, an interval in a video sequence can be described by multiple action labels. An detected action interval is determined both by the unary local detector and the relations with other actions. We use a wavelet feature to represent the action sequence, and design a composite temporal logic descriptor to describe the action relations. The model parameters are trained by structural SVM learning. Given a long video sequence, a sequential decision window search algorithm is designed to detect the actions. Experiments on our new collected concurrent action dataset demonstrate the strength of our method.


title: Relative sub-image based features for leaf recognition using support vector machine

abstract: In this paper, we extract our proposed RSC features from leaf images and use SVM classifier to implement an automated leaf recognition system for plant leaf identification and classification. Automatic plant species identification and classification is helpful in biology, forest and agriculture to study and discover new species in plant in botanical gardens and is also used to recognize the medicinal plants to prepare herbal medicines. Here, 300 leaf features are extracted from a single leaf of 624 leaf dataset to classify 23 different kinds of plant species with an average accuracy of 95%. Compared with other approaches, our proposed algorithm has less time complexity and is easy to implementation with higher accuracy.


title: Genome engineering using the CRISPR-Cas9 system

abstract: Targeted nucleases are powerful tools for mediating genome alteration with high precision. The RNA-guided Cas9 nuclease from the microbial clustered regularly interspaced short palindromic repeats (CRISPR) adaptive immune system can be used to facilitate efficient genome engineering in eukaryotic cells by simply specifying a 20-nt targeting sequence within its guide RNA. Here we describe a set of tools for Cas9-mediated genome editing via nonhomologous end joining (NHEJ) or homology-directed repair (HDR) in mammalian cells, as well as generation of modified cell lines for downstream functional studies. To minimize off-target cleavage, we further describe a double-nicking strategy using the Cas9 nickase mutant with paired guide RNAs. This protocol provides experimentally derived guidelines for the selection of target sites, evaluation of cleavage efficiency and analysis of off-target activity. Beginning with target design, gene modifications can be achieved within as little as 1-2 weeks, and modified clonal cell lines can be derived within 2-3 weeks.


title: Locally Adaptive Translation for Knowledge Graph Embedding

abstract: Knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space. Existing methods, e.g., TransE and TransH, learn embedding representation by defining a global margin-based loss function over the data. However, the optimal loss function is determined during experiments whose parameters are examined among a closed set of candidates. Moreover, embeddings over two knowledge graphs with different entities and relations share the same set of candidate loss functions, ignoring the locality of both graphs. This leads to the limited performance of embedding related applications. In this paper, we propose a locally adaptive translation method for knowledge graph embedding, called TransA, to find the optimal loss function by adaptively determining its margin over different knowledge graphs. Experiments on two benchmark data sets demonstrate the superiority of the proposed method, as compared to the-state-of-the-art ones.


title: Schema Theory for Genetic Programming with One-Point Crossover and Point Mutation

abstract: In this paper we first review the main results obtained in the theory of schemata in Genetic Programming (GP) emphasising their strengths and weaknesses. Then we propose a new, simpler definition of the concept of schema for GP which is quite close to the original concept of schema in genetic algorithms (GAs). Along with a new form of crossover, one-point crossover, and point mutation this concept of schema has been used to derive an improved schema theorem for GP which describes the propagation of schemata from one generation to the next. In the paper we discuss this result and show that our schema theorem is the natural counterpart for GP of the schema theorem for GAs, to which it asymptotically converges.


title: Performance analysis of dense small cell networks with generalized fading

abstract: In this paper, we propose a unified framework to analyze the performance of dense small cell networks (SCNs) in terms of the coverage probability and the area spectral efficiency (ASE). In our analysis, we consider a practical path loss model that accounts for both non-line-of-sight (NLOS) and line-of-sight (LOS) transmissions. Furthermore, we adopt a generalized fading model, in which Rayleigh fading, Rician fading and Nakagami-m fading can be treated in a unified framework. The analytical results of the coverage probability and the ASE are derived, using a generalized stochastic geometry analysis. Different from existing work that does not differentiate NLOS and LOS transmissions, our results show that NLOS and LOS transmissions have a significant impact on the coverage probability and the ASE performance, particularly when the SCNs grow dense. Furthermore, our results establish for the first time that the performance of the SCNs can be divided into four regimes, according to the intensity (aka density) of BSs, where in each regime the performance is dominated by different factors.


title: Reducing common mode EMI noise in two-switch forward converter

abstract: This paper addresses the common mode (CM) electromagnetic interference (EMI) noise issues in the twoswitch forward converter. Two-switch forward converter has low CM noise with its symmetric primary side structure. However, the asymmetric parasitic capacitances and secondary side noise source have adverse effects on the symmetry and degrade the CM noise reduction. In order to solve the issues, CM noise model of the converter and transformer is first derived. Symmetry conditions are then summarized as the guideline for achieving CM noise reduction. Methods are proposed to minimize the CM noise by controlling the transformer termination, parasitic capacitances and structure. Experimental results validated the proposed techniques.


title: Cellular LTE-A Technologies for the Future Internet-of-Things: Physical Layer Features and Challenges

abstract: Human-generated information has been the main interest of the wireless communication technologies designs for decades. However, we are currently witnessing the emerge of an entirely different paradigm of communication introduced by machines, and hence, the name machine type communication (MTC). Such paradigm arises as a result of the new applications included in the Internet-of-Things (IoT) framework. Among the enabling technologies of the IoT, cellular-based communication is the most promising and more efficient. This is justified by the currently well-developed and mature radio access networks, along with the large capacities and flexibility of the offered data rates to support a large variety of applications. On the other hand, several radio-access-network groups put efforts to optimize the 3GPP LTE standard to accommodate for the new challenges by introducing new communication categories paving the way to support the machine-to-machine communication within the IoT framework. In this paper, we provide a step-by-step tutorial discussing the development of MTC design across different releases of LTE and the newly introduced user equipment categories, namely, MTC category (CAT-M) and narrowband IoT category (CAT-N). We start by briefly discussing the different physical channels of the legacy LTE. Then we provide a comprehensive and up-to-date background for the most recent standard activities to specify CAT-M and CAT-N technologies. We also emphasize on some of necessary concepts used in the new specifications, such as the narrowband concept used in CAT-M and the frequency hopping. Finally, we identify and discuss some of the open research challenges related to the implementation of the new technologies in real life scenarios.


Tuberculosis control, and the where and why of artificial intelligence


title: Effective Use of Bidirectional Language Modeling for Transfer Learning in Biomedical Named Entity Recognition

abstract: Biomedical named entity recognition (NER) is a fundamental task in text mining of medical documents and has many applications. Deep learning based approaches to this task have been gaining increasing attention in recent years as their parameters can be learned end-to-end without the need for hand-engineered features. However, these approaches rely on high-quality labeled data, which is expensive to obtain. To address this issue, we investigate how to use unlabeled text data to improve the performance of NER models. Specifically, we train a bidirectional language model (BiLM) on unlabeled data and transfer its weights to "pretrain" an NER model with the same architecture as the BiLM, which results in a better parameter initialization of the NER model. We evaluate our approach on four benchmark datasets for biomedical NER and show that it leads to a substantial improvement in the F1 scores compared with the state-of-the-art approaches. We also show that BiLM weight transfer leads to a faster model training and the pretrained model requires fewer training examples to achieve a particular F1 score.


A Neural Question Answering Model Based on Semi-Structured Tables


A Full End-to-End Semantic Role Labeler, Syntactic-agnostic Over Syntactic-aware?


title: Action-Centered Exposure Therapy (ACET): A New Approach to the Use of Virtual Reality to the Care of People with Post-Traumatic Stress Disorder

abstract: Post-Traumatic Stress Disorder (PTSD) can be seen as the result of dysfunctional beliefs that associate stimuli with a danger or a threat leading to anxious reactions. Exposure therapy is so far considered to be the most effective treatment, and research suggests that it is mainly based on a habituation process. Based on learning theories, it appears that a passive systemic exposure to traumatic stimuli should not be the best option for the treatment of PTSD. We hypothesis that an active learning of safer and healthier coping strategies combined with systematic exposure should be more effective in reducing the psychological distress associated with PTSD. In this paper, we describe the theoretical foundations of this approach that focuses on the action and activity of the patient in his or her exposure environment. In this approach, we take advantage of Virtual Reality technologies and learning mechanics of serious games to allow the patient to learn new safe associations while promoting the empowerment. We named this action-centered exposure therapy (ACET). This approach exploits behaviorism, cognitivism, and constructivism learning theories. With the different benefits of virtual reality technologies, this approach would easily integrate with in-virtuo exposure therapy and would allow us to exploit as much as possible the enormous potential of these technologies. As a first step toward validation, we present a case study that supports the ACET approach.


title: Improving Conditional Sequence Generative Adversarial Networks by Stepwise Evaluation

abstract: Sequence generative adversarial networks (SeqGAN) have been used to improve conditional sequence generation tasks, for example, chit-chat dialogue generation. To stabilize the training of SeqGAN, Monte Carlo tree search (MCTS) or reward at every generation step (REGS) is used to evaluate the goodness of a generated subsequence. MCTS is computationally intensive, but the performance of REGS is worse than MCTS. In this paper, we propose stepwise GAN (StepGAN), in which the discriminator is modified to automatically assign scores quantifying the goodness of each subsequence at every generation step. StepGAN has significantly less computational costs than MCTS. We demonstrate that StepGAN outperforms previous GAN-based methods on both synthetic experiment and chit-chat dialogue generation.


title: Anomaly Detection for Cybersecurity of the Substations

abstract: Cybersecurity of the substations in a power system is a major issue as the substations become increasingly dependent on computer and communication networks. This paper is concerned with anomaly detection in the computer network environment of a substation. An anomaly inference algorithm is proposed for early detection of cyber-intrusions at the substations. The potential scenario of simultaneous intrusions launched over multiple substations is considered. The proposed detection method considers temporal anomalies. Potential intrusion events are ranked based on the credibility impact on the power system. Snapshots of anomaly entities at substations are described. Simulation results using the modified IEEE 118-bus system have shown the effectiveness of the proposed method for systematic identification. The result of this research is a tool to detect cyber-intrusions that are likely to cause significant damages to the power grid.


Formation of luminescent impurities during the polymerization of nylon 66


title: A user-specific and selective multimodal biometric fusion strategy by ranking subjects

abstract: The recognition performance of a biometric system varies significantly from one enrolled user to another. As a result, there is a need to tailor the system to each user. This study investigates a relatively new fusion strategy that is both user-specific and selective. By user-specific, we understand that each user in a biometric system has a different set of fusion parameters that have been tuned specifically to a given enrolled user. By selective, we mean that only a subset of modalities may be chosen for fusion. The rationale for this is that if one biometric modality is sufficiently good to recognize a user, fusion by multimodal biometrics would not be necessary, we advance the state of the art in user-specific and selective fusion in the following ways: (1) provide thorough analyses of (a) the effect of pre-processing the biometric output (prior to applying a user-specific score normalization procedure) in order to improve its central tendency and (b) the generalisation ability of user-specific parameters; (2) propose a criterion to rank the users based solely on a training score dataset in such a way that the obtained rank order will maximally correlate with the rank order that is obtained if it were to be computed on the test set; and, (3) experimentally demonstrate the performance gain of a user-specific and -selective fusion strategy across fusion data sets at different values of ''pruning rate'' that control the percentage of subjects for whom fusion is not required. Fifteen sets of multimodal fusion experiments carried out on the XM2VTS score-level benchmark database show that even though our proposed user-specific and -selective fusion strategy, its performance compares favorably with the conventional fusion system that considers all information.


title: Optimal trajectory for swim-out acoustic decoy to countermeasure torpedo

abstract: The optimal trajectory of a swim-out acoustic decoy defending against an acoustic homing torpedo is studied. First, the dynamic models of the torpedo and submarine, and several trajectory models of the decoy are established. The factors that affect the countermeasure effectiveness are analyzed. Then based on the assessment criterion, genetic algorithm is used to obtain the optimal parameters of the trajectory models under different cases. Simulation results show that the helical and circular models of decoy trajectory are dominated by three other models, i.e., straight line, second turn-angle and snake trajectory.


title: Knowledge transfer via multiple model local structure mapping

abstract: The effectiveness of knowledge transfer using classification algorithms depends on the difference between the distribution that generates the training examples and the one from which test examples are to be drawn. The task can be especially difficult when the training examples are from one or several domains different from the test domain. In this paper, we propose a locally weighted ensemble framework to combine multiple models for transfer learning, where the weights are dynamically assigned according to a model's predictive power on each test example. It can integrate the advantages of various learning algorithms and the labeled information from multiple training domains into one unified classification model, which can then be applied on a different domain. Importantly, different from many previously proposed methods, none of the base learning method is required to be specifically designed for transfer learning. We show the optimality of a locally weighted ensemble framework as a general approach to combine multiple models for domain transfer. We then propose an implementation of the local weight assignments by mapping the structures of a model onto the structures of the test domain, and then weighting each model locally according to its consistency with the neighborhood structure around the test example. Experimental results on text classification, spam filtering and intrusion detection data sets demonstrate significant improvements in classification accuracy gained by the framework. On a transfer learning task of newsgroup message categorization, the proposed locally weighted ensemble framework achieves 97% accuracy when the best single model predicts correctly only on 73% of the test examples. In summary, the improvement in accuracy is over 10% and up to 30% across different problems.


title: Supreme Court decision on violent video games was based on the First Amendment, not scientific evidence.

abstract: Comments on the article, "Violent video games and the Supreme Court: Lessons for the scientific community in the wake of Brown v. Entertainment Merchants Association," by C. J. Ferguson (see record 2013-04752-001). The commentators agree with Ferguson that the U.S. Supreme Court case involving violent video games offers scientists a unique opportunity to reflect on violent video game research and findings. However, they disagree with many of the points he made. Due to space limits, they focus on five major areas of disagreement: 1) The Supreme Court decision was based on the First Amendment, not scientific evidence, 2) comparison of two amicus briefs, 3) some disagreement in the field does mean that the field is evenly divided, 4) what constitutes a trivial effect, and 5) laboratory measures of aggression are not trivial. (PsycINFO Database Record (c) 2014 APA, all rights reserved). Language: en


Prosopopeia — Playing on the Edge of Reality


title: Active collaborative permutation learning

abstract: We consider the problem of Collaborative Permutation Recovery, i.e. recovering multiple permutations over objects (e.g. preference rankings over different options) from limited pairwise comparisons. We tackle both the problem of how to recover multiple related permutations from limited observations, and the active learning problem of which pairwise comparison queries to ask so as to allow better recovery. There has been much work on recovering single permutations from pairwise comparisons, but we show that considering several related permutations jointly we can leverage their relatedness so as to reduce the number of comparisons needed compared to reconstructing each permutation separately. To do so, we take a collaborative filtering / matrix completion approach and use a trace-norm or max-norm regularized matrix learning model. Our approach can also be seen as a collaborative learning version of Jamieson and Nowak's recent work on constrained permutation recovery, where instead of basing the recovery on known features, we learn the best features de novo.


title: Digital Steganography: Hiding data within Data

abstract: Digital steganography is the art of inconspicuously hiding data within data. Steganography's goal in general is to hide data well enough that unintended recipients do not suspect the steganographic medium of containing hidden data. The software and links mentioned in this article are just a sample of the steganography tools currently available. As privacy concerns continue to develop along with the digital communication domain, steganography will undoubtedly play a growing role in society. For this reason, it is important that we are aware of digital steganography technology and its implications. Equally important are the ethical concerns of using steganography and steganalysis. Steganography enhances rather than replaces encryption. Messages are not secure simply by virtue of being hidden. Likewise, steganography is not about keeping your message from being known - it's about keeping its existence from being known.


title: Characterizing structural relationships in scenes using graph kernels

abstract: Modeling virtual environments is a time consuming and expensive task that is becoming increasingly popular for both professional and casual artists. The model density and complexity of the scenes representing these virtual environments is rising rapidly. This trend suggests that data-mining a 3D scene corpus could be a very powerful tool enabling more efficient scene design. In this paper, we show how to represent scenes as graphs that encode models and their semantic relationships. We then define a kernel between these relationship graphs that compares common virtual substructures in two graphs and captures the similarity between their corresponding scenes. We apply this framework to several scene modeling problems, such as finding similar scenes, relevance feedback, and context-based model search. We show that incorporating structural relationships allows our method to provide a more relevant set of results when compared against previous approaches to model context search.


title: Extraction and Classification of Facemarks with Kernel Methods

abstract: To reduce disturbance and interference signals, and particularly recurring disturbance signals due to vibration or out-of-round conditions of rotating elements coupled to a speed transducer, a low-pass filter (11) is connected to the speed transducer and, in addition, a high-pass filter (12) which, however, is disconnected upon sensing that the speed of the wheel or of the vehicle is below a predetermined limit, or that the anti brake-lock system unit (13) has responded. Thus, disturbance signals, and particularly periodic disturbance signal which might cause oscillatory conditions to arise in the filter and transducer circuitry, are effectively suppressed. To further increase the reliability of response, the output from the filter circuits is connected to threshold circuits with a variable threshold level in which comparators compare a peak signal with a weighted, then existing signal, so that the comparison level threshold is continuously shifted based on previously occurring peaks, so that disturbance signals are reliably excluded from affecting signal processing within the anti brake-lock system unit (13).


title: FilaBavi, a demographic surveillance site, an epidemiological field laboratory in Vietnam

abstract: Background: This Supplement to the Scandinavian Journal of Public Health brings together a collection of community-based studies for which the common denominator is a demographic surveillance site, FilaBavi, established in northern Vietnam in 1999. This introductory paper sets out some of the background to FilaBavi as well as setting the scene for the individual studies.


title: Influence of Syndrome of Qi Deficiency with Blood Stasis on the Anti-thrombosis Effect after Percutaneous Coronary Intervention

abstract: Objective To explore the independent risk factors for the major cardiovascular events (MCVE) after percutaneous coronary intervention (PCI). Methods Totally 123 coronary heart disease (CHD) patients successful treated by PCI were given standard anti-thrombosis treatment,and they were classified into the qi deficiency with blood stasis group (49 cases) and non-qi deficiency with blood stasis group (74 cases) according to TCM syndrome differentiation. Seven days later,all patients were examined with blood test to observe their platelet count,maximum platelet aggregation (MPA),plasma hereditary false hemophilia factor vWF,platelet membranous glycoprotein Ⅱ b/Ⅲ a (GPⅡ b/Ⅲ a),prothrombin fragment 1+2 (F1+2),and fibrinogen (Fg). Using Logistic regression analysis to screen out the independent risk factors for MCVE one year after PCI. Results The MPA,vWF,and GPⅡb/Ⅲa of the qi deficiency with blood stasis group were higher than those of the non-qi deficiency with blood stasis group (P0.05),while the platelet count,F1+2,and Fg of the two groups were without statistical difference (P0.05). Conclusion There still exists the obvious platelet activation and vascular endothelial injury in qi deficiency with blood stasis after PCI,. This is one of the independent risk factor for MCVE after PCI.


title: An information extraction customizer

abstract: When an information extraction system is applied to a new task or domain, we must specify the classes of entities and relations to be extracted. This is best done by a subject matter expert, who may have little training in NLP. To meet this need, we have developed a toolset which is able to analyze a corpus and aid the user in building the specifications of the entity and relation types.


title: Path planning and collision avoidance for robots

abstract: An optimal control problem to find the fastest collision-free trajectory of a robot surrounded by obstacles is presented. ::: The collision avoidance is based on linear programming arguments and expressed as state constraints. The optimal control problem is ::: solved with a sequential programming method. In order to decrease the number of unknowns and constraints a backface culling active set ::: strategy is added to the resolution technique.


title: Color constancy using 3D scene geometry

abstract: The aim of color constancy is to remove the effect of the color of the light source. As color constancy is inherently an ill-posed problem, most of the existing color constancy algorithms are based on specific imaging assumptions such as the grey-world and white patch assumptions.


Universality Results for Pop-Ups


Text to Speech Conversion Using Raspberry-Pi for Embedded System


title: Sources of preanalytical error in pharmacokinetic analyses - focus on intravenous drug administration and collection of blood samples.

abstract: INTRODUCTION ::: Pharmacokinetic (PK) studies for long-established drugs are generally performed outside the well-standardized settings of pharmaceutical industry trials. Instead, such studies are usually performed within daily clinical practice of hospitals. ::: ::: ::: AREAS COVERED ::: This article describes aspects of intravenous (i.v.) drug administration and blood sampling that contribute to potential sources of preanalytical errors for PK investigations. Parameters that bias determination of start and end time of i.v. infusions, as well as consistent rate of drug delivery, are discussed. Causes for drug loss in the infusion device, including adsorption and insufficient flushing, are outlined. The advantages and disadvantages of different blood sampling techniques are reviewed, with an emphasis on pediatric studies. ::: ::: ::: EXPERT OPINION ::: For PK studies that are integrated into the general hospital routine, a variety of potential sources of error exist. Potential pitfalls depend on the specific drug and trial characteristics and they must be anticipated and discussed in advance. Working procedures need to be developed that address the anticipated problems and in detail describe procedures that need compliance between bed and bench.


title: Modeling the Evolution of Users’ Preferences and Social Links in Social Networking Services

abstract: Sociologists have long converged that the evolution of a S ocial N etworking S ervice(SNS) is driven by the interplay between users’ preferences (reflected in user-item interaction behavior) and the social network structure (reflected in user-user interaction behavior). Nevertheless, traditional approaches either modeled these two kinds of behaviors in isolation or relied on a static assumption of a SNS. Thus, it is still unclear how do the roles of the dynamic social network structure and users’ historical preferences affect the evolution of SNSs. Furthermore, can transforming the underlying social theories in the platform evolution modeling process benefit both behavior prediction tasks? In this paper, we incorporate the underlying social theories to explain and model the evolution of users’ two kinds of behaviors in SNSs. Specifically, we present two kinds of representations for users’ behaviors: a direct (latent) representation that presumes users’ behaviors are represented directly (latently) by their historical behaviors. Under each representation, we associate each user's two kinds of behaviors with two vectors at each time. Then, for each representation, we propose the corresponding learning model to fuse the interplay between users’ two kinds of behaviors. Finally, extensive experimental results demonstrate the effectiveness of our proposed models for both user preference prediction and social link suggestion.


Computer Vision-Aided Road Traffic Monitoring


title: A 20Gb/s NRZ/PAM-4 1V transmitter in 40nm CMOS driving a Si-photonic modulator in 0.13µm CMOS

abstract: The need for more bandwidth driven by streaming video and other data intensive applications has been steadily pushing the optical link speed to the 40G/100G domain. Compared to VCSEL and ring resonator, Mach-Zehnder Interferometer (MZI) is the best solution for long distance (>500m), high data rate (>28Gb/s) optical communications [1-3]. However, high power consumption, low link density and high cost seriously prevent traditional MZI from being the next generation of optical link technology. To fundamentally reduce the cost of MZI, it is highly desirable to make the process CMOS compatible with high efficiency, thus the modulation voltage, size, and power can be reduced to a level where advanced sub-1V CMOS circuits can be used as the driver. This paper presents two CMOS-MZI-based optical transmitters, NRZ or configurable PAM-N (N = 4,16), featuring 20Gb/s data rate and sub-pJ/bit modulation energy (PAM-4) using a 1V supply. The fully CMOS compatible photonic device is highly cost-effective in terms of integration, manufacturability and scalability.


title: Unsupervised Natural Language Generation with Denoising Autoencoders

abstract: Generating text from structured data is important for various tasks such as question answering and dialog systems. We show that in at least one domain, without any supervision and only based on unlabeled text, we are able to build a Natural Language Generation (NLG) system with higher performance than supervised approaches. In our approach, we interpret the structured data as a corrupt representation of the desired output and use a denoising auto-encoder to reconstruct the sentence. We show how to introduce noise into training examples that do not contain structured data, and that the resulting denoising auto-encoder generalizes to generate correct sentences when given structured data.


Breaking the mold of machine learning: technical perspective


title: Tracking control based on neural network strategy for robot manipulator

abstract: Abstract This study presents a sliding-mode neural-network (SMNN) control system for the tracking control of an n rigid-link robot manipulator to achieve high-precision position control. The aim of this study is to overcome some of the shortcomings of conventional robust controllers such as a model-based adaptive controller requires the system dynamics in detail; the fuzzy rule learning scheme has a latent stability problem; an adaptive control scheme for robot manipulator via fuzzy compensator requires strict constrained conditions and prior system knowledge. In the SMNN control system, a neural network controller is developed to mimic an equivalent control law in the sliding-mode control, and a robust controller is designed to curb the system dynamics on the sliding surface for guaranteeing the asymptotic stability property. Moreover, an adaptive bound estimation algorithm is employed to estimate the upper bound of uncertainties. All adaptive learning algorithms in the SMNN control system are derived from the sense of Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system whether the uncertainties occur or not. Computer simulations of a two-link robot manipulator verify the validity of the proposed control strategy in the possible presence of uncertainties and different trajectories. The proposed SMNN control scheme possesses two salient merits: (1) it guarantees the stability of the controlled system, and (2) no constrained conditions and prior knowledge of the controlled plant is required in the design process. This new intelligent methodology provides the designer with an alternative choice to control an n rigid-link robot manipulator.


title: Distant freehand pointing and clicking on very large, high resolution displays

abstract: We explore the design space of freehand pointing and clicking interaction with very large high resolution displays from a distance. Three techniques for gestural pointing and two for clicking are developed and evaluated. In addition, we present subtle auditory and visual feedback techniques to compensate for the lack of kinesthetic feedback in freehand interaction, and to promote learning and use of appropriate postures.


title: Arabic Diacritization with Recurrent Neural Networks

abstract: Arabic, Hebrew, and similar languages are typically written without diacritics, leading to ambiguity and posing a major challenge for core language processing tasks like speech recognition. Previous approaches to automatic diacritization employed a variety of machine learning techniques. However, they typically rely on existing tools like morphological analyzers and therefore cannot be easily extended to new genres and languages. We develop a recurrent neural network with long shortterm memory layers for predicting diacritics in Arabic text. Our language-independent approach is trained solely from diacritized text without relying on external tools. We show experimentally that our model can rival state-of-the-art methods that have access to additional resources.


title: 12-Way 100 kW Reentrant Cavity-Based Power Combiner With Doorknob Couplers

abstract: We present radio frequency (RF) and thermal characterization of a compact 12-way power combiner designed for operation at 352 MHz at a power level of 100 kW with 5% duty factor. The combiner is based on a reentrant cavity with 12 input doorknob couplers and one output coupler that is integrated with the post of the cavity and forms doorknob type geometry. We introduce convenient design formulas that allow easy identification of a suitable parameter space, which is then refined with numerical simulations. Low-power RF measurements of a prototype show 0.2% insertion loss and a relative rms amplitude imbalance between the ports of 0.1% and phase imbalance of 0.036° rms. The matching is better than −25 dB over a 3-dB bandwidth around the design frequency. We also tested the combiner up to 200 kW and found the RF loss to be comparable to that of the low-power measurement. In a long test run at 100 kW with 5% duty factor, the combiner temperature stabilized at 10° above ambient.


title: Collaborative Recommender Systems for Building Automation

abstract: Building Automation Systems (BASs) can save building owners money by reducing energy consumption while simultaneously preserving occupant comfort. There are algorithms that optimize this tradeoff, such as detecting which appliances are turned on without requiring expensive status detectors to be attached to each appliance. However, better ways are needed to determine which algorithms are best-suited to a particular building. This paper explores the idea of allowing building managers to automatically communicate among themselves and exchange ratings of individual monitoring and control algorithms in such a way that each building manager can then obtain predicted ratings for all algorithms that he has not yet tried personally. We allow individual algorithms to be replaced by using a blackboard architecture to loosen the coupling between them. We propose a recommender system that operates on a database of contributed ratings to predict ratings of untried algorithms. To explore this approach, we developed a prototype that seamlessly interacts with both emulated physical buildings and buildings simulated in software and we implemented several of the control algorithms described in previous works. We demonstrate a recommender system that selects between algorithms in various types of buildings.


title: A docitive Q-learning approach towards joint resource allocation and power control in self-organised femtocell networks

abstract: Femtocell is a technology that contributes towards the escalation of coverage as well as throughput. By virtue of uncertain deployment of femtocells, self-organisation is a viable solution for resource allocation. In this study, we are projecting a docitive Q-learning DQL paradigm for joint resource allocation and power control JRAPC. Moreover, the proposed learning paradigm is compared with independent Q-learning for the same JRAPC problem. In the proposed DQL paradigm, femto base stations, which are agents, learn the strategies by exploiting Q-learning and share their learned strategies with their neighbours. Concerning the shared channel environment, the problem function is formulated as the maximisation of femtocell capacity while maintaining the quality of service requirement of the macrousers. The impact of the proposed DQL paradigm is investigated on system capacity and femtocell capacity. Furthermore, comparison is carried out with the considered independent learning paradigm in terms of convergence, min-max capacity and the effect of femtocell density. Also, the fairness index is computed to have further insight. The results illustrate that DQL-based JRAPC outperforms its counterpart. Copyright © 2014 John Wiley & Sons, Ltd.


title: Trends, Tips, Tolls: A Longitudinal Study of Bitcoin Transaction Fees

abstract: The Bitcoin protocol supports optional direct payments from transaction partners to miners. These “fees” are supposed to substitute miners’ minting rewards in the long run. Acknowledging their role for the stability of the system, the right level of transaction fees is a hot topic of normative debates. This paper contributes empirical evidence from a historical analysis of agents’ revealed behavior concerning their payment of transaction fees. We identify several regime shifts, which can be largely explained by changes in the default client software or actions of big intermediaries in the ecosystem. Overall, it seems that rules dominate ratio, a state that is sustainable only if fees remain negligible.


title: Text detection and recognition using enhanced MSER detection and a novel OCR technique

abstract: Detection and recognition of text from any natural scene image is challenging but essential extensively for extracting information from the image. In this paper, we propose an accurate and effective algorithm for detecting enhanced Maximally Stable Extremal Regions (MSERs) as main character candidates and these character candidates are filtered by stroke width variation for removing regions where the stroke width exhibits too much variation. For the detection of text regions, firstly some preprocessing is applied to the natural image and then after detecting MSERs, an intersection of canny edge and MSER region is produced to locate regions that are even more likely to belong to text. Finally, the selected text region is taken as an input of a novel Optical Character Recognition (OCR) technique to make the text editable and usable. The evaluation results substantiates 77.47% of the f-measure on the ICDAR 2011 dataset which is better than the previous performance 76.22%.


title: A MOS Parametric Integrator With Improved Linearity for SC $\Sigma\Delta$ Modulators

abstract: This brief presents a double complimentary metal–oxide–semiconductor (MOS) parametric integrator (DCMPI) with improved linearity characteristics for wideband switched-capacitor sigma-delta ( $\Sigma\Delta$ ) modulators. Compared with the MOS parametric integrator [1] , the proposed circuit achieves similar power, noise, and speed performance, while its linearity characteristics and transfer function are improved. The main nonideality improvements in the proposed DCMPI are presented through the mathematical formulas and verified through the postlayout simulations performed in the Spectre/Cadence electrical simulator using the BSIM3v3 complementary MOS (CMOS) model in the standard 0.18- $\mu\hbox{m}$ CMOS process.


title: What Triggers Impulse Purchase Behavior: The Moderating Effects of User Expertise and Product Type

abstract: Exposing consumers to persuasive cues can cause them to make impulse purchases. Persuasive cues can be heuristic cue or systematic cue. Heuristic cue uses simple rules to process the information such as identity of the source or other non-content cues. Systematic cue emphasizes detailed processing of message content and uses messagerelevant content or arguments to assist in decision making. The features of heuristic cue and systematic cue are investigated to see how they can impact one’s impulse purchase behavior. The amount of expertise a consumer has in a specific product type (search or experience) will shape the relationships between persuasive cues and impulse purchase. The findings contribute to the impulse purchase literature and help merchants and website designers decide on what persuasive cues to provide consumers without overloading them with unnecessary information. To consumers, they will have better grip on their own impulse purchase behavior when exposed to persuasive cues.


title: Coronary Heart Disease Diagnosis using Deep Neural Networks

abstract: According to the World Health Organization, cardiovascular disease (CVD) is the top cause of death worldwide. In 2015, over 30% of global deaths was due to CVD, leading to over 17 million deaths, a global health burden. Of those deaths, over 7 million were caused by heart disease, and greater than 75% of deaths due to CVD were in developing countries. In the United States alone, 25% of deaths is attributed to heart disease, killing over 630,000 Americans annually. Among heart disease conditions, coronary heart disease is the most common, causing over 360,000 American deaths due to heart attacks in 2015. Thus, coronary heart disease is a public health issue. In this research paper, an enhanced deep neural network (DNN) learning was developed to aid patients and healthcare professionals and to increase the accuracy and reliability of heart disease diagnosis and prognosis in patients. The developed DNN learning model is based on a deeper multilayer perceptron architecture with regularization and dropout using deep learning. The developed DNN learning model includes a classification model based on training data and a prediction model for diagnosing new patient cases using a data set of 303 clinical instances from patients diagnosed with coronary heart disease at the Cleveland Clinic Foundation. The testing results showed that the DNN classification and prediction model achieved the following results: diagnostic accuracy of 83.67%, sensitivity of 93.51%, specificity of 72.86%, precision of 79.12%, F-Score of 0.8571, area under the ROC curve of 0.8922, Kolmogorov-Smirnov (K-S) test of 66.62%, diagnostic odds ratio (DOR) of 38.65, and 95% confidence interval for the DOR test of [38.65, 110.28]. Therefore, clinical diagnoses of coronary heart disease were reliably and accurately derived from the developed DNN classification and prediction models. Thus, the models can be used to aid healthcare professionals and patients throughout the world to advance both public health and global health, especially in developing countries and resource-limited areas with fewer cardiac specialists available.


title: From NLP (natural language processing) to MLP (machine language processing)

abstract: Natural Language Processing (NLP) in combination with Machine Learning techniques plays an important role in the field of automatic text analysis. Motivated by the successful use of NLP in solving text classification problems in the area of e-Participation and inspired by our prior work in the field of polymorphic shellcode detection we gave classical NLP-processes a trial in the special case of malicious code analysis. Any malicious program is based on some kind of machine language, ranging from manually crafted assembler code that exploits a buffer overflow to high level languages such as Javascript used in web-based attacks. We argue that well known NLP analysis processes can be modified and applied to the malware analysis domain. Similar to the NLP process we call this process Machine Language Processing (MLP). In this paper, we use our e-Participation analysis architecture, extract the various NLP techniques and adopt them for the malware analysis process. As proof-of-concept we apply the adopted framework to malicious code examples from Metasploit.


8 LOW-DISTORTION EMBEDDINGS OF FINITE METRIC SPACES


title: The Smartphone Psychology Manifesto

abstract: By 2025, when most of today’s psychology undergraduates will be in their mid-30s, more than 5 billion people on our planet will be using ultra-broadband, sensor-rich smartphones far beyond the abilities of today’s iPhones, Androids, and Blackberries. Although smartphones were not designed for psychological research, they can collect vast amounts of ecologically valid data, easily and quickly, from large global samples. If participants download the right “psych apps,” smartphones can record where they are, what they are doing, and what they can see and hear and can run interactive surveys, tests, and experiments through touch screens and wireless connections to nearby screens, headsets, biosensors, and other peripherals. This article reviews previous behavioral research using mobile electronic devices, outlines what smartphones can do now and will be able to do in the near future, explains how a smartphone study could work practically given current technology (e.g., in studying ovulatory cycle effects on w...


title: Trunk muscle recruitment patterns in specific chronic low back pain populations.

abstract: BACKGROUND ::: It is hypothesized that injury or degeneration of osteoligamentous spinal structures would require compensation by trunk musculature and alterations in motor control to maintain spine stability. While, biomechanical modeling has supported this hypothesis, studies of muscle recruitment patterns in chronic low back pain patients both with and without significant osteoligamentous damage have been limited. This study utilized a non-randomized case-control design to investigate trunk muscle recruitment patterns around the neutral spine position between subgroups of patients with chronic mechanical low back pain and asymptomatic controls. ::: ::: ::: METHODS ::: Twenty subjects with chronic low back pain attributed to clinical lumbar instability were matched to 20 asymptomatic controls. In addition 12 patients with non-specific chronic low back pain were studied. Surface EMG from five trunk muscles was analyzed to determine activation levels and patterns of recruitment during a standing reach under two different loading conditions. ::: ::: ::: FINDINGS ::: The chronic low back pain group with symptoms attributed to clinical instability demonstrated significantly higher activation levels of the external oblique and rectus abdominus muscles and lower abdominal synergist ratios than the control group. No significant differences were found between patient subgroups. ::: ::: ::: INTERPRETATION ::: While these data demonstrate altered muscle recruitment patterns in patients with chronic low back pain, the changes are not consistent with Panjabi's theory suggesting that these alterations are driven by passive subsystem damage. However, the higher activation of global abdominal musculature and altered synergist patterns may represent a motor control pattern that has consequences for continued dysfunction and chronic pain.


title: A similarity metric method of obfuscated malware using function-call graph

abstract: Code obfuscating technique plays a significant role to produce new obfuscated malicious programs, generally called malware variants, from previously encountered malwares. However, the traditional signature-based malware detecting method is hard to recognize the up-to-the-minute obfuscated malwares. This paper proposes a method to identify the malware variants based on the function-call graph. Firstly, the function-call graphs were created from the disassembled codes of program; then the caller---callee relationships of functions and the operational code (opcode) information about functions, combining the graph coloring techniques were used to measure the similarity metric between two function-call graphs; at last, the similarity metric was utilized to identify the malware variants from known malwares. The experimental results show that the proposed method is able to identify the obfuscated malicious softwares effectively.


3 D Lidar-Camera Intrinsic and Extrinsic Calibration : Observability Analysis and Analytical Least Squares-based Initialization


title: Modeling the adoption and use of social media by nonprofit organizations

abstract: This study examines what drives organizational adoption and use of social media through a model built around four key factors – strategy, capacity, governance and environment. Using Twitter, Facebook, and other data on 100 large US nonprofit organizations, the model is employed to examine the determinants of three key facets of social media utilization: (1) adoption, (2) frequency of use and (3) dialogue. We find that organizational strategies, capacities, governance features and external pressures all play a part in these social media adoption and utilization outcomes. Through its integrated, multi-disciplinary theoretical perspective, this study thus helps foster understanding of which types of organizations are able and willing to adopt and juggle multiple social media accounts, to use those accounts to communicate more frequently with their external publics, and to build relationships with those publics through the sending of dialogic messages.


title: Motivations for social networking at work

abstract: The introduction of a social networking site inside of a large enterprise enables a new method of communication between colleagues, encouraging both personal and professional sharing inside the protected walls of a company intranet. Our analysis of user behavior and interviews presents the case that professionals use internal social networking to build stronger bonds with their weak ties and to reach out to employees they do not know. Their motivations in doing this include connecting on a personal level with coworkers, advancing their career with the company, and campaigning for their projects.


title: Sizing and optimization of a fully autonomous hybrid PV-wind power system

abstract: In this paper, we will present a design study and optimization of a hybrid power system for the electrification of isolated sites, and which combines two renewable energy sources, solar and wind, with storage battery to ensure full autonomy of the system. The aim is to dimension the optimal configuration PV/Wind/Batteries, which fill the need of a daily electrical demand, following the approach which allows: study the climate conditions at the site, the definition of all the specific elements of the system, the technical and economic characteristics, and then the simulation software Homer. And in final phase the evaluation of the energy performance of the system, the economic analysis of the optimal combination and a sensitivity analysis to optimize the dimensioning. The achieved results for a study of three regions of Morocco, show that the feasibility of implementing hybrid system depends closely to the energy resources of the implantation site, and the purchase price of the system components. Adding to this the use of single-source systems, solar or wind energy with batteries, are optimal for small electrical charges, on the other hand the PV/Wind/Batteries system is more suitable for big systems that requires big energy power in order to ensure more autonomy of the system.


title: Linear transform for simultaneous diagonalization of covariance and perceptual metric matrix in image coding

abstract: Two types ofredundancies are contained in images: statistical redundancy and psychovisual redundancy. Image representation techniques for image coding should remove both redundancies in order to obtain good results. In order to establish an appropriate representation, the standard approach to transform coding only considers the statistical redundancy, whereas the psychovisual factors are introduced after the selection ofthe representation as a simple scalar weighting in the transform domain. In this work, we take into account the psychovisual factors in the de8nition of the representation together with the statistical factors, by means of the perceptual metric and the covariance matrix, respectively. In general the ellipsoids described by these matrices are not aligned. Therefore, the optimal basis for image representation should simultaneously diagonalize both matrices. This approach to the basis selection problem has several advantages in the particular application ofimage coding. As the transform domain is Euclidean (by de8nition), the quantizer design is highly simpli8ed and at the same time, the use ofscalar quantizers is truly justi8ed. The proposed representation is compared to covariance-based representations such as the DCT and the KLT or PCA using standard JPEG-like and Max-Lloyd quantizers. ? 2003 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.


title: Effectiveness of automatic translations for cross-lingual ontology mapping

abstract: Accessing or integrating data lexicalized in different languages is a challenge. Multilingual lexical resources play a fundamental role in reducing the language barriers to map concepts lexicalized in different languages. In this paper we present a large-scale study on the effectiveness of automatic translations to support two key cross-lingual ontology mapping tasks: the retrieval of candidate matches and the selection of the correct matches for inclusion in the final alignment. We conduct our experiments using four different large gold standards, each one consisting of a pair of mapped wordnets, to cover four different families of languages. We categorize concepts based on their lexicalization (type of words, synonym richness, position in a subconcept graph) and analyze their distributions in the gold standards. Leveraging this categorization, we measure several aspects of translation effectiveness, such as word-translation correctness, word sense coverage, synset and synonym coverage. Finally, we thoroughly discuss several findings of our study, which we believe are helpful for the design of more sophisticated cross-lingual mapping algorithms.


Radoop: Analyzing Big Data with RapidMiner and


title: Reflections on the use of chord diagrams in social network visualization in process mining

abstract: Data Visualization is an important area of research including different techniques to enhance the capability of people to understand and use data-driven information. The chord diagram is a technique that aims to support the visualization of relations among different participants in a social network. Although this technique is widely used and adopted in many disciplines, it is not currently implemented in Business Process Management (BPM). In this paper, we show the potential of the visualizing social network in BPM area using the chord diagram. The result shows the potential benefits and strength of this technique to discover social network patterns in BPM area.


title: Understanding Hidden Memories of Recurrent Neural Networks

abstract: Recurrent neural networks (RNNs) have been successfully applied to various natural language processing (NLP) tasks and achieved better results than conventional methods. However, the lack of understanding of the mechanisms behind their effectiveness limits further improvements on their architectures. In this paper, we present a visual analytics method for understanding and comparing RNN models for NLP tasks. We propose a technique to explain the function of individual hidden state units based on their expected response to input texts. We then co-cluster hidden state units and words based on the expected response and visualize co-clustering results as memory chips and word clouds to provide more structured knowledge on RNNs' hidden states. We also propose a glyph-based sequence visualization based on aggregate information to analyze the behavior of an RNN's hidden state at the sentence-level. The usability and effectiveness of our method are demonstrated through case studies and reviews from domain experts.


title: LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation

abstract: FlowNet2, the state-of-the-art convolutional neural network (CNN) for optical flow estimation, requires over 160M parameters to achieve accurate flow estimation. In this paper we present an alternative network that outperforms FlowNet2 on the challenging Sintel final pass and KITTI benchmarks, while being 30 times smaller in the model size and 1.36 times faster in the running speed. This is made possible by drilling down to architectural details that might have been missed in the current frameworks: (1) We present a more effective flow inference approach at each pyramid level through a lightweight cascaded network. It not only improves flow estimation accuracy through early correction, but also permits seamless incorporation of descriptor matching in our network. (2) We present a novel flow regularization layer to ameliorate the issue of outliers and vague flow boundaries by using a feature-driven local convolution. (3) Our network owns an effective structure for pyramidal feature extraction and embraces feature warping rather than image warping as practiced in FlowNet2. Our code and trained models are available at https://github.com/twhui/LiteFlowNet.


title: Transfer joint embedding for cross-domain named entity recognition

abstract: Named Entity Recognition (NER) is a fundamental task in information extraction from unstructured text. Most previous machine-learning-based NER systems are domain-specific, which implies that they may only perform well on some specific domains (e.g., Newswire) but tend to adapt poorly to other related but different domains (e.g., Weblog). Recently, transfer learning techniques have been proposed to NER. However, most transfer learning approaches to NER are developed for binary classification, while NER is a multiclass classification problem in nature. Therefore, one has to first reduce the NER task to multiple binary classification tasks and solve them independently. In this article, we propose a new transfer learning method, named Transfer Joint Embedding (TJE), for cross-domain multiclass classification, which can fully exploit the relationships between classes (labels), and reduce domain difference in data distributions for transfer learning. More specifically, we aim to embed both labels (outputs) and high-dimensional features (inputs) from different domains (e.g., a source domain and a target domain) into a unified low-dimensional latent space, where 1) each label is represented by a prototype and the intrinsic relationships between labels can be measured by Euclidean distance; 2) the distance in data distributions between the source and target domains can be reduced; 3) the source domain labeled data are closer to their corresponding label-prototypes than others. After the latent space is learned, classification on the target domain data can be done with the simple nearest neighbor rule in the latent space. Furthermore, in order to scale up TJE, we propose an efficient algorithm based on stochastic gradient descent (SGD). Finally, we apply the proposed TJE method for NER across different domains on the ACE 2005 dataset, which is a benchmark in Natural Language Processing (NLP). Experimental results demonstrate the effectiveness of TJE and show that TJE can outperform state-of-the-art transfer learning approaches to NER.


title: Artificial Neural Networks ’ Applications in Management 1,2 2

abstract: 3 Abstract: Finding more effective solution and tools for complicated managerial problems is one of the most important and dominant subjects in management studies. With the advancement of computer and communication technology, the tools that are using for management decisions have undergone a massive change. Artificial Neural Networks (ANNs) are one of these tools that have become a critical component of business intelligence. In this article we describe the basic of neural networks as well as a review of selected works done in application of ANNs in management sciences.


title: Cognitive Biases in New Technology Appropriation : An experiment on the impact of judgmental and presentational priming

abstract: This paper extends individual-based theories of adaptive structuration by exploring the role of cognitive biases in shaping the individual sensemaking of a new technology (i.e., an online product configurator). We question whether cognitive biases intervene in the sensemaking process to influence the ensuing appropriation of the technology. We experimentally trigger cognitive biases by priming participants (N=645) with positive/negative judgments about the technology and with textual/visual instructions about its features. As outcome variables, we measure subjective and objective faithfulness of appropriation (FOA), and satisfaction with the technology. Results indicate that negative judgment lowers users’ satisfaction, but at the same time increases their objective FOA. Compared to textual priming, visual priming leads to higher satisfaction, and triggers an illusion of having appropriated the technology faithfully – although without influencing objective FOA. We conclude by showing implications for IS scholars and practitioners.


title: A Comparative Analysis of Persona Clustering Methods

abstract: Current and future information systems require a better understanding of the interactions between users and systems in order to improve system use, and ultimately, success. The use of personas as design tools is becoming more widespread as academicians and practitioners discover its benefits. This paper presents an empirical study comparing the performance of existing qualitative and quantitative clustering techniques at the task of identifying personas and grouping system users into those personas. A method based on Factor (Principal Component) Analysis outperforms two others using Latent Semantic Analysis and Multivariate Cluster Analysis.


title: Identifying Frequent User Tasks from Application Logs

abstract: In the light of continuous growth in log analytics, application logs remain a valuable source to understand and analyze patterns in user behavior. Today, almost every major software company employs analysts to reveal user insights from log data. To understand the tasks and challenges of the analysts, we conducted a background study with a group of analysts from a major software company. A fundamental analytics objective that we recognized through this study involves identifying frequent user tasks from application logs. More specifically, analysts are interested in identifying operation groups that represent meaningful tasks performed by many users inside applications. This is challenging, primarily because of the nature of modern application logs, which are long, noisy and consist of events from high-cardinality set. In this paper, we address these challenges to design a novel frequent pattern ranking technique that extracts frequent user tasks from application logs. Our experimental study shows that our proposed technique significantly outperforms state of the art for real-world data.


title: Multi-swarm Optimization in Dynamic Environments

abstract: Many real-world problems are dynamic, requiring an optimization algorithm which is able to continuously track a changing optimum over time. In this paper, we present new variants of Particle Swarm Optimization (PSO) specifically designed to work well in dynamic environments. The main idea is to extend the single population PSO and Charged Particle Swarm Optimization (CPSO) methods by constructing interacting multi-swarms. In addition, a new algorithmic variant, which broadens the implicit atomic analogy of CPSO to a quantum model, is introduced. The multi-swarm algorithms are tested on a multi-modal dynamic function – the moving peaks benchmark – and results are compared to the single population approach of PSO and CPSO, and to results obtained by a state-of-the-art evolutionary algorithm, namely self-organizing scouts (SOS). We show that our multi-swarm optimizer significantly outperforms single population PSO on this problem, and that multi-quantum swarms are superior to multi-charged swarms and SOS.


title: Autonomous driving in urban environments: approaches, lessons and challenges

abstract: The development of autonomous vehicles for urban driving has seen rapid progress in the past 30 years. This paper provides a summary of the current state of the art in autonomous driving in urban environments, based primarily on the experiences of the authors in the 2007 DARPA Urban Challenge (DUC). The paper briefly summarizes the approaches that different teams used in the DUC, with the goal of describing some of the challenges that the teams faced in driving in urban environments. The paper also highlights the long-term research challenges that must be overcome in order to enable autonomous driving and points to opportunities for new technologies to be applied in improving vehicle safety, exploiting intelligent road infrastructure and enabling robotic vehicles operating in human environments.


title: Synthesis of Secure FPGA Implementations

abstract: This paper describes the synthesis of Dynamic Differential Logic to increase the resistance of FPGA implementations against Differential Power Analysis. The synthesis procedure is developed and a detailed description is given of how EDA tools should be used appropriately to implement a secure digital design flow. Compared with an existing technique to implement Dynamic Differential Logic on FPGA, the technique saves a factor 2 in slice utilization. Experimental results also indicate that a secure version of the AES encryption algorithm can now be implemented with a mere 50% increase in time delay and 90% increase in slice utilization when compared with a normal non-secure single ended implementation.


title: Sparse Compositional Metric Learning

abstract: We propose a new approach for metric learning by framing it as learning a sparse combination of locally discriminative metrics that are inexpensive to generate from the training data. This flexible framework allows us to naturally derive formulations for global, multi-task and local metric learning. The resulting algorithms have several advantages over existing methods in the literature: a much smaller number of parameters to be estimated and a principled way to generalize learned metrics to new testing data points. To analyze the approach theoretically, we derive a generalization bound that justifies the sparse combination. Empirically, we evaluate our algorithms on several datasets against state-of-the-art metric learning methods. The results are consistent with our theoretical findings and demonstrate the superiority of our approach in terms of classification performance and scalability.


title: Identifying Key Users in Online Social Networks: A PageRank Based Approach

abstract: Online social networks evolved into a global mainstream medium that generates an increasing social and economic impact. However, many online social networks face the question how to leverage on their fast growing popularity to achieve sustainable revenues. In that context, particularly more effective advertising strategies and sophisticated customer loyalty programs to foster users' retention are needed. Thereby, key users in terms of users' connectivity and communication activity play a decisive role. However, quantitative approaches for the identification of key users in online social networks merging concepts and findings from research on users' connectivity and communication activity are missing. Based on the design science research paradigm, we therefore propose a novel PageRank based approach bringing together both research streams. To demonstrate its practical applicability, we use a publicly available dataset of Facebook.com. Finally, we evaluate our novel PageRank based approach in comparison to existing approaches, which could alternatively be used.


title: Empathy with Inanimate Objects and the Uncanny Valley

abstract: The term "uncanny valley" goes back to an article of the Japanese roboticist Masahiro Mori (Mori 1970, 2005). He put forward the hypothesis that humanlike objects like certain kinds of robots elicit emotional responses similar to real humans proportionate to their degree of human likeness. Yet, if a certain degree of similarity is reached emotional responses become all of a sudden very repulsive. The corresponding recess in the supposed function is called the uncanny valley. The present paper wants to propose a philosophical explanation why we feel empathy with inanimate objects in the first place, and why the uncanny valley occurs when these objects become very humanlike. The core of this explanation--which is informed by the recently developing empirical research on the matter--will be a form of empathy involving a kind of imaginative perception. However, as will be shown, imaginative perception fails in cases of very humanlike objects.


title: Topics on Knowledge Management: An Empirical Insight into Articles Published in the International Journal of Knowledge Management

abstract: The International Journal of Knowledge Management (IJKM) has continuously attracted high-quality contributions. The combination of proper journal management and marketing led to the ongoing success of the journal. Within this contribution, we analyze the content of 176 papers published from 2005 until 2013. Furthermore, the authors raise upcoming research questions. The main goal of this work is to present the current status of the research on Knowledge Management, while offering other scholars a robust basis for future research endeavors.


title: Declarative Cleaning of Inconsistencies in Information Extraction

abstract: The population of a predefined relational schema from textual content, commonly known as Information Extraction (IE), is a pervasive task in contemporary computational challenges associated with Big Data. Since the textual content varies widely in nature and structure (from machine logs to informal natural language), it is notoriously difficult to write IE programs that unambiguously extract the sought information. For example, during extraction, an IE program could annotate a substring as both an address and a person name. When this happens, the extracted information is said to be inconsistent, and some way of removing inconsistencies is crucial to compute the final output. Industrial-strength IE systems like GATE and IBM SystemT therefore provide a built-in collection of cleaning operations to remove inconsistencies from extracted relations. These operations, however, are collected in an ad hoc fashion through use cases. Ideally, we would like to allow IE developers to declare their own policies. But existing cleaning operations are defined in an algorithmic way, and hence it is not clear how to extend the built-in operations without requiring low-level coding of internal or external functions. We embark on the establishment of a framework for declarative cleaning of inconsistencies in IE through principles of database theory. Specifically, building upon the formalism of document spanners for IE, we adopt the concept of prioritized repairs, which has been recently proposed as an extension of the traditional database repairs to incorporate priorities among conflicting facts. We show that our framework captures the popular cleaning policies, as well as the POSIX semantics for extraction through regular expressions. We explore the problem of determining whether a cleaning declaration is unambiguous (i.e., always results in a single repair) and whether it increases the expressive power of the extraction language. We give both positive and negative results, some of which are general and some of which apply to policies used in practice.


title: Flow-based Brute-force Attack Detection

abstract: Brute-force attacks are a prevalent phenomenon that is getting harderto successfully detect on a network level due to increasing volume and en-cryption of network traffic and growing ubiquity of hi ...


title: Watch What You Wear: Preliminary Forensic Analysis of Smart Watches

abstract: This work presents preliminary forensic analysis of two popular smart watches, the Samsung Gear 2 Neo and LG G. These wearable computing devices have the form factor of watches and sync with smart phones to display notifications, track footsteps and record voice messages. We posit that as smart watches are adopted by more users, the potential for them becoming a haven for digital evidence will increase thus providing utility for this preliminary work. In our work, we examined the forensic artifacts that are left on a Samsung Galaxy S4 Active phone that was used to sync with the Samsung Gear 2 Neo watch and the LG G watch. We further outline a methodology for physically acquiring data from the watches after gaining root access to them. Our results show that we can recover a swath of digital evidence directly form the watches when compared to the data on the phone that is synced with the watches. Furthermore, to root the LG G watch, the watch has to be reset to its factory settings which is alarming because the process may delete data of forensic relevance. Although this method is forensically intrusive, it may be used for acquiring data from already rooted LG watches. It is our observation that the data at the core of the functionality of at least the two tested smart watches, messages, health and fitness data, e-mails, contacts, events and notifications are accessible directly from the acquired images of the watches, which affirms our claim that the forensic value of evidence from smart watches is worthy of further study and should be investigated both at a high level and with greater specificity and granularity.


title: Diversified Stress Testing of RDF Data Management Systems

abstract: The Resource Description Framework (RDF) is a standard for conceptually describing data on the Web, and SPARQL is the query language for RDF. As RDF data continue to be published across heterogeneous domains and integrated at Web-scale such as in the Linked Open Data (LOD) cloud, RDF data management systems are being exposed to queries that are far more diverse and workloads that are far more varied. The first contribution of our work is an in-depth experimental analysis that shows existing SPARQL benchmarks are not suitable for testing systems for diverse queries and varied workloads. To address these shortcomings, our second contribution is the Waterloo SPARQL Diversity Test Suite (WatDiv) that provides stress testing tools for RDF data management systems. Using WatDiv, we have been able to reveal issues with existing systems that went unnoticed in evaluations using earlier benchmarks. Specifically, our experiments with five popular RDF data management systems show that they cannot deliver good performance uniformly across workloads. For some queries, there can be as much as five orders of magnitude difference between the query execution time of the fastest and the slowest system while the fastest system on one query may unexpectedly time out on another query. By performing a detailed analysis, we pinpoint these problems to specific types of queries and workloads.


title: Discovering Simple Mappings Between Relational Database Schemas and Ontologies

abstract: Ontologies proliferate with the growth of the Semantic Web. However, most of data on the Web are still stored in relational databases. Therefore, it is important to establish interoperability between relational databases and ontologies for creating a Web of data. An effective way to achieve interoperability is finding mappings between relational database schemas and ontologies. In this paper, we propose a new approach to discovering simple mappings between a relational database schema and an ontology. It exploits simple mappings based on virtual documents, and eliminates incorrect mappings via validating mapping consistency. Additionally, it also constructs a special type of semantic mappings, called contextual mappings, which is useful for practical applications. Experimental results demonstrate that our approach performs well on several data sets from real world domains.


title: Direct Lewis Acid Catalyzed Conversion of Enantioenriched N-Acyloxazolidinones to Chiral Esters, Amides, and Acids.

abstract: The identification of Yb(OTf)3 through a multivariable high-throughput experimentation strategy has enabled a unified protocol for the direct conversion of enantioenriched N-acyloxazolidinones to the corresponding chiral esters, amides, and carboxylic acids. This straightforward and catalytic method has shown remarkable chemoselectivity for substitution at the acyclic N-acyl carbonyl for a diverse array of N-acyloxazolidinone substrates. The ionic radius of the Lewis acid catalyst was demonstrated as a key driver of catalyst performance that led to the identification of a robust and scalable esterification of a pharmaceutical intermediate using catalytic Y(OTf)3.


title: Low-Power Design of a Self-powered Piezoelectric Energy Harvesting System With Maximum Power Point Tracking

abstract: A low-power energy harvesting system targeting to harvest several milliwatts from vibration is presented in this paper. Several low-power design schemes to reduce power dissipation of the proposed system are described, and sources of power loss are analyzed to improve the power efficiency. A discontinuous conduction mode (DCM) flyback converter with the constant on-time modulation is adopted for our system. The DCM operation of a flyback converter is chosen as for maximum power point tracking (MPPT) to be implemented with a single current sensor. The constant on-time modulation lowers the clock frequency of the controller by more than an order of magnitude for our system, which reduces the dynamic power dissipation of the controller. MPPT, executed by a microcontroller unit (MCU), is achieved through dynamic resistive matching, and the MPPT is executed at intermittent time intervals due to a relatively slow change of the operating condition. When MPPT is not active, the MCU operates at a lower clock frequency to save power. Experimental results indicate that the proposed system harvests up to 8.4 mW power under 0.5-g base acceleration with four parallel piezoelectric cantilevers and achieves 72% power efficiency around the resonant frequency of 47 Hz.


title: Intellegence autonomous parking control system of four-wheeled vehicle

abstract: A four-wheeled vehicle autonomous parking control system has been designed. This paper presents mathematical formulation of the vehicle that provides planning the minimum length parking trajectory and minimum parking maneuver space. Control system of the autonomous park assistant is based on the fuzzy logic. Experimental results were obtained with prototype of the four-wheeled vehicle.


title: Path integration and cognitive mapping in a continuous attractor neural network model

abstract: A minimal synaptic architecture is proposed for how the brain might perform path integration by computing the next internal representation of self-location from the current representation and from the perceived velocity of motion. In the model, a place-cell assembly called a “chart” contains a two-dimensional attractor set called an “attractor map” that can be used to represent coordinates in any arbitrary environment, once associative binding has occurred between chart locations and sensory inputs. In hippocampus, there are different spatial relations among place fields in different environments and behavioral contexts. Thus, the same units may participate in many charts, and it is shown that the number of uncorrelated charts that can be encoded in the same recurrent network is potentially quite large. According to this theory, the firing of a given place cell is primarily a cooperative effect of the activity of its neighbors on the currently active chart. Therefore, it is not particularly useful to think of place cells as encoding any particular external object or event. Because of its recurrent connections, hippocampal field CA3 is proposed as a possible location for this “multichart” architecture; however, other implementations in anatomy would not invalidate the main concepts. The model is implemented numerically both as a network of integrate-and-fire units and as a “macroscopic” (with respect to the space of states) description of the system, based on a continuous approximation defined by a system of stochastic differential equations. It provides an explanation for a number of hitherto perplexing observations on hippocampal place fields, including doubling, vanishing, reshaping in distorted environments, acquiring directionality in a two-goal shuttling task, rapid formation in a novel environment, and slow rotation after disorientation. The model makes several new predictions about the expected properties of hippocampal place cells and other cells of the proposed network.


title: Mono-pulse Comparator Network System with hybrid rings

abstract: The Mono-pulse Comparator Network System (MCNS) with hybrid rings at frequency 2.4GHz is presented in this paper. The main objective is to provide the four feed signals for mono-pulse antenna. The methodology that is used for this work is the HFSS simulation, Photo-lithographic process and testing using SNA-2550.


title: Induction of visual orientation modules in auditory cortex

abstract: Modules of neurons sharing a common property are a basic organizational feature of mammalian sensory cortex. Primary visual cortex (V1) is characterized by orientation modules--groups of cells that share a preferred stimulus orientation--which are organized into a highly ordered orientation map. Here we show that in ferrets in which retinal projections are routed into the auditory pathway, visually responsive neurons in 'rewired' primary auditory cortex are also organized into orientation modules. The orientation tuning of neurons within these modules is comparable to the tuning of cells in V1 but the orientation map is less orderly. Horizontal connections in rewired cortex are more patchy and periodic than connections in normal auditory cortex, but less so than connections in V1. These data show that afferent activity has a profound influence on diverse components of cortical circuitry, including thalamocortical and local intracortical connections, which are involved in the generation of orientation tuning, and long-range horizontal connections, which are important in creating an orientation map.


title: Molecular definitions of autophagy and related processes

abstract: Over the past two decades, the molecular machinery that underlies autophagic responses has been characterized with ever increasing precision in multiple model organisms. Moreover, it has become clear that autophagy and autophagy-related processes have profound implications for human pathophysiology. However, considerable confusion persists about the use of appropriate terms to indicate specific types of autophagy and some components of the autophagy machinery, which may have detrimental effects on the expansion of the field. Driven by the overt recognition of such a potential obstacle, a panel of leading experts in the field attempts here to define several autophagy-related terms based on specific biochemical features. The ultimate objective of this collaborative exchange is to formulate recommendations that facilitate the dissemination of knowledge within and outside the field of autophagy research.