title: Algorithm 832: UMFPACK V4.3---an unsymmetric-pattern multifrontal method

abstract: An ANSI C code for sparse LU factorization is presented that combines a column pre-ordering strategy with a right-looking unsymmetric-pattern multifrontal numerical factorization. The pre-ordering and symbolic analysis phase computes an upper bound on fill-in, work, and memory usage during the subsequent numerical factorization. User-callable routines are provided for ordering and analyzing a sparse matrix, computing the numerical factorization, solving a system with the LU factors, transposing and permuting a sparse matrix, and converting between sparse matrix representations. The simple user interface shields the user from the details of the complex sparse factorization data structures by returning simple handles to opaque objects. Additional user-callable routines are provided for printing and extracting the contents of these opaque objects. An even simpler way to use the package is through its MATLAB interface. UMFPACK is incorporated as a built-in operator in MATLAB 6.5 as x = Abb when A is sparse and unsymmetric.


title: Topological trajectory clustering with relative persistent homology

abstract: Cloud Robotics techniques based on Learning from Demonstrations suggest promising alternatives to manual programming of robots and autonomous vehicles. One challenge is that demonstrated trajectories may vary dramatically: it can be very difficult, if not impossible, for a system to learn control policies unless the trajectories are clustered into meaningful consistent subsets. Metric clustering methods, based on a distance measure, require quadratic time to compute a pairwise distance matrix and do not naturally distinguish topologically distinct trajectories. This paper presents an algorithm for topological clustering based on relative persistent homology, which, for a fixed underlying simplicial representation and discretization of trajectories, requires only linear time in the number of trajectories. The algorithm incorporates global constraints formalized in terms of the topology of sublevel or superlevel sets of a function and can be extended to incorporate probabilistic motion models. In experiments with real automobile and ship GPS trajectories as well as pedestrian trajectories extracted from video, the algorithm clusters trajectories into meaningful consistent subsets and, as we show in an experiment with ship trajectories, results in a faster and more efficient clustering than a metric clustering by Frechet distance.


title: How to Identify an Infection Source with Limited Observations

abstract: A rumor spreading in a social network or a disease propagating in a community can be modeled as an infection spreading in a network. Finding the infection source is a challenging problem, which is made more difficult in many applications where we have access only to a limited set of observations. We consider the problem of estimating an infection source for a Susceptible-Infected model, in which not all infected nodes can be observed. When the network is a tree, we show that an estimator for the source node associated with the most likely infection path that yields the limited observations is given by a Jordan center, i.e., a node with minimum distance to the set of observed infected nodes. We also propose approximate source estimators for general networks. Simulation results on various synthetic networks and real world networks suggest that our estimators perform better than distance, closeness, and betweenness centrality based heuristics .


title: Light Propagation with Phase Discontinuities: Generalized Laws of Reflection and Refraction

abstract: Conventional optical components rely on gradual phase shifts accumulated during light propagation to shape light beams. New degrees of freedom are attained by introducing abrupt phase changes over the scale of the wavelength. A two-dimensional array of optical resonators with spatially varying phase response and subwavelength separation can imprint such phase discontinuities on propagating light as it traverses the interface between two media. Anomalous reflection and refraction phenomena are observed in this regime in optically thin arrays of metallic antennas on silicon with a linear phase variation along the interface, which are in excellent agreement with generalized laws derived from Fermat’s principle. Phase discontinuities provide great flexibility in the design of light beams, as illustrated by the generation of optical vortices through use of planar designer metallic interfaces.


Ultra-Low Power Sensor Devices for Monitoring Physical Activity and Respiratory Frequency in Farmed Fish


title: Black Box Variational Inference

abstract: Variational inference has become a widely used method to approximate posteriors in complex latent variables models. However, deriving a variational inference algorithm generally requires significant model-specific analysis, and these efforts can hinder and deter us from quickly developing and exploring a variety of models for a problem at hand. In this paper, we present a "black box" variational inference algorithm, one that can be quickly applied to many models with little additional derivation. Our method is based on a stochastic optimization of the variational objective where the noisy gradient is computed from Monte Carlo samples from the variational distribution. We develop a number of methods to reduce the variance of the gradient, always maintaining the criterion that we want to avoid difficult model-based derivations. We evaluate our method against the corresponding black box sampling based methods. We find that our method reaches better predictive likelihoods much faster than sampling methods. Finally, we demonstrate that Black Box Variational Inference lets us easily explore a wide space of models by quickly constructing and evaluating several models of longitudinal healthcare data.


title: Synthesized Classifiers for Zero-Shot Learning

abstract: Given semantic descriptions of object classes, zeroshot learning aims to accurately recognize objects of the unseen classes, from which no examples are available at the training stage, by associating them to the seen classes, from which labeled examples are provided. We propose to tackle this problem from the perspective of manifold learning. Our main idea is to align the semantic space that is derived from external information to the model space that concerns itself with recognizing visual features. To this end, we introduce a set of "phantom" object classes whose coordinates live in both the semantic space and the model space. Serving as bases in a dictionary, they can be optimized from labeled data such that the synthesized real object classifiers achieve optimal discriminative performance. We demonstrate superior accuracy of our approach over the state of the art on four benchmark datasets for zero-shot learning, including the full ImageNet Fall 2011 dataset with more than 20,000 unseen classes.


title: EX2: Exploration with Exemplar Models for Deep Reinforcement Learning

abstract: Deep reinforcement learning algorithms have been shown to learn complex tasks using highly general policy classes. However, sparse reward problems remain a significant challenge. Exploration methods based on novelty detection have been particularly successful in such settings but typically require generative or predictive models of the observations, which can be difficult to train when the observations are very high-dimensional and complex, as in the case of raw images. We propose a novelty detection algorithm for exploration that is based entirely on discriminatively trained exemplar models, where classifiers are trained to discriminate each visited state against all others. Intuitively, novel states are easier to distinguish against other states seen during training. We show that this kind of discriminative modeling corresponds to implicit density estimation, and that it can be combined with count-based exploration to produce competitive results on a range of popular benchmark tasks, including state-of-the-art results on challenging egocentric observations in the vizDoom benchmark.


title: Expression and regulation of intergenic long noncoding RNAs during T cell development and differentiation

abstract: Intergenic long noncoding RNAs (lincRNAs) regulate gene expression in various tissues. Zhao and colleagues identify 1,524 lincRNA clusters in thymocytes and mature T cell subsets and reveal dynamic and cell-specific patterns of lincRNA expression during T cell differentiation.


title: Nonoperative Treatment of Thoracic and Lumbar Spine Fractures: A Prospective Randomized Study of Different Treatment Options

abstract: OBJECTIVES ::: To evaluate and compare nonoperative treatment methods for traumatic thoracic and lumbar compression fractures and burst fractures. ::: ::: ::: DESIGN ::: Prospective randomized controlled trial with long-term follow-up. ::: ::: ::: SETTING ::: Two general hospitals in the Netherlands. ::: ::: ::: PATIENTS/PARTICIPANTS ::: Patients with a traumatic thoracic or lumbar spine fracture, without neurologic damage, with less than 50% loss of height of the anterior column and less than 30% reduction of the spinal canal were included. ::: ::: ::: INTERVENTION ::: Patients in the compression group were randomized to physical therapy and postural instructions, a brace for 6 weeks, or a Plaster of Paris cast for 6 or 12 weeks. Patients in the burst group received a brace or a Plaster of Paris cast, both for 12 weeks. ::: ::: ::: MAIN OUTCOME MEASUREMENTS ::: Follow-up examinations included radiographs, Visual Analogue Scores for toleration of treatment and persistent pain, and an Oswestry Disability Index at long-term follow-up. ::: ::: ::: RESULTS ::: There were 133 patients: 108 in the compression group and 25 in the burst group. For compression fractures, physical therapy and brace were considered the most tolerable. Brace therapy scored significantly better on the Visual Analogue Scores for residual pain and on the Oswestry Disability Index. None of the treatments had any significant effect on the residual deformity measurements. For burst fractures, no significant differences were found. ::: ::: ::: CONCLUSIONS ::: Brace treatment with supplementary physical therapy is the treatment of choice for patients with compression fractures of the thoracic and lumbar spine. Furthermore, more than 20% of all patients had moderate or severe back pain at long-term follow-up.


title: Robust Convolutional Neural Networks under Adversarial Noise

abstract: Recent studies have shown that Convolutional Neural Networks (CNNs) are vulnerable to a small perturbation of input called "adversarial examples". In this work, we propose a new feedforward CNN that improves robustness in the presence of adversarial noise. Our model uses stochastic additive noise added to the input image and to the CNN models. The proposed model operates in conjunction with a CNN trained with either standard or adversarial objective function. In particular, convolution, max-pooling, and ReLU layers are modified to benefit from the noise model. Our feedforward model is parameterized by only a mean and variance per pixel which simplifies computations and makes our method scalable to a deep architecture. From CIFAR-10 and ImageNet test, the proposed model outperforms other methods and the improvement is more evident for difficult classification tasks or stronger adversarial noise.


title: 3D Surface Reconstruction Using Graph Cuts with Surface Constraints

abstract: We describe a graph cut algorithm to recover the 3D object surface using both silhouette and foreground color information. The graph cut algorithm is used for optimization on a color consistency field. Constraints are added to improve its performance. These constraints are a set of predetermined locations that the true surface of the object is likely to pass through. They are used to preserve protrusions and to pursue concavities respectively in the first and the second phase of the algorithm. We also introduce a method for dealing with silhouette uncertainties arising from background subtraction on real data. We test the approach on synthetic data with different numbers of views (8, 16, 32, 64) and on a real image set containing 30 views of a toy squirrel.


title: Scalable distributed inference of dynamic user interests for behavioral targeting

abstract: Historical user activity is key for building user profiles to predict the user behavior and affinities in many web applications such as targeting of online advertising, content personalization and social recommendations. User profiles are temporal, and changes in a user's activity patterns are particularly useful for improved prediction and recommendation. For instance, an increased interest in car-related web pages may well suggest that the user might be shopping for a new vehicle.In this paper we present a comprehensive statistical framework for user profiling based on topic models which is able to capture such effects in a fully \emph{unsupervised} fashion. Our method models topical interests of a user dynamically where both the user association with the topics and the topics themselves are allowed to vary over time, thus ensuring that the profiles remain current. We describe a streaming, distributed inference algorithm which is able to handle tens of millions of users. Our results show that our model contributes towards improved behavioral targeting of display advertising relative to baseline models that do not incorporate topical and/or temporal dependencies. As a side-effect our model yields human-understandable results which can be used in an intuitive fashion by advertisers.


title: Beyond Bilinear: Generalized Multi-modal Factorized High-order Pooling for Visual Question Answering

abstract: Visual question answering (VQA) is challenging because it requires a simultaneous understanding of both visual content of images and textual content of questions. To support the VQA task, we need to find good solutions for the following three issues: 1) fine-grained feature representations for both the image and the question; 2) multi-modal feature fusion that is able to capture the complex interactions between multi-modal features; 3) automatic answer prediction that is able to consider the complex correlations between multiple diverse answers for the same question. For fine-grained image and question representations, a `co-attention' mechanism is developed by using a deep neural network architecture to jointly learn the attentions for both the image and the question, which can allow us to reduce the irrelevant features effectively and obtain more discriminative features for image and question representations. For multi-modal feature fusion, a generalized Multi-modal Factorized High-order pooling approach (MFH) is developed to achieve more effective fusion of multi-modal features by exploiting their correlations sufficiently, which can further result in superior VQA performance as compared with the state-of-the-art approaches. For answer prediction, the KL (Kullback-Leibler) divergence is used as the loss function to achieve more accurate characterization of the complex correlations between multiple diverse answers with same or similar meaning, which can allow us to achieve faster convergence rate and obtain slightly better accuracy on answer prediction. A deep neural network architecture is designed to integrate all these aforementioned modules into one unified model for achieving superior VQA performance. With an ensemble of 9 models, we achieve the state-of-the-art performance on the large-scale VQA datasets and win the runner-up in the VQA Challenge 2017.


title: Taxonomy of cloud computing services

abstract: Cloud computing is a highly discussed topic, and many big players of the software industry are entering the development of cloud services. Several companies want to explore the possibilities and benefits of cloud computing, but with the amount of cloud computing services increasing quickly, the need for a taxonomy framework rises. This paper describes the available cloud computing services, and proposes a tree-structured taxonomy based on their characteristics, to easily classify cloud computing services making it easier to compare them.


title: Towards Cross-Domain Learning for Social Video Popularity Prediction

abstract: Previous research on online media popularity prediction concluded that the rise in popularity of online videos maintains a conventional logarithmic distribution. However, recent studies have shown that a significant portion of online videos exhibit bursty/sudden rise in popularity, which cannot be accounted for by video domain features alone. In this paper, we propose a novel transfer learning framework that utilizes knowledge from social streams (e.g., Twitter) to grasp sudden popularity bursts in online content. We develop a transfer learning algorithm that can learn topics from social streams allowing us to model the social prominence of video content and improve popularity predictions in the video domain. Our transfer learning framework has the ability to scale with incoming stream of tweets, harnessing physical world event information in real-time. Using data comprising of 10.2 million tweets and 3.5 million YouTube videos, we show that social prominence of the video topic (context) is responsible for the sudden rise in its popularity where social trends have a ripple effect as they spread from the Twitter domain to the video domain. We envision that our cross-domain popularity prediction model will be substantially useful for various media applications that could not be previously solved by traditional multimedia techniques alone.


title: Pricing strategy and execution: an overlooked way to increase revenues and profits

abstract: Purpose – This article advocates putting pricing strategy on the CEO's agenda and considers the new concept of pricing execution – the development of pricing strategy and the implementation of the strategy through a combination of leadership, management practice, and software – from the point of view of the senior management team.Design/methodology/approach – New software tools give companies a new way of setting, optimizing, and enforcing pricing changes within the organization.Findings – There has been an explosion of applications in the pricing management space. Pricing execution offers both growing and mature companies a lower‐risk approach to revenue, margin, profit, and shareholder‐value growth than innovation and acquisitions.Research limitations/implications – A proprietary 2005 Deloitte study of the available applications concludes that no one vendor offers a complete integrated solution.Practical implications – Using software to improve pricing measurement, combined with better pricing execution...


title: Deobfuscation: reverse engineering obfuscated code

abstract: In recent years, code obfuscation has attracted attention as a low cost approach to improving software security by making it difficult for attackers to understand the inner workings of proprietary software systems. This paper examines techniques for automatic deobfuscation of obfuscated programs, as a step towards reverse engineering such programs. Our results indicate that much of the effects of code obfuscation, designed to increase the difficulty of static analyses, can be defeated using simple combinations of straightforward static and dynamic analyses. Our results have applications to both software engineering and software security. In the context of software engineering, we show how dynamic analyses can be used to enhance reverse engineering, even for code that has been designed to be difficult to reverse engineer. For software security, our results serve as an attack model for code obfuscators, and can help with the development of obfuscation techniques that are more resilient to straightforward reverse engineering.


title: Experiment-driven Characterization of Full-Duplex Wireless Systems

abstract: We present an experiment-based characterization of passive suppression and active self-interference cancellation mechanisms in full-duplex wireless communication systems. In particular, we consider passive suppression due to antenna separation at the same node, and active cancellation in analog and/or digital domain. First, we show that the average amount of cancellation increases for active cancellation techniques as the received self-interference power increases. Our characterization of the average cancellation as a function of the self-interference power allows us to show that for a constant signal-to-interference ratio at the receiver antenna (before any active cancellation is applied), the rate of a full-duplex link increases as the self-interference power increases. Second, we show that applying digital cancellation after analog cancellation can sometimes increase the self-interference, and thus digital cancellation is more effective when applied selectively based on measured suppression values. Third, we complete our study of the impact of self-interference cancellation mechanisms by characterizing the probability distribution of the self-interference channel before and after cancellation.


title: The Convergence of Player Experience Questionnaires

abstract: Player experience is an important field of digital games research to understand how games influence players. A common way to directly measure players' reported experiences is through questionnaires. However, the large number of questionnaires currently in use introduces several challenges both in terms of selecting suitable measures and comparing results across studies. In this paper, we review some of the most widely known and used questionnaires and focus on the immersive experience questionnaire (IEQ), the game engagement questionnaire (GEQ), and the player experience of need satisfaction (PENS), with the aim to position each of them in relation to each other. This was done through an online survey, in which we gathered 270 responses from players about their most recent experience of a digital game. Our findings show considerable convergence between these three questionnaires and that there is room to refine them into a more widely applicable measure of general game engagement.


title: Teaching Machines to Read and Comprehend

abstract: Teaching machines to read natural language documents remains an elusive challenge. Machine reading systems can be tested on their ability to answer questions posed on the contents of documents that they have seen, but until now large scale training and test datasets have been missing for this type of evaluation. In this work we define a new methodology that resolves this bottleneck and provides large scale supervised reading comprehension data. This allows us to develop a class of attention based deep neural networks that learn to read real documents and answer complex questions with minimal prior knowledge of language structure.


title: An enhanced pulse-based dead-time compensation technique for PWM-VSI drives

abstract: A dead-time delay is imperative in pulse width modulated (PWM) voltage source inverters in order to prevent a shoot through in the DC link. This delay causes the dead time effect and it results in output waveform distortions. This leads to various undesired characteristics. A simple yet an accurate pulse based dead time compensation technique is presented in this paper which compensates the dead time effect accurately without any phase delays at the output. Proposed method is developed by analyzing both magnitude and phase distortions in a pulse by pulse manner. Simulation results of the proposed method are provided in order to verify the validity of the method.


title: ncFANs: a web server for functional annotation of long non-coding RNAs

abstract: Recent interest in the non-coding transcriptome has resulted in the identification of large numbers of long non-coding RNAs (lncRNAs) in mammalian genomes, most of which have not been functionally characterized. Computational exploration of the potential functions of these lncRNAs will therefore facilitate further work in this field of research. We have developed a practical and user-friendly web interface called ncFANs (non-coding RNA Function ANnotation server), which is the first web service for functional annotation of human and mouse lncRNAs. On the basis of the re-annotated Affymetrix microarray data, ncFANs provides two alternative strategies for lncRNA functional annotation: one utilizing three aspects of a coding-non-coding gene co-expression (CNC) network, the other identifying condition-related differentially expressed lncRNAs. ncFANs introduces a highly efficient way of re-using the abundant pre-existing microarray data. The present version of ncFANs includes re-annotated CDF files for 10 human and mouse Affymetrix microarrays, and the server will be continuously updated with more re-annotated microarray platforms and lncRNA data. ncFANs is freely accessible at http://www.ebiomed.org/ncFANs/ or http://www.noncode.org/ncFANs/.


title: Remembering and Forgetting: Narrative as Cultural Memory

abstract: This paper has two objectives: one is to explore the dialectics of remembering and forgetting, an issue traditionally neglected in psychological memory research; the other is to question the widespread dichotomy of individual and social memory. To do so, a cultural-historical perspective is outlined that allows us to conceive of individual memory as an inextricable part of an overarching cultural discourse, the discourse of cultural memory. In this discourse, narrative practices are of central importance because they combine various cultural symbol systems, integrating them within one symbolic space. In order to explain and illustrate this conception of narrative, a historical memorial and work of art is examined. Three narrative orders of this artwork are distinguished—the linguistic, semiotic and performative or discursive—and discussed as particular forms of meaning construction. Together, they constitute a mnemonic system, a symbolic space of remembering and forgetting in which the time orders of past...


title: Detect What You Can: Detecting and Representing Objects Using Holistic Models and Body Parts

abstract: Detecting objects becomes difficult when we need to deal with large shape deformation, occlusion and low resolution. We propose a novel approach to i) handle large deformations and partial occlusions in animals (as examples of highly deformable objects), ii) describe them in terms of body parts, and iii) detect them when their body parts are hard to detect (e.g., animals depicted at low resolution). We represent the holistic object and body parts separately and use a fully connected model to arrange templates for the holistic object and body parts. Our model automatically decouples the holistic object or body parts from the model when they are hard to detect. This enables us to represent a large number of holistic object and body part combinations to better deal with different "detectability" patterns caused by deformations, occlusion and/or low resolution. We apply our method to the six animal categories in the PASCAL VOC dataset and show that our method significantly improves state-of-the-art (by 4.1% AP) and provides a richer representation for objects. During training we use annotations for body parts (e.g., head, torso, etc.), making use of a new dataset of fully annotated object parts for PASCAL VOC 2010, which provides a mask for each part.


title: Sequencediscriminative training of deep neural networks

abstract: Sequence-discriminative training of deep neural networks (DNNs) is investigated on a standard 300 hour American En- glish conversational telephone speech task. Different sequence- discriminative criteria — maximum mutual information (MMI), minimum phone error (MPE), state-level minimum Bayes risk (sMBR), and boosted MMI — are compared. Two different heuristics are investigated to improve the performance of the DNNs trained using sequence-based criteria — lattices are re- generated after the first iteration of training; and, for MMI and BMMI, the frames where the numerator and denominator hy- potheses are disjoint are removed from the gradient compu- tation. Starting from a competitive DNN baseline trained us- ing cross-entropy, different sequence-discriminative criteria are shown to lower word error rates by 7-9% relative, on aver- age. Little difference is noticed between the different sequence- based criteria that are investigated. The experiments are done using the open-source Kaldi toolkit, which makes it possible for the wider community to reproduce these results. Index Terms: speech recognition, deep learning, sequence- criterion training, neural networks, reproducible research


title: A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks

abstract: Intrusion detection plays an important role in ensuring information security, and the key technology is to accurately identify various attacks in the network. In this paper, we explore how to model an intrusion detection system based on deep learning, and we propose a deep learning approach for intrusion detection using recurrent neural networks (RNN-IDS). Moreover, we study the performance of the model in binary classification and multiclass classification, and the number of neurons and different learning rate impacts on the performance of the proposed model. We compare it with those of J48, artificial neural network, random forest, support vector machine, and other machine learning methods proposed by previous researchers on the benchmark data set. The experimental results show that RNN-IDS is very suitable for modeling a classification model with high accuracy and that its performance is superior to that of traditional machine learning classification methods in both binary and multiclass classification. The RNN-IDS model improves the accuracy of the intrusion detection and provides a new research method for intrusion detection.


Coporate Governance Mechanisms and Accounting Conservatism


title: Caching based socially-aware D2D communications in wireless content delivery networks: a hypergraph framework

abstract: The emergence of the D2D communications paradigm has transformed the way in which cellular networks are operated. Previous work in D2D communications, however, mainly focused on interference management and capacity maximization of both D2D links and cellular links. Recent literature has observed that the QoE can be greatly enhanced by caching contents in mobile devices, with a carefully designed caching strategy. In this article, we propose a novel hypergraph framework that designs the caching based D2D communication scheme by taking social ties among users and common interests into consideration. We first present the considered features of the D2D transmission scheme, social characteristics consideration, and interest similarity impact. The key concepts of hypergraph and related techniques, such as hypergraph coloring and multidimensional matching, are explained. Then some design issues with simulation results in the proposed framework are discussed in detail, which show the potential of the proposed approach. By jointly considering social ties, common interests, and the D2D transmission scheme, we believe the proposed framework explores new opportunities and future directions in caching based socially-aware D2D communications.


The Impacts of E-service Quality on Customers' Repurchase Intention in Platform Online Retailing: An Empirical Investigation


title: Classification of document pages using structure-based features

abstract: Searching for documents by their type or genre is a natural way to enhance the effectiveness of document retrieval. The layout of a document contains a significant amount of information that can be used to classify it by type in the absence of domain-specific models. Our approach to classification is based on “visual similarity” of layout structure and is implemented by building a supervised classifier, given examples of each class. We use image features such as percentages of text and non-text (graphics, images, tables, and rulings) content regions, column structures, relative point sizes of fonts, density of content area, and statistics of features of connected components which can be derived without class knowledge. In order to obtain class labels for training samples, we conducted a study where subjects ranked document pages with respect to their resemblance to representative page images. Class labels can also be assigned based on known document types, or can be defined by the user. We implemented our classification scheme using decision tree classifiers and self-organizing maps.


title: Selection of robot pre-grasps using box-based shape approximation

abstract: Grasping is a central issue of various robot applications, especially when unknown objects have to be manipulated by the system. In earlier work, we have shown the efficiency of 3D object shape approximation by box primitives for the purpose of grasping. A point cloud was approximated by box primitives [1]. In this paper, we present a continuation of these ideas and focus on the box representation itself. On the number of grasp hypotheses from box face normals, we apply heuristic selection integrating task, orientation and shape issues. Finally, an off-line trained neural network is applied to chose a final best hypothesis as the final grasp. We motivate how boxes as one of the simplest representations can be applied in a more sophisticated manner to generate task-dependent grasps.


title: Psychological Stress and Welfare in Fish

abstract: The ability to respond to stress is vital to the survival of any living organism, though sustained reactions can become detrimental to the health and welfare of animals. Stress responses of vertebrates are known through several studies in their physiological, behavioural and psychological components, under acute and chronic contexts. In fish, the physiological and behavioural aspects of stress are considerably well known phenomena and show striking similarities to those of other vertebrates. However, the psychological component is not well known. Some authors deny mental experiences to fish on the basis of their lack of neocortex. Nevertheless, recent studies have shown neuroendocrine, cognitive and emotional processes in fish that are not only equivalent to other vertebrates, but also allow inferring some forms of mental representation. The integration of psychological elements in fish stress physiology is insufficiently studied, but, as discussed in this article, there is already indirect evidence to admit that some form of stimuli appraisal can take place in fish. This fact has profound implications on the regulation of the stress response, as well as on fish welfare and its management


title: GENERAL DESIGN OF N-WAY MULTI-FREQUENCY UNEQUAL SPLIT WILKINSON POWER DIVIDER USING TRANSMISSION LINE TRANSFORMERS

abstract: In this paper, a new N-way multi-frequency unequal split Wilkinson power divider (WPD) is proposed. The dividers are composed of multi-section transmission line transformers (TLT) and isolation resistors, which provide high isolation and very good input/output ports matching simultaneously at arbitrary design frequencies. To verify the validity of the design, several multi-frequency power dividers are designed and simulated. Speciflcally, a 3-way unequal split dual-frequency WPD operating at 900 and 1800MHz, a 3-way unequal split triple-frequency WPD operating at 1, 2, 3GHz, and a 4-way equal split quad-frequency WPD operating at 1, 2, 3, 4GHz, are designed.


title: Instance Weighting for Patient-Specific Risk Stratification Models

abstract: Accurate risk models for adverse outcomes can provide important input to clinical decision-making. Surprisingly, one of the main challenges when using machine learning to build clinically useful risk models is the small amount of data available. Risk models need to be developed for specific patient populations, specific institutions, specific procedures, and specific outcomes. With each exclusion criterion, the amount of relevant training data decreases, until there is often an insufficient amount to learn an accurate model. This difficulty is compounded by the large class imbalance that is often present in medical applications. In this paper, we present an approach to address the problem of small data using transfer learning methods in the context of developing risk models for cardiac surgeries. We explore ways to build surgery-specific and hospital-specific models (the target task) using information from other kinds of surgeries and other hospitals (source tasks). We propose a novel method to weight examples based on their similarity to the target task training examples to take advantage of the useful examples while discounting less relevant ones. We show that incorporating appropriate source data in training can lead to improved performance over using only target task training data, and that our method of instance weighting can lead to further improvements. Applied to a surgical risk stratification task, our method, which used data from two institutions, performed comparably to the risk model published by the Society for Thoracic Surgeons, which was developed and tested on over one hundred thousand surgeries from hundreds of institutions.


title: Skeleton-Based Abnormal Gait Detection

abstract: Human gait analysis plays an important role in musculoskeletal disorder diagnosis. Detecting anomalies in human walking, such as shuffling gait, stiff leg or unsteady gait, can be difficult if the prior knowledge of such a gait pattern is not available. We propose an approach for detecting abnormal human gait based on a normal gait model. Instead of employing the color image, silhouette, or spatio-temporal volume, our model is created based on human joint positions (skeleton) in time series. We decompose each sequence of normal gait images into gait cycles. Each human instant posture is represented by a feature vector which describes relationships between pairs of bone joints located in the lower body. Such vectors are then converted into codewords using a clustering technique. The normal human gait model is created based on multiple sequences of codewords corresponding to different gait cycles. In the detection stage, a gait cycle with normality likelihood below a threshold, which is determined automatically in the training step, is assumed as an anomaly. The experimental results on both marker-based mocap data and Kinect skeleton show that our method is very promising in distinguishing normal and abnormal gaits with an overall accuracy of 90.12%.


title: Enlightening Deep Neural Networks with Knowledge of Confounding Factors

abstract: Deep learning techniques have demonstrated significant capacity in modeling some of the most challenging real world problems of high complexity. Despite the popularity of deep models, we still strive to better understand the underlying mechanism that drives their success. Motivated by observations that neurons in trained deep nets predict attributes indirectly related to the training tasks, we recognize that a deep network learns representations more general than the task at hand to disentangle impacts of multiple confounding factors governing the data, in order to isolate the effects of the concerning factors and optimize a given objective. Consequently, we propose a general framework to augment training of deep models with information on auxiliary explanatory data variables, in an effort to boost this disentanglement and train deep networks that comprehend the data interactions and distributions more accurately, and thus improve their generalizability. We incorporate information on prominent auxiliary explanatory factors of the data population into existing architectures as secondary objective/loss blocks that take inputs from hidden layers during training. Once trained, these secondary circuits can be removed to leave a model with the same architecture as the original, but more generalizable and discerning thanks to its comprehension of data interactions. Since pose is one of the most dominant confounding factors for object recognition, we apply this principle to instantiate a pose-aware deep convolutional neural network and demonstrate that auxiliary pose information indeed improves the classification accuracy in our experiments on SAR target classification tasks.


title: Perceptual selectivity for color and form

abstract: Three visual-search experiments tested whether the preattentive parallel stage can-selectively guide the attentive stage to a particular known-to-be-relevant target feature. Subjects searched multielement displays for a salient green circle that had a unique form when surrounded by green nontarget squares or had a unique color when surrounded by red nontarget circles. In the distractor conditions, a salient item in the other dimension was present as well. As an extension of earlier findings (Theeuwes, 1991), the results showed that complete top-down selectivity toward a particular feature was not possible, not even after extended and consistent practice. The results reveal that selectivity depends on the relative discriminability of the stimulus dimensions: the presence of an irrelevant item with a unique color interferes with parallel search for a unique form, and vice versa.


title: The Turtles Project: Design and Implementation of Nested Virtualization

abstract: In classical machine virtualization, a hypervisor runs multiple operating systems simultaneously, each on its own virtual machine. In nested virtualization, a hypervisor can run multiple other hypervisors with their associated virtual machines. As operating systems gain hypervisor functionality--Microsoft Windows 7 already runs Windows XP in a virtual machine--nested virtualization will become necessary in hypervisors that wish to host them. We present the design, implementation, analysis, and evaluation of high-performance nested virtualization on Intel x86-based systems. The Turtles project, which is part of the Linux/KVM hypervisor, runs multiple unmodified hypervisors (e.g., KVM and VMware) and operating systems (e.g., Linux and Windows). Despite the lack of architectural support for nested virtualization in the x86 architecture, it can achieve performance that is within 6-8% of single-level (non-nested) virtualization for common workloads, through multi-dimensional paging for MMU virtualization and multi-level device assignment for I/O virtualization.


title: Improving security and capacity for Arabic text steganography using ‘Kashida’ extensions

abstract: Steganography is a method of hiding data within a cover media so that other individuals fail to realize their existence. In this paper, a new approach for steganography in Arabic texts is proposed. The main idea is that each Arabic word may have some characters which can be extended by ‘Kashida’. The ranks ‘locations’ of such characters and the inserted Kashida, construct a coding method to represent a block of secret bits. Different scenarios have been proposed based on the maximum number of Kashida possible to be inserted per word. The approach was compared to some existing Arabic text steganography approaches in terms of capacity and security. It is shown that this proposed approach outperforms the others with interesting promising results.


title: A robust compliant grasper via shape deposition manufacturing

abstract: Joint compliance can enable successful robot grasping despite uncertainties in target object location. Compliance also enhances manipulator robustness by minimizing contact forces in the event of unintended contacts or impacts. In this paper, we describe the design, fabrication, and evaluation of a novel compliant robotic grasper constructed using polymer-based shape deposition manufacturing. Joints are formed by elastomeric flexures, and actuator and sensor components are embedded in tough rigid polymers. The result is a robot gripper with the functionality of conventional metal prototypes for grasping in unstructured environments but with robustness properties that allow for large forces due to inadvertent contact.


title: A Generic Deep-Learning-Based Approach for Automated Surface Inspection

abstract: Automated surface inspection (ASI) is a challenging task in industry, as collecting training dataset is usually costly and related methods are highly dataset-dependent. In this paper, a generic approach that requires small training data for ASI is proposed. First, this approach builds classifier on the features of image patches, where the features are transferred from a pretrained deep learning network. Next, pixel-wise prediction is obtained by convolving the trained classifier over input image. An experiment on three public and one industrial data set is carried out. The experiment involves two tasks: 1) image classification and 2) defect segmentation. The results of proposed algorithm are compared against several best benchmarks in literature. In the classification tasks, the proposed method improves accuracy by 0.66%–25.50%. In the segmentation tasks, the proposed method reduces error escape rates by 6.00%–19.00% in three defect types and improves accuracies by 2.29%–9.86% in all seven defect types. In addition, the proposed method achieves 0.0% error escape rate in the segmentation task of industrial data.


title: A Hybrid HMM/DNN Approach to Keyword Spotting of Short Words

abstract: An HMM/DNN framework is proposed to address the issues of short-word detection. The first-stage keyword hypothesizer is redesigned with a context-aware keyword model and a 9state filler model to reduce the miss rate from 80% to 6% and increase the figure-of-merit (FOM) from 6.08% to 21.88% for short words. The hypothesizer is followed by a MLP-based second-stage keyword verifier to further reduce its putative hits. To enhance short word detection, three new techniques, including an HMM-based feature transformation for the MLPs, knowledge-based features, and deep neural networks, are incorporated into redesigning the verifier. With a set of nine short keywords from the TIMIT set the best FOM we had achieved for the proposed KWS system was 42.79%, which is comparable with that of 42.6% for long content words and much better than the FOM of 18.4% for short keywords reported in previous research [10].


title: Hate Speech Detection with Comment Embeddings

abstract: We address the problem of hate speech detection in online user comments. Hate speech, defined as an "abusive speech targeting specific group characteristics, such as ethnicity, religion, or gender", is an important problem plaguing websites that allow users to leave feedback, having a negative impact on their online business and overall user experience. We propose to learn distributed low-dimensional representations of comments using recently proposed neural language models, that can then be fed as inputs to a classification algorithm. Our approach addresses issues of high-dimensionality and sparsity that impact the current state-of-the-art, resulting in highly efficient and effective hate speech detectors.


title: Genomics, gene expression and DNA arrays

abstract: Experimental genomics in combination with the growing body of sequence information promise to revolutionize the way cells and cellular processes are studied. Information on genomic sequence can be used experimentally with high-density DNA arrays that allow complex mixtures of RNA and DNA to be interrogated in a parallel and quantitative fashion. DNA arrays can be used for many different purposes, most prominently to measure levels of gene expression (messenger RNA abundance) for tens of thousands of genes simultaneously. Measurements of gene expression and other applications of arrays embody much of what is implied by the term ‘genomics’; they are broad in scope, large in scale, and take advantage of all available sequence information for experimental design and data interpretation in pursuit of biological understanding.


title: Architecture for Modular Data Centers

abstract: Several factors are driving high-scale deployments of large data centers built upon commodity components. These commodity clusters are far cheaper than mainframe systems of the past but they bring serious heat and power density issues. Also the high failure rate of the individual components drives significant administrative costs. This proposal outlines an architecture for data center design based upon 20'x8'x8' modules that substantially changes how these systems are acquired, administered, and then later recycled.


title: Greedy Algorithms for Steiner Forest

abstract: In the Steiner Forest problem, we are given terminal pairs $\{s_i, t_i\}$, and need to find the cheapest subgraph which connects each of the terminal pairs together. In 1991, Agrawal, Klein, and Ravi, and Goemans and Williamson gave primal-dual constant-factor approximation algorithms for this problem; until now, the only constant-factor approximations we know are via linear programming relaxations. ::: We consider the following greedy algorithm: Given terminal pairs in a metric space, call a terminal "active" if its distance to its partner is non-zero. Pick the two closest active terminals (say $s_i, t_j$), set the distance between them to zero, and buy a path connecting them. Recompute the metric, and repeat. Our main result is that this algorithm is a constant-factor approximation. ::: We also use this algorithm to give new, simpler constructions of cost-sharing schemes for Steiner forest. In particular, the first "group-strict" cost-shares for this problem implies a very simple combinatorial sampling-based algorithm for stochastic Steiner forest.


title: Emergent elasticity in the neural code for space

abstract: Upon encountering a novel environment, an animal must construct a consistent environmental map, as well as an internal estimate of its position within that map, by combining information from two distinct sources: self-motion cues and sensory landmark cues. How do known aspects of neural circuit dynamics and synaptic plasticity conspire to accomplish this feat? Here we show analytically how a neural attractor model that combines path integration of self-motion cues with Hebbian plasticity in synaptic weights from landmark cells can self-organize a consistent map of space as the animal explores an environment. Intriguingly, the emergence of this map can be understood as an elastic relaxation process between landmark cells mediated by the attractor network. Moreover, our model makes several experimentally testable predictions, including (i) systematic path-dependent shifts in the firing fields of grid cells toward the most recently encountered landmark, even in a fully learned environment; (ii) systematic deformations in the firing fields of grid cells in irregular environments, akin to elastic deformations of solids forced into irregular containers; and (iii) the creation of topological defects in grid cell firing patterns through specific environmental manipulations. Taken together, our results conceptually link known aspects of neurons and synapses to an emergent solution of a fundamental computational problem in navigation, while providing a unified account of disparate experimental observations.


title: NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference

abstract: Motivation: Reconstruction of gene regulatory networks (GRNs) is of utmost interest to biologists and is vital for understanding the complex regulatory mechanisms within the cell. Despite various methods developed for reconstruction of GRNs from gene expression profiles, they are notorious for high false positive rate owing to the noise inherited in the data, especially for the dataset with a large number of genes but a small number of samples. Results: In this work, we present a novel method, namely NARROMI, to improve the accuracy of GRN inference by combining ordinary differential equation-based recursive optimization (RO) and information theory-based mutual information (MI). In the proposed algorithm, the noisy regulations with low pairwise correlations are first removed by using MI, and the redundant regulations from indirect regulators are further excluded by RO to improve the accuracy of inferred GRNs. In particular, the RO step can help to determine regulatory directions without prior knowledge of regulators. The results on benchmark datasets from Dialogue for Reverse Engineering Assessments and Methods challenge and experimentally determined GRN of Escherichia coli show that NARROMI significantly outperforms other popular methods in terms of false positive rates and accuracy. Availability: All the source data and code are available at: http://csb.


title: Android (Nougats) security issues and solutions

abstract: Android mobile operating system, Google developed, Linux Kernel based with basic motive to serve for devices with touchscreen like tablets and smartphones. Due to weak OS security it is vulnerable to various security attacks therefor to restrict access of third-party applications off critical resources the security has been built upon a permission based mechanism. Permissions are declarations by developers and user is demanded to accept. This paper highlights Share User ID permission misuse following two-factor authentication failure etc.


title: Automatic wordnet development for low-resource languages using cross-lingual WSD

abstract: Wordnets are an effective resource for natural language processing and information retrieval, especially for semantic processing and meaning related tasks. So far, wordnets have been constructed for many languages. However, the automatic development of wordnets for low-resource languages has not been well studied. In this paper, an Expectation-Maximization algorithm is used to create high quality and large scale wordnets for poor-resource languages. The proposed method benefits from possessing cross-lingual word sense disambiguation and develops a wordnet by only using a bi-lingual dictionary and a monolingual corpus. The proposed method has been executed with Persian language and the resulting wordnet has been evaluated through several experiments. The results show that the induced wordnet has a precision score of 90% and a recall score of 35%.


title: Classification of Rubber Tree Leaf Diseases Using Multilayer Perceptron Neural Network

abstract: This paper presents about classification of rubber tree leaf diseases through automation and utilizing primary RGB color model. Several rubber tree leaf diseases are been studied for digital RGB color extraction where three sets of rubber tree leaf diseases image are digitally captured under standard and control environment. The identified regions of interest (ROI) of these diseases images are then processed to quantify the normalized indices from the RGB color distribution. This system involved the process of image classification by using artificial neural network where 600 samples were used as training while another 200 samples were for testing. The optimized ANN model in this work has two method which based only on the dominant pixel RGB (mean) and applying principle component analysis (PCA) on the pixel gradation values of each image. The optimized model was evaluated and validated through analysis of the performance indicators. Findings in this work have shown that both models have produced about 70% in diagnostic accuracy with more than 80% achievement for sensitivity. However, model with the applied PCA has lower network size.


title: Observing Human-Object Interactions: Using Spatial and Functional Compatibility for Recognition

abstract: Interpretation of images and videos containing humans interacting with different objects is a daunting task. It involves understanding scene or event, analyzing human movements, recognizing manipulable objects, and observing the effect of the human movement on those objects. While each of these perceptual tasks can be conducted independently, recognition rate improves when interactions between them are considered. Motivated by psychological studies of human perception, we present a Bayesian approach which integrates various perceptual tasks involved in understanding human-object interactions. Previous approaches to object and action recognition rely on static shape or appearance feature matching and motion analysis, respectively. Our approach goes beyond these traditional approaches and applies spatial and functional constraints on each of the perceptual elements for coherent semantic interpretation. Such constraints allow us to recognize objects and actions when the appearances are not discriminative enough. We also demonstrate the use of such constraints in recognition of actions from static images without using any motion information.


title: U-Sem: Semantic Enrichment, User Modeling and Mining of Usage Data on the Social Web

abstract: With the growing popularity of Social Web applications, more and more user data is published on the Web everyday. Our research focuses on investigating ways of mining data from such platforms that can be used for modeling users and for semantically augmenting user profiles. This process can enhance adaptation and personalization in various adaptive Web-based systems. In this paper, we present the U-Sem people modeling service, a framework for the semantic enrichment and mining of people's profiles from usage data on the Social Web. We explain the architecture of our people modeling service and describe its application in an adult e-learning context as an example. Versions: Mar 21, 10:10, Mar 25, 09:37


title: Cable Tension Control and Analysis of Reel Transparency for 6-DOF Haptic Foot Platform on a Cable-Driven Locomotion Interface

abstract: A Cable-Driven Locomotion Interface provides a low inertia haptic interface and is used as a way of enabling the user to walk and interact with virtual surfaces. These surfaces generate Cartesian wrenches which must be optimized for each motorized reel in order to reproduce a haptic ensation in both feet. However, the use of wrench control requires a measure of the cable tensions applied to the moving platform. The latter measure may be inaccurate if it is based on sensors located near the reel. Moreover, friction hysteresis from the reel moving parts needs to be compensated for with an evaluation of low angular velocity of the motor shaft. Also, the pose of the platform is not known precisely due to cable sagging and mechanical deformation. This paper presents a non-ideal motorized reel design with its corresponding control strategy that aims at overcoming the aforementioned issues. A transfert function of the reel based on frequency responses in function of cable tension and cable length is presented with an optimal adaptative PIDF controller. Dynamic and static reel transparencies are evaluated experimentally with a cost function to optimize and with an analysis of friction ::: respectively. Finally, a hybrid position/tension control is discussed with an analysis of the stability for achieving a complete functionality of the haptic platform.


Capability Myths Demolished


title: On Motion Planning in Changing, Partially Predictable Environments

abstract: We present a framework for analyzing and computing motion plans for a robot that operates in an environment that both varies over time and is not completely predictable. We first classify sources of motion-planning uncertainty into four cat egories, and argue that the problems addressed in this article belong to a fundamental category that has received little atten tion. We treat the changing environment in a flexible manner by combining traditional configuration-space concepts with a Markov process that models the environment. For this context, we then propose the use of a motion strategy, which provides a motion command for the robot for each contingency that it might confront. We allow the specification of a desired per formance criterion, such as time or distance, and determine a motion strategy that is optimal with respect to that criterion. We demonstrate the breadth of our framework by applying it to a variety of motion-planning problems. Examples are com puted for problems that involve a changing ...


title: The use of religious metaphors by UK newspapers to describe and denigrate climate change

abstract: British newspapers have denigrated anthropogenic climate change by misrepresenting scientific consensus and/or framing climate change within unsympathetic discourses. One aspect of the latter that has not been studied is the use of metaphor to disparage climate change science and proponents. This article analyses 122 British newspaper articles published using a religious metaphor between summer 2003 and 2008. Most were critical of climate change, especially articles in conservative newspapers The Daily Telegraph, Daily Mail and The Times. Articles used religion as a source of metaphor to denigrate climate change in two ways: (1) undermining its scientific status by presenting it as irrational faith-based religion, and proponents as religious extremists intolerant of criticism; (2) mocking climate change using notions of sin, e.g. describing ‘green’ behaviours as atonement or sacrifice. We argue that the religious metaphor damages constructive debate by emphasizing morality and how climate change is discussed, and detracting attention from the content of scientific data and theories.


title: On Ontologies And Enterprise Modelling

abstract: From a design perspective, an enterprise model should provide the language used to explicitly define an enterprise. We need to be able to explore alternative models in the design of enterprises spanning organisation structure and behaviour. In order to reason about alternative designs for enterprises, we need to reason about different possible sets of constraints for enterprises within the model. We need to ask the questions -can a process be performed in a different way, or can we achieve some goal in a different way? Can we relax the constraints in the enterprise such that we can improve performance or achieve new goals?


title: Reducing conservativeness in safety guarantees by learning disturbances online: Iterated guaranteed safe online learning

abstract: Reinforcement learning has proven itself to be a powerful technique in robotics, however it has not often been employed to learn a controller in a hardware-in-the-loop environment due to the fact that spurious training data could cause a robot to take an unsafe (and potentially catastrophic) action. One approach to overcoming this limitation is known as Guaranteed Safe Online Learning via Reachability (GSOLR), in which the controller being learned is wrapped inside another controller based on reachability analysis that seeks to guarantee safety against worst-case disturbances. This paper proposes a novel improvement to GSOLR which we call Iterated Guaranteed Safe Online Learning via Reachability (IGSOLR), in which the worst-case disturbances are modeled in a state-dependent manner (either parametrically or nonparametrically), this model is learned online, and the safe sets are periodically recomputed (in parallel with whatever machine learning is being run online to learn how to control the system). As a result the safety of the system automatically becomes neither too liberal nor too conservative, depending only on the actual system behavior. This allows the machine learning algorithm running in parallel the widest possible latitude in performing its task while still guaranteeing system safety. In addition to explaining IGSOLR, we show how it was used in a real-world example, namely that of safely learning an altitude controller for a quadrotor helicopter. The resulting controller, which was learned via hardware-inthe-loop reinforcement learning, out-performs our original handtuned controller while still maintaining safety. To our knowledge, this is the first example in the robotics literature of an algorithm in which worst-case disturbances are learned online in order to guarantee system safety.


title: Effects of fermentation temperature on the strain population of Saccharomyces cerevisiae.

abstract: The influence of fermentation temperature (from 15 to 35 degrees C) on a mixed strain population was studied. Mitochondrial DNA analysis was used to differentiate Saccharomyces cerevisiae strains and the frequency of each strain during the alcoholic fermentation was determined. The chemical analyses of resulting wines were carried out. The temperature determined how Saccharomyces strains developed and how effectively they fermented. Some strains performed better at high temperatures and others at low temperatures. The maximal population size was similar at all temperatures. At low temperatures, however, it was reached later though it remained constant throughout the alcoholic fermentation. On the other hand, viable cells decreased at high temperatures, especially at 35 degrees C. Obviously, the composition of the wines changed as the temperature of fermentation changed. At low temperatures, alcohol yield was higher. Secondary metabolites to alcoholic fermentation increased as the temperature increased. Glycerol levels were directly affected by temperature.


title: Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks

abstract: In general, natural language is governed by a tree structure: smaller units (e.g., phrases) are nested within larger units (e.g., clauses). This is a strict hierarchy: when a larger constituent ends, all of the smaller constituents that are nested within it must also be closed. While the standard LSTM allows different neurons to track information at different time scales, the architecture does not impose a strict hierarchy. This paper proposes to add such a constraint to the system by ordering the neurons; a vector of"master"input and forget gates ensure that when a given unit is updated, all of the units that follow it in the ordering are also updated. To this end, we propose a new RNN unit: ON-LSTM, which achieves good performance on four different tasks: language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference.


title: Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper

abstract: Feature selection is often an essential data processing step prior to applying a learning algorithm. The removal of irrelevant and redundant information often improves the performance of machine learning algorithms. There are two common approaches: a wrapper uses the intended learning algorithm itself to evaluate the usefulness of features, while a fllter evaluates features according to heuristics based on general characteristics of the data. The wrapper approach is generally considered to produce better feature subsets but runs much more slowly than a fllter. This paper describes a new fllter approach to feature selection that uses a correlation based heuristic to evaluate the worth of feature subsets When applied as a data preprocessing step for two common machine learning algorithms, the new method compares favourably with the wrapper but requires much less computation.


title: An overview of coaching with limitations

abstract: As agent relationships become more complex, one challenging relationship that merits study is that of coach or adviser to another agent. Our research on coaching refers to one autonomous agent providing advice to another autonomous agent about how to act. Here we describe the current state of the coach agent framework we are developing. One of our goals is to design a coach agent that can provide advice to many differently built and structured advice taking agents. A coach must then adapt its advice to the capabilities and limitations of the agents it is coaching. This paper explores the effect of advice giving in the presence of limitations.


title: Pocket-size augmented reality system for flight control

abstract: Head-up displays (HUDs) have become common equipment in aircraft cockpits. One of the uses of HUDs is to provide a specific visual interface for pilots in the form of what is called a "tunnel-in-the-sky" (i.e. 3D geometry for the navigation path displayed on a flat screen). According to recent studies the "tunnel-in-the-sky" approach does not provide crucial advantages in comparison with more traditional methods of presenting navigation information to pilots. Our research considers a stereoscopic version of the 3D "tunnel-in-the-sky" realized as an augmented reality (AR) pocket-size system with see-through light-weight AR glasses. The system consists of low-cost items and does not suffer from the drawbacks tied with existing synthetic/enhanced vision systems for pilots. The results of the experiments with desktop simulators of different AR pilot's interfaces (2D, 3D and stereo 3D conditions) proved the effectiveness of the proposed stereo AR solution. A flight test of the prototype of the developed AR system was carried out on Cessna 172 aircraft and is showed in the accompanying video.


title: Sentic LDA: Improving on LDA with semantic similarity for aspect-based sentiment analysis

abstract: The advent of the Social Web has provided netizens with new tools for creating and sharing, in a time- and cost-efficient way, their contents, ideas, and opinions with virtually the millions of people connected to the World Wide Web. This huge amount of information, however, is mainly unstructured as specifically produced for human consumption and, hence, it is not directly machine-processable. In order to enable a more efficient passage from unstructured information to structured data, aspect-based opinion mining models the relations between opinion targets contained in a document and the polarity values associated with these. Because aspects are often implicit, however, spotting them and calculating their respective polarity is an extremely difficult task, which is closer to natural language understanding rather than natural language processing. To this end, Sentic LDA exploits common-sense reasoning to shift LDA clustering from a syntactic to a semantic level. Rather than looking at word co-occurrence frequencies, Sentic LDA leverages on the semantics associated with words and multi-word expressions to improve clustering and, hence, outperform state-of-the-art techniques for aspect extraction.


title: Three-level neutral-point-clamped inverters in transformerless PV systems — State of the art

abstract: Nowadays, the three-level neutral point clamped (3L-NPC) inverter has become more attractive among multilevel inverters topologies, especially in transformerless grid connected photovoltaic systems. Due to the unbalance between the two DC load-side capacitor voltages, the development and improvement of such topology were important topics in recent studies. New structures were derived focusing on better distribution of losses, increasing efficiency, decreasing the switching devices losses and the conduction losses. For this reason, seven derived topologies are discussed in this paper and compared among the conventional inverter. In addition, a part of this study focuses on modifying modulation techniques in order to comply with the neutral point fluctuation problem. Several carrier-based PWM and space vector modulation techniques are considered, discussed and compared.


title: Toward Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models

abstract: In many machine learning domains (such as scene understanding), several related sub-tasks (such as scene categorization, depth estimation, object detection) operate on the same raw data and provide correlated outputs. Each of these tasks is often notoriously hard, and state-of-the-art classifiers already exist for many sub-tasks. It is desirable to have an algorithm that can capture such correlation without requiring to make any changes to the inner workings of any classifier. ::: ::: We propose Feedback Enabled Cascaded Classification Models (FE-CCM), that maximizes the joint likelihood of the sub-tasks, while requiring only a 'black-box' interface to the original classifier for each sub-task. We use a two-layer cascade of classifiers, which are repeated instantiations of the original ones, with the output of the first layer fed into the second layer as input. Our training method involves a feedback step that allows later classifiers to provide earlier classifiers information about what error modes to focus on. We show that our method significantly improves performance in all the sub-tasks in two different domains: (i) scene understanding, where we consider depth estimation, scene categorization, event categorization, object detection, geometric labeling and saliency detection, and (ii) robotic grasping, where we consider grasp point detection and object classification.


title: A study on jamming performance evaluation of noise and deception jammer against SAR satellite

abstract: SAR satellite has pulse compression gain through the process including range and azimuth. Efficient jammers against the SAR satellite with simulated elements were evaluated in the view of power and SAR image. In this paper, J/S is analyzed for SAR with RF propagation equation firstly. Several jamming signals against SAR signal were added into SAR image through pulse compression process. Objective jamming performance of the signals was evaluated using euclidean distance.


title: Data Mining Techniques: For Marketing, Sales, and Customer Support

abstract: From the Publisher: ::: Data Mining Techniques thoroughly acquaints you with the new generation of data mining tools and techniques and shows you how to use them to make better business decisions. One of the first practical guides to mining business data, it describes techniques for detecting customer behavior patterns useful in formulating marketing, sales, and customer support strategies. While database analysts will find more than enough technical information to satisfy their curiosity, technically savvy business and marketing managers will find the coverage eminently accessible. Here's your chance to learn all about how leading companies across North America are using data mining to beat the competition; how each tool works, and how to pick the right one for the job; seven powerful techniques - cluster detection, memory-based reasoning, market basket analysis, genetic algorithms, link analysis, decision trees, and neural nets, and how to prepare data sources for data mining, and how to evaluate and use the results you get. Data Mining Techniques shows you how to quickly and easily tap the gold mine of business solutions lying dormant in your information systems.


title: Size and power of tests for a zero random effect variance or polynomial regression in additive and linear mixed models

abstract: Several tests for a zero random effect variance in linear mixed models are compared. This testing problem is non-regular because the tested parameter is on the boundary of the parameter space. Size and power of the different tests are investigated in an extensive simulation study that covers a variety of important settings. These include testing for polynomial regression versus a general smooth alternative using penalized splines. Among the test procedures considered, three are based on the restricted likelihood ratio test statistic (RLRT), while six are different extensions of the linear model F-test to the linear mixed model. Four of the tests with unknown null distributions are based on a parametric bootstrap, the other tests rely on approximate or asymptotic distributions. The parametric bootstrap-based tests all have a similar performance. Tests based on approximate F-distributions are usually the least powerful among the tests under consideration. The chi-square mixture approximation for the RLRT is confirmed to be conservative, with corresponding loss in power. A recently developed approximation to the distribution of the RLRT is identified as a rapid, powerful and reliable alternative to computationally intensive parametric bootstrap procedures. This novel method extends the exact distribution available for models with one random effect to models with several random effects.


title: An ensemble heterogeneous classification methodology for discovering health-related knowledge in social media messages

abstract: Display Omitted Propose 5 heterogeneous features for identifying health messages in social media.Propose ensemble methods that allow base classifiers to make collective decisions.Validate the proposed methods based on empirical evaluations.Generalize the proposed methodology across multiple social media domains. ObjectivesThe role of social media as a source of timely and massive information has become more apparent since the era of Web 2.0.Multiple studies illustrated the use of information in social media to discover biomedical and health-related knowledge.Most methods proposed in the literature employ traditional document classification techniques that represent a document as a bag of words.These techniques work well when documents are rich in text and conform to standard English; however, they are not optimal for social media data where sparsity and noise are norms.This paper aims to address the limitations posed by the traditional bag-of-word based methods and propose to use heterogeneous features in combination with ensemble machine learning techniques to discover health-related information, which could prove to be useful to multiple biomedical applications, especially those needing to discover health-related knowledge in large scale social media data.Furthermore, the proposed methodology could be generalized to discover different types of information in various kinds of textual data. MethodologySocial media data is characterized by an abundance of short social-oriented messages that do not conform to standard languages, both grammatically and syntactically.The problem of discovering health-related knowledge in social media data streams is then transformed into a text classification problem, where a text is identified as positive if it is health-related and negative otherwise.We first identify the limitations of the traditional methods which train machines with N-gram word features, then propose to overcome such limitations by utilizing the collaboration of machine learning based classifiers, each of which is trained to learn a semantically different aspect of the data.The parameter analysis for tuning each classifier is also reported. Data setsThree data sets are used in this research.The first data set comprises of approximately 5000 hand-labeled tweets, and is used for cross validation of the classification models in the small scale experiment, and for training the classifiers in the real-world large scale experiment.The second data set is a random sample of real-world Twitter data in the US.The third data set is a random sample of real-world Facebook Timeline posts. EvaluationsTwo sets of evaluations are conducted to investigate the proposed model's ability to discover health-related information in the social media domain: small scale and large scale evaluations.The small scale evaluation employs 10-fold cross validation on the labeled data, and aims to tune parameters of the proposed models, and to compare with the stage-of-the-art method.The large scale evaluation tests the trained classification models on the native, real-world data sets, and is needed to verify the ability of the proposed model to handle the massive heterogeneity in real-world social media. FindingsThe small scale experiment reveals that the proposed method is able to mitigate the limitations in the well established techniques existing in the literature, resulting in performance improvement of 18.61% (F-measure).The large scale experiment further reveals that the baseline fails to perform well on larger data with higher degrees of heterogeneity, while the proposed method is able to yield reasonably good performance and outperform the baseline by 46.62% (F-Measure) on average.


title: 3D-Printed Origami Packaging With Inkjet-Printed Antennas for RF Harvesting Sensors

abstract: This paper demonstrates the combination of additive manufacturing techniques for realizing complex 3D origami structures for high frequency applications. A 3D-printed compact package for enclosing radio frequency (RF) electronics is built, that features on-package antennas for RF signal reception (for harvesting or communication) at orthogonal orientations. Conventional 3D printing technologies often require significant amounts of time and supporting material to realize certain structures, such as hollow packages. In this work, instead of fabricating the package in its final 3D form, it is 3D-printed as a planar structure with “smart” shape-memory hinges that allow origami folding to a 3D shape after heating. This significantly reduces fabrication time and effectively eliminates the need for supporting material, thus minimizing the overall manufacturing cost. Metallization on the package is performed by directly inkjet printing conductive inks on top of the 3D-printed surface with a modified inkjet-printed process without the need for surface treatment or processing. Inkjet-printed on-package conductive features are successfully fabricated, that are combined with RF energy harvesting electronics to showcase the proof-of-concept of utilizing origami techniques to build fully 3D RF systems. The methodologies presented in this paper will be enabling the manufacturing of numerous real-time shape-changing 3D complex structures for electromagnetic applications.


PAID I EVALUATING ATTENTION NETWORKS FOR ANAPHORA RESOLUTION


title: Articulated part-based model for joint object detection and pose estimation

abstract: Despite recent successes, pose estimators are still somewhat fragile, and they frequently rely on a precise knowledge of the location of the object. Unfortunately, articulated objects are also very difficult to detect. Knowledge about the articulated nature of these objects, however, can substantially contribute to the task of finding them in an image. It is somewhat surprising, that these two tasks are usually treated entirely separately. In this paper, we propose an Articulated Part-based Model (APM) for jointly detecting objects and estimating their poses. APM recursively represents an object as a collection of parts at multiple levels of detail, from coarse-to-fine, where parts at every level are connected to a coarser level through a parent-child relationship (Fig. 1(b)-Horizontal). Parts are further grouped into part-types (e.g., left-facing head, long stretching arm, etc) so as to model appearance variations (Fig. 1(b)-Vertical). By having the ability to share appearance models of part types and by decomposing complex poses into parent-child pairwise relationships, APM strikes a good balance between model complexity and model richness. Extensive quantitative and qualitative experiment results on public datasets show that APM outperforms state-of-the-art methods. We also show results on PASCAL 2007 - cats and dogs - two highly challenging articulated object categories.


title: Measurement of an ozone generator using a phase-shifted PWM full bridge inverter

abstract: This paper proposes the measurement of an ozone generator using a phase-shifted pulse width modulation (PWM) full bridge inverter as a power supply. The method of electrode parameter measurement for an ozone generator is presented. An electrode set is represented by an equivalent circuit by parallel capacitor and resistor. The test was performed with high-frequency high-voltage ac power supply. Then voltage and charge characteristics were obtained leading to calculating values of the parallel capacitor and resistor. The validity of the obtained parameters is verified with a comparison between simulation and experimental results. It has proved that the proposed equivalent circuit with parameters obtained from the proposed method is valid which is in agreement with experimental results. It can be used for power supply and electrode design for generating ozone gas. The correctness of the proposed technique is verified by both simulation and experimental results.


title: A Survey on Virtual Reality for Individuals with Autism Spectrum Disorder: Design Considerations

abstract: In this article, state of the art on virtual reality (VR) for individuals with autism spectrum disorder (ASD) with a focus on training/targeted intervention is discussed and reflected upon to explore areas for more future benefits. We present advantages of VR for individuals with ASD. We identify challenges and design issues for future training applications regarding individuals with ASD. We discuss and present design guidelines accumulated in the literature so far, mostly based on observations in user studies exploring the usefulness of VR as a training tool for individuals with ASD, with a systematic literature review. We present and apply a new taxonomy that classifies previous VR works on training individuals with ASD according to immersive and regular (non-immersive) VR systems and types of social, life and safety skills based on a systematic literature review. We explore the common design considerations of the previous VR studies for training individuals with ASD. Finally, based on the systematic literature reviews, we identify key gaps in the research on this topic and present future research considerations.


title: Updates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff

abstract: AI systems are being deployed to support human decision making in high-stakes domains such as healthcare and criminal justice. In many cases, the human and AI form a team, in which the human makes decisions after reviewing the AI’s inferences. A successful partnership requires that the human develops insights into the performance of the AI system, including its failures. We study the influence of updates to an AI system in this setting. While updates can increase the AI’s predictive performance, they may also lead to behavioral changes that are at odds with the user’s prior experiences and confidence in the AI’s inferences. We show that updates that increase AI performance may actually hurt team performance. We introduce the notion of the compatibility of an AI update with prior user experience and present methods for studying the role of compatibility in human-AI teams. Empirical results on three high-stakes classification tasks show that current machine learning algorithms do not produce compatible updates. We propose a re-training objective to improve the compatibility of an update by penalizing new errors. The objective offers full leverage of the performance/compatibility tradeoff across different datasets, enabling more compatible yet accurate updates.


title: Rule extraction as a formal method for the verification and validation of neural networks

abstract: The term formal method refers to the use of techniques from formal logic and discrete math in the specification, design, and construction of computer systems and software. These techniques enable the formalization of software for development and testing so that it may be verified and validated in a more thorough way. Although not specifically identified in the literature as a verification and validation (V&V) formal method technique, neural network rule extraction fits the basic definition by using techniques from formal logic to formalize neural network software so that it may be examined more completely. This paper identifies several areas where rule extraction can be an effective tool for the V&V of neural networks.


title: Real time speech emotion recognition using RGB image classification and transfer learning

abstract: This paper describes a real-time Speech Emotion Recognition (SER) task formulated as an image classification problem. The shift to an image classification paradigm provided the advantage of using an existing Deep Neural Network (AlexNet) pre-trained on a very large number of images, and thus eliminating the need for a lengthy network training process. Two alternative multi-class SER systems, AlexNet-SVM and FTAlexNet, were investigated. Both systems were shown to achieve state-of-the-art results when tested on a popular Berlin Emotional Speech (EMO-DB) database. Transformation from speech to image classification was achieved by creating RGB images depicting speech spectrograms. The ALEXNet-SVM method passes the spectrogram images as inputs to a pre-trained Convolutional Neural Network (AlexNet) to provide features for the Support Vector Machine (SVM) classifier, whereas the FTAlexNet method simply applies the images to a fine tuned AlexNet to provide emotional class labels. The FTAlexNet offers slightly higher accuracy compared to the AlexNet-SVM, while the AlexNet-SVM requires a lower number of computations due to the elimination of the neural network training procedure. A real-time demo is given on: https://www.youtube.com/watch?v=fuMpF3cUqDU&t=6s.


title: Novel low power full adder cells in 180nm CMOS technology

abstract: This paper proposes four low power adder cells using different XOR and XNOR gate architectures. Two sets of circuit designs are presented. One implements full adders with 3 transistors (3-T) XOR and XNOR gates. The other applies Gate-Diffusion-Input (GDI) technique to full adders. Simulations are performed by using Hspice based on 180nm CMOS technology. In comparison with Static Energy Recovery Full (SERF) adder cell module, the proposed four full adder cells demonstrate their advantages, including lower power consumption, smaller area, and higher speed.


title: Performance evaluation of SQL and MongoDB databases for big e-commerce data

abstract: With the advent of big data phenomenon in the world of data and its related technologies, the developments on the NoSQL databases are highly regarded. It has been claimed that these databases outperform their SQL counterparts. The aim of this study is to investigate the claim by evaluating the document-oriented MongoDB database with SQL in terms of the performance of common aggregated and non-aggregate queries. We designed a set of experiments with a huge number of operations such as read, write, delete, and select from various aspects in the two databases and on the same data for a typical e-commerce schema. The results show that MongoDB performs better for most operations excluding some aggregate functions. The results can be a good source for commercial and non-commercial companies eager to change the structure of the database used to provide their line-of-business services.


Unilateral Idiopathic Calcinosis Cutis: A Case Report


title: Evolving an adaptive industrial software system to use architecture-based self-adaptation

abstract: Although architecture-based self-adaptation has been widely used, there is still little understanding about the validity and tradeoffs of incorporating it into real-world software-intensive systems which already feature built-in adaptation mechanisms. In this paper, we report on our experience in integrating Rainbow, a platform for architecture-based self-adaptation, and an industrial middleware employed to monitor and manage highly populated networks of devices. Specifically, we reflect on aspects such as the effort required for framework customization and legacy code refactoring, performance improvement, and the impact of architecture-based self-adaptation on system evolution.


title: Non-control-data attacks are realistic threats

abstract: Most memory corruption attacks and Internet worms follow a familiar pattern known as the control-data attack. Hence, many defensive techniques are designed to protect program control flow integrity. Although earlier work did suggest the existence of attacks that do not alter control flow, such attacks are generally believed to be rare against real-world software. The key contribution of this paper is to show that non-control-data attacks are realistic. We demonstrate that many real-world applications, including FTP, SSH, Telnet, and HTTP servers, are vulnerable to such attacks. In each case, the generated attack results in a security compromise equivalent to that due to the control-data attack exploiting the same security bug. Non-control-data attacks corrupt a variety of application data including user identity data, configuration data, user input data, and decision-making data. The success of these attacks and the variety of applications and target data suggest that potential attack patterns are diverse. Attackers are currently focused on control-data attacks, but it is clear that when control flow protection techniques shut them down, they have incentives to study and employ non-control-data attacks. This paper emphasizes the importance of future research efforts to address this realistic threat.


title: Autogenous bone grafts in oral implantology—is it still a “gold standard”? A consecutive review of 279 patients with 456 clinical procedures

abstract: BackgroundThis study assessed the clinical outcomes of graft success rate and early implant survival rate after preprosthetic alveolar ridge reconstruction with autologous bone grafts.MethodsA consecutive retrospective study was conducted on all patients who were treated at the military outpatient clinic of the Department of Oral and Plastic Maxillofacial Surgery at the military hospital in Ulm (Germany) in the years of 2009 until 2011 with autologous bone transplantation prior to secondary implant insertion. Intraoral donor sites (crista zygomatico-alveolaris, ramus mandible, symphysis mandible, and anterior sinus wall) and extraoral donor site (iliac crest) were used. A total of 279 patients underwent after a healing period of 3–5 months routinely computer tomography scans followed by virtual implant planning. The implants were inserted using guided oral implantation as described by Naziri et al. All records of all the consecutive patients were reviewed according to patient age, history of periodontitis, smoking status, jaw area and dental situation, augmentation method, intra- and postoperative surgical complications, and surgeon’s qualifications. Evaluated was the augmentation surgical outcome regarding bone graft loss and early implant loss postoperatively at the time of prosthodontic restauration as well a follow-up period of 2 years after loading.ResultsA total of 279 patients underwent 456 autologous augmentation procedures in 546 edentulous areas. One hundred thirteen crista zygomatico-alveolaris grafts, 104 ramus mandible grafts, 11 symphysis grafts, 116 grafts from the anterior superior iliac crest, and 112 sinus lift augmentations with bone scrapes from the anterior facial wall had been performed. There was no drop out or loss of follow-up of any case that had been treated in our clinical center in this 3-year period. Four hundred thirty-six (95.6%) of the bone grafts healed successfully, and 20 grafts (4.4%) in 20 patients had been lost. Fourteen out of 20 patients with total graft failure were secondarily re-augmented, and six patients wished no further harvesting procedure. In the six patients, a partial graft resorption was detected at the time of implantation and additional simultaneous augmentation during implant insertion was necessary. No long-term nerve injury occurred. Five hundred twenty-five out of 546 initially planned implants in 259 patients could be inserted into successfully augmented areas, whereas 21 implants in 20 patients due to graft loss could not be inserted. A final rehabilitation as preplanned with dental implants was possible in 273 of the 279 patients. The early implant failure rate was 0.38% concerning two out of the 525 inserted implants which had to be removed before the prosthodontic restoration. Two implants after iliac crest augmentation were lost within a period of 2 years after loading, concerning a total implant survival rate after 2 years of occlusal loading rate of 99.6% after autologous bone augmentation prior to implant insertion.ConclusionsThis review demonstrates the predictability of autologous bone material in alveolar ridge reconstructions prior to implant insertion, independent from donor and recipient site including even autologous bone chips for sinus elevation. Due to the low harvesting morbidity of autologous bone grafts, the clinical results of our study indicate that autologous bone grafts still remain the “gold standard” in alveolar ridge augmentation prior to oral implantation.


title: Liver X receptor β: maintenance of epidermal expression in intrinsic and extrinsic skin aging

abstract: Aging in human skin is the composite of time-dependent intrinsic aging plus photoaging induced by chronic exposure to ultraviolet radiation. Nuclear hormone receptors coordinate diverse processes including metabolic homeostasis. Liver X receptor β (LXRβ) is a close human homologue of daf-12, a regulator of nematode longevity. LXRβ is positively regulated by sirtuin-1 and resveratrol, while LXRβ-null mice show transcriptional profiles similar to those seen in aged human skin. In these studies, we examined LXRβ expression in aged and photoaged human skin. Volunteers were recruited to assess intrinsic aging and photoaging. Epidermal LXRβ mRNA was examined by in situ hybridization while protein was identified by immunofluorescence. No significant changes were observed in either LXRβ mRNA or protein expression between young and aged volunteers (mRNA p = 0.90; protein p = 0.26). Similarly, LXRβ protein expression was unaltered in photoaged skin (p = 0.75). Our data therefore suggest that, while not playing a major role in skin aging, robust cutaneous expression implies a fundamental role for LXRβ in epidermal biology.