title: Detecting Adversarial Samples from Artifacts

abstract: Deep neural networks (DNNs) are powerful nonlinear architectures that are known to be robust to random perturbations of the input. However, these models are vulnerable to adversarial perturbations--small input changes crafted explicitly to fool the model. In this paper, we ask whether a DNN can distinguish adversarial samples from their normal and noisy counterparts. We investigate model confidence on adversarial samples by looking at Bayesian uncertainty estimates, available in dropout neural networks, and by performing density estimation in the subspace of deep features learned by the model. The result is a method for implicit adversarial detection that is oblivious to the attack algorithm. We evaluate this method on a variety of standard datasets including MNIST and CIFAR-10 and show that it generalizes well across different architectures and attacks. Our findings report that 85-93% ROC-AUC can be achieved on a number of standard classification tasks with a negative class that consists of both normal and noisy samples.


A Fast, Automated, N-Dimensional Phase Unwrapping Algorithm


title: Sensitivity of Monthly Heart Rate and Psychometric Measures for Monitoring Physical Performance in Highly Trained Young Handball Players

abstract: ▼ aim of the present study was to examine whether monthly resting heart rate (HR), HR variability (HRV) and psychometric measures can be used to monitor changes in physical performance in highly-trained adolescent handball players. Data were collected in 37 adolescent players (training 10 ± 2.1 h.wk − 1 ) on 11 occasions from September to May during the in-season period, and included an estimation of training status (resting HR and HRV, the profile of mood state (POMS) questionnaire), and 3 physical performance tests (a 10-m sprint, a counter movement jump and a graded aerobic intermittent test, 30-15 Intermittent Fitness Test). The sensitivity of HR and psychometric measures to changes in physical performance was poor ( 75 %), irrespective of the markers and the performance measures. Finally, the differ ence in physical performance between players with better vs. worse estimated training status were all almost certainly trivial. The present results highlight the limitation of monthly measures of resting HR, HRV and perceived mood and fatigue for predicting in-season changes in physical performance in highly-trained adolescent handball players. This suggests that more frequent monitoring might be required, and/or that other markers might need to be considered.


title: Aerial 3D reconstruction with line-constrained dynamic programming

abstract: Aerial imagery of an urban environment is often characterized by significant occlusions, sharp edges, and textureless regions, leading to poor 3D reconstruction using conventional multi-view stereo methods. In this paper, we propose a novel approach to 3D reconstruction of urban areas from a set of uncalibrated aerial images. A very general structural prior is assumed that urban scenes consist mostly of planar surfaces oriented either in a horizontal or an arbitrary vertical orientation. In addition, most structural edges associated with such surfaces are also horizontal or vertical. These two assumptions provide powerful constraints on the underlying 3D geometry. The main contribution of this paper is to translate the two constraints on 3D structure into intra-image-column and inter-image-column constraints, respectively, and to formulate the dense reconstruction as a 2-pass Dynamic Programming problem, which is solved in complete parallel on a GPU. The result is an accurate cloud of 3D dense points of the underlying urban scene. Our algorithm completes the reconstruction of 1M points with 160 available discrete height levels in under a hundred seconds. Results on multiple datasets show that we are capable of preserving a high level of structural detail and visual quality.


title: Evaluation and Mitigation of Soft-Errors in Neural Network-Based Object Detection in Three GPU Architectures

abstract: In this paper, we evaluate the reliability of the You Only Look Once (YOLO) object detection framework. We have exposed to controlled neutron beams GPUs designed with three different architectures (Kepler, Maxwell, and Pascal) running Darknet, a Convolutional Neural Network for automotive applications, detecting objects in both Caltech and Visual Object Classes data sets. By analyzing the neural network corrupted output, we can distinguish between tolerable errors and critical errors, i.e., errors that could impact on real-time system execution.Additionally, we propose an Algorithm-Based Fault-Tolerance (ABFT) strategy to apply to the matrix multiplication kernels of neural networks able to detect and correct 50% to 60% of radiation induced corruptions. We experimentally validate our hardening solution and compare its efficiency and efficacy with the available ECC.


title: Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery

abstract: Thanks to recent advances in CNNs, solid improvements have been made in semantic segmentation of high resolution remote sensing imagery. However, most of the previous works have not fully taken into account the specific difficulties that exist in remote sensing tasks. One of such difficulties is that objects are small and crowded in remote sensing imagery. To tackle with this challenging task we have proposed a novel architecture called local feature extraction (LFE) module attached on top of dilated front-end module. The LFE module is based on our findings that aggressively increasing dilation factors fails to aggregate local features due to sparsity of the kernel, and detrimental to small objects. The proposed LFE module solves this problem by aggregating local features with decreasing dilation factor. We tested our network on three remote sensing datasets and acquired remarkably good results for all datasets especially for small objects.


title: AA-DBSCAN: an approximate adaptive DBSCAN for finding clusters with varying densities

abstract: Clustering is a typical data mining technique that partitions a dataset into multiple subsets of similar objects according to similarity metrics. In particular, density-based algorithms can find clusters of different shapes and sizes while remaining robust to noise objects. DBSCAN, a representative density-based algorithm, finds clusters by defining the density criterion with global parameters, $$ \varepsilon $$ ::: -distance and $$ MinPts $$ ::: . However, most density-based algorithms, including DBSCAN, find clusters incorrectly because the density criterion is fixed to the global parameters and misapplied to clusters of varying densities. Although studies have been conducted to determine optimal parameters or to improve clustering performance using additional parameters and computations, running time for clustering has been significantly increased, particularly when the dataset is large. In this study, we focus on minimizing the additional computation required to determine the parameters by using the approximate adaptive $$ \varepsilon $$ ::: -distance for each density while finding the clusters with varying densities that DBSCAN cannot find. Specifically, we propose a new tree structure based on a quadtree to define a dataset density layer. In addition, we propose approximate adaptive DBSCAN (AA-DBSCAN) and kAA-DBSCAN that have clustering performance similar to those of existing algorithms for finding clusters with varying densities while significantly reducing the running time required to perform clustering. We evaluate the proposed algorithms, AA-DBSCAN and kAA-DBSCAN, via extensive experiments using the state-of-the-art algorithms. Experimental results demonstrate an improvement in clustering performance and reduction in running time of the proposed algorithms.


title: Recent advances in differential evolution – An updated survey

abstract: Abstract Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed since the first comprehensive survey article was published on DE by Das and Suganthan in 2011. Several developments have been reported on various aspects of the algorithm in these 5 years and the research on and with DE have now reached an impressive state. Considering the huge progress of research with DE and its applications in diverse domains of science and technology, we find that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research. The purpose of this paper is to summarize and organize the information on these current developments on DE. Beginning with a comprehensive foundation of the basic DE family of algorithms, we proceed through the recent proposals on parameter adaptation of DE, DE-based single-objective global optimizers, DE adopted for various optimization scenarios including constrained, large-scale, multi-objective, multi-modal and dynamic optimization, hybridization of DE with other optimizers, and also the multi-faceted literature on applications of DE. The paper also presents a dozen of interesting open problems and future research issues on DE.


title: Analysis, Experimental Results, and Range Adaptation of Magnetically Coupled Resonators for Wireless Power Transfer

abstract: Wireless power technology offers the promise of cutting the last cord, allowing users to seamlessly recharge mobile devices as easily as data are transmitted through the air. Initial work on the use of magnetically coupled resonators for this purpose has shown promising results. We present new analysis that yields critical insight into the design of practical systems, including the introduction of key figures of merit that can be used to compare systems with vastly different geometries and operating conditions. A circuit model is presented along with a derivation of key system concepts, such as frequency splitting, the maximum operating distance (critical coupling), and the behavior of the system as it becomes undercoupled. This theoretical model is validated against measured data and shows an excellent average coefficient of determination of 0.9875. An adaptive frequency tuning technique is demonstrated, which compensates for efficiency variations encountered when the transmitter-to-receiver distance and/or orientation are varied. The method demonstrated in this paper allows a fixed-load receiver to be moved to nearly any position and/or orientation within the range of the transmitter and still achieve a near-constant efficiency of over 70% for a range of 0-70 cm.


title: Building Knowledge through Families of Experiments

abstract: Experimentation in software engineering is necessary but difficult. One reason is that there are a large number of context variables and, so, creating a cohesive understanding of experimental results requires a mechanism for motivating studies and integrating results. It requires a community of researchers that can replicate studies, vary context variables, and build models that represent the common observations about the discipline. The paper discusses the experience of the authors, based upon a collection of experiments, in terms of a framework for organizing sets of related studies. With such a framework, experiments can be viewed as part of common families of studies, rather than being isolated events. Common families of studies can contribute to important and relevant hypotheses that may not be suggested by individual experiments. A framework also facilitates building knowledge in an incremental manner through the replication of experiments within families of studies. To support the framework, the paper discusses the experiences of the authors in carrying out empirical studies, with specific emphasis on persistent problems encountered in experimental design, threats to validity, criteria for evaluation, and execution of experiments in the domain of software engineering.


title: Verification of Workflow Nets

abstract: Workflow management systems will change the architecture of future information systems dramatically. The explicit representation of business procedures is one of the main issues when introducing a workflow management system. In this paper we focus on a class of Petri nets suitable for the representation, validation and verification of these procedures. We will show that the correctness of a procedure represented by such a Petri net can be verified by using standard Petri-net-based techniques. Based on this result we provide a comprehensive set of transformation rules which can be used to construct and modify correct procedures.


title: Happily (n)ever after: Aging in the context of oxidative stress, proteostasis loss and cellular senescence

abstract: Abstract Aging is a complex phenomenon and its impact is becoming more relevant due to the rising life expectancy and because aging itself is the basis for the development of age-related diseases such as cancer, neurodegenerative diseases and type 2 diabetes. Recent years of scientific research have brought up different theories that attempt to explain the aging process. So far, there is no single theory that fully explains all facets of aging. The damage accumulation theory is one of the most accepted theories due to the large body of evidence found over the years. Damage accumulation is thought to be driven, among others, by oxidative stress. This condition results in an excess attack of oxidants on biomolecules, which lead to damage accumulation over time and contribute to the functional involution of cells, tissues and organisms. If oxidative stress persists, cellular senescence is a likely outcome and an important hallmark of aging. Therefore, it becomes crucial to understand how senescent cells function and how they contribute to the aging process. This review will cover cellular senescence features related to the protein pool such as morphological and molecular hallmarks, how oxidative stress promotes protein modifications, how senescent cells cope with them by proteostasis mechanisms, including antioxidant enzymes and proteolytic systems. We will also highlight the nutritional status of senescent cells and aged organisms (including human clinical studies) by exploring trace elements and micronutrients and on their importance to develop strategies that might increase both, life and health span and postpone aging onset.


LINEAR PREDICTION-BASED DEREVERBERATION WITH ADVANCED SPEECH ENHANCEMENT AND RECOGNITION TECHNOLOGIES FOR THE REVERB CHALLENGE


title: Reading Skills and Development for Effective use of Library Resources

abstract: The estimable value of reading calls for the need to master the skills by the individual. A competent reader is obviously a competent learner. Since reading is the tool for all subjects in all content fields, it is pertinent to master the skills at all developmental stages of a child. This paper presented the analysis of reading skills from birth to adulthood; from nursery to the university level. The skills when properly mastered would provide easy access to effective use of all library resources. The reading of a learner would thereby reinforced cognitively and affectively, making a reader emerge not only as a competent reader but also an intellectual.


title: Low self-esteem prospectively predicts depression in adolescence and young adulthood.

abstract: Low self-esteem and depression are strongly correlated in cross-sectional studies, yet little is known about their prospective effects on each other. The vulnerability model hypothesizes that low self-esteem serves as a risk factor for depression, whereas the scar model hypothesizes that low self-esteem is an outcome, not a cause, of depression. To test these models, the authors used 2 large longitudinal data sets, each with 4 repeated assessments between the ages of 15 and 21 years and 18 and 21 years, respectively. Cross-lagged regression analyses indicated that low self-esteem predicted subsequent levels of depression, but depression did not predict subsequent levels of self-esteem. These findings held for both men and women and after controlling for content overlap between the self-esteem and depression scales. Thus, the results supported the vulnerability model, but not the scar model, of self-esteem and depression.


Treating the High Risk Adolescent: A Survey of Effective Programs and Interventions


title: Sparse representations for facial expressions recognition via l1 optimization

abstract: In this paper, the principles of sparse signal representation theory are explored in order to perform facial expressions recognition from frontal views. Motivated by the success such methods have demonstrated in the face recognition problem, we formulate the feature extraction procedure in order to achieve facial expression recognition as an l 1 optimization problem. We show that the straightforward application of these methods to expressive images imposes certain difficulties. The use of difference images (i.e., the images that are derived from the subtraction of the neutral image from the expressive one) for sparse facial expression representations is justified. The use of expressive facial grids for similar tasks is also studied. Finally, the robustness of the proposed representations under facial image occlusion is shown and the efficacy of the proposed method in a series of experiments is demonstrated.


title: Strong regeneration ability from rhizome fragments in two invasive clonal plants (Solidago canadensis and S. gigantea)

abstract: The two rhizomatous perennials Solidago canadensis and S. gigantea belong to the most widespread alien plants in Europe. Anecdotal observations suggest that they disperse also by rhizome fragments. For testing their resprouting ability, rhizome fragments of different sizes from both species were buried at four different soil depths (0, 5, 10 and 20 cm, respectively) in a common garden. Rhizome fragments of S. canadensis ranged 3–6 cm in length, those of S. gigantea 5–20 cm in length. Resprouting plants were harvested after 3 months and growth related traits measured to assess their vitality. Resprouting rate in S. gigantea was far higher than in S. canadensis (85 and 19%, respectively). In S. gigantea, fragments of all sizes resprouted from all soil depths whereas none rhizome of S. canadensis emerged from 20 cm burial depth. In S. gigantea, size related traits showed significant interactions between fragment size and burial depth, but not relative shoot growth rate. At all burial depths, vitality of plants emerging from small rhizomes was lower than plants emerging from large rhizomes. Effects of rhizome size became stronger with increasing burial depth. The results show that both species are able to resprout from buried rhizome fragments, and that succesful regeneration is more likely to occur in S. gigantea. Managing these species should avoid any activities promoting rhizome fragmentation and dispersal of fragments.


title: Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays

abstract: Given image labels as the only supervisory signal, we focus on harvesting, or mining, thoracic disease localizations from chest X-ray images. Harvesting such localizations from existing datasets allows for the creation of improved data sources for computer-aided diagnosis and retrospective analyses. We train a convolutional neural network (CNN) for image classification and propose an attention mining (AM) strategy to improve the model's sensitivity or saliency to disease patterns. The intuition of AM is that once the most salient disease area is blocked or hidden from the CNN model, it will pay attention to alternative image regions, while still attempting to make correct predictions. However, the model requires to be properly constrained during AM, otherwise, it may overfit to uncorrelated image parts and forget the valuable knowledge that it has learned from the original image classification task. To alleviate such side effects, we then design a knowledge preservation (KP) loss, which minimizes the discrepancy between responses for X-ray images from the original and the updated networks. Furthermore, we modify the CNN model to include multi-scale aggregation (MSA), improving its localization ability on small-scale disease findings, e.g., lung nodules. We experimentally validate our method on the publicly-available ChestX-ray14 dataset, outperforming a class activation map (CAM)-based approach, and demonstrating the value of our novel framework for mining disease locations.


title: MetaAnchor: Learning to Detect Objects with Customized Anchors

abstract: We propose a novel and flexible anchor mechanism named MetaAnchor for object detection frameworks. Unlike many previous detectors model anchors via a predefined manner, in MetaAnchor anchor functions could be dynamically generated from the arbitrary customized prior boxes. Taking advantage of weight prediction, MetaAnchor is able to work with most of the anchor-based object detection systems such as RetinaNet. Compared with the predefined anchor scheme, we empirically find that MetaAnchor is more robust to anchor settings and bounding box distributions; in addition, it also shows the potential on transfer tasks. Our experiment on COCO detection task shows that MetaAnchor consistently outperforms the counterparts in various scenarios.


title: A deep-learning-based approach for automated wagon component inspection

abstract: Inspecting objects in the industry aims to guarantee product quality allowing problems to be corrected and damaged products to be discarded. Inspection is also widely used in railway maintenance, where wagon components need to be checked due to efficiency and safety concerns. In some organizations, hundreds of wagons are inspected visually by a human inspector, which leads to quality issues and safety risks for the inspectors. This paper describes a wagon component inspection approach using Deep Learning techniques to detect a particular damaged component: the shear pad. We compared our approach for convolutional neural networks with the state of art classification methods to distinguish among three shear pads conditions: absent, damaged, and undamaged shear pad. Our results are very encouraging showing empirical evidence that our approach has better performance than other classification techniques.


title: Understanding Visual Ads by Aligning Symbols and Objects using Co-Attention

abstract: We tackle the problem of understanding visual ads where given an ad image, our goal is to rank appropriate human generated statements describing the purpose of the ad. This problem is generally addressed by jointly embedding images and candidate statements to establish correspondence. Decoding a visual ad requires inference of both semantic and symbolic nuances referenced in an image and prior methods may fail to capture such associations especially with weakly annotated symbols. In order to create better embeddings, we leverage an attention mechanism to associate image proposals with symbols and thus effectively aggregate information from aligned multimodal representations. We propose a multihop co-attention mechanism that iteratively refines the attention map to ensure accurate attention estimation. Our attention based embedding model is learned end-to-end guided by a max-margin loss function. We show that our model outperforms other baselines on the benchmark Ad dataset and also show qualitative results to highlight the advantages of using multihop co-attention.


title: A survey on robotic devices for upper limb rehabilitation

abstract: The existing shortage of therapists and caregivers assisting physically disabled individuals at home is expected to increase and become serious problem in the near future. The patient population needing physical rehabilitation of the upper extremity is also constantly increasing. Robotic devices have the potential to address this problem as noted by the results of recent research studies. However, the availability of these devices in clinical settings is limited, leaving plenty of room for improvement. The purpose of this paper is to document a review of robotic devices for upper limb rehabilitation including those in developing phase in order to provide a comprehensive reference about existing solutions and facilitate the development of new and improved devices. In particular the following issues are discussed: application field, target group, type of assistance, mechanical design, control strategy and clinical evaluation. This paper also includes a comprehensive, tabulated comparison of technical solutions implemented in various systems.


title: Linking rhizosphere microbiome composition of wild and domesticated Phaseolus vulgaris to genotypic and root phenotypic traits

abstract: Plant domestication was a pivotal accomplishment in human history, but also led to a reduction in genetic diversity of crop species compared to their wild ancestors. How this reduced genetic diversity affected plant–microbe interactions belowground is largely unknown. Here, we investigated the genetic relatedness, root phenotypic traits and rhizobacterial community composition of modern and wild accessions of common bean (Phaseolus vulgaris) grown in agricultural soil from the highlands of Colombia, one of the centers of common bean diversification. Diversity Array Technology-based genotyping and phenotyping of local common bean accessions showed significant genetic and root architectural differences between wild and modern accessions, with a higher specific root length for the wild accessions. Canonical Correspondence Analysis indicated that the divergence in rhizobacterial community composition between wild and modern bean accessions is associated with differences in specific root length. Along the bean genotypic trajectory, going from wild to modern, we observed a gradual decrease in relative abundance of Bacteroidetes, mainly Chitinophagaceae and Cytophagaceae, and an increase in relative abundance of Actinobacteria and Proteobacteria, in particular Nocardioidaceae and Rhizobiaceae, respectively. Collectively, these results establish a link between common bean domestication, specific root morphological traits and rhizobacterial community assembly.


title: Design for Intuitive Use - Testing Image Schema Theory for User Interface Design

abstract: Demand for interactive products that are intuitive to use is high. Intuitive use in this paper is understood as the subconscious application of prior knowledge by the user. This definition is explicated by a continuum model of prior knowledge. Theories from several disciplines of application are surveyed whether they can contribute to the field of ‘Design for Intuitive Use’. Two sets of requirements are posed against which candidate theories have to be validated: (1) the claims the theory makes about intuitive use must be shown to be valid within the context of user interface design, and (2) the application of the theory has to support the user interface design process. Image schema theory which draws on knowledge on the sensorimotor stage of the knowledge continuum was put to the test by using rigorous psychological experimentation and by analysing user interfaces in different domains of application. These analyses showed that the application of image schema theory as a design language may be a powerful tool for the evaluation and improvement phases of a human-centred design process. By collecting the results of these analyses in an online catalogue, designers are provided with a tool that helps them with producing design solutions. Ongoing studies are investigating whether image schemas are also useful for the early phases of the design process such as requirements gathering. Although the studies are not finished yet it is concluded that the image schema approach offers value to and insight in designing user interfaces that are intuitive to use.


title: Person identification using anthropometric and gait data from kinect sensor

abstract: Uniquely identifying individuals using anthropometric and gait data allows for passive biometric systems, where cooperation from the subjects being identified is not required. In this paper, we report on experiments using a novel data set composed of 140 individuals walking in front of a Microsoft Kinect sensor. We provide a methodology to extract anthropometric and gait features from this data and show results of applying different machine learning algorithms on subject identification tasks. Focusing on KNN classifiers, we discuss how accuracy varies in different settings, including number of individuals in a gallery, types of attributes used and number of considered neighbors. Finally, we compare the obtained results with other results in the literature, showing that our approach has comparable accuracy for large galleries.


title: Continuum regression for cross-modal multimedia retrieval

abstract: Understanding the relationship among different modalities is a challenging task. The frequently used canonical correlation analysis (CCA) and its variants have proved effective for building a common space in which the correlation between different modalities is maximized. In this paper, we show that CCA and its variants may cause information dissipation when switching the modals, and thus propose to use the continuum regression (CR) model to handle this problem. In particular, the CR model with a fixed variance coefficient of 1/2 is adopted here. We also apply the multinomial logistic regression model for further classification task. To evaluate the CR model, we perform a series of cross-modal retrieval experiments in terms of two kinds of modals, namely image and text. Compared with previous methods, experimental results show that the CR model has achieved the best retrieval precision, which demonstrates the potential of our method for real internet search applications.


title: PhaseBeat: Exploiting CSI Phase Data for Vital Sign Monitoring with Commodity WiFi Devices

abstract: Vital signs, such as respiration and heartbeat, are useful to health monitoring since such signals provide important clues of medical conditions. Effective solutions are needed to provide contact-free, easy deployment, low-cost, and long-term vital sign monitoring. In this paper, we present PhaseBeat to exploit channel state information (CSI) phase difference data to monitor breathing and heartbeat with commodity WiFi devices. We provide a rigorous analysis of the CSI phase difference data with respect to its stability and periodicity. Based on the analysis, we design and implement the PhaseBeat system with off-the-shelf WiFi devices, and conduct an extensive experimental study to validate its performance. Our experimental results demonstrate the superior performance of PhaseBeat over existing approaches in various indoor environments.


Mayer-Rokitansky-Küster-Hauser Syndrome with Imperforate Anus : A Rare Association and an Innovative Surgical Management


title: Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm

abstract: Valiant (1984) and others have studied the problem of learning various classes of Boolean functions from examples. Here we discuss incremental learning of these functions. We consider a setting in which the learner responds to each example according to a current hypothesis. Then the learner updates the hypothesis, if necessary, based on the correct classification of the example. One natural measure of the quality of learning in this setting is the number of mistakes the learner makes. For suitable classes of functions, learning algorithms are available that make a bounded number of mistakes, with the bound independent of the number of examples seen by the learner. We present one such algorithm that learns disjunctive Boolean functions, along with variants for learning other classes of Boolean functions. The basic method can be expressed as a linear-threshold algorithm. A primary advantage of this algorithm is that the number of mistakes grows only logarithmically with the number of irrelevant attributes in the examples. At the same time, the algorithm is computationally efficient in both time and space.


title: Range Doppler detection for automotive FMCW radars

abstract: The FMCW-Radar-Principle is widely used for automotive radar systems. The basic idea for FMCW-Radars is to generate a linear frequency ramp as transmit signal. The difference frequency between the transmitted and received signal is determined after downconversion. In order to detect range and velocity together, the information, extracted from one frequency ramp, is not enough, because it is ambiguous. Several subsequent ramps have to be generated to remove the ambiguity between the frequency portion produced by range and the doppler frequency. In this paper two methods are presented to perform this basic signal processing step. The two methods have the Fast Fourier Transform as basic calculation step in common, but the number of FFTs and their length are different. The requirements on bandwidth for the IF-Hardware and A/D-Converters are also determined by the algorithm for Range-Doppler-Detection. The CFAR (Constant False Alarm Rate)-Algorithm for target detection must also be adapted to the chosen method. Both methods have been verified with a 24 GHz radar prototype. The Radar-Frontend has been built with a newly developed SiGe-Radar-Chipset on Infineon's B7HF200-Process with a transition frequency of 200 GHz.


title: Extending UTAUT2 to explore consumer adoption of mobile payments

abstract: There is a growing interest in studying the adoption of m-payments but literature on the subject is still in its infancy and no empirical research relating to this has been conducted in the context of the UK to date. The aim of this study is to unveil the current situation in m-payment adoption research and provide future research direction through the development of a research model for the examination of factors affecting m-payment adoption in the UK context. Following an extensive search of the literature, this study finds that 186 relationships between independent and dependent variables have been analysed by 32 existing empirical m-payment and m-banking adoption studies. From analysis of these relationships the most significant factors found to influence adoption are uncovered and an extension of UTAUT2 with the addition of perceived risk and trust is proposed to increase the applicability of UTAUT2 to the m-payment context.


title: Learning the Relative Importance of Objects from Tagged Images for Retrieval and Cross-Modal Search

abstract: We introduce an approach to image retrieval and auto-tagging that leverages the implicit information about object importance conveyed by the list of keyword tags a person supplies for an image. We propose an unsupervised learning procedure based on Kernel Canonical Correlation Analysis that discovers the relationship between how humans tag images (e.g., the order in which words are mentioned) and the relative importance of objects and their layout in the scene. Using this discovered connection, we show how to boost accuracy for novel queries, such that the search results better preserve the aspects a human may find most worth mentioning. We evaluate our approach on three datasets using either keyword tags or natural language descriptions, and quantify results with both ground truth parameters as well as direct tests with human subjects. Our results show clear improvements over approaches that either rely on image features alone, or that use words and image features but ignore the implied importance cues. Overall, our work provides a novel way to incorporate high-level human perception of scenes into visual representations for enhanced image search.


title: Segmentation of breast ultrasound images based on active contours using neutrosophic theory

abstract: PURPOSE ::: Ultrasound imaging is an effective approach for diagnosing breast cancer, but it is highly operator-dependent. Recent advances in computer-aided diagnosis have suggested that it can assist physicians in diagnosis. Definition of the region of interest before computer analysis is still needed. Since manual outlining of the tumor contour is tedious and time-consuming for a physician, developing an automatic segmentation method is important for clinical application. ::: ::: ::: METHODS ::: The present paper represents a novel method to segment breast ultrasound images. It utilizes a combination of region-based active contour and neutrosophic theory to overcome the natural properties of ultrasound images including speckle noise and tissue-related textures. First, due to inherent speckle noise and low contrast of these images, we have utilized a non-local means filter and fuzzy logic method for denoising and image enhancement, respectively. This paper presents an improved weighted region-scalable active contour to segment breast ultrasound images using a new feature derived from neutrosophic theory. ::: ::: ::: RESULTS ::: This method has been applied to 36 breast ultrasound images. It generates true-positive and false-positive results, and similarity of 95%, 6%, and 90%, respectively. ::: ::: ::: CONCLUSION ::: The purposed method indicates clear advantages over other conventional methods of active contour segmentation, i.e., region-scalable fitting energy and weighted region-scalable fitting energy.


title: The cognitive neuroscience of sleep: neuronal systems, consciousness and learning

abstract: Sleep can be addressed across the entire hierarchy of biological organization. We discuss neuronal-network and regional forebrain activity during sleep, and its consequences for consciousness and cognition. Complex interactions in thalamocortical circuits maintain the electroencephalographic oscillations of non-rapid eye movement (NREM) sleep. Functional neuroimaging affords views of the human brain in both NREM and REM sleep, and has informed new concepts of the neural basis of dreaming during REM sleep — a state that is characterized by illogic, hallucinosis and emotionality compared with waking. Replay of waking neuronal activity during sleep in the rodent hippocampus and in functional images of human brains indicates possible roles for sleep in neuroplasticity. Different forms and stages of learning and memory might benefit from different stages of sleep and be subserved by different forebrain regions.


title: DynaMat: a dynamic view management system for data warehouses

abstract: Pre-computation and materialization of views with aggregate functions is a common technique in Data Warehouses. Due to the complex structure of the warehouse and the different profiles of the users who submit queries, there is need for tools that will automate the selection and management of the materialized data. In this paper we present DynaMat, a system that dynamically materializes information at multiple levels of granularity in order to match the demand (workload) but also takes into account the maintenance restrictions for the warehouse, such as down time to update the views and space availability. DynaMat unifies the view selection and the view maintenance problems under a single framework using a novel “goodness” measure for the materialized views. DynaMat constantly monitors incoming queries and materializes the best set of views subject to the space constraints. During updates, DynaMat reconciles the current materialized view selection and refreshes the most beneficial subset of it within a given maintenance window. We compare DynaMat against a system that is given all queries in advance and the pre-computed optimal static view selection. The comparison is made based on a new metric, the Detailed Cost Savings Ratio introduced for quantifying the benefits of view materialization against incoming queries. These experiments show that DynaMat's dynamic view selection outperforms the optimal static view selection and thus, any sub-optimal static algorithm that has appeared in the literature.


title: Analysis of Ranging Precision in an FMCW Radar Measurement Using a Phase-Locked Loop

abstract: The standard deviation in a frequency modulated continuous wave radar distance measurement using a charge pump phase-locked loop (PLL) is calculated analytically. The phase noise of the PLL is modeled as an Ornstein–Uhlenbeck process resulting in a Lorentzian spectrum. We calculate the distance error as a function of the receiver noise bandwidth and the target distance. Depending on the frequency estimation algorithm and the target distance, the rms distance error due to PLL phase noise increases by about 6–9 dB with doubling the target distance. By contrast, the white noise in the radar receiver raises the distance error by about 12 dB in the far field with distance doubling, making this error contribution dominant for large target distances. These findings are verified by measurements on a scalable 61/122-GHz radar sensor platform.


Politics of Social Research: An Inquiry into the Ethics and Responsibilities of Social Scientists:


title: A research framework of smart education

abstract: The development of new technologies enables learners to learn more effectively, efficiently, flexibly and comfortably. Learners utilize smart devices to access digital resources through wireless network and to immerse in both personalized and seamless learning. Smart education, a concept that describes learning in digital age, has gained increased attention. This paper discusses the definition of smart education and presents a conceptual framework. A four-tier framework of smart pedagogies and ten key features of smart learning environments are proposed for foster smart learners who need master knowledge and skills of the 21st century learning. The smart pedagogy framework includes class-based differentiated instruction, group-based collaborative learning, individual-based personalized learning and mass-based generative learning. Furthermore, a technological architecture of smart education, which emphasizes the role of smart computing, is proposed. The tri-tier architecture and key functions are all presented. Finally, challenges of smart education are discussed.


title: A Model of a Generic Natural Language Interface for Querying Database

abstract: Extracting information from database is typically done by using a structured language such as SQL (Structured Query Language). But non expert users can’t use this later. Wherefore using Natural Language (NL) for communicating with database can be a powerful tool. But without any help, computers can’t understand this language; that is why it is essential to develop an interface able to translate user’s query given in NL into an equivalent one in Database Query Language (DBQL). This paper presents a model of a generic natural language query interface for querying database. This model is based on machine learning approach which allows interface to automatically improve its knowledge base through experience. The advantage of this interface is that it functions independently of the database language, content and model. Experimentations are realized to study the performance of this interface and make necessary corrections for its amelioration


title: TDTOS - T-shirt Design and Try-On System

abstract: In this paper, a new framework for T-shirt design and try-on simulation based on FPGA is presented. Users can not only gain realistic try-on experience, but also design the T-shirts all on their own. The design approach of this system consists of three major parts. First, collect relevant information from the camera and identify the position of the clothes. Second, process the retrieved data and modulate the color of the clothes with folds and shadows remained. Third, place built-in or user-designed pictures onto the clothes and simulate their deformation while the user moves arbitrarily. In comparison with existing virtual clothes fitting systems, our system provides the flexibility of designing customized pictures on the T-shirt with realistic virtual try-on simulation in real- time.


title: Floyd warshall algorithm with FIS Sugeno for search evacuation route optimization

abstract: Evacuation route is very important for the community especially those who are living in disaster-prone areas since the route used to perform the evacuation to evacuate from the place of the disaster to a safer place. The example of this case is the eruption of Mount Merapi. The eruption of Mount Merapi in 2010 causes casualties. One factor that can affect the number of casualties is the shortest path with the determination of the evacuation route. This research aims to know the shortest evacuation lines by using a combination of Fuzzy and Floyd Warshall algorithm. The output of the fuzzy logic which is a weighting value of each route, processed with Floyd Warshall algorithm. Floyd Warshall algorithm uses calculations to all nodes with the matrix and graph outputs hyphen is the smallest of all nodes. The result obtained from the combination of both methods is the optimal path. The output of the fuzzy logic which is a weighting value of each route, processed with Floyd Warshall. Floyd Warshall algorithm uses calculations to all nodes with the matrix and graph outputs hyphen is the smallest of all nodes. The results of the test show that the program can demonstrate the steps to find the evacuation routes and suggest which node is passed for the shortest distance. Based on the study, the results of the evacuation route are the same for the two algorithms. Floyd Warshall algorithm's running time is faster than Exhaustive algorithm at 270 of the whole experiments conducted. It is concluded that Floyd Warshall algorithm is more suitable for evacuation route planning of Merapi eruption casualties in Sleman Regency.


title: Nonvolatile memory design based on ferroelectric FETs

abstract: Ferroelectric FETs (FEFETs) offer intriguing possibilities for the design of low power nonvolatile memories by virtue of their three-terminal structure coupled with the ability of the ferroelectric (FE) material to retain its polarization in the absence of an electric field. Utilizing the distinct features of FEFETs, we propose a 2-transistor (2T) FEFET-based nonvolatile memory with separate read and write paths. With proper co-design at the device, cell and array levels, the proposed design achieves non-destructive read and lower write power at iso-write speed compared to standard FE-RAM. In addition, the FEFET-based memory exhibits high distinguishability with six orders of magnitude difference in the read currents corresponding to the two states. Comparative analysis based on experimentally calibrated models shows significant improvement of access energy-delay. For example, at a fixed write time of 550ps, the write voltage and energy are 58.5% and 67.7% lower than FERAM, respectively. These benefits are achieved with 2.4 times the area overhead. Further exploration of the proposed FEFET memory in energy harvesting nonvolatile processors shows an average improvement of 27% in forward progress over FERAM.


title: Sentiment analysis in tickets for IT support

abstract: Sentiment analysis has been adopted in software engineeringfor problems such as software usability and sentimentof developers in open-source projects. This paper proposesa method to evaluate the sentiment contained in tickets forIT (Information Technology) support.IT tickets are broadin coverage (e.g. infrastructure, software), and involve errors,incidents, requests, etc. The main challenge is to automaticallydistinguish between factual information, whichis intrinsically negative (e.g. error description), from thesentiment embedded in the description. Our approach isto automatically create a Domain Dictionary that containsterms with sentiment in the IT context, used to filter termsin ticket for sentiment analysis. We experiment and evaluatethree approaches for calculating the polarity of terms intickets. Our study was developed using 34,895 tickets fromfive organizations, from which we randomly selected 2,333tickets to compose a Gold Standard. Our best results displayan average precision and recall of 82.83% and 88.42%, whichoutperforms the compared sentiment analysis solutions.


title: Robust and Collective Entity Disambiguation through Semantic Embeddings

abstract: Entity disambiguation is the task of mapping ambiguous terms in natural-language text to its entities in a knowledge base. It finds its application in the extraction of structured data in RDF (Resource Description Framework) from textual documents, but equally so in facilitating artificial intelligence applications, such as Semantic Search, Reasoning and Question & Answering. We propose a new collective, graph-based disambiguation algorithm utilizing semantic entity and document embeddings for robust entity disambiguation. Robust thereby refers to the property of achieving better than state-of-the-art results over a wide range of very different data sets. Our approach is also able to abstain if no appropriate entity can be found for a specific surface form. Our evaluation shows, that our approach achieves significantly (>5%) better results than all other publicly available disambiguation algorithms on 7 of 9 datasets without data set specific tuning. Moreover, we discuss the influence of the quality of the knowledge base on the disambiguation accuracy and indicate that our algorithm achieves better results than non-publicly available state-of-the-art algorithms.


title: Temporal Information Retrieval

abstract: The study of temporal dynamics and its impact can be framed within the so-called temporal IR approaches, which explain how user behavior, document content and scale vary with time, and how we can use them in our favor in order to improve retrieval effectiveness. This half-day tutorial will outline research issues with respect to temporal dynamics, and provide a comprehensive overview of temporal IR approaches, essentially regarding processing dynamic content, temporal information extraction, temporal query analysis, and time-aware retrieval and ranking. The tutorial is structured into two sessions. During the first session, we will explain the general and wide aspects associated to temporal dynamics by focusing on the web domain, from content and structural changes to variations of user behavior and interactions. We will begin with temporal indexing and query processing. Next step, we will explain current approaches to time-aware retrieval and ranking, which can be classified into different types based on two main notions of relevance with respect to time, namely, recency-based ranking, and time-dependent ranking. In the latter session, we will describe research issues centered on determining the temporal intent of queries, and time-aware query enhancement, e.g., temporal relevance feedback, and time-aware query reformulation. In addition, we present applications in related research areas, e.g., exploration, summarization, and clustering of search results, as well as future event retrieval and prediction. To this end, we conclude our tutorial and outline future directions. This tutorial targets graduate students, researchers and practitioners in the field of information retrieval. The goal is to provide an overview as well as an important context that enables further research on and practical applications within this area.


title: A Novel High-speed Adder-Subtractor Design based on CNFET

abstract: Carbon Nanotube filed-effect transistor (CNFET) is one of the promising alternatives to the MOS transistors. The geometrydependent threshold voltage is one of the CNFET characteristics, which is used in the proposed design. In this paper, we present a novel high speed Adder-subtractor cell using CNFETs based on XOR gates and multiplexer. Presented design uses fourteen transistors, ten for full adder and four to modify the cell for subtraction. Simulation results show significant improvement in terms of delay and area saving with 48% and 11% respectively compared to the latest design. Simulations were carried out using HSPICE based on CNFET model with optimized design parameters.


title: Disorder induced magnetoresistance in a two dimensional electron system

abstract: We predict and demonstrate that a disorder-induced carrier density inhomogeneity causes magnetoresistance (MR) in a two-dimensional electron system. Our experiments on graphene show a quadratic MR persisting far from the charge neutrality point. Effective medium calculations show that for charged impurity disorder, the low-field MR is a universal function of the ratio of carrier density to fluctuations in carrier density, a power law when this ratio is large, in excellent agreement with experiment. The MR is generic and should occur in other materials with large carrier density inhomogeneity.


title: Pyramidal implementation of the Lucas Kanade feature tracker

abstract: 1 Problem Statement Let I and J be two 2D grayscaled images. The two quantities I(x) = I(x, y) and J(x) = J(x, y) are then the grayscale value of the two images are the location x = [x y] , where x and y are the two pixel coordinates of a generic image point x. The image I will sometimes be referenced as the first image, and the image J as the second image. For practical issues, the images I and J are discret function (or arrays), and the upper left corner pixel coordinate vector is [0 0] . Let nx and ny be the width and height of the two images. Then the lower right pixel coordinate vector is [nx − 1 ny − 1] . Consider an image point u = [ux uy] on the first image I. The goal of feature tracking is to find the location v = u + d = [ux+dx uy +dy] on the second image J such as I(u) and J(v) are “similar”. The vector d = [dx dy] is the image velocity at x, also known as the optical flow at x. Because of the aperture problem, it is essential to define the notion of similarity in a 2D neighborhood sense. Let ωx and ωy two integers. We define the image velocity d as being the vector that minimizes the residual function defined as follows:


title: Attention networks for image-to-text

abstract: The paper approaches the problem of image-to-text with attention-based encoder-decoder networks that are trained to handle sequences of characters rather than words. We experiment on lines of text from a popular handwriting database with different attention mechanisms for the decoder. The model trained with softmax attention achieves the lowest test error, outperforming several other RNN-based models. Our results show that softmax attention is able to learn a linear alignment whereas the alignment generated by sigmoid attention is linear but much less precise.


title: With a flick of the wrist: stretch sensors as lightweight input for mobile devices

abstract: With WristFlicker, we detect wrist movement through sets of stretch sensors embedded in clothing. Our system supports wrist rotation (pronation/supination), and both wrist tilts (flexion/extension and ulnar/radial deviation). Each wrist movement is measured by two opposing stretch sensors, mimicking the counteracting movement of muscles. We discuss interaction techniques that allow a user to control a music player through this lightweight input.


title: Cognitive-affective correlates and predictors of superficial dyspareunia

abstract: This study investigated the role of cognitive-affective variables related to sexuality, chronic pain, individual and relational well-being in superficial dyspareunia. Although symptomatic women (n = 80) differed from complaint-free controls (n = 62) on all variables, sexuality related measures had the most important contribution into the prediction of group membership. Dyspareunia subgroups based on the presence/absence of a concomitant diagnosis of provoked vestibulodynia were only distinguishable on pain intensity but not on variables related to sexuality and psychological well-being. The present findings underscore the relevance of psychosexual factors in women with superficial dyspareunia.


A Survey on Interactive Lenses in Visualization


title: Symbolic Execution of Obfuscated Code

abstract: Symbolic and concolic execution nd important applications in a number of security-related program analyses, including analysis of malicious code. However, malicious code tend to very often be obfuscated, and current concolic analysis techniques have trouble dealing with some of these obfuscations, leading to imprecision and/or excessive resource usage. This paper discusses three such obfuscations: two of these are already found in obfuscation tools used by malware, while the third is a simple variation on an existing obfuscation technique. We show empirically that existing symbolic analyses are not robust against such obfuscations, and propose ways in which the problems can be mitigated using a combination of ne-grained bit-level taint analysis and architecture-aware constraint generations. Experimental results indicate that our approach is eective in allowing symbolic and concolic execution to handle such obfuscations.


title: Exemplar-Based Image and Video Stylization Using Fully Convolutional Semantic Features

abstract: Color and tone stylization in images and videos strives to enhance unique themes with artistic color and tone adjustments. It has a broad range of applications from professional image postprocessing to photo sharing over social networks. Mainstream photo enhancement softwares, such as Adobe Lightroom and Instagram, provide users with predefined styles, which are often hand-crafted through a trial-and-error process. Such photo adjustment tools lack a semantic understanding of image contents and the resulting global color transform limits the range of artistic styles it can represent. On the other hand, stylistic enhancement needs to apply distinct adjustments to various semantic regions. Such an ability enables a broader range of visual styles. In this paper, we first propose a novel deep learning architecture for exemplar-based image stylization, which learns local enhancement styles from image pairs. Our deep learning architecture consists of fully convolutional networks for automatic semantics-aware feature extraction and fully connected neural layers for adjustment prediction. Image stylization can be efficiently accomplished with a single forward pass through our deep network. To extend our deep network from image stylization to video stylization, we exploit temporal superpixels to facilitate the transfer of artistic styles from image exemplars to videos. Experiments on a number of data sets for image stylization as well as a diverse set of video clips demonstrate the effectiveness of our deep learning architecture.


title: A method for modeling clouds based on atmospheric fluid dynamics

abstract: The simulation of natural phenomena such as clouds, smoke, fire and water is one of the most important research areas in computer graphics. In particular, clouds play an important role in creating images of outdoor scenes. The proposed method is based on the physical simulation of atmospheric fluid dynamics which characterizes the shape of clouds. To take account of the dynamics, we used a method called the coupled map lattice (CML). CML is an extended method of cellular automaton and is computationally inexpensive. The proposed method can create various types of clouds and can also realize the animation of these clouds. Moreover, we have developed an interactive system for modeling various types of clouds.


title: Biofuel cells--recent advances and applications.

abstract: In 2006, the journal Biosensors and Bioelectronics published a special issue devoted exclusively to biofuel cells, including several research papers and an extensive review of the field [Bullen, R.A., Arnot, T.C., Lakeman, J.B., Walsh, F.C., 2006. Biosens. Bioelectron.]. Within this review a brief description will firstly be given of the history of biofuel cells together with coverage of some of the major historical advances. The review is intended, however, to largely concentrate on and give an overview of the advances made in recent years in this area together with a discussion surrounding the practical application of biofuel cells. There are several classes of biofuel cells: we shall firstly discuss the recent advances in biofuel cells that convert chemical fuels to produce electrical power by use of catalytic enzymes. This will be followed by a section on similar cells where micro-organisms rather than enzymes are used to convert the fuel to energy. Thirdly we shall consider hybrid biofuel cells that combine the utilisation of photochemical chemistries and biological systems for the generation of electricity. Finally we will discuss some of the proposed uses of biofuel cells together with a short consideration of future research possibilities and applications of these systems.


title: Design, Analysis and Characterisation of Spoof Surface Plasmon Polaritons based Wideband Bandpass Filter at Microwave Frequency

abstract: This paper presents the wideband bandpass filter (BPF) in the microwave frequency domain. The realisation approach is based on spoof surface plasmon polaritons (SSPPs) phenomenon using plasmonic metamaterial. A novel unit cell is designed for filter design using an LC resonator concept. Then SSPPs BPF is realised using an optimised mode converter and five unit cells. This paper includes a brief design detail of the proposed novel unit cell. The passband of BPF is achieved at approximately 1.20 - 5.80 GHz, 3dB bandwidth is tentatively 4.60 GHz and the insertion loss is less than 2 dB approximately over the passband. The overall dimension of fabricated filter is (90 x 45) mm. A basic schematic of transmission line representation is also proposed to evaluate the BPF structure.


title: Multiple lane boundary detection using a combination of low-level image features

abstract: In this paper, the authors propose a vision-based multiple lane boundaries detection and estimation structure that fuses the edge features and the high intensity features. The approach utilizes a camera as the only input sensor. The application of a Kalman filter for information fusion and tracking significantly improves the reliability and robustness of the system. The authors test their system on roads with different driving scenarios, including day, night, heavy traffic, rain, confusing textures and shadows. The feasibility of their approach is demonstrated by quantitative evaluation using manually labeled video clips.


title: Towards trusted cloud computing

abstract: Cloud computing infrastructures enable companies to cut costs by outsourcing computations on-demand. However, clients of cloud computing services currently have no means of verifying the confidentiality and integrity of their data and computation. ::: ::: To address this problem we propose the design of a trusted cloud computing platform (TCCP). TCCP enables Infrastructure as a Service (IaaS) providers such as Amazon EC2 to provide a closed box execution environment that guarantees confidential execution of guest virtual machines. Moreover, it allows users to attest to the IaaS provider and determine whether or not the service is secure before they launch their virtual machines.


title: Design of Compact F-Shaped Slot Triple-Band Antenna for WLAN/WiMAX Applications

abstract: This communication presents a small, low-profile planar triple-band microstrip antenna for WLAN/WiMAX applications. The goal of this communication is to combine WLAN and WiMAX communication standards simultaneously into a single device by designing a single antenna that can excite triple-band operation. The designed antenna has a compact size of $19 \times 25\;\text{mm}^{2}$ ( $0.152 \lambda_{0}\;\times 0.2 \lambda_{0}$ ). The proposed antenna consists of F-shaped slot radiators and a defected ground plane. Since only two F-shaped slots are etched on either sides of the radiator for triple-band operation, the radiator is very compact in size and simple in structure. The antenna shows three distinct bands I from 2.0 to 2.76, II from 3.04 to 4.0, and III from 5.2 to 6.0 GHz, which covers entire WLAN (2.4/5.2/5.8 GHz) and WiMAX (2.5/3.5/5.5) bands. To validate the proposed design, an experimental prototype has been fabricated and tested. Thus, the simulation results along with the measurements show that the antenna can simultaneously operate over WLAN (2.4/5.2/5.8 GHz) and WiMAX (2.5/3.5/5.5 GHz) frequency bands.


title: Real-Time Surface Extraction and Visualization of Medical Images using OpenCL and GPUs

abstract: Marching Cubes (MC) is an algorithm that extracts surfaces from volumetric scalar data. It is used extensively in visualization and analysis of medical data from modalities like CT and MR, usually after a 3D segmentation of the structures of interest have been performed. Implementations of MC on CPUs are slow, using several seconds (even minutes) to extract the surface before sending it to the Graphics Processing Unit (GPU) for rendering. Fast surface extraction implementations are very beneficial in medical applications, where large datasets are used and time is crucial. Analysis of medical image data often entails changing different parameters, thus real-time implementations are very desirable. MC is a completely dataparallel algorithm, making it ideal for execution on GPUs. GPU processing enables the result to be rendered on screens in a few milliseconds. In this paper, a MC implementation written in OpenCL that runs entirely on the GPU is presented. We show that our implementation uses a more efficient storage scheme than previous GPU implementations, and that this enables real-time processing of large medical datasets. Our implementation also shows that GPU implementations written in OpenCL has the potential of being just as fast and efficient as CUDA or shader implementations.


title: The functional basis of face evaluation.

abstract: People automatically evaluate faces on multiple trait dimensions, and these evaluations predict important social outcomes, ranging from electoral success to sentencing decisions. Based on behavioral studies and computer modeling, we develop a 2D model of face evaluation. First, using a principal components analysis of trait judgments of emotionally neutral faces, we identify two orthogonal dimensions, valence and dominance, that are sufficient to describe face evaluation and show that these dimensions can be approximated by judgments of trustworthiness and dominance. Second, using a data-driven statistical model for face representation, we build and validate models for representing face trustworthiness and face dominance. Third, using these models, we show that, whereas valence evaluation is more sensitive to features resembling expressions signaling whether the person should be avoided or approached, dominance evaluation is more sensitive to features signaling physical strength/weakness. Fourth, we show that important social judgments, such as threat, can be reproduced as a function of the two orthogonal dimensions of valence and dominance. The findings suggest that face evaluation involves an overgeneralization of adaptive mechanisms for inferring harmful intentions and the ability to cause harm and can account for rapid, yet not necessarily accurate, judgments from faces.


title: Unit Dependency Graph and its Application to Arithmetic Word Problem Solving

abstract: Math word problems provide a natural abstraction to a range of natural language understanding problems that involve reasoning about quantities, such as interpreting election results, news about casualties, and the financial section of a newspaper. Units associated with the quantities often provide information that is essential to support this reasoning. This paper proposes a principled way to capture and reason about units and shows how it can benefit an arithmetic word problem solver. This paper presents the concept of Unit Dependency Graphs (UDGs), which provides a compact representation of the dependencies between units of numbers mentioned in a given problem. Inducing the UDG alleviates the brittleness of the unit extraction system and allows for a natural way to leverage domain knowledge about unit compatibility, for word problem solving. We introduce a decomposed model for inducing UDGs with minimal additional annotations, and use it to augment the expressions used in the arithmetic word problem solver of (Roy and Roth 2015) via a constrained inference framework. We show that introduction of UDGs reduces the error of the solver by over 10 %, surpassing all existing systems for solving arithmetic word problems. In addition, it also makes the system more robust to adaptation to new vocabulary and equation forms .


title: A framework for performance modeling and prediction

abstract: Cycle-accurate simulation is far too slow for modeling the expected performance of full parallel applications on large HPC systems. And just running an application on a system and observing wallclock time tells you nothing about why the application performs as it does (and is anyway impossible on yet-to-be-built systems). Here we present a framework for performance modeling and prediction that is faster than cycle-accurate simulation, more informative than simple benchmarking, and is shown useful for performance investigations in several dimensions.


title: Exploring crowd consistency in a mechanical turk survey

abstract: Crowdsourcing can provide a platform for evaluating software engineering research. In this paper, we aim to explore characteristics of the worker population on Amazon's Mechanical Turk, a popular micro task crowdsourcing environment, and measure the percentage of workers who are potentially qualified to perform software- or computer science- related tasks. Through a baseline survey and two replications, we measure workers' answer consistency as well as the consistency of sample characteristics. In the end, we deployed 1,200 total surveys that were completed by 1,064 unique workers. Our results show that 24% of the study participants have a computer science or IT background and most people are payment driven when choosing tasks. The sample characteristics can vary significantly, even on large samples with 300 participants. Additionally, we often observed inconsistency in workers' answers for those who completed two surveys; approximately 30% answered at least one question inconsistently between the two survey submissions. This implies a need for replication and quality controls in crowdsourced experiments.


title: Comparative primate neurobiology and the evolution of brain language systems

abstract: Human brain specializations supporting language can be identified by comparing human with non-human primate brains. Comparisons with chimpanzees are critical in this endeavor. Human brains are much larger than non-human primate brains, but human language capabilities cannot be entirely explained by brain size. Human brain specializations that potentially support our capacity for language include firstly, wider cortical minicolumns in both Broca's and Wernicke's areas compared with great apes; secondly, leftward asymmetries in Broca's area volume and Wernicke's area minicolumn width that are not found in great apes; and thirdly, arcuate fasciculus projections beyond Wernicke's area to a region of expanded association cortex in the middle and inferior temporal cortex involved in processing word meaning.


title: Parallel functional arrays

abstract: The goal of this paper is to develop a form of functional arrays (sequences) that are as efficient as imperative arrays, can be used in parallel, and have well defined cost-semantics. The key idea is to consider sequences with functional value semantics but non-functional cost semantics. Because the value semantics is functional, "updating" a sequence returns a new sequence. We allow operations on "older" sequences (called interior sequences) to be more expensive than operations on the "most recent" sequences (called leaf sequences). We embed sequences in a language supporting fork-join parallelism. Due to the parallelism, operations can be interleaved non-deterministically, and, in conjunction with the different cost for interior and leaf sequences, this can lead to non-deterministic costs for a program. Consequently the costs of programs can be difficult to analyze. The main result is the derivation of a deterministic cost dynamics which makes analyzing the costs easier. The theorems are not specific to sequences and can be applied to other data types with different costs for operating on interior and leaf versions. We present a wait-free concurrent implementation of sequences that requires constant work for accessing and updating leaf sequences, and logarithmic work for accessing and linear work for updating interior sequences. We sketch a proof of correctness for the sequence implementation. The key advantages of the present approach compared to current approaches is that our implementation requires no changes to existing programming languages, supports nested parallelism, and has well defined cost semantics. At the same time, it allows for functional implementations of algorithms such as depth-first search with the same asymptotic complexity as imperative implementations.


Novel Shifted Real Spectrum for Exact Signal Reconstruction.


title: The mechanisms of manual therapy in the treatment of musculoskeletal pain: a comprehensive model.

abstract: Prior studies suggest manual therapy (MT) as effective in the treatment of musculoskeletal pain; however, the mechanisms through which MT exerts its effects are not established. In this paper we present a comprehensive model to direct future studies in MT. This model provides visualization of potential individual mechanisms of MT that the current literature suggests as pertinent and provides a framework for the consideration of the potential interaction between these individual mechanisms. Specifically, this model suggests that a mechanical force from MT initiates a cascade of neurophysiological responses from the peripheral and central nervous system which are then responsible for the clinical outcomes. This model provides clear direction so that future studies may provide appropriate methodology to account for multiple potential pertinent mechanisms.


A learnercentric view of mobile seamless learning


title: Attractor Dynamics in Feedforward Neural Networks

abstract: We study the probabilistic generative models parameterized by feedforward neural networks. An attractor dynamics for probabilistic inference in these models is derived from a mean field approximation for large, layered sigmoidal networks. Fixed points of the dynamics correspond to solutions of the mean field equations, which relate the statistics of each unit to those of its Markov blanket. We establish global convergence of the dynamics by providing a Lyapunov function and show that the dynamics generate the signals required for unsupervised learning. Our results for feedforward networks provide a counterpart to those of Cohen-Grossberg and Hopfield for symmetric networks.


title: Supporting Easy Physical-to-Virtual Creation of Mobile VR Maze Games: A New Genre

abstract: With the fast development of virtual reality games, one of the key research questions is how players may express their creativity and participate in the process of game design. In this paper, we present a new game genre which combines user-controlled game design in physical space with game play in virtual space on a mobile device. The new system supports authoring by anyone, creating virtual reality games that can be easily modified or developed for physical space, and be used anywhere by novice end-users without any knowledge of tracking technology. We present the design and implementation of the system, as well as a user experiment. Findings illustrate that the proposed system promotes participation and provides a richer, more interactive and engaging experience.


title: A remote mobile collaborative AR system for learning in physics

abstract: This research demo describes the implementation of a mobile AR-supported educational course application, AR Circuit, which is designed to promote the effectiveness of remote collaborative learning for physics. The application employs the TCP/IP protocol enabling multiplayer functionality in a mobile AR environment. One phone acts as the server and the other acts as the client. The server phone will capture the video frames, process the video frame, and send the current frame and the markers transformation matrices to the client phone.


Do Character-Level Neural Network Language Models Capture Knowledge of Multiword Expression Compositionality?


title: The Good, the Bad, and the Angry: An experimental study on the heterogeneity of people's (dis)honest behavior

abstract: We introduce a novel decision problem to experimentally study situations where people know that they can lie, but do not initially know the economic consequences: they have to invest time to find them out. We report numerous findings, the most intriguing of which is that, in our setting, people can be divided in three types: the Good, who act honestly independently of the corresponding payoff; the Bad, who maximize their payoff without even checking the payoff for telling the truth; the Angry, who first look at the payoff for telling the truth and then lie only if that is low.


title: CheckInside: a fine-grained indoor location-based social network

abstract: Existing location-based social networks (LBSNs), e.g. Foursquare, depend mainly on GPS or network-based localization to infer users' locations. However, GPS is unavailable indoors and network-based localization provides coarse-grained accuracy. This limits the accuracy of current LBSNs in indoor environments, where people spend 89% of their time. This in turn affects the user experience, in terms of the accuracy of the ranked list of venues, especially for the small-screens of mobile devices; misses business opportunities; and leads to reduced venues coverage. In this paper, we present CheckInside: a system that can provide a fine-grained indoor location-based social network. CheckInside leverages the crowd-sensed data collected from users' mobile devices during the check-in operation and knowledge extracted from current LBSNs to associate a place with its name and semantic fingerprint. This semantic fingerprint is used to obtain a more accurate list of nearby places as well as automatically detect new places with similar signatures. A novel algorithm for handling incorrect check-ins and inferring a semantically-enriched floorplan is proposed as well as an algorithm for enhancing the system performance based on the user implicit feedback. Evaluation of CheckInside in four malls over the course of six weeks with 20 participants shows that it can provide the actual user location within the top five venues 99% of the time. This is compared to 17% only in the case of current LBSNs. In addition, it can increase the coverage of current LBSNs by more than 25%.


title: Augmentative Communication and Early Intervention

abstract: The use of augmentative and alternative communication (AAC) services and supports with infants and young children has been limited, owing to a number of myths about the appropriateness of AAC use with this population. This article will provide an overview of some of the myths that have hampered the inclusion of AAC into early intervention service delivery and refutes them. It will then examine some of the realities that must be considered when delivering AAC services and supports to young children. Key words: augmentative communication, severe disabilities, speech and language intervention


title: The Application of MIMO to Non-Orthogonal Multiple Access

abstract: This paper considers the application of multiple-input multiple-output (MIMO) techniques to nonorthogonal multiple access (NOMA) systems. A new design of precoding and detection matrices for MIMO-NOMA is proposed and its performance is analyzed for the case with a fixed set of power allocation coefficients. To further improve the performance gap between MIMO–NOMA and conventional orthogonal multiple access schemes, user pairing is applied to NOMA and its impact on the system performance is characterized. More sophisticated choices of power allocation coefficients are also proposed to meet various quality-of-service requirements. Finally, computer simulation results are provided to facilitate the performance evaluation of MIMO–NOMA and also demonstrate the accuracy of the developed analytical results.


title: Fog Assisted Cloud Models for Smart Grid Architectures- Comparison Study and Optimal Deployment

abstract: Cloud Computing (CC) serves to be a key driver for fulfilling the store and compute requirements of a modern Smart Grid (SG). However, since the datacenters are deployed in concentrated and far remote areas, it fails to guarantee the quality of experience (QoE) attributes for the SG services, viz. latency, bandwidth, energy consumption, and network cost. Fog Computing (FC) extends the processing capabilities into the edge of the network, offering location-awareness, low latency, and latency-sensitive analytics for mission critical requirements of SG. In this work, we first examine the current state of cloud based SG architectures and highlight the motivation(s) for adopting FC as technology enabler for sustainable and real-time SG analytics. Then we present a hierarchical FC architecture for supporting integration of massive number of IoT devices into future SG. Following this architecture we proposed a cost optimization framework that jointly investigates data consumer association, workload distribution, virtual machine placement and QoS constraints towards viable deployment of FC model over SG networks. The formulated MINLP problem is then solved using Modified Differential Evolution (MDE) algorithm. Comprehensive evaluation of proposed framework on real world parameters shows that for an infrastructure with nearly 50% applications requesting real-time services, the overall service latency for fog computing get reduced to almost half of that of generic cloud paradigm. It is also observed that the fog assisted cloud framework lowers the aggregated electricity consumption of the pure cloud computing paradigm by more than 40%.


title: Optimal Boolean Matrix Decomposition: Application to Role Engineering

abstract: A decomposition of a binary matrix into two matrices gives a set of basis vectors and their appropriate combination to form the original matrix. Such decomposition solutions are useful in a number of application domains including text mining, role engineering as well as knowledge discovery. While a binary matrix can be decomposed in several ways, however, certain decompositions better characterize the semantics associated with the original matrix in a succinct but comprehensive way. Indeed, one can find different decompositions optimizing different criteria matching various semantics. In this paper, we first present a number of variants to the optimal Boolean matrix decomposition problem that have pragmatic implications. We then present a unified framework for modeling the optimal binary matrix decomposition and its variants using binary integer programming. Such modeling allows us to directly adopt the huge body of heuristic solutions and tools developed for binary integer programming. Although the proposed solutions are applicable to any domain of interest, for providing more meaningful discussions and results, in this paper, we present the binary matrix decomposition problem in a role engineering context, whose goal is to discover an optimal and correct set of roles from existing permissions, referred to as the role mining problem (RMP). This problem has gained significant interest in recent years as role based access control has become a popular means of enforcing security in databases. We consider several variants of the above basic RMP, including the min-noise RMP, delta-approximate RMP and edge-RMP. Solutions to each of them aid security administrators in specific scenarios. We then model these variants as Boolean matrix decomposition and present efficient heuristics to solve them.


title: A survey of point-based POMDP solvers

abstract: The past decade has seen a significant breakthrough in research on solving partially observable Markov decision processes (POMDPs). Where past solvers could not scale beyond perhaps a dozen states, modern solvers can handle complex domains with many thousands of states. This breakthrough was mainly due to the idea of restricting value function computations to a finite subset of the belief space, permitting only local value updates for this subset. This approach, known as point-based value iteration, avoids the exponential growth of the value function, and is thus applicable for domains with longer horizons, even with relatively large state spaces. Many extensions were suggested to this basic idea, focusing on various aspects of the algorithm--mainly the selection of the belief space subset, and the order of value function updates. In this survey, we walk the reader through the fundamentals of point-based value iteration, explaining the main concepts and ideas. Then, we survey the major extensions to the basic algorithm, discussing their merits. Finally, we include an extensive empirical analysis using well known benchmarks, in order to shed light on the strengths and limitations of the various approaches.


title: E-Learning Environment for Hearing Impaired Students.

abstract: The usage of technology within the educational department has become more vital by each year passing. One of the most popular technological approaches used is the e-learning environment. The usage of e-learning environment in education involves a wide range of types of students, and this includes the hearing impaired ones. Some adjustment or enhancement needs to be implemented within the e-learning environment, based on the needs or the adaptability of the hearing impaired students accordingly. This paper reviews some of the past researches on the usage of the e-learning environment for hearing impaired students for the past decade or so. INTRODUCTION There are a lot of disabled individuals who have the right to get the best education as they can get, just like their normal peers. Hearing impaired (HI) individuals are among those of people with disabilities that deserve the same rights. Hearing impaired individuals, particularly hearing impaired students, usually acquire the same level of mental capability as the normal hearing students in terms of studying. The term ‘deaf and dumb’ is not practically to be used since the hearing impaired students are only lacking of their hearing capability not their intelligence level (Schwartz, 2002). Therefore, they will be undertaking the same subjects or courses in any education departments or institutions as those taught to the normal hearing students of the same age. However, a problem could occur for the hearing impaired students if the technique used by the teachers or instructors in teaching them is as the same used for normal hearing students. Thus, the usage of technology is vital in preparing the HI students with appropriate learning environment, since by using technology, the HI students could access sound in their own suitable way (Berndsen & Luckner, 2010). E-learning environment is one of the most used techniques for educational purpose and this includes the education for HI students. However, most e-learning environment available does not particularly can be useful to those students due to its feature which is lacking in terms of adaptability. They often encounter problem in accessing the information available in terms of understanding it and using it in a proper manner (Fichten, Ferraro, Asuncion, Chwojka, Nguyen, Klomp & Wolforth, 2009). Therefore, in order to assist these HI students in accessing the information adequately, the e-learning environment needs to be developed and designed according to the needs of the HI students by adding or enhancing some features within the e-learning environment. DISCUSSION ON SELECTED PAPERS In this section, we present the review of past researches on the usage of the e-learning environment for hearing impaired students starting from the year 2005 until the year of 2012. Table 1 below shows the list of the studies related to the issue concerned. We begin the review with the brief explanation on the each study, followed by the approaches, limitations and also some valuable directions for future studies. Table 1: List of studies on the hearing impaired e-learning environment Author(s) Year Participants


title: Liquidity, Stock Return and Trading Volume: Measuring Causality at Multiple Horizons

abstract: The relationships between trading volume, stock return and illiquidity have long been measured separately. To offer a comprehensive study in Chinese stock market, we examine these causal relationships empirically and apply the measure of strength of causality for different horizons and directions on one of the most representative index of the market -- the SSE 50 index from it launching day 2004 till now. Since the asymptotic distribution of causality is hard to calculate, we sue a simulation-based method to estimate the confidence interval. Therefore, we notice the following causal relationships: (i) Causality is stronger at short horizons, and gradually drops down as the horizon increases; (ii) Causalities run from return and volume to illiquidity rather than the opposite direction. The former is much stronger than the latter at the beginning horizons and overwhelms at almost all following horizons; (iii) Causality trends on multi-horizons running from trade volume and return to illiquidity resemble each other, and indicate the same causal pattern mentioned in (i); (iv) Stock return and trade volume have mutual causal relationships, but the return have a stronger effect on trade volume.


title: A Terahertz Detector Array in a SiGe HBT Technology

abstract: This paper presents HBT terahertz power detectors implemented in an experimental 0.25- μm SiGe process technology with a peak fT/fmax of 280/435 GHz. Based on the nonlinearity of the HBT base-emitter junction, the detector operates as a square-law down converter, mixing terahertz frequencies directly to dc. Fifteen detectors have been arranged in a 3 × 5-pixel array with differential on-chip ring antennas. The array has been assembled with a hyper-hemispherical silicon lens. Referred to the collecting aperture of the lens, a maximum optical current responsivity RI of 1 A/W and a minimum noise equivalent power (NEP) of about 50 pW/√{Hz} have been measured at 0.7 THz with a 125-kHz chopping frequency. The detectors have been characterized from 0.65 to 1 THz.


title: Fast and robust multiframe super resolution

abstract: Super-resolution reconstruction produces one or a set of high-resolution images from a set of low-resolution images. In the last two decades, a variety of super-resolution methods have been proposed. These methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses their short-comings. We propose an alternate approach using L1 norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models. This computationally inexpensive method is robust to errors in motion and blur estimation and results in images with sharp edges. Simulation results confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods.


title: 3-D facial pose and gaze point estimation using a robust real-time tracking paradigm

abstract: Facial pose and gaze point are fundamental to any visually directed human-machine interface. In this paper we propose a system capable of tracking a face and estimating the 3-D pose and the gaze point all in a real-time video stream of the head. This is done by using a 3-D model together with multiple triplet triangulation of feature positions assuming an affine projection. Using feature-based tracking the calculation of a 3-D eye gaze direction vector is possible even with head rotation and using a monocular camera. The system is also able to automatically initialise the feature tracking and to recover from total tracking failures which can occur when a person becomes occluded or temporarily leaves the image.


title: The Pervasive Effects of an Antibiotic on the Human Gut Microbiota, as Revealed by Deep 16S rRNA Sequencing

abstract: The human intestinal microbiota is essential to the health of the host and plays a role in nutrition, development, metabolism, pathogen resistance, and regulation of immune responses. Antibiotics may disrupt these coevolved interactions, leading to acute or chronic disease in some individuals. Our understanding of antibiotic-associated disturbance of the microbiota has been limited by the poor sensitivity, inadequate resolution, and significant cost of current research methods. The use of pyrosequencing technology to generate large numbers of 16S rDNA sequence tags circumvents these limitations and has been shown to reveal previously unexplored aspects of the “rare biosphere.” We investigated the distal gut bacterial communities of three healthy humans before and after treatment with ciprofloxacin, obtaining more than 7,000 full-length rRNA sequences and over 900,000 pyrosequencing reads from two hypervariable regions of the rRNA gene. A companion paper in PLoS Genetics (see Huse et al., doi: 10.1371/journal.pgen.1000255) shows that the taxonomic information obtained with these methods is concordant. Pyrosequencing of the V6 and V3 variable regions identified 3,300–5,700 taxa that collectively accounted for over 99% of the variable region sequence tags that could be obtained from these samples. Ciprofloxacin treatment influenced the abundance of about a third of the bacterial taxa in the gut, decreasing the taxonomic richness, diversity, and evenness of the community. However, the magnitude of this effect varied among individuals, and some taxa showed interindividual variation in the response to ciprofloxacin. While differences of community composition between individuals were the largest source of variability between samples, we found that two unrelated individuals shared a surprising degree of community similarity. In all three individuals, the taxonomic composition of the community closely resembled its pretreatment state by 4 weeks after the end of treatment, but several taxa failed to recover within 6 months. These pervasive effects of ciprofloxacin on community composition contrast with the reports by participants of normal intestinal function and with prior assumptions of only modest effects of ciprofloxacin on the intestinal microbiota. These observations support the hypothesis of functional redundancy in the human gut microbiota. The rapid return to the pretreatment community composition is indicative of factors promoting community resilience, the nature of which deserves future investigation.


title: A new, fast method for 2D polygon clipping: analysis and software implementation

abstract: This paper presents a new 2D polygon clipping method, based on an extension to the Sutherland-Cohen 2D line clipping method. After discussing three basic polygon clipping algorithms, a different approach is proposed, explaining the principles of a new algorithm and presenting it step by step. An example implementation of the algorithm is given along with some results. A comparison between the proposed method, the Liang and Barsky algorithm, and the Sutherland-Hodgman algorithm is also given, showing performances up to eight times the speed of the Sutherland-Hodgman algorithm, and up to three times the Liang and Barsky algorithm. The algorithm proposed here can use floating point or integer operations; this can be useful for fast or simple implementations.


title: Sequence-to-Sequence RNNs for Text Summarization

abstract: In this work, we cast text summarization as a sequence-to-sequence problem and apply the attentional encoder-decoder RNN that has been shown to be successful for Machine Translation (Bahdanau et al. (2014)). Our experiments show that the proposed architecture significantly outperforms the state-of-the art model of Rush et al. (2015) on the Gigaword dataset without any additional tuning. We also propose additional extensions to the standard architecture, which we show contribute to further improvement in performance.


title: Anomaly detection approach using Hidden Markov Model

abstract: Anomaly detection is an important problem that has been researched within diverse research areas. Numerous methods and approaches based on Hidden Markov Model regarding the anomaly detection have been proposed and reported in the literature. However, the potential applications using Hidden Markov Model classification based anomaly detection technique have not yet been fully explored and still in its infancy. This paper investigates the capabilities the use of Hidden Markov Model in anomaly detection for discrete sequences.


title: Hamsa: fast signature generation for zero-day polymorphic worms with provable attack resilience

abstract: Zero-day polymorphic worms pose a serious threat to the security of Internet infrastructures. Given their rapid propagation, it is crucial to detect them at edge networks and automatically generate signatures in the early stages of infection. Most existing approaches for automatic signature generation need host information and are thus not applicable for deployment on high-speed network links. In this paper, we propose Hamsa, a network-based automated signature generation system for polymorphic worms which is fast, noise-tolerant and attack-resilient. Essentially, we propose a realistic model to analyze the invariant content of polymorphic worms which allows us to make analytical attack-resilience guarantees for the signature generation algorithm. Evaluation based on a range of polymorphic worms and polymorphic engines demonstrates that Hamsa significantly outperforms Polygraph (J. Newsome et al., 2005) in terms of efficiency, accuracy, and attack resilience.