title: Checking Business Process Evolution

abstract: Business processes support the modeling and the implementation of software as workflows of local and inter-process activities. Taking over structuring and composition, evolution has become a central concern in software development. We advocate it should be taken into account as soon as the modeling of business processes, which can thereafter be made executable using process engines or model-to-code transformations. We show here that business process evolution needs formal analysis in order to compare different versions of processes, identify precisely the differences between them, and ensure the desired consistency. To reach this objective, we first present a model transformation from the BPMN standard notation to the LNT process algebra. We then propose a set of relations for comparing business processes at the formal model level. With reference to related work, we propose a richer set of comparison primitives supporting renaming, refinement, property- and context-awareness. Thanks to an implementation of our approach that can be used through a Web application, we put the checking of evolution within the reach of business process designers.


title: A Review on the Computational Methods for Emotional State Estimation from the Human EEG

abstract: A growing number of affective computing researches recently developed a computer system that can recognize an emotional state of the human user to establish affective human-computer interactions. Various measures have been used to estimate emotional states, including self-report, startle response, behavioral response, autonomic measurement, and neurophysiologic measurement. Among them, inferring emotional states from electroencephalography (EEG) has received considerable attention as EEG could directly reflect emotional states with relatively low costs and simplicity. Yet, EEG-based emotional state estimation requires well-designed computational methods to extract information from complex and noisy multichannel EEG data. In this paper, we review the computational methods that have been developed to deduct EEG indices of emotion, to extract emotion-related features, or to classify EEG signals into one of many emotional states. We also propose using sequential Bayesian inference to estimate the continuous emotional state in real time. We present current challenges for building an EEG-based emotion recognition system and suggest some future directions.


title: Implementation of Alternating Median Based Round Robin Scheduling Algorithm

abstract: The scheduling of tasks run by CPU directly affects theperformance of our computer system. Different schedulingalgorithms satisfy a limited spectrum of the scheduling criteriathat focus on reducing either of response time or number ofcontext switches and waiting time. This paper introduces a novelscheduling algorithm that meets the scheduling criteria at abroader spectrum by giving first priority to the reduction of thenumber of context switches and waiting time and secondpriority to reduction of response time. The proposed algorithmalso offers a lesser overhead of time quantum calculation. Thepaper provides the comparison between the proposed andsimilar algorithms through the numerical analysis andverification of the results. The paper also empirically evaluatesthe working and behavior of the proposed algorithm.


title: GLAC Net: GLocal Attention Cascading Networks for Multi-image Cued Story Generation

abstract: The task of multi-image cued story generation, such as visual storytelling dataset (VIST) challenge, is to compose multiple coherent sentences from a given sequence of images. The main difficulty is how to generate image-specific sentences within the context of overall images. Here we propose a deep learning network model, GLAC Net, that generates visual stories by combining global-local (glocal) attention and context cascading mechanisms. The model incorporates two levels of attention, i.e., overall encoding level and image feature level, to construct image-dependent sentences. While standard attention configuration needs a large number of parameters, the GLAC Net implements them in a very simple way via hard connections from the outputs of encoders or image features onto the sentence generators. The coherency of the generated story is further improved by conveying (cascading) the information of the previous sentence to the next sentence serially. We evaluate the performance of the GLAC Net on the visual storytelling dataset (VIST) and achieve very competitive results compared to the state-of-the-art techniques.


title: A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care

abstract: Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.


title: Fisher Vector Faces in the Wild.

abstract: Several recent papers on automatic face verification have significantly raised the performance bar by developing novel, specialised representations that outperform standard features such as SIFT for this problem. This paper makes two contributions: first, and somewhat surprisingly, we show that Fisher vectors on densely sampled SIFT features, i.e. an off-the-shelf object recognition representation, are capable of achieving state-of-the-art face verification performance on the challenging “Labeled Faces in the Wild” benchmark; second, since Fisher vectors are very high dimensional, we show that a compact descriptor can be learnt from them using discriminative metric learning. This compact descriptor has a better recognition accuracy and is very well suited to large scale identification tasks.


title: Architecture of Field-Programmable Gate Arrays

abstract: A survey of field-programmable gate array (FPGA) architectures and the programming technologies used to customize them is presented. Programming technologies are compared on the basis of their volatility, size parasitic capacitance, resistance, and process technology complexity. FPGA architectures are divided into two constituents: logic block architectures and routing architectures. A classification of logic blocks based on their granularity is proposed, and several logic blocks used in commercially available FPGAs are described. A brief review of recent results on the effect of logic block granularity on logic density and performance of an FPGA is then presented. Several commercial routing architectures are described in the context of a general routing architecture model. Finally, recent results on the tradeoff between the flexibility of an FPGA routing architecture, its routability, and its density are reviewed. >


title: Work in progress - developing warm-up questions for strength of materials

abstract: This paper outlines our preliminary efforts to develop warm-up questions for Strength of Materials. These are conceptual questions that students answer before the topics the questions cover are introduced in class. The warm ups give instructors an idea of what students conceptions are before discussion begins, so the instructor can directly address students' specific difficulties as the topic is introduced. The questions that we are developing are still in flux, but our preliminary results indicate that while students remember some relevant information from the prerequisite Materials Science course, they have trouble knowing when to apply it.


title: Deep Learning for Emotion Recognition on Small Datasets using Transfer Learning

abstract: This paper presents the techniques employed in our team's submissions to the 2015 Emotion Recognition in the Wild contest, for the sub-challenge of Static Facial Expression Recognition in the Wild. The objective of this sub-challenge is to classify the emotions expressed by the primary human subject in static images extracted from movies. We follow a transfer learning approach for deep Convolutional Neural Network (CNN) architectures. Starting from a network pre-trained on the generic ImageNet dataset, we perform supervised fine-tuning on the network in a two-stage process, first on datasets relevant to facial expressions, followed by the contest's dataset. Experimental results show that this cascading fine-tuning approach achieves better results, compared to a single stage fine-tuning with the combined datasets. Our best submission exhibited an overall accuracy of 48.5% in the validation set and 55.6% in the test set, which compares favorably to the respective 35.96% and 39.13% of the challenge baseline.


title: ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset

abstract: This paper describes the fourth edition of the Handwritten Text Recognition (HTR) competition that was prepared this time in the context of the International Conference on Document Analysis and Recognition (ICDAR) 2017. Previous editions of this competition were conducted, first, with datasets from the tranScriptorium project in ICFHR 2014, and ICDAR 2015, and then, with datasets from the "Recognition and Enrichment of Archival Documents (READ)" European project in ICFHR 2016. This competition aims to bring together researchers working on off-line HTR and provides them a suitable benchmark to compare their techniques on the task of transcribing typical and difficult historical handwritten documents. The competition proposed for ICDAR 2017 aims at introducing a usual scenario for some collections in which there exist transcripts at page level for many pages useful for training, but these transcripts are not aligned with line images. Two tracks with different conditions on the use of training data were proposed. Most of the data comes from the Alfred Escher Letter Collection. But handwritten images were drawn from other German collections written by several hands.


title: AquaOptical: A lightweight device for high-rate long-range underwater point-to-point communication

abstract: This paper describes AquaOptical, an underwater optical communication system. Three optical modems have been developed: a long range system, a short range system, and a hybrid. We describe their hardware and software architectures and highlight trade-offs. We present pool and ocean experiments with each system. In clear water AquaOptical was tested to achieve a data rate of 1.2Mbit/sec at distances up to 30m. The system was not tested beyond 30m. In water with visibility estimated at 3m AquaOptical achieved communication at data rates of 0.6Mbit/sec at distances up to 9m.


title: Drowsiness monitoring by steering and lane data based features under real driving conditions

abstract: Experts state that driver drowsiness is responsible for about 30% of severe traffic accidents. Driver monitoring systems, such as the Mercedes-Benz Attention Assist aim to reduce these road-crashes caused by fatigued drivers using standard equipment sensors. In this article, new measures (features) for detecting drowsiness are proposed in addition to promising features in literature. Most studies in literature are based on driving simulator data, whereas this article focuses on real world driving. External influences such as road condition, road bumps and cross-wind are furthermore taken into account. The presented results are based on a large selection of the Mercedes-Benz drowsiness database which covers over 1.2 million kilometers of measurements. Features are analyzed for their correlation with the subjective Karolinska Sleepiness Scale (KSS). The performance of a combination of features is assessed by sophisticated classifiers and dimension reduction techniques. Even after these improvements, the classification results do not reach the results obtained in a driving simulator.


title: Rapid eye movement sleep deprivation modifies expression of long-term potentiation in visual cortex of immature rats

abstract: During rapid eye movement (REM) sleep, activity of non-retinal origin is propagated into central visual-system pathways in a manner similar, in pattern and intensity, to central visual-system activity that is exogenously generated in waking. It has been hypothesized that REM sleep, which is more abundantly represented early in life than later, functions to provide adjunct 'afferent' input for shaping synaptic connectivity during brain maturation. Here we present data that support this proposal. Recent studies have described a developmentally regulated form of in vitro long-term potentiation (LTP) in the visual cortex that is experience- and age-dependent. In immature rats, suppression of retinal activation of the visual system by removal of visual experience (dark rearing) extends the age when the developmentally regulated form of LTP can be produced. This study tests whether suppression of REM-state activation of the visual system also lengthens the developmental period in which this specific form of LTP can be elicited. Young rats were deprived of REM sleep by the multiple-small-platforms-over-water method during the typically latest week for induction of such LTP in slices of visual cortex. After this week, we could still induce LTP in slices from nearly all the REM-sleep-deprived rats (8/9) but not from age-matched rats that had not lost REM sleep (0/5). The control rats had been housed on large platforms that allow the animals to obtain REM sleep. Only body weights and the concentration of thyrotrophin-releasing hormone in the hypothalamus distinguished home-caged, normal-sleeping controls from both groups of platform animals. On all measures, stress levels were not dissimilar in the two platforms groups. After 7 days of behavioral suppression of REM sleep in immature rats, and consequent reduction of the intense, extra-retinal activity endogenously generated during this sleep state, we found that the period was extended in which developmentally regulated synaptic plasticity (LTP) could be elicited in slices of visual neocortex. These studies support the role of REM sleep and its associated neuronal activity in brain maturation.


title: Switched tank converters

abstract: This paper presents a new class of Switched Tank Converters (abbreviated as STCs) for high efficiency high density non-isolated DC-DC application where large voltage step down (up) ratios are required. Distinguished from switched capacitor converters, the STCs uniquely employ LC resonant tanks to partially replace the flying capacitors for energy transfer. Full soft charging, soft switching and minimal device voltage stresses are achieved under all operating conditions. The STCs feature very high efficiency, density and robustness against component non-idealities over a wide range. Furthermore, thanks to the full resonant operation, multiple STCs can operate in parallel with inherent droop current sharing, offering the best scalability and control simplicity. These attributes of STC make it a disruptive and robust technology viable for industry's high volume adoption. A novel equivalent DCX building block principle is introduced to simplify the analysis of STC. A 98.92% efficiency STC product evaluation board (4-to-1, 650W) has been developed and demonstrated for the next-gen 48V bus conversion on data center server boards.


title: 'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions

abstract: Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, European law provides individuals limited rights to 'meaningful information about the logic' behind significant, autonomous decisions such as loan approvals, insurance quotes, and CV filtering. We undertake three experimental studies examining people's perceptions of justice in algorithmic decision-making under different scenarios and explanation styles. Dimensions of justice previously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions. Qualitative analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity. Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different styles---under repeated exposure of one style, scenario effects obscure any explanation effects. Our results suggests there may be no 'best' approach to explaining algorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions.


title: Object Detection Based on Multi-Layer Convolution Feature Fusion and Online Hard Example Mining

abstract: Object detection is a significant issue in visual surveillance. Faster region-based convolutional neural network (R-CNN) is a typical object detection algorithm of deep learning; however, neither its generalization ability nor its detection accuracy of small object is high. In this paper, an effective object detection algorithm is proposed for the small and occluded objects, which is based on multi-layer convolution feature fusion (MCFF) and online hard example mining (OHEM). First, the candidate regions are generated with region proposal network optimized by MCFF. Then, an effective OHEM algorithm is employed to train the region-based ConvNet detector. The hard examples are automatically selected to improve training efficiency. The avoidance of invalid examples accelerates the convergence speed of the model training. The experiments are performed on KITTI data set in intelligent traffic scenario. The proposed method outperforms the popular methods, such as Faster R-CNN, Regionlets, in terms of the overall detection accuracy. Furthermore, our method is good at the detection of small and occluded objects.


title: Morningness-eveningness and risk taking.

abstract: ABSTRACT. Depending on individual differences in biological rhythms and diurnal preferences, people have long been described as either “larks” or “owls.” Larks and owls differ greatly in personality aspects, but from the behavioral perspective it is unclear whether they have significant differences in terms of risky behaviors. Whether morning types or evening types are consistently more risk-taking or risk-averse in different domains remains unknown. This study adopted a general American adult sample to systematically investigate the relationship between chronotype and individuals’ risky behaviors in different domains. By using different methods to measure risky behaviors in different domains, the current research obtained convergent results that morningness was negatively related only to financially risky behaviors for American adults. More specifically, by using the composite scale of morningness and the domain-specific risk attitude scale, Study 1 showed that for American adults, morning types were les...


title: Reinforcement Learning for Semantic Segmentation in Indoor Scenes

abstract: Future advancements in robot autonomy and sophistication of robotics tasks rest on robust, efficient, and task-dependent semantic understanding of the environment. Semantic segmentation is the problem of simultaneous segmentation and categorization of a partition of sensory data. The majority of current approaches tackle this using multi-class segmentation and labeling in a Conditional Random Field (CRF) framework or by generating multiple object hypotheses and combining them sequentially. In practical settings, the subset of semantic labels that are needed depend on the task and particular scene and labelling every single pixel is not always necessary. We pursue these observations in developing a more modular and flexible approach to multi-class parsing of RGBD data based on learning strategies for combining independent binary object-vs-background segmentations in place of the usual monolithic multi-label CRF approach. Parameters for the independent binary segmentation models can be learned very efficiently, and the combination strategy---learned using reinforcement learning---can be set independently and can vary over different tasks and environments. Accuracy is comparable to state-of-art methods on a subset of the NYU-V2 dataset of indoor scenes, while providing additional flexibility and modularity.


title: ROSF: Leveraging Information Retrieval and Supervised Learning for Recommending Code Snippets

abstract: When implementing unfamiliar programming tasks, developers commonly search code examples and learn usage patterns of APIs from the code examples or reuse them by copy-pasting and modifying. For providing high-quality code examples, previous studies present several methods to recommend code snippets mainly based on information retrieval. In this paper, to provide better recommendation results, we propose ROSF, Recommending code Snippets with multi-aspect Features, a novel method combining both information retrieval and supervised learning. In our method, we recommend Top- K code snippets for a given free-form query based on two stages, i.e., coarse-grained searching and fine-grained re-ranking. First, we generate a code snippet candidate set by searching a code snippet corpus using an information retrieval method. Second, we predict probability values of the code snippets for different relevance scores in the candidate set by the learned prediction model from a training set, re-rank these candidate code snippets according to the probability values, and recommend the final results to developers. We conduct several experiments to evaluate our method in a large-scale corpus containing 921,713 real-world code snippets. The results show that ROSF is an effective method for code snippets recommendation and outperforms the-state-of-the-art methods by 20-41percent in Precision and 13-33 percent in NDCG.


title: Application of data mining techniques in a personalized diet recommendation system for cancer patients

abstract: Cancer is one of the top fatal diseases in the world. When cancer patients undergo treatment, they need a different diet to help them withstand the side-effects of treatment, as well as to provide sufficient nutrition to boost the recovery cycle. Most of the dietician systems currently in the market are more in the advice area rather than recommending specific diet menus for users. These systems usually provide advice in a general form, for example: “Cancer patients should eat foods with high protein”. Such recommendations are insufficient to assist cancer patients with the physical preparation necessary to withstand the side effects of treatment, or with ensuring they take adequate nutrients for their body. We propose a Personalized Diet Recommendation System for Cancer Patients to help patients manage their daily food intake. The proposed system integrates the data mining techniques of Case-based Reasoning, Rule-based Reasoning and Genetic Algorithm. Case-based Reasoning is used to suggest a set of diet plans taken from the cases existing in the system, whereas Rule-based Reasoning is used to filter out irrelevant cases from the system and select the most appropriate case to be suggested to the patient. The Genetic Algorithm technique ensures that the diet menus suggested are customized according to each patient's personal health conditions. The output of the diet plan system is in the form of a list of specific nutritional values to be taken daily, and a menu recommendation suggesting actual dishes for the patient.


title: The effects of recommendations’ presentation on persuasion and satisfaction in a movie recommender system

abstract: The explosive growth of Internet applications and content, during the last decade, has revealed an increasing need for information filtering and recommendation. Most research in the area of recommendation systems has focused on designing and implementing efficient algorithms that provide accurate recommendations. However, the selection of appropriate recommendation content and the presentation of information are equally important in creating successful recommender applications. This paper addresses issues related to the presentation of recommendations in the movies domain. The current work reviews previous research approaches and popular recommender systems, and focuses on user persuasion and satisfaction. In our experiments, we compare different presentation methods in terms of recommendations' organization in a list (i.e. top N-items list and structured overview) and recommendation modality (i.e. simple text, combination of text and image, and combination of text and video). The most efficient presentation methods, regarding user persuasion and satisfaction, proved to be the "structured overview" and the "text and video" interfaces, while a strong positive correlation was also found between user satisfaction and persuasion in all experimental conditions.


BY JEFFREY DEAN AND SANJAY GHEMAWAT MapReduce: A Flexible Data Processing Tool


title: Internet-of-Things and Smart Homes for Elderly Healthcare: An End User Perspective

abstract: Although an Internet-of-Things-based smart home solution can provide an improved and better approach to healthcare management, yet its end user adoption is very low. With elderly people as the main target, these conservative users pose a serious challenge to the successful implementation of smart home healthcare services. The objective of this research was to develop and test a theoretical framework empirically for determining the core factors that can affect the elderly users’ acceptance of smart home services for healthcare. Accordingly, an online survey was conducted with 254 elderly people aged 55 years and above across four Asian countries. Partial least square structural equation modeling was applied to analyze the effect of eight hypothesized predicting constructs. The user perceptions were measured on a conceptual level rather than the actual usage intention toward a specific service. Performance expectancy, effort expectancy, expert advice, and perceived trust have a positive impact on the behavioral intention. The same association is negative for technology anxiety and perceived cost. Facilitating conditions and social influence do not have any effect on the behavioral intention. The model could explain 81.4% of the total variance in the dependent variable i.e., behavioral intention. Effort expectancy is the leading predictor of smart homes for healthcare acceptance among the elderly. Together with expert advice, perceived trust, and perceived cost, these four factors represent the key influence of the elderly peoples’ acceptance behavior. This paper provides the groundwork to explore the process of the actual adoption of smart home services for healthcare by the elderly people with potential future research areas.


title: PONDER DEEPLY OVER THE SUCCESSFUL IMPENDING XIUYAN-HAICHENG EARTHQUAKE PREDICTION

abstract: In this paper, we summarize the main points of bases and radical principles impending the Xiuyan-Haicheng earthquake prediction. We also discuss about the seismic parameters application and predictive value of premonitory Anomaly after a main earthquake, and precursor significance of recent earthquakes in North China. Further more, we also analyse the risk of the earthquake prediction.


Concurrency Control and Security Issue in Distributed Database System


title: BioPortal: ontologies and integrated data resources at the click of a mouse

abstract: Biomedical ontologies provide essential domain knowledge to drive data integration, information retrieval, data annotation, natural-language processing and decision support. BioPortal (http://bioportal.bioontology.org) is an open repository of biomedical ontologies that provides access via Web services and Web browsers to ontologies developed in OWL, RDF, OBO format and Protege frames. BioPortal functionality includes the ability to browse, search and visualize ontologies. The Web interface also facilitates community-based participation in the evaluation and evolution of ontology content by providing features to add notes to ontology terms, mappings between terms and ontology reviews based on criteria such as usability, domain coverage, quality of content, and documentation and support. BioPortal also enables integrated search of biomedical data resources such as the Gene Expression Omnibus (GEO), ClinicalTrials.gov, and ArrayExpress, through the annotation and indexing of these resources with ontologies in BioPortal. Thus, BioPortal not only provides investigators, clinicians, and developers ‘one-stop shopping’ to programmatically access biomedical ontologies, but also provides support to integrate data from a variety of biomedical resources.


title: The Marginal Value of Adaptive Gradient Methods in Machine Learning

abstract: Adaptive optimization methods, which perform local optimization with a metric constructed from the history of iterates, are becoming increasingly popular for training deep neural networks. Examples include AdaGrad, RMSProp, and Adam. We show that for simple overparameterized problems, adaptive methods often find drastically different solutions than gradient descent (GD) or stochastic gradient descent (SGD). We construct an illustrative binary classification problem where the data is linearly separable, GD and SGD achieve zero test error, and AdaGrad, Adam, and RMSProp attain test errors arbitrarily close to half. We additionally study the empirical generalization capability of adaptive methods on several state-of-the-art deep learning models. We observe that the solutions found by adaptive methods generalize worse (often significantly worse) than SGD, even when these solutions have better training performance. These results suggest that practitioners should reconsider the use of adaptive methods to train neural networks.


title: Unraveling the mysteries of anxiety and its disorders from the perspective of emotion theory.

abstract: The ascendance of emotion theory, recent advances in cognitive science and neuroscience, and increasingly important findings from developmental psychology and learning make possible an integrative account of the nature and etiology of anxiety and its disorders. This model specifies an integrated set of triple vulnerabilities: a generalized biological (heritable) vulnerability, a generalized psychological vulnerability based on early experiences in developing a sense of control over salient events, and a more specific psychological vulnerability in which one learns to focus anxiety on specific objects or situations. The author recounts the development of anxiety and related disorders based on these triple vulnerabilities and discusses implications for the classification of emotional disorders.


title: Learning a Deep Hybrid Model for Semi-Supervised Text Classification

abstract: We present a novel fine-tuning algorithm in a deep hybrid architecture for semisupervised text classification. During each increment of the online learning process, the fine-tuning algorithm serves as a top-down mechanism for pseudo-jointly modifying model parameters following a bottom-up generative learning pass. The resulting model, trained under what we call the Bottom-Up-Top-Down learning algorithm, is shown to outperform a variety of competitive models and baselines trained across a wide range of splits between supervised and unsupervised training data.


title: BER performance of OFDM system with adaptive modulation

abstract: Orthogonal Frequency Division Multiplexing (OFDM) is a multicarrier transmission scheme. OFDM transmits data by using a large number of narrow bandwidth carriers. In an OFDM transmission system, each subcarrier is attenuated individually under the frequency-selective and fast fading channel if the same fixed transmission scheme is used for all OFDM subcarriers thus it results in highest attenuation and hence poor performance. The purpose of this paper is to introduce the adaptive modulation to get an understanding of the differences between fixed and adaptive modulations schemes. In this work adaptive modulation is implemented by dividing whole subcarriers into blocks of adjacent subcarriers. Based on calculated average instantaneous signal to noise (SNR) same modulation scheme is applied to all subcarriers of same block. Here average bit error rate (BER) performance of OFDM system under fixed modulation and adaptive modulation is observed. Average BER performance of these modulation techniques is observed with various inverse fast Fourier transform (IFFT) size and using simpler adaptive quadrature amplitude modulation (QAM) schemes. The simulation results show that BER performance of OFDM system using adaptive modulation is better than fixed modulation.


title: Design of parallel coupled microstrip bandpass filter for FM Wireless applications

abstract: Design, analysis and optimization of a parallel-coupled microstrip bandpass filter for FM Wireless applications is presented in this paper. The filter is designed and optimized at a center frequency of 6 GHz. Half wavelength long resonators and admittance inverters are used to design the filter. A brief description of coupled microstrip lines and immittance inverters is also included. Design equations to compute physical dimensions of the filter are given in the paper. The filter is simulated using ADS (Advanced Design System) design software and implemented on Roger 4003C substrate.


title: When the prey gets too big: an uncommon road accident involving a motorcyclist, a car and a bird

abstract: We present the postmortem findings of a fatal road accident involving a motorcyclist, a car, and a common buzzard. Both the motorcyclist and the bird died on the scene of the accident and were examined by postmortem full-body CT and autopsy. In addition, a facial injury of the motorcyclist was compared with the dimensions of the buzzard's beak and claws by 3D scan technologies. Blood splatters collected on the bird's beak, feet, and tail were examined by DNA analysis. The overall findings suggested a collision of a common buzzard with a motorcyclist in full speed, causing the motorcyclist to lose control of his vehicle and crash with an approaching car on the oncoming lane.


title: A posteriori environment analysis with Pushdown Delta CFA

abstract: Flow-driven higher-order inlining is blocked by free variables, yet current theories of environment analysis cannot reliably cope with multiply-bound variables. One of these, I CFA, is a promising theory based on stack change but is undermined by its finite-state model of the stack. We present Pushdown I CFA which takes a I CFA-approach to pushdown models of control flow and can cope with multiply-bound variables, even in the face of recursion.


title: A complete ensemble empirical mode decomposition with adaptive noise

abstract: In this paper an algorithm based on the ensemble empirical mode decomposition (EEMD) is presented. The key idea on the EEMD relies on averaging the modes obtained by EMD applied to several realizations of Gaussian white noise added to the original signal. The resulting decomposition solves the EMD mode mixing problem, however it introduces new ones. In the method here proposed, a particular noise is added at each stage of the decomposition and a unique residue is computed to obtain each mode. The resulting decomposition is complete, with a numerically negligible error. Two examples are presented: a discrete Dirac delta function and an electrocardiogram signal. The results show that, compared with EEMD, the new method here presented also provides a better spectral separation of the modes and a lesser number of sifting iterations is needed, reducing the computational cost.


title: Internet traffic engineering by optimizing OSPF weights

abstract: Open shortest path first (OSPF) is the most commonly used intra-domain Internet routing protocol. Traffic flow is routed along shortest paths, splitting flow at nodes where several outgoing links are on shortest paths to the destination. The weights of the links, and thereby the shortest path routes, can be changed by the network operator. The weights could be set proportional to their physical distances, but often the main goal is to avoid congestion, i.e., overloading of links, and the standard heuristic recommended by Cisco is to make the weight of a link inversely proportional to its capacity. Our starting point was a proposed AT&T WorldNet backbone with demands projected from previous measurements. The desire was to optimize the weight setting based on the projected demands. We showed that optimizing the weight settings for a given set of demands is NP-hard, so we resorted to a local search heuristic. Surprisingly it turned out that for the proposed AT&T WorldNet backbone, we found weight settings that performed within a few percent from that of the optimal general routing where the flow for each demand is optimally distributed over all paths between source and destination. This contrasts the common belief that OSPF routing leads to congestion and it shows that for the network and demand matrix studied we cannot get a substantially better load balancing by switching to the proposed more flexible multi-protocol label switching (MPLS) technologies. Our techniques were also tested on synthetic internetworks, based on a model of Zegura et al., (1996), for which we did not always get quite as close to the optimal general routing.


title: Clustering Using an Improved Krill Herd Algorithm

abstract: In recent years, metaheuristic algorithms have been widely used in solving clustering problems because of their good performance and application effects. Krill herd algorithm (KHA) is a new effective algorithm to solve optimization problems based on the imitation of krill individual behavior, and it is proven to perform better than other swarm intelligence algorithms. However, there are some weaknesses yet. In this paper, an improved krill herd algorithm (IKHA) is studied. Modified mutation operators and updated mechanisms are applied to improve global optimization, and the proposed IKHA can overcome the weakness of KHA and performs better than KHA in optimization problems. Then, KHA and IKHA are introduced into the clustering problem. In our proposed clustering algorithm, KHA and IKHA are used to find appropriate cluster centers. Experiments were conducted on University of California Irvine (UCI) standard datasets, and the results showed that the IKHA clustering algorithm is the most effective.


title: Semantic object classes in video: A high-definition . . .

abstract: Visual object analysis researchers are increasingly experimenting with video, because it is expected that motion cues should help with detection, recognition, and other analysis tasks. This paper presents the Cambridge-driving Labeled Video Database (CamVid) as the first collection of videos with object class semantic labels, complete with metadata. The database provides ground truth labels that associate each pixel with one of 32 semantic classes. The database addresses the need for experimental data to quantitatively evaluate emerging algorithms. While most videos are filmed with fixed-position CCTV-style cameras, our data was captured from the perspective of a driving automobile. The driving scenario increases the number and heterogeneity of the observed object classes. Over 10min of high quality 30Hz footage is being provided, with corresponding semantically labeled images at 1Hz and in part, 15Hz. The CamVid Database offers four contributions that are relevant to object analysis researchers. First, the per-pixel semantic segmentation of over 700 images was specified manually, and was then inspected and confirmed by a second person for accuracy. Second, the high-quality and large resolution color video images in the database represent valuable extended duration digitized footage to those interested in driving scenarios or ego-motion. Third, we filmed calibration sequences for the camera color response and intrinsics, and computed a 3D camera pose for each frame in the sequences. Finally, in support of expanding this or other databases, we present custom-made labeling software for assisting users who wish to paint precise class-labels for other images and videos. We evaluate the relevance of the database by measuring the performance of an algorithm from each of three distinct domains: multi-class object recognition, pedestrian detection, and label propagation.


title: Fast Cholesky factorization on GPUs for batch and native modes in MAGMA

abstract: Abstract This paper presents a GPU-accelerated Cholesky factorization for two different modes of operation. The first one is the batch mode, where many independent factorizations on small matrices can be performed concurrently. This mode supports fixed size and variable size problems, and is found in many scientific applications. The second mode is the native mode, where one factorization is performed on a large matrix without any CPU involvement, which allows the CPU do other useful work. We show that, despite the different workloads, both modes of operation share a common code-base that uses the GPU only. We also show that the developed routines achieve significant speedups against a multicore CPU using the MKL library, and against a GPU implementation by cuSOLVER. This work is part of the MAGMA library.


title: Shape and motion under varying illumination: unifying structure from motion, photometric stereo, and multiview stereo

abstract: We present an algorithm for computing optical flow, shape, motion, lighting, and albedo from an image sequence of a rigidly-moving Lambertian object under distant illumination. The problem is formulated in a manner that subsumes structure from motion, multiview stereo, and photometric stereo as special cases. The algorithm utilizes both spatial and temporal intensity variation as cues: the former constrains flow and the latter constrains surface orientation; combining both cues enables dense reconstruction of both textured and textureless surfaces. The algorithm works by iteratively estimating affine camera parameters, illumination, shape, and albedo in an alternating fashion. Results are demonstrated on videos of hand-held objects moving in front of a fixed light and camera.


Cooperative Information Systems


MISC: A data set of information-seeking conversations


title: Temporal Item Embedding with Static Similarity Regularization for Sequential Recommendation

abstract: Recommender systems have attracted a significant amount of research interests in recent years. Traditional methods such as content-based approaches and collaborative filtering approaches mainly focus on modeling the general user preference by using the user's whole purchase history. However, except for user preference, the sequential information should also be considered because human behavior exhibits sequential patterns. The methods considering both sequential item relationship and user preference are called sequential recommendation methods, which are mostly base on Markov Chains. Due to the difficulty of parameter estimation, most prior work only considers the latest interaction, which is insufficient according to our observations. To that end, in this paper, we propose a temporal item embedding method based on word2vec framework to model long purchase history, with each purchased item regarded as a word in sentence. Meanwhile, inspired by the collaborative filtering in traditional recommendations, we assume that similar items should have similar embeddings and propose static similarity regularization (SSR) in recommendation. The regularized item embedding can capture user preference and sequential item relationship simultaneously. Experiments on real-world datasets show that the proposed approach outperforms a spectrum of state-of-the-art algorithms.


title: A Complete End-to-End Speaker Verification System Using Deep Neural Networks: From Raw Signals to Verification Result

abstract: End-to-end systems using deep neural networks have been widely studied in the field of speaker verification. Raw audio signal processing has also been widely studied in the fields of automatic music tagging and speech recognition. However, as far as we know, end-to-end systems using raw audio signals have not been explored in speaker verification. In this paper, a complete end-to-end speaker verification system is proposed, which inputs raw audio signals and outputs the verification results. A pre-processing layer and the embedded speaker feature extraction models were mainly investigated. The proposed pre-emphasis layer was combined with a strided convolution layer for pre-processing at the first two hidden layers. In addition, speaker feature extraction models using convolutionallayer and long short-term memory are proposed to be embedded in the proposed end-to-end system.


Analyzing Regression Test Selection Techniques


title: Distributed Subgradient Methods for Multi-Agent Optimization

abstract: We study a distributed computation model for optimizing a sum of convex objective functions corresponding to multiple agents. For solving this (not necessarily smooth) optimization problem, we consider a subgradient method that is distributed among the agents. The method involves every agent minimizing his/her own objective function while exchanging information locally with other agents in the network over a time-varying topology. We provide convergence results and convergence rate estimates for the subgradient method. Our convergence rate results explicitly characterize the tradeoff between a desired accuracy of the generated approximate optimal solutions and the number of iterations needed to achieve the accuracy.


The relationship between the feedback environment and creativity: a self-motives perspective


title: Double-tiered switched capacitor battery charge equalizer with chain structure

abstract: The switched capacitor equalizer is one of the simplest voltage equalizing methods. However this equalizer has disadvantages which are prolonged equalization time and large difference of equalization time caused by the position of problematic energy storage cell. This paper proposes novel switched capacitor equalizer which has the advantages of the switched capacitor with chain structure and the double-tiered switched capacitor. The proposed circuit has many paths of charge exchange, which reduces the equalization time and constantly keeps the performance above a certain level regardless of the position of the problematic cell. The performance of the proposed circuit was confirmed with simulation and circuit experiments.


title: GUS, a frame-driven dialog system

abstract: GUS is the first of a series of experimental computer systems that we intend to construct as part of a program of research on language understanding. In large measure, these systems will fill the role of periodic progress reports, summarizing what we have learned, assessing the mutual coherence of the various lines of investigation we have been following, and suggesting where more emphasis is needed in future work. GUS (Genial Understander System) is intended to engage a sympathetic and highly cooperative human in an English dialog, directed towards a specific goal within a very restricted domain of discourse. As a starting point, GUS was restricted to the role of a travel agent in a conversation with a client who wants to make a simple return trip to a single city in California. ::: ::: There is good reason for restricting the domain of discourse for a computer system which is to engage in an English dialog. Specializing the subject matter that the system can talk about permits it to achieve some measure of realism without encompassing all the possibilities of human knowledge or of the English language. It also provides the user with specific motivation for participating in the conversation, thus narrowing the range of expectations that GUS must have about the user's purposes. A system restricted in this way will be more able to guide the conversation within the boundaries of its competence.


title: GTmoPass: two-factor authentication on public displays using gaze-touch passwords and personal mobile devices

abstract: As public displays continue to deliver increasingly private and personalized content, there is a need to ensure that only the legitimate users can access private information in sensitive contexts. While public displays can adopt similar authentication concepts like those used on public terminals (e.g., ATMs), authentication in public is subject to a number of risks. Namely, adversaries can uncover a user's password through (1) shoulder surfing, (2) thermal attacks, or (3) smudge attacks. To address this problem we propose GTmoPass, an authentication architecture that enables Multi-factor user authentication on public displays. The first factor is a knowledge-factor: we employ a shoulder-surfing resilient multimodal scheme that combines gaze and touch input for password entry. The second factor is a possession-factor: users utilize their personal mobile devices, on which they enter the password. Credentials are securely transmitted to a server via Bluetooth beacons. We describe the implementation of GTmoPass and report on an evaluation of its usability and security, which shows that although authentication using GTmoPass is slightly slower than traditional methods, it protects against the three aforementioned threats.


title: A contextual change account of the directed forgetting effect

abstract: The authors propose that the costs and benefits of directed forgetting in the list method result from an internal context change that occurs between the presentations of 2 lists in response to a “forget” instruction. In Experiment 1 of this study, costs and benefits akin to those found in directed forgetting were obtained in the absence of a forget instruction by a direct manipulation of cognitive context change. Experiment 2 of this study replicated those findings using a different cognitive context manipulation and investigated the effects of context reinstatement at the time of recall. Context reinstatement reduced the memorial costs and benefits of context change in the condition where context had been manipulated and in the standard forget condition. The results are consistent with a context change account of directed forgetting. Directed forgetting is a phenomenon first studied by R. A. Bjork, LaBerge, and LeGrand (1968) whereby people appear to be able to intentionally forget information, making it less accessible to later attempts at recall and reducing interference from that information. The paradigm involves two variations: the item method, which seems to reflect differential encoding of items, and the list method, which does not depend on differential encoding of items (Basden, Basden, & Gargano, 1993). The present work concerns the mechanism of directed forgetting with the list method. Participants are presented two lists of items to study but, immediately after List 1, half of the participants are instructed to forget List 1 (the “forget” group), whereas the remaining half are told to continue remembering List 1 (the “remember” group). The final test requires recall of both lists. Typically, the forget group recalls fewer items from the first list than does the remember group—a finding referred to as the costs of directed forgetting


title: Learning Single-View 3D Reconstruction with Adversarial Training

abstract: Single-view 3D shape reconstruction is an important but challenging problem, mainly for two reasons. First, as shape annotation is very expensive to acquire, current methods rely on synthetic data, in which ground-truth 3D annotation is easy to obtain. However, this results in domain adaptation problem when applied to natural images. The second challenge is that it exists multiple shapes that can explain a given 2D image. In this paper, we propose a framework to improve over these challenges using adversarial training. On one hand, we impose domain-confusion between natural and synthetic image representations to reduce the distribution gap. On the other hand, we impose the reconstruction to be `realistic' by forcing it to lie on a (learned) manifold of realistic object shapes. Moreover, our experiments show that these constraints improve performance by a large margin over a baseline reconstruction model. We achieve results competitive with the state of the art using only RGB images and with a much simpler architecture.


Supervised and Unsupervised Transfer Learning for Activity Recognition from Simple In-home Sensors


title: Design and Development of Portable Spirometer

abstract: Spirometer as main device to perfom spirometry test is needed to make clinical diagnosis of Chronic Obstructive Pulmonary Disease (COPD), a limitation airflow disease. Spirometer will produce Forced Vital Capacity (FVC), maximum volume of air that can be exhaled during a forced maneuver and produce Forced Expired Volume in one second (FEVl), volume expired in the first second of maximal expiration after a maximal inspiration as the main factor to diagnosis COPD. Spirometry test generally performed at a health clinic or medical offices but nowdays home spirometry with portable devices is slowly gaining acceptance. But current portable home based spirometers have no coaching, feedback, or quality control mechanisms from physicians to ensure acceptable measurements. This study creates android messaging, javafx desktop and website based information system integrated to portable spirometer made from MPX5100DP sensor to calculate the pressure during FVC, and Arduino nano to keep track and maintaince spirometry test.


title: Movie Revenue Prediction Using Regression and Clustering

abstract: Among many movies that have been released, some generate high profit while the others do not. This paper studies the relationship between movie factors and its revenue and build prediction models. Besides analysis on aggregate data, we also divide data into groups using different methods and compare accuracy across these techniques as well as explore whether clustering techniques could help improve accuracy. Specifically, two major steps were employed. Initially, linear regression, polynomial regression and support vector regression (SVR) were applied on the entire movie data to predict the movie revenue. Then, clustering techniques, such as by genre, using Expectation Maximization (EM) and using K-means were applied to divide data into groups before regression analyses are executed. To compare accuracy among different techniques, R-square and the root-mean-square error (RMSE) were used as a performance indicator. Our study shows that generally linear regression without clustering offers the model with the highest R-square, while linear regression with EM clustering yields the lowest RMSE.


Biodiversity in Agricultural Landscapes of Southeastern Brazil


title: Multimodal Measurement of Depression Using Deep Learning Models

abstract: This paper addresses multi-modal depression analysis. We propose a multi-modal fusion framework composed of deep convolutional neural network (DCNN) and deep neural network (DNN) models. Our framework considers audio, video and text streams. For each modality, handcrafted feature descriptors are input into a DCNN to learn high-level global features with compact dynamic information, then the learned features are fed to a DNN to predict the PHQ-8 scores. For multi-modal fusion, the estimated PHQ-8 scores from the three modalities are integrated in a DNN to obtain the final PHQ-8 score. Moreover, in this work, we propose new feature descriptors for text and video. For the text descriptors, we select the participant»s answers to the questions associated with psychoanalytic aspects of depression, such as sleep disorder, and make use of the Paragraph Vector (PV) to learn the distributed representations of these sentences. For the video descriptors, we propose a new global descriptor, the Histogram of Displacement Range (HDR), calculated directly from the facial landmarks to measure their displacements and speed. Experiments have been carried out on the AVEC2017 depression sub-challenge dataset. The obtained results show that the proposed depression recognition framework obtains very promising accuracy, with the root mean square error (RMSE) as 4.653, mean absolute error (MAE) as 3.980 on the development set, and RMSE as 5.974, MAE as 5.163 on the test set.


title: Robust Downbeat Tracking Using an Ensemble of Convolutional Networks

abstract: In this paper, we present a novel state-of-the-art system for automatic downbeat tracking from music signals. The audio signal is first segmented in frames which are synchronized at the tatum level of the music. We then extract different kind of features based on harmony, melody, rhythm, and bass content to feed convolutional neural networks that are adapted to take advantage of the characteristics of each feature. This ensemble of neural networks is combined to obtain one downbeat likelihood per tatum. The downbeat sequence is finally decoded with a flexible and efficient temporal model which takes advantage of the assumed metrical continuity of a song. We then perform an evaluation of our system on a large base of nine datasets, compare its performance to four other published algorithms and obtain a significant increase of 16.8% points compared to the second-best system, for altogether a moderate cost in test and training. The influence of each step of the method is studied to show its strengths and shortcomings.


title: Metamaterial matching of high-permittivity coatings for 79 GHz radar sensors

abstract: Future automotive radar sensors in the 79 GHz regime are intended to be integrated behind painted, plastic fascia. This integration has to be carefully studied, concerning reflections coming from the bumper in line of sight of the sensor. To reduce these reflections, impedance matching of multilayer structures with high permittivities (e.g. metallic paints) is mandatory. Besides conventional matching techniques, metamaterials are a possibility to realize a matching layer. Based on an adopted parameter extraction algorithm for multilayer dielectrics, a bandwidth evaluation of a possible metamaterial design is shown. The measurement results show that for a bandwidth up to 3.8 GHz the metamaterial outperforms the benchmark.


title: Improving Restaurants by Extracting Subtopics from Yelp Reviews

abstract: In this paper, we describe latent subtopics discovered from Yelp restaurant reviews by running an online Latent Dirichlet Allocation (LDA) algorithm. The goal is to point out demand of customers from a large amount of reviews, with high dimensionality. These topics can provide meaningful insights to restaurants about what customers care about in order to increase their Yelp ratings, which directly affects their revenue. We used the open dataset from the Yelp Dataset Challenge with over 158,000 restaurant reviews. To find latent subtopics from reviews, we adopted Online LDA, a generative probabilistic model for collections of discrete data such as text corpora. We present the breakdown of hidden topics over all reviews, predict stars per hidden topics discovered, and extend our findings to that of temporal information regarding restaurants peak hours. Overall, we have found several interesting insights and a method which could definitely prove useful to restaurant owners.


title: Analysis and Design of Conformal Tapered Leaky-Wave Antennas

abstract: The theory to analyze and design tapered conformal line-source leaky-wave antennas is described in this letter. The deformation in the radiation pattern associated to the bending of the antenna is accurately predicted, and useful expressions are derived to taper the leaky-mode complex propagation constant in order to restore the high-directive radiated beam scanning at the desired angle. The analysis method and the design approach are validated with full-wave simulations of a curved tapered leaky-wave antenna in hybrid technology.


title: Application of Artificial Neural Network (ANN) for Reservoir Water Level Forecasting

abstract: Future water level forecast helps in knowing the water storage capacity of reservoir which can further be employed for irrigation, water supply, hydropower, etc. These soft computing techniques can map an input-output pattern without the prior knowledge of the criteria that influence the forecast procedure. Amongst the all, the artificial neural network (ANN) is one of the most accurate models that is used in water resource management. The principal inputs that are used to compute water level at t+1 time are: Inflow, water level at t time and released water. The statistics parameters room mean square error (RMSE), coefficient of correlation (R), coefficient of determination (R 2 ) and discrepancy ratio (D) were used to get best model out of three alternatives. The objective is to define the ANN model by applying different types of network tools like Feed Forward distributed time delay, layer recurrent and NARX on a set of input data to forecast the 10 day ahead water level for the case study, Sukhi Reservoir Project, Gujarat, India. The study reveals that, among the three algorithms applied, ANN using Feed Forward distributed time delay is an appropriate predictor for real- time Water Level forecasting of study area.


title: Social participation and survival at older ages: Is the effect driven by activity content or context?

abstract: This study tested the hypothesis that time spent on social activities (i.e., in direct interaction with others) and time spent in social contexts (i.e., while others are present) is associated with survival in persons aged 70 and older. An observational study with mortality follow-ups was carried out in the former West Berlin, Germany (Berlin Aging Study). The sample was stratified by age and sex, and consisted of 473 persons aged 70 to 103 years. Social activity and social context measures were assessed in 1990–1993 by structured interviews in the participants’ homes. Cox regression was used to model survival from time of interview. The main outcome measure was survival on 1 August 2003. Time spent on social activities was revealed as a predictor of survival only in analyses that did not control for confounding factors. In contrast, time spent in context “with friends” was significantly related to increased survival (relative risk=0.76, 95% confidence interval 0.59 to 0.99) even after several confounding factors were controlled for. This study suggests that time spent with friends affords a survival advantage among older adults, above and beyond the effects of other leisure activities. Future research on social participation and survival may benefit from an examination of the interaction between activity content and social context.


Language production : Grammatical encoding


title: Ranking Through Clustering: An Integrated Approach to Multi-Document Summarization

abstract: Multi-document summarization aims to create a condensed summary while retaining the main characteristics of the original set of documents. Under such background, sentence ranking has hitherto been the issue of most concern. Since documents often cover a number of topic themes with each theme represented by a cluster of highly related sentences, sentence clustering has been explored in the literature in order to provide more informative summaries. For each topic theme, the rank of terms conditional on this topic theme should be very distinct, and quite different from the rank of terms in other topic themes. Existing cluster-based summarization approaches apply clustering and ranking in isolation, which leads to incomplete, or sometimes rather biased, analytical results. A newly emerged framework uses sentence clustering results to improve or refine the sentence ranking results. Under this framework, we propose a novel approach that directly generates clusters integrated with ranking in this paper. The basic idea of the approach is that ranking distribution of sentences in each cluster should be quite different from each other, which may serve as features of clusters and new clustering measures of sentences can be calculated accordingly. Meanwhile, better clustering results can achieve better ranking results. As a result, ranking and clustering by mutually and simultaneously updating each other so that the performance of both can be improved. The effectiveness of the proposed approach is demonstrated by both the cluster quality analysis and the summarization evaluation conducted on the DUC 2004-2007 datasets.


title: Removal of fixed impulse noise from digital images using Bezier curve based interpolation

abstract: In this paper, we propose a Bezier curve based interpolation technique to eliminate the fixed impulse noise from digital images as well as maintaining the edges of the image. To eliminate the noise, we make the noisy image to pass through two steps, where in the first step we found out the pixels affected by the impulse noise, and in second step, edge protecting process is done using Bezier interpolation technique. Promising results were obtained for images having more than 80 percent of the image pixels are affected. Our proposed algorithm is producing better results in comparison to existing algorithms.


title: New Trellis Code Design for Spatial Modulation

abstract: Spatial modulation (SM), in which multiple antennas are used to convey information besides the conventional M-ary signal constellations, is a new multiple-input multiple-output (MIMO) transmission technique, which has recently been proposed as an alternative to V-BLAST (vertical Bell Labs layered space-time). In this paper, a novel MIMO transmission scheme, called spatial modulation with trellis coding (SM-TC), is proposed. Similar to the conventional trellis coded modulation (TCM), in this scheme, a trellis encoder and an SM mapper are jointly designed to take advantage of the benefits of both. A soft decision Viterbi decoder, which is fed with the soft information supplied by the optimal SM decoder, is used at the receiver. A pairwise error probability (PEP) upper bound is derived for the SM-TC scheme in uncorrelated quasi-static Rayleigh fading channels. From the PEP upper bound, code design criteria are given and then used to obtain new 4-, 8- and 16-state SM-TC schemes using quadrature phase-shift keying (QPSK) and 8-ary phase-shift keying (8-PSK) modulations for 2,3 and 4 bits/s/Hz spectral efficiencies. It is shown via computer simulations and also supported by a theoretical error performance analysis that the proposed SM-TC schemes achieve significantly better error performance than the classical space-time trellis codes and coded V-BLAST systems at the same spectral efficiency, yet with reduced decoding complexity.


title: Hybrid Dimensionality Reduction Method Based on Support Vector Machine and Independent Component Analysis

abstract: This paper presents a new hybrid dimensionality reduction method to seek projection through optimization of both structural risk (supervised criterion) and data independence (unsupervised criterion). Classification accuracy is used as a metric to evaluate the performance of the method. By minimizing the structural risk, projection originated from the decision boundaries directly improves the classification performance from a supervised perspective. From an unsupervised perspective, projection can also be obtained based on maximum independence among features (or attributes) in data to indirectly achieve better classification accuracy over more intrinsic representation of the data. Orthogonality interrelates the two sets of projections such that minimum redundancy exists between the projections, leading to more effective dimensionality reduction. Experimental results show that the proposed hybrid dimensionality reduction method that satisfies both criteria simultaneously provides higher classification performance, especially for noisy data sets, in relatively lower dimensional space than various existing methods.


title: Have We Forsaken Reading Theory in the Name of “Quick Fix” Interventions for Children With Reading Disability?

abstract: Our contribution to this special issue on reading theory questions the effectiveness of the prevailing interventions intended to improve word-reading and reading comprehension skills in children with reading disability (RD). Our hypothesis is that we as a field may have inadvertently diluted reading theory in ways that compromise the power of intervention programs. For both word reading and reading comprehension we argue that current intervention programs target instruction at a knowledge level below that which is necessary to foster reading skill development that is “generative” in children with RD. Further, we contend that current interventions for children with RD fail to mimic and promote the inductive learning mechanisms that characterize typical reading development. Thus, we return to reading theory in an attempt to identify ways that current interventions may be reconceptualized to treat word-reading and reading comprehension deficits. In doing so, we call for the development of a new generation of...


title: Evolving fuzzy rule based controllers using genetic algorithms

abstract: Abstract The synthesis of genetics-based machine learning and fuzzy logic is beginning to show promise as a potent tool in solving complex control problems in multi-variate non-linear systems. In this paper an overview of current research applying the genetic algorithm to fuzzy rule based control is presented. A novel approach to genetics-based machine learning of fuzzy controllers, called a Pittsburgh Fuzzy Classifier System # 1 (P-FCS1) is proposed. P-FCS1 is based on the Pittsburgh model of learning classifier systems and employs variable length rule-sets and simultaneously evolves fuzzy set membership functions and relations. A new crossover operator which respects the functional linkage between fuzzy rules with overlapping input fuzzy set membership functions is introduced. Experimental results using P-FCS 1 are reported and compared with other published results. Application of P-FCS1 to a distributed control problem (dynamic routing in computer networks) is also described and experimental results are presented.


title: Cannabidiol--recent advances.

abstract: The aim of this review is to present some of the recent publications on cannabidiol (CBD; 2), a major non-psychoactive constituent of Cannabis, and to give a general overview. Special emphasis is laid on biochemical and pharmacological advances, and on novel mechanisms recently put forward, to shed light on some of the pharmacological effects that can possibly be rationalized through these mechanisms. The plethora of positive pharmacological effects observed with CBD make this compound a highly attractive therapeutic entity.


title: Automatic feature learning for vulnerability prediction

abstract: Code flaws or vulnerabilities are prevalent in software systems and can potentially cause a variety of problems including deadlock, information loss, or system failure. A variety of approaches have been developed to try and detect the most likely locations of such code vulnerabilities in large code bases. Most of them rely on manually designing features (e.g. complexity metrics or frequencies of code tokens) that represent the characteristics of the code. However, all suffer from challenges in sufficiently capturing both semantic and syntactic representation of source code, an important capability for building accurate prediction models. In this paper, we describe a new approach, built upon the powerful deep learning Long Short Term Memory model, to automatically learn both semantic and syntactic features in code. Our evaluation on 18 Android applications demonstrates that the prediction power obtained from our learned features is equal or even superior to what is achieved by state of the art vulnerability prediction models: 3%--58% improvement for within-project prediction and 85% for cross-project prediction.


title: What's in a URL: Fast Feature Extraction and Malicious URL Detection

abstract: Phishing is an online social engineering attack with the goal of digital identity theft carried out by pretending to be a legitimate entity. The attacker sends an attack vector commonly in the form of an email, chat session, blog post etc., which contains a link (URL) to a malicious website hosted to elicit private information from the victims. We focus on building a system for URL analysis and classification to primarily detect phishing attacks. URL analysis is attractive to maintain distance between the attacker and the victim, rather than visiting the website and getting features from it. It is also faster than Internet search, retrieving content from the destination website and network-level features used in previous research. We investigate several facets of URL analysis, e.g., performance analysis on both balanced and unbalanced datasets in a static as well as live experimental setup and online versus batch learning.


title: Moving Object Tracking using Gaussian Mixture Model and Optical Flow

abstract: In this paper, we propose a new tracking method that uses Gaussian Mixture Model (GMM) and Optical Flow approach for object tracking. The GMM approach consists of three different Gaussian distributions, the average, standard deviation and weight respectively. There are two important steps to establish the background for model, and background updates which separate the foreground and background. This paper combines the GMM and Optical Flow object tracking. The advantages of Optical Flow are quick calculations and the disadvantage is a lack of complete object tracking. The advantage of GMM is complete results of the operation the disadvantage is not a complete object tracking, GMM result of the operation complete but disadvantages include computing for a long time with more noise. These two methods can complement each other and image filtering results in the successful tracking of objects. It has variety of uses such as video communication and compression, traffic control, medical imaging and video editing.


title: Bacterial Community Variation in Human Body Habitats Across Space and Time

abstract: Elucidating the biogeography of bacterial communities on the human body is critical for establishing healthy baselines from which to detect differences associated with diseases. To obtain an integrated view of the spatial and temporal distribution of the human microbiota, we surveyed bacteria from up to 27 sites in seven to nine healthy adults on four occasions. We found that community composition was determined primarily by body habitat. Within habitats, interpersonal variability was high, whereas individuals exhibited minimal temporal variability. Several skin locations harbored more diverse communities than the gut and mouth, and skin locations differed in their community assembly patterns. These results indicate that our microbiota, although personalized, varies systematically across body habitats and time; such trends may ultimately reveal how microbiome changes cause or prevent disease.


title: Recurrent Falls in Parkinson's Disease: A Systematic Review

abstract: Most people with Parkinson’s disease (PD) fall and many experience recurrent falls. The aim of this review was to examine the scope of recurrent falls and to identify factors associated with recurrent fallers. A database search for journal articles which reported prospectively collected information concerning recurrent falls in people with PD identified 22 studies. In these studies, 60.5% (range 35 to 90%) of participants reported at least one fall, with 39% (range 18 to 65%) reporting recurrent falls. Recurrent fallers reported an average of 4.7 to 67.6 falls per person per year (overall average 20.8 falls). Factors associated with recurrent falls include: a positive fall history, increased disease severity and duration, increased motor impairment, treatment with dopamine agonists, increased levodopa dosage, cognitive impairment, fear of falling, freezing of gait, impaired mobility and reduced physical activity. The wide range in the frequency of recurrent falls experienced by people with PD suggests that it would be beneficial to classify recurrent fallers into sub-groups based on fall frequency. Given that there are several factors particularly associated with recurrent falls, fall management and prevention strategies specifically targeting recurrent fallers require urgent evaluation in order to inform clinical practice.


title: A novel plasticity rule can explain the development of sensorimotor intelligence

abstract: Grounding autonomous behavior in the nervous system is a fundamental challenge for neuroscience. In particular, self-organized behavioral development provides more questions than answers. Are there special functional units for curiosity, motivation, and creativity? This paper argues that these features can be grounded in synaptic plasticity itself, without requiring any higher-level constructs. We propose differential extrinsic plasticity (DEP) as a new synaptic rule for self-learning systems and apply it to a number of complex robotic systems as a test case. Without specifying any purpose or goal, seemingly purposeful and adaptive rhythmic behavior is developed, displaying a certain level of sensorimotor intelligence. These surprising results require no system-specific modifications of the DEP rule. They rather arise from the underlying mechanism of spontaneous symmetry breaking, which is due to the tight brain body environment coupling. The new synaptic rule is biologically plausible and would be an interesting target for neurobiological investigation. We also argue that this neuronal mechanism may have been a catalyst in natural evolution.


title: Positive feedback technique for DC-gain enhancement of folded cascode Op-Amps

abstract: In this paper, a novel folded cascode operational amplifier is proposed which improves DC-gain using positive feedback technique. This method does not affect the unity-gain frequency, stability, power dissipation, and output voltage swing of the conventional folded cascode Op-Amp. The proposed Op-Amp was designed in a standard 0.18µm TSMC 1.8V CMOS technology. Simulation results show a DC-gain enhancement of 25dB and 513MHz unity gain bandwidth for the presented Op-Amp. HSPICE simulation results confirm the theoretical estimated improvements.


RNN-Based Sequence-Preserved Attention for Dependency Parsing


title: Forensic Analysis of the ChatSecure Instant Messaging Application on Android Smartphones

abstract: We present the forensic analysis of the artifacts generated on Android smartphones by ChatSecure, a secure Instant Messaging application that provides strong encryption for transmitted and locally-stored data to ensure the privacy of its users.We show that ChatSecure stores local copies of both exchanged messages and files into two distinct, AES-256 encrypted databases, and we devise a technique able to decrypt them when the secret passphrase, chosen by the user as the initial step of the encryption process, is known.Furthermore, we show how this passphrase can be identified and extracted from the volatile memory of the device, where it persists for the entire execution of ChatSecure after having been entered by the user, thus allowing one to carry out decryption even if the passphrase is not revealed by the user.Finally, we discuss how to analyze and correlate the data stored in the databases used by ChatSecure to identify the IM accounts used by the user and his/her buddies to communicate, as well as to reconstruct the chronology and contents of the messages and files that have been exchanged among them.For our study we devise and use an experimental methodology, based on the use of emulated devices, that provides a very high degree of reproducibility of the results, and we validate the results it yields against those obtained from real smartphones.


title: An electronic animal activity recorder

abstract: On decrit une methode pour discerner et enregistrer l'activite generale des animaux de petite taille, loges dans des cages en matiere plastique, en employant un transmetteur et des antennes accordees. Le systeme utilise les caracteristiques du champ magnetique autour d'une source de courant electrique et la capacite de resistance des objets contigus.


title: A 25 kW industrial prototype wireless electric vehicle charger

abstract: A 25 kW wireless charger design for electric vehicles is presented in this paper. The wireless charger consist of three phase power factor corrector (PFC), three phase resonant inverter, primary and secondary coils with series resonant compensation, and a rectifier. In the proposed design, PFC provides a constant voltage DC Bus and the whole output regulation is done by the resonant inverter. It simplifies the rectifier structure to the simple full-bridge topology. The design is verified with experiments at the output power from 0 W to 25 kW. The measured system efficiency was up to 91%.


title: Policy Search by Dynamic Programming

abstract: We consider the policy search approach to reinforcement learning. We show that if a "baseline distribution" is given (indicating roughly how often we expect a good policy to visit each state), then we can derive a policy search algorithm that terminates in a finite number of steps, and for which we can provide non-trivial performance guarantees. We also demonstrate this algorithm on several grid-world POMDPs, a planar biped walking robot, and a double-pole balancing problem.


title: Emotional design in multimedia learning: Effects of shape and color on affect and learning

abstract: We examine design factors that may evoke positive emotions in learners and investigate the effects of these positive emotions on learning. Recent research showed that the emotional design of multimedia learning materialcaninducepositiveemotionsinlearnersthatinturnfacilitatecomprehensionandtransfer.Wesought to replicate these results with a different population and different mood induction procedure and examine individual emotions, and to decompose the effects of the design elements of colorand shape. Study 1 showed that well-designed materials induced positive emotions and facilitated comprehension, though transfer performance was not affected by emotional design. Study 2 found that round face-like shapes both alone and in conjunctionwithwarm color induced positive emotions.Warm colors alone, however, did not affectlearners’ emotions.Comprehensionwasfacilitatedbywarmcolorsandroundface-likeshapes,independentlyaswellas together. Transfer was facilitated by round face-like shapes when used with neutral colors.


title: Complex digital filters with asymmetrical characteristics

abstract: Proposes two frequency transformations. Both map an infinite impulse response (IIR) low-pass response into a complex single-band-pass response with asymmetrical transition width. Such a response can reduce the necessary order. One method is detailed in a complex transmission line network, and robustly implemented by a ladder wave digital filter. Though the transformed network is not 'one-real', an alternative combination of voltage-based serial sections and current-based parallel sections leads to a high modularity. Another method is established in the digital domain as a multiple performance of low-pass to low-pass and shifting transformations. >


Spring 5-2012 Best Intentions : Health Monitoring Technology and Children .


Augmented Reality Textbook for Future Blended Education


title: From databases to dataspaces: a new abstraction for information management

abstract: The development of relational database management systems served to focus the data management community for decades, with spectacular results. In recent years, however, the rapidly-expanding demands of "data everywhere" have led to a field comprised of interesting and productive efforts, but without a central focus or coordinated agenda. The most acute information management challenges today stem from organizations (e.g., enterprises, government agencies, libraries, "smart" homes) relying on a large number of diverse, interrelated data sources, but having no way to manage their dataspaces in a convenient, integrated, or principled fashion. This paper proposes dataspaces and their support systems as a new agenda for data management. This agenda encompasses much of the work going on in data management today, while posing additional research objectives.


title: A Non-Inclusive Memory Permissions architecture for protection against cross-layer attacks

abstract: Protecting modern computer systems and complex software stacks against the growing range of possible attacks is becoming increasingly difficult. The architecture of modern commodity systems allows attackers to subvert privileged system software often using a single exploit. Once the system is compromised, inclusive permissions used by current architectures and operating systems easily allow a compromised high-privileged software layer to perform arbitrary malicious activities, even on behalf of other software layers. This paper presents a hardware-supported page permission scheme for the physical pages that is based on the concept of non-inclusive sets of memory permissions for different layers of system software such as hypervisors, operating systems, and user-level applications. Instead of viewing privilege levels as an ordered hierarchy with each successive level being more privileged, we view them as distinct levels each with its own set of permissions. Such a permission mechanism, implemented as part of a processor architecture, provides a common framework for defending against a range of recent attacks. We demonstrate that such a protection can be achieved with negligible performance overhead, low hardware complexity and minimal changes to the commodity OS and hypervisor code.


title: A Research Agenda Acknowledging the Persistence of Passwords

abstract: Despite countless attempts and near-universal desire to replace them, passwords are more widely used and firmly entrenched than ever. The authors' exploration leads them to argue that no silver bullet will meet all requirements-not only will passwords be with us for some time, but in many instances, they're the solution that best fits the scenario of use. Among broad authentication research directions to follow, they first suggest better means to concretely identify actual requirements (surprisingly overlooked to date) and weight their relative importance in target scenarios. Second, for scenarios where passwords appear to be the best-fit solution, they suggest designing better means to support them. The authors also highlight the need for more systematic research and how the premature conclusion that passwords are dead has led to the neglect of important research questions.


title: Design criteria for shoot-through elimination in Trench Field Plate Power MOSFET

abstract: We studied shoot-through phenomena in buried-source-type Trench Field Plate Power MOSFET. We proposed a new analytical circuit model explaining transient gate and buried-source voltages in this structure under the high dV DS /dt. The new analytical circuit model was verified by TCAD mixed-mode simulation and experimental results, and it was found that there are two types of shoot-through mechanism, for the first time. One is channel conduction mode caused by the displacement gate current from the buried-source, and the other is dynamic avalanche breakdown induced by buried-source voltage increase. We also showed that the reduction of buried-source interconnection resistance is effective for suppressing both types of shoot-through phenomena. It is demonstrated that DC-DC buck converter efficiency is improved by reducing resistance of the buried-source interconnection.


title: Finite-element time-step coupled Generator,Load, AVR, and brushless exciter modeling

abstract: This paper presents a model of a small brushless generating set that incorporates independently validated subsystem models. The new model links: a finite-element and time-stepping circuit model of the main alternator and load, a real-time model of the analog automatic voltage regulator (AVR) and a model based on the self and mutual inductances of the exciter machine along with the rectifier diode bridge. The composite model can be used to study the transient and steady-state performance of the whole generating set under different load conditions including synchronous operation in parallel with mains. The predicted transient behavior of an isolated generating set when subjected to various loads is presented, the implications with regard to induction motor starting are discussed, and voltage recovery and the function of each component are demonstrated.


title: Survey of clustering algorithms

abstract: Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.


title: Location-aided routing (LAR) in mobile ad hoc networks

abstract: A mobile ad hoc network consists of wireless hosts that may move often. Movement of hosts results in a change in routes, requiring some mechanism for determining new routes. Several routing protocols have already been proposed for ad hoc networks. This report suggests an approach to utilize location information (for instance, obtained using the global positioning system) to improve performance of routing protocols for ad hoc networks.


title: Foveated Depth-of-Field Filtering in Head-Mounted Displays

abstract: In recent years, a variety of methods have been introduced to exploit the decrease in visual acuity of peripheral vision, known as foveated rendering. As more and more computationally involved shading is requested and display resolutions increase, maintaining low latencies is challenging when rendering in a virtual reality context. Here, foveated rendering is a promising approach for reducing the number of shaded samples. However, besides the reduction of the visual acuity, the eye is an optical system, filtering radiance through lenses. The lenses create depth-of-field (DoF) effects when accommodated to objects at varying distances. The central idea of this article is to exploit these effects as a filtering method to conceal rendering artifacts. To showcase the potential of such filters, we present a foveated rendering system, tightly integrated with a gaze-contingent DoF filter. Besides presenting benchmarks of the DoF and rendering pipeline, we carried out a perceptual study, showing that rendering quality is rated almost on par with full rendering when using DoF in our foveated mode, while shaded samples are reduced by more than 69%.


title: Novel 11-level Multi-level Inverter

abstract: Inverters play a major role in converting the DC power obtained from renewable energy resources into AC. Conventional inverters produce a square wave with a THD of 48.34%. To reduce harmonics, multilevel inverters are a better option than large-sized L-C filters. Increased number of levels results in an output with close resemblance to a sine wave. But, increase in levels increases the number of switches and control complexity. So, a balance has to be struck between the THD in the output and the number of components to be used. This paper proposes a novel topology with a reduced number of switches, to obtain a similar performance to that of existing cascaded Hbridge configurations, which use Carrier-Based Sinusoidal Pulse Width Modulation (CBSPWM) technique. The designed power and pulse generator circuit are validated by simulation and the outputs are presented.


title: PLM software selection model for project management using hierarchical decision modeling with criteria from PMBOK knowledge areas

abstract: This paper presents a methodology for product lifecycle management (plm) software selection utilizing the nine knowledge areas of the project management body of knowledge (pmbok). The methodology utilizes a five process gate approach where the plm offerings are researched, sorted, paired down, evaluated, and implemented. The primary decision model utilizes hierarchical decision modeling (hdm). Prioritizations are assigned to the pmbok knowledge areas utilizing a pair-wise comparison survey and then assessed across the different plm system offerings. The proposed decision methodology is meant to serve only as a guide to assist with selection of plm software from a project management perspective. To validate the model, several case studies are presented that apply the selection methodology to different industries: semiconductor, information technology (it), and automotive supply.


title: EURASIP Journal on Applied Signal Processing 2003:12, 1229–1237 c ○ 2003 Hindawi Publishing Corporation Nonlinear System Identification Using Neural Networks Trained with Natural Gradient Descent

abstract: We use natural gradient (NG) learning neural networks (NNs) for modeling and identifying nonlinear systems with memory. The nonlinear system is comprised of a discrete-time linear filter H followed by a zero-memory nonlinearity g(ċ). The NN model is composed of a linear adaptive filter Q followed by a two-layer memoryless nonlinear NN. A Kalman filter-based technique and a search-and-converge method have been employed for the NG algorithm. It is shown that the NG descent learning significantly outperforms the ordinary gradient descent and the Levenberg-Marquardt (LM) procedure in terms of convergence speed and mean squared error (MSE) performance.


title: A hybrid approach for recommendation system with added feedback component

abstract: With the increasing E-Commerce and online shopping there is a need for recommendation systems which help the customers in decision making and to suggest potential goods of purchase. In domains such as automobiles there are many websites but most of them are not having enhanced recommendation systems to enable easy decision making. Thus we have taken the initiative of building a dataset with multiple parameters based on a survey of the communities needs using potential blogs and created a recommendation system using user based and item based collaborative filtering. In addition to the combined collaborative filtering techniques we propose a framework which includes a feedback analysis to improve the recommendation system. The enhanced model aids the customers in decision making. We have proposed the feedback system at two levels. One is external feedback where the comments are gathered from public platforms like social media and automobile websites. The other is internal feedback i.e. the feedback is taken from users who have been provided with recommended items. The opinions extracted from such varied comments broadens the system and results. Our proposed hybrid model with feedback analysis has improvised the current system by providing better suggestions to customers.


title: Improving Deep Neural Networks with Probabilistic Maxout Units

abstract: We present a probabilistic variant of the recently introduced maxout unit. The success of deep neural networks utilizing maxout can partly be attributed to favorable performance under dropout, when compared to rectified linear units. It however also depends on the fact that each maxout unit performs a pooling operation over a group of linear transformations and is thus partially invariant to changes in its input. Starting from this observation we ask the question: Can the desirable properties of maxout units be preserved while improving their invariance properties ? We argue that our probabilistic maxout (probout) units successfully achieve this balance. We quantitatively verify this claim and report classification performance matching or exceeding the current state of the art on three challenging image classification benchmarks (CIFAR-10, CIFAR-100 and SVHN).


title: Sensation seeking and danger invulnerability: Paths to college student risk-taking

abstract: Abstract This study examines the roles of sensation seeking and invulnerability as predictors of health compromising behaviors in a multiethnic sample of 1690 emerging adult college students (mean age = 19.8, range 18–25 years) from nine US colleges and universities. Participants completed the Arnett Sensation Seeking Inventory and the Adolescent Invulnerability Scale; and reported how often they had participated in a set of health compromising risk behaviors (i.e., substance use, impaired driving, and sexual behaviors) in the 30 days prior to assessment. Sensation seeking and danger invulnerability scores were moderately correlated ( r = .30). Findings from a series of multivariate Poisson regression analyses suggest that when considered simultaneously as predictors, sensation seeking appears to be a general risk factor associated with engagement in a variety of risk behaviors, whereas danger invulnerability is primarily a factor in those risk behaviors that are less common among peers (e.g., hard drug use, casual sex, and driving while intoxicated).