title: Automated diagnosis of human intestinal parasites using optical microscopy images

abstract: Intestinal parasitosis constitutes a serious health problem in most tropical countries. The diagnosis of enteroparasites in laboratory routine relies on the examination of stool samples using optical microscopy and the error rates usually range from moderate to high. Approaches based on automatic image analysis have been proposed, but the methods are usually specific for some species, some of them are computationally expensive, and image acquisition and focus are manual. We present a solution to automate the diagnosis of the 15 most common species of enteroparasites in Brazil, using a sensitive parasitological technique, a motorized microscope with digital camera for automatic image acquisition and focus, and fast image analysis methods. The results indicate that our solution is effective and suitable for laboratory routine, in which the exam must be concluded in a few minutes.


title: On the feasiblity of ISAR processing of stepped frequency waveforms

abstract: Inverse synthetic aperture radar imaging of moving targets with a stepped frequency waveform presents unique challenges. Intra-step target motion introduces phase discontinuities between frequency bands, which in turn produce degraded range side lobes. Frequency stitching of the stepped-frequency waveform to emulate a contiguous bandwidth can dramatically reduce the effective pulse repetition frequency, which then may impact the maximize target size that can be unambiguously measured and imaged via ISAR. This paper analyzes these effects and validates results via simulated data.


title: Motion sensor technologies in education

abstract: This paper attempts to raise a discussion regarding motion sensor technologies, mainly seen as peripherals of contemporary video game consoles, by examining their exploitation within educational context. An overview of the existing literature is presented, while attempting to categorize the educational approaches which involve motion sensor technologies, in two parts. The first one concerns the education of people with special needs. The utilization of motion sensor technologies, incorporated by game consoles, in the education of such people is examined. The second one refers to various educational approaches in regular education, under which not so many research approaches, but many teaching ideas can be found. The aim of the paper is to serve as a reference point for every individual/group, willing to explore the Sensor-Based Games Based Learning (SBGBL) research area, by providing a complete and structured literature review.


title: PixMix: A real-time approach to high-quality Diminished Reality

abstract: Diminished Reality (DR) allows to remove objects from a video stream while preseving a frame to frame coherence. Some approaches apply a pseudo-DR, allowing for the removal of objects only, while their background can be observed by a second camera. Most real DR approaches are highly computational expensive, not even allowing for interactive rates and/or apply significant restrictions regarding the uniformity of the background, or allow linear camera movements or even a static camera only. In this paper we will present a real-time capable Diminished Reality approach for high-quality image manipulation. Our approach achieves a significantly better performance and image quality for almost planar but non-trivial image backgrounds. Our Diminished Reality pipeline provides coherent video streams even for nonlinear camera movements due to the integration of homography based object tracking.


title: Ontogenetic variations in auditory arousal threshold during sleep

abstract: Abstract Developmental variations in auditory arousal thresholds during sleep were investigated in four groups of normal male subjects--children, preadolescents, adolescents, and young adults. Arousal thresholds were determined during NREM and REM sleep for tones presented via earphone insert on a single night following two adaptation nights of undisturbed sleep. Age-related relationships were observed for both awakening frequency and stimulus intensity required to effect awakening, with awakenings occurring more frequently in response to lower stimulus intensities with increasing age. Although stimulus intensities required for awakening were high and statistically equivalent across sleep stages in nonadults, higher intensity stimuli were required in Stage 4 relative to Stage 2 and REM sleep in adults. These results confirm previous observations of marked resistance to awakening during sleep in preadolescent children and suggest that processes underlying awakening from sleep undergo systematic modification during ontogenetic development.


title: A blast from the past: The terror management function of nostalgia

abstract: According to terror management theory, people turn to meaning-providing structures to cope with the knowledge of inevitable mortality. Recent theory and research suggest that nostalgia is a meaning-providing resource and thus may serve such an existential function. The current research tests and supports this idea. In Experiments 1 and 2, nostalgia proneness was measured and mortality salience manipulated. In Experiment 1, when mortality was salient, the more prone to nostalgia participants were, the more they perceived life to be meaningful. In Experiment 2, when mortality was salient, the more prone to nostalgia participants were, the less death thoughts were accessible. In Experiment 3, nostalgia and mortality salience were manipulated. It was found that nostalgia buffered the effects of mortality salience on death-thought accessibility.


title: HyperNF: building a high performance, high utilization and fair NFV platform

abstract: Network Function Virtualization has been touted as the silver bullet for tackling a number of operator problems, including vendor lock-in, fast deployment of new functionality, converged management, and lower expenditure since packet processing runs on inexpensive commodity servers. The reality, however, is that, in practice, it has proved hard to achieve the stable, predictable performance provided by hardware middleboxes, and so operators have essentially resorted to throwing money at the problem, deploying highly underutilized servers (e.g., one NF per CPU core) in order to guarantee high performance during peak periods and meet SLAs. In this work we introduce HyperNF, a high performance NFV framework aimed at maximizing server performance when concurrently running large numbers of NFs. To achieve this, HyperNF implements hypercall-based virtual I/O, placing packet forwarding logic inside the hypervisor to significantly reduce I/O synchronization overheads. HyperNF improves throughput by 10%-73% depending on the NF, is able to closely match resource allocation specifications (with deviations of only 3.5%), and to efficiently cope with changing traffic loads.


title: Trace Norm Regularised Deep Multi-Task Learning

abstract: We propose a framework for training multiple neural networks simultaneously. The parameters from all models are regularised by the tensor trace norm, so that each neural network is encouraged to reuse others' parameters if possible -- this is the main motivation behind multi-task learning. In contrast to many deep multi-task learning models, we do not predefine a parameter sharing strategy by specifying which layers have tied parameters. Instead, our framework considers sharing for all shareable layers, and the sharing strategy is learned in a data-driven way.


title: Mining Association Rules with Item Constraints

abstract: The problem of discovering association rules has received considerable research attention and several fast algorithms for mining association rules have been developed. In practice, users are often interested in a subset of association rules. For example, they may only want rules that contain a specific item or rules that contain children of a specific item in a hierarchy. While such constraints can be applied as a post-processing step, integrating them into the mining algorithm can dramatically reduce the execution time. We consider the problem of integrating constraints that are Boolean expressions over the presence or absence of items into the association discovery algorithm. We present three integrated algorithms for mining association rules with item constraints and discuss their tradeoffs.


title: Face Recognition Using Convolutional Neural Network and Simple Logistic Classifier

abstract: In this paper, a hybrid system is presented in which a convolutional neural network (CNN) and a Logistic regression classifier (LRC) are combined. A CNN is trained to detect and recognize face images, and a LRC is used to classify the features learned by the convolutional network. Applying feature extraction using CNN to normalized data causes the system to cope with faces subject to pose and lighting variations. LRC which is a discriminative classifier is used to classify the extracted features of face images. Discriminant analysis is more efficient when the normality assumptions are satisfied. The comprehensive experiments completed on Yale face database shows improved classification rates in smaller amount of time.


Building Trust in Decentralized Peer-to-Peer Electronic Communities


title: The sketchy database: learning to retrieve badly drawn bunnies

abstract: We present the Sketchy database, the first large-scale collection of sketch-photo pairs. We ask crowd workers to sketch particular photographic objects sampled from 125 categories and acquire 75,471 sketches of 12,500 objects. The Sketchy database gives us fine-grained associations between particular photos and sketches, and we use this to train cross-domain convolutional networks which embed sketches and photographs in a common feature space. We use our database as a benchmark for fine-grained retrieval and show that our learned representation significantly outperforms both hand-crafted features as well as deep features trained for sketch or photo classification. Beyond image retrieval, we believe the Sketchy database opens up new opportunities for sketch and image understanding and synthesis.


title: Identity Development Theories in Student Affairs: Origins, Current Status, and New Approaches

abstract: Enhancing the development of students has long been a primary role of student affairs practitioners. Identity development theories help practitioners to understand how students go about discovering their “abilities, aptitude and objectives” while assisting them to achieve their “maximum effectiveness” (American Council on Education, 1937, p. 69). The tasks involved in discovering abilities, goals, and effectiveness are part of creating a sense of identity that allows the student to enter adult life. Identity is shaped by how one organizes experiences within the environment (context) that revolves around oneself (Erikson, 1959/1994). Across academic disciplines, the view of how individuals organize experiences takes on varying definitions. Within the student affairs literature, identity is commonly understood as one’s personally held beliefs about the self in relation to social groups (e.g., race, ethnicity, religion, sexual orientation) and the ways one expresses that relationship. Identity is also commonly understood to be socially constructed; that is, one’s sense of self and beliefs about one’s own social group as well others are constructed through interactions with the broader social context in which dominant values dictate norms and expectations (see Gergen, 1991; McEwen, 2003). Examples of these broader social contexts include both institutions such as education and work, as well as systems of power and inequality such as race, social class, and gender (Anderson & Collins, 2007). Social construction of identity occurs in different contexts on campus such as in how student organizations are created and which students are drawn to them, or in the social identities among those in leadership positions and those not, as well as in issues of institutional fit within access and retention. One of the components of identity development that arises quickly on most campuses is the process of students learning how to balance their needs with those of others (Kegan, 1982, 1994; Kroger, 2004). In working to create community and mutual respect on campus, student affairs professionals help students to understand this balance between self and others as well as expose students to the varied nature of what is encompassed in the “other.” A common program used to illustrate this process revolves around diversity issues. These programs often focus on exposure to other social groups and an understanding of how history supports society’s view of these groups. This influence of the other contributes to the social construction of identity; in other words, the context and interactions with others—including other people, societal norms, and/or expectations that evolve from culture—influence how one constructs one’s identity (Jones, 1997; McEwen, 2003; Torres, 2003; Weber, 1998). In addition, several researchers embrace a developmental approach to describe the shift that occurs when students


title: A new force-feedback arm exoskeleton for haptic interaction in virtual environments

abstract: The paper presents the mechanical design of the L-EXOS, a new exoskeleton for the human arm. The exoskeleton is a tendon driven wearable haptic interface with 5 dof 4 actuated ones, and is characterized by a workspace very close to the one of the human arm. The design has been optimized to obtain a solution with reduced mass and high stiffness, by employing special mechanical components and carbon fiber structural parts. The devised exoskeleton is very effective for simulating the touch by hand of large objects or the manipulation within the whole workspace of the arm. The main features of the first prototype that has been developed at PERCRO are presented, together with an indication of the achieved and tested performance.


title: Assessment of genetic diversity and phylogenetic relationships in Asparagus species related to Asparagus officinalis

abstract: Eight species of the genus Asparagus, members of the group of European species closely related to A. officinalis, were analysed using internal transcribed spacer (ITS), and expressed sequence tag-derived simple sequence repeat (EST-SSR) markers, as well as cytological observations of their hybrids, to study their phylogenetic relationships and the possibility of broadening the narrow genetic base of cultivated varieties. Phylogenetic analysis using ITS data revealed two major clades: clade I consisting of A. acutifolius and clade II (referred to in this study as the ‘officinalis group’) comprised of sequences derived from species closely related to A. officinalis; but the different species within the ‘officinalis group’ could not be clearly separated. In contrast, cluster analysis of EST-SSR marker data showed six major clades and clearly separated each population, grouping most of the genotypes from each population together. That is, EST-SSR markers were found to be more informative than ITS markers about the relationships within the ‘officinalis group’, indicating that EST-SSR markers are more useful than ITS sequences for establishing phylogenetic relationships at the intrageneric level. All the crosses carried out at the same ploidy level were successful. The high crossability, together with the regular meiotic behaviour and high pollen and seed fertility observed in the interspecific hybrids analysed, suggest relatively close relationships between the species studied. We conclude that the group of species classified in the ‘officinalis group’ are in the primary gene pool, indicating that these species could be used to increase the genetic diversity of the cultivated species. In addition, the tetraploid landrace “Morado de Huetor” could be employed as a bridge to generate new cultivated germplasm.


title: Stator structure design and analysis of variable reluctance resolver for hybrid-vehicle motor drive

abstract: Variable-reluctance (VR) resolver is often applied in Electric Vehicles as the motor speed and position sensor for its outstanding reliability and durability. This paper introduces the operation principles and optimized design guidelines of the variable-reluctance (VR) resolver. The number of the stator teeth is determined based on the theory of fractional slot sinusoidal windings, and a special stator structure with slotted teeth is proposed. Simulation and Experiments are carried out to test the angular position accuracy, verifying the feasibility and effectiveness of the optimization methods.


Vehicle Traffic Light Control Using SARSA


title: Design of the series fed microstrip patch planar array antenna by the parato genetic algorithm

abstract: Since the series fed microstrip patch array antenna has low feeding loss, it is especially effective for the millimeter wave application. In order to realize an array antenna with a high gain, low sidelobe level, and low VSWR, we have optimized the width and element spacing for each patch antenna by the parato genetic algorithm. As for such an evolution method as genetic algorithm, the calculation load is enormous. In this paper, to reduce the load, these parameters are optimized by a coarse mesh. After optimization, we check the performance by a fine mesh. By numerical simulation of a planar array, we confirmed the effectiveness of the proposed method.


title: A business intelligence system

abstract: An automatic system is being developed to disseminate information to the various sections of any industrial, scientific or government organization. This intelligence system will utilize data-processing machines for auto-abstracting and auto-encoding of documents and for creating interest profiles for each of the "action points" in an organization. Both incoming and internally generated documents are automatically abstracted, characterized by a word pattern, and sent automatically to appropriate action points. This paper shows the flexibility of such a system in identifying known information, in finding who needs to know it and in disseminating it efficiently either in abstract form or as a complete document.


Deep Learning with Separable Convolutions


title: Multi-Prototype Vector-Space Models of Word Meaning

abstract: Current vector-space models of lexical semantics create a single "prototype" vector to represent the meaning of a word. However, due to lexical ambiguity, encoding word meaning with a single vector is problematic. This paper presents a method that uses clustering to produce multiple "sense-specific" vectors for each word. This approach provides a context-dependent vector representation of word meaning that naturally accommodates homonymy and polysemy. Experimental comparisons to human judgements of semantic similarity for both isolated words as well as words in sentential contexts demonstrate the superiority of this approach over both prototype and exemplar based vector-space models.


title: Vascular gene transfer driven by endoglin and ICAM-2 endothelial-specific promoters

abstract: The involvement of the vascular endothelium in a large number of diseases supports the importance of vascular-specific gene delivery for their treatment. The hereditary hemorrhagic telangiectasia type 1 is an example of a vascular inherited disease (OMIM 187300). This is an autosomal dominant vascular disorder originated by mutations in the endoglin gene and associated with frequent epistaxis, telangiectases, gastrointestinal bleedings, and arteriovenous malformations in brain, lung and liver. Here, we address for the first time the possibility of using in vivo gene transfer to target endoglin expression to the vasculature. The promoter of the endothelial gene, ICAM-2, was used to generate transgenic animals which demonstrated endothelial expression of endoglin. Next, the promoters of the human endothelial genes, endoglin and ICAM-2, were inserted upstream of the human endoglin cDNA, and the resulting constructs were systemically or locally delivered, demonstrating endoglin expression in the vessel walls of liver, lung and skin. These gene transfer experiments represent an initial step in the treatment of the hereditary hemorrhagic telangiectasia type 1 by gene therapy, and suggest that endoglin and ICAM-2 promoters can be used to deliver other genes to the endothelium specifically.


Forecasting at Scale


title: The effectiveness of combining rolling deformation with Wire–Arc Additive Manufacture on β-grain refinement and texture modification in Ti–6Al–4V

abstract: Abstract In Additive Manufacture (AM), with the widely used titanium alloy Ti–6Al–4V, the solidification conditions typically result in undesirable, coarse-columnar, primary β grain structures. This can result in a strong texture and mechanical anisotropy in AM components. Here, we have investigated the efficacy of a new approach to promote β grain refinement in Wire–Arc Additive Manufacture (WAAM) of large scale parts, which combines a rolling step sequentially with layer deposition. It has been found that when applied in-process, to each added layer, only a surprisingly low level of deformation is required to greatly reduce the β grain size. From EBSD analysis of the rolling strain distribution in each layer and reconstruction of the prior β grain structure, it has been demonstrated that the normally coarse centimetre scale columnar β grain structure could be refined down to


title: Dense Two-Frame Stereo Correspondence by Self-Organizing Neural Network

abstract: This work aims at defining an extension of a competitive method for matching correspondences in stereoscopic image analysis. The method we extended was proposed by Venkatesh, Y.V. et al where the authors extend a Self-Organizing Map by changing the neural weights updating phase in order to solve the correspondence problem within a two-frame area matching approach and producing dense disparity maps. In the present paper we have extended the method mentioned by adding some details that lead to better results. Experimental studies were conducted to evaluate and compare the solution proposed.


title: A wireless and batteryless neural headstage with optical stimulation and electrophysiological recording

abstract: This paper presents a miniature Optogenetics headstage for wirelessly stimulating the brain of rodents with an implanted LED while recording electrophysiological data from a two-channel custom readout. The headstage is powered wirelessly using an inductive link, and is built using inexpensive commercial off-the-shelf electronic components, including a RF microcontroller and a printed antenna. This device has the capability to drive one light-stimulating LED and, at the same time, capture and send back neural signals recorded from two microelectrode readout channels. Light stimulation uses flexible patterns that allow for easy tuning of light intensity and stimulation periods. For driving the LED, a low-pass filtered digitally-generated PWM signal is employed for providing a flexible pulse generation method that alleviates the need for D/A converters. The proposed device can be powered wirelessly into an animal chamber using inductive energy transfer, which enables compact, light-weight and cost-effective smart animal research systems. The device dimensions are 15×25×17 mm; it weighs 7.4 grams and has a data transmission range of more than 2 meters. Different types of LEDs with different power consumptions can be used for this system. The power consumption of the system without the LED is 94.52 mW.


title: Collapse informatics and practice: Theory, method, and design

abstract: What happens if efforts to achieve sustainability failq Research in many fields argues that contemporary global industrial civilization will not persist indefinitely in its current form, and may, like many past human societies, eventually collapse. Arguments in environmental studies, anthropology, and other fields indicate that this transformation could begin within the next half-century. While imminent collapse is far from certain, it is prudent to consider now how to develop sociotechnical systems for use in these scenarios. We introduce the notion of collapse informatics—the study, design, and development of sociotechnical systems in the abundant present for use in a future of scarcity. We sketch the design space of collapse informatics and a variety of example projects. We ask how notions of practice—theorized as collective activity in the “here and now”—can shift to the future since collapse has yet to occur.


title: Evaluating Multi-label Classification of Incident-related Tweet

abstract: Microblogs are an important source of information in emergency management as lots of situational information is shared, both by citizens and ocial sources. It has been shown that incident-related information can be identified in the huge amount of available information using machine learning. Nevertheless, the currently used classification techniques only assign a single label to a micropost, resulting in a loss of important information that would be valuable for crisis management. With this paper we contribute the first in-depth analysis of multi-label classification of incident-related tweets. We present an approach assigning multiple labels to these messages, providing additional information about the situation at-hand. An evaluation shows that multi-label classification is applicable for detecting multiple labels with an exact match of 84.35%. Thus, it is a valuable means for classifying incident-related tweets. Furthermore, we show that correlation between labels can be taken into account for these kinds of classification tasks.


title: On the analysis and design of software for reinforcement learning, with a survey of existing systems

abstract: Reinforcement Learning (RL) is a very complex domain and software for RL is correspondingly complex. We analyse the scope, requirements, and potential for RL software, discuss relevant design issues, survey existing software, and make recommendations for designers. We argue that broad and flexible libraries of reusable software components are valuable from a scientific, as well as practical, perspective, as they allow precise control over experimental conditions, encourage comparison of alternative methods, and allow a fuller exploration of the RL domain.


title: Compressing Deep Neural Networks using a Rank-Constrained Topology

abstract: We present a general approach to reduce the size of feedforward deep neural networks (DNNs). We propose a rankconstrained topology, which factors the weights in the input layer of the DNN in terms of a low-rank representation: unlike previous work, our technique is applied at the level of the filters learned at individual hidden layer nodes, and exploits the natural two-dimensional time-frequency structure in the input. These techniques are applied on a small-footprint DNN-based keyword spotting task, where we find that we can reduce model size by 75% relative to the baseline, without any loss in performance. Furthermore, we find that the proposed approach is more effective at improving model performance compared to other popular dimensionality reduction techniques, when evaluated with a comparable number of parameters.


title: Urban Social Media Demographics: An Exploration of Twitter Use in Major American Cities

abstract: This article explores intersections between place, race/ethnicity, and gender amongst American Twitter users and makes an argument that studying the intensity of tweets provides insights into how and why particular groups tweet. Given recent events in American political life such as the shooting in Ferguson, Missouri and the reactions by young, urban African Americans on Twitter, understanding the role of race, place, gender, and age is important. We observed the time between tweets of urban American Twitter users and explored whether the medium may be providing traditionally marginalized groups, such as young Black men, with potential avenues for mobilizing communication and access to resources.


title: Preanalytical quality improvement. In pursuit of harmony, on behalf of European Federation for Clinical Chemistry and Laboratory Medicine (EFLM) Working group for Preanalytical Phase (WG-PRE)

abstract: Laboratory diagnostics develop through differ- ent phases that span from test ordering (pre-preanalytical phase), collection of diagnostic specimens (preanalytical phase), sample analysis (analytical phase), results report- ing (postanalytical phase) and interpretation (post-posta- nalytical phase). Although laboratory medicine seems less vulnerable than other clinical and diagnostic areas, the chance of errors is not negligible and may adversely impact on quality of testing and patient safety. This article, which continues a biennial tradition of collective papers on preanalytical quality improvement, is aimed to provide further contributions for pursuing quality and harmony in the preanalytical phase, and is a synopsis of lectures of the third European Federation of Clinical Chemistry and Laboratory Medicine (EFLM)-Becton Dickinson (BD) Euro- pean Conference on Preanalytical Phase meeting entitled 'Preanalytical quality improvement. In pursuit of har- mony' (Porto, 20-21 March 2015). The leading topics that will be discussed include unnecessary laboratory testing, management of test request, implementation of the Euro- pean Union (EU) Directive on needlestick injury preven- tion, harmonization of fasting requirements for blood sampling, influence of physical activity and medical contrast media on in vitro diagnostic testing, recent evi- dence about the possible lack of necessity of the order of draw, the best practice for monitoring conditions of time and temperature during sample transportation, along


title: Missing value imputation on missing completely at random data using multilayer perceptrons.

abstract: Data mining is based on data files which usually contain errors in the form of missing values. This paper focuses on a methodological framework for the development of an automated data imputation model based on artificial neural networks. Fifteen real and simulated data sets are exposed to a perturbation experiment, based on the random generation of missing values. These data set sizes range from 47 to 1389 records. A perturbation experiment was performed for each data set where the probability of missing value was set to 0.05. Several architectures and learning algorithms for the multilayer perceptron are tested and compared with three classic imputation procedures: mean/mode imputation, regression and hot-deck. The obtained results, considering different performance measures, not only suggest this approach improves the quality of a database with missing values, but also the best results are clearly obtained using the Multilayer Perceptron model in data sets with categorical variables. Three learning rules (Levenberg-Marquardt, BFGS Quasi-Newton and Conjugate Gradient Fletcher-Reeves Update) and a small number of hidden nodes are recommended.


title: On popularity prediction of videos shared in online social networks

abstract: Popularity prediction, with both technological and economic importance, has been extensively studied for conventional video sharing sites (VSSes), where the videos are mainly found via searching, browsing, or related links. Recent statistics however suggest that online social network (OSN) users regularly share video contents from VSSes, which has contributed to a significant portion of the accesses; yet the popularity prediction in this new context remains largely unexplored. In this paper, we present an initial study on the popularity prediction of videos propagated in OSNs along friendship links. We conduct a large-scale measurement and analysis of viewing patterns of videos shared in one of largest OSNs in China, and examine the performance of typical views-based prediction models. We find that they are generally ineffective, if not totally fail, especially when predicting the early peaks and later bursts of accesses, which are common during video propagations in OSNs. To overcome these limits, we track the propagation process of videos shared in a Facebook-like OSN in China, and analyze the user viewing and sharing behaviors. We accordingly develop a novel propagation-based video popularity prediction solution, namely SoVP. Instead of relying solely on the early views for prediction, SoVP considers both the intrinsic attractiveness of a video and the influence from the underlying propagation structure. The effectiveness of SoVP, particularly for predicting the peaks and bursts, have been validated through our trace-driven experiments.


title: Brief review of invariant texture analysis methods

abstract: This paper considers invariant texture analysis. Texture analysis approaches whose performances are not affected by translation, rotation, affine, and perspective transform are addressed. Existing invariant texture analysis algorithms are carefully studied and classified into three categories: statistical methods, model based methods, and structural methods. The importance of invariant texture analysis is presented first. Each approach is reviewed according to its classification, and its merits and drawbacks are outlined. The focus of possible future work is also suggested.


title: Discovering Event Evolution Graphs From News Corpora

abstract: Given the advance of Internet technologies, we can now easily extract hundreds or thousands of news stories of any ongoing incidents from newswires such as CNN.com, but the volume of information is too large for us to capture the blueprint. Information retrieval techniques such as topic detection and tracking are able to organize news stories as events, in a flat hierarchical structure, within a topic. However, they are incapable of presenting the complex evolution relationships between the events. We are interested to learn not only what the major events are but also how they develop within the topic. It is beneficial to identify the seminal events, the intermediary and ending events, and the evolution of these events. In this paper, we propose to utilize the event timestamp, event content similarity, temporal proximity, and document distributional proximity to model the event evolution relationships between events in an incident. An event evolution graph is constructed to present the underlying structure of events for efficient browsing and extracting of information. Case study and experiments are presented to illustrate and show the performance of our proposed technique. It is found that our proposed technique outperforms the baseline technique and other comparable techniques in previous work.


title: On the Use of Stochastic Hessian Information in Optimization Methods for Machine Learning

abstract: This paper describes how to incorporate sampled curvature information in a Newton-CG method and in a limited memory quasi-Newton method for statistical learning. The motivation for this work stems from supervised machine learning applications involving a very large number of training points. We follow a batch approach, also known in the stochastic optimization literature as a sample average approximation approach. Curvature information is incorporated in two subsampled Hessian algorithms, one based on a matrix-free inexact Newton iteration and one on a preconditioned limited memory BFGS iteration. A crucial feature of our technique is that Hessian-vector multiplications are carried out with a significantly smaller sample size than is used for the function and gradient. The efficiency of the proposed methods is illustrated using a machine learning application involving speech recognition.


Pomerleau, D.A. (1991) Efficient TrairJ of Artificial Neural Networks for Autonomous


title: Improving performance aligning business analytics with process orientation

abstract: Abstract How to align business analytics with process orientation initiatives and how management can influence a synergistic accomplishment of these two elements in order to streamline organizational performance is a foremost managerial issue. By means of a quantitative research effort, a study was designed to investigate the relationships among these constructs. Empirical data were collected in 2011 from a survey of 368 large and mid-sized industrial and service companies headquartered in Brazil. A quantitative analysis was performed, and both the reliability and validity of the scales were evaluated prior to using structural equation modeling. The results demonstrate that the direct effects among BPO, analytical indicators and performance can be taken as statistically significant. The findings also demonstrate that BPO and analytical indicators can be taken as predictors of performance. The paper discusses these results and their managerial implications, as well as some research paths for future studies on the topic.


PEDRO MIRASSOL TOMÉ ANALOG NEURAL PREDISTORTION OF POWER AMPLIFIERS


title: Smartwatch Wearing Behavior Analysis

abstract: Smartwaches are the representative wearable or body-worn devices that provide convenient and easy information access. There is a growing body of research work on enabling novel interaction techniques and understanding user experiences of smartwatches. However, there is still lack of user experience research on wearing behaviors of smartwatches, which is critical for wearable device and service design. In this work, we investigate how college students wear smartwatches and what factors affect wearing behaviors by analyzing a longitudinal activity dataset collected from 50 smartwatch users for 203 days. Our results show that there are several temporal usage patterns and distinct groups of usage patterns. The factors affecting wearing behaviors are contextual, nuanced, and multifaceted. Our findings provide diverse design implications for improving wearability of smartwatches and leveraging smartwatches for behavioral changes.


title: Real Time Automated Fabric Defect Detection System using Microcontroller

abstract: In recent times the uses of wireless devices are fetching ubiquitous and these networks have turn into an increasingly important part of global communication architecture. Information gathering is the problem of sending one packet each from a subset of the nodes to a single sink node in the network. In view of the fact that the vast majority of data applications make use of the TCP/IP protocols, optimizing TCP performance over these networks would have a broad and significant impact on the userperceived data application performance. Most of the wireless networks have fixed infrastructure and the need for mobile wireless networks are growing rapidly, which requires higher data rates. Due to high data rate in wireless networks the TCP performance has a broad and significant impact on data applications. Rapid advances in consumer electronics have led to the anomaly that consumer offthe-shelf gaming hardware and software provide better interactive graphics than military and other specialized systems costing orders of magnitude more. In order to improve the performance of TCP/IP, a UTSAF agent is implemented along with the Transmission Control Protocol (TCP). Performance has been analyzed for the TCP/IP with and without UTSAF agent for the metrics throughput and latency.


title: Achieving the Secrecy Capacity of Wiretap Channels Using Polar Codes

abstract: Suppose that Alice wishes to send messages to Bob through a communication channel C1, but her transmissions also reach an eavesdropper Eve through another channel C2. This is the wiretap channel model introduced by Wyner in 1975. The goal is to design a coding scheme that makes it possible for Alice to communicate both reliably and securely. Reliability is measured in terms of Bob's probability of error in recovering the message, while security is measured in terms of the mutual information between the message and Eve's observations. Wyner showed that the situation is characterized by a single constant Cs, called the secrecy capacity, which has the following meaning: for all e >; 0, there exist coding schemes of rate R ≥ Cs-e that asymptotically achieve the reliability and security objectives. However, his proof of this result is based upon a random-coding argument. To date, despite consider able research effort, the only case where we know how to construct coding schemes that achieve secrecy capacity is when Eve's channel C2 is an erasure channel, or a combinatorial variation thereof. Polar codes were recently invented by Arikan; they approach the capacity of symmetric binary-input discrete memoryless channels with low encoding and decoding complexity. In this paper, we use polar codes to construct a coding scheme that achieves the secrecy capacity for a wide range of wiretap channels. Our construction works for any instantiation of the wiretap channel model, as long as both C1 and C2 are symmetric and binary-input, and C2 is degraded with respect to C1. Moreover, we show how to modify our construction in order to provide strong security, in the sense defined by Maurer, while still operating at a rate that approaches the secrecy capacity. In this case, we cannot guarantee that the reliability condition will also be satisfied unless the main channel C1 is noiseless, although we believe it can be always satisfied in practice.


title: That's So Annoying!!!: A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using #petpeeve Tweets

abstract: We propose a novel data augmentation approach to enhance computational behavioral analysis using social media text. In particular, we collect a Twitter corpus of the descriptions of annoying behaviors using the #petpeeve hashtags. In the qualitative analysis, we study the language use in these tweets, with a special focus on the fine-grained categories and the geographic variation of the language. In quantitative analysis, we show that lexical and syntactic features are useful for automatic categorization of annoying behaviors, and frame-semantic features further boost the performance; that leveraging large lexical embeddings to create additional training instances significantly improves the lexical model; and incorporating frame-semantic embedding achieves the best overall performance.


title: The Polyvagal Theory: phylogenetic contributions to social behavior

abstract: The scientific legacy of Paul MacLean provides important insights into the neural substrate of adaptive social behavior in mammals. Through his research and visionary conceptualizations, current investigators can legitimately study social behavior from a neurobiological perspective. His research and writings provided three important contributions. First, he emphasized the importance of evolution as an organizing principle that shaped both the structure of the nervous system and the adaptive social behavior. Second, by defining the limbic system, he legitimized the biological perspective in the study of emotion. Third, he recognized the important role of the vagal afferents in the regulation of higher brain structures. The paper will focus on the Polyvagal Theory. The Polyvagal Theory is a new conceptualization of the role of vagus and employs several features that MacLean emphasized including the importance of evolution, limbic structures and vagal afferents. The Polyvagal Theory builds on these early findings by MacLean and focuses on the link between phylogenetic changes in the autonomic nervous system and social behavior. By focusing on the phylogenetic changes in the structure of the vagus and the role that vagus plays in the neural regulation of visceral state, new insights regarding social behavior emerge. Moreover, by articulating the phylogenetically organized hierarchy of neural circuits, insights into benefits of social behavior become evident as do an understanding of the behavioral and physiological features associated with stress and psychiatric disorders.


title: A comparison of methods to test mediation and other intervening variable effects.

abstract: A Monte Carlo study compared 14 methods to test the statistical significance of the intervening variable effect. An intervening variable (mediator) transmits the effect of an independent variable to a dependent variable. The commonly used R. M. Baron and D. A. Kenny (1986) approach has low statistical power. Two methods based on the distribution of the product and 2 difference-in-coefficients methods have the most accurate Type I error rates and greatest statistical power except in 1 important case in which Type I error rates are too high. The best balance of Type I error and statistical power across all cases is the test of the joint significance of the two effects comprising the intervening variable effect.


title: On fast surface reconstruction methods for large and noisy point clouds

abstract: In this paper we present a method for fast surface reconstruction from large noisy datasets. Given an unorganized 3D point cloud, our algorithm recreates the underlying surface's geometrical properties using data resampling and a robust triangulation algorithm in near realtime. For resulting smooth surfaces, the data is resampled with variable densities according to previously estimated surface curvatures. Incremental scans are easily incorporated into an existing surface mesh, by determining the respective overlapping area and reconstructing only the updated part of the surface mesh. The proposed framework is flexible enough to be integrated with additional point label information, where groups of points sharing the same label are clustered together and can be reconstructed separately, thus allowing fast updates via triangular mesh decoupling. To validate our approach, we present results obtained from laser scans acquired in both indoor and outdoor environments.


title: Development of smart public transportation system in Jakarta city based on integrated IoT platform

abstract: Travel security, traffic congestion and the efficiency of the global transport sector are key issues in some developing countries. Jakarta is one of the big cities and is the capital of Indonesia. This study aims to provide solutions to congestion problems in the capital city by maximizing the intelligent transportation system, convenient and safe for passengers. This research finds the fact that people's need for Trans Jakarta is relatively high but in fact it is not directly proportional to the public interest towards the use of Trans Jakarta buses. This study finds issues related to the needs and expectations of the community using Trans Jakarta facilities. Not only convenience and security and cheap price offered but also the ease and speed in transacting. This research provides ideas related to the facilities provided by Trans Jakarta as one of public transportation owned by the government. Combining smart phones with the use of internet-based technology (IOT). This research proposed an integrated system (integration platform). The solution with IOT-based technology enhancements makes it easy to present all information about bus availability, bus scheduling, booking places and easy online payment. This study uses a comparative study related to the needs of passengers in using Trans Jakarta. This study found the need of passengers for the existence of an integrated system that presents all information and ease of public transportation access in the city of Jakarta.


title: Independent examination of the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV): what does the WAIS-IV measure?

abstract: Published empirical evidence for the Wechsler Adult Intelligence Scale—Fourth Edition (WAIS-IV) does not address some essential questions pertaining to the applied practice of intellectual assessment. In this study, the structure and cross-age invariance of the latest WAIS-IV revision were examined to (a) elucidate the nature of the constructs measured and (b) determine whether the same constructs are measured across ages. Results suggest that a Cattell-Horn-Carroll (CHC)-inspired structure provides a better description of test performance than the published scoring structure does. Broad CHC abilities measured by the WAIS―IV include crystallized ability (Gc), fluid reasoning (Gf), visual processing (Gv), short-term memory (Gsm), and processing speed (Gs), although some of these abilities are measured more comprehensively than are others. Additionally, the WAIS-IV provides a measure of quantitative reasoning (QR). Results also suggest a lack of cross-age invariance resulting from age-related differences in factor loadings. Formulas for calculating CHC indexes and suggestions for interpretation are provided.


title: A moving target approach for identifying malicious sensors in control systems

abstract: In this paper, we consider the problem of attack identification in cyber-physical systems (CPS). Attack identification is often critical for the recovery and performance of a CPS that is targeted by malicious entities, allowing defenders to construct algorithms which bypass harmful nodes. Previous work has characterized limitations in the perfect identification of adversarial attacks on deterministic LTI systems. For instance, a system must remain observable after removing any 2q sensors to only identify q attacks. However, the ability for an attacker to create an unidentifiable attack requires knowledge of the system model. In this paper, we aim to limit the adversary's knowledge of the system model with the goal of accurately identifying all sensor attacks. Such a scheme will allow systems to withstand larger attacks or system operators to allocate fewer sensing devices to a control system while maintaining security. We explore how changing the dynamics of the system as a function of time allows us to actively identify malicious/faulty sensors in a control system. We discuss the design of time varying system matrices to meet this goal and evaluate performance in deterministic and stochastic systems.


title: A Taxonomy and Survey of Cloud Resource Orchestration Techniques

abstract: Cloud services and applications prove indispensable amid today’s modern utility-based computing. The cloud has displayed a disruptive and growing impact on everyday computing tasks. However, facilitating the orchestration of cloud resources to build such cloud services and applications is yet to unleash its entire magnitude of power. Accordingly, it is paramount to devise a unified and comprehensive analysis framework to accelerate fundamental understanding of cloud resource orchestration in terms of concepts, paradigms, languages, models, and tools. This framework is essential to empower effective research, comprehension, comparison, and selection of cloud resource orchestration models, languages, platforms, and tools. This article provides such a comprehensive framework while analyzing the relevant state of the art in cloud resource orchestration from a novel and holistic viewpoint.


title: A Compact Repetitive Unipolar Nanosecond-Pulse Generator for Dielectric Barrier Discharge Application

abstract: Dielectric barrier discharge excitated by pulsed power is a promising approach for producing nonthermal plasma at atmospheric pressure, but pulsed power generators vary widely in performance and should be chosen according to application requirements. In this paper, a repetitive unipolar nanosecond-pulse generator is constructed using resonant charging and one-stage magnetic compression circuits, where IGBT and magnetic switches are the key units, respectively. The generator is capable of providing repetitive pulses with a voltage of up to 30 kV and duration of 70 ns at a 300 resistive load. Output pulse voltage can be adjusted by varying ac input voltage or trigger pulse-width. This compact and convenient generator has been used successfully to produce stable dielectric barrier discharge.


Patent Big Data Analysis by R Data Language for Technology Management


title: Detection of leukemia and its types using image processing and machine learning

abstract: Leukemia (blood cancer) begins in the bone marrow and causes the formation of a large number of abnormal cells. The most common types of leukemia known are Acute lymphoblastic leukemia (ALL), Acute myeloid leukemia (AML), Chronic lymphocytic leukemia (CLL) and Chronic myeloid leukemia (CML). This thesis makes an effort to devise a methodology for the detection of Leukemia using image processing techniques, thus automating the detection process. The dataset used comprises of 220 blood smear images of leukemic and non leukemic patients. The Image segmentation algorithms that have been used are k means clustering algorithm, Marker controlled watershed algorithm and HSV color based segmentation algorithm. The morphological components of normal and Leukemic lymphocytes differ significantly; hence various features have been extracted from the segmented lymphocyte images. The leukemia is further classified into its types and sub types by making use of the SVM classifier, which is a Machine Learning classifier. This thesis aims at detecting leukemia and determine whether it is AML, CML, CLL or ALL; thus taking the classification process one step further in the field of research, because most of the previous methods have been limited to the detection of leukemia or classifying it into one or two subtypes.


title: ARTICLES Placeless Organizations: Collaborating for Transformation

abstract: This article defines and discusses placeless organizations as sites and generators of learning on a large scale. The emphasis is on how placeless organizations structure themselves to carry out social transformation—necessarily involving intensive learning—on a national or global scale. The argument is made that place is not a necessary component of such activity and that lack of a sense of place may be beneficial to the work. The article is intended to contribute to elaborating the cultural-historical dimensions of activity theory by examining a social framework within which significant learning activity occurs.


title: NASA's L-Band Digital Beamforming Synthetic Aperture Radar

abstract: The Digital Beamforming Synthetic Aperture Radar (DBSAR) is a state-of-the-art L-band radar that employs advanced radar technology and a customized data acquisition and real-time processor in order to enable multimode measurement techniques in a single radar platform. DBSAR serves as a test bed for the development, implementation, and testing of digital beamforming radar techniques applicable to Earth science and planetary measurements. DBSAR flew its first field campaign on board the National Aeronautics and Space Administration P3 aircraft in October 2008, demonstrating enabling techniques for scatterometry, synthetic aperture, and altimetry.


title: An In-Memory VLSI Architecture for Convolutional Neural Networks

abstract: This paper presents an energy-efficient and high throughput architecture for convolutional neural networks (CNN). Architectural and circuit techniques are proposed to address the dominant energy and delay costs associated with data movement in CNNs. The proposed architecture employs a deep in-memory architecture, to embed energy-efficient low swing mixed-signal computations in the periphery of the SRAM bitcell array. An efficient data access pattern and a mixed-signal multiplier are proposed to exploit data reuse opportunities in convolution. Silicon-validated energy, delay, and behavioral models of the proposed architecture are developed and employed to perform large-scale system simulations. System-level simulations using these models show >97% detection accuracy on the MNIST data set, along with $4.9\times $ and $2.4\times $ improvements in energy efficiency and throughput, respectively, leading to $11.9\times $ reduction in energy-delay product as compared with a conventional (SRAM + digital processor) architecture.


title: Two Decades of Terror Management Theory: A Meta-Analysis of Mortality Salience Research

abstract: A meta-analysis was conducted on empirical trials investigating the mortality salience (MS) hypothesis of terror management theory (TMT). TMT postulates that investment in cultural worldviews and self-esteem serves to buffer the potential for death anxiety; the MS hypothesis states that, as a consequence, accessibility of death-related thought (MS) should instigate increased worldview and self-esteem defense and striving. Overall, 164 articles with 277 experiments were included. MS yielded moderate effects (r = .35) on a range of worldview- and self-esteem-related dependent variables (DVs), with effects increased for experiments using (a) American participants, (b) college students, (c) a longer delay between MS and the DV, and (d) people-related attitudes as the DV. Gender and self-esteem may moderate MS effects differently than previously thought. Results are compared to other reviews and examined with regard to alternative explanations of TMT. Finally, suggestions for future research are offered.


title: A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration

abstract: Although the correlation filter-based trackers achieve the competitive results both on accuracy and robustness, there is still a need to improve the overall tracking capability. In this paper, we presented a very appealing tracker based on the correlation filter framework. To tackle the problem of the fixed template size in kernel correlation filter tracker, we suggest an effective scale adaptive scheme. Moreover, the powerful features including HoG and color-naming are integrated together to further boost the overall tracking performance. The extensive empirical evaluations on the benchmark videos and VOT 2014 dataset demonstrate that the proposed tracker is very promising for the various challenging scenarios. Our method successfully tracked the targets in about 72% videos and outperformed the state-of-the-art trackers on the benchmark dataset with 51 sequences.


title: Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition

abstract: Machine learning is enabling a myriad innovations, including new algorithms for cancer diagnosis and self-driving cars. The broad use of machine learning makes it important to understand the extent to which machine-learning algorithms are subject to attack, particularly when used in applications where physical security or safety is at risk. In this paper, we focus on facial biometric systems, which are widely used in surveillance and access control. We define and investigate a novel class of attacks: attacks that are physically realizable and inconspicuous, and allow an attacker to evade recognition or impersonate another individual. We develop a systematic method to automatically generate such attacks, which are realized through printing a pair of eyeglass frames. When worn by the attacker whose image is supplied to a state-of-the-art face-recognition algorithm, the eyeglasses allow her to evade being recognized or to impersonate another individual. Our investigation focuses on white-box face-recognition systems, but we also demonstrate how similar techniques can be used in black-box scenarios, as well as to avoid face detection.


title: Broad-band mirror (1.12-1.62 /spl mu/m) using a subwavelength grating

abstract: We report the first experimental demonstration of a novel single-layer subwavelength grating (SWG) that has >500-nm-wide reflection spectrum from 1.12-1.62 /spl mu/m and very high reflectivity (>98.5%). This SWG is scalable for different wavelengths by simply changing the grating dimensions, which, thus, facilitates monolithic integration of devices over a wide range of wavelengths.


title: Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors

abstract: Human activity recognition (HAR) is a classification task for recognizing human movements. Methods of HAR are of great interest as they have become tools for measuring occurrences and durations of human actions, which are the basis of smart assistive technologies and manual processes analysis. Recently, deep neural networks have been deployed for HAR in the context of activities of daily living using multichannel time-series. These time-series are acquired from body-worn devices, which are composed of different types of sensors. The deep architectures process these measurements for finding basic and complex features in human corporal movements, and for classifying them into a set of human actions. As the devices are worn at different parts of the human body, we propose a novel deep neural network for HAR. This network handles sequence measurements from different body-worn devices separately. An evaluation of the architecture is performed on three datasets, the Oportunity, Pamap2, and an industrial dataset, outperforming the state-of-the-art. In addition, different network configurations will also be evaluated. We find that applying convolutions per sensor channel and per body-worn device improves the capabilities of convolutional neural network (CNNs).


Reinforcement Learning For Automated Trading


title: Simple telemedicine for developing regions: camera phones and paper-based microfluidic devices for real-time, off-site diagnosis.

abstract: This article describes a prototype system for quantifying bioassays and for exchanging the results of the assays digitally with physicians located off-site. The system uses paper-based microfluidic devices for running multiple assays simultaneously, camera phones or portable scanners for digitizing the intensity of color associated with each colorimetric assay, and established communications infrastructure for transferring the digital information from the assay site to an off-site laboratory for analysis by a trained medical professional; the diagnosis then can be returned directly to the healthcare provider in the field. The microfluidic devices were fabricated in paper using photolithography and were functionalized with reagents for colorimetric assays. The results of the assays were quantified by comparing the intensities of the color developed in each assay with those of calibration curves. An example of this system quantified clinically relevant concentrations of glucose and protein in artificial urine. The combination of patterned paper, a portable method for obtaining digital images, and a method for exchanging results of the assays with off-site diagnosticians offers new opportunities for inexpensive monitoring of health, especially in situations that require physicians to travel to patients (e.g., in the developing world, in emergency management, and during field operations by the military) to obtain diagnostic information that might be obtained more effectively by less valuable personnel.


title: Design method for a reconfigurable mechanism for finger rehabilitation

abstract: This paper presents a design method for a reconfigurable single degree-of-freedom mechanism for robotic assisted finger therapy following a stroke. The mechanism is a four-bar linkage that in combination with variable link lengths is capable of reproducing a power grasp finger motion for a wide variety of finger sizes. This is accomplished through an optimization procedure that determines the parameters of the four-bar linkage needed to fit the sampled range of finger trajectories. The linkage is located behind the hand and attaches to the medial phalanx of the finger just above the distal interphalangeal joint. In addition, the mechanism is designed so that it does not interfere with finger motion and so that the subject‘s fingertips and palm are free to touch real objects and experience tactile feedback. In future implementations, the mechanism could be used for a single finger or in parallel with other similar mechanisms to exercise multiple fingers simultaneously. Although the specific application presented here is the four-bar mechanism and finger power grasp motion, the developed design methods may be applied to a much broader range of mechanisms and applications where scalability for human-machine interface is required.


title: Learning for larger datasets with the Gaussian process latent variable model

abstract: In this paper we apply the latest techniques in sparse Gaussian process regression (GPR) to the Gaussian process latent variable model (GPLVM). We review three techniques and discuss how they may be implemented in the context of the GP-LVM. Each approach is then implemented on a well known benchmark data set and compared with earlier attempts to sparsify the model.


title: Design of circularly polarized Giuseppe Peano fractal patch antenna by the theory of Characteristic Mode

abstract: The Characteristic Mode (CM) Theory has been used in a variety of applications including radiation and scattering analysis and pattern synthesis. CM theory provides a systematic approach to antenna design and is used here to design the Giuseppe Peano (GP) fractal microstrip antennas. In this paper, three GP fractal antennas with linear and circular polarization are designed by CM theory. Each GP fractal antenna is first analyzed by CM theory to find the antenna CMs. Coaxial probe feed location is then selected appropriately to excite desired CMs with respect to their modal currents (surface currents). The goal of this work is to use the CM theory to inform the synthesis of dual band and circularly polarized GP fractal antennas. GP fractal antennas are fabricated and measured to evaluate and prove their stated properties and specifications.


title: Security and Privacy Challenges of a Digital Government

abstract: A digital government can be viewed as an amalgam of heterogeneous information systems that exchange high-volume information among government agencies and public and private sectors engaged in government business. This gives rise to several daunting multidomain security challenges as well as concern for citizen privacy. The success of a digital government infrastructure depends on how well it meets these challenges and its preparedness against numerous potential threats ranging from simple act of hacking to cyber-terrorism. In this chapter, we outline these crucial security and privacy issues and present various solutions that are available and need to be further investigated.


title: Fall Detection for Elderly from Partially Observed Depth-Map Video Sequences Based on View-Invariant Human Activity Representation

abstract: This paper presents a new approach for fall detection from partially-observed depth-map video sequences. The proposed approach utilizes the 3D skeletal joint positions obtained from the Microsoft Kinect sensor to build a view-invariant descriptor for human activity representation, called the motion-pose geometric descriptor (MPGD). Furthermore, we have developed a histogram-based representation (HBR) based on the MPGD to construct a length-independent representation of the observed video subsequences. Using the constructed HBR, we formulate the fall detection problem as a posterior-maximization problem in which the posteriori probability for each observed video subsequence is estimated using a multi-class SVM (support vector machine) classifier. Then, we combine the computed posteriori probabilities from all of the observed subsequences to obtain an overall class posteriori probability of the entire partially-observed depth-map video sequence. To evaluate the performance of the proposed approach, we have utilized the Kinect sensor to record a dataset of depth-map video sequences that simulates four fall-related activities of elderly people, including: walking, sitting, falling form standing and falling from sitting. Then, using the collected dataset, we have developed three evaluation scenarios based on the number of unobserved video subsequences in the testing videos, including: fully-observed video sequence scenario, single unobserved video subsequence of random lengths scenarios and two unobserved video subsequences of random lengths scenarios. Experimental results show that the proposed approach achieved an average recognition accuracy of 93 . 6 % , 77 . 6 % and 65 . 1 % , in recognizing the activities during the first, second and third evaluation scenario, respectively. These results demonstrate the feasibility of the proposed approach to detect falls from partially-observed videos.


title: Hardware trojan attacks in FPGA devices: threat analysis and effective counter measures

abstract: Reconfigurable hardware including Field programmable gate arrays (FPGAs) are being used in a wide range of embedded applications including signal processing, multimedia, and security. FPGA device production is often outsourced to off-shore facilities for economic reasons. This opens up the opportunities for insertion of malicious design alterations in the foundry, referred to as hardware Trojan attacks, to cause logical and physical malfunction. The vulnerability of these devices to hardware attacks raises security concerns regarding hardware and design assurance. In this paper, we analyze hardware Trojan attacks in FPGA considering diverse activation and payload characteristics and derive a taxonomy of Trojan attacks in FPGA. To our knowledge, this is the first effort to analyze Trojan threats in FPGA hardware. Next, we propose a novel redundancy-based protection approach based on Trojan tolerance that modifies the application mapping process to provide high-level of protection against Trojans of varying forms and sizes. We show that the proposed approach incurs significantly higher security at lower overhead than conventional fault-tolerance schemes by exploiting the nature of Trojans and reconfiguration of FPGA resources.


title: MONITORING LAND USE DYNAMICS OF PERI-URBAN AGRICULTUTRE IN CENTRAL KENYA WITH RAPIDEYE SATELLITE IMAGERY

abstract: Abstract. The poster submitted to the ISPRS Congress 2016 in Prague illustrates the concept behind the research project in its initial stage. The project concerns recent dynamics of urban and peri-urban agriculture (PUA) in middle-size cities of central Kenya. On the date of submission, only general research ideas were presented due to the unavailability of remote sensing data at the early stage of the project.


title: An Improved Quality-Guided Phase-Unwrapping Algorithm Based on Priority Queue

abstract: Phase unwrapping is one of the key problems in reconstructing the elevation map of a scene from interferometric synthetic aperture radar or interferometric synthetic aperture sonar (InSAS) data. In this letter, an improved quality-guided phase-unwrapping algorithm is proposed, which depends on the quality value and its surrounding quality information to guide the path of unwrapping more accurately. In order to design highly efficient quality-guided algorithm, a guided map deduced from the quality map is introduced and taken as the new quality map which the quality-guided algorithm should completely depend on. A quantized quality-guided method is designed, which adopts the quantized new quality map and the priority queue designed to optimize the process of unwrapping. The quantized quality map establishes the relation between the quality values and the indexes of array, which can save the time of inserting one pixel into the priority queue according to its integral quality value. Priority queue keeps all the pixels by their quantized quality in a nondecreasing order by maintaining the doubly linked lists in an increasing order. An optimized strategy is used to accelerate the process of finding the pixel with the highest quality value in the priority queue. Tests performed on real InSAS data and simulated interferograms confirm the accuracy and efficiency of the proposed algorithm, and the improved algorithm is suitable for our real-time processing InSAS system.


9 Using Virtual Reality in Experimental Psychology


title: Fault Diagnosis of Networked Control Systems

abstract: Networked Control Systems (NCSs) deal with feedback control systems with loops closed via data communication networks. Control over a network has many advantages compared with traditionally controlled systems, such as a lower implementation cost, reduced wiring, simpler installation and maintenance and higher reliability. Nevertheless, the network-induced delay, packet dropout, asynchronous behavior and other specificities of networks will degrade the performance of closed-loop systems. In this context, it is necessary to develop a new theory for systems that operate in a distributed and asynchronous environment. Research on Fault Detection and Isolation (FDI) for NCSs has received increasing attention in recent years. This paper reviews the state of the art in this topic.


title: Shedding Light on Photophobia

abstract: Photophobia is a common yet debilitating symptom seen in many ophthalmic and neurologic disorders. Despite its prevalence, it is poorly understood and difficult to treat. However, the past few years have seen significant advances in our understanding of this symptom. We review the clinical characteristics and disorders associated with photophobia, discuss the anatomy and physiology of this phenomenon, and conclude with a practical approach to diagnosis and treatment.


title: A Dynamic Secret-Based Encryption Scheme for Smart Grid Wireless Communication

abstract: Integrating information network into power system is the key for realizing the vision of smart grid, but also introduces many security problems. Wireless communication offers the benefits of low cost, rapid deployment, shared communication medium, and mobility; at the same time, it causes many security and privacy challenges. In this paper, the concept of dynamic secret is applied to design an encryption scheme for smart grid wireless communication. Between two parties of communication, the previous packets are coded as retransmission sequence, where retransmitted packet is marked as “1” and the other is marked as “0.” During the communication, the retransmission sequence is generated at both sides to update the dynamic encryption key. Any missing or misjudging in retransmission sequence would prevent the adversary from achieving the keys. In our experiments, a smart grid platform is built, employing the ZigBee protocol for wireless communication. And a dynamic secret-based encryption demo system is designed based on this platform. The experiment results show that the retransmission and packet loss in ZigBee communication are inevitable and unpredictable, and it is impossible for the adversary to track the updating of the dynamic encryption key.


Diversity and abundance of lactobacilli during ulcerative colitis in North Indian patients : a case control study


title: Base Station Operation and User Association Mechanisms for Energy-Delay Tradeoffs in Green Cellular Networks

abstract: Energy-efficiency, one of the major design goals in wireless cellular networks, has received much attention lately, due to increased awareness of environmental and economic issues for network operators. In this paper, we develop a theoretical framework for BS energy saving that encompasses dynamic BS operation and the related problem of user association together. Specifically, we formulate a total cost minimization that allows for a flexible tradeoff between flow-level performance and energy consumption. For the user association problem, we propose an optimal energy-efficient user association policy and further present a distributed implementation with provable convergence. For the BS operation problem (i.e., BS switching on/off), which is a challenging combinatorial problem, we propose simple greedy-on and greedy-off algorithms that are inspired by the mathematical background of submodularity maximization problem. Moreover, we propose other heuristic algorithms based on the distances between BSs or the utilizations of BSs that do not impose any additional signaling overhead and thus are easy to implement in practice. Extensive simulations under various practical configurations demonstrate that the proposed user association and BS operation algorithms can significantly reduce energy consumption.


title: OntoEdit: Guiding Ontology Development by Methodology and Inferencing

abstract: Ontologies now play an important role for many knowledge-intensive applications for which they provide a source of precisely defined terms. The terms are used for concise communication across people and applications. OntoEdit is an ontology editor that has been developed keeping five main objectives in mind: 1. Ease of use. 2. Methodology-guided development of ontologies. 3. Ontology development with help of inferencing. 4. Development of ontology axioms. 5. Extensibility through plug-in structure. This paper is about the first four of these items.


title: Typologies of Extreme Longevity Myths

abstract: Purpose. Political, national, religious, and other motivations have led the media and even scientists to errantly accept extreme longevity claims prima facie. We describe various causes of false claims of extraordinary longevity. Design and Methods. American Social Security Death Index files for the period 1980–2009 were queried for individuals with birth and death dates yielding ages 110+ years of age. Frequency was compared to a list of age-validated supercentenarians maintained by the Gerontology Research Group who died during the same time period. Age claims of 110+ years and the age validation experiences of the authors facilitated a list of typologies of false age claims. Results. Invalid age claim rates increase with age from 65% at age 110-111 to 98% by age 115 to 100% for 120+ years. Eleven typologies of false claims were: Religious Authority Myth, Village Elder Myth, Fountain of Youth Myth (substance), Shangri-La Myth (geographic), Nationalist Pride, Spiritual Practice, Familial Longevity, Individual and/or Family Notoriety, Military Service, Administrative Entry Error, and Pension-Social Entitlement Fraud. Conclusions. Understanding various causes of false extreme age claims is important for placing current, past, and future extreme longevity claims in context and for providing a necessary level of skepticism.


title: Emotion recognition in human-computer interaction

abstract: In this paper, we outline the approach we have developed to construct an emotion-recognising system. It is based on guidance from psychological studies of emotion, as well as from the nature of emotion in its interaction with attention. A neural network architecture is constructed to be able to handle the fusion of different modalities (facial features, prosody and lexical content in speech). Results from the network are given and their implications discussed, as are implications for future direction for the research.


title: A neurocognitive perspective on language: The declarative/procedural model

abstract: What are the psychological, computational and neural underpinnings of language? Are these neurocognitive correlates dedicated to language? Do different parts of language depend on distinct neurocognitive systems? Here I address these and other issues that are crucial for our understanding of two fundamental language capacities: the memorization of words in the mental lexicon, and the rule-governed combination of words by the mental grammar. According to the declarative/procedural model, the mental lexicon depends on declarative memory and is rooted in the temporal lobe, whereas the mental grammar involves procedural memory and is rooted in the frontal cortex and basal ganglia. I argue that the declarative/procedural model provides a new framework for the study of lexicon and grammar.


title: A security framework for the analysis and design of software attestation

abstract: Software attestation has become a popular and challenging research topic at many established security conferences with an expected strong impact in practice. It aims at verifying the software integrity of (typically) resource-constrained embedded devices. However, for practical reasons, software attestation cannot rely on stored cryptographic secrets or dedicated trusted hardware. Instead, it exploits side-channel information, such as the time that the underlying device needs for a specific computation. As traditional cryptographic solutions and arguments are not applicable, novel approaches for the design and analysis are necessary. This is certainly one of the main reasons why the security goals, properties and underlying assumptions of existing software attestation schemes have been only vaguely discussed so far, limiting the confidence in their security claims. Thus, putting software attestation on a solid ground and having a founded approach for designing secure software attestation schemes is still an important open problem. We provide the first steps towards closing this gap. Our first contribution is a security framework that formally captures security goals, attacker models and various system and design parameters. Moreover, we present a generic software attestation scheme that covers most existing schemes in the literature. Finally, we analyze its security within our framework, yielding sufficient conditions for provably secure software attestation schemes. We expect that such a consolidating work allows for a meaningful security analysis of existing schemes, supports the design of secure software attestation schemes and will inspire new research in this area.


title: Personality and intelligence: gender, the Big Five, self-estimated and psychometric intelligence

abstract: This paper reports on two studies that investigated the relationship between the Big Five personality traits, self-estimates of intelligence (SEI), and scores on two psychometrically validated intelligence tests. In study 1 a total of 100 participants completed the NEO-PI-R, the Wonderlic Personnel Test and the Baddeley Reasoning Test, and estimated their own intelligence on a normal distribution curve. Multiple regression showed that psychometric intelligence was predicted by Conscientiousness and SEI, while SEI was predicted by gender, Neuroticism (notably anxiety) and Agreeableness (notably modesty). Personality was a better predictor of SEI than of psychometric intelligence itself. Study 2 attempted to explore the relationship between SEI and psychometric intelligence. A total of 130 participants completed the NEO-PI-R, the Baddeley Reasoning Test, and the S & M Spatial intelligence test. In addition, SEI and participants conceptions of intelligence were also examined. In combination with gender and previous IQ test experience, these variables were found to predict about 11% of the variance in SEI. SEI was the only significant predictor of psychometrically measured intelligence. Inconsistencies between results of the two studies, theoretical and applied implications, and limitations of this work are discussed.


title: Nonverbal social withdrawal in depression: Evidence from manual and automatic analyses

abstract: The relationship between nonverbal behavior and severity of depression was investigated by following depressed participants over the course of treatment and video recording a series of clinical interviews. Facial expressions and head pose were analyzed from video using manual and automatic systems. Both systems were highly consistent for FACS action units (AUs) and showed similar effects for change over time in depression severity. When symptom severity was high, participants made fewer affiliative facial expressions (AUs 12 and 15) and more non-affiliative facial expressions (AU 14). Participants also exhibited diminished head motion (i.e., amplitude and velocity) when symptom severity was high. These results are consistent with the Social Withdrawal hypothesis: that depressed individuals use nonverbal behavior to maintain or increase interpersonal distance. As individuals recover, they send more signals indicating a willingness to affiliate. The finding that automatic facial expression analysis was both consistent with manual coding and revealed the same pattern of findings suggests that automatic facial expression analysis may be ready to relieve the burden of manual coding in behavioral and clinical science.


title: Evaluating link prediction methods

abstract: Link prediction is a popular research area with important applications in a variety of disciplines, including biology, social science, security, and medicine. The fundamental requirement of link prediction is the accurate and effective prediction of new links in networks. While there are many different methods proposed for link prediction, we argue that the practical performance potential of these methods is often unknown because of challenges in the evaluation of link prediction, which impact the reliability and reproducibility of results. We describe these challenges, provide theoretical proofs and empirical examples demonstrating how current methods lead to questionable conclusions, show how the fallacy of these conclusions is illuminated by methods we propose, and develop recommendations for consistent, standard, and applicable evaluation metrics. We also recommend the use of precision-recall threshold curves and associated areas in lieu of receiver operating characteristic curves due to complications that arise from extreme imbalance in the link prediction classification problem.


title: Anytime QoS optimization over the PlanGraph for web service composition

abstract: Automatic service composition is the generation of a business process to fulfill business goals that cannot be fulfilled by individual services. Planning algorithms are frequently used to solve this problem. In addition to satisfying functional goals, recent research is geared towards selecting the best services to optimize the QoS of the result business process. In this paper, we combine a systematic search algorithm like Dijkstra's algorithm with a planning algorithm, GraphPlan, to achieve both functional goals and QoS optimization at the same time. In addition, we make our algorithm an anytime algorithm that has the advantage of getting better solutions if it keeps running for a longer time.


title: Effect of High-Speed and Plyometric Training for 13-Year-Old Male Soccer Players on Acceleration and Agility Performance

abstract: Acceleration, sprint and agility performance are crucial in sports like soccer. There are few studies regarding the effect of training on youth soccer players in agility performance and in sprint distances shorter than 30 meter. Therefore, the aim of the recent study was to examine the effect of a high-intensity sprint and plyometric training program on 13-year-old male soccer players. A training group of 14 adolescent male soccer players, mean age (±SD) 13.5 years (±0.24) followed an eight week intervention program for one hour per week, and a group of 12 adolescent male soccer players of corresponding age, mean age 13.5 years (±0.23) served as control a group. Pre- and post-tests assessed 10-m linear sprint, 20-m linear sprint and agility performance. Results showed a significant improvement in agility performance, pre 8.23 s (±0.34) to post 7.69 s (± 0.34) (p<0.01), and a significant improvement in 0-20m linear sprint, pre 3.54s (±0.17) to post 3.42s (±0.18) (p<0.05). In 0-10m sprint the participants also showed an improvement, pre 2.02s (±0.11) to post 1.96s (± 0.11), however this was not significant. The correlation between 10-m sprint and agility was r = 0.53 (p<0.01), and between 20-m linear sprint and agility performance, r = 0.67 (p<0.01). The major finding in the study is the significant improvement in agility performance and in 0-20 m linear sprint in the intervention group. These findings suggest that organizing the training sessions with short-burst high-intensity sprint and plyometric exercises interspersed with adequate recovery time, may result in improvements in both agility and in linear sprint performance in adolescent male soccer players. Another finding is the correlation between linear sprint and agility performance, indicating a difference when compared to adults.


title: Gaze and eye contact: A research review.

abstract: Research on gaze and eye contact was organized within the framework of Patterson's (1982) sequential functional model of nonverbal exchange. Studies were reviewed showing how gaze functions to (a) provide information, (b) regulate interaction, (c) express intimacy, (d) exercise social control, and (


title: Expertise retrieval in bibliographic network: a topic dominance learning approach

abstract: Expert finding in bibliographic networks has received increased interests in recent years. This task concerns with finding relevant researchers for a given topic. Motivated by the observation that rarely do all coauthors contribute to a paper equally, in this paper, we propose a discriminative method to realize leading authors contributing in a scientific publication. Specifically, we cast the problem of expert finding in a bibliographic network to find leading experts in a research group, which is easier to solve. According to some observations, we recognize three feature groups that can discriminate relevant and irrelevant experts. Experimental results on a real dataset, and an automatically generated one that is gathered from Microsoft academic search show that the proposed model significantly improves the performance of expert finding in terms of all common Information Retrieval evaluation metrics.


title: Organizational Website Usability and Attractiveness Effects on Viewer Impressions

abstract: Previous research on the rapidly growing field of Internet recruitment has operated under the implicit assumption that recruitment websites can and do influence viewers’ opinions of recruiting organizations. This study empirically tested this assumption using a pre/post-test experimental design. Results indicated that viewers’ ratings on company familiarity, favorability, image as an employer, and organizational attractiveness were affected by organizations’ recruitment websites. Moreover, changes in viewers’ evaluations of these companies were directly related to the usability and attractiveness of their websites. Specifically, greater increases in favorable evaluations from the pre- to post-test measures occurred with companies that maintained websites that were easy to navigate and that were appealing in terms of their colors, fonts, and images.


title: Co-design of augmented reality book for collaborative learning experience in primary education

abstract: Through co-design of Augmented Reality (AR) based teaching material, this research aims to enhance collaborative learning experience in primary school education. It will introduce an interactive AR Book based on primary school textbook using tablets as the real time interface. The development of this AR Book employs co-design methods to involve children, teachers, educators and HCI experts from the early stages of the design process. Research insights from the co-design phase will be implemented in the AR Book design. The final outcome of the AR Book will be evaluated in the classroom to explore its effect on the collaborative experience of primary school students. The research aims to answer the question — Can Augmented Books be designed for primary school students in order to support collaboration? This main research question is divided into two sub-questions as follows — How can co-design methods be applied in designing Augmented Book with and for primary school children? And what is the effect of the proposed Augmented Book on primary school students' collaboration? This research will not only present a practical application of co-designing AR Book for and with primary school children, it will also clarify the benefit of AR for education in terms of collaborative experience.


title: The effects of organizational learning culture and job satisfaction on motivation to transfer learning and turnover intention. Human Resource Development Quarterly

abstract: Although organizational learning theory and practice have been clarified by practitioners and scholars over the past several years, there is much to be explored regarding interactions between organizational learning culture and employee learning and performance outcomes. This study examined the relationship of organizational learning culture, job satisfaction, and organizational outcome variables with a sample of information technology (IT) employees in the United States. It found that learning organizational culture is associated with IT employee job satisfaction and motivation to transfer learning. Turnover intention was found to be negatively influenced by organizational learning culture and job satisfaction. Suggestions for future study of learning organizational culture in association with job satisfaction and performance-related outcomes are discussed. With the current expansion of the global economy and the fast-changing evolution of technology and innovation, organizations are facing an ongoing need for employee learning and development. As knowledge increasingly becomes a key factor for productivity, it has also become a currency for competitive success. Understanding factors that contribute to organizational learning and the transfer of knowledge to the workplace environment are essential to


title: Dynamic Coordination in Fleet Management Systems: Toward Smart Cyber Fleets

abstract: Fleet management systems are commonly used to coordinate mobility and delivery services in a broad variety of domains. However, their traditional top-down control architecture becomes a bottleneck in open and dynamic environments, where scalability, proactiveness, and autonomy are becoming key factors for their success. Here, the authors present an abstract event-based architecture for fleet management systems that supports tailoring dynamic control regimes for coordinating fleet vehicles, and illustrate it for the case of medical emergency management. Then, they go one step ahead in the transition toward automatic or driverless fleets, by conceiving fleet management systems in terms of cyber-physical systems, and putting forward the notion of cyber fleets.


title: Mobile Core-Banking Server: Cashless, Branchless and Wireless Retail Banking for the Mass Market

abstract: Banking is essential to modern economies and nearly half of the world's people are unbanked and underserved by banks. The high operating costs of banks passed on to retail customers are driving them away who otherwise should be in the financial system. Without having access to the financial system the unbanked and rural poor have little means to engage in mainstream economic activities. A mobile phone-based banking system was designed to provide retail banking services to the world's unbanked population and rural poor of 2.2 billion based on a cashless banking system that reduces operating cost and reduces lending risks and therefore can lower the current exorbitant rates charged on retail lending and micro-credit. A core-banking server on a low-cost mobile device (an Android tablet) could serve over 15,000 micro-banking customers. The system definitely appears to be suitable for most retail banks or microfinance institutions.


title: Utility of capsule endoscopy for evaluating anthelmintic efficacy in fully conscious dogs.

abstract: The current accepted standard for evaluating the efficacy of gastrointestinal anthelmintic drugs is necropsy of infected animals followed by a comparison of worm counts between treated and non-treated groups. In this study capsule endoscopy, a minimally invasive method of imaging the small intestine of humans, is evaluated as a possible alternative to necropsy for the purposes of worm quantification in dogs. Eighteen Beagle dogs were included in this study. These dogs were part of a separate trial intended to determine the efficacy of various candidate parasiticides against Ancylostoma caninum via the necropsy standard. Dogs were inoculated with A. caninum L3s 4 weeks prior to treatment with one of the candidate compounds; a control group (n=8) received no treatment. Capsule endoscopy was performed 6-14 days post-treatment, followed by necropsy the following day. Seventeen dogs had complete examinations, i.e. the capsule traversed the small intestine and reached the colon within the battery life of the capsule. A strong correlation (r(s)=0.87, P<0.0001) was observed between the worm counts acquired by capsule endoscopy and necropsy. There was no clear relationship between the ability of the capsule endoscope to detect hookworms and either visibility of the intestinal lumen or small intestinal transit time. Generation of a virtual spatial record of hookworm location from the capsule endoscopy data revealed a temporal trend, with the majority of worms present in the proximal small intestine in the morning versus the central to distal small intestine in the afternoon. Worm distribution as determined by capsule endoscopy closely resembled post-mortem findings. In conclusion, capsule endoscopy shows promise as an alternative to necropsy for the enumeration of A. caninum in the canine small intestine, although further work is required to improve completion rates and optimise intestinal examination.


title: EML-NET:An Expandable Multi-Layer NETwork for Saliency Prediction

abstract: Abstract Existing advanced saliency systems have been proposed using Convolutional Neural Networks (CNNs), the prior knowledge of objectness is crucial for saliency detection. However, we show that the use of objectness may also limit the power of CNNs on the images which do not contain a salient object. Besides, one previous study has shown applying a deeper CNN model may not improve the performance, the reason behind could be due to the limited training data. In this work, we aim at investigating the effect of prior knowledge from other domains and proposing a multi-modality system. Our work shows the depth of the model still plays an important role in saliency detection when the training data is large enough. To do this in a sophisticated manner can be complex, and also result in unwieldy networks or produce competing objectives that are hard to balance. For the scalability, our multi-modality system is trained in an almost end-to-end piece-wise fashion. The encoder and decoder components are separately trained to deal with complexity tied to the computational paradigm and required space. Therefore, our system can be easily extended to include a variety of prior knowledge for saliency detection. Besides, we also propose a combined saliency loss based on modifications of Pearson correlation and normalized scanpath saliency. Our experiment shows the combined loss can train a CNN model more comprehensively for saliency detection. We denote our expandable multi-layer network as EML-NET and our method achieves the state-of-the-art results on the public saliency benchmarks, SALICON, MIT300 and CAT2000.