title: A New and Practical Design of Cancellable Biometrics: Index-of-Max Hashing.

abstract: Despite a variety of theoretical-sound techniques have been proposed to generate cancellable biometric templates, there is rarely practical solution that satisfies non-invertibility, revocability, non-linkability and performance simultaneously. In this paper, we propose a locality sensitive hashing inspired cancellable biometrics framework, namely "Index-of-Max" (IoM) hashing. Briefly, IoM hashing non-linearly transforms a realvalued feature vector into discrete index hashed code. We demonstrate two realizations from IoM hashing framework, namely Gaussian Random Projection based and Uniformly Random Permutation based hashing schemes. The discrete indices representation nature of IoM hashed codes enjoy several merits such as it empowers strong concealment to biometric information. This contributes to the solid ground of noninvertibility guarantee. IoM hashing is insensitive to the magnitude of features, hence are more robust against biometric features variation and its magnitude-independence trait makes the resultant hash codes scale-invariant, which is critical for matching and feature alignment. The experimental results demonstrate reasonable accuracy performance on benchmark FVC2002 and FVC2004 fingerprint databases. Moreover, the analyses justify its resilience to the existing and newly introduced security and privacy attacks as well as satisfy the revocability and unlinkability criteria of cancellable biometrics. Besides, the implementation of IoM hashing is also incredibly simple for practical applications.


title: Face Aging Effect Simulation Using Hidden Factor Analysis Joint Sparse Representation

abstract: Face aging simulation has received rising investigations nowadays, whereas it still remains a challenge to generate convincing and natural age-progressed face images. In this paper, we present a novel approach to such an issue using hidden factor analysis joint sparse representation. In contrast to the majority of tasks in the literature that integrally handle the facial texture, the proposed aging approach separately models the person-specific facial properties that tend to be stable in a relatively long period and the age-specific clues that gradually change over time. It then transforms the age component to a target age group via sparse reconstruction, yielding aging effects, which is finally combined with the identity component to achieve the aged face. Experiments are carried out on three face aging databases, and the results achieved clearly demonstrate the effectiveness and robustness of the proposed method in rendering a face with aging effects. In addition, a series of evaluations prove its validity with respect to identity preservation and aging effect generation.


title: Frontotemporal hairline recession in a postmenopausal woman

abstract: A 69-year-old postmenopausal woman consulted for frontal hair loss for two years. She had started menopause at the age of 50 years old and had been taking bisphosphonates for her osteoporosis for two years. Her clinical history, including gynecological data, was otherwise negative. Anamnestic data ruled out the possibility of traction alopecia. Dermatological examination revealed a Fitzpatrick skin type III. She had a linear frontotemporal recession with perifollicular erythema, lonely hairs on the frontal region, and scarring alopecia ( Fig-ure 1). The patient had a total loss of eyebrows but she did not have body hair loss. There were no other skin or mucosal abnormalities. Thyroid hormone function was also normal. Dermoscopy with a non-contact polarizing FotoFinder der-matoscope x20 (FotoFinder Systems, Inc, Bad Birnbach, Germany) revealed perifollicular erythema and very mild perifollicular scaling in addition to hair shaft dystrophy and broken hair. Furthermore, dermoscopy noted the presence of white dots coexisting with irregular white and pink areas devoid of hair follicular openings ( Figure 2 ). No prior topical treatment was used before our consultation. A 4 mm scalp punch biopsy from the frontal hairline was performed. His-


title: Offline time is quality time. Comparing within-group self-disclosure in mobile messaging applications and face-to-face interactions

abstract: In contrast to the prominent individualistic view on self-disclosure, this study focuses on self-disclosure in groups of prior acquaintances that both meet offline and communicate online. It compares within-group self-disclosure between offline face-to-face (FtF) interactions and online communication via mobile messaging applications (MMAs). An online-survey (N?=?357) was conducted to test for differences between within-group self-disclosure online and offline across four dimensions (amount, depth, breadth, valence). The results show that there is more amount, more breadth and more depth for offline within-group self-disclosure, but it is less positively valenced than online within-group self-disclosure. Interestingly, the mere frequency of communication is higher in an MMA environment. In spite of the permanent availability of the online communication sphere, group members do not permanently disclose personal information to each other online. Thus, for within-group self-disclosure, offline time seems quality time. We compare offline and online within-group self-disclosure among prior acquaintances.Groups communicate more often via mobile messaging applications.Face-to-face, there is a higher amount, more breadth, and more depth of within-group self-disclosure.In MMAs, there is more positively valenced within-group self-disclosure.For within-group self-disclosure, offline time is quality time.


title: SUEFUL-7: A 7DOF upper-limb exoskeleton robot with muscle-model-oriented EMG-based control

abstract: This paper proposes an electromyography (EMG) signal based control method for a seven degrees of freedom (7DOF) upper-limb motion assist exoskeleton robot (SUEFUL-7). The SUEFUL-7 is able to assist the motions of shoulder vertical and horizontal flexion/extension, shoulder internal/external rotation, elbow flexion/extension, forearm supination/pronation, wrist flexion/extension, and wrist radial/ulnar deviation of physically weak individuals. In the proposed control method, an impedance controller is applied to the muscle-model-oriented control method by considering the end effecter force vector. Impedance parameters are adjusted in real time by considering the upper-limb posture and EMG activity levels. Experiments have been performed to evaluate the effectiveness of the proposed robotic system.


title: Deep Attributes Driven Multi-Camera Person Re-identification

abstract: The visual appearance of a person is easily affected by many factors like pose variations, viewpoint changes and camera parameter differences. This makes person Re-Identification (ReID) among multiple cameras a very challenging task. This work is motivated to learn mid-level human attributes which are robust to such visual appearance variations. And we propose a semi-supervised attribute learning framework which progressively boosts the accuracy of attributes only using a limited number of labeled data. Specifically, this framework involves a three-stage training. A deep Convolutional Neural Network (dCNN) is first trained on an independent dataset labeled with attributes. Then it is fine-tuned on another dataset only labeled with person IDs using our defined triplet loss. Finally, the updated dCNN predicts attribute labels for the target dataset, which is combined with the independent dataset for the final round of fine-tuning. The predicted attributes, namely deep attributes exhibit superior generalization ability across different datasets. By directly using the deep attributes with simple Cosine distance, we have obtained surprisingly good accuracy on four person ReID datasets. Experiments also show that a simple distance metric learning modular further boosts our method, making it significantly outperform many recent works.


title: Multivariate discretization of continuous variables for set mining

abstract: Many algorithms in data mining can be formulated as a set mining problem where the goal is to nd conjunctions (or disjunctions) of terms that meet user speci ed constraints. Set mining techniques have been largely designed for categorical or discrete data where variables can only take on a xed number of values. However, many data sets also contain continuous variables and a common method of dealing with these is to discretize them by breaking them into ranges. Most discretization methods are univariate and consider only a single feature at a time (sometimes in conjunction with the class variable). We argue that this is a sub-optimal approach for knowledge discovery as univariate discretization can destroy hidden patterns in data. Discretization should consider the e ects on all variables in the analysis and that two regions X and Y should only be in the same cell after discretization if the instances in those regions have similar multivariate distributions (Fx Fy) across all variables and combinations of variables. We present a bottom up merging algorithm to discretize continuous variables based on this rule. Our experiments indicate that the approach is feasible, that it does not destroy hidden patterns and that it generates meaningful intervals.


A literature survey on algorithms for multi-label learning


title: Internet addiction: coping styles, expectancies, and treatment implications

abstract: Internet addiction (IA) has become a serious mental health condition in many countries. To better understand the clinical implications of IA, this study tested statistically a new theoretical model illustrating underlying cognitive mechanisms contributing to development and maintenance of the disorder. The model differentiates between a generalized Internet addiction (GIA) and specific forms. This study tested the model on GIA on a population of general Internet users. The findings from 1019 users show that the hypothesized structural equation model explained 63.5% of the variance of GIA symptoms, as measured by the short version of the Internet Addiction Test. Using psychological and personality testing, the results show that a person's specific cognitions (poor coping and cognitive expectations) increased the risk for GIA. These two factors mediated the symptoms of GIA if other risk factors were present such as depression, social anxiety, low self-esteem, low self-efficacy, and high stress vulnerability to name a few areas that were measured in the study. The model shows that individuals with high coping skills and no expectancies that the Internet can be used to increase positive or reduce negative mood are less likely to engage in problematic Internet use, even when other personality or psychological vulnerabilities are present. The implications for treatment include a clear cognitive component to the development of GIA and the need to assess a patient's coping style and cognitions and improve faulty thinking to reduce symptoms and engage in recovery.


title: Data-driven Natural Language Generation: Paving the Road to Success

abstract: We argue that there are currently two major bottlenecks to the commercial use of statistical machine learning approaches for natural language generation (NLG): (a) The lack of reliable automatic evaluation metrics for NLG, and (b) The scarcity of high quality in-domain corpora. We address the first problem by thoroughly analysing current evaluation metrics and motivating the need for a new, more reliable metric. The second problem is addressed by presenting a novel framework for developing and evaluating a high quality corpus for NLG training.


title: Artistic Minimal Rendering with Lines and Blocks

abstract: Many non-photorealistic rendering techniques exist to produce artistic effects from given images. Inspired by various artists, interesting effects can be produced by using a minimal rendering, where the minimum refers to the number of tones as well as the number and complexity of the primitives used for rendering. Our method is based on various computer vision techniques, and uses a combination of refined lines and blocks (potentially simplified), as well as a small number of tones, to produce abstracted artistic rendering with sufficient elements from the original image. We also considered a variety of methods to produce different artistic styles, such as colour and 2-tone drawings, and use semantic information to improve renderings for faces. By changing some intuitive parameters a wide range of visually pleasing results can be produced. Our method is fully automatic. We demonstrate the effectiveness of our method with extensive experiments and a user study.


title: Design and optimization of microstrip Hairpin-Line bandpass filter using DOE methodology

abstract: This paper proposes a novel methodology for the optimization of Hairpin-Line bandpass filter. The proposed methodology is based on well-known Plackett-Burman Design of Experiment technique. First, a five section hairpin resonator is designed to operate at a center frequency of 2.4GHz with a bandwidth of 300MHz using Genesys software. Plackett-Burman Design of Experiment methodology is then applied to this filter for further optimization. Implementation of proposed approach shows 61% improvement in return loss and 15% improvement in insertion loss of the filter as compared to the filter designed using Genesys software.


title: A Decentralized Public Key Infrastructure with Identity Retention.

abstract: Public key infrastructures (PKIs) enable users to look up and verify one another’s public keys based on identities. Current approaches to PKIs are vulnerable because they do not offer sufficiently strong guarantees of identity retention; that is, they do not effectively prevent one user from registering a public key under another’s already-registered identity. In this paper, we leverage the consistency guarantees provided by cryptocurrencies such as Bitcoin and Namecoin to build a PKI that ensures identity retention. Our system, called Certcoin, has no central authority and thus requires the use of secure distributed dictionary data structures to provide efficient support for key lookup.


title: μPnP: plug and play peripherals for the internet of things

abstract: Internet of Things (IoT) applications require diverse sensors and actuators. However, contemporary IoT devices provide limited support for the integration of third-party peripherals. To tackle this problem, we introduce μPnP: a hardware and software solution for plug-and-play integration of embedded peripherals with IoT devices. μPnP provides support for: driver development, automatic integration of third-party peripherals, discovery and remote access to peripheral services. This is achieved through a low-cost hardware identification approach, a lightweight driver language and a multicast network architecture. Evaluation shows that μPnP has a minimal memory footprint, reduces development effort and provides true plug-and-play integration at orders of magnitude less energy than USB.


The burden on image analysts in medical fields has led to the automated processing of pictorial data . Here , a device called the cytocomputer searches for genetic mutations . Biomedical Image Processing


title: The DLR Robot Motion Simulator Part I: Design and setup

abstract: In recent years a new generation of motion simulators, based on serial kinematics industrial robots, emerged as alternative to the currently prevalent Steward-platforms. This paper presents the newest addition to this: The DLR Robot Motion Simulator.


title: Deep Brain Stimulation Signal Classification Using Deep Belief Networks

abstract: An approach to modeling complex real-world data such as biomedical signals is to develop pattern recognition techniques and robust features that capture the relevant information. In this paper, we use a deep belief network (DBN) to predict subcortical structures of patients with Parkinson's disease based on microelectrode records (MER) obtained during deep brain stimulation (DBS). We report on experiments using a data set involving 52 MER for the structures: zona incerta (Zi), subthalamic nucleus (STN), thalamus nucleus (TAL), and substantia nigra (SNR). The results show that our chosen features and network architecture produces a 99.5% accuracy of detection and classification of the subcortical structures under study. Based on the results we conclude that deep belief networks could be used to predict subcortical structure–mainly the STN for neurostimulation.


title: MPR to Support The Sales of ICT Education

abstract: In the era of globalization the development of economy in each country is determined on how they develop and utilize Information and Communication Technology (ICT) in their business activity. However, Marketing Public Relations (MPR) is also the most appropriate strategy for Education institute, because in nature education institute has to create long term relationship with their student. Therefore MPR should at least encompass three disciplines of studies and professions, in which are strategic management, marketing and public relations. For BINUS Center, awareness can be gain through referral activity. Advocacy or referral have dominant effect in building trust (believe), other than that publication also play a role in creating a trustworthy image towards BINUS Center. Marketing Public Relation is an effective marketing strategy for ICT education. Based on the quantitative research the majority of BINUS Center target market is in the age bracket 18-22 years old. In correlation with that fact the qualitative research has shown that MPR is effective in supporting the enrollment decision towards BINUS Center.


title: EmotionCheck: A Wearable Device to Regulate Anxiety through False Heart Rate Feedback

abstract: Previous studies indicate that the way we perceive our bodily signals, such as our heart rate, can influence how we feel. Inspired by these studies, we built EmotionCheck, which is a wearable device that can change users' perception of their heart rate through subtle vibrations on the wrist. The results of an experiment with 67 participants show that the EmotionCheck device can help users regulate their anxiety through false feedback of a slow heart rate.


title: Solution to Detect, Classify, and Report Illicit Online Marketing and Sales of Controlled Substances via Twitter: Using Machine Learning and Web Forensics to Combat Digital Opioid Access

abstract: BACKGROUND ::: On December 6 and 7, 2017, the US Department of Health and Human Services (HHS) hosted its first Code-a-Thon event aimed at leveraging technology and data-driven solutions to help combat the opioid epidemic. The authors—an interdisciplinary team from academia, the private sector, and the US Centers for Disease Control and Prevention—participated in the Code-a-Thon as part of the prevention track. ::: ::: ::: OBJECTIVE ::: The aim of this study was to develop and deploy a methodology using machine learning to accurately detect the marketing and sale of opioids by illicit online sellers via Twitter as part of participation at the HHS Opioid Code-a-Thon event. ::: ::: ::: METHODS ::: Tweets were collected from the Twitter public application programming interface stream filtered for common prescription opioid keywords in conjunction with participation in the Code-a-Thon from November 15, 2017 to December 5, 2017. An unsupervised machine learning–based approach was developed and used during the Code-a-Thon competition (24 hours) to obtain a summary of the content of the tweets to isolate those clusters associated with illegal online marketing and sale using a biterm topic model (BTM). After isolating relevant tweets, hyperlinks associated with these tweets were reviewed to assess the characteristics of illegal online sellers. ::: ::: ::: RESULTS ::: We collected and analyzed 213,041 tweets over the course of the Code-a-Thon containing keywords codeine, percocet, vicodin, oxycontin, oxycodone, fentanyl, and hydrocodone. Using BTM, 0.32% (692/213,041) tweets were identified as being associated with illegal online marketing and sale of prescription opioids. After removing duplicates and dead links, we identified 34 unique “live” tweets, with 44% (15/34) directing consumers to illicit online pharmacies, 32% (11/34) linked to individual drug sellers, and 21% (7/34) used by marketing affiliates. In addition to offering the “no prescription” sale of opioids, many of these vendors also sold other controlled substances and illicit drugs. ::: ::: ::: CONCLUSIONS ::: The results of this study are in line with prior studies that have identified social media platforms, including Twitter, as a potential conduit for supply and sale of illicit opioids. To translate these results into action, authors also developed a prototype wireframe for the purposes of detecting, classifying, and reporting illicit online pharmacy tweets selling controlled substances illegally to the US Food and Drug Administration and the US Drug Enforcement Agency. Further development of solutions based on these methods has the potential to proactively alert regulators and law enforcement agencies of illegal opioid sales, while also making the online environment safer for the public.


title: ANN Approach Based On Back Propagation Network and Probabilistic Neural Network to Classify Brain Cancer

abstract: This paper presents the artificial neural network approach namely Back propagation network (BPNs) and probabilistic neural network (PNN). It is used to classify the type of tumor in MRI images of different patients with Astrocytoma type of brain tumor. The image processing techniques have been developed for detection of the tumor in the MRI images. Gray Level Co-occurrence Matrix (GLCM) is used to achieve the feature extraction. The whole system worked in two modes firstly Training/Learning mode and secondly Testing/Recognition mode.


title: An Empirical Performance Evaluation of GPU-Enabled Graph-Processing Systems

abstract: Graph processing is increasingly used in knowledge economies and in science, in advanced marketing, social networking, bioinformatics, etc. A number of graph-processing systems, including the GPU-enabled Medusa and Totem, have been developed recently. Understanding their performance is key to system selection, tuning, and improvement. Previous performance evaluation studies have been conducted for CPU-based graph-processing systems, such as Graph and GraphX. Unlike them, the performance of GPU-enabled systems is still not thoroughly evaluated and compared. To address this gap, we propose an empirical method for evaluating GPU-enabled graph-processing systems, which includes new performance metrics and a selection of new datasets and algorithms. By selecting 9 diverse graphs and 3 typical graph-processing algorithms, we conduct a comparative performance study of 3 GPU-enabled systems, Medusa, Totem, and MapGraph. We present the first comprehensive evaluation of GPU-enabled systems with results giving insight into raw processing power, performance breakdown into core components, scalability, and the impact on performance of system-specific optimization techniques and of the GPU generation. We present and discuss many findings that would benefit users and developers interested in GPU acceleration for graph processing.


title: On the Influence of Emotional Valence Shifts on the Spread of Information in Social Networks

abstract: In this paper, we present a study on 4.4 million Twitter messages related to 24 systematically chosen real-world events. For each of the 4.4 million tweets, we first extracted sentiment scores based on the eight basic emotions according to Plutchik's wheel of emotions. Subsequently, we investigated the effects of shifts in the emotional valence on the spread of information. We found that in general OSN users tend to conform to the emotional valence of the respective real-world event. However, we also found empirical evidence that prospectively negative real-world events exhibit a significant amount of shifted emotions in the corresponding tweets (i.e. positive messages). To explain this finding, we use the theory of social connection and emotional contagion. To the best of our knowledge, this is the first study that provides empirical evidence for the undoing hypothesis in online social networks (OSNs). The undoing hypothesis postulates that positive emotions serve as an antidote during negative events.


Survey Paper on Phishing Detection : Identification of Malicious URL Using Bayesian Classification on Social Network Sites


title: Cell Transplantation: A Possible Alternative to Orthotopic Liver Transplant (OLT)

abstract: The progress made in the field of liver organ transplantation has revolutionized the treatment of a wide spectrum of liver diseases. Orthotopic liver transplantation (OLT), which requires removal of the entire native liver and transplantation of a high quality graft, has become an almost routine procedure with 1-year survival rates higher than 80%. However, with the ensuing interminable increase in the waiting list, the current major limitation is the considerable shortage in organ donors and the need of timely availability of suitable livers. As a result, although death rate after surgery is slowly decreasing, the number of total deaths in waiting list patients is steadily rising. Several solutions have been proposed to overcome this problem, such as legislative measures, mass media campaigns, optimization of available organ allocation, or innovative surgical techniques such as split-liver, living donor, non-heart beating donor, and domino transplantation. However, these measures have been met with only limited success in providing enough liver grafts (Neuberger, 2000; Thalheimer & Capra, 2002). Hence, the research community endeavoured to establish clinical alternatives to liver transplantation. Cell-based therapies are emerging as an alternative to whole-organ transplantation, which has shown initial promise in both animal models and clinical cases. This novel technique may provide functional liver support while the native liver regenerates in patients of acute liver failure, may provide a short-term “bridge” to sustain critical patients until OLT, or may aid in replacing a missing enzyme function in metabolic conditions with the aim of avoiding OLT. Some of the most promising cells types that could be used in this emerging field are hepatocytes, embryonic stem cells (ESC), mesenchymal stromal cells (MSC), amnion epithelial (AE) cells, and induced pluripotent stem cells (iPSC).


title: Safe and Reliable Path Planning for the Autonomous Vehicle Verdino

abstract: This paper introduces a local planner which computes a set of commands, allowing an autonomous vehicle to follow a given trajectory. To do so, the platform relies on a localization system, a map and a cost map which represents the obstacles in the environment. The presented method computes a set of tentative trajectories, using a schema based on a Fren?t frame obtained from the global planner. These trajectories are then scored using a linear combination of weighted cost functions. In the presented approach, new weights are introduced in order to satisfy the specificities of our autonomous platform, Verdino. A study on the influence of the defined weights in the final behavior of the vehicle is introduced. From these tests, several configurations have been chosen and ranked according to two different proposed behaviors. The method has been tested both in simulation and in real conditions.


title: Human-Agent Dialogues on Health Topics - An Evaluation Study

abstract: A common conversation between an older adult and a nurse about health-related issues includes topics such as troubles with sleep, reasons for walking around nighttime, pain conditions, etc. This di ...


title: Deep Cross-Modal Audio-Visual Generation

abstract: Cross-modal audio-visual perception has been a long-lasting topic in psychology and neurology, and various studies have discovered strong correlations in human perception of auditory and visual stimuli. Despite works in computational multimodal modeling, the problem of cross-modal audio-visual generation has not been systematically studied in the literature. In this paper, we make the first attempt to solve this cross-modal generation problem leveraging the power of deep generative adversarial training. Specifically, we use conditional generative adversarial networks to achieve cross-modal audio-visual generation of musical performances. We explore different encoding methods for audio and visual signals, and work on two scenarios: instrument-oriented generation and pose-oriented generation. Being the first to explore this new problem, we compose two new datasets with pairs of images and sounds of musical performances of different instruments. Our experiments using both classification and human evaluations demonstrate that our model has the ability to generate one modality, i.e., audio/visual, from the other modality, i.e., visual/audio, to a good extent. Our experiments on various design choices along with the datasets will facilitate future research in this new problem space.


title: NICO: An Open-Source Interface, Bridging the Gap Between Musician and Tesla Coil

abstract: This paper describes the design and implementation of a new interface, Nico, which allows new and existing musical devices to control Tesla Coils. The interface uses an Arduino microcontroller to convert OSC and MIDI control data to TTL pulses that command the behavior of a Tesla Coil. This paper will give some background on the Tesla Coil, outline the design and capabilities of the interface, and will discuss the performance possibilities that Nico creates.


title: Related Fact Checks: a tool for combating fake news

abstract: The emergence of"Fake News"and misinformation via online news and social media has spurred an interest in computational tools to combat this phenomenon. In this paper we present a new"Related Fact Checks"service, which can help a reader critically evaluate an article and make a judgment on its veracity by bringing up fact checks that are relevant to the article. We describe the core technical problems that need to be solved in building a"Related Fact Checks"service, and present results from an evaluation of an implementation.


title: Learning versus performance: an integrative review.

abstract: The primary goal of instruction should be to facilitate long-term learning—that is, to create relatively permanent changes in comprehension, understanding, and skills of the types that will support long-term retention and transfer. During the instruction or training process, however, what we can observe and measure is performance, which is often an unreliable index of whether the relatively long-term changes that constitute learning have taken place. The time-honored distinction between learning and performance dates back decades, spurred by early animal and motor-skills research that revealed that learning can occur even when no discernible changes in performance are observed. More recently, the converse has also been shown—specifically, that improvements in performance can fail to yield significant learning—and, in fact, that certain manipulations can have opposite effects on learning and performance. We review the extant literature in the motor- and verbal-learning domains that necessitates the distinc...


title: Feature-based time-series analysis

abstract: This work presents an introduction to feature-based time-series analysis. The time series as a data type is first described, along with an overview of the interdisciplinary time-series analysis literature. I then summarize the range of feature-based representations for time series that have been developed to aid interpretable insights into time-series structure. Particular emphasis is given to emerging research that facilitates wide comparison of feature-based representations that allow us to understand the properties of a time-series dataset that make it suited to a particular feature-based representation or analysis algorithm. The future of time-series analysis is likely to embrace approaches that exploit machine learning methods to partially automate human learning to aid understanding of the complex dynamical patterns in the time series we measure from the world.


title: Sequential Correspondence of Multiple Visual Cues for 3-D Building Reconstruction

abstract: Traditional building height estimation from a stereo pair of satellite images relies heavily on the selection of building primitives by the human operator. It is labor-intensive and highly inefficient. In this work, we propose a system to automate the estimation of 3-D building height from an IKONOS stereo pair. Minimum user intervention is required to outline the building top in the left image. Stereo correspondence is automatically established through a multi-scale feature (coarse-to-fine) matching, which is capable of achieving sub-pixel accuracy. The height of the building top is computed using the Rational Polynomial Coefficient (RPC) model. Experimental results show that the proposed method is efficient, robust and it produces accurate building height estimation.


title: DISTRIBUTION OF ORGANOCHLORINE PESTICIDES AND HEAVY METAL RESIDUES IN FISH AND SHELLFISH FROM CALICUT REGION, KERALA, INDIA

abstract: Abstract Fish collected from five different locations from the Calicut region, India were analysed for the levels of organochlorine pesticides (OCPs) and heavy metal (HM) residues in order to elucidate the status of these chemical contaminants in fish and shellfish meant for human consumption. The highest concentrations of OCPs detected in the edible portion of fish were 10.47, 70.57 and 28.35 ng g −1 wet weight, in marine, brackish water and freshwater, respectively. BHC and heptachlor epoxide formed the major share of OCPs in the marine fish while BHCs contributed to the major share in the freshwater and brackish water fish. The DDT (sum of DDT and its metabolites) ranged from 0.05 to 8 ng g −1 in the samples irrespective of the habitat. Among the HMs, Cu, Zn, Mn and Cr were present in the samples irrespective of the place of collection. About 22% of the samples from the freshwater area contained marginally higher lead content above the legal limit of 0.2–0.4 μg g −1 . The Cr content ranged from 0.1 to 1.1 μg g −1 in the fish with marginally higher content in the fish from freshwater and marine regions (0.55–1.1 μg g −1 ). Mercury was detected in the samples (55%) from marine habitat only. The concentrations of OCPs and HMs in the samples, in general, were below the maximum residual level recommended by various organizations.


title: Contributions of Theodor Wilhelm Engelmann on phototaxis, chemotaxis, and photosynthesis

abstract: Theodor Wilhelm Engelmann (1843-1909), who had a creative life in music, muscle physiology, and microbiology, developed a sensitive method for tracing the photosynthetic oxygen production of unicellular plants by means of bacterial aerotaxis (chemotaxis). He discovered the absorption spectrum of bacteriopurpurin (bacteriochlorophyll a) and the scotophobic response, photokinesis, and photosynthesis of purple bacteria.


Design Efficiency Analysis Towards Product Improvement for Eco-Friendly Using DFMA Method


title: View Synthesis for Advanced 3D Video Systems

abstract: Interest in 3D video applications and systems is growing rapidly and technology is maturating. It is expected that multiview autostereoscopic displays will play an important role in home user environments, since they support multiuser 3D sensation and motion parallax impression. The tremendous data rate cannot be handled efficiently by representation and coding formats such as MVC or MPEG-C Part 3. Multiview video plus depth (MVD) is a new format that efficiently supports such advanced 3DV systems, but this requires high-quality intermediate view synthesis. For this, a new approach is presented that separates unreliable image regions along depth discontinuities from reliable image regions, which are treated separately and fused to the final interpolated view. In contrast to previous layered approaches, our algorithm uses two boundary layers and one reliable layer, performs image-based 3D warping only, and was generically implemented, that is, does not necessarily rely on 3D graphics support. Furthermore, different hole-filling and filtering methods are added to provide high-quality intermediate views. As a result, high-quality intermediate views for an existing 9-view auto-stereoscopic display as well as other stereo- and multiscopic displays are presented, which prove the suitability of our approach for advanced 3DV systems.


title: Deep learning assessment of tumor proliferation in breast cancer histological images

abstract: Current analysis of tumor proliferation, the most salient breast cancer prognostic biomarker, is limited to subjective mitosis counting by pathologists in localized regions of tissue images. This study presents the first data-driven integrative approach to characterize the severity of tumor growth and spread on a categorical and molecular level, utilizing multiple biologically salient deep learning classifiers to develop a comprehensive prognostic model. Our approach achieves pathologist-level performance on three-class categorical tumor severity prediction. It additionally pioneers prediction of molecular expression data from a tissue image, obtaining a Spearman's rank correlation coefficient of 0.60 with ex vivo mean calculated RNA expression. Furthermore, our framework is applied to identify over two hundred unprecedented biomarkers critical to the accurate assessment of tumor proliferation, validating our proposed integrative pipeline as the first to holistically and objectively analyze histopathological images.


title: The 2014 General Video Game Playing Competition

abstract: This paper presents the framework, rules, games, controllers, and results of the first General Video Game Playing Competition, held at the IEEE Conference on Computational Intelligence and Games in 2014. The competition proposes the challenge of creating controllers for general video game play, where a single agent must be able to play many different games, some of them unknown to the participants at the time of submitting their entries. This test can be seen as an approximation of general artificial intelligence, as the amount of game-dependent heuristics needs to be severely limited. The games employed are stochastic real-time scenarios (where the time budget to provide the next action is measured in milliseconds) with different winning conditions, scoring mechanisms, sprite types, and available actions for the player. It is a responsibility of the agents to discover the mechanics of each game, the requirements to obtain a high score and the requisites to finally achieve victory. This paper describes all controllers submitted to the competition, with an in-depth description of four of them by their authors, including the winner and the runner-up entries of the contest. The paper also analyzes the performance of the different approaches submitted, and finally proposes future tracks for the competition.


title: Row-Reduced Column Generation for Degenerate Master Problems

abstract: Column generation for solving linear programs with a huge number of variables alternates between solving a master problem and a pricing subproblem to add variables to the master problem as needed. The method is known to often suffer from degeneracy in the master problem. Inspired by recent advances in coping with degeneracy in the primal simplex method, we propose a row-reduced column generation method that may take advantage of degenerate solutions. The idea is to reduce the number of constraints to the number of strictly positive basic variables in the current master problem solution. The advantage of this row-reduction is a smaller working basis, and thus a faster re-optimization of the master problem. This comes at the expense of a more involved pricing subproblem, itself eventually solved by column generation, that needs to generate weighted subsets of variables that are said compatible with the row-reduction, if possible. Such a subset of variables gives rise to a strict improvement in the objective function value if the weighted combination of the reduced costs is negative. We thus state, as a by-product, a necessary and sufficient optimality condition for linear programming.


title: Tracking EEG changes in response to alpha and beta binaural beats

abstract: Abstract A binaural beat can be produced by presenting two tones of a differing frequency, one to each ear. Such auditory stimulation has been suggested to influence behaviour and cognition via the process of cortical entrainment. However, research so far has only shown the frequency following responses in the traditional EEG frequency ranges of delta, theta and gamma. Hence a primary aim of this research was to ascertain whether it would be possible to produce clear changes in the EEG in either the alpha or beta frequency ranges. Such changes, if possible, would have a number of important implications as well as potential applications. A secondary goal was to track any observable changes in the EEG throughout the entrainment epoch to gain some insight into the nature of the entrainment effects on any changes in an effort to identify more effective entrainment regimes. Twenty two healthy participants were recruited and randomly allocated to one of two groups, each of which was exposed to a distinct binaural beat frequency for ten 1-minute epochs. The first group listened to an alpha binaural beat of 10 Hz and the second to a beta binaural beat of 20 Hz. EEG was recorded from the left and right temporal regions during pre-exposure baselines, stimulus exposure epochs and post-exposure baselines. Analysis of changes in broad-band and narrow-band amplitudes, and frequency showed no effect of binaural beat frequency eliciting a frequency following effect in the EEG. Possible mediating factors are discussed and a number of recommendations are made regarding future studies, exploring entrainment effects from a binaural beat presentation.


title: A current source PWM inverter with instantaneous current control capability [AC motors]

abstract: A novel approach for the control of a current-source PWM (pulsewidth-modulated) inverter is proposed to achieve instantaneous current control capability. The scheme provides instantaneous current response in the AC motor by altering the modulation index of the current-source PWM patterns. To achieve this task, optimum programmed PWM patterns with selective elimination of lower-order harmonics in the motor line currents are used. Output filter capacitors are provided to bypass the current harmonics in the inverter output and allow for the instantaneous changes in line currents. This method of control provides sinusoidal output voltages and sinusoidal currents in the AC motor. The scheme guarantees fast response characteristics combined with high-performance steady-state behavior. A design procedure for the current-source PWM and selection of programmed PWM current patterns for a variable-speed situation are illustrated. Selected results are verified experimentally. >


title: Design and Optimization of Resonance-Based Efficient Wireless Power Delivery Systems for Biomedical Implants

abstract: Resonance-based wireless power delivery is an efficient technique to transfer power over a relatively long distance. This technique typically uses four coils as opposed to two coils used in conventional inductive links. In the four-coil system, the adverse effects of a low coupling coefficient between primary and secondary coils are compensated by using high-quality (Q) factor coils, and the efficiency of the system is improved. Unlike its two-coil counterpart, the efficiency profile of the power transfer is not a monotonically decreasing function of the operating distance and is less sensitive to changes in the distance between the primary and secondary coils. A four-coil energy transfer system can be optimized to provide maximum efficiency at a given operating distance. We have analyzed the four-coil energy transfer systems and outlined the effect of design parameters on power-transfer efficiency. Design steps to obtain the efficient power-transfer system are presented and a design example is provided. A proof-of-concept prototype system is implemented and confirms the validity of the proposed analysis and design techniques. In the prototype system, for a power-link frequency of 700 kHz and a coil distance range of 10 to 20 mm, using a 22-mm diameter implantable coil resonance-based system shows a power-transfer efficiency of more than 80% with an enhanced operating range compared to ~40% efficiency achieved by a conventional two-coil system.


title: Integrated Transformer-Coupled Balun Bandpass Filters With an Optimal Common-Mode Rejection Ratio

abstract: This paper describes a compact transformer-coupled balun bandpass filter design based on integrated circuit technology. Using a planar transformer with high-density fully symmetrical wiring not only greatly reduces the component size, but also provides a superior balance at the differential output ports. In addition, applying the tapped feed-line structure to the balun bandpass filter is accomplished by carefully studying the impedance-matching method and transmission-zero mechanism. Interestingly, the proposed balun bandpass filter can guarantee an optimal common-mode rejection ratio level in a specific operating frequency range by precisely controlling the fractional bandwidth of the filter passband.


title: Eye Detection Using Morphological and Color Image Processing

abstract: Eye detection is required in many applications like eye-gaze tracking, iris detection, video conferencing, auto-stereoscopic displays, face detection and face recognition. This paper proposes a novel technique for eye detection using color and morphological image processing. It is observed that eye regions in an image are characterized by low illumination, high density edges and high contrast as compared to other parts of the face. The method proposed is based on assumption that a frontal face image (full frontal) is available. Firstly, the skin region is detected using a color based training algorithm and six-sigma technique operated on RGB, HSV and NTSC scales. Further analysis involves morphological processing using boundary region detection and detection of light source reflection by an eye, commonly known as an eye dot. This gives a finite number of eye candidates from which noise is subsequently removed. This technique is found to be highly efficient and accurate for detecting eyes in frontal face images.


title: 3D Mask Face Anti-spoofing with Remote Photoplethysmography

abstract: 3D mask spoofing attack has been one of the main challenges in face recognition. Among existing methods, texture-based approaches show powerful abilities and achieve encouraging results on 3D mask face anti-spoofing. However, these approaches may not be robust enough in application scenarios and could fail to detect imposters with hyper-real masks. In this paper, we propose a novel approach to 3D mask face anti-spoofing from a new perspective, by analysing heartbeat signal through remote Photoplethysmography (rPPG). We develop a novel local rPPG correlation model to extract discriminative local heartbeat signal patterns so that an imposter can better be detected regardless of the material and quality of the mask. To further exploit the characteristic of rPPG distribution on real faces, we learn a confidence map through heartbeat signal strength to weight local rPPG correlation pattern for classification. Experiments on both public and self-collected datasets validate that the proposed method achieves promising results under intra and cross dataset scenario.


title: Latent semantic modeling for slot filling in conversational understanding

abstract: In this paper, we propose a new framework for semantic template filling in a conversational understanding (CU) system. Our method decomposes the task into two steps: latent n-gram clustering using a semi-supervised latent Dirichlet allocation (LDA) and sequence tagging for learning semantic structures in a CU system. Latent semantic modeling has been investigated to improve many natural language processing tasks such as syntactic parsing or topic tracking. However, due to several complexity problems caused by issues involving utterance length or dialog corpus size, it has not been analyzed directly for semantic parsing tasks. In this paper, we propose extending the LDA by introducing prior knowledge we obtain from semantic knowledge bases. Then, the topic posteriors obtained from the new LDA model are used as additional constraints to a sequence learning model for the semantic template filling task. The experimental results show significant performance gains on semantic slot filling models when features from latent semantic models are used in a conditional random field (CRF).


title: Measuring Service Quality: Servqual vs. Servperf Scales

abstract: Quality has come to be recognized as a strategic tool for attaining operational efficiency and improved business performance. This is true for both the goods and services sectors. However, the problem with management of service quality in service firms is that quality is not easily identifiable and measurable due to inherent characteristics of services which make them different from goods. Various definitions of the term ‘service quality’ have been proposed in the past and, based on different definitions, different scales for measuring service quality have been put forward. SERVQUAL and SERVPERF constitute two major service quality measurement scales. The consensus, however, continues to elude till date as to which one is superior.An ideal service quality scale is one that is not only psychometrically sound but is also diagnostically robust enough to provide insights to the managers for corrective actions in the event of quality shortfalls. Empirical studies evaluating validity, reliability, and methodolo...


title: Don't make excuses! Discouraging neutralization to reduce IT policy violation

abstract: Past research on information technology (IT) security training and awareness has focused on informing employees about security policies and formal sanctions for violating those policies. However, research suggests that deterrent sanctions may not be the most powerful influencer of employee violations. Often, employees use rationalizations, termed neutralization techniques, to overcome the effects of deterrence when deciding whether or not to violate a policy. Therefore, neutralization techniques often are stronger than sanctions in predicting employee behavior. For this study, we examine ''denial of injury,'' ''metaphor of the ledger,'' and ''defense of necessity'' as relevant justifications for violating password policies that are commonly used in organizations as used in (Siponen and Vance, 2010). Initial research on neutralization in IS security has shown that results are consistent regardless of which type of neutralization is considered (Siponen and Vance, 2010). In this study, we investigate whether IT security communication focused on mitigating neutralization, rather than deterrent sanctions, can reduce intentions to violate security policies. Additionally, considering the effects of message framing in persuading individuals against security policy violations are largely unexamined, we predict that negatively-framed communication will be more persuasive than positively-framed communication. We test our hypotheses using the factorial survey method. Our results suggest that security communication and training that focuses on neutralization techniques is just as effective as communication that focuses on deterrent sanctions in persuading employees not to violate policies, and that both types of framing are equally effective.


title: Some unique properties of eigenvector centrality

abstract: Abstract Eigenvectors, and the related centrality measure Bonacich's c(β), have advantages over graph-theoretic measures like degree, betweenness, and closeness centrality: they can be used in signed and valued graphs and the beta parameter in c(β) permits the calculation of power measures for a wider variety of types of exchange. Degree, betweenness, and closeness centralities are defined only for classically simple graphs—those with strictly binary relations between vertices. Looking only at these classical graphs, where eigenvectors and graph–theoretic measures are competitors, eigenvector centrality is designed to be distinctively different from mere degree centrality when there are some high degree positions connected to many low degree others or some low degree positions are connected to a few high degree others. Therefore, it will not be distinctively different from degree when positions are all equal in degree (regular graphs) or in core-periphery structures in which high degree positions tend to be connected to each other.


title: A Critical Review of Robustness in Power Grids Using Complex Networks Concepts

abstract: This paper reviews the most relevant works that have investigated robustness in power grids using Complex Networks (CN) concepts. In this broad field there are two different approaches. The first one is based solely on topological concepts, and uses metrics such as mean path length, clustering coefficient, efficiency and betweenness centrality, among many others. The second, hybrid approach consists of introducing (into the CN framework) some concepts from Electrical Engineering (EE) in the effort of enhancing the topological approach, and uses novel, more efficient electrical metrics such as electrical betweenness, net-ability, and others. There is however a controversy about whether these approaches are able to provide insights into all aspects of real power grids. The CN community argues that the topological approach does not aim to focus on the detailed operation, but to discover the unexpected emergence of collective behavior, while part of the EE community asserts that this leads to an excessive simplification. Beyond this open debate it seems to be no predominant structure (scale-free, small-world) in high-voltage transmission power grids, the vast majority of power grids studied so far. Most of them have in common that they are vulnerable to targeted attacks on the most connected nodes and robust to random failure. In this respect there are only a few works that propose strategies to improve robustness such as intentional islanding, restricted link addition, microgrids and Energies 2015, 8 9212 smart grids, for which novel studies suggest that small-world networks seem to be the best topology.


title: Secure Data Mining in Cloud Using Homomorphic Encryption

abstract: With the advancement in technology, industry, e-commerce and research a large amount of complex and pervasive digital data is being generated which is increasing at an exponential rate and often termed as big data. Traditional Data Storage systems are not able to handle Big Data and also analyzing the Big Data becomes a challenge and thus it cannot be handled by traditional analytic tools. Cloud Computing can resolve the problem of handling, storage and analyzing the Big Data as it distributes the big data within the cloudlets. No doubt, Cloud Computing is the best answer available to the problem of Big Data storage and its analyses but having said that, there is always a potential risk to the security of Big Data storage in Cloud Computing, which needs to be addressed. Data Privacy is one of the major issues while storing the Big Data in a Cloud environment. Data Mining based attacks, a major threat to the data, allows an adversary or an unauthorized user to infer valuable and sensitive information by analyzing the results generated from computation performed on the raw data. This thesis proposes a secure k-means data mining approach assuming the data to be distributed among different hosts preserving the privacy of the data. The approach is able to maintain the correctness and validity of the existing k-means to generate the final results even in the distributed environment.


title: Transaction Repair for Multi-Version Concurrency Control

abstract: The optimistic variants of Multi-Version Concurrency Control (MVCC) avoid blocking concurrent transactions at the cost of having a validation phase. Upon failure in the validation phase, the transaction is usually aborted and restarted from scratch. The "abort and restart" approach becomes a performance bottleneck for use cases with high contention objects or long running transactions. In addition, restarting from scratch creates a negative feedback loop in the system, because the system incurs additional overhead that may create even more conflicts. In this paper, we propose a novel approach for conflict resolution in MVCC for in-memory databases. This low overhead approach summarizes the transaction programs in the form of a dependency graph. The dependency graph also contains the constructs used in the validation phase of the MVCC algorithm. Then, when encountering conflicts among transactions, our mechanism quickly detects the conflict locations in the program and partially re-executes the conflicting transactions. This approach maximizes the reuse of the computations done in the initial execution round, and increases the transaction processing throughput.


title: Tics and Tourette: a clinical, pathophysiological and etiological review

abstract: PURPOSE OF REVIEW ::: Describe developments in the etiological understanding of Tourette syndrome. ::: ::: ::: RECENT FINDINGS ::: Tourette syndrome is a complex heterogenous clinical syndrome, which is not a unitary entity. Pathophysiological models describe gamma-aminobutyric acid-ergic-associated disinhibition of cortico-basal ganglia motor, sensory and limbic loops. MRI studies support basal ganglia volume loss, with additional white matter and cerebellar changes. Tourette syndrome cause likely involves multiple vulnerability genes and environmental factors. Only recently have some vulnerability gene findings been replicated, including histidine decarboxylase and neurexin 1, yet these rare variants only explain a small proportion of patients. Planned large genetic studies will improve genetic understanding. The role of inflammation as a contributor to disease expression is now supported by large epidemiological studies showing an association with maternal autoimmunity and childhood infection. Investigation of blood cytokines, blood mRNA and brain mRNA expression support the role of a persistent immune activation, and there are similarities with the immune literature of autistic spectrum disorder. Current treatment is symptomatic, although there is a better appreciation of factors that influence treatment response. ::: ::: ::: SUMMARY ::: At present, therapeutics is focused on symptom-based treatments, yet with improved etiological understanding, we will move toward disease-modifying therapies in the future.


Vection and visually induced motion sickness: how are they related?


title: Strategic fit in transitional economies: The case of China’s electronics industry

abstract: The strategic fit paradigm, originally derived from observations in market-based economies, asserts that an appropriate match between environment and strategy has significant and positive implications for business performance. Based on a random sample of Chinese electronics firms, this study systematically examines the applicability of the strategic fit paradigm in China’s centrally planned economy in transition. Results indicate that while environment-strategy coalignment is evident, coalignment only improves performance under certain environmental conditions.


title: Non-invasive tracking and monitoring glucose content using near infrared spectroscopy

abstract: Diabetes is deliberate as one of the major contributors of crystallize infirmity and death in non-infectious diseases. Common method for determination of blood glucose concentration is using a self-monitoring glucose meter. This process requires pricking the finger and pry out the blood from the forearm and doing the chemical analysis with the help of disposable test strips. The pain and difficulty caused by this method have lead to the evolution of a noninvasive method. This method makes use of a near infrared sensor for transmission and reception of rays from fingertip. Near-infrared (NIR) is passing through the fingertip, before and after obstructing the flow of blood. By explicate the variation in received signal intensity achieved after reflection in both the cases, glucose present in blood can be predicted and wirelessly transmitted to a remote PC. The results show the potential of glucose measurement using Near-infrared.


A manual of intergroup relations


title: An Adaptive Control for UPS to Compensate Unbalance and Harmonic Distortion Using a Combined Capacitor/Load Current Sensing

abstract: This paper investigates the control of an uninterruptible power supply (UPS) using a combined measurement of capacitor and load currents in the same current sensor arrangement. The purpose of this combined measurement is, on one hand, to reach a similar performance as that obtained in the inductor current controller with load current feedforward and, on the other hand, to easily obtain an estimate of the inductor current for overcurrent protection capability. Based on this combined current measurement, a voltage controller based on resonant harmonic filters is investigated in order to compensate for unbalance and harmonic distortion on the load. Adaptation is included to cope with uncertainties in the system parameters. It is shown that after transformations the proposed controller gets a simple and practical form that includes a bank of resonant filters, which is in agreement with the internal model principle and corresponds to similar approaches proposed recently. The controller is based on a frequency-domain description of the periodic disturbances, which include both symmetric components, namely, the negative and positive sequence. Experimental results on the output stage of a three-phase three-wire UPS are presented to assess the performance of the proposed algorithm


title: Weight-loss induced changes in plasma factor VII coagulant activity and relation to the factor VII Arg/Gln353 polymorphism in moderately obese adults

abstract: Elevated plasma factor VII coagulant activity (factor Vllc) may be an independent risk factor for coronary heart disease. Several cross-sectional studies suggest that a polymorphism of the factor VII gene (Arg-Gln 353 ) interacts with plasma triglyceride level in determining factor VIIc. but prospective data are lacking. Factor VII genotype. factor VIIc, and triglyceride level were measured in moderately obese adults aged 25 to 45 who underwent a six-month clinical trial to evaluate strategies for weight loss. A total of men and 50 women who experienced substantial weight loss (mean: 10 kg) provided samples for genetic analysis. Overall, 78% of participants were homozygous for the Arg 353 allele. while the remaining 22% were heterozygous (Arg/Gln 353 ). At the baseline examination, heterozygotes had lower mean factor VIIc than Arg 353 homozygotes (92% vs. 112%; p 120%). Cross-sectional and longitudinal associations between plasma factor Vllc and triglyceride level were not dependent on genotype. These data confirm that the Gln 353 allele is associated with lower factor VII coagulant activity in moderately obese adults, but they do not support the hypothesis that the Arg-Gln 353 polymorphism interacts with plasma triglyceride level in determining factor Vllc.


title: Civic crowdfunding: how do offline communities engage online?

abstract: Civic crowdfunding is a sub-type of crowdfunding whereby citizens contribute to funding community-based projects ranging from physical structures to amenities. Though civic crowdfunding has great potential for impact, it remains a developing field in terms of project success and widespread adoption. To explore how technology shapes interactions and outcomes within civic projects, our research addresses two interrelated questions: how do offline communities engage online across civic crowdfunding projects, and, what purpose does this activity serve both projects and communities? These questions are explored through discussion of types of offline communities and description of online activity across civic crowdfunding projects. We conclude by considering the implications of this knowledge for civic crowdfunding and its continued research.


A Conversational Agent Powered by Online Learning ( Extended Abstract )


Fast Author Name Disambiguation in CiteSeer


title: Globalization and health: a framework for analysis and action.

abstract: Globalization is a key challenge to public health, especially in developing countries, but the linkages between globalization and health are complex. Although a growing amount of literature has appeared on the subject, it is piecemeal, and suffers from a lack of an agreed framework for assessing the direct and indirect health effects of different aspects of globalization. This paper presents a conceptual framework for the linkages between economic globalization and health, with the intention that it will serve as a basis for synthesizing existing relevant literature, identifying gaps in knowledge, and ultimately developing national and international policies more favourable to health. The framework encompasses both the indirect effects on health, operating through the national economy, household economies and health-related sectors such as water, sanitation and education, as well as more direct effects on population-level and individual risk factors for health and on the health care system. Proposed also is a set of broad objectives for a programme of action to optimize the health effects of economic globalization. The paper concludes by identifying priorities for research corresponding with the five linkages identified as critical to the effects of globalization on health.


title: EMULATOR vs REAL PHONE: Android Malware Detection Using Machine Learning

abstract: The Android operating system has become the most popular operating system for smartphones and tablets leading to a rapid rise in malware. Sophisticated Android malware employ detection avoidance techniques in order to hide their malicious activities from analysis tools. These include a wide range of anti-emulator techniques, where the malware programs attempt to hide their malicious activities by detecting the emulator. For this reason, countermeasures against anti-emulation are becoming increasingly important in Android malware detection. Analysis and detection based on real devices can alleviate the problems of anti-emulation as well as improve the effectiveness of dynamic analysis. Hence, in this paper we present an investigation of machine learning based malware detection using dynamic analysis on real devices. A tool is implemented to automatically extract dynamic features from Android phones and through several experiments, a comparative analysis of emulator based vs. device based detection by means of several machine learning algorithms is undertaken. Our study shows that several features could be extracted more effectively from the on-device dynamic analysis compared to emulators. It was also found that approximately 24% more apps were successfully analysed on the phone. Furthermore, all of the studied machine learning based detection performed better when applied to features extracted from the on-device dynamic analysis.


title: A Multi-Horizon Quantile Recurrent Forecaster

abstract: We propose a framework for general probabilistic multi-step time series regression. Specifically, we exploit the expressiveness and temporal nature of Recurrent Neural Networks, the nonparametric nature of Quantile Regression and the efficiency of Direct Multi-Horizon Forecasting. A new training scheme for recurrent nets is designed to boost stability and performance. We show that the approach accommodates both temporal and static covariates, learning across multiple related series, shifting seasonality, future planned event spikes and cold-starts in real life large-scale forecasting. The performance of the framework is demonstrated in an application to predict the future demand of items sold on Amazon.com, and in a public probabilistic forecasting competition to predict electricity price and load.


title: Visual Grounding via Accumulated Attention

abstract: Visual Grounding (VG) aims to locate the most relevant object or region in an image, based on a natural language query. The query can be a phrase, a sentence or even a multi-round dialogue. There are three main challenges in VG: 1) what is the main focus in a query; 2) how to understand an image; 3) how to locate an object. Most existing methods combine all the information curtly, which may suffer from the problem of information redundancy (i.e. ambiguous query, complicated image and a large number of objects). In this paper, we formulate these challenges as three attention problems and propose an accumulated attention (A-ATT) mechanism to reason among them jointly. Our A-ATT mechanism can circularly accumulate the attention for useful information in image, query, and objects, while the noises are ignored gradually. We evaluate the performance of A-ATT on four popular datasets (namely Refer-COCO, ReferCOCO+, ReferCOCOg, and Guesswhat?!), and the experimental results show the superiority of the proposed method in term of accuracy.


title: Lower-Extremity Muscle Activation during the Star Excursion Balance Tests

abstract: Objective: To identify integrated EMG (I-EMG) activity of 6 lower-extremity muscles during the 8 Star Excursion Balance Tests (SEBTs). Design and Setting: Repeated measures, laboratory setting. Subjects: 10 healthy young adults. Interventions: The SEBTs require the subject to balance on the stance leg and maximally reach with the contralateral foot along each of 8 lines extending from a common axis at 45° intervals. Measures: I-EMG activity of the vastus medialis obliquus (VMO), vastus lateralis (VL), medial hamstring (MH), biceps femoris (BF), anterior tibialis (AT), and gas-trocnemius. Results: Significant differences were found in all muscles (P < .05) except the gastrocnemius (P = .08). VMO and VL activity tended to be greatest with anteriorly directed excursions, whereas the MH and BF activity were greatest with posteriorly directed excursions. AT activity was lowest with the lateral excursion. Conclusions: Performance of the different SEBTs results in different lower-extremity muscle-activation patt...


title: DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems

abstract: Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data. We have seen wide adoption of DL in many safety-critical scenarios. However, a plethora of studies have shown that the state-of-the-art DL systems suffer from various vulnerabilities which can lead to severe consequences when applied to real-world applications. Currently, the testing adequacy of a DL system is usually measured by the accuracy of test data. Considering the limitation of accessible high quality test data, good accuracy performance on test data can hardly provide confidence to the testing adequacy and generality of DL systems. Unlike traditional software systems that have clear and controllable logic and functionality, the lack of interpretability in a DL system makes system analysis and defect detection difficult, which could potentially hinder its real-world deployment. In this paper, we propose DeepGauge, a set of multi-granularity testing criteria for DL systems, which aims at rendering a multi-faceted portrayal of the testbed. The in-depth evaluation of our proposed testing criteria is demonstrated on two well-known datasets, five DL systems, and with four state-of-the-art adversarial attack techniques against DL. The potential usefulness of DeepGauge sheds light on the construction of more generic and robust DL systems.


title: CAN - Ethernet gateway for automotive applications

abstract: This paper presents the design and practical implementation of a gateway interface between two networks, CAN and Ethernet. Data packets detected over a network will be routed to the other network, with the possibility of using various filters. Since there is no standard yet for this purpose, the paper also proposes a method to pack one or more CAN messages in an Ethernet frame. For testing purposes of the interface gateway and its implementation a series of graphical software tools running on a PC client have been developed. These tools are able to send and decode messages using the specified packaging. At the end of the paper a graphical application demonstrates the functionality of the gateway implementation and the benefits of the proposed CAN-Ethernet routing method.


title: A Deep Joint Network for Session-based News Recommendations with Contextual Augmentation

abstract: Session-based recommendations have drawn more and more attention in many recommendation settings of modern online services. Unlike many other domains such as books and music, news recommendations suffer from new challenges of fast updating rate and recency issues of news articles and lack of user profiles. In this paper, we proposed a method that combines user click events within session and news contextual features to predict next click behavior of a user. The model consists of two different kinds of hierarchical neutral networks to learn article contextual properties and temporal sequential patterns in streams of clicks. Character-level embedding over input features is adopted to allow integrating different types of data and reduce engineering computation. Besides, we also introduced a time-decay method to compute the freshness of news articles within a time slide. Experimental results on two real-world datasets show significant improvements over several baselines and state-of-the-art methods on session-based neural networks.


title: Figurative language comprehension in individuals with autism spectrum disorder: A meta-analytic review

abstract: We present a meta-analysis of studies that compare figurative language comprehension in individuals with autism spectrum disorder and in typically developing controls who were matched based on chronological age or/and language ability. A total of 41 studies and 45 independent effect sizes were included based on predetermined inclusion criteria. Group matching strategy, age, types of figurative language, and cross-linguistic differences were examined as predictors that might explain heterogeneity in effect sizes. Overall, individuals with autism spectrum disorder showed poorer comprehension of figurative language than their typically developing peers (Hedges’ g = –0.57). A meta-regression analysis showed that group matching strategy and types of figurative language were significantly related to differences in effect sizes, whereas chronological age and cross-linguistic differences were not. Differences between the autism spectrum disorder and typically developing groups were small and nonsignificant when t...


title: GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

abstract: Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal). While this can be addressed as a supervised learning problem, a significantly more challenging problem is that of detecting the unknown/unseen anomaly case that takes us instead into the space of a one-class, semi-supervised learning paradigm. We introduce such a novel anomaly detection model, by using a conditional generative adversarial network that jointly learns the generation of high-dimensional image space and the inference of latent space. Employing encoder-decoder-encoder sub-networks in the generator network enables the model to map the input image to a lower dimension vector, which is then used to reconstruct the generated output image. The use of the additional encoder network maps this generated image to its latent representation. Minimizing the distance between these images and the latent vectors during training aids in learning the data distribution for the normal samples. As a result, a larger distance metric from this learned data distribution at inference time is indicative of an outlier from that distribution—an anomaly. Experimentation over several benchmark datasets, from varying domains, shows the model efficacy and superiority over previous state-of-the-art approaches.


title: Successive Interference Cancellation in Heterogeneous Networks

abstract: At present, operators address the explosive growth of mobile data demand by densification of the cellular network so as to reduce the transmitter-receiver distance and to achieve higher spectral efficiency. Due to such network densification and the intense proliferation of wireless devices, modern wireless networks are interference-limited, which motivates the use of interference mitigation and coordination techniques. In this work, we develop a statistical framework to evaluate the performance of multi-tier heterogeneous networks with successive interference cancellation (SIC) capabilities, accounting for the computational complexity of the cancellation scheme and relevant network related parameters such as random location of the access points (APs) and mobile users, and the characteristics of the wireless propagation channel. We explicitly model the consecutive events of canceling interferers and we derive the success probability to cancel the $n$ -th strongest signal and to decode the signal of interest after $n$ cancellations. When users are connected to the AP which provides the maximum average received signal power, the analysis indicates that the performance gains of SIC diminish quickly with $n$ and the benefits are modest for realistic values of the signal-to-interference ration (SIR). We extend the statistical model to include several association policies where distinct gains of SIC are expected: (i) maximum instantaneous SIR association, (ii) minimum load association, and (iii) range expansion. Numerical results show the effectiveness of SIC for the considered association policies. This work deepens the understanding of SIC by defining the achievable gains for different association policies in multi-tier heterogeneous networks.


title: Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge

abstract: This paper explores the task of translating natural language queries into regular expressions which embody their meaning. In contrast to prior work, the proposed neural model does not utilize domain-specific crafting, learning to translate directly from a parallel corpus. To fully explore the potential of neural models, we propose a methodology for collecting a large corpus of regular expression, natural language pairs. Our resulting model achieves a performance gain of 19.6% over previous state-of-the-art models.


title: Facile synthesis of soluble graphene via a green reduction of graphene oxide in tea solution and its biocomposites.

abstract: The chemical reduction of graphene oxide (GO) typically involves highly toxic reducing agents that are harmful to human health and environment, and complicated surface modification is often needed to avoid aggregation of the reduced GO during reduction process. In this paper, a green and facile strategy is reported for the fabrication of soluble reduced GO. The proposed method is based on the reduction of exfoliated GO in green tea solution by making use of the reducing capability and the aromatic rings of tea polyphenol (TP) that contained in tea solution. The measurements of the resultant graphene confirm the efficient removal of the oxygen-containing groups in GO. The strong interactions between the reduced graphene and the aromatic TPs guarantee the good dispersion of the reduced graphene in both aqueous and a variety of organic solvents. These features endow this green approach with great potential in constructing of various graphene-based materials, especially for high-performance biorelated materials as demonstrated in this study of chitosan/graphene composites.


title: Analysis and comparison of MIMO radar waveforms

abstract: Choosing a proper waveform is a critical task for the implementation of multiple-input multiple-output (MIMO) radars. In addition to the general requirements for radar waveforms such as good resolution, low sidelobes, etc, MIMO radar waveforms also should possess good orthogonality. In this paper we give a brief overview of MIMO radar waveforms, which are classified into four categories: (1) time division multiple access (TDMA), (2) frequency division multiple access (FDMA), (3) Doppler division multiple access (DDMA), and (4) code division multiple access (CDMA). A special circulating MIMO waveform is also addressed The properties as well as application limitations of different waveforms are analyzed and compared. Some simulations results are also presented to illustrate the respective performance of different waveforms.


title: Towards Deviceless Edge Computing: Challenges, Design Aspects, and Models for Serverless Paradigm at the Edge

abstract: The serverless paradigm has been rapidly adopted by developers of cloud-native applications, mainly because it relieves them from the burden of provisioning, scaling, and operating the underlying infrastructure. In this chapter, we propose a novel computing paradigm—Deviceless Edge Computing— that extends the serverless paradigm to the edge of the network, enabling IoT and Edge devices, such as gateways and micro clouds, to be seamlessly integrated as application execution infrastructure. We propose a reference architecture for the Deviceless Edge Computing. We also analyze the main requirements and challenges to realize this novel computing paradigm from two main points of view: (1) required support for application development, in terms of programming models, and (2) required runtime support for deviceless applications, in terms of main deviceless platform mechanisms. Finally, we show how our existing work in the area of Edge Computing and IoT serves as starting point and as one of main enablers for realizing the emerging Deviceless Edge Computing.


Effective Front-End Project Management – A Key Element in Achieving Project Success in Developing Countries


title: Unbounded cache model for online language modeling with open vocabulary

abstract: Recently, continuous cache models were proposed as extensions to recurrent neural network language models, to adapt their predictions to local changes in the data distribution. These models only capture the local context, of up to a few thousands tokens. In this paper, we propose an extension of continuous cache models, which can scale to larger contexts. In particular, we use a large scale non-parametric memory component that stores all the hidden activations seen in the past. We leverage recent advances in approximate nearest neighbor search and quantization algorithms to store millions of representations while searching them efficiently. We conduct extensive experiments showing that our approach significantly improves the perplexity of pre-trained language models on new distributions, and can scale efficiently to much larger contexts than previously proposed local cache models.


title: Detection and classification of peer-to-peer traffic: A survey

abstract: The emergence of new Internet paradigms has changed the common properties of network data, increasing the bandwidth consumption and balancing traffic in both directions. These facts raise important challenges, making it necessary to devise effective solutions for managing network traffic. Since traditional methods are rather ineffective and easily bypassed, particular attention has been paid to the development of new approaches for traffic classification. This article surveys the studies on peer-to-peer traffic detection and classification, making an extended review of the literature. Furthermore, it provides a comprehensive analysis of the concepts and strategies for network monitoring.


title: Comparison of somatosensory evoked fields to airpuff and electric stimuli

abstract: We recorded somatosensory evoked magnetic fields (SEFs) from 6 healthy subjects with a 122-channel whole-scalp SQUID gradiometer. In separate experiments, airpuff stimuli were delivered to the dorsum of the proximal phalanx of the middle finger, and electric stimuli were delivered to the median nerve at the wrist; the interstimulus interval was 3 sec and left and right hands were stimulated in subsequent sessions. Airpuffs evoked clear and reproducible responses in all subjects. First responses were recorded over the SI cortex. All subjects showed SII responses both to contra- and ipsilateral airpuffs. The posterior parietal source, identified previously to electric stimulation, was activated also by airpuffs, but only in the right hemisphere. The earliest responses from SI were smaller in amplitude and longer in latency to airpuffs than to electric stimuli; the long-latency responses arising from the other somatosensory areas did not differ significantly.


title: Blockchain challenges and opportunities: a survey

abstract: Blockchain has numerous benefits such as decentralisation, persistency, anonymity and auditability. There is a wide spectrum of blockchain applications ranging from cryptocurrency, financial services, risk management, internet of things (IoT) to public and social services. Although a number of studies focus on using the blockchain technology in various application aspects, there is no comprehensive survey on the blockchain technology in both technological and application perspectives. To fill this gap, we conduct a comprehensive survey on the blockchain technology. In particular, this paper gives the blockchain taxonomy, introduces typical blockchain consensus algorithms, reviews blockchain applications and discusses technical challenges as well as recent advances in tackling the challenges. Moreover, this paper also points out the future directions in the blockchain technology.


title: Query expansion using lexical-semantic relations

abstract: Applications such as office automation, news filtering, help facilities in complex systems, and the like require the ability to retrieve documents from full-text databases where vocabulary problems can be particularly severe. Experiments performed on small collections with single-domain thesauri suggest that expanding query vectors with words that are lexically related to the original query words can ameliorate some of the problems of mismatched vocabularies. This paper examines the utility of lexical query expansion in the large, diverse TREC collection. Concepts are represented by WordNet synonym sets and are expanded by following the typed links included in WordNet. Experimental results show this query expansion technique makes little difference in retrieval effectiveness if the original queries are relatively complete descriptions of the information being sought even when the concepts to be expanded are selected by hand. Less well developed queries can be significantly improved by expansion of hand-chosen concepts. However, an automatic procedure that can approximate the set of hand picked synonym sets has yet to be devised, and expanding by the synonym sets that are automatically generated can degrade retrieval performance.


title: BI-based organizations: a sensemaking perspective

abstract: Business intelligence (BI) offers opportunities for managers to master vast data resources for operational and strategic gains, and allows BI-based organizations to generate significant business value. While several researchers emphasized the importance of BI to assist making quality decisions, no study explored the use of BI for improved understanding of business before such decisions are made and assessing the impact of the actions derived from these decisions. To fill this gap we use the theory of organizational sensemaking. The presented research uses hermeneutic phenomenology to study the experiences of decision-makers in using BI-generated insights to guide their actions while altering business processes, structures and information. The study emphasizes the necessity of using BI in the creation and maintenance of individual and organizational identity, as well as, enactment of this identity on the business and its environment, which need to be molded in response to changing circumstances.


title: JFIX: semantics-based repair of Java programs via symbolic PathFinder

abstract: Recently there has been a proliferation of automated program repair (APR) techniques, targeting various programming languages. Such techniques can be generally classified into two families: syntactic- and semantics-based. Semantics-based APR, on which we focus, typically uses symbolic execution to infer semantic constraints and then program synthesis to construct repairs conforming to them. While syntactic-based APR techniques have been shown success- ful on bugs in real-world programs written in both C and Java, semantics-based APR techniques mostly target C programs. This leaves empirical comparisons of the APR families not fully explored, and developers without a Java-based semantics APR technique. We present JFix, a semantics-based APR framework that targets Java, and an associated Eclipse plugin. JFix is implemented atop Symbolic PathFinder, a well-known symbolic execution engine for Java programs. It extends one particular APR technique (Angelix), and is designed to be sufficiently generic to support a variety of such techniques. We demonstrate that semantics-based APR can indeed efficiently and effectively repair a variety of classes of bugs in large real-world Java programs. This supports our claim that the framework can both support developers seeking semantics-based repair of bugs in Java programs, as well as enable larger scale empirical studies comparing syntactic- and semantics-based APR targeting Java. The demonstration of our tool is available via the project website at: https://xuanbachle.github.io/semanticsrepair/


The Digital Workplace Is Key to Digital Innovation


title: Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information

abstract: In marketing analytics applications in OR, the modeler often faces the problem of selecting key variables from a large number of possibilities. For example, SKU level retail store sales are affected by inter and intra category effects which potentially need to be considered when deciding on promotional strategy and producing operational forecasts. But no research has yet put this well accepted concept into forecasting practice: an obvious obstacle is the ultra-high dimensionality of the variable space. This paper develops a four steps methodological framework to overcome the problem. It is illustrated by investigating the value of both intra- and inter-category SKU level promotional information in improving forecast accuracy. The method consists of the identification of potentially influential categories, the building of the explanatory variable space, variable selection and model estimation by a multistage LASSO regression, and the use of a rolling scheme to generate forecasts. The success of this new method for dealing with high dimensionality is demonstrated by improvements in forecasting accuracy compared to alternative methods of simplifying the variable space. The empirical results show that models integrating more information perform significantly better than the baseline model when using the proposed methodology framework. In general, we can improve the forecasting accuracy by 12.6 percent over the model using only the SKU's own predictors. But of the improvements achieved, 95 percent of it comes from the intra-category information, and only 5 percent from the inter-category information. The substantive marketing results also have implications for promotional category management.


title: The Sybil Attack

abstract: Large-scale peer-to-peer systems face security threats from faulty or hostile remote computing elements. To resist these threats, many such systems employ redundancy. However, if a single faulty entity can present multiple identities, it can control a substantial fraction of the system, thereby undermining this redundancy. One approach to preventing these "Sybil attacks" is to have a trusted agency certify identities. This paper shows that, without a logically centralized authority, Sybil attacks are always possible except under extreme and unrealistic assumptions of resource parity and coordination among entities.


title: Predicting Adverse Drug Events Using Heterogeneous Event Sequences

abstract: Adverse drug events (ADEs) are known to be severely under-reported in electronic health record (EHR) systems. One approach to mitigate this problem is to employ machine learning methods to detect and signal for potentially missing ADEs, with the aim of increasing reporting rates. There are, however, many challenges involved in constructing prediction models for this task, since data present in health care records is heterogeneous, high dimensional, sparse and temporal. Previous approaches typically employ bag-of-items representations of clinical events that are present in a record, ignoring the temporal aspects. In this paper, we study the problem of classifying heterogeneous and multivariate event sequences using a novel algorithm building on the well known concept of ensemble learning. The proposed approach is empirically evaluated using 27 datasets extracted from a real EHR database with different ADEs present. The results indicate that the proposed approach, which explicitly models the temporal nature of clinical data, can be expected to outperform, in terms of the trade-off between precision and recall, models that do no consider the temporal aspects.


title: CLIN.CHEM.37/9, 1633-1638 (1991) Absorption Spectra of Human Fetal and Adult Oxyhemoglobin, De-Oxyhemoglobin,

abstract: We determined the millimolar absorptivities of the four clinically relevant derivatives of fetal and adult human hemoglobin in the visible and near-infrared spectral range (450-1000 nm). As expected, spectral absorption curves of similar shape were found, but the small differences between fetal and adult hemoglobin absorptivity were important enough that they should be taken into account in multicomponent analysis of hemoglobin derivatives. Common pulse oximeters, however, involving light of 660 and 940 nm, are so insensitive to the presence of fetal hemoglobin that they can be used safely in neonates. The error in pulse oximetry caused by the presence of carboxyhemoglobin is insubstantial, but methemoglobin gives either an understimation or an overestimation at high or low oxygen saturation, respectively, the turning point being near 70% saturation.


title: From names to entities using thematic context distance

abstract: Name ambiguity arises from the polysemy of names and causes uncertainty about the true identity of entities referenced in unstructured text. This is a major problem in areas like information retrieval or knowledge management, for example when searching for a specific entity or updating an existing knowledge base. We approach this problem of named entity disambiguation (NED) using thematic information derived from Latent Dirichlet Allocation (LDA) to compare the entity mention's context with candidate entities in Wikipedia represented by their respective articles. We evaluate various distances over topic distributions in a supervised classification setting to find the best suited candidate entity, which is either covered in Wikipedia or unknown. We compare our approach to a state of the art method and show that it achieves significantly better results in predictive performance, regarding both entities covered in Wikipedia as well as uncovered entities. We show that our approach is in general language independent as we obtain equally good results for named entity disambiguation using the English, the German and the French Wikipedia.


title: Evidence of Online Shopping: A Consumer Perspective

abstract: Although electronic commerce (e-retailing) has received considerable research attentions these years, little research has examined the effect of e-commerce's Web presentation on retail customer shopping experiences for Jordanian online retail shoppers. Five factors were employed to define their effect on the purchase decision through internet. Questionnaire used to collect data, and distributed to a stratified sample of 215 respondents. Data for 181 questionnaires were valid and analyzed by using descriptive and analytical statistics. We found that promotion (r = 0.344) and security (r = 0.316) are the most influential factors on consumer purchase decision through internet while eprice(r=0.028) was not related significantly with the consumer purchasing decision. The Infrastructure for internet (r = 0.175) and product and service characteristics (r = 0.264) were moderately related to the consumer’ decision process.


Patterns and dynamics of users' behavior and interaction: Network analysis of an online community


title: Clickers in college classrooms : Fostering learning with questioning methods in large lecture classes q

abstract: What can be done to promote student–instructor interaction in a large lecture class? One approach is to use a personal response system (or ‘‘clickers”) in which students press a button on a hand-held remote control device corresponding to their answer to a multiple choice question projected on a screen, then see the class distribution of answers on a screen, and discuss the thinking that leads to the correct answer. Students scored significantly higher on the course exams in a college-level educational psychology class when they used clickers to answer 2 to 4 questions per lecture (clicker group), as compared to an identical class with in-class questions presented without clickers (no-clicker group, d = 0.38) or with no in-class questions (control group, d = 0.40). The clicker treatment produced a gain of approximately 1/3 of a grade point over the no-clicker and control groups, which did not differ significantly from each other. Results are consistent with the generative theory of learning, which predicts students in the clicker group are more cognitively engaged during learning.