title: A huge, undescribed soil ciliate (Protozoa: Ciliophora) diversity in natural forest stands of Central Europe

abstract: We investigated 12 natural forest stands in eastern Austria for soil ciliate diversity, viz., eight beech forests and two lowland and Pinus nigra forests each. The stands span a wide range of climatic (e.g., 543–1759 mm precipitation, 160–1035 m above sea-level) and abiotic (e.g., pH 4–7.4) factors. Samples were taken twice in autumn and late spring and analysed with the non-flooded Petri dish method. Species were identified in vivo, in silver preparations, and in the scanning electron microscope. A total of 233 species were found, of which 30 were undescribed, a surprising number showing our ignorance of soil ciliate diversity, even in Central Europe. Species number varied highly from 45 (acidic beech on silicate) to 120 (floodplain forest) and was strongly correlated with pH and overall habitat quality, as measured by climate, the C/P quotient (ratio of r-selected colpodean and k-selected polyhymenophorean ciliates), and the proportion of mycophagous ciliate species; multivariate analysis showed further important variables, viz., the general nutrient status (glucose, nitrogen, C/ N ratio) and microbial (urease) activity. The highest species number occurred in one of the two floodplain soils, supporting the intermediate disturbance hypothesis. The three main forest types could be clearly distinguished by their ciliate communities, using similarity indices and multidimensional scaling. Individual numbers varied highly from 135−1 (lowland forest) to 10,925 ml−1 (beech on silicate) soil percolate and showed, interestingly, a weak correlation with soil protozoan phospholipid fatty acids. Eight of the 30 new species found and a forgotten species, Arcuospathidium coemeterii (Kahl 1943) nov. comb., are described in detail, as examples of how species were recognized and soil protozoan diversity should be analyzed: Latispathidium truncatum bimicronucleatum, Protospathidium fusioplites, Erimophrya sylvatica, E. quadrinucleata, Paragonostomum simplex, Periholosticha paucicirrata, P. sylvatica, and Australocirrus zechmeisterae.


title: Chatbot for university related FAQs

abstract: Chatbots are programs that mimic human conversation using Artificial Intelligence (AI). It is designed to be the ultimate virtual assistant, entertainment purpose, helping one to complete tasks ranging from answering questions, getting driving directions, turning up the thermostat in smart home, to playing one's favorite tunes etc. Chatbot has become more popular in business groups right now as they can reduce customer service cost and handles multiple users at a time. But yet to accomplish many tasks there is need to make chatbots as efficient as possible. To address this problem, in this paper we provide the design of a chatbot, which provides an efficient and accurate answer for any query based on the dataset of FAQs using Artificial Intelligence Markup Language (AIML) and Latent Semantic Analysis (LSA). Template based and general questions like welcome/ greetings and general questions will be responded using AIML and other service based questions uses LSA to provide responses at any time that will serve user satisfaction. This chatbot can be used by any University to answer FAQs to curious students in an interactive fashion.


title: BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services

abstract: Recent studies have shown the latency and energy consumption of deep neural networks can be significantly improved by splitting the network between the mobile device and cloud. This paper introduces a new deep learning architecture, called BottleNet, for reducing the feature size needed to be sent to the cloud. Furthermore, we propose a training method for compensating for the potential accuracy loss due to the lossy compression of features before transmitting them to the cloud. BottleNet achieves on average 30x improvement in end-to-end latency and 40x improvement in mobile energy consumption compared to the cloud-only approach with negligible accuracy loss.


title: Evaluation of Non-Intrusive Approaches for Wiener-Askey Generalized Polynomial Chaos

abstract: Polynomial chaos expansions (PCE) are an attractive technique for uncertainty quantification (UQ) due to their strong mathematical basis and ability to produce functional representations of stochastic variability. When tailoring the orthogonal polynomial bases to match the forms of the input uncertainties in a Wiener-Askey scheme, excellent convergence properties can be achieved for general probabilistic analysis problems. Non-intrusive PCE methods allow the use of simulations as black boxes within UQ studies, and involve the calculation of chaos expansion coefficients based on a set of response function evaluations. These methods may be characterized as being either Galerkin projection methods, using sampling or numerical integration, or regression approaches (also known as point collocation or stochastic response surfaces), using linear least squares. Numerical integration methods may be further categorized as either tensor product quadrature or sparse grid Smolyak cubature and as either isotropic or anisotropic. Experience with these approaches is presented for algebraic and PDE-based benchmark test problems, demonstrating the need for accurate, efficient coefficient estimation approaches that sca le for problems with significant numbers of random variables.


title: BOLD - Binary online learned descriptor for efficient image matching

abstract: In this paper we propose a novel approach to generate a binary descriptor optimized for each image patch independently. The approach is inspired by the linear discriminant embedding that simultaneously increases inter and decreases intra class distances. A set of discriminative and uncorrelated binary tests is established from all possible tests in an offline training process. The patch adapted descriptors are then efficiently built online from a subset of tests which lead to lower intra class distances thus a more robust descriptor. A patch descriptor consists of two binary strings where one represents the results of the tests and the other indicates the subset of the patch-related robust tests that are used for calculating a masked Hamming distance. Our experiments on three different benchmarks demonstrate improvements in matching performance, and illustrate that per-patch optimization outperforms global optimization.


title: Ecological models based on people's knowledge: a multi-step fuzzy cognitive mapping approach

abstract: Abstract Many types of ecological or environmental problems would benefit from models based on people’s knowledge. To create ecological models with both expert and local people’s knowledge, a multi-step fuzzy cognitive mapping approach is proposed. A cognitive map can be made of almost any system or problem. Cognitive maps are qualitative models of a system, consisting of variables and the causal relationships between those variables. We describe how our cognitive mapping research has been used in real environmental management applications. This research includes examining the perceptions of different stakeholders in an environmental conflict, obtaining the perceptions of different stakeholders to facilitate the development of participatory environmental management plans, and determining the wants and desires for resettlement of people displaced by a large scale dam project. Based on our research, which involved six separate studies, we have found that interviewees complete their cognitive maps in 40–90 min on average. These maps contain an average of 23±2 S.D. variables with 37±3 S.D. connections. People generally put more forcing functions into their maps than utility variables. Fuzzy cognitive mapping offers many advantages for ecological modeling including the ability to include abstract and aggregate variables in models, the ability to model relationships which are not known with certainty, the ability to model complex relationships which are full of feedback loops, and the ease and speed of obtaining and combining different knowledge sources and of running different policy options.


title: Let me Join Two Worlds! Analyzing the Integration of Web and Native Technologies in Hybrid Mobile Apps

abstract: We can notice that security problems of inappropriate integration of native and web technologies in hybrid mobile applications (apps) have been covered in the related state-of-the-art research. However, analyzing hybrid mobile apps' unique behaviors has been seldom addressed. In this paper, we explore the influence of native and web technologies integration in hybrid mobile apps on the generated profile of mobile applications. Specifically, we analyze the type of Security Sensitive APIs (SS-APIs) exposed to web content and identify the corresponding usage patterns by systematically tracking function-call-graphs of a large number of hybrid and native mobile apps. Our investigations indicate that the generated profiles for hybrid and native mobile apps are considerably different. Using our proposed tool, called Hybrid-scanner, for tracking and analyzing internal behaviors of hybrid mobile apps, we show that there is more trace of API calling for triggering a specific SS-API in a hybrid mobile app in comparison with Android native mobile apps. In addition, we have found that almost 40% of SS-APIs in hybrid mobile apps are invoked by third-party libraries, e.g. advertisement libraries. This knowledge, however, is crucial for designing appropriate malware detection or vulnerability mitigation strategies. Based on our results, we discuss two main approaches in Android malware analysis field and enumerate some suggestions which should be considered in order to successfully detect malicious behaviors in such new type of apps.


Stability Improvement of 8 T Sram Cell Using Schmitt Trigger


title: Graph mode-based contextual kernels for robust SVM tracking

abstract: Visual tracking has been typically solved as a binary classification problem. Most existing trackers only consider the pairwise interactions between samples, and thereby ignore the higher-order contextual interactions, which may lead to the sensitivity to complicated factors such as noises, outliers, background clutters and so on. In this paper, we propose a visual tracker based on support vector machines (SVMs), for which a novel graph mode-based contextual kernel is designed to effectively capture the higher-order contextual information from samples. To do so, we first create a visual graph whose similarity matrix is determined by a baseline visual kernel. Second, a set of high-order contexts are discovered in the visual graph. The problem of discovering these high-order contexts is solved by seeking modes of the visual graph. Each graph mode corresponds to a vertex community termed as a high-order context. Third, we construct a contextual kernel that effectively captures the interaction information between the high-order contexts. Finally, this contextual kernel is embedded into SVMs for robust tracking. Experimental results on challenging videos demonstrate the effectiveness and robustness of the proposed tracker.


title: A Random Network Generator with Finely Tunable Clustering Coefficient for Small-World Social Networks

abstract: Many social networks share two generic distinct features: power law distributions of degrees and a high clustering. In some cases, it is difficult to obtain the structure information of real networks. Network generators provide a way to generate test networks for simulation. We present a random network generator to generate test networks with prescribed power law distributions of degrees and a finely tunable average clustering coefficient. The generator is composed of three steps. First, the degree sequences are generated following the given degree power law exponents. Second, the generator constructs a test network with these degree sequences. Third, the test network is modified to meet the prescribed average clustering coefficient as closely as possible. Experiments show the impact of the clustering coefficient on network connectivity using this generator. The comparison with existing random network generators is presented.


title: Hereditary Nonpolyposis Colorectal Cancer in 95 Families: Differences and Similarities between Mutation-Positive and Mutation-Negative Kindreds

abstract: Hereditary nonpolyposis colorectal cancer (HNPCC) describes the condition of a disparate group of families that have in common a predisposition to colorectal cancer in the absence of a premalignant phenotype. The genetic basis of this disease has been linked to mutations in genes associated with DNA mismatch repair. A large proportion of families harbor changes in one of two genes, hMSH2 and hMLH1. Approximately 35% of families in which the diagnosis is based on the Amsterdam criteria do not appear to harbor mutations in DNA-mismatch-repair genes. In this report we present data from a large series of families with HNPCC and indicate that there are subtle differences between families that harbor germline changes in hMSH2 and families that harbor hMLH1 mutations. Furthermore, there are differences between the mutation-positive group (hMSH2 and hMLH1 combined) of families and the mutation-negative group of families. The major findings identified in this study focus primarily on the extracolonic disease profile observed between the mutation-positive families and the mutation-negative families. Breast cancer was not significantly overrepresented in the hMSH2 mutation-positive group but was overrepresented in the hMLH1 mutation-positive group and in the mutation-negative group. Prostate cancer was not overrepresented in the mutation-positive groups but was overrepresented in the mutation-negative group. In age at diagnosis of colorectal cancer, there was no difference between the hMSH2 mutation-positive group and the hMLH1 mutation-positive group, but there was a significant difference between these two groups and the mutation-negative group.


title: Empirical Studies on Usability of mHealth Apps: A Systematic Literature Review

abstract: The release of smartphones and tablets, which offer more advanced communication and computing capabilities, has led to the strong emergence of mHealth on the market. mHealth systems are being used to improve patients' lives and their health, in addition to facilitating communication between doctors and patients. Researchers are now proposing mHealth applications for many health conditions such as dementia, autism, dysarthria, Parkinson's disease, and so on. Usability becomes a key factor in the adoption of these applications, which are often used by people who have problems when using mobile devices and who have a limited experience of technology. The aim of this paper is to investigate the empirical usability evaluation processes described in a total of 22 selected studies related to mHealth applications by means of a Systematic Literature Review. Our results show that the empirical evaluation methods employed as regards usability could be improved by the adoption of automated mechanisms. The evaluation processes should also be revised to combine more than one method. This paper will help researchers and developers to create more usable applications. Our study demonstrates the importance of adapting health applications to users' need.


title: Load Forecasting via Deep Neural Networks

abstract: Abstract Nowadays, electricity plays a vital role in national economic and social development. Accurate load forecasting can help power companies to secure electricity supply and scheduling and reduce wastes since electricity is difficult to store. In this paper, we propose a novel Deep Neural Network architecture for short term load forecasting. We integrate multiple types of input features by using appropriate neural network components to process each of them. We use Convolutional Neural Network components to extract rich features from historical load sequence and use Recurrent Components to model the implicit dynamics. In addition, we use Dense layers to transform other types of features. Experimental results on a large data set containing hourly loads of a North China city show the superiority of our method. Moreover, the proposed method is quite flexible and can be applied to other time series prediction tasks.


title: Better than my loved ones: Social comparison tendencies among narcissists

abstract: Narcissists pursue superiority and status at frequent costs to their relationships, and social comparisons seem central to these pursuits. Critically, these comparison tendencies should distinguish narcissism from healthy self-esteem. We tested this hypothesis in a study examining individual differences in everyday comparison activity. Narcissists, relative to those with high self-esteem, (1) made more frequent social comparisons, particularly downward ones, (2) were more likely to think they were better-off than other important individuals in their lives, and (3) perceived themselves superior to these important individuals on agentic traits. However, narcissists’ positive emotional reactions to these self-flattering comparisons were based on their high self-esteem. These results suggest that comparison processes play an important role in narcissists’ endless pursuit of status and admiration.


title: A network on chip architecture and design methodology

abstract: We propose a packet switched platform for single chip systems which scales well to an arbitrary number of processor like resources. The platform, which we call Network-on-Chip (NOC), includes both the architecture and the design methodology. The NOC architecture is a m/spl times/n mesh of switches and resources are placed on the slots formed by the switches. We assume a direct layout of the 2-D mesh of switches and resources providing physical- and architectural-level design integration. Each switch is connected to one resource and four neighboring switches, and each resource is connected to one switch. A resource can be a processor core, memory, an FPGA, a custom hardware block or any other intellectual property (IP) block, which fits into the available slot and complies with the interface of the NOC. The NOC architecture essentially is the onchip communication infrastructure comprising the physical layer, the data link layer and the network layer of the OSI protocol stack. We define the concept of a region, which occupies an area of any number of resources and switches. This concept allows the NOC to accommodate large resources such as large memory banks, FPGA areas, or special purpose computation resources such as high performance multi-processors. The NOC design methodology consists of two phases. In the first phase a concrete architecture is derived from the general NOC template. The concrete architecture defines the number of switches and shape of the network, the kind and shape of regions and the number and kind of resources. The second phase maps the application onto the concrete architecture to form a concrete product.


title: Personalized online education - a crowdsourcing challenge

abstract: Interest in online education is surging, as dramatized bythe success of Khan Academy and recent Stanford online courses, but the technology for online education isin its infancy. Crowdsourcing mechanisms will likelybe essential in order to reach the full potential of thismedium. This paper sketches some of the challengesand directions we hope HCOMP researchers will address.


title: Development of a Home Automation System Using Wireless Sensor/Actuator Nodes

abstract: This work presents the design and implementation of a wireless home monitoring and automation system consisting of wireless sensor/actuator nodes, wireless camera, and a home server. The low-cost wireless sensor/actuator node features temperature, light intensity and motion sensors, and actuator driver circuits for the control of motors, heaters, and lights. Server and client programs used to monitor and control the home were also developed. The home server receives and processes sensor readings, such as temperature and light intensity readings, and also transmits user commands to wireless nodes. The system provides ambient condition monitoring, graphing of sensor data, intrusion detection, automated device control, and video monitoring in order to achieve improved security and comfort in the home. In addition, users have the flexibility of determining sensor-actuator interaction at run-time. The developed system could also put the home in various configurable modes based on user requests, time or environmental cues.


title: A Switched-Capacitor RF Power Amplifier

abstract: A fully integrated switched-capacitor power amplifier (SCPA) utilizes switched-capacitor techniques in an EER/Polar architecture. It operates on the envelope of a nonconstant envelope modulated signal as an RF-DAC in order to amplify the signal efficiently. The measured maximum output power and PAE are 25.2 dBm and 45%, respectively. When amplifying an 802.11g 64-QAM orthogonal frequency-division multiplexing (OFDM) signal, the measured error vector magnitude is 2.6% and the average output power and power-added efficiencies are 17.7 dBm and 27%, respectively.


title: Polypheny-DB: Towards a Distributed and Self-Adaptive Polystore

abstract: Cloud providers are more and more confronted with very diverse and heterogeneous requirements their customers impose on the management of data. First, these requirements stem from service-level agreements that specify a desired degree of availability and a guaranteed latency. As a consequence, Cloud providers replicate data across data centers or availability zones and/or partition data and place it close to the location of their customers. Second, the workload at each Cloud data center or availability zone is diverse and may significantly change over time – e. g., an OLTP workload during regular business hours and OLAP analyzes over night. For this, polystore and multistore databases have recently been introduced as they are intrinsically able to cope with such mixed and varying workloads. While the problem of heterogeneous requirements on data management in the Cloud is either addressed at global level by replicating and partitioning data across data centers or at local level by providing polystore systems in a Cloud data center, there is no integrated solution that leverages the benefits of both approaches. In this paper, we present the Polypheny-DB vision of a distributed polystore system that seamlessly combines replication and partitioning with local polystores and that is able to dynamically adapt all parts of the system when the workload changes. We present the basic building blocks for both parts of the system and we discuss open challenges towards the implementation of the Polypheny-DB vision.


Acquiring Predicate Paraphrases from News Tweets


title: Improving the effectiveness of peer feedback for learning

abstract: Abstract The present study examined the effectiveness of (a) peer feedback for learning, more specifically of certain characteristics of the content and style of the provided feedback, and (b) a particular instructional intervention to support the use of the feedback. A quasi-experimental repeated measures design was adopted. Writing assignments of 43 students of Grade 7 in secondary education showed that receiving ‘justified’ comments in feedback improves performance, but this effect diminishes for students with better pretest performance. Justification was superior to the accuracy of comments. The instructional intervention of asking assessees to reflect upon feedback after peer assessment did not increase learning gains significantly.


title: Grid synchronization of power converters using multiple second order generalized integrators

abstract: This paper presents a new frequency-adaptive synchronization method for grid-connected power converters which allows estimating not only the positive- and negative-sequence components of the power signal at the fundamental frequency, but also other sequence components at higher frequencies. The proposed system is called the MSOGI-FLL since it is based on a decoupled network consisting of multiple second order generalized integrators (MSOGI) which are frequency-adaptive by using a frequency-locked loop (FLL). In this paper, the MSOGI-FLL is analyzed and its performance is evaluated by both simulations and experiments.


title: Computational methods for modeling facial aging: A survey

abstract: Facial aging, a new dimension that has recently been added to the problem of face recognition, poses interesting theoretical and practical challenges to the research community. The problem which originally generated interest in the psychophysics and human perception community has recently found enhanced interest in the computer vision community. How do humans perceive age? What constitutes an age-invariant signature that can be derived from faces? How compactly can the facial growth event be described? How does facial aging impact recognition performance? In this paper, we give a thorough analysis on the problem of facial aging and further provide a complete account of the many interesting studies that have been performed on this topic from different fields. We offer a comparative analysis of various approaches that have been proposed for problems such as age estimation, appearance prediction, face verification, etc. and offer insights into future research on this topic.


Forensic audio and visual evidence 2004-2007 : A Review


title: Urine Metabolite Profiles Predictive of Human Kidney Allograft Status

abstract: Noninvasive diagnosis and prognostication of acute cellular rejection in the kidney allograft may help realize the full benefits of kidney transplantation. To investigate whether urine metabolites predict kidney allograft status, we determined levels of 749 metabolites in 1516 urine samples from 241 kidney graft recipients enrolled in the prospective multicenter Clinical Trials in Organ Transplantation-04 study. A metabolite signature of the ratio of 3-sialyllactose to xanthosine in biopsy specimen-matched urine supernatants best discriminated acute cellular rejection biopsy specimens from specimens without rejection. For clinical application, we developed a high-throughput mass spectrometry-based assay that enabled absolute and rapid quantification of the 3-sialyllactose-to-xanthosine ratio in urine samples. A composite signature of ratios of 3-sialyllactose to xanthosine and quinolinate to X-16397 and our previously reported urinary cell mRNA signature of 18S ribosomal RNA, CD3ε mRNA, and interferon-inducible protein-10 mRNA outperformed the metabolite signatures and the mRNA signature. The area under the receiver operating characteristics curve for the composite metabolite-mRNA signature was 0.93, and the signature was diagnostic of acute cellular rejection with a specificity of 84% and a sensitivity of 90%. The composite signature, developed using solely biopsy specimen-matched urine samples, predicted future acute cellular rejection when applied to pristine samples taken days to weeks before biopsy. We conclude that metabolite profiling of urine offers a noninvasive means of diagnosing and prognosticating acute cellular rejection in the human kidney allograft, and that the combined metabolite and mRNA signature is diagnostic and prognostic of acute cellular rejection with very high accuracy.


title: Efficient Non-Consecutive Feature Tracking for Structure-from-Motion

abstract: Structure-from-motion (SfM) is an important computer vision problem and largely relies on the quality of feature tracking. In image sequences, if disjointed tracks caused by objects moving in and out of the view, occasional occlusion, or image noise, are not handled well, the corresponding SfM could be significantly affected. In this paper, we address the non-consecutive feature point tracking problem and propose an effective method to match interrupted tracks. Our framework consists of steps of solving the feature 'dropout' problem when indistinctive structures, noise or even large image distortion exist, and of rapidly recognizing and joining common features located in different subsequences. Experimental results on several challenging and large-scale video sets show that our method notably improves SfM.


title: Online generative model personalization for hand tracking

abstract: We present a new algorithm for real-time hand tracking on commodity depth-sensing devices. Our method does not require a user-specific calibration session, but rather learns the geometry as the user performs live in front of the camera, thus enabling seamless virtual interaction at the consumer level. The key novelty in our approach is an online optimization algorithm that jointly estimates pose and shape in each frame, and determines the uncertainty in such estimates. This knowledge allows the algorithm to integrate per-frame estimates over time, and build a personalized geometric model of the captured user. Our approach can easily be integrated in state-of-the-art continuous generative motion tracking software. We provide a detailed evaluation that shows how our approach achieves accurate motion tracking for real-time applications, while significantly simplifying the workflow of accurate hand performance capture. We also provide quantitative evaluation datasets at http://gfx.uvic.ca/datasets/handy


title: Overview of the Author Identification Task at PAN 2015

abstract: The author identification task at PAN-2014 focuses on author verification. Similar to PAN-2013 we are given a set of documents by the same author along with exactly one document of questioned authorship, and the task is to determine whether the known and the questioned documents are by the same author or not. In comparison to PAN-2013, a significantly larger corpus was built comprising hundreds of documents in four natural languages (Dutch, English, Greek, and Spanish) and four genres (essays, reviews, novels, opinion articles). In addition, more suitable performance measures are used focusing on the accuracy and the confidence of the predictions as well as the ability of the submitted methods to leave some problems unanswered in case there is great uncertainty. To this end, we adopt the c@1 measure, originally proposed for the question answering task. We received 13 software submissions that were evaluated in the TIRA framework. Analytical evaluation results are presented where one language-independent approach serves as a challenging baseline. Moreover, we continue the successful practice of the PAN labs to examine meta-models based on the combination of all submitted systems. Last but not least, we provide statistical significance tests to demonstrate the important differences between the submitted approaches.


title: Automated aircraft instrument reading using real time video analysis

abstract: Automated Dial Reading (ADR) using image processing is a challenging task that has to deal with the dynamics of real time environment. Literature contains limited research work for ADR that is based on background subtraction, object tracking, and pattern recognition. These methods suffer from dynamic environment such as: varying light intensity, poor resolution, and vibrations in capturing device. A valuable contribution to the existing dial reading approaches is made in this paper by deploying convolution method which plays a significant role in needle/hand recognition within a dial. Proposed dial reading approach is successfully used and tested reading analogue aircraft instruments facilitated by the Flight Guardian1 (FG) project for automated reading of the cockpit devices in dynamic environments. Performance is evaluated by statistical analysis of the experimental results that proved the robustness of the proposed method.


A Study on Test Coverage in Software Testing


title: A multi-layered and kill-chain based security analysis framework for cyber-physical systems

abstract: This paper introduces a novel framework for understanding cyber attacks and the related risks to cyber-physical systems. The framework consists of two elements, a three-layered logical model and reference architecture for cyber-physical systems, and a meta-model of cyber-physical system attacks that is referred to as the cyber-physical system kill-chain. The layered reference architecture provides a systematic basis for studying how the causal chain associated with cyber perturbations can be traced all the way to physical perturbations. The cyber-physical system kill-chain describes the progressive stages of attacks to illuminate the steps required for an attacker to launch a successful attack against a cyber-physical system. The proposed framework offers a novel approach for comprehensively studying the elements of cyber-physical system attacks, including the attacker objectives, cyber exploitation, control-theoretic properties and physical system properties. The framework is evaluated using a simulated unmanned aerial system and the results of the evaluation are discussed. The longer-term goal is to use the framework as a means to deduce cyber-physical system security properties and to enumerate the principles for designing systems that are resilient to cyber attacks.


title: Detection of Face Morphing Attacks by Deep Learning

abstract: Identification by biometric features has become more popular in the last decade. High quality video and fingerprint sensors have become less expensive and are nowadays standard components in many mobile devices. Thus, many devices can be unlocked via fingerprint or face verification. The state of the art accuracy of biometric facial recognition systems prompted even systems that need high security standards like border control at airports to rely on biometric systems. While most biometric facial recognition systems perform quite accurate under a controlled environment, they can easily be tricked by morphing attacks. The concept of a morphing attack is to create a synthetic face image that contains characteristics of two different individuals and to use this image on a document or as reference image in a database. Using this image for authentication, a biometric facial recognition system accepts both individuals. In this paper, we propose a morphing attack detection approach based on convolutional neural networks. We present an automatic morphing pipeline to generate morphing attacks, train neural networks based on this data and analyze their accuracy. The accuracy of different well-known network architectures are compared and the advantage of using pretrained networks compared to networks learned from scratch is studied.


An Empirical Study of Building a Strong Baseline for Constituency Parsing


title: Coupled Support Vector Machines for Supervised Domain Adaptation

abstract: Popular domain adaptation (DA) techniques learn a classifier for the target domain by sampling relevant data points from the source and combining it with the target data. We present a Support Vector Machine (SVM) based supervised DA technique, where the similarity between source and target domains is modeled as the similarity between their SVM decision boundaries. We couple the source and target SVMs and reduce the model to a standard single SVM. We test the Coupled-SVM on multiple datasets and compare our results with other popular SVM based DA approaches.


title: Overlapping Community Detection in Social Network Using Disjoint Community Detection

abstract: With increasing popularity and complexity of social networks, community detection in these networks has become an important research area. Several algorithms are available to detect overlapping community structures based on different approaches. Here we propose a two step genetic algorithm to detect overlapping communities based on node representation. First, we find disjoint communities and these disjoint communities are used to find overlapping communities. We use modularity as our optimization function. Experiments are performed on both artificial and real networks to verify efficiency and scalability of our algorithm.


title: A multi-dimensional meter-adaptive method for automatic segmentation of music

abstract: Music structure appears on a wide variety of temporal levels (notes, bars, phrases, etc). Its highest-level expression is therefore dependent on music's lower-level organization, especially beats and bars. We propose a method for automatic structure segmentation that uses musically meaningful information and is content-adaptive. It relies on a meter-adaptive signal representation that prevents from the use of empirical parameters. Moreover, our method is designed to combine multiple signal features to account for various musical dimensions. Finally, it also combines multiple structural principles that yield complementary results. The resulting algorithm proves to already outperform state-of-the-art methods, especially within small tolerance windows, and yet offers several encouraging improvement directions.


title: A modal analysis of presupposition and modal subordination

abstract: In this paper I will give a modal two-dimensional analysis of presupposition and modal subordination. I will think of presupposition as a non-veridical propositional attitude. This allows me to evaluate what is presupposed and what is asserted at different dimensions without getting into the binding problem. What is presupposed will be represented by an accessibility relation between possible worlds. The major part of the paper consists of a proposal to account for the dependence of the interpretation of modal expressions, i.e. modal subordination, in terms of an accessibility relation as well. Moreover, I show how such an analysis can be extended from the propositional to the predicate logical level.


title: Predictive Entropy Search for Efficient Global Optimization of Black-box Functions

abstract: We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum. PES codifies this intractable acquisition function in terms of the expected reduction in the differential entropy of the predictive distribution. This reformulation allows PES to obtain approximations that are both more accurate and efficient than other alternatives such as Entropy Search (ES). Furthermore, PES can easily perform a fully Bayesian treatment of the model hyperparameters while ES cannot. We evaluate PES in both synthetic and real-world applications, including optimization problems in machine learning, finance, biotechnology, and robotics. We show that the increased accuracy of PES leads to significant gains in optimization performance.


title: Engaging high school students using chatbots

abstract: Chatbots have been used in different scenarios for getting people interested in CS for decades. However, their potential for teaching basic concepts and their engaging effect has not been measured. In this paper we present a software platform called Chatbot designed to foster engagement while teaching basic CS concepts such as variables, conditionals and finite state automata, among others. We carried out two experiences using Chatbot and the well known platform Alice: 1) an online nation-wide competition, and 2) an in-class 15-lesson pilot course in 2 high schools. Data shows that retention and girl interest are higher with Chatbot than with Alice, indicating student engagement.


title: The 'hedonic' in human-computer interaction: history, contributions, and future research directions

abstract: Over the recent years, the notion of a non-instrumental, hedonic quality of interactive products received growing interest. Based on a review of 151 publications, we summarize more than ten years research on the hedonic to provide an overview of definitions, assessment tools, antecedents, consequences, and correlates. We highlight a number of contributions, such as introducing experiential value to the practice of technology design and a better prediction of overall quality judgments and product acceptance. In addition, we suggest a number of areas for future research, such as providing richer, more nuanced models and tools for quantitative and qualitative analysis, more research on the consequences of using hedonic products and a better understanding of when the hedonic plays a role and when not.


title: Meta learning Framework for Automated Driving

abstract: The success of automated driving deployment is highly depending on the ability to develop an efficient and safe driving policy. The problem is well formulated under the framework of optimal control as a cost optimization problem. Model based solutions using traditional planning are efficient, but require the knowledge of the environment model. On the other hand, model free solutions suffer sample inefficiency and require too many interactions with the environment, which is infeasible in practice. Methods under the Reinforcement Learning framework usually require the notion of a reward function, which is not available in the real world. Imitation learning helps in improving sample efficiency by introducing prior knowledge obtained from the demonstrated behavior, on the risk of exact behavior cloning without generalizing to unseen environments. In this paper we propose a Meta learning framework, based on data set aggregation, to improve generalization of imitation learning algorithms. Under the proposed framework, we propose MetaDAgger, a novel algorithm which tackles the generalization issues in traditional imitation learning. We use The Open Race Car Simulator (TORCS) to test our algorithm. Results on unseen test tracks show significant improvement over traditional imitation learning algorithms, improving the learning time and sample efficiency in the same time. The results are also supported by visualization of the learnt features to prove generalization of the captured details.


title: Intermodulation between Digital and Analogue Channels in a Coherent Optical Subcarrier Multiplexed System

abstract: This paper analyzes the impact of mutual interference among digital and analogue channels in a coherent subcarrier multipled system. The result shows that the performance of the digital channels in such a hybrid system may be better than that those in a pure digital system if the modulation index of the analog channel is less than half of the modulation index of the digital channel


title: Refractive indices of diverse data set of polymers: A computational QSPR based study

abstract: Abstract In silico prediction of physico-chemical characteristics of materials has become an important tool in modern chemist’s arsenal to design functional materials with desired properties. Here a QSPR model was developed for prediction of refractive indices (n) in a diverse set of organic polymers. Theoretical descriptors used in the model were generated from the structure of the repeating units of the polymers. A four–variable model was developed for prediction of refractive indices with R2 = 0.932 and Q2LOO = 0.914. The predictive ability and robustness of the proposed model was validated using various methods, including cross-validation and y-randomization experiments. The R2ext for the test set was found to be 0.882, which confirms the statistical significance of the model. For refractive index, the polarizability and sp2 hybridized carbon atoms were the most influential properties. In addition, four small virtual libraries of the novel polymers were designed and their n values were predicted using the developed model.


title: A multiresolution spline with application to image mosaics

abstract: We define a multiresolution spline technique for combining two or more images into a larger image mosaic. In this procedure, the images to be splined are first decomposed into a set of band-pass filtered component images. Next, the component images in each spatial frequency hand are assembled into a corresponding bandpass mosaic. In this step, component images are joined using a weighted average within a transition zone which is proportional in size to the wave lengths represented in the band. Finally, these band-pass mosaic images are summed to obtain the desired image mosaic. In this way, the spline is matched to the scale of features within the images themselves. When coarse features occur near borders, these are blended gradually over a relatively large distance without blurring or otherwise degrading finer image details in the neighborhood of th e border.


title: Peak criterion for choosing Gaussian kernel bandwidth in Support Vector Data Description

abstract: Support Vector Data Description (SVDD) is a machine learning technique used for single class classification and outlier detection. SVDD formulation with kernel function provides a flexible boundary around data. The value of kernel function parameters affects the nature of data boundary. For example, it is observed that with Gaussian kernel, as the value of kernel bandwidth is lowered, the data boundary changes from spherical to wiggly. The spherical data boundary leads to underfitting and extremely wiggly data boundary leads to overfitting. In this paper we propose an empirical criteria to obtain a good value of Gaussian kernel bandwidth which provides a smooth boundary capturing the essential visual features of the data.


title: Effect of Graft Shape in Lateral Column Lengthening on Tarsal Bone Position and Subtalar and Talonavicular Contact Pressure in a Cadaveric Flatfoot Model

abstract: Background:Lateral column lengthening (LCL) has been used for correction of flatfoot deformity. The purpose of this study was to determine the effect of LCL graft shape on tarsal bone position and talonavicular and subtalar joint pressure.Methods:A flatfoot model was created in 6 cadaveric specimens. Corrective LCL was performed using a rectangular graft or a trapezoidal graft with the broad surface oriented dorsally, laterally, or plantarly. Bony surface markers were digitized to calculate angular parameters used in the evaluation of flatfoot deformity. Contact pressure and area in the subtalar and talonavicular joints were also recorded. All measurements were carried out under multiple axial loads in the intact and flatfoot conditions, and following LCL with each graft shape.Results:Flatfoot creation resulted in significant changes in arch collapse and forefoot abduction. LCL with a rectangular graft best corrected these parameters, while a laterally oriented trapezoidal graft provided some correction. ...


title: A DC Motor Driven by a DC/DC Boost Converter-Inverter: Modeling and Simulation

abstract: In this paper the new topology DC/DC Boost power converter-inverter-DC motor that allows bidirectional rotation of the motor shaft is presented. In this direction, the system mathematical model is developed considering its different operation modes. Afterwards, the model validation is performed via numerical simulations by using Matlab-Simulink.


title: A 90–240 MHz Hysteretic Controlled DC-DC Buck Converter With Digital Phase Locked Loop Synchronization

abstract: This paper reports a digital phase locked loop (D-PLL) based frequency locking technique for high frequency hysteretic controlled dc-dc buck converters. The proposed converter achieves constant operating frequency over a wide output voltage range, eliminating the dependence of switching frequency on duty cycle or voltage conversion range. The D-PLL is programmable over a wide range of parameters and can be synchronized to a clock reference to ensure proper frequency lock and switching operation outside undesirable power supply resonance bands. The stability and loop dynamics of the proposed converter is analyzed using an analog equivalent PLL behavioral model which describes the dc-dc converter as a voltage-controlled oscillator (VCO). We demonstrate a 90-240 MHz single phase converter with fast hysteretic control and output conversion range of 33%-80%. The converter achieves an efficiency of 80% at 180 MHz, a load response of 40 ns for a 120 mA current step and a peak-to-peak ripple less than 25 mV. The circuit was implemented in 130 nm digital CMOS process.


title: Infrared Colorization Using Deep Convolutional Neural Networks

abstract: This paper proposes a method for transferring the RGB color spectrum to near-infrared (NIR) images using deep multi-scale convolutional neural networks. A direct and integrated transfer between NIR and RGB pixels is trained. The trained model does not require any user guidance or a reference image database in the recall phase to produce images with a natural appearance. To preserve the rich details of the NIR image, its high frequency features are transferred to the estimated RGB image. The presented approach is trained and evaluated on a real-world dataset containing a large amount of road scene images in summer. The dataset was captured by a multi-CCD NIR/RGB camera, which ensures a perfect pixel to pixel registration.


title: CrowdTruth: Machine-Human Computation Framework for Harnessing Disagreement in Gathering Annotated Data

abstract: In this paper we introduce the CrowdTruth open-source software framework for machine-human computation, that implements a novel approach to gathering human annotation data for a variety of media (e.g. text, image, video). The CrowdTruth approach embodied in the software captures human semantics through a pipeline of four processes: a) combining various machine processing of media in order to better understand the input content and optimize its suitability for micro-tasks, thus optimize the time and cost of the crowdsourcing process; b) providing reusable human-computing task templates to collect the maximum diversity in the human interpretation, thus collect richer human semantics; c) implementing 'disagreement metrics', i.e. CrowdTruth metrics, to support deep analysis of the quality and semantics of the crowdsourcing data; and d) providing an interface to support data and results visualization. Instead of the traditional inter-annotator agreement, we use their disagreement as a useful signal to evaluate the data quality, ambiguity and vagueness. We demonstrate the applicability and robustness of this approach to a variety of problems across multiple domains. Moreover, we show the advantages of using open standards and the extensibility of the framework with new data modalities and annotation tasks.


title: An automatic methodology to evaluate personalized information retrieval systems

abstract: Due to the information overload we are faced with nowadays, personalization services are becoming almost essential, in order to find relevant information tailored to each individual or group of people with common interests. Therefore, it is very important to be able to build efficient and robust personalization techniques to be part of these services. The evaluation step is a crucial stage in their development and improvement, so much more research is needed to overcome this issue. We have proposed an automatic evaluation methodology for personalized information retrieval systems (ASPIRE), which joins the advantages of both system-centred (repeatable, comparable and generalizable results) and user-centred (considers the user) evaluation approaches, and makes the evaluation process easy and fast. Its reliability and robustness have been assessed by means of a user-oriented evaluation. ASPIRE may be considered as an interesting alternative to the costly and difficult user studies, able to discriminate between either different personalization techniques or different parameter configurations of a given personalization method.


title: Orthogonal Beamforming for SDMA Downlink with Limited Feedback

abstract: On a multi-antenna downlink channel, separation of multiple users by transmit beamforming enables simultaneous transmission from the base station to the users, resulting in high sum throughput. This paper proposes and analyzes a practical algorithm for joint scheduling and orthogonal beamforming, which is enabled by feedback of quantized channel state information (CSI). In this approach, each user quantizes CSI using a codebook comprised of multiple orthonormal vector sets and sends back quantized CSI. Using feedback CSI, the base station jointly selects a set of orthogonal beamforming vectors and schedules a subset of feedback users for downlink transmission such that the throughput is maximized. For moderate to large numbers of users, the proposed algorithm achieves higher sum capacities than the conventional ones.


title: WreckWatch : Automatic Traffic Accident Detection and Notification with Smartphones

abstract: Traffic accidents are one of the leading causes of fatalities in the US. An important indicator of survival rates after an accident is the time between the accident and when emergency medical personnel are dispatched to the scene. Eliminating the time between when an accident occurs and when first responders are dispatched to the scene decreases mortality rates by 6%. One approach to eliminating the delay between accident occurrence and first responder dispatch is to use in-vehicle automatic accident detection and notification systems, which sense when traffic accidents occur and immediately notify emergency personnel. These in-vehicle systems, however, are not available in all cars and are expensive to retrofit for older vehicles. This paper describes how smartphones, such as the iPhone and Google Android platforms, can automatically detect traffic accidents using accelerometers and acoustic data, immediately notify a central emergency dispatch server after an accident, and provide situational awareness through photographs, GPS coordinates, VOIP communication channels, and accident data recording. This paper provides the following contributions to the study of detecting traffic accidents via smartphones: (1) we present a formal model for accident detection that combines sensors and context data, (2) we show how smartphone sensors, network connections, and web services can be used to provide situational awareness to first responders, and (3) we provide empirical results demonstrating the efficacy of different approaches employed by smartphone accident detection systems to prevent false positives.


title: A Structural Representation Learning for Multi-relational Networks

abstract: Most of the existing multi-relational network embedding methods, e.g., TransE, are formulated to preserve pair-wise connectivity structures in the networks. With the observations that significant triangular connectivity structures and parallelogram connectivity structures found in many real multi-relational networks are often ignored and that a hard-constraint commonly adopted by most of the network embedding methods is inaccurate by design, we propose a novel representation learning model for multi-relational networks which can alleviate both fundamental limitations. Scalable learning algorithms are derived using the stochastic gradient descent algorithm and negative sampling. Extensive experiments on real multi-relational network datasets of WordNet and Freebase demonstrate the efficacy of the proposed model when compared with the state-of-the-art embedding methods.


Cyber Defense Capability Model: A Foundation Taxonomy


title: Injectable biodegradable poly(ester-co-ether) methacrylate monomers for bone tissue engineering and drug delivery applications.

abstract: The aim of this project was to produce strong, fast photocuring polymer adhesives and composites for biomedical applications that degrade and can release drugs at a controllable rate after set. Five ABA triblock poly(lactide-co-propylene glycol-co- lactide)s with 7, 17 or 34 propylene glycol and 2, 4 or 8 lactic acid units in each B and A block respectively, end capped with methacrylate groups, were prepared. Using FTIR, Raman and NMR, the relative lactide, polypropylene glycol and methacrylate levels in the monomer were proved controllable and as expected from reactant ratios. Polymerisation rates upon blue light exposure for 60, 120 or 240 s using 0.5, 1 or 2 wt% initiators were determined. The shortest monomer with 0.5 wt% initiator achieved 96 % conversion by 120 s after start of 60 s illumination, forming a semi-rigid polymer that in water degraded almost linearly with time with 19 wt% material loss over 14 weeks. Raising initiator concentration reduced polymerisation rate on the lower surface of samples. Increasing the number of lactic acid units in each A block from 2 to 8 enhanced water sorption and increased average total mass loss in 14 weeks to 60 wt% but degradation rate decreased with time. Monomers produced with longer polypropylene glycol B blocks required longer periods of light exposure for full cure and the final more flexible polymers exhibited slower non-linear degradation. Drug release was controlled by varying monomer composition and drug loading level. With hydrophobic ketoprofen, release was more enhanced from rapid-eroding, high water-absorbing polymers. Release of the more water-soluble chlorhexidine diacetate and prednisolone were affected more by polymer swelling and drug diffusion rates through the polymer. Two highly soluble phosphate glasses (67 wt%) were added to one monomer producing composites without losing the rapid set capacity of the polymer. Within a few days in water the glass was leached out providing a means to generate a porous structure. Replacement of phosphate glass with p-tricalcium phosphate and monocalcium phosphate monohydrate filler increased the composite modulus by an order of magnitude upon water sorption, buffered the acidic polymer degradation products and raised the polymer erosion rate significantly. Through further investigations, these polymers and composites should potentially provide a new range of injectable biodegradable slow drug-releasing adhesive materials for various applications in bone tissue engineering and drug delivery.


title: Adaptive sampling and reconstruction using greedy error minimization

abstract: We introduce a novel approach for image space adaptive sampling and reconstruction in Monte Carlo rendering. We greedily minimize relative mean squared error (MSE) by iterating over two steps. First, given a current sample distribution, we optimize over a discrete set of filters at each pixel and select the filter that minimizes the pixel error. Next, given the current filter selection, we distribute additional samples to further reduce MSE. The success of our approach hinges on a robust technique to select suitable per pixel filters. We develop a novel filter selection procedure that robustly solves this problem even with noisy input data. We evaluate our approach using effects such as motion blur, depth of field, interreflections, etc. We provide a comparison to a state-of-the-art algorithm based on wavelet shrinkage and show that we achieve significant improvements in numerical error and visual image quality. Our approach is simple to implement, requires a single user parameter, and is compatible with standard Monte Carlo rendering.


title: An evolutionary game-theoretic modeling for heterogeneous information diffusion

abstract: In this paper, we model and analyze the information diffusion in heterogeneous social networks from an evolutionary game perspective. Users interact with each other according to their individual fitness, which are heterogeneous among different user types. We first study a model where in each social interaction the payoff of a user is independent of the type of the interacted user. In such a case, we derive the information diffusion dynamics of each type of users as well as that of the overall network. The evolutionarily stable states (ESSs) of the dynamics are determined accordingly. Afterwards, we investigate a more general model where in each interaction the payoff of a user depends on the type of the interacted user. We show that the local influence dynamics change much more quickly than the global strategy population dynamics and the former keeps track of the latter throughout the information diffusion process. Based on this observation, the global strategy population dynamics are derived. Finally, simulations are conducted to verify the theoretical results.


title: The influence of parental practices on child promotive and preventive food consumption behaviors: a systematic review and meta-analysis

abstract: Background ::: The family is an important social context where children learn and adopt eating behaviors. Specifically, parents play the role of health promoters, role models, and educators in the lives of children, influencing their food cognitions and choices. This study attempts to systematically review empirical studies examining the influence of parents on child food consumption behavior in two contexts: one promotive in nature (e.g., healthy food), and the other preventive in nature (e.g., unhealthy food).


title: Epoch Synchronous Non Overlap Add (ESNOLA) Method based Concatenative Speech Synthesis

abstract: In the last decade there has been a shift towards development of speech synthesizer using concatenative synthesis technique instead of parametric synthesis. There are a number of different methodologies for concatenative synthesis like TDPSOLA, PSOLA, and MBROLA. This paper, describes a concatenative speech synthesis system based on Epoch Synchronous Non Over Lapp Add (ESNOLA) technique, for standard colloquial Bengali, which uses the partnemes as the smallest signal units for concatenation. The system provided full control for prosody and intonation.


title: Advances in Instance Selection for Instance-Based Learning Algorithms

abstract: The basic nearest neighbour classifier suffers from the indiscriminate storage of all presented training instances. With a large database of instances classification response time can be slow. When noisy instances are present classification accuracy can suffer. Drawing on the large body of relevant work carried out in the past 30 years, we review the principle approaches to solving these problems. By deleting instances, both problems can be alleviated, but the criterion used is typically assumed to be all encompassing and effective over many domains. We argue against this position and introduce an algorithm that rivals the most successful existing algorithm. When evaluated on 30 different problems, neither algorithm consistently outperforms the other: consistency is very hard. To achieve the best results, we need to develop mechanisms that provide insights into the structure of class definitions. We discuss the possibility of these mechanisms and propose some initial measures that could be useful for the data miner.


title: Celebrity Recommendation with Collaborative Social Topic Regression

abstract: Recently how to recommend celebrities to the public becomes an interesting problem on the social network websites, such as Twitter and Tencent Weibo. In this paper, we proposed a unified hierarchical Bayesian model to recommend celebrities to the general users. Specifically, we proposed to leverage both social network and descriptions of celebrities to improve the prediction ability and recommendation interpretability. In our model, we combine topic model with matrix factorization for both social network of celebrities and user following action matrix. It works by regularizing celebrity factors through celebrity's social network and descriptive words associated with each celebrity. We also proposed to incorporate different confidences for different dyadic contexts to handle the situation that only positive observations exist. We conducted experiments on two real-world datasets from Twitter and Tencent Weibo, which are the largest and second largest microblog websites in USA and China, respectively. The experiment results show that our model achieves a higher performance and provide more effective results than the state-of-art methods especially when recommending new celebrities. We also show that our model captures user intertests more precisely and gives better recommendation interpretability.


title: “Incivility, social undermining, bullying…oh my!”: A call to reconcile constructs within workplace aggression research

abstract: Research in the field of workplace aggression has rapidly developed in the last two decades, and with this growth has come an abundance of overlapping constructs that fall under the broad rubric of workplace aggression. While researchers have conceptually distinguished these constructs, it is unclear whether this proliferation of constructs is adding appreciably to our knowledge, or whether it is constraining the questions we ask. In this paper, I consider five example constructs (i.e., abusive supervision, bullying, incivility, social undermining, and interpersonal conflict) and argue that the manner in which we have differentiated these (and other) aggression constructs does not add appreciably to our knowledge of workplace aggression. I then provide supplementary meta-analytic evidence to show that there is not a predictable pattern of outcomes from these constructs, and propose a restructuring of the manner in which we conceptualize workplace aggression. Copyright © 2010 John Wiley & Sons, Ltd.


title: Android Inter-App Communication Threats and Detection Techniques

abstract: Abstract With the digital breakthrough, smart phones have become very essential component for many routine tasks like shopping, paying bills, transferring money, instant messaging, emails etc. Mobile devices are very attractive attack surface for cyber thieves as they hold personal details (accounts, locations, contacts, photos) and have potential capabilities for eavesdropping (with cameras/microphone, wireless connections). Android, being the most popular, is the target of malicious hackers who are trying to use Android app as a tool to break into and control device. Android malware authors use many anti-analysis techniques to hide from analysis tools. Academic researchers and commercial anti-malware companies are putting great effort to detect such malicious apps. They are making use of the combinations of static, dynamic and behavior-based analysis techniques. Despite of all the security mechanisms provided by Android, apps can carry out malicious actions through inter-app communication. One such inter-app communication threats is collusion. In collusion, malicious functionality is divided across multiple apps. Each participating app accomplishes its part and communicate information to another app through Inter Component Communication (ICC). ICC does not require any special permissions. Also there is no compulsion to inform user about the communication. Each participating app needs to request a minimal set of privileges, which may make it appear benign to current state-of-the-art techniques that analyze one app at a time. There are many surveys on app analysis techniques in Android; however they focus on single-app analysis. This survey highlights several inter-app communication threats, in particular collusion among multiple-apps. In this paper, we present Android vulnerabilities that may be exploited for carrying privilege escalation attacks, privacy leakage and collusion attacks. We cover the existing threat analysis, scenarios, and a detailed comparison of tools for intra- and inter-app analysis. To the best of our knowledge this is the first survey on inter-app communication threats, app collusion and state-of-the-art detection tools in Android.


title: Learning to Track and Identify Players from Broadcast Sports Videos

abstract: Tracking and identifying players in sports videos filmed with a single pan-tilt-zoom camera has many applications, but it is also a challenging problem. This paper introduces a system that tackles this difficult task. The system possesses the ability to detect and track multiple players, estimates the homography between video frames and the court, and identifies the players. The identification system combines three weak visual cues, and exploits both temporal and mutual exclusion constraints in a Conditional Random Field (CRF). In addition, we propose a novel Linear Programming (LP) Relaxation algorithm for predicting the best player identification in a video clip. In order to reduce the number of labeled training data required to learn the identification system, we make use of weakly supervised learning with the assistance of play-by-play texts. Experiments show promising results in tracking, homography estimation, and identification. Moreover, weakly supervised learning with play-by-play texts greatly reduces the number of labeled training examples required. The identification system can achieve similar accuracies by using merely 200 labels in weakly supervised learning, while a strongly supervised approach needs a least 20,000 labels.


title: Human interactions and personal space in collaborative virtual environments

abstract: As humans start to spend more time in collaborative virtual environments (CVEs) it becomes important to study their interactions in such environments. One aspect of such interactions is personal space. To begin to address this, we have conducted empirical investigations in a non immersive virtual environment: an experiment to investigate the influence on personal space of avatar gender, and an observational study to further explore the existence of personal space. Experimental results give some evidence to suggest that avatar gender has an influence on personal space although the participants did not register high personal space invasion anxiety, contrary to what one might expect from personal space invasion in the physical world. The observational study suggests that personal space does exist in CVEs, as the users tend to maintain, in a similar way to the physical world, a distance when they are interacting with each other. Our studies provide an improved understanding of personal space in CVEs and the results can be used to further enhance the usability of these environments.


title: Frequency-minimal moving target defense using software-defined networking

abstract: With the increase of cyber attacks such as DoS, there is a need for intelligent counter-strategies to protect critical cloud-hosted applications. The challenge for the defense is to minimize the waste of cloud resources and limit loss of availability, yet have effective proactive and reactive measures that can thwart attackers. In this paper we address the defense needs by leveraging moving target defense protection within Software-Defined Networking-enabled cloud infrastructure. Our novelty is in the frequency minimization and consequent location selection of target movement across heterogeneous virtual machines based on attack probability, which in turn minimizes cloud management overheads. We evaluate effectiveness of our scheme using a large-scale GENI testbed for a just-in-time news feed application setup. Our results show low attack success rate and higher performance of target application in comparison to the existing static moving target defense schemes that assume homogenous virtual machines.


title: A 2.45 GHz Phased Array Antenna Unit Cell Fabricated Using 3-D Multi-Layer Direct Digital Manufacturing

abstract: This paper reports on the design, fabrication and characterization of a 3-D printed RF front end for a 2.45 GHz phased array unit cell. The printed unit cell, which includes a circularly-polarized dipole antenna, a miniaturized capacitive-loaded open-loop resonator filter and a 4-bit phase shifter, is fabricated using a direct digital manufacturing (DDM) approach that integrates fused deposition of thermoplastic substrates with micro-dispensing for deposition of conductive traces. The individual components are combined in a passive phased array antenna unit cell comprised of seven stacked substrate layers with seven conductor layers. The measured return loss of the unit cell is ${>}12$ dB across the 2.45 GHz ISM band and the measured gain is ${-}11$ dBi including all components. Experimental and simulation-based characterization is performed to investigate electrical properties of as-printed materials, in particular the inhomogeneity of printed thick-film conductors and substrate surface roughness. The results demonstrate the strong potential for fully-printed RF front ends for light weight, low cost, conformal and readily customized applications.


title: BAF and FI-BAF: Efficient and Publicly Verifiable Cryptographic Schemes for Secure Logging in Resource-Constrained Systems

abstract: Audit logs are an integral part of modern computer systems due to their forensic value. Protecting audit logs on a physically unprotected machine in hostile environments is a challenging task, especially in the presence of active adversaries. It is critical for such a system to have forward security and append-only properties such that when an adversary compromises a logging machine, she cannot forge or selectively delete the log entries accumulated before the compromise. Existing public-key-based secure logging schemes are computationally costly. Existing symmetric secure logging schemes are not publicly verifiable and open to certain attacks. In this article, we develop a new forward-secure and aggregate signature scheme called Blind-Aggregate-Forward (BAF), which is suitable for secure logging in resource-constrained systems. BAF is the only cryptographic secure logging scheme that can produce publicly verifiable, forward-secure and aggregate signatures with low computation, key/signature storage, and signature communication overheads for the loggers, without requiring any online trusted third party support. A simple variant of BAF also allows a fine-grained verification of log entries without compromising the security or computational efficiency of BAF. We prove that our schemes are secure in Random Oracle Model (ROM). We also show that they are significantly more efficient than all the previous publicly verifiable cryptographic secure logging schemes.


Overall satisfaction, sexual function, and the durability of neophallus dimensions following staged female to male genital gender confirming surgery: the Institute of Urology, London U.K. experience


title: Test-cost sensitive classification on data with missing values

abstract: In the area of cost-sensitive learning, inductive learning algorithms have been extended to handle different types of costs to better represent misclassification errors. Most of the previous works have only focused on how to deal with misclassification costs. In this paper, we address the equally important issue of how to handle the test costs associated with querying the missing values in a test case. When an attribute contains a missing value in a test case, it may or may not be worthwhile to take the extra effort in order to obtain a value for that attribute, or attributes, depending on how much benefit the new value bring about in increasing the accuracy. In this paper, we consider how to integrate test-cost-sensitive learning with the handling of missing values in a unified framework that includes model building and a testing strategy. The testing strategies determine which attributes to perform the test on in order to minimize the sum of the classification costs and test costs. We show how to instantiate this framework in two popular machine learning algorithms: decision trees and naive Bayesian method. We empirically evaluate the test-cost-sensitive methods for handling missing values on several data sets.


title: ASETS: A SDN Empowered Task Scheduling System for HPCaaS on the Cloud

abstract: With increasing demands for High Performance Computing (HPC), new ideas and methods are emerged to utilize computing resources more efficiently. Cloud Computing appears to provide benefits such as resource pooling, broad network access and cost efficiency for the HPC applications. However, moving the HPC applications to the cloud can face several key challenges, primarily, the virtualization overhead, multi-tenancy and network latency. Software-Defined Networking (SDN) as an emerging technology appears to pave the road and provide dynamic manipulation of cloud networking such as topology, routing, and bandwidth allocation. This paper presents a new scheme called ASETS which targets dynamic configuration and monitoring of cloud networking using SDN to improve the performance of HPC applications and in particular task scheduling for HPC as a service on the cloud (HPCaaS). Further, SETSA, (SDN-Empowered Task Scheduler Algorithm) is proposed as a novel task scheduling algorithm for the offered ASETS architecture. SETSA monitors the network bandwidth to take advantage of its changes when submitting tasks to the virtual machines. Empirical analysis of the algorithm in different case scenarios show that SETSA has significant potentials to improve the performance of HPCaaS platforms by increasing the bandwidth efficiency and decreasing task turnaround time. In addition, SETSAW, (SETSA Window) is proposed as an improvement of the SETSA algorithm.


title: Estimating crop yields with deep learning and remotely sensed data

abstract: This paper describes Illinois corn yield estimation using deep learning and another machine learning, SVR. Deep learning is a technique that has been attracting attention in recent years of machine learning, it is possible to implement using the Caffe. High accuracy estimation of crop yield is very important from the viewpoint of food security. However, since every country prepare data inhomogeneously, the implementation of the crop model in all regions is difficult. Deep learning is possible to extract important features for estimating the object from the input data, so it can be expected to reduce dependency of input data. The network model of two InnerProductLayer was the best algorithm in this study, achieving RMSE of 6.298 (standard value). This study highlights the advantages of deep learning for agricultural yield estimating.


title: Coordination of Generation Scheduling with PEVs Charging in Industrial Microgrids

abstract: Conventional industrial microgrids (IMGs) consist of factories with distributed energy resources (DERs) and electric loads that rely on combined heat and power (CHP) systems while the developing IMGs are expected to also include renewable DERs and plug-in electric vehicles (PEVs) with different vehicle ratings and charging characteristics. This paper presents an electricity and heat generation scheduling method coordinated with PEV charging in an IMG considering photovoltaic (PV) generation systems coupled with PV storages. The proposed method is based on dynamic optimal power flow (DOPF) over a 24-hour period and includes security-constrained optimal power flow (SCOPF), IMG's factories constraints, PV storage constraints and PEVs dynamic charging constraints. It will utilize the generators waste heat to fulfill thermal requirements while considering the status of renewable DERs to decrease the overall cost of IMGs. To demonstrate the effectiveness of the proposed method, detailed simulation results are presented and analyzed for an 18-bus IMG consisting of 12 factories and 6 types of PEVs without/with PV generation systems operating in grid-connected and stand-alone modes. The main contribution is including PEVs with dynamic constraints that have changed the nature of scheduling formulation from a simple hourly OPF to a dynamic OPF.


title: Dynamic intuitionistic fuzzy multi-attribute decision making

abstract: The dynamic multi-attribute decision making problems with intuitionistic fuzzy information are investigated. The notions of intuitionistic fuzzy variable and uncertain intuitionistic fuzzy variable are defined, and two new aggregation operators: dynamic intuitionistic fuzzy weighted averaging (DIFWA) operator and uncertain dynamic intuitionistic fuzzy weighted averaging (UDIFWA) operator are presented. Some methods, including the basic unit-interval monotonic (BUM) function based method, normal distribution based method, exponential distribution based method and average age method, are introduced to determine the weight vectors associated with these operators. A procedure based on the DIFWA operator is developed to solve the dynamic intuitionistic fuzzy multi-attribute decision making (DIF-MADM) problems where all the decision information about attribute values takes the form of intuitionistic fuzzy numbers collected at different periods, and a procedure based on the UDIFWA operator is developed for DIF-MADM under interval uncertainty in which all the decision information about attribute values takes the form of interval-valued intuitionistic fuzzy numbers collected at different periods. Finally, a practical case is used to illustrate the developed procedures.


title: Anonymized data: generation, models, usage

abstract: Data anonymization techniques have been the subject of intense investigation in recent years, for many kinds of structured data, including tabular, graph and item set data. They enable publication of detailed information, which permits ad hoc queries and analyses, while guaranteeing the privacy of sensitive information in the data against a variety of attacks. In this tutorial, we aim to present a unified framework of data anonymization techniques, viewed through the lens of uncertainty. Essentially, anonymized data describes a set of possible worlds, one of which corresponds to the original data. We show that anonymization approaches such as suppression, generalization, perturbation and permutation generate different working models of uncertain data, some of which have been well studied, while others open new directions for research. We demonstrate that the privacy guarantees offered by methods such as k-anonymization and l-diversity can be naturally understood in terms of similarities and differences in the sets of possible worlds that correspond to the anonymized data. We describe how the body of work in query evaluation over uncertain databases can be used for answering ad hoc queries over anonymized data in a principled manner. A key benefit of the unified approach is the identification of a rich set of new problems for both the Data Anonymization and the Uncertain Data communities.


ProTracer: Towards Practical Provenance Tracing by Alternating Between Logging and Tainting


People don’t trust driverless cars. Researchers are trying to change that


title: Deformable Models of Ears in-the-Wild for Alignment and Recognition

abstract: Ears have been discovered to have biometric importance for identifying people and/or verifying their identity. This is largely because of their complex inner shape structure, which is not only unique but also long-lasting regardless of ageing. In this paper, we make two important contributions relevant to analysis of ear in imagery captured in unconstrained conditions. That is, we present (a) the first, to the best of our knowledge, annotated database with ear landmarks and use it in order to build statistical deformable ear models in-the-wild and (b) a database of 2058 labelled unconstrained ear images with 231 subjects and use it for ear recognition/verification. We perform extensive comparisons for ear alignment using many state-of-the-art techniques and extensive experiments. Finally, we conducted extensive experiments for ear recognition using both handcrafted, as well as learned features (i.e., using deep learning). All annotated data and code will be publicly available.


title: Emotional Intensity analysis in Bipolar subjects

abstract: The massive availability of digital repositories of human thought opens radical novel way of studying the human mind. Natural language processing tools and computational models have evolved such that many mental conditions are predicted by analysing speech. Transcription of interviews and discourses are analyzed using syntactic, grammatical or sentiment analysis to infer the mental state. Here we set to investigate if classification of Bipolar and control subjects is possible. We develop the Emotion Intensity Index based on the Dictionary of Affect, and find that subjects categories are distinguishable. Using classical classification techniques we get more than 75\% of labeling performance. These results sumed to previous studies show that current automated speech analysis is capable of identifying altered mental states towards a quantitative psychiatry.


title: End-to-end Learning of Deterministic Decision Trees

abstract: Conventional decision trees have a number of favorable properties, including interpretability, a small computational footprint and the ability to learn from little training data. However, they lack a key quality that has helped fuel the deep learning revolution: that of being end-to-end trainable, and to learn from scratch those features that best allow to solve a given supervised learning problem. Recent work (Kontschieder 2015) has addressed this deficit, but at the cost of losing a main attractive trait of decision trees: the fact that each sample is routed along a small subset of tree nodes only. We here propose a model and Expectation-Maximization training scheme for decision trees that are fully probabilistic at train time, but after a deterministic annealing process become deterministic at test time. We also analyze the learned oblique split parameters on image datasets and show that Neural Networks can be trained at each split node. In summary, we present the first end-to-end learning scheme for deterministic decision trees and present results on par with or superior to published standard oblique decision tree algorithms.


title: The impact of visual attributes on online image diffusion

abstract: Little is known on how visual content affects the popularity on social networks, despite images being now ubiquitous on the Web, and currently accounting for a considerable fraction of all content shared. Existing art on image sharing focuses mainly on non-visual attributes. In this work we take a complementary approach, and investigate resharing from a mainly visual perspective. Two sets of visual features are proposed, encoding both aesthetical properties (brightness, contrast, sharpness, etc.), and semantical content (concepts represented by the images). We collected data from a large image-sharing service (Pinterest) and evaluated the predictive power of different features on popularity (number of reshares). We found that visual properties have low predictive power compared that of social cues. However, after factoring-out social influence, visual features show considerable predictive power, especially for images with higher exposure, with over 3:1 accuracy odds when classifying highly exposed images between very popular and unpopular.


title: Automatic Summarization of Bug Reports

abstract: Software developers access bug reports in a project's bug repository to help with a number of different tasks, including understanding how previous changes have been made and understanding multiple aspects of particular defects. A developer's interaction with existing bug reports often requires perusing a substantial amount of text. In this article, we investigate whether it is possible to summarize bug reports automatically so that developers can perform their tasks by consulting shorter summaries instead of entire bug reports. We investigated whether existing conversation-based automated summarizers are applicable to bug reports and found that the quality of generated summaries is similar to summaries produced for e-mail threads and other conversations. We also trained a summarizer on a bug report corpus. This summarizer produces summaries that are statistically better than summaries produced by existing conversation-based generators. To determine if automatically produced bug report summaries can help a developer with their work, we conducted a task-based evaluation that considered the use of summaries for bug report duplicate detection tasks. We found that summaries helped the study participants save time, that there was no evidence that accuracy degraded when summaries were used and that most participants preferred working with summaries to working with original bug reports.


title: A unique trend of murder–suicide in the Jamnagar region of Gujarat, India (A retrospective study of 5 years)

abstract: Abstract Jamnagar region, Gujarat state, enjoys a relatively low incidence of homicide in India. In the 5 year period from 2000 to 2004, 8 mothers committed 13 murders involving 3 male and 10 female victims and in every case it was followed by suicide of the assailant mothers. During the study the annual incidence of murder–suicide was about 1.8 cases. All the assailants were mothers and the victims were their small children in the age group of 6 months to 7 years. Five incidents took place in rural areas and three in urban areas. It was prevalent only in low socio-economic families. Methods both for killing and suicide were either burning or drowning. All the mothers were legally married and living with the family. Family and family related matters were the main motives for killing. In one case there was history of depression of the mother due to her previous miscarriage. Alcohol consumption or drug abuse was not seen even in a single case. All cases fell in the altruistic category of filicide–suicide.


MATHEMATICAL MODELS OF PHYSICAL RELIABILITY THEORY


title: A Minimum Delay Routing Algorithm Using Distributed Computation

abstract: An algorithm is defined for establishing routing tables in the individual nodes of a data network. The routing table at a node i specifies, for each other node j , what fraction of the traffic destined for node j should leave node i on each of the links emanating from node i . The algorithm is applied independently at each node and successively updates the routing table at that node based on information communicated between adjacent nodes about the marginal delay to each destination. For stationary input traffic statistics, the average delay per message through the network converges, with successive updates of the routing tables, to the minimum average delay over all routing assignments. The algorithm has the additional property that the traffic to each destination is guaranteed to be loop free at each iteration of the algorithm. In addition, a new global convergence theorem for noncontinuous iteration algorithms is developed.


title: Design of iMacros-based Data Crawler and the Behavioral Analysis of Facebook Users

abstract: Obtaining the desired dataset is still a prime challenge faced by researchers while analyzing Online Social Network (OSN) sites. Application Programming Interfaces (APIs) provided by OSN service providers for retrieving data impose several unavoidable restrictions which make it difficult to get a desirable dataset. In this paper, we present an iMacros technology-based data crawler called IMcrawler, capable of collecting every piece of information which is accessible through a browser from the Facebook website within the legal framework which permits access to publicly shared user content on OSNs. The proposed crawler addresses most of the challenges allied with web data extraction approaches and most of the APIs provided by OSN service providers. Two broad sections have been extracted from Facebook user profiles, namely, Personal Information and Wall Activities. The present work is the first attempt towards providing the detailed description of crawler design for the Facebook website.


title: SDN controllers: A comparative study

abstract: The control plane is an essential part of the SDN architecture, so it is very important to give proper attention to any proposal or design of an SDN controller. During the past few years, several controllers have been developed and several studies have been done to evaluate, compare and test the performance of these controllers. In this paper, new controllers are tested, such as ONOS and Libfluid-based controllers (raw, base), using Cbench, an OpenFlow testing tool. Even though the results show that MUL, Beacon, and Maestro (in latency mode) are the best performing controllers; however, the selection of the best-fitted controller should be based on several criteria, per user requirements.


Logic-Based Tools for the Analysis and Representation of Legal Contracts


title: Retrieving Information from Social Media using Ontology

abstract: People have access to more data in single day than most people that have access to data in the previous decade. This data is created in many forms and it highlights the development of Big Data. Big Data in organizations have transformed the way organizations across industries implement new approach to handle huge amount of data. Organizations rely to this data to achieve specific business priorities. The challenge is how to retrieve this data to be considered relevant for the specific organization activities because determining relevant data is a key to deliver information from massive amounts of data. The aim of this paper is to integrate organizational data and social data using an ontology to retrieve relevant information for efficient decision-making. We investigate how external data such as social media can support internal data such as organizational data in relation to the organizational goals. The results from the case study demonstrate how we incorporate social data and organizational data. This paper demonstrates that ontology provide a platform to integrate social data and organizational data.


title: Pressages: augmenting phone calls with non-verbal messages

abstract: ForcePhone is a mobile synchronous haptic communication system. During phone calls, users can squeeze the side of the device and the pressure level is mapped to vibrations on the recipient's device. The pressure/vibrotactile messages supported by ForcePhone are called pressages. Using a lab-based study and a small field study, this paper addresses the following questions: how can haptic interpersonal communication be integrated into a standard mobile device? What is the most appropriate feedback design for pressages? What types of non-verbal cues can be represented by pressages? Do users make use of pressages during their conversations? The results of this research indicate that such a system has value as a communication channel in real-world settings with users expressing greetings, presence and emotions through pressages.


title: Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks

abstract: Semantic labeling (or pixel-level land-cover classification) in ultrahigh-resolution imagery (<10 cm) requires statistical models able to learn high-level concepts from spatial data, with large appearance variations. Convolutional neural networks (CNNs) achieve this goal by learning discriminatively a hierarchy of representations of increasing abstraction. In this paper, we present a CNN-based system relying on a downsample-then-upsample architecture. Specifically, it first learns a rough spatial map of high-level representations by means of convolutions and then learns to upsample them back to the original resolution by deconvolutions. By doing so, the CNN learns to densely label every pixel at the original resolution of the image. This results in many advantages, including: 1) the state-of-the-art numerical accuracy; 2) the improved geometric accuracy of predictions; and 3) high efficiency at inference time. We test the proposed system on the Vaihingen and Potsdam subdecimeter resolution data sets, involving the semantic labeling of aerial images of 9- and 5-cm resolution, respectively. These data sets are composed by many large and fully annotated tiles, allowing an unbiased evaluation of models making use of spatial information. We do so by comparing two standard CNN architectures with the proposed one: standard patch classification, prediction of local label patches by employing only convolutions, and full patch labeling by employing deconvolutions. All the systems compare favorably or outperform a state-of-the-art baseline relying on superpixels and powerful appearance descriptors. The proposed full patch labeling CNN outperforms these models by a large margin, also showing a very appealing inference time.


title: Combining Text Mining and Data Mining for Bug Report Classification

abstract: Misclassification of bug reports inevitably sacrifices the performance of bug prediction models. Manual examinations can help reduce the noise but bring a heavy burden for developers instead. In this paper, we propose a hybrid approach by combining both text mining and data mining techniques of bug report data to automate the prediction process. The first stage leverages text mining techniques to analyze the summary parts of bug reports and classifies them into three levels of probability. The extracted features and some other structured features of bug reports are then fed into the machine learner in the second stage. Data grafting techniques are employed to bridge the two stages. Comparative experiments with previous studies on the same data -- three large-scale open source projects -- consistently achieve a reasonable enhancement (from 77.4% to 81.7%, 73.9% to 80.2% and 87.4% to 93.7%, respectively) over their best results in terms of overall performance. Additional comparative empirical experiments on other two popular open source repositories confirm the findings and demonstrate the benefits of our approach.


title: Replicated softmax: an undirected topic model

abstract: We introduce a two-layer undirected graphical model, called a "Replicated Softmax", that can be used to model and automatically extract low-dimensional latent semantic representations from a large unstructured collection of documents. We present efficient learning and inference algorithms for this model, and show how a Monte-Carlo based method, Annealed Importance Sampling, can be used to produce an accurate estimate of the log-probability the model assigns to test data. This allows us to demonstrate that the proposed model is able to generalize much better compared to Latent Dirichlet Allocation in terms of both the log-probability of held-out documents and the retrieval accuracy.