title: Phosphorescent iridium(III)-bis-N-heterocyclic carbene complexes as mitochondria-targeted theranostic and photodynamic anticancer agents.

abstract: Mitochondria-targeted compounds represent a promising approach to target tumors selectively and overcome resistance to current anticancer therapies. In this work, three cyclometalated iridium(III) complexes (1-3) containing bis-N-heterocyclic carbene (NHC) ligands have been explored as theranostic and photodynamic agents targeting mitochondria. These complexes display rich photophysical properties, which greatly facilitates the study of their intracellular fate. All three complexes are more cytotoxic than cisplatin against the cancer cells screened. 1-3 can penetrate into human cervical carcinoma (HeLa) cells quickly and efficiently, and they can carry out theranostic functions by simultaneously inducing and monitoring the morphological changes in mitochondria. Mechanism studies show that these complexes exert their anticancer efficacy by initiating a cascade of events related to mitochondrial dysfunction. Additionally, they display up to 3 orders of magnitude higher cytotoxicity upon irradiation at 365 nm, which is so far the highest photocytotoxic responses reported for iridium complexes.


title: Antioxidant activity and quantitative estimation of azadirachtin and nimbin in Azadirachta Indica A. Juss grown in foothills of Nepal

abstract: The leaf and bark fraction extracts of Azadirachta indica A. Juss. (neem) grown in the foothills (subtropical region) of Nepal were evaluated for their antioxidant activity, total phenolic (TP) and total flavonid (TF) contents. HPLC method was employed to quantify the amount of azadirachtin and nimbin present in the seed, leaf and the bark extracts of neem. The result showed that the highest azadirachtin content was found in the methanolic extract of the seed (3300 ig/g dw). Similarly, the hexane fraction of bark showed the highest nimbin content (271 ig/g dw) followed by the methanolic extract (260 ig/g dw). Antioxidant activity was determined by measuring 1,1-diphenyl-2-picrylhydrazyl free radical scavenging activity, hydroxyl radical scavenging activity, DNA protection assay, metal chelating and the inhibition of peroxidation using linoleic acid system and their results were found at different magnitudes of potency. The results of TP content expressed in tannic acid equivalents ranged from 66.63 to 629.04 ig/mg in the bark extracts and 23.85 to 237.00 ig/mg in the leaf extracts. Likewise, the content of TF expressed in quercetine equivalents ranged from 12.87 to 17.07 ig/mg in the bark and 13.72 to 93.17 ig/mg in the leaf extracts.


title: A Gravitational Black Hole Algorithm for Autonomous UCAV Mission Planning in 3D Realistic Environments

abstract: This article addresses a novel approach to 3D mission planning of UCAVs in constrained environments. To solve this NP-hard problem, black hole algorithm (BH) is improved by considering stars gravities information. By modelling UCAV properties, aerospace constraints and DTM of environment, proposed mission planner based on black hole optimization algorithm is proposed. Also it provides a comparative study for efficiency evaluation of evolutionary 3D mission planners based on ACO, BA, DE, ES, GA, BH and PSO optimization algorithms. Then mission planning task of UCAV is performed. Simulations show the advantage of proposed gravitational BH mission planner. General Terms UCAV mission Planning, Artificial Intelligence, Autonomous Navigation, Mission Planning, Black Hole


title: Role of the Osmotic Stress Regulatory Pathway in Morphogenesis and Secondary Metabolism in Filamentous Fungi

abstract: Environmental stimuli trigger an adaptative cellular response to optimize the probability of survival and proliferation. In eukaryotic organisms from mammals to fungi osmotic stress, mainly through the action of the high osmolarity glycerol (HOG) pathway, leads to a response necessary for adapting and surviving hyperosmotic environments. In this review we show that the osmoadaptative response is conserved but not identical in different fungi. The osmoadaptative response system is also intimately linked to morphogenesis in filamentous fungi, including mycotoxin producers. Previous studies indicate that the response to osmotic stress is also coupled to the biosynthesis of natural products, including mycotoxins.


title: Towards application development for the internet of things

abstract: The Internet of Things (IoT) integrates the physical world with the existing Internet, and is rapidly gaining popularity, thanks to the increased adoption of smart phones and sensing devices. One of the important challenges in this domain is to enable domain experts to easily specify applications for the IoT. As a first step towards developing a suitable programming abstraction, in this paper we present a domain model for applications in the Internet of Things, based on a survey of recently proposed IoT applications from the real world that represent a wide class of behaviors found in IoT use cases.


title: Teaching operating systems administration with user mode linux

abstract: User Mode Linux is a virtual machine running on a GNU-Linux operating system. It is the right choice for teaching operating systems' administration, as it does not need any dedicated hardware. It runs at user level (no need for root, i.e. administrator, access or possible security threats) and it does not have the performance problems of an emulator. This paper describes how to set up a laboratory for teaching operating systems' administration.


title: Fuzzy Longest Common Subsequence Matching With FCM Using R

abstract: Capturing the interdependencies between real valued time series can be achieved by finding common similar patterns. The abstraction of time series makes the process of finding similarities closer to the way as humans do. Therefore, the abstraction by means of a symbolic levels and finding the common patterns attracts researchers. One particular algorithm, Longest Common Subsequence, has been used successfully as a similarity measure between two sequences including real valued time series. In this paper, we propose Fuzzy Longest Common Subsequence matching for time series.


title: Understanding happiness in cities using Twitter: Jobs, children, and transport

abstract: The demographics and landscape of cities are changing rapidly, and there is an emphasis to better understand the factors which influence citizen happiness in order to design smarter urban systems. Few studies have attempted to understand how large-scale sentiment maps to urban human geography. Inferring sentiment from social media data is one such scalable solution. In this paper, we apply natural language processing (NLP) techniques to 0.4 million geo-tagged Tweets in the Greater London area to understand the influence of socioeconomic and urban geography parameters on happiness. Our results not only verify established thinking: that job opportunities correlate with positive sentiments; but also reveal two insights: (1) happiness is negatively correlated with number of children, and (2) happiness has a U-shaped (parabolic) relationship with access to public transportation. The latter implies that the happiest people are those who have good access to public transport, or such poor access that they use private transportation. The number of jobs, children, and transportation availability are every day facets of urban living and individually account for up to 47% of the variations in people's happiness. Our results show that they influence happiness more significantly than long term socioeconomic parameters such as degradation, education, income, housing, and crime. This study will enable urban planners and system designers to move beyond the traditional cost-benefit methodology and to incorporate citizens' happiness.


title: Model compression as constrained optimization, with application to neural nets. Part I: general framework

abstract: Compressing neural nets is an active research problem, given the large size of state-of-the-art nets for tasks such as object recognition, and the computational limits imposed by mobile devices. We give a general formulation of model compression as constrained optimization. This includes many types of compression: quantization, low-rank decomposition, pruning, lossless compression and others. Then, we give a general algorithm to optimize this nonconvex problem based on the augmented Lagrangian and alternating optimization. This results in a "learning-compression" algorithm, which alternates a learning step of the uncompressed model, independent of the compression type, with a compression step of the model parameters, independent of the learning task. This simple, efficient algorithm is guaranteed to find the best compressed model for the task in a local sense under standard assumptions. ::: We present separately in several companion papers the development of this general framework into specific algorithms for model compression based on quantization, pruning and other variations, including experimental results on compressing neural nets and other models.


title: The CNN as a Guided Multilayer RECOS Transform [Lecture Notes]

abstract: There is a resurging interest in developing a neural-network-based solution to the supervised machine-learning problem. The convolutional neural network (CNN) will be studied in this lecture note. We introduce a rectified-correlations on a sphere (RECOS) [1] transform as a basic building block of CNNs. It consists of two main concepts: 1) data clustering on a sphere and 2) rectification. We then interpret a CNN as a network that implements the guided multilayer RECOS transform with two highlights. First, we compare the traditional single-layer and modern multilayer signal-analysis approaches, point out key areas that enable the multilayer approach, and provide a full explanation to the operating principle of CNNs. Second, we discuss how guidance is provided by labels through backpropagation (BP) in the training.


title: Standardization of the in-car gesture interaction space

abstract: Driven by technological advancements, gesture interfaces have recently found their way into vehicular prototypes of various kind. Unfortunately, their application is less than perfect and detailed information about preferred gesture execution regions, spatial extent, and time behavior are not available yet. Providing car (interior) manufacturer with gesture characteristics would allow them to design future in-vehicle concepts in a way to not interfere with gestural interaction. To tackle the problem, this research aims as preliminary work for a later standardization of the diverse properties of gestures and gesture classes similarly to what is already standardized in norms such as ISO 3958/4040 for placement and reachability of traditional controls and indicators. We have set up a real driving experiment recording trajectories and time behavior of gestures related to car and media control tasks. Data evaluation reveals that most of the subjects perform gestures in the same region (bounded by a "triangle" steering wheel, rear mirror, and gearshift) and with similar spatial extent (on average below 2 sec.). The generated density plots can be further used for an initial discussion about gesture execution in the passenger compartment. The final aim is to propose a new standard on permitted gesture properties (time, space) in the car.


title: Avoiding and Treating Blindness From Fillers: A Review of the World Literature.

abstract: BACKGROUND ::: As the popularity of soft tissue fillers increases, so do the reports of adverse events. The most serious complications are vascular in nature and include blindness. ::: ::: ::: OBJECTIVE ::: To review the cases of blindness after filler injection, to highlight key aspects of the vascular anatomy, and to discuss prevention and management strategies. ::: ::: ::: METHODS ::: A literature review was performed to identify all the cases of vision changes from filler in the world literature. ::: ::: ::: RESULTS ::: Ninety-eight cases of vision changes from filler were identified. The sites that were high risk for complications were the glabella (38.8%), nasal region (25.5%), nasolabial fold (13.3%), and forehead (12.2%). Autologous fat (47.9%) was the most common filler type to cause this complication, followed by hyaluronic acid (23.5%). The most common symptoms were immediate vision loss and pain. Most cases of vision loss did not recover. Central nervous system complications were seen in 23.5% of the cases. No treatments were found to be consistently successful in treating blindness. ::: ::: ::: CONCLUSION ::: Although the risk of blindness from fillers is rare, it is critical for injecting physicians to have a firm knowledge of the vascular anatomy and to understand key prevention and management strategies.


title: Data Mining Techniques for Optimization of Liver Disease Classification

abstract: Patients with liver disease continue to increase and the symptoms of the disease is difficult to detect. Therefore many people suffer from liver damage but they feel healthy, it causes many medical practitioners to often fail to detect the disease. Failure to detect can mislead to improper medication and medical treatment. Therefore accurate detection is necessary to help the medical practitioner to give proper medication and medical treatment. Some researchers have been using data mining techniques to classify liver disease. The problem is, it is not easy to have the same consensus for a better algorithm in classifying liver disease. This study aims to identify if the patients have the liver disease based on the 10 important attributes of liver disease using a Decision Tree, Naive Bayes, and NBTree algorithms. The result shows NBTree algorithm has the highest accuracy, however the Naive Bayes algorithm gives the fastest computation time. This study presents promising results in giving recommendation if the patients have the disease.


title: GMS: Grid-Based Motion Statistics for Fast, Ultra-Robust Feature Correspondence

abstract: Incorporating smoothness constraints into feature matching is known to enable ultra-robust matching. However, such formulations are both complex and slow, making them unsuitable for video applications. This paper proposes GMS (Grid-based Motion Statistics), a simple means of encapsulating motion smoothness as the statistical likelihood of a certain number of matches in a region. GMS enables translation of high match numbers into high match quality. This provides a real-time, ultra-robust correspondence system. Evaluation on videos, with low textures, blurs and wide-baselines show GMS consistently out-performs other real-time matchers and can achieve parity with more sophisticated, much slower techniques.


title: Combining Weakly and Webly Supervised Learning for Classifying Food Images

abstract: Food classification from images is a fine-grained classification problem. Manual curation of food images is cost, time and scalability prohibitive. On the other hand, web data is available freely but contains noise. In this paper, we address the problem of classifying food images with minimal data curation. We also tackle a key problems with food images from the web where they often have multiple cooccuring food types but are weakly labeled with a single label. We first demonstrate that by sequentially adding a few manually curated samples to a larger uncurated dataset from two web sources, the top-1 classification accuracy increases from 50.3% to 72.8%. To tackle the issue of weak labels, we augment the deep model with Weakly Supervised learning (WSL) that results in an increase in performance to 76.2%. Finally, we show some qualitative results to provide insights into the performance improvements using the proposed ideas.


title: A Psychometric Toolbox for Testing Validity and Reliability

abstract: Purpose: To review the concepts of reliability and validity, provide examples of how the concepts have been used in nursing research, provide guidance for improving the psychometric soundness of instruments, and report suggestions from editors of nursing journals for incorporating psychometric data into manuscripts. Methods: CINAHL, MEDLINE, and PsycINFO databases were searched using key words: validity, reliability, and psychometrics. Nursing research articles were eligible for inclusion if they were published in the last 5 years, quantitative methods were used, and statistical evidence of psychometric properties were reported. Reports of strong psychometric properties of instruments were identified as well as those with little supporting evidence of psychometric soundness. Findings: Reports frequently indicated content validity but sometimes the studies had fewer than five experts for review. Criterion validity was rarely reported and errors in the measurement of the criterion were identified. Construct validity remains underreported. Most reports indicated internal consistency reliability (α) but few reports included reliability testing for stability. When retest reliability was asserted, time intervals and correlations were frequently not included. Conclusions: Planning for psychometric testing through design and reducing nonrandom error in measurement will add to the reliability and validity of instruments and increase the strength of study findings. Underreporting of validity might occur because of small sample size, poor design, or lack of resources. Lack of information on psychometric properties and misapplication of psychometric testing is common in the literature.


title: Texting while driving: A study of 1211 U.S. adults with the Distracted Driving Survey

abstract: Texting and other cell-phone related distracted driving is estimated to account for thousands of motor vehicle collisions each year but studies examining the specific cell phone reading and writing activities of drivers are limited. The objective of this study was to describe the frequency of cell-phone related distracted driving behaviors. A national, representative, anonymous panel of 1211 United States drivers was recruited in 2015 to complete the Distracted Driving Survey (DDS), an 11-item validated questionnaire examining cell phone reading and writing activities and at what speeds they occur. Higher DDS scores reflect more distraction. DDS scores were analyzed by demographic data and self-reported crash rate. Nearly 60% of respondents reported a cell phone reading or writing activity within the prior 30 days, with reading texts (48%), writing texts (33%) and viewing maps (43%) most frequently reported. Only 4.9% of respondents had enrolled in a program aimed at reducing cell phone related distracted driving. DDS scores were significantly correlated to crash rate (p < 0.0001), with every one point increase associated with an additional 7% risk of a crash (p < 0.0001). DDS scores were inversely correlated to age (p < 0.0001). The DDS demonstrated high internal consistency (Cronbach's alpha = 0.94). High rates of cell phone-related distraction are reported here in a national sample. Distraction is associated with crash rates and occurs across all age groups, but is highest in younger drivers. The DDS can be used to evaluate the impact of public health programs aimed at reducing cell-phone related distracted driving.


title: Data science at SoBigData: the European research infrastructure for social mining and big data analytics

abstract: Most people have become “big data” producers in their daily life. Our desires, opinions, sentiments, social links as well as our mobile phone calls and GPS track leave traces of our behaviours. To transform these data into knowledge, value is a complex task of data science. This paper shows how the SoBigData Research Infrastructure supports data science towards the new frontiers of big data exploitation. Our research infrastructure serves a large community of social sensing and social mining researchers and it reduces the gap between existing research centres present at European level. SoBigData integrates resources and creates an infrastructure where sharing data and methods among text miners, visual analytics researchers, socio-economic scientists, network scientists, political scientists, humanities researchers can indeed occur. The main concepts related to SoBigData Research Infrastructure are presented. These concepts support virtual and transnational (on-site) access to the resources. Creating and supporting research communities are considered to be of vital importance for the success of our research infrastructure, as well as contributing to train the new generation of data scientists. Furthermore, this paper introduces the concept of exploratory and shows their role in the promotion of the use of our research infrastructure. The exploratories presented in this paper represent also a set of real applications in the context of social mining. Finally, a special attention is given to the legal and ethical aspects. Everything in SoBigData is supervised by an ethical and legal framework.


title: Research Directions for the Internet of Things

abstract: Many technical communities are vigorously pursuing research topics that contribute to the Internet of Things (IoT). Nowadays, as sensing, actuation, communication, and control become even more sophisticated and ubiquitous, there is a significant overlap in these communities, sometimes from slightly different perspectives. More cooperation between communities is encouraged. To provide a basis for discussing open research problems in IoT, a vision for how IoT could change the world in the distant future is first presented. Then, eight key research topics are enumerated and research problems within these topics are discussed.


title: A stochastic rank ordered logit model for rating multi-competitor games and sports

abstract: Many games and sports, including races, involve outcomes in which competitors are rank ordered. In some sports, competitors may play in multiple events over long periods of time, and it is natural to assume that their abilities change over time. We propose a Bayesian state-space framework for rank ordered logit models to rate competitor abilities over time from the results of multi-competitor games. Our approach assumes competitors’ performances follow independent extreme value distributions, with each competitor’s ability evolving over time as a Gaussian random walk. The model accounts for the possibility of ties, an occurrence that is not atypical in races in which some of the competitors may not finish and therefore tie for last place. Inference can be performed through Markov chain Monte Carlo (MCMC) simulation from the posterior distribution. We also develop a filtering algorithm that is an approximation to the full Bayesian computations. The approximate Bayesian filter can be used for updating competitor abilities on an ongoing basis. We demonstrate our approach to measuring abilities of 268 women from the results of women’s Alpine downhill skiing competitions recorded over the period 2002–2013.


title: Throughput-delay trade-off in wireless networks

abstract: Gupta and Kumar (2000) introduced a random network model for studying the way throughput scales in a wireless network when the nodes are fixed, and showed that the throughput per source-destination pair is /spl otimes/(1//spl radic/nlogn). Grossglauser and Tse (2001) showed that when nodes are mobile it is possible to have a constant or /spl otimes/(1) throughput scaling per source-destination pair. The focus of this paper is on characterizing the delay and determining the throughput-delay trade-off in such fixed and mobile ad hoc networks. For the Gupta-Kumar fixed network model, we show that the optimal throughput-delay trade-off is given by D(n) = /spl otimes/(nT(n)), where T(n) and D(n) are the throughput and delay respectively. For the Grossglauser-Tse mobile network model, we show that the delay scales as /spl otimes/(n/sup 1/2//v(n)), where v(n) is the velocity of the mobile nodes. We then describe a scheme that achieves the optimal order of delay for any given throughput. The scheme varies (i) the number of hops, (ii) the transmission range and (iii) the degree of node mobility to achieve the optimal throughput-delay trade-off. The scheme produces a range of models that capture the Gupta-Kumar model at one extreme and the Grossglauser-Tse model at the other. In the course of our work, we recover previous results of Gupta and Kumar, and Grossglauser and Tse using simpler techniques, which might be of a separate interest.


title: Coreference Resolution for Russian: Taking Stock and Moving Forward

abstract: Coreference resolution is targeted at collecting all mentions of an entity within a text into a single cluster. Various approaches for this task have already been proposed but the problem is still challenging. RU-EVAL-2014, an evaluation of anaphora and coreference resolution, renewed interest for this task for the Russian language. We try to follow this trend; our work can be viewed as an extension to recent approaches. Thorough manual feature engineering allows us to significantly improve current state-of-the-art results. Additionally, we compare applicability of various clustering algorithms to building coreference groups. That is essentially the second part of the approach.


title: Minimum cost model energy code envelope requirements

abstract: This paper describes the analysis underlying development of the U.S. Department of Energy’s proposed revisions of the Council of American Building Officials (CABO) 1993 Model Energy Code (MEC) building thermal envelope requirements for single-family and low-rise multifamily residences. This analysis resulted in revised MEC envelope conservation levels based on an objective methodology that determined the minimum-cost combination of energy efficiency measures (EEMs) for residences in different locations around the United States.


title: Decentralized Algorithm for Randomized Task Allocation in Fog Computing Systems

abstract: Fog computing is identified as a key enabler for using various emerging applications by battery powered and computationally constrained devices. In this paper, we consider devices that aim at improving their performance by choosing to offload their computational tasks to nearby devices or to an edge cloud. We develop a game theoretical model of the problem and use a variational inequality theory to compute an equilibrium task allocation in static mixed strategies. Based on the computed equilibrium strategy, we develop a decentralized algorithm for allocating the computational tasks among nearby devices and the edge cloud. We use the extensive simulations to provide insight into the performance of the proposed algorithm and compare its performance with the performance of a myopic best response algorithm that requires global knowledge of the system state. Despite the fact that the proposed algorithm relies on average system parameters only, our results show that it provides a good system performance close to that of the myopic best response algorithm.


title: A dataset for inter-sentence relation extraction using distant supervision

abstract: © LREC 2018 - 11th International Conference on Language Resources and Evaluation. All rights reserved. This paper presents a benchmark dataset for the task of inter-sentence relation extraction. The paper explains the distant supervision method followed for creating the dataset for inter-sentence relation extraction, involving relations previously used for standard intra-sentence relation extraction task. The study evaluates baseline models such as bag-of-words and sequence based recurrent neural network models on the developed dataset and shows that recurrent neural network models are more useful for the task of intra-sentence relation extraction. Comparing the results of the present work on iner-sentence relation extraction with previous work on intra-sentence relation extraction, the study suggests the need for more sophisticated models to handle long-range information between entities across sentences.


title: Learning to Rank with Multiple Objective Functions

abstract: We investigate the problem of learning to rank with document retrieval from the perspective of learning for multiple objective functions. We present solutions to two open problems in learning to rank: first, we show how multiple measures can be combined into a single graded measure that can be learned. This solves the problem of learning from a 'scorecard' of measures by making such scorecards comparable, and we show results where a standard web relevance measure (NDCG) is used for the top-tier measure, and a relevance measure derived from click data is used for the second-tier measure; the second-tier measure is shown to significantly improve while leaving the top-tier measure largely unchanged. Second, we note that the learning-to-rank problem can itself be viewed as changing as the ranking model learns: for example, early in learning, adjusting the rank of all documents can be advantageous, but later during training, it becomes more desirable to concentrate on correcting the top few documents for each query. We show how an analysis of these problems leads to an improved, iteration-dependent cost function that interpolates between a cost function that is more appropriate for early learning, with one that is more appropriate for late-stage learning. The approach results in a significant improvement in accuracy with the same size models. We investigate these ideas using LambdaMART, a state-of-the-art ranking algorithm.


title: Architecture of an IoT-based system for football supervision (IoT Football)

abstract: Football, also called soccer, is one of the most popular sports in the world, if one considers the number of fans as well as the number of players. However, footballers face serious injuries during the match and even during training. Concussion, hypoglycemia, swallowing the tongue and shortness of breath are examples of the health problems footballers face, and in extreme cases, may lead to death. In addition, many sport clubs and sport academies spend millions of dollars contracting new professional footballers or even developing new professional footballers. The Internet of Things (IoT) is a new paradigm that combines various technologies to enhance our lives. Today's technology can protect footballers by diagnosing any health problems, which may occur during the match or training session, which, if detected early, may prevent any adverse effects on their long-term health. This paper proposes an IoT-based architecture for the sport of football, called IoT Football. Our proposal aims to embed sensing devices (e.g. sensors and RFID), telecommunication technologies (e.g. ZigBee) and cloud computing in the sport of football in order monitor the health of footballers and reduce the occurrence of adverse health conditions. The aim is to integrate the IoT environment, in particular the IoT application, into the field of sport in the form of a new application.


title: A Deep Learning Approach to Human Activity Recognition Based on Single Accelerometer

abstract: In this paper, we propose an acceleration-based human activity recognition method using popular deep architecture, Convolution Neural Network (CNN). In particular, we construct a CNN model and modify the convolution kernel to adapt the characteristics of tri-axial acceleration signals. Also, for comparison, we use some widely used methods to accomplish the recognition task on the same dataset. The large dataset we constructed consists of 31688 samples from eight typical activities. The experiment results show that the CNN works well, which can reach an average accuracy of 93.8% without any feature extraction methods.


title: Meaning as collective use: predicting semantic hashtag categories on twitter

abstract: This paper sets out to explore whether data about the usage of hashtags on Twitter contains information about their semantics. Towards that end, we perform initial statistical hypothesis tests to quantify the association between usage patterns and semantics of hashtags. To assess the utility of pragmatic features - which describe how a hashtag is used over time - for semantic analysis of hashtags, we conduct various hashtag stream classification experiments and compare their utility with the utility of lexical features. Our results indicate that pragmatic features indeed contain valuable information for classifying hashtags into semantic categories. Although pragmatic features do not outperform lexical features in our experiments, we argue that pragmatic features are important and relevant for settings in which textual information might be sparse or absent (e.g., in social video streams).


title: Activity recognition from user-annotated acceleration data

abstract: In this work, algorithms are developed and evaluated to de- tect physical activities from data acquired using five small biaxial ac- celerometers worn simultaneously on different parts of the body. Ac- celeration data was collected from 20 subjects without researcher su- pervision or observation. Subjects were asked to perform a sequence of everyday tasks but not told specifically where or how to do them. Mean, energy, frequency-domain entropy, and correlation of acceleration data was calculated and several classifiers using these features were tested. De- cision tree classifiers showed the best performance recognizing everyday activities with an overall accuracy rate of 84%. The results show that although some activities are recognized well with subject-independent training data, others appear to require subject-specific training data. The results suggest that multiple accelerometers aid in recognition because conjunctions in acceleration feature values can effectively discriminate many activities. With just two biaxial accelerometers - thigh and wrist - the recognition performance dropped only slightly. This is the first work to investigate performance of recognition algorithms with multiple, wire-free accelerometers on 20 activities using datasets annotated by the subjects themselves.


title: Mechanical design, modelling and control of a novel aerial manipulator

abstract: In this paper a novel aerial manipulation system is proposed. The mechanical structure of the system, the number of thrusters and their geometry will be derived from technical optimization problems. The aforementioned problems are defined by taking into consideration the desired actuation forces and torques applied to the end-effector of the system. The framework of the proposed system is designed in a CAD Package in order to evaluate the system parameter values. Following this, the kinematic and dynamic models are developed and an adaptive backstepping controller is designed aiming to control the exact position and orientation of the end-effector in the Cartesian space. Finally, the performance of the system is demonstrated through a simulation study, where a manipulation task scenario is investigated.


title: Highly linear three-way Doherty amplifier with uneven power drive for repeater system

abstract: We have demonstrated a high linear three-way Doherty amplifier by applying uneven power drive and optimizing the peaking biases and load impedances. The amplifier has been implemented at 2.14GHz using 190-W peak envelope power laterally diffused metal-oxide-semiconductor field-effect transistors. For comparison, a class AB biased amplifier is tested as it's counterpart. The two-tone signal and forward-link wideband code-division multiple access (WCDMA) signal have been selected as test signals. At 42dBm (12.5-dB backed-off output power), there are large improvements in the third- and fifth-order intermodulation distortions. For the forward-link four-carrier WCDMA signal, the adjacent channel leakage ratio (ACLR) performances at 5-MHz and 10-MHz offsets are -52.5dBc and -53.4dBc, respectively, and satisfy the generally medium high power amplifier linearity target without using any other linearization circuits. In comparison with the class AB amplifier, the three-way Doherty amplifier with uneven power drive has 9.8-dB lower ACLR at 5-MHz offset while maintaining a comparable drain efficiency of 10.2%.


title: Optical tracking using projective invariant marker pattern properties

abstract: We describe a novel optical tracker algorithm for the tracking of interaction devices in virtual and augmented reality. The tracker uses invariant properties of marker patterns to efficiently identify and reconstruct the pose of these interaction devices. Since invariant properties are sensitive to noise in the 2D marker positions, an offline training session is used to determine deviations in these properties. These deviations are taken into account when searching for the patterns once the tracker is used.


title: Data leakage mitigation for discretionary access control in collaboration clouds

abstract: With the growing popularity of cloud computing, more and more enterprises are migrating their collaboration platforms from in-enterprise systems to Software as a Service (SaaS) applications. While SaaS collaboration has numerous advantages, it also raises new security challenges. In particular, since SaaS collaboration is increasingly used across enterprise boundaries, organizations are concerned that sensitive information may be leaked to outsiders due to their employees' inadvertent mistakes on information sharing. In this article, we propose to mitigate the data leakage problem in SaaS collaboration systems by reducing human errors. Built on top of the discretionary access control model in existing collaboration systems, we have designed a series of mechanisms to provide defense in depth against information leakage. First, we allow enterprises to encode their organizational security rules as mandatory access control policies, so as to impose coarse-grained restrictions on their employees' discretionary sharing decisions. Second, we design an attribute-based recommender that suggests and prioritizes potential recipients for users' files, reducing errors in the choices of recipients. Third, our system actively examines abnormal recipients entered by a file owner, providing the last line of defense before a file is shared. We have implemented a prototype of our solution and performed experiments on data collected from real-world collaboration systems.


title: Exploratory Data Analysis of Acceleration Signals to Select Light-Weight and Accurate Features for Real-Time Activity Recognition on Smartphones

abstract: Smartphone-based activity recognition (SP-AR) recognizes users' activities using the embedded accelerometer sensor. Only a small number of previous works can be classified as online systems, i.e., the whole process (pre-processing, feature extraction, and classification) is performed on the device. Most of these online systems use either a high sampling rate (SR) or long data-window (DW) to achieve high accuracy, resulting in short battery life or delayed system response, respectively. This paper introduces a real-time/online SP-AR system that solves this problem. Exploratory data analysis was performed on acceleration signals of 6 activities, collected from 30 subjects, to show that these signals are generated by an autoregressive (AR) process, and an accurate AR-model in this case can be built using a low SR (20 Hz) and a small DW (3 s). The high within class variance resulting from placing the phone at different positions was reduced using kernel discriminant analysis to achieve position-independent recognition. Neural networks were used as classifiers. Unlike previous works, true subject-independent evaluation was performed, where 10 new subjects evaluated the system at their homes for 1 week. The results show that our features outperformed three commonly used features by 40% in terms of accuracy for the given SR and DW.


title: Sparse Sensing With Co-Prime Samplers and Arrays

abstract: This paper considers the sampling of temporal or spatial wide sense stationary (WSS) signals using a co-prime pair of sparse samplers. Several properties and applications of co-prime samplers are developed. First, for uniform spatial sampling with M and N sensors where M and N are co-prime with appropriate interelement spacings, the difference co-array has O(MN) freedoms which can be exploited in beamforming and in direction of arrival estimation. An M -point DFT filter bank and an N-point DFT filter bank can be used at the outputs of the two sensor arrays and their outputs combined in such a way that there are effectively MN bands (i.e., MN narrow beams with beamwidths proportional to 1/MN), a result following from co-primality. The ideas are applicable to both active and passive sensing, though the details and tradeoffs are different. Time domain sparse co-prime samplers also generate a time domain co-array with O(MN) freedoms, which can be used to estimate the autocorrelation at much finer lags than the sample spacings. This allows estimation of power spectrum of an arbitrary signal with a frequency resolution proportional to 2π/(MNT) even though the pairs of sampled sequences xc(NTn) and xc(MTn) in the time domain can be arbitrarily sparse - in fact from the sparse set of samples xc(NTn) and xc(MTn) one can estimate O(MN) frequencies in the range |ω| <; π/T. It will be shown that the co-array based method for estimating sinusoids in noise offers many advantages over methods based on the use of Chinese remainder theorem and its extensions. Examples are presented throughout to illustrate the various concepts.


title: Autism , Asperger syndrome and brain mechanisms for the attribution of mental states to animated shapes

abstract: Summary Ten able adults with autism or Asperger syndrome and 10 normal volunteers were PET scanned while watching animated sequences. The animations depicted two triangles moving about on a screen in three different conditions: moving randomly, moving in a goal-directed fashion (chasing, fighting), and moving interactively with implied intentions (coaxing, tricking). The last condition frequently elicited descriptions in terms of mental states that viewers attributed to the triangles (mentalizing). The autism group gave fewer and less accurate descriptions of these latter animations, but equally accurate descriptions of the other animations compared with controls. While viewing animations that elicited mentalizing, in contrast to randomly moving shapes, the normal group showed increased activation in a previously identified mentalizing network (medial prefrontal cortex, superior temporal sulcus at the temporoparietal junction and temporal poles). The autism group showed less activation than the normal group in all these regions. However, one additional region, extrastriate cortex, which was highly active when watching animations that elicited mentalizing, showed the same amount of increased activation in both groups. In the autism group this extrastriate region showed reduced functional connectivity with the superior temporal sulcus at the temporo-parietal junction, an area associated with the processing of biological motion as well as with mentalizing. This finding suggests a physiological cause for the mentalizing dysfunction in autism: a bottleneck in the interaction between higher order and lower order perceptual processes.


Model of Learning Computational Thinking


title: Simple, Robust and Optimal Ranking from Pairwise Comparisons

abstract: We consider data in the form of pairwise comparisons of n items, with the goal of precisely identifying the top k items for some value of k < n, or alternatively, recovering a ranking of all the items. We analyze the Copeland counting algorithm that ranks the items in order of the number of pairwise comparisons won, and show it has three attractive features: (a) its computational efficiency leads to speed-ups of several orders of magnitude in computation time as compared to prior work; (b) it is robust in that theoretical guarantees impose no conditions on the underlying matrix of pairwise-comparison probabilities, in contrast to some prior work that applies only to the BTL parametric model; and (c) it is an optimal method up to constant factors, meaning that it achieves the information-theoretic limits for recovering the top k-subset. We extend our results to obtain sharp guarantees for approximate recovery under the Hamming distortion metric, and more generally, to any arbitrary error requirement that satisfies a simple and natural monotonicity condition.


title: Reliability of hybrid bond interconnects

abstract: Hybrid bonding, with wafer-level bonding to form oxide-oxide bonds and Cu-Cu bonds, is a promising technology for 3D integrated circuits. In this study, we describe the design, processing. and characterization of test structures formed using hybrid bonding for wafers built in two different technologies; a 180 nm Al BEOL technology and a 110nm Cu BEOL technology. The reliability evaluation shows good results for package level and wafer level stresses.


title: Accelerating relational database operations using both CPU and GPU co-processor

abstract: Data is evolving and the number of existing data sources is vastly growing. Therefore, there is a compelling need for effective techniques to store, retrieve and process such massive data. Significant speed-ups at a small cost can be achieved by deploying co-processors such as GPUs. To this end, in this paper, we propose a new hybrid query processing technique that makes use of the capabilities of CPUs and GPUs. The proposed approach breaks down each SQL statement into smaller parts during the parsing process. It then automatically manages the distribution of different query parts to be executed either on the CPU or parallel on the GPU and CPU. To achieve this, we developed and implemented the proposed approach on a SQL server database using the .Net framework instead of working under the Linux environment. The performance of the proposed approach is validated using different workloads and the results demonstrate that the proposed GPU-based query processor achieved speedup up to 39 as fast as multi-core CPUs.


title: Results of the WNUT2017 Shared Task on Novel and Emerging Entity Recognition

abstract: This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization), but recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms. Take for example the tweet "so.. kktny in 30 mins?!" -- even human experts find the entity 'kktny' hard to detect and resolve. The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities. The task as described in this paper evaluated the ability of participating entries to detect and classify novel and emerging named entities in noisy text.


Virtual youth research: an exploration of methodologies and ethical dilemmas from a British perspective


title: Exploring Markov Logic Networks for Question Answering

abstract: Elementary-level science exams pose significant knowledge acquisition and reasoning challenges for automatic question answering. We develop a system that reasons with knowledge derived from textbooks, represented in a subset of firstorder logic. Automatic extraction, while scalable, often results in knowledge that is incomplete and noisy, motivating use of reasoning mechanisms that handle uncertainty. Markov Logic Networks (MLNs) seem a natural model for expressing such knowledge, but the exact way of leveraging MLNs is by no means obvious. We investigate three ways of applying MLNs to our task. First, we simply use the extracted science rules directly as MLN clauses and exploit the structure present in hard constraints to improve tractability. Second, we interpret science rules as describing prototypical entities, resulting in a drastically simplified but brittle network. Our third approach, called Praline, uses MLNs to align lexical elements as well as define and control how inference should be performed in this task. Praline demonstrates a 15% accuracy boost and a 10x reduction in runtime as compared to other MLNbased methods, and comparable accuracy to word-based baseline approaches.


title: High temperature (≫200°C) isolated gate drive topologies for Silicon Carbide (SiC) JFET

abstract: Volume and weight limitations for components in hybrid electrical vehicle (HEV) propulsion systems demand highly-compact and highly-efficient power electronics. The application of silicon carbide (SiC) semiconductor technology in conjunction with high temperature (HT) operation allows the power density of the DC-DC converters and inverters to be increased. Elevated ambient temperatures of above 200degC also affects the gate drives attached to the power semiconductors. This paper focuses on the selection of HT components and discusses different gate drive topologies for SiC JFETs with respect to HT operation capability, limitations, dynamic performance and circuit complexity. An experimental performance comparison of edge-triggered and phase-difference HT drivers with a conventional room temperature JFET gate driver is given. The proposed edge-triggered gate driver offers high switching speeds and a cost effective implementation. Switching tests at 200degC approve an excellent performance at high temperature and a low temperature drift of the driver output voltage.


title: Cost Efficient Design of Fault Tolerant Geo-Distributed Data Centers

abstract: Many critical e-commerce and financial services are deployed on geo-distributed data centers for scalability and availability. Recent market surveys show that failure of a data center is inevitable resulting in a huge financial loss. Fault-tolerance in distributed data centers is typically handled by provisioning spare capacity to mask failure at a site. We argue that the operating cost and data replication cost (for data availability) must be considered in spare capacity provisioning along with minimizing the number of servers. Since the operating cost and client demand vary across space and time, we propose cost-aware capacity provisioning to minimize the total cost of ownership (TCO) for fault-tolerant data centers. We formulate the problem of spare capacity provisioning in fault-tolerant distributed data centers using mixed integer linear programming (MILP), with an objective of minimizing the TCO. The model accounts for heterogeneous client demand, data replication strategies (single and multiple site), variation in electricity price and carbon tax, and delay constraints while computing the spare capacity. Solving the MILP using real-world data, we observed a saving in the TCO to the tune of 35% compared to a model that minimizes the total number of servers and 43% compared to the model that minimizes the average response time. We demonstrate that our model is beneficial when the cost of electricity, carbon tax, and bandwidth vary significantly across the locations, which seems to be the problem for most of the operators.


Automated Imaging and Analysis of a Novel Comet Assay to Enable High Throughput Genotoxicity Testing


Supporting children and young people’s psychosocial recovery post-disaster: Internal and external protective factors


title: Simulation for the Social Scientist

abstract: What can computer simulation contribute to the social sciences? Which of the many approaches to simulation would be best for my social science project? How do I design, carry out and analyse the results from a computer simulation? This is a practical textbook on the techniques of building computer simulations to assist understanding of social and economic issues and problems. Interest in social simulation has been growing rapidly worldwide as a result of increasingly powerful hardware and software and also a rising interest in the application of ideas of complexity, evolution, adaptation and chaos in the social sciences. This authoritative book details all the common approaches to social simulation, to provide social scientists with an appreciation of the literature and allow those with some programming skills to create their own simulations.New for this edition is a chapter on how to use simulation as a tool. A new chapter on multi-agent systems has also been added to support the fact that multi-agent modelling has become the preferred approach to simulation. Social scientists in a wide range of fields will find this book an essential tool for research, particularly in sociology, economics, anthropology, geography, organizational theory, political science, social policy, cognitive psychology and cognitive science. It will also appeal to computer scientists interested in distributed artificial intelligence, multi-agent systems and agent technologies.


title: FallDeFi: Ubiquitous Fall Detection using Commodity Wi-Fi Devices

abstract: Falling or tripping among elderly people living on their own is recognized as a major public health worry that can even lead to death. Fall detection systems that alert caregivers, family members or neighbours can potentially save lives. In the past decade, an extensive amount of research has been carried out to develop fall detection systems based on a range of different detection approaches, i.e, wearable and non-wearable sensing and detection technologies. In this paper, we consider an emerging non-wearable fall detection approach based on WiFi Channel State Information (CSI). Previous CSI based fall detection solutions have considered only time domain approaches. Here, we take an altogether different direction, time-frequency analysis as used in radar fall detection. We use the conventional Short-Time Fourier Transform (STFT) to extract time-frequency features and a sequential forward selection algorithm to single out features that are resilient to environment changes while maintaining a higher fall detection rate. When our system is pre-trained, it has a 93% accuracy and compared to RTFall and CARM, this is a 12% and 15% improvement respectively. When the environment changes, our system still has an average accuracy close to 80% which is more than a 20% to 30% and 5% to 15% improvement respectively.


title: Motion-based calibration of multimodal sensor arrays

abstract: This paper formulates a new pipeline for automated extrinsic calibration of multi-sensor mobile platforms. The new method can operate on any combination of cameras, navigation sensors and 3D lidars. Current methods for extrinsic calibration are either based on special markers and/or chequerboards, or they require a precise parameters initialisation for the calibration to converge. These two limitations prevent them from being fully automatic. The method presented in this paper removes these restrictions. By combining information extracted from both, platform's motion estimates and external observations, our approach eliminates the need for special markers and also removes the need for manual initialisation. A third advantage is that the motion-based automatic initialisation does not require overlapping field of view between sensors. The paper also provides a method to estimate the accuracy of the resulting calibration. We illustrate the generalisation of our approach and validate its performance by showing results with two contrasting datasets. The first dataset was collected in a city with a car platform, and the second one was collected in a tree-crop farm with a Segway platform.


The Earth Observing System: A Space-based Program for Assessing Mankind's Impact on the Global Environment


title: Predicting different conceptualizations of system use: Acceptance in hedonic volitional context (Facebook)

abstract: This research examines the relationship between the predictors of use and the different conceptualizations of system use in a hedonic volitional setting (Facebook). Using the unified theory of acceptance and use of technology (UTAUT) model, an investigation into the three aspects of system use: the user, system and task were carried out. Results from a cross-sectional survey of 449 students show that behavioral intention has a significant influence on all aspects and dimensions of system use including cognitive absorption and deep structure use. Performance expectancy, effort expectancy and social influence are significantly related to system use. From the component model, performance expectancy is only significant with deep structure use. Hedonic performance expectancy is found to be significantly related to cognitive absorption. Results also demonstrate that predictors of usage have a significant relationship with the user aspect of system use. The variance explained in usage conceptualized as the user/task aspects is much higher than that of the system/task aspects or one-dimensional measures. Overall, conceptualizing system use using the user/task aspects offers greater explanatory power in Facebook use.


title: Supervised Discrete Hashing

abstract: Recently, learning based hashing techniques have attracted broad research interests because they can support efficient storage and retrieval for high-dimensional data such as images, videos, documents, etc. However, a major difficulty of learning to hash lies in handling the discrete constraints imposed on the pursued hash codes, which typically makes hash optimizations very challenging (NP-hard in general). In this work, we propose a new supervised hashing framework, where the learning objective is to generate the optimal binary hash codes for linear classification. By introducing an auxiliary variable, we reformulate the objective such that it can be solved substantially efficiently by employing a regularization algorithm. One of the key steps in this algorithm is to solve a regularization sub-problem associated with the NP-hard binary optimization. We show that the sub-problem admits an analytical solution via cyclic coordinate descent. As such, a high-quality discrete solution can eventually be obtained in an efficient computing manner, therefore enabling to tackle massive datasets. We evaluate the proposed approach, dubbed Supervised Discrete Hashing (SDH), on four large image datasets and demonstrate its superiority to the state-of-the-art hashing methods in large-scale image retrieval.


title: The Strengths and Difficulties Questionnaire: a pilot study on the validity of the self-report version

abstract: The self-report version of the Strengths and Difficulties Questionnaire (SDQ) was administered to two samples of 11–16 year olds: 83 young people in the community and 116 young people attending a mental health clinic. The questionnaire discriminated satisfactorily between the two samples. For example, the clinic mean for the total difficulties score was 1.4 standard deviations above the community mean, with clinic cases being over six times more likely to have a score in the abnormal range. The correlations between self-report SDQ scores and teacher or parent-rated SDQ scores compared favourably with the average cross-informant correlations in previous studies of a range of measures. The self-report SDQ appears promising and warrants further evaluation.


TOWARDS AUTOMATED FOOTBALL ANALYSIS: ALGORITHMS AND DATA STRUCTURES


Smartphone use and smartphone addiction among young people in Switzerland


title: Image-based reconstruction and synthesis of dense foliage

abstract: Flora is an element in many computer-generated scenes. But trees, bushes and plants have complex geometry and appearance, and are difficult to model manually. One way to address this is to capture models directly from the real world. Existing techniques have focused on extracting macro structure such as the branching structure of trees, or the structure of broad-leaved plants with a relatively small number of surfaces. This paper presents a finer scale technique to demonstrate for the first time the processing of densely leaved foliage - computation of 3D structure, plus extraction of statistics for leaf shape and the configuration of neighboring leaves. Our method starts with a mesh of a single exemplar leaf of the target foliage. Using a small number of images, point cloud data is obtained from multi-view stereo, and the exemplar leaf mesh is fitted non-rigidly to the point cloud over several iterations. In addition, our method learns a statistical model of leaf shape and appearance during the reconstruction phase, and a model of the transformations between neighboring leaves. This information is useful in two ways - to augment and increase leaf density in reconstructions of captured foliage, and to synthesize new foliage that conforms to a user-specified layout and density. The result of our technique is a dense set of captured leaves with realistic appearance, and a method for leaf synthesis. Our approach excels at reconstructing plants and bushes that are primarily defined by dense leaves and is demonstrated with multiple examples.


LIDAR-based 3 D Object Perception


title: Spectral Efficiency of MIMO Millimeter-Wave Links with Single-Carrier Modulation for 5G Networks

abstract: Future wireless networks will extensively rely upon bandwidths centered on carrier frequencies larger than 10GHz. Indeed, recent research has shown that, despite the large path-loss, millimeter wave (mmWave) frequencies can be successfully exploited to transmit very large data-rates over short distances to slowly moving users. Due to hardware complexity and cost constraints, single-carrier modulation schemes, as opposed to the popular multi-carrier schemes, are being considered for use at mmWave frequencies. This paper presents preliminary studies on the achievable spectral efficiency on a wireless MIMO link operating at mmWave in a typical 5G scenario. Two different single-carrier modem schemes are considered, i.e. a traditional modulation scheme with linear equalization at the receiver, and a single-carrier modulation with cyclic prefix, frequency-domain equalization and FFT-based processing at the receiver. Our results show that the former achieves a larger spectral efficiency than the latter. Results also confirm that the spectral efficiency increases with the dimension of the antenna array, as well as that performance gets severely degraded when the link length exceeds 100 meters and the transmit power falls below 0dBW. Nonetheless, mmWave appear to be very suited for providing very large data-rates over short distances.


title: Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks

abstract: Face detection and alignment in unconstrained environment are challenging due to various poses, illuminations, and occlusions. Recent studies show that deep learning approaches can achieve impressive performance on these two tasks. In this letter, we propose a deep cascaded multitask framework that exploits the inherent correlation between detection and alignment to boost up their performance. In particular, our framework leverages a cascaded architecture with three stages of carefully designed deep convolutional networks to predict face and landmark location in a coarse-to-fine manner. In addition, we propose a new online hard sample mining strategy that further improves the performance in practice. Our method achieves superior accuracy over the state-of-the-art techniques on the challenging face detection dataset and benchmark and WIDER FACE benchmarks for face detection, and annotated facial landmarks in the wild benchmark for face alignment, while keeps real-time performance.


title: LSTM with working memory

abstract: LSTM is arguably the most successful RNN architecture for many tasks that involve sequential information. In the past few years there have been several proposed improvements to LSTM. We propose an improvement to LSTM which allows communication between memory cells in different blocks and allows an LSTM layer to carry out internal computation within its memory.


title: Pavement crack detection using the Gabor filter

abstract: Crack is a common form of pavement distress and it carries significant information on the condition of roads. The detection of cracks is essential to perform pavement maintenance and rehabilitation. Many of the highways agencies, in different countries, are still employing conventional, costly and very time consuming techniques which involve direct human intervention and assessment. Although automated recognition has been successfully performed for many pavement distresses, crack detection remains, to this date, a topic where reservations exist. A novel approach to automatically distinguish cracks in digital pavement images is proposed in this paper. The Gabor filter is proven to be a highly potential technique for multidirectional crack detection that was not done previously using the Gabor filter. Image analysis using the Gabor function is directly related to the mammalian visual perception, hence the choice of this method for crack detection. Results reported in this paper concentrate on pavement images with high levels of surface texture that makes crack detection difficult. An initial detection precision of up to 95% has been reported in this paper showing a good promise in the proposed method.


title: User experience: challenges and opportunities

abstract: User expectations, motivations and feelings when using a product or encountering a system have urged the need to investigate beyond the traditional functionality and usability concerns by assessing and designing for the user experience. Having this realized, we can ensure positive user experience as well as desirable products. This paper aims at providing an overview of the main concepts and principles related to user experience. The topics discussed in this paper cover both theories from the academia as well as empirical studies from the industry. Due to misconception of user experience, it was found that industry depends on and uses the traditional usability methods in the product development as user experience practices. As a result, this paper discusses and elaborates the relationship between conventional usability and user experience in addition to the variety of methods which can be used for user experience evaluation during different time spans. Furthermore, user experience extends over different areas such as human computer interaction, and product design and development. Knowledge and concepts related to user experience are scattered due to the dynamic concepts of user experience. In other words, authors tend to discuss the user experience from their own perspective and interest. Hence, this paper is trying to provide and establish a holistic understanding of the user experience by covering different topics related to user experience. The output of this paper may provide and assist researchers and practitioners to establish a base to aid in proper understanding of user experience, its concepts and principles.


title: Aggregating crowdsourced binary ratings

abstract: In this paper we analyze a crowdsourcing system consisting of a set of users and a set of binary choice questions. Each user has an unknown, fixed, reliability that determines the user's error rate in answering questions. The problem is to determine the truth values of the questions solely based on the user answers. Although this problem has been studied extensively, theoretical error bounds have been shown only for restricted settings: when the graph between users and questions is either random or complete. In this paper we consider a general setting of the problem where the user--question graph can be arbitrary. We obtain bounds on the error rate of our algorithm and show it is governed by the expansion of the graph. We demonstrate, using several synthetic and real datasets, that our algorithm outperforms the state of the art.


0 Volume Ray Casting in WebGL


title: Scientific workflow design for mere mortals

abstract: Recent years have seen a dramatic increase in research and development of scientific workflow systems. These systems promise to make scientists more productive by automating data-driven and compute-intensive analyses. Despite many early achievements, the long-term success of scientific workflow technology critically depends on making these systems useable by ''mere mortals'', i.e., scientists who have a very good idea of the analysis methods they wish to assemble, but who are neither software developers nor scripting-language experts. With these users in mind, we identify a set of desiderata for scientific workflow systems crucial for enabling scientists to model and design the workflows they wish to automate themselves. As a first step towards meeting these requirements, we also show how the collection-oriented modeling and design (comad) approach for scientific workflows, implemented within the Kepler system, can help provide these critical, design-oriented capabilities to scientists.


Beyond the Prince : Race and Gender Role Portrayal in


title: Fused-layer CNN accelerators

abstract: Deep convolutional neural networks (CNNs) are rapidly becoming the dominant approach to computer vision and a major component of many other pervasive machine learning tasks, such as speech recognition, natural language processing, and fraud detection. As a result, accelerators for efficiently evaluating CNNs are rapidly growing in popularity. The conventional approaches to designing such CNN accelerators is to focus on creating accelerators to iteratively process the CNN layers. However, by processing each layer to completion, the accelerator designs must use off-chip memory to store intermediate data between layers, because the intermediate data are too large to fit on chip. In this work, we observe that a previously unexplored dimension exists in the design space of CNN accelerators that focuses on the dataflow across convolutional layers. We find that we are able to fuse the processing of multiple CNN layers by modifying the order in which the input data are brought on chip, enabling caching of intermediate data between the evaluation of adjacent CNN layers. We demonstrate the effectiveness of our approach by constructing a fused-layer CNN accelerator for the first five convolutional layers of the VGGNet-E network and comparing it to the state-of-the-art accelerator implemented on a Xilinx Virtex-7 FPGA. We find that, by using 362KB of on-chip storage, our fused-layer accelerator minimizes off-chip feature map data transfer, reducing the total transfer by 95%, from 77MB down to 3.6MB per image.


title: GPU-accelerated synthetic aperture radar backprojection in CUDA

abstract: Pleasingly parallel algorithms such as filtered back-projection have been documented to enjoy significant speedups when ported to run on a graphics processor instead of a standard CPU. Presented here is a two-dimensional SAR backprojection implementation for a single GPU using the NVIDIA CUDA framework. Given that input range projections may be too large to fit in graphics memory, our implementation allows the partitioning of the data along range as well as aperture. We show the efficacy of some CUDA optimizations, and apply it to two public datasets, one of which has varying start frequency.


title: Embedded wafer level ball grid array (eWLB) technology for system integration

abstract: Silicon front-end and assembly and packaging technology more and more merge. In addition interconnect density reaches limits for advanced CMOS technology. In this paper we introduce the fan-out embedded wafer level packaging technology, which is an example to link front-end and packaging technology and offers additional freedom for interconnect design. We demonstrate capabilites for system integration of the eWLB technology, which includes system on chip (SoC) integration and system in package (SiP) integration like side by side and stacking of devices. We highlight the importance of understanding properties of new materials, which influence warpage or heat dissipation. We also show the excellent performance of the eWLB package for mm-wave applications.


title: Microbial communities associated with human decomposition and their potential use as postmortem clocks

abstract: Most forensic research that is used to better understand how to estimate the postmortem interval (PMI) entails the study of the physiochemical characteristics of decomposition and the effects that environmental factors have on the decomposition process. Forensic entomology exploits the life cycles of arthropods like Diptera (blow flies or flesh flies) and Coleoptera (beetles) deposited on the decaying carcass to determine PMI. Forensic taphonomy, from the Greek word taphos meaning burial, studies the creation of the fossils of decomposed cadavers to ascertain information as to the nature and time of death. Compared to other areas of taphonomy, there have been relatively few forensic science studies that have investigated the impact of human decomposition on the microbial changes occurring on or in a corpse or in the soil communities underneath a body. Such research may facilitate the critical determination of PMI. Therefore, the scope of this review is to provide a concise summary of the current progress in the newly emerging field of microbial diversity and the next-generation metagenomic sequencing approaches for assessing these communities in humans and in the soil beneath decomposing human.


title: Flexible UHF/VHF Vivaldi antenna for broadband and gas balloon applications

abstract: A Vivaldi flexible antenna with a −6 dB bandwidth from 150 MHz to 2000 MHz is introduced. The antenna is fabricated on a 60∗60 cm2 silicone substrate. In this paper, we present the design, realization and performances of this wide band and directive antenna. The proposed structure is lightweight, easy to realize and does not require any matching network. The targeted application is the radio-localization of signal source emissions from helium gas balloon. Six antennas are integrated on the bottom side of the balloon and the information is recovered with a cable that serves to its stabilization.


title: Solid-state time-of-flight range camera

abstract: The concept of a real-time range camera without moving parts is described, based on the time-of-flight (TOF) principle. It operates with modulated visible and near-infrared radiation, which is detected and demodulated simultaneously by a 2-D array of lock-in pixels employing the charge-coupled device principle. Each pixel individually measures the amplitude, offset and phase of the received radiation. The theoretical resolution limit of this TOF range camera is derived, which depends on the square root of the detected background radiation and the inverse of the modulation amplitude. Actual measurements of 3-D sequences acquired at 10 range images per second show excellent agreement between our theory and the observed results. A range resolution of a few centimeters over a range of 10 m, with an illumination power of a few hundreds of milliwatts is obtained in laboratory scenes for noncooperative, diffusely reflecting objects.


title: Toward Formal Modeling of Affective Agents in a BDI Architecture

abstract: When designing agents to simulate human behavior, the incorporation of personality, emotions and mood into the agent reasoning process provides the agent with a closer to human behavior. We have designed an Open Affective Agent Architecture (O3A) based on widely accepted psychological theories. O3A offers a flexible way of integrating the affective characteristics of agents into their logic. We extend the operational semantic of the BDI agent language AgentSpeak modifying the traditional BDI reasoning cycle to incorporate affective components. This informal semantic description allows an agent to have a representation of not only the current state of the environment, but also of the agent affective state.


title: The distributed human neural system for face perception

abstract: Face perception, perhaps the most highly developed visual skill in humans, is mediated by a distributed neural system in humans that is comprised of multiple, bilateral regions. We propose a model for the organization of this system that emphasizes a distinction between the representation of invariant and changeable aspects of faces. The representation of invariant aspects of faces underlies the recognition of individuals, whereas the representation of changeable aspects of faces, such as eye gaze, expression, and lip movement, underlies the perception of information that facilitates social communication. The model is also hierarchical insofar as it is divided into a core system and an extended system. The core system is comprised of occipitotemporal regions in extrastriate visual cortex that mediate the visual analysis of faces. In the core system, the representation of invariant aspects is mediated more by the face-responsive region in the fusiform gyrus, whereas the representation of changeable aspects is mediated more by the face-responsive region in the superior temporal sulcus. The extended system is comprised of regions from neural systems for other cognitive functions that can be recruited to act in concert with the regions in the core system to extract meaning from faces.


1 Personalized E-learning System Architecture Using 2 Data Mining Approach 3


title: A $V$ -Band Switched Beam-Forming Antenna Module Using Absorptive Switch Integrated With 4$\,\times\,$ 4 Butler Matrix in 0.13- $\mu{\hbox {m}}$ CMOS

abstract: A beam-forming antenna module is demonstrated using an integrated CMOS beam-former chip and a simple two-metal layer printed circuit board at V-band. The beam-former circuit integrates an absorptive single-pole four-throw switch together with a 4 × 4 Butler matrix using a 0.13-μm CMOS process. The entire insertion loss of the integrated beam former integrated circuit (IC) is around 7.5 dB at 60 GHz, among which 3 dB is attributed to the Butler matrix. The overall phase error is within ±12%. The antenna module employs backside radiation structure using series-fed patch antenna arrays to suppress parasitic radiation. The measured radiation pattern shows good agreement with the simulation. To the best of our knowledge, this is the first demonstration of the beam-forming antenna module using a single-chip CMOS switched beam-former IC at V-band.


title: Force Requirements for Artificial Muscle to Create an Eyelid Blink With Eyelid Sling

abstract: Objective: To determine the force requirements, optimal vector, and appropriate materials of a novel eyelid sling device that will be used to rehabilitate eyelid closure (blink) in congenital or acquired permanent facial paralysis with an artificial muscle. Methods: The force required to close the eyelids in human cadavers (n=6) were measured using a load cell system. The eyelid sling using either expanded polytetrafluoroethylene (ePTFE) or temporalis muscle fascia was implanted. The ideal vector of force and placement within the eyelid for a natural eyelid closure were compared. Results: The eyelid sling concept was successful at creating eyelid closure in a cadaver model using an upper eyelid sling attached to the distal tarsal plate. Less force was necessary to create eyelid closure using a temporalismusclefasciasling(627±128mN)thanfortheePTFE eyelid sling (1347±318 mN).


title: End-to-end pedestrian collision warning system based on a convolutional neural network with semantic segmentation

abstract: Traditional pedestrian collision warning systems sometimes raise alarms even when there is no danger (e.g., when all pedestrians are walking on the sidewalk). These false alarms can make it difficult for drivers to concentrate on their driving. In this paper, we propose a novel framework for an end-to-end pedestrian collision warning system based on a convolutional neural network. Semantic segmentation information is used to train the convolutional neural network and two loss functions, such as cross entropy and Euclidean losses, are minimized. Finally, we demonstrate the effectiveness of our method in reducing false alarms and increasing warning accuracy compared to a traditional histogram of oriented gradients (HoG)-based system.


title: Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor

abstract: In this paper, we introduce a new radiomic descriptor, Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) for capturing subtle differences between benign and pathologic phenotypes which may be visually indistinguishable on routine anatomic imaging. CoLlAGe seeks to capture and exploit local anisotropic differences in voxel-level gradient orientations to distinguish similar appearing phenotypes. CoLlAGe involves assigning every image voxel an entropy value associated with the co-occurrence matrix of gradient orientations computed around every voxel. The hypothesis behind CoLlAGe is that benign and pathologic phenotypes even though they may appear similar on anatomic imaging, will differ in their local entropy patterns, in turn reflecting subtle local differences in tissue microarchitecture. We demonstrate CoLlAGe’s utility in three clinically challenging classification problems: distinguishing (1) radiation necrosis, a benign yet confounding effect of radiation treatment, from recurrent tumors on T1-w MRI in 42 brain tumor patients, (2) different molecular sub-types of breast cancer on DCE-MRI in 65 studies and (3) non-small cell lung cancer (adenocarcinomas) from benign fungal infection (granulomas) on 120 non-contrast CT studies. For each of these classification problems, CoLlAGE in conjunction with a random forest classifier outperformed state of the art radiomic descriptors (Haralick, Gabor, Histogram of Gradient Orientations).


title: Detecting a pothole using deep convolutional neural network models for an adaptive shock observing in a vehicle driving

abstract: A pothole is a one of the greatest threat to vehicle drives. It causes an accident by sudden steering of the vehicle wheel, forcing an enormous stress on a vehicle tire or making a hard turning in a vehicle by late detection. It is crucial to find where a pothole is on the pavement. As the number of pavement increases, detecting a pothole becomes a great challenge in a modern society. Several methods suggest detecting potholes using sensors. However, these methods require an installation on the vehicle in order to collect data of the pavement. Meanwhile, other methods are using smartphone sensors to reduce a cost of deployment and get an advantage of sensitive sensors without a complex installation on the vehicle. For this reason, a method using a smartphone camera with the artificial neural network becomes a way in detecting a pothole on a pavement. In this paper, we investigate the performance in detecting potholes with an image classification method based on the deep convolutional neural network models.


title: PSYCHOLOGICAL SCIENCE Research Article PARTITION PRIMING IN JUDGMENT UNDER UNCERTAINTY

abstract: We show that likelihood judgments are biased toward an ignorance-prior probability that assigns equal credence to each mutually exclusive event considered by the judge. The value of the ignorance prior depends crucially on how the set of possibilities (i.e., the state space) is subjectively partitioned by the judge. For instance, asking “what is the probability that Sunday will be hotter than any other day next week?” facilitates a two-fold case partition, (Sunday hotter, Sunday not hotter), thus priming an ignorance prior of 1/2. In contrast, asking “what is the probability that the hottest day of the week will be Sunday?” facilitates a seven-fold class partition, (Sunday hottest, Monday hottest, etc.), priming an ignorance prior of 1/7. In four studies, we observed systematic partition dependence: Judgments made by participants presented with either case or class formulations of the same query were biased toward the corresponding ignorance prior.


title: Single-Phase T-Type Inverter Performance Benchmark Using Si IGBTs, SiC MOSFETs, and GaN HEMTs

abstract: In this paper, benchmark of Si IGBT, SiC MOSFET, and Gallium nitride (GaN) HEMT power switches at 600-V class is conducted in single-phase T-type inverter. Gate driver requirements, switching performance, inverter efficiency performance, heat sink volume, output filter volume, and dead-time effect for each technology is evaluated. Gate driver study shows that GaN has the lowest gate driver losses above 100 kHz and below 100 kHz, SiC has lowest gate losses. GaN has the best switching performance among three technologies that allows high efficiency at high-frequency applications. GaN-based inverter operated at 160-kHz switching frequency with 97.3% efficiency at 2.5-kW output power. Performance of three device technologies at different temperature, switching frequency, and load conditions shows that heat sink volume of the converter can be reduced by 2.5 times by switching from Si to GaN solution at 60 $^{\circ }$ C case temperature, and for SiC and GaN, heat sink volume can be reduced by 2.36 and 4.92 times, respectively, by increasing heat sink temperature to 100 $^{\circ }$ C. Output filter volume can be reduced by 43% with 24, 26, and 61 W increase in device power loss for GaN-, SiC-, and Si-based converters, respectively. WBG devices allow reduction of harmonic distortion at output current from 3.5% to 1.5% at 100 kHz.


title: Tangible Computing in Everyday Life: Extending Current Frameworks for Tangible User Interfaces with Personal Objects

abstract: This paper describes an extension to Ullmer and Ishii’s TUI-categorization [41]. The reason for adding new categories is based on their omission of their associative TUIs and our work in this area of personal objects. The benefit of using personal objects instead of generic objects is that in the first instance users already have mental models or personal links between experiences, the related media and these objects. In addition, a Graspable or Tangible User Interface with personal objects can support existing media systems, instead of designing new ones that have to be learned by users.


title: Efficient and Scalable Socware Detection in Online Social Networks

abstract: Online social networks (OSNs) have become the new vector for cybercrime, and hackers are finding new ways to propagate spam and malware on these platforms, which we refer to as socware. As we show here, socware cannot be identified with existing security mechanisms (e.g., URL blacklists), because it exploits different weaknesses and often has different intentions. ::: ::: In this paper, we present MyPageKeeper, a Facebook application that we have developed to protect Facebook users from socware. Here, we present results from the perspective of over 12K users who have installed MyPageKeeper and their roughly 2.4 million friends. Our work makes three main contributions. First, to enable protection of users at scale, we design an efficient socware detection method which takes advantage of the social context of posts. We find that our classifier is both accurate (97% of posts flagged by it are indeed socware and it incorrectly flags only 0.005% of benign posts) and efficient (it requires 46 ms on average to classify a post). Second, we show that socware significantly differs from traditional email spam or web-based malware. For example, website blacklists identify only 3% of the posts flagged by MyPageKeeper, while 26% of flagged posts point to malicious apps and pages hosted on Facebook (which no current antivirus or blacklist is designed to detect). Third, we quantify the prevalence of socware by analyzing roughly 40 million posts over four months; 49% of our users were exposed to at least one socware post in this period. Finally, we identify a new type of parasitic behavior, which we refer to as "Like-as-a-Service", whose goal is to artificially boost the number of "Likes" of a Facebook page.


title: Fabric Defect Detection Method Based on Gabor Filter Mask

abstract: In this paper, a fabric defect detection method based on Gabor filter masks is proposed. In this method, one even symmetric Gabor filter mask and one odd symmetric Gabor filter mask derived from the impulse response of the optimal Gabor filter are used. The optimal Gabor filter is designed to match with the texture features of defect-free fabric image, whose parameters are obtained by using the genetic algorithm. The performance of the proposed method is evaluated off-line by using a group of fabric sample images containing many kinds of fabric defects. The experimental results exhibit its accurate defect detection with low false alarms. And the effectiveness and robustness of the proposed method are confirmed.


title: Sparseness Meets Deepness: 3D Human Pose Estimation from Monocular Video

abstract: This paper addresses the challenge of 3D full-body human pose estimation from a monocular image sequence. Here, two cases are considered: (i) the image locations of the human joints are provided and (ii) the image locations of joints are unknown. In the former case, a novel approach is introduced that integrates a sparsity-driven 3D geometric prior and temporal smoothness. In the latter case, the former case is extended by treating the image locations of the joints as latent variables. A deep fully convolutional network is trained to predict the uncertainty maps of the 2D joint locations. The 3D pose estimates are realized via an Expectation-Maximization algorithm over the entire sequence, where it is shown that the 2D joint location uncertainties can be conveniently marginalized out during inference. Empirical evaluation on the Human3.6M dataset shows that the proposed approaches achieve greater 3D pose estimation accuracy over state-of-the-art baselines. Further, the proposed approach outperforms a publicly available 2D pose estimation baseline on the challenging PennAction dataset.


title: You Only Look Once: Unified, Real-Time Object Detection

abstract: We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. ::: Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is far less likely to predict false detections where nothing exists. Finally, YOLO learns very general representations of objects. It outperforms all other detection methods, including DPM and R-CNN, by a wide margin when generalizing from natural images to artwork on both the Picasso Dataset and the People-Art Dataset.


title: L2-Net: Deep Learning of Discriminative Patch Descriptor in Euclidean Space

abstract: The research focus of designing local patch descriptors has gradually shifted from handcrafted ones (e.g., SIFT) to learned ones. In this paper, we propose to learn high performance descriptor in Euclidean space via the Convolutional Neural Network (CNN). Our method is distinctive in four aspects: (i) We propose a progressive sampling strategy which enables the network to access billions of training samples in a few epochs. (ii) Derived from the basic concept of local patch matching problem, we empha-size the relative distance between descriptors. (iii) Extra supervision is imposed on the intermediate feature maps. (iv) Compactness of the descriptor is taken into account. The proposed network is named as L2-Net since the output descriptor can be matched in Euclidean space by L2 distance. L2-Net achieves state-of-the-art performance on the Brown datasets [16], Oxford dataset [18] and the newly proposed Hpatches dataset [11]. The good generalization ability shown by experiments indicates that L2-Net can serve as a direct substitution of the existing handcrafted descriptors. The pre-trained L2-Net is publicly available.


title: A Multi-Broadband Planar Antenna for GSM/ UMTS/LTE and WLAN/WiMAX Handsets

abstract: A compact multi-broadband planar antenna is proposed for wireless handsets. The proposed antenna covers multiple broad frequency bands, including the 900-MHz band with a bandwidth of 29% (790-1061 MHz), the 2-GHz band with a bandwidth of 51% (1650-2775 MHz), and the 3.5/5-GHz band with a bandwidth of 68% (3132-6382 MHz). The multi-broadband antenna consists of a folded monopole coupling to an S-shaped strip for the 900-MHz band, a C-shaped monopole coupling with a C-shaped strip for the 2-GHz band, and a trapezoidal monopole with an inverted-F monopole for the 3.5/5-GHz band. All these monopoles with coupled strips are printed on a planar substrate with a compact size of 15 mm×60 mm×0.5 mm, suitable for emerging ultra-slim GSM/UMTS/LTE and WLAN/WiMAX handsets.


title: The Hofstede model: Applications to global branding and advertising strategy and research

abstract: Recent years have seen increasing interest in the consequences of culture for global marketing and advertising. Many recent studies point at the necessity of adapting branding and advertising strategies to the culture of the consumer. In order to understand cultural differences, several models have been developed of which the Hofstede model is the most used. This article describes elements of this model that are most relevant to branding and advertising, and reviews studies that have used the model for aspects of international branding and for advertising research. It provides some cautious remarks about applying the model. Suggestions for more cross-cultural research are added.


title: Deep multimodal learning for Audio-Visual Speech Recognition

abstract: In this paper, we present methods in deep multimodal learning for fusing speech and visual modalities for Audio-Visual Automatic Speech Recognition (AV-ASR). First, we study an approach where uni-modal deep networks are trained separately and their final hidden layers fused to obtain a joint feature space in which another deep network is built. While the audio network alone achieves a phone error rate (PER) of 41% under clean condition on the IBM large vocabulary audio-visual studio dataset, this fusion model achieves a PER of 35.83% demonstrating the tremendous value of the visual channel in phone classification even in audio with high signal to noise ratio. Second, we present a new deep network architecture that uses a bilinear softmax layer to account for class specific correlations between modalities. We show that combining the posteriors from the bilinear networks with those from the fused model mentioned above results in a further significant phone error rate reduction, yielding a final PER of 34.03%.


title: Heavy metals in urban soils with various types of land use in Beijing, China.

abstract: Heavy metal concentrations of Cd, Cr, Cu, Ni, Pb and Zn were investigated for 127 urban soil samples collected from business area (BA), classical garden (CG), culture and education area (CEA), public green space (PGS), residential area (RA) and roadside area (RSA) in Beijing. The distribution of Cd, Cu, Pb and Zn was mainly affected by anthropogenic sources, with their mean concentrations much higher than the background values of Beijing, while Cr and Ni were from natural sources. Among the 6 types of land use, the concentrations of Cd, Cu, Pb and Zn in CG were significantly higher than those in the other 5 types of land use (p<0.05), which were due to their historical use such as pigments, wood preservation and brassware. For the other 5 types of land use except CG, the mean concentration of Cd in RSA was significantly higher than those in BA, CEA, PGS and RA (p<0.05), suggesting Cd was mainly from traffic sources. The distribution maps revealed that the concentrations of Cu, Pb and Zn showed decreasing trends from the center to the suburb of Beijing, they increased with the age of the urban area.


title: Feasibility study of a giant Magneto-Resistance based respiration rate monitor

abstract: This paper reports a simple and reliable electronic technique for the estimation of respiration rate (RR). Giant Magneto-Resistance (GMR) based sensors are employed to extract a plethysmograph signal from the subject. This signal is filtered and processed further through simple signal processing stages to obtain RR indication. The feasibility of the system has been studied on a prototype built and associated experimentation on 20 volunteers. The developed system provided clean plethysmographic signals, whose relevant features are shown to possess a relation with RR. Test results shows that the worst-case error in RR estimation of the system is merely 2 breaths per minute. Further, the capability of the system for detecting breathing cessations is studied and results reported. The system can be extended to function as a heart-rate (HR) monitor. Some basic results which proves the working and accuracy of system as a HR estimator is also given in the paper. The proposed system can be used for effective estimation of the above two vital signs in clinics, automotives, wearable devices, etc.


Identification and Characterization of a Novel Association between Dietary Potassium and Risk of Crohn’s Disease and Ulcerative Colitis


title: Development of dynamic protection against timing channels

abstract: Information systems face many threats, such as covert channels, which declassify hidden information by, e.g., analyzing the program execution time. Such threats exist at various stages of the execution of instructions. Even if software developers are able to neutralize these threats in source code, new attack vectors can arise in compiler-generated machine code from these representations. Existing approaches for preventing vulnerabilities have numerous restrictions related to both their functionality and the range of threats that can be found and removed. This study presents a technique for removing threats and generating safer code using dynamic compilation in an execution environment by combining information from program analysis of the malicious code and re-compiling such code to run securely. The proposed approach stores summary information in the form of rules that can be shared among analyses. The annotations enable us to conduct the analyses to mitigate threats. Developers can update the analyses and control the volume of resources that are allocated to perform these analyses by changing the precision. The authors’ experiments show that the binary code created by applying the suggested method is of high quality.