title: Site-Level Web Template Extraction Based on DOM Analysis

abstract: One of the main development resources for website engineers are Web templates. Templates allow them to increase productivity by plugin content into already formatted and prepared pagelets. For the final user templates are also useful, because they provide uniformity and a common look and feel for all webpages. However, from the point of view of crawlers and indexers, templates are an important problem, because templates usually contain irrelevant information such as advertisements, menus, and banners. Processing and storing this information leads to a waste of resources (storage space, bandwidth, etc.). It has been measured that templates represent between 40 % and 50 % of data on the Web. Therefore, identifying templates is essential for indexing tasks. In this work we propose a novel method for automatic web template extraction that is based on similarity analysis between the DOM trees of a collection of webpages that are detected using an hyperlink analysis. Our implementation and experiments demonstrate the usefulness of the technique.


title: IS/IT Investment Evaluation and Benefits Realization Issues in Australia

abstract: Information systems/information technology represents substantial financial investment for many organizations. It has been shown that IS/IT investments in many organizations are huge and increasing rapidly every year and yet there is still a lack of understanding of the impact of the proper IS/IT investment evaluation processes and practices in these organizations. Thus, a survey and an in-depth case study of these practices and processes in two large governmental departments with several major IS/IT projects was conducted. The key issues presented here in some detail are of interest to practitioners concerned with making decisions about project investment and realizing benefits, as well as researchers. ACM Classification: H.0 (Information Systems – General)


title: Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning

abstract: Active learning (AL) aims to enable training high performance classifiers with low annotation cost by predicting which subset of unlabelled instances would be most beneficial to label. The importance of AL has motivated extensive research, proposing a wide variety of manually designed AL algorithms with diverse theoretical and intuitive motivations. In contrast to this body of research, we propose to treat active learning algorithm design as a meta-learning problem and learn the best criterion from data. We model an active learning algorithm as a deep neural network that inputs the base learner state and the unlabelled point set and predicts the best point to annotate next. Training this active query policy network with reinforcement learning, produces the best non-myopic policy for a given dataset. The key challenge in achieving a general solution to AL then becomes that of learner generalisation, particularly across heterogeneous datasets. We propose a multi-task dataset-embedding approach that allows dataset-agnostic active learners to be trained. Our evaluation shows that AL algorithms trained in this way can directly generalize across diverse problems.


title: A survey of cryptographic algorithms for IoT devices

abstract: The future of Internet is “Internet of Things” where trillions of physical objects, most of them with low or extremely low resources, communicate with each other without human intervention. Light weight cryptography includes cryptographic algorithms specifically meant for extremely constrained resources. They can be applied not only for encryption but also for hashing and authentication under environments that are highly constrained. □ In this paper, we first discuss the need of light weight cryptography and their design differences with normal block ciphers. An overview of some of the light weight cryptographic algorithms is discussed after that. Also, we look into different types of attacks that has been studied on some of these ciphers. Finally, we compare the performance of some of these ciphers on Windows and Embedded platform.


title: Simple High-Performance Metal-Plating Procedure for Stereolithographically 3-D-Printed Waveguide Components

abstract: This letter presents a simple and cheap metal-plating procedure for plastic 3-D printed microwave components. The devices are built using a Stereolithographic printer and then metalized by a two-step process consisting in a silver-painted substrate followed by copper electrodeposition. The method achieves good adhesion of the metal to the plastic body and high conductivity. Resonators and $X$ -band filters have been fabricated and tested, showing excellent performance in terms of dimensional accuracy and low loss, with quality factors better than 6500. The technique can be usefully employed for fast and cheap prototyping of microwave components at least up to the $X$ -band.


title: 3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks

abstract: Human activity understanding with 3D/depth sensors has received increasing attention in multimedia processing and interactions. This work targets on developing a novel deep model for automatic activity recognition from RGB-D videos. We represent each human activity as an ensemble of cubic-like video segments, and learn to discover the temporal structures for a category of activities, i.e. how the activities to be decomposed in terms of classification. Our model can be regarded as a structured deep architecture, as it extends the convolutional neural networks (CNNs) by incorporating structure alternatives. Specifically, we build the network consisting of 3D convolutions and max-pooling operators over the video segments, and introduce the latent variables in each convolutional layer manipulating the activation of neurons. Our model thus advances existing approaches in two aspects: (i) it acts directly on the raw inputs (grayscale-depth data) to conduct recognition instead of relying on hand-crafted features, and (ii) the model structure can be dynamically adjusted accounting for the temporal variations of human activities, i.e. the network configuration is allowed to be partially activated during inference. For model training, we propose an EM-type optimization method that iteratively (i) discovers the latent structure by determining the decomposed actions for each training example, and (ii) learns the network parameters by using the back-propagation algorithm. Our approach is validated in challenging scenarios, and outperforms state-of-the-art methods. A large human activity database of RGB-D videos is presented in addition.


title: Principal Component Analysis for clustering gene expression data

abstract: MOTIVATION ::: There is a great need to develop analytical methodology to analyze and to exploit the information contained in gene expression data. Because of the large number of genes and the complexity of biological networks, clustering is a useful exploratory technique for analysis of gene expression data. Other classical techniques, such as principal component analysis (PCA), have also been applied to analyze gene expression data. Using different data analysis techniques and different clustering algorithms to analyze the same data set can lead to very different conclusions. Our goal is to study the effectiveness of principal components (PCs) in capturing cluster structure. Specifically, using both real and synthetic gene expression data sets, we compared the quality of clusters obtained from the original data to the quality of clusters obtained after projecting onto subsets of the principal component axes. ::: ::: ::: RESULTS ::: Our empirical study showed that clustering with the PCs instead of the original variables does not necessarily improve, and often degrades, cluster quality. In particular, the first few PCs (which contain most of the variation in the data) do not necessarily capture most of the cluster structure. We also showed that clustering with PCs has different impact on different algorithms and different similarity metrics. Overall, we would not recommend PCA before clustering except in special circumstances.


title: Input feature selection for classification problems

abstract: Feature selection plays an important role in classifying systems such as neural networks (NNs). We use a set of attributes which are relevant, irrelevant or redundant and from the viewpoint of managing a dataset which can be huge, reducing the number of attributes by selecting only the relevant ones is desirable. In doing so, higher performances with lower computational effort is expected. In this paper, we propose two feature selection algorithms. The limitation of mutual information feature selector (MIFS) is analyzed and a method to overcome this limitation is studied. One of the proposed algorithms makes more considered use of mutual information between input attributes and output classes than the MIFS. What is demonstrated is that the proposed method can provide the performance of the ideal greedy selection algorithm when information is distributed uniformly. The computational load for this algorithm is nearly the same as that of MIFS. In addition, another feature selection algorithm using the Taguchi method is proposed. This is advanced as a solution to the question as to how to identify good features with as few experiments as possible. The proposed algorithms are applied to several classification problems and compared with MIFS. These two algorithms can be combined to complement each other's limitations. The combined algorithm performed well in several experiments and should prove to be a useful method in selecting features for classification problems.


title: IMLS-SLAM: Scan-to-Model Matching Based on 3D Data

abstract: The Simultaneous Localization And Mapping (SLAM) problem has been well studied in the robotics community, especially using mono, stereo cameras or depth sensors. 3D depth sensors, such as Velodyne LiDAR, have proved in the last 10 years to be very useful to perceive the environment in autonomous driving, but few methods exist that directly use these 3D data for odometry. We present a new low-drift SLAM algorithm based only on 3D LiDAR data. Our method relies on a scan-to-model matching framework. We first have a specific sampling strategy based on the LiDAR scans. We then define our model as the previous localized LiDAR sweeps and use the Implicit Moving Least Squares (IMLS) surface representation. We show experiments with the Velodyne HDL32 with only 0.40% drift over a 4 km acquisition without any loop closure (i.e., 16 m drift after 4 km). We tested our solution on the KITTI benchmark with a Velodyne HDL64 and ranked among the best methods (against mono, stereo and LiDAR methods) with a global drift of only 0.69%.


title: Mining Trackman Golf Data

abstract: Recently, innovative technology like Trackman has made it possible to generate data describing golf swings. In this application paper, we analyze Trackman data from 275 golfers using descriptive statistics and machine learning techniques. The overall goal is to find non-trivial and general patterns in the data that can be used to identify and explain what separates skilled golfers from poor. Experimental results show that random forest models, generated from Trackman data, were able to predict the handicap of a golfer, with a performance comparable to human experts. Based on interpretable predictive models, descriptive statistics and correlation analysis, the most distinguishing property of better golfers is their consistency. In addition, the analysis shows that better players have superior control of the club head at impact and generally hit the ball straighter. A very interesting finding is that better players also tend to swing flatter. Finally, an outright comparison between data describing the club head movement and ball flight data, indicates that a majority of golfers do not hit the ball solid enough for the basic golf theory to apply.


title: The Key Factors of Successful Internet Crowdfunding Projects - An Empirical Study Based on Different Platforms

abstract: Crowdfunding, as a new conception in fundraising, provides access to get fund in the background of Internet. This paper has conducted a research on the key factors to highly successful crowdfunding based on two biggest crowdfunding platforms in China - JingDong crowdfunding platform and TaoBao crowdfunding platform. By using Logistic regression model, we find that different platform designs and different mechanisms have effect on the project success. Meanwhile, quality signals that the founders show and their own experience do have a positive association with high success of projects. Other factors that are visible directly on crowdfunding website are also been discussed.


title: A Multimodal Deep Learning Method for Android Malware Detection Using Various Features

abstract: With the widespread use of smartphones, the number of malware has been increasing exponentially. Among smart devices, android devices are the most targeted devices by malware because of their high popularity. This paper proposes a novel framework for android malware detection. Our framework uses various kinds of features to reflect the properties of android applications from various aspects, and the features are refined using our existence-based or similarity-based feature extraction method for effective feature representation on malware detection. Besides, a multimodal deep learning method is proposed to be used as a malware detection model. This paper is the first study of the multimodal deep learning to be used in the android malware detection. With our detection model, it was possible to maximize the benefits of encompassing multiple feature types. To evaluate the performance, we carried out various experiments with a total of 41 260 samples. We compared the accuracy of our model with that of other deep neural network models. Furthermore, we evaluated our framework in various aspects including the efficiency in model updates, the usefulness of diverse features, and our feature representation method. In addition, we compared the performance of our framework with those of other existing methods including deep learning-based methods.


title: Model driven development of context aware software systems

abstract: This paper presents the first results of an ongoing work towards the realization of a model driven development framework for context awareness. Its core element consists of a domain specific modeling language called CAMEL (Context Awareness ModEling Language), and defined as a UML extension. CAMEL can be used to enrich a UML model of an application with elements related to contexts and context dependent behaviors. The resulting UML+CAMEL model is the starting point for model transformation aimed at generating executable code or other artifacts. CAMEL is implemented by an Eclipse plugin.


title: Saudi Arabia stock prices forecasting using artificial neural networks

abstract: In this paper, we have proposed artificial neural network for the prediction of Saudi stock market. The proposed predictions model, with its high degree of accuracy, could be used as investment advisor for the investors and traders in the Saudi stock market. The proposed model is based mainly on Saudi Stock market historical data covering a large span of time. Achieving reasonable accuracy rate of predication models will surely facilitate an increased confidence in the investment in the Saudi stock market. We have only used the closing price of the stock as the stock variable considered for input to the system. The number of windows gap to determine the numbers of previous data to be used in predicting the next day closing price data has been choosing based on heuristics. Our results indicated that the proposed ANN model predicts the next day closing price stock market value with a low RMSE and high correlation coefficient of up to 99.9% for the test set, which is an indication that the model adequately mimics the trend of the market in its prediction. This performance is really encouraging and thus the proposed system will impact positively the analysis and prediction of Saudi stock market in general.


title: Effect of Transdermal Teriparatide Administration on Bone Mineral Density in Postmenopausal Women

abstract: Context: Treatment of osteoporosis with an anabolic agent, teriparatide [human PTH 1-34 (TPTD)], is effective in reducing incident fractures, but patient resistance to daily sc injections has limited its use. A novel transdermal patch, providing a rapid, pulse delivery of TPTD, may provide a desirable alternative. ::: ::: Objective: The aim of the study was to determine the safety and efficacy of a novel transdermal TPTD patch compared to placebo patch and sc TPTD 20-μg injection in postmenopausal women with osteoporosis. ::: ::: Design: Our study consisted of 6-month, randomized, placebo-controlled, positive control, multidose daily administration. ::: ::: Patients: We enrolled 165 postmenopausal women (mean age, 64 yr) with osteoporosis. ::: ::: Interventions: A TPTD patch with a 20-, 30-, or 40-μg dose or a placebo patch was self-administered daily for 30-min wear time, or 20 μg of TPTD was injected daily. ::: ::: Outcomes: The primary efficacy measure was mean percentage change in lumbar spine bone mineral density (BMD) from baseline at 6 months. ::: ::: Results: TPTD delivered by transdermal patch significantly increased lumbar spine BMD vs. placebo patch in a dose-dependent manner at 6 months (P < 0.001). TPTD 40-μg patch increased total hip BMD compared to both placebo patch and TPTD injection (P < 0.05). Bone turnover markers (procollagen type I N-terminal propeptide and C-terminal cross-linked telopeptide of type I collagen) increased from baseline in a dose-dependent manner in all treatment groups and were all significantly different from placebo patch (P < 0.001). All treatments were well tolerated, and no prolonged hypercalcemia was observed. ::: ::: Conclusion: Transdermal patch delivery of TPTD in postmenopausal women with osteoporosis for 6 months is safe and effective in increasing lumbar spine and total hip BMD.


title: FOSTERING INTER-TEAM KNOWLEDGE SHARING EFFECTIVENESS IN AGILE SOFTWARE DEVELOPMENT

abstract: Agile methods have impacted on software development by providing several changes on how software is developed. Agile values, principles and practices foster intra-team knowledge sharing. However, they do not inform how to cope with that across organizational levels. This study seeks to understand inter-team knowledge sharing effectiveness in agile software development organizations that employ practices for this endeavor. Using grounded theory, we analyzed data from four Brazilian agile software organizations and got feedback from an expert in agile methods implementation. Our main contribution is a conceptual model that explains how effectiveness depends on applying purposeful practices, along with organizational conditions and stimuli, that foster knowledge sharing across agile software development teams. This understanding represents an innovative focus within agile context and provides what to consider when striving on this path. Yet, it yields opportunities for further studies in refining and extending the model to several organizational contexts. Scaling agility toward organizational level is the next horizon for agile software development. Cross-team knowledge sharing leverages this endeavor and also reflects the way agile software organizations are coping with enterprise agility and the consideration of knowledge as a resource for organizational competitiveness.


title: Head movement-based driver drowsiness detection: A review of state-of-art techniques

abstract: Driver fatigue is one of the most common reasons for deadly road accidents around the world. Continuous monotonous driving for long hours without rest causes drowsiness and consequently fatal road accidents. Automatic driver drowsiness detection can prevent a vast number of sleep persuaded road accidents, and hence can save precious lives. Number of techniques for driver drowsiness detection has been examined in the recent past. This paper presents a survey of these techniques. These techniques detect the driver drowsiness by observing the driving pattern. Abnormalities in driving pattern is hypothesized as a drowsiness state of driver. Various measures such as subjective, behavioral, physiological, and vehicular have been used for this purpose. The comparative analysis of these techniques indicates that behavioral measures are easy to acquire and does not disturb the driver as they are non-invasive. Among various behavioral measures, head movement measure is found to be most precise and effective.


Modular mechanisms for Bayesian optimization


title: Vortex Visualization in Ultra Low Reynolds Number Insect Flight

abstract: We present the visual analysis of a biologically inspired CFD simulation of the deformable flapping wings of a dragonfly as it takes off and begins to maneuver, using vortex detection and integration-based flow lines. The additional seed placement and perceptual challenges introduced by having multiple dynamically deforming objects in the highly unsteady 3D flow domain are addressed. A brief overview of the high speed photogrammetry setup used to capture the dragonfly takeoff, parametric surfaces used for wing reconstruction, CFD solver and underlying flapping flight theory is presented to clarify the importance of several unsteady flight mechanisms, such as the leading edge vortex, that are captured visually. A novel interactive seed placement method is used to simplify the generation of seed curves that stay in the vicinity of relevant flow phenomena as they move with the flapping wings. This method allows a user to define and evaluate the quality of a seed's trajectory over time while working with a single time step. The seed curves are then used to place particles, streamlines and generalized streak lines. The novel concept of flowing seeds is also introduced in order to add visual context about the instantaneous vector fields surrounding smoothly animate streak lines. Tests show this method to be particularly effective at visually capturing vortices that move quickly or that exist for a very brief period of time. In addition, an automatic camera animation method is used to address occlusion issues caused when animating the immersed wing boundaries alongside many geometric flow lines. Each visualization method is presented at multiple time steps during the up-stroke and down-stroke to highlight the formation, attachment and shedding of the leading edge vortices in pairs of wings. Also, the visualizations show evidence of wake capture at stroke reversal which suggests the existence of previously unknown unsteady lift generation mechanisms that are unique to quad wing insects.


title: A Scalable Recipe Recommendation System for Mobile Application

abstract: There are more and more users to use mobile application to get the cooking recipe information, but to recommend right recipe for each user is challenging for the complex food and diet information. In this paper, a hybrid recommendation system is proposed for personalized recipe mobile application. We describe a scalable recommendation system to process the massive data from mobile applications based on the spark clusters, and design a hybrid recommendation algorithm, which combines content-based filtering and collaborative filtering to improve the recipe recommended effectiveness. Our experiments reveal that the recommendation system has scalable computational capability.


title: Balanced realization and model reduction for unstable systems

abstract: This paper proposes a new balanced realization and model reduction method for possibly unstable systems by introducing some new controllability and observability Gramians. These Gramians can be related to minimum control energy and minimum estimation error. In contrast to Gramians defined in the literature for unstable systems, these Gramians can always be computed for systems without imaginary axis poles and they reduce to the standard controllability and observability Gramians when the systems are stable. The proposed balanced model reduction method enjoys the similar error bounds as does for the standard balanced model reduction. Furthermore, the new error bounds and the actual approximation errors seem to be much smaller than the ones using the methods given in the literature for unstable systems. Copyright © 1999 John Wiley & Sons, Ltd.


title: ‘TuskBot’: Design of the mobile stair climbing 2 by 2 wheels robot platform with novel passive structure ‘Tusk’

abstract: In this paper, we present ‘Tuskbot’, a wheel-based robot with a novel structure called ‘Tusk’, passive and protruded elements in the front part of the robot, which can create an angle-of-attack when it climbs stairs. The robot can easily overcome stairs with the help of Tusk, which does not require additional active mechanisms. We formulated a simplified mathematical model of the structure based on the geometrical relationship between the wheels and the stairs in each phase during the stair-climb. To test the model and Tusk structure, we calculated the length of each link and the angle of Tusk from the dimension of stair and radius of wheels, and built the robot accordingly. The results demonstrate the validity of the model and the structure.


title: Evaluation of XBee-Pro transmission range for Wireless Sensor Network's node under forested environments based on Received Signal Strength Indicator (RSSI)

abstract: Wireless sensor networks (WSN) have been proposed to develop a monitoring system for forest in which XBee is one of prospective device for this implementation due to its simple set-up, low cost, low power consumption, and relative wide transmission coverage. Regarding the coverage area of WSN in forest is generally wide and the radio transmission is highly attenuated by foliage, the coverage range of device must be defined to optimize the number of nodes in the WSN systems. This paper delivers the empirical study of XBee transmission range in the forest based on Received Signal Strength Indicator (RSSI). The measurements have conducted in two different type of forest, i.e. heterogeneous tropical forest and homogeneous pine forest. The measurement results found highly degrades of transmission range compared to non-forest environment with the maximum distance of transmission are 250 m and 330 m for both aforementioned types of forest consecutively.


title: Dynamic Game Balancing : an Evaluation of User Satisfaction

abstract: User satisfaction in computer games seems to be influenced by game balance, the level of challenge faced by the user. This work presents an evaluation, performed by human players, of dynamic game balancing approaches. The results indicate that adaptive approaches are more effective. This paper also enumerates some issues encountered in evaluating users' satisfaction, in the context of games, and depicts some learned lessons.


title: Pathogen Recognition and Innate Immunity

abstract: Microorganisms that invade a vertebrate host are initially recognized by the innate immune system through germline-encoded pattern-recognition receptors (PRRs). Several classes of PRRs, including Toll-like receptors and cytoplasmic receptors, recognize distinct microbial components and directly activate immune cells. Exposure of immune cells to the ligands of these receptors activates intracellular signaling cascades that rapidly induce the expression of a variety of overlapping and unique genes involved in the inflammatory and immune responses. New insights into innate immunity are changing the way we think about pathogenesis and the treatment of infectious diseases, allergy, and autoimmunity.


title: Mechatronic model of a novel slotless permanent magnet DC-motor with air gap winding design

abstract: This paper presents a mechatronic model of a novel slotless permanent magnet DC-motor with air gap winding. Besides technical advantages of this type of motor like high power density, high torque, very low weight and high efficiency, the motor design allows a very precise and efficient modelling with limited effort. A nonlinear model of magnetic field density can be extracted from a detailed nonlinear FE-model build in ANSYS/Maxwell, approximated by Fourier series and then used to model driving torque and back EMF, representing the coupling between electrical and mechanical subsystems. Analytically founded numerical models for driving torque and back EMF will be given. Real geometry of the phase winding is taken into account to improve model accuracy. The electrical subsystem will be described as coupled three phase system, whose parameters can also be extracted from the nonlinear FE-model with high accuracy. Together with a mechanical model of the rotor a MATLAB/Simulink model is build and extended by models of the hall sensors to detect rotor position and commutation logic to control the HEX-Bridge during operation. Finally, results of a complex simulation model, based on the parameters of the prototype of a wheel-hub motor, implementing the new motor design, are getting shown. Simulation results compare very well to measured data. Simulation time is very short due to the efficient approximation of magnetic flux density.


title: Quality function deployment: A literature review

abstract: Abstract This paper presents a literature review of quality function deployment (QFD) based on a reference bank of about 650 QFD publications established through searching various sources. The origination and historical development of QFD, especially in Japan and the US, are briefly accounted first, followed by a partial list of QFD organizations, softwares, and online resources. Then a categorical analysis is conducted about QFD’s functional fields, applied industries and methodological development. Ten informative QFD publications are also suggested, particularly for those who are not yet familiar with QFD. It is hoped that the paper can serve the needs of researchers and practitioners for easy references of QFD studies and applications, and hence promote QFD’s future development.


An Efficient Concurrency Control Technique for Mobile Database Environment


title: IoT-Based Wireless Polysomnography Intelligent System for Sleep Monitoring

abstract: Polysomnography (PSG) is considered the gold standard in the diagnosis of obstructive sleep apnea (OSA). The diagnosis of OSA requires an overnight sleep experiment in a laboratory. However, due to limitations in relation to the number of labs and beds available, patients often need to wait a long time before being diagnosed and eventually treated. In addition, the unfamiliar environment and restricted mobility when a patient is being tested with a polysomnogram may disturb their sleep, resulting in an incomplete or corrupted test. Therefore, it is posed that a PSG conducted in the patient’s home would be more reliable and convenient. The Internet of Things (IoT) plays a vital role in the e-Health system. In this paper, we implement an IoT-based wireless polysomnography system for sleep monitoring, which utilizes a battery-powered, miniature, wireless, portable, and multipurpose recorder. A Java-based PSG recording program in the personal computer is designed to save several bio-signals and transfer them into the European data format. These PSG records can be used to determine a patient’s sleep stages and diagnose OSA. This system is portable, lightweight, and has low power-consumption. To demonstrate the feasibility of the proposed PSG system, a comparison was made between the standard PSG-Alice 5 Diagnostic Sleep System and the proposed system. Several healthy volunteer patients participated in the PSG experiment and were monitored by both the standard PSG-Alice 5 Diagnostic Sleep System and the proposed system simultaneously, under the supervision of specialists at the Sleep Laboratory in Taipei Veteran General Hospital. A comparison of the results of the time-domain waveform and sleep stage of the two systems shows that the proposed system is reliable and can be applied in practice. The proposed system can facilitate the long-term tracing and research of personal sleep monitoring at home.


title: Design Optimization, Modeling, and Control of Unmanned Aerial Vehicle Lifted By Coandă Effect

abstract: The Coandă effect is a natural phenomenon, which is that flowing fluid on a certain surface is pulled to the surface due to the viscosity of the fluid. This phenomenon can be utilized to produce the lift force of a flying object. This paper presents a new type of unmanned aerial vehicle (UAV) lifted by the Coandă effect, called SCoandă. We introduce the empirical study of its design and implementations. The principle of the Coandă effect is briefly explained in the first section. The design optimization and control of the UAV, and implementation issues are explained. The structural design of SCoandă is devised to use the maximum lift force produced by the Coandă effect. Since there are a plenty of cases about the shape of SCoandă, it has been limited to oval. It is investigated through computational fluid dynamics. As the result, an optimized design is determined among several candidates and appropriate structural materials are selected via tests. Also, dynamic modeling is suggested under several conditions of SCoandă before simulations are conducted to build up a controller and determine gains according to the dynamic modeling. Finally, the UAV is manufactured and its performances are successfully demonstrated in the indoor and the outdoor environments.


title: Application layer protocols for the Internet of Things: A survey

abstract: The “Internet of things” (IoT) concept nowadays is one of the hottest trends for research in any given field; since IoT is about interactions between multiple devices, things, and objects. This interaction opens different directions of enhancement and development in many fields, such as architecture, dependencies, communications, protocols, security, applications and big data. The results will be outstanding and we will be able to reach the desired change and improvements we seek in the fields that affect our lives. The critical goal of Internet of things (IoT) is to ensure effective communication between objects and build a sustained bond among them using different types of applications. The application layer is responsible for providing services and determines a set of protocols for message passing at the application level. This survey addresses a set of application layer protocols that are being used today for IoT, to affirm a reliable tie among objects and things.


title: Online and Offline Information for Omnichannel Retailing

abstract: This paper studies how retailers can effectively deliver online and offline information to omnichannel consumers who strategically choose whether to gather information online or offline and whether to buy products online or offline. Information resolves two types of uncertainty: product value uncertainty i.e., consumers realize valuations when they inspect the product in store, but may end up returning the product when they purchase online and availability uncertainty i.e., store visits are futile when consumers encounter stockouts. We consider three information mechanisms: physical showrooms allow consumers to learn valuations anytime they visit the store, even during stockouts; virtual showrooms give consumers online access to an imperfect signal of their valuations; availability information provides real-time information about whether the store has a product in stock. Our main results follow. First, physical showrooms may prompt retailers to reduce store inventory, which increases availability risk and discourages store patronage. Second, virtual showrooms may increase online returns and hurt profits, if they induce excessive customer migration from store to online channels. Third, availability information may be redundant when availability risk is low and may render physical showrooms ineffective when implemented jointly. Finally, when customers are homogeneous, these mechanisms may not exhibit significant complementarities and the optimal information structure may involve choosing only one of the three.


title: Progressive degradation of rock properties and time-dependent behavior of deep tunnels

abstract: Time-dependent response of deep tunnels is studied considering the progressive degradation of the mechanical properties of the rock mass. The constitutive model is based on a rock-aging law for the uniaxial strength of the rock and for the Young’s modulus. A semi-analytical solution is developed for the stresses and displacements around a deep circular tunnel taking into account the face advance. The evolution of the plastic and damage zones over time is determined. Numerical examples are presented for the case of Saint-Martin-La-Porte access adit in France of the Lyon–Turin Base Tunnel. The computed results which are compared with the field data in terms of the convergence of tunnel wall and of the displacements inside the rock mass monitored by multi-point extensometers show the efficiency of the approach to simulate the time-dependent deformation of a tunnel excavated in squeezing ground. Simple relationships are proposed to evaluate the parameters of the constitutive model directly from those of the empirical convergence law presented in previous work.


title: Using Pop-Up Windows to Improve Multimedia Learning

abstract: The aim of the present study is to evaluate the effects on learning of the spatial integration of textual information incorporated into illustrations in the form of pop-up windows that are opened by the user. Three groups of students viewed illustrated texts depicting the functioning of the heart and the replication of the AIDS virus either with textual information presented below the picture, with textual information integrated within the picture, or with textual information integrated within the picture with pop-up windows. The results showed that the integrated formats were more effective than the separated format when we tested the retention of textual information, comprehension and the matching of textual elements to the appropriate illustrated elements. Furthermore, they indicated that the group working with pop-up windows performed better than the integrated groups on the retention test for illustrated information as well as when asked to find correct solutions to problems in a comprehension test. Consequently, these findings support the use of pop-up windows in learning with this kind of illustrated explanatory text.


title: Hybrid BiLSTM-Siamese network for FAQ Assistance

abstract: We describe an automated assistant for answering frequently asked questions; our system has been deployed, and is currently answering HR-related queries in two different areas (leave management and health insurance) to a large number of users. The needs of a large global corporate lead us to model a frequently asked question (FAQ) to be an equivalence class of actually asked questions, for which there is a common answer (certified as being consistent with the organization's policy). When a new question is posed to our system, it finds the class of question, and responds with the answer for the class. At this point, the system is either correct (gives correct answer); or incorrect (gives wrong answer); or incomplete (says "I don't know''). We employ a hybrid deep-learning architecture in which a BiLSTM-based classifier is combined with second BiLSTM-based Siamese network in an iterative manner: Questions for which the classifier makes an error during training are used to generate a set of misclassified question-question pairs. These, along with correct pairs, are used to train the Siamese network to drive apart the (hidden) representations of the misclassified pairs. We present experimental results from our deployment showing that our iteratively trained hybrid network: (a) results in better performance than using just a classifier network, or just a Siamese network; (b) performs better than state-of-the art sentence classifiers in the two areas in which it has been deployed, in terms of both accuracy as well as precision-recall tradeoff; and (c) also performs well on a benchmark public dataset. We also observe that using question-question pairs in our hybrid network, results in marginally better performance than using question-to-answer pairs. Finally, estimates of precision and recall from the deployment of our automated assistant suggest that we can expect the burden on our HR department to drop from answering about 6000 queries a day to about 1000.


title: Learning to Reach: A Mathematical Model.

abstract: This article presents a mathematical model of the development of reaching that assumes that the major problem facing infants is their lack of lower level motor control and that infants learn to adjust their reaching strategies as a consequence of their previous experience and to match their current level of control. The model hypothesizes that infant reaches are a series of submovements, with the goal being to get the hand to the target in the face of errors in executed submovements. To relate actual infant reaches to this model, reaching data were decomposed into submovements, using a polynomial fitting algorithm that assumed minimum-jerk submovements. The model makes quantitative predictions about the course of development that are supported by existing results. The validity of the model's underlying assumptions was assessed by comparing the directional variability ofthe submovements with the variability assumed in the model.


title: Diagnosing Advanced Persistent Threats: A Position Paper

abstract: When a computer system is hacked, analyzing the root-cause (for example entry-point of penetration) is a diagnostic process. An audit trail, as defined in the National Information Assurance Glossary, is a securityrelevant chronological (set of) record(s), and/or destination and source of records that provide evidence of the sequence of activities that have affected, at any time, a specific operation, procedure, or event. After detecting an intrusion, system administrators manually analyze audit trails to both isolate the root-cause and perform damage impact assessment of the attack. Due to the sheer volume of information and low-level activities in the audit trails, this task is rather cumbersome and time intensive. In this position paper, we discuss our ideas to automate the analysis of audit trails using machine learning and model-based reasoning techniques. Our approach classifies audit trails into the high-level activities they represent, and then reasons about those activities and their threat potential in real-time and forensically. We argue that, by using the outcome of this reasoning to explain complex evidence of malicious behavior, we are equipping system administrators with the proper tools to promptly react to, stop, and mitigate attacks.


title: Failure prediction based on log files using Random Indexing and Support Vector Machines

abstract: Research problem: The impact of failures on software systems can be substantial since the recovery process can require unexpected amounts of time and resources. Accurate failure predictions can help in mitigating the impact of failures. Resources, applications, and services can be scheduled to limit the impact of failures. However, providing accurate predictions sufficiently ahead is challenging. Log files contain messages that represent a change of system state. A sequence or a pattern of messages may be used to predict failures. Contribution: We describe an approach to predict failures based on log files using Random Indexing (RI) and Support Vector Machines (SVMs). Method: RI is applied to represent sequences: each operation is characterized in terms of its context. SVMs associate sequences to a class of failures or non-failures. Weighted SVMs are applied to deal with imbalanced datasets and to improve the true positive rate. We apply our approach to log files collected during approximately three months of work in a large European manufacturing company. Results: According to our results, weighted SVMs sacrifice some specificity to improve sensitivity. Specificity remains higher than 0.80 in four out of six analyzed applications. Conclusions: Overall, our approach is very reliable in predicting both failures and non-failures.


title: Automatic sperms counting using adaptive local threshold and ellipse detection

abstract: A male fertility is influenced by several factors. One of which is the quality of the sperm. Over the last 50 years, human sperm quality decreases. It became the driving force for a male to examine his sperm. It is therefore necessary to have a method to check the sperm quality which is not only accurate, but fast and cheap. Sperm quality can be measured by, one of which, sperm count. In some medical laboratories sperm count is still using manual assessment which makes it subjective, non repetitive, inaccurate, and time consuming. By using multimedia microscope that can record the sperm into a video, one can process it using image processing and computer vision method. In this paper, we propose a method to detect and count sperm in a video taken by a multimedia microscope using an adaptive local threshold and enhanced ellipse detection. The result is 90.97% accuracy compared to the manual assessment.


title: Kinematic analysis and design optimization of a cable-driven universal joint module

abstract: Cable-driven parallel manipulators (CDPMs) are a special class of parallel manipulators that are driven by cables instead of rigid links. So CDPMs have a light-weight structure with large reachable workspace. The aim of this paper is to provide the kinematic analysis and the design optimization of a cable-driven 2-DOF module, comprised of a passive universal joint, for a reconfigurable system. This universal joint module can be part of a modular reconfigurable system where various cable-driven modules can be attached serially into many different configurations. Based on a symmetric design approach, six topological configurations are enumerated with three or four cables arrangements. With a variable constrained axis, the structure matrix of the universal joint has to be formulated with respect to the intermediate frame. The orientation workspace of the universal joint is a submanifold of SO(3). Therefore, the workspace representation is a plane in R2. With the integral measure for the submanifold expressed as a cosine function of one of the angles of rotation, an equivolumetric method is employed to numerically calculate the workspace volume. The orientation workspace volume of the universal joint module is found to be 2π. Optimization results show that the 4-1 cable arrangement produces the largest workspace with better Global Conditioning Index.


title: A Robust and Binarization-Free Approach for Text Line Detection in Historical Documents

abstract: Text line extraction from complex handwritten documents, especially for historical collections, is still an unsolved problem. There is a strong demand for reliable and robust approaches since text line extraction is a crucial pre-processing step for modern text recognition and keyword spotting systems. We propose a binarization-free system which employs a newly developed clustering approach based on so-called 'superpixels'. Although multiple ways of generating superpixels were developed in the past, we demonstrate that even a standard method yields impressive results. Our clustering approach is applicable to various scenarios by making use of general characteristics of text lines (e.g., curvilinearity, interline spacings, local homogeneity), and without adapting its parametrization. State-of-the-art results are achieved by the same parametrization for 8 different well-established benchmarking datasets. These datasets cover historical and modern texts as well as images with diverse resolutions and fonts. The system is developed for detecting text lines in complex scenarios. It is not tuned to assign foreground pixels to detected text lines. Thus, superior performance is achieved for the historical datasets for which no pixel hit accuracy of 95% is required. Remarkably, for the dataset of the ICDAR 2015 Competition on Text Line Detection in Historical Documents, the average cost per text line was reduced from 9.77 (winning team) to 8.19.


title: AC/DC reversible mixed inverter with built-in inrush-current limitation and cut-off stand-by losses

abstract: A new inverter using two thyristors in place of two freewheeling diodes is presented. This inverter allows a reversible DC/AC converter to be implemented. This converter can control the inrush-current and allow bridge full-disconnection at stand-by to suppress undesired losses. This is achieved by a full-silicon solution without the need of any inrush-current limiting resistor and or mechanical relays. In this paper, PSPICE simulation results are presented to validate the converter operation. Experimental results are given to discuss the on-state threshold voltage difference between a slow diode, using in a classic inverter, and a thyristor used in this new topology.


title: Filtered OFDM: A new waveform for future wireless systems

abstract: A spectrally-localized waveform is proposed based on filtered orthogonal frequency division multiplexing (f-OFDM). By allowing the filter length to exceed the cyclic prefix (CP) length of OFDM and designing the filter appropriately, the proposed f-OFDM waveform can achieve a desirable frequency localization for bandwidths as narrow as a few tens of subcarriers, while keeping the inter-symbol interference/inter-carrier interference (ISI/ICI) within an acceptable limit. Enabled by the proposed f-OFDM, an asynchronous filtered orthogonal frequency division multiple access (f-OFDMA)/filtered discrete-Fourier transform-spread OFDMA (f-DFT-S-OFDMA) scheme is introduced, which uses the spectrum shaping filter at each transmitter for side lobe leakage elimination and a bank of filters at the receiver for inter-user interference rejection. Per-user downsampling and short fast Fourier transform (FFT) are used at the receiver to ensure a reasonable complexity of implementation. The proposed scheme removes the inter-user time-synchronization overhead required in the synchronous OFDMA/DFT-S-OFDMA. The performance of the asynchronous f-OFDMA is evaluated and compared with that of the universal-filtered OFDM (UF-OFDM), proposed in [1], [2].


title: Principles and practice in reporting structural equation analyses

abstract: Principles for reporting analyses using structural equation modeling are reviewed, with the goal of supplying readers with complete and accurate information. It is recommended that every report give a detailed justification of the model used, along with plausible alternatives and an account of identifiability. Nonnormality and missing data problems should also be addressed. A complete set of parameters and their standard errors is desirable, and it will often be convenient to supply the correlation matrix and discrepancies, as well as goodness-of-fit indices, so that readers can exercise independent critical judgment. A survey of fairly representative studies compares recent practice with the principles of reporting recommended here.


title: MPPT Scheme for a PV-Fed Single-Phase Single-Stage Grid-Connected Inverter Operating in CCM With Only One Current Sensor

abstract: The cost and efficiency of a photovoltaic (PV)-based grid-connected system depends upon the number of components and stages involved in the power conversion. This has led to the development of several single-stage configurations that can perform voltage transformation, maximum power point tracking (MPPT), inversion, and current shaping-all in one stage. Such configurations would usually require at least a couple of current and voltage sensors and a relatively complex control strategy. With a view to minimize the overall cost and control complexity, this paper presents a novel MPPT scheme with reduced number of sensors. The proposed scheme is applicable to any single-stage, single-phase grid-connected inverter operating in continuous conduction mode (CCM). The operation in CCM is desirable as it drastically reduces the stress on the components. Unlike other MPPT methods, which sense both PV array's output current and voltage, only PV array's output voltage is required to be sensed to implement MPPT. Only one current sensor is used for shaping the buck-boost inductor current as well as for MPPT. The information about power output of the array is obtained indirectly from array's voltage and the inductor current amplitude. Detailed analysis and the flowchart of the algorithm for the proposed scheme are included. Simulation and experimental results are also presented to highlight the usefulness of the scheme.


title: A Linearization Scheme for Thermistor-Based Sensing in Biomedical Studies

abstract: Temperature is one of the basic biophysical quantity monitored for various biomedical systems. Moreover, the variation of temperature is also an important parameter which can be used for estimating other measurands, such as respiratory airflow. This paper proposes a simple operational amplifier-based astable multivibrator circuit for linearization of the characteristic of a negative temperature coefficient thermistor constituting one of the timing resistors. The circuit has been combined with a lookup table to get the unknown temperature value from multivibrator output. Moreover, the same system topology can be used as a linearizer for measurement of respiratory airflow. The performance of the composite system has been verified experimentally. A linearity of approximately ±0.75% has been achieved over 30 °C–110 °C in the case of temperature measurement and ±1.2% for airflow of 10–60 LPM. Better results can also be achieved with the introduction of interpolation algorithms, but at a higher computational and component cost. The compactness of the complete system makes it a good candidate for embedded sensing applications in biomedical systems, such as point-of-care monitoring or in sleep study.


title: Web-based database for facial expression analysis

abstract: In the last decade, the research topic of automatic analysis of facial expressions has become a central topic in machine vision research. Nonetheless, there is a glaring lack of a comprehensive, readily accessible reference set of face images that could be used as a basis for benchmarks for efforts in the field. This lack of easily accessible, suitable, common testing resource forms the major impediment to comparing and extending the issues concerned with automatic facial expression analysis. In this paper, we discuss a number of issues that make the problem of creating a benchmark facial expression database difficult. We then present the MMI facial expression database, which includes more than 1500 samples of both static images and image sequences of faces in frontal and in profile view displaying various expressions of emotion, single and multiple facial muscle activation. It has been built as a Web-based direct-manipulation application, allowing easy access and easy search of the available images. This database represents the most comprehensive reference set of images for studies on facial expression analysis to date.


title: Design and implementation of PLC-based monitoring control system for induction motor

abstract: The implementation of a monitoring and control system for the induction motor based on programmable logic controller (PLC) technology is described. Also, the implementation of the hardware and software for speed control and protection with the results obtained from tests on induction motor performance is provided. The PLC correlates the operational parameters to the speed requested by the user and monitors the system during normal operation and under trip conditions. Tests of the induction motor system driven by inverter and controlled by PLC prove a higher accuracy in speed regulation as compared to a conventional V/f control system. The efficiency of PLC control is increased at high speeds up to 95% of the synchronous speed. Thus, PLC proves themselves as a very versatile and effective tool in industrial control of electric drives.


title: RecycleNet: Intelligent Waste Sorting Using Deep Neural Networks

abstract: Waste management and recycling is the fundamental part of a sustainable economy. For more efficient and safe recycling, it is necessary to use intelligent systems instead of employing humans as workers in the dump-yards. This is one of the early works demonstrating the efficiency of latest intelligent approaches. In order to provide the most efficient approach, we experimented on well-known deep convolutional neural network architectures. For training without any pre-trained weights, Inception-Resnet, Inception-v4 outperformed all others with 90% test accuracy. For transfer learning and fine-tuning of weight parameters using ImageNet, DenseNet121 gave the best result with 95% test accuracy. One disadvantage of these networks, however, is that they are slightly slower in prediction time. To enhance the prediction performance of the models we altered the connection patterns of the skip connections inside dense blocks. Our model RecycleNet is carefully optimized deep convolutional neural network architecture for classification of selected recyclable object classes. This novel model reduced the number of parameters in a 121 layered network from 7 million to about 3 million.


title: 2018 PIRM Challenge on Perceptual Image Super-resolution

abstract: This paper reports on the 2018 PIRM challenge on perceptual super-resolution (SR), held in conjunction with the Perceptual Image Restoration and Manipulation (PIRM) workshop at ECCV 2018. In contrast to previous SR challenges, our evaluation methodology jointly quantifies accuracy and perceptual quality, therefore enabling perceptual-driven methods to compete alongside algorithms that target PSNR maximization. Twenty-one participating teams introduced algorithms which well-improved upon the existing state-of-the-art methods in perceptual SR, as confirmed by a human opinion study. We also analyze popular image quality measures and draw conclusions regarding which of them correlates best with human opinion scores. We conclude with an analysis of the current trends in perceptual SR, as reflected from the leading submissions.


An Efficient Prediction of Breast Cancer Data using Data Mining Techniques


title: Volume Visualization: A Technical Overview with a Focus on Medical Applications

abstract: With the increasing availability of high-resolution isotropic three- or four-dimensional medical datasets from sources such as magnetic resonance imaging, computed tomography, and ultrasound, volumetric image visualization techniques have increased in importance. Over the past two decades, a number of new algorithms and improvements have been developed for practical clinical image display. More recently, further efficiencies have been attained by designing and implementing volume-rendering algorithms on graphics processing units (GPUs). In this paper, we review volumetric image visualization pipelines, algorithms, and medical applications. We also illustrate our algorithm implementation and evaluation results, and address the advantages and drawbacks of each algorithm in terms of image quality and efficiency. Within the outlined literature review, we have integrated our research results relating to new visualization, classification, enhancement, and multimodal data dynamic rendering. Finally, we illustrate issues related to modern GPU working pipelines, and their applications in volume visualization domain.


SemEval-2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiMSUM)


title: A New PID Neural Network Controller Design for Nonlinear Processes

abstract: In this paper, a novel adaptive tuning method of PID neural network (PIDNN) controller for nonlinear process is proposed. The method utilizes an improved gradient descent method to adjust PIDNN parameters where the margin stability will be employed to get high tracking performance and robustness with regard to external load disturbance and parameter variation. Simulation results show the effectiveness of the proposed algorithm compared with other well-known learning methods.


Channel State Information Fingerprinting Based Indoor Localization: a Deep Learning Approach


abstract: The interpretation of structure from motion is examined from a computional point of view. The question addressed is how the three dimensional structure and motion of objects can be inferred from the two dimensional transformations of their projected images when no three dimensional information is conveyed by the individual projections. The following scheme is proposed: (i) divide the image into groups of four elements each; (ii) test each group for a rigid interpretation; (iii) combine the results obtained in (ii). It is shown that this scheme will correctly decompose scenes containing arbitrary rigid objects in motion, recovering their three dimensional structure and motion. The analysis is based primarily on the "structure from motion" theorem which states that the structure of four non-coplanar points is recoverable from three orthographic projections. The interpretation scheme is extended to cover perspective projections, and its psychological relevance is discussed.


title: Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes

abstract: There is a previously identified equivalence between wide fully connected neural networks (FCNs) and Gaussian processes (GPs). This equivalence enables, for instance, test set predictions that would have resulted from a fully Bayesian, infinitely wide trained FCN to be computed without ever instantiating the FCN, but by instead evaluating the corresponding GP. In this work, we derive an analogous equivalence for multi-layer convolutional neural networks (CNNs) both with and without pooling layers, and achieve state of the art results on CIFAR10 for GPs without trainable kernels. We also introduce a Monte Carlo method to estimate the GP corresponding to a given neural network architecture, even in cases where the analytic form has too many terms to be computationally feasible. Surprisingly, in the absence of pooling layers, the GPs corresponding to CNNs with and without weight sharing are identical. As a consequence, translation equivariance in finite-channel CNNs trained with stochastic gradient descent (SGD) has no corresponding property in the Bayesian treatment of the infinite channel limit - a qualitative difference between the two regimes that is not present in the FCN case. We confirm experimentally, that while in some scenarios the performance of SGD-trained finite CNNs approaches that of the corresponding GPs as the channel count increases, with careful tuning SGD-trained CNNs can significantly outperform their corresponding GPs, suggesting advantages from SGD training compared to fully Bayesian parameter estimation.


title: Verena: End-to-End Integrity Protection for Web Applications

abstract: Web applications rely on web servers to protect the integrity of sensitive information. However, an attacker gaining access to web servers can tamper with the data and query computation results, and thus serve corrupted web pages to the user. Violating the integrity of the web page can have serious consequences, affecting application functionality and decision-making processes. Worse yet, data integrity violation may affect physical safety, as in the case of medical web applications which enable physicians to assign treatment to patients based on diagnostic information stored at the web server. This paper presents Verena, a web application platform that provides end-to-end integrity guarantees against attackers that have full access to the web and database servers. In Verena, a client's browser can verify the integrity of a web page by verifying the results of queries on data stored at the server. Verena provides strong integrity properties such as freshness, completeness, and correctness for a common set of database queries, by relying on a small trusted computing base. In a setting where there can be many users with different write permissions, Verena allows a developer to specify an integrity policy for query results based on our notion of trust contexts, and then enforces this policy efficiently. We implemented and evaluated Verena on top of the Meteor framework. Our results show that Verena can support real applications with modest overhead.


title: Detection of bypass fraud based on speaker recognition

abstract: In telecommunication industry, fraud becomes a serious problem that affects telecommunications service providers all around the world. As a significant amount of revenue losses to fraud every year, so an efficient system to detect fraud activities is greatly required. A well-known fraud which affects GSM and PSTN service providers is Bypass fraud. It is used to avoid a charge of international calls. However, a fraud cannot be completely eradicated, an early detection of a fraud will minimize the loss in revenue. In this paper a new approach of detection system of Bypass Fraud is proposed. The proposed system based on the speaker recognition techniques to detect any fraud based on the user profiling.


title: Event Detection and Summarization in Soccer Videos Using Bayesian Network and Copula

abstract: Semantic video analysis and automatic concept extraction play an important role in several applications; including content-based search engines, video indexing, and video summarization. As the Bayesian network is a powerful tool for learning complex patterns, a novel Bayesian network-based method is proposed for automatic event detection and summarization in soccer videos. The proposed method includes efficient algorithms for shot boundary detection, shot view classification, mid-level visual feature extraction, and construction of the related Bayesian network. The method contains of three main stages. In the first stage, the shot boundaries are detected. Using the hidden Markov model, the video is segmented into large and meaningful semantic units, called play-break sequences. In the next stage, several features are extracted from each of these units. Finally, in the last stage, in order to achieve high level semantic features (events and concepts), the Bayesian network is used. The basic part of the method is constructing the Bayesian network, for which the structure is estimated using the Chow-Liu tree. The joint distributions of random variables of the network are modeled by applying the Farlie-Gumbel-Morgenstern family of Copulas. The performance of the proposed method is evaluated on a dataset with about 9 h of soccer videos. The method is capable of detecting seven different events in soccer videos; namely, goal, card, goal attempt, corner, foul, offside, and nonhighlights. Experimental results show the effectiveness and robustness of the proposed method on detecting these events.


title: Energy Management in Smart Cities Based on Internet of Things: Peak Demand Reduction and Energy Savings

abstract: Around the globe, innovation with integrating information and communication technologies (ICT) with physical infrastructure is a top priority for governments in pursuing smart, green living to improve energy efficiency, protect the environment, improve the quality of life, and bolster economy competitiveness. Cities today faces multifarious challenges, among which energy efficiency of homes and residential dwellings is a key requirement. Achieving it successfully with the help of intelligent sensors and contextual systems would help build smart cities of the future. In a Smart home environment Home Energy Management plays a critical role in finding a suitable and reliable solution to curtail the peak demand and achieve energy conservation. In this paper, a new method named as Home Energy Management as a Service (HEMaaS) is proposed which is based on neural network based Q-learning algorithm. Although several attempts have been made in the past to address similar problems, the models developed do not cater to maximize the user convenience and robustness of the system. In this paper, authors have proposed an advanced Neural Fitted Q-learning method which is self-learning and adaptive. The proposed method provides an agile, flexible and energy efficient decision making system for home energy management. A typical Canadian residential dwelling model has been used in this paper to test the proposed method. Based on analysis, it was found that the proposed method offers a fast and viable solution to reduce the demand and conserve energy during peak period. It also helps reducing the carbon footprint of residential dwellings. Once adopted, city blocks with significant residential dwellings can significantly reduce the total energy consumption by reducing or shifting their energy demand during peak period. This would definitely help local power distribution companies to optimize their resources and keep the tariff low due to curtailment of peak demand.


title: PRIMA-1MET induces death in soft-tissue sarcomas cell independent of p53

abstract: Background ::: The aim of this study was to explore the efficacy and define mechanisms of action of PRIMA-1MET as a TP53 targeted therapy in soft-tissue sarcoma (STS) cells.


title: From Byzantine Consensus to BFT State Machine Replication: A Latency-Optimal Transformation

abstract: We present a new algorithm for state machine replication that is built around a leader-driven consensus primitive. This algorithm requires only two additional communication steps between replicas and clients if the consensus leader is correct and the system is synchronous, being thus the first latency-optimal transformation from Byzantine consensus to BFT state machine replication. We also discuss how to make simple modifications to leader-driven consensus algorithms in order to make them compatible with our transformation.


title: Simple Testing Can Prevent Most Critical Failures: An Analysis of Production Failures in Distributed Data-Intensive Systems.

abstract: Large, production-quality distributed systems still fail periodically, sometimes catastrophically where most or all users experience an outage or data loss. Conventional wisdom has it that these failures can only manifest themselves on large production clusters and are extremely difficult to prevent a priori, because these systems are designed to be fault tolerant and are well-tested. By investigating 198 user-reported failures that occurred on production-quality distributed systems, we found that almost all (92%) of the catastrophic system failures are the result of incorrect handling of non-fatal errors, and, surprisingly, many of them are caused by trivial mistakes such as error handlers that are empty or that contain expressions like "FIXME" or "TODO" in the comments. We therefore developed a simple static checker, Aspirator, capable of locating trivial bugs in error handlers; it found 143 new bugs and bad practices that have been fixed or confirmed by the developers.


A Kernel-Based Nonlinear Representor with Application to Eigenface Classification


title: Prediction of Indian election using sentiment analysis on Hindi Twitter

abstract: Sentiment analysis is considered to be a category of machine learning and natural language processing. It is used to extricate, recognize, or portray opinions from different content structures, including news, audits and articles and categorizes them as positive, neutral and negative. It is difficult to predict election results from tweets in different Indian languages. We used Twitter Archiver tool to get tweets in Hindi language. We performed data (text) mining on 42,235 tweets collected over a period of a month that referenced five national political parties in India, during the campaigning period for general state elections in 2016. We made use of both supervised and unsupervised approaches. We utilized Dictionary Based, Naive Bayes and SVM algorithm to build our classifier and classified the test data as positive, negative and neutral. We identified the sentiment of Twitter users towards each of the considered Indian political parties. The results of the analysis for Naive Bayes was the BJP (Bhartiya Janta Party), for SVM it was the BJP (Bhartiya Janta Party) and for the Dictionary Approach it was the Indian Nathional Congress. SVM predicted a 78.4% chance that the BJP would win more elections in the general election due to the positive sentiment they received in tweets. As it turned out, BJP won 60 out of 126 constituencies in the 2016 general election, far more than any other political party as the next party (the Indian National Congress) only won 26 out of 126 constituencies.


title: Measurement of the Thickness of the Urethrovaginal Space in Women with or without Vaginal Orgasm

abstract: INTRODUCTION ::: The physiology and anatomy of female sexual function are poorly understood. The differences in sexual function among women may be partly attributed to anatomical factors. ::: ::: ::: AIM ::: The purpose of this study was to use ultrasonography to evaluate the anatomical variability of the urethrovaginal space in women with and without vaginal orgasm. ::: ::: ::: METHODS ::: Twenty healthy, neurologically intact volunteers were recruited from a population of women who were a part of a previous published study. All women underwent a complete urodynamic evaluation and those with clinical and urodynamic urinary incontinence, idiopathic detrusor overactivity, or micturition disorders, as well as postmenopausal women and those with sexual dysfunction were excluded. The reported experience of vaginal orgasm was investigated. ::: ::: ::: MAIN OUTCOME MEASURE ::: The urethrovaginal space thickness as measured by ultrasound was chosen as the indicator of urogenital anatomical variability. Designated evaluators carried out the measurements in a blinded fashion. ::: ::: ::: RESULTS ::: The urethrovaginal space and distal, middle, and proximal urethrovaginal segments were thinner in women without vaginal orgasm. A direct correlation between the presence of vaginal orgasm and the thickness of urethrovaginal space was found. Women with a thicker urethrovaginal space were more likely to experience vaginal orgasm (r = 0.884; P = 0.015). A direct and significant correlation between the thickness of each urethrovaginal segment and the presence of vaginal orgasm was found, with the best correlation observed for the distal segment (r = 0.863; P < 0.0001). Interobserver agreement between the designated evaluators was excellent (r = 0.87; P < 0.001). ::: ::: ::: CONCLUSIONS ::: The measurement of the space within the anterior vaginal wall by ultrasonography is a simple tool to explore anatomical variability of the human clitoris-urethrovaginal complex, also known as the G-spot, which can be correlated to the ability to experience the vaginally activated orgasm.


title: Interactions between autophagy receptors and ubiquitin-like proteins form the molecular basis for selective autophagy.

abstract: Selective autophagy ensures recognition and removal of various cytosolic cargoes. Hence, aggregated proteins, damaged organelles, or pathogens are enclosed into the double-membrane vesicle, the autophagosome, and delivered to the lysosome for degradation. This process is mediated by selective autophagy receptors, such as p62/SQSTM1. These proteins recognize autophagic cargo and, via binding to small ubiquitin-like modifiers (UBLs)--Atg8/LC3/GABARAPs and ATG5--mediate formation of selective autophagosomes. Recently, it was found that UBLs can directly engage the autophagosome nucleation machinery. Here, we review recent findings on selective autophagy and propose a model for selective autophagosome formation in close proximity to cargo.


title: Low profile H-plane horn antenna based on half mode substrate integrated waveguide technique

abstract: In this paper a half mode substrate integrated waveguide (HMSIW) H-plane horn antenna is proposed. A probe is used as the feed line of the antenna in order to avoid parasitic radiation and to reduce conductor loss. The proposed antenna is numerically investigated using HFSS software. Radiation properties of the proposed antenna including reflection coefficient and radiation patterns are reported. The obtained results are compared with numerical ones obtained for a substrate integrated waveguide (SIW) H-plane horn antenna. Results illustrate that radiation characteristics of the proposed HMSIW horn antenna and SIW horn are in good agreement while size of the proposed HMSIW antenna is around 67% of the SIW horn antenna size.


title: Joint Intra and Multiple Description Coding for Packet Loss Resilient Video Transmission

abstract: Multiple description coding (MDC) is a technique for video transmission over error prone networks where the descriptions are routed over multiple paths. Intra coding such as MDC provides error resiliency but coding in this mode must be decided with care since it degrades the compression ratio. In this paper, we present our investigation results for a new intra coding approach in MDC. We have found that, in MDC streams, the best policy is to encode selective frames as I-frame instead of coding some macroblocks of frames in intra mode. In order to find the most suitable I-frame positions within a given video stream, we developed a cost function based on which intra/inter frame type is decided. The MDC scheme with the proposed intra coding criterion, with and without redundancy optimization, is implemented in the H.264/AVC reference software, JM16.0. Based on the experimental performance evaluation, we show that our method achieves higher average PSNR compared to the other optimized MDCs found in the literature.


title: What is Wrong with Topic Modeling? (and How to Fix it Using Search-based Software Engineering)

abstract: Abstract Context Topic modeling finds human-readable structures in unstructured textual data. A widely used topic modeling technique is Latent Dirichlet allocation. When running on different datasets, LDA suffers from “order effects”, i.e., different topics are generated if the order of training data is shuffled. Such order effects introduce a systematic error for any study. This error can relate to misleading results; specifically, inaccurate topic descriptions and a reduction in the efficacy of text mining classification results. Objective To provide a method in which distributions generated by LDA are more stable and can be used for further analysis. Method We use LDADE, a search-based software engineering tool which uses Differential Evolution (DE) to tune the LDA’s parameters. LDADE is evaluated on data from a programmer information exchange site (Stackoverflow), title and abstract text of thousands of Software Engineering (SE) papers, and software defect reports from NASA. Results were collected across different implementations of LDA (Python+Scikit-Learn, Scala+Spark) across Linux platform and for different kinds of LDAs (VEM, Gibbs sampling). Results were scored via topic stability and text mining classification accuracy. Results In all treatments: (i) standard LDA exhibits very large topic instability; (ii) LDADE’s tunings dramatically reduce cluster instability; (iii) LDADE also leads to improved performances for supervised as well as unsupervised learning. Conclusion Due to topic instability, using standard LDA with its “off-the-shelf” settings should now be depreciated. Also, in future, we should require SE papers that use LDA to test and (if needed) mitigate LDA topic instability. Finally, LDADE is a candidate technology for effectively and efficiently reducing that instability.


title: The architecture of emergent self-organizing maps to reduce projection errors

abstract: There are mainly two types of Emergent Self-Organizing Maps (ESOM) grid structures in use: hexgrid (honeycomb like) and quadgrid (trellis like) maps. In addition to that, the shape of the maps may be square or rectangular. This work investigates the effects of these different map layouts. Hexgrids were found to have no convincing advantage over quadgrids. Rectangular maps, however, are distinctively superior to square maps. Most surprisingly, rectangular maps outperform square maps for isotropic data, i.e. data sets with no particular primary direction.


title: Patients' and relatives' experience of difficulties following severe traumatic brain injury: The sub-acute stage

abstract: The present study aimed to (1) identify the difficulties most frequently reported by individuals with severe traumatic brain injury (TBI) at the time of discharge from a sub-acute rehabilitation brain injury unit as well as difficulties reported by their relatives, (2) compare patients' and relatives' reports of patient difficulties, and (3) explore the role of injury severity, disability and other factors on subjective experience of difficulties. The primary measure was the European Brain Injury Questionnaire (EBIQ) administered to patients and to one of their close relatives at discharge. Results from 52 patients and 50 relatives indicate that the most frequent complaints in both groups related to somatic and cognitive problems. Relatives reported significantly more difficulties than patients on all subscales of the EBIQ. However, the level of complaints in both patients and relatives was low compared to other studies using the EBIQ. Furthermore, the effects of injury severity and general level of funct...


title: High Power Density Series Resonant Inverter Using an Auxiliary Switched Capacitor Cell for Induction Heating Applications

abstract: This paper proposes a unique topology of voltage-fed high-frequency series load resonant inverter with a lossless snubber capacitor and an auxiliary switched cell for induction heating appliances. The main objective of this paper is to demonstrate how high power density can be achieved by including a switched capacitor cell with the capacitor-clamped half-bridge zero voltage switching high-frequency inverter circuit using the PWM control scheme. The operation principle of the proposed inverter circuit is based upon an asymmetrical duty cycle pulsewidth modulated (PWM) control scheme. The operating performances of high-frequency ac regulation and power conversion efficiency characteristics are shown through experiments with their soft-switching operating ranges.


Wave Propagation and Scattering in Random Media


title: Joint Cell Nuclei Detection and Segmentation in Microscopy Images Using 3D Convolutional Networks

abstract: We propose a 3D convolutional neural network to simultaneously segment and detect cell nuclei in confocal microscopy images. Mirroring the co-dependency of these tasks, our proposed model consists of two serial components: the first part computes a segmentation of cell bodies, while the second module identifies the centers of these cells. Our model is trained end-to-end from scratch on a mouse parotid salivary gland stem cell nuclei dataset comprising 107 image stacks from three independent cell preparations, each containing several hundred individual cell nuclei in 3D. In our experiments, we conduct a thorough evaluation of both detection accuracy and segmentation quality, on two different datasets. The results show that the proposed method provides significantly improved detection and segmentation accuracy compared to state-of-the-art and benchmark algorithms. Finally, we use a previously described test-time drop-out strategy to obtain uncertainty estimates on our predictions and validate these estimates by demonstrating that they are strongly correlated with accuracy.


title: A Markov based image forgery detection approach by analyzing CFA artifacts

abstract: The image acquisition device, the light is filtered through a Color Filter Array (CFA), where each pixel captures only one color (from Red, Green, and Blue), while others are calibrated. This process is known as interpolation process, and the artifacts introduced are called CFA or interpolation artifacts. The structure of these artifacts in the image is disturbed while a forgery is introduced in an image. In this paper, a high-order statistical approach is proposed to detect the inconsistencies in the artifacts of different parts of the image to expose any forgery present. The Markov Transition Probability Matrix (MTPM) is employed to develop various features that will detect the presence or absence of CFA artifacts in a particular region of the image. The Markov random process is applied because it provides an enhanced efficiency and reduced computational complexity for the forgery detection model. The algorithm is tested on 2 × 2 pixel block of the image which provides the results of a fine quality. There is no prior information of the location of the forged region of the image. The algorithm is tested on various images, taken from various social networking websites. The proposed forgery detection technique outperforms the existing state-of-the-art techniques for the different forgery scenarios by providing an average accuracy of 90.58%.


title: Semi-Supervised Collective Classification via Hybrid Label Regularization

abstract: Many classification problems involve data instances that are interlinked with each other, such as webpages connected by hyperlinks. Techniques for collective classification (CC) often increase accuracy for such data graphs, but usually require a fully-labeled training graph. In contrast, we examine how to improve the semi-supervised learning of CC models when given only a sparsely-labeled graph, a common situation. We first describe how to use novel combinations of classifiers to exploit the different characteristics of the relational features vs. the non-relational features. We also extend the ideas of label regularization to such hybrid classifiers, enabling them to leverage the unlabeled data to bias the learning process. We find that these techniques, which are efficient and easy to implement, significantly increase accuracy on three real datasets. In addition, our results explain conflicting findings from prior related studies.


title: Capacity of a Single Spiking Neuron Channel

abstract: Information transfer through a single neuron is a fundamental component of information processing in the brain, and computing the information channel capacity is important to understand this information processing. The problem is difficult since the capacity depends on coding, characteristics of the communication channel, and optimization over input distributions, among other issues. In this letter, we consider two models. The temporal coding model of a neuron as a communication channel assumes the output is τ where τ is a gamma-distributed random variable corresponding to the interspike interval, that is, the time it takes for the neuron to fire once. The rate coding model is similar; the output is the actual rate of firing over a fixed period of time. Theoretical studies prove that the distribution of inputs, which achieves channel capacity, is a discrete distribution with finite mass points for temporal and rate coding under a reasonable assumption. This allows us to compute numerically the capacity of a neuron. Numerical results are in a plausible range based on biological evidence to date.


title: A Descriptive/Comparative Study of the Evolution of Process Models of Software Development Life Cycles (PM-SDLCs)

abstract: A relevant knowledge [24] (and consequently research area) is the study of software lifecycle process models (PM-SDLCs). Such process models have been defined in three abstraction levels: (i) full organizational software lifecycles process models (e.g. ISO 12207, ISO 15504, CMMI/SW); (ii) lifecycles frameworks models (e.g. waterfall, spiral, RAD, and others) and (iii) detailed software development life cycles process (e.g. unified process, TSP, MBASE, and others). This paper focuses on (ii) and (iii) levels and reports the results of a descriptive/comparative study of 13 PM-SDLCs that permits a plausible explanation of their evolution in terms of common, distinctive, and unique elements as well as of the specification rigor and agility attributes. For it, a conceptual research approach and a software process lifecycle meta-model are used. Findings from the conceptual analysis are reported. Paper ends with the description of research limitations and recommendations for further research.


title: Designing mobile interfaces for novice and low-literacy users

abstract: While mobile phones have found broad application in bringing health, financial, and other services to the developing world, usability remains a major hurdle for novice and low-literacy populations. In this article, we take two steps to evaluate and improve the usability of mobile interfaces for such users. First, we offer an ethnographic study of the usability barriers facing 90 low-literacy subjects in India, Kenya, the Philippines, and South Africa. Then, via two studies involving over 70 subjects in India, we quantitatively compare the usability of different points in the mobile design space. In addition to text interfaces such as electronic forms, SMS, and USSD, we consider three text-free interfaces: a spoken dialog system, a graphical interface, and a live operator. Our results confirm that textual interfaces are unusable by first-time low-literacy users, and error prone for literate but novice users. In the context of healthcare, we find that a live operator is up to ten times more accurate than text-based interfaces, and can also be cost effective in countries such as India. In the context of mobile banking, we find that task completion is highest with a graphical interface, but those who understand the spoken dialog system can use it more quickly due to their comfort and familiarity with speech. We synthesize our findings into a set of design recommendations.


title: Educational robotics for the formation of programming skills and computational thinking in childish

abstract: Currently, the education sector has been transformed because of the incorporation of information and communication technologies as a valuable support for the formation of digital skills and significant learning. In this article, we present how educational robotics can be integrated into a school environment allowing the achievement of curricular objectives and the development of programming skills and computational thinking. The study was oriented towards a population of 131 students of early childhood education, belonging to a concerted educational center of the autonomous community of Castilla y Leon, during the 2016–2017 academic period. It describes the resources used, the teaching-learning activities developed and shows what was the acceptance expressed by teachers and students about the incorporation of educational robotics in the classroom.


title: Online Computation of Mutual Information and Word Context Entropy

abstract: Mutual information (MI) has been extensively used to measure the co-occurrence strength between two words in the field of natural language processing. Similarly, the word context entropy is also a useful measure to determine the distribution of words in contexts, and can be used to calculate word similarity. Calculating scores for both measures usually relies on a large text corpus to obtain a reliable estimation. However, calculation based on a static corpus may not reflect the dynamic nature of languages. In this paper, we consider the web documents as a text corpus, and develop an efficient online calculator for both mutual information and word context entropy. The major advantage of the online computation is that the web corpus not only is large enough to obtain a reliable estimation but also can reflect the dynamic nature of languages.


Stance Detection for the Fake News Challenge with Attention and Conditional Encoding


Implementation of HDR panorama stitching algorithm


title: Development of integrated preamplifier for high-frequency ultrasonic transducers and low-power handheld receiver

abstract: This paper describes the design of a front-end circuit consisting of an integrated preamplifier with a Sallen-Key Butterworth filter for very-high-frequency ultrasonic transducers and a low-power handheld receiver. This preamplifier was fabricated using a 0.18-μm 7WL SiGe bi-polar complementary metal oxide semiconductor (BiCMOS) process. The Sallen-Key filter is used to increase the voltage gain of the front-end circuit for high-frequency transducers which are generally low in sensitivity. The measured peak voltage gain of the frontend circuits for the BiCMOS preamplifier with the Sallen-Key filter was 41.28 dB at 100 MHz with a-6-dB bandwidth of 91%, and the dc power consumption of the BiCMOS preamplifier was 49.53 mW. The peak voltage gain of the front-end circuits for the CMOS preamplifier with the Sallen-Key filter was 39.52 dB at 100 MHz with a-6-dB bandwidth of 108%, and the dc power consumption of the CMOS preamplifier was 43.57 mW. Pulse-echo responses and wire phantom images with a single-element ultrasonic transducer have been acquired to demonstrate the performance of the front-end circuit.


title: Multilevel Converters: An Enabling Technology for High-Power Applications

abstract: Multilevel converters are considered today as the state-of-the-art power-conversion systems for high-power and power-quality demanding applications. This paper presents a tutorialonthistechnology,coveringtheoperatingprincipleand the different power circuit topologies, modulation methods, technical issues and industry applications. Special attention is given to established technology already found in industry with more in-depth and self-contained information, while recent advances and state-of-the-art contributions are addressed with useful references. This paper serves as an introduction to the subject for the not-familiarized reader, as well as an update or reference for academics and practicing engineers working in the field of industrial and power electronics.


title: Dynamic Control of Explore/Exploit Trade-Off In Bayesian Optimization

abstract: Bayesian optimization offers the possibility of optimizing black-box operations not accessible through traditional techniques. The success of Bayesian optimization methods such as Expected Improvement (EI) are significantly affected by the degree of trade-off between exploration and exploitation. Too much exploration can lead to inefficient optimization protocols, whilst too much exploitation leaves the protocol open to strong initial biases, and a high chance of getting stuck in a local minimum. Typically, a constant margin is used to control this trade-off, which results in yet another hyper-parameter to be optimized. We propose contextual improvement as a simple, yet effective heuristic to counter this - achieving a one-shot optimization strategy. Our proposed heuristic can be swiftly calculated and improves both the speed and robustness of discovery of optimal solutions. We demonstrate its effectiveness on both synthetic and real world problems and explore the unaccounted for uncertainty in the pre-determination of search hyperparameters controlling explore-exploit trade-off.


title: The Digital Music Lab: A Big Data Infrastructure for Digital Musicology

abstract: In musicology and music research generally, the increasing availability of digital music, storage capacities, and computing power enable and require new and intelligent systems. In the transition from traditional to digital musicology, many techniques and tools have been developed for the analysis of individual pieces of music, but large-scale music data that are increasingly becoming available require research methods and systems that work on the collection-level and at scale. Although many relevant algorithms have been developed during the past 15 years of research in Music Information Retrieval, an integrated system that supports large-scale digital musicology research has so far been lacking. In the Digital Music Lab (DML) project, a collaboration among music librarians, musicologists, computer scientists, and human-computer interface specialists, the DML software system has been developed that fills this gap by providing intelligent large-scale music analysis with a user-friendly interactive interface supporting musicologists in their exploration and enquiry. The DML system empowers musicologists by addressing several challenges: distributed processing of audio and other music data, management of the data analysis process and results, remote analysis of data under copyright, logical inference on the extracted information and metadata, and visual web-based interfaces for exploring and querying the music collections. The DML system is scalable and based on Semantic Web technology and integrates into Linked Data with the vision of a distributed system that enables music research across archives, libraries, and other providers of music data. A first DML system prototype has been set up in collaboration with the British Library and I Like Music Ltd. This system has been used to analyse a diverse corpus of currently 250,000 music tracks. In this article, we describe the DML system requirements, design, architecture, components, and available data sources, explaining their interaction. We report use cases and applications with initial evaluations of the proposed system.


Internet Addiction : Personality Traits Associated with Its Development


title: The use of CORDIC in software defined radios: a tutorial

abstract: CORDIC is a versatile algorithm widely used for VLSI implementation of digital signal processing applications. This article presents a tutorial of how to use CORDIC to implement different communication subsystems that can be found in a software defined radio. Specifically, it shows how to use CORDIC to implement direct digital synthesizers, AM, PM, and FM analog modulators and ASK, PSK and FSK modulators, up-/down-converters of in-phase and quadrature signals, full mixers for complex signals, and phase detection for synchronizers. The article also shows some tricks to efficiently implement the algorithm