title: Comparison of Machine Learning Algorithms for Skin Disease Classification Using Color and Texture Features

abstract: Machine learning algorithms are being used widely in biomedical fields for segmentation and diagnosis. These algorithms use features derived from images as input to make a decision. So, choosing proper feature extraction methods combined with suitable machine learning (ML) algorithms is very important to achieve good classification accuracy. During the literature survey, we found that there is a lack of information about machine learning algorithms for skin disease classification. To address this problem, we have collected Chronic Eczema, Lichen planus and Plaque psoriasis images using a digital camera and extracted Red, Green and Blue (RGB) color features and Gray Level Co-occurrence Matrix (GLCM) texture features. Different combinations of features with four popular ML algorithms were considered to compare classifier performances. Out of the four algorithms tested, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) showed highest classification accuracy.


title: Age and Gender Estimation of Unfiltered Faces

abstract: This paper concerns the estimation of facial attributes—namely, age and gender—from images of faces acquired in challenging, in the wild conditions. This problem has received far less attention than the related problem of face recognition, and in particular, has not enjoyed the same dramatic improvement in capabilities demonstrated by contemporary face recognition systems. Here, we address this problem by making the following contributions. First, in answer to one of the key problems of age estimation research—absence of data—we offer a unique data set of face images, labeled for age and gender, acquired by smart-phones and other mobile devices, and uploaded without manual filtering to online image repositories. We show the images in our collection to be more challenging than those offered by other face-photo benchmarks. Second, we describe the dropout-support vector machine approach used by our system for face attribute estimation, in order to avoid over-fitting. This method, inspired by the dropout learning techniques now popular with deep belief networks, is applied here for training support vector machines, to the best of our knowledge, for the first time. Finally, we present a robust face alignment technique, which explicitly considers the uncertainties of facial feature detectors. We report extensive tests analyzing both the difficulty levels of contemporary benchmarks as well as the capabilities of our own system. These show our method to outperform state-of-the-art by a wide margin.


title: Accurate circuit synthesis of low-pass corrugated waveguide filters

abstract: This paper presents a synthesis method for low-pass corrugated filters in rectangular waveguide. Using this method, excellent filter dimensions can be obtained in negligible CPU times, even for a demanding set of specifications. In contrast to previous works, low-pass filters with improved power handling capabilities can be successfully synthesized. The procedure is based on a low-pass prototype able to represent the real filtering structure. First, the structure discontinuities are characterized using an electromagnetical simulator, and the retrieved data is included in the prototype model. Then, the resulting prototype is optimized to obtain a suitable ideal response. From the prototype, the structure physical dimensions are synthesized. This paper aims to show the good performance provided by the proposed method and the benefits which can be obtained from its use.


title: Strategic supply chain framework for the automotive industry

abstract: st century has led to companies facing issues ranging from globalisation, economic uncertainty to new technologies and increasing consumer demands. In the automobile industry, as manufacturers design and build vehicles globally, their supply chains become increasingly complex with challenges that often stand in the way of profitability and higher shareholder value such as long order-to-delivery lead times, unreliable production schedules, excess inventory across the supply chain, lengthy demand planning cycles and lack of visibility of suppliers. The effect of the global economic meltdown increased the pressure on automotive executives to make right decisions about their supply chain for better performance. In a highly challenging and competitive environment such as today, where supply chain is a popular tool for improving the organisational competitiveness, an efficient and effective supply chain strategy is a must for automotive manufacturers and their component manufacturers so as to meet changing consumer demands. The paper explores the concept of lean and agility as generic supply chain strategies and presents a strategic supply chain framework for the automobile industry as a possible strategy to respond to changing consumer demand.


title: 0.7 V supply self-biased nanoWatt MOS-only threshold voltage monitor

abstract: This work presents a self-biased MOSFET threshold voltage V T0 monitor. The threshold condition is defined based on a current-voltage relationship derived from a continuous physical model. The model is valid for any operating condition, from weak to strong inversion, and under triode or saturation regimes. The circuit consists in balancing two self-cascode cells operating at different inversion levels, where one of the transistors that compose these cells is biased at the threshold condition. The circuit is MOSFET-only (can be implemented in any standard digital process), and it operates with a power supply of less than 1 V, consuming tenths of nW. We propose a process independent design methodology, evaluating different trade-offs of accuracy, area and power consumption. Schematic simulation results, including Monte Carlo variability analysis, support the V T0 monitoring behavior of the circuit with good accuracy on a 180 nm process.


title: Recruitment of an area involved in eye movements during mental arithmetic

abstract: Throughout the history of mathematics, concepts of number and space have been tightly intertwined. We tested the hypothesis that cortical circuits for spatial attention contribute to mental arithmetic in humans. We trained a multivariate classifier algorithm to infer the direction of an eye movement, left or right, from the brain activation measured in the posterior parietal cortex. Without further training, the classifier then generalized to an arithmetic task. Its left versus right classification could be used to sort out subtraction versus addition trials, whether performed with symbols or with sets of dots. These findings are consistent with the suggestion that mental arithmetic co-opts parietal circuitry associated with spatial coding.


title: Incremental Learning of Concept Drift in Nonstationary Environments

abstract: We introduce an ensemble of classifiers-based approach for incremental learning of concept drift, characterized by nonstationary environments (NSEs), where the underlying data distributions change over time. The proposed algorithm, named Learn++.NSE, learns from consecutive batches of data without making any assumptions on the nature or rate of drift; it can learn from such environments that experience constant or variable rate of drift, addition or deletion of concept classes, as well as cyclical drift. The algorithm learns incrementally, as other members of the Learn++ family of algorithms, that is, without requiring access to previously seen data. Learn++.NSE trains one new classifier for each batch of data it receives, and combines these classifiers using a dynamically weighted majority voting. The novelty of the approach is in determining the voting weights, based on each classifier's time-adjusted accuracy on current and past environments. This approach allows the algorithm to recognize, and act accordingly, to the changes in underlying data distributions, as well as to a possible reoccurrence of an earlier distribution. We evaluate the algorithm on several synthetic datasets designed to simulate a variety of nonstationary environments, as well as a real-world weather prediction dataset. Comparisons with several other approaches are also included. Results indicate that Learn++.NSE can track the changing environments very closely, regardless of the type of concept drift. To allow future use, comparison and benchmarking by interested researchers, we also release our data used in this paper.


title: SIFT-based ear recognition by fusion of detected keypoints from color similarity slice regions

abstract: Ear biometric is considered as one of the most reliable and invariant biometrics characteristics in line with iris and fingerprint characteristics. In many cases, ear biometrics can be compared with face biometrics regarding many physiological and texture characteristics. In this paper, a robust and efficient ear recognition system is presented, which uses Scale Invariant Feature Transform (SIFT) as feature descriptor for structural representation of ear images. In order to make it more robust to user authentication, only the regions having color probabilities in a certain ranges are considered for invariant SIFT feature extraction, where the K-L divergence is used for keeping color consistency. Ear skin color model is formed by Gaussian mixture model and clustering the ear color pattern using vector quantization. Finally, K-L divergence is applied to the GMM framework for recording the color similarity in the specified ranges by comparing color similarity between a pair of reference model and probe ear images. After segmentation of ear images in some color slice regions, SIFT keypoints are extracted and an augmented vector of extracted SIFT features are created for matching, which is accomplished between a pair of reference model and probe ear images. The proposed technique has been tested on the IITK Ear database and the experimental results show improvements in recognition accuracy while invariant features are extracted from color slice regions to maintain the robustness of the system.


title: Man-in-the-Middle Attack to the HTTPS Protocol

abstract: Web-based applications rely on the HTTPS protocol to guarantee privacy and security in transactions ranging from home banking, e-commerce, and e-procurement to those that deal with sensitive data such as career and identity information. Users trust this protocol to prevent unauthorized viewing of their personal, financial, and confidential information over the Web.


title: A mobility strategy for improving opportunity

abstract: Abstract Postwar changes in the metropolitan settlement structure have profoundly altered the geography of opportunity in the older cities of the Northeast and Midwest. Poverty is concentrated in once‐central cities, while employment continues to deconcentrate to the suburbs. In the face of these changes, antipoverty policy has become a geographic exercise of moving people and resources from certain places to other places. The three main strategies for confronting inner‐city poverty are dispersal, development, and mobility. These strategies are discussed and compared, with particular emphasis on the mobility approach. The article argues that the mobility approach—which connects poor inner‐city residents to suburban employment opportunities without changing the location of households or firms—is the most promising near‐term strategy for combating urban poverty.


title: Efficient Fuzz Testing Leveraging Input, Code, and Execution

abstract: Any kind of smart testing technique must be very efficient to be competitive with random fuzz testing. State-of the-art test generators are largely inferior to random testing in real world applications. This work proposes to gather and evaluate lightweight analyses that can enable the creation of an efficient and sufficiently effective analysis-assisted fuzz tester. The analyses shall leverage information sources apart from the program under test itself, such as e.g. descriptions of the targeted input format in the form of extended context-free grammars, or hardware counters. As the main contributions, an efficient framework for building fuzzers around given analyses will be created, and with its help analyses will be identified and categorized according to their performance.


Deep Spatial Pyramid Match Kernel for Scene Classification


title: Sketch of Big Data Real-Time Analytics Model

abstract: Big Data has drawn huge attention from researchers in information sciences, decision makers in governments and enterprises. However, there is a lot of potential and highly useful value hidden in the huge volume of data. Data is the new oil, but unlike oil data can be refined further to create even more value. Therefore, a new scientific paradigm is born as data-intensive scientific discovery, also known as Big Data. The growth volume of real-time data requires new techniques and technologies to discover insight value. In this paper we introduce the Big Data real-time analytics model as a new technique. We discuss and compare several Big Data technologies for real-time processing along with various challenges and issues in adapting Big Data. Real-time Big Data analysis based on cloud computing approach is our future research direction.


title: Efficient distance-based outlier detection on uncertain datasets of Gaussian distribution

abstract: Uncertain data management, querying and mining have become important because the majority of real world data is accompanied with uncertainty these days. Uncertainty in data is often caused by the deficiency in underlying data collecting equipments or sometimes manually introduced to preserve data privacy. This work discusses the problem of distance-based outlier detection on uncertain datasets of Gaussian distribution. The Naive approach of distance-based outlier on uncertain data is usually infeasible due to expensive distance function. Therefore a cell-based approach is proposed in this work to quickly identify the outliers. The infinite nature of Gaussian distribution prevents to devise effective pruning techniques. Therefore an approximate approach using bounded Gaussian distribution is also proposed. Approximating Gaussian distribution by bounded Gaussian distribution enables an approximate but more efficient cell-based outlier detection approach. An extensive empirical study on synthetic and real datasets show that our proposed approaches are effective, efficient and scalable.


title: Analysis of Customer Behavior using Clustering and Association Rules

abstract: The analysis of customer behavior is used to maintain good relationship with customers. It maximizes the customer satisfaction. We can also improve customer loyalty and retention. The aim of this paper is to develop a very useful trend for launching products with configurations for customers of different gender based on past transactions. Based on the previous transactions of the customers, prediction is done and data is estimated with the help of clustering and association rules. This paper proposes an effective method to extract knowledge from transactions records which is very useful for increasing the sales. Customer details are segmented using k-means and then Apriori algorithm is applied to identify customer behavior. This is followed by the identification of product associations within segments. This paper aims to develop a new trend and launch a new series of products using the previous transactions of the customers. General Terms Clustering analysis, Association rules


Bilingualism in legal education: A Malaysian experience


title: A relative color approach to color discrimination for malignant melanoma detection in dermoscopy images

abstract: Early detection of malignant melanoma can be lifesaving (1–3) and may be aided by digital image analysis (4–10). Certain colors are associated with melanoma in dermoscopy images, including blue and gray in veil structures, dark gray, blue and black in peppering associated with regression, white in scar-like regression, erythema associated with inflammation, brown in dots, globules and blotches as well as the pigment network, and black in dots (11, 12). Automatic detection of colors in clinical images (13–18) and dermoscopy images (5, 6, 19) using a number of techniques has been presented, but delineation of the range of benign vs. malignant coloring in digital dermoscopy images has not previously been attempted. In previous research, a data-driven relative color histogram analysis technique was examined for determining colors characteristic of melanomas in clinical images (16, 17). The use of relative color (13), in which the background color is subtracted from the image color, has been proposed as a technique to avoid color distortion in the imaging process as well as a method of accounting for variations in normal skin color. ::: ::: This research presents the relative color histogram technique that is applied to discrimination of benign and malignant colors in dermoscopy images. An overview of the color feature analysis algorithm used in the present study is presented in Fig. 1. Experiments presented here compare detection of clustered colors, as seen in the dermoscopy image lesion structures listed above, with detection of colors without regard to structure. Additional experiments are performed to evaluate the histogram analysis technique based on diagnostic accuracy achieved with: (1) different numbers of lesion images used as training data to identify melanoma colors and (2) different areas of the lesion selected for color analysis. ::: ::: ::: ::: Fig. 1 ::: ::: Overview of algorithm for color feature calculations and lesion discrimination. ::: ::: ::: ::: The remaining sections of this paper include: (1) the techniques used to calculate the percent melanoma color and color clustering ratio features, (2) experiments performed for melanoma and benign lesion discrimination, (3) experimental results, (4) discussion, and (5) conclusions from the study.


title: A High-Reliability, Low-Power Magnetic Full Adder

abstract: Recently, ultra-low power circuits based on logic-in magnetic tunnel junction (MTJ) memory structure have been studied thanks to its non-volatility, infinite endurance, high access speed, and easy integration with CMOS process. However, this type of circuit suffers from low reliability both in memory cell and sensing amplifier circuits, which greatly limits its practical applications for logic computation. In this paper, we present a new design of magnetic full adder (MFA) to overcome this issue based on the thermally assisted switching (TAS) MTJ cell and pre-charge sensing amplifier (PCSA) circuit. By using CMOS 65 nm design kit and a precise TAS-MTJ model, mixed simulations have been performed to demonstrate its high reliability keeping low power and small die area.


title: Bolt-on causal consistency

abstract: We consider the problem of separating consistency-related safety properties from availability and durability in distributed data stores via the application of a "bolt-on" shim layer that upgrades the safety of an underlying general-purpose data store. This shim provides the same consistency guarantees atop a wide range of widely deployed but often inflexible stores. As causal consistency is one of the strongest consistency models that remain available during system partitions, we develop a shim layer that upgrades eventually consistent stores to provide convergent causal consistency. Accordingly, we leverage widely deployed eventually consistent infrastructure as a common substrate for providing causal guarantees. We describe algorithms and shim implementations that are suitable for a large class of application-level causality relationships and evaluate our techniques using an existing, production-ready data store and with real-world explicit causality relationships.


title: Whole-genome sequencing of liver cancers identifies etiological influences on mutation patterns and recurrent mutations in chromatin regulators

abstract: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. We sequenced and analyzed the whole genomes of 27 HCCs, 25 of which were associated with hepatitis B or C virus infections, including two sets of multicentric tumors. Although no common somatic mutations were identified in the multicentric tumor pairs, their whole-genome substitution patterns were similar, suggesting that these tumors developed from independent mutations, although their shared etiological backgrounds may have strongly influenced their somatic mutation patterns. Statistical and functional analyses yielded a list of recurrently mutated genes. Multiple chromatin regulators, including ARID1A, ARID1B, ARID2, MLL and MLL3, were mutated in ∼50% of the tumors. Hepatitis B virus genome integration in the TERT locus was frequently observed in a high clonal proportion. Our whole-genome sequencing analysis of HCCs identified the influence of etiological background on somatic mutation patterns and subsequent carcinogenesis, as well as recurrent mutations in chromatin regulators in HCCs.


Natural Language Detectors Emerge in Individual Neurons


title: I.: Large-scale interactive ontology matching: Algorithms and implementation

abstract: In this paper we present the ontology matching system LogMap 2, a much improved version of its predecessor LogMap. LogMap 2 supports user interaction during the matching process, which is essential for use cases requiring very accurate mappings. Interactivity, however, imposes very strict scalability requirements; we are able to satisfy these requirements by providing real-time user response even for large-scale ontologies. Finally, LogMap 2 implements scalable reasoning and diagnosis algorithms, which minimise any logical inconsistencies introduced by the matching process.


title: Dimensioning of cable-driven parallel robot actuators, gearboxes and winches according to the twist feasible workspace

abstract: Cable Driven Parallel Robots (CDPRs) are a particular class of parallel robots whose legs consist of cables. CDPRs are composed of several components, e.g. winches, pulleys and actuators. The design of a CDPR requires the dimensioning of all these components, according to the task to be performed. The dimensioning of the actuators, the gearboxes and the winches are strictly related to the performances of the CDPR in terms of the platform static and kinematic equilibrium. This paper introduces a new tool, the so called Twist Feasible Workspace (TFW), built in order to analyze the workspace of the platform twists. A pose is said to be twist feasible if the platform of the CDPR can assume a given range of linear and rotational velocities while satisfying the cable speed limits imposed by the actuators and the transmission systems. The size of the TFW is used as an optimization criterion for the dimensioning of the actuators and the winches.


title: Quantum-inspired genetic algorithms

abstract: A novel evolutionary computing method-quantum inspired genetic algorithms-is introduced, where concepts and principles of quantum mechanics are used to inform and inspire more efficient evolutionary computing methods. The basic terminology of quantum mechanics is introduced before a comparison is made between a classical genetic algorithm and a quantum inspired method for the travelling salesperson problem. It is informally shown that the quantum inspired genetic algorithm performs better than the classical counterpart for a small domain. The paper concludes with some speculative comments concerning the relationship between quantum inspired genetic algorithms and various complexity classes.


title: Partner relationship satisfaction and maternal emotional distress in early pregnancy

abstract: BACKGROUND ::: Recognition of maternal emotional distress during pregnancy and the identification of risk factors for this distress are of considerable clinical- and public health importance. The mental health of the mother is important both for herself, and for the physical and psychological health of her children and the welfare of the family. The first aim of the present study was to identify risk factors for maternal emotional distress during pregnancy with special focus on partner relationship satisfaction. The second aim was to assess interaction effects between relationship satisfaction and the main predictors. ::: ::: ::: METHODS ::: Pregnant women enrolled in the Norwegian Mother and Child Cohort Study (n = 51,558) completed a questionnaire with questions about maternal emotional distress, relationship satisfaction, and other risk factors. Associations between 37 predictor variables and emotional distress were estimated by multiple linear regression analysis. ::: ::: ::: RESULTS ::: Relationship dissatisfaction was the strongest predictor of maternal emotional distress (β = 0.25). Other predictors were dissatisfaction at work (β = 0.11), somatic disease (β = 0.11), work related stress (β = 0.10) and maternal alcohol problems in the preceding year (β = 0.09). Relationship satisfaction appeared to buffer the effects of frequent moving, somatic disease, maternal smoking, family income, irregular working hours, dissatisfaction at work, work stress, and mother's sick leave (P < 0.05). ::: ::: ::: CONCLUSIONS ::: Dissatisfaction with the partner relationship is a significant predictor of maternal emotional distress in pregnancy. A good partner relationship can have a protective effect against some stressors.


title: Parasite element materials and their effect on blade antenna performance

abstract: In this paper, method for increasing the performance of a blade antenna by changing the materials of the parasite grounded element is introduced. Return loss, reflection coefficient, directionality depends of a blade antenna depends on the parasite element. Changing the materials of the parasite element shows different results. The whole work is done in search of the suitable material for the parasite element. The antenna is designed in Antenna Magus Software and EM simulation is done in the CST Microwave studio.


title: Neural Network Predicting Remote Vehicle Movement with Vehicle-to-Vehicle Data

abstract: This paper presents a neural network developed for predicting the path of a remote vehicle using post facto created vehicle-to-vehicle (V2V) data and uses that prediction to determine whether it is safe to change lanes. The data was collected in a 2013 experiment involving various drivers traveling on public roads in Ann Arbor, MI. The trips were on suburban roads, city roads and divided highways over a two day period. The vehicular satellite global positioning system (GPS) data from movement over this period was gathered and post-processed to find vehicle paths within 10 meters of one another. The path traces of the two vehicles were combined to simulate what a V2V network would have provided to properly equipped vehicles if such a network and vehicles existed on real road networks demonstrating natural driving behavior.This research harnesses this data to determine the increased effectiveness of a neural network predicting the future path of surrounding vehicles and lane change safety when a V2V network is available. The most studied maneuver is overtaking. To a lesser extent, this paper also provides a view into how a neural network predicts remote vehicle behaviors using a host vehicle equipped with only perceptive hardware and no given information from the remote vehicle.


title: Evaluation of Musical Features for Emotion Classification

abstract: Because music conveys and evokes feelings, a wealth of research has been performed on music emotion recognition. Previous research has shown that musical mood is linked to features based on rhythm, timbre, spectrum and lyrics. For example, sad music correlates with slow tempo, while happy music is generally faster. However, only limited success has been obtained in learning automatic classifiers of emotion in music. In this paper, we collect a ground truth data set of 2904 songs that have been tagged with one of the four words “happy”, “sad”, “angry” and “relaxed”, on the Last.FM web site. An excerpt of the audio is then retrieved from 7Digital.com, and various sets of audio features are extracted using standard algorithms. Two classifiers are trained using support vector machines with the polynomial and radial basis function kernels, and these are tested with 10-fold cross validation. Our results show that spectral features outperform those based on rhythm, dynamics, and, to a lesser extent, harmony. We also find that the polynomial kernel gives better results than the radial basis function, and that the fusion of different feature sets does not always lead to improved classification.


title: An Analysis of the Mozilla Jetpack Extension Framework

abstract: The Jetpack framework is Mozilla's newly-introduced extension development technology. Motivated primarily by the need to improve how scriptable extensions (also called addons in Firefox parlance) are developed, the Jetpack framework structures addons as a collection of modules. Modules are isolated from each other, and communicate with other modules via cleanly-defined interfaces. Jetpack also recommends that each module satisfy the principle of least authority (POLA). The overall goal of the Jetpack framework is to ensure that the effects of any vulnerabilities are contained within a module. Its modular structure also facilitates code reuse across addons. ::: ::: In this paper, we study the extent to which the Jetpack framework achieves its goals. Specifically, we use static analysis to study capability leaks in Jetpack modules and addons. We implemented Beacon, a static analysis tool to identify the leaks and used it to analyze 77 core modules from the Jetpack framework and another 359 Jetpack addons. In total, Beacon analyzed over 600 Jetpack modules and detected 12 capability leaks in 4 core modules and another 24 capability leaks in 7 Jetpack addons. Beacon also detected 10 over-privileged core modules. We have shared the details with Mozilla who have acknowledged our findings.


title: How to Predict Mood? Delving into Features of Smartphone-Based Data

abstract: Smartphones are increasingly utilized in society and enable scientists to record a wide range of behavioral and environmental information. These information, referred to as Unobtrusive Ecological Momentary Assessment Data, might support prediction procedures regarding the mood level of users and simultaneously contribute to an enhancement of therapy strategies. In this paper, we analyze how the mood level of healthy clients is affected by unobtrusive measures and how this kind of data contributes to the prediction performance of various statistical models (Bayesian methods, Lasso procedures, etc.). We conduct analyses on a non-user and a user level. We then compare the models by utilizing introduced performance measures. Our findings indicate that the prediction performance increases when considering individual users. However, the implemented models only perform slightly better than the introduced mean model. Indicated by feature selection methods, we assume that more meaningful variables regarding the outcome can potentially increase prediction performance.


Impact of Social Media on Social Anxiety: A Systematic Review


title: Vulnerability of Remote Keyless-Entry Systems Against Pulsed Electromagnetic Interference and Possible Improvements

abstract: Remote keyless-entry systems are systems that are widely used to control access to vehicles or buildings. The system is increasingly secured against hacking attacks by use of encryption and code algorithms. However, there are effective hacker attacks that rely on jamming the wireless link from the key fob to the receiver, while the attacker is able to receive the signal from the key fob. In this paper, we show that typical envelope receivers that are often used in remote keyless-entry systems are highly vulnerable to pulsed interference as compared to continuous interference. The effects of pulsed interference on envelope detectors are analyzed through both simulations and measurements. An improved receiver design would use synchronous receivers instead, which are not very sensitive against pulsed interference.


title: Social effects of oxytocin in humans: context and person matter

abstract: Building on animal research, the past decade has witnessed a surge of interest in the effects of oxytocin on social cognition and prosocial behavior in humans. This work has generated considerable excitement about identifying the neurochemical underpinnings of sociality in humans, and discovering compounds to treat social functioning deficits. Inspection of the literature, however, reveals that the effects of oxytocin in the social domain are often weak and/or inconsistent. We propose that this literature can be informed by an interactionist approach in which the effects of oxytocin are constrained by features of situations and/or individuals. We show how this approach can improve understanding of extant research, suggest novel mechanisms through which oxytocin might operate, and refine predictions about oxytocin pharmacotherapy.


title: Fiction: Simulation of Social Worlds

abstract: Fiction is the simulation of selves in interaction. People who read it improve their understanding of others. This effect is especially marked with literary fiction, which also enables people to change themselves. These effects are due partly to the process of engagement in stories, which includes making inferences and becoming emotionally involved, and partly to the contents of fiction, which include complex characters and circumstances that we might not encounter in daily life. Fiction can be thought of as a form of consciousness of selves and others that can be passed from an author to a reader or spectator, and can be internalized to augment everyday cognition.


title: Face detection by structural models

abstract: Despite the successes in the last two decades, the state-of-the-art face detectors still have problems in dealing with images in the wild due to large appearance variations. Instead of leaving appearance variations directly to statistical learning algorithms, we propose a hierarchical part based structural model to explicitly capture them. The model enables part subtype option to handle local appearance variations such as closed and open month, and part deformation to capture the global appearance variations such as pose and expression. In detection, candidate window is fitted to the structural model to infer the part location and part subtype, and detection score is then computed based on the fitted configuration. In this way, the influence of appearance variation is reduced. Besides the face model, we exploit the co-occurrence between face and body, which helps to handle large variations, such as heavy occlusions, to further boost the face detection performance. We present a phrase based representation for body detection, and propose a structural context model to jointly encode the outputs of face detector and body detector. Benefit from the rich structural face and body information, as well as the discriminative structural learning algorithm, our method achieves state-of-the-art performance on FDDB, AFW and a self-annotated dataset, under wide comparisons with commercial and academic methods. (C) 2013 Elsevier B.V. All rights reserved.


title: An Experimental Evaluation of Point-of-interest Recommendation in Location-based Social Networks

abstract: Point-of-interest (POI) recommendation is an important service to Location-Based Social Networks (LBSNs) that can benefit both users and businesses. In recent years, a number of POI recommender systems have been proposed, but there is still a lack of systematical comparison thereof. In this paper, we provide an all-around evaluation of 12 state-of-the-art POI recommendation models. From the evaluation, we obtain several important findings, based on which we can better understand and utilize POI recommendation models in various scenarios. We anticipate this work to provide readers with an overall picture of the cutting-edge research on POI recommendation.


title: Compensation Of Geometric And Elastic Deflection Errors In Large Manipulators Based On Experimental Measurements: Application To A High Accuracy Medical Manipulator

abstract: A model is developed to compensate for end effectormanipulator errors. The objective is to achieve very high accuracy. The method explicitly decomposes measured end-point error data into generalized geometric and elastic errors. It is computationally simple and requires only identification of parameters which are functions of only one variable. It is shown that the measured data requirements are relatively low. The method is applied to a high accuracy 6 DOF medical robot that positions patients for cancer therapy. Experimental results demonstrate the high effectiveness of the compensation method.


title: IEEE 802.11ah: Advantages in standards and further challenges for sub 1 GHz Wi-Fi

abstract: The rapid developments in Internet-of-Things (IoT) and Machine-to-Machine (M2M) communication make it necessary to design communication systems operating in different wireless spectrum as an alternative to highly congested wireless access systems. In addition, the deployment of wireless smart meter devices is ramping up and it is expected that such devices will flood the market in the near future competing for the same wireless spectrum. The IEEE 802.11ah standardization task group is developing a global Wireless LAN (WLAN) standard that will allow wireless access using carrier frequencies below 1 GHz in the ISM (Industrial, Scientific, and Medical) band and will help Wi-Fi-enabled devices to get guaranteed access for short-burst data transmissions, such as meter data. In addition to exploiting the underutilized sub 1 GHz spectrum the improved coverage range allows new applications to emerge such as wide area based sensor networks, sensor backhaul systems and potential Wi-Fi off-loading functions. This paper summarizes the IEEE 802.11ah standardization activities in progress and discusses advantages and challenges in the design of physical layer (PHY) and media access control (MAC) schemes in the sub 1 GHz band.


title: IEEE 802.11-saturation throughput analysis

abstract: To satisfy the emerging need of wireless data communications, the IEEE is currently standardizing the 802.11 protocol for wireless local area networks. This standard adopts a CSMA/CA medium access control protocol with exponential backoff. We present a simple analytical model to compute the saturation throughput performance in the presence of a finite number of terminals and in the assumption of ideal channel conditions. The model applies to both basic and request-to-send/clear-to-send (RTS/CTS) access mechanisms. Comparison with simulation results shows that the model is extremely accurate in predicting the system throughput.


title: Potential of Augmented Reality for Intelligent Transportation Systems

abstract: Rapid advances in wireless communication technologies coupled with ongoing massive development in vehicular networking standards and innovations in computing, sensing, and analytics have paved the way for intelligent transportation systems (ITS) to develop rapidly in the near future. ITS provides a complete solution for the efficient and intelligent management of real-time traffic, wherein sensory data is collected from within the vehicles (i.e., via their onboard units) as well as data exchanged between the vehicles, between the vehicles and their supporting roadside infrastructure/network, among the vehicles and vulnerable pedestrians, subsequently paving the way for the realization of the futuristic Internet of Vehicles. The traditional intent of an ITS system is to detect, monitor, control, and subsequently reduce traffic congestion based on a real-time analysis of the data pertinent to certain patterns of the road traffic, including traffic density at a geographical area of interest, precise velocity of vehicles, current and predicted travelling trajectories and times, etc. However, merely relying on an ITS framework is not an optimal solution. In case of dense traffic environments, where communication broadcasts from hundreds of thousands of vehicles could potentially choke the entire network (and so could lead to fatal accidents in the case of autonomous vehicles that depend on reliable communications for their operational safety), a fall back to the traditional decentralized vehicular ad hoc network (VANET) approach becomes necessary. It is therefore of critical importance to enhance the situational awareness of vehicular drivers so as to enable them to make quick but well-founded manual decisions in such safety-critical situations.


RapidChain : A Fast Blockchain Protocol via Full


title: Learning Visual Knowledge Memory Networks for Visual Question Answering

abstract: Visual question answering (VQA) requires joint comprehension of images and natural language questions, where many questions can't be directly or clearly answered from visual content but require reasoning from structured human knowledge with confirmation from visual content. This paper proposes visual knowledge memory network (VKMN) to address this issue, which seamlessly incorporates structured human knowledge and deep visual features into memory networks in an end-to-end learning framework. Comparing to existing methods for leveraging external knowledge for supporting VQA, this paper stresses more on two missing mechanisms. First is the mechanism for integrating visual contents with knowledge facts. VKMN handles this issue by embedding knowledge triples (subject, relation, target) and deep visual features jointly into the visual knowledge features. Second is the mechanism for handling multiple knowledge facts expanding from question and answer pairs. VKMN stores joint embedding using key-value pair structure in the memory networks so that it is easy to handle multiple facts. Experiments show that the proposed method achieves promising results on both VQA v1.0 and v2.0 benchmarks, while outperforms state-of-the-art methods on the knowledge-reasoning related questions.


Artificial error generation for translation-based grammatical error correction


title: A Comparative Study of Wireless Sensor Networks and Their Routing Protocols

abstract: Recent developments in the area of micro-sensor devices have accelerated advances in the sensor networks field leading to many new protocols specifically designed for wireless sensor networks (WSNs). Wireless sensor networks with hundreds to thousands of sensor nodes can gather information from an unattended location and transmit the gathered data to a particular user, depending on the application. These sensor nodes have some constraints due to their limited energy, storage capacity and computing power. Data are routed from one node to other using different routing protocols. There are a number of routing protocols for wireless sensor networks. In this review article, we discuss the architecture of wireless sensor networks. Further, we categorize the routing protocols according to some key factors and summarize their mode of operation. Finally, we provide a comparative study on these various protocols.


title: An IoT-cloud Based Wearable ECG Monitoring System for Smart Healthcare

abstract: Public healthcare has been paid an increasing attention given the exponential growth human population and medical expenses. It is well known that an effective health monitoring system can detect abnormalities of health conditions in time and make diagnoses according to the gleaned data. As a vital approach to diagnose heart diseases, ECG monitoring is widely studied and applied. However, nearly all existing portable ECG monitoring systems cannot work without a mobile application, which is responsible for data collection and display. In this paper, we propose a new method for ECG monitoring based on Internet-of-Things (IoT) techniques. ECG data are gathered using a wearable monitoring node and are transmitted directly to the IoT cloud using Wi-Fi. Both the HTTP and MQTT protocols are employed in the IoT cloud in order to provide visual and timely ECG data to users. Nearly all smart terminals with a web browser can acquire ECG data conveniently, which has greatly alleviated the cross-platform issue. Experiments are carried out on healthy volunteers in order to verify the reliability of the entire system. Experimental results reveal that the proposed system is reliable in collecting and displaying real-time ECG data, which can aid in the primary diagnosis of certain heart diseases.


title: Cost Minimization of Charging Stations With Photovoltaics: An Approach With EV Classification

abstract: This paper proposes a novel electric vehicle (EV) classification scheme for a photovoltaic (PV) powered EV charging station (CS) that reduces the effect of intermittency of electricity supply as well as reducing the cost of energy trading of the CS. Since not all EV drivers would like to be environmentally friendly, all vehicles in the CS are divided into three categories: 1) premium, 2) conservative, and 3) green, according to their charging behavior. Premium and conservative EVs are considered to be interested only in charging their batteries, with noticeably higher rate of charging for premium EVs. Green vehicles are more environmentally friendly, and thus assist the CS to reduce its cost of energy trading by allowing the CS to use their batteries as distributed storage. A different charging scheme is proposed for each type of EV, which is adopted by the CS to encourage more EVs to be green. A basic mixed integer programming (MIP) technique is used to facilitate the proposed classification scheme. It is shown that the uncertainty in PV generation can be effectively compensated, along with minimization of total cost of energy trading to the CS, by consolidating more green EVs. Real solar and pricing data are used for performance analysis of the system. It is demonstrated that the total cost to the CS reduces considerably as the percentage of green vehicles increases, and also that the contributions of green EVs in winter are greater than those in summer.


title: TWIN: Personality-based Intelligent Recommender System

abstract: The research work of the third author is partially funded by the WIQ-EI (IRSES grant n. 269180) and DIANA APPLICATIONS (TIN2012-38603-C02-01), and done in the framework of the VLC/Campus Microcluster on Multimodal Interaction in Intelligent Systems.


title: Development and evaluation of a questionnaire to assess knowledge, attitudes, and behaviors towards hearing loss prevention

abstract: AbstractObjective: To develop and evaluate a questionnaire assessing knowledge, attitudes, and behaviors (KAB) as they pertain to hearing conservation, using the constructs of the health belief model (HBM). Design: The KAB was completed by 235 participants. Relationships between knowledge and attitudes about hearing and hearing conservation, participation in noisy activities, and use of hearing protection were examined. Study sample: 117 males and 118 females aged between 18 and 80 years (mean = 42.3, SD = 4.1) recruited from the Portland VA Medical Center, local universities, and a community college. Results: Knowledge scores ranged from 15.6% to 93.8%. Factor analyses revealed six attitude factors, interpreted as measuring perceived susceptibility, perceived severity, perceived benefits, perceived barriers, perceived self-efficacy, and cues to action. Over 95% of participants routinely participated in at least one noisy activity but few used hearing protection while doing so. The attitude scores of indi...


title: Waveguide Pseudoelliptic Filters Using Slant and Transverse Rectangular Ridge Resonators

abstract: This paper presents a novel class of resonators suitable for the design of waveguide pseudoelliptic filters. The basic structure is a rectangular ridge arbitrarily located and oriented within a rectangular waveguide sized to allow the propagation of the fundamental TE10 mode. The ridge has the capability to provide both a reflection and a transmission zero, thus behaving as a singlet. The coupling coefficients of the resonator can be controlled by varying the position and orientation of the ridge, or by additional elements such as irises or ridge taps. Compact pseudoelliptic filters can be designed by cascading multiple ridges and half a wavelength waveguide sections. A practical design procedure is presented. The experimental results of a fifth-order filter with three resonant ridges validate the proposed approach.


title: Targeted Delivery of Doxorubicin by HPMA Copolymer-Hyaluronan Bioconjugates

abstract: AbstractPurpose. Overexpression of hyaluronan (HA) receptors on cancer cells results in enhanced endocytotic uptake of the drug conjugate. An N-(2-hydroxypropyl)methacrylamide (HPMA)-HA polymeric drug delivery system was used for targeted delivery of doxorubicin to cancer cells. ::: Methods. HA-doxorubicin (DOX) bioconjugates (HA-DOX), and HPMA copolymer-DOX conjugates containing HA as a side chain (HPMA-HA-DOX) were synthesized. The cytotoxicity of the polymer-drug conjugate was evaluated via in vitro cell culture. The internalization of the conjugate was visualized by fluorescence microscopy. ::: Results. Cytotoxicity of HPMA-HA-DOX targeted bioconjugate was higher against human breast cancer (HBL-100), ovarian cancer (SKOV-3), and colon cancer (HCT-116) cells when compared to the non-targeted HPMA-DOX conjugate. Fluorescence confocal microscopy revealed that the targeted HPMA-HA-DOX conjugates were internalized more efficiently by cancer cells relative to the non-targeted HPMA-DOX conjugate. Both HPMA-DOX and HPMA-HA-DOX showed minimal cytotoxicity toward mouse fibroblast NIH 3T3 cells. The internalization of polymer conjugates was correlated with their cytotoxicity. ::: Conclusions. Selective delivery of anti-cancer agents to cancer cells was achieved by biochemical targeting. The HA-modified HPMA copolymer showed improved toxicity due to receptor-mediated uptake of the macromolecular drug.


title: Novel Design of Wilkinson Power Dividers With Arbitrary Power Division Ratios

abstract: In this paper, a stub-loaded transmission line that is used to design Wilkinson power dividers with arbitrary power division ratios is discussed in depth. Power division is always required in most wireless/wireline communications such as industrial systems and consumer electronics. However, the design of an unequal Wilkinson power divider, particularly with a large division ratio, becomes a challenge in engineering because of the impractical realization of the high- or low-impedance transmission lines on a dielectric substrate. In this paper, we first introduce a transmission-line structure and perform theoretical analysis. The derivations reveal that the studied structure can be utilized to design Wilkinson power dividers with equal or unequal power divisions. For demonstration purposes, two power dividers with division ratios of 1 : 1 and 1 : 9, respectively, and operating in the popular industrial, scientific, and radio-frequency identification frequency bands are implemented by loading distributed transmission-line elements, namely, open- or short-circuited stubs. The demonstrators are also fabricated and validated by measurements, and good agreement between the experimental results and the simulated responses is observed.


ℓ1-magic: Recovery of Sparse Signals via Convex Programming


On the Viability of Distributed Consensus by Proof of Space


title: DupLESS: Server-Aided Encryption for Deduplicated Storage ∗

abstract: Cloud storage service providers such as Dropbox, Mozy, and others perform deduplication to save space by only storing one copy of each file uploaded. Should clients conventionally encrypt their files, however, savings are lost. Message-locked encryption (the most prominent manifestation of which is convergent encryption) resolves this tension. However it is inherently subject to brute-force attacks that can recover files falling into a known set. We propose an architecture that provides secure deduplicated storage resisting brute-force attacks, and realize it in a system called DupLESS. In DupLESS, clients encrypt under message-based keys obtained from a key-server via an oblivious PRF protocol. It enables clients to store encrypted data with an existing service, have the service perform deduplication on their behalf, and yet achieves strong confidentiality guarantees. We show that encryption for deduplicated storage can achieve performance and space savings close to that of using the storage service with plaintext data.


title: A review on artificial intelligence techniques for developing intelligent honeypot

abstract: Honeypot is a closely monitored computer resource that emulates behaviors of production host within a network in order to lure and attract the attackers. The workability and effectiveness of a deployed honeypot depends on its technical configuration. Since honeypot is a resource that is intentionally made attractive to the attackers, it is crucial to make it intelligent and self-manageable. This research reviews at artificial intelligence techniques such as expert system and case-based reasoning, in order to build an intelligent honeypot.


title: Analysing Trends in Computer Science Research: A Preliminary Study Using The Microsoft Academic Graph

abstract: Research in Computer Science (CS) evolves rapidly in a dynamic fashion. New research area may emerge and attract researchers, while older areas may have lesser interest from researchers. Studying how trends evolve in CS can be interesting from several dimensions. Furthermore, it can be used to craft research agendas. In this paper, we present trend analysis on research area in CS. We also look at citation trend analysis. Our analysis is performed using the Microsoft Academic Graph dataset. We propose the FoS score to measure the level of interest in any particular research area or topic. We apply the FoS score to investigate general publication trends, citation trends, evolution of research areas, and relation between research areas in CS.


title: Exclusive Use Spectrum Access Trading Models in Cognitive Radio Networks: A Survey

abstract: Spectrum frequency is a valuable resource for wireless communication but very limited in its availability. Due to the extensive use and ever increasing demand of spectrum bands by wireless devices and newer applications, unlicensed band is becoming congested, while licensed bands are found mostly underutilized. To solve this problem of spectrum scarcity, cognitive radio (CR) devices can share licensed bands opportunistically in several ways. We analyze the three main dynamic sharing models (commons, shared-use, and exclusive-use) proposed in literature with extensive analysis of the exclusive-use model, which is the most promising as it provides benefits to both licensed and unlicensed users. In this model, CR-enabled service providers, also known as secondary service providers, can buy or lease spectrum from licensed, known as primary service providers, for both short and long duration and gain exclusive rights to access the spectrum. In this survey paper, exclusive-use trading approaches, namely, game theory, market equilibrium, and classical, hybrid and other models are reviewed extensively and their characteristics and differences are highlighted and compared. We also propose possible future research directions on exclusive-use CR model.


title: Recovering Particle Diversity in a Rao-Blackwellized Particle Filter for SLAM After Actively Closing Loops

abstract: Acquiring models of the environment belongs to the fundamental tasks of mobile robots. Approaches addressing the problem of simultaneous localization and mapping (SLAM) typically process the perceived sensor data and do not influence the motion of the mobile robot. In this paper, we present an approach to actively closing loops during exploration. It applies a Rao-Blackwellized particle filter to maintain multiple hypotheses about potential trajectories of the robot and corresponding maps. To prevent the particle filter from becoming overly confident, we present a technique to recover the particle diversity after successfully closing a loop. This way the particle depletion problem is avoided. The combination of our approach with the active loop closing strategy allows to deal with multiple nested loops. Experimental results presented in this paper illustrate the advantage of our method over pervious approaches to mapping with Rao-Blackwellized particle filters.


title: High-efficiency dual-axis solar tracking developement using Arduino

abstract: The renewable-energy sector is fast gaining ground as a new growth area for numerous countries with the vast potential it presents environmentally and economically. Solar energy plays an important role as a primary source of energy, especially for rural area. This paper presents the design and development of high-efficiency dual-axis solar tracking system using Arduino platform. Furthermore, the ultimate objective of this project is to trace the maximum sunlight source to power the solar panel. The project is divided into two stages, which are hardware and software development. In hardware development, five light dependent resistor (LDR) has been used for capturing maximum light source. Two servo motors have been used to move the solar panel at maximum light source location sensing by LDR. Moreover, the code is constructed using C programming language and targeted to Arduino UNO controller. The efficiency of the system has been tested and compared with static solar panel on several time intervals, and it shows the system react the best at the 10-minutes intervals with consistent voltage generated. Therefore, the system has been proven working for capturing the maximum sunlight source for high efficiency solar harvesting applications.


title: Learn-to-Score: Efficient 3D Scene Exploration by Predicting View Utility

abstract: Camera equipped drones are nowadays being used to explore large scenes and reconstruct detailed 3D maps. When free space in the scene is approximately known, an offline planner can generate optimal plans to efficiently explore the scene. However, for exploring unknown scenes, the planner must predict and maximize usefulness of where to go on the fly. Traditionally, this has been achieved using handcrafted utility functions. We propose to learn a better utility function that predicts the usefulness of future viewpoints. Our learned utility function is based on a 3D convolutional neural network. This network takes as input a novel volumetric scene representation that implicitly captures previously visited viewpoints and generalizes to new scenes. We evaluate our method on several large 3D models of urban scenes using simulated depth cameras. We show that our method outperforms existing utility measures in terms of reconstruction performance and is robust to sensor noise.


title: Contrast enhancement of night images

abstract: Night images obtained from a surveillance camera have low visibility compared to daytime images. Images captured at night have low brightness, low contrast and high noise. A modified contrast enhancement (CE) algorithm was proposed and developed in luminance-chrominance space. Only the luminance channel obtained by PCA transform is processed as it contains the most valuable information. Daytime images are simulated with various degrees of contrast and Poisson noise using MATLAB. CE algorithm is applied in three scales to obtain good brightness and contrast of the images. Images are denoised by using bilateral filter that smoothes the noise while preserving edges. The brightness and contrast of the night images have been enhanced significantly and the noise is reduced effectively, preserving the details of the images. Finally, the performance of the proposed algorithm is illustrated by processing images under various lighting conditions without introducing halo and ghosting artifacts. Structural similarity and visual contrast measures demonstrate that the proposed method is more effective over other existing methods.


title: A High-Voltage Pulse Generator With Continuously Variable Pulsewidth Based on a Modified PFN

abstract: The amount of stored energy is an important issue in the low-droop and high-precision pulse generation. Pulse forming network (PFN) simulating an open-ended transmission line seems to be useful due to its minimum stored energy. The drawbacks of the PFN include low-output pulse quality and constant pulsewidth, which limit their applications. Another limitation of generating rectangular pulses using PFN is that half of the charged voltage appears in the output when delivering the entire stored energy to the matched load. This paper proposes a structure of PFN-based pulse generator that overcomes the above-mentioned limitations. For continuous pulsewidth adjustment, a lossless scheme is presented. By increasing the ratio of the load magnitude to the characteristic impedance, the amplitude of the output pulse voltage would be approximately equal to that of the charged voltage. Therefore, the semiconductor switches can be utilized in the PFN pulse generator easily. In addition, the pulse quality is enhanced and faster rising time is obtained in comparison with the conventional PFNs. In this proposal, the stored energy quantity is limited to three times the energy needed for the output pulse in the case of $R_{L}=10Z_{0}$ . To verify the proposed structure, a prototype is simulated using PSPICE software, and the experimental results are presented.


title: Analysis of selected fungi variation and its dependence on season and mountain range in southern Poland—key factors in drawing up trial guidelines for aeromycological monitoring

abstract: The aim of the study was to identify fungal spores, in particular plant pathogenic fungi, occurring in the air in selected mountain ranges. The results revealed not only the array of fungal species migrating with air currents from the Czech Republic and Slovakia but also how the season of the year affects the distribution of spores. Such studies may lay a foundation for future aeromycological monitoring, in accordance with the requirements for integrated plant protection. Aeromycological research was carried out between 2013 and 2016 at 3-month intervals in mountainous areas along the southern borders of Poland: the Bieszczady, the Pieniny, the Giant Mountains (Karkonosze) and the Babia Gora Massif. The research relied on impact method employing Air Ideal 3P sampler, which, by drawing in atmospheric air, also collects fungal spores. Regardless of altitudinal zonation, the changing weather conditions appeared to be the main reason for the variations in the number of the fungal spores under study in those years.


title: Using Heuristics to Evaluate the Overall User Experience of Video Games and Advanced Interaction Games

abstract: This chapter describes an approach to evaluating user experience in video games and advanced interaction games (tabletop games) by using heuristics. We provide a short overview of computer games with a focus on advanced interaction games and explain the concept of user-centred design for games. Furthermore, we describe the history of heuristics for video games and the role of user experience of games in general. We propose a framework consisting of three sets of heuristics (game play/game story, virtual interface and tabletop specific) to detect the most critical issues in video games as well as advanced interaction games. To assess its applicability, we compare the results of expert evaluations of five current games with the user experience-based ratings of various game review sites. In the conclusion, we provide an outlook on possible extensions of our approach.


title: A Meta-Instrument for Interactive, On-the-Fly Machine Learning

abstract: Supervised learning methods have long been used to allow musical interface designers to generate new mappings by example. We propose a method for harnessing machine learning algorithms within a radically interactive paradigm, in which the designer may repeatedly generate examples, train a learner, evaluate outcomes, and modify parameters in real-time within a single software environment. We describe our meta-instrument, the Wekinator, which allows a user to engage in on-the-fly learning using arbitrary control modalities and sound synthesis environments. We provide details regarding the system implementation and discuss our experiences using the Wekinator for experimentation and performance.


title: Global optimizations and tabu search based on memory

abstract: Based on the idea of tabu search that Glover et al put forward, a new tabu search, named Memory Tabu Search (MTS), is proposed for solving the multiple-minima problem of continuous functions. Two convergence theorems, which show that MTS asymptotically converges to the global optimal solutions in probability one under suitable conditions, are given. Numerical results illustrate that this algorithm is efficient, robust and easy to implement.


title: Age Related Differences of Executive Functioning Problems in Everyday Life of Children and Adolescents in the Autism Spectrum

abstract: Numerous studies investigated executive functioning (EF) problems in people with autism spectrum disorders (ASD) using laboratory EF tasks. As laboratory task performances often differ from real life observations, the current study focused on EF in everyday life of 118 children and adolescents with ASD (6–18 years). We investigated age-related and individual differences in EF problems as reported by parents on the Behavioral Rating Inventory Executive Functions (BRIEF: Gioia et al. in Behavior rating inventory of executive function. Psychological Assessment Resources, Odesse 2000), and examined the association with autism severity. Inhibition problems were mostly found in the youngest group (6- to 8-year-olds), whereas problems with planning where more evident for 12- to 14-year-olds as compared to 9- to 11-year-olds. In a subsample of participants meeting the ADOS ASD cut-off criteria the age related differences in planning were absent, while problems with cognitive flexibility were less apparent in 15- to 18-year-olds, compared to 9- to 11-, and 12- to 14-year olds. EF problems surpassing the clinical cutoff were only observed in 20 % (planning) to 51 % (cognitive flexibility) of the children and adolescents, and no relation was found with ASD symptom severity. This underlines the heterogeneous nature of ASD.


title: Cybersecurity in banking and financial sector: Security analysis of a mobile banking application

abstract: The goal of providing a totally secure mobile based internet banking environment continues to be a moving target exacerbated by the free and readily available flow of information on the Internet at the disposal of would be attackers. As new security measures are devised, new attack vectors are developed to thwart these measures. A trusted device segregated from the Internet and potential malware seems like a simple, straight-forward solution. With the rapid development of smart phones and the plethora of smart phone applications becoming developed, the lines between the cellular network and the internet are being blurred, rendering the phone a not-so-trusted device. In this paper we show how existing smart phone banking applications can be tampered to capture user information and password.


title: Learning Outcome Enhancement via Serious Game: Implementing Game-Based Learning Framework in Blended Learning Environment

abstract: This study aims to enhance learning outcome with serious game. Game-Based Learning (GBL) framework was implemented in a blended learning environment on a specific topic namely Java programming at the foundation level. Quantitative and qualitative approaches were used as research methodologies, which included a learning satisfaction survey, interviews, and classroom observations. The research findings showed that serious game in higher education could increase the students' interest, which in turn enhance their academic achievement in studies.


On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts - Towards Memetic Algorithms


title: A new method for person identification in a biometric security system based on brain EEG signal processing

abstract: Recently, researches in a biometric security tend to use new types of biometric that based on physiological signals, such as EEG and ECG signals, rather than more traditional biological traits. Since it is very hard to fake an EEG signature or to attack an EEG biometric system, this paper presented a biometric security system that based on EEG signal processing. A new investigated method for person identification using the EEG Brain Signal processing is introduced. The proposed method based on executing a voting scheme between the 3 feature extraction methods which achieved maximum classification rates in the preliminary test. Preliminary test used Discrete Fourier Transform (DFT) and Wavelet packet decomposition (WPD) for features extraction with two different measures with each of them, thus a total of 4 different methods, produced 4 different features sets. Classification rates were 93%, 87% & 93% using the 3 recommended features sets. After executing the proposed voting scheme, classification rate increased to 100%, for 3 subjects' experiment which surpassed the results from the previous works in this application. Multi-layer Perceptron Neural Network trained by a standard back propagation algorithm is used as a classifier. Taking into account of reducing distraction to subjects, 4 channels only were used in the experiment and the subject need only to sit with eyes closed and quiet, which free the physical requirements of users and the condition of applying environment.


title: Privacy of the Internet of Things: A Systematic Literature Review (Extended Discussion)

abstract: The Internet of Things' potential for major privacy invasion is a concern. This paper reports on a systematic literature review of privacy-preserving solutions appearing in the research literature and in the media. We analysed proposed solutions in terms of the techniques they deployed and the extent to which they satisfied core privacy principles. We found that very few solutions satisfied all core privacy principles. We also identified a number of key knowledge gaps in the course of the analysis. In particular, we found that most solution providers assumed that end users would be willing to expend effort to preserve their privacy; that they would be motivated to act to preserve their privacy. The validity of this assumption needs to be proved, since it cannot simply be assumed that people would necessarily be willing to engage with these solutions. We suggest this as a topic for future research.


title: A bidirectional drug repositioning approach for Parkinson's disease through network-based inference.

abstract: Parkinson's Disease (PD) is one of the most prevailing neurodegenerative disorders. Novel computational approaches are required to find new ways of using the existing drugs or drug repositioning, as currently there exists no cure for PD. We proposed a new bidirectional drug repositioning method that consists of Top-down and Bottom-up approaches and finally gives information about significant repositioning drug candidates. This method takes into account of the topological significance of drugs in the tripartite Indication-drug-target network (IDTN) as well the significance of their targets in the PD-specific protein-protein interaction network (PPIN). 9 non-Parkinsonian drugs have been proposed as the significant repositioning candidates for PD. In order to find out the efficiency of the repositioning candidates we introduced a parameter called the On-target ratio (OTR). The average OTR value of final repositioning candidates has been found to be higher than that of known PD specific drugs.


title: Towards Network-level Efficiency for Cloud Storage Services

abstract: Cloud storage services such as Dropbox, Google Drive, and Microsoft OneDrive provide users with a convenient and reliable way to store and share data from anywhere, on any device, and at any time. The cornerstone of these services is the data synchronization (sync) operation which automatically maps the changes in users' local filesystems to the cloud via a series of network communications in a timely manner. If not designed properly, however, the tremendous amount of data sync traffic can potentially cause (financial) pains to both service providers and users. This paper addresses a simple yet critical question: Is the current data sync traffic of cloud storage services efficiently used? We first define a novel metric named TUE to quantify the Traffic Usage Efficiency} of data synchronization. Based on both real-world traces and comprehensive experiments, we study and characterize the TUE of six widely used cloud storage services. Our results demonstrate that a considerable portion of the data sync traffic is in a sense wasteful, and can be effectively avoided or significantly reduced via carefully designed data sync mechanisms. All in all, our study of TUE of cloud storage services not only provides guidance for service providers to develop more efficient, traffic-economic services, but also helps users pick appropriate services that best fit their needs and budgets.


title: The Traveling Wave Power Divider/Combiner

abstract: A power divider/combiner circuit designed for application to octave bandwidth microstrip power transistor amplifier is presented. Circuit characteristics, design and advantages are discussed. Experimental results on a 4-way divider are presented and compared with theory.


title: Oral Reading Fluency in Second Language Reading.

abstract: This study investigated the role of oral reading fluency in second language reading. Two hundred and fifty-five high school students in South Korea were assessed on three oral reading fluency (ORF) variables and six other reading predictors. The relationship between ORF and other reading predictors was examined through an exploratory factor analysis (EFA). Next, the contribution of ORF to silent reading comprehension was investigated through multiple regression analyses (MRA) with ORF variables as predictors of reading comprehension. EFA identified two factors (fluency and comprehension) and showed that passage reading fluency crossloaded with both factors. MRA results indicated that the three ORF variables collectively explained 21.2% of variance in silent reading comprehension. Oral passage reading fluency alone explained 20.9% of variance in silent reading comprehension. After controlling for pseudoword reading and word reading fluency, oral passage reading fluency still accounted for an additional 12.4% of the remaining reading variance.


On Creativity: Towards an Understanding of Creativity and its Measurements


title: Do Student Samples Provide an Accurate Estimate of the General Public?

abstract: Most psychological studies rely on student samples. Students are usually considered as more homogenous than representative samples both within and across countries. However, little is known about the nature of the differences between student and representative samples. This is an important gap, also because knowledge about the degree of difference between student and representative samples may allow to infer from the former to the latter group. Across 59 countries and 12 personality (Big-5) and attitudinal variables we found that differences between students and general public were partly substantial, incoherent, and contradicted previous findings. Two often used cultural variables, embeddedness and intellectual autonomy, failed to explain the differences between both groups across countries. We further found that students vary as much as the general population both between and within countries. In summary, our results indicate that generalizing from students to the general public can be problematic when personal and attitudinal variables are used, as students vary mostly randomly from the general public. Findings are also discussed in terms of the replication crisis within psychology.


title: Modeling of DFIG-Based WTs for Small-Signal Stability Analysis in DVC Timescale in Power Electronized Power Systems

abstract: This paper presents a dynamic modeling methodology of a DFIG-based wind turbine (WT) for small-signal stability analysis in DC-link voltage control (DVC) timescale in power electronized power systems. The DVC timescale (around 100 ms) is determined by DVC, terminal voltage control, active power control, and phase-locked loop in DFIG WT. Motion equation concept is introduced and extended to describe DFIG WT external characteristics in the concerned timescale. The relation between the active/reactive power imbalances and phase/magnitude dynamics of defined synthetic internal voltage (inner potential) vector are developed. The model in DVC timescale is similar to synchronous generator rotor motion equation that is in electromechanical timescale (around 1 s) and familiar to power engineers. With the developed model, characteristics of equivalent inertia, damping and synchronizing coefficients of DFIG WT in DVC timescale can be understood, and the dynamic interactions among multiple DFIG WTs, as well as between DFIG WT and other grid-connected devices in DVC timescale can be fully interpreted. Comparisons of eigenvalues show that the proposed model can hold the main behaviors of concern. Applications on the stability analyses of DFIG WT interconnected with VSC-HVDC system and two-DFIG WT system are taken as examples to validate the feasibility of the proposed model.


title: Automatic Intra-Register Vectorization for the Intel® Architecture

abstract: Recent extensions to the Intel® Architecture feature the SIMD technique to enhance the performance of computational intensive applications that perform the same operation on different elements in a data set. To date, much of the code that exploits these extensions has been hand-coded. The task of the programmer is substantially simplified, however, if a compiler does this exploitation automatically. The high-performance Intel® C++/Fortran compiler supports automatic translation of serial loops into code that uses the SIMD extensions to the Intel® Architecture. This paper provides a detailed overview of the automatic vectorization methods used by this compiler together with an experimental validation of their effectiveness.


title: TSCA: A Temporal-Spatial Real-Time Charging Scheduling Algorithm for On-Demand Architecture in Wireless Rechargeable Sensor Networks

abstract: The collaborative charging issue in Wireless Rechargeable Sensor Networks (WRSNs) is a popular research problem. With the help of wireless power transfer technology, electrical energy can be transferred from wireless charging vehicles (WCVs) to sensors, providing a new paradigm to prolong network lifetime. Existing techniques on collaborative charging usually take the periodical and deterministic approach, but neglect influences of non-deterministic factors such as topological changes and node failures, making them unsuitable for large-scale WRSNs. In this paper, we develop a t emporal- s patial c harging scheduling a lgorithm, namely TSCA, for the on-demand charging architecture. We aim to minimize the number of dead nodes while maximizing energy efficiency to prolong network lifetime. First, after gathering charging requests, a WCV will compute a feasible movement solution. A basic path planning algorithm is then introduced to adjust the charging order for better efficiency. Furthermore, optimizations are made in a global level. Then, a node deletion algorithm is developed to remove low efficient charging nodes. Lastly, a node insertion algorithm is executed to avoid the death of abandoned nodes. Extensive simulations show that, compared with state-of-the-art charging scheduling algorithms, our scheme can achieve promising performance in charging throughput, charging efficiency, and other performance metrics.


DEVELOPMENT OF A DLLRF USING COMERCIAL UTCA PLATFORM


title: The magnetization transfer characteristics of human breast tissues: an in vitro NMR study.

abstract: A series of freshly excised human breast tissues was analysed using a nuclear magnetic resonance spectrometer and then subjected to routine histopathology examination. Tissues comprised normal parenchymal, adipose, fibrocystic, fibroadenoma and malignant types. An inversion-recovery sequence performed both with and without magnetization transfer allowed T1, T1s, M0 and Ms values to be obtained. From this information, the magnetization transfer rate constant, K, was calculated for each tissue sample. These data show that T1s provided greater discrimination between neoplasic and normal tissues than did T1. However, neither T1s nor K values provided a means of discriminating between benign and malignant disease.


title: AETAS: A System for Semanticizing Temporal Expressions from Unstructured Contents

abstract: AETAS is an online tool for converting text into RDF linked data with resolution of temporal expressions. AETAS follows fully SOA architecture and is accessible via web-service. It implements a novel approach for semantic representation and linked temporal graphs built from natural language sentences. In this paper, we present a demonstration tool, which combines the normalized temporal expressions with linguistic semantic frames and creates a linked RDF graph where time is defined as an individual dimension. The tool is based on SUTime which identifies and normalizes the temporal expressions and on FreeLing, a linguistic processor which extracts the semantics of sentences. The output of AETAS is a set of time-enriched triples that can be stored in a RDF database for later \(\tau \)-SPARQL querying.


title: Learning words from knowledgeable versus ignorant speakers: links between preschoolers' theory of mind and semantic development.

abstract: Two studies addressed whether children consider speakers’ knowledge states when establishing initial wordreferent links. In Study 1, forty-eight 3- and 4-year-olds were taught two novel words by a speaker who expressed either knowledge or ignorance about the words’ referents. Children showed better word learning when the speaker was knowledgeable. In Study 2, forty-eight 3- and 4-year-olds were taught two novel words by a speaker who expressed uncertainty about their referents. Whether the uncertainty truly reflected ignorance, however, differed across conditions. In one condition, the speaker said he made the object himself and thus, he was knowledgeable. In the other condition, the speaker stated that the object was made by a friend and thus, expressed ignorance about it. Four-year-olds learned better in the speaker-made than in the friend-made condition; 3-year-olds, however, showed relatively poor learning in both conditions. These findings suggest that theory-of-mind developments impact word learning.


Rank Aggregation Revisited


title: An Efficient Approach to Encoding Context for Spoken Language Understanding

abstract: In task-oriented dialogue systems, spoken language understanding, or SLU, refers to the task of parsing natural language user utterances into semantic frames. Making use of context from prior dialogue history holds the key to more effective SLU. State of the art approaches to SLU use memory networks to encode context by processing multiple utterances from the dialogue at each turn, resulting in significant trade-offs between accuracy and computational efficiency. On the other hand, downstream components like the dialogue state tracker (DST) already keep track of the dialogue state, which can serve as a summary of the dialogue history. In this work, we propose an efficient approach to encoding context from prior utterances for SLU. More specifically, our architecture includes a separate recurrent neural network (RNN) based encoding module that accumulates dialogue context to guide the frame parsing sub-tasks and can be shared between SLU and DST. In our experiments, we demonstrate the effectiveness of our approach on dialogues from two domains.


title: An Effective Voltage Controller for Quasi-Z-Source Inverter-Based STATCOM With Constant DC-Link Voltage

abstract: A quasi-Z-source inverter (qZSI) could achieve buck/boost conversion as well as dc to ac inversion in a single-stage topology, which reduces the structure cost when compared to a traditional two-stage inverter. Specifically, the buck/boost conversion was accomplished via shoot-through state which took place across all phase legs of the inverter. In this paper, instead of using traditional dual-loop-based proportional integral (PI)-P controller, a type 2 based closed-loop voltage controller with novel dc-link voltage reference algorithm was proposed to fulfill the dc-link voltage tracking control of a single-phase qZSI regardless of any loading conditions, without the need of inner inductor current loop. A dc–ac boost inverter with similar circuit parameters as a qZSI was used to verify the flexibility of the proposed controller. The dynamic and transient performances of the proposed controller were investigated to evaluate its superiority against the aforementioned conventional controller. The integrated proposed controller and qZSI topology was then employed in static synchronous compensator application to perform reactive power compensation at the point of common coupling. The effectiveness of the proposed approach was verified through both simulation and experimental studies.


title: Stress Detection in Computer Users Based on Digital Signal Processing of Noninvasive Physiological Variables

abstract: � Abstract— A stress detection system is developed based on the physiological signals monitored by non-invasive and non-intrusive sensors. The development of this emotion recognition system involved three stages: experiment setup for physiological sensing, signal preprocessing for the extraction of affective features and affective recognition using a learning system. Four signals: Galvanic Skin Response (GSR), Blood Volume Pulse (BVP), Pupil Diameter (PD) and Skin Temperature (ST) are monitored and analyzed to differentiate affective states in a computer user. A Support Vector Machine is used to perform the supervised classification of affective states between "stress" and "relaxed". Results indicate that the physiological signals monitored do, in fact, have a strong correlation with the changes in emotional state of our experimental subjects when stress stimuli are applied to the interaction environment. It was also found that the pupil diameter was the most significant affective state indicator, compared to the other three physiological signals monitored.


Detecting Review Spammer Groups via Bipartite Graph Projection


title: StatSnowball: a statistical approach to extracting entity relationships

abstract: Traditional relation extraction methods require pre-specified relations and relation-specific human-tagged examples. Bootstrapping systems significantly reduce the number of training examples, but they usually apply heuristic-based methods to combine a set of strict hard rules, which limit the ability to generalize and thus generate a low recall. Furthermore, existing bootstrapping methods do not perform open information extraction (Open IE), which can identify various types of relations without requiring pre-specifications. In this paper, we propose a statistical extraction framework called Statistical Snowball (StatSnowball), which is a bootstrapping system and can perform both traditional relation extraction and Open IE. StatSnowball uses the discriminative Markov logic networks (MLNs) and softens hard rules by learning their weights in a maximum likelihood estimate sense. MLN is a general model, and can be configured to perform different levels of relation extraction. In StatSnwoball, pattern selection is performed by solving an l1-norm penalized maximum likelihood estimation, which enjoys well-founded theories and efficient solvers. We extensively evaluate the performance of StatSnowball in different configurations on both a small but fully labeled data set and large-scale Web data. Empirical results show that StatSnowball can achieve a significantly higher recall without sacrificing the high precision during iterations with a small number of seeds, and the joint inference of MLN can improve the performance. Finally, StatSnowball is efficient and we have developed a working entity relation search engine called Renlifang based on it.


title: Anthropometric study of penile length in self-declared Brazilians regarding the color of the skin as white or black: The study of a Myth

abstract: This is an observational transversal cohort study in which we aim to analyze the Brazilian penis length and compare the penis size of the men self-declared as white (SDW) or black (SDB) skin color. Subjects were asked for self-declare according to their skin color, after that they have been invited to participate in a semi-structured interview so as to have their perception evaluated regarding their penis size and their self-esteem as well. Eventually, their penis length was measured with an anthropometric ruler. The men´s mean penis length who declared themselves as black skin color was 16.5 ± 1.7 cm (penis length in real fully-stretched flaccid length) and the men´s mean penis length who declared themselves as white skin color was 15.8 ± 1.6 cm (p < 0.001).The majority of either SDB (94.0%) or SDW (89.4%) are satisfied with their penis size (p = 0.464). We have shown that the man´s mean penis length who identifies himself as black is just a little bit bigger than the one who identifies himself as white. However, there were no significant difference between groups regarding self-assessment of genital body image.


title: Music therapy for patients receiving spine surgery

abstract: AIMS AND OBJECTIVES ::: The aim of this study was to evaluate the effects of music therapy on anxiety, postoperative pain and physiological reactions to emotional and physical distress in patients undergoing spinal surgery. ::: ::: ::: BACKGROUND ::: Surgery-related anxiety and pain are the greatest concern of surgical patients, especially for those undergoing major procedures. ::: ::: ::: DESIGN ::: A quasi-experimental study design was conducted in a medical centre in Taiwan from April-July 2006. ::: ::: ::: METHODS ::: Sixty patients were recruited. The study group listened to selected music from the evening before surgery to the second day after surgery. The control group did not listen to music. Patients' levels of anxiety and pain were measured with visual analogue scales (VAS). Physiological measures, including heart rate, blood pressure and 24-hour urinalysis, were performed. ::: ::: ::: RESULTS ::: The average age of the 60 patients was 62·18 (SD 18·76) years. The mean VAS score for degree of anxiety in the study group was 0·8-2·0, compared with 2·1-5·1 in the control group. The mean VAS score for degree of pain in the study group was 1·7-3·0, compared with 4·4-6·0 in the control group. The differences between the two groups in VAS scores for both anxiety (p = 0·018-0·001) and pain (p = 0·001) were statistically significant. One hour after surgery, the mean blood pressure was significantly lower in the study group than in the control group (p = 0·014), but no significant differences were found between the two groups in urine cortisol (p = 0·145-0·495), norepinephrine (p = 0·228-0·626) or epinephrine values (p = 0·074-0·619). ::: ::: ::: CONCLUSIONS ::: Music therapy has some positive effects on levels of anxiety and pain in patients undergoing spinal surgery. ::: ::: ::: RELEVANCE TO CLINICAL PRACTICE ::: Complementary music therapy can alleviate pain and anxiety in patients before and after spinal surgery.


title: A Survey of Autonomous Human Affect Detection Methods for Social Robots Engaged in Natural HRI

abstract: In Human-Robot Interactions (HRI), robots should be socially intelligent. They should be able to respond appropriately to human affective and social cues in order to effectively engage in bi-directional communications. Social intelligence would allow a robot to relate to, understand, and interact and share information with people in real-world human-centered environments. This survey paper presents an encompassing review of existing automated affect recognition and classification systems for social robots engaged in various HRI settings. Human-affect detection from facial expressions, body language, voice, and physiological signals are investigated, as well as from a combination of the aforementioned modes. The automated systems are described by their corresponding robotic and HRI applications, the sensors they employ, and the feature detection techniques and affect classification strategies utilized. This paper also discusses pertinent future research directions for promoting the development of socially intelligent robots capable of recognizing, classifying and responding to human affective states during real-time HRI.


title: The relationship of the fronto-temporal branches of the facial nerve to the fascias of the temporal region: a literature review applied to practical anatomical dissection

abstract: The understanding of the course of the facial nerve and its relationship to the different connective tissue layers in the temporal area is paramount to preserving this nerve during surgery. But the use of different nomenclatures for anatomical structures such as for the different fascial layers or fat pads in the temporal region as well as the difference in description of the course of the fronto-temporal branches of the facial nerve in relationship to the fascial layers can lead to confusion. Therefore we have reviewed the literature about this topic and tried to apply the information to practical anatomical dissection.


title: Stochastic Optimal Planning of Battery Energy Storage Systems for Isolated Microgrids

abstract: A novel stochastic planning framework is proposed to determine the optimal battery energy storage system (BESS) capacity and the year of installation in an isolated microgrid using a new representation of the BESS energy diagram. The BESS enhances microgrid operation via load leveling and reserve provisions in conjunction with the spinning reserve from dispatchable distributed generation (DG) units. A decomposition-based approach is proposed to solve the problem of stochastic planning of BESS under uncertainty. The optimal decisions minimize the net present value (NPV) of total expected costs over a multiyear horizon taking into consideration the optimal BESS operation considering a novel matrix representing BESS energy capacity degradation. The proposed approach is solved in two stages as mixed-integer linear programming problems to ensure the convergence of the stochastic optimization problem. The optimal ratings of the BESS are determined in the first stage, while the optimal installation year is determined in the second stage. The approach ensures utilizing the allocated budget effectively by performing several iterations. Extensive studies considering four types of BESS technologies for deterministic, Monte Carlo Simulations, and stochastic cases are presented to demonstrate the effectiveness of the proposed approach.


title: ECG biometric authentication based on non-fiducial approach using kernel methods

abstract: Identity recognition faces several challenges especially in extracting an individual's unique features from biometric modalities and pattern classifications. Electrocardiogram (ECG) waveforms, for instance, have unique identity properties for human recognition, and their signals are not periodic. At present, in order to generate a significant ECG feature set, non-fiducial methodologies based on an autocorrelation (AC) in conjunction with linear dimension reduction methods are used. This paper proposes a new non-fiducial framework for ECG biometric verification using kernel methods to reduce both high autocorrelation vectors' dimensionality and recognition system after denoising signals of 52 subjects with Discrete Wavelet Transform (DWT). The effects of different dimensionality reduction techniques for use in feature extraction were investigated to evaluate verification performance rates of a multi-class Support Vector Machine (SVM) with the One-Against-All (OAA) approach. The experimental results demonstrated higher test recognition rates of Gaussian OAA SVMs on random unknown ECG data sets with the use of the Kernel Principal Component Analysis (KPCA) as compared to the use of the Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA). A new design of experimental evaluation setup for identity recognition is concerned.The use of mother wavelet functions is extended for denoising ECG signals.A novel non-fiducial verification framework is modelled by kernel-based methods.The effect of different feature extractor models is examined by recognition rates.Window and subject recognition rates are tested on random unknown data sets.


title: Design of efficient algorithm for secured key exchange over Cloud Computing

abstract: Cloud computing framework is accepted by most of the enterprises. It provides on-demand service, broad network access, elasticity etc. But the usability of these characteristic get obstructed by many security challenges. As the cloud service user uploads their confidential data over the cloud platform, it must be transmitted in a secure way. For this, it should be transmitted through a protected communication channel. A practical solution to this is a Diffie-Hellman key exchange algorithm. It is used to exchange the cryptographical keys securely over the public channel. In this paper, the extended version of the Diffie-Hellman algorithm is suggested which can prevent the system against Mim attack and plain-text attack. This algorithm is oriented towards removing the extended version of Diffie-Hellman algorithm. It uses one model based on arbitrary numbers and logarithms. This is considered to provide security improvement to the communication channel and also countermeasure the attack. Also, a cloud based model is presented for engineering educational domain. Above algorithm is used in this architecture to provide more security to the user data. This architecture attains various security parameters such as confidentiality, data integrity and individualization.