title: Virtual Teams versus Face‐to‐Face Teams: An Exploratory Study of a Web‐based Conference System*

abstract: Many organizations are forming “virtual teams” of geographically distributed knowledge workers to collaborate on a variety of workplace tasks. But how effective are these virtual teams compared to traditional face-to-face groups? Do they create similar teamwork and is information exchanged as effectively? An exploratory study of a World Wide Web-based asynchronous computer conference system known as Meeting Web™ is presented and discussed. It was found that teams using this computer-mediated communication system (CMCS) could not outperform traditional (face-to-face) teams under otherwise comparable circumstances. Further, relational links among team members were found to be a significant contributor to the effectiveness of information exchange. Though virtual and face-to-face teams exhibit similar levels of communication effectiveness, face-to-face team members report higher levels of satisfaction. Therefore, the paper presents steps that can be taken to improve the interaction experience of virtual teams. Finally, guidelines for creating and managing virtual teams are suggested, based on the findings of this research and other authoritative sources.


title: Beyond the Lambertian assumption: A generative model for Apparent BRDF fields of faces using anti-symmetric tensor splines

abstract: Human faces are neither exactly Lambertian nor entirely convex and hence most models in literature which make the Lambertian assumption, fall short when dealing with specularities and cast shadows. In this paper, we present a novel anti-symmetric tensor spline (a spline for tensor-valued functions) based method for the estimation of the Apparent BRDF (ABRDF) field for human faces that seamlessly accounts for specularities and cast shadows. Furthermore, unlike other methods, it does not require any 3D information to build the model and can work with as few as 9 images. In order to validate the accuracy of our anti-symmetric tensor spline model, we present a novel approximation of the ABRDF using a continuous mixture of single-lobed spherical functions. We demonstrate the effectiveness of our anti-symmetric tensor-spline model in comparison to other popular models in the literature, by presenting extensive results for face relighting and face recognition using the Extended Yale B database.


title: Hand Gesture Recognition using Computer Vision

abstract: The use of the gesture system in our daily life as a natural human-human interaction has inspired the researchers to simulate and utilize this gift in human-machine interaction which is appealing and can take place the bore interaction ones that existed such as television, radio, and various home appliances as well as virtual reality will worth and deserve its name. This kind of interaction ensures promising and satisfying outcomes if applied in systematic approach, and supports unadorned human hand when transferring the message to these devices which is easiest, comfort and desired rather than the communication that requires frills to deliver the message to such devices. With the rapid emergence of 3d applications and virtual environments in computer system the need for a new type of interaction device arises. This is because the traditional devices such as mouse, keyboard and joystick become inefficient and cumbersome within this virtual environments.in other words evolution of user interfaces shapes the change in the HumanComputer Interaction (HCI).Intuitive and naturalness characteristics of “Hand Gestures” in the HCI have been the driving force and motivation to develop an interaction device which can replace current unwieldy tools. A survey on the methods of analysing, modelling and recognizing hand gestures in the context of the HCI is provided in this paper.


title: Insensitive Arc in Domination of Fuzzy Graph

abstract: A connected fuzzy graph is arc insensitive if the domination number is unchanged when any single arc is removed. The minimum number of arcs required by such arc insensitive fuzzy graph is determined. Similarly we determine the minimum number of arcs required for the fuzzy graph to remain connected after the removal of any arc, when the dominating set remain fixed. Mathematics Subject Classification: 03E72, 05C69, 05C72


title: Deep Convolutional Network Cascade for Facial Point Detection

abstract: We propose a new approach for estimation of the positions of facial key points with three-level carefully designed convolutional networks. At each level, the outputs of multiple networks are fused for robust and accurate estimation. Thanks to the deep structures of convolutional networks, global high-level features are extracted over the whole face region at the initialization stage, which help to locate high accuracy key points. There are two folds of advantage for this. First, the texture context information over the entire face is utilized to locate each key point. Second, since the networks are trained to predict all the key points simultaneously, the geometric constraints among key points are implicitly encoded. The method therefore can avoid local minimum caused by ambiguity and data corruption in difficult image samples due to occlusions, large pose variations, and extreme lightings. The networks at the following two levels are trained to locally refine initial predictions and their inputs are limited to small regions around the initial predictions. Several network structures critical for accurate and robust facial point detection are investigated. Extensive experiments show that our approach outperforms state-of-the-art methods in both detection accuracy and reliability.


title: Extracting Optimal Performance from Dynamic Time Warping

abstract: Dynamic Time Warping (DTW) is a distance measure that compares two time series after optimally aligning them. DTW is being used for decades in thousands of academic and industrial projects despite the very expensive computational complexity, O(n2). These applications include data mining, image processing, signal processing, robotics and computer graphics among many others. In spite of all this research effort, there are many myths and misunderstanding about DTW in the literature, for example "it is too slow to be useful" or "the warping window size does not matter much." In this tutorial, we correct these misunderstandings and we summarize the research efforts in optimizing both the efficiency and effectiveness of both the basic DTW algorithm, and of the higher-level algorithms that exploit DTW such as similarity search, clustering and classification. We will discuss variants of DTW such as constrained DTW, multidimensional DTW and asynchronous DTW, and optimization techniques such as lower bounding, early abandoning, run-length encoding, bounded approximation and hardware optimization. We will discuss a multitude of application areas including physiological monitoring, social media mining, activity recognition and animal sound processing. The optimization techniques are generalizable to other domains on various data types and problems.


title: Modeling the auditory scene: predictive regularity representations and perceptual objects

abstract: Predictive processing of information is essential for goal-directed behavior. We offer an account of auditory perception suggesting that representations of predictable patterns, or 'regularities', extracted from the incoming sounds serve as auditory perceptual objects. The auditory system continuously searches for regularities within the acoustic signal. Primitive regularities may be encoded by neurons adapting their response to specific sounds. Such neurons have been observed in many parts of the auditory system. Representations of the detected regularities produce predictions of upcoming sounds as well as alternative solutions for parsing the composite input into coherent sequences potentially emitted by putative sound sources. Accuracy of the predictions can be utilized for selecting the most likely interpretation of the auditory input. Thus in our view, perception generates hypotheses about the causal structure of the world.


title: Amalgam or composite fillings — which material lasts longer?

abstract: DATA SOURCES ::: Cochrane Oral Health Group's Trials Register, the Cochrane Central Register of Controlled Trials (CENTRAL), Medline, Embase and LILACS. Hand searched relevant journals and attempted contact with authors of unpublished studies and manufacturers. ::: ::: ::: STUDY SELECTION ::: Randomised controlled trials comparing dental resin composites with dental amalgams in permanent posterior teeth with a minimum follow up of three years were eligible. ::: ::: ::: DATA EXTRACTION AND SYNTHESIS ::: Data were extracted independently by a minimum of two review authors using specially designed data extraction forms. Risk of bias was assessed using the Cochrane risk of bias tool. Relative risks and 95% CIs were extracted for dichotomous data and mean difference (MD) or standardised mean difference (SMD) for continuous data. Relative risk was combined in a meta-analysis. ::: ::: ::: RESULTS ::: Seven studies were included. Two were parallel and five split-mouth design. Data from 871 participants were available from the two parallel studies but several of the split-mouth studies did not report the number of participants. All studies were considered at high risk of bias. Only data from the two parallel studies were included in the primary meta-analysis. Failure rates in these studies were recorded for between five and seven years. The risk ratio of failure for composite versus amalgam was 1.89 with 95% CI of 1.52-2.35. The increased failure was primarily due to caries rather than fracture. ::: ::: ::: CONCLUSIONS ::: There is low-quality evidence to suggest that resin composites lead to higher failure rates and risk of secondary caries than amalgam restorations.


Rehabilitation Institute of Chicago Care and Comfort Caregiver Questionnaire ( RIC CareQ ) Improvements in Children With Cerebral Palsy Following Intrathecal Baclofen : Use of the


title: Insights into Human Behavior from Lesions to the Prefrontal Cortex

abstract: The prefrontal cortex (PFC), a cortical region that was once thought to be functionally insignificant, is now known to play an essential role in the organization and control of goal-directed thought and behavior. Neuroimaging, neurophysiological, and modeling techniques have led to tremendous advances in our understanding of PFC functions over the last few decades. It should be noted, however, that neurological, neuropathological, and neuropsychological studies have contributed some of the most essential, historical, and often prescient conclusions regarding the functions of this region. Importantly, examination of patients with brain damage allows one to draw conclusions about whether a brain area is necessary for a particular function. Here, we provide a broad overview of PFC functions based on behavioral and neural changes resulting from damage to PFC in both human patients and nonhuman primates.


T.B.TESEMA et al.: DATA MINING USING ADAPTIVE REGRESSION TREES RULE MINING AND CLASSIFICATION OF ROAD TRAFFIC ACCIDENTS USING ADAPTIVE REGRESSION TREES


title: Wearables and the Internet of Things for Health: Wearable, Interconnected Devices Promise More Efficient and Comprehensive Health Care

abstract: In our recent book Health-e Everything: Wearables and the Internet of Things for Health, we capture in an interactive e-book format some global thought-leader perspectives as well as early examples of case studies and novel innovations that are driving this emerging technology domain. Here, we provide a brief snapshot of key findings related to these novel technologies and use cases, which are driving both health care practitioners and health consumers (patients). As technologists, having a firm understanding of customer-driven innovation and the actual user benefits of interconnective devices for health will help us engineer better solutions that are more targeted to the triple aim of better, faster, and cheaper health solutions.


title: A CPW-Fed Compact UWB Microstrip Antenna

abstract: A novel coplanar waveguide (CPW)-fed compact ultrawideband (UWB) microstrip antenna is proposed for ultrawideband applications. The proposed antenna posseses a method to minimize the monopole antenna by loading of inverted L-strip over the conventional monopole patch antenna to lower the height of the antenna. The ground was vertically extended toward two sides of the single radiator. Therefore, the large space around the radiator that is usually wasted can be effectively saved. The antenna is practically fabricated and simulated. Measured results show a good agreement with simulated results. The prototype with overall size of $25 \times 25 \times 1.6\ {\hbox{mm}}^3$ achieves good impedance matching, constant gain, stable radiation patterns, and constant group delay over an operating bandwidth of 2.6-13.04 GHz (10.44 GHz).


title: Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection

abstract: To better detect pedestrians of various scales, deep multi-scale methods usually detect pedestrians of different scales by different in-network layers. However, the semantic levels of features from different layers are usually inconsistent. In this paper, we propose a multi-branch and high-level semantic network by gradually splitting a base network into multiple different branches. As a result, the different branches have the same depth and the output features of different branches have similarly high-level semantics. Due to the difference of receptive fields, the different branches are suitable to detect pedestrians of different scales. Meanwhile, the multi-branch network does not introduce additional parameters by sharing convolutional weights of different branches. To further improve detection performance, skip-layer connections among different branches are used to add context to the branch of relatively small receptive filed, and dilated convolution is incorporated into part branches to enlarge the resolutions of output feature maps. When they are embedded into Faster RCNN architecture, the weighted scores of proposal generation network and proposal classification network are further proposed. Experiments on KITTI dataset, Caltech pedestrian dataset, and Citypersons dataset demonstrate the effectiveness of proposed method. On these pedestrian datasets, the proposed method achieves state-of-the-art detection performance. Moreover, experiments on COCO benchmark show the proposed method is also suitable for general object detection.


title: A Geometric Approach to Active Learning for Convolutional Neural Networks

abstract: Convolutional neural networks (CNNs) have been successfully applied to many recognition and learning tasks using a universal recipe; training a deep model on a very large dataset of supervised examples. However, this approach is rather restrictive in practice since collecting a large set of labeled images is very expensive. One way to ease this problem is coming up with smart ways for choosing images to be labelled from a very large collection (i.e. active learning) In this paper, we first show that uncertainty based active learning heuristics are not effective for CNNs even in an oracle setting. Our counterintuitive empirical results make us question these heuristics and inspire us to come up with a simple but effective method, choosing a set of images to label such that they cover the set of unlabeled images as closely as possible. We further present a theoretical justification for this geometric heuristic by giving a bound over the generalization error of CNNs. Our experiments show that the proposed method significantly outperforms existing approaches in image classification experiments by a large margin.


title: 2011 terminology of the vulva of the International Federation for Cervical Pathology and Colposcopy.

abstract: h Abstract Objective. This study aimed to present the clinical and colposcopic terminology of the vulva (including the anus) of the International Federation of Cervical Pathology and Colposcopy. Materials and Methods. The terminology has been developed by the International Federation of Cervical Pathology and Colposcopy Nomenclature Committee during 2009Y2011. Results. The terminology is part of a comprehensive terminology of the lower genital tract, allowing for standardization of nomenclature by colposcopists, clinicians, and researchers taking care of women with lesions in these areas. The terminology includes basic definitions and normal findings that are important for the clinician lacking experience with management of vulvar disease. This terminology introduces definitions for abnormal findings recently accepted by the International Society for the Study of Vulvovaginal Disease and includes patterns to identify malignancy. Conclusions. The terminology differs from past terminologies in that it includes colposcopic patterns and anal colposcopy. Nevertheless, the role of the colposcope in the management of vulvar disease is limited. h


title: PEQUAL - E-commerce websites quality evaluation methodology

abstract: Website quality evaluation is an important research task. Evolution and a growing set of available methods are observed. The article presents the authors' evaluation methodology of the quality of websites named PEQUAL. The formal foundation of the proposed methodology is the broadening of the classical EQUAL method with aspects of preference modelling and evaluation aggregation used in Multi-Criteria Decision Analysis (MCDA). Its empirical verification has been carried out for top e-commerce websites. The conducted research has revealed significant practical possibilities of analysis and interpretation of obtained final rankings.


title: Classification of wine quality with imbalanced data

abstract: We propose a data analysis approach to classify wine into different quality categories. A data set of white wines of 4898 observations obtained from the Minho region in Portugal was used in our analysis. As the occurrence of events in the data set was imbalanced with about 93% of the observations are from one category, we applied the Synthetic Minority Over-Sampling Technique (SMOTE) to over sample the minority class. The balanced data was used to model a classifier that categorizes a wine into three categories as high quality, normal quality, and poor quality. Three different classification techniques were used: decision tree, adaptive boosting (AdaBoost), and random forest. Our experiments show that the random forest technique seems to produce the desired results with the least percentage of error.


title: Mutations in MYH7 cause Multi-minicore Disease (MmD) with variable cardiac involvement

abstract: Abstract Central Core Disease (CCD) and Multi-minicore Disease (MmD) (the “core myopathies”) have been mainly associated with mutations in the skeletal muscle ryanodine receptor ( RYR1 ) and the selenoprotein N ( SEPN1 ) gene. A proportion of cases remain unresolved. Mutations in MYH7 encoding the beta myosin heavy chain protein have been implicated in cardiac and, less frequently, skeletal muscle disorders. Here we report four patients from two families with a histopathological diagnosis of MmD, presenting in childhood with slowly progressive muscle weakness, more proximal in Family 1 and more distal in Family 2, and variable degrees of cardiorespiratory impairment evolving later in life. There was also a strong family history of sudden death in the first family. Muscle biopsies obtained in early childhood showed multiple minicores as the most prominent feature. Sequencing of the MYH7 gene revealed heterozygous missense mutations, c.4399C>G; p.Leu1467Val (exon 32) in Family 1 and c.4763G>C; p.Arg1588Pro (exon 34) in Family 2. These findings suggest MYH7 mutations as another cause of a myopathy with multiple cores, in particular if associated with dominant inheritance and cardiac involvement. However, clinical features previously associated with this genetic background, namely a more distal distribution of weakness and an associated cardiomyopathy, may only evolve over time.


title: Image Retrieval with Fisher Vectors of Binary Features

abstract: Recently, the Fisher vector representation of local features has attracted much attention because of its effectiveness in both image classification and image retrieval. Another trend in the area of image retrieval is the use of binary feature such as ORB, FREAK, and BRISK. Considering the significant performance improvement in terms of accuracy in both image classification and retrieval by the Fisher vector of continuous feature descriptors, if the Fisher vector were also to be applied to binary features, we would receive the same benefits in binary feature based image retrieval and classification. In this paper, we derive the closed-form approximation of the Fisher vector of binary features which are modeled by the Bernoulli mixture model. In experiments, it is shown that the Fisher vector representation improves the accuracy of image retrieval by 25% compared with a bag of binary words approach.


Maximizing the Spread of Influence through a Social Network


title: Multi-task learning in deep neural networks for improved phoneme recognition

abstract: In this paper we demonstrate how to improve the performance of deep neural network (DNN) acoustic models using multi-task learning. In multi-task learning, the network is trained to perform both the primary classification task and one or more secondary tasks using a shared representation. The additional model parameters associated with the secondary tasks represent a very small increase in the number of trained parameters, and can be discarded at runtime. In this paper, we explore three natural choices for the secondary task: the phone label, the phone context, and the state context. We demonstrate that, even on a strong baseline, multi-task learning can provide a significant decrease in error rate. Using phone context, the phonetic error rate (PER) on TIMIT is reduced from 21.63% to 20.25% on the core test set, and surpassing the best performance in the literature for a DNN that uses a standard feed-forward network architecture.


title: Blur detection for digital images using wavelet transform

abstract: With the prevalence of digital cameras, the number of digital images increases quickly, which raises the demand for image quality assessment in terms of blur. Based on the edge type and sharpness analysis, using the Harr wavelet transform, a new blur detection scheme is proposed in this paper, which can determine whether an image is blurred or not and to what extent an image is blurred. Experimental results demonstrate the effectiveness of the proposed scheme.


title: Loneliness and Internet use

abstract: Abstract The Internet is becoming increasingly influential, but some observers have noted that heavy Internet users seem alienated from normal social contacts and may even cut these off as the Internet becomes the predominate social factor in their lives. Kraut, Patterson, Lundmark, Kiesler, Mukopadhyay, and Scherlis [American Psychologist 53 (1998) 65] carried out a longitudinal study from which they concluded that Internet use leads to loneliness among its users. However, their study did not take into account that the population of Internet users is not uniform and comprises many different personality types. People use the Internet in a variety of ways in keeping with their own personal preference. Therefore, the results of this interaction between personality and Internet use are likely to vary among different individuals and similarly the impact on user well-being will not be uniform. One of the personality characteristics that has been found to influence Internet use is that of extroversion and neuroticism [Hamburger & Ben-Artzi, Computers in Human Behavior 16 (2000) 441]. For this study, 89 participants completed questionnaires pertaining to their own Internet use and feelings of loneliness and extroversion and neuroticism. The results were compared to two models (a) the Kraut et al. (1998) model which argues that Internet use leads to loneliness (b) an alternative model which argues that it is those people who are already lonely who spend time on the Internet. A satisfactory goodness of fit was found for the alternative model. Building on these results, several different directions are suggested for continuing research in this field.


title: Trace ratio criterion for feature selection

abstract: Fisher score and Laplacian score are two popular feature selection algorithms, both of which belong to the general graph-based feature selection framework. In this framework, a feature subset is selected based on the corresponding score (subset-level score), which is calculated in a trace ratio form. Since the number of all possible feature subsets is very huge, it is often prohibitively expensive in computational cost to search in a brute force manner for the feature subset with the maximum subset-level score. Instead of calculating the scores of all the feature subsets, traditional methods calculate the score for each feature, and then select the leading features based on the rank of these feature-level scores. However, selecting the feature subset based on the feature-level score cannot guarantee the optimum of the subset-level score. In this paper, we directly optimize the subset-level score, and propose a novel algorithm to efficiently find the global optimal feature subset such that the subset-level score is maximized. Extensive experiments demonstrate the effectiveness of our proposed algorithm in comparison with the traditional methods for feature selection.


title: Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables

abstract: Summary Background Diabetes is presently classified into two main forms, type 1 and type 2 diabetes, but type 2 diabetes in particular is highly heterogeneous. A refined classification could provide a powerful tool to individualise treatment regimens and identify individuals with increased risk of complications at diagnosis. Methods We did data-driven cluster analysis (k-means and hierarchical clustering) in patients with newly diagnosed diabetes (n=8980) from the Swedish All New Diabetics in Scania cohort. Clusters were based on six variables (glutamate decarboxylase antibodies, age at diagnosis, BMI, HbA 1c , and homoeostatic model assessment 2 estimates of β-cell function and insulin resistance), and were related to prospective data from patient records on development of complications and prescription of medication. Replication was done in three independent cohorts: the Scania Diabetes Registry (n=1466), All New Diabetics in Uppsala (n=844), and Diabetes Registry Vaasa (n=3485). Cox regression and logistic regression were used to compare time to medication, time to reaching the treatment goal, and risk of diabetic complications and genetic associations. Findings We identified five replicable clusters of patients with diabetes, which had significantly different patient characteristics and risk of diabetic complications. In particular, individuals in cluster 3 (most resistant to insulin) had significantly higher risk of diabetic kidney disease than individuals in clusters 4 and 5, but had been prescribed similar diabetes treatment. Cluster 2 (insulin deficient) had the highest risk of retinopathy. In support of the clustering, genetic associations in the clusters differed from those seen in traditional type 2 diabetes. Interpretation We stratified patients into five subgroups with differing disease progression and risk of diabetic complications. This new substratification might eventually help to tailor and target early treatment to patients who would benefit most, thereby representing a first step towards precision medicine in diabetes. Funding Swedish Research Council, European Research Council, Vinnova, Academy of Finland, Novo Nordisk Foundation, Scania University Hospital, Sigrid Juselius Foundation, Innovative Medicines Initiative 2 Joint Undertaking, Vasa Hospital district, Jakobstadsnejden Heart Foundation, Folkhalsan Research Foundation, Ollqvist Foundation, and Swedish Foundation for Strategic Research.


title: Pervasive Computing: Vision and Challenges

abstract: This article discusses the challenges in computer systems research posed by the emerging field of pervasive computing. It first examines the relationship of this new field to its predecessors: distributed systems and mobile computing. It then identifies four new research thrusts: effective use of smart spaces, invisibility, localized scalability, and masking uneven conditioning. Next, it sketches a couple of hypothetical pervasive computing scenarios, and uses them to identify key capabilities missing from today's systems. The article closes with a discussion of the research necessary to develop these capabilities.


title: Robust, Deep and Inductive Anomaly Detection

abstract: PCA is a classical statistical technique whose simplicity and maturity has seen it find widespread use as an anomaly detection technique. However, it is limited in this regard by being sensitive to gross perturbations of the input, and by seeking a linear subspace that captures normal behaviour. The first issue has been dealt with by robust PCA, a variant of PCA that explicitly allows for some data points to be arbitrarily corrupted, however, this does not resolve the second issue, and indeed introduces the new issue that one can no longer inductively find anomalies on a test set. This paper addresses both issues in a single model, the robust autoencoder. This method learns a nonlinear subspace that captures the majority of data points, while allowing for some data to have arbitrary corruption. The model is simple to train and leverages recent advances in the optimisation of deep neural networks. Experiments on a range of real-world datasets highlight the model's effectiveness.


title: Collective entity resolution in relational data

abstract: Many databases contain uncertain and imprecise references to real-world entities. The absence of identifiers for the underlying entities often results in a database which contains multiple references to the same entity. This can lead not only to data redundancy, but also inaccuracies in query processing and knowledge extraction. These problems can be alleviated through the use of entity resolution. Entity resolution involves discovering the underlying entities and mapping each database reference to these entities. Traditionally, entities are resolved using pairwise similarity over the attributes of references. However, there is often additional relational information in the data. Specifically, references to different entities may cooccur. In these cases, collective entity resolution, in which entities for cooccurring references are determined jointly rather than independently, can improve entity resolution accuracy. We propose a novel relational clustering algorithm that uses both attribute and relational information for determining the underlying domain entities, and we give an efficient implementation. We investigate the impact that different relational similarity measures have on entity resolution quality. We evaluate our collective entity resolution algorithm on multiple real-world databases. We show that it improves entity resolution performance over both attribute-based baselines and over algorithms that consider relational information but do not resolve entities collectively. In addition, we perform detailed experiments on synthetically generated data to identify data characteristics that favor collective relational resolution over purely attribute-based algorithms.


title: Evaporative cooling by water spray systems: CFD simulation, experimental validation and sensitivity analysis

abstract: Evaporative cooling by water spray is increasingly used as an efficient and environmentally-friendly approach to enhance thermal comfort in built environments. The complex two-phase flow in a water spray system is influenced by many factors such as continuous phase velocity, temperature and relative humidity patterns, droplet characteristics and continuous phase-droplet and droplet-droplet interactions. Computational Fluid Dynamics (CFD) can be a valuable tool for assessing the potential and performance of evaporative cooling by water spray systems in outdoor and indoor urban environments. This paper presents a systematic evaluation of the Lagrangian-Eulerian approach for evaporative cooling provided by the use of a water spray system with a hollow-cone nozzle configuration. The evaluation is based on grid-sensitivity analysis and validated using wind-tunnel measurements. This paper also presents a sensitivity analysis focused on the impact of the turbulence model for the continuous phase, the drag coefficient model, the number of particle streams for the discrete phase and the nozzle spray angle. The results show that CFD simulation of evaporation by the LagrangianEulerian (3D steady RANS) approach, in spite of its limitations, can accurately predict the evaporation process, with local deviations from the wind-tunnel measurements within 10% for dry bulb temperature, 5% for wet bulb temperature and 7% for the specific enthalpy. The average deviations for all three variables are less than 3% in absolute values. The results of this paper are intended to support future CFD studies of evaporative cooling by water spray systems in outdoor and indoor urban environments.


title: To context-aware learner modeling based on ontology

abstract: One of the main motivations for better learner modeling is to make distance learning system intelligent so that they can present adaptive learning content to each learner according to his needs. This aspect of intelligence is mainly ensured by the exploitation of the learner model. However, the multiplicity of information about the learner makes their modeling a very complex activity and constitutes an open line of research, which has given rise to several proposals from different points of view. In this trend, this paper presents a new context-aware learner modeling approach enriched from existing models and based on the exploitation of ontology.


title: Code-reuse attacks: new frontiers and defenses

abstract: Code-reuse attacks are software exploits in which an attacker directs control flow through existing code with a malicious result. For example, return-oriented programming is an effective code-reuse attack in which short code sequences ending in a ret instruction are found within existing binaries and executed in arbitrary order by taking control of the stack. This allows for Turing-complete behavior in the target program without the need for injecting attack code, thus significantly negating current code injection defense efforts (e.g., W⊕X). On the other hand, its inherent characteristics, such as the reliance on the stack and the consecutive execution of return-oriented gadgets, have prompted a variety of defenses to detect or prevent it from happening. ::: This document introduces two novel code-reuse attacks. The first, jump-oriented programming, eliminates the reliance on the stack and ret instructions seen in return-oriented programming without sacrificing expressive power. This attack still builds and chains normal functional gadgets, each performing certain primitive operations, except these gadgets end in an indirect branch rather than ret. Without the convenience of using ret to unify them, the attack relies on a dispatcher gadget to dispatch and execute the functional gadgets. We have successfully identified the availability of these jump-oriented gadgets in the GNU libc library and demonstrated the technique on both the x86 and MIPS architectures. Our experience with an example shellcode attack demonstrates the practicality and effectiveness of this technique. ::: The second attack presented, Turing-complete return-into-libc , demonstrates that it is possible to attain arbitrary computation even when only chaining entire functions as opposed to short gadgets. This has negative implications for certain defenses, and more importantly corrects the record on the capabilities of the existing return-into-libc technique. ::: To mitigate the threats presented by the above exploits, this document proposes a novel defense technique called control flow locking, which ensures that the control flow graph of an application is deviated from at most once, and that this deviation cannot be used to craft a malicious system call. This defense thwarts the existing code-reuse attacks, and the implementation presented shows performance overhead competitive with existing techniques, achieving significant gains in several benchmarks. Control flow locking represents a general solution to the problem of code-reuse attacks with a performance penalty small enough to justify deployment in real-world situations.


title: A Qualitative Analysis of Online Gaming Addicts in Treatment

abstract: Online gaming addiction is a relatively under-researched area and there have been few studies examining online gamers in treatment. This paper reports the findings from a qualitative interview study of nine players undergoing treatment for their addictive playing of Massively Multiplayer Online Role Playing Games (MMORPGs). A face-to-face interview study with nine online gaming addicts was carried out using Grounded Theory. The six most reported phenomena by the participants were: (i) entertainment search, (ii) virtual friendship, (iii) escapism and/or dissociation, (iv) game context, (v) control versus no control, and (vi) conflict. The findings suggest that players’ initial gaming motivation is because of three factors: (i) entertainment, (ii) escapism, and/or (iii) virtual friendship. MMORPG addiction appears once the playing time significantly increases, coupled with a loss of control and a narrow behavior focus. These factors lead to problems and result in psychological dependence and serious life conflicts. The consequences of MMORPG addiction are similar to the consequences of more established substance addictions including salience, mood modification, loss of control, craving, and serious adverse effects. Additionally, in some cases, tolerance and relapse may also be present.


title: Digital disease detection: Application of machine learning in community health informatics

abstract: Health informatics is a new research area which is interdisciplinary amongst information science, computer science and healthcare. The concept of health informatics is to develop a new way to manipulate healthcare data from various resources and devices by optimizing the method of data acquisition, data storage, data processing, and data visualization. Community health informatics can be described as the systematic application of information and computer science to obtain valuable data for solving health problems and providing it to health policy makers. The challenge of community health informatics is to maximize the efficiency and efficacy of big data analysis. This discussion paper aims to present the various applications of machine learning and software engineering approaches that implemented in digital disease detection.


Monte Carlo Methods and Importance Sampling


title: Modelling of a special class of spherical parallel manipulators with Euler parameters

abstract: A method of workspace modelling for spherical parallel manipulators (SPMs) of symmetrical architecture is developed by virtue of Euler parameters in the paper. The adoption of Euler parameters in the expression of spatial rotations of SPMs helps not only to eliminate the possible singularity in the rotation matrix, but also to formulate all equations in polynomials, which are more easily manipulated. Moreover, a homogeneous workspace can be obtained with Euler parameters for the SPMs, which facilitates the evaluation of dexterity. In this work, the problem of workspace modelling and analysis is formulated in terms of Euler parameters. An equation dealing with boundary surfaces is derived and branches of boundary surface are identified. Evaluation of dexterity is explored to quantitatively describe the capability of a manipulator to attain orientations. The singularity identification is also addressed. Examples are included to demonstrate the application of the proposed method.


title: A new optimised De Bruijn coding strategy for structured light patterns

abstract: Coded structured light is an optical technique based on active stereovision that obtains the shape of objects. One shot techniques are based on projecting a unique light pattern with an LCD projector so that grabbing an image with a camera, a large number of correspondences can be obtained. Then, a 3D reconstruction of the illuminated object can be recovered by means of triangulation. The most used strategy to encode one-shot patterns is based on De Bruijn sequences. In This work a new way to design patterns using this type of sequences is presented. The new coding strategy minimises the number of required colours and maximises both the resolution and the accuracy.


title: Integrated Lane and Vehicle Detection, Localization, and Tracking: A Synergistic Approach

abstract: In this paper, we introduce a synergistic approach to integrated lane and vehicle tracking for driver assistance. The approach presented in this paper results in a final system that improves on the performance of both lane tracking and vehicle tracking modules. Further, the presented approach introduces a novel approach to localizing and tracking other vehicles on the road with respect to lane position, which provides information on higher contextual relevance that neither the lane tracker nor vehicle tracker can provide by itself. Improvements in lane tracking and vehicle tracking have been extensively quantified. Integrated system performance has been validated on real-world highway data. Without specific hardware and software optimizations, the fully implemented system runs at near-real-time speeds of 11 frames per second.


title: Designing and implementation of autonomous hexarotor as unmanned aerial vehicle

abstract: In order to increasing the stability and performances of the hexarotor, the autonomous control method needs to be implemented, which based on the parameters from the sensors; like accelerometer, gyroscope, compass, and global positioning system (GPS) sensor. Based on that, we designed a flight controller that combined these sensors using our proposed control algorithm which based on proportional integrator differentiator (PID) and have been implemented on the experimental tests, which shown good results even when the hexarotor is flying during the strong wind condition. And also, we designed the hexarotor frame structure in order to reduce the take-off weight and accommodate the experimental tests.


Unidentified educational costs in a university teaching hospital: an initial study.


title: Automatic feature selection in neuroevolution

abstract: Feature selection is the process of finding the set of inputs to a machine learning algorithm that will yield the best performance. Developing a way to solve this problem automatically would make current machine learning methods much more useful. Previous efforts to automate feature selection rely on expensive meta-learning or are applicable only when labeled training data is available. This paper presents a novel method called FS-NEAT which extends the NEAT neuroevolution method to automatically determine an appropriate set of inputs for the networks it evolves. By learning the network's inputs, topology, and weights simultaneously, FS-NEAT addresses the feature selection problem without relying on meta-learning or labeled data. Initial experiments in an autonomous car racing simulation demonstrate that FS-NEAT can learn better and faster than regular NEAT. In addition, the networks it evolves are smaller and require fewer inputs. Furthermore, FS-NEAT's performance remains robust even as the feature selection task it faces is made increasingly difficult.


title: INDUSTRIALIZATION AND URBANIZATION AND ITS PATHS' SELECTION OF LIAONING TRADITIONAL INDUSTRIAL BASE

abstract: Liaoning's heavy industry plays a leading role in its urban development and urbanization. Then, following change of the economic system and reformation of all state-owned enterprises, Liaoning's industrialization rate, especially the proportion of the heavy industry in the industrial economy as a whole, has appeared a certain degree descent since the late 1970s. On the contrary, its urbanization rate has begun continual rising after having experienced a process of descent from 1960s to 1970s. The changing directions of the urbanization rate and the industrialization rate are from consistence to reverse. To study this phenomenon, we make a thorough inquiry on the relations between the urbanization and the industrialization or the non-agriculturalization on the base of looking back and pondering the ways of its urbanization and industrialization, and put forward some new trains of thought of urbanization of Liaoning traditional industrial base at last.


title: Efficient 3D Pointing Selection in Cluttered Virtual Environments

abstract: In this article, we study the impact of such eye-hand visibility mismatch on selection tasks performed with hand-rooted pointing techniques. We propose a new mapping for ray control, called Ray Casting from the Eye (RCE), which attempts to overcome this mismatch's negative effects. In essence, RCE combines the benefits of image-plane techniques (the absence of visibility mismatch and continuity of the ray movement in screen space) with the benefits of ray control through hand rotation (requiring less physical hand movement). This article builds on a previous study on the impact of eye-to-hand separation on 3D pointing selection. Here, we provide empirical evidence that RCE clearly outperforms classic ray casting (RC) selection, both in sparse and cluttered scenes.


title: MPPT of PV Systems Under Partial Shaded Conditions Through a Colony of Flashing Fireflies

abstract: This paper reports the development of a maximum power-point tracking (MPPT) method for photovoltaic (PV) systems under partially shaded conditions using firefly algorithm. The major advantages of the proposed method are simple computational steps, faster convergence, and its implementation on a low-cost microcontroller. The proposed scheme is studied for two different configurations of PV arrays under partial shaded conditions and its tracking performance is compared with traditional perturb and observe (P&O) method and particle swarm optimization (PSO) method under identical conditions. The improved performance of the algorithm in terms of tracking efficiency and tracking speed is validated through simulation and experimental studies.


title: Characterization of Silicided Polysilicon Fuse Implemented in 65nm Logic CMOS Technology

abstract: NiSi electrically programmable fuses (eFUSE) were fabricated and investigated using 65 nm logic CMOS technology. The optimization of fuse program was achieved by analyzing electrical and physical responses of fuse bits for various conditions. Controlled electromigration of Ni during fuse program was identified as a key factor in achieving reliably high post-program fuse resistance.


A literature Review on Prediction of Chronic Kidney Disease Using Data Mining Techniques


title: Alcohol Use, Misuse, and Abuse Among Nursing Students: A Photovoice Study.

abstract: Rates and frequencies of alcohol consumption remain unknown among nursing students, and risk and protective factors associated with alcohol misuse are poorly understood. Nursing curricula often lack content on substance use disorders among nurses, which is reported to begin prior to or during college.The aims of the study were to examine nursing students' perceptions of the risk and protective factors associated with alcohol behaviors among themselves and their peers and to identify substance use policies and their influence.We used the Photovoice method, which employs participatory action research. Participants were screened for alcohol misuse via AUDIT-C, and thoughts and perceptions were obtained regarding their alcohol consumption behaviors through self-expression and group advocacy. Data were collected from four focus groups that included nine undergraduate nursing students.Data reflected the following key issues: stress, environmental influences, societal acceptance, and availability of alcohol. Participants identified that the following problems place them at risk for alcohol misuse: lack of addiction/alcohol education; nursing program expectations increase stress/anxiety; unhealthy habits, social isolation, and individual influences; peer influence/the college experience; and ineffective and unenforced campus policies. Protective factors included university policies; life experiences; and nursing program policies, responsibilities, peer influences, and perceived reputation.This action research informed a dialogue with colleagues regarding nursing students' stressors and resulting professional ramifications. Recommendations for future work include investigation of expressed social isolation from university peers and its effects on their alcohol consumption behaviors and increase alcohol education with emphasis on adaptive coping strategies in a stressful professional role in Bachelor of Science in Nursing curricula.


title: A compact printed monopole antenna with symmetrical i and rectangular shaped slots for bluetooth/WLAN/WIMAX applications

abstract: A compact dual band microstrip fed printed monopole antenna is designed and presented in this paper for Bluetooth/WLAN/WiMAX applications. Two I with rectangular shape slot have been etched on a Roger RT/duroid substrate to form desired monopole antenna. The radiator of the antenna is very compact with an area of only 9.1 × 10.2 mm2. The size of the I & rectangular slots of dual radiating elements were adjusted so as to achieve two different resonant modes for dual band operation. The simulation result shows that the proposed antenna has 10-dB impedance bandwidth of 219.6 MHz and 3.20 GHz which cover the Bluetooth, WLAN 2.4/5.8 GHz and the WiMAX 2.5/3.5 GHz bands. The overall proposed antenna size is 15 × 17 × 0.762 mm 3 with the obtained VSWR less than 1.5.


Ethical perspectives on e-commerce: an empirical investigation


title: High-performance and energy-efficient mobile web browsing on big/little systems

abstract: Internet web browsing has reached a critical tipping point. Increasingly, users rely more on mobile web browsers to access the Internet than desktop browsers. Meanwhile, webpages over the past decade have grown in complexity by more than tenfold. The fast penetration of mobile browsing and everricher webpages implies a growing need for high-performance mobile devices in the future to ensure continued end-user browsing experience. Failing to deliver webpages meeting hard cut-off constraints could directly translate to webpage abandonment or, for e-commerce websites, great revenue loss. However, mobile devices' limited battery capacity limits the degree of performance that mobile web browsing can achieve. In this paper, we demonstrate the benefits of heterogeneous systems with big/little cores each with different frequencies to achieve the ideal trade-off between high performance and energy efficiency. Through detailed characterizations of different webpage primitives based on the hottest 5,000 webpages, we build statistical inference models that estimate webpage load time and energy consumption. We show that leveraging such predictive models lets us identify and schedule webpages using the ideal core and frequency configuration that minimizes energy consumption while still meeting stringent cut-off constraints. Real hardware and software evaluations show that our scheduling scheme achieves 83.0% energy savings, while only violating the cut-off latency for 4.1% more webpages as compared with a performance-oriented hardware strategy. Against a more intelligent, OS-driven, dynamic voltage and frequency scaling scheme, it achieves 8.6% energy savings and 4.0% performance improvement simultaneously.


title: Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks

abstract: We propose a parallel-data-free voice-conversion (VC) method that can learn a mapping from source to target speech without relying on parallel data. The proposed method is general purpose, high quality, and parallel-data free and works without any extra data, modules, or alignment procedure. It also avoids over-smoothing, which occurs in many conventional statistical model-based VC methods. Our method, called CycleGAN-VC, uses a cycle-consistent adversarial network (CycleGAN) with gated convolutional neural networks (CNNs) and an identity-mapping loss. A CycleGAN learns forward and inverse mappings simultaneously using adversarial and cycle-consistency losses. This makes it possible to find an optimal pseudo pair from unpaired data. Furthermore, the adversarial loss contributes to reducing over-smoothing of the converted feature sequence. We configure a CycleGAN with gated CNNs and train it with an identity-mapping loss. This allows the mapping function to capture sequential and hierarchical structures while preserving linguistic information. We evaluated our method on a parallel-data-free VC task. An objective evaluation showed that the converted feature sequence was near natural in terms of global variance and modulation spectra. A subjective evaluation showed that the quality of the converted speech was comparable to that obtained with a Gaussian mixture model-based method under advantageous conditions with parallel and twice the amount of data.


title: Radiatus: a Shared-Nothing Server-Side Web Architecture

abstract: Web applications are a frequent target of successful attacks. In most web frameworks, the damage is amplified by the fact that application code is responsible for security enforcement. In this paper, we design and evaluate Radiatus, a shared-nothing web framework where application-specific computation and storage on the server is contained within a sandbox with the privileges of the end-user. By strongly isolating users, user data and service availability can be protected from application vulnerabilities. To make Radiatus practical at the scale of modern web applications, we introduce a distributed capabilities system to allow fine-grained secure resource sharing across the many distributed services that compose an application. We analyze the strengths and weaknesses of a shared-nothing web architecture, which protects applications from a large class of vulnerabilities, but adds an overhead of 60.7% per server and requires an additional 31MB of memory per active user. We demonstrate that the system can scale to 20K operations per second on a 500-node AWS cluster.


title: Acoustic monitoring and classification of bee swarm activity using MFCC feature extraction and HMM acoustic modeling

abstract: This paper presents a proposed system for acoustic monitoring and classification of bee activity, based on audio modality. Beekeeping is one of the agriculture sectors, where ICT solutions provide great benefit for production and animal welfare. The presented system is a general classifier, which can classify normal and swarm bee activity. The captured audio signal is transformed into mel-frequency cepstral coefficients, which are used to train one state hidden Markov models. The activity classification is carried out in a form of recognition architecture, based on principles applied to speech modality. The acoustic models were trained in a two-stage approach using the open audio data provided by the Open Source Beehives Project. The highest classification accuracy of 80.89% was achieved with the proposed general classification approach.


title: Record-low on-Resistance for 0.35 μm based integrated XtreMOSTM Transistors

abstract: Experimental dataareshownforintegrated smart powertransistors breaking thesilicon limitat IOOV.The performance isclose tothemuchlowersuper-junction limit for thegivendevicepitch. The deviceusesstandard trench technology, andisimplemented ina 0.35gm smartpower process. Keysteps toimprove thedevice performance yielding a recordperformance of30mOhm*mm2fora Vbdof94V,are highlighted inthepaper. I.INTRODUCTION


title: Multi criteria preventive maintenance scheduling through Arena based simulation modeling

abstract: Line performance and equipment utilization have been major points of interest for many companies due to their direct impact on productivity. Achieving the highest possible utilization while maximizing throughput will improve the line performance; will also show significant increase on the line productivity. There are many variables that affect the line utilization and performance and preventive maintenance schedule is one of them. In this paper a multi criteria decision making approach will be implemented to select the preventive maintenance schedule that gives the best utility and performance values. To demonstrate the selection process a bread packaging line is used as a case study. Environmental conditions and line behavior are developed and simulated by using an Arena-based simulation model. The Arena model is to be used as a support tool for the multi criteria decision making process.


title: A Multilayer LTCC Solution for Integrating 5G Access Point Antenna Modules

abstract: An integrated solution for the development of multilayer antenna modules for fifth-generation (5G) communications, based on low temperature cofired ceramic (LTCC), is presented. The design exploits the 3-D integration capabilities of the LTCC process, enabling the realization of a full-corporate feed network (CFN) in vertical configuration. A novel implementation of the CFN employing dielectric-embedded parallel plate waveguides (PPWs) is proposed. The PPW lines are delimited by via-rows. As opposed to standard substrate-integrated waveguide feed networks, guided fields are orthogonal to the via-rows and propagate along the vertical axis of the structure. The CFN feeds four long slots, without any coupling structure, and provides broadband operation. The final prototype comprises 18 LTCC tapes, with a total thickness of 3.4 mm. The measured −10-dB impedance bandwidth spans from 51.2 to 66 GHz (>25.2%). The module generates a fixed broadside beam, but multibeam operation in $H$ -plane can be easily achieved. In the 50–66-GHz band, the peak gain is 14.25 dBi and the average first side-lobe level in $H$ -plane is −20.6 dB. The proposed technology and the design concept are suited for highly integrated millimeter-wave systems, such as access points in the future $V$ -band high data-rate wireless networks.


title: DL-Lite Ontology Revision Based on An Alternative Semantic Characterization

abstract: Ontology engineering and maintenance require (semi-)automated ontology change operations. Intensive research has been conducted on TBox and ABox changes in description logics (DLs), and various change operators have been proposed in the literature. Existing operators largely fall into two categories: syntax-based and model-based. While each approach has its advantages and disadvantages, an important topic that has rarely been explored is how to achieve a balance between syntax-based and model-based approaches. Also, most existing operators are specially designed for either TBox change or ABox change, and cannot handle the general ontology revision task—given a DL knowledge base (KB, a pair consisting of a TBox and an ABox), how to revise it by a set of TBox and ABox axioms (i.e., a new DL KB). In this article, we introduce an alternative structure for DL-Lite, called a featured interpretation, and show that featured models provide a finite and tight characterization to the classical semantics of DL-Lite. A key issue for defining a change operator is the so-called expressibility, that is, whether a set of models (or featured models here) is axiomatizable in DLs. It is indeed much easier to obtain expressibility results for featured models than for classical DL models. As a result, the new semantics determined by featured models provides a method for defining and studying various changes of DL-Lite KBs that involve both TBoxes and ABoxes. To demonstrate the usefulness of the new semantic characterization in ontology change, we define two revision operators for DL-Lite KBs using featured models and study their properties. In particular, we show that our two operators both satisfy AGM postulates. We show that the complexity of our revisions is ΠP2-complete, that is, on the same level as major revision operators in propositional logic, which further justifies the feasibility of our revision approach for DL-Lite. Also, we develop algorithms for these DL-Lite revisions.


title: Distributed Kalman filtering and Network Tracking Capacity

abstract: We propose and study a new distributed Kalman filter algorithm that can track unstable dynamics with bounded mean-squared error (MSE). The Network Tracking Capacity (NTC) of this algorithm depends only on the diffusion rate of the network and is independent of the local observation patterns, only requiring global observability. We analyze and compare the NTC for different network models.


title: X-Tree diff+: efficient change detection algorithm in XML documents

abstract: As web documents proliferate fast, the need fo real-time computation of change (edit script) between web documents increases. Though fast heuristic algorithms have been proposed recently, the qualities of edit scripts produced by them are not satisfactory. In this paper, we propose X-tree Diff+ which produces better quality of edit scripts by introducing a tuning step based on the notion of consistency of matching. We also add copy operation to provide users more convenience. Tuning and copy operation increase matching ratio drastically. X-tree Diff+ produces better quality of edit scripts and runs fast equivalent to the time complexity of fastest heuristic algorithms


title: Requirements and languages for the semantic representation of manufacturing systems

abstract: HighlightsThe requirements that the languages for the semantic and ontological modelling of the manufacturing domain should present are collected.A review of the manufacturing domain semantic modelling is performed.A possible role of ontologies in the manufacturing domain is envisioned, that is to be used as knowledge bases that support Service oriented Control Architectures based on Web Services within the open automation paradigm.An industrial case is presented that shows the possible use in a real context of the ontology for manufacturing domain. In the last years, attention has been devoted to the development of ontologies, which are ICT conceptual models allowing a formal and shared representation of a particular domain of discourse, and to the use of these representations in a variety of contexts, among which also the industrial engineering can be counted. Within the industrial engineering field, the manufacturing domain has not yet seen a wide application of ontologies. This paper firstly shows the use of ontologies for the semantic annotation of a Web Service-based architecture for the control of manufacturing systems; and then contributes to the research field of manufacturing domain ontologies by proposing a thorough literature review and analysis of the available languages supporting such objective. The paper collects the main requirements that semantic languages must meet to be used in the manufacturing domain with the outlined purpose. In fact, the available semantic languages are several and characterized by different features: the paper identifies the most proper ones for the manufacturing domain representation thanks to their analysis against the main requirements. Lastly, the paper shows how the discussed topics are deployed in a real industrial example.


title: Wideband LTCC 60-GHz Antenna Array With a Dual-Resonant Slot and Patch Structure

abstract: This paper presents a wideband 60-GHz antenna array with a dual-resonant slot-patch structure. A multilayered low-temperature co-fired ceramic substrate was used for antenna fabrication. A half-wavelength resonant slot was designed with a backed substrate-integrated waveguide (SIW) cavity to enhance the front radiation. The inverted microstrip center-fed structure was designed for easy signal excitation and superior impedance matching. A parasitic patch was also applied to enhance for bandwidth and gain enhancement. The effects of the SIW cavity-backed slot antenna with and without parasitic patches were empirically examined. The simulated results show that adding parasitic patches increased the resonance of the poles and improved antenna gain by 1.85 dB and bandwidth by 9%. A 2 × 2 dual-resonant slot-patch antenna array was designed to further enhance the gain and bandwidth. The equipment setup for on-chip measurements of gain and radiation patterns was established. The measured S11 showed a wide bandwidth of 23%. The measured gain for the four-element antenna array was 9 dBi with slight fluctuations over the 57-64-GHz frequency range.


title: The School Profiling Guide: Attitude Measurement.

abstract: ABSTRACT This guide is part of a series designed to assist school personnel to profile their data to support local school decisions. The profiling process can be used in school improvement programs. This particular guide describes the measurement of student attitudes toward schooling, about one's schools performance, about feelings toward school as a place to learn, or about others' judgments of one's school performance. Criteria for attitude test selection are outlined, including test validity, community standards of appropriateness, provision of information by tae test publisher, norms, age appropriateness, score reports, and instructions for interpretation. Planning of the data collection is recommended. Testing should occur within a week of the achievement testing, if attitudes and achievement are to be compared. Decisions must be made about who to test, whether the responses need be anonymous, additional biographicrl information needed, and scoring procedures. Standard procedures nould be used for administering tests and recording data. Prc,;,Ltdures for reporting and interpreting the data are illustrated, using Scott Foresman's School Attitude Measure as an example. Suggestions for evaluating the findings and developing gcals are included. (GDC)


title: Towards Vir

abstract: Honeynet represents the new theory in network protection. Unlike purely defensive methods such as Intrusion Detection Systems (IDS), Data Encryption and Firewalls that try to avoid interaction with the attacker, Honeynet is based on the idea of interaction with the attacker. During this interaction, the Honeynet records all of the attacker's actions and tools used without the attacker knowing, representing them to the security analyzers to study and prepare defensive methods. A hybrid honeynet including both the high-interaction and low-interaction honeypots was developed and implemented utilizing virtualization technology, representing a typical laboratory environment.


title: Laryngeal Tumor Detection and Classification in Endoscopic Video

abstract: The development of the narrow-band imaging (NBI) has been increasing the interest of medical specialists in the study of laryngeal microvascular network to establish diagnosis without biopsy and pathological examination. A possible solution to this challenging problem is presented in this paper, which proposes an automatic method based on anisotropic filtering and matched filter to extract the lesion area and segment blood vessels. Lesion classification is then performed based on a statistical analysis of the blood vessels’ characteristics, such as thickness, tortuosity, and density. Here, the presented algorithm is applied to 50 NBI endoscopic images of laryngeal diseases and the segmentation and classification accuracies are investigated. The experimental results show the proposed algorithm provides reliable results, reaching an overall classification accuracy rating of 84.3%. This is a highly motivating preliminary result that proves the feasibility of the new method and supports the investment in further research and development to translate this study into clinical practice. Furthermore, to our best knowledge, this is the first time image processing is used to automatically classify laryngeal tumors in endoscopic videos based on tumor vascularization characteristics. Therefore, the introduced system represents an innovation in biomedical and health informatics.


title: A novel approach for graph isomorphism: Handling large graphs

abstract: Graph Isomorphism has been proved as most crucial and very difficult process in Pattern Matching. The Graph Isomorphism problem is to check if two graphs are similar or not based on different properties like degree, vertex, edges etc. Two graphs are Isomorphic if they satisfy above properties. A Novel Approach is proposed for Graph Isomorphism Detection Problem (GIDP) based on two different methods. First method is to match an Input Graph with a Model Graph and second method is to match an input graph to the set of Model Graphs (Database of Model Graphs). This Novel Approach is used to solve Isomorphic Problems in an efficient way. Numbers of experiments are performed on large graphs and compared its performance with well-established algorithms like Ullman and VF2.


title: Using social psychology to motivate contributions to online communities

abstract: Under-contribution is a problem for many online communities. Social psychology theories of social loafing and goal-setting can provide mid-level design principles to address this problem. We tested the design principles in two field experiments. In one, members of an online movie recommender community were reminded of the uniqueness of their contributions and the benefits that follow from them. In the second, they were given a range of individual or group goals for contribution. As predicted by theory, individuals contributed when they were reminded of their uniqueness and when they were given specific and challenging goals, but other predictions were not borne out. The paper ends with suggestions and challenges for mining social science theories as well as implications for design.


title: Review of uncertainty reasoning approaches as guidance for maritime and offshore safety-based assessment

abstract: AbstractMany different formal techniques have been developed over the past two decades for dealing with uncertain information for decision making. In this paper we review some of the most important ones, i.e., Bayesian theory of probability, Dempster-Shafer theory of evidence, and fuzzy set theory, describe how they work and in what ways they differ from one another, and show their strength and weakness respectively as well as their connection. We also consider hybrid approaches which combine two or more approximate reasoning techniques within a single reasoning framework. These have been proposed to address limitations in the use of individual techniques. The study is intended to provide guidance in the process of developing frameworks for maritime and offshore safety-based decision analysis using different methods for reasoning under uncertainty.


title: Unknowable Manipulators: Social Network Curator Algorithms

abstract: For a social networking service to acquire and retain users, it must find ways to keep them engaged. By accurately gauging their preferences, it is able to serve them with the subset of available content that maximises revenue for the site. Without the constraints of an appropriate regulatory framework, we argue that a sufficiently sophisticated curator algorithm tasked with performing this process may choose to explore curation strategies that are detrimental to users. In particular, we suggest that such an algorithm is capable of learning to manipulate its users, for several qualitative reasons: 1. Access to vast quantities of user data combined with ongoing breakthroughs in the field of machine learning are leading to powerful but uninterpretable strategies for decision making at scale. 2. The availability of an effective feedback mechanism for assessing the short and long term user responses to curation strategies. 3. Techniques from reinforcement learning have allowed machines to learn automated and highly successful strategies at an abstract level, often resulting in non-intuitive yet nonetheless highly appropriate action selection. In this work, we consider the form that these strategies for user manipulation might take and scrutinise the role that regulation should play in the design of such systems.


title: Detecting Stance in Tweets And Analyzing its Interaction with Sentiment

abstract: One may express favor (or disfavor) towards a target by using positive or negative language. Here for the first time we present a dataset of tweets annotated for whether the tweeter is in favor of or against pre-chosen targets, as well as for sentiment. These targets may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets. We develop a simple stance detection system that outperforms all 19 teams that participated in a recent shared task competition on the same dataset (SemEval-2016 Task #6). Additionally, access to both stance and sentiment annotations allows us to conduct several experiments to tease out their interactions. We show that while sentiment features are useful for stance classification, they alone are not sufficient. We also show the impacts of various features on detecting stance and sentiment, respectively.


title: Learning Syntax by Automata Induction

abstract: In this paper we propose an explicit computer model for learning natural language syntax based on Angluin's (1982) efficient induction algorithms, using a complete corpus of grammatical example sentences. We use these results to show how inductive inference methods may be applied to learn substantial, coherent subparts of at least one natural language – English – that are not susceptible to the kinds of learning envisioned in linguistic theory. As two concrete case studies, we show how to learn English auxiliary verb sequences (such as could be taking, will have been taking) and the sequences of articles and adjectives that appear before noun phrases (such as the very old big deer). Both systems can be acquired in a computationally feasible amount of time using either positive examples, or, in an incremental mode, with implicit negative examples (examples outside a finite corpus are considered to be negative examples). As far as we know, this is the first computer procedure that learns a full-scale range of noun subclasses and noun phrase structure. The generalizations and the time required for acquisition match our knowledge of child language acquisition for these two cases. More importantly, these results show that just where linguistic theories admit to highly irregular subportions, we can apply efficient automata-theoretic learning algorithms. Since the algorithm works only for fragments of language syntax, we do not believe that it suffices for all of language acquisition. Rather, we would claim that language acquisition is nonuniform and susceptible to a variety of acquisition strategiess this algorithm may be one these.


title: Breaking News Detection and Tracking in Twitter

abstract: Twitter has been used as one of the communication channels for spreading breaking news. We propose a method to collect, group, rank and track breaking news in Twitter. Since short length messages make similarity comparison difficult, we boost scores on proper nouns to improve the grouping results. Each group is ranked based on popularity and reliability factors. Current detection method is limited to facts part of messages. We developed an application called “Hotstream” based on the proposed method. Users can discover breaking news from the Twitter timeline. Each story is provided with the information of message originator, story development and activity chart. This provides a convenient way for people to follow breaking news and stay informed with real-time updates.


title: Stochastic spectral methods for efficient Bayesian solution of inverse problems

abstract: We present a reformulation of the Bayesian approach to inverse problems, that seeks to accelerate Bayesian inference by using polynomial chaos (PC) expansions to represent random variables. Evaluation of integrals over the unknown parameter space is recast, more efficiently, as Monte Carlo sampling of the random variables underlying the PC expansion. We evaluate the utility of this technique on a transient diffusion problem arising in contaminant source inversion. The accuracy of posterior estimates is examined with respect to the order of the PC representation, the choice of PC basis, and the decomposition of the support of the prior. The computational cost of the new scheme shows significant gains over direct sampling.


title: Submodular Function Maximization for Group Elevator Scheduling

abstract: We propose a novel approach for group elevator scheduling by formulating it as the maximization of submodular function under a matroid constraint. In particular, we propose to model the total waiting time of passengers using a quadratic Boolean function. The unary and pairwise terms in the function denote the waiting time for single and pairwise allocation of passengers to elevators, respectively. We show that this objective function is submodular. The matroid constraints ensure that every passenger is allocated to exactly one elevator. We use a greedy algorithm to maximize the submodular objective function, and derive provable guarantees on the optimality of the solution. We tested our algorithm using Elevate 8, a commercial-grade elevator simulator that allows simulation with a wide range of elevator settings. We achieve significant improvement over the existing algorithms.


Julian: A Novel


title: Bandwidth Enhancement of CPW-Fed Circle-Like Slot Antenna With Dual Band-Notched Characteristic

abstract: A novel single-layer dual band-notched printed circle-like slot antenna for ultrawideband (UWB) applications is presented. The proposed antenna comprises a circle-like slot, a trident-shaped feed line, and two nested C-shaped stubs. By using a trident-shaped feed line, much wider impedance bandwidth is obtained. Due to inserting a pair of nested C-shaped stubs on the back surface of the substrate, two frequency band-notches of 5.1-6.2 (WLAN) and 3-3.8 GHz (WiMAX) are achieved. The nested stubs are connected to the tuning stub using two cylindrical via pins. The designed antenna has a total size of 26 × 30 mm2 and operates over the frequency band between 2.5 and 25 GHz. Throughout this letter, experimental results of the impedance bandwidth, gain, and radiation patterns are compared and discussed .


Inference and Paraphrase by Computer


title: POWERFUL: Mobile app fingerprinting via power analysis

abstract: Which apps a mobile user has and how they are used can disclose significant private information about the user. In this paper, we present the design and evaluation of POWERFUL, a new attack which can fingerprint sensitive mobile apps (or infer sensitive app usage) by analyzing the power consumption profiles on Android devices. POWERFUL works on the observation that distinct apps and their different usage patterns all lead to distinguishable power consumption profiles. Since the power profiles on Android devices require no permission to access, POWERFUL is very difficult to detect and can pose a serious threat against user privacy. Extensive experiments involving popular and sensitive apps in Google Play Store show that POWERFUL can identify the app used at any particular time with accuracy up to 92.9%, demonstrating the feasibility of POWERFUL.


title: Solving Systems of Random Quadratic Equations via Truncated Amplitude Flow

abstract: This paper puts forth a new algorithm, termed \emph{truncated amplitude flow} (TAF), to recover an unknown $n$-dimensional real-/complex-valued vector $\bm{x}$ from $m$ quadratic equations of the form $y_i=|\langle\bm{a}_i,\bm{x}\rangle|^2$. This problem is known to be \emph{NP-hard} in general. We prove that as soon as the number of equations $m$ is on the order of the number of unknowns $n$, TAF recovers the solution exactly (up to a global unimodular constant) with high probability and complexity growing linearly with the time required to read the data. Our method adopts the \emph{amplitude-based} cost function and proceeds in two stages: In stage one, we introduce an \emph{orthogonality-promoting} initialization that is obtained with a few simple power iterations. Stage two refines the initial estimate by successive updates of scalable \emph{truncated generalized gradient iterations}. The former is in sharp contrast to existing spectral initializations, while the latter handles the rather challenging nonconvex and nonsmooth amplitude-based cost function. In particular for real-valued vectors, our gradient truncation rule provably eliminates the erroneously estimated signs with high probability to markedly improve upon its untruncated version. Numerical tests demonstrate that our initialization method returns more accurate and robust estimates relative to its spectral counterparts. Furthermore, even under the same initialization, our amplitude-based refinement outperforms Wirtinger-based alternatives, corroborating the superior performance of TAF over state-of-the-art algorithms.


title: Detecting Advanced Persistent Threats using Fractal Dimension based Machine Learning Classification

abstract: Advanced Persistent Threats (APTs) are a new breed of internet based smart threats, which can go undetected with the existing state of-the-art internet traffic monitoring and protection systems. With the evolution of internet and cloud computing, a new generation of smart APT attacks has also evolved and signature based threat detection systems are proving to be futile and insufficient. One of the essential strategies in detecting APTs is to continuously monitor and analyze various features of a TCP/IP connection, such as the number of transferred packets, the total count of the bytes exchanged, the duration of the TCP/IP connections, and details of the number of packet flows. The current threat detection approaches make extensive use of machine learning algorithms that utilize statistical and behavioral knowledge of the traffic. However, the performance of these algorithms is far from satisfactory in terms of reducing false negatives and false positives simultaneously. Mostly, current algorithms focus on reducing false positives, only. This paper presents a fractal based anomaly classification mechanism, with the goal of reducing both false positives and false negatives, simultaneously. A comparison of the proposed fractal based method with a traditional Euclidean based machine learning algorithm (k-NN) shows that the proposed method significantly outperforms the traditional approach by reducing false positive and false negative rates, simultaneously, while improving the overall classification rates.


title: Advanced Concurrency Control in Java

abstract: Developing concurrent applications is not a trivial task. As programs grow larger and become more complex, advanced concurrency control mechanisms are needed to ensure that application consistency is not compromised. Managing mutual exclusion on a per-object basis is not sufficient to guarantee isolation of sets of semantically-related actions. In this paper, we consider ‘atomic blocks’, a simple and lightweight concurrency control paradigm that enables arbitrary blocks of code to access multiple shared objects in isolation. We evaluate various strategies for implementing atomic blocks in Java, in such a way that concurrency control is transparent to the programmer, isolation is preserved, and concurrency is maximized. We discuss these concurrency control strategies and evaluate them in terms of complexity and performance. Copyright © 2002 John Wiley & Sons, Ltd.


title: A chemically functionalizable nanoporous material

abstract: Although zeolites and related materials combine nanoporosity with high thermal stability, they are difficult to modify or derivatize in a systematic way. A highly porous metal coordination polymer [Cu3(TMA)2(H2O)3]n (where TMA is benzene-1,3,5-tricarboxylate) was formed in 80 percent yield. It has interconnected [Cu2(O2CR)4] units (where R is an aromatic ring), which create a three-dimensional system of channels with a pore size of 1 nanometer and an accessible porosity of about 40 percent in the solid. Unlike zeolites, the channel linings can be chemically functionalized; for example, the aqua ligands can be replaced by pyridines. Thermal gravimetric analysis and high-temperature single-crystal diffractometry indicate that the framework is stable up to 240 degreesC.


title: Entropy-Complexity Characterization of Brain Development in Chickens

abstract: Abstract: Electroencephalography (EEG) reﬂects the electrical activity of the brain, whichcan be considered chaotic and ruled by a nonlinear dynamics. Chickens exhibit a protractedperiod of maturation, and this temporal separation of the synapse formation and maturationphases is analogous to human neural development, though the changes in chickens occur inweeks compared to years in humans. The development of synaptic networks in the chickenbrain can be regarded as occurring in two broadly deﬁned phases. We speciﬁcally describethe chicken brain development phases in the causality entropy-complexity plane H C ,showing that the complexity of the electrical activity can be characterized by estimating theintrinsic correlational structure of the EEG signal. This allows us to identify the dynamicsof the developing chicken brain within the zone of a chaotic dissipative behavior in the plane H C .Keywords: EEG signals; statistical complexity measure; chicken brain; neural maturationPACS classiﬁcations: 02.50.-r; 05.45. Tp;87.19.La


title: Quantifying the accuracy of relational statements in Wikipedia: a methodology

abstract: An initial evaluation of the English Wikipedia indicates that it may provide accurate data for disambiguating and finding relations among named entities.


title: Dominant Resource Fairness : Fair Allocation of Multiple Resource Types

abstract: We consider the problem of fair resource allocation in a system containing different resource types, where each user may have different demands for each resource. To address this problem, we propose Dominant Resource Fairness (DRF), a generalization of max-min fairness to multiple resource types. We show that DRF, unlike other possible policies, satisfies several highly desirable properties. First, DRF incentivizes users to share resources, by ensuring that no user is better off if resources are equally partitioned among them. Second, DRF is strategy-proof, as a user cannot increase her allocation by lying about her requirements. Third, DRF is envy-free, as no user would want to trade her allocation with that of another user. Finally, DRF allocations are Pareto efficient, as it is not possible to improve the allocation of a user without decreasing the allocation of another user. We have implemented DRF in the Mesos cluster resource manager, and show that it leads to better throughput and fairness than the slot-based fair sharing schemes in current cluster schedulers.


title: A review of urban computing for mobile phone traces: current methods, challenges and opportunities

abstract: In this work, we present three classes of methods to extract information from triangulated mobile phone signals, and describe applications with different goals in spatiotemporal analysis and urban modeling. Our first challenge is to relate extracted information from phone records (i.e., a set of time-stamped coordinates estimated from signal strengths) with destinations by each of the million anonymous users. By demonstrating a method that converts phone signals into small grid cell destinations, we present a framework that bridges triangulated mobile phone data with previously established findings obtained from data at more coarse-grained resolutions (such as at the cell tower or census tract levels). In particular, this method allows us to relate daily mobility networks, called motifs here, with trip chains extracted from travel diary surveys. Compared with existing travel demand models mainly relying on expensive and less-frequent travel survey data, this method represents an advantage for applying ubiquitous mobile phone data to urban and transportation modeling applications. Second, we present a method that takes advantage of the high spatial resolution of the triangulated phone data to infer trip purposes by examining semantic-enriched land uses surrounding destinations in individual’s motifs. In the final section, we discuss a portable computational architecture that allows us to manage and analyze mobile phone data in geospatial databases, and to map mobile phone trips onto spatial networks such that further analysis about flows and network performances can be done. The combination of these three methods demonstrate the state-of-the-art algorithms that can be adapted to triangulated mobile phone data for the context of urban computing and modeling applications.


title: Point Signatures: A New Representation for 3D Object Recognition

abstract: Few systems capable of recognizing complex objects with free-form (sculptured) surfaces have been developed. The apparent lack of success is mainly due to the lack of a competent modelling scheme for representing such complex objects. In this paper, a new form of point representation for describing 3D free-form surfaces is proposed. This representation, which we call the point signature, serves to describe the structural neighbourhood of a point in a more complete manner than just using the 3D coordinates of the point. Being invariant to rotation and translation, the point signature can be used directly to hypothesize the correspondence to model points with similar signatures. Recognition is achieved by matching the signatures of data points representing the sensed surface to the signatures of data points representing the model surface. ::: ::: The use of point signatures is not restricted to the recognition of a single-object scene to a small library of models. Instead, it can be extended naturally to the recognition of scenes containing multiple partially-overlapping objects (which may also be juxtaposed with each other) against a large model library. No preliminary phase of segmenting the scene into the component objects is required. In searching for the appropriate candidate model, recognition need not proceed in a linear order which can become prohibitive for a large model library. For a given scene, signatures are extracted at arbitrarily spaced seed points. Each of these signatures is used to vote for models that contain points having similar signatures. Inappropriate models with low votes can be rejected while the remaining candidate models are ordered according to the votes they received. In this way, efficient verification of the hypothesized candidates can proceed by testing the most likely model first. Experiments using real data obtained from a range finder have shown fast recognition from a library of fifteen models whose complexities vary from that of simple piecewise quadric shapes to complicated face masks. Results from the recognition of both single-object and multiple-object scenes are presented.


title: A self-adjusting knee exoskeleton for robot-assisted treatment of knee injuries

abstract: In this study, we present a novel active device for robot-assisted rehabilitation that accommodates transitional movements of the knee joint as well as its rotation, enabling a perfect match between human joint axes and the device axes. Automatically adjusting its joint axes, the proposed device is not only capable of guaranteeing ergonomy and comfort throughout the therapy, but also extends the usable range of motion for the knee joint. Moreover, the adjustability feature significantly shortens the setup time required to attach the patient to the exoskeleton, allowing more effective time be spend on exercises instead of wasting it for adjustments. The proposed system is different from the similar works in literature in that it supports both passive translational movements of the knee joint and independent active control of these degrees of freedom. In particular, we introduce implementation details of a prototype that features compact design and combines the power of three actuators to achieve high rotational torques, detail the model based impedance controller utilized to adjust interaction forces and present the experimental characterization of the exoskeleton.


A Fast Dense Spectral–Spatial Convolution Network Framework for Hyperspectral Images Classification


title: Topological Graphic Passwords And Their Matchings Towards Cryptography

abstract: Graphical passwords (GPWs) are convenient for mobile equipments with touch screen. Topological graphic passwords (Topsnut-gpws) can be saved in computer by classical matrices and run quickly than the existing GPWs. We research Topsnut-gpws by the matching of view, since they have many advantages. We discuss: configuration matching partition, coloring/labelling matching partition, set matching partition, matching chain, etc. And, we introduce new graph labellings for enriching Topsnut-matchings and show these labellings can be realized for trees or spanning trees of networks. In theoretical works we explore Graph Labelling Analysis, and show first that every graph admits our extremal labellings and set-type labellings in graph theory. Many of the graph labellings mentioned are related with problems of set matching partitions to number theory, and yield new objects and new problems to graph theory.


title: Using metrics to evaluate software system maintainability

abstract: Software metrics have been much criticized in the last few years, sometimes justly but more often unjustly, because critics misunderstand the intent behind the technology. Software complexity metrics, for example, rarely measure the "inherent complexity" embedded in software systems, but they do a very good job of comparing the relative complexity of one portion of a system with another. In essence, they are good modeling tools. Whether they are also good measuring tools depends on how consistently and appropriately they are applied. >


title: Relinquishing the truth.

abstract: In this essay, the author reflects on a case involving a client who presents to therapy asking for professional guidance as to whether she should tell her adult child of her true paternity. The child was the product of a paternal rape. The author explores the impact of this case on her thinking about truth as prerequisite to healing.


title: Diversified query expansion using conceptnet

abstract: Search result diversification (SRD) aims to select diverse documents from the search results in order to cover as many search intents as possible. A prerequisite is that the search results contain diverse documents. For this purpose, we investigate a new approach to SRD by diversifying the query. Expansion terms are selected from ConceptNet so as to cover as diverse aspects as possible. The experimental results on several TREC data sets show that our method can outperform the existing state-of-the-art approaches that do not diversify the query.


title: Evolutionary-Neural Hybrid Agents for Architecture Search

abstract: Neural Architecture Search has shown potential to automate the design of neural networks. Deep Reinforcement Learning based agents can learn complex architectural patterns, as well as explore a vast and compositional search space. On the other hand, evolutionary algorithms offer higher sample efficiency, which is critical for such a resource intensive application. In order to capture the best of both worlds, we propose a class of Evolutionary-Neural hybrid agents (Evo-NAS). We show that the Evo-NAS agent outperforms both neural and evolutionary agents when applied to architecture search for a suite of text and image classification benchmarks. On a high-complexity architecture search space for image classification, the Evo-NAS agent surpasses the accuracy achieved by commonly used agents with only 1/3 of the search cost.


title: A Novel Miniaturized Vivaldi Antenna Using Tapered Slot Edge With Resonant Cavity Structure for Ultrawideband Applications

abstract: In this letter, a novel tapered slot edge with resonant cavity (TSERC) structure is adopted to improve the design of a planar printed conventional Vivaldi antenna. The proposed modified structure has the capacity to extend the low-end bandwidth limitation. In addition, the directivity and antenna gain of the TSERC structure Vivaldi antenna has been significantly improved when compared to a conventional Vivaldi antenna of the same size at lower frequencies. Compared to the conventional Vivaldi antenna, the TSERC structure lowers the gain at the higher frequencies. A prototype of the modified Vivaldi antenna was fabricated and tested. The measured results were found to be in good agreement with the simulated, which validates the feasibility of this novel design.


Mediation and the Estimation of Indirect Effects in Political Communication Research


title: Educational data mining and its role in determining factors affecting students academic performance: A systematic review

abstract: Education helps people develop as individuals. It not only helps build social skills but also enhances the problem solving and decision making skills of individuals. With the growing number of schools, colleges and universities around the globe, education now has taken new dimension. The main focus of higher Educational Institutes is to improve the overall quality and effectiveness of education. Predicting the performance of student and Predicting the Future in this world of Big Data is the need of Today in Education. The crude data from educational organizations act as a gold mine which facilitate the analysts and designers to make a system that will improve the overall teaching and learning process. This new field coined as Educational Data mining helps in Predicting the Future and Changing the Future. In this paper we have studied what is Educational Data Mining and what are the Application areas of EDM, various techniques of EDM and the factors affecting the students' Academic Performance and the Teaching-Learning process.