title: Physical Activity in Overweight and Obese Adolescents: Systematic Review of the Effects on Physical Fitness Components and Cardiovascular Risk Factors

abstract: BACKGROUND ::: The increasing prevalence of obesity in the pediatric age range has become a major concern. Studies have investigated the role of physical activity (PA) to prevent obesity in this population. However, previous reviews did not focus on the effects of PA in overweight/obese adolescents on physical fitness and risk factors for cardiovascular disease altogether. ::: ::: ::: OBJECTIVE ::: The present systematic review analyzed trials investigating the effect of PA on aerobic capacity, muscle strength, body composition, hemodynamic variables, biochemical markers, and endothelial function in obese/overweight adolescents. ::: ::: ::: METHODS ::: PubMed, LILACS, Web of Science, Scopus (including Embase), and SPORTDiscus databases were searched for relevant reports without time limits. Inclusion criteria included studies published in English, with overweight and obese adolescents aged 12-17 years. The review was registered (Number CRD42013004632) on PROSPERO, the International Prospective Register of Systematic Reviews. ::: ::: ::: RESULTS ::: The results indicated that PA is associated with significant and beneficial changes in fat percentage, waist circumference, systolic blood pressure, insulin, low-density lipoprotein cholesterol, and total cholesterol, as well as with small non-significant changes in diastolic blood pressure, glucose, and high-density lipoprotein cholesterol. ::: ::: ::: CONCLUSION ::: Although limited, results from controlled trials suggest that PA intervention may improve physical fitness and risk factors for cardiovascular disease in adolescents who are overweight or obese.


title: A Novel Quad-Polarization Agile Patch Antenna

abstract: In this communication we present a novel polarization-agile microstrip antenna design. To dynamically change the polarization state, the radiating patch is fed by a tunable quasi-lumped coupler. The whole structure can be dynamically altered to radiate electromagnetic waves with vertical linear, horizontal linear, right-handed circular or left-handed circular polarization simply by changing the operating mode of the quasi-lumped coupler. Due to its topology the coupler is simply reconfigured by switching the bias of two varactor diodes via a very simple DC bias circuitry: no additional capacitors or inductors are required. A prototype is fabricated with a 0.762-mm-thick upper layer substrate for the radiating element and a 0.130-mm-thick layer substrate for the coupler circuit, both with the same dielectric material relative permittivity of 2.22. The simulated and measured scattering parameters, the axial ratio in circular radiation-mode and the cross-polarization level in linear mode, the gain and the radiation patterns are presented. The agile polarization capabilities of this new antenna, as demonstrated in this communication, underscore its suitability for modern wireless communications in a multi-path propagation environment.


Combining Unigrams and Bigrams in Semi-Supervised Text Classification


title: iGraph: an incremental data processing system for dynamic graph

abstract: With the popularity of social network, the demand for real-time processing of graph data is increasing. However, most of the existing graph systems adopt a batch processing mode, therefore the overhead of maintaining and processing of dynamic graph is significantly high. In this paper, we design iGraph, an incremental graph processing system for dynamic graph with its continuous updates. The contributions of iGraph include: 1) a hash-based graph partition strategy to enable fine-grained graph updates; 2) a vertexbased graph computing model to support incremental data processing; 3) detection and rebalance methods of hotspot to address the workload imbalance problem during incremental processing. Through the general-purpose API, iGraph can be used to implement various graph processing algorithms such as PageRank. We have implemented iGraph on Apache Spark, and experimental results show that for real life datasets, iGraph outperforms the original GraphX in respect of graph update and graph computation.


Possible influence of cosmic rays on the changes in the ozone content of the stratosphere


Flexible Information Management, Exploration and Analysis in SAP HANA


title: Discrete Wigner Function Derivation of the Aaronson-Gottesman Tableau Algorithm

abstract: The Gottesman-Knill theorem established that stabilizer states and operations can be efficiently simulated classically. For qudits with dimension three and greater, stabilizer states and Clifford operations have been found to correspond to positive discrete Wigner functions and dynamics. We present a discrete Wigner function-based simulation algorithm for odd-$d$ qudits that has the same time and space complexity as the Aaronson-Gottesman algorithm. We show that the efficiency of both algorithms is due to the harmonic evolution in the symplectic structure of discrete phase space. The differences between the Wigner function algorithm and Aaronson-Gottesman are likely due only to the fact that the Weyl-Heisenberg group is not in $SU(d)$ for $d=2$ and that qubits have state-independent contextuality. This may provide a guide for extending the discrete Wigner function approach to qubits.


title: An efficient preprocessing method for supervised sentiment analysis by converting sentences to numerical vectors: a twitter case study

abstract: Along with the significant growth of social media, individuals and companies are increasingly receiving public opinions which direct their decisions. Opinion mining, which is considered as a sub-field of natural language processing, information retrieval, and text mining, is the process of understanding the users’ views from their comment, which have been represented as unstructured texts. Emergence of online social media has led to the production of a huge amount of user comments on websites, and thus, has raised opinion mining as a very useful and challenging problem. In this paper, an efficient preprocessing method for opinion mining is presented and will be used for analyzing users’ comments on Twitter social network. For this purpose, different text preprocessing techniques have been used on the dataset to achieve an acceptable standard text. Word2vec method which is a fast and accurate method have been also exploited to convert the words’ arrays to numerical vectors. Machine learning methods, with supervised learning approach, have been applied on the obtained data after this fast and accurate preprocessing phase. Python and RapidMiner have been used to implement different opinion mining methods and the results of these implementations have been compared and evaluated. The experimental results show that the combined use of the preprocessing method of this paper and support vector machine and artificial neural network have the highest accuracy compared to other methods.


title: More than Words: Syntactic Packaging and Implicit Sentiment

abstract: Work on sentiment analysis often focuses on the words and phrases that people use in overtly opinionated text. In this paper, we introduce a new approach to the problem that focuses not on lexical indicators, but on the syntactic "packaging" of ideas, which is well suited to investigating the identification of implicit sentiment, or perspective. We establish a strong predictive connection between linguistically well motivated features and implicit sentiment, and then show how computational approximations of these features can be used to improve on existing state-of-the-art sentiment classification results.


title: Inertial navigation systems for mobile robots

abstract: A low-cost solid-state inertial navigation system (INS) for mobile robotics applications is described. Error models for the inertial sensors are generated and included in an extended Kalman filter (EKF) for estimating the position and orientation of a moving robot vehicle. Two different solid-state gyroscopes have been evaluated for estimating the orientation of the robot. Performance of the gyroscopes with error models is compared to the performance when the error models are excluded from the system. Similar error models have been developed for each axis of a solid-state triaxial accelerometer and for a conducting-bubble tilt sensor which may also be used as a low-cost accelerometer. An integrated inertial platform consisting of three gyroscopes, a triaxial accelerometer and two tilt sensors is described. >


title: Deep Learning Based Technique for Plagiarism Detection in Arabic Texts

abstract: Plagiarism detection is very important especially for academician, researchers and students. Although, there are many plagiarism detection tools, it is still challenging task because of huge amount of online documents. In this research, we propose to use word2vec model to detect the semantic similarity between words in Arabic language which can help in detecting plagiarism. Word2vec is a deep learning technique that is used to represent words as features of vectors with high precision. The quality of vectors representation depends on the quality of corpus used in training phase. In this paper, we used OSAC corpus for training word2vec model. Moreover cosine similarity measure is used to compute the similarity between words' vectors. The similarity measures show how simple changes in text such as changing one word, or changing the position of verbs and nouns results with similarity value equal to 99% which provide the possibility to detect plagiarism even if the test is altered by replacing words by their synonyms or changing the words order


title: Anomaly Monitoring Framework Based on Intelligent Data Analysis

abstract: Real-time data processing has become an increasingly important challenge as the need for faster analysis of big data widely manifests itself. In this research, several Computational Intelligence methods have been applied for identifying possible anomalies in two real world sensor-based datasets. By achieving similar results to those of well respected methods, the proposed framework shows a promising potential for anomaly detection and its lightweight, real-time features make it applicable to a range of in-situ data analysis scenarios.


title: IMPLEMENTATION AND EVALUATION OF IMAGE PROCESSING TECHNIQUES ON A VISION NAVIGATION LINE FOLLOWER ROBOT

abstract: In a fast growing industrial world, carriers are required to carry products from one manufacturing plant to another which are usually in different buildings or separate blocks. This study intends to automate this sector using vision controlled mobile robots instead of laying railway tracks which are both expensive and inconvenient. To achieve this purpose an autonomous robot with computer vision as its primary sensor for gaining information about its environment for path following is developed. The proposed Line Follower Robot (LFR) consists of web cam mounted on the vehicle and connected to Matlab platform. A PID control algorithm will be applied to adjust the robot on the line. The proposed LFR is accomplished through the following stages: Firstly, the image is acquired using the web cam. The acquired RGB image is converted to another color coordinates for testing and comparing to choose the best color space. After that, the image contrast is enhanced using histogram equalization and then Wiener, Lee and Kuan filters are implemented to decide the best filter to be implemented. Subsequently, the basic morphological operations are carried out to choose the suitable operation to be utilized. The results are evaluated qualitatively and quantitatively from the points of Peak Signal-to-Noise Ratio (PSNR), entropy and image smoothness. The results show that the closing process is more suitable for the vision enhancement purpose, as well as the wiener filter gives the best result as regards the time and efficiency. Besides, the demonstrated LFR is capable of tracking a pre specified colored line as long as it is different from the surroundings.


title: Graph-Based Methods for Natural Language Processing and Understanding—A Survey and Analysis

abstract: This survey and analysis presents the functional components, performance, and maturity of graph-based methods for natural language processing and natural language understanding and their potential for mature products. Resulting capabilities from the methods surveyed include summarization, text entailment, redundancy reduction, similarity measure, word sense induction and disambiguation, semantic relatedness, labeling (e.g., word sense), and novelty detection. Estimated scores for accuracy, coverage, scalability, and performance are derived from each method. This survey and analysis, with tables and bar graphs, offers a unique abstraction of functional components and levels of maturity from this collection of graph-based methodologies.


title: Augmented Lagrangian based Sparse Representation Method with Dictionary Updating for Image Deblurring

abstract: This paper presents an efficient alternating direction method with patch-based dictionary updating, ADMDU-DEB, for sparse representation regularization framework of image deblurring. The main idea of the proposed method is to reformulate the variational problem as a linear equality constrained problem and then minimize its augmented Lagrangian function. The alternating direction method decouples the minimization by alternately iterating the pixel-based regularization and the patch-based sparse representation. Typically, accelerated sparse coding and simple dictionary updating applied in the sparse representation stage enable the whole algorithm to converge at a relatively small number of iterations. Additionally, the approach is readily extended to solve the same kind of variational problem with a nonnegativity constraint. Experimental results on benchmark test images consistently validate the superiority of the proposed approach and demonstrate that it achieves very competitive deblurring performance, co...


title: Weakly Supervised Localization using Deep Feature Maps

abstract: Object localization is an important computer vision problem with a variety of applications. The lack of large scale object-level annotations and the relative abundance of image-level labels makes a compelling case for weak supervision in the object localization task. Deep Convolutional Neural Networks are a class of state-of-the-art methods for the related problem of object recognition. In this paper, we describe a novel object localization algorithm which uses classification networks trained on only image labels. This weakly supervised method leverages local spatial and semantic patterns captured in the convolutional layers of classification networks. We propose an efficient beam search based approach to detect and localize multiple objects in images. The proposed method significantly outperforms the state-of-the-art in standard object localization data-sets with a 8 point increase in mAP scores.


title: Martial Arts, Dancing and Sports dataset: A challenging stereo and multi-view dataset for 3D human pose estimation

abstract: Human pose estimation is one of the most popular research topics in the past two decades, especially with the introduction of human pose datasets for benchmark evaluation. These datasets usually capture simple daily life actions. Here, we introduce a new dataset, the Martial Arts, Dancing and Sports (MADS), which consists of challenging martial arts actions (Tai-chi and Karate), dancing actions (hip-hop and jazz), and sports actions (basketball, volleyball, football, rugby, tennis and badminton). Two martial art masters, two dancers and an athlete performed these actions while being recorded with either multiple cameras or a stereo depth camera. In the multi-view or single-view setting, we provide three color views for 2D image-based human pose estimation algorithms. For depth-based human pose estimation, we provide stereo-based depth images from a single view. All videos have corresponding synchronized and calibrated ground-truth poses, which were captured using a Motion Capture system. We provide initial baseline results on our dataset using a variety of tracking frameworks, including a generative tracker based on the annealing particle filter and robust likelihood function, a discriminative tracker using twin Gaussian processes [1], and hybrid trackers, such as Personalized Depth Tracker [2]. The results of our evaluation suggest that discriminative approaches perform better than generative approaches when there are enough representative training samples, and that the generative methods are more robust to diversity of poses, but can fail to track when the motion is too quick for the effective search range of the particle filter. The data and the accompanying code will be made available to the research community. We propose a new dataset called the Martial Arts, Dancing and Sports dataset for 3D human pose estimation.The dataset contains challenging actions from Tai-chi, Karate, jazz, hip-hop and sports.It contains 30 multi-view videos and 30 stereo depth videos, with a total of 53,000 frames.We provide initial results using several baseline algorithms.


title: Critical Infrastructure Integration Modeling and Simulation

abstract: The protection of critical infrastructures, such as electrical power grids, has become a primary concern of many nation states in recent years. Critical infrastructures involve multi-dimensional, highly complex collections of technologies, processes, and people, and as such, are vulnerable to potentially catastrophic failures on many levels. Moreover, cross-infrastructure dependencies can give rise to cascading and escalating failures across multiple infrastructures. In order to address the problem of critical infrastructure protection, our research is developing innovative approaches to modeling critical infrastructures, with emphasis on analyzing the ramifications of cross-infrastructure dependencies. This paper presents an initial overview of the research and of the modeling environment under development.


title: AutoMoDe: A novel approach to the automatic design of control software for robot swarms

abstract: We introduce AutoMoDe: a novel approach to the automatic design of control software for robot swarms. The core idea in AutoMoDe recalls the approach commonly adopted in machine learning for dealing with the bias–variance tradedoff: to obtain suitably general solutions with low variance, an appropriate design bias is injected. AutoMoDe produces robot control software by selecting, instantiating, and combining preexisting parametric modules—the injected bias. The resulting control software is a probabilistic finite state machine in which the topology, the transition rules and the values of the parameters are obtained automatically via an optimization process that maximizes a task-specific objective function. As a proof of concept, we define AutoMoDe-Vanilla, which is a specialization of AutoMoDe for the e-puck robot. We use AutoMoDe-Vanilla to design the robot control software for two different tasks: aggregation and foraging. The results show that the control software produced by AutoMoDe-Vanilla (i) yields good results, (ii) appears to be robust to the so called reality gap, and (iii) is naturally human-readable.


title: HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads

abstract: The production environment for analytical data management applications is rapidly changing. Many enterprises are shifting away from deploying their analytical databases on high-end proprietary machines, and moving towards cheaper, lower-end, commodity hardware, typically arranged in a shared-nothing MPP architecture, often in a virtualized environment inside public or private "clouds". At the same time, the amount of data that needs to be analyzed is exploding, requiring hundreds to thousands of machines to work in parallel to perform the analysis. ::: ::: There tend to be two schools of thought regarding what technology to use for data analysis in such an environment. Proponents of parallel databases argue that the strong emphasis on performance and efficiency of parallel databases makes them well-suited to perform such analysis. On the other hand, others argue that MapReduce-based systems are better suited due to their superior scalability, fault tolerance, and flexibility to handle unstructured data. In this paper, we explore the feasibility of building a hybrid system that takes the best features from both technologies; the prototype we built approaches parallel databases in performance and efficiency, yet still yields the scalability, fault tolerance, and flexibility of MapReduce-based systems.


title: Identifying Important Citations Using Contextual Information from Full Text

abstract: In this paper we address the problem of classifying cited work into important and non-important to the developments presented in a research publication. This task is vital for the algorithmic techniques that detect and follow emerging research topics and to qualitatively measure the impact of publications in increasingly growing scholarly big data. We consider cited work as important to a publication if that work is used or extended in some way. If a reference is cited as background work or for the purpose of comparing results, the cited work is considered to be non-important. By employing five classification techniques (Support Vector Machine, Naive Bayes, Decision Tree, K-Nearest Neighbors and Random Forest) on an annotated dataset of 465 citations, we explore the effectiveness of eight previously published features and six novel features (including context based, cue words based and textual based). Within this set, our new features are among the best performing. Using the Random Forest classifier we achieve an overall classification accuracy of 0.91 AUC.


Best practices and boundary conditions


title: Speech emotion recognition based on HMM and SVM

abstract: Automatic emotion recognition in speech is a current research area with a wide range of applications in human-machine interactions. This paper uses two classification methods, the hidden Markov model (HMM) and the support vector machine (SVM), to classify five emotional states: anger, happiness, sadness, surprise and a neutral state. In the HMM method, 39 candidate instantaneous features were extracted, and the sequential forward selection (SFS) method was used to find the best feature subset. The classification performance of the selected feature subset was then compared with that of the Mel frequency cepstrum coefficients (MFCC). Within the method based on SVM, a new vector measuring the difference between Mel frequency scale sub-bands energies is proposed. The performance of the K-nearest neighbors (KNN) classifier using the proposed vector was also investigated. Both gender dependent and gender independent experiments were conducted on the Danish emotional speech (DES) database. The recognition rates by the HMM classifier were 98.9% for female subjects, 100% for male subjects, and 99.5% for gender independent cases. When the SVM classifier and the proposed feature vector were employed, correct classification rates of 89.4%, 93.6% and 88.9% were obtained for male, female and gender independent cases respectively.


title: MUTO: the modular unified tagging ontology

abstract: Tagging has become a popular indexing method within the last years and can be considered one of the cornerstones of the Social Web. Several ontologies have been developed that aim to formally describe tagging and folksonomies in order to improve their interoperability and processability. However, each of these ontologies covers different aspects of the domain; finding the right ontology for a certain purpose and aligning it correctly with other ontologies is difficult. This paper critically reviews available tagging ontologies and presents a unified vocabulary that combines the 'best of' these ontologies in one consistent schema. A central goal was to ensure high concept reuse by simultaneously avoiding redundancies. A modular design was chosen to reduce complexity and prevent inconsistencies. It groups the different elements of tagging and separates advanced concepts from the core ontology. Key design decisions are justified and modeling alternatives are discussed, not only to explain the unified ontology but also to contribute to a better understanding of the conceptual space of tagging and folksonomies.


title: A Fuzzy Approach for Placement of Capacitors with Considering Harmonic Distortions

abstract: This paper presents a fuzzy-based approach for the placement of capacitor banks in a harmonics-polluted distribution system. In the problem the objective function is the total system power loss, while the constraints are the RMS voltage and the total harmonic voltage distortion (THDv) limits at each bus, as well as the limit of the available capacitor units to be placed. In the study the system power loss, the bus RMS voltage, the THDv at each bus, and the capacitor site suitability are modeled as fuzzy membership functions. A useful solution algorithm is then proposed to find the best capacitor locations by testing an actual distribution system. Numerical experiences show that the proposed method yields favorable results for the capacitor placement problem.


title: Classification of fashion article images using convolutional neural networks

abstract: In this paper, we propose a state-of-the-art model for classification of fashion article images. We trained convolutional neural network based deep learning architectures to classify images in the Fashion-MNIST dataset. We have proposed three different convolutional neural network architectures and used batch normalization and residual skip connections for ease and acceleration of learning process. Our model shows impressive results on the benchmark dataset of Fashion-MNIST. Comparisons show that our proposed model reports improved accuracy of around 2% over the existing state-of-the-art systems in literature.


title: Hymenal anomalies in twins—review of the literature and case report

abstract: Subocclusive hymenal variants, such as microperforate or septate hymen, impair somatic functions (e.g., vaginal intercourse or menstrual hygiene) and can negatively impact the quality of life of young women. We know little about the prevalence and inheritance of subocclusive hymenal variants. So far, eight cases of familial occurrence of occlusive hymenal anomalies (imperforate hymen) have been reported. In one of these cases, monozygotic twins were affected. We are reporting the first case of subocclusive hymenal variants (microperforate hymen and septate hymen) in 16-year-old white dizygotic twins. In addition, we review and discuss the current evidence. Conclusion: The mode of inheritance of hymenal variants has not been determined so far. Because surgical corrections of hymenal variants should be carried out in asymptomatic patients (before menarche), gynecologists and pediatricians should keep in mind that familial occurrences may occur.


title: Robust multiple structures estimation with j-linkage

abstract: This paper tackles the problem of fitting multiple instances of a model to data corrupted by noise and outliers. The proposed solution is based on random sampling and conceptual data representation. Each point is represented with the characteristic function of the set of random models that fit the point. A tailored agglomerative clustering, called J-linkage, is used to group points belonging to the same model. The method does not require prior specification of the number of models, nor it necessitate parameters tuning. Experimental results demonstrate the superior performances of the algorithm.


title: Perceived barriers and facilitators to mental health help-seeking in young people: a systematic review

abstract: BackgroundAdolescents and young adults frequently experience mental disorders, yet tend not to seek help. This systematic review aims to summarise reported barriers and facilitators of help-seeking in young people using both qualitative research from surveys, focus groups, and interviews and quantitative data from published surveys. It extends previous reviews through its systematic research methodology and by the inclusion of published studies describing what young people themselves perceive are the barriers and facilitators to help-seeking for common mental health problems.MethodsTwenty two published studies of perceived barriers or facilitators in adolescents or young adults were identified through searches of PubMed, PsycInfo, and the Cochrane database. A thematic analysis was undertaken on the results reported in the qualitative literature and quantitative literature.ResultsFifteen qualitative and seven quantitative studies were identified. Young people perceived stigma and embarrassment, problems recognising symptoms (poor mental health literacy), and a preference for self-reliance as the most important barriers to help-seeking. Facilitators were comparatively under-researched. However, there was evidence that young people perceived positive past experiences, and social support and encouragement from others as aids to the help-seeking process.ConclusionsStrategies for improving help-seeking by adolescents and young adults should focus on improving mental health literacy, reducing stigma, and taking into account the desire of young people for self-reliance.


title: A tetrahedral space-filling curve for non-conforming adaptive meshes

abstract: We introduce a space-filling curve for triangular and tetrahedral red-refinement that can be computed using bitwise interleaving operations similar to the well-known Z-order or Morton curve for cubical meshes. To store sufficient information for random access, we define a low-memory encoding using 10 bytes per triangle and 14 bytes per tetrahedron. We present algorithms that compute the parent, children, and face-neighbors of a mesh element in constant time, as well as the next and previous element in the space-filling curve and whether a given element is on the boundary of the root simplex or not. Our presentation concludes with a scalability demonstration that creates and adapts selected meshes on a large distributed-memory system.


Architecture-based runtime software evolution


Baseline Knowledge About Vaccines and Vaccine-Preventable Diseases Among Older Adults: Preliminary Baseline Analysis of the Vaccine Education Through Pharmacists and Senior Centers (VEPSC) Study


title: Investigation of DELPHI-inspired compact thermal model for modeling planar transformer — Network concept

abstract: Planar Magnetic Components, as called PMC, are now interesting alternatives to classic high frequency transformers. Based on the combination of a low profile magnetic core and Printed Circuit Board (PCB), these devices allow large reduction in terms of weight and size, which exacerbates power density constraints. In order to circumvent the potential thermal issues of a multi-layer embedding of flatten high-current-density copper windings at the heart of a PCB substrate, an investigation is conducted to define the proper way for building a simple thermal model that can take into account the electro-thermo-magnetic combined effects. At first, to better understand the mutual interactions of conjugated copper and iron losses as well as the heat conduction capability of the core structure, a set of numerical simulations were performed to calibrate a fine Detailed Thermal Model (DTM). Then the concept of Compact Thermal Model dedicated to planar transformer devices, inspired by DELPHI'S method, is defined for minimizing the computation time while keeping a high accuracy level to monitor the sensitive temperatures of primary and secondary windings as well as those of core structure. The presented results demonstrate the ability to deduce such a resistor network from time-consuming simulations. Designers can then perform trivial calculations to analyse the thermal impact of planar transformers.


title: BirdVis: Visualizing and Understanding Bird Populations

abstract: Birds are unrivaled windows into biotic processes at all levels and are proven indicators of ecological well-being. Understanding the determinants of species distributions and their dynamics is an important aspect of ecology and is critical for conservation and management. Through crowdsourcing, since 2002, the eBird project has been collecting bird observation records. These observations, together with local-scale environmental covariates such as climate, habitat, and vegetation phenology have been a valuable resource for a global community of educators, land managers, ornithologists, and conservation biologists. By associating environmental inputs with observed patterns of bird occurrence, predictive models have been developed that provide a statistical framework to harness available data for predicting species distributions and making inferences about species-habitat associations. Understanding these models, however, is challenging because they require scientists to quantify and compare multiscale spatialtemporal patterns. A large series of coordinated or sequential plots must be generated, individually programmed, and manually composed for analysis. This hampers the exploration and is a barrier to making the cross-species comparisons that are essential for coordinating conservation and extracting important ecological information. To address these limitations, as part of a collaboration among computer scientists, statisticians, biologists and ornithologists, we have developed BirdVis, an interactive visualization system that supports the analysis of spatio-temporal bird distribution models. BirdVis leverages visualization techniques and uses them in a novel way to better assist users in the exploration of interdependencies among model parameters. Furthermore, the system allows for comparative visualization through coordinated views, providing an intuitive interface to identify relevant correlations and patterns. We justify our design decisions and present case studies that show how BirdVis has helped scientists obtain new evidence for existing hypotheses, as well as formulate new hypotheses in their domain.


title: The behaviour of the LSPMSM in asynchronous operation

abstract: The paper proposes a study of synchronization capability of line-start permanent magnet synchronous motor with small saliency ratio and very high cage resistance. The exemplified method is for a motor with the rated power of 3.5 kW, the synchronous speed of 3000 rpm, and the saliency ratio of 1.08.


title: Factors influencing the response rate in social question and answering behavior

abstract: With the increasing growth and popularity of social networking sites, social question and answering has become a venue for individuals to seek and share information. This study evaluates eleven extrinsic factors that may influence the response rate in social question and answering. These factors include the number of followers, the frequency of posting, the number of at-mentioned recipients, whether or not a question contains an at-mentioned verified account, unverified account, hashtag, emoticon, expression of gratitude, repeated punctuation or interjections, as well as the topic and the posting time of a question. We collected and analyzed over 10,000 questions from Sina Weibo. Eight out of all eleven features were found to significantly predict the number of responses received. We believe that our study is of significant value in providing insights for the design and development of future social question and answering tools, as well as enhancing the collaboration among social network users in supporting social information seeking activities.


title: Context-sensitive information retrieval using implicit feedback

abstract: A major limitation of most existing retrieval models and systems is that the retrieval decision is made based solely on the query and document collection; information about the actual user and search context is largely ignored. In this paper, we study how to exploit implicit feedback information, including previous queries and clickthrough information, to improve retrieval accuracy in an interactive information retrieval setting. We propose several context-sensitive retrieval algorithms based on statistical language models to combine the preceding queries and clicked document summaries with the current query for better ranking of documents. We use the TREC AP data to create a test collection with search context information, and quantitatively evaluate our models using this test set. Experiment results show that using implicit feedback, especially the clicked document summaries, can improve retrieval performance substantially.


title: Adaptive Comanagement and Its Relationship to Environmental Governance

abstract: We provide a systematic review of the adaptive comanagement (ACM) literature to (i) investigate how the concept of governance is considered and (ii) examine what insights ACM offers with reference to six key concerns in environmental governance literature: accountability and legitimacy; actors and roles; fit, interplay, and scale; adaptiveness, flexibility, and learning; evaluation and monitoring; and, knowledge. Findings from the systematic review uncover a complicated relationship with evidence of conceptual closeness as well as relational ambiguities. The findings also reveal several specific contributions from the ACM literature to each of the six key environmental governance concerns, including applied strategies for sharing power and responsibility and value of systems approaches in understanding problems of fit. More broadly, the research suggests a dissolving or fuzzy boundary between ACM and governance, with implications for understanding emerging approaches to navigate social-ecological system change. Future research opportunities may be found at the confluence of ACM and environmental governance scholarship, such as identifying ways to build adaptive capacity and encouraging the development of more flexible governance arrangements.


title: Automobile Brake-by-Wire Control System Design and Analysis

abstract: The automobile brake-by-wire (BBW) system, which is also called the electromechanical brake system, has become a promising vehicle braking control scheme that enables many new driver interfaces and enhanced performances without a mechanical or hydraulic backup. In this paper, we survey BBW control systems with focuses on fault tolerance design and vehicle braking control schemes. At first, the system architecture of BBW systems is described. Fault tolerance design is then discussed to meet the high requirements of reliability and safety of BBW systems. A widely used braking model and several braking control schemes are investigated. Although previous work focused on antilock and antislip braking controls on a single wheel basis, we present a whole-vehicle control scheme to enhance vehicle stability and safety. Simulations based on the whole-vehicle braking model validate a proposed fuzzy logic control scheme in the lateral and yaw stability controls of vehicles.


title: Integrated GPS/INS System for Pedestrian Navigation in a Signal Degraded Environment

abstract: This paper evaluates the performance of a shoe/foot mounted inertial system for pedestrian navigation application. Two different grades of inertial sensors are used, namely a medium cost tactical grade Honeywell HG1700 inertial measurement unit (IMU) and a low-cost MEMS-based Crista IMU (Cloud Cap Technology). The inertial sensors are used in two different ways for computing the navigation solution. The first method is a conventional integration algorithm where IMU measurements are processed through a set of mechanization equation to compute a six degree-offreedom (DOF) navigation solution. Such a system is referred to as an Inertial Navigation System (INS). The integration of this system with GPS is performed using a tightly coupled integration scheme. Since the sensor is placed on the foot, the designed integrated system exploits the small period for which foot comes to rest at each step (stance-phase of the gait cycle) and uses Zero Velocity Update (ZUPT) to keep the INS errors bounded in the absence of GPS. An algorithm for detecting the stance-phase using the pattern of three-dimensional acceleration is discussed. In the second method, the navigation solutions is computed using the fact that a pedestrian takes one step at a time, and thus positions can be computed by propagating the step-length in the direction of pedestrian motion. This algorithm is termed as pedestrian dead-reckoning (PDR) algorithm. The IMU measurement in this algorithm is used to detect the step, estimate the step-length, and determine the heading for solution propagation. Different algorithms for stridelength estimation and step-detection are discussed in this paper. The PDR system is also integrated with GPS through a tightly coupled integration scheme. The performance of both the systems is evaluated through field tests conducted in challenging GPS environments using both inertial sensors. The specific focus is on the system performance under long GPS outages of duration upto 30 minutes.


title: Visual Tracking via Boolean Map Representations

abstract: Abstract In this paper, we present a simple yet effective Boolean map based representation that exploits connectivity cues for visual tracking. We describe a target object with histogram of oriented gradients and raw color features, of which each one is characterized by a set of Boolean maps generated by uniformly thresholding their values. The Boolean maps effectively encode multi-scale connectivity cues of the target with different granularities. The fine-grained Boolean maps capture spatially structural details that are effective for precise target localization while the coarse-grained ones encode global shape information that are robust to large target appearance variations. Finally, all the Boolean maps form together a robust representation that can be approximated by an explicit feature map of the intersection kernel, which is fed into a logistic regression classifier with online update, and the target location is estimated within a particle filter framework. The proposed representation scheme is computationally efficient and facilitates achieving favorable performance in terms of accuracy and robustness against the state-of-the-art tracking methods on the OTB50 and VOT2016 benchmark datasets.


An Analysis of the Cryptocurrency Industry


title: Combining multiple matchers for a high security fingerprint verification system

abstract: Abstract Integration of various fingerprint matching algorithms is a viable method to improve the performance of a fingerprint verification system. Different fingerprint matching algorithms are often based on different representations of the input fingerprints and hence complement each other. We use the logistic transform to integrate the output scores from three different fingerprint matching algorithms. Experiments conducted on a large fingerprint database confirm the effectiveness of the proposed integration scheme.


title: DOES ELASTIC TAPING ON THE TRICEPS SURAE FACILITATE THE ABILITY OF VERTICAL JUMP

abstract: T.S Hsieh, P.L Wu, J.H Liao, T.Y Kuo, T.Y Wu, C.Y Huang, S.K Chang, H. Chai, F.C Su Institute of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan Graduate Institute and Department of Physical Therapy, National Cheng Kung University, Tainan, Taiwan; School and Graduate Institute of Physical Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan email: fcsu@mail.ncku.edu.tw INTRODUCTION Elastic taping applied on the triceps surae has been commonly used by the athletes to improve the performance of lower extremity. It has been described by the athletes that elastic taping to the triceps surae would make them “feel” stronger in jumping. However, less objective and solid evidences have been documented. For athletes, vertical jump is an indicator of performance capacity of lower extremity, [1]. The purpose of this study was to investigate the effect of elastic taping on the triceps surae during maximal vertical jump.


title: Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging

abstract: Abstract Introduction White matter hyperintensities (WMHs) are areas of abnormal signal on magnetic resonance images (MRIs) that characterize various types of histopathological lesions. The load and location of WMHs are important clinical measures that may indicate the presence of small vessel disease in aging and Alzheimer's disease (AD) patients. Manually segmenting WMHs is time consuming and prone to inter-rater and intra-rater variabilities. Automated tools that can accurately and robustly detect these lesions can be used to measure the vascular burden in individuals with AD or the elderly population in general. Many WMH segmentation techniques use a classifier in combination with a set of intensity and location features to segment WMHs, however, the optimal choice of classifier is unknown. Methods We compare 10 different linear and nonlinear classification techniques to identify WMHs from MRI data. Each classifier is trained and optimized based on a set of features obtained from co-registered MR images containing spatial location and intensity information. We further assess the performance of the classifiers using different combinations of MRI contrast information. The performances of the different classifiers were compared on three heterogeneous multi-site datasets, including images acquired with different scanners and different scan-parameters. These included data from the ADC study from University of California Davis, the NACC database and the ADNI study. The classifiers (naive Bayes, logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors, bagging, and boosting) were evaluated using a variety of voxel-wise and volumetric similarity measures such as Dice Kappa similarity index (SI), Intra-Class Correlation (ICC), and sensitivity as well as computational burden and processing times. These investigations enable meaningful comparisons between the performances of different classifiers to determine the most suitable classifiers for segmentation of WMHs. In the spirit of open-source science, we also make available a fully automated tool for segmentation of WMHs with pre-trained classifiers for all these techniques. Results Random Forests yielded the best performance among all classifiers with mean Dice Kappa (SI) of 0.66±0.17 and ICC=0.99 for the ADC dataset (using T1w, T2w, PD, and FLAIR scans), SI=0.72±0.10, ICC=0.93 for the NACC dataset (using T1w and FLAIR scans), SI=0.66±0.23, ICC=0.94 for ADNI1 dataset (using T1w, T2w, and PD scans) and SI=0.72±0.19, ICC=0.96 for ADNI2/GO dataset (using T1w and FLAIR scans). Not using the T2w/PD information did not change the performance of the Random Forest classifier (SI=0.66±0.17, ICC=0.99). However, not using FLAIR information in the ADC dataset significantly decreased the Dice Kappa, but the volumetric correlation did not drastically change (SI=0.47±0.21, ICC=0.95). Conclusion Our investigations showed that with appropriate features, most off-the-shelf classifiers are able to accurately detect WMHs in presence of FLAIR scan information, while Random Forests had the best performance across all datasets. However, we observed that the performances of most linear classifiers and some nonlinear classifiers drastically decline in absence of FLAIR information, with Random Forest still retaining the best performance.


title: Fabrication of body-tied FinFETs (Omega MOSFETs) using bulk Si wafers

abstract: Nano scale body-tied FinFETs have been firstly fabricated. They have fin top width of 30 nm, fin bottom width of 61 nm, fin height of 99 nm, and gate length of 60 nm. This Omega MOSFET shows excellent transistor characteristics, such as very low subthreshold swing, Drain Induced Barrier Lowering (DIBL) of 24 mV/V, almost no body bias effect, and orders of magnitude lower I/sub SUB//I/sub D/ than planar type DRAM cell transistors.


title: Towards 3D object maps for autonomous household robots

abstract: This paper describes a mapping system that acquires 3D object models of man-made indoor environments such as kitchens. The system segments and geometrically reconstructs cabinets with doors, tables, drawers, and shelves, objects that are important for robots retrieving and manipulating objects in these environments. The system also acquires models of objects of daily use such glasses, plates, and ingredients. The models enable the recognition of the objects in cluttered scenes and the classification of newly encountered objects. Key technical contributions include (1) a robust, accurate, and efficient algorithm for constructing complete object models from 3D point clouds constituting partial object views, (2) feature-based recognition procedures for cabinets, tables, and other task-relevant furniture objects, and (3) automatic inference of object instance and class signatures for objects of daily use that enable robots to reliably recognize the objects in cluttered and real task contexts. We present results from the sensor-based mapping of a real kitchen.


title: A Branch and Bound Algorithm for Feature Subset Selection

abstract: A feature subset selection algorithm based on branch and bound techniques is developed to select the best subset of m features from an n-feature set. Existing procedures for feature subset selection, such as sequential selection and dynamic programming, do not guarantee optimality of the selected feature subset. Exhaustive search, on the other hand, is generally computationally unfeasible. The present algorithm is very efficient and it selects the best subset without exhaustive search. Computational aspects of the algorithm are discussed. Results of several experiments demonstrate the very substantial computational savings realized. For example, the best 12-feature set from a 24-feature set was selected with the computational effort of evaluating only 6000 subsets. Exhaustive search would require the evaluation of 2 704 156 subsets.


title: Industry 4.0 as a part of smart cities

abstract: In this paper, we propose the conjunction of the Smart City Initiative and the concept of Industry 4.0. The term smart city has been a phenomenon of the last years, which is very inflected especially since 2008 when the world was hit by the financial crisis. The main reasons for the emergence of the Smart City Initiative are to create a sustainable model for cities and preserve quality of life of their citizens. The topic of the smart city cannot be seen only as a technical discipline, but different economic, humanitarian or legal aspects must be involved as well. In the concept of Industry 4.0, the Internet of Things (IoT) shall be used for the development of so-called smart products. Sub-components of the product are equipped with their own intelligence. Added intelligence is used both during the manufacturing of a product as well as during subsequent handling, up to continuous monitoring of the product lifecycle (smart processes). Other important aspects of the Industry 4.0 are Internet of Services (IoS), which includes especially intelligent transport and logistics (smart mobility, smart logistics), as well as Internet of Energy (IoE), which determines how the natural resources are used in proper way (electricity, water, oil, etc.). IoT, IoS, IoP and IoE can be considered as an element that can create a connection of the Smart City Initiative and Industry 4.0 — Industry 4.0 can be seen as a part of smart cities. Interconnection of these systems can be expected to change — transport processes from design logistic processes through to their online optimization with respect to the chosen objective function and the latest information from the transport infrastructure. Linking information from process-based Industry 4.0 with intelligent transport systems of the smart city could create very effective, demand-oriented and higher productivity of manufacturing enterprises as well as sustainable development of society.


title: Interference management in OFDMA femtocell networks: issues and approaches

abstract: One of the effective techniques of improving the coverage and enhancing the capacity and data rate in cellular wireless networks is to reduce the cell size (i.e., cell splitting) and transmission distances. Therefore, the concept of deploying femtocells over macrocell has recently attracted growing interests in academia, industry, and standardization forums. Various technical challenges towards mass deployment of femtocells have been addressed in recent literature. Interference mitigation between neighboring femtocells and between the femtocell and macrocell is considered to be one of the major challenges in femtocell networks because femtocells share the same licensed frequency spectrum with macrocell. Further, the conventional radio resource management techniques for hierarchical cellular system is not suitable for femtocell networks since the positions of the femtocells are random depending on the users' service requirement. In this article, we provide a survey of the different state-of-the-art approaches for interference and resource management in orthogonal frequency-division multiple access (OFDMA)-based femtocell networks. A qualitative comparison among the different approaches is provided. To this end, open challenges in designing interference management schemes for OFDMA femtocell networks are discussed.


title: Implementation of IoT system using block chain with authentication and data protection

abstract: In a block-chain IoT environment, when data or device authentication information is put on a block chain, personal information may be leaked through the proof-of-work process or address search. In this paper, we apply Zero-Knowledge proof to a smart meter system to prove that a prover without disclosing information such as public key, and we have studied how to enhance anonymity of block chain for privacy protection.


title: Profile of minocycline and its potential in the treatment of schizophrenia

abstract: Accumulating evidence suggests that neuroinflammation affecting microglia plays an important role in the etiology of schizophrenia, and appropriate control of microglial activation may be a promising therapeutic strategy for schizophrenia. Minocycline, a second-generation tetracycline that inhibits microglial activation, has been shown to have a neuroprotective effect in various models of neurodegenerative disease, including anti-inflammatory, antioxidant, and antiapoptotic properties, and an ability to modulate glutamate-induced excitotoxicity. Given that these mechanisms overlap with neuropathologic pathways, minocycline may have a potential role in the adjuvant treatment of schizophrenia, and improve its negative symptoms. Here, we review the relevant studies of minocycline, ranging from preclinical research to human clinical trials.


Impact Assessment of Urethral Meatus Morphology and Penile Biometry in Transurethral Prostate and Bladder Surgery


title: SCMFS: A File System for Storage Class Memory and its Extensions

abstract: This paper considers the problem of how to implement a file system on Storage Class Memory (SCM), that is directly connected to the memory bus, byte addressable and is also non-volatile. In this paper, we propose a new file system, called SCMFS, which is implemented on the virtual address space. In SCMFS, we utilize the existing memory management module in the operating system to do the block management and keep the space always contiguous for each file. The simplicity of SCMFS not only makes it easy to implement, but also improves the performance. We have implemented a prototype in Linux and evaluated its performance through multiple benchmarks.


title: Active-Impedance Control of a Lower-Limb Assistive Exoskeleton

abstract: We propose a novel control method for lower-limb assist that produces a virtual modification of the mechanical impedance of the human limbs. This effect is accomplished by making the exoskeleton display active impedance properties. Active impedance control emphasizes control of the exoskeleton's dynamics and regulation of the transfer of energy between the exoskeleton and the user. Its goal is improving the dynamic response of the human limbs without sacrificing the user's control authority. The proposed method is an alternative to myoelectrical exoskeleton control, which is based on estimating muscle torques from electromyographical (EMG) activity. Implementation of an EMG-based controller is a complex task that involves modeling the user's musculoskeletal system and requires recalibration. In contrast, active impedance control is less dependent on estimation of the user's attempted motion, thereby avoiding conflicts resulting from inaccurate estimation. In this paper we also introduce a new form of human assist based on improving the kinematic response of the limbs. Reduction of average muscle torques is a common goal of research in human assist. However, less emphasis has been placed so far on improving the user's agility of motion. We aim to use active impedance control to attain such effects as increasing the user's average speed of motion, and improving their acceleration capabilities in order to compensate for perturbations from the environment.


title: Loc-Auth: Location-Enabled Authentication Through Attribute-Based Encryption

abstract: Traditional user authentication involves entering a username and password into a system. Strong authentication security demands, among other requirements, long, frequently hard-to-remember passwords. Two-factor authentication aids in the security, even though, as a side effect, might worsen user experience. We depict a mobile sign-on scheme that benefits from the dynamic relationship between a user's attributes, the service the user wishes to utilize, and location (where the user is, and what services are available there) as an authentication factor. We demonstrate our scheme employing Bluetooth Low Energy beacons for location awareness and the expressiveness of Attribute-Based Encryption to capture and leverage the described relationship. Bluetooth Low Energy beacons broadcast encrypted messages with encoded access policies. Within range of the beacons, a user with appropriate attributes is able to decrypt the broadcast message and obtain parameters that allow the user to perform a short or simplified login.


title: A Hybrid Data Mining Approach for Intrusion Detection on Imbalanced NSL-KDD Dataset

abstract: Intrusion detection systems aim to detect malicious viruses from computer and network traffic, which is not possible using common firewall. Most intrusion detection systems are developed based on machine learning techniques. Since datasets which used in intrusion detection are imbalanced, in the previous methods, the accuracy of detecting two attack classes, R2L and U2R, is lower than that of the normal and other attack classes. In order to overcome this issue, this study employs a hybrid approach. This hybrid approach is a combination of synthetic minority oversampling technique (SMOTE) and cluster center and nearest neighbor (CANN). Important features are selected using leave one out method (LOO). Moreover, this study employs NSL KDD dataset. Results indicate that the proposed method improves the accuracy of detecting U2R and R2L attacks in comparison to the baseline paper by 94% and 50%, respectively


title: Fundamentals of Computer Security

abstract: From the Publisher: ::: The book studies modern concepts of Computer Security. It is selfcontained in the sense that it introduces the basic mathematical background necessary to follow computer security concepts. Next we examine modern developments in Cryptography starting from private-key and public-key encryption, going through hashing, digital signatures, authentication, secret sharing, group-oriented cryptography, pseudorandomness, key establishment protocols, zero-knowledge protocols, identification and finishing with an introduction to modern e-business systems based on digital cash. Intrusion detection and access control provide examples of security systems implemented as a part of operating system. Database and network security is also discussed.


title: The SAP HANA Database -- An Architecture Overview

abstract: Requirements of enterprise applications have become much more demanding. They require the computation of complex reports on transactional data while thousands of users may read or update records of the same data. The goal of the SAP HANA database is the integration of transactional and analytical workload within the same database management system. To achieve this, a columnar engine exploits modern hardware (multiple CPU cores, large main memory, and caches), compression of database content, maximum parallelization in the database kernel, and database extensions required by enterprise applications, e.g., specialized data structures for hierarchies or support for domain specific languages. In this paper we highlight the architectural concepts employed in the SAP HANA database. We also report on insights gathered with the SAP HANA database in real-world enterprise application scenarios.


title: HeartToGo: A Personalized medicine technology for cardiovascular disease prevention and detection

abstract: To date, cardiovascular disease (CVD) is the first leading cause of global death. The Electrocardiogram (ECG) is the most widely adopted clinical tool that measures and records the electrical activity of the heart from the body surface. The mainstream resting ECG machines for CVD diagnosis and supervision can be ineffective in detecting abnormal transient heart activities, which may not occur during an individual's hospital visit. Common Holter-based portable solutions offer 24-hour ECG recording, containing hundreds of thousands of heart beats that not only are tedious and time-consuming to analyze manually but also miss the capability to provide any real-time feedback. In this study, we seek to establish a cell phone-based personalized medicine technology for CVD, capable of performing continuous monitoring and recording of ECG in real time, generating individualized cardiac health summary report in layman's language, automatically detecting abnormal CVD conditions and classifying them at any place and anytime. Specifically, we propose to develop an artificial neural network (ANN)-based machine learning technique, combining both individualized medical information and clinical ECG database data, to train the cell phone to learn to adapt to its user's physiological conditions to achieve better ECG feature extraction and more accurate CVD classification results.


title: Enabling Safe Autonomous Driving in Real-World City Traffic Using Multiple Criteria Decision Making

abstract: This paper addresses the topic of real-time decision making for autonomous city vehicles, i.e., the autonomous vehicles' ability to make appropriate driving decisions in city road traffic situations. The paper explains the overall controls system architecture, the decision making task decomposition, and focuses on how Multiple Criteria Decision Making (MCDM) is used in the process of selecting the most appropriate driving maneuver from the set of feasible ones. Experimental tests show that MCDM is suitable for this new application area.


title: Robust extraction of P300 using constrained ICA for BCI applications

abstract: P300 is a positive event-related potential used by P300-brain computer interfaces (BCIs) as a means of communication with external devices. One of the main requirements of any P300-based BCI is accuracy and time efficiency for P300 extraction and detection. Among many attempted techniques, independent component analysis (ICA) is currently the most popular P300 extraction technique. However, since ICA extracts multiple independent components (ICs), its use requires careful selection of ICs containing P300 responses, which limits the number of channels available for computational efficiency. Here, we propose a novel procedure for P300 extraction and detection using constrained independent component analysis (cICA) through which we can directly extract only P300-relevant ICs. We tested our procedure on two standard datasets collected from healthy and disabled subjects. We tested our procedure on these datasets and compared their respective performances with a conventional ICA-based procedure. Our results demonstrate that the cICA-based method was more reliable and less computationally expensive, and was able to achieve 97 and 91.6% accuracy in P300 detection from healthy and disabled subjects, respectively. In recognizing target characters and images, our approach achieved 95 and 90.25% success in healthy and disabled individuals, whereas use of ICA only achieved 83 and 72.25%, respectively. In terms of information transfer rate, our results indicate that the ICA-based procedure optimally performs with a limited number of channels (typically three), but with a higher number of available channels (>3), its performance deteriorates and the cICA-based one performs better.


title: Anomaly-Based Detection and Classification of Attacks in Cyber-Physical Systems

abstract: Cyber-physical systems are found in industrial and production systems, as well as critical infrastructures. Due to the increasing integration of IP-based technology and standard computing devices, the threat of cyber-attacks on cyber-physical systems has vastly increased. Furthermore, traditional intrusion defense strategies for IT systems are often not applicable in operational environments. In this paper we present an anomaly-based approach for detection and classification of attacks in cyber-physical systems. To test our approach, we set up a test environment with sensors, actuators and controllers widely used in industry, thus, providing system data as close as possible to reality. First, anomaly detection is used to define a model of normal system behavior by calculating outlier scores from normal system operations. This valid behavior model is then compared with new data in order to detect anomalies. Further, we trained an attack model, based on supervised attacks against the test setup, using the naive Bayes classifier. If an anomaly is detected, the classification process tries to classify the anomaly by applying the attack model and calculating prediction confidences for trained classes. To evaluate the statistical performance of our approach, we tested the model by applying an unlabeled dataset, which contains valid and anomalous data. The results show that this approach was able to detect and classify such attacks with satisfactory accuracy.


title: Requirements for Energy Efficient Edge Computing: A Survey

abstract: Internet of Things is evolving heavily in these times. One of the major obstacle is energy consumption in the IoT devices (sensor nodes and wireless gateways). The IoT devices are often battery powered wireless devices and thus reducing the energy consumption in these devices is essential to lengthen the lifetime of the device without battery change. It is possible to lengthen battery lifetime by efficient but lightweight sensor data analysis in close proximity of the sensor. Performing part of the sensor data analysis in the end device can reduce the amount of data needed to transmit wirelessly. Transmitting data wirelessly is very energy consuming task. At the same time, the privacy and security should not be compromised. It requires effective but computationally lightweight encryption schemes. This survey goes thru many aspects to consider in edge and fog devices to minimize energy consumption and thus lengthen the device and the network lifetime.


title: Visual Text Analytics: Techniques for Linguistic Information Visualization

abstract: Visual Text Analytics has been an active area of interdisciplinary research (http://textvis.lnu.se/). This interactive tutorial is designed to give attendees an introduction to the area of information visualization, with a focus on linguistic visualization. After an introduction to the basic principles of information visualization and visual analytics, this tutorial will give an overview of the broad spectrum of linguistic and text visualization techniques, as well as their application areas [3]. This will be followed by a hands-on session that will allow participants to design their own visualizations using tools (e.g., Tableau), libraries (e.g., d3.js), or applying sketching techniques [4]. Some sample datasets will be provided by the instructor. Besides general techniques, special access will be provided to use the VisArgue framework [1] for the analysis of selected datasets.


title: What does social media say about your stress

abstract: With the rise of social media such as Twitter, people are more willing to convey their stressful life events via these platforms. In a sense, it is feasible to detect stress from social media data for proactive health care. In psychology, stress is composed of stressor and stress level, where stressor further comprises of stressor event and subject. By far, little attention has been paid to estimate exact stressor and stress level from social media data, due to the following challenges: 1) stressor subject identification, 2) stressor event detection, and 3) data collection and representation. To address these problems, we devise a comprehensive scheme to measure a user's stress level from his/her social media data. In particular, we first build a benchmark dataset and extract a rich set of stress-oriented features. We then propose a novel hybrid multi-task model to detect the stressor event and subject, which is capable of modeling the relatedness among stressor events as well as stressor subjects. At last, we lookup an expert-defined stress table with the detected subject and event to estimate the stressor and stress level. Extensive experiments on real-world datasets well verify the effectiveness of our scheme.


title: Losses in hybrid and active magnetic bearings applied to Long Term Flywheel Energy Storage

abstract: In this work, Radial Active Magnetic Bearings (RAMB) and PM-biased Hybrid Radial Magnetic Bearings (HRMB) were designed and optimized in the case of the Flywheel Long Term Energy Storage (LTFES). Taking into account the amplitude of external disturbance as well as unbalance force as load capacity, the effect of losses (non including losses in the power electronic) on the both RAMB and PM-biased HRMB was studied. Flywheel discharge times have been computed and drawn for a flywheel rotating at a nominal speed of 9000 rpm and self-discharging down to half-speed (4500 rpm). The evolution of this time duration versus mass of bearings is shown for different values of unbalance forces for these two magnetic bearing configurations. (6 pages)


title: Integrating Trust and Computer Self-Efficacy with TAM: An Empirical Assessment of Customers ’ Acceptance of Banking Information Systems (BIS) in Jamaica

abstract: Financial institutions all over the world are providing banking services via information systems, such as: automated teller machines (ATMs), Internet banking, and telephone banking, in an effort to remain competitive as well as enhancing customer service. However, the acceptance of such banking information systems (BIS) in developing countries remains open. The classical Technology Acceptance Model (TAM) has been well validated over hundreds of studies in the past two decades. This study contributed to the extensive body of research of technology acceptance by attempting to validate the integration of trust and computer self-efficacy (CSE) constructs into the classical TAM model. Moreover, the key uniqueness of this work is in the context of BIS in a developing country, namely Jamaica. Based on structural equations modeling using data of 374 customers from three banks in Jamaica, this study results indicated that the classic TAM provided a better fit than the extended TAM with Trust and CSE. However, the results also indicated that trust is indeed a significant construct impacting both perceived usefulness and perceived ease-of-use. Additionally, test for gender differences indicated that across all study participants, only trust was found to be significantly different between male and female bank customers. Conclusions and recommendations for future research are also provided.


2 Rhesus hemolytic disease of the newborn : postnatal management , associated morbidity and long-term outcome


title: Spirometry in the asthmatic child: is FEF25-75 a more sensitive test than FEV1/FVC?

abstract: The use of spirometry in the assessment of children with asthma is taking on new importance with the realization that considerable airway obstruction may exist in the absence of clinically detectable abnormalities. There has been controversy over the value and relative sensitivity of various spirometry indices. This study evaluated the forced expiratory flow between 25% and 75% of vital capacity (FEF25-75), forced expired volume in 1 second (FEV1), and the ratio between the FEV1 and the forced vital capacity (FVC) in 100 asthmatic children aged 6-17 years, 29 of whom were wheezing at the time of the evaluation. All children with clinical wheezing had a FEV25-75 < 2 standard deviations (SD) below the mean (-2 SD), whereas 8 had a normal FEV1. The majority of the wheezing children had abnormalities of all 3 indices, whether expressed as < or = -2 SD or, in the case of the FEV1/FVC, arbitrarily taken as < 80%. Sixty-seven children of the entire study group had at least 1 abnormal spirometric index, but 38 of these had no clinical abnormalities. Twelve children had a reduced FEF25-75 as the only abnormality. These results suggest that FEF25-75 is a sensitive index of airway obstruction.


title: A Survey of Crowdsourcing Systems

abstract: Crowd sourcing is evolving as a distributed problem-solving and business production model in recent years. In crowd sourcing paradigm, tasks are distributed to networked people to complete such that a company's production cost can be greatly reduced. In 2003, Luis von Ahn and his colleagues pioneered the concept of "human computation", which utilizes human abilities to perform computation tasks that are difficult for computers to process. Later, the term "crowdsourcing" was coined by Jeff Howe in 2006. Since then, a lot of work in crowd sourcing has focused on different aspects of crowd sourcing, such as computational techniques and performance analysis. In this paper, we give a survey on the literature on crowd sourcing which are categorized according to their applications, algorithms, performances and datasets. This paper provides a structured view of the research on crowd sourcing to date.


title: A Novel Reconfigurable Metal Rim Integrated Open Slot Antenna for Octa-Band Smartphone Applications

abstract: In this paper, a novel reconfigurable open slot antenna has been proposed for LTE smartphone applications to cover a wide bandwidth of 698–960 and 1710–2690 MHz. The antenna is located at the bottom portion of the mobile phone and is integrated with metal rim, thereby occupying a small space and providing mechanical stability to the mobile phone. Varactor diode is used to cover the lower band frequencies, so as to achieve a good frequency coverage and antenna miniaturization. The operational principles of the antenna are studied and the final design is optimized, fabricated, and tested. It has achieved the desired impedance bandwidth and the total efficiency of minimum 50% in free space throughout the required bands. The antenna performance with mobile phone components and human hand is also been studied. Furthermore, the SAR in a human head is investigated and is found to be within allowable SAR limits. Finally a multiple-input multiple-output antenna configuration with high isolation is proposed; it has an identical reconfigurable open slot antenna integrated at the top edge of the mobile phone acting as the secondary antenna for 698–960 and 1710–2690 MHz. Thus the proposed antenna is an excellent candidate for LTE smartphones and mobile devices.


title: New baseline correction algorithm for text-line recognition with bidirectional recurrent neural networks

abstract: Many preprocessing techniques have been proposed for isolated word recognition. However, recently, recognition systems have dealt with text blocks and their compound text lines. In this paper, we propose a new preprocessing approach to efficiently correct baseline skew and fluctuations. Our approach is based on a sliding window within which the vertical position of the baseline is estimated. Segmentation of text lines into subparts is, thus, avoided. Experiments conducted on a large publicly available database (Rimes), with a BLSTM (bidirectional long short-term memory) recurrent neural network recognition system, show that our baseline correction approach highly improves performance.


title: ISOLATED WORDS RECOGNITION USING MFCC, LPC AND NEURAL NETWORK

abstract: Automatic speech recognition is an important topic of speech processing. This paper presents the use of an Artificial Neural Network (ANN) for isolated word recognition. The Pre-processing is done and voiced speech is detected based on energy and zero crossing rates (ZCR). The proposed approach used in speech recognition is Mel Frequency Cepstral Coefficients (MFCC) and combine features of both MFCC and Linear Predictive Coding (LPC). The back-propagation is used as a classifier. The recognition accuracy is increased when combine features of both LPC and MFCC are used as compared to only MFCC approach using Neural Network as a classifier..


Which cognitive abilities underlie computational thinking? Criterion validity of the Computational Thinking Test


title: Inner control loops approach to control the islanded photovoltaic microgrid

abstract: The intermittent character of the photovoltaic generator, power electronic converters and load dynamic are the main factors leading operation instability in islanded microgrids. The necessity of the immediate control within the micro-source is very important since it impacts the interaction between micro-sources in the microgrid. This control level must be characterized by a fast and accurate response in order to control the operation point of the photovoltaic generator of the microgrid, and also the injected currents into the microgrid, as well as the amplitude and the frequency of the voltage at the photovoltaic connecting point at AC side. However, its performance depends on the control structure and the connecting mode of the photovoltaic generator. In this present paper, we propose a new structure of the internal control applied in islanded photovoltaic microgrids. It advocates the use of the close control multi-objective controller which is developed and implemented with Matlab/Simulink. Accordingly, the simulation results obtained show that this control approach improves the stability operation of the islanded microgrid while allowing maximum power transfer from DC to AC side.


title: Adaptive load shedding based on combined frequency and voltage stability assessment using synchrophasor measurements

abstract: Under frequency load shedding (UFLS) and under voltage load shedding (UVLS) are attracting more attention, as large disturbances occur more frequently than in the past. Usually, these two schemes work independently from each other, and are not designed in an integrated way to exploit their combined effect on load shedding. Besides, reactive power is seldom considered in the load shedding process. To fill this gap, we propose in this paper a new centralized, adaptive load shedding algorithm, which uses both voltage and frequency information provided by phasor measurement units (PMUs). The main contribution of the new method is the consideration of reactive power together with active power in the load shedding strategy. Therefore, this method addresses the combined voltage and frequency stability issues better than the independent approaches. The new method is tested on the IEEE 39-Bus system, in order to compare it with other methods. Simulation results show that, after large disturbance, this method can bring the system back to a new stable steady state that is better from the point of view of frequency and voltage stability, and loadability.


abstract: Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that 'similar" points in input space are mapped to nearby points on the manifold. We present a method - called Dimensionality Reduction by Learning an Invariant Mapping (DrLIM) - for learning a globally coherent nonlinear function that maps the data evenly to the output manifold. The learning relies solely on neighborhood relationships and does not require any distancemeasure in the input space. The method can learn mappings that are invariant to certain transformations of the inputs, as is demonstrated with a number of experiments. Comparisons are made to other techniques, in particular LLE.


title: Conceptual and Database Modelling of Graph Databases

abstract: Comparing graph databases with traditional, e.g., relational databases, some important database features are often missing there. Particularly, a graph database schema including integrity constraints is not explicitly defined, also a conceptual modelling is not used at all. It is hard to check a consistency of the graph database, because almost no integrity constraints are defined. In the paper, we discuss these issues and present current possibilities and challenges in graph database modelling. Also a conceptual level of a graph database design is considered. We propose a sufficient conceptual model and show its relationship to a graph database model. We focus also on integrity constraints modelling functional dependencies between entity types, which reminds modelling functional dependencies known from relational databases and extend them to conditional functional dependencies.


Interactivity, Game Creation, Design, Learning, and Innovation


title: ANNETT-O: An Ontology for Describing Artificial Neural Network Evaluation, Topology and Training

abstract: Deep learning models, while effective and versatile, are becoming increasingly complex, often including multiple overlapping networks of arbitrary depths, multiple objectives and non-intuitive training methodologies. This makes it increasingly difficult for researchers and practitioners to design, train and understand them. In this paper we present ANNETT-O, a much-needed, generic and computer-actionable vocabulary for researchers and practitioners to describe their deep learning configurations, training procedures and experiments. The proposed ontology focuses on topological, training and evaluation aspects of complex deep neural configurations, while keeping peripheral entities more succinct. Knowledge bases implementing ANNETT-O can support a wide variety of queries, providing relevant insights to users. In addition to a detailed description of the ontology, we demonstrate its suitability to the task via a number of hypothetical use-cases of increasing complexity.


“No Free Rides, No Excuses”: Film Stereotypes of Urban Working Class Students


Dave the debater: a retrieval-based and generative argumentative dialogue agent


title: Silicon germanium programmable circuits for gigahertz applications

abstract: Implementation of a silicon germanium (SiGe) field programmable gate array (FPGA) has been described. The reconfigurable basic cell (BC) that evolved from the Xilinx XC6200 has been redesigned to achieve high speed with lower power consumption. The propagation delay of the BC in comparison to the BC implemented in the earlier generation SiGe process has been reduced to 18% of its original value (from 240 to 42 ps) and the power consumption has been comparably reduced. The range of power reduction is from 13% of its original value when the BC is fully turned on down to 2% when the power saving scheme is applied. A 20times20 SiGe FPGA with physical dimensions of 4.5times4.8 mm has been fabricated using the IBM 120 GHz (7HP) process. To deliver a 10 GHz clock, an H tree has been designed and implemented with reduced skew. To demonstrate its performance, a 4:1 multiplexer (MUX) has been mapped for comparison with various CMOS FPGAs. The SiGe FPGA can achieve an 8 Gbps transmission rate, which is a 40 times improvement over the same implementation on a Xilinx Virtex CMOS FPGA. Other comparisons between the SiGe FPGA and commercial FPGAs have also been included. From simulations and measurements, the SiGe FPGAs have been shown to have high performance that can successfully tackle gigahertz applications


title: Accelerating Comparative Genomics Workflows in a Distributed Environment with Optimized Data Partitioning

abstract: The advent of new sequencing technology has generated massive amounts of biological data at unprecedented rates. High-throughput bioinformatics tools are required to keep pace with this. Here, we implement a workflow-based model for parallelizing the data intensive task of genome alignment and variant calling with BWA and GATK's Haplotype Caller. We explore different approaches of partitioning data and how each affect the run time. We observe granularity-based partitioning for BWA and alignment-based partitioning for Halo type Caller to be the optimal choices for the pipeline. We identify the various challenges encountered while developing such an application and provide an insight into addressing them. We report significant performance improvements, from 12 days to 4 hours, while running the BWA-GATK pipeline using 100 nodes for analyzing high-coverage oak tree data.


title: Interpregnancy weight change and risk of adverse pregnancy outcomes: a population-based study

abstract: BACKGROUND ::: Maternal obesity has been positively associated with risk of adverse pregnancy outcomes, but evidence of a causal relation is scarce. Causality would be lent support if temporal changes in weight affected risk of adverse pregnancy outcomes. ::: ::: ::: METHODS ::: We examined the associations between change in prepregnancy body-mass index (BMI) from the first to the second pregnancies, and the risk of adverse outcomes during the second pregnancy in a nationwide Swedish study of 151 025 women who had their first two consecutive singleton births between 1992 and 2001. ::: ::: ::: FINDINGS ::: Compared with women whose BMI changed between -1.0 and 0.9 units, the adjusted odds ratios for adverse pregnancy outcomes for those who gained 3 or more units during an average 2 years were: pre-eclampsia, 1.78 (95% CI 1.52-2.08); gestational hypertension 1.76 (1.39-2.23); gestational diabetes 2.09 (1.68-2.61); caesarean delivery 1.32 (1.22-1.44); stillbirth 1.63 (1.20-2.21); and large-for-gestational-age birth 1.87 (1.72-2.04). The associations were linearly related to the amount of weight change and were also noted in women who had a healthy prepregnancy BMI for both pregnancies. ::: ::: ::: INTERPRETATION ::: These findings lend support to a causal relation between being overweight or obese and risks of adverse pregnancy outcomes. Additionally they suggest that modest increases in BMI before pregnancy could result in perinatal complications, even if a woman does not become overweight. Our results provide robust epidemiological evidence for advocating weight loss in overweight and obese women who are planning to become pregnant and, to prevent weight gain before pregnancy in women with healthy BMIs.


title: The Leabra Cognitive Architecture: How to Play 20 Principles with Nature and Win!

abstract: This chapter provides a synthetic review of a long-term effort to produce an internally consistent theory of the neural basis of human cognition, the Leabra cognitive architecture, which explains a great deal of brain and behavioral data. In a highly influential commentary, Allen Newell (1973) first issued a call for a more comprehensive, principled approach to studying cognition. “You can’t play 20 questions with nature, and win,” he said, alluding to the old parlor guessing game involving 20 yes or no questions. His point was that cognition, and the brain that gives rise to it, is just too complex and multidimensional a system to ever hope that a series of narrowly framed experiments and/or models would ever be able to characterize it. Instead, a single cognitive architecture should be used to simulate a wide range of data at many levels in a cumulative manner. However, these cognitive architectures tend to be complex and difficult to fully comprehend. In an attempt to most clearly and simply present the Leabra biologicallybased cognitive architecture, we articulate 20 principles that motivate its design, at multiple levels of analysis.


title: A Self-Supervised Terrain Roughness Estimator for Off-Road Autonomous Driving

abstract: We present a machine learning approach for estimating the second derivative of a drivable surface, its roughness. Robot perception generally focuses on the first derivative, obstacle detection. However, the second derivative is also important due to its direct relation (with speed) to the shock the vehicle experiences. Knowing the second derivative allows a vehicle to slow down in advance of rough terrain. Estimating the second derivative is challenging due to uncertainty. For example, at range, laser readings may be so sparse that significant information about the surface is missing. Also, a high degree of precision is required in projecting laser readings. This precision may be unavailable due to latency or error in the pose estimation. We model these sources of error as a multivariate polynomial. Its coefficients are learned using the shock data as ground truth -- the accelerometers are used to train the lasers. The resulting classifier operates on individual laser readings from a road surface described by a 3D point cloud. The classifier identifies sections of road where the second derivative is likely to be large. Thus, the vehicle can slow down in advance, reducing the shock it experiences. The algorithm is an evolution of one we used in the 2005 DARPA Grand Challenge. We analyze it using data from that route.


title: Segmentation of Offline Handwritten Bengali Script

abstract: Character segmentation has long been one of the most critical areas of optical character recognition process. Through this operation, an image of a sequence of characters, which may be connected in some cases, is decomposed into sub-images of individual alphabetic symbols. In this paper, segmentation of cursive handwritten script of world's fourth popular language, Bengali, is considered. Unlike English script, Bengali handwritten characters and its components often encircle the main character, making the conventional segmentation methodologies inapplicable. Experimental results, using the proposed segmentation technique, on sample cursive handwritten data containing 218 ideal segmentation points show a success rate of 97.7%. Further feature-analysis on these segments may lead to actual recognition of handwritten cursive Bengali script.


From STEM to STEAM: Toward a Human-Centered Education


title: Role of Nitric Oxide Synthases in Parkinson’s Disease: A Review on the Antioxidant and Anti-inflammatory Activity of Polyphenols

abstract: Natural polyphenols can exert protective action on a number of pathological conditions including neurodegenerative disorders. The neuroprotective effects of many polyphenols rely on their ability to permeate brain barrier and here directly scavenge pathological concentration of reactive oxygen and nitrogen species and chelate transition metal ions. Importantly, polyphenols modulate neuroinflammation by inhibiting the expression of inflammatory genes and the level of intracellular antioxidants. Parkinson’s disease (PD) is a neurodegenerative disorder characterized by several abnormalities including inflammation, mitochondrial dysfunction, iron accumulation and oxidative stress. There is considerable evidence showing that cellular oxidative damage occurring in PD might result also from the actions of altered production of nitric oxide (NO). Indeed, high levels of neuronal and inducible NO synthase (NOS) were found in substantia nigra of patients and animal models of PD. Here, we evaluate the involvement of NOS/NO in PD and explore the neuroprotective activity of natural polyphenol compounds in terms of anti-inflammatory and antioxidant action.


title: Network Architecture of Bidirectional Visible Light Communication and Passive Optical Network

abstract: Network architectures to combine the fiber network outside the building and the visible light communication (VLC) access network inside the building are important for effectively distributing the high-capacity data to different users. In this paper, we propose a bidirectional network combining the VLC access network and the time–wavelength-division-multiplexed passive optical network (TWDM-PON). Orthogonal frequency division multiplexed modulation is used since it offers high spectral efficiency for both the TWDM-PON and the VLC access network. Low-cost phosphor-based white-light light-emitting diodes are (LEDs) used; hence, the VLC access network can combine with the lighting systems.


title: Data Trustworthiness in Wireless Sensor Networks

abstract: Wireless Sensor Networks (WSN) comprises of tiny wireless sensor nodes installed in the terrain for continuous observation of physical or environmental conditions. Finding data trustworthiness is a prime pre-processing action in WSN because of harsh environment producing faulty data and insecure data transfer over WSN. The trustworthy of the data generated from sensor nodes play an important role to make critical decision. In this work, we propose a Data Trust Management Scheme (DTMS) to address the issue by assigning the trust score to data items. The proposed DTMS detects the data fault with the help of temporal and spatial correlations. The provenance data is used to evaluate the trust score of data item by similarity of value and provenance. The data trust score is utilized for making decision. Implementation of the proposed DTMS is done by simulations. Results show that the proposed DTMS detects untrustworthy data and score the data items which are useful for taking critical decisions.


title: Dual-Band and Dual-Circularly Polarized Shared-Aperture Array Antennas With Single-Layer Substrate

abstract: This paper presents a new approach to implement planar shared-aperture dual-band dual-circular polarization (CP) array antennas. The antennas can be fabricated on a single-layer substrate and extended to a larger array easily. In this approach, each array element is obtained by connecting two patches working at different frequencies directly. To form arrays with higher gain, two kinds of feed networks are described, which can be applied in systems where narrowband and wideband are needed, respectively. One is using the conventional feed network and the other is using the sequential rotation technique to further improve the CP axial ratio (AR) performance. Two prototype arrays with $4 \times 4$ elements are fabricated and tested in $X/Ku$ bands. Experimental results show that good CP characteristics are obtained, which agree well with the simulation results. For the first narrow-band prototype array, the 3-dB AR bandwidth is around 1.5% for both bands. For the second array using the sequential rotation technique, the bandwidth of return loss and AR are wider. In the lower band centered at 12.1 GHz, the ${-}10\hbox{-}\text{dB}$ return loss bandwidth is 8.3% and the 3-dB AR bandwidth is 14.2% [right-hand CP (RHCP)]; in the higher band centered at 17.4 GHz, the corresponding data are 18.9% and 14.9% [left-hand CP (LHCP)], respectively.


title: Bayesian Model for Multiple Change-Points Detection in Multivariate Time Series

abstract: This paper addresses the issue of detecting change-points in time series. The proposed model, called the Bernoulli Detector, is presented first in a univariate context. This approach differs from existing counterparts by making only assumptions on the nature of the change-points, and does not depend on hypothesis on the distribution of the data, contrary to the parametric methods. It relies on the combination of a local robust statistical test, based on the computation of ranks and acting on individual time segments, with a global Bayesian framework able to optimize the change-points configurations from multiple local statistics, provided as $p$ -values. The control of the detection of a single change-point is proved even for small samples. The interest of such a generalizable nonparametric approach is shown on simulated data by the good performances attained for Gaussian noise as well as in presence of outliers, without adapting the model. The model is extended to the multivariate case by introducing the probabilities that the change-points affect simultaneously several time series. The method presents then the advantage to detect both unique and shared change-points for each signal. We finally illustrate our algorithm with real datasets from energy monitoring and genomic. Segmentations are compared to state-of-the-art approaches like the group lasso and the BARD algorithm.


title: Real time control of ball and beam system with model reference adaptive control strategy using MIT rule

abstract: During real time control of a dynamic system, one needs to design control systems with advanced control strategies to handle inherent nonlinearities and disturbances. This paper deals with the designing of a model reference adaptive control system with the use of MIT rule for real time control of a ball and beam system. This paper uses the gradient theory to develop MIT rule in which one or more parameters of adaptive controller needs to be adjusted so that the plant could track the reference model. A linearized model of ball and beam system is used in this paper to design the controller on MATLAB and the designed controller is then applied for real time control of ball and beam system. Simulations carried on SIMULINK and MATLAB show good performance of the designed adaptive controller in real time.