title: Strepsiptera, Phylogenomics and the Long Branch Attraction Problem

abstract: Insect phylogeny has recently been the focus of renewed interest as advances in sequencing techniques make it possible to rapidly generate large amounts of genomic or transcriptomic data for a species of interest. However, large numbers of markers are not sufficient to guarantee accurate phylogenetic reconstruction, and the choice of the model of sequence evolution as well as adequate taxonomic sampling are as important for phylogenomic studies as they are for single-gene phylogenies. Recently, the sequence of the genome of a strepsipteran has been published and used to place Strepsiptera as sister group to Coleoptera. However, this conclusion relied on a data set that did not include representatives of Neuropterida or of coleopteran lineages formerly proposed to be related to Strepsiptera. Furthermore, it did not use models that are robust against the long branch attraction artifact. Here we have sequenced the transcriptomes of seven key species to complete a data set comprising 36 species to study the higher level phylogeny of insects, with a particular focus on Neuropteroidea (Coleoptera, Strepsiptera, Neuropterida), especially on coleopteran taxa considered as potential close relatives of Strepsiptera. Using models robust against the long branch attraction artifact we find a highly resolved phylogeny that confirms the position of Strepsiptera as a sister group to Coleoptera, rather than as an internal clade of Coleoptera, and sheds new light onto the phylogeny of Neuropteroidea.


title: Robust Watermarking of Compressed and Encrypted JPEG2000 Images

abstract: Digital asset management systems (DAMS) generally handle media data in a compressed and encrypted form. It is sometimes necessary to watermark these compressed encrypted media items in the compressed-encrypted domain itself for tamper detection or ownership declaration or copyright management purposes. It is a challenge to watermark these compressed encrypted streams as the compression process would have packed the information of raw media into a low number of bits and encryption would have randomized the compressed bit stream. Attempting to watermark such a randomized bit stream can cause a dramatic degradation of the media quality. Thus it is necessary to choose an encryption scheme that is both secure and will allow watermarking in a predictable manner in the compressed encrypted domain. In this paper, we propose a robust watermarking algorithm to watermark JPEG2000 compressed and encrypted images. The encryption algorithm we propose to use is a stream cipher. While the proposed technique embeds watermark in the compressed-encrypted domain, the extraction of watermark can be done in the decrypted domain. We investigate in detail the embedding capacity, robustness, perceptual quality and security of the proposed algorithm, using these watermarking schemes: Spread Spectrum (SS), Scalar Costa Scheme Quantization Index Modulation (SCS-QIM), and Rational Dither Modulation (RDM).


title: The integrated curriculum in medical education: AMEE Guide No. 96

abstract: The popularity of the term "integrated curriculum" has grown immensely in medical education over the last two decades, but what does this term mean and how do we go about its design, implementation, and evaluation? Definitions and application of the term vary greatly in the literature, spanning from the integration of content within a single lecture to the integration of a medical school's comprehensive curriculum. Taking into account the integrated curriculum's historic and evolving base of knowledge and theory, its support from many national medical education organizations, and the ever-increasing body of published examples, we deem it necessary to present a guide to review and promote further development of the integrated curriculum movement in medical education with an international perspective. We introduce the history and theory behind integration and provide theoretical models alongside published examples of common variations of an integrated curriculum. In addition, we identify three areas of particular need when developing an ideal integrated curriculum, leading us to propose the use of a new, clarified definition of "integrated curriculum", and offer a review of strategies to evaluate the impact of an integrated curriculum on the learner. This Guide is presented to assist educators in the design, implementation, and evaluation of a thoroughly integrated medical school curriculum.


title: Predicting Development Effort from User Stories

abstract: In this paper, we propose a method for predicting development effort based on user stories. Such approach is well suited for Agile software projects where requirements are developed along with the project and only sketched in a rough manner. We apply the proposed method to two industrial Agile software projects of very different size and structure. We show that such effort estimation works reasonably well if user stories are written in a structured way.


title: The Prisonized Old Head: Intergenerational Socialization and the Fusion of Ghetto and Prison Culture:

abstract: Over the last four decades, the United States’ criminal justice system has undergone a historic expansion, which has disproportionately impacted poor urban neighborhoods. The meteoric rise in the p...


title: Information-Flow Security for a Core of JavaScript

abstract: Tracking information flow in dynamic languages remains an important and intricate problem. This paper makes substantial headway toward understanding the main challenges and resolving them. We identify language constructs that constitute a core of Java Script: objects, higher-order functions, exceptions, and dynamic code evaluation. The core is powerful enough to naturally encode native constructs as arrays, as well as functionalities of Java Script's API from the document object model (DOM) related to document tree manipulation and event processing. As the main contribution, we develop a dynamic type system that guarantees information-flow security for this language.


title: Evaluation of pooling operations in convolutional architectures for object recognition

abstract: A common practice to gain invariant features in object recognition models is to aggregate multiple low-level features over a small neighborhood. However, the differences between those models makes a comparison of the properties of different aggregation functions hard. Our aim is to gain insight into different functions by directly comparing them on a fixed architecture for several common object recognition tasks. Empirical results show that a maximum pooling operation significantly outperforms subsampling operations. Despite their shift-invariant properties, overlapping pooling windows are no significant improvement over nonoverlapping pooling windows. By applying this knowledge, we achieve state-of-the-art error rates of 4.57% on the NORB normalized-uniform dataset and 5.6% on the NORB jittered-cluttered dataset.


title: Best Practices for Data Centers: Lessons Learned from Benchmarking 22 Data Centers

abstract: Over the past few years, the authors benchmarked 22 data center buildings. From this effort, we have determined that data centers can be over 40 times as energy intensive as conventional office buildings. Studying the more efficient of these facilities enabled us to compile a set of “best-practice” technologies for energy efficiency. These best practices include: improved air management, emphasizing control and isolation of hot and cold air streams; rightsizing central plants and ventilation systems to operate efficiently both at inception and as the data center load increases over time; optimized central chiller plants, designed and controlled to maximize overall cooling plant efficiency, central air-handling units, in lieu of distributed units; “free cooling” from either air-side or water-side economizers; alternative humidity control, including elimination of control conflicts and the use of direct evaporative cooling; improved uninterruptible power supplies; high-efficiency computer power supplies; on-site generation combined with special chillers for cooling using the waste heat; direct liquid cooling of racks or computers; and lowering the standby losses of standby generation systems. Other benchmarking findings include power densities from 5 to nearly 100 Watts per square foot; though lower than originally predicted, these densities are growing. A 5:1 variation in cooling effectiveness index (ratio of cooling power to computer power) was found, as well as large variations in power distribution efficiency and overall center performance (ratio of computer power to total building power). These observed variations indicate the potential of energy savings achievable through the implementation of best practices in the design and operation of data centers.


title: Pharmacological Profile of Lurasidone, a Novel Antipsychotic Agent with Potent 5-Hydroxytryptamine 7 (5-HT7) and 5-HT1A Receptor Activity

abstract: Lurasidone [(3aR,4S,7R,7aS)-2-[(1R,2R)-2-[4-(1,2-benzisothiazol-3-yl)piperazin-1-ylmethyl]cyclohexylmethyl]hexahydro-4,7-methano-2H-isoindole-1,3-dione hydrochloride; SM-13496] is an azapirone derivative and a novel antipsychotic candidate. The objective of the current studies was to investigate the in vitro and in vivo pharmacological properties of lurasidone. Receptor binding affinities of lurasidone and several antipsychotic drugs were tested under comparable assay conditions using cloned human receptors or membrane fractions prepared from animal tissue. Lurasidone was found to have potent binding affinity for dopamine D(2), 5-hydroxytryptamine 2A (5-HT(2A)), 5-HT(7), 5-HT(1A), and noradrenaline alpha(2C) receptors. Affinity for noradrenaline alpha(1), alpha(2A), and 5-HT(2C) receptors was weak, whereas affinity for histamine H(1) and muscarinic acetylcholine receptors was negligible. In vitro functional assays demonstrated that lurasidone acts as an antagonist at D(2) and 5-HT(7) receptors and as a partial agonist at the 5-HT(1A) receptor subtype. Lurasidone showed potent effects predictive of antipsychotic activity, such as inhibition of methamphetamine-induced hyperactivity and apomorphine-induced stereotyped behavior in rats, similar to other antipsychotics. Furthermore, lurasidone had only weak extrapyramidal effects in rodent models. In animal models of anxiety disorders and depression, treatment with lurasidone was associated with significant improvement. Lurasidone showed a preferential effect on the frontal cortex (versus striatum) in increasing dopamine turnover. Anti-alpha(1)-noradrenergic, anticholinergic, and central nervous system (CNS) depressant actions of lurasidone were also very weak. These results demonstrate that lurasidone possesses antipsychotic activity and antidepressant- or anxiolytic-like effects with potentially reduced liability for extrapyramidal and CNS depressant side effects.


Multiple Intelligences Learning-Based For Islamic Teaching In The Elementary School


title: Architecture of a cyber defense competition

abstract: This paper describes the effort involve in executing cyber defense exercise while focusing on the white cell and red forces activities during the 2003 inter-academy cyber defense exercise (CDE). These exercise components were led by the National Security Agency and were comprised of security professionals from Carnegie Mellon University's CERT, the United States Air Force, and the United States Army. This hands-on experience provided the capstone educational experience for information assurance students at the U.S. service academies. The white cell developed the scenarios and anomalies, established the scoring criteria, refereed the exercise, and determined the winner based on the effectiveness of each academy to minimize the impact to their networks from the red forces network intelligence gathering, intrusion, attack and evaluation. To understand better all that is involved, this paper takes advantage of the authors three years of experience in directing the activities associated with the planning and execution of the 2003 exercise.


title: A Fog Operating System for User-Oriented IoT Services: Challenges and Research Directions

abstract: As the proliferation of mobile devices has ignited cloud computing, it is expected that increasing development and deployment of IoT services will expedite the era of fog computing. Fog computing brings computing, storage, and networking even closer to end users and devices for services with better QoS. We introduce FogOS, a fog computing architecture for IoT services. We take the perspective of designing an operating system, practicing the architectural lessons from the long history of operating systems. We focus on addressing the challenges raised by the diversity and heterogeneity of IoT services and edge devices that are owned by individuals and different owners, and presenting how FogOS is designed to effectively and efficiently provide and manage such IoT services. We provide a city-scale surveillance use case to demonstrate FogOS in action.


title: Multi-task deep neural network for multi-label learning

abstract: This paper proposes a multi-task deep neural network (MT-DNN) architecture to handle the multi-label learning problem, in which each label learning is defined as a binary classification task, i.e., a positive class for “an instance owns this label” and a negative class for “an instance does not own this label”. Multi-label learning is accordingly transformed to multiple binary-class classification tasks. Considering that a deep neural nets (DNN) architecture can learn good intermediate representations shared across tasks, we generalize one classification task of traditional DNN into multiple binary classification tasks through defining the output layer with a negative class node and a positive class node for each label. After a similar pretraining process to deep belief nets, we redefine the label assignment error of MT-DNN and perform the back-propagation algorithm to fine-tune the network. To evaluate the proposed model, we carry out image annotation experiments on two public image datasets, with 2000 images and 30,000 images respectively. The experiments demonstrate that the proposed model achieves the state-of-the-art performance.


Degenerative myopathy in ratites


title: Scanning physical interaction behavior of 3D objects

abstract: We describe a system for constructing computer models of several aspects of physical interaction behavior, by scanning the response of real objects. The behaviors we can successfully scan and model include deformation response, contact textures for interaction with force-feedback, and contact sounds. The system we describe uses a highly automated robotic facility that can scan behavior models of whole objects. We provide a comprehensive view of the modeling process, including selection of model structure, measurement, estimation, and rendering at interactive rates. The results are demonstrated with two examples: a soft stuffed toy which has significant deformation behavior, and a hard clay pot which has significant contact textures and sounds. The results described here make it possible to quickly construct physical interaction models of objects for applications in games, animation, and e-commerce.


title: The Power of Testing Memory -- Basic Research and Implications for Educational Practice

abstract: A powerful way of improving one's memory for material is to be tested on that material. Tests enhance later retention more than additional study of the material, even when tests are given without feedback. This surpris- ing phenomenon is called the testing effect, and although it has been studied by cognitive psychologists sporadically over the years, today there is a renewed effort to learn why testing is effective and to apply testing in educational settings. In this article, we selectively review laboratory studies that reveal the power of testing in improving re- tention and then turn to studies that demonstrate the basic effects in educational settings. We also consider the related concepts of dynamic testing and formative assess- ment as other means of using tests to improve learning. Finally, we consider some negative consequences of testing that may occur in certain circumstances, though these negative effects are often small and do not cancel out the large positive effects of testing. Frequent testing in the classroom may boost educational achievement at all levels of education. In contemporary educational circles, the concept of testing has a dubious reputation, and many educators believe that testing is overemphasized in today's schools. By ''testing,'' most com- mentators mean using standardized tests to assess students. During the 20th century, the educational testing movement produced numerous assessment devices used throughout edu- cation systems in most countries, from prekindergarten through graduate school. However, in this review, we discuss primarily the kind of testing that occurs in classrooms or that students engage in while studying (self-testing). Some educators argue


title: Depression Scale Recognition from Audio, Visual and Text Analysis

abstract: Depression is a major mental health disorder that is rapidly affecting lives worldwide. Depression not only impacts emotional but also physical and psychological state of the person. Its symptoms include lack of interest in daily activities, feeling low, anxiety, frustration, loss of weight and even feeling of self-hatred. This report describes work done by us for Audio Visual Emotion Challenge (AVEC) 2017 during our second year BTech summer internship. With the increase in demand to detect depression automatically with the help of machine learning algorithms, we present our multimodal feature extraction and decision level fusion approach for the same. Features are extracted by processing on the provided Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) database. Gaussian Mixture Model (GMM) clustering and Fisher vector approach were applied on the visual data; statistical descriptors on gaze, pose; low level audio features and head pose and text features were also extracted. Classification is done on fused as well as independent features using Support Vector Machine (SVM) and neural networks. The results obtained were able to cross the provided baseline on validation data set by 17% on audio features and 24.5% on video features.


title: A topology control approach for utilizing multiple channels in multi-radio wireless mesh networks

abstract: We consider the channel assignment problem in a multi-radio wireless mesh network that involves assigning channels to radio interfaces for achieving efficient channel utilization. We propose the notion of a traffic-independent base channel assignment to ease coordination and enable dynamic, efficient and flexible channel assignment. We present a novel formulation of the base channel assignment as a topology control problem, and show that the resulting optimization problem is NP-complete. We then develop a new greedy heuristic channel assignment algorithm (termed CLICA) for finding connected, low interference topologies by utilizing multiple channels. Our extensive simulation studies show that the proposed CLICA algorithm can provide large reduction in interference (even with a small number of radios per node), which in turn leads to significant gains in both link layer and multihop performance in 802.11-based multi-radio mesh networks.


title: Efficiently Creating 3D Training Data for Fine Hand Pose Estimation

abstract: While many recent hand pose estimation methods critically rely on a training set of labelled frames, the creation of such a dataset is a challenging task that has been overlooked so far. As a result, existing datasets are limited to a few sequences and individuals, with limited accuracy, and this prevents these methods from delivering their full potential. We propose a semi-automated method for efficiently and accurately labeling each frame of a hand depth video with the corresponding 3D locations of the joints: The user is asked to provide only an estimate of the 2D reprojections of the visible joints in some reference frames, which are automatically selected to minimize the labeling work by efficiently optimizing a sub-modular loss function. We then exploit spatial, temporal, and appearance constraints to retrieve the full 3D poses of the hand over the complete sequence. We show that this data can be used to train a recent state-of-the-art hand pose estimation method, leading to increased accuracy. The code and dataset can be found on our website this https URL


title: POMDP-Based Statistical Spoken Dialog Systems : A Review This paper presents the theory and practice of belief tracking , policy optimization , parameter estimation , and fast learning

abstract: Statistical dialog systems (SDSs) are motivated by the need for a data-driven framework that reduces the cost of laboriously handcrafting complex dialog managers and that provides robustness against the errors created by speech re- cognizers operating in noisy environments. By including an explicit Bayesian model of uncertainty and by optimizing the policy via a reward-driven process, partially observable Markov decision processes (POMDPs) provide such a frame- work. However, exact model representation and optimization is computationally intractable. Hence, the practical application of POMDP-based systems requires efficient algorithms and carefully constructed approximations. This review article pro- vides an overview of the current state of the art in the devel- opment of POMDP-based spoken dialog systems.


GQ ( λ ) : A general gradient algorithm for temporal-difference prediction learning with eligibility traces


Three-Dimensional CT Image Segmentation by Combining 2 D Fully Convolutional Network with 3 D Majority Voting


Princess Vera Gedroits: military surgeon, poet, and author.


title: Machine learning framework for analysis of transport through complex networks in porous, granular media: A focus on permeability.

abstract: We present a data-driven framework to study the relationship between fluid flow at the macroscale and the internal pore structure, across the micro- and mesoscales, in porous, granular media. Sphere packings with varying particle size distribution and confining pressure are generated using the discrete element method. For each sample, a finite element analysis of the fluid flow is performed to compute the permeability. We construct a pore network and a particle contact network to quantify the connectivity of the pores and particles across the mesoscopic spatial scales. Machine learning techniques for feature selection are employed to identify sets of microstructural properties and multiscale complex network features that optimally characterize permeability. We find a linear correlation (in log-log scale) between permeability and the average closeness centrality of the weighted pore network. With the pore network links weighted by the local conductance, the average closeness centrality represents a multiscale measure of efficiency of flow through the pore network in terms of the mean geodesic distance (or shortest path) between all pore bodies in the pore network. Specifically, this study objectively quantifies a hypothesized link between high permeability and efficient shortest paths that thread through relatively large pore bodies connected to each other by high conductance pore throats, embodying connectivity and pore structure.


title: Design and implementation of low cost ECG monitoring system for the patient using smart device

abstract: Cardiovascular disease (CSD) has become the leading cause of death worldwide in recent years. This CSD is the most challenging problem for detection or identification in early stages of patients. This research work approaches to develop an ECG signal generator at very low cost for the patients who can receive his/her ECG signal and detect the probability of cardiovascular diseases instantly. This ECG signal is transmitted via Bluetooth/Wi-Fi/Zigbee module to smart device with support software simulation where feature extraction and detection algorithm is setup for cardiovascular disease. This network can be connected with the doctors and hospitals to get the fastest treatment. In this paper, we have also proposed extraction and detection algorithm for detecting of CSD. This proposed idea is to contribute to bring under control heart diseases and also act as an expected results in health care service to patients in remote area.


title: Fast brief practical DFA minimization

abstract: Minimization of deterministic finite automata has traditionally required complicated programs and correctness proofs, and taken O(nklogn) time, where n is the number of states and k the size of the alphabet. Here a short, memory-efficient program is presented that runs in O(n+mlogm), or even in O(n+mlogn), time, where m is the number of transitions. The program is complete with input, output, and the removal of irrelevant parts of the automaton. Its invariant-style correctness proof is relatively short.


title: Pruning Convolutional Neural Networks for Resource Efficient Transfer Learning

abstract: We propose a new framework for pruning convolutional kernels in neural networks to enable efficient inference, focusing on transfer learning where large and potentially unwieldy pretrained networks are adapted to specialized tasks. We interleave greedy criteria-based pruning with fine-tuning by backpropagation - a computationally efficient procedure that maintains good generalization in the pruned network. We propose a new criterion based on an efficient first-order Taylor expansion to approximate the absolute change in training cost induced by pruning a network component. After normalization, the proposed criterion scales appropriately across all layers of a deep CNN, eliminating the need for per-layer sensitivity analysis. The proposed criterion demonstrates superior performance compared to other criteria, such as the norm of kernel weights or average feature map activation.


title: Crop Yield Prediction Using Deep Neural Networks

abstract: Crop yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions. Accurate yield prediction requires fundamental understanding of the functional relationship between yield and these interactive factors, and to reveal such relationship requires both comprehensive datasets and powerful algorithms. In the 2018 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the genotype and yield performances of 2,267 maize hybrids planted in 2,247 locations between 2008 and 2016 and asked participants to predict the yield performance in 2017. As one of the winning teams, we designed a deep neural network (DNN) approach that took advantage of state-of-the-art modeling and solution techniques. Our model was found to have a superior prediction accuracy, with a root-mean-square-error (RMSE) being 12\% of the average yield and 50\% of the standard deviation for the validation dataset using predicted weather data. With perfect weather data, the RMSE would be reduced to 11\% of the average yield and 46\% of the standard deviation. We also performed feature selection based on the trained DNN model, which successfully decreased the dimension of the input space without significant drop in the prediction accuracy. Our computational results suggested that this model significantly outperformed other popular methods such as Lasso, shallow neural networks (SNN), and regression tree (RT). The results also revealed that environmental factors had a greater effect on the crop yield than genotype.


title: MatchZoo: A Toolkit for Deep Text Matching

abstract: In recent years, deep neural models have been widely adopted for text matching tasks, such as question answering and information retrieval, showing improved performance as compared with previous methods. In this paper, we introduce the MatchZoo toolkit that aims to facilitate the designing, comparing and sharing of deep text matching models. Specifically, the toolkit provides a unified data preparation module for different text matching problems, a flexible layer-based model construction process, and a variety of training objectives and evaluation metrics. In addition, the toolkit has implemented two schools of representative deep text matching models, namely representation-focused models and interaction-focused models. Finally, users can easily modify existing models, create and share their own models for text matching in MatchZoo.


title: Improving the generalization of disease stage classification with deep CNN for Glioma histopathological images

abstract: In the field of histopathology, computer-assisted diagnosis systems are important in obtaining patient-specific diagnosis for various diseases and help define precision medicine. Therefore, many studies on automatic analysis methods for digital pathology images have been reported. One of the severe brain tumors is the Glioma can provide unique insights into identifying and grading disease stages. However, the number of tissue samples to be examined is enormous, and is a burden to pathologists because of the tedious manual evaluation required for efficient decision-making and diagnosis. Therefore, there is a strong demand for quick and automatic analysis to do that. In this study, we consider feature extraction and disease stage classification for Glioma images using automatic image analysis methods with deep learning techniques. By devising a custom made deep convolutional neural network (CNN) for disease stage classification we apply it on image data available on the cancer genome atlas for brain glioma in histopathology.


title: A taxonomy of computer worms

abstract: To understand the threat posed by computer worms, it is necessary to understand the classes of worms, the attackers who may employ them, and the potential payloads. This paper describes a preliminary taxonomy based on worm target discovery and selection strategies, worm carrier mechanisms, worm activation, possible payloads, and plausible attackers who would employ a worm.


title: A Security Risk Assessment Model for Business Process Deployment in the Cloud

abstract: Managing security risks on information systems is essential to guarantee their security while handling costs. However, the complexity of risk assessments is greatly increased when data is spread on multiple environments. In this paper we present a security risk assessment model for distributing business processes in a multi-cloud environment. We aim at offering the full power of cloud computing to composite applications while shielding companies from the complexity related to security risk assesments in the Cloud. We also want to give them the capablility to automatically generate secure and cost-effective applications across multiple clouds. Our approach is based on existing risk assessment methodologies, while using the industry recognized IT standards.


title: What is the most important approach in current drug discovery: doing the right things or doing things right?

abstract: Doing the right things or doing things right: what is the most important focus for current drug discovery to secure delivery of new drugs of sustainable value to patients, healthcare professionals and healthcare providers? Some of the challenges faced today in drug discovery are addressed here: the relationship between R&D speed, cost and quality; how selection of performance metrics can affect the quality of the R&D output; the importance of leadership and management; how process orientation can affect, for example, creativity and innovation; the importance of selecting the right pharmacologic target and the right chemical lead; and why the use of drug-target kinetic and thermodynamic data to drive lead selection and lead optimization could increase success rates.


title: Methodology and Framework for Predicting Helicopter Rolling Element Bearing Failure

abstract: The enhanced ability to predict the remaining useful life of helicopter drive train components offers potential improvement in the safety, maintainability, and reliability of a helicopter fleet. Current existing helicopter health and usage monitoring systems provide diagnostic information that indicates when the condition of a drive train component is degraded; however, prediction techniques are not currently used. Although various algorithms exist for providing remaining life predictions, prognostic techniques have not fully matured. This particular study addresses remaining useful life predictions for the helicopter oil-cooler bearings. The paper proposes a general methodology of how to perform rolling element bearing prognostics, and presents the results using a robust regression curve fitting approach. The proposed methodology includes a series of processing steps prior to the prediction routine, including feature extraction, feature selection, and health assessment. This approach provides a framework for including prediction algorithms into existing health and usage monitoring systems. A case study with the data collected by Impact Technology, LLC. is analysed using the proposed methodology. Future work would consider using the same methodology, but comparing the accuracy of this prediction method with Bayesian filtering techniques, usage based methods, and other time series prediction methods.


title: Page Segmentation for Historical Handwritten Documents Using Fully Convolutional Networks

abstract: Page segmentation is a fundamental and challenging task in document image analysis due to the layout diversity. In this work, we propose a pixel-wise segmentation method for historical handwritten documents using fully convolutional network (FCN). The document image is segmented into different regions by classifying pixels into different categories: background, main text body, comments, and decorations. By supervised learning on document images with pixel-wise labels, the FCN can extract discriminative features and perform pixel-wise segmentation accurately. After pixel-wise classification, post-processing steps are taken to reduce noises, correct wrong segmentations and find out overlapping regions. Experimental results on the public dataset DIVA-HisDB containing challenging medieval manuscripts demonstrate the effectiveness and superiority of the proposed method, which yields pixel-level accuracy of above 99%.


title: An online complete coverage algorithm for cleaning robots based on boustrophedon motions and A* search

abstract: This paper presents an online complete coverage algorithm for cleaning robots based on boustrophedon motions combined with A* search algorithm. In our approach, while performing a boustrophedon motion to cover an unvisited area in the workspace, the robot detects and stores backtracking points. To execute the next boustrophedon motion, A* search is employed as a backtracking mechanism which guides the robot to the nearest backtracking point. Experimental results prove that the proposed algorithm ensures the complete coverage of the workspace in the finite moving steps of the robot. Furthermore, our proposed approach is efficient in terms of the covered path length and the number of boustrophedon motions.


Chord Detection Using Deep Learning


title: Towards a unified architecture for in-RDBMS analytics

abstract: The increasing use of statistical data analysis in enterprise applications has created an arms race among database vendors to offer ever more sophisticated in-database analytics. One challenge in this race is that each new statistical technique must be implemented from scratch in the RDBMS, which leads to a lengthy and complex development process. We argue that the root cause for this overhead is the lack of a unified architecture for in-database analytics. Our main contribution in this work is to take a step towards such a unified architecture. A key benefit of our unified architecture is that performance optimizations for analytics techniques can be studied generically instead of an ad hoc, per-technique fashion. In particular, our technical contributions are theoretical and empirical studies of two key factors that we found impact performance: the order data is stored, and parallelization of computations on a single-node multicore RDBMS. We demonstrate the feasibility of our architecture by integrating several popular analytics techniques into two commercial and one open-source RDBMS. Our architecture requires changes to only a few dozen lines of code to integrate a new statistical technique. We then compare our approach with the native analytics tools offered by the commercial RDBMSes on various analytics tasks, and validate that our approach achieves competitive or higher performance, while still achieving the same quality.


Implementation of Photometric Stereo with General , Unknown Lighting


title: Reng, Bifurcation of minimal surfaces in Riemannian manifolds

abstract: We study the bifurcation of closed minimal surfaces in Riemannian manifolds through higher order variations of the area functional and relate it to elementary catastrophes.


title: Interaction with virtual game through hand gesture recognition

abstract: Hand gesture recognition systems for virtual reality applications provides the users an enhanced interaction experience as it integrates the virtual and the real world object. Growth in virtual environments based upon computer systems and development of user interfaces influence the changes in the Human-Computer Interaction (HCI). Gesture recognition based interaction interface, endow with more realistic and immersive interaction compared to the traditional devices. The system enables a physically realistic mode of interaction to the virtual environment. The Hand gesture recognition system based interface proposed and implemented in this paper consists of a detection, tracking and recognition module. For the implementation of these modules various image processing algorithms as Camshift, Lucas Kanade, Haar like features etc has been employed. Comprehensive user acceptability has been considered to exhibit the accuracy, usefulness and ease of use to the proposed and implemented hand gesture recognition system. Hand gesture communication based vocabulary offers many variations ranging from simple action of using our finger to point at to using hands for moving objects around to the rather complex one like expression of the feelings. The proposed hand gesture recognition system offers intensions to traditional input devices for interaction with the virtual environments. The gesture based interaction interface being proposed here can be substantially applied towards many applications like Virtual Reality, Sign Language and Games. Though the present paper considered games as the application domain.


Improving Distributed Word Representation and Topic Model by Word-Topic Mixture Model


title: IOA: Improving SVM Based Sentiment Classification Through Post Processing

abstract: This paper describes our systems for expression-level and message-level sentiment analysis ‐ two subtasks of SemEval-2015 Task 10 on sentiment analysis in Twitter. First we built two baseline systems for the two subtasks using SVM with a variety of features. Then we improved the systems through model iteration and probability-output weighting respectively. Our submissions are ranked the 3rd and 2nd among eleven teams on the 2015 test set and progress test set in subtask A and the 7th and 4th among 40 teams on the two test sets respectively in subtask B.


title: A Fuzzy Grey Cognitive Maps-based intelligent security system

abstract: Fuzzy Grey Cognitive Map (FGCM) is an innovative soft computing technique mixing Fuzzy Cognitive Maps and Grey Systems Theory. FGCMs are supervised learning fuzzy-neural systems typically modeled with signed fuzzy grey weighted digraphs, generally involving feedbacks. It is hard to find an accurate mathematical model to describe this decision-making because it includes a high uncertainty and the factors involved interact each other. FGCMs are able to capture and imitate the nature of human being in describing, representing and developing models. They are good at processing fuzzy and grey information and have adaptive, intelligent features. This paper presents a FGCM-based decision support tool, which synthetically takes the related factors into account, offering objective parameters for selecting the fitter surveillance asset. The proposed method is robust, adaptive and simple.


title: Autoencoder, Principal Component Analysis and Support Vector Regression for Data Imputation

abstract: Data collection often results in records that have missing values or variables. This investigation compares 3 different data imputation models and identifies their merits by using accuracy measures. Autoencoder Neural Networks, Principal components and Support Vector regression are used for prediction and combined with a genetic algorithm to then impute missing variables. The use of PCA improves the overall performance of the autoencoder network while the use of support vector regression shows promising potential for future investigation. Accuracies of up to 97.4 % on imputation of some of the variables were achieved.


title: First Person Action-Object Detection with EgoNet

abstract: Unlike traditional third-person cameras mounted on robots, a first-person camera, captures a person's visual sensorimotor object interactions from up close. In this paper, we study the tight interplay between our momentary visual attention and motor action with objects from a first-person camera. We propose a concept of action-objects---the objects that capture person's conscious visual (watching a TV) or tactile (taking a cup) interactions. Action-objects may be task-dependent but since many tasks share common person-object spatial configurations, action-objects exhibit a characteristic 3D spatial distance and orientation with respect to the person. ::: We design a predictive model that detects action-objects using EgoNet, a joint two-stream network that holistically integrates visual appearance (RGB) and 3D spatial layout (depth and height) cues to predict per-pixel likelihood of action-objects. Our network also incorporates a first-person coordinate embedding, which is designed to learn a spatial distribution of the action-objects in the first-person data. We demonstrate EgoNet's predictive power, by showing that it consistently outperforms previous baseline approaches. Furthermore, EgoNet also exhibits a strong generalization ability, i.e., it predicts semantically meaningful objects in novel first-person datasets. Our method's ability to effectively detect action-objects could be used to improve robots' understanding of human-object interactions.


Web Users Clustering


title: A theory of multiclass boosting

abstract: Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary classification is well understood, in the multiclass setting, the "correct" requirements on the weak classifier, or the notion of the most efficient boosting algorithms are missing. In this paper, we create a broad and general framework, within which we make precise and identify the optimal requirements on the weak-classifier, as well as design the most effective, in a certain sense, boosting algorithms that assume such requirements.


title: Generative Adversarial Perturbations

abstract: In this paper, we propose novel generative models for creating adversarial examples, slightly perturbed images resembling natural images but maliciously crafted to fool pre-trained models. We present trainable deep neural networks for transforming images to adversarial perturbations. Our proposed models can produce image-agnostic and image-dependent perturbations for both targeted and non-targeted attacks. We also demonstrate that similar architectures can achieve impressive results in fooling classification and semantic segmentation models, obviating the need for hand-crafting attack methods for each task. Using extensive experiments on challenging high-resolution datasets such as ImageNet and Cityscapes, we show that our perturbations achieve high fooling rates with small perturbation norms. Moreover, our attacks are considerably faster than current iterative methods at inference time.


title: Buck converter controlled by Arduino Uno

abstract: The paper presents a buck converter controlled with microcontroller integrated on Arduino Uno board. The control is implemented by use of PI controller embedded on Arduino Uno board. Open loop control-to-output transfer function is obtained from measured step response. The PI controller and feedback divider transfer functions are synthesized to get desired loop gain. As a verification of analysis, input voltage step responses of regulated and unregulated buck converter are compared and improvements are identified.


title: Learning from One Example through Shared Densities on Transforms

abstract: We define a process called congealing in which elements of a dataset (images) are brought into correspondence with each other jointly, producing a data-defined model. It is based upon minimizing the summed component-wise (pixel-wise) entropies over a continuous set of transforms on the data. One of the biproducts of this minimization is a set of transform, one associated with each original training sample. We then demonstrate a procedure for effectively bringing test data into correspondence with the data-defined model produced in the congealing process. Subsequently; we develop a probability density over the set of transforms that arose from the congealing process. We suggest that this density over transforms may be shared by many classes, and demonstrate how using this density as "prior knowledge" can be used to develop a classifier based on only a single training example for each class.


title: Secure Pub-Sub: Blockchain-Based Fair Payment With Reputation for Reliable Cyber Physical Systems

abstract: The cyber physical system (CPS) has gained considerable success in large-scale distributed integration environment. In such systems, the sensor devices collect data which would be disseminated via reliable manner to all interested co-operant entities from the physical world. However, highly unreliable environment of CPS, for example, a number of limitations of existing network middle wares, makes secure and reliable data distribution services a challenge issue. In this paper, we propose a new architecture called secure pub-sub (SPS) without middle ware, i.e., blockchain-based fair payment with reputation. In SPS, publishers publish a topic on the blockchain and subscribers specify an interest message by making a deposit to subscribing the topic. Then, if the interest message matches the topic, the publisher transmits the encrypted content of the topic to the blockchain such that the subscribers can decrypt the ciphertext to obtain the content, and mark the publisher as its reputation. Finally, the publisher receives the payment from the subscriber. The new proposal provides confidentiality and reliability of data, anonymity of subscribers and payment fairness between the publishers and subscribers. Different from the traditional pub-sub services, no trusted third party is involved in our system due to employing blockchain technique. The security of the proposed SPS is analyzed as well. The implementation of the protocol on Ethereum of smart contract demonstrates the validity of SPS.


Classification of Epileptic EEG Signals using Wavelet-EMD-Domain Features and Improved Multi-class SVM 1


title: A Reconfigurable 40-to-67 dB SNR, 50-to-6400 Hz Frame-Rate, Column-Parallel Readout IC for Capacitive Touch-Screen Panels

abstract: This paper describes a capacitive touch-screen panel (TSP) readout IC that provides a reconfigurable SNR and frame rate with high noise immunity and touch sensitivity. The readout IC mitigates severe noise interference with a lock-in sensing architecture. In addition, a band-pass filtering effect by the TSP and charge amplifier further improves the noise immunity. A differential sensing scheme is employed to enhance the touch sensitivity and to reject a common-mode noise. A column-parallel incremental ΣΔ ADC is adopted to provide a high resolution and frame rate. Furthermore, a multiple sampling and averaging effect of the ADC enhances the noise immunity. The readout IC reconfigures its SNR and frame rate by adjusting the configurable resolution of the employed ADC. The test chip is fabricated in a 0.18 μm CMOS process and occupies a 2.2 mm 2 active area. The test chip achieves a 60 dB SNR and 200 Hz frame rate with a 12×8 TSP. The SNR can be adjusted from 40 dB to 67 dB, while the frame rate is then inversely scaled from 50 Hz to 6.4 kHz. The test chip supports a supply voltage range from 2.1 V to 3.3 V and consumes 6.26 mW from a 3.3 V supply.


title: Proximal Hamstring Tendinopathy: Clinical Aspects of Assessment and Management.

abstract: Synopsis Proximal hamstring tendinopathy (PHT) typically manifests as deep buttock pain at the hamstring common origin. Both athletic and nonathletic populations are affected by PHT. Pain and dysfunction are often long-standing and limit sporting and daily functions. There is limited evidence regarding diagnosis, assessment, and management; for example, there are no randomized controlled trials investigating rehabilitation of PHT. Some of the principles of management established in, for example, Achilles and patellar tendinopathy would appear to apply to PHT but are not as well documented. This narrative review and commentary will highlight clinical aspects of assessment and management of PHT, drawing on the available evidence and current principles of managing painful tendinopathy. The management outline presented aims to guide clinicians as well as future research. J Orthop Sports Phys Ther 2016;46(6):483-493. Epub 15 Apr 2016. doi:10.2519/jospt.2016.5986.


Using Convolutional Neural Networks to Classify Hate-Speech


RegionAAL - The Styrian AAL-test-region in Graz, Deutschlandsberg and Leibnitz


title: Implementation of MPPT control using fuzzy logic in solar-wind hybrid power system

abstract: The renewable energy sources such as Solar energy and Wind energy are complementary by nature. Utilizing these natural resources to produce power will reduce the power demand on the conventional power generation sector. One of the applications of Solar-Wind hybrid power system (SWHPS) is to reduce the amount of power consumed from the conventional power generation to charge the storage reserves present in the system. The SWHPS comprises of Photovoltaic array, wind turbine, Permanent Magnet Synchronous generator (PMSG), controller and converter. The efficiency of the SWHPS depends on the MPPT controller, which makes the Photovoltaic (PV) and wind power generation system to operate at its maximum power. In PV system Perturb & Observe (P&O) algorithm is used as control logic for the Maximum Power Point Tracking (MPPT) controller and Hill Climb Search (HCS) algorithm is used as MPPT control logic for the Wind power system in order to maximizing the power generated. This paper presents a comparative analysis of MPPT controller built using P&O for PV system and HCS for Wind power system, with MPPT controller implemented using Fuzzy Logic control (FLC) in the both the renewable sources in the hybrid system. The performance of the different implementation of MPPT controllers in the hybrid system are investigated in this paper in MATLAB, Simulink. The SWHPS with the FLC based MPPT has shown to have a better, faster control as compared with the other controllers.


title: A vision-aided inertial navigation system for agile high-speed flight in unmapped environments: Distribution statement A: Approved for public release, distribution unlimited

abstract: Small, lightweight flight vehicles, such as consumer-grade quadrotors, are becoming increasingly common. These vehicles' on-board state estimators are typically reliant upon frequent and accurate updates from external systems such as the Global Positioning System (GPS) to provide state estimates required for stable flight. However, in many cases GPS signals may be unavailable or unreliable, and loss of GPS can cause these vehicles to go unstable or crash, potentially putting operators, bystanders, and property in danger. Thus reliance on GPS severely limits the robustness and operational capabilities of lightweight flight vehicles. This paper introduces the Smoothing And Mapping With In-ertial State Estimation (SAMWISE) navigation system. SAM-WISE is a vision-aided inertial navigation system capable of providing high-rate, low-latency state estimates to enable high-dynamic flight through obstacle-laden unmapped indoor and outdoor environments. SAMWISE offers a flexible framework for inertial navigation with nonlinear measurements, such as those produced by visual feature trackers, by utilizing an incremental smoother to efficiently optimize a set of nonlinear measurement constraints, estimating the vehicle trajectory in a sliding window in real-time with a slight processing delay. To overcome this delay and consistently produce state estimates at the high rates necessary for agile flight, we propose a novel formulation in which the smoother runs in a background thread while a low-latency inertial strapdown propagator outputs position, attitude, and velocity estimates at high-rate. We additionally propose a novel measurement buffering approach to seamlessly handle delayed measurements, measurements produced at inconsistent rates, and sensor data requiring significant processing time, such as camera imagery. We present experimental results high-speed flight with a fully autonomous quadrotor using SAMWISE for closed-loop state estimation from flight demonstrations during the DARPA Fast Lightweight Autonomy (FLA) program in April and November of 2016. SAMWISE achieved less than 1% position error and up to 5.5 m/s (12 mph) flight in a simulated indoor warehouse environment using a scanning-lidar, inertial measurement unit, and laser altimeter during the first FLA milestone event in April 2016. In November 2016, SAMWISE achieved approximately 3% error and up to 20 m/s (45 mph) flight in an open outdoor environment with large obstacles during the second FLA milestone event. The results of these flight tests demonstrate that our navigation system works robustly at high speed across multiple distinct environments.


title: Noise-robust bioimpedance approach for cardiac output measurement.

abstract: OBJECTIVE ::: Congestive heart failure is a problem effecting millions of Americans. A continuous, non-invasive, telemonitoring device that can accurately monitor cardiac metrics could greatly help this population, reducing unnecessary hospitalizations and cost. ::: ::: ::: APPROACH ::: Machine Learning (ML) algorithms trained on electrical-impedance tomography (EIT) data are presented for portable cardiac monitoring. The approach was validated on a simulated thorax and a measured tank experiment. A highly detailed 4D chest model (finite element method mesh and conductivity profiles) was developed utilizing the 4D XCAT phantom to provide realistic data. The ML algorithms were trained using databases that assumed the presence of poorly contacting electrodes without any assumptions of knowing which electrodes would be bad in the experiment. The trained ML algorithms were compared to EIT evaluated with and without removing bad electrodes. ::: ::: ::: MAIN RESULTS ::: A regression Support Vector Machine (rSVM) and a Deep Neural Network (DNN) were found to be the most accurate and robust to poorly contacting electrodes while not needing to know which electrodes were in poor contact in the simulated and measured experiments, respectively. ::: ::: ::: SIGNIFICANCE ::: Although, the ML algorithms are not always better than EIT (with bad electrodes removed), the comparable results without needing priori knowledge of which electrodes are bad is seen as a very promising feature. An evaluation of computational costs showed that the DNN required comparable computational to the other methods while requiring less memory, which could make the DNNs an attractive algorithm for a low-power, portable system. This work represents an important validation of the method using measured data, and model development, which is needed to apply this method on real clinical data. Additionally, the developed 4D simulated thorax model could be an important tool within the EIT community.


Pediatric oncology: A treatise for the clinician


title: MUSIC COMPOSITION BY ONOMATOPOEIA

abstract: We introduce a simple music notation system based on onomatopoeia. Although many text-based music notation systems have been proposed, most of them are cryptic and much more difficult to understand than standard graphical musical scores. Our music notation system, called the Sutoton notation, is based on onomatopoeia and note names which are easily pronounceable by humans with no extra training. Although being simple, Sutoton notation has been used in a variety of systems and loved by many hobbyists.


title: A study on factors that affect users' behavioral intention to transfer usage from the offline to the online channel

abstract: Increasingly more service providers offer both offline and online services simultaneously, but consumers still seem to prefer using offline services rather than online ones. Our research focuses on factors that influence users' intention to transfer their usage from the offline to the online channel that offer similar services. Drawing on the valence framework and prior research related to habit, innovativeness, and Internet experience, we propose a research model that incorporates motivators and inhibitors of usage transfer from both the offline and online channels. Using banking services in China as the research context, our study reveals that innovativeness in new technology and relative benefit have positive effects on users' intention to transfer usage. Conversely, habits that consumers form in the offline channel have a negative effect on the intention to transfer usage. Moreover, our findings indicate that Internet experience moderates the relationship between relative benefit and consumers' intention to transfer usage from offline to online services. These results provide a better understanding of consumers' usage-transfer behavior and offer suggestions to providers in boosting their consumers' use of online services.


title: Advanced Signal Processing for Vital Sign Extraction With Applications in UWB Radar Detection of Trapped Victims in Complex Environments

abstract: Ultra-wideband (UWB) radar plays an important role in search and rescue at disaster relief sites. Identifying vital signs and locating buried survivors are two important research contents in this field. In general, it is hard to identify a human's vital signs (breathing and heartbeat) in complex environments due to the low signal-to-noise ratio of the vital sign in radar signals. In this paper, advanced signal-processing approaches are used to identify and to extract human vital signs in complex environments. First, we apply Curvelet transform to remove the source-receiver direct coupling wave and background clutters. Next, singular value decomposition is used to de-noise in the life signals. Finally, the results are presented based on FFT and Hilbert-Huang transform to separate and to extract human vital sign frequencies, as well as the micro-Doppler shift characteristics. The proposed processing approach is first tested by a set of synthetic data generated by FDTD simulation for UWB radar detection of two trapped victims under debris at an earthquake site of collapsed buildings. Then, it is validated by laboratory experiments data. The results demonstrate that the combination of UWB radar as the hardware and advanced signal-processing algorithms as the software has potential for efficient vital sign detection and location in search and rescue for trapped victims in complex environment.


title: Information and Influence Propagation in Social Networks

abstract: Research on social networks has exploded over the last decade. To a large extent, this has been fueled by the spectacular growth of social media and online social networking sites, which continue growing at a very fast pace, as well as by the increasing availability of very large social network datasets for purposes of research. A rich body of this research has been devoted to the analysis of the propagation of information, influence, innovations, infections, practices and customs through networks. Can we build models to explain the way these propagations occur? How can we validate our models against any available real datasets consisting of a social network and propagation traces that occurred in the past? These are just some questions studied by researchers in this area. Information propagation models find applications in viral marketing, outbreak detection, finding key blog posts to read in order to catch important stories, finding leaders or trendsetters, information feed ranking, etc. A number of algorithmic problems arising in these applications have been abstracted and studied extensively by researchers under the garb of influence maximization. This book starts with a detailed description of well-established diffusion models, including the independent cascade model and the linear threshold model, that have been successful at explaining propagation phenomena. We describe their properties as well as numerous extensions to them, introducing aspects such as competition, budget, and time-criticality, among many others. We delve deep into the key problem of influence maximization, which selects key individuals to activate in order to influence a large fraction of a network. Influence maximization in classic diffusion models including both the independent cascade and the linear threshold models is computationally intractable, more precisely #P-hard, and we describe several approximation algorithms and scalable heuristics that have been proposed in the literature. Finally, we also deal with key issues that need to be tackled in order to turn this research into practice, such as learning the strength with which individuals in a network influence each other, as well as the practical aspects of this research including the availability of datasets and software tools for facilitating research. We conclude with a discussion of various research problems that remain open, both from a technical perspective and from the viewpoint of transferring the results of research into industry strength applications. Table of Contents: Acknowledgments / Introduction / Stochastic Diffusion Models / Influence Maximization / Extensions to Diffusion Modeling and Influence Maximization / Learning Propagation Models / Data and Software for Information/Influence: Propagation Research / Conclusion and Challenges / Bibliography / Authors' Biographies / Index


title: Supporting primary health care nurse practitioners’ transition to practice

abstract: OBJECTIVE To examine role transition and support requirements for nurse practitioner (NP) graduates in their first year of practice from the perspectives of the NPs and coparticipants familiar with the NPs’ practices; and to make recommendations for practice, education, and policy. DESIGN Descriptive qualitative design informed by focused ethnography and narrative analysis using semistructured, in-depth, qualitative interviews. SETTING Primary health care (PHC) settings in Ontario in which NPs worked. PARTICIPANTS Twenty-three NPs who had graduated from the Ontario Primary Health Care Nurse Practitioner program, and 21 coparticipants including family physicians, NPs, and managers who were familiar with the NPs’ practices. METHODS Anglophone and francophone NPs in their first year of practice in PHC settings were contacted by e-mail or letter. Participating NPs nominated colleagues in the workplace who could comment on their practice. Interviews were conducted within the first 3 months, at 6 months, and at 12 months of the NPs’ first year of practice and were transcribed verbatim and coded. Job descriptions and organizational charts demonstrating the NPs’ organization positions were also analyzed. The researchers collaboratively analyzed the interviews using a systematic data analysis protocol. MAIN FINDINGS Familiarity of colleagues and employers with the NP role and scope of practice was an important element in successful NP role transition. Lack of preparation for integrating NPs into clinical settings and lack of infrastructure, orientation, mentorship, and awareness of the NP role and needs made the transition difficult for many. One-third of the NPs had changed employment, identifying interprofessional conflict or problems with acceptance of their role in new practice environments as reasons for the change. CONCLUSION The transition of NP graduates in Ontario was complicated by the health care environment being ill-prepared to receive them owing to rapid changes in PHC. Strategies for mentorship and for the integration of new NPs into PHC settings are available and need to be implemented by health professionals and administrators. Recommendations for family physicians to support NP graduate transition into practice are provided.


title: Approximate Incremental Big-Data Harmonization

abstract: The needs of `big data analytics' increasingly require IT organizations to ingest, process, and extract business insights from ever larger volumes of data that arrive far more rapidly than before, as well as from new sources such as social media, mobile devices, and sensors. However, in order to extract insights from diverse information feeds from multiple, often unrelated sources, these first need to be correlated or harmonized to a common level of granularity. We formally define this commonly arising data harmonization problem. We show how to correlate disparate data sources using map-reduce, but in an approximate and/or incremental manner as often required in practice. We motivate our techniques through a real-life enterprise data-harmonization case study for which we describe our performance results on big-data technologies, namely, Map Reduce, Hadoop and PIG.


title: State space modeling and control of the dc-dc multilevel boost converter

abstract: The dc-dc multilevel boost converter topology was recently proposed with features such as high voltage gain without using an extreme duty ratio, few components and self dc-link balancing. This paper presents the state space modeling and a linear controller based on the pole placement technique for the dc-dc multilevel boost converter. Nonlinear and linear models are derived. Simulation results obtained with the averaging model in Matlab and with the simulated circuit in Synopsys Saber software are presented to prove the principle of the proposition.


title: Reinforcement Learning for Dialog Management using Least-Squares Policy Iteration and Fast Feature Selection

abstract: Reinforcement learning (RL) is a promising technique for creating a dialog manager. RL accepts features of the current dialog state and seeks to find the best action given those features. Although it is often easy to posit a large set of potentially useful features, in practice, it is difficult to find the subset which is large enough to contain useful information yet compact enough to reliably learn a good policy. In this paper, we propose a method for RL optimization which automatically performs feature selection. The algorithm is based on least-squares policy iteration, a state-of-the-art RL algorithm which is highly sampleefficient and can learn from a static corpus or on-line. Experiments in dialog simulation show it is more stable than a baseline RL algorithm taken from a working dialog system.


title: pyOpt: a Python-based object-oriented framework for nonlinear constrained optimization

abstract: We present pyOpt, an object-oriented framework for formulating and solving nonlinear constrained optimization problems in an efficient, reusable and portable manner. The framework uses object-oriented concepts, such as class inheritance and operator overloading, to maintain a distinct separation between the problem formulation and the optimization approach used to solve the problem. This creates a common interface in a flexible environment where both practitioners and developers alike can solve their optimization problems or develop and benchmark their own optimization algorithms. The framework is developed in the Python programming language, which allows for easy integration of optimization software programmed in Fortran, C, C+?+, and other languages. A variety of optimization algorithms are integrated in pyOpt and are accessible through the common interface. We solve a number of problems of increasing complexity to demonstrate how a given problem is formulated using this framework, and how the framework can be used to benchmark the various optimization algorithms.


title: Measuring Post Traumatic Stress Disorder in Twitter

abstract: Traditional mental health studies rely on information primarily collected through personal contact with a health care professional. Recent work has shown the utility of social media data for studying depression, but there have been limited evaluations of other mental health conditions. We consider post traumatic stress disorder (PTSD), a serious condition that affects millions worldwide, with especially high rates in military veterans. We also present a novel method to obtain a PTSD classifier for social media using simple searches of available Twitter data, a significant reduction in training data cost compared to previous work. We demonstrate its utility by examining differences in language use between PTSD and random individuals, building classifiers to separate these two groups and by detecting elevated rates of PTSD at and around U.S. military bases using our classifiers.


Evolutionary Theoretical Game for Cooperative Spectrum Sensing in Cognitive Radio Networks : A Survey


title: Validation of the Psychometric Properties of the Self‐Compassion Scale. Testing the Factorial Validity and Factorial Invariance of the Measure among Borderline Personality Disorder, Anxiety Disorder, Eating Disorder and General Populations

abstract: Background ::: During the last years, there has been a growing interest in self-compassion. Empirical evidences show that self-compassion is associated with psychological benefits among young adults and it might be considered a buffer factor in several mental disorders. ::: ::: Aims ::: The aim of this study was to validate the psychometric properties of the Self-compassion Scale (SCS: Neff, 2003a) after the initial lack of replicating the original six-factor structure. ::: ::: Method ::: Data were collected from the overall database of a research centre (56 men and 305 women; mean age = 25.19) and comprised four groups: borderline personality disorder, anxiety disorder, eating disorder and general population. ::: ::: Results ::: Confirmatory factor analysis supported a two-factor model (self-compassionate attitude versus self-critical attitude) with good internal consistencies, construct-related validity and external validity. Configural, weak measurement and structural invariance of the two-factor model of SCS were also shown. ::: ::: Conclusions ::: Findings support the generalizability of the two-factor model and show that both properties and interpretations of scores on self-compassion are equivalent across these population groups. Copyright © 2015 John Wiley & Sons, Ltd. ::: ::: Key Practitioner Message ::: A two-factor structure of SCS with strong psychometric validity was supported in clinical and non-clinical samples. ::: Helping individuals with limited experiences of compassion to develop positive internal processing systems seems to be related with better mental health, self-acceptance and self-nurturing abilities. ::: The non-probabilistic sampling limits the generalization of our conclusions.


title: The catalytic reduction of nitric oxide by ammonia over a clean and vanadium oxide-coated platinum foil

abstract: Abstract The reduction of NO by NH3 could be readily monitored over a 2-cm 2 area platinum foil in the temperature range of 450–560 K. The reaction is first order in NO and zero order in NH 3 partial pressures, and the rate is given by r NO = 5 x 107 [NO] exp(- E/RT ) (mol/cm 2 .s) with E = 113 ± 20 kJ/mol. Vanadium oxide forms three-dimensional islands on Pt with a stoichiometry near V 2 0 4 . Its presence does not alter the activation energy appreciably but reduces the rate by blocking the active platinum sites.


title: Critical Success Factors for Enterprise Resource Planning Implementation and Upgrade

abstract: Seven categories of critical success factors were identified from the ERP literature: (1) business plan and vision; (2) change management; (3) communication; (4) ERP team composition, skills and compensation; (5) management support and championship; (6) project management; (7) system analysis, selection and technical implementation. We conducted a case study of two organizations that had implemented and upgraded ERP systems. We adopted Markus and Tanis' four-phase model and compared the importance of these critical success factors across the phases of ERP implementation and upgrade. The importance of these factors across the phases of ERP implementation and upgrade is very similar. ‘Business Plan and Vision’ and ‘Top Management Support and Championship’ are critical during the Chartering phase. ‘ERP Team Composition, Skills and Compensation,’ ‘Project Management’ and ‘System Analysis, Selection and Technical Implementation’ are most important during the Project phase. ‘Change Management’ and ‘Communicatio...


title: Biometrics and Face Recognition Techniques

abstract: Biometrics is a growing technology, which has been widely used in forensics, secured access and prison security. A biometric system is fundamentally a pattern recognition system that recognizes a person by determining the authentication by using his different biological features i.e. Fingerprint, retina-scan, iris scan, hand geometry, and face recognition are leading physiological biometrics and behavioral characteristic are Voice recognition, keystroke-scan, and signature-scan. In this paper different biometrics techniques such as Iris scan, retina scan and face recognition techniques are discussed. Keyword: Biometric, Biometric techniques, Eigenface, Face recognition.


title: Generating fuzzy rules by learning from examples

abstract: A general method is developed to generate fuzzy rules from numerical data. The method consists of five steps: divide the input and output spaces of the given numerical data into fuzzy regions; generate fuzzy rules from the given data; assign a degree of each of the generated rules for the purpose of resolving conflicts among the generated rules; create a combined fuzzy rule base based on both the generated rules and linguistic rules of human experts; and determine a mapping from input space to output space based on the combined fuzzy rule base using a defuzzifying procedure. The mapping is proved to be capable of approximating any real continuous function on a compact set to arbitrary accuracy. Applications to truck backer-upper control and time series prediction problems are presented. >


title: Effective localization of humanoid with fish-eye lens using field line detection

abstract: This paper presents a real-time technique for efficient localization of a humanoid robot in a soccer field using a fish-eye lens. We propose a novel method that uses node merging to provide a robust and an accurate estimate of positions of the intersection points in the field lines. The distances to these points are calculated using Inverse Perspective Mapping and fed to the particle filter for the purpose of localization. The proposed algorithms have been implemented and tested on the humanoid robot platform AcYut, being developed at the Centre for Robotics and Intelligent Systems (CRIS), BITS Pilani.


title: Dependence-based program analysis

abstract: Program analysis and optimization can be speeded up through the use of the dependence flow graph (DFG), a representation of program dependences which generalizes def-use chains and static single assignment (SSA) form. In this paper, we give a simple graph-theoretic description of the DFG and show how the DFG for a program can be constructed in O(EV) time. We then show how forward and backward dataflow analyses can be performed efficiently on the DFG, using constant propagation and elimination of partial redundancies as examples. These analyses can be framed as solutions of dataflow equations in the DFG. Our construction algorithm is of independent interest because it can be used to construct a program's control dependence graph in O(E) time and its SSA representation in O(EV) time, which are improvements over existing algorithms.


title: A dynamic graph visualization perspective on eye movement data

abstract: During eye tracking studies, vast amounts of spatio-temporal data in the form of eye gaze trajectories are recorded. Finding insights into these time-varying data sets is a challenging task. Visualization techniques such as heat maps or gaze plots help find patterns in the data but highly aggregate the data (heat maps) or are difficult to read due to overplotting (gaze plots). In this paper, we propose transforming eye movement data into a dynamic graph data structure to explore the visualization problem from a new perspective. By aggregating gaze trajectories of participants over time periods or Areas of Interest (AOIs), a fair trade-off between aggregation and details is achieved. We show that existing dynamic graph visualizations can be used to display the transformed data and illustrate the approach by applying it to eye tracking data recorded for investigating the readability of tree diagrams.


Better Security for Queries on Encrypted Databases


title: Dynamic modeling of a fixed-wing VTOL UAV

abstract: This paper presents a transition-flight mathematical model of a civil tilt-rotor VTOL unmanned aerial vehicle (UAV) TURAC. Forces and moments acting on the UAV body are calculated using Newton's second law. Aerodynamic effects of free airstream and propeller airstream are defined separately. CFD analyses are performed to specify aerodynamic coefficients for transitional flight regime. The trim point is mathematically defined with respect to angle of attack, tilt angle, airspeed, thrust of tilt-rotor and coaxial fan are defined during transitional flight. A transitional flight scenario is developed with force and moment equations.


title: Nurturing Collaborative Relations Building Trust in Interorganizational Collaboration

abstract: Many organizations aspire to gain collaborative advantage by working in partnerships across organizational, sectoral, and even national boundaries. Such collaborations, however, are difficult to manage, and the likelihood of disappointing outputs is high. To create advantage, practitioners need to engage in a continuous process of nurturing the collaborative processes. One issue that appears significant in the nurturing process is trust. This article incorporates a synthesis of research on trust with research on other aspects of collaboration. It develops a trust-building loop and examines its validity to inform practice. It argues that trust building is problematic and that management of trust implies both the ability to cope in situations where trust is lacking and the ability to build trust in situations where this is possible. It summarizes pragmatic implications in a tool for thinking about and addressing trust management in different collaborative situations.


title: Large Scale Conditional Covariance Matrix Modeling, Estimation and Testing,” University of California at San Diego working paper

abstract: A new representation of the diagonal Vech model is given using the Hadamard product. Sufficient conditions on parameter matrices are provided to ensure the positive definiteness of covariance matrices from the new representation. Based on this, some new and simple models are discussed. A set of diagnostic tests for multivariate ARCH models is proposed. The tests are able to detect various model misspecifications by examing the orthogonality of the squared normalized residuals. A small Monte-Carlo study is carried out to check the small sample performance of the test. An empirical example is also given as guidance for model estimation and selection in the multivariate framework. For the specific data set considered, it is found that the simple one and two parameter models and the constant conditional correlation model perform fairly well.


title: Control Strategy of Two Capacitor Voltages for Separate MPPTs in Photovoltaic Systems Using Neutral-Point-Clamped Inverters

abstract: The centralized topology with three-level inverters are widely used in photovoltaic (PV) systems due to their less installation costs and complexity. However, the common maximum power point tracking (MPPT) is a disadvantage of the centralized topology particularly under the partial shading and panel mismatch conditions. In this system, if PV modules are separately connected to the split capacitor voltage, the MPPT efficiency can be improved by the proposed control strategy. By the proposed method, the two capacitor voltages can be controlled asymmetrically to perform the separate MPPTs of each PV module connected to separate capacitors. The outputs can be generated without distortion even if two capacitors are asymmetrically regulated. Simulation and experimental results verify the validity and feasibility of the proposed methods.


title: Differential Microstrip and Slot-Ring Antennas for Millimeter-Wave Silicon Systems

abstract: Differential on-chip microstrip and slot-ring antennas with a quartz superstrate are presented for wafer-scale silicon systems. The antennas are fed at the nonradiating edge, which is compatible with differential coupled-lines, and are built on a 0.13-μ m CMOS process with a layout that meets all the metal density rules. A high radiation efficiency is achieved using a 100- μm quartz superstrate placed on top of the silicon chip. Both antennas have a measured gain varies from about 2 to 3 dBi at 91-94 GHz, with a - 10-dB S11 bandwidth of 7-8 GHz and a simulated radiation efficiency of >;50%. The designs are compatible with single- and multielement transceivers, and with wafer-scale imaging systems and power-combining arrays.


abstract: The Idea of a University, Defined and Illustrated [1873] John Henry Newman 549pp Inaugural Address Delivered to the University of St. Andrews [1867] John Stuart Mill 106pp On the Principles of English University Education [1837] William Whewell 186pp A Discourse on the Studies of the University [1833] Adam Sedgwick 116pp Professor Sedgwick's Discourse on the Studies of the University [1835] John Stuart Mill 64pp University Education [1851] Henry Philip Tappan 120pp German Universities: A Narrative of Personal Experience Together with Recent Statistical Information, Practical Suggestions, and a Comparison of the German, English and American Systems of Higher Education [1874] James Morgan Hart 405pp


title: Situation and Perspective of Knowledge Engineering

abstract: Knowledge Engineering was in the past primarily concerned with building and developing knowledge-based systems, an objective which puts Knowledge Engineering in a niche of the world-wide research efforts at best. This has changed dramatically: Knowledge Engineering is now a key technology in the upcoming knowledge society. Companies are recognizing knowledge as their key assets, which have to be exploited and protected in a fast changing, global and competitive economy. This situation has led to the application of Knowledge Engineering techniques in Knowledge Management. The demand for more efficient (business to) business processes requires the interconnection and interoperation of different information systems. But information access and integration is not an algorithmic task that is easy to solve: much knowledge is required to resolve the semantic differences of data residing in two information systems. Thus Knowledge Engineering has become a major technique for information integration. And, last but not least the fast growing World Wide Web generates an ever-increasing demand for more efficient knowledge exploitation and creation techniques. Here again Knowledge Engineering technologies may become the key technology for solving the problem. In this paper we discuss these recent developments and describe our view of the future.


title: Vehicular roof antenna cavity for coverage at low elevation angles

abstract: Chassis cavities have recently been proposed as a new mounting position for vehicular antennas. Cavities can be concealed and potentially offer more space for antennas than shark-fin modules mounted on top of the roof. An antenna cavity for the front or rear edge of the vehicle roof is designed, manufactured and measured for 5.9 GHz. The cavity offers increased radiation in the horizontal plane and to angles below horizon, compared to cavities located in the roof center.


Block-LDA: Jointly modeling entity-annotated text and entityentity links


title: Improving Machine Learning Approaches To Coreference Resolution

abstract: We present a noun phrase coreference system that extends the work of Soon et al. (2001) and, to our knowledge, produces the best results to date on the MUC-6 and MUC-7 coreference resolution data sets --- F-measures of 70.4 and 63.4, respectively. Improvements arise from two sources: extra-linguistic changes to the learning framework and a large-scale expansion of the feature set to include more sophisticated linguistic knowledge.


The Evolution of Game Controllers and Control Schemes and their Effect on their games


title: Relationships among smartphone addiction, stress, academic performance, and satisfaction with life

abstract: Results of several studies have suggested that smartphone addiction has negative effects on mental health and well-being. To contribute to knowledge on this topic, our study had two aims. One was to investigate the relationship between risk of smartphone addiction and satisfaction with life mediated by stress and academic performance. The other aim was to explore whether satisfaction with life mediated by stress and academic performance facilitates smartphone addiction. To identify test subjects, systematic random sampling was implemented. A total of 300 university students completed an online survey questionnaire that was posted to the student information system. The survey questionnaire collected demographic information and responses to scales including the Smartphone Addiction Scale - Short Version, the Perceived Stress Scale, and the Satisfaction with Life Scale. Data analyses included Pearson correlations between the main variables and multivariate analysis of variances. The results showed that smartphone addiction risk was positively related to perceived stress, but the latter was negatively related to satisfaction with life. Additionally, a smartphone addiction risk was negatively related to academic performance, but the latter was positively related to satisfaction with life. Stress mediates the relationship between smartphone addiction and satisfaction with life.Academic performance mediates the relationship b/w smartphone addiction & satisfaction with life.There is a zero order correlation between smartphone addiction and satisfaction with life.


title: Modeling and yield estimation of SRAM sub-system for different capacities subjected to parametric variations

abstract: Process variations have become a major challenge with the advancement in CMOS technologies. The performance of memory sub-systems such as Static Random Access Memory (SRAMs) is heavily dependent on these variations. Also, the VLSI industry requires the SRAM bit cell to qualify in the order of less than 0.1ppb to achieve higher Yield (Y). This paper proposes an efficient qualitative statistical analysis and Yield estimation method of SRAM sub-system which considers deviations due to variations in process parameters in bit line differential and input offset of sense amplifier (SA) all together. The Yield of SRAM is predicted for different capacities of SRAM array by developing a statistical model of memory sub-system in 65nm bulk CMOS technology. For the sub-system with 64 bit cells, it is estimated that the probability of failure is 4.802 ∗ 10−13 in a read cycle of frequency 1GHz. Furthermore, the probability of failure for 8MB capacity is 5.035 ∗ 10−7 while for 2GB capacity it increases to 1.289 ∗ 10−5. It is also observed that as the load on one SA per column is doubled, the probability of failure of memory slice increases by 70%. The proposed technique estimates the Yield(Y) for SRAM array to be more than 99.9999.


title: ELEVATE: A Framework for Entity-level Event Diffusion Prediction into Foreign Language Communities

abstract: The accessibility to news via the Web or other "traditional" media allows a rapid diffusion of information into almost every part of the world. These reports cover the full spectrum of events, ranging from locally relevant ones up to those that gain global attention. The societal impact of an event can be relatively easily "measured" by the attention it attracts (e.g. in the number of responses it receives and/or provokes) in the news or social media. However, this does not necessarily reflect its inter-cultural impact and its diffusion into other communities. In order to address the issue of predicting the spread of information into foreign language communities we introduce the ELEVATE framework. ELEVATE exploits entity information from Web contents and harnesses location related data for language-related event diffusion prediction. Our experiments on event spreading across Wikipedia communities of different language demonstrate the viability of our approach and improvement over state-of-the-art approaches.


title: A Survey of Vision-Based Hand Gesture Recognition System

abstract: The task of hand gesture recognition is highly challenging due to complex background (i.e. disturbance), presence of non-gesture hand motions, and various illumination environments. The proposed techniques begin by detecting the hand, tracking the hands movements and analysing the variations in the hand locations (i.e. Motion detection), and finally recognizing the appropriate gesture. Since the introduction of new gesture recognition technologies, interactive hand-gesture devices (such as smart televisions and displays) have been rapidly emerging.


title: Meta-Transfer Learning for Few-Shot Learning

abstract: Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As deep neural networks (DNNs) tend to overfit using a few samples only, meta-learning typically uses shallow neural networks (SNNs), thus limiting its effectiveness. In this paper we propose a novel few-shot learning method called meta-transfer learning (MTL) which learns to adapt a deep NN for few shot learning tasks. Specifically, "meta" refers to training multiple tasks, and "transfer" is achieved by learning scaling and shifting functions of DNN weights for each task. In addition, we introduce the hard task (HT) meta-batch scheme as an effective learning curriculum for MTL. We conduct experiments using (5-class, 1-shot) and (5-class, 5-shot) recognition tasks on two challenging few-shot learning benchmarks: miniImageNet and Fewshot-CIFAR100. Extensive comparisons to related works validate that our meta-transfer learning approach trained with the proposed HT meta-batch scheme achieves top performance. An ablation study also shows that both components contribute to fast convergence and high accuracy.


title: Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques

abstract: Objective: This work presents a system for a simultaneous non-invasive estimate of the blood glucose level (BGL) and the systolic (SBP) and diastolic (DBP) blood pressure, using a photoplethysmograph (PPG) and machine learning techniques. The method is independent of the person whose values are being measured and does not need calibration over time or subjects. Methodology: The architecture of the system consists of a photoplethysmograph sensor, an activity detection module, a signal processing module that extracts features from the PPG waveform, and a machine learning algorithm that estimates the SBP, DBP and BGL values. The idea that underlies the system is that there is functional relationship between the shape of the PPG waveform and the blood pressure and glucose levels. Results: As described in this paper we tested this method on 410 individuals without performing any personalized calibration. The results were computed after cross validation. The machine learning techniques tested were: ridge linear regression, a multilayer perceptron neural network, support vector machines and random forests. The best results were obtained with the random forest technique. In the case of blood pressure, the resulting coefficients of determination for reference vs. prediction were R"S"B"P^2=0.91, R"D"B"P^2=0.89, and R"B"G"L^2=0.90. For the glucose estimation, distribution of the points on a Clarke error grid placed 87.7% of points in zone A, 10.3% in zone B, and 1.9% in zone D. Blood pressure values complied with the grade B protocol of the British Hypertension society. Conclusion: An effective system for estimate of blood glucose and blood pressure from a photoplethysmograph is presented. The main advantage of the system is that for clinical use it complies with the grade B protocol of the British Hypertension society for the blood pressure and only in 1.9% of the cases did not detect hypoglycemia or hyperglycemia.


title: Recursive Least Squares Dictionary Learning Algorithm

abstract: We present the recursive least squares dictionary learning algorithm, RLS-DLA, which can be used for learning overcomplete dictionaries for sparse signal representation. Most DLAs presented earlier, for example ILS-DLA and K-SVD, update the dictionary after a batch of training vectors has been processed, usually using the whole set of training vectors as one batch. The training set is used iteratively to gradually improve the dictionary. The approach in RLS-DLA is a continuous update of the dictionary as each training vector is being processed. The core of the algorithm is compact and can be effectively implemented. The algorithm is derived very much along the same path as the recursive least squares (RLS) algorithm for adaptive filtering. Thus, as in RLS, a forgetting factor ? can be introduced and easily implemented in the algorithm. Adjusting ? in an appropriate way makes the algorithm less dependent on the initial dictionary and it improves both convergence properties of RLS-DLA as well as the representation ability of the resulting dictionary. Two sets of experiments are done to test different methods for learning dictionaries. The goal of the first set is to explore some basic properties of the algorithm in a simple setup, and for the second set it is the reconstruction of a true underlying dictionary. The first experiment confirms the conjectural properties from the derivation part, while the second demonstrates excellent performance.


title: Managerial succession and firm performance

abstract: We examine CEO turnover and firm financial performance. Accounting measures of performance relative to other firms deteriorate prior to CEO turnover and improve thereafter. The degree of improvement is positively related to the level of institutional shareholdings, the presence of an outsider-dominated board, and the appointment of an outsider (rather than an insider) CEO. Turnover announcements are associated with significantly positive average abnormal stock returns, which are in turn significantly positively related to subsequent changes in accounting measures of performance. This suggests that investors view turnover announcements as good news presaging performance improvements.


title: Meaningful keyword search in relational databases with large and complex schema

abstract: Keyword search over relational databases offers an alternative way to SQL to query and explore databases that is effective for lay users who may not be well versed in SQL or the database schema. This becomes more pertinent for databases with large and complex schemas. An answer in this context is a join tree spanning tuples containing the query's keywords. As there are potentially many answers to the query, and the user is often only interested in seeing the top-k answers, how to rank the answers based on their relevance is of paramount importance. We focus on the relevance of join as the fundamental means to rank answers. We devise means to measure relevance of relations and foreign keys in the schema over the information content of the database. This can be done offline with no need for external models. We compare the proposed measures against a gold standard we derive from a real workload over TPC-E and evaluate the effectiveness of our methods. Finally, we test the performance of our measures against existing techniques to demonstrate a marked improvement, and perform a user study to establish naturalness of the ranking of the answers.