Pushover Analysis of 4 Storey ’ s Reinforced Concrete Building

The earthquakes in the Indian subcontinent have led to an increase in the seismic zoning factor over many parts of the country. Also, ductility has become an issue for all building that was designed and detailed using earlier versions of the codes. Under such circumstances, seismic qualification of building has become extremely important. The structural engineering profession has been using the nonlinear static procedure (NSP) or pushover analysis. Modeling for such analysis requires the determination of the nonlinear properties of each component in the structure, quantified by strength and deformation capacities, which depend on the modeling assumptions. Pushover analysis is carried out for either user-defined nonlinear hinge properties or default-hinge properties, available in some programs based on the FEMA-356 and ATC-40 guidelines. This paper aims to evaluate the zone –IV selected reinforced concrete building to conduct the non-linear static analysis (Pushover Analysis). The pushover analysis shows the pushover curves, capacity spectrum, plastic hinges and performance level of the building. The non-linear static analysis gives better understanding and more accurate seismic performance of buildings of the damage or failure element.


A Low-Profile Wideband Substrate-Integrated Waveguide Cavity-Backed E-Shaped Patch Antenna for the Q-LINKPAN Applications

A low-profile substrate-integrated waveguide cavity-backed E-shaped patch antenna is proposed for the Q-LINKPAN application in this paper. In order to expand the operating bandwidth, a co-planar waveguide (CPW) is used to feed the proposed antenna with a metallized via for generating one more resonant mode. In addition, a substrate-integrated cavity is employed to suppress the surface wave and improve the radiation efficiency. A differential feeding network is used in the design to improve the symmetry of the E-plane radiation pattern and the H-plane cross polarization. A  $2 \times 2$  prototype is designed, fabricated, and measured for a demonstration. The measured results show that the prototype has a 10 dB impedance bandwidth of 34.4%, a gain of around 12.5 dBi with a narrow E-plane radiation beam within 37.5–46 GHz for long distance applications, and a gain of around 8 dBi with a broad E-plane radiation beam within 47–53 GHz for short distance applications. The proposed technique can be used to develop compact planar antenna for meeting both the short- and long-rang communication requirements of the emerging Q-LINKPAN wireless system.


Knowledge Collaboration in Online Communities

O communities (OCs) are a virtual organizational form in which knowledge collaboration can occur in unparalleled scale and scope, in ways not heretofore theorized. For example, collaboration can occur among people not known to each other, who share different interests and without dialogue. An exploration of this organizational form can fundamentally change how we theorize about knowledge collaboration among members of organizations. We argue that a fundamental characteristic of OCs that affords collaboration is their fluidity. This fluidity engenders a dynamic flow of resources in and out of the community—resources such as passion, time, identity, social disembodiment of ideas, socially ambiguous identities, and temporary convergence. With each resource comes both a negative and positive consequence, creating a tension that fluctuates with changes in the resource. We argue that the fluctuations in tensions can provide an opportunity for knowledge collaboration when the community responds to these tensions in ways that encourage interactions to be generative rather than constrained. After offering numerous examples of such generative responses, we suggest that this form of theorizing—induced by online communities—has implications for theorizing about the more general case of knowledge collaboration in organizations.


IP Lookup using Two-level Indexing and B-Trees

Networks are expanding very fast and the number of clients is increasing dramatically, this causes the router forwarding table to become very large and present more demand on faster router operations. In this paper, we address the problem of packet forwarding in the routers aiming to increase the speed of address lookup and minimize the memory required for storing the forwarding table. We propose a new algorithm that makes use of two-level indexing and Btrees. We test the approach and compare it to other famous IP lookup approaches. The preliminary simulations show 20% less memory requirements and the lookup speed scaling linearly with increasing table size.


mTOR Signaling in Growth Control and Disease

The mechanistic target of rapamycin (mTOR) signaling pathway senses and integrates a variety of environmental cues to regulate organismal growth and homeostasis. The pathway regulates many major cellular processes and is implicated in an increasing number of pathological conditions, including cancer, obesity, type 2 diabetes, and neurodegeneration. Here, we review recent advances in our understanding of the mTOR pathway and its role in health, disease, and aging. We further discuss pharmacological approaches to treat human pathologies linked to mTOR deregulation.


Counterintuitive Behavior of Social Systems

This paper addresses several social concerns: population trends; quality of urban life; policies for urban growth; and the unexpected, ineffective, or detrimental results often generated by government programs. Society becomes frustrated as repeated attacks on deficiencies in social systems lead only to worse symptoms. Legislation is debated and passed with great hope, but many programs prove to be ineffective. Results are often far short of expectations. Because dynamic behavior of social systems is not understood, government programs often cause exactly the reverse of desired results. The field of system dynamics now can explain how such contrary results happen. Fundamental reasons cause people to misjudge behavior of social systems. Orderly processes in creating human judgment and intuition lead people to wrong decisions when faced with complex and highly interacting systems. Until we reach a much better public understanding of social systems, attempts to develop corrective programs for social troubles will continue to be disappointing. This paper cautions against continuing to depend on the same past approaches that have led to present feelings of frustration. New methods developed over the last 30 years will lead to a better understanding of social systems and thereby to more effective policies for guiding the future. 1 Updated March, 1995. This paper was first copyrighted © 1971 by Jay W. Forrester. It is based on testimony for the Subcommittee on Urban Growth of the Committee on Banking and Currency, U.S. House of Representatives, on October 7, 1970. The original text appeared in the January, 1971, issue of the Technology Review published by the Alumni Association of the Massachusetts Institute of Technology. All figures are taken from World Dynamics by Jay W. Forrester, Productivity Press, Portland, Oregon. 2 Germeshausen Professor Emeritus and Senior Lecturer, Massachusetts Institute of Technology, Cambridge, MA, USA Copyright © 1995 Jay W. Forrester


Overview of narrowband IoT in LTE Rel-13

In 3GPP Rel-13, a narrowband system, named Narrowband Internet of Things (NB-IoT), has been introduced to provide low-cost, low-power, wide-area cellular connectivity for the Internet of Things. This system, based on Long Term Evolution (LTE) technology, supports most LTE functionalities albeit with essential simplifications to reduce device complexity. Further optimizations to increase coverage, reduce overhead and reduce power consumption while increasing capacity have been introduced as well. The design objectives of NB-IoT include low-complexity devices, high coverage, long device battery life, and massive capacity. Latency is relaxed although a delay budget of 10 seconds is the target for exception reports. This paper provides an overview of NB-IoT design, including salient features from the physical and higher layers. Illustrative results with respect to performance objectives are also provided. Finally, NB-IoT enhancements in LTE Rel-14 are briefly outlined.


Parenting stress in mothers and fathers of toddlers with autism spectrum disorders: associations with child characteristics.

Elevated parenting stress is observed among mothers of older children with autism spectrum disorders (ASD), but little is known about parents of young newly-diagnosed children. Associations between child behavior and parenting stress were examined in mothers and fathers of 54 toddlers with ASD (mean age = 26.9 months). Parents reported elevated parenting stress. Deficits/delays in children's social relatedness were associated with overall parenting stress, parent-child relationship problems, and distress for mothers and fathers. Regulatory problems were associated with maternal stress, whereas externalizing behaviors were associated with paternal stress. Cognitive functioning, communication deficits, and atypical behaviors were not uniquely associated with parenting stress. Clinical assessment of parental stress, acknowledging differences in parenting experiences for mothers and fathers of young children with ASD, is needed.


Dynamic time warping constraint learning for large margin nearest neighbor classification

Nearest neighbor (NN) classifier with dynamic time warping (DTW) is considered to be an effective method for time series classification. The performance of NN-DTW is dependent on the DTW constraints because the NN classifier is sensitive to the used distance function. For time series classification, the global path constraint of DTW is learned for optimization of the alignment of time series by maximizing the nearest neighbor hypothesis margin. In addition, a reduction technique is combined with a search process to condense the prototypes. The approach is implemented and tested on UCR datasets. Experimental results show the effectiveness of the proposed method. ! 2011 Elsevier Inc. All rights reserved.


DreamSketch: Early Stage 3D Design Explorations with Sketching and Generative Design

We present DreamSketch, a novel 3D design interface that combines the free-form and expressive qualities of sketching with the computational power of generative design algorithms. In DreamSketch, a user coarsely defines the problem by sketching the design context. Then, a generative design algorithm produces multiple solutions that are augmented as 3D objects in the sketched context. The user can interact with the scene to navigate through the generated solutions. The combination of sketching and generative algorithms enables designers to explore multiple ideas and make better informed design decisions during the early stages of design. Design study sessions with designers and mechanical engineers demonstrate the expressive nature and creative possibilities of DreamSketch.


Practical Bayesian Optimization of Machine Learning Algorithms

The use of machine learning algorithms frequently involves careful tuning of learning parameters and model hyperparameters. Unfortunately, this tuning is often a “black art” requiring expert experience, rules of thumb, or sometimes bruteforce search. There is therefore great appeal for automatic approaches that can optimize the performance of any given learning algorithm to the problem at hand. In this work, we consider this problem through the framework of Bayesian optimization, in which a learning algorithm’s generalization performance is modeled as a sample from a Gaussian process (GP). We show that certain choices for the nature of the GP, such as the type of kernel and the treatment of its hyperparameters, can play a crucial role in obtaining a good optimizer that can achieve expertlevel performance. We describe new algorithms that take into account the variable cost (duration) of learning algorithm experiments and that can leverage the presence of multiple cores for parallel experimentation. We show that these proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization for many algorithms including latent Dirichlet allocation, structured SVMs and convolutional neural networks.


Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid

Naive Bayes induction algorithms were previously shown to be surprisingly accurate on many classi cation tasks even when the conditional independence assumption on which they are based is violated How ever most studies were done on small databases We show that in some larger databases the accuracy of Naive Bayes does not scale up as well as decision trees We then propose a new algorithm NBTree which in duces a hybrid of decision tree classi ers and Naive Bayes classi ers the decision tree nodes contain uni variate splits as regular decision trees but the leaves contain Naive Bayesian classi ers The approach re tains the interpretability of Naive Bayes and decision trees while resulting in classi ers that frequently out perform both constituents especially in the larger databases tested


Bayesian Optimization with Robust Bayesian Neural Networks

Bayesian optimization is a prominent method for optimizing expensive-to-evaluate black-box functions that is widely applied to tuning the hyperparameters of machine learning algorithms. Despite its successes, the prototypical Bayesian optimization approach – using Gaussian process models – does not scale well to either many hyperparameters or many function evaluations. Attacking this lack of scalability and flexibility is thus one of the key challenges of the field. We present a general approach for using flexible parametric models (neural networks) for Bayesian optimization, staying as close to a truly Bayesian treatment as possible. We obtain scalability through stochastic gradient Hamiltonian Monte Carlo, whose robustness we improve via a scale adaptation. Experiments including multi-task Bayesian optimization with 21 tasks, parallel optimization of deep neural networks and deep reinforcement learning show the power and flexibility of this approach.


Taking the Human Out of the Loop: A Review of Bayesian Optimization

Big Data applications are typically associated with systems involving large numbers of users, massive complex software systems, and large-scale heterogeneous computing and storage architectures. The construction of such systems involves many distributed design choices. The end products (e.g., recommendation systems, medical analysis tools, real-time game engines, speech recognizers) thus involve many tunable configuration parameters. These parameters are often specified and hard-coded into the software by various developers or teams. If optimized jointly, these parameters can result in significant improvements. Bayesian optimization is a powerful tool for the joint optimization of design choices that is gaining great popularity in recent years. It promises greater automation so as to increase both product quality and human productivity. This review paper introduces Bayesian optimization, highlights some of its methodological aspects, and showcases a wide range of applications.


Efficient and Robust Automated Machine Learning

The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts. To be effective in practice, such systems need to automatically choose a good algorithm and feature preprocessing steps for a new dataset at hand, and also set their respective hyperparameters. Recent work has started to tackle this automated machine learning (AutoML) problem with the help of efficient Bayesian optimization methods. Building on this, we introduce a robust new AutoML system based on scikit-learn (using 15 classifiers, 14 feature preprocessing methods, and 4 data preprocessing methods, giving rise to a structured hypothesis space with 110 hyperparameters). This system, which we dub AUTO-SKLEARN, improves on existing AutoML methods by automatically taking into account past performance on similar datasets, and by constructing ensembles from the models evaluated during the optimization. Our system won the first phase of the ongoing ChaLearn AutoML challenge, and our comprehensive analysis on over 100 diverse datasets shows that it substantially outperforms the previous state of the art in AutoML. We also demonstrate the performance gains due to each of our contributions and derive insights into the effectiveness of the individual components of AUTO-SKLEARN.


The biology of cancer: metabolic reprogramming fuels cell growth and proliferation.

Cell proliferation requires nutrients, energy, and biosynthetic activity to duplicate all macromolecular components during each passage through the cell cycle. It is therefore not surprising that metabolic activities in proliferating cells are fundamentally different from those in nonproliferating cells. This review examines the idea that several core fluxes, including aerobic glycolysis, de novo lipid biosynthesis, and glutamine-dependent anaplerosis, form a stereotyped platform supporting proliferation of diverse cell types. We also consider regulation of these fluxes by cellular mediators of signal transduction and gene expression, including the phosphatidylinositol 3-kinase (PI3K)/Akt/mTOR system, hypoxia-inducible factor 1 (HIF-1), and Myc, during physiologic cell proliferation and tumorigenesis.


Plantar fasciitis: evidence-based review of diagnosis and therapy.

Plantar fasciitis causes heel pain in active as well as sedentary adults of all ages. The condition is more likely to occur in persons who are obese or in those who are on their feet most of the day. A diagnosis of plantar fasciitis is based on the patient's history and physical findings. The accuracy of radiologic studies in diagnosing plantar heel pain is unknown. Most interventions used to manage plantar fasciitis have not been studied adequately; however, shoe inserts, stretching exercises, steroid injection, and custom-made night splints may be beneficial. Extracorporeal shock wave therapy may effectively treat runners with chronic heel pain but is ineffective in other patients. Limited evidence suggests that casting or surgery may be beneficial when conservative measures fail.


Penetration state transition analysis: A rule-based intrusion detection approach

| This paper presents a new approach to representing and detecting computer penetrations in real-time. The approach, called state transition analysis, models penetrations as a series of state changes that lead from an initial secure state to a target compromised state. State transition diagrams, the graphical representation of penetrations, identify precisely the requirements for and the compromise of a penetration and present only the critical events that must occur for the successful completion of the penetration. State transition diagrams are written to correspond to the states of an actual computer system, and these diagrams form the basis of a rule-based expert system for detecting penetrations, called the State Transition Analysis Tool (STAT). The design and implementation of a UNIX-speci c prototype of this expert system, called USTAT, is also presented. This prototype provides a further illustration of the overall design and functionality of this intrusion detection approach. Lastly, STAT is compared to the functionality of comparable intrusion detection tools. Keywords|Security, Intrusion Detection, Expert Systems


Detection and counting of pothole using image processing techniques

Pothole are the primary cause of accidents, hence identification and classification using image processing techniques is very important. In this paper image pre-processing based on difference of Gaussian-Filtering and clustering based image segmentation methods are implemented for better results. From the results the K-Means clustering based segmentation was preferred for its fastest computing time and edge detection based segmentation is preferred for its specificity. The main goal of this paper is to identify a better method which is highly efficient and accurate compared to the conventional methods. Different image pre-processing and segmentation methods for pothole detection where reviewed using performance measures.


Robust facial expression recognition using local binary patterns

A novel low-computation discriminative feature space is introduced for facial expression recognition capable of robust performance over a rang of image resolutions. Our approach is based on the simple local binary patterns (LBP) for representing salient micro-patterns of face images. Compared to Gabor wavelets, the LBP features can be extracted faster in a single scan through the raw image and lie in a lower dimensional space, whilst still retaining facial information efficiently. Template matching with weighted Chi square statistic and support vector machine are adopted to classify facial expressions. Extensive experiments on the Cohn-Kanade Database illustrate that the LBP features are effective and efficient for facial expression discrimination. Additionally, experiments on face images with different resolutions show that the LBP features are robust to low-resolution images, which is critical in real-world applications where only low-resolution video input is available.


1 FEEDFORWARD NEURAL NETWORKS : AN INTRODUCTION

1 A neural network is a massively parallel distributed processor that has a natural propensity for storing experiential knowledge and making it available for use. It resembles the brain in two respects (Haykin 1998): 1. Knowledge is acquired by the network through a learning process. 2. Interconnection strengths known as synaptic weights are used to store the knowledge. Basically, learning is a process by which the free parameters (i.e., synaptic weights and bias levels) of a neural network are adapted through a continuing process of stimulation by the environment in which the network is embedded. The type of learning is determined by the manner in which the parameter changes take place. In a general sense, the learning process may be classified as follows:


Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention

Multimedia content is dominating today's Web information. The nature of multimedia user-item interactions is 1/0 binary implicit feedback (e.g., photo likes, video views, song downloads, etc.), which can be collected at a larger scale with a much lower cost than explicit feedback (e.g., product ratings). However, the majority of existing collaborative filtering (CF) systems are not well-designed for multimedia recommendation, since they ignore the implicitness in users' interactions with multimedia content. We argue that, in multimedia recommendation, there exists item- and component-level implicitness which blurs the underlying users' preferences. The item-level implicitness means that users' preferences on items (e.g. photos, videos, songs, etc.) are unknown, while the component-level implicitness means that inside each item users' preferences on different components (e.g. regions in an image, frames of a video, etc.) are unknown. For example, a 'view'' on a video does not provide any specific information about how the user likes the video (i.e.item-level) and which parts of the video the user is interested in (i.e.component-level). In this paper, we introduce a novel attention mechanism in CF to address the challenging item- and component-level implicit feedback in multimedia recommendation, dubbed Attentive Collaborative Filtering (ACF). Specifically, our attention model is a neural network that consists of two attention modules: the component-level attention module, starting from any content feature extraction network (e.g. CNN for images/videos), which learns to select informative components of multimedia items, and the item-level attention module, which learns to score the item preferences. ACF can be seamlessly incorporated into classic CF models with implicit feedback, such as BPR and SVD++, and efficiently trained using SGD. Through extensive experiments on two real-world multimedia Web services: Vine and Pinterest, we show that ACF significantly outperforms state-of-the-art CF methods.


3D Mesh Compression: Survey, Comparisons, and Emerging Trends

3D meshes are commonly used to represent virtual surface and volumes. However, their raw data representations take a large amount of space. Hence, 3D mesh compression has been an active research topic since the mid 1990s. In 2005, two very good review articles describing the pioneering works were published. Yet, new technologies have emerged since then. In this article, we summarize the early works and put the focus on these novel approaches. We classify and describe the algorithms, evaluate their performance, and provide synthetic comparisons. We also outline the emerging trends for future research.


Prescribed performance control of quadruped robot

Due to the complex and high nonlinear structure of the quadruped robots, the dynamic control of quadruped robots has long been a big challenge for the researchers. In this paper, a guaranteed performance adaptive control algorithm is proposed for tracking control of a quadruped robot with parameter uncertainties. The controller is designed based on the sliding mode theory, and incorporates a transformed error, which includes the performance indices. Parameter uncertainties are estimated by a linear network. By choosing a proper Lyapunov function, the prescribed performance with the proposed controller is proven in the scenes of the parameter uncertainties and the environment constraint. Numerical simulations demonstrate the effectiveness and superiority of the proposed controller.


Largest inscribed rectangles in convex polygons

We consider approximation algorithms for the problem of computing an inscribed rectangle having largest area in a convex polygon on n vertices. If the order of the vertices of the polygon is given, we present a randomized algorithm that computes an inscribed rectangle with area at least (1− ) times the optimum with probability t in time O( 1 log n) for any constant t < 1. We further give a deterministic approximation algorithm that computes an inscribed rectangle of area at least (1− ) times the optimum in running time O( 1 2 log n) and show how this running time can be slightly improved.


Self-enucleation in a young schizophrenic patient--a case report.

Self-enucleation represents an extreme but fortunately rare form of deliberate self-harm. Case reports of patients who self-enucleate reveal some common features. A case of auto-enucleation in a young schizophrenic patient and a short discussion on deliberate self-harm are presented.


Random Forests and VGG-NET: An Algorithm for the ISIC 2017 Skin Lesion Classification Challenge

This manuscript briefly describes an algorithm developed for the ISIC 2017 Skin Lesion Classification Competition. In this task, participants are asked to complete two independent binary image classification tasks that involve three unique diagnoses of skin lesions (melanoma, nevus, and seborrheic keratosis). In the first binary classification task, participants are asked to distinguish between (a) melanoma and (b) nevus and seborrheic keratosis. In the second binary classification task, participants are asked to distinguish between (a) seborrheic keratosis and (b) nevus and melanoma. The other phases of the competition are not considered. Our proposed algorithm consists of three steps: preprocessing, classification using VGG-NET [2] and Random Forests [3], and calculation of a final score.


A MATLAB-based simulation program for indoor visible light communication system

We report a simulation program for indoor visible light communication environment based on MATLAB and Simulink. The program considers the positions of the transmitters and the reflections at each wall. For visible light communication environment, the illumination light-emitting diode is used not only as a lighting device, but also as a communication device. Using the simulation program, the distributions of illuminance and root-mean-square delay spread are analyzed at bottom surface.


Cognitive Biases in Visualizations


Automatic creation of WordNets from parallel corpora

In this paper we present the evaluation results for the creation of WordNets for five languages (Spanish, French, German, Italian and Portuguese) using an approach based on parallel corpora. We have used three very large parallel corpora for our experiments: DGT-TM, EMEA and ECB. The English part of each corpus is semantically tagged using Freeling and UKB. After this step, the process of WordNet creation is converted into a word alignment problem, where we want to align WordNet synsets in the English part of the corpus with lemmata on the target language part of the corpus. The word alignment algorithm used in these experiments is a simple most frequent translation algorithm implemented into the WN-Toolkit. The obtained precision values are quite satisfactory, but the overall number of extracted synset-variant pairs is too low, leading into very poor recall values. In the conclusions, the use of more advanced word alignment algorithms, such as Giza++, Fast Align or Berkeley aligner is suggested.


Preliminary safety assessment for a sectorless ATM concept

In a sectorless air traffic management concept the airspace is no longer divided into sectors but regarded as one piece. A number of aircraft, not necessarily in the same airspace region, are assigned to each air traffic controller who is then responsible for these aircraft from their entry into the airspace to their exit. These individually assigned flights can be anywhere in the airspace and therefore in different traffic situations. This means that air traffic controllers will manage flights which may not be geographically connected. Such a concept change will necessitate also a significant change in the controllers' routines and the support tools. Naturally, the question of safety arises regarding new procedures and systems. This paper provides a preliminary safety assessment for a sectorless air traffic management concept. The assessment is based on the Single European Sky ATM Research (SESAR) Safety Reference Material which was originally developed for SESAR purposes. This success-based approach stresses the positive contribution of a new concept while traditional approaches mainly consider the negative effect of possible hazards and failures. Based on validation activities including realtime simulations we have developed safety acceptance criteria and safety objectives for a sectorless air traffic management (ATM) concept. Starting from these we have sketched the safety performance requirement model and deduce the first safety requirements for normal conditions, abnormal conditions and in the case of internal system failures.


Two decades of terror management theory: a meta-analysis of mortality salience research.

A meta-analysis was conducted on empirical trials investigating the mortality salience (MS) hypothesis of terror management theory (TMT). TMT postulates that investment in cultural worldviews and self-esteem serves to buffer the potential for death anxiety; the MS hypothesis states that, as a consequence, accessibility of death-related thought (MS) should instigate increased worldview and self-esteem defense and striving. Overall, 164 articles with 277 experiments were included. MS yielded moderate effects (r = .35) on a range of worldview- and self-esteem-related dependent variables (DVs), with effects increased for experiments using (a) American participants, (b) college students, (c) a longer delay between MS and the DV, and (d) people-related attitudes as the DV. Gender and self-esteem may moderate MS effects differently than previously thought. Results are compared to other reviews and examined with regard to alternative explanations of TMT. Finally, suggestions for future research are offered.


A Fast and Compact Saliency Score Regression Network Based on Fully Convolutional Network

Visual saliency detection aims at identifying the most visually distinctive parts in an image, and serves as a pre-processing step for a variety of computer vision and image processing tasks. To this end, the saliency detection procedure must be as fast and compact as possible and optimally processes input images in a real time manner. It is an essential application requirement for the saliency detection task. However, contemporary detection methods often utilize some complicated procedures to pursue feeble improvements on the detection precession, which always take hundreds of milliseconds and make them not easy to be applied practically. In this paper, we tackle this problem by proposing a fast and compact saliency score regression network which employs fully convolutional network, a special deep convolutional neural network, to estimate the saliency of objects in images. It is an extremely simplified endto-end deep neural network without any pre-processings and post-processings. When given an image, the network can directly predict a dense full-resolution saliency map (image-to-image prediction). It works like a compact pipeline which effectively simplifies the detection procedure. Our method is evaluated on six public datasets, and experimental results show that it can achieve comparable or better precision performance than the state-of-the-art methods while get a significant improvement in detection speed (35 FPS, processing in real time).


The Evolution to Modern Phased Array Architectures

Phased array technology has been evolving steadily with advances in solid-state microwave integrated circuits, analysis and design tools, and reliable fabrication practices. With significant government investments, the technologies have matured to a point where phased arrays are widely used in military systems. Next-generation phased arrays will employ high levels of digitization, which enables a wide range of improvements in capability and performance. Digital arrays leverage the rapid commercial evolution of digital processor technology. The cost of phased arrays can be minimized by utilizing high-volume commercial microwave manufacturing and packaging techniques. Dramatic cost reductions are achieved by employing a tile array architecture, which greatly reduces the number of printed circuit boards and connectors in the array.


Spectrum Survey in Singapore: Occupancy Measurements and Analyses

We study the 24-hour spectrum usage pattern in Singapore in the frequency bands ranging from 80 MHz to 5850 MHz. The objectives are to find how the scarce radio spectrum allocated to different services is utilized in Singapore and identify the bands that could be accessed for future opportunistic use due to their low or no active utilization. The results from the spectrum measurements taken over 12 weekday periods reveal that a significant amount of spectrum in Singapore has very low occupancy all the time. The occupancy is quantified as the amount of spectrum detected above a certain received power threshold. The outcome of this study suggests that Singapore has a great potential for employing emerging spectrum sharing technology such as the cognitive radio technology to accommodate enormous demands for future wireless services. However, this study of spectrum survey is preliminary in its nature and future long term studies need to be performed to determine any potential secondary usage on those channels that have low or no active utilization.


Convolutional Neural Networks vs. Convolution Kernels: Feature Engineering for Answer Sentence Reranking

In this paper, we study, compare and combine two state-of-the-art approaches to automatic feature engineering: Convolution Tree Kernels (CTKs) and Convolutional Neural Networks (CNNs) for learning to rank answer sentences in a Question Answering (QA) setting. When dealing with QA, the key aspect is to encode relational information between the constituents of question and answer in learning algorithms. For this purpose, we propose novel CNNs using relational information and combined them with relational CTKs. The results show that (i) both approaches achieve the state of the art on a question answering task, where CTKs produce higher accuracy and (ii) combining such methods leads to unprecedented high results.


Electronic textiles: a platform for pervasive computing

In our era of converging technologies, it is significant that the second Industrial Revolution should meet the first on common ground: textiles. Modern data processing is, after all, an offshoot of technology first introduced in the 18th century to automate the production of woven textiles. The punched-card system developed by J.-M. Jacquard in 1804 to regulate the weaving of patterned fabrics was later adapted by the English inventor C. Babbage for his proposed analytical engine, the precursor of all binary-based digital computers. Now researchers are delving into a new technology that literally weaves a computing platform into wearable fabrics, creating electronic textiles, or e-textiles, that offer the wearer ready access to information anytime, anywhere, through the medium of an unobtrusive interface. This new field of research draws together an interdisciplinary cadre of specialists in information technology, computing and communications, microtechnology, textiles, and application domains. Their objective is to develop an economical process for the manufacture of large-area, flexible, conformable information systems that can be used in such diverse areas as medicine, sports, space research, firefighting, and law enforcement. In this paper, the authors explore the synergies accruing from wedding the capabilities of textiles with those of computing. In addition to the twin dimensions of functionality and aesthetics, clothing can now be woven to accommodate a third dimension: intelligence. Clothing, the authors point out, meets the user’s requirements for interactivity, connectivity, individuality, and adaptability. It is also probably the most universal of human–computer interfaces: it is familiar; it can be tailored to individual sizes, tastes, and budgets; and it is adaptable to varying climatic conditions. Textiles also provide the ultimate flexibility in systems design by offering a broad range of fibers, yarns, fabrics, and manufacturing techniques. Fabrics also have the spatial capacity needed for hosting the large numbers of sensors and processors demanded for covering broad terrains, such as a battlefield. The size of clothing further enables designers the latitude to build in redundant systems to ensure fault tolerance.


Synchromesh Torque Estimation in an Electric Vehicle's Clutchless Automated Manual Transmission Using Unknown Input Observer

This paper studies the estimation of the frictional torque of the synchromesh during the gear shifting operation in an electric vehicle equipped with a clutchless automated manual transmission (AMT). The clutchless drivetrain of the electric vehicle is discussed and the dynamical model of the powertrain from the electric traction motor to the synchromesh system of a two-speed AMT is developed. In order to estimate the frictional torque of the synchromesh, which is indeed an unknown input to the dynamical system, it is assumed to be generated by a fictitious autonomous system. Thereafter, the augmented state- space representation of the actual and fictitious state variables, which forms the basis for the observer design, is provided and the observability of this augmented system is discussed. A deterministic Luenberger observer and a stochastic Kalman-Bucy filter are designed in order to estimate the frictional torque of the synchromesh. The estimation is based on the measuring angular velocities of the electric motor and synchro ring together with the known electromagnetic torque of the traction motor. The performance of the observers is assessed experimentally by means of a test rig. The results demonstrate the satisfactory performance of the stochastic observer when the system encounters process and measurement noise which are likely to happen in practice.


Automatic Cloud I/O Configurator for I/O Intensive Parallel Applications

As the cloud platform becomes a promising alternative to traditional HPC (high performance computing) centers or in-house clusters, the I/O bottleneck problem is highlighted in this new environment, typically with top-of-the-line compute instances but sub-par communication and I/O facilities. It has been observed that changing the cloud I/O system configurations, such as choices of file systems, number of I/O servers and their placement strategies, etc., will lead to a considerable variation in the performance and cost efficiency of I/O intensive parallel applications. However, storage system configuration is tedious and error-prone to do manually, even for expert users, leading to solutions that are grossly over-provisioned (low cost inefficiency), substantially under-performing (poor performance) or, in the worst case, both. This paper proposes ACIC, a system which automatically searches for optimized I/O system configurations from many candidates for each individual application running on a given cloud platform. ACIC takes advantage of machine learning models to perform performance/cost predictions. To tackle the high-dimensional parameter exploration space, we enable affordable, reusable, and incremental training on cloud platforms, guided by the Plackett and Burman Matrices for experiment design. Our evaluation results with four representative parallel applications indicate that ACIC consistently identifies optimal or near-optimal configurations among a large group of candidate settings. The top ACIC-recommended configuration is capable of improving the applications' performance by a factor of up to 10.5 (3.1 on average), and cost saving of up to 89 percent (51 percent on average), compared with a commonly used baseline I/O configuration. In addition, we carried out a small-scale user study for one of the test applications, which found that ACIC consistently beat the user and even the application's developer, often by a significant margin, in selecting optimized configurations.


Evolutionary algorithm in Forex trade strategy generation

This paper shows an evolutionary algorithm application to generate profitable strategies to trade futures contracts on foreign exchange market (Forex). Strategy model in approach is based on two decision trees, responsible for taking the decisions of opening long or short positions on Euro/US Dollar currency pair. Trees take into consideration only technical analysis indicators, which are connected by logic operators to identify border values of these indicators for taking profitable decision(s). We have tested the efficiency of presented approach on learning and test time-frames of various characteristics.


Enhanced 3GPP system for Machine Type Communications and Internet of Things

The Third Generation Partnership Project (3GPP) has been working on developing specifications on Machine Type Communications (MTC), also known as Machine to Machine Communications (M2M), which is all part of Internet of Things (IoT), a technology that enables machines and devices to be inter-connected via the internet. This paper presents recent M2M/IoT feature enhancements in 3GPP. These features include architectural enhancements for inclusion of a service exposure framework, group based IoT management, efficient monitoring mechanisms for IoT devices, optimization of IoT device power consumption, and high latency communications. These M2M/IoT related functionalities and enhancements are a continuing effort within 3GPP to make the mobile network fulfil the ever changing M2M/IoT requirements, and in so doing allow operators/service providers to offer unique services.


On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators

This paper shows, by means of an operator called a splitting operator, that the Douglas-Rachford splitting method for finding a zero of the sum of two monotone operators is a special case of the proximal point algorithm, Therefore, applications of Douglas -Rachford splitting, such as the alternating direction method of multipliers for convex programming decomposit ion, are also special cases of the proximal point algorithm. This observation allows the unification and generalization of a variety of convex programming algorithms. By introducing a modified version of the proximal point algorithm, we derive a new, generalized alternating direction method of multipliers for convex programming. Advances of this sort illustrate the power and generality gained by adopting monotone operator theory as a conceptual framework.


Mikro Bloglardaki Finans Toplulukları için Kullanıcı Ağırlıklandırılmış Duygu Analizi Yöntemi

Nowadays, sentiment analysis is a popular research area in computer science. which aims to determine a person’s or a group’s mood, behaviour and opinion about any topic with using textual documents. With the proliferation of social micro-blogging sites, digital opinion text data is increased. Thus, many sentiment analysis researches are performed on these public data in different sociological fields, such as finance, economy and politics. In this paper, a novel sentiment analysis method is proposed on micro-blogging sites which uses new user metrics. Proposed method is used to measure financial community’s sentiment polarity on micro-blogging sites. In addition to that we analyze the correlation between the mood of financial community and the behavior of the Borsa Istanbul 100 index weekly by Pearson correlation coefficient method. Our test results show that this novel sentiment analysis method improves to the accuracy of linear relationship between the behavior of the stock market and the sentiment polarity of the financial community.


Testing predictions from personality neuroscience. Brain structure and the big five.

We used a new theory of the biological basis of the Big Five personality traits to generate hypotheses about the association of each trait with the volume of different brain regions. Controlling for age, sex, and whole-brain volume, results from structural magnetic resonance imaging of 116 healthy adults supported our hypotheses for four of the five traits: Extraversion, Neuroticism, Agreeableness, and Conscientiousness. Extraversion covaried with volume of medial orbitofrontal cortex, a brain region involved in processing reward information. Neuroticism covaried with volume of brain regions associated with threat, punishment, and negative affect. Agreeableness covaried with volume in regions that process information about the intentions and mental states of other individuals. Conscientiousness covaried with volume in lateral prefrontal cortex, a region involved in planning and the voluntary control of behavior. These findings support our biologically based, explanatory model of the Big Five and demonstrate the potential of personality neuroscience (i.e., the systematic study of individual differences in personality using neuroscience methods) as a discipline.


Hand Gesture Recognition for Real Time Human Machine Interaction System

Real Time Human-machine Interaction system using hand gesture Recognition to handle the mouse event , media player , image viewer .Users have to repeat same mouse and keyboard actions, inducing waste of time. Gestures have long been considered as an interaction technique that can potentially deliver more natural. A fast gesture recognition scheme is proposed to be an interface for the human-machine interaction (HMI) of systems. The system presents some lowcomplexity algorithms and gestures to reduce the gesture recognition complexity and be more suitable for controlling real-time computer systems. In this paper we use the webcam for capturing the image. After capturing the image it converts into the binary image. A gesture is a specific combination of hand position.


Solution aversion: On the relation between ideology and motivated disbelief.

There is often a curious distinction between what the scientific community and the general population believe to be true of dire scientific issues, and this skepticism tends to vary markedly across groups. For instance, in the case of climate change, Republicans (conservatives) are especially skeptical of the relevant science, particularly when they are compared with Democrats (liberals). What causes such radical group differences? We suggest, as have previous accounts, that this phenomenon is often motivated. However, the source of this motivation is not necessarily an aversion to the problem, per se, but an aversion to the solutions associated with the problem. This difference in underlying process holds important implications for understanding, predicting, and influencing motivated skepticism. In 4 studies, we tested this solution aversion explanation for why people are often so divided over evidence and why this divide often occurs so saliently across political party lines. Studies 1, 2, and 3-using correlational and experimental methodologies-demonstrated that Republicans' increased skepticism toward environmental sciences may be partly attributable to a conflict between specific ideological values and the most popularly discussed environmental solutions. Study 4 found that, in a different domain (crime), those holding a more liberal ideology (support for gun control) also show skepticism motivated by solution aversion.


Lava: Hardware Design in Haskell

Lava is a tool to assist circuit designers in specifying, designing, verifying and implementing hardware. It is a collection of Haskell modules. The system design exploits functional programming language features, such as monads and type classes, to provide multiple interpretations of circuit descriptions. These interpretations implement standard circuit analyses such as simulation, formal verification and the generation of code for the production of real circuits.Lava also uses polymorphism and higher order functions to provide more abstract and general descriptions than are possible in traditional hardware description languages. Two Fast Fourier Transform circuit examples illustrate this.


Locality preserving indexing for document representation

Document representation and indexing is a key problem for document analysis and processing, such as clustering, classification and retrieval. Conventionally, Latent Semantic Indexing (LSI) is considered effective in deriving such an indexing. LSI essentially detects the most representative features for document representation rather than the most discriminative features. Therefore, LSI might not be optimal in discriminating documents with different semantics. In this paper, a novel algorithm called Locality Preserving Indexing (LPI) is proposed for document indexing. Each document is represented by a vector with low dimensionality. In contrast to LSI which discovers the global structure of the document space, LPI discovers the local structure and obtains a compact document representation subspace that best detects the essential semantic structure. We compare the proposed LPI approach with LSI on two standard databases. Experimental results show that LPI provides better representation in the sense of semantic structure.


Dear Sir or Madam, May I Introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer

Style transfer is the task of automatically transforming a piece of text in one particular style into another. A major barrier to progress in this field has been a lack of training and evaluation datasets, as well as benchmarks and automatic metrics. In this work, we create the largest corpus for a particular stylistic transfer (formality) and show that techniques from the machine translation community can serve as strong baselines for future work. We also discuss challenges of using automatic metrics.


A Deep Policy Inference Q-Network for Multi-Agent Systems

We present DPIQN, a deep policy inference Q-network that targets multi-agent systems composed of controllable agents, collaborators, and opponents that interact with each other. We focus on one challenging issue in such systems—modeling agents with varying strategies—and propose to employ “policy features” learned from raw observations (e.g., raw images) of collaborators and opponents by inferring their policies. DPIQN incorporates the learned policy features as a hidden vector into its own deep Q-network (DQN), such that it is able to predict better Q values for the controllable agents than the state-of-the-art deep reinforcement learningmodels. We further propose an enhanced version of DPIQN, called deep recurrent policy inference Q-network (DRPIQN), for handling partial observability. Both DPIQN and DRPIQN are trained by an adaptive training procedure, which adjusts the network’s attention to learn the policy features and its own Q-values at different phases of the training process. We present a comprehensive analysis of DPIQN and DRPIQN, and highlight their effectiveness and generalizability in various multi-agent settings. Our models are evaluated in a classic soccer game involving both competitive and collaborative scenarios. Experimental results performed on 1 vs. 1 and 2 vs. 2 games show that DPIQN and DRPIQN demonstrate superior performance to the baseline DQN and deep recurrent Q-network (DRQN) models. We also explore scenarios in which collaborators or opponents dynamically change their policies, and show that DPIQN and DRPIQN do lead to better overall performance in terms of stability and mean scores.


Scalable Modified Kneser-Ney Language Model Estimation

We present an efficient algorithm to estimate large modified Kneser-Ney models including interpolation. Streaming and sorting enables the algorithm to scale to much larger models by using a fixed amount of RAM and variable amount of disk. Using one machine with 140 GB RAM for 2.8 days, we built an unpruned model on 126 billion tokens. Machine translation experiments with this model show improvement of 0.8 BLEU point over constrained systems for the 2013 Workshop on Machine Translation task in three language pairs. Our algorithm is also faster for small models: we estimated a model on 302 million tokens using 7.7% of the RAM and 14.0% of the wall time taken by SRILM. The code is open source as part of KenLM.


A dual-band bagley power divider using modified Π-network

A new methodology to design a dual-band 3-way Bagley power divider is presented. The general method of using π-network as well as the proposed modified π-network has been discussed. The proposed π-network mimics a λ/2 line at two different frequencies. Design equations in closed form are obtained by using transmission line concepts and simple network analysis techniques. The proposed design is validated using Agilent ADS. This modification also leads to wider bandwidth as observed by a careful simulation.


Self-organizing semantic maps

Self-organized formation of topographic maps for abstract data, such as words, is demonstrated in this work. The semantic relationships in the data are reflected by their relative distances in the map. Two different simulations, both based on a neural network model that implements the algorithm of the selforganizing feature maps, are given. For both, an essential, new ingredient is the inclusion of the contexts, in which each symbol appears, into the input data. This enables the network to detect the “logical similarity” between words from the statistics of their contexts. In the first demonstration, the context simply consists of a set of attribute values that occur in conjunction with the words. In the second demonstration, the context is defined by the sequences in which the words occur, without consideration of any associated attributes. Simple verbal statements consisting of nouns, verbs, and adverbs have been analyzed in this way. Such phrases or clauses involve some of the abstractions that appear in thinking, namely, the most common categories, into which the words are then automatically grouped in both of our simulations. We also argue that a similar process may be at work in the brain.


Multi-View Unsupervised Feature Selection with Adaptive Similarity and View Weight

With the advent of multi-view data, multi-view learning has become an important research direction in both machine learning and data mining. Considering the difficulty of obtaining labeled data in many real applications, we focus on the multi-view unsupervised feature selection problem. Traditional approaches all characterize the similarity by fixed and pre-defined graph Laplacian in each view separately and ignore the underlying common structures across different views. In this paper, we propose an algorithm named Multi-view Unsupervised Feature Selection with Adaptive Similarity and View Weight (ASVW) to overcome the above mentioned problems. Specifically, by leveraging the learning mechanism to characterize the common structures adaptively, we formulate the objective function by a common graph Laplacian across different views, together with the sparse <inline-formula><tex-math notation="LaTeX">$\ell _{2,p}$</tex-math> <alternatives><inline-graphic xlink:href="hou-ieq1-2681670.gif"/></alternatives></inline-formula>-norm constraint designed for feature selection. We develop an efficient algorithm to address the non-smooth minimization problem and prove that the algorithm will converge. To validate the effectiveness of ASVW, comparisons are made with some benchmark methods on real-world datasets. We also evaluate our method in the real sports action recognition task. The experimental results demonstrate the effectiveness of our proposed algorithm.


Evolution of Trajectories: A Novel Representation for Deep Action Recognition

Achieving high classification accuracy remains a challenge for human action recognition approaches that are based on convolutional neural networks (CNN). CNN-based action recognition methods on resource-constrained edge systems are currently unable to train on whole videos due to infeasible computational and memory requirements. On the other hand, approaches that utilize video-level supervision with sparse-sampling designs run the risk of learning local features shared with multiple similar classes. Additionally, features captured by motion estimation algorithms for temporal stream CNN's are already reduced in dimension, increasing the possibility of label mismatch in methods that rely on short-term features. To address the aforementioned points, we design a novel temporal representation to capture a 1) long-term interval of motion and 2) integrate the trajectory of motion captured therein. We compare our approach with current motion representations and demonstrate its efficacy for examples containing local features with high inter-class similarity. We implement our representation as part of two and three-stream CNN's and conduct experiments on one of the popular and most difficult action recognition datasets: HMDB51. Our results show that, for methods employing sparse-sampling designs, our approach surpasses the current state-of-the-art approaches achieving 71.76% on HMDB51.


BranchScope: A New Side-Channel Attack on Directional Branch Predictor

We present BranchScope - a new side-channel attack where the attacker infers the direction of an arbitrary conditional branch instruction in a victim program by manipulating the shared directional branch predictor. The directional component of the branch predictor stores the prediction on a given branch (taken or not-taken) and is a different component from the branch target buffer (BTB) attacked by previous work. BranchScope is the first fine-grained attack on the directional branch predictor, expanding our understanding of the side channel vulnerability of the branch prediction unit. Our attack targets complex hybrid branch predictors with unknown organization. We demonstrate how an attacker can force these predictors to switch to a simple 1-level mode to simplify the direction recovery. We carry out BranchScope on several recent Intel CPUs and also demonstrate the attack against an SGX enclave.


How GaN Power Transistors Drive High-Performance Lidar: Generating ultrafast pulsed power with GaN FETs

Light detection and ranging (lidar) is a versatile light-based remote sensing technology that recently has been the subject of great attention. It has shown up in a number of media venues and has even led to public debate about the engineering choices of a well-known electric car company, Tesla Motors [1]. While this article is not going to enter the fray, it will provide some background on lidar and discuss its strong connection to power electronics technologies.


Color Transform Based Approach for Disease Spot Detection on Plant Leaf

In this research, an algorithm for disease spot segmentation using image processing techniques in plant leaf is implemented. This is the first and important phase for automatic detection and classification of plant diseases. Disease spots are different in color but not in intensity, in comparison with plant leaf color. So we color transform of RGB image can be used for better segmentation of disease spots. In this paper a comparison of the effect of CIELAB, HSI and YCbCr color space in the process of disease spot detection is done. Median filter is used for image smoothing. Finally threshold can be calculated by applying Otsu method on color component to detect the disease spot. An algorithm which is independent of background noise, plant type and disease spot color was developed and experiments were carried out on different “Monocot” and “Dicot” family plant leaves with both, noise free (white) and noisy background.


Review of control algorithms for robotic ankle systems in lower-limb orthoses, prostheses, and exoskeletons.

This review focuses on control strategies for robotic ankle systems in active and semiactive lower-limb orthoses, prostheses, and exoskeletons. Special attention is paid to algorithms for gait phase identification, adaptation to different walking conditions, and motion intention recognition. The relevant aspects of hardware configuration and hardware-level controllers are discussed as well. Control algorithms proposed for other actuated lower-limb joints (knee and/or hip), with potential applicability to the development of ankle devices, are also included.


Exoskeleton robot for rehabilitation of elbow and forearm movements

To perform essential daily activities the movement of shoulder, elbow, and wrist play a vital role and therefore proper functioning of upper-limb is very much essential. We therefore have been developing an exoskeleton robot (ExoRob) to rehabilitate and to ease upper limb motion. Toward to make a complete (i.e., 7DOF) upper-arm motion assisted robotic exoskeleton this paper focused on the development of a 2DOF exoskeleton robot to rehabilitate the elbow and forearm movements. The proposed 2DOF ExoRob is supposed to be worn on the lateral side of forearm and provide naturalistic range movements of elbow (flexion/extension) and forearm (pronation/supination) motions. This paper also focuses on the modeling and control of the proposed ExoRob. A kinematic model of the ExoRob has been developed based on modified Denavit-Hartenberg notations. Nonlinear sliding mode control technique is employed in dynamic simulation of the proposed ExoRob, where trajectory tracking that corresponds to typical rehab (passive) exercises has been carried out to evaluate the effectiveness of the developed model and controller. Simulated results show that the controller is able to maneuver the ExoRob efficiently to track the desired trajectories, which in this case consisted in passive arm movements. These movements are widely used in rehab therapy and could be performed efficiently with the developed ExoRob and the controller.


Robot-aided neurorehabilitation.

Our goal is to apply robotics and automation technology to assist, enhance, quantify, and document neurorehabilitation. This paper reviews a clinical trial involving 20 stroke patients with a prototype robot-aided rehabilitation facility developed at the Massachusetts Institute of Technology, Cambridge, (MIT) and tested at Burke Rehabilitation Hospital, White Plains, NY. It also presents our approach to analyze kinematic data collected in the robot-aided assessment procedure. In particular, we present evidence 1) that robot-aided therapy does not have adverse effects, 2) that patients tolerate the procedure, and 3) that peripheral manipulation of the impaired limb may influence brain recovery. These results are based on standard clinical assessment procedures. We also present one approach using kinematic data in a robot-aided assessment procedure.


An Alternative Way to Analyze Workflow Graphs

At the CAiSE conference in Heidelberg in 1999, Wasim Sadiq and Maria Orlowska presented an algorithm to verify workflow graphs [19]. The algorithm uses a set of reduction rules to detect structural conflicts. This paper shows that the set of reduction rules presented in [19] is not complete and proposes an alternative algorithm. The algorithm translates workflow graphs into so-called WF-nets. WF-nets are a class of Petri nets tailored towards workflow analysis. As a result, Petri-net theory and tools can be used to verify workflow graphs. In particular, our workflow verification tool Woflan [21] can be used to detect design errors. It is shown that the absence of structural conflicts, i.e., deadlocks and lack of synchronization, conforms to soundness of the corresponding WF-net [2]. In contrast to the algorithm presented in [19], the algorithm presented in this paper is complete. Moreover, the complexity of this alternative algorithm is given.


Smart-Contract Based System Operations for Permissioned Blockchain

Enterprises have paid attention to blockchain (BC), recently permissioned BC characterized with smart-contract, where busi-ness transactions among inter-authorized companies (forming consortium) can automatically be executed based on distributed consensus protocol over user-defined business logics pre-built with program codes. A single BC system will be built across mul-tiple management domains having different operational policies, e.g., datacenter of each organization; this will trigger a problem that its system operations (e.g., backup) will become time-consuming and costly due to the difficulty in unifying and/or adjusting operational policy, schedule, etc. Toward solving the problem, we propose an operations execution method for BC systems; a primary idea is to define operations as smart-contract so that unified and synchronized cross-organizational operations can be executed effectively by using BC-native features. We de-sign the proposed method as hybrid architecture including in-BC consensus establishment and out-BC event-based instruction execution, in order to be adaptable to the recent heterogeneous BC architecture. Performance evaluation using a prototype with Hyperledger Fabric v1.0 shows that the proposed method can start executing operations within 5 seconds. Furthermore, cost evaluation using model-based estimation shows that the total yearly cost of monthly operations on a 5-organizational BC sys-tem could be reduced by 61 percent compared to a conventional manual method.


Neural Map: Structured Memory for Deep Reinforcement Learning

A critical component to enabling intelligent reasoning in partially observable environments is memory. Despite this importance, Deep Reinforcement Learning (DRL) agents have so far used relatively simple memory architectures, with the main methods to overcome partial observability being either a temporal convolution over the past k frames or an LSTM layer. More recent work (Oh et al., 2016) has went beyond these architectures by using memory networks which can allow more sophisticated addressing schemes over the past k frames. But even these architectures are unsatisfactory due to the reason that they are limited to only remembering information from the last k frames. In this paper, we develop a memory system with an adaptable write operator that is customized to the sorts of 3D environments that DRL agents typically interact with. This architecture, called the Neural Map, uses a spatially structured 2D memory image to learn to store arbitrary information about the environment over long time lags. We demonstrate empirically that the Neural Map surpasses previous DRL memories on a set of challenging 2D and 3D maze environments and show that it is capable of generalizing to environments that were not seen during training.


A portable powered ankle-foot orthosis for rehabilitation.

Innovative technological advancements in the field of orthotics, such as portable powered orthotic systems, could create new treatment modalities to improve the functional out come of rehabilitation. In this article, we present a novel portable powered ankle-foot orthosis (PPAFO) to provide untethered assistance during gait. The PPAFO provides both plantar flexor and dorsiflexor torque assistance by way of a bidirectional pneumatic rotary actuator. The system uses a portable pneumatic power source (compressed carbon dioxide bottle) and embedded electronics to control the actuation of the foot. We collected pilot experimental data from one impaired and three nondisabled subjects to demonstrate design functionality. The impaired subject had bilateral impairment of the lower legs due to cauda equina syndrome. We found that data from nondisabled walkers demonstrated the PPAFO's capability to provide correctly timed plantar flexor and dorsiflexor assistance during gait. Reduced activation of the tibialis anterior during stance and swing was also seen during assisted nondisabled walking trials. An increase in the vertical ground reaction force during the second half of stance was present during assisted trials for the impaired subject. Data from nondisabled walkers demonstrated functionality, and data from an impaired walker demonstrated the ability to provide functional plantar flexor assistance.


Learning Semantic Composition to Detect Non-compositionality of Multiword Expressions

Non-compositionality of multiword expressions is an intriguing problem that can be the source of error in a variety of NLP tasks such as language generation, machine translation and word sense disambiguation. We present methods of non-compositionality detection for English noun compounds using the unsupervised learning of a semantic composition function. Compounds which are not well modeled by the learned semantic composition function are considered noncompositional. We explore a range of distributional vector-space models for semantic composition, empirically evaluate these models, and propose additional methods which improve results further. We show that a complex function such as polynomial projection can learn semantic composition and identify non-compositionality in an unsupervised way, beating all other baselines ranging from simple to complex. We show that enforcing sparsity is a useful regularizer in learning complex composition functions. We show further improvements by training a decomposition function in addition to the composition function. Finally, we propose an EM algorithm over latent compositionality annotations that also improves the performance.


Usability Problem Description and the Evaluator Effect in Usability Testing

Previous usability evaluation method (UEM) comparison studies have noted an evaluator effect on problem detection in heuristic evaluation, with evaluators differing in problems found and problem severity judgments. There have been few studies of the evaluator effect in usability testing (UT), task-based testing with end-use rs. UEM comparison studies focus on counting usability problems detected, but we also need to assess the content of usability problem descriptions (UPDs) to more fully mea sure evaluation effectiveness. The goals of this research were to develop UPD gui delines, explore the evaluator effect in UT, and evaluate the usefulness of the guidelines for grading UPD content. Ten guidelines for writing UPDs were developed by consulting usability practitioners through two questionnaires and a card sort. These guidelines are ( briefly): be clear and avoid jargon, describe problem severity, provide backing data, describe problem causes, describe user actions, provide a solution, consider politics and diplomacy, be professional and scientific, describe your methodology, and help the re ader sympathize with the user. A fourth study compared usability reports collect ed from 44 evaluators, both practitioners and graduate students, watching the same 10-minute UT session recording. Three judges measured problem detection for each evaluator a nd graded the reports for following 6 of the UPD guidelines. There was support for existence of an evaluator effect, even when watching prerecorded sessions, with low to moderate individual thoroughness of problem detection across all/severe problems (22%/34%), reliability of problem detection (37%/50%) and reliability of severity judgments (57% for severe ratings). Practiti oners received higher grades averaged across the 6 guidelines than students did, suggesting that the guide lin s may be useful for grading reports. The grades for the guidelines were not cor related with thoroughness, suggesting that the guideline grades complement measures of problem detection. A simulation of evaluators working in groups found a 34% increase in severe problems found by adding a second evaluator. The simulation also found that thoroughness of individual evaluators would have been overestimated if the study had included a small number of evaluators. The final recommendations are to use multipl e evaluators in UT, and to assess both problem detection and description when measuring evaluation effectiveness. iii ACKNOWLEDGEMENTS I would like to thank my advisory committee, Tonya Smith-Jackson, John Burton, Rex Hartson, Brian Kleiner, and Maury Nussbaum. In particular, Tonya Smith -Jackson provided suggestions for experimental design and statistical analysis, and Rex Hartson and Tonya Smith-Jackson provided suggestions for the judging procedure used to analyze the usability problems collected from evaluators. I also owe a special thanks to Laurian Hobby, John Howarth, and Pardha Pyla, each of whom generously donated over 50 hours of their time to read and evaluate hundreds of usability problems. The final study would not have been possible without them. Thanks also to my thesis advisor, Bob Williges, who guided me when I began studying usability methods. This dissertation would not have been possible without the support of my husband, Rob Capra, who shared the journey with me as we both completed our dissertations within a month of each other. I might not have discovered Human Factors or pursued a PhD without him. He is my partner in research, and many important ideas gre w out of discussions of our research at school, at home, and over meals. He is my partner in life, and I love him with all my heart. Many other people contributed to this research. Terence Andre shared the usability movies and reports from studies run by him and his students. Rob Capra assi sted with data coding in the first and fourth studies. Suzanne Aref suggested using factor analysis to cluster the items in the card sort. Joe Dumas shared his experience s analyzing the CUE-4 reports, provided criteria for identifying descriptions that discuss t he same usability problem, and helped recruit practitioners. Rolf Molich gave permiss ion to use the report template and severity rating scales from the CUE studies. The co mparison of usability diagnosis to medical diagnosis was refined through discussions with Rex Hartson and Steve Belz. Thanks to the dozens of usability practitioners and graduate students who volunteered to participate in my studies or provided feedback as pilot participants, especially the practitioners that took extra time above and beyond the study requirem nts to discuss their usability practices and reporting habits. Thanks to my famil and friends for years of encouragement and patience, and fellow students for support and commiseration along the way. Thanks to my former colleagues at what is now AT &T Labs for introducing me to Human Factors and sparking my interest in the field. Thanks to IMDb, Inc. for permission to include screen shots of their website in this document. Information courtesy of: The Internet Movie Database (http://www.imdb.com/ ). Used with permission. Portions of this research were conducted while I was supported by the Alexander E. Walter Fellowship from the Grado Department of Industrial and Systems Eng ineering (2001-2004). iv TABLE OF CONTENTS ABSTRACT . ................................................................................................................. ii ACKNOWLEDGEMENTS............................................................................................. iii TABLE OF CONTENTS................................................................................................. iv LIST OF TABLES .........................................................................................................viii LIST OF FIGURES .......................................................................................................... x LIST OF EQUATIONS ..................................................................................................xii CHAPTER


Driver fatigue detection based on saccadic eye movements

The correct determination of driver's level of fatigue has been of vital importance for the safety of driving. There are various methods, such as analyzing facial expression, eyelid activity, and head movements to assess the fatigue level of drivers. This paper describes the design and prototype implementation of a driver fatigue level determination system based on detection of saccadic eye movements. Driver's eye movement speed is used to assess driver's fatigue level. The information about eyes is obtained via infrared led camera device. Movements of pupils were recorded in two driving scenarios with different traffic density. In the first scenario, the traffic density was set to low while the second scenario was based on high density and aggressive traffic. Based on the movements of pupils, the data on saccadic eye movement was analyzed to determine fatigue level of the driver. Acceleration, speed, and size of pupils at both traffic scenarios were compared with data mining techniques, such as segmentation adaptive peak, entropy, and data distribution analyses. Significantly different levels of fatigue were found between the tired and vigorous driver for the different types of scenarios.


Emotion, plasticity, context, and regulation: perspectives from affective neuroscience.

The authors present an overview of the neural bases of emotion. They underscore the role of the prefrontal cortex (PFC) and amygdala in 2 broad approach- and withdrawal-related emotion systems. Components and measures of affective style are identified. Emphasis is given to affective chronometry and a role for the PFC in this process is proposed. Plasticity in the central circuitry of emotion is considered, and implications of data showing experience-induced changes in the hippocampus for understanding psychopathology and stress-related symptoms are discussed. Two key forms of affective plasticity are described--context and regulation. A role for the hippocampus in context-dependent normal and dysfunctional emotional responding is proposed. Finally, implications of these data for understanding the impact on neural circuitry of interventions to promote positive affect and on mechanisms that govern health and disease are considered.


On Analyzing User Topic-Specific Platform Preferences Across Multiple Social Media Sites

Topic modeling has traditionally been studied for single text collections and applied to social media data represented in the form of text documents. With the emergence of many social media platforms, users find themselves using different social media for posting content and for social interaction. While many topics may be shared across social media platforms, users typically show preferences of certain social media platform(s) over others for certain topics. Such platform preferences may even be found at the individual level. To model social media topics as well as platform preferences of users, we propose a new topic model known as MultiPlatform-LDA (MultiLDA). Instead of just merging all posts from different social media platforms into a single text collection, MultiLDA keeps one text collection for each social media platform but allowing these platforms to share a common set of topics. MultiLDA further learns the user-specific platform preferences for each topic. We evaluate MultiLDA against TwitterLDA, the state-of-the-art method for social media content modeling, on two aspects: (i) the effectiveness in modeling topics across social media platforms, and (ii) the ability to predict platform choices for each post. We conduct experiments on three real-world datasets from Twitter, Instagram and Tumblr sharing a set of common users. Our experiments results show that the MultiLDA outperforms in both topic modeling and platform choice prediction tasks. We also show empirically that among the three social media platforms, “Daily matters” and “Relationship matters” are dominant topics in Twitter, “Social gathering”, “Outing” and “Fashion” are dominant topics in Instagram, and “Music”, “Entertainment” and “Fashion” are dominant topics in Tumblr.


A3: a coding guideline for HCI+autism research using video annotation

Due to the profile of strengths and weaknesses indicative of autism spectrum disorders (ASD), technology may play a key role in ameliorating communication difficulties with this population. This paper documents coding guidelines established through cross-disciplinary work focused on facilitating communication development in children with ASD using computerized feedback. The guidelines, referred to as A3 (pronounced A-Cubed) or Annotation for ASD Analysis, define and operationalize a set of dependent variables coded via video annotation. Inter-rater reliability data are also presented from a study currently in-progress, as well as related discussion to help guide future work in this area. The design of the A3 methodology is well-suited for the examination and evaluation of the behavior of low-functioning subjects with ASD who interact with technology.


River flow time series prediction with a range-dependent neural network

Artificial neural networks provide a promising alternative to hydrological time series modelling. However, there are still many fundamental problems requiring further analyses, such as structure identification, parameter estimation, generalization, performance improvement, etc. Based on a proposed clustering algorithm for the training pairs, a new neural network, namely the range-dependent neural network (RDNN) has been developed for better accuracy in hydrological time series prediction. The applicability and potentials of the RDNN in daily streamflow and annual reservoir inflow prediction are examined using data from two watersheds in China. Empirical comparisons of the predictive accuracy, in terms of the model efficiency R and absolute relative errors (ARE), between the RDNN, backpropagation (BP) networks and the threshold auto-regressive (TAR) model are made. The case studies demonstrated that the RDNN network performed significantly better than the BP network, especially for reproducing low-flow events.


Short circuit III in high power IGBTs

Short circuit III is the occurrence of a short circuit across the load during the conducting mode of the freewheeling diode. This has the same importance for the application of high power IGBTs as the known short circuit II. Different to short circuit II, the IGBT of the same module is now turned-on starting from very low voltage across its terminals and showing a forward recovery voltage before saturating. After that a dynamic short circuit peak current occurs similar to the one seen in SC II. During the short circuit, a reverse recovery process occurs at the freewheeling diode with a very high voltage slope.


Cancer phenotype as the outcome of an evolutionary game between normal and malignant cells

Background:There is variability in the cancer phenotype across individuals: two patients with the same tumour may experience different disease life histories, resulting from genetic variation within the tumour and from the interaction between tumour and host. Until now, phenotypic variability has precluded a clear-cut identification of the fundamental characteristics of a given tumour type.Methods:Using multiple myeloma as an example, we apply the principles of evolutionary game theory to determine the fundamental characteristics that define the phenotypic variability of a tumour.Results:Tumour dynamics is determined by the frequency-dependent fitness of different cell populations, resulting from the benefits and costs accrued by each cell type in the presence of others. Our study shows how the phenotypic variability in multiple myeloma bone disease can be understood through the theoretical approach of a game that allows the identification of key genotypic features in a tumour and provides a natural explanation for phenotypic variability. This analysis also illustrates how complex biochemical signals can be translated into cell fitness that determines disease dynamics.Conclusion:The present paradigm is general and extends well beyond multiple myeloma, and even to non-neoplastic disorders. Furthermore, it provides a new perspective in dealing with cancer eradication. Instead of trying to kill all cancer cells, therapies should aim at reducing the fitness of malignant cells compared with normal cells, allowing natural selection to eradicate the tumour.


Machine Learning Applied to Cyber Operations


Demanding Customers : Consumerist Patients and Quality of Care

Consumerism arises when patients acquire and use medical information from sources other than their physicians. This practice has been hailed as a means of improving quality. This need not be the result. Our theoretical model identifies a channel through which consumerism may reduce quality: consumerist patients place additional demands on their doctors’ time, thus imposing a negative externality on other patients. Relative to a world in which consumerism does not exist, consumerism may harm other consumerists, non-consumerists, or both. Data from a large national survey of physicians confirm the negative effects of consumerism: high levels of consumerist patients are associated with lower perceived quality among physicians.


Sequential Optimization and Reliability Assessment Method for Efficient Probabilistic Design

Probabilistic design, such as reliability-based design and robust design, offers tool making reliable decisions with the consideration of uncertainty associated with de variables/parameters and simulation models. Since a probabilistic optimization ofte volves a double-loop procedure for the overall optimization and iterative probabili assessment, the computational demand is extremely high. In this paper, the seq optimization and reliability assessment (SORA) is developed to improve the efficien probabilistic optimization. The SORA method employs a single-loop strategy with a s of cycles of deterministic optimization and reliability assessment. In each cycle, op zation and reliability assessment are decoupled from each other; the reliability asses is only conducted after the deterministic optimization to verify constraint feasibility un uncertainty. The key to the proposed method is to shift the boundaries of violated straints (with low reliability) to the feasible direction based on the reliability informati obtained in the previous cycle. The design is quickly improved from cycle to cycle an computational efficiency is improved significantly. Two engineering applications, reliability-based design for vehicle crashworthiness of side impact and the integr reliability and robust design of a speed reducer, are presented to demonstrate the tiveness of the SORA method. @DOI: 10.1115/1.1649968 #


LIPN at SemEval-2017 Task 10: Filtering Candidate Keyphrases from Scientific Publications with Part-of-Speech Tag Sequences to Train a Sequence Labeling Model

This paper describes the system used by the team LIPN in SemEval 2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications. The team participated in Scenario 1, that includes three subtasks, Identification of keyphrases (Subtask A), Classification of identified keyphrases (Subtask B) and Extraction of relationships between two identified keyphrases (Subtask C). The presented system was mainly focused on the use of part-of-speech tag sequences to filter candidate keyphrases for Subtask A. Subtasks A and B were addressed as a sequence labeling problem using Conditional Random Fields (CRFs) and even though Subtask C was out of the scope of this approach, one rule was included to identify synonyms.


Network densification: the dominant theme for wireless evolution into 5G

This article explores network densification as the key mechanism for wireless evolution over the next decade. Network densification includes densification over space (e.g, dense deployment of small cells) and frequency (utilizing larger portions of radio spectrum in diverse bands). Large-scale cost-effective spatial densification is facilitated by self-organizing networks and intercell interference management. Full benefits of network densification can be realized only if it is complemented by backhaul densification, and advanced receivers capable of interference cancellation.


Ripple-Based Control of Switching Regulators—An Overview

Switching regulators with ripple-based control (i.e., ¿ripple regulators¿) are conceptually simple, have fast transient responses to both line and load perturbations, and some versions operate with a switching frequency that is proportional to the load current under the discontinuous conduction mode. These characteristics make the ripple regulators well-suited, especially for power management applications in computers and portable electronic devices. Ripple regulators also have some drawbacks, including (in some versions) a poorly defined switching frequency, noise-induced jitter, inadequate dc regulation, and a tendency for fast-scale instability. This paper presents an overview of the various ripple-based control techniques, discusses their merits and limitations, and introduces techniques for reducing the noise sensitivity and the sensitivity to capacitor parameters, improving the frequency stability and the dc regulation, and avoiding fast-scale instability.


Maximum likelihood analysis of conflicting observations in social sensing

This article addresses the challenge of truth discovery from noisy social sensing data. The work is motivated by the emergence of social sensing as a data collection paradigm of growing interest, where humans perform sensory data collection tasks. Unlike the case with well-calibrated and well-tested infrastructure sensors, humans are less reliable, and the likelihood that participants' measurements are correct is often unknown a priori. Given a set of human participants of unknown trustworthiness together with their sensory measurements, we pose the question of whether one can use this information alone to determine, in an analytically founded manner, the probability that a given measurement is true. In our previous conference paper, we offered the first maximum likelihood solution to the aforesaid truth discovery problem for corroborating observations only. In contrast, this article extends the conference paper and provides the first maximum likelihood solution to handle the cases where measurements from different participants may be conflicting. The article focuses on binary measurements. The approach is shown to outperform our previous work used for corroborating observations, the state-of-the-art fact-finding baselines, as well as simple heuristics such as majority voting.


GENETIC ALGORITHM APPLIED TO OPTIMIZATION OF THE SHIP HULL FORM WITH RESPECT TO SEAKEEPING PERFORMANCE

Hull form optimization from a hydrodynamic performance point of view is an important aspect in preliminary ship design. This study presents a computational method to estimate the ship seakeeping in regular head waves. In the optimization process, the genetic algorithm (GA) is linked to the computational method to obtain an optimum hull form by taking into account the displacement as a design constraint. New hull forms are obtained from the wellknown S60 hull and the classical Wigley hull taken as initial hulls in the optimization process at two Froude numbers (Fn=0.2 and Fn=0.3). The optimization variables are a combination of ship hull offsets and main dimensions. The objective function of the optimization procedure includes the peak values for vertical absolute motion at the centre of gravity (CG) and the bow point (0.15Lwl) behind the forward perpendicular (FP).


User experiences with web-based 3D virtual travel destination marketing portals: the need for visual indication of interactive 3D elements

The tourism sector has found virtual reality technology to be a good way to market travel destinations for consumers. In this paper, we describe two user studies with three web-based 3D virtual travel destination marketing portals. These three portals were developed to support and attract wintertime tourism into the region by offering a possibility to experience in advance, a virtual snowy scenery with different activities, for example downhill skiing. In both user studies with 21 subjects the focus was on user experience with the 3D virtual travel destination marketing portals. In the second study also the visual design aspects within these portals were studied. Our studies indicate that 3D virtual travel destination marketing portals can enhance 2D web pages, if they offer the possibility to explore the location freely and through different kinds of virtual activities. Also our studies support prior findings of the efficiency of glow effect for indicating interactive 3D elements within a 3D virtual environment.


Frontomaxillary facial angle in trisomy 21 fetuses at 16-24 weeks of gestation.

OBJECTIVES
To establish a normal range for the frontomaxillary facial (FMF) angle by three-dimensional (3D) ultrasound imaging and to examine the FMF angle in trisomy 21 fetuses at 16-24 weeks of gestation.

METHODS
We measured the FMF angle using 3D volumes of the fetal profile obtained with the transducer parallel to the long axis of the nose and at 45 degrees to the palate, which had been acquired from 150 normal fetuses and 23 fetuses with trisomy 21.

RESULTS
In the normal group there was no significant association between the FMF angle and gestational age; the mean FMF angle was 83.9 degrees (range, 76.9-90.2 degrees ) and the 95(th) centile was 88.5 degrees . In 15 (65.2%) of the fetuses with trisomy 21 the FMF angle was greater than 88.5 degrees . Repeatability studies demonstrated that in 95% of cases the difference between two measurements of FMF angle by the same operator and different operators was less than 5 degrees .

CONCLUSIONS
In the majority of second-trimester fetuses with trisomy 21 the FMF angle is increased.


Multi-Product Utility Maximization for Economic Recommendation

Basic economic relations such as substitutability and complementarity between products are crucial for recommendation tasks, since the utility of one product may depend on whether or not other products are purchased. For example, the utility of a camera lens could be high if the user possesses the right camera (complementarity), while the utility of another camera could be low because the user has already purchased one (substitutability). We propose \emph{multi-product utility maximization} (MPUM) as a general approach to recommendation driven by economic principles. MPUM integrates the economic theory of consumer choice with personalized recommendation, and focuses on the utility of \textit{sets} of products for individual users. MPUM considers what the users already have when recommending additional products. We evaluate MPUM against several popular recommendation algorithms on two real-world E-commerce datasets. Results confirm the underlying economic intuition, and show that MPUM significantly outperforms the comparison algorithms under top-K evaluation metrics.


Towards IP over LPWANs technologies: LoRaWAN, DASH7, NB-IoT

In this paper, we discuss a set of solutions that are proposed in order to run IPv6 over IoT and we investigate its applicability over the three Low Power Wide Area Networks (LPWANs) technologies: LoRaWAN, DASH7, NB-IoT. LPWANs are wireless technologies that are used to connect things to the Internet. These technologies are characterized by their large coverage area, long battery operation, low bandwidth, small frame payload size, and the use of asymmetric and non-synchronized communication. Based on this investigation, we highlight the schemes that can be adopted for IP-based LPWANs technologies.


The SIMON and SPECK lightweight block ciphers

The Simon and Speck families of block ciphers were designed specifically to offer security on constrained devices, where simplicity of design is crucial. However, the intended use cases are diverse and demand flexibility in implementation. Simplicity, security, and flexibility are ever-present yet conflicting goals in cryptographic design. This paper outlines how these goals were balanced in the design of Simon and Speck.