Plant Disease Prediction using Image Processing Techniques-A Review

Agriculture has become much more than simply a means to feed ever growing populations. It is very important where in more than 70% population depends on agriculture in India. That means it feeds great number of people. The plant diseases effect the humans directly or indirectly by health or also economically. To detect these plant diseases we need a fast automatic way. Diseases are analyzed by different digital image processing techniques. In this paper, we have done survey on different digital image processing techniques to detect the plant diseases.


Peeking Behind the Curtains of Serverless Platforms

Serverless computing is an emerging paradigm in which an application’s resource provisioning and scaling are managed by third-party services. Examples include AWS Lambda, Azure Functions, and Google Cloud Functions. Behind these services’ easy-to-use APIs are opaque, complex infrastructure and management ecosystems. Taking on the viewpoint of a serverless customer, we conduct the largest measurement study to date, launching more than 50,000 function instances across these three services, in order to characterize their architectures, performance, and resource management efficiency. We explain how the platforms isolate the functions of different accounts, using either virtual machines or containers, which has important security implications. We characterize performance in terms of scalability, coldstart latency, and resource efficiency, with highlights including that AWS Lambda adopts a bin-packing-like strategy to maximize VM memory utilization, that severe contention between functions can arise in AWS and Azure, and that Google had bugs that allow customers to use resources for free.


Characterizing concept drift

Most machine learning models are static, but the world is dynamic, and increasing online deployment of learned models gives increasing urgency to the development of efficient and effective mechanisms to address learning in the context of non-stationary distributions, or as it is commonly called concept drift. However, the key issue of characterizing the different types of drift that can occur has not previously been subjected to rigorous definition and analysis. In particular, while some qualitative drift categorizations have been proposed, few have been formally defined, and the quantitative descriptions required for precise and objective understanding of learner performance have not existed. We present the first comprehensive framework for quantitative analysis of drift. This supports the development of the first comprehensive set of formal definitions of types of concept drift. The formal definitions clarify ambiguities and identify gaps in previous definitions, giving rise to a new comprehensive taxonomy of concept drift types and a solid foundation for research into mechanisms to detect and address concept drift.


Conclusions of Ghaheri's Study That Laser Surgery for Posterior Tongue and Lip Ties Improves Breastfeeding Are Not Substantiated.

Editor’s Note: The diagnosis and treatment of tongue and lip ties remains a topic of much and at times heated debate. The following correspondences, which take opposite sides of the argument as to the value of laser surgery, are symptomatic of this still unresolved and hotly contested subject. Breastfeeding Medicine is of the opinion that it should serve as a platform for the presentation of both sides of the issue so as to provide information that will assist clinicians in their management decision.


' s personal copy A comparative study on feature reduction approaches in Hindi and Bengali named entity recognition

Features used for named entity recognition (NER) are often high dimensional in nature. These cause overfitting when training data is not sufficient. Dimensionality reduction leads to performance enhancement in such situations. There are a number of approaches for dimensionality reduction based on feature selection and feature extraction. In this paper we perform a comprehensive and comparative study on different dimensionality reduction approaches applied to the NER task. To compare the performance of the various approaches we consider two Indian languages namely Hindi and Bengali. NER accuracies achieved in these languages are comparatively poor as yet, primarily due to scarcity of annotated corpus. For both the languages dimensionality reduction is found to improve performance of the classifiers. A Comparative study of the effectiveness of several dimensionality reduction techniques is presented in detail in this paper. 2011 Elsevier B.V. All rights reserved.


Designing Persian Floral Patterns using Circle Packing

In this paper, we present a novel approach toward generating floral patterns. We extract the essence of a pattern aside from its appearance and geometry into combinatorial elements. As a result, existing patterns can be reshaped while preserving their essence. Furthermore, we can create new patterns that adhere to high level concepts such as imperfect symmetry and visual balance. By decomposing floral patterns into a configuration of circles and angles, we can reconstruct this patterns on different surfaces given a conformal mapping.


Object discovery in 3D scenes via shape analysis

We present a method for discovering object models from 3D meshes of indoor environments. Our algorithm first decomposes the scene into a set of candidate mesh segments and then ranks each segment according to its “objectness” - a quality that distinguishes objects from clutter. To do so, we propose five intrinsic shape measures: compactness, symmetry, smoothness, and local and global convexity. We additionally propose a recurrence measure, codifying the intuition that frequently occurring geometries are more likely to correspond to complete objects. We evaluate our method in both supervised and unsupervised regimes on a dataset of 58 indoor scenes collected using an Open Source implementation of Kinect Fusion [1]. We show that our approach can reliably and efficiently distinguish objects from clutter, with Average Precision score of .92. We make our dataset available to the public.


MC64: A Web Platform to Test Bioinformatics Algorithms in a Many-Core Architecture

New analytical methodologies, like the so-called “next-generation sequencing” (NGS), allow the sequencing of full genomes with high speed and reduced price. Yet, such technologies generate huge amounts of data that demand large raw computational power. Many-core technologies can be exploited to overcome the involved bioinformatics bottleneck. Indeed, such hardware is currently in active development. We have developed parallel bioinformatics algorithms for many-core microprocessors containing 64 cores each. Thus, the MC64 web platform allows executing high-performance alignments (NeedlemanWunsch, Smith-Waterman and ClustalW) of long sequences. The MC64 platform can be accessed via web browsers, allowing easy resource integration into thirdparty tools. Furthermore, the results obtained from the MC64 include timeperformance statistics that can be compared with other platforms.


Research on Fast Charge Method for Lead-Acid Electric Vehicle Batteries

Electric vehicle (EV) is environment friendly and high efficient. But the shortages of traction battery limited the rapid development of EV. Battery as a key part of EV has aroused lots of engineers to explore the management method and fast charge method is a key technology of battery management for electric vehicle. Constant current-constant voltage (CC-CV) and multistage constant current-constant voltage (MCC-CV) are two traditional charging ways. According to the dynamic circuit model of Lead-acid battery and fast charge theory, on the basic of CC-CV and MCC-CV method, explored the fast charge method for Lead-acid battery of electric vehicle. Compare experiment result of the fast charge method and traditional method. The two major parameters like temperature rise and the capacity-time ratio are considered in order to compare the result. Lots of experiment result support that the negative pulse could eliminate the polarization effective. The MCC-CV with negative pulse method proves working efficient and practical. Keywords-Fast charge; Electric Vehicle; Lead-acid battery;


Deep neural networks on graph signals for brain imaging analysis

Brain imaging data such as EEG or MEG are high-dimensional spatiotemporal data often degraded by complex, non-Gaussian noise. For reliable analysis of brain imaging data, it is important to extract discriminative, low-dimensional intrinsic representation of the recorded data. This work proposes a new method to learn the low-dimensional representations from the noise-degraded measurements. In particular, our work proposes a new deep neural network design that integrates graph information such as brain connectivity with fully-connected layers. Our work leverages efficient graph filter design using Chebyshev polynomial and recent work on convolutional nets on graph-structured data. Our approach exploits graph structure as the prior side information, localized graph filter for feature extraction and neural networks for high capacity learning. Experiments on real MEG datasets show that our approach can extract more discriminative representations, leading to improved accuracy in a supervised classification task.


Lossy compression on IoT big data by exploiting spatiotemporal correlation

As the volume of data generated by various deployed IoT devices increases, storing and processing IoT big data becomes a huge challenge. While compression, especially lossy ones, can drastically reduce data volume, finding an optimal balance between the volume reduction and the information loss is not an easy task given that the data collected by diverse sensors exhibit different characteristics. Motivated by this, we present a feasibility analysis of lossy compression on agricultural sensor data by comparing fidelity of reconstructed data from various signal processing algorithms and temporal difference encoding. Specifically, we evaluated five real-world sensor data from weather stations as one of major IoT applications. Our experimental results indicate that Discrete Cosine Transform (DCT) and Fast Walsh-Hadamard Transform (FWHT) generate higher compression ratios than others. In terms of information loss, Lossy Delta Encoding (LDE) significantly outperforms others nonetheless. We also observe that, as compression factor is increased, error rates for all compression algorithms also increase. However, the impact of introduced error is much severe in DCT and FWHT while LDE was able to maintain a relatively lower error rate than other methods.


Knowledge discovery from database Using an integration of clustering and classification

Clustering and classification are two important techniques of data mining. Classification is a supervised learning problem of assigning an object to one of several pre-defined categories based upon the attributes of the object. While, clustering is an unsupervised learning problem that group objects based upon distance or similarity. Each group is known as a cluster. In this paper we make use of a large database ‘Fisher’s Iris Dataset’ containing 5 attributes and 150 instances to perform an integration of clustering and classification techniques of data mining. We compared results of simple classification technique (using J48 classifier) with the results of integration of clustering and classification technique, based upon various parameters using WEKA (Waikato Environment for Knowledge Analysis), a Data Mining tool. The results of the experiment show that integration of clustering and classification gives promising results with utmost accuracy rate and robustness even when the data set is containing missing values. KeywordsData Mining; J48; KMEANS; WEKA; Fisher’s Iris dataset;


Nonlinear control for output regulation of ball and plate system

This paper proposes nonlinear control methods for output regulation of ball and plate system. Positions of the ball are regulated with double feedback loops. Recursive backstepping design is employed for the external feedback loop, while switching control scheme is used in the inner feedback loop. System performance was tuned by backstepping parameters. Simulation results show that the proposed nonlinear control works wells in both stabilization and tracking control. Asymptotical stabilities are also achieved under unknown external disturbance in the experiments.


A Benchmark for Automatic Acral Melanoma Preliminary Screening

Despite recent progress in artificial intelligence and computer vision, the lack of data for acral melanoma, a particular type of skin disease under nails, makes it difficult to develop its automatic visual diagnosis system. This paper introduces a large data set of dermoscopic images for acral melanoma, which is annotated by senior dermatologists. It contains 6,066 images of two categories: subungual hematomas and other acral melanoma symptoms of malignant tendency (i.e., just bleeding under nails or more critical disorder that requires treatment). We hope this benchmark data set will encourage further research on acral melanoma recognition and will continue to maintain this data set to better serve it. We address the classification task using various computer vision algorithms from conventional techniques to cutting edge deep learning. Currently, we achieve 0.928 accuracy.


Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology

Valid measurement scales for predicting user acceptance of computers are in short supply. Most subjective measures used in practice are unvalidated, and their relationship to system usage is unknown. The present research develops and validates new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance. Definitions for these two variables were used to develop scale items that were pretested for content validity and then tested for reliability and construct validity in two studies involving a total of 152 users and four application programs. The measures were refined and streamlined, resulting in two six-item scales with reliabilities of .98 for usefulness and .94 for ease of use. The scales exhibited high convergent, discriminant, and factorial validity. Perceived usefulness was significantly correlated with both selfreported current usage (r=.63, Study 1) and self-predicted future usage (r =.85, Study 2). Perceived ease of use was also significantly correlated with current usage (r= .45, Study 1) and future usage (r =.59, Study 2). In both studies, usefulness had a significantly greater correlation with usage behavior than did ease of use. Regression analyses suggest that perceived ease of use may actually be a causal antecedent to perceived usefulness, as opposed to a parallel, direct determinant of system usage. Implications are drawn for future research on user acceptance.


BEHIND ADVERTISING : THE LANGUAGE OF PERSUASION Kenechukwu

Advertising is a force that makes it possible to sell more merchandise due to its persuasive nature. As a tool of marketing communication, advertising is the structured and composed, non-personal communication of information. It is usually paid for and usually persuasive in nature. The language of persuasion is employed for successful advertising campaign thereby, boosting patronage of idea, product or service. The persuasive nature of advertising however, has generated criticisms and controversies. Advertising has been vigorously attacked on the ground that it is unnecessary and wasteful and that through it, people are induced to buy worthless products. It is further argued by the objectors that much advertising is misleading and untruthful. Thus, this paper examines how consumers can be persuaded to patronise goods and services advertised.


Mixed-Initiative Real-Time Topic Modeling & Visualization for Crisis Counseling

Text-based counseling and support systems have seen an increasing proliferation in the past decade. We present Fathom, a natural language interface to help crisis counselors on Crisis Text Line, a new 911-like crisis hotline that takes calls via text messaging rather than voice. Text messaging opens up the opportunity for software to read the messages as well as people, and to provide assistance for human counselors who give clients emotional and practical support. Crisis counseling is a tough job that requires dealing with emotionally stressed people in possibly life-critical situations, under time constraints. Fathom is a system that provides topic modeling of calls and graphical visualization of topic distributions, updated in real time. We develop a mixed-initiative paradigm to train coherent topic and word distributions and use them to power real-time visualizations aimed at reducing counselor cognitive overload. We believe Fathom to be the first real-time computational framework to assist in crisis counseling.


A review of central retinal artery occlusion: clinical presentation and management

Central retinal artery occlusion (CRAO) is an ophthalmic emergency and the ocular analogue of cerebral stroke. Best evidence reflects that over three-quarters of patients suffer profound acute visual loss with a visual acuity of 20/400 or worse. This results in a reduced functional capacity and quality of life. There is also an increased risk of subsequent cerebral stroke and ischaemic heart disease. There are no current guideline-endorsed therapies, although the use of tissue plasminogen activator (tPA) has been investigated in two randomized controlled trials. This review will describe the pathophysiology, epidemiology, and clinical features of CRAO, and discuss current and future treatments, including the use of tPA in further clinical trials.


New edge-directed interpolation

This paper proposes an edge-directed interpolation algorithm for natural images. The basic idea is to first estimate local covariance coefficients from a low-resolution image and then use these covariance estimates to adapt the interpolation at a higher resolution based on the geometric duality between the low-resolution covariance and the high-resolution covariance. The edge-directed property of covariance-based adaptation attributes to its capability of tuning the interpolation coefficients to match an arbitrarily oriented step edge. A hybrid approach of switching between bilinear interpolation and covariance-based adaptive interpolation is proposed to reduce the overall computational complexity. Two important applications of the new interpolation algorithm are studied: resolution enhancement of grayscale images and reconstruction of color images from CCD samples. Simulation results demonstrate that our new interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional linear interpolation.


A Neural Algorithm of Artistic Style

Werner Reichardt Centre for Integrative Neuroscience and Institute of Theoretical Physics, University of Tübingen, Germany Bernstein Center for Computational Neuroscience, Tübingen, Germany Graduate School for Neural Information Processing, Tübingen, Germany Max Planck Institute for Biological Cybernetics, Tübingen, Germany Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA ∗To whom correspondence should be addressed; E-mail: leon.gatys@bethgelab.org


PCA-based feature selection scheme for machine defect classification

The sensitivity of various features that are characteristic of a machine defect may vary considerably under different operating conditions. Hence it is critical to devise a systematic feature selection scheme that provides guidance on choosing the most representative features for defect classification. This paper presents a feature selection scheme based on the principal component analysis (PCA) method. The effectiveness of the scheme was verified experimentally on a bearing test bed, using both supervised and unsupervised defect classification approaches. The objective of the study was to identify the severity level of bearing defects, where no a priori knowledge on the defect conditions was available. The proposed scheme has shown to provide more accurate defect classification with fewer feature inputs than using all features initially considered relevant. The result confirms its utility as an effective tool for machine health assessment.


Uncovering cabdrivers' behavior patterns from their digital traces

Recognizing high-level human behavior and decisions from their digital traces are critical issues in pervasive computing systems. In this paper, we develop a novel methodology to reveal cabdrivers’ operation patterns by analyzing their continuous digital traces. For the first time, we systematically study large scale cabdrivers’ behavior in a real and complex city context through their daily digital traces. We identify a set of valuable features, which are simple and effective to classify cabdrivers, delineate cabdrivers’ operation patterns and compare the different cabdrivers’ behavior. The methodology and steps could spatially and temporally quantify, visualize, and examine different cabdrivers’ operation patterns. Drivers were categorized into top drivers and ordinary drivers by their daily income. We use the daily operations of 3000 cabdrivers in over 48 million of trips and 240 million kilometers to uncover: (1) spatial selection behavior, (2) context-aware spatio-temporal operation behavior, (3) route choice behavior, and (4) operation tactics. Though we focused on cabdriver operation patterns analysis from their digital traces, the methodology is a general empirical and analytical methodology for any GPS-like trace analysis. Our work demonstrates the great potential to utilize the massive pervasive data sets to understand human behavior and high-level intelligence. Published by Elsevier Ltd.


Predicting SMEs' adoption of enterprise systems

Purpose – The purpose of this paper is to develop a model that can be used to predict which small to medium-sized enterprises (SMEs) are more likely to become adopters of enterprise systems (ERP, CRM, SCM and e-procurement). Design/methodology/approach – Direct interviews were used to collect data from a random sample of SMEs located in the Northwest of England. Using logistic regression, 102 responses were analysed. Findings – The results reveal that the factors influencing SMEs’ adoption of enterprise systems are different from the factors influencing SMEs’ adoption of other previously studied information systems (IS) innovations. SMEs were found to be more influenced by technological and organisational factors than environmental factors. Moreover, the results indicate that firms with a greater perceived relative advantage, a greater ability to experiment with these systems before adoption, greater top management support, greater organisational readiness and a larger size are predicted to become adopters of enterprise systems. Research limitations/implications – Although this study focused on the factors that influence SMEs’ adoption of a set of enterprise systems (i.e. ERP, CRM, SCM and e-procurement), it fails to differentiate between factors that influence each of these systems. Practical implications – The model can be used to assist software vendors not only to develop marketing strategies that can target potential adopters, but also to develop strategies to increase the adoption of ES among SMEs. Originality/value – The paper contributes to the continuing research in IS innovations adoption/diffusion in the small business context.


Minimising semantic drift with Mutual Exclusion Bootstrapping

Iterative bootstrapping techniques are commonly used to extract lexical semantic resources from raw text. Their major weakness is that, without costly human intervention, the extracted terms (often rapidly) drift from the meaning of the original seed terms. In this paper we propose Mutual Exclusion bootstrapping (MEB) in which multiple semantic classes compete for each extracted term. This significantly reduces the problem of semantic drift by providing boundaries for the semantic classes. We demonstrate the superiority of MEB to standard bootstrapping in extracting named entities from the Google Web 1T 5-grams. Finally, we demonstrate that MEB is a multi-way cut problem over semantic classes, terms and contexts.


Time-dependent visual adaptation for fast realistic image display

Human vision takes time to adapt to large changes in scene intensity, and these transient adjustments have a profound effect on visual appearance. This paper offers a new operator to include these appearance changes in animations or interactive real-time simulations, and to match a user's visual responses to those the user would experience in a real-world scene.
Large, abrupt changes in scene intensities can cause dramatic compression of visual responses, followed by a gradual recovery of normal vision. Asymmetric mechanisms govern these time-dependent adjustments, and offer adaptation to increased light that is much more rapid than adjustment to darkness. We derive a new tone reproduction operator that simulates these mechanisms. The operator accepts a stream of scene intensity frames and creates a stream of color display images.
All operator components are derived from published quantitative measurements from physiology, psychophysics, color science, and photography. ept intentionally simple to allow fast computation, the operator is meant for use with real-time walk-through renderings, high dynamic range video cameras, and other interactive applications. We demonstrate its performance on both synthetically generated and acquired “real-world” scenes with large dynamic variations of illumination and contrast.


Narrowband Internet of Things: Evolutions, Technologies, and Open Issues

We are on the threshold of the explosive growth in the global Internet-of-Things (IoT) market. Comparing with the legacy human-centric applications, machine type communication scenarios exhibit totally different characteristics, such as low throughput, delay insensitivity, occasional transmission, and deep coverage. Meanwhile, it also requires the terminal devices to be cheap enough and sustain long battery life. These demands hasten the prosperity of low power wide area (LPWA) technologies. Narrowband IoT (NB-IoT) is the newest Long Term Evolution (LTE) specification ratified by the third generation partner project as one of the LPWA solutions to achieve the objectives of super coverage, low power, low cost, and massive connection. Working in the licensed frequency band, it is designed to reuse and coexist with the existing LTE cellular networks, which endows it with outstanding advantages in decreasing network installation cost and minimizing product time-to-market. In this backdrop, it has been extensively regarded as one of the most promising technologies toward the IoT landscape. However, as a new LTE standard, there are still a lot of challenges that need to overcome. This paper surveys its evolutions, technologies, and issues, spanning from performance analysis, design optimization, combination with other leading technologies, to implementation and application. The goal is to deliver a holistic understanding for the emerging wireless communication system, in helping to spur further research in accelerating the broad use of NB-IoT.


Texture classification and segmentation using multiresolution simultaneous autoregressive models


k -Anonymous Data Mining: A Survey

Data mining technology has attracted significant interest as a means of identifying patterns and trends from large collections of data. It is however evident that the collection and analysis of data that include personal information may violate the privacy of the individuals to whom information refers. Privacy protection in data mining is then becoming a crucial issue that has captured the attention of many researchers. In this chapter, we first describe the concept of k-anonymity and illustrate different approaches for its enforcement. We then discuss how the privacy requirements characterized by k-anonymity can be violated in data mining and introduce possible approaches to ensure the satisfaction of k-anonymity in data mining.


An expert evaluation of word-sized visualizations for analyzing eye movement data

Word-sized visualizations for eye movement data allow analysts to compare a variety of experiment conditions or participants at the sametime. Weimplementedasetofsuchword-sizedvisualizations as part of an analysis framework. We want to ﬁnd out which of the visualizations is most suitable for different analysis tasks. To this end, we applied the framework to data from an eye tracking study on the reading behavior of users studying metro maps. In anexpertevaluationwithﬁveanalysts,weidentiﬁeddistinguishing characteristics of the different word-sized visualizations.


Comparing Business and Household Sector Innovation in Consumer Products : Findings from a Representative Study in the United Kingdom

In a first survey of its type, we measure development and modification of consumer products by product users in a representative sample of 1,173 UK consumers aged 18 and over. We estimate this previously unmeasured type of innovation to be quite large: 6.1% of UK consumers – nearly 2.9 million individuals have engaged in consumer product innovation during the prior three years. In aggregate, consumers’ annual product development expenditures are more than 1.4 times larger than the annual consumer product R&D expenditures of all firms in the UK combined. Consumers engage in many small projects which seem complementary to the innovation efforts of incumbent producers. Consumer innovators very seldom protect their innovations via intellectual property, and 17% diffuse to others. These results imply that official statistics partly miss relevant innovation activity, and that existing companies should reconfigure their product development systems to find and build upon prototypes developed by consumers. 1 MIT Sloan School of Management, Cambridge MA, USA. 2 RSM Erasmus University, Rotterdam, The Netherlands. 3 EIM Business and Policy Research, Zoetermeer, The Netherlands. 4 Centre for Research in Innovation Management, University of Brighton, United Kingdom. * Corresponding author. E-mail: evhippel@mit.edu. Phone: +1 617-253-7155


A study on human gait analysis

Human gait is one of the most important biometric which has so far been neglected for use in medical diagnostics. In this paper, we make a feasibility study on human gait acquired from a wearable sensor based biometric suit called as Intelligent Gait Oscillation Detector (IGOD). This suit measures simultaneous gait oscillation from eight major joints (two knees, two hips, two elbows and two shoulders) of a human body. Techniques for analyzing and understanding the human gait patterns were developed. Variance in the gait oscillation was studied with respect to gait speed varying from 3km/hr to 5km/hr. Gender variance (male/female) gait oscillation has also been studied. A comprehensive analysis on human gait affected by knee joint movement and hip joint oscillation has been addressed with the arm swing effects. This analysis will provide us with an insight on human bipedal locomotion and its stability. We plan to create a repository of human gait oscillations which could extensively be analyzed for person identification and detecting walking problems in patients, which is detection of disease in the medical field.


Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!

Argumentation mining (AM) requires the identification of complex discourse structures and has lately been applied with success monolingually. In this work, we show that the existing resources are, however, not adequate for assessing cross-lingual AM, due to their heterogeneity or lack of complexity. We therefore create suitable parallel corpora by (human and machine) translating a popular AM dataset consisting of persuasive student essays into German, French, Spanish, and Chinese. We then compare (i) annotation projection and (ii) bilingual word embeddings based direct transfer strategies for cross-lingual AM, finding that the former performs considerably better and almost eliminates the loss from cross-lingual transfer. Moreover, we find that annotation projection works equally well when using either costly human or cheap machine translations. Our code and data are available at http://github.com/UKPLab/ coling2018-xling_argument_mining.


A Brief Review of Digital Games and Learning

Digital games have caught the attention of scholars across a variety of disciplines. Today, scholars from fields as diverse as media design, literature, computer science, education, and theatre studies have together contributed to the understanding of this emergent medium and its phenomenon as a learning tool The unsatisfying experience of directly adopting game formats into educational contexts has motivated researchers to study successful (commercial) digital games, in particular to examine the play activities of digital games and the principles underlying the design of digital games in order to develop concepts and strategies for rich virtual learning contexts. In this review, provided is a brief account of academic arguments regarding digital games and learning.


Adversarial examples: A survey

Adversarial examples are a phenomenon that have gathered a lot of attention in recent studies. The fact that the addition of very small, but carefully crafted perturbations to the inputs of sophisticated and high performing machine learning models may cause them to make significant errors, is both fascinating and important. A survey of findings connected with adversarial examples is presented and discussed in this paper.


Sex-work harm reduction

Sex work is an extremely dangerous profession. The use of harm-reduction principles can help to safeguard sex workers' lives in the same way that drug users have benefited from drug-use harm reduction. Sex workers are exposed to serious harms: drug use, disease, violence, discrimination, debt, criminalisation, and exploitation (child prostitution, trafficking for sex work, and exploitation of migrants). Successful and promising harm-reduction strategies are available: education, empowerment, prevention, care, occupational health and safety, decriminalisation of sex workers, and human-rights-based approaches. Successful interventions include peer education, training in condom-negotiating skills, safety tips for street-based sex workers, male and female condoms, the prevention-care synergy, occupational health and safety guidelines for brothels, self-help organisations, and community-based child protection networks. Straightforward and achievable steps are available to improve the day-to-day lives of sex workers while they continue to work. Conceptualising and debating sex-work harm reduction as a new paradigm can hasten this process.


Challenges for the Self-Safety in Autonomous Vehicles

The combination of multiple functions having different and complementary capabilities enables the emergence of Autonomous Vehicles. Their deployment is limited by the level of complexity they represent together with the challenges encountered in real environments with strong safety concerns. Thus a major concern prior to massive deployment is on how to ensure the safety of autonomous vehicles despite likely internal (e.g. malfunctions) and external (e.g., aggressive behaviors) disturbances they might undergo. This paper presents the challenges that undergoes the design and development of autonomous vehicles with respect to their functional architecture and adaptive behaviors from a safety perspective. For the purpose of the rationales, we define needs and requirements that lead to the formulation of an architectural framework. Our approach is based on paradigms and technologies from non-automotive domains to address non-functional system properties like safety, reliability and security. The notion of micro-services is also introduced for the self-safety of autonomous vehicles. These are part of the proposed framework that should facilitate the analysis, design, development and validation for the adequate composition and orchestration of services aimed to warrant the required non-functional properties, such as safety. In the present paper, we introduce the structural and behavioral adaptations of the framework to offer a holistic and scalable vision of the safety over the system.


Toward a Semantic Forum for Active Collaborative Learning

Online discussion forums provide open workspace allowing learners to share information, exchange ideas, address problems and discuss on specific themes. But the substantial impediment to its promotion as effective elearning facility lies in the continuously increasing messages but with discrete and incoherent structure as well as the loosely-tied learners with response-freeness. To motivate and facilitate active collaborative learning, this paper describes the design of a semantic forum with semantic link networking on discussion transcripts. Based on domain ontology and text mining technologies, messages are automatically processed for structural modeling with semantic association and special interest groups are automatically discovered for topic-centric social context measurement, which lays the foundation for the fulfillment of distinctive functionalities in the semantic forum (i.e. semantic search, relational navigation and recommendation). Compared with traditional forums, the semantic forum has three outstanding features. First, it deals with the structural incoherence and content isolation within forums. Second, it enables active learning by providing learners with relational navigation to meet their learning demands. Third, it supports social context based ranking to recommend learning companions or transcripts for collaborative problem-solving.


Unique Signatures of Histograms for Local Surface Description

This paper deals with local 3D descriptors for surface matching. First, we categorize existing methods into two classes: Signatures and Histograms. Then, by discussion and experiments alike, we point out the key issues of uniqueness and repeatability of the local reference frame. Based on these observations, we formulate a novel comprehensive proposal for surface representation, which encompasses a new unique and repeatable local reference frame as well as a new 3D descriptor. The latter lays at the intersection between Signatures and Histograms, so as to possibly achieve a better balance between descriptiveness and robustness. Experiments on publicly available datasets as well as on range scans obtained with Spacetime Stereo provide a thorough validation of our proposal.


Automatic collision avoidance during parking and maneuvering — An optimal control approach

In order to reduce the great number of parking incidences and other collisions in low speed scenarios, an obstacle avoidance algorithm is proposed. Since collision avoidance can be achieved by sole braking when driving slowly this algorithm's objective is a comfort orientated braking routine. Therefore, an optimization problem is formulated leading to jerk and time optimal trajectories for braking. In addition to that, based on the prediction of the future vehicle motion, this algorithm compensates for a significant actuator time delay. Using an occupancy grid for representing the static vehicle environment and the current driving state, possible collision points are determined not only for the vehicle front or rear, but also for both vehicle sides, where no sensors are located. The algorithm's performance is demonstrated in a real-world scenario.


Mercury BLASTN: Faster DNA Sequence Comparison using a Streaming Hardware Architecture

Motivation: Large-scale DNA sequence comparison, as implemented by BLAST and related algorithms, is one of the pillars of modern genomic analysis. One way to accelerate these computations is with a streaming architecture, in which processors are arranged in a pipeline that replicates the multistage structure of the algorithm. To achieve high performance, the processor hardware implementing the critical seed matching and ungapped extension stages of BLAST should be specialized to execute these stages as quickly as possible. However, accelerating these stages requires solving two key problems: first, the seed matching stage is not of a form which has traditionally been amenable to hardware acceleration; and second, the accelerated implementation of BLAST should retain sensitivity at least comparable to that of the original software. Results: We describe Mercury BLASTN, an FPGA-based implementation of BLAST for DNA. Mercury BLASTN combines a Bloom filtering approach to seed matching with a modified ungapped extension algorithm to overcome barriers to placing the early stages of BLAST onto hardware. On a previous-generation FPGA hardware platform, Mercury BLASTN runs 5 to 11 times faster than NCBI BLASTN current-generation general-purpose CPUs, with the prospect of a further eight-fold speedup on current-generation FPGAs. Moreover, its sensitivity to significant DNA sequence alignments is 99% of that observed with software NCBI BLASTN. Availability: Academic users should contact the authors for information on acquiring a prototype of the Mercury BLASTN system. Contact: jbuhler@cse.wustl.edu


Comprehensive Survey on T-SDN: Software-Defined Networking for Transport Networks

Paradoxically, with an ever-increasing traffic demand, today transport-network operators experience a progressive erosion of their margins. The alarms of change are set, and software define networking (SDN) is coming to the rescue with the promise of reducing capital expenditures and operational expenses. Driven by economic needs and network innovation facilities, today transport SDN (T-SDN) is a reality. It gained big momentum in the last years, however, in the networking industry, the transport network will be perhaps the last segment to embrace SDN, mainly due to the heterogeneous nature and complexity of the optical equipment composing it. This survey guides the reader through a fascinating technological adventure that provides an organic analysis of the T-SDN development and evolution considering contributions from: academic research, standardization bodies, industrial development, open source projects, and alliances among them. After creating a comprehensive picture of T-SDN, we provide an analysis of many open issues that are expected to need significant future work, and give our vision in this path toward a fully programmable and dynamic transport network.


Gated Recurrent Capsules for Visual Word Embeddings

The caption retrieval task can be defined as follows: given a set of images I and a set of describing sentences S, for each image i in I we ought to find the sentence in S that best describes i. The most commonly applied method to solve this problem is to build a multimodal space and to map each image and each sentence to that space, so that they can be compared easily. A non-conventional model called Word2VisualVec has been proposed recently: instead of mapping images and sentences to a multimodal space, they mapped sentences directly to a space of visual features. Advances in the computation of visual features let us infer that such an approach is promising. In this paper, we propose a new Recurrent Neural Network model following that unconventional approach based on Gated Recurrent Capsules (GRCs), designed as an extension of Gated Recurrent Units (GRUs). We show that GRCs outperform GRUs on the caption retrieval task. We also state that GRCs present a great potential for other applications.


Improving Convergence Speed and Scalability in OSPF: A Survey

Open Shortest Path First (OSPF), a link state routing protocol, is a popular interior gateway protocol (IGP) in the Internet. Wide spread deployment and years of experience running the protocol have motivated continuous improvements in its operation as the nature and demands of the routing infrastructures have changed. Modern routing domains need to maintain a very high level of service availability. Hence, OSPF needs to achieve fast convergence to topology changes. Also, the ever-growing size of routing domains, and possible presence of wireless mobile adhoc network (MANET) components, requires highly scalable operation on part of OSPF to avoid routing instability. Recent years have seen significant efforts aimed at improving OSPF's convergence speed as well as scalability and extending OSPF to achieve seamless integration of mobile adhoc networks with conventional wired networks. In this paper, we present a comprehensive survey of these efforts.


Error-Related EEG Potentials Generated During Simulated Brain–Computer Interaction

Brain-computer interfaces (BCIs) are prone to errors in the recognition of subject's intent. An elegant approach to improve the accuracy of BCIs consists in a verification procedure directly based on the presence of error-related potentials (ErrP) in the electroencephalogram (EEG) recorded right after the occurrence of an error. Several studies show the presence of ErrP in typical choice reaction tasks. However, in the context of a BCI, the central question is: ldquoAre ErrP also elicited when the error is made by the interface during the recognition of the subject's intent?rdquo We have thus explored whether ErrP also follow a feedback indicating incorrect responses of the simulated BCI interface. Five healthy volunteer subjects participated in a new human-robot interaction experiment, which seem to confirm the previously reported presence of a new kind of ErrP. However, in order to exploit these ErrP, we need to detect them in each single trial using a short window following the feedback associated to the response of the BCI. We have achieved an average recognition rate of correct and erroneous single trials of 83.5% and 79.2%, respectively, using a classifier built with data recorded up to three months earlier.


ERP components on reaction errors and their functional significance: a tutorial

Some years ago we described a negative (Ne) and a later positive (Pe) deflection in the event-related brain potentials (ERPs) of incorrect choice reactions [Falkenstein, M., Hohnsbein, J., Hoormann, J., Blanke, L., 1990. In: Brunia, C.H.M., Gaillard, A.W.K., Kok, A. (Eds.), Psychophysiological Brain Research. Tilburg Univesity Press, Tilburg, pp. 192-195. Falkenstein, M., Hohnsbein, J., Hoormann, J., 1991. Electroencephalography and Clinical Neurophysiology, 78, 447-455]. Originally we assumed the Ne to represent a correlate of error detection in the sense of a mismatch signal when representations of the actual response and the required response are compared. This hypothesis was supported by the results of a variety of experiments from our own laboratory and that of Coles [Gehring, W. J., Goss, B., Coles, M.G.H., Meyer, D.E., Donchin, E., 1993. Psychological Science 4, 385-390. Bernstein, P.S., Scheffers, M.K., Coles, M.G.H., 1995. Journal of Experimental Psychology: Human Perception and Performance 21, 1312-1322. Scheffers, M.K., Coles, M. G.H., Bernstein, P., Gehring, W.J., Donchin, E., 1996. Psychophysiology 33, 42-54]. However, new data from our laboratory and that of Vidal et al. [Vidal, F., Hasbroucq, T., Bonnet, M., 1999. Biological Psychology, 2000] revealed a small negativity similar to the Ne also after correct responses. Since the above mentioned comparison process is also required after correct responses it is conceivable that the Ne reflects this comparison process itself rather than its outcome. As to the Pe, our results suggest that this is a further error-specific component, which is independent of the Ne, and hence associated with a later aspect of error processing or post-error processing. Our new results with different age groups argue against the hypotheses that the Pe reflects conscious error processing or the post-error adjustment of response strategies. Further research is necessary to specify the functional significance of the Pe.


Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects

Braincomputer interfaces (BCIs) allow their users to communicate or control external devices using brain signals rather than the brain's normal output pathways of peripheral nerves and muscles. Motivated by the hope of restoring independence to severely disabled individuals and by interest in further extending human control of external systems, researchers from many fields are engaged in this challenging new work. BCI research and development has grown explosively over the past two decades. Efforts have begun recently to provide laboratory-validated BCI systems to severely disabled individuals for real-world applications. In this paper, we discuss the current status and future prospects of BCI technology and its clinical applications. We will define BCI, review the BCI-relevant signals from the human brain, and describe the functional components of BCIs. We will also review current clinical applications of BCI technology and identify potential users and potential applications. Lastly, we will discuss current limitations of BCI technology, impediments to its widespread clinical use, and expectations for the future.


Anterior cingulate cortex, error detection, and the online monitoring of performance.

An unresolved question in neuroscience and psychology is how the brain monitors performance to regulate behavior. It has been proposed that the anterior cingulate cortex (ACC), on the medial surface of the frontal lobe, contributes to performance monitoring by detecting errors. In this study, event-related functional magnetic resonance imaging was used to examine ACC function. Results confirm that this region shows activity during erroneous responses. However, activity was also observed in the same region during correct responses under conditions of increased response competition. This suggests that the ACC detects conditions under which errors are likely to occur rather than errors themselves.


QR code location based reverse car-searching route recommendation model

To deal with the reverse car-searching issue in large buildings and parking lots, a Quick Response Code (QR code) based reverse car-searching route recommendation model is designed. By scanning the deployed QR codes, a Smartphone can pinpoint the host location and parking location efficiently. Based on the submitted location information, the central control system can finally return the recommended routes, which facilitates a host to reach the parking location effectively. In our model, the reverse car-searching route is divided into two parts: choosing the optimal exports (elevator) and computing the shortest walking distance route. Based on the optimal export selection algorithm and regional shortest path algorithm, our model can choose the prior exports (elevator) effectively, and then recommend the optimal walking route in the buildings and parking lots. The simulation shows that this low-cost system can effectively solve the reverse car-searching problem in large buildings and parking lots, save the driver's car-searching time and improve the utilization rate of parking facilities.


Ant supervised by PSO and 2-Opt algorithm, AS-PSO-2Opt, applied to Traveling Salesman Problem

AS-PSO-2Opt is a new enhancement of the AS-PSO method. In the classical AS-PSO, the Ant heuristic is used to optimize the tour length of a Traveling Salesman Problem, TSP, and PSO is applied to optimize three parameters of ACO, (α, β, ρ). The AS-PSO-2Opt consider a post processing resuming path redundancy, helping to improve local solutions and to decrease the probability of falling in local minimum. Applied to TSP, the method allowed retrieving a valuable path solution and a set of fitted parameters for ACO. The performance of the AS-PSO-2Opt is tested on nine different TSP test benches. Experimental results based on a statistical analysis showed that the new proposal performs better than key state of art methods using Genetic algorithm, Neural Network and ACO algorithm. The AS-PSO-2Opt performs better than close related methods such as PSO-ACO-3Opt [9] and ACO with ABC [19] for various test benches.


Using association rules for ontology extraction from a Quran corpus

Ontology can be seen as a formal representation of knowledge. They have been investigated in many arti ficial intelligence studies including semantic web, softwa re engineering, and information retrieval. The aim of ontology is t o develop knowledge representations that can be shared and re used. This research project is concerned with the use of assoc iati n rules to extract the Quran ontology. The manual acquisition of ontologies from Quran verses can be very costly; therefore, we ne d an intelligent system for Quran ontology construction using patternbased schemes and associations rules to discover Qu ran concepts and semantics relations from Quran verses. Our system i s based on the combination of statistics and linguistics methods t o extract concepts and conceptual relations from Quran. In particular, linguistic pattern-based approach is exploited to extract spec ific concepts from the Quran, while the conceptual relations are found based on association rules technique. The Quran ontology wil l offer a new and powerful representation of Quran knowledge, and the association rules will help to represent the relations between all classes of connected concepts in the Quran ontology. Keywords— Ontology; Concepts Extraction ; Quran; Association Rules; Text Mining.


Learning Image Descriptors with Boosting

We propose a novel and general framework to learn compact but highly discriminative floating-point and binary local feature descriptors. By leveraging the boosting-trick we first show how to efficiently train a compact floating-point descriptor that is very robust to illumination and viewpoint changes. We then present the main contribution of this paper—a binary extension of the framework that demonstrates the real advantage of our approach and allows us to compress the descriptor even further. Each bit of the resulting binary descriptor, which we call BinBoost, is computed with a boosted binary hash function, and we show how to efficiently optimize the hash functions so that they are complementary, which is key to compactness and robustness. As we do not put any constraints on the weak learner configuration underlying each hash function, our general framework allows us to optimize the sampling patterns of recently proposed hand-crafted descriptors and significantly improve their performance. Moreover, our boosting scheme can easily adapt to new applications and generalize to other types of image data, such as faces, while providing state-of-the-art results at a fraction of the matching time and memory footprint.


SMART VOTING SYSTEM WITH FACE RECOGNITION

Manual voting system has been deployed for many yea rs in our country. However in many parts of our cou ntry people cannot attend the voting because of several r asons. In order to solve these problems there is a need of online election voting system in addition to manual voting system. The long-term goal of this project is to g reatly reduce the cost and complexity of running elections and increase th e accuracy of results by removing the direct involv ement of humans in gathering and counting of votes. The voters will be a le to give their votes at any field areas by usi ng the system if they prefer online voting In this system, the user votes using android applic ation which can be downloaded over internet. The us er has to login before giving his vote. Authentication will b e done using SMS confirmation and face recognition. Users can view parties, enter votes, can verify vote via SMS or email and can view result after result is declared.


The Nature, Importance, and Difficulty of Machine Ethics

The question of whether machine ethics exists or might exist in the future is difficult to answer if we can't agree on what counts as machine ethics. Some might argue that machine ethics obviously exists because humans are machines and humans have ethics. Others could argue that machine ethics obviously doesn't exist because ethics is simply emotional expression and machines can't have emotions. A wide range of positions on machine ethics are possible, and a discussion of the issue could rapidly propel us into deep and unsettled philosophical issues. Perhaps, understandably, few in the scientific arena pursue the issue of machine ethics. As we expand computers' decision-making roles in practical matters, such as computers driving cars, ethical considerations are inevitable. Computer scientists and engineers must examine the possibilities for machine ethics because, knowingly or not, they've already engaged in some form of it. Before we can discuss possible implementations of machine ethics, however, we need to be clear about what we're asserting or denying


Integrating Conflicting Data: The Role of Source Dependence

Many data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, require integrating data from multiple sources. Each of these sources provides a set of values and different sourc es can often provide conflicting values. To present quality data to users, it is critical that data integration systems can resolve conflicts and discover true values. Typically, we expect a true value to be provided by more sources than any particular false one, so we can take the value provided by the majority of the sources as the truth. Unfortunately, a false value can be spread through copying and that makes truth discovery extremely tricky. In this paper, we consider how to find true values from conflicting information when there are a large number of sources, among which some may copy from others. We present a novel approach that considers pendencebetween data sources in truth discovery. Intuitively, if two data sources provide a large number of common values and many of these values are rarely provided by other sources ( e.g., particular false values), it is very likely that one copies from the other. We apply Bayesian analysis to decide dependence between sources and design an algorithm that iteratively detects dependence and discovers truth from conflicting information. We also extend our model by considering accuracyof data sources and similarity between values. Our experiments on synthetic data as well as real-world data show that our algorithm can significantly improve accuracy of truth discovery and is scalable when there are a large number of data sources.


Teenager ’ s Addiction to Smart Phones and Its Integrated Therapy Method

The fundamental cause of teenagers’ addiction to smart phone is due to stress arising from family and school. Smartphone can cause many problems including musculoskeletal symptom which can develop due to staying in one position for a long time. Furthermore, starring at a small screen for an extended time can cause decreased eye sight, along with amblyopia. Smart phone addiction causes depression and anxiety which will lead to severe mental problems. This will lead to problems associated to one’s social life and human network. Fundamental treatment of cognitive and emotional elements needed to become harmonized as a whole in order to increase the effectiveness of the treatment results. The therapy has been verified to cure the behavioral and emotional aspect related to depression, sense of control, and assimilation in school. From these integrated treatment approaches, this report compared the treatment’s upsides and downsides and also, assessed the possibility the integrated treatment process. We can presume that this report provided the opportunity and support for the creation of a fundamental treatment method that can help teenagers live a healthy life by overcoming addiction to smart phones.


Communication-efficient algorithms for statistical optimization

We study two communication-efficient algorithms for distributed statistical optimization on large-scale data. The first algorithm is an averaging method that distributes the N data samples evenly to m machines, performs separate minimization on each subset, and then averages the estimates. We provide a sharp analysis of this average mixture algorithm, showing that under a reasonable set of conditions, the combined parameter achieves mean-squared error that decays as O(N-1 + (N/m)-2). Whenever m ≤ √N, this guarantee matches the best possible rate achievable by a centralized algorithm with access to all N samples. The second algorithm is a novel method, based on an appropriate form of bootstrap. Requiring only a single round of communication, it has mean-squared error that decays as O(N-1 + (N/m)-3), and so is more robust to the amount of parallelization.


Reinforcement Learning of Coordination in Cooperative Multi-Agent Systems

We report on an investigation of reinforcement learning techniques for the learning of coordination in cooperative multi-agent systems. Specifically, we focus on a novel action selection strategy for Q-learning (Watkins 1989). The new technique is applicable to scenarios where mutual observation of actions is not possible.To date, reinforcement learning approaches for such <i>independent </i> agents did not guarantee convergence to the optimal joint action in scenarios with high miscoordination costs. We improve on previous results (Claus & Boutilier 1998) by demonstrating empirically that our extension causes the agents to converge almost always to the optimal joint action even in these difficult cases.


Characterization of log periodic planar dipole array antenna

This paper proposes a analysis approach for characterizing the broadband Log Periodic Planar Dipole Array (LPPDA) antenna useful for C-band applications. A simplest geometry of the edge fed Log Periodic Planar Dipole Array is designed and developed. Parameters like length, width of the elements, and spacing between elements are the varied using defined scale factor (τ) and a spacing factor (σ), keeping others constant and their impact was noted down. A parametric analysis is performed. using CST software. A transmission line equivalent circuit of the proposed LPPDA is constructed. Log periodicity behaviour of the proposed antenna is checked by plotting input impedance with respect to logarithm of frequency. An antenna is fabricated and measured in laboratory.


Using Suffix Arrays to Compute Term Frequency and Document Frequency for All Substrings in a Corpus

Bigrams and trigrams are commonly used in statistical natural language processing; this paper will describe techniques for working with much longer n-grams. Suffix arrays (Manber and Myers 1990) were first introduced to compute the frequency and location of a substring (n-gram) in a sequence (corpus) of length N. To compute frequencies over all N(N+1)/2 substrings in a corpus, the substrings are grouped into a manageable number of equivalence classes. In this way, a prohibitive computation over substrings is reduced to a manageable computation over classes. This paper presents both the algorithms and the code that were used to compute term frequency (tf) and document frequency (df) for all n-grams in two large corpora, an English corpus of 50 million words of Wall Street Journal and a Japanese corpus of 216 million characters of Mainichi Shimbun. The second half of the paper uses these frequencies to find interesting substrings. Lexicographers have been interested in n-grams with high mutual information (MI) where the joint term frequency is higher than what would be expected by chance, assuming that the parts of the n-gram combine independently. Residual inverse document frequency (RIDF) compares document frequency to another model of chance where terms with a particular term frequency are distributed randomly throughout the collection. MI tends to pick out phrases with noncompositional semantics (which often violate the independence assumption) whereas RIDF tends to highlight technical terminology, names, and good keywords for information retrieval (which tend to exhibit nonrandom distributions over documents). The combination of both MI and RIDF is better than either by itself in a Japanese word extraction task.


QDFS: A quality-aware distributed file storage service based on HDFS

On the basis of the Hadoop distributed file system (HDFS), this paper presents the design and implementation of QDFS, a distributed file storage service system that employs a backup policy based on recovery volumes and a quality-aware data distribution strategy. The experimental results show that QDFS increases the storage utilization ratio and improves the storage performance by being aware of the quality of service of the DataNodes. The improvements of QDFS compared with HDFS make the Hadoop distributed computing model more suitable to unstable wide area network environments.


Effects of psychological and social factors on organic disease: a critical assessment of research on coronary heart disease.

An extensive research literature in the behavioral sciences and medicine suggests that psychological and social factors may play a direct role in organic coronary artery disease (CAD) pathology. However, many in the medical and scientific community regard this evidence with skepticism. This chapter critically examines research on the impact of psychological and psychosocial factors on the development and outcome of coronary heart disease, with particular emphasis on studies employing verifiable outcomes of CAD morbidity or mortality. Five key variables identified as possible psychosocial risk factors for CAD are addressed: acute and chronic stress, hostility, depression, social support, and socioeconomic status. Evidence regarding the efficacy of psychosocial interventions is also presented. It is suggested that, taken as a whole, evidence for a psychological and social impact on CAD morbidity and mortality is convincing. However, continued progress in this area requires multidisciplinary research integrating expertise in cardiology and the behavioral sciences, and more effective efforts to communicate research findings to a biomedical audience.


Learning Theory and Instructional Design

Designing effective instruction goes beyond systematically executing various steps within an instructional design model. Among a host of considerations, effective instructional design should take into consideration the theoretical bases in which it is grounded. This is not to say that learning theory offers instructional designers answers to design problems but instead, offers clarity, direction and focus throughout the instructional design process. Merrill (2001, p. 294) explains that a “theoretical tool, in and of itself, is not an instructional design theory but defines instructional components that can be used to define instructional prescriptions more precisely.” Likewise, Merriam and Caffarella (1999, p. 250) make the point that “[learning] theories do not give us solutions, but they do direct our attention to those variables that are crucial in finding solutions.” Thus, understanding theoretical frameworks and properly incorporating them within the scope of instructional design is important for designers to effectively prepare and present instruction as well as for organizational entities to more precisely and efficiently address training-appropriate issues.


Oil content, tocopherol composition and fatty acid patterns of the seeds of 51 Cannabis sativa L. genotypes

The oil content, the tocopherol composition, the plastochromanol-8 (P-8) content and the fatty acid composition (19 fatty acids) of the seed of 51 hemp (Cannabis sativa L.) genotypes were studied in the 2000 and 2001 seasons. The oil content of the hemp seed ranged from 26.25% (w/w) to 37.50%. Analysis of variance revealed significant effects of genotype, year and of the interaction (genotype × year) on the oil content. The oil contents of the 51 genotypes in 2000 and 2001 were correlated (r = 0.37**) and averaged 33.19 ± 1.45% in 2000 and 31.21 ± 0.96% in 2001. The γ-tocopherol, α-tocopherol, δ-tocopherol, P-8- and β-tocopherol contents of the 51 genotypes averaged 21.68 ± 3.19, 1.82 ± 0.49, 1.20 ± 0.40, 0.18 ± 0.07 and 0.16 ± 0.04 mg 100g−1 of seeds, respectively (2000 and 2001 data pooled). Hierarchical clustering of the fatty acid data did not group the hemp genotypes according to their geographic origin. The γ-linolenic acid yield of hemp (3–30 kg ha−1) was similar to the γ-linolenic acid yield of plant species that are currently used as sources of γ-linolenic acid (borage (19–30 kg ha−1), evening primrose (7–30 kg ha−1)). The linoleic acid yield of hemp (129–326 kg ha−1) was similar to flax (102–250 kg ha−1), but less than in sunflower (868–1320 kg ha−1). Significant positive correlations were detected between some fatty acids and some tocopherols. Even though the average content of P-8 in hemp seeds was only 1/120th of the average γ-tocopherol content, P-8 content was more closely correlated with the unsaturated fatty acid content than γ-tocopherol or any other tocopherol fraction. The average broad-sense heritabilities of the oil content, the antioxidants (tocopherols and P-8) and the fatty acids were 0.53, 0.14 and 0.23, respectively. The genotypes Fibrimon 56, P57, Juso 31, GB29, Beniko, P60, FxT, Félina 34, Ramo and GB18 were capable of producing the largest amounts of high quality hemp oil.


Learning to Remember Translation History with a Continuous Cache

Existing neural machine translation (NMT) models generally translate sentences in isolation, missing the opportunity to take advantage of document-level information. In this work, we propose to augment NMT models with a very light-weight cache-like memory network, which stores recent hidden representations as translation history. The probability distribution over generated words is updated online depending on the translation history retrieved from the memory, endowing NMT models with the capability to dynamically adapt over time. Experiments on multiple domains with different topics and styles show the effectiveness of the proposed approach with negligible impact on the computational cost.


A fast method of fog and haze removal

Fog and haze degrade the quality of preview and captured image by reducing the contrast and saturation. As a result the visibility of scene or object degrades. The objective of the present work is to enhance the visibility, saturation, contrast and reduce the noise in a foggy image. We propose a method that uses single frame for enhancing foggy images using multilevel transmission map. The method is fast and free from noise or artifacts that generally arise in such enhancement techniques. A comparison with existing methods shows that the proposed method performs better in terms of both processing time and quality. The proposed method works in real time for VGA resolution. The proposed work also presents a scheme to remove fog, rain and snow in real-time.


Snapshots in a flash with ioSnap

Snapshots are a common and heavily relied upon feature in storage systems. The high performance of flash-based storage systems brings new, more stringent, requirements for this classic capability. We present ioSnap, a flash optimized snapshot system. Through careful design exploiting common snapshot usage patterns and flash oriented optimizations, including leveraging native characteristics of Flash Translation Layers, ioSnap delivers low-overhead snapshots with minimal disruption to foreground traffic. Through our evaluation, we show that ioSnap incurs negligible performance overhead during normal operation, and that common-case operations such as snapshot creation and deletion incur little cost. We also demonstrate techniques to mitigate the performance impact on foreground I/O during intensive snapshot operations such as activation. Overall, ioSnap represents a case study of how to integrate snapshots into a modern, well-engineered flash-based storage system.


Analysis of back-end flash in a 1.5b/stage pipelined ADC

An analysis of the impact of last stage flash in a conventional pipeline ADC is performed in this paper. The performance of a pipeline ADC can be altered significantly by calibrating the comparators in the back-end flash. Also, realizing that the input to the back-end flash (in a pipeline ADC) is not uniformly distributed, this paper proposes alternative back-end flash references to improve the overall performance of the ADC. An analysis of the performance of the pipeline with large offsets in the MDAC stages is also presented in this paper.


A Preliminary Taxonomy of Crowdsourcing

Many firms are now asking how they can benefit from the new form of outsourcing labelled “crowdsourcing”. Like many other forms of outsourcing, crowdsourcing is now being “talked up” by a somewhat credulous trade press. However, the term crowdsourcing has been used to describe several related, but different phenomena, and what might be successful with one form of crowdsourcing may not be with another. In this paper the notion of crowdsourcing is decomposed to create a taxonomy that expands our understanding of what is meant by the term. This taxonomy focuses on the different capability levels of crowdsourcing suppliers; different motivations; and different allocation of benefits. The management implications of these distinctions are then considered in light of what we know about other forms of outsourcing.


Being John Malkovich

Given a photo of person A, we seek a photo of person B with similar pose and expression. Solving this problem enables a form of puppetry, in which one person appears to control the face of another. When deployed on a webcam-equipped computer, our approach enables a user to control another person’s face in real-time. This image-retrievalinspired approach employs a fully-automated pipeline of face analysis techniques, and is extremely general—we can puppet anyone directly from their photo collection or videos in which they appear. We show several examples using images and videos of celebrities from the Internet.


Common-Duty-Ratio Control of Input-Parallel Output-Parallel (IPOP) Connected DC–DC Converter Modules With Automatic Sharing of Currents

Input-parallel output-parallel (IPOP) connected converter systems allow the use of low-power converter modules for high-power applications, with interleaving control scheme resulting in smaller filter size, better dynamic performance, and higher power density. In this paper, a new IPOP converter system is proposed, which consists of multiple dual-active half-bridge (DAHB) dc-dc converter modules. Moreover, by applying a common-duty-ratio control scheme, without a dedicated current-sharing controller, the automatic sharing of input currents or load currents is achieved in the IPOP converter even in the presence of substantial differences of 10% in various module parameters. The current-sharing performance of the proposed control method is analyzed using both a small-signal model and a steady-state dc model of the IPOP system. It is concluded that the equal sharing of currents among modules can be achieved by reducing the mismatches in various module parameters, which is achievable in practice. The current-sharing performance of the IPOP converter is also verified by Saber simulation and a 400-W experimental prototype consisting of two DAHB modules. The common-duty-ratio control method can be extended to any IPOP system that consists of three or more converter modules, including traditional dual-active bridge dc-dc converters, which have a characteristic of current source.


Unobtrusive User-Authentication on Mobile Phones Using Biometric Gait Recognition

The need for more security on mobile devices is increasing with new functionalities and features made available. To improve the device security we propose gait recognition as a protection mechanism. Unlike previous work on gait recognition, which was based on the use of video sources, floor sensors or dedicated high-grade accelerometers, this paper reports the performance when the data is collected with a commercially available mobile device containing low-grade accelerometers. To be more specific, the used mobile device is the Google G1 phone containing the AK8976A embedded accelerometer sensor. The mobile device was placed at the hip on each volunteer to collect gait data. Preproccesing, cycle detection and recognition-analysis were applied to the acceleration signal. The performance of the system was evaluated having 51 volunteers and resulted in an equal error rate (EER) of 20%.


Estimation of Planar Curves, Surfaces, and Nonplanar Space Curves Defined by Implicit Equations with Applications to Edge and Range Image Segmentation

This paper addresses the problem of parametric representation and estimation of complex planar curves in 2-D, surfaces in 3-D and nonplanar space curves in 3-D. Curves and surfaces can be defined either parametrically or implicitly, and we use the latter representation. A planar curve is the set of zeros of a smooth function of two variables X-Y, a surface is the set of zeros of a smooth function of three variables X-~-Z, and a space curve is the intersection of two surfaces, which are the set of zeros of two linearly independent smooth functions of three variables X-!/-Z. For example, the surface of a complex object in 3D can be represented as a subset of a single implicit surface, with similar results for planar and space curves. We show how this unified representation can be used for object recognition, object position estimation, and segmentation of objects into meaningful subobjects, that is, the detection of “interest regions” that are more complex than high curvature regions and, hence, more useful as features for object recognition. Fitting implicit curves and surfaces to data would be ideally based on minimizing the mean square distance from the data points to the curve or surface. Since the distance from a point to a curve or surface cannot be computed exactly by direct methods, the approximate distance, which is a first-order approximation of the real distance, is introduced, generalizing and unifying previous results. We fit implicit curves and surfaces to data minimizing the approximate mean square distance, which is a nonlinear least squares problem. We show that in certain cases, this problem reduces to the generalized eigenvector fit, which is the minimization of the sum of squares of the values of the functions that define the curves or surfaces under a quadratic constraint function of the data. This fit is computationally reasonable to compute, is readily parallelizable, and, hence, is easily computed in real time. In general, the generalized eigenvector lb provides a very good initial estimate for the iterative minimization of the approximate mean square distance. Although we are primarily interested in the 2-D and 3-D cases, the methods developed herein are dimension independent. We show that in the case of algebraic curves and surfaces, i.e., those defined by sets of zeros of polynomials, the minimizers of the approximate mean square distance and the generalized eigenvector fit are invariant with respect to similarity transformations. Thus, the generalized eigenvector lit is independent of the choice of coordinate system, which is a very desirable property for object recognition, position estimation, and the stereo matching problem. Finally, as applications of the previous techniques, we illustrate the concept of “interest regions” Manuscript received April 13, 1988; revised April 30, 1991. This work was supported by an IBM Fellowship and NSF Grant IRI-8715774. The author was with the Laboratory for Engineering Man/Machine Systems, Department of Engineering, Brown University, Providence, RI 02912. He is now with the Exploratory Computer Vision Group, IBM T. J. Watson Research Center, Yorktown Heights, NY 10598. IEEE Log Number 9102093. for object recognition and describe a variable-order segmentation algorithm that applies to the three cases of interest.


What Is Coefficient Alpha ? An Examination of Theory and Applications

Psychological research involving scale construction has been hindered considerably by a widespread lack of understanding of coefficient alpha and reliability theory in general. A discussion of the assumptions and meaning of coefficient alpha is presented. This discussion is followed by a demonstration of the effects of test length and dimensionality on alpha by calculating the statistic for hypothetical tests with varying numbers of items, numbers of orthogonal dimensions, and average item intercorrelations. Recommendations for the proper use of coefficient alpha are offered.


Biometric authentication on a mobile device: a study of user effort, error and task disruption

We examine three biometric authentication modalities -- voice, face and gesture -- as well as password entry, on a mobile device, to explore the relative demands on user time, effort, error and task disruption. Our laboratory study provided observations of user actions, strategies, and reactions to the authentication methods. Face and voice biometrics conditions were faster than password entry. Speaking a PIN was the fastest for biometric sample entry, but short-term memory recall was better in the face verification condition. None of the authentication conditions were considered very usable. In conditions that combined two biometric entry methods, the time to acquire the biometric samples was shorter than if acquired separately but they were very unpopular and had high memory task error rates. These quantitative results demonstrate cognitive and motor differences between biometric authentication modalities, and inform policy decisions in selecting authentication methods.


Is Smart Beta Really Smart ?

BURTON G. MALKIEL is the Chemical Bank Chairman’s Professor Emeritus of Economics at Princeton University in Princeton, NJ. bmalkiel@princeton.edu There is a popular new investment strategy in portfolio management called smart beta. With a catchy title and a promise of improved portfolio performance, the strategy has already attracted hundreds of billions of dollars and is growing by leaps and bounds. Unfortunately, smart beta portfolios do not consistently outperform and when they do produce appealing results, they f lunk the risk test.


Exploring the Regularity of Sparse Structure in Convolutional Neural Networks

Sparsity helps reduce the computational complexity of deep neural networks by skipping zeros. Taking advantage of sparsity is listed as a high priority in next generation DNN accelerators such as TPU[1]. The structure of sparsity, i.e., the granularity of pruning, affects the efficiency of hardware accelerator design as well as the prediction accuracy. Coarse-grained pruning creates regular sparsity patterns, making it more amenable for hardware acceleration but more challenging to maintain the same accuracy. In this paper we quantitatively measure the trade-off between sparsity regularity and prediction accuracy, providing insights in how to maintain accuracy while having more a more structured sparsity pattern. Our experimental results show that coarse-grained pruning can achieve a sparsity ratio similar to unstructured pruning without loss of accuracy. Moreover, due to the index saving effect, coarse-grained pruning is able to obtain a better compression ratio than fine-grained sparsity at the same accuracy threshold. Based on the recent sparse convolutional neural network accelerator (SCNN), our experiments further demonstrate that coarse-grained sparsity saves∼ 2× the memory references compared to fine-grained sparsity. Since memory reference is more than two orders of magnitude more expensive than arithmetic operations, the regularity of sparse structure leads to more efficient hardware design.


Lazy Modeling of Variants of Token Swapping Problem and Multi-agent Path Finding through Combination of Satisfiability Modulo Theories and Conflict-based Search

We address item relocation problems in graphs in this paper. We assume items placed in vertices of an undirected graph with at most one item per vertex. Items can be moved across edges while various constraints depending on the type of relocation problem must be satisfied. We introduce a general problem formulation that encompasses known types of item relocation problems such as multi-agent path finding (MAPF) and token swapping (TSWAP). In this formulation we express two new types of relocation problems derived from token swapping that we call token rotation (TROT) and token permutation (TPERM). Our solving approach for item relocation combines satisfiability modulo theory (SMT) with conflict-based search (CBS). We interpret CBS in the SMT framework where we start with the basic model and refine the model with a collision resolution constraint whenever a collision between items occurs in the current solution. The key difference between the standard CBS and our SMT-based modification of CBS (SMT-CBS) is that the standard CBS branches the search to resolve the collision while in SMT-CBS we iteratively add a single disjunctive collision resolution constraint. Experimental evaluation on several benchmarks shows that the SMT-CBS algorithm significantly outperforms the standard CBS. We also compared SMT-CBS with a modification of the SAT-based MDD-SAT solver that uses an eager modeling of item relocation in which all potential collisions are eliminated by constrains in advance. Experiments show that lazy approach in SMT-CBS produce fewer constraint than MDD-SAT and also achieves faster solving run-times.


Data Mining in Healthcare : Current Applications and Issues By

The successful application of data mining in highly visible fields like e-business, marketing and retail have led to the popularity of its use in knowledge discovery in databases (KDD) in other industries and sectors. Among these sectors that are just discovering data mining are the fields of medicine and public health. This research paper provides a survey of current techniques of KDD, using data mining tools for healthcare and public health. It also discusses critical issues and challenges associated with data mining and healthcare in general. The research found a growing number of data mining applications, including analysis of health care centers for better health policy-making, detection of disease outbreaks and preventable hospital deaths, and detection of fraudulent insurance claims.


Using the Kinect for detecting tremors: Challenges and opportunities

The Kinect sensor is an attachment for the Xbox gaming console which allows players to interact with games through body movement. This paper explores the possibility of using the Kinect in a clinical setting for detection of Parkinson's, postural, flapping, titubation and voice tremors. All of the physical tremors were simulated and the ability of the Kinect to capture them was evaluated. The Kinect was also used to record voice data from real voice tremors patients. Physical and voice data were gathered from healthy persons for comparison. The results showed that the Kinect could reliably detect voice, postural and Parkinson's tremors. A very consistent graph could be obtained repeatedly for both Parkinson's and postural tremors. For voice tremors there was also a consistent pattern that differentiated a normal voice from one with a tremor. We have therefore proven that the Kinect can consistently record Parkinson's, postural and voice tremors.


Automated Pyramid Scoring of Summaries using Distributional Semantics

The pyramid method for content evaluation of automated summarizers produces scores that are shown to correlate well with manual scores used in educational assessment of students’ summaries. This motivates the development of a more accurate automated method to compute pyramid scores. Of three methods tested here, the one that performs best relies on latent semantics.


Large eddy simulations of the HyShot II scramjet

The present work is part of a broad effort toward predictive simulations of complex flows at high Reynolds numbers. The main objective of the Stanford PSAAP Center (Pre-dictive Science Academic Alliance Program) is to predict the reacting flow in the HyShot II scramjet experiment carried out in the HEG facility of the German Aerospace Agency DLR (cf. Laurence et al. 2011). Specifically, the objective is to predict the best-estimate of the flow, to estimate the uncertainties from a range of sources in this prediction, and, finally , to quantify the margin to engine unstart. Unstart occurs in supersonic internal flows when the flow becomes choked (e.g., due to excessive friction or heat addition), which initiates an unsteady unstart process that eventually leads to subsonic flow throughout. The unstart process involves a propagation of shock waves upstream in the combustor, the speed of which depends strongly on the amount of heat addition. The experiments by Laurence et al. (2011) indicate propagation velocities of 31 m/s at equivalence ratio (ER) of 0.5, 93 m/s at ER=0.7, and above 200 m/s at ER=1.1. For this problem with fixed mass flux of incoming air, the equivalence ratio is proportional to the fuel mass flux. The length of the HyShot combustor is about 300 mm; thus the unstart process takes about 10 ms at ER=0.5. The experiments were carried out in a shock tube with a test time of about 2.5 ms; thus the unstart process is significantly longer than the test time near the unstart boundary. For this reason, it is clear that shock tube experiments can not give a precise unstart bound. An alternative approach is to use a validated large eddy simulation (LES) method to explore and determine the unstart bound. This is the approach taken here, and the motivation of the work described in this brief. The complexity of the flow (turbulence, shocks, mixing, combustion) implies that a careful multi-stage validation plan is needed. Moreover, the available quantitative experimental data for the HyShot case is limited to measurements of pressure (mean and rms) and heat transfer (although these are highly uncertain) along two lines in the combustor. Therefore, validation on the HyShot problem alone is not sufficient. The first step in the validation is to compute a supersonic flat plate boundary layer as a basic check on the LES method and, specifically, the modeling of the friction and heat transfer at …


Context-free Attacks Using Keyboard Acoustic Emanations

The emanations of electronic and mechanical devices have raised serious privacy concerns. It proves possible for an attacker to recover the keystrokes by acoustic signal emanations. Most existing malicious applications adopt context-based approaches, which assume that the typed texts are potentially correlated. Those approaches often incur a high cost during the context learning stage, and can be limited by randomly typed contents (e.g., passwords). Also, context correlations can increase the risk of successive false recognition. We present a context-free and geometry-based approach to recover keystrokes. Using off-the-shelf smartphones to record acoustic emanations from keystrokes, this design estimates keystrokes' physical positions based on the Time Difference of Arrival (TDoA) method. We conduct extensive experiments and the results show that more than 72.2\% of keystrokes can be successfully recovered.


Marriage, honesty, and stability

Many centralized two-sided markets form a matching between participants by running a stable marriage algorithm. It is a well-known fact that no matching mechanism based on a stable marriage algorithm can guarantee truthfulness as a dominant strategy for participants. However, as we will show in this paper, in a probabilistic setting where the preference lists of one side of the market are composed of only a constant (independent of the the size of the market) number of entries, each drawn from an arbitrary distribution, the number of participants that have more than one stable partner is vanishingly small. This proves (and generalizes) a conjecture of Roth and Peranson [23]. As a corollary of this result, we show that, with high probability, the truthful strategy is the best response for a given player when the other players are truthful. We also analyze equilibria of the deferred acceptance stable marriage game. We show that the game with complete information has an equilibrium in which a (1 - o(1)) fraction of the strategies are truthful in expectation. In the more realistic setting of a game of incomplete information, we will show that the set of truthful strategies form a (1 + o(1))-approximate Bayesian-Nash equilibrium. Our results have implications in many practical settings and were inspired by the work of Roth and Peranson [23] on the National Residency Matching Program.


A personalized recommender system for pervasive social networks

The current availability of interconnected portable devices, and the advent of the Web 2.0, raise the problem of supporting anywhere and anytime access to a huge amount of content, generated and shared by mobile users. On the one hand, users tend to be always connected for sharing experiences and conducting their social interactions with friends and acquaintances, through so-called Mobile Social Networks, further improving their social inclusion. On the other hand, the pervasiveness of communication infrastructures spreading data (cellular networks, direct device-to-device contacts, interactions with ambient devices as in the Internet-of-Things) makes compulsory the deployment of solutions able to filter off undesired information and to select what content should be addressed to which users, for both (i) better user experience, and (ii) resource saving of both devices and network. In this work, we propose a novel framework for pervasive social networks, called Pervasive PLIERS (p-PLIERS), able to discover and select, in a highly personalized way, contents of interest for single mobile users. p-PLIERS exploits the recently proposed PLIERS tag-based recommender system [2] as context a reasoning tool able to adapt recommendations to heterogeneous interest profiles of different users. p-PLIERS effectively operates also when limited knowledge about the network is maintained. It is implemented in a completely decentralized environment, in which new contents are continuously generated and diffused through the network, and it relies only on the exchange of single nodes’ knowledge during proximity contacts and through device-to-device communications. We evaluated p-PLIERS by simulating its behavior in three different scenarios: a big event (Expo 2015), a conference venue (ACM KDD’15), and a working day in the city of Helsinki. For each scenario, we used real or synthetic mobility traces and we extracted real datasets from Twitter interactions to characterize the generation and sharing of user contents.


Recent Advancements in Retinal Vessel Segmentation

Retinal vessel segmentation is a key step towards the accurate visualization, diagnosis, early treatment and surgery planning of ocular diseases. For the last two decades, a tremendous amount of research has been dedicated in developing automated methods for segmentation of blood vessels from retinal fundus images. Despite the fact, segmentation of retinal vessels still remains a challenging task due to the presence of abnormalities, varying size and shape of the vessels, non-uniform illumination and anatomical variability between subjects. In this paper, we carry out a systematic review of the most recent advancements in retinal vessel segmentation methods published in last five years. The objectives of this study are as follows: first, we discuss the most crucial preprocessing steps that are involved in accurate segmentation of vessels. Second, we review most recent state-of-the-art retinal vessel segmentation techniques which are classified into different categories based on their main principle. Third, we quantitatively analyse these methods in terms of its sensitivity, specificity, accuracy, area under the curve and discuss newly introduced performance metrics in current literature. Fourth, we discuss the advantages and limitations of the existing segmentation techniques. Finally, we provide an insight into active problems and possible future directions towards building successful computer-aided diagnostic system.


Deep Learning with Low Precision by Half-Wave Gaussian Quantization

The problem of quantizing the activations of a deep neural network is considered. An examination of the popular binary quantization approach shows that this consists of approximating a classical non-linearity, the hyperbolic tangent, by two functions: a piecewise constant sign function, which is used in feedforward network computations, and a piecewise linear hard tanh function, used in the backpropagation step during network learning. The problem of approximating the widely used ReLU non-linearity is then considered. An half-wave Gaussian quantizer (HWGQ) is proposed for forward approximation and shown to have efficient implementation, by exploiting the statistics of of network activations and batch normalization operations. To overcome the problem of gradient mismatch, due to the use of different forward and backward approximations, several piece-wise backward approximators are then investigated. The implementation of the resulting quantized network, denoted as HWGQ-Net, is shown to achieve much closer performance to full precision networks, such as AlexNet, ResNet, GoogLeNet and VGG-Net, than previously available low-precision networks, with 1-bit binary weights and 2-bit quantized activations.


Resource Allocation Optimization for Device-to-Device Communication Underlaying Cellular Networks

Device-to-Device (D2D) communication will become an important technology in future networks with the increase of the requirements of local communication services. The interference between cellular communication and D2D communication can be coordinated by proper power control and resource allocation. In this paper, we analyze the resource allocation methods for D2D communication underlaying cellular networks. A novel resource allocation method that D2D can reuse the resources of more than one cellular user is proposed. After that, we discuss the selection of the optimal resource allocation method from the proposed method and the conventional methods. Finally, the performance of different methods is evaluated through numerical simulation. The simulation results show that the proposed method is the optimal resource allocation method when the D2D pair locates at the most part of the cell area in both uplink and downlink. The proposed method can improve the sum throughput of cellular communication and D2D communication significantly.