Digital photography with flash and no-flash image pairs

Digital photography has made it possible to quickly and easily take a pair of images of low-light environments: one with flash to capture detail and one without flash to capture ambient illumination. We present a variety of applications that analyze and combine the strengths of such flash/no-flash image pairs. Our applications include denoising and detail transfer (to merge the ambient qualities of the no-flash image with the high-frequency flash detail), white-balancing (to change the color tone of the ambient image), continuous flash (to interactively adjust flash intensity), and red-eye removal (to repair artifacts in the flash image). We demonstrate how these applications can synthesize new images that are of higher quality than either of the originals.


A Fully Projective Formulation to Improve the Accuracy of Lowe's Pose-Estimation Algorithm

Both the original version of David Lowe's innuential and classic algorithm for tracking known objects and a reformulation of it implemented by Ishii et al. rely on (diierent) approximated imaging models. Removing their simplifying assumptions yields a fully pro-jective solution with signiicantly improved accuracy and convergence, and arguably better computation{ time properties.


The Design and Analysis of Computer Algorithms

Addison-Wesley Pub. Co., 1974, , 470 pages. With this text, you gain an understanding of the fundamental concepts of algorithms, the very heart of computer science. It introduces the basic data structures and programming techniques often used in efficient algorithms. Covers use of lists, push-down stacks, queues, trees, and graphs. Later chapters go into sorting, searching and graphing algorithms, the string-matching algorithms, and the Schonhage-Strassen integer-multiplication algorithm. Provides numerous graded exercises at the end of each chapter. 0201000296B04062001.


Derivatives of Regular Expressions

Kleene's regular expressions, which can be used for describing sequential circuits, were defined using three operators (union, concatenation and iterate) on sets of sequences. Word descriptions of problems can be more easily put in the regular expression language if the language is enriched by the inclusion of other logical operations. However, il~ the problem of converting the regular expression description to a state diagram, the existing methods either cannot handle expressions with additional operators, or are made quite complicated by the presence of such operators. In this paper the notion of a derivative of a regular expression is introduced atld the properties of derivatives are discussed. This leads, in a very natural way, to the construction of a state diagram from a regular expression containing any number of logical operators.


Fast Pattern Matching in Strings

テキストの中から与えられたパターンを見つけ出すと いう,いわゆる文字列探索のアルゴリズムを調べようと アルゴリズムの教科書をひもとくと,必ずといってよい ほど,力まかせの方法,Knuth,Morris,Prattによる 方法,BoyerとMooreによる方法 の 3つが紹介され ている.本論文は 2番目の方法-著者の頭文字をとっ て KMPアルゴリズムと省略される について述べて いる. 文字列探索が必要とされる代表的な例に,テキストエ ディタの探索コマンドがある.実際のテキストエディタ の探索コマンドでは,探索する文字列がきわめて短い場 合には力まかせの方法が用いられることがある以外は, 第 3の方法これも著者の頭文字から BMアルゴリズ ムと省略されるが利用される場合が多いようではあ るが,KMPアルゴリズムは BMアルゴリズムと並び,ア イディア 1つで計算量が改善されるというアルゴリズム の面白さを,我々に十分教えてくれる. 本論文は,KMPアルゴリズムの動作を例を用いて解 説することから始まり,具体的なアルゴリズムの提示, アルゴリズムの改良と拡張,計算量に関する理論的な考 察と続く.アルゴリズムを提案する論文としてはここま でで十分なのだが,本論文はさらに,長さが偶数の回文 文字列の話,KMPアルゴリズム発明の歴史,ついには 後書きと称して BMアルゴリズムに関する考察にまで話 が及ぶという,特異とも思える構成となっている. ここで述べられている歴史によると,KMPアルゴリズ ムは,元々はMorris,および,Knuth,Prattチームによ り独立に発明された.Morrisは,1969年に CDC 6400と いう計算機上のテキストエディタを作成していた際,有 限オートマトン理論を利用して,このアルゴリズムの原 形を考えた.またこれとは別に,Knuthは 1970年に, 長さが偶数の回文文字列の 2方向プッシュダウンオート マトンによる認識の理論の調査をしているうちに,その 背後の文字列探索アルゴリズムの存在に気づき,本アル ゴリズムの発明に至ったという.これは,Knuthにとっ て,オートマトン理論が現実のプログラミングの問題の 解決に役に立った初めての経験であったらしい.Robert Sedgewickは,その有名なアルゴリズムの教科書 の文 字列探索の章の中で,このことを「理論的な結果が思い がけず直接実用と結びついた稀な実例である」と指摘し ているが,実際この経験は,Knuthにとってきわめて印 象深いものであったに違いない.そう考えると,上で述 べたような本論文の特異とも思える構成も,著者による 思いの発露と思えて興味深い.ちなみに,本論文が掲載 されている SIAM Journal on Computingの同じ号には, Sedgewickの論文も掲載されている. Morrisがこのアルゴリズムを思いついた背景には, CDC 6400でのバッファリング処理が複雑であるため, テキストの「後戻り」を必要としない方法が求められて いたことがあった.最近のように主記憶をふんだんに使 うことができなかったという不自由さが,新しいアルゴ リズムの発明に結びついたといえよう. 本論文の KMPアルゴリズムの改良の章には,番兵を 用いた高速化の話も述べられている.力ずく的に記述さ れたプログラムが多い現在では,番兵は以前ほどは使わ れなくなってしまったが,本論文が発表された 1970年代 後半では,番兵は美しく高速なプログラムを記述するた めの重宝な技術であった.このようなところにも,時代 の流れを感じさせるものがある. KMPアルゴリズムは,素朴なアルゴリズムから賢いア ルゴリズムをプログラム変換によって導出する例題とし てもよく用いられる.このような題材としても KMPア ルゴリズムが役に立つことになろうとは,著者たちは想 定していなかったであろう.


Representation of Events in Nerve Nets and Finite Automata


Finite Automata and Their Decision Problems

Finite automata are considered in this paper a s instruments for classifying finite tapes. Each onetape automaton defines a set of tapes, a two-tape automaton defines a set of pairs of tapes, et cetera. The structure of the defined sets is studied. Various generalizations of the notion of an automaton are introduced and their relation to the classical automata is determined. Some decision problems concerning automata are shown to be solvable by effective algorithms; others turn out to be unsolvable by algorithms.


Improving Video Game Development: Facilitating Heterogeneous Team Collaboration through Flexible Software Processes

Based on our observations in the Austrian video game software development practices we identified a lack of systematic processes and method support and inefficient collaboration between various involved disciplines, i.e. engineers and artists. Video game development includes heterogeneous disciplines, e.g. creative arts, game and content design, and software. Nevertheless, improvement of team collaboration and process support is an ongoing challenge to enable a comprehensive view on game development projects. Lessons learned from software engineering practices can help game developers to increase game development processes within a heterogeneous environment. Based on a state of the practice survey in the Austrian games industry, this paper presents (a) first results with focus on process and method support and (b) suggests a candidate flexible process approach based on Scrum to improve video game development and team collaboration. The results showed (a) a trend to highly flexible software processes involving various disciplines and (b) identified the suggested flexible process approach as feasible and useful for project application.


Compression of multiple depth maps for IBR

Image-based rendering techniques include those with geometry and those without. Geometric information in the form of a depth map aligned with the image holds a lot of promise for IBR due to the several methods available to capture it. It can improve the quality of generated views using a limited number of views. Compression of light fields or multiple images has attracted a lot of attention in the past. Compression of multiple depth maps of the same scene has not been explored much in the literature. We propose a method for compressing multiple depth maps in this paper using geometric proxy. Different quality of rendering and compression ratio can be achieved by varying different parameters. Experiments show the effectiveness of the compression technique on several model data.


Smart contract-based campus demonstration of decentralized transactive energy auctions

Transactive energy paradigms will enable the exchange of energy from a distributed set of prosumers. While prosumers have access to distributed energy resources, these resources are intermittently available. There is a need for distributed markets to enable the exchange of energy in transactive environments, however, the large number of potential prosumers introduces challenges in the establishment of trust between prosumers. Markets for transactive environments create other challenges, such as establishing clearing prices for energy and exchanging money between prosumers. Blockchains provide a unique technology to address this distributed trust problem through the use of a distributed ledger, cryptocurrencies, and the execution of smart contracts. This paper introduces a smart contract that implements a transactive energy auction that operates without the need for a trusted entitys oversight. The auction mechanism implements a Vickrey second price auction, which guarantees bidders will submit honest bids. The contract is implemented on transactive agents on the WSU campus interacting with a 72kW PV array and the Ethereum blockchain. The contract is then used to execute auctions based on the energy from the the PV array and simulated building loads to demonstrate the auctions operations.


RAMSYS: Resource-Aware Asynchronous Data Transfer with Multicore SYStems

High-speed data transfer is vital to data-intensive computing that often requires moving large data volumes efficiently within a local data center and among geographically dispersed facilities. Effective utilization of the abundant resources in modern multicore environments for data transfer remains a persistent challenge, particularly, for Non-Uniform Memory Access (NUMA) systems wherein the locality of data accessing is an important factor. This requires rethinking how to exploit parallel access to data and to optimize the storage and network I/Os. We address this challenge and present a novel design of asynchronous processing and resource-aware task scheduling in the context of high-throughput data replication. Our software allocates multiple sets of threads to different stages of the processing pipeline, including storage I/O and network communication, based on their capacities. Threads belonging to each stage follow an asynchronous model, and attain high performance via multiple locality-aware and peer-aware mechanisms, such as task grouping, buffer sharing, affinity control and communication protocols. Our design also integrates high performance features to enhance the scalability of data transfer in several scenarios, e.g., file-level sorting, block-level asynchrony, and thread-level pipelining. Our experiments confirm the advantages of our software under different types of workloads and dynamic environments with contention for shared resources, including a 28-160 percent increase in bandwidth for transferring large files, 1.7-66 times speed-up for small files, and up to 108 percent larger throughput for mixed workloads compared with three state of the art alternatives, GridFTP , BBCP and Aspera.


A Review of the Status of Brain Structure Research in Transsexualism

The present review focuses on the brain structure of male-to-female (MtF) and female-to-male (FtM) homosexual transsexuals before and after cross-sex hormone treatment as shown by in vivo neuroimaging techniques. Cortical thickness and diffusion tensor imaging studies suggest that the brain of MtFs presents complex mixtures of masculine, feminine, and demasculinized regions, while FtMs show feminine, masculine, and defeminized regions. Consequently, the specific brain phenotypes proposed for MtFs and FtMs differ from those of both heterosexual males and females. These phenotypes have theoretical implications for brain intersexuality, asymmetry, and body perception in transsexuals as well as for Blanchard's hypothesis on sexual orientation in homosexual MtFs. Falling within the aegis of the neurohormonal theory of sex differences, we hypothesize that cortical differences between homosexual MtFs and FtMs and male and female controls are due to differently timed cortical thinning in different regions for each group. Cross-sex hormone studies have reported marked effects of the treatment on MtF and FtM brains. Their results are used to discuss the early postmortem histological studies of the MtF brain.


Pregnancy-related low back pain.

Pregnancy related low back pain is a common complaint among pregnant women. It can potentially have a negative impact on their quality of life. The aim of this article is to present a current review of the literature concerning this issue.By using PubMed database and low back pain, pelvic girdle pain, pregnancy as keywords, abstracts and original articles in English investigating the diagnosis treatment of back pain during pregnancy were searched and analyzedLow back pain could present as either a pelvic girdle pain between the posterior iliac crest and the gluteal fold or as a lumbar pain over and around the lumbar spine. The source of the pain should be diagnosed and differentiated early.The appropriate treatment aims to reduce the discomfort and the impact on the pregnant womans quality of life. This article reveals the most common risk factors, as well as treatment methods, which may help to alleviate the pain. Some suggestions for additional research are also discussed.


Refining the relationship between personality and subjective well-being.

Understanding subjective well-being (SWB) has historically been a core human endeavor and presently spans fields from management to mental health. Previous meta-analyses have indicated that personality traits are one of the best predictors. Still, these past results indicate only a moderate relationship, weaker than suggested by several lines of reasoning. This may be because of commensurability, where researchers have grouped together substantively disparate measures in their analyses. In this article, the authors review and address this problem directly, focusing on individual measures of personality (e.g., the Neuroticism-Extroversion-Openness Personality Inventory; P. T. Costa & R. R. McCrae, 1992) and categories of SWB (e.g., life satisfaction). In addition, the authors take a multivariate approach, assessing how much variance personality traits account for individually as well as together. Results indicate that different personality and SWB scales can be substantively different and that the relationship between the two is typically much larger (e.g., 4 times) than previous meta-analyses have indicated. Total SWB variance accounted for by personality can reach as high as 39% or 63% disattenuated. These results also speak to meta-analyses in general and the need to account for scale differences once a sufficient research base has been generated.


A Randomized Algorithm for CCA

We present RandomizedCCA, a randomized algorithm for computing canonical analysis, suitable for large datasets stored either out of core or on a distributed file system. Accurate results can be obtained in as few as two data passes, which is relevant for distributed processing frameworks in which iteration is expensive (e.g., Hadoop). The strategy also provides an excellent initializer for standard iterative solutions.


Scene classification based on single-layer SAE and SVM

Scene classification aims to group images into semantic categories. It is a challenging problem in computer vision due to the difficulties of intra-class variability and inter-class similarity. In this paper, a scene classification approach based on single-layer sparse autoencoder (SAE) and support vector machine (SVM) is proposed. This approach consists of two steps: SAE-based feature learning step and SVM-based classification step. In the first step, a single-layer SAE network is constructed and trained by the patches which are sampled randomly from the source images. The feature representation of images is learned by the trained single-layer SAE network. Meanwhile, a pooling operation is used to reduce the dimension of the learned feature vectors. In the second step, in order to improve the classification accuracy, the parameters of SVM are optimized by a particle swarm optimization (PSO) based algorithm. The one-versus-one strategy is employed for the multiple scene classification problem. To show the efficiency of the proposed approach, several public data sets are employed. The results reveal that the proposed approach achieves better classification accuracy than the existing state-of-the-art methods. 2014 Elsevier Ltd. All rights reserved.


An Ultra-Wideband Horizontally Polarized Omnidirectional Circular Connected Vivaldi Antenna Array

An ultra-wideband horizontally polarized omnidirectional antenna is proposed. The connected Vivaldi antenna array mechanism is exploited to achieve the ultra-wide bandwidths of both the impedance and the radiation patterns simultaneously. Sixteen Vivaldi antenna elements are connected with each other and distributed along a circular substrate to form the proposed antenna. The designed antenna exhibits a relative impedance bandwidth of 159.97% (1.28–11.51 GHz) for <inline-formula> <tex-math notation="LaTeX">$\vert S_{11}\vert <-10$ </tex-math></inline-formula> dB. Meanwhile, uniform omnidirectional radiation patterns with a gain variation of less than 1.5 dB have been obtained within the frequency band of 1.25–7.6 GHz (142.73%).


A Social-Network-Based Cryptocurrency Wallet-Management Scheme

Effective cryptocurrency key management has become an urgent requirement for modern cryptocurrency. Although a large body of cryptocurrency wallet-management schemes has been proposed, they are mostly constructed for specific application scenarios and often suffer from weak security. In this paper, we propose a more effective, usable, and secure cryptocurrency wallet-management system based on semi-trusted social networks, therein allowing users to collaborate with involved parties to achieve some powerful functions and recovery under certain circumstances. Furthermore, we employ an identity-based hierarchical key-insulated encryption scheme to achieve time-sharing authorization and present a semi-trusted portable social-network-based wallet-management scheme that provides the features of security-enhanced storage, portable login on different devices, no-password authentication, flexible key delegation, and so on. The performance analysis shows that our proposed schemes require minimal additional overhead and have low time delays, making them sufficiently efficient for real-world deployment.


A Framework for Analysis of Road Accidents

The road accident data analysis use data mining and machine learning techniques, focusing on identifying factors that affect the severity of an accident. There are a variety of reasons that contribute to accidents. Some of them are internal to the driver but many are external. For example, adverse weather conditions like fog, rainfall or snowfall cause partial visibility and it may become difficult as well as risky to drive on such roads. It is expected that the findings from this paper would help civic authorities to take proactive actions on likely crash prone weather and traffic conditions.


Low delay MPEG DASH streaming over the WebRTC data channel

Dynamic Adaptive Streaming over HTTP (MPEG-DASH) is becoming the de-facto data format and streaming solution for over-the-top (OTT) video on-demand streaming. The underlying HTTP transport has its limitations and known penalties in slow start, under-utilization, and other inefficiency. An alternate approach to DASH is the new transport schemes like HTTP/2.0 and WebSocket. In this work, we explore WebRTC as a streaming transport to carry DASH data and demonstrate that it is competitive in serving low delay streaming applications with fast channel switching, with proper video data coding and streaming signaling/control solutions.


Single Image 3D Interpreter Network

Understanding 3D object structure from a single image is an important but difficult task in computer vision, mostly due to the lack of 3D object annotations in real images. Previous work tackles this problem by either solving an optimization task given 2D keypoint positions, or training on synthetic data with ground truth 3D information. In this work, we propose 3D INterpreter Network (3D-INN), an endto-end framework which sequentially estimates 2D keypoint heatmaps and 3D object structure, trained on both real 2D-annotated images and synthetic 3D data. This is made possible mainly by two technical innovations. First, we propose a Projection Layer, which projects estimated 3D structure to 2D space, so that 3D-INN can be trained to predict 3D structural parameters supervised by 2D annotations on real images. Second, heatmaps of keypoints serve as an intermediate representation connecting real and synthetic data, enabling 3D-INN to benefit from the variation and abundance of synthetic 3D objects, without suffering from the difference between the statistics of real and synthesized images due to imperfect rendering. The network achieves state-of-the-art performance on both 2D keypoint estimation and 3D structure recovery. We also show that the recovered 3D information can be used in other vision applications, such as 3D rendering and image retrieval.


Real time face recognition engine using compact features for electronics key

In this research, a design and implementation of real-time face recognition engine using compact features vector extracted by linear binary pattern (LBP) and zoned discrete cosine transforms (DCT) analysis is proposed for electronics key. The function of LBP is to normalize the lighting variations of the input face image and the function of zoned DCT is to define the local descriptors of the face image. In order to get more compact features vector, the predictive LDA is employed for dimensional reduction. The aims of this research is to develop fast and strong face recognition that can be implemented for electronic key which will be implemented for substituting current security system (PIN and password). In addition, the recognition engine is also designed for real time face recognition which can work on hardware having limited resources such as Intel atom computer, raspberry pi, and android smart phone. The experimental results show that the proposed engine provide high enough recognition rate, and small false rejection rate (FRR) and false acceptance rate (FAR). In addition, the engine needs short processing time.


Web page revisitation revisited: implications of a long-term click-stream study of browser usage

This paper presents results of an extensive long-term click-stream study of Web browser usage. Focusing on character and challenges of page revisitation, previous findings from seven to thirteen years ago are updated. The term page re-visit had to be differentiated, since the recurrence rate--the key measure for the share of page revisits--turns out to strongly depend on interpretation. We identify different types of revisitation that allow assessing the quality of current user support and developing concepts for new tools.
 Individual navigation strategies differ dramatically and are strongly influenced by personal habits and type of site visited. Based on user action logs and interviews, we distinguished short-term revisits (backtrack or undo) from medium-term (re-utilize or observe) and long-term revisits (rediscover). We analyze current problems and provide suggestions for improving support for different revisitation types.


Streaming End-to-end Speech Recognition For Mobile Devices

End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition. E2E models, however, present numerous challenges: In order to be truly useful, such models must decode speech utterances in a streaming fashion, in real time; they must be robust to the long tail of use cases; they must be able to leverage user-specific context (e.g., contact lists); and above all, they must be extremely accurate. In this work, we describe our efforts at building an E2E speech recognizer using a recurrent neural network transducer. In experimental evaluations, we find that the proposed approach can outperform a conventional CTC-based model in terms of both latency and accuracy in a number of evaluation categories.


Human facial illustrations: Creation and psychophysical evaluation

We present a method for creating black-and-white illustrations from photographs of human faces. In addition an interactive technique is demonstrated for deforming these black-and-white facial illustrations to create caricatures which highlight and exaggerate representative facial features. We evaluate the effectiveness of the resulting images through psychophysical studies to assess accuracy and speed in both recognition and learning tasks. These studies show that the facial illustrations and caricatures generated using our techniques are as effective as photographs in recognition tasks. For the learning task we find that illustrations are learned two times faster than photographs and caricatures are learned one and a half times faster than photographs. Because our techniques produce images that are effective at communicating complex information, they are useful in a number of potential applications, ranging from entertainment and education to low bandwidth telecommunications and psychology research.


Information-centric sensor networks for cognitive IoT: an overview

Information-centric sensor networks (ICSNs) are a paradigm of wireless sensor networks that focus on delivering information from the network based on user requirements, rather than serving as a point-to-point data communication network. Introducing learning in such networks can help to dynamically identify good data delivery paths by correlating past actions and results, make intelligent adaptations to improve the network lifetime, and also improve the quality of information delivered by the network to the user. However, there are several factors and limitations that must be considered while choosing a learning strategy. In this paper, we identify some of these factors and explore various learning techniques that have been applied to sensor networks and other applications with similar requirements in the past. We provide our recommendation on the learning strategy based on how well it complements the needs of ICSNs, while keeping in mind the cost, computation, and operational overhead limitations.


3-D Path Planning and Target Trajectory Prediction for the Oxford Aerial Tracking System

For the Oxford Aerial Tracking System (OATS) we are developing a robot helicopter that can track moving ground objects. Here we describe algorithms for the device to perform path planning and trajectory prediction. The path planner uses superquadratic potential fields and incorporates a height change mechanism that is triggered where necessary and in order to avoid local minima traps. The goal of the trajectory prediction system is to reliably predict the target trajectory during occlusion caused by ground obstacles. For this we use two artificial neural networks in parallel whose retraining is automatically triggered if major changes in the target behaviour pattern are detected. Simulation results for both systems are presented.


Convolutional Neural Network-Based Human Detection in Nighttime Images Using Visible Light Camera Sensors

Because intelligent surveillance systems have recently undergone rapid growth, research on accurately detecting humans in videos captured at a long distance is growing in importance. The existing research using visible light cameras has mainly focused on methods of human detection for daytime hours when there is outside light, but human detection during nighttime hours when there is no outside light is difficult. Thus, methods that employ additional near-infrared (NIR) illuminators and NIR cameras or thermal cameras have been used. However, in the case of NIR illuminators, there are limitations in terms of the illumination angle and distance. There are also difficulties because the illuminator power must be adaptively adjusted depending on whether the object is close or far away. In the case of thermal cameras, their cost is still high, which makes it difficult to install and use them in a variety of places. Because of this, research has been conducted on nighttime human detection using visible light cameras, but this has focused on objects at a short distance in an indoor environment or the use of video-based methods to capture multiple images and process them, which causes problems related to the increase in the processing time. To resolve these problems, this paper presents a method that uses a single image captured at night on a visible light camera to detect humans in a variety of environments based on a convolutional neural network. Experimental results using a self-constructed Dongguk night-time human detection database (DNHD-DB1) and two open databases (Korea advanced institute of science and technology (KAIST) and computer vision center (CVC) databases), as well as high-accuracy human detection in a variety of environments, show that the method has excellent performance compared to existing methods.


Understanding mobile SNS continuance usage in China from the perspectives of social influence and privacy concern

Retaining users and facilitating continuance usage are crucial to the success of mobile social network services (SNS). This research examines the continuance usage of mobile SNS in China by integrating both the perspectives of social influence and privacy concern. Social influence includes three processes: compliance, identification and internalization, which are respectively represented by subjective norm, social identity, and group norm. The results indicate that these three factors and privacy concern have significant effects on continuance usage. The results suggest that service providers should address the issues of social influence and privacy concern to encourage mobile SNS continuance usage. 2014 Elsevier Ltd. All rights reserved.


Chaffing and Winnowing: Confidentiality without Encryption

• Encryption: transforming the message to a ciphertext such that an adversary who overhears the ciphertext can not determine the message sent. The legitimate receiver possesses a secret decryption key that allows him to reverse the encryption transformation and retrieve the message. The sender may have used the same key to encrypt the message (with symmetric encryption schemes) or used a different, but related key (with public-key schemes). DES and RSA are familiar examples of encryption schemes.


Simulated Annealing Algorithm for 2D Image Compression

In this paper a new sign coding approximation method for the wavelet coefficients in a 2D image codec based on a simulated annealing metaheuristic is presented. The efficiency of the proposed algorithm versus a genetic algorithm using benchmarks of Kodak is compared and showing that the proposed sign prediction algorithm is efficient and provides a significant reduction of wavelet coefficients sign information in the final bit-stream. The results show that, by including sign coding capabilities to a nonembedded encoder, the sign compression gain is up to 17.35%, being the rate-distortion (R/D) performance improvement up to 0.25 dB.


The Big-Data-Driven Intelligent Wireless Network: Architecture, Use Cases, Solutions, and Future Trends

The concept of using big data (BD) for wireless communication network optimization is no longer new. However, previous work has primarily focused on long-term policies in the network, such as network planning and management. Apart from this, the source of the data collected for analysis/model training is mostly limited to the core network (CN). In this article, we introduce a novel data-driven intelligent radio access network (RAN) architecture that is hierarchical and distributed and operates in real time. We also identify the required data and respective workflows that facilitate intelligent network optimizations. It is our strong belief that the wireless BD (WBD) and machine-learning/artificial-intelligence (AI)-based methodology applies to all layers of the communication system. To demonstrate the superior performance gains of our proposed methodology, two use cases are analyzed with system-level simulations; one is the neural-network-aided optimization for Transmission Control Protocol (TCP), and the other is prediction-based proactive mobility management.


A deep reinforcement learning based framework for power-efficient resource allocation in cloud RANs

Cloud Radio Access Networks (RANs) have become a key enabling technique for the next generation (5G) wireless communications, which can meet requirements of massively growing wireless data traffic. However, resource allocation in cloud RANs still needs to be further improved in order to reach the objective of minimizing power consumption and meeting demands of wireless users over a long operational period. Inspired by the success of Deep Reinforcement Learning (DRL) on solving complicated control problems, we present a novel DRL-based framework for power-efficient resource allocation in cloud RANs. Specifically, we define the state space, action space and reward function for the DRL agent, apply a Deep Neural Network (DNN) to approximate the action-value function, and formally formulate the resource allocation problem (in each decision epoch) as a convex optimization problem. We evaluate the performance of the proposed framework by comparing it with two widely-used baselines via simulation. The simulation results show it can achieve significant power savings while meeting user demands, and it can well handle highly dynamic cases.


Ternary CAM Power and Delay Model: Extensions and Uses

Applications in computer networks often require high throughput access to large data structures for lookup and classification. While advanced algorithms exist to speed these search primitives on network processors and even custom application-specific integrated circuits (ASICs), achieving tight bounds on worst case performance with standard memories often requires a very careful analysis of all possible access patterns. An alternative, and often times more simple solution, is possible if a ternary CAM (TCAM) is used to perform a fully parallel search across the entire data set. Unfortunately, this parallelism means that large portions of the chip are switching during each cycle, causing large amounts of power to be consumed. While researchers at all levels of design (from algorithms to circuits) have begun to explore new ways of managing the power consumption, quantifying design alternatives is difficult due to a lack of available models. In this paper, we examine the structure of a modern TCAM and present a simple, yet accurate, power and delay model. We present techniques to estimate the dynamic power consumption and leakage power of a TCAM structure and validate the model using a combination of industrial TCAM datasheets and prior published works. Such a model is a critical first step in bridging the intellectual divide between circuit-level and algorithm-level optimizations. To demonstrate the utility of our model, we present an extensive analysis of the model by varying various architectural parameters and describe how our model can be easily extended to handle several circuit optimizations in the TCAM structure. In addition, we present a comparative study of SRAM and TCAM energy consumption to directly quantify the many design options which will be very useful for network designers to explore various power management schemes.


Socially compliant mobile robot navigation via inverse reinforcement learning

Mobile robots are increasingly populating our human environments. To interact with humans in a socially compliant way, these robots need to understand and comply with mutually accepted rules. In this paper, we present a novel approach to model the cooperative navigation behavior of humans. We model their behavior in terms of a mixture distribution that captures both the discrete navigation decisions, such as going left or going right, as well as the natural variance of human trajectories. Our approach learns the model parameters of this distribution that match, in expectation, the observed behavior in terms of user-defined features. To compute the feature expectations over the resulting high-dimensional continuous distributions, we use Hamiltonian Markov chain Monte Carlo sampling. Furthermore, we rely on a Voronoi graph of the environment to efficiently explore the space of trajectories from the robot’s current position to its target position. Using the proposed model, our method is able to imitate the behavior of pedestrians or, alternatively, to replicate a specific behavior that was taught by tele-operation in the target environment of the robot. We implemented our approach on a real mobile robot and demonstrate that it is able to successfully navigate in an office environment in the presence of humans. An extensive set of experiments suggests that our technique outperforms state-of-the-art methods to model the behavior of pedestrians, which makes it also applicable to fields such as behavioral science or computer graphics.


A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights

We derive a second-order ordinary differential equation (ODE), which is the limit of Nesterov’s accelerated gradient method. This ODE exhibits approximate equivalence to Nesterov’s scheme and thus can serve as a tool for analysis. We show that the continuous time ODE allows for a better understanding of Nesterov’s scheme. As a byproduct, we obtain a family of schemes with similar convergence rates. The ODE interpretation also suggests restarting Nesterov’s scheme leading to an algorithm, which can be rigorously proven to converge at a linear rate whenever the objective is strongly convex.


Global Aesthetics Consensus: Avoidance and Management of Complications from Hyaluronic Acid Fillers—Evidence- and Opinion-Based Review and Consensus Recommendations

BACKGROUND
Although the safety profile of hyaluronic acid fillers is favorable, adverse reactions can occur. Clinicians and patients can benefit from ongoing guidance on adverse reactions to hyaluronic acid fillers and their management.

METHODS
A multinational, multidisciplinary group of experts in cosmetic medicine convened the Global Aesthetics Consensus Group to review the properties and clinical uses of Hylacross and Vycross hyaluronic acid products and develop updated consensus recommendations for early and late complications associated with hyaluronic acid fillers.

RESULTS
The consensus panel provided specific recommendations focusing on early and late complications of hyaluronic acid fillers and their management. The impact of patient-, product-, and technique-related factors on such reactions was described. Most of these were noted to be mild and transient. Serious adverse events are rare. Early adverse reactions to hyaluronic acid fillers include vascular infarction and compromise; inflammatory reactions; injection-related events; and inappropriate placement of filler material. Among late reactions are nodules, granulomas, and skin discoloration. Most adverse events can be avoided with proper planning and technique. Detailed understanding of facial anatomy, proper patient and product selection, and appropriate technique can further reduce the risks. Should adverse reactions occur, the clinician must be prepared and have tools available for effective treatment.

CONCLUSIONS
Adverse reactions with hyaluronic acid fillers are uncommon. Clinicians should take steps to further reduce the risk and be prepared to treat any complications that arise.


Let a Thousand Flowers Bloom? An Early Look at Large Numbers of Software App Developers and Patterns of Innovation

It is often presumed that bringing more members on board a multi-sided platform will stimulate value creation. Here I study the thousands of software producers building applications (“apps”) on leading handheld computer platforms (1999-2004). Consistent with past theory, I find a lock-step link between numbers of producers and varieties of software titles. The narrow, unchanging scope of producers and a series of other patterns are consistent with a pronounced role of specialization and heterogeneity of producers. I also find that while adding producers making different types of software stimulated investment incentives, consistent with network effects, adding producers making similar software crowdedout innovation incentives. The latter of these two effects dominates in this context. The patterns also indicate nonrandom generation and sorting of producers onto platforms, with later cohorts generating systematically less compelling software than earlier cohorts of entrants. Overall, added producers led innovation to become more dependent on population-level diversity, variation and experimentation—while drawing less on the heroic efforts and investments of any one individual innovator.


Simultaneous video defogging and stereo reconstruction

We present a method to jointly estimate scene depth and recover the clear latent image from a foggy video sequence. In our formulation, the depth cues from stereo matching and fog information reinforce each other, and produce superior results than conventional stereo or defogging algorithms. We first improve the photo-consistency term to explicitly model the appearance change due to the scattering effects. The prior matting Laplacian constraint on fog transmission imposes a detail-preserving smoothness constraint on the scene depth. We further enforce the ordering consistency between scene depth and fog transmission at neighboring points. These novel constraints are formulated together in an MRF framework, which is optimized iteratively by introducing auxiliary variables. The experiment results on real videos demonstrate the strength of our method.


Xbase: implementing domain-specific languages for Java

Xtext is an open-source framework for implementing external, textual domain-specific languages (DSLs). So far, most DSLs implemented with Xtext and similar tools focus on structural aspects such as service specifications and entities. Because behavioral aspects are significantly more complicated to implement, they are often delegated to general-purpose programming languages. This approach introduces complex integration patterns and the DSL's high level of abstraction is compromised.
 We present Xbase as part of Xtext, an expression language that can be reused via language inheritance in any DSL implementation based on Xtext. Xbase expressions provide both control structures and program expressions in a uniform way. Xbase is statically typed and tightly integrated with the Java type system. Languages extending Xbase inherit the syntax of a Java-like expression language as well as language infrastructure components, including a parser, an unparser, a linker, a compiler and an interpreter. Furthermore, the framework provides integration into the Eclipse IDE including debug and refactoring support.
 The application of Xbase is presented by means of a domain model language which serves as a tutorial example and by the implementation of the programming language Xtend. Xtend is a functional and object-oriented general purpose language for the Java Virtual Machine (JVM). It is built on top of Xbase which is the reusable expression language that is the foundation of Xtend.


Impulsive corporal punishment by mothers and antisocial behavior and impulsiveness of children.

This study tested the hypothesis that corporal punishment (CP), such as spanking or slapping a child for purposes of correcting misbehavior, is associated with antisocial behavior (ASB) and impulsiveness by the child. The data were obtained through interviews with a probability sample of 933 mothers of children age 2-14 in two small American cities. Analyses of variance found that the more CP experienced by the child, the greater the tendency for the child to engage in ASB and to act impulsively. These relationships hold even after controlling for family socioeconomic status, the age and sex of the child, nurturance by the mother, and the level of noncorporal interventions by the mother. There were also significant interaction effects of CP with impulsiveness by the mother. When CP was carried out impulsively, it was most strongly related to child impulsiveness and ASB; when CP was done when the mother was under control, the relationship to child behavior problems was reduced but still present. In view of the fact that there is a high risk of losing control when engaged in CP, even by parents who are not usually impulsive, and the fact that impulsive CP is so strongly associated with child behavior problems, the results of this study suggest that CP is an important risk factor for children developing a pattern of impulsive and antisocial behavior which, in turn, may contribute to the level of violence and other crime in society.


Design and simulation of self-tuning PID-type fuzzy adaptive control for an expert HVAC system

The modelling, numerical simulation and intelligent control of an expert HVAC (heating, ventilating and air-conditioning) system having two different zones with variable flow-rate were performed by considering the ambient temperature in this study. The sub-models of the system were obtained by deriving heat transfer equations of heat loss of two zones by conduction and convection, cooling unit and fan. All models of the variable flow-rate HVAC system were generated by using MATLAB/SIMULINK, and proportional-integral-derivative (PID) parameters were obtained by using Fuzzy sets. For comfortable of people the temperatures of the two different zones were decreased to 5 C from the ambient temperature. The successful results were obtained by applying self-tuning proportional-integral-derivative (PID)-type fuzzy adaptive controller if comparing with the fuzzy PD-type and the classical PID controller. The obtained results were presented in a graphical form. 2008 Elsevier Ltd. All rights reserved.


A Procedure for Measuring Microplastics using Pressurized Fluid Extraction.

A method based on pressurized fluid extraction (PFE) was developed for measuring microplastics in environmental samples. This method can address some limitations of the current microplastic methods and provide laboratories with a simple analytical method for quantifying common microplastics in a range of environmental samples. The method was initially developed by recovering 101% to 111% of spiked plastics on glass beads and was then applied to a composted municipal waste sample with spike recoveries ranging from 85% to 94%. The results from municipal waste samples and soil samples collected from an industrial area demonstrated that the method is a promising alternative for determining the concentration and identity of microplastics in environmental samples.


DCFont: an end-to-end deep chinese font generation system

Building a complete personalized Chinese font library for an ordinary person is a tough task due to the existence of huge amounts of characters with complicated structures. Yet, existing automatic font generation methods still have many drawbacks. To address the problem, this paper proposes an end-to-end learning system, DCFont, to automatically generate the whole GB2312 font library that consists of 6763 Chinese characters from a small number (e.g., 775) of characters written by the user. Our system has two major advantages. On the one hand, the system works in an end-to-end manner, which means that human interventions during offline training and online generating periods are not required. On the other hand, a novel deep neural network architecture is designed to solve the font feature reconstruction and handwriting synthesis problems through adversarial training, which requires fewer input data but obtains more realistic and high-quality synthesis results compared to other deep learning based approaches. Experimental results verify the superiority of our method against the state of the art.


Cyber Security Resource Allocation: A Markov Decision Process Approach

An effective defense-in-depth in cyber security applies multiple layers of defense throughout a system. The goalis to defend a system against cyber-attack using severalindependent methods. Therefore, a cyber-attack that is able to penetrate one layer of defense may be unsuccessful in other layers. Common layers of cyber defense include: attack avoidance, prevention, detection, survivability and recovery. It follows that in security-conscious organizations, the cyber security investment portfolio is divided into different layers of defense. For instance, a two-way division is agility and recovery. Cyber agility pursues attack avoidance techniques such that cyber-attacks are rendered as ineffective, whereas cyber recovery seeks to fight-through successful attacks. We show that even when the primary focus is on the agility of a system, recovery should be an essential point during implementation because the frequency of attacks will degrade the system and a quick and fast recovery is necessary. However, there is not yet an optimum mechanism to allocate limited cyber security resourcesinto the different layers. We propose an approach using theMarkov Decision Process (MDP) framework for resourcesallocation between the two end layers: agility and recovery.


Protein folding and misfolding

The manner in which a newly synthesized chain of amino acids transforms itself into a perfectly folded protein depends both on the intrinsic properties of the amino-acid sequence and on multiple contributing influences from the crowded cellular milieu. Folding and unfolding are crucial ways of regulating biological activity and targeting proteins to different cellular locations. Aggregation of misfolded proteins that escape the cellular quality-control mechanisms is a common feature of a wide range of highly debilitating and increasingly prevalent diseases.


Machine Translation Approaches: Issues and Challenges

In the modern world, there is an increased need for language translations owing to the fact that language is an effective medium of communication. The demand for translation has become more in recent years due to increase in the exchange of information between various regions using different regional languages. Accessibility to web document in other languages, for instance, has been a concern for information Professionals. Machine translation (MT), a subfield under Artificial Intelligence, is the application of computers to the task of translating texts from one natural (human) language to another. Many approaches have been used in the recent times to develop an MT system. Each of these approaches has its own advantages and challenges. This paper takes a look at these approaches with the few of identifying their individual features, challenges and the best domain they are best suited to.


Gay and bisexual identity development among female-to-male transsexuals in North America: emergence of a transgender sexuality.

We studied a North American sample of female-to-male (FtM) transsexuals sexually attracted to men, aiming to understand their identity and sexuality in the context of a culture of transgender empowerment. Sex-reassigned FtM transsexuals, 18 years or older and attracted to men, were recruited via an FtM community conference and listserv. Participants (N = 25) responded to open-ended questions about identity development, sexual behavior, and social support. Data were analyzed by content analysis. Scores for sexual identity, self esteem, sexual functioning, and psychological adjustment were compared to those of a comparison group (N = 76 nontransgender gay and bisexual men). Of the 25 FtMs, 15 (60%) identified as gay, 8 (32%) as bisexual, and 2 (8%) as queer. All were comfortable with their gender identity and sexual orientation. The FtM group was more bisexual than the nontransgender gay and bisexual controls. No significant group differences were found in self esteem, sexual satisfaction, or psychological adjustment. For some FtMs, sexual attractions and experiences with men affirmed their gender identity; for others, self-acceptance of a transgender identity facilitated actualization of their attractions toward men. Most were "out" as transgender among friends and family, but not on the job or within the gay community. Disclosure and acceptance of their homosexuality was limited. The sexual identity of gay and bisexual FtMs appears to mirror the developmental process for nontransgender homosexual men and women in several ways; however, participants also had experiences unique to being both transgender and gay/bisexual. This signals the emergence of a transgender sexuality.


Autonomous Vehicle-target Assignment: a Game-theoretical Formulation

We consider an autonomous vehicle-target assignment problem where a group of vehicles are expected to optimally assign themselves to a set of targets. We introduce a gametheoretical formulation of the problem in which the vehicles are viewed as self-interested decision makers. Thus, we seek the optimization of a global utility function through autonomous vehicles that are capable of making individually rational decisions to optimize their own utility functions. The first important aspect of the problem is to choose the utility functions of the vehicles in such a way that the objectives of the vehicles are localized to each vehicle yet aligned with a global utility function. The second important aspect of the problem is to equip the vehicles with an appropriate negotiation mechanism by which each vehicle pursues the optimization of its own utility function. We present several design procedures and accompanying caveats for vehicle utility design. We present two new negotiation mechanisms, namely, “generalized regret monitoring with fading memory and inertia” and “selective spatial adaptive play,” and provide accompanying proofs of their convergence. Finally, we present simulations that illustrate how vehicle negotiations can consistently lead to near-optimal assignments provided that the utilities of the vehicles are designed appropriately. DOI: 10.1115/1.2766722


Information Systems and Healthcare XVI: Physician Adoption of Electronic Medical Records: Applying the UTAUT Model in a Healthcare Context

This study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) to the phenomenon of physician adoption of electronic medical records (EMR) technology. UTAUT integrates eight theories of individual acceptance into one comprehensive model designed to assist in understanding what factors either enable or hinder technology adoption and use. As such, it provides a useful lens through which to view what is currently taking place in the healthcare industry regarding EMR adoption. This is mutually beneficial to both the healthcare and MIS communities, as UTAUT offers valuable practical insight to the healthcare industry in explaining why EMR technology has not been more widely adopted as well as what prescriptions may facilitate future adoption, while offering the MIS community the opportunity to strengthen existing theory through an illustration of its application.


SiGe BiCMOS and eWLB packaging technologies for automotive radar solutions

In this paper the evolution of silicon based automotive radar for the 76-81 GHz range is described. Starting with SiGe bare-die chips in 2009, today packaged MMICs are available for low-cost radar applications. Future SiGe BiCMOS technology will enable highly integrated single chip radars with superior performance at lower power consumption. This will pave the way for automotive radar safety for everyone and will be an important step towards autonomous driving.


W-band scalable phased arrays for imaging and communications

This article discusses the benefits and challenges associated with the design of multi-function scalable phased arrays at millimeter wave frequencies. First, applications for phased arrays with tens to hundreds of elements are discussed. Existing solutions for scaling silicon-based phased arrays from microwave to terahertz frequencies are reviewed. The challenges and tradeoffs associated with multiple integration options for W-band phased arrays are analyzed, with special consideration given to packaging and antenna performance. Finally, a solution based on SiGe ICs and organic packages for a 64-element dual-polarized 94 GHz phased array is described, along with associated measurement results.


A 77GHz automotive radar receiver in a wafer level package

In this paper, a 77-GHz radar receiver is presented, which comes in a wafer level package and thus eliminates the need for wire bonding yielding significant cost reduction. The high integration level available in the productive Silicon-Germanium (SiGe) technology used in this paper allows for implementation of in-system monitoring of the receiver conversion parameters. This facilitates the realization of ISO 26262 compliant radar sensors for automotive safety applications.


A compact 4-chip package with 64 embedded dual-polarization antennas for W-band phased-array transceivers

A fully-integrated antenna-in-package (AiP) solution for W-band scalable phased-array systems is demonstrated. We present a fully operational compact W-band transceiver package with 64 dual-polarization antennas embedded in a multilayer organic substrate. This package has 12 metal layers, a size of 16.2 mm × 16.2 mm, and 292 ball-grid-array (BGA) pins with 0.4 mm pitch. Four silicon-germanium (SiGe) transceiver ICs are flip-chip attached to the package. Extensive full-wave electromagnetic simulation and radiation pattern measurements have been performed to optimize the antenna performance in the package environment, with excellent model-to-hardware correlation achieved. Enabled by detailed circuit-package co-design, a half-wavelength spacing, i.e., 1.6 mm at 94 GHz, is maintained between adjacent antenna elements to support array scalability at both the package and board level. Effective isotropic radiated power (EIRP) and radiation patterns are also measured to demonstrate the 64-element spatial power combining.


Low-Cost High-Gain and Broadband Substrate- Integrated-Waveguide-Fed Patch Antenna Array for 60-GHz Band

A low-cost high-gain and broadband substrate integrated waveguide (SIW)-fed patch antenna array is demonstrated at the 60-GHz band. A single-layered SIW feeding network with wideband T-junctions and wideband high-gain cavity-backed patch antennas are employed to achieve high gain and wideband performance simultaneously. Although the proposed antenna array has a multilayered structure, it can be fabricated by conventional low-cost single-layered printed circuit board (PCB) technology and then realized by stacking and fixing all of single layers together. The simulated and measured impedance bandwidths of a 4 × 4 antenna array are 27.5% and 22.6% for 10 dB. The discrepancy between simulation and measurement is analyzed. A gain up to 19.6 dBi, and symmetrical unidirectional radiation patterns with low cross polarization are also achieved. With advantages of low fabrication cost and good performances, the proposed antenna array is a promising candidate for millimeter-wave wireless communication systems.


Design and Sensorless Control of a Novel Axial-Flux Permanent Magnet Machine for In-Wheel Applications

This paper proposes an axial-flux-modulation permanent magnet (PM) machine and its sensorless control strategy for in-wheel application in electrical vehicles. A Vernier structure is integrated with the axial-flux PM machine to include the magnetic gear effect and improve output torque. The sensorless control strategy of the proposed machine, including initial rotor position estimation and rotating position estimation, is proposed for flux-modulation motors in this paper. The initial rotor position estimation is based on the technique of rectangular pulse voltage injection and the rotating rotor position estimation is based on the sliding mode observer (SMO). The saturation effect on inductances, which is the theoretical basis of the rectangular pulse voltage injection, makes the stator parameter variation in different loads and affects the SMO estimation. To overcome this problem, a novel online parameter self-adjustment procedure for the SMO is introduced. The machine design and its sensorless control performance are verified by simulation and prototype experiments.


Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding

In 1994, Karl Sims' evolved virtual creatures showed the potential of evolutionary algorithms to produce natural, complex morphologies and behaviors [30]. One might assume that nearly 20 years of improvements in computational speed and evolutionary algorithms would produce far more impressive organisms, yet the creatures evolved in the field of artificial life today are not obviously more complex, natural, or intelligent. Fig. 2 demonstrates an example of similar complexity in robots evolved 17 years apart.


Statistical limits of spiked tensor models

We study the statistical limits of both detecting and estimating a rank-one deformation of a symmetric random Gaussian tensor. We establish upper and lower bounds on the critical signal-to-noise ratio, under a variety of priors for the planted vector: (i) a uniformly sampled unit vector, (ii) i.i.d. ±1 entries, and (iii) a sparse vector where a constant fraction ρ of entries are i.i.d. ±1 and the rest are zero. For each of these cases, our upper and lower bounds match up to a 1+o(1) factor as the order d of the tensor becomes large. For sparse signals (iii), our bounds are also asymptotically tight in the sparse limit ρ → 0 for any fixed d (including the d = 2 case of sparse PCA). Our upper bounds for (i) demonstrate a phenomenon reminiscent of the work of Baik, Ben Arous and Péché: an ‘eigenvalue’ of a perturbed tensor emerges from the bulk at a strictly lower signal-to-noise ratio than when the perturbation itself exceeds the bulk; we quantify the size of this effect. We also provide some general results for larger classes of priors. In particular, the large d asymptotics of the threshold location differs between problems with discrete priors versus continuous priors. Finally, for priors (i) and (ii) we carry out the replica prediction from statistical physics, which is conjectured to give the exact information-theoretic threshold for any fixed d. Of independent interest, we introduce a new improvement to the second moment method for contiguity, on which our lower bounds are based. Our technique conditions away from rare ‘bad’ events that depend on interactions between the signal and noise. This enables us to close √ 2-factor gaps present in several previous works.


Mobile application testing matrix and challenges

The adoption of smart phones and the usages of mobile applications are increasing rapidly. Consequently, within limited time-range, mobile Internet usages have managed to take over the desktop usages particularly since the first smart phone-touched application released by iPhone in 2007. This paper is proposed to provide solution and answer the most demandable questions related to mobile application automated and manual testing limitations. Moreover, Mobile application testing requires agility and physically testing. Agile testing is to detect bugs through automated tools, whereas the compatibility testing is more to ensure that the apps operates on mobile OS (Operation Systems) as well as on the different real devices. Moreover, we have managed to answer automated or manual questions through two mobile application case studies MES (Mobile Exam System) and MLM (Mobile Lab Mate) by creating test scripts for both case studies and our experiment results have been discussed and evaluated on whether to adopt test on real devices or on emulators? In addition to this, we have introduced new mobile application testing matrix for the testers and some enterprises to obtain knowledge from.


X-ray: Automating Root-Cause Diagnosis of Performance Anomalies in Production Software

Troubleshooting the performance of production software is challenging. Most existing tools, such as profiling, tracing, and logging systems, reveal what events occurred during performance anomalies. However, users of such tools must infer why these events occurred; e.g., that their execution was due to a root cause such as a specific input request or configuration setting. Such inference often requires source code and detailed application knowledge that is beyond system administrators and end users. This paper introduces performance summarization, a technique for automatically diagnosing the root causes of performance problems. Performance summarization instruments binaries as applications execute. It first attributes performance costs to each basic block. It then uses dynamic information flow tracking to estimate the likelihood that a block was executed due to each potential root cause. Finally, it summarizes the overall cost of each potential root cause by summing the per-block cost multiplied by the cause-specific likelihood over all basic blocks. Performance summarization can also be performed differentially to explain performance differences between two similar activities. X-ray is a tool that implements performance summarization. Our results show that X-ray accurately diagnoses 17 performance issues in Apache, lighttpd, Postfix, and PostgreSQL, while adding 2.3% average runtime overhead.


An Improving Deception Detection Method in Computer-Mediated Communication

Online deception is disrupting our daily life, organizational process, and even national security. Existing deception detection approaches followed a traditional paradigm by using a set of cues as antecedents, and used a variety of data sets and common classification models to detect deception, which were demonstrated to be an accurate technique, but the previous results also showed the necessity to expand the deception feature set in order to improve the accuracy. In our study, we propose a novel feature selection method of the combination of CHI statistics and hypothesis testing, and achieve the accuracy level of 86% and F-measure of 0.84 by using the novel feature sets and SVM classification models, which exceeds the previous experiment results.


When learning about the real world is better done virtually : A study of substituting computer simulations for laboratory equipment

This paper examines the effects of substituting a computer simulation for real laboratory equipment in the second semester of a large-scale introductory physics course. The direct current circuit laboratory was modified to compare the effects of using computer simulations with the effects of using real light bulbs, meters, and wires. Two groups of students, those who used real equipment and those who used a computer simulation that explicitly modeled electron flow, were compared in terms of their mastery of physics concepts and skills with real equipment. Students who used the simulated equipment outperformed their counterparts both on a conceptual survey of the domain and in the coordinated tasks of assembling a real circuit and describing how it worked.


Progressive Gaussian filtering using explicit likelihoods

In this paper, we introduce a new sample-based Gaussian filter. In contrast to the popular Nonlinear Kalman Filters, e.g., the UKF, we do not rely on linearizing the measurement model. Instead, we take up the Gaussian progressive filtering approach introduced by the PGF 42 but explicitly rely on likelihood functions. Progression means, we incorporate the information of a new measurement gradually into the state estimate. The advantages of this filtering method are on the one hand the avoidance of sample degeneration and on the other hand an adaptive determination of the number of likelihood evaluations required for each measurement update. By this means, less informative measurements can be processed quickly, whereas measurements containing much information automatically receive more emphasis by the filter. These properties allow the new filter to cope with the demanding problem of very narrow likelihood functions in an efficient way.


Distributed Edge Cloud R-CNN for Real Time Object Detection

Cloud computing infrastructures have become the de-facto platform for data driven machine learning applications. However, these centralized models of computing are unqualified for dispersed high-volume real-time edge data intensive applications such as real time object detection, where video streams may be captured at multiple geographical locations. While many recent advancements in object detection have been made using Convolutional Neural Networks, these performance improvements only focus on a single, contiguous object detection model. In this paper, we propose a distributed Edge-Cloud R-CNN pipeline. By splitting the object detection pipeline into components and dynamically distributing these components in the cloud, we can achieve optimal performance to enable real time object detection. As a proof of concept, we evaluate the performance of the proposed system on a distributed computing platform including cloud servers and edge-embedded devices for real-time object detection on live video streams.


A Novel Multi-Stack Device Structure and its Analysis for High Power CMOS Switch Design

A novel multi-stack CMOS device structure is proposed, the operation of the structure is fully analyzed for high power CMOS switch design. The structure is also implemented in a standard 0.18-um triple-well CMOS process, and its performance is fully characterized. The proposed switch device incorporates multi-stack NMOS switches, one of which has a switch at the bulk and the others of which have a connection between the bulk and the source in order to provide high power handling capability to the transmit switch side. In order to demonstrate the improvement of power handling capability, performance of the conventional structure and the proposed structure are fully analyzed, compared from various simulation results, and verified with measurement results in detail. Experimental data show that the isolation of the proposed test structure is 25 dB higher at 30 dBm input power level than that of conventional structures, which was caused by the significant reduction of leakage current of the switch device in OFF state. In addition, insertion loss of the Rx switch can be maintained as 1.5 dB by applying body switching technique in 900 MHz.


A Dual-Band Dual-Polarized Nested Vivaldi Slot Array With Multilevel Ground Plane

In this paper, we present a systematic approach to the design, optimization and characterization of a broadband 5:1 bandwidth (0.8 to 4.0 GHz) antenna subarray. The array element is an optimized-taper antipodal Vivaldi slot with a bandwidth of 2.5:1. Two such elements of different sizes and with 0.4 GHz (10% of the highest frequency) overlapping bandwidths are arrayed in a nested lattice above a multilevel ground plane that shields the feeds and electronics. Return loss, radiation patterns, cross-polarization and mutual coupling are measured from 0.5–5.0 GHz. This array demonstrates E plane patterns with 50 and 45 3-dB beamwidths in the lower and upper frequency bands, respectively. The coupling between the elements is characterized for different relative antenna positions in all three dimensions.


Online and Linear-Time Attention by Enforcing Monotonic Alignments

Recurrent neural network models with an attention mechanism have proven to be extremely effective on a wide variety of sequence-tosequence problems. However, the fact that soft attention mechanisms perform a pass over the entire input sequence when producing each element in the output sequence precludes their use in online settings and results in a quadratic time complexity. Based on the insight that the alignment between input and output sequence elements is monotonic in many problems of interest, we propose an end-to-end differentiable method for learning monotonic alignments which, at test time, enables computing attention online and in linear time. We validate our approach on sentence summarization, machine translation, and online speech recognition problems and achieve results competitive with existing sequence-tosequence models.


User involvement: a review of the benefits and challenges

Users can be involved in the design process in several ways. The author focuses on user-centred design, participatory design, ethnography, and contextual design. She briefly describes each approach and cites the corresponding literature. The expected benefits of these approaches highly overlap. Methods based on early user involvement promise to avoid the implementation of costly features that users do not want or cannot use. Participatory methods promise to improve the user satisfaction and the acceptance of a system. Nearly all approaches intend to improve the overall usability of the system and by this means increase the productivity and decrease the training costs and the need of user support.


BabyTalk: Understanding and Generating Simple Image Descriptions

We present a system to automatically generate natural language descriptions from images. This system consists of two parts. The first part, content planning, smooths the output of computer vision-based detection and recognition algorithms with statistics mined from large pools of visually descriptive text to determine the best content words to use to describe an image. The second step, surface realization, chooses words to construct natural language sentences based on the predicted content and general statistics from natural language. We present multiple approaches for the surface realization step and evaluate each using automatic measures of similarity to human generated reference descriptions. We also collect forced choice human evaluations between descriptions from the proposed generation system and descriptions from competing approaches. The proposed system is very effective at producing relevant sentences for images. It also generates descriptions that are notably more true to the specific image content than previous work.


Urogenital tract disorders in children suspected of being sexually abused.

INTRODUCTION
Child sexual abuse (CSA) is generally defined as child exploitation that leads to achievement of sexual satisfaction. According to data from European countries, sexual abuse of children affects 10-40% of girls and 5-20% of boys.

MATERIAL AND METHODS
The Medline, and Web of Science databases were searched with no date limitation on May 2015 using the terms 'child abuse' in conjunction with 'urinary tract', 'urologist', 'urological dysfunction', 'urologic symptoms', 'LUTS' or 'urinary infection'.

RESULTS
Awareness of the CSA problem among paediatricians and urologists is very important, because they are often the only physicians who are able to recognize the problem. CSA diagnosis is possible only through the proper collection of a medical history and a thorough physical examination. Urologists have to remember that children exposed to sexual abuse rarely exhibit abnormal genital findings. In fact, absence of genital findings is the rule rather than the exception. In most cases, the final diagnosis of sexual abuse is based on the child's history and behavior, along with the onset and exacerbation of urologic symptoms.

CONCLUSIONS
In this article, we present a review of studies and literature concerning urinary symptoms in sexually abused children to clarify the problem for a broad group of urologists. We present common symptoms and premises that can point to the right diagnosis and basic guidelines of proceeding after suspicion of abuse.


The Three Layers Evaluation Model for Computer-Generated Plots

This paper describes a model for evaluating a computer-generated plot. The main motivation of this project is to provide MEXICA, our plot generator, with the capacity of evaluating its own outputs as well as assessing narratives generated by other agents that can be employed to enrich its knowledge base. We present a description of our computer model as well as an explanation of our first prototype. Then, we show the results of assessing three computergenerated narratives. The outcome suggests that we are in the right direction, although much more work


THE EFFECTS OF MYOFASCIAL RELEASE VS STATIC STRETCHING ON HAMSTRINGS RANGE OF MOTION

The purpose of this study was to investigate the effects of three days of foam rolling on the hamstrings range of motion in comparison with static stretching. Lower extremity injuries are prevalent in strength training and sports today. Poor flexibility has been found to increase the risk of overuse injuries and significantly affect the individual's level of function and performance. Self myofascial release (SMR), foam rolling, is a recent modality clinically used to increase flexibility. On the other hand, there are few research studies on the technique. Twenty college students participated in this study. Ten participants were in the static stretching group, while ten participants were in the SMR group. Participants received the treatment three times in one week with at least 48 hours between treatments. The treatments were static stretching and SMR for three minutes of stretching the hamstrings muscles. The wall sit-and-reach test was used to measure hamstrings range of motion. Measurements were made at the beginning of the study and after each treatment. iv The acute stretching programs increased hamstrings range of motion in the self myofascial release group (28.9%) and static group (33.2%) respectively. The Group by Time ANOVA for flexibility revealed that there was no main effect of Group (F(1,18) = 3.629, p = 0.0729), but that there was a main effect of Time (F(3,54) = 32.130, p =.0001). At the same time there was no Group by Time interaction (F(3,54) = 1.152, p =.3366). These data suggest that self myofascial release compared to static stretching did not have a greater effect on hamstrings range of motion, but both groups increased range of motion from pretest to posttest.


Cultural Congruent Care : A Reflection on Patient Outcome

The growing diverse multicultural population in the United States requires that healthcare workers develop cultural knowledge, awareness, and sensitivity to help diverse patient population receive quality health care. Personal belief has been identified as a significant contributor to professional care delivery and may influence the provision of culturally competent care. However, the relationship between cultural competence and the individual cognitive perception has been an uneasy one. This article explores the moral foundations of cultural competence and ultimately its impact on patient care outcome.


Global supply chain design : A literature review and critique

In this paper, we review decision support models for the design of global supply chains, and assess the fit between the research literature in this area and the practical issues of global supply chain design. The classification scheme for this review is based on ongoing and emerging issues in global supply chain management and includes review dimensions for (1) decisions addressed in the model, (2) performance metrics, (3) the degree to which the model supports integrated decision processes, and (4) globalization considerations. We conclude that although most models resolve a difficult feature associated with globalization, few models address the practical global supply chain design problem in its entirety. We close the paper with recommendations for future research in global supply chain modeling that is both forward-looking and practically oriented. 2005 Elsevier Ltd. All rights reserved.


Approximation capabilities of multilayer feedforward networks


The HiBench benchmark suite: Characterization of the MapReduce-based data analysis

The MapReduce model is becoming prominent for the large-scale data analysis in the cloud. In this paper, we present the benchmarking, evaluation and characterization of Hadoop, an open-source implementation of MapReduce. We first introduce HiBench, a new benchmark suite for Hadoop. It consists of a set of Hadoop programs, including both synthetic micro-benchmarks and real-world Hadoop applications. We then evaluate and characterize the Hadoop framework using HiBench, in terms of speed (i.e., job running time), throughput (i.e., the number of tasks completed per minute), HDFS bandwidth, system resource (e.g., CPU, memory and I/O) utilizations, and data access patterns.


The Google file system

We have designed and implemented the Google File System, a scalable distributed file system for large distributed data-intensive applications. It provides fault tolerance while running on inexpensive commodity hardware, and it delivers high aggregate performance to a large number of clients. While sharing many of the same goals as previous distributed file systems, our design has been driven by observations of our application workloads and technological environment, both current and anticipated, that reflect a marked departure from some earlier file system assumptions. This has led us to reexamine traditional choices and explore radically different design points. The file system has successfully met our storage needs. It is widely deployed within Google as the storage platform for the generation and processing of data used by our service as well as research and development efforts that require large data sets. The largest cluster to date provides hundreds of terabytes of storage across thousands of disks on over a thousand machines, and it is concurrently accessed by hundreds of clients. In this paper, we present file system interface extensions designed to support distributed applications, discuss many aspects of our design, and report measurements from both micro-benchmarks and real world use.


A Constrained, Weighted-L1 Minimization Approach for Joint Discovery of Heterogeneous Neural Connectivity Graphs

Determining functional brain connectivity is crucial to understanding the brain and neural differences underlying disorders such as autism. Recent studies have used Gaussian graphical models to learn brain connectivity via statistical dependencies across brain regions from neuroimaging. However, previous studies often fail to properly incorporate priors tailored to neuroscience, such as preferring shorter connections. To remedy this problem, the paper here introduces a novel, weighted-`1, multi-task graphical model (W-SIMULE). This model elegantly incorporates a flexible prior, along with a parallelizable formulation. Additionally, W-SIMULE extends the often-used Gaussian assumption, leading to considerable performance increases. Here, applications to fMRI data show that W-SIMULE succeeds in determining functional connectivity in terms of (1) log-likelihood, (2) finding edges that differentiate groups, and (3) classifying different groups based on their connectivity, achieving 58.6% accuracy on the ABIDE dataset. Having established W-SIMULE’s effectiveness, it links four key areas to autism, all of which are consistent with the literature. Due to its elegant domain adaptivity, W-SIMULE can be readily applied to various data types to effectively estimate connectivity.


Taming Subgraph Isomorphism for RDF Query Processing

RDF data are used to model knowledge in various areas such as l ife sciences, Semantic Web, bioinformatics, and social graphs . The size of real RDF data reaches billions of triples. This calls for a framework for efficiently processing RDF data. The core func tion of processing RDF data is subgraph pattern matching. There h av been two completely different directions for supporting ef ficient subgraph pattern matching. One direction is to develop spec ialized RDF query processing engines exploiting the properties of R DF data for the last decade, while the other direction is to deve lop fficient subgraph isomorphism algorithms for general, labele d graphs for over 30 years. Although both directions have a similar go al (i.e., finding subgraphs in data graphs for a given query grap h), they have been independently researched without clear reas on. We argue that a subgraph isomorphism algorithm can be easily mo dified to handle the graph homomorphism, which is the RDF patter n matching semantics, by just removing the injectivity const raint. In this paper, based on the state-of-the-art subgraph isomorp hism algorithm, we propose an in-memory solution, TurboHOM++, which is tamed for the RDF processing, and we compare it with the rep resentative RDF processing engines for several RDF benchmark s in a server machine where billions of triples can be loaded in mem ory. In order to speed upTurboHOM++, we also provide a simple yet effective transformation and a series of optimization tech niques. Extensive experiments using several RDF benchmarks show th at TurboHOM++ consistently and significantly outperforms the representative RDF engines. Specifically, TurboHOM++ outperforms its competitors by up tofiveorders of magnitude.


Identifying and Accounting for Task-Dependent Bias in Crowdsourcing

Models for aggregating contributions by crowd workers have been shown to be challenged by the rise of taskspecific biases or errors. Task-dependent errors in assessment may shift the majority opinion of even large numbers of workers to an incorrect answer. We introduce and evaluate probabilistic models that can detect and correct task-dependent bias automatically. First, we show how to build and use probabilistic graphical models for jointly modeling task features, workers’ biases, worker contributions and ground truth answers of tasks so that task-dependent bias can be corrected. Second, we show how the approach can perform a type of transfer learning among workers to address the issue of annotation sparsity. We evaluate the models with varying complexity on a large data set collected from a citizen science project and show that the models are effective at correcting the task-dependent worker bias. Finally, we investigate the use of active learning to guide the acquisition of expert assessments to enable automatic detection and correction of worker bias.


A soft robotic approach to robust and dexterous grasping

In this paper, we present a compliant robotic gripper, Edgy-2, with 4-DOF dexterity, enabling four grasping modes: parallel grasping, power grasping, finger-tip pinch and fully-actuated grasping. The roboticfinger is based on soft-rigid-hybrid structures, combining fiber-reinforced soft pneumatic actuators with rigid joints, which exhibit reliable structural rigidity and grasping robustness while maintaining excellent adaptability and inherent compliance. With both grasping dexterity and grasping robustness, the Edgy-2 achieves excellent grasping reliability with various daily objects, from a fragile cherry to a 2 kg water bottled water. The relationship of design parameters and grasping strength is presented with analytical models. The performance of a prototype Edgy-2 is verified by dedicated experiments. The proposed hybrid dexterous grasping approach can be easily extended into different end-effector designs according to application requirements. The proposed mechanism for grasping provides excellent human-robot interaction safety and reliability.


Which NoSQL Database? A Performance Overview

NoSQL data stores are widely used to store and retrieve possibly large amounts of data, typically in a key-value format. There are many NoSQL types with different performances, and thus it is important to compare them in terms of performance and verify how the performance is related to the database type. In this paper, we evaluate five most popular NoSQL databases: Cassandra, HBase, MongoDB, OrientDB and Redis. We compare those databases in terms of query performance, based on reads and updates, taking into consideration the typical workloads, as represented by the Yahoo! Cloud Serving Benchmark. This comparison allows users to choose the most appropriate database according to the specific mechanisms and application needs. TYPE OF PAPER AND


Pattern Recognition

In this paper a novel reconstruction method is presented that uses the topological relationship of detected image features to create a highly abstract but semantically rich 3D model of the reconstructed scenes. In the first step, a combined image-based reconstruction of points and lines is performed based on the current state of art structure from motion methods. Subsequently, connected planar threedimensional structures are reconstructed by a novel method that uses the topological relationships between the detected image features. The reconstructed 3D models enable a simple extraction of geometric shapes, such as rectangles, in the scene.


SD-Map - A Fast Algorithm for Exhaustive Subgroup Discovery

In this paper we present the novel SD-Map algorithm for exhaustive but efficient subgroup discovery. SD-Map guarantees to identify all interesting subgroup patterns contained in a data set, in contrast to heuristic or samplingbased methods. The SD-Map algorithm utilizes the well-known FP-growth method for mining association rules with adaptations for the subgroup discovery task. We show how SD-Map can handle missing values, and provide an experimental evaluation of the performance of the algorithm using synthetic data.


Demystifying Learning Analytics in Personalised Learning

This paper presents learning analytics as a mean to improve students’ learning. Most learning analytics tools are developed by in-house individual educational institutions to meet the specific needs of their students. Learning analytics is defined as a way to measure, collect, analyse and report data about learners and their context, for the purpose of understanding and optimizing learning. The paper concludes by highlighting framework of learning analytics in order to improve personalised learning. In addition, it is an endeavour to define the characterising features that represents the relationship between learning analytics and personalised learning environment. The paper proposes that learning analytics is dependent on personalised approach for both educators and students. From a learning perspective, students can be supported with specific learning process and reflection visualisation that compares their respective performances to the overall performance of a course. Furthermore, the learners may be provided with personalised recommendations for suitable learning resources, learning paths, or peer students through recommending system. The paper’s contribution to knowledge is in considering personalised learning within the context framework of learning analytics.


Credit card incidents and control systems

Credit and debit cards have spread and skyrocketed all around the world to become the most popular means of payments in many countries. Despite their enormous popularity, cards are not free of risk. Technology development and e-commerce have exponentially increased internal credit card incidents. This paper identifies and quantifies the different types of credit card fraud and puts into question the effectiveness of the role assigned to cardholders in its detection. © 2012 Elsevier Ltd. All rights reserved.


A unified view of gradient-based attribution methods for Deep Neural Networks

Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that has gain increasing attention over the last few years. While several methods have been proposed to explain network predictions, only few attempts to analyze them from a theoretical perspective have been made in the past. In this work we analyze various state-of-the-art attribution methods and prove unexplored connections between them. We also show how some methods can be reformulated and more conveniently implemented. Finally, we perform an empirical evaluation with six attribution methods on a variety of tasks and architectures and discuss their strengths and limitations.


Cyclic deadlock prediction and avoidance for zone-controlled AGV system

Automated guided vehicles (AGVs) are now becoming popular in automated materials handling systems, flexible manufacturing systems and even containers handling applications at seaports. Its performance affects the efficiency of the entire system. Deadlock formation is a serious problem as it stalls the AGVS. The objective of this paper is to develop an efficient AGV deadlock prediction and avoidance algorithm for container port operations. Deadlock in a broad sense is a situation in which at least a part of the system stalls. There are a lot of situations in which the system may stall and most of these situations can be avoided through the control and navigation system. This paper discusses the development of an efficient strategy for predicting and avoiding the deadlocks in a large scale AGVS A variety of deadlock-detecting algorithms are available, but these methods work mainly for manufacturing system where the network layout is simple and uses only a small number of AGVs. The AGVS under study has complex layout and involves close to 80 AGVs. The proposed solution is implemented using the Automod simulation software, and the performance of the technique is presented. Simulation shows that all the potential deadlock situations can be detected and avoided via this methodology. Also, the proposed strategy is computationally efficient and is able to provide the movement decision to the vehicles within the 1.5-s sampling time. r 2002 Published by Elsevier Science B.V.


The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems

We propose a deep learning based method, the Deep Ritz Method, for numerically solving variational problems, particularly the ones that arise from partial differential equations. The Deep Ritz method is naturally nonlinear, naturally adaptive and has the potential to work in rather high dimensions. The framework is quite simple and fits well with the stochastic gradient descent method used in deep learning. We illustrate the method on several problems including some eigenvalue problems.


The effect of music background on the emotional appraisal of film sequences

In this study the effects of musical background on the emotional appraisal of film sequences was investigated. Four pairs of polar emotions defined in Plutchik’s model were used as basic emotional qualities: joy-sadness, anticipation-surprise, fear-anger, and trustdisgust. In the preliminary study eight film sequences and eight music themes were selected as the best representatives of all eight Plutchik’s emotions. In the main experiment the participant judged the emotional qualities of film-music combinations on eight seven-point scales. Half of the combinations were congruent (e.g. joyful film joyful music), and half were incongruent (e.g. joyful film sad music). Results have shown that visual information (film) had greater effects on the emotion appraisal than auditory information (music). The modulation effects of music background depend on emotional qualities. In some incongruent combinations (joysadness) the modulations in the expected directions were obtained (e.g. joyful music reduces the sadness of a sad film), in some cases (anger-fear) no modulation effects were obtained, and in some cases (trust-disgust, anticipation-surprise) the modulation effects were in an unexpected direction (e.g. trustful music increased the appraisal of disgust of a disgusting film). These results suggest that the appraisals of conjoint effects of emotions depend on the medium (film masks the music) and emotional quality (three types of modulation effects).


Dynamic surface self-reconstruction is the key of highly active perovskite nano-electrocatalysts for water splitting.

The growing need to store increasing amounts of renewable energy has recently triggered substantial R&D efforts towards efficient and stable water electrolysis technologies. The oxygen evolution reaction (OER) occurring at the electrolyser anode is central to the development of a clean, reliable and emission-free hydrogen economy. The development of robust and highly active anode materials for OER is therefore a great challenge and has been the main focus of research. Among potential candidates, perovskites have emerged as promising OER electrocatalysts. In this study, by combining a scalable cutting-edge synthesis method with time-resolved X-ray absorption spectroscopy measurements, we were able to capture the dynamic local electronic and geometric structure during realistic operando conditions for highly active OER perovskite nanocatalysts. Ba0.5Sr0.5Co0.8Fe0.2O3-δ as nano-powder displays unique features that allow a dynamic self-reconstruction of the material's surface during OER, that is, the growth of a self-assembled metal oxy(hydroxide) active layer. Therefore, besides showing outstanding performance at both the laboratory and industrial scale, we provide a fundamental understanding of the operando OER mechanism for highly active perovskite catalysts. This understanding significantly differs from design principles based on ex situ characterization techniques.


Applying multi-agent technique in multi-section flexible manufacturing system

In the highly competitive market, cooperative multi-agent transaction and negotiation mechanism have become an important research topic. This paper uses multi-agent technology to construct a multi-section flexible manufacturing system (FMS) model, and utilizes simulation to build a manufacturing environment based on JADE framework for multi-agent to combine with dispatching rules, such as shortest imminent processing time (SIPT), first come first serve (FCFS) earliest due date (EDD), and Buffer Sequence. This paper finds that using multi-agent technique for multi-section FMS model can enhance the production efficiency in practice. Meanwhile, in this study, multi-agent systems combined with dynamic dispatching can be used to identify the best dispatching rules combination for achieving largest throughput, and thus it can provide the reference for production scheduling in advance. 2010 Elsevier Ltd. All rights reserved.