query: Efficient String Matching: An Aid to Bibliographic Search

positive: 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.

positive: 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.

positive: 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.

positive: Representation of Events in Nerve Nets and Finite Automata

positive: 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.

negative: 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.

negative: 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.

negative: 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.

negative: 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.

negative: 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.

negative: 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.

negative: 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.

negative: 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.

negative: 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.

negative: 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%).

negative: 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.

negative: 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.

negative: 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.

negative: 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.

negative: 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.

negative: 1 Pavement distress detection methods : a review 2 The road pavement condition affects safety and comfort, traffic and travel times, vehicles 8 operating cost and emission levels. In order to optimize the road pavement management and 9 guarantee satisfactory mobility conditions for all the road users, the Pavement Management 10 System (PMS) is an effective tool for the road manager. An effective PMS requires the availability of 11 pavement distress data, the possibility of data maintenance and updating, in order to evaluate the 12 best maintenance program. In the last decade, many researches have been focused on pavement 13 distress detection, using a huge variety of technological solutions for both data collection and 14 information extraction and qualification. This paper presents a literature review of data collection 15 systems and processing approach aimed at the pavement condition evaluation. Both commercial 16 solutions and research approaches have been included. The main goal is to draw a framework of 17 the actual existing solutions, considering them from a different point of view in order to identify 18 the most suitable for further research and technical improvement, also considering the automated 19 and semi-automated emerging technologies. An important attempt is to evaluate the aptness of the 20 data collection and extraction to the type of distress, considering the distress detection, 21 classification and quantification phases of the procedure. 22

negative:
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.

negative: 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.

negative: 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.

negative: 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.

negative: 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.

negative: 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.

negative: 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.

negative: 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.

negative: 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.

negative: OpenFst: A General and Efficient Weighted Finite-State Transducer Library We describe OpenFst, an open-source library for weighted finite-state transducers (WFSTs). OpenFst consists of a C++ template library with efficient WFST representations and over twenty-five operations for constructing, combining, optimizing, and searching them. At the shell-command level, there are corresponding transducer file representations and programs that operate on them. OpenFst is designed to be both very efficient in time and space and to scale to very large problems. This library has key applications speech, image, and natural language processing, pattern and string matching, and machine learning. We give an overview of the library, examples of its use, details of its design that allow customizing the labels, states, and weights and the lazy evaluation of many of its operations. Further information and a download of the OpenFst library can be obtained from http://www.openfst.org.

negative: A method to infer emotions from facial Action Units We present a robust method to map detected facial Action Units (AUs) to six basic emotions. Automatic AU recognition is prone to errors due to illumination, tracking failures and occlusions. Hence, traditional rule based methods to map AUs to emotions are very sensitive to false positives and misses among the AUs. In our method, a set of chosen AUs are mapped to the six basic emotions using a learned statistical relationship and a suitable matching technique. Relationships between the AUs and emotions are captured as template strings comprising the most discriminative AUs for each emotion. The template strings are computed using a concept called discriminative power. The Longest Common Subsequence (LCS) distance, an approach for approximate string matching, is applied to calculate the closeness of a test string of AUs with the template strings, and hence infer the underlying emotions. LCS is found to be efficient in handling practical issues like erroneous AU detection and helps to reduce false predictions. The proposed method is tested with various databases like CK+, ISL, FACS, JAFFE, MindReading and many real-world video frames. We compare our performance with rule based techniques, and show clear improvement on both benchmark databases and real-world datasets.

negative: AIDE: Fast and Communication Efficient Distributed Optimization In this paper, we present two new communication-efficient methods for distributed minimization of an average of functions. The first algorithm is an inexact variant of the DANE algorithm [20] that allows any local algorithm to return an approximate solution to a local subproblem. We show that such a strategy does not affect the theoretical guarantees of DANE significantly. In fact, our approach can be viewed as a robustification strategy since the method is substantially better behaved than DANE on data partition arising in practice. It is well known that DANE algorithm does not match the communication complexity lower bounds. To bridge this gap, we propose an accelerated variant of the first method, called AIDE, that not only matches the communication lower bounds but can also be implemented using a purely first-order oracle. Our empirical results show that AIDE is superior to other communication efficient algorithms in settings that naturally arise in machine learning applications.

negative:
Systematic literature studies: Database searches vs. backward snowballing Systematic studies of the literature can be done in different ways. In particular, different guidelines propose different first steps in their recommendations, e.g. start with search strings in different databases or start with the reference lists of a starting set of papers.
 In software engineering, the main recommended first step is using search strings in a number of databases, while in information systems, snowballing has been recommended as the first step. This paper compares the two different search approaches for conducting literature review studies.
 The comparison is conducted by searching for articles addressing "Agile practices in global software engineering". The focus of the paper is on evaluating the two different search approaches.
 Despite the differences in the included papers, the conclusions and the patterns found in both studies are quite similar. The strengths and weaknesses of each first step are discussed separately and in comparison with each other.
 It is concluded that none of the first steps is outperforming the other, and the choice of guideline to follow, and hence the first step, may be context-specific, i.e. depending on the area of study.

negative: Intelligent Tutoring Systems and Learning Outcomes : A Meta-Analysis Intelligent Tutoring Systems (ITS) are computer programs that model learners’ psychological states to provide individualized instruction. They have been developed for diverse subject areas (e.g., algebra, medicine, law, reading) to help learners acquire domain-specific, cognitive and metacognitive knowledge. A meta-analysis was conducted on research that compared the outcomes from students learning from ITS to those learning from non-ITS learning environments. The meta-analysis examined how effect sizes varied with type of ITS, type of comparison treatment received by learners, type of learning outcome, whether knowledge to be learned was procedural or declarative, and other factors. After a search of major bibliographic databases, 107 effect sizes involving 14,321 participants were extracted and analyzed. The use of ITS was associated with greater achievement in comparison with teacher-led, large-group instruction (g .42), non-ITS computer-based instruction (g .57), and textbooks or workbooks (g .35). There was no significant difference between learning from ITS and learning from individualized human tutoring (g –.11) or small-group instruction (g .05). Significant, positive mean effect sizes were found regardless of whether the ITS was used as the principal means of instruction, a supplement to teacher-led instruction, an integral component of teacher-led instruction, or an aid to homework. Significant, positive effect sizes were found at all levels of education, in almost all subject domains evaluated, and whether or not the ITS provided feedback or modeled student misconceptions. The claim that ITS are relatively effective tools for learning is consistent with our analysis of potential publication bias.

negative: Rotation-discriminating template matching based on Fourier coefficients of radial projections with robustness to scaling and partial occlusion We consider brightness/contrast-invariant and rotation-discriminating template matching that searches an image to analyze A for a query image Q. We propose to use the complex coefficients of the discrete Fourier transform of the radial projections to compute new rotation-invariant local features. These coefficients can be efficiently obtained via FFT. We classify templates in “stable” and “unstable” ones and argue that any local feature-based template matching may fail to find unstable templates. We extract several stable sub-templates of Q and find them in A by comparing the features. The matchings of the sub-templates are combined using the Hough transform. As the features of A are computed only once, the algorithm can find quickly many different sub-templates in A, and it is suitable for: finding many query images in A; multi-scale searching and partial occlusion-robust template matching.

negative: Snakes and Strings: New Robotic Components for Rescue Operations The Japanese government is establishing an International Rescue Complex to promote research and development of key technologies for realization of practical search-and-rescue robots, anticipating for future large-scale earthquakes and other catastrophic disasters. This paper proposes a new paradigm called “snakes and strings”, for developing practical mobile robot systems that may be useful in such situations. “Snakes” stands for snake-like robots, which can skillfully move among the debris of the collapsed buildings. “Strings”, on the other hand, means robotic systems using strings or tethers, such as proposed in the “hyper-tether” research [9]. Theters can continuously supply energy, accomplish reliable communication link, and also exhibit high traction force. This paper will present many new mechanical implementations of snake-like robots developed in our lab., and also explain in detail the new paradigm.


query: Active-Metric Learning for Classification of Remotely Sensed Hyperspectral Images

positive: Query by Committee We propose an algorithm called query by commitee, in which a committee of students is trained on the same data set. The next query is chosen according to the principle of maximal disagreement. The algorithm is studied for two toy models: the high-low game and perceptron learning of another perceptron. As the number of queries goes to infinity, the committee algorithm yields asymptotically finite information gain. This leads to generalization error that decreases exponentially with the number of examples. This in marked contrast to learning from randomly chosen inputs, for which the information gain approaches zero and the generalization error decreases with a relatively slow inverse power law. We suggest that asymptotically finite information gain may be an important characteristic of good query algorithms.

positive: Adaptive Manifold Learning Manifold learning algorithms seek to find a low-dimensional parameterization of high-dimensional data. They heavily rely on the notion of what can be considered as local, how accurately the manifold can be approximated locally, and, last but not least, how the local structures can be patched together to produce the global parameterization. In this paper, we develop algorithms that address two key issues in manifold learning: 1) the adaptive selection of the local neighborhood sizes when imposing a connectivity structure on the given set of high-dimensional data points and 2) the adaptive bias reduction in the local low-dimensional embedding by accounting for the variations in the curvature of the manifold as well as its interplay with the sampling density of the data set. We demonstrate the effectiveness of our methods for improving the performance of manifold learning algorithms using both synthetic and real-world data sets.

positive: Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning Advances in hyperspectral sensing provide new capability for characterizing spectral signatures in a wide range of physical and biological systems, while inspiring new methods for extracting information from these data. HSI data often lie on sparse, nonlinear manifolds whose geometric and topological structures can be exploited via manifold-learning techniques. In this article, we focused on demonstrating the opportunities provided by manifold learning for classification of remotely sensed data. However, limitations and opportunities remain both for research and applications. Although these methods have been demonstrated to mitigate the impact of physical effects that affect electromagnetic energy traversing the atmosphere and reflecting from a target, nonlinearities are not always exhibited in the data, particularly at lower spatial resolutions, so users should always evaluate the inherent nonlinearity in the data. Manifold learning is data driven, and as such, results are strongly dependent on the characteristics of the data, and one method will not consistently provide the best results. Nonlinear manifold-learning methods require parameter tuning, although experimental results are typically stable over a range of values, and have higher computational overhead than linear methods, which is particularly relevant for large-scale remote sensing data sets. Opportunities for advancing manifold learning also exist for analysis of hyperspectral and multisource remotely sensed data. Manifolds are assumed to be inherently smooth, an assumption that some data sets may violate, and data often contain classes whose spectra are distinctly different, resulting in multiple manifolds or submanifolds that cannot be readily integrated with a single manifold representation. Developing appropriate characterizations that exploit the unique characteristics of these submanifolds for a particular data set is an open research problem for which hierarchical manifold structures appear to have merit. To date, most work in manifold learning has focused on feature extraction from single images, assuming stationarity across the scene. Research is also needed in joint exploitation of global and local embedding methods in dynamic, multitemporal environments and integration with semisupervised and active learning.

positive: Stochastic Neighbor Embedding We describe a probabilistic approach to the task of placing objects, described by high-dimensional vectors or by pairwise dissimilarities, in a low-dimensional space in a way that preserves neighbor identities. A Gaussian is centered on each object in the high-dimensional space and the densities under this Gaussian (or the given dissimilarities) are used to define a probability distribution over all the potential neighbors of the object. The aim of the embedding is to approximate this distribution as well as possible when the same operation is performed on the low-dimensional “images” of the objects. A natural cost function is a sum of Kullback-Leibler divergences, one per object, which leads to a simple gradient for adjusting the positions of the low-dimensional images. Unlike other dimensionality reduction methods, this probabilistic framework makes it easy to represent each object by a mixture of widely separated low-dimensional images. This allows ambiguous objects, like the document count vector for the word “bank”, to have versions close to the images of both “river” and “finance” without forcing the images of outdoor concepts to be located close to those of corporate concepts.

positive: Nonlinear Component Analysis as a Kernel Eigenvalue Problem A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear mapfor instance, the space of all possible five-pixel products in 16 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.

negative: Leveraging graph topology and semantic context for pharmacovigilance through twitter-streams Adverse drug events (ADEs) constitute one of the leading causes of post-therapeutic death and their identification constitutes an important challenge of modern precision medicine. Unfortunately, the onset and effects of ADEs are often underreported complicating timely intervention. At over 500 million posts per day, Twitter is a commonly used social media platform. The ubiquity of day-to-day personal information exchange on Twitter makes it a promising target for data mining for ADE identification and intervention. Three technical challenges are central to this problem: (1) identification of salient medical keywords in (noisy) tweets, (2) mapping drug-effect relationships, and (3) classification of such relationships as adverse or non-adverse. We use a bipartite graph-theoretic representation called a drug-effect graph (DEG) for modeling drug and side effect relationships by representing the drugs and side effects as vertices. We construct individual DEGs on two data sources. The first DEG is constructed from the drug-effect relationships found in FDA package inserts as recorded in the SIDER database. The second DEG is constructed by mining the history of Twitter users. We use dictionary-based information extraction to identify medically-relevant concepts in tweets. Drugs, along with co-occurring symptoms are connected with edges weighted by temporal distance and frequency. Finally, information from the SIDER DEG is integrate with the Twitter DEG and edges are classified as either adverse or non-adverse using supervised machine learning. We examine both graph-theoretic and semantic features for the classification task. The proposed approach can identify adverse drug effects with high accuracy with precision exceeding 85 % and F1 exceeding 81 %. When compared with leading methods at the state-of-the-art, which employ un-enriched graph-theoretic analysis alone, our method leads to improvements ranging between 5 and 8 % in terms of the aforementioned measures. Additionally, we employ our method to discover several ADEs which, though present in medical literature and Twitter-streams, are not represented in the SIDER databases. We present a DEG integration model as a powerful formalism for the analysis of drug-effect relationships that is general enough to accommodate diverse data sources, yet rigorous enough to provide a strong mechanism for ADE identification.

negative: IMPLICIT SHAPE MODELS FOR OBJECT DETECTION IN 3D POINT CLOUDS We present a method for automatic object localization and recognition in 3D point clouds representing outdoor urban scenes. The method is based on the implicit shape models (ISM) framework, which recognizes objects by voting for their center locations. It requires only few training examples per class, which is an important property for practical use. We also introduce and evaluate an improved version of the spin image descriptor, more robust to point density variation and uncertainty in normal direction estimation. Our experiments reveal a significant impact of these modifications on the recognition performance. We compare our results against the state-of-the-art method and get significant improvement in both precision and recall on the Ohio dataset, consisting of combined aerial and terrestrial LiDAR scans of 150,000 m of urban area in total.

negative: Multi-task Learning for Maritime Traffic Surveillance from AIS Data Streams In a world of global trading, maritime safety, security and efficiency are crucial issues. We propose a multi-task deep learning framework for vessel monitoring using Automatic Identification System (AIS) data streams. We combine recurrent neural networks with latent variable modeling and an embedding of AIS messages to a new representation space to jointly address key issues to be dealt with when considering AIS data streams: massive amount of streaming data, noisy data and irregular timesampling. We demonstrate the relevance of the proposed deep learning framework on real AIS datasets for a three-task setting, namely trajectory reconstruction, anomaly detection and vessel type identification.

negative: The Dysphagia Outcome and Severity Scale The Dysphagia Outcome and Severity Scale (DOSS) is a simple, easy-to-use, 7-point scale developed to systematically rate the functional severity of dysphagia based on objective assessment and make recommendations for diet level, independence level, and type of nutrition. Intra- and interjudge reliabilities of the DOSS was established by four clinicians on 135 consecutive patients who underwent a modified barium swallow procedure at a large teaching hospital. Patients were assigned a severity level, independence level, and nutritional level based on three areas most associated with final recommendations: oral stage bolus transfer, pharyngeal stage retention, and airway protection. Results indicate high interrater (90%) and intrarater (93%) agreement with this scale. Implications are suggested for use of the DOSS in documenting functional outcomes of swallowing and diet status based on objective assessment.

negative: Depth Map Super-Resolution Considering View Synthesis Quality Accurate and high-quality depth maps are required in lots of 3D applications, such as multi-view rendering, 3D reconstruction and 3DTV. However, the resolution of captured depth image is much lower than that of its corresponding color image, which affects its application performance. In this paper, we propose a novel depth map super-resolution (SR) method by taking view synthesis quality into account. The proposed approach mainly includes two technical contributions. First, since the captured low-resolution (LR) depth map may be corrupted by noise and occlusion, we propose a credibility based multi-view depth maps fusion strategy, which considers the view synthesis quality and interview correlation, to refine the LR depth map. Second, we propose a view synthesis quality based trilateral depth-map up-sampling method, which considers depth smoothness, texture similarity and view synthesis quality in the up-sampling filter. Experimental results demonstrate that the proposed method outperforms state-of-the-art depth SR methods for both super-resolved depth maps and synthesized views. Furthermore, the proposed method is robust to noise and achieves promising results under noise-corruption conditions.

negative: A New Approach to Linear Filtering and Prediction Problems AN IMPORTANT class of theoretical and practical problems in communication and control is of a statistical nature. Such problems are: (i) Prediction of random signals; (ii) separation of random signals from random noise; (iii) detection of signals of known form (pulses, sinusoids) in the presence of random noise. In his pioneering work, Wiener [1]3 showed that problems (i) and (ii) lead to the so-called Wiener-Hopf integral equation; he also gave a method (spectral factorization) for the solution of this integral equation in the practically important special case of stationary statistics and rational spectra. Many extensions and generalizations followed Wiener’s basic work. Zadeh and Ragazzini solved the finite-memory case [2]. Concurrently and independently of Bode and Shannon [3], they also gave a simplified method [2) of solution. Booton discussed the nonstationary Wiener-Hopf equation [4]. These results are now in standard texts [5-6]. A somewhat different approach along these main lines has been given recently by Darlington [7]. For extensions to sampled signals, see, e.g., Franklin [8], Lees [9]. Another approach based on the eigenfunctions of the WienerHopf equation (which applies also to nonstationary problems whereas the preceding methods in general don’t), has been pioneered by Davis [10] and applied by many others, e.g., Shinbrot [11], Blum [12], Pugachev [13], Solodovnikov [14]. In all these works, the objective is to obtain the specification of a linear dynamic system (Wiener filter) which accomplishes the prediction, separation, or detection of a random signal.4 ——— 1 This research was supported in part by the U. S. Air Force Office of Scientific Research under Contract AF 49 (638)-382. 2 7212 Bellona Ave. 3 Numbers in brackets designate References at end of paper. 4 Of course, in general these tasks may be done better by nonlinear filters. At present, however, little or nothing is known about how to obtain (both theoretically and practically) these nonlinear filters. Contributed by the Instruments and Regulators Division and presented at the Instruments and Regulators Conference, March 29– Apri1 2, 1959, of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS. NOTE: Statements and opinions advanced in papers are to be understood as individual expressions of their authors and not those of the Society. Manuscript received at ASME Headquarters, February 24, 1959. Paper No. 59-IRD—11. A New Approach to Linear Filtering and Prediction Problems

negative: Detection of exudates in fundus photographs using convolutional neural networks Diabetic retinopathy is one of the leading causes of preventable blindness in the developed world. Early diagnosis of diabetic retinopathy enables timely treatment and in order to achieve it a major effort will have to be invested into screening programs and especially into automated screening programs. Detection of exudates is very important for early diagnosis of diabetic retinopathy. Deep neural networks have proven to be a very promising machine learning technique, and have shown excellent results in different compute vision problems. In this paper we show that convolutional neural networks can be effectively used in order to detect exudates in color fundus photographs.

negative: Random Forests Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.

negative: Multi-task Learning Of Deep Neural Networks For Audio Visual Automatic Speech Recognition Multi-task learning (MTL) involves the simultaneous training of two or more related tasks over shared representations. In this work, we apply MTL to audio-visual automatic speech recognition(AV-ASR). Our primary task is to learn a mapping between audio-visual fused features and frame labels obtained from acoustic GMM/HMM model. This is combined with an auxiliary task which maps visual features to frame labels obtained from a separate visual GMM/HMM model. The MTL model is tested at various levels of babble noise and the results are compared with a base-line hybrid DNN-HMM AVASR model. Our results indicate that MTL is especially useful at higher level of noise. Compared to base-line, upto 7% relative improvement in WER is reported at -3 SNR dB1.

negative: Ethnicity estimation with facial images We have advanced an effort to develop vision based human understanding technologies for realizing human-friendly machine interfaces. Visual information, such as gender, age ethnicity, and facial expression play an important role in face-to-face communication. This paper addresses a novel approach for ethnicity classification with facial images. In this approach, the Gabor wavelets transformation and retina sampling are combined to extract key facial features, and support vector machines that are used for ethnicity classification. Our system, based on this approach, has achieved approximately 94% for ethnicity estimation under various lighting conditions.

negative: Bullying and victimization in adolescence: concurrent and stable roles and psychological health symptoms. From an initial sample of 1278 Italian students, the authors selected 537 on the basis of their responses to a self-report bully and victim questionnaire. Participants' ages ranged from 13 to 20 years (M = 15.12 years, SD = 1.08 years). The authors compared the concurrent psychological symptoms of 4 participant groups (bullies, victims, bully/victims [i.e., bullies who were also victims of bullying], and uninvolved students). Of participants, 157 were in the bullies group, 140 were in the victims group, 81 were in the bully/victims group, and 159 were in the uninvolved students group. The results show that bullies reported a higher level of externalizing problems, victims reported more internalizing symptoms, and bully/victims reported both a higher level of externalizing problems and more internalizing symptoms. The authors divided the sample into 8 groups on the basis of the students' recollection of their earlier school experiences and of their present role. The authors classified the participants as stable versus late bullies, victims, bully/victims, or uninvolved students. The authors compared each stable group with its corresponding late group and found that stable victims and stable bully/victims reported higher degrees of anxiety, depression, and withdrawal than did the other groups. The authors focus their discussion on the role of chronic peer difficulties in relation to adolescents' symptoms and well-being.

negative: Side gate HiGT with low dv/dt noise and low loss This paper presents a novel side gate HiGT (High-conductivity IGBT) that incorporates historical changes of gate structures for planar and trench gate IGBTs. Side gate HiGT has a side-wall gate, and the opposite side of channel region for side-wall gate is covered by a thick oxide layer to reduce Miller capacitance (Cres). In addition, side gate HiGT has no floating p-layer, which causes the excess Vge overshoot. The proposed side gate HiGT has 75% smaller Cres than the conventional trench gate IGBT. The excess Vge overshoot during turn-on is effectively suppressed, and Eon + Err can be reduced by 34% at the same diode's recovery dv/dt. Furthermore, side gate HiGT has sufficiently rugged RBSOA and SCSOA.

negative: Incremental Dialogue System Faster than and Preferred to its Nonincremental Counterpart Current dialogue systems generally operate in a pipelined, modular fashion on one complete utterance at a time. Evidence from human language understanding shows that human understanding operates incrementally and makes use of multiple sources of information during the parsing process, including traditionally “later” components such as pragmatics. In this paper we describe a spoken dialogue system that understands language incrementally, provides visual feedback on possible referents during the course of the user’s utterance, and allows for overlapping speech and actions. We further present findings from an empirical study showing that the resulting dialogue system is faster overall than its nonincremental counterpart. Furthermore, the incremental system is preferred to its nonincremental counterpart – beyond what is accounted for by factors such as speed and accuracy. These results indicate that successful incremental understanding systems will improve both performance and usability.

negative: The balanced scorecard: a foundation for the strategic management of information systems Ž . The balanced scorecard BSC has emerged as a decision support tool at the strategic management level. Many business leaders now evaluate corporate performance by supplementing financial accounting data with goal-related measures from the following perspectives: customer, internal business process, and learning and growth. It is argued that the BSC concept can be adapted to assist those managing business functions, organizational units and individual projects. This article develops a Ž . balanced scorecard for information systems IS that measures and evaluates IS activities from the following perspectives: business value, user orientation, internal process, and future readiness. Case study evidence suggests that a balanced IS scorecard can be the foundation for a strategic IS management system provided that certain development guidelines are followed, appropriate metrics are identified, and key implementation obstacles are overcome. q 1999 Elsevier Science B.V. All rights reserved.

negative: Adversarial Machine Learning at Scale Adversarial examples are malicious inputs designed to fool machine learning models. They often transfer from one model to another, allowing attackers to mount black box attacks without knowledge of the target model’s parameters. Adversarial training is the process of explicitly training a model on adversarial examples, in order to make it more robust to attack or to reduce its test error on clean inputs. So far, adversarial training has primarily been applied to small problems. In this research, we apply adversarial training to ImageNet (Russakovsky et al., 2014). Our contributions include: (1) recommendations for how to succesfully scale adversarial training to large models and datasets, (2) the observation that adversarial training confers robustness to single-step attack methods, (3) the finding that multi-step attack methods are somewhat less transferable than singlestep attack methods, so single-step attacks are the best for mounting black-box attacks, and (4) resolution of a “label leaking” effect that causes adversarially trained models to perform better on adversarial examples than on clean examples, because the adversarial example construction process uses the true label and the model can learn to exploit regularities in the construction process.

negative: A Combined Model- and Learning-Based Framework for Interaction-Aware Maneuver Prediction This paper presents a novel online-capable interaction-aware intention and maneuver prediction framework for dynamic environments. The main contribution is the combination of model-based interaction-aware intention estimation with maneuver-based motion prediction based on supervised learning. The advantages of this framework are twofold. On one hand, expert knowledge in the form of heuristics is integrated, which simplifies the modeling of the interaction. On the other hand, the difficulties associated with the scalability and data sparsity of the algorithm due to the so-called curse of dimensionality can be reduced, as a reduced feature space is sufficient for supervised learning. The proposed algorithm can be used for highly automated driving or as a prediction module for advanced driver assistance systems without the need of intervehicle communication. At the start of the algorithm, the motion intention of each driver in a traffic scene is predicted in an iterative manner using the game-theoretic idea of stochastic multiagent simulation. This approach provides an interpretation of what other drivers intend to do and how they interact with surrounding traffic. By incorporating this information into a Bayesian network classifier, the developed framework achieves a significant improvement in terms of reliable prediction time and precision compared with other state-of-the-art approaches. By means of experimental results in real traffic on highways, the validity of the proposed concept and its online capability is demonstrated. Furthermore, its performance is quantitatively evaluated using appropriate statistical measures.

negative: Frequency Tracking Control for a Capacitor-Charging Parallel Resonant Converter with Phase-Locked Loop This study investigates a phase-locked loop (PLL) controlled parallel resonant converter (PRC) for a pulse power capacitor charging application. The dynamic nature of the capacitor charging is such that it causes a shift in the resonant frequency of the PRC. Using the proposed control method, the PRC can be optimized to operate with its maximum power capability and guarantee ZVS operation, even when the input voltage and resonant tank parameters vary. The detailed implementation of the PLL controller, as well as the determination of dead-time and leading time, is presented in this paper. Simulation and experimental results verify the performance of the proposed control method.

negative: A Novel Connectionist System for Unconstrained Handwriting Recognition Recognizing lines of unconstrained handwritten text is a challenging task. The difficulty of segmenting cursive or overlapping characters, combined with the need to exploit surrounding context, has led to low recognition rates for even the best current recognizers. Most recent progress in the field has been made either through improved preprocessing or through advances in language modeling. Relatively little work has been done on the basic recognition algorithms. Indeed, most systems rely on the same hidden Markov models that have been used for decades in speech and handwriting recognition, despite their well-known shortcomings. This paper proposes an alternative approach based on a novel type of recurrent neural network, specifically designed for sequence labeling tasks where the data is hard to segment and contains long-range bidirectional interdependencies. In experiments on two large unconstrained handwriting databases, our approach achieves word recognition accuracies of 79.7 percent on online data and 74.1 percent on offline data, significantly outperforming a state-of-the-art HMM-based system. In addition, we demonstrate the network's robustness to lexicon size, measure the individual influence of its hidden layers, and analyze its use of context. Last, we provide an in-depth discussion of the differences between the network and HMMs, suggesting reasons for the network's superior performance.

negative: Enhancing computer vision to detect face spoofing attack utilizing a single frame from a replay video attack using deep learning Recently, automatic face recognition has been applied in many web and mobile applications. Developers integrate and implement face recognition as an access control into these applications. However, face recognition authentication is vulnerable to several attacks especially when an attacker presents a 2-D printed image or recorded video frames in front of the face sensor system to gain access as a legitimate user. This paper introduces a non-intrusive method to detect face spoofing attacks that utilize a single frame of sequenced frames. We propose a specialized deep convolution neural network to extract complex and high features of the input diffused frame. We tested our method on the Replay Attack dataset which consists of 1200 short videos of both real-access and spoofing attacks. An extensive experimental analysis was conducted that demonstrated better results when compared to previous static algorithms results.

negative: ISAR - radar imaging of targets with complicated motion ISAR imaging is described for general motion of a radar target. ISAR imaging may be seen as a 3D to 2D projection, and the importance of the ISAR image projection plane is stated. For general motion, ISAR images are often smeared when using FFT processing. Time frequency methods are used to analyze such images, and to form sharp images. A given smeared image is shown to be the result of changes both in scale and in the projection plane orientation.

negative: Closing the Loop: Evaluating a Measurement Instrument for Maturity Model Design To support the systematic improvement of business intelligence (BI) in organizations, we have designed and refined a BI maturity model (BIMM) and a respective measurement instrument (MI) in prior research. In this study, we devise an evaluation strategy, and evaluate the validity of the designed measurement artifact. Through cluster analysis of maturity assessments of 92 organizations, we identify characteristic BI maturity scenarios and representative cases for the relevant scenarios. For evaluating the designed instrument, we compare its results with insights obtained from in-depth interviews in the respective companies. A close match between our model's quantitative maturity assessments and the maturity levels from the qualitative analyses indicates that the MI correctly assesses BI maturity. The applied evaluation approach has the potential to be re-used in other design research studies where the validity of utility claims is often hard to prove.

negative: An architecture for scalable, universal speech recognition This thesis describes MultiSphinx, a concurrent architecture for scalable, low-latency automatic speech recognition. We first consider the problem of constructing a universal “core” speech recognizer on top of which domain and task specific adaptation layers can be constructed. We then show that when this problem is restricted to that of expanding the search space from a “core” vocabulary to a superset of this vocabulary across multiple passes of search, it allows us to effectively “factor” a recognizer into components of roughly equal complexity. We present simple but effective algorithms for constructing the reduced vocabulary and associated statistical language model from an existing system. Finally, we describe the MultiSphinx decoder architecture, which allows multiple passes of recognition to operate concurrently and incrementally, either in multiple threads in the same process, or across multiple processes on separate machines, and which allows the best possible partial results, including confidence scores, to be obtained at any time during the recognition process.

negative: ' SMART ’ CANE FOR THE VISUALLY IMPAIRED : DESIGN AND CONTROLLED FIELD TESTING OF AN AFFORDABLE OBSTACLE DETECTION SYSTEM (USA) respectively. Since graduation, they have been associated with this project in voluntary and individual capacity. Sandeep Singh Gujral, an occupational therapist was an intern at the IIT Delhi from August-November 2009 and conducted user training for the trials.

negative: Forecasting Nike's sales using Facebook data This paper tests whether accurate sales forecasts for Nike are possible from Facebook data and how events related to Nike affect the activity on Nike's Facebook pages. The paper draws from the AIDA sales framework (Awareness, Interest, Desire, and Action) from the domain of marketing and employs the method of social set analysis from the domain of computational social science to model sales from Big Social Data. The dataset consists of (a) selection of Nike's Facebook pages with the number of likes, comments, posts etc. that have been registered for each page per day and (b) business data in terms of quarterly global sales figures published in Nike's financial reports. An event study is also conducted using the Social Set Visualizer (SoSeVi). The findings suggest that Facebook data does have informational value. Some of the simple regression models have a high forecasting accuracy. The multiple regressions have a lower forecasting accuracy and cause analysis barriers due to data set characteristics such as perfect multicollinearity. The event study found abnormal activity around several Nike specific events but inferences about those activity spikes, whether they are purely event-related or coincidences, can only be determined after detailed case-by-case text analysis. Our findings help assess the informational value of Big Social Data for a company's marketing strategy, sales operations and supply chain.

negative: Wiktionary-Based Word Embeddings Vectorial representations of words have grown remarkably popular in natural language processing and machine translation. The recent surge in deep learning-inspired methods for producing distributed representations has been widely noted even outside these fields. Existing representations are typically trained on large monolingual corpora using context-based prediction models. In this paper, we propose extending pre-existing word representations by exploiting Wiktionary. This process results in a substantial extension of the original word vector representations, yielding a large multilingual dictionary of word embeddings. We believe that this resource can enable numerous monolingual and cross-lingual applications, as evidenced in a series of monolingual and cross-lingual semantic experiments that we have conducted.

negative: Aerial Mapping of Forests Affected by Pathogens Using UAVs, Hyperspectral Sensors, and Artificial Intelligence The environmental and economic impacts of exotic fungal species on natural and plantation forests have been historically catastrophic. Recorded surveillance and control actions are challenging because they are costly, time-consuming, and hazardous in remote areas. Prolonged periods of testing and observation of site-based tests have limitations in verifying the rapid proliferation of exotic pathogens and deterioration rates in hosts. Recent remote sensing approaches have offered fast, broad-scale, and affordable surveys as well as additional indicators that can complement on-ground tests. This paper proposes a framework that consolidates site-based insights and remote sensing capabilities to detect and segment deteriorations by fungal pathogens in natural and plantation forests. This approach is illustrated with an experimentation case of myrtle rust (Austropuccinia psidii) on paperbark tea trees (Melaleuca quinquenervia) in New South Wales (NSW), Australia. The method integrates unmanned aerial vehicles (UAVs), hyperspectral image sensors, and data processing algorithms using machine learning. Imagery is acquired using a Headwall Nano-Hyperspec ® camera, orthorectified in Headwall SpectralView ® , and processed in Python programming language using eXtreme Gradient Boosting (XGBoost), Geospatial Data Abstraction Library (GDAL), and Scikit-learn third-party libraries. In total, 11,385 samples were extracted and labelled into five classes: two classes for deterioration status and three classes for background objects. Insights reveal individual detection rates of 95% for healthy trees, 97% for deteriorated trees, and a global multiclass detection rate of 97%. The methodology is versatile to be applied to additional datasets taken with different image sensors, and the processing of large datasets with freeware tools.

negative: AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification Aerial scene classification, which aims to automatically label an aerial image with a specific semantic category, is a fundamental problem for understanding high-resolution remote sensing imagery. In recent years, it has become an active task in the remote sensing area, and numerous algorithms have been proposed for this task, including many machine learning and data-driven approaches. However, the existing data sets for aerial scene classification, such as UC-Merced data set and WHU-RS19, contain relatively small sizes, and the results on them are already saturated. This largely limits the development of scene classification algorithms. This paper describes the Aerial Image data set (AID): a large-scale data set for aerial scene classification. The goal of AID is to advance the state of the arts in scene classification of remote sensing images. For creating AID, we collect and annotate more than 10000 aerial scene images. In addition, a comprehensive review of the existing aerial scene classification techniques as well as recent widely used deep learning methods is given. Finally, we provide a performance analysis of typical aerial scene classification and deep learning approaches on AID, which can be served as the baseline results on this benchmark.

negative: Remote Sensing and Geospatial Technological Applications for Site-specific Management of Fruit and Nut Crops: A Review Site-specific crop management (SSCM) is one facet of precision agriculture which is helping increase production with minimal input. It has enhanced the cost-benefit scenario in crop production. Even though the SSCM is very widely used in row crop agriculture like corn, wheat, rice, soybean, etc. it has very little application in cash crops like fruit and nut. The main goal of this review paper was to conduct a comprehensive review of advanced technologies, including geospatial technologies, used in site-specific management of fruit and nut crops. The review explores various remote sensing data from different platforms like satellite, LIDAR, aerial, and field imaging. The study analyzes the use of satellite sensors, such as Quickbird, Landsat, SPOT, and IRS imagery as well as hyperspectral narrow-band remote sensing data in study of fruit and nut crops in blueberry, citrus, peach, apple, etc. The study also explores other geospatial technologies such as GPS, GIS spatial modeling, advanced image processing techniques, and information technology for suitability study, orchard delineation, and classification accuracy assessment. The study also provides an example of a geospatial model developed in ArcGIS ModelBuilder to automate the blueberry production suitability analysis. The GIS spatial model is developed using various crop characteristics such as chilling hours, soil permeability, drainage, and pH, and land cover to determine the best sites for growing OPEN ACCESS Remote Sensing 2010, 2 1974 blueberry in Georgia, U.S. The study also provides a list of spectral reflectance curves developed for some fruit and nut crops, blueberry, crowberry, redblush citrus, orange, prickly pear, and peach. The study also explains these curves in detail to help researchers choose the image platform, sensor, and spectrum wavelength for various fruit and nut crops SSCM.

negative: Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery Learning efficient image representations is at the core of the scene classification task of remote sensing imagery. The existing methods for solving the scene classification task, based on either feature coding approaches with low-level hand-engineered features or unsupervised feature learning, can only generate mid-level image features with limited representative ability, which essentially prevents them from achieving better performance. Recently, the deep convolutional neural networks (CNNs), which are hierarchical architectures trained on large-scale datasets, have shown astounding performance in object recognition and detection. However, it is still not clear how to use these deep convolutional neural networks for high-resolution remote sensing (HRRS) scene classification. In this paper, we investigate how to transfer features from these successfully pre-trained CNNs for HRRS scene classification. We propose two scenarios for generating image features via extracting CNN features from different layers. In the first scenario, the activation vectors extracted from fully-connected layers are regarded as the final image features; in the second scenario, we extract dense features from the last convolutional layer at multiple scales and then encode the dense features into global image features through commonly used feature coding approaches. Extensive experiments on two public scene classification datasets demonstrate that the image features obtained by the two proposed scenarios, even with a simple linear classifier, can result in remarkable performance and improve the state-of-the-art by a significant margin. The results reveal that the features Remote Sens. 2015, 7 14681 from pre-trained CNNs generalize well to HRRS datasets and are more expressive than the lowand mid-level features. Moreover, we tentatively combine features extracted from different CNN models for better performance.

negative: Capsule Networks for Hyperspectral Image Classification Convolutional neural networks (CNNs) have recently exhibited an excellent performance in hyperspectral image classification tasks. However, the straightforward CNNbased network architecture still finds obstacles when effectively exploiting the relationships between hyperspectral imaging (HSI) features in the spectral–spatial domain, which is a key factor to deal with the high level of complexity present in remotely sensed HSI data. Despite the fact that deeper architectures try to mitigate these limitations, they also find challenges with the convergence of the network parameters, which eventually limit the classification performance under highly demanding scenarios. In this paper, we propose a new CNN architecture based on spectral–spatial capsule networks in order to achieve a highly accurate classification of HSIs while significantly reducing the network design complexity. Specifically, based on Hinton’s capsule networks, we develop a CNN model extension that redefines the concept of capsule units to become spectral– spatial units specialized in classifying remotely sensed HSI data. The proposed model is composed by several building blocks, called spectral–spatial capsules, which are able to learn HSI spectral–spatial features considering their corresponding spatial positions in the scene, their associated spectral signatures, and also their possible transformations. Our experiments, conducted Manuscript received June 9, 2018; revised August 22, 2018; accepted September 19, 2018. This work was supported in part by the Ministerio de Educación (Resolución de 26 de diciembre de 2014 y de 19 de noviembre de 2015, de la Secretaría de Estado de Educación, Formación Profesional y Universidades, por la que se convocan ayudas para la formación de profesorado universitario, de los subprogramas de Formación y de Movilidad incluidos en el Programa Estatal de Promoción del Talento y su Empleabilidad, and en el marco del Plan Estatal de Investigación Científica y Técnica y de Innovación 2013–2016), in part by the Junta de Extremadura (decreto 297/2014, ayudas para la realización de actividades de investigación y desarrollo tecnológico, and de divulgación y de transferencia de conocimiento por los Grupos de Investigación de Extremadura) under Grant GR15005, in part by the Generalitat Valenciana under Contract APOSTD/2017/007, in part by the Spanish Ministry of Economy under Project ESP2016-79503-C2-2P and Project TIN2015-63646-C5-5-R, in part by the National Natural Science Foundation of China under Grant 61771496, in part by the National Key Research and Development Program of China under Grant 2017YFB0502900, and in part by the Guangdong Provincial Natural Science Foundation under Grant 2016A030313254. (Corresponding author: Jun Li.) M. E. Paoletti, J. M. Haut, J. Plaza, and A. Plaza are with the Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, Escuela Politécnica, University of Extremadura, 10003 Cáceres, Spain (e-mail: mpaoletti@unex.es; juanmariohaut@unex.es; jplaza@unex.es; aplaza@unex.es). R. Fernandez-Beltran and F. Pla are with the Institute of New Imaging Technologies, Universitat Jaume I, 12071 Castellón de la Plana, Spain (e-mail: rufernan@uji.es; pla@uji.es). J. Li is with the Guangdong Provincial Key Laboratory of Urbanization and Geosimulation, Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China (e-mail: lijun48@mail.sysu.edu.cn). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TGRS.2018.2871782 using five well-known HSI data sets and several state-of-theart classification methods, reveal that our HSI classification approach based on spectral–spatial capsules is able to provide competitive advantages in terms of both classification accuracy and computational time.

negative: Early Detection and Quantification of Verticillium Wilt in Olive Using Hyperspectral and Thermal Imagery over Large Areas Automatic methods for an early detection of plant diseases (i.e., visible symptoms at early stages of disease development) using remote sensing are critical for precision crop protection. Verticillium wilt (VW) of olive caused by Verticillium dahliae can be controlled only if detected at early stages of development. Linear discriminant analysis (LDA) and support vector machine (SVM) classification methods were applied to classify V. dahliae severity using remote sensing at large scale. High-resolution thermal and hyperspectral imagery were acquired with a manned platform which flew a 3000-ha commercial olive area. LDA reached an overall accuracy of 59.0% and a κ of 0.487 while SVM obtained a higher overall accuracy, 79.2% with a similar κ, 0.495. However, LDA better classified trees at initial and low severity levels, reaching accuracies of 71.4 and 75.0%, respectively, in comparison with the 14.3% and 40.6% obtained by SVM. Normalized canopy temperature, chlorophyll fluorescence, structural, xanthophyll, chlorophyll, carotenoid and disease indices were found to be the best indicators for early and advanced stage infection by VW. These results demonstrate that the methods developed in other studies at orchard scale are valid for flights in large areas comprising several olive orchards differing in soil and crop management characteristics.

negative: Exploring Models and Data for Remote Sensing Image Caption Generation Inspired by recent development of artificial satellite, remote sensing images have attracted extensive attention. Recently, notable progress has been made in scene classification and target detection. However, it is still not clear how to describe the remote sensing image content with accurate and concise sentences. In this paper, we investigate to describe the remote sensing images with accurate and flexible sentences. First, some annotated instructions are presented to better describe the remote sensing images considering the special characteristics of remote sensing images. Second, in order to exhaustively exploit the contents of remote sensing images, a large-scale aerial image data set is constructed for remote sensing image caption. Finally, a comprehensive review is presented on the proposed data set to fully advance the task of remote sensing caption. Extensive experiments on the proposed data set demonstrate that the content of the remote sensing image can be completely described by generating language descriptions. The data set is available at https://github.com/201528014227051/RSICD_optimal.