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Question: Rice disease identification using pattern recognition techniques

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The techniques of machine vision are extensively applied to agricultural science, and it has great perspective especially in the plant protection field, which ultimately leads to crops management. The paper describes a software prototype system for rice disease detection based on the infected images of various rice plants. Images of the infected rice plants are captured by digital camera and processed using image growing, image segmentation techniques to detect infected parts of the plants. Then the infected part of the leaf has been used for the classification purpose using neural network. The methods evolved in this system are both image processing and soft computing technique applied on number of diseased rice plants.

Plant recognition based on digital leaf image has received as particular attention in computer vision and intelligence system, due its important implication in automatic plant identification. Plant species have the unique leaf characteristics such as the shape, texture, margin, and colour, which different each other. This study presents a novel method for automation plant recognition using Generalized Relevance Learning Vector Quantization (GRLVQ). GRLVQ is a competitive based learning algorithm which is integrating features extraction and classification phases. The experimental result shows that GRLVQ has better performance than the predecessor algorithm.


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Question: AN EXHAUSTIVE STUDY ON ASSOCIATION RULE MINING

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Association Rule Mining is a data mining method that enables to find out frequent item set, interesting pattern, interesting correlation among set of items in a transactional database or data repositories. This paper establishes the preparatory of basic concept about Association Rule Mining. Broadly, Association Rule Mining can be classified into Apriori, Partitioning, and Frequent Pattern Tree Algorithms. This study is to show the Benefits and Limitations.

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PROBLEMS CONNECTED WITH THE USE OF THE CORRELATION COEFFICIENT IN ANALYZING ENGINEERING DATA AND EXAMPLES OF ITS MISUSE ARE DISCUSSED. IN GENERAL, THE USE OF REGRESSION ANALYSIS SERVES THE ENGINEER BETTER. PITFALLS CONNECTED WITH COMPUTER ANALYSIS OF DATA ARE ALSO POINTED OUT. /AUTHOR/


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Question: Feature Selection for Time Series Forecasting: A Case Study

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The integration of feature selection techniques within the modeling process of a time series forecaster can improve dealing with some usual important problems in this type of tasks, such as noise reduction, the curse of dimensionality and reducing the complexity of both the problem and the solution. In this paper we show how a convenient combination of feature selection procedures with soft computing techniques can be used to solve satisfactorily a real world problem. The problem is a rather hard one and consists of forecasting the amount of incoming calls for an emergency call center, so that the center managers can make a better resource planning.

Traditional Management and Management Engineering.- Dynamic Supply and Demand Balance Problems.- Linear and Probabilistic Resource Optimization and Allocation Problems.- Forecasting of Time Series.- Business Intelligence and Data Mining.- The Use of Game Theory.- Summary of Some Fundamental Management Engineering Principles.- Concluding Remarks.


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Question: Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering

A popular recent approach to answering open-domain questions is to first search for question-related passages and then apply reading comprehension models to extract answers. Existing methods usually extract answers from single passages independently. But some questions require a combination of evidence from across different sources to answer correctly. In this paper, we propose two models which make use of multiple passages to generate their answers. Both use an answer-reranking approach which reorders the answer candidates generated by an existing state-of-the-art QA model. We propose two methods, namely, strength-based re-ranking and coverage-based re-ranking, to make use of the aggregated evidence from different passages to better determine the answer. Our models have achieved state-of-the-art results on three public open-domain QA datasets: Quasar-T, SearchQA and the open-domain version of TriviaQA, with about 8 percentage points of improvement over the former two datasets.

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Foundations of Semantic Web Reasoning.- Composing Frameworks and Components for Families of Semantic Web Applications.- Semantic Web Logic Programming Tools.- Web Rules Need Two Kinds of Negation.- Reasoning in Practice.- Towards the Adaptive Semantic Web.- On Reasoning on Time and Location on the Web.- Reasoning about Communicating Agents in the Semantic Web.- Query and Rule-Languages.- A Visual Language for Web Querying and Reasoning.- XML Document Adaptation Queries (XDAQ): An Approach to Adaptation Reasoning Using Web Query Languages.- On Types for XML Query Language Xcerpt.- Integrating Description Logics and Answer Set Programming.- Semantics and Knowledge Representation.- Extracting Mathematical Semantics from Documents.- Reasoning in Attempto Controlled English.- Systematics and Architecture for a Resource Representing Knowledge about Named Entities.


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Question: A 77-GHz FMCW Radar System Using On-Chip Waveguide Feeders in 65-nm CMOS

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E.g. ['Represent the abstract for retrieving the scientific paper title', "Like many other states, Texas is raising the speed limit on its freeways and interstates to 113 kph (70 mph) following the federal government's repeal of the 88 kph (55 mph) national maximum speed limit. Texas' new speed limit is based on the 85th percentile speed rule, which means that 85 percent of motorists will be driving at or below a reasonable speed for existing conditions. Research has shown that motorists select their speeds based on the driving environment--roadway geometry, traffic conditions, weather, and pedestrians. A common argument against raising the speed limit is that more accidents will occur; however, this argument has not held up under scrutiny."] == ['Represent the title for retrieving the corresponding scientific abstract', 'HOW ARE SPEED LIMITS SET? AN EXAMPLE FROM TXDOT']

This paper presents a 77-GHz radar system using a transmitter (Tx) and receiver (Rx) integrated with on-chip waveguide feeders in 65-nm CMOS. The newly proposed on-chip waveguide feeder shows the insertion loss of about 2 dB and more than 30% bandwidth. Additionally, both the Tx and Rx are integrated with internal $\times$ 10 frequency multipliers. Therefore, a radar system can be easily implemented without sensitive millimeter-wave packaging technology by mounting the Tx and Rx chips on the waveguide aperture. The interconnections for the low-frequency reference and baseband signals can be realized on the low-cost FR-4 printed circuit board. The radar system shows 9-dBm output power and 13-dB down-conversion gain from the waveguide port.

Preface.Acknowledgements.Glossary of Abbreviations.Glossary of Symbols.1. An Introduction to Optical Communication Systems.2. Principles of Distributed Feedback Semiconductor Laser Diodes: Coupled Wave Theory.3. Structural Impacts on the Solutions of Coupled Wave Equations: An Overview.4. Transfer Matrix Modelling in DFB Semiconductor Lasers.5. Threshold Analysis and Optimization of Various DFB LDs Using the Transfer Matrix Method.6. Above-Threshold Characteristics of DFB Laser Diodes: A TMM Approach.7. Above-Threshold Analysis of Various DFB Lase r Structures Using the TMM.8. Circuit and Transmission-Line Laser Modelling (TLLM) Techniques.9. Analysis of DFB Laser Diode Characteristics Based on the Transmission-Line Modelling (TLLM).10. Wavelength Tunable Optical Filters Based on DFB Laser Structures.11. Other Wavelenght Tunable Optical Filters Based on the DFB Laser Structure.12. Conclusion, Summary and Suggestions.Index.


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Question: Delay Tolerant Networking for smart city through drones

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Sensors in future smart cities will continuously monitor the environment in order to prevent critical situations and waste of resources or to offer new services to end users. Likely, the existing networks will not be able to sustain such a traffic without huge investments in the telecommunication infrastructure. One possible solution to overcome these problems is to apply the Delay Tolerant Network (DTN) paradigm, that is to exploit the movement of objects, possibly carrying data toward the final destination. Depending on location of data sources in the area and on paths followed by moving objects some sources could be isolated. In order to fix this issue, we use a drone, flying at proper instant of time over the isolated sources to deliver their data to the final destination. The impact of the presence of a drone as relay of the DTN is investigated accounting for realistic constraints, such as limited drone flight duration.

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Ambient Intelligence (AmI) refers to electronic environments that are sensitive and responsive to the presence of people. Ambient intelligence is closely related to the long term vision of an intelligent service system in which technologies are able to automate a platform embedding the required devices for powering context aware, personalized, adaptive and anticipatory services. Ambient intelligence is a vision on the future of consumer electronics, telecommunications and computing that was originally developed in the late 1990s for the time frame 2010-2020.This paper on Vibrant Ambient Intelligence with Agents based Service Oriented Approach focuses on the ambient intelligence environment of managing the traffic congestion control in Coimbatore city through VAISTC4 proposal without crippling the mobility of users using Mobile Internet Protocol Version 6(IPV6). It also discusses on the benefit of coupling service-oriented approach and multi-agent systems towards more appropriate interactions with users.Wi...


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Question: An Investigation of Customization in ERP System Implementations

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This research investigates why certain enterprise resource planning (ERP) system adopters have pursued high levels of software customization during implementation despite the generally accepted best-practice heuristic of limiting customization. Qualitative data from ERP adoption projects and consultants working with ERP implementations have been collected. This study empirically identifies customization drivers and explains their relationship to customization. The results suggest that high customization may occur because of: unnecessary redevelopment of functionality that is available in the ERP system standard, resistance to change based on cultural issues and low project acceptance, insufficient weight given to the implementation team's recommendations, and the implementation team's lack of opposition to customization requests. The results of this study also explain how these problems occur and why they lead to overcustomization.

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How to Think About R&D Quality. Organizing for Quality in R&D. Measuring R&D Quality. Assessing R&D Quality Status. Intellectual Property Management Process Improvement. Implementing Quality in Research: A Case Example. Views and Lessons Learned at the Top. References. Index.


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Question: Recycling zinc oxide varistor blocks for electro-active silicone composites

The recycling process toward the elimination of varistor wastes into new electro-active silicon composites has been investigated. We studied the dependence of the E-J characteristics on the aggregate content in the polymer matrix. Formulations with 40 vol.% metal oxide varistor aggregates in the 100-200 μm range exhibit reliable nonlinear behavior with a switching voltage of 280±30 V/mm. The breakdown voltage of the composite decreases as both filler's diameter and filler's volume fraction increases in the 550-220 V/mm and 440-280 V/mm range, respectively. This paves the way for the valorization of varistors based zinc oxide (ZnO) ceramics.

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Piezoelectric sensor can produce voltage when deflected (function as an energy harvester) while piezoelectric actuator can deflect when a voltage is applied. Different device applications have different requirements on the thickness and in-plane geometry of the Lead Zirconate Titanate(PZT) piezoelectric layers and thus have their own processing difficulties. In this paper, PZT-Au-Si cantilever is fabricated by eutectic bonding and dicing process.The properties of lapped PZT ceramics and silicon cantilever is also evaluated. The PZT-Au-Si cantilever applications for both piezoelectric actuators and energy harvesters have been confirmed.


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Question: Traffic accident detection through a hydrodynamic lens

In this paper we present a novel method for automatic traffic accident detection, based on Smoothed Particles Hydrodynamics (SPH). In our method, a motion flow field is obtained from the video through dense optical flow extraction. Then a thermal diffusion process (TDP) is exploited to turn the motion flow field into a coherent motion field. Approximating the moving particles to individuals, their interaction forces, represented as endothermic reactions, are computed using the enthalpy measure, thus obtaining the potential particles of interest. Furthermore, we exploit SPH that accumulates the contribution of each particle in a weighted form, based on a kernel function. The experimental evaluation is conducted on a set of video sequences collected from Youtube, and the obtained results are compared against a state of the art technique.

This thesis presents an experimental evaluation of the road grip estimator RT3 Curve manufactured by Halliday Technologies Inc. RT3 Curve can be pictured as a small trailer that is coupled behind a ...


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Question: Motivation in online learning: Testing a model of self-determination theory

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As high attrition rates becomes a pressing issue of online learning and a major concern of online educators, it is important to investigate online learner motivation, including its antecedents and outcomes. Drawing on Deci and Ryan's self-determination theory, this study proposed and tested a model for online learner motivation in two online certificate programs (N=262). Results from structural equation modeling provided evidence for the mediating effect of need satisfaction between contextual support and motivation/self-determination; however, motivation/self-determination failed to predict learning outcomes. Additionally, this study supported SDT's main theorizing that intrinsic motivation, extrinsic motivation, and amotivation are distinctive constructs, and found that the direct effect and indirect effects of contextual support exerted opposite impacts on learning outcomes. Implications for online learner support were discussed.

Informed by the Cognitive Mediation Model of media learning, this study explores how self-efficacy and information-processing strategies jointly impact the learning of health knowledge. Using survey data (N=1409), the study examines the roles that self-efficacy, motivation of media use, news attention, and elaboration play in acquiring knowledge about swine flu during the 2009 global pandemic crisis. Results support the hypothesized relationships among self-efficacy, motivation, attention to and elaboration of swine flu news, and knowledge about the flu. Implications of the findings to advance the research in mediated cognitive learning are discussed.


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Question: Projective Bundle Adjustment from Arbitrary Initialization Using the Variable Projection Method

Bundle adjustment is used in structure-from-motion pipelines as final refinement stage requiring a sufficiently good initialization to reach a useful local mininum. Starting from an arbitrary initialization almost always gets trapped in a poor minimum. In this work we aim to obtain an initialization-free approach which returns global minima from a large proportion of purely random starting points. Our key inspiration lies in the success of the Variable Projection (VarPro) method for affine factorization problems, which have close to 100 % chance of reaching a global minimum from random initialization. We find empirically that this desirable behaviour does not directly carry over to the projective case, and we consequently design and evaluate strategies to overcome this limitation. Also, by unifying the affine and the projective camera settings, we obtain numerically better conditioned reformulations of original bundle adjustment algorithms.

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Introduction. Instruments. Depth and Transverse Resolution. Phase Imaging. Applications. Appendix A: Vector Field Theory for Depth and Transverse Resolution of a CSOM.


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Question: A survey on extractive text summarization

Text Summarization is the process of obtaining salient information from an authentic text document. In this technique, the extracted information is achieved as a summarized report and conferred as a concise summary to the user. It is very crucial for humans to understand and to describe the content of the text. Text Summarization techniques are classified into abstractive and extractive summarization. The extractive summarization technique focuses on choosing how paragraphs, important sentences, etc produces the original documents in precise form. The implication of sentences is determined based on linguistic and statistical features. In this work, a comprehensive review of extractive text summarization process methods has been ascertained. In this paper, the various techniques, populous benchmarking datasets and challenges of extractive summarization have been reviewed. This paper interprets extractive text summarization methods with a less redundant summary, highly adhesive, coherent and depth information.

The web has become a large knowledge provider for society, allowing people to not just consume information but also produce it. Collaborative documents bring some significant advantages and decentralization, but they also raise questions concerning its quality. In this work, we explore the quality assessment on collaborative documents using these documents' topics. The proposed approach improved in 3.2% the accuracy of quality assesment of Wikipedia content. Then, the main contribution in this paper is an analysis of how we can use topic modelling in order to improve quality prediction performance.


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Question: Correctly Rounded Arbitrary-Precision Floating-Point Summation

We present a fast algorithm together with its low-level implementation of correctly rounded arbitrary-precision floating-point summation. The arithmetic is the one used by the GNU MPFR library: radix 2; no subnormals; each variable (each input and the output) has its own precision. We also give a worst-case complexity of this algorithm and describe how the implementation is tested.

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What is a Macro How do Macros Work A Few Short Macros Using the Macro Debugger (Rel 2.2) and STEP Mode Using MacroLearn (Rel 2.2) and Macro Record (Rel 3) Using the Macro Run Keyin Rel 2.2 and 3 The Macro Programming Commands The Art of Range Naming Creating a Macro Library in Rel 2.2 and 3 Togglesand Pop-Ups Self-Modifying Macros The If Command Counters and the For Loop The Print Macros Using String Formulas Day-of-the-Week and Mail Merge Using Multidimensional Worksheets in 3.0 Macros Special Applications and Higher Level Programming.


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Question: Live-fly, large-scale field experimentation for large numbers of fixed-wing UAVs

In this paper, we present extensive advances in live-fly field experimentation capabilities of large numbers of fixed-wing aerial robots, and highlight both the enabling technologies as well as the challenges addressed in such largescale flight operations. We showcase results from recent field tests, including the autonomous launch, flight, and landing of 50 UAVs, which illuminate numerous operational lessons learned and generate rich multi-UAV datasets. We detail the design and open architecture of the testbed, which intentionally leverages low-cost and open-source components, aimed at promoting continued advances and alignment of multi-robot systems research and practice.

Robotic Sensor Networks composed of robots and wireless sensing devices hold the potential to revolutionize environmental sciences by enabling researchers to collect data across expansive environments, over long, sustained periods of time. We report progress on building such systems for two applications. The first application is on monitoring invasive fish (common carp) in inland lakes. In the second application, the robots act as data mules and collect data from sparsely deployed wireless sensors.


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Question: Clamp-and-Forget: A self-sustainable non-invasive wireless sensor node for smart metering applications

Measuring and managing the power consumption of household appliances, as well as that of industrial machineries, is becoming more and more important to improve the distribution and usage of the electrical energy and to reduce the energy bill. We present the design of a non-invasive wireless current meter which can measure AC current up to 60 A RMS exploiting a small clamp-on inductive sensor. The novelty of the design is a subsystem consisting of a harvesting circuit designed to extract energy from the same current transducer used for measurements. Experiments have been conducted to validate the approach, to assess the accuracy of the sensing system and deviations due to the energy harvester, and to determine the condition which permits us to achieve the energy neutrality and thus, a self-sustainable smart meter.

A packaged self-powered system by hybridizing nanogenerators (PSNGS) is demonstrated. The performance of the PSNGS is tested in a biofluid and used for powering an electronic thermometer. Select waterproof universal connectors are designed and fabricated for energy and signal transmission. This PSNGS and the connectors can significantly advance the development of self-powered implanted medical devices and wearable/portable electronics.


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Question: Design and analysis of cable-connected metallic bellows as Variable Buoyancy Modules

Design of a variable buoyancy system with metallic bellows for underwater applications is presented here. Metallic bellow with a Linear Actuator is proposed as the Variable Buoyancy module. The metallic bellow is approximated as a cylinder and modelled mathematically to study the behavior of the module underwater. The response of the system at different initial orientation and the velocity of the system at different positive buoyancy are studied. The dynamics of the module, when anchored to an underwater platformand the motion characteristics of the module with various buoyancy levels are studied. Two similar modules are then cascaded one above the other and their performance is analyzed with alternative sequence of actuation.

Research on Active Vibration Control System (AVCS) is being carried out to reduce structural vibrations caused by unwanted vibrations in many application areas such as in space, aircraft structures, satellites, automobiles and civil structures (bridges), particularly at low frequencies. In this paper; we have discussed Active Vibration Control technique by using open loop control system. Result from ANSYS and experimental results from FFT analyzer were compared. The experimental results are presented for the cantilever beam excited at one of its natural frequency using active vibration control system. For open loop control system, less reduction were observed and the reasons were discussed for it. Keywords—Piezoelectric(PZT), Active Vibration Control(AVC), Cantilever beam, Actuator, Electro-dynamic shaker,ANSYS, FFT


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Question: Inductive wireless power transfer for electric vehicle dynamic charging

The aim of the paper is to present an overview of inductive wireless power transfer (WPT) technologies for the application of electric vehicles (EVs) wireless charging. The basic principles of this technology are introduced followed by a classification of power electronic architectures and the magnetic coupler design. The advantages and limitations of each technology for EV dynamic charging are discussed. The recent progress, technical issues, challenges and state-of-art researches are introduced. An efficient design of WPT charging system is proposed and studied in VEDECOM institute. The innovative solution is an optimized magnetic coupler associated with the use of new semiconductors components the most challenging EV charging application's requirements.

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Abstract With the standardization of communication done, reliable power sources are the main challenge faced by wireless system in industrial automation. While primary cells are suitable power sources, they still require maintenance after several years which increases the total cost of a wireless network. In this paper we analyze the state of the art with primary cells and the standard electronics and show possible approaches to increase the service life of wireless industrial devices.


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Question: Novel in-wheel axial-flux segmented switched reluctance motor

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In this paper a novel axial-flux segmented rotor switched reluctance motor (AFSSRM) with a stator sandwiched by two rotors with a particular form of the segments is presented. The machine has magnetic short paths with no flux reversal and the active magnetic parts are made of soft magnetic composites. An in-wheel AFSSRM, specially intended for direct drive of light motorcycles, is designed. Electromagnetic analysis of the AFSSRM is carried out using 3D finite element. Matlab-Simulink simulations of the whole in-wheel AFSSRM drive are used to verify its behavior.

A directly coupled linear permanent magnet generator of longitudinal flux-type is investigated. The generator will be used for power take-off in a wave energy converter. A combined field- and circ ...


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Question: Corpus-Oriented Grammar Development for Acquiring a Head-Driven Phrase Structure Grammar from the Penn Treebank

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This paper describes a method of semi-automatically acquiring an English HPSG grammar from the Penn Treebank. First, heuristic rules are employed to annotate the treebank with partially-specified derivation trees of HPSG. Lexical entries are automatically extracted from the annotated corpus by inversely applying HPSG schemata to partially-specified derivation trees. Predefined HPSG schemata assure the acquired lexicon to conform to the theoretical formulation of HPSG. Experimental results revealed that this approach enabled us to develop an HPSG grammar with significant robustness at small cost.

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I Classification.- 1 Introduction.- 2 Lexical Analysis: A New Approach.- 3 Author's Motivation for a Speech Act Classification.- 4 Description and Explanation of the Method.- 5 Survey of the Resulting Speech Act Classification.- 6 Prospects and Limitations.- 7 References.- II Lexicon Sections.- Directions for Using Lexicon Section I.- 8 Lexicon Section I Semantic Classification of Speech Act Verbs.- Directions for Using Lexicon Section II.- 9 Lexicon Section II Alphabetic Index of Speech Act Verbs.


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Question: Disordered speech recognition using acoustic and sEMG signals.

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Parallel isolated word corpora were collected from healthy speakers and individuals with speech impairment due to stroke or cerebral palsy. Surface electromyographic (sEMG) signals were collected for both vocalized and mouthed speech production modes. Pioneering work on disordered speech recognition using the acoustic signal, the sEMG signals, and their fusion are reported. Results indicate that speakerdependent isolated-word recognition from the sEMG signals of articulator muscle groups during vocalized disorderedspeech production was highly effective. However, word recognition accuracy for mouthed speech was much lower, likely related to the fact that some disordered speakers had considerable difficulty producing consistent mouthed speech. Further development of the sEMG-based speech recognition systems is needed to increase usability and robustness.

A discriminative feature extraction based on Principal component analysis (PCA) and Gaussian mixture models is presented to increase the discrimative capability of modified two dimentional root cepstrum analysis (MTDRC). The exprimental results show that F-ratio tests indicate better separability of phonemes by using discriminative feature extraction than MTDRC.


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Question: Spreadsheet data manipulation using examples

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Millions of computer end users need to perform tasks over large spreadsheet data, yet lack the programming knowledge to do such tasks automatically. We present a programming by example methodology that allows end users to automate such repetitive tasks. Our methodology involves designing a domain-specific language and developing a synthesis algorithm that can learn programs in that language from user-provided examples. We present instantiations of this methodology for particular domains of tasks: (a) syntactic transformations of strings using restricted forms of regular expressions, conditionals, and loops, (b) semantic transformations of strings involving lookup in relational tables, and (c) layout transformations on spreadsheet tables. We have implemented this technology as an add-in for the Microsoft Excel Spreadsheet system and have evaluated it successfully over several benchmarks picked from various Excel help forums.

A problem that the writer faced during the course of a actual subject was analyzed, and a viable solution was proposed to settle this problem. Implementation of importing and exporting data of an office document to SQL Server field based on ADO Stream object as well as the ADO Stream object and its main technique were introduced.


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Question: Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

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Objectives: A modified variable step block backward differentiation formulae (MVS-BBDF) method is introduced in this paper as another alternative way for solving ordinary differential equations (ODEs). Methods: We demonstrated the detailed formulation of the corrector formulae for MVS-BBDF method which is carried out using Maple software. Then, to validate the performance of the introduced method, we applied it to stiff ODEs problem. Findings: The performance of the method in terms of maximum error and number of total steps taken during the computation are compared with the performance of ode15s and ode23s solver in MATLAB. Consequently, the efficiency of MVS-BBDF shows that it is able to outperform both Matlab’s ODE solver since it produces better accuracy and manages to reduce the number of total step.

Objectives ::: There has been continuous development in the area of stereoscopic medical imaging devices, and many stereoscopic imaging devices have been realized and applied in the medical field. In this article, we review past and current trends pertaining to the application stereo-imaging technologies in the medical field.


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Question: Age of language acquisition and cortical language organization in multilingual patients undergoing awake brain mapping

OBJECTIVEMost knowledge regarding the anatomical organization of multilingualism is based on aphasiology and functional imaging studies. However, the results have still to be validated by the gold standard approach, namely electrical stimulation mapping (ESM) during awake neurosurgical procedures. In this ESM study the authors describe language representation in a highly specific group of 13 multilingual individuals, focusing on how age of acquisition may influence the cortical organization of language.METHODSThirteen patients who had a high degree of proficiency in multiple languages and were harboring lesions within the dominant, left hemisphere underwent ESM while being operated on under awake conditions. Demographic and language data were recorded in relation to age of language acquisition (for native languages and early- and late-acquired languages), neuropsychological pre- and postoperative language testing, the number and location of language sites, and overlapping distribution in terms of language...

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Purpose To assess the utility of a wide range of language measures (phonology, morphology, syntax, and semantics) for the identification of adults with developmental language impairment. Method Mea...


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Question: A block-based noise level estimation from X-ray images in SVD domain

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Accurate and fast estimation of noise levels from medical images has numerous applications in medical image processing, including image enhancement, image segmentation and feature extraction. In this paper, a block-based noise level estimation algorithm in SVD domain is proposed. The proposed algorithm employs the non-overlapping block image segmentation to estimate homogenous image regions. Each homogenous block is used to obtain an independent noise level estimates in SVD domain. For any particular image, the overall noise level estimate is ascertained by averaging over the set of noise level estimates associated with the homogenous image blocks. In this paper, the optimal size of image segmentation blocks is evaluated systematically over a large dataset of x-ray images. The experimental results show that the proposed method offers numerous advantages over some alternative SVD domain method.

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The article introduces the m×n S shape scanning field division method and the arbitrary scanning field division method.


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Question: Nested Sequential Monte Carlo Methods

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We propose nested sequential Monte Carlo (NSMC), a methodology to sample from sequences of probability distributions, even where the random variables are high-dimensional. NSMC generalises the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm. Furthermore, NSMC can in itself be used to produce such properly weighted samples. Consequently, one NSMC sampler can be used to construct an efficient high-dimensional proposal distribution for another NSMC sampler, and this nesting of the algorithm can be done to an arbitrary degree. This allows us to consider complex and high-dimensional models using SMC. We show results that motivate the efficacy of our approach on several filtering problems with dimensions in the order of 100 to 1 000.

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Images, Windows and Operators. Methodical Fundamentals. Algorithmic Fundamentals. Coordinate Transformations and Geometrical Operators. Gray Scale Transformations and Point Operators. Window Functions and Local Operators. Global Operators. Glossary. Index.


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Question: A conceptual framework for assessing electronic banking continued use

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For instance, <<['represent this scientific intro in order to find the title of the paper.', 'Based on ecological characteristics of land types,and by using Zhengning County,Gansu Province located on the Loess Plateau as a study area,the coupled relation between ecological suitability and ecological system of land types was studied.The research indicated that there is a satisfactory coupled relation between land types and ecological systems,which provides the main basis for land ecological evaluation and construction of rational ecological system structure.With restructuring rational ecological system and increasing comprehensive ecological functions in Zhengning County as their objectives,the authors proposed ecological construction strategies and measures for the county,which is also helpful to the ecological construction on the Loess Plateau.']>>  to "['Represent the title for retrieving the corresponding scientific abstract', 'Analysis of Ecological Suitability and Rational Ecological System Structure of Land Types:A Case Study in Zhengning County,Gansu Province']"

The development and advancement of electronic banking (e-banking) had raised the level competitiveness in the market for financial products and services. More and more banks and other financial institutions adopted such technology to improve service quality and enhance customer satisfaction and loyalty. Despite the increasing number e-banking users globally, little is known on how this technology can help banks to retain customers. Furthermore, it is only recently that researchers have started to understand the importance of consumers' continued use. Therefore, it is the aim of this paper to propose a framework based on an extensive review of current literature in related areas of e-banking domain and continued use. The main benefits of the proposed framework are towards understanding factors that may influence the e-banking continuance usage.

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The author sets out to achieve a better measurement of interorganisational power in distribution channels.


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Question: Control strategies in object manipulation tasks

The remarkable manipulative skill of the human hand is not the result of rapid sensorimotor processes, nor of fast or powerful effector mechanisms. Rather, the secret lies in the way manual tasks are organized and controlled by the nervous system. At the heart of this organization is prediction. Successful manipulation requires the ability both to predict the motor commands required to grasp, lift, and move objects and to predict the sensory events that arise as a consequence of these commands.

This paper describes a robots system BeRoSH which incorporates a behaviour-based dynamic cooperation strategy for multiple behaviour-based robots. This behaviour-based dynamic cooperation strategy is realized in two steps: designing the distributed robot's cooperative behavioural attributes according to its abilities, and organizing these behavioural attributes. Through the behaviour design, this strategy allows the dynamic cooperation control to be distributed equally to each robot. In organizing the robots' abilities, the concept of form closure is introduced as the basic strategy. An experimental robot system and experimental results of object transportation and assembly work are also shown.


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The query could be '['Represent this title from a paper to retrieve its scientific short abstract', 'Integrating conservation and sustainable use of biodiversity : Four Examples of Ecosystem Management Areas in Germany and Sweden']' and should be close to '['Represent this abstract from a paper to retrieve its scientific title', 'The loss of biodiversity is recognised as a tremendous threat to ecosystems, but its maintenance is challenging. One important issue is seen as decisive for its success: the integration of sustaina ...']' but very far from '['Represent this scientific intro in order to find the title of the paper', 'Environmental Challenges, Regulations and Institutions in Saudi Arabia.- Environmental Concerns and Policies in Saudi Arabia.- Environmental Investments.- Incentive Structures.- Environmental Business Channels.- The Way Forward.']'

Question: AutoPart: Parameter-Free Graph Partitioning and Outlier Detection

Graphs arise in numerous applications, such as the analysis of the Web, router networks, social networks, co-citation graphs, etc. Virtually all the popular methods for analyzing such graphs, for example, k-means clustering, METIS graph partitioning and SVD/PCA, require the user to specify various parameters such as the number of clusters, number of partitions and number of principal components. We propose a novel way to group nodes, using information-theoretic principles to choose both the number of such groups and the mapping from nodes to groups. Our algorithm is completely parameter-free, and also scales practically linearly with the problem size. Further, we propose novel algorithms which use this node group structure to get further insights into the data, by finding outliers and computing distances between groups. Finally, we present experiments on multiple synthetic and real-life datasets, where our methods give excellent, intuitive results.

These invited talks discuss the following: STG Decomposition and Its Correctness; Asynchronous Network-On-Chip and its Potential.


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Question: Adaptive Object Detection Using Adjacency and Zoom Prediction

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State-of-the-art object detection systems rely on an accurate set of region proposals. Several recent methods use a neural network architecture to hypothesize promising object locations. While these approaches are computationally efficient, they rely on fixed image regions as anchors for predictions. In this paper we propose to use a search strategy that adaptively directs computational resources to sub-regions likely to contain objects. Compared to methods based on fixed anchor locations, our approach naturally adapts to cases where object instances are sparse and small. Our approach is comparable in terms of accuracy to the state-of-the-art Faster R-CNN approach while using two orders of magnitude fewer anchors on average. Code is publicly available.


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Question: Predictive-corrective incompressible SPH

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We present a novel, incompressible fluid simulation method based on the Lagrangian Smoothed Particle Hydrodynamics (SPH) model. In our method, incompressibility is enforced by using a prediction-correction scheme to determine the particle pressures. For this, the information about density fluctuations is actively propagated through the fluid and pressure values are updated until the targeted density is satisfied. With this approach, we avoid the computational expenses of solving a pressure Poisson equation, while still being able to use large time steps in the simulation. The achieved results show that our predictive-corrective incompressible SPH (PCISPH) method clearly outperforms the commonly used weakly compressible SPH (WCSPH) model by more than an order of magnitude while the computations are in good agreement with the WCSPH results.

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Stohastic description temporally variable subsurface flow spatially variable subsurface flow transport processes in heterogeneous media geostatistical methods and parameter estimation.


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Question: Connecting Devices to Cookies via Filtering, Feature Engineering, and Boosting

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We present a supervised machine learning system capable of matching internet devices to web cookies through filtering, feature engineering, binary classification, and post processing. The system builds a reasonably sized training and testing data set through filtering and feature engineering. We build 415 features in total. Some of these features were engineered to be O(n) time, stand alone classifiers for this problem. Other features use various natural language processing (NLP) techniques. Meta features are created by ridge regression and Adaboost. Then binary classification through two different gradient boosting (XGBoost with logarithmic loss) models is performed. A post processing pipeline connects devices and cookies in a way that maximizes F_0.5 score. Our machine learning system obtained a private F_0.5 score of 0.849562 for a final rank of 12th/340 on the ICDM 2015: Drawbridge Cross-Device Connections challenge.

Getting Started Creating and Building a Database Building Up Your Command Library Creating Reports and Labels Variables Writing Simple Programs and Beyond Writing Simple Programs Writing Menu Programs Additional Functions and Commands Advanced Applications Putting It All Together Appendixes Index.


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Question: Efficient Bayesian Network Learning for System Optimization in Reliability Engineering

We present a new Bayesian network modeling that learns the behavior of an unknown system from real data and can be used for reliability engineering and optimization processes in industrial systems. The suggested approach relies on quantitative criteria for addressing the trade-off between the complexity of a learned model and its prediction accuracy. These criteria are based on measures from Information Theory as they predetermine both the accuracy as well as the complexity of the model. We illustrate the proposed method by a classical example of system reliability engineering. Using computer experiments, we show how in a targeted Bayesian network learning, a tremendous reduction in the model complexity can be accomplished, while maintaining most of the essential information for optimizing the system.

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Partial table of contents: FUNDAMENTALS. Reliability Physics and Failure Mechanisms. Statistical Distributions. MODELLING. The Load-Strength Concept. Intrinsic Reliability - The Generic Case. Extrinsic Reliability. EVALUATION. Lifetesting. Field Failure Data Analysis. Reliability Prediction of Electronic Equipment. ASSURANCE. Reliability Screening: Burn-in. Reliability Indicator Screening. Appendices. Index.


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Question: Recurrent Neural Networks for Online Video Popularity Prediction

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"['Represent this scientific intro in order to find the title of the paper', "Having flexible payment options ensures that patients will have all of the motivation and information that they need to accept care in your office. An HCCC simply offers you the chance to make more expensive treatment available to those who need or want it. The small cost of using a HCCC program more than pays for itself--in fact, it costs less than one-third of what you would spend if you were to bill these patients instead. Having consistent financial policies and flexible payment options can dramatically increase your office's productivity and profitability, while expressing your commitment to customer service and high-quality dentistry."]" == "['Represent this paper title in order to find a scientific intro to what it is about', 'Providing patient-friendly financial options.']"

In this paper, we address the problem of popularity prediction of online videos shared in social media. We prove that this challenging task can be approached using recently proposed deep neural network architectures. We cast the popularity prediction problem as a classification task and we aim to solve it using only visual cues extracted from videos. To that end, we propose a new method based on a Long-term Recurrent Convolutional Network (LRCN) that incorporates the sequentiality of the information in the model. Results obtained on a dataset of over 37’000 videos published on Facebook show that using our method leads to over 30% improvement in prediction performance over the traditional shallow approaches and can provide valuable insights for content creators.

Why Data Mining is Important: What is Customer-Centric Data Mining? How Data Mining Can Enhance Your Services and Products Data Mining Can Solve Your Most Difficult Problems The Data Mining Process Pillars of the Data Mining Framework: The Information Technology of Business Intelligence The Data in Data Mining The Mathematics of Data Mining Data Mining Techniques: Knowledge Discovery Data Mining Techniques: More Knowledge Discovery Data Mining Techniques: Predictive Models Data Mining Management: Common Reasons Data Mining Projects Fail Overcoming Obstacles Successful Data Mining Project Management Data Mining in Practice--Case Studies: Data Mining in: Customer Service, Retail, Insurance, Financial Services, Health Care and Medicine, Telecommunications, Transportation and Logistics, Energy, and Government Glossary Bibliography Appendixes: Vendor Information "STAT 101" Techniques Listed by Methodology Phase


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Question: Distance metric learning with eigenvalue fine tuning

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Distance metric learning focuses on learning one global or multiple local distance functions to draw similar instances close to each other and push away dissimilar ones. Most existing work has to do matrix projection to learn distance functions. In this paper, we present a novel distance function learning model which is based on eigenvalue fine tuning. Our model not only is able to learn the global distance function but also can be easily adopted into local metric learning tasks. From the perspective of dimension reduction, the proposed model can measure how much information has been preserved after feature transformation. Moreover, we connect our model with principal components analysis to improve its performance by introducing the label information. Experimental results have demonstrated the effectiveness of the proposed method.

Multi instance multi label learning is a framework in which objects are represented as bags of instances and labels are provided at the bag level. Instance annotation is the problem of assigning labels to the instances in a bag given only the bag label. Recently, OR-ed logistic regression (OR-LR) model and an EM based inference method have been proposed for instance annotation. Due to the linear nature of the logistic regression function, OR-LR performance on linearly inseparable data is limited. This paper addresses this problem by proposing a regularized kernel-based extension to the OR-LR framework. Experiments show that the kernel-based OR-LR algorithm achieves a significant improvement in classification accuracy over the linear OR-LR from 3% to 9% on audio bird song and image annotation datasets and two synthetic datasets.


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Question: A 200-245 GHz Balanced Frequency Doubler with Peak Output Power of +2 dBm

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This paper presents a wideband 90 nm SiGe BiCMOS frequency multiplier at 200-245 GHz. The balanced multiplier results in a low first harmonic component, and uses a reflector at the base nodes to reflect the second harmonics to transistors for improved efficiency. The measured output power is > -2 dBm at 200-245 GHz with a peak value of +2 dBm at 224-228 GHz and a conversion gain of -15 dB. To the author's knowledge, this is the highest power wideband doubler at 200-250 GHz.

Preface.Acknowledgements.Glossary of Abbreviations.Glossary of Symbols.1. An Introduction to Optical Communication Systems.2. Principles of Distributed Feedback Semiconductor Laser Diodes: Coupled Wave Theory.3. Structural Impacts on the Solutions of Coupled Wave Equations: An Overview.4. Transfer Matrix Modelling in DFB Semiconductor Lasers.5. Threshold Analysis and Optimization of Various DFB LDs Using the Transfer Matrix Method.6. Above-Threshold Characteristics of DFB Laser Diodes: A TMM Approach.7. Above-Threshold Analysis of Various DFB Lase r Structures Using the TMM.8. Circuit and Transmission-Line Laser Modelling (TLLM) Techniques.9. Analysis of DFB Laser Diode Characteristics Based on the Transmission-Line Modelling (TLLM).10. Wavelength Tunable Optical Filters Based on DFB Laser Structures.11. Other Wavelenght Tunable Optical Filters Based on the DFB Laser Structure.12. Conclusion, Summary and Suggestions.Index.


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Question: Big Data Validation and Quality Assurance -- Issuses, Challenges, and Needs

With the fast advance of big data technology and analytics solutions, big data computing and service is becoming a very hot research and application subject in academic research, industry community, and government services. Nevertheless, there are increasing data quality problems resulting in erroneous data costs in enterprises and businesses. Current research seldom discusses how to effectively validate big data to ensure data quality. This paper provides informative discussions for big data validation and quality assurance, including the essential concepts, focuses, and validation process. Moreover, the paper presents a comparison among big data validation tools and several major players in industry are discussed. Furthermore, the primary issues, challenges, and needs are discussed.

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How to Think About R&D Quality. Organizing for Quality in R&D. Measuring R&D Quality. Assessing R&D Quality Status. Intellectual Property Management Process Improvement. Implementing Quality in Research: A Case Example. Views and Lessons Learned at the Top. References. Index.


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Question: Efficient techniques for dynamic vehicle detection

Fast detection of moving vehicles is crucial for safe autonomous urban driving. We present the vehicle detection algorithm developed for our entry in the Urban Grand Challenge, an autonomous driving race organized by the U.S. Government in 2007. The algorithm provides reliable detection of moving vehicles from a high-speed moving platform using laser range finders. We present the notion of motion evidence, which allows us to overcome the low signal-to-noise ratio that arises during rapid detection of moving vehicles in noisy urban environments. We also present and evaluate an array of optimization techniques that enable accurate detection in real time. Experimental results show empirical validation on data from the most challenging situations presented at the Urban Grand Challenge as well as other urban settings.

A new framework for description and analysis of uncertainty in dynamic systems is briefly described.


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Question: Flying-capacitor boost converter

This paper introduces the flying-capacitor dc-dc boost converter. In the proposed topology, a high voltage gain ratio and improved conversion efficiency are achieved with low inductor core and copper losses. The input current ripple of the new converter is low while its ripple frequency is high, yielding a low-sized input filter. Furthermore, the voltage stresses on the power switches and diodes of the proposed converter are considerably low. It is shown that the proposed converter can be extended to higher stages to achieve a higher voltage gain ratio at enhanced efficiency. The principle of operation of the new converter is proposed, and the comparison with the conventional boost topology is presented. A complete analysis of this converter in the steady state and small signal operation is performed as well. Simulation results and a design example are also given to demonstrate the effectiveness of the proposed power converter.

Reduction of switching loss, EMI( electromagnetic interference) and input power factor increase for the switched mode power supply is a matter of great importance Especially in case of battery charger for electrical vehicle requires !ugh power factor and efficiency, so apply power factor correction circuit and the switching method of reduction of switching loss Also battery for using load, isolation of input and output stage is necessary. ::: In this paper show the computer simulation and expenment of isolated single stage single phase AC/DC Full Bridge Boost Converter And introduce to an electrical vehicle system.


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Question: Heterogeneous network-based trust analysis: a survey

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Different information sources publish information with different degrees of correctness and originality. False information can often result in considerable damage. Hence, trustworthinessof information is an important issue in this datadriven world economy. Reputation of different agents in a network has been studied earlier in a variety of domains like e-commerce, social sciences, sensor networks, and P2P networks. Recently there has been work in the data mining community on performing trust analysis based on the data provided by multiple information providers for different objects, and such agents and their provided information about data objects form a multi-typed heterogeneous network. The trust analysis under such a framework is considered as heterogeneous network-based trust analysis. This paper will survey heterogeneous network-based trust analysis models and their applications. We would conclude with a summary and some thoughts on future research in the area.

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In this chapter, we propose a systematic architecture on data-driven personalized QoE management. A framework of the QoE management architecture is firstly introduced, which consists of two modules namely (1) training module and (2) control module. We also depict two models for the prediction of user preference, including Bayesian Graphic Model and Context Aware Matrix Factorization Model. A preliminary use case is deployed to demonstrate and evaluate the proposed architecture. Simulation results illustrate the superior performance of proposed architecture compared with traditional water-filling method.


Question: Learning for larger datasets with the Gaussian process latent variable model

In this paper we apply the latest techniques in sparse Gaussian process regression (GPR) to the Gaussian process latent variable model (GPLVM). We review three techniques and discuss how they may be implemented in the context of the GP-LVM. Each approach is then implemented on a well known benchmark data set and compared with earlier attempts to sparsify the model.

In many data mining applications, the data manifold is of lower dimension than the dimension of the input space. In this paper, it is proposed to take advantage of this additional information in the frame of variational mixtures. The responsibilities computed in the VBE step are constrained according to a discrepancy measure between the Euclidean and the geodesic distance. The methodology is applied to variational Gaussian mixtures as a particular case and outperforms the standard approach, as well as Parzen windows, on both artificial and real data.


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Question: Proposal: A Design of E-learning User Authentication System

The user authentication is very crucial in developing an e-learning system. Emerging standards for distance learning and education influence in a major way the development of e-learning systems. E-learning system must be secured against manipulation from the side of the students and also it protects user's privacy. This paper examines privacy and security issues associated with e-learning. It presents the basic principles behind privacy practices and legislation. Concrete solutions for improving the degree of security achievable are shown. A design of an e-learning user authentication system is introduced. This framework prevents manipulation from the side of the students during learning, thus allowing a reliable control of learning success.

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AbstractThe following sections are included:IntroductionThe Information Sharing to Make Use of an ICT SystemThe Evaluation Function of the ICT SystemThe Value Propagation Chain Caused by the ICT SystemActive Use of the Evaluation Function in KPMAn Example of Visualizing the Effect of Risk Distribution on InvestmentSample calculationDiscussionConclusionReferences


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Question: SAGE: whitebox fuzzing for security testing

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SAGE has had a remarkable impact at Microsoft.

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This paper expounds the problems of the ob- ject- oriented database- MIS on Borland C++for Windows, and makes researching on currentnature in detail. [


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Question: Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields

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The Fast Style Transfer methods have been recently proposed to transfer a photograph to an artistic style in real-time. This task involves controlling the stroke size in the stylized results, which remains an open challenge. In this paper, we present a stroke controllable style transfer network that can achieve continuous and spatial stroke size control. By analyzing the factors that influence the stroke size, we propose to explicitly account for the receptive field and the style image scales. We propose a StrokePyramid module to endow the network with adaptive receptive fields, and two training strategies to achieve faster convergence and augment new stroke sizes upon a trained model respectively. By combining the proposed runtime control strategies, our network can achieve continuous changes in stroke sizes and produce distinct stroke sizes in different spatial regions within the same output image.

These invited talks discuss the following: STG Decomposition and Its Correctness; Asynchronous Network-On-Chip and its Potential.


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Question: A predictive model for identifying students with dropout profiles in online courses

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Online education often deals with the problem related to the high students’ dropout rate during a course in many areas. There is huge amount of historical data about students in online courses. Hence, a relevant problem on this context is to examine those data, aiming at finding effective mechanisms to understand student profiles, identifying those students with characteristics to drop out at early stage in the course. In this paper, we address this problem by proposing predictive models to provide educational managers with the duty to identify students whom are in the dropout bound. Four classification algorithms with different classification methods were used during the evaluation, in order to find the model with the highest accuracy in prediction the profile of dropouts students. Data for model generation were obtained from two data sources available from University. The results showed the model generated by using SVM algorithm as the most accurate among those selected, with 92.03% of accuracy.

This research examines the effectiveness of a training intervention program designed to improve strategies for learning from texts and to determine whether the success of this intervention depends ...


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Question: Towards quality metrics for OpenStreetMap

Volunteered Geographic Information (VGI) is currently a "hot topic" in the GIS community. The OpenStreetMap (OSM) project is one of the most popular and well supported examples of VGL Traditional measures of spatial data quality are often not applicable to OSM as in many cases it is not possible to access ground-truth spatial data for all regions mapped by OSM. We investigate to develop measures of quality for OSM which operate in an unsupervised manner without reference to a "trusted" source of ground-truth data. We provide results of analysis of OSM data from several European countries. The results highlight specific quality issues in OSM. Results of comparing OSM with ground-truth data for Ireland are also presented.

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The chapter deals with the issue of context definition in ::: adaptive cartography for the flood event.


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Question: Radiation pattern measurement system for millimeter-wave antenna fed by contact probe

This paper presents a radiation pattern measurement system in order to evaluate mm-wave antenna fed by contact probe. It reduces the transmission loss by using a waveguide as a revolving arm. Three cut patterns can be measured by changing the orientation of the revolving arm. A differential feeding dipole antenna with a reflection plate was evaluated by using this measurement system. The measurement result of half-power beam width agrees well with the simulation result and the dynamic range is more than 20dB. By covering the possible scatterers in the anechoic chamber by planar electromagnetic absorber, the ripple in the half-power beam width is suppressed below ±0.5dB.

Previously, we demonstrated the integration of an electrically small EZ antenna with a mesoband high power microwave (HPM) quarter-wave oscillator source. This design was confirmed with both computer modeling and experimental results for an EZ antenna with ka = 0.47. We also have reported results from computer simulations of a dual-band HPM EZ antenna system with resonances near 500 MHz and 1.5 GHz. In this paper we discuss the design of an EZ antenna array fed via the same mesoband source through a radial waveguide designed for handling high power levels.


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Question: A Survey of Definitions and Models of Exploratory Search

Exploratory search has an unclear and open-ended definition. The complexity of the task and the difficulty of defining this activity are reflected in the limits of existing evaluation methods for exploratory search systems. In order to improve them, we intend to design an evaluation method based on a user-centered model of exploratory search. In this work, we identified and defined the characteristics of exploratory search and used them as an information seeking model evaluation grid. We tested this analytic grid on two information seeking models: Ellis' and Marchionini's models. The results show that Marchonini's model does not match our evaluation method's requirements whereas on the other hand Ellis' model could be adapted to better suit exploratory search.

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1. An Introduction to Geographical Information Systems and Participatory Geographical Information Science 2. A Micro-Macro Framework for Participatory Decision Situations 3. Methods and Tools for Participatory Spatial Decision Support 4. Social Behavioural Research Strategies for Investigating the Use of Participatory Geographical Information Systems 5. Collaborative Spatial Decision Making in Primary Health Care Management 6. Transportation Improvement Program Decision Making 7. Collaborative Decision Making About Habitat Restoration 8. Conclusions and Prospects for Future Research


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Question: Evidence relating to Object-Oriented software design: A survey

There is little empirical knowledge of the effectiveness of the object-oriented paradigm. To conduct a systematic review of the literature describing empirical studies of this paradigm. We undertook a Mapping Study of the literature. 138 papers have been identified and classified by topic, form of study involved, and source. The majority of empirical studies of OO concentrate on metrics, relatively few consider effectiveness.

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The current paper presents a solution of the Program Understanding: A Reengineering Case for the Transformation Tool Contest using the VIATRA2 model transformation tool.


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Question: Chaotic Time Series Prediction Based on a Novel Robust Echo State Network

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In this paper, a robust recurrent neural network is presented in a Bayesian framework based on echo state mechanisms. Since the new model is capable of handling outliers in the training data set, it is termed as a robust echo state network (RESN). The RESN inherits the basic idea of ESN learning in a Bayesian framework, but replaces the commonly used Gaussian distribution with a Laplace one, which is more robust to outliers, as the likelihood function of the model output. Moreover, the training of the RESN is facilitated by employing a bound optimization algorithm, based on which, a proper surrogate function is derived and the Laplace likelihood function is approximated by a Gaussian one, while remaining robust to outliers. It leads to an efficient method for estimating model parameters, which can be solved by using a Bayesian evidence procedure in a fully autonomous way. Experimental results show that the proposed method is robust in the presence of outliers and is superior to existing methods.

A. Filippidis, M. Russo, L.C. Jain: Novel Extension of ART2 in Surface Landmine Detection.- M. Blume: An Efficient Mapping of Fuzzy ART onto a Neural Architecture.- M. Blume, D.A. Van Blerkom: Fuzzy ARTMAP Modifications for Intersecting Class Distributions.- A.-H. Tan: Supervised Adaptive Resonance Theory and Rules.- G. Bartfai, R. White: Incremental Learning and Optimization of Hierarchical Clusterings with ART-Based Modular Networks.- C.P. Lim, R.F. Harrison: ART-Based Autonomous Learning Systems: Part I - Architectures and Algorithms.- C.P. Lim, R.F. Harrison: ART-Based Autonomous Learning Systems: Part II - Applications.- T. Serrano-Gotarredona, B. Linares-Barranco: Adaptive Resonance Theory Microchips.


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Question: The GRASP Multiple Micro UAV Testbed

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In the last five years, advances in materials, electronics, sensors, and batteries have fueled a growth in the development of microunmanned aerial vehicles (MAVs) that are between 0.1 and 0.5 m in length and 0.1-0.5 kg in mass [1]. A few groups have built and analyzed MAVs in the 10-cm range [2], [3]. One of the smallest MAV is the Picoftyer with a 60-mmpropellor diameter and a mass of 3.3 g [4]. Platforms in the 50-cm range are more prevalent with several groups having built and flown systems of this size [5]-[7]. In fact, there are severalcommercially available radiocontrolled (PvC) helicopters and research-grade helicopters in this size range [8].

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Group 5's collection of small collectrions Placeholder Description for the Liberal Arts Collection


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Question: Real-time texture error detection on textured surfaces with compressed sensing

We present a real-time approach to detect and localise defects in grey-scale textures within a Compressed Sensing framework. Inspired by recent results in texture classification, we use compressed local grey-scale patches for texture description. In a first step, a Gaussian Mixture model is trained with the features extracted from a handful of defect-free texture samples. In a second step, the novelty detection of texture samples is performed by comparing each pixel to the likelihood obtained in the training process. The inspection stage is embedded into a multi-scale framework to enable real-time defect detection and localisation. The performance of compressed grey-scale patches for texture error detection is evaluated on two independent datasets. The proposed method is able to outperform the performance of non-compressed grey-scale patches in terms of accuracy and speed.

In this paper, a new method for efficient generation of video hologram for 3-D video is proposed by combined use of redundant data of 3-D video and look-up table techniques.


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Question: Humanoid robot Lola: Design and walking control

In this paper we present the humanoid robot LOLA, its mechatronic hardware design, simulation and real-time walking control. The goal of the LOLA-project is to build a machine capable of stable, autonomous, fast and human-like walking. LOLA is characterized by a redundant kinematic configuration with 7-DoF legs, an extremely lightweight design, joint actuators with brushless motors and an electronics architecture using decentralized joint control. Special emphasis was put on an improved mass distribution of the legs to achieve good dynamic performance. Trajectory generation and control aim at faster, more flexible and robust walking. Center of mass trajectories are calculated in real-time from footstep locations using quadratic programming and spline collocation methods. Stabilizing control uses hybrid position/force control in task space with an inner joint position control loop. Inertial stabilization is achieved by modifying the contact force trajectories.

Three-dimensional structure handling reactions and synthesis property prediction and analysis integrated systems.


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Question: TrustStore: Making Amazon S3 Trustworthy with Services Composition

The enormous amount of data generated in daily operations and the increasing demands for data accessibility across organizations are pushing individuals and organizations to outsource their data storage to cloud storage services. However, the security and the privacy of the outsourced data goes beyond the data owners' control. We propose a service composition approach to preserve privacy for data stored in untrusted storage service. A virtual file system, called Trust Store, is prototyped to demonstrate this concept. It allows users utilize untrusted storage service provider with confidentiality and integrity of the data preserved. We deployed the prototype with Amazon S3 and evaluate its performance.

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This chapter contains sections titled: About SourceForge, What Developers Say about Success and Abandonment, Our Success/Abandonment Classification System, Making Our Classification Work in the Real World, Conclusion


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Question: An Energy-Efficient Adaptive Sensing Framework for Gait Monitoring Using Smart Insole

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Gait analysis is an important process to gauge human motion. Recently, longitudinal gait analysis received much attention from the medical and healthcare domains. The challenge in studies over extended time periods is the battery life. Due to the continuous sensing and computing, wearable gait devices cannot fulfill a full-day work schedule. In this paper, we present an energy-efficient adaptive sensing framework to address this problem. Through presampling for content understanding, a selective sensing and sparsity-based signal reconstruction method is proposed. In particular, we develop and implement the new sensing scheme in a smart insole system to reduce the number of samples, while still preserving the information integrity of gait parameters. Experimental results show the effectiveness of our method in data point reduction. Our proposed method improves the battery life to 10.47 h, while normalized mean square error is within 10%.

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Part 1: Application Mapping and Communication Infrastructure: Virtualization in NOCs--Enhanced MPSOC Robustness and Performance Verification.-HW Support to Exploit Parallelism in Homogeneous and Heterogeneous Multicore System-on-Chip.-PALLAS: Mapping Applications onto Manycore.-Part 2: Reconfigurable Hardware in Multiprocessor Systems: Adaptive Multiprocessor System on Chip Architecture.-Designing FPGA Systems with Many Processors.-Part 3: Physical Design of Multiprocessor Systems: Design tools and methods for chip physical design.-Challenges in Physical Design for Multi- and Manycore Hardware Architectures.


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Question: Latent semantic indexing and convolutional neural network for multi-label and multi-class text classification

The classification of a real text should not be necessarily treated as a binary or multi-class classification, since the text may belong to one or more labels. This type of problem is called multi-label classification. In this paper, we propose the use of latent semantic indexing to text representation, convolutional neural networks to feature extraction and a single multi layer perceptron for multi-label classification in real text data. The experiments show that the model outperforms state of the art techniques when the dataset has long documents, and we observe that the precision is poor when the size of the texts is small.

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We present a generic model to describe image and video content by a combination of semantic entities and low level features for semantically meaningful and fast retrieval. The proposed model includes semantic entities such as Object, Event and Actors to express relations between the first two. The use of Actors entity increases the efficiency of certain types of search, while the use of semantic and linguistic roles increases the expression capability of the model. The model also contains links to high-level media segments such as actions and interactions, and low level media segments such as elementary motion and reaction units, as well as low-level features such as motion parameters and trajectories. Based on this model, we propose image and video retrieval combining semantic and low-level information. The retrieval performance of our system is tested by using query-by-annotation, query-by-example, query-by-sketch, and a combination of them.


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Question: An Empirical Study of Language CNN for Image Captioning

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Language models based on recurrent neural networks have dominated recent image caption generation tasks. In this paper, we introduce a language CNN model which is suitable for statistical language modeling tasks and shows competitive performance in image captioning. In contrast to previous models which predict next word based on one previous word and hidden state, our language CNN is fed with all the previous words and can model the long-range dependencies in history words, which are critical for image captioning. The effectiveness of our approach is validated on two datasets: Flickr30K and MS COCO. Our extensive experimental results show that our method outperforms the vanilla recurrent neural network based language models and is competitive with the state-of-the-art methods.


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Question: Automated Tissue Image Analysis Using Pattern Recognition

Automated tissue image analysis aims to develop algorithms for a variety of histological applications. This has important implications in the diagnostic grading of cancer such as in breast and pros ...

Why Data Mining is Important: What is Customer-Centric Data Mining? How Data Mining Can Enhance Your Services and Products Data Mining Can Solve Your Most Difficult Problems The Data Mining Process Pillars of the Data Mining Framework: The Information Technology of Business Intelligence The Data in Data Mining The Mathematics of Data Mining Data Mining Techniques: Knowledge Discovery Data Mining Techniques: More Knowledge Discovery Data Mining Techniques: Predictive Models Data Mining Management: Common Reasons Data Mining Projects Fail Overcoming Obstacles Successful Data Mining Project Management Data Mining in Practice--Case Studies: Data Mining in: Customer Service, Retail, Insurance, Financial Services, Health Care and Medicine, Telecommunications, Transportation and Logistics, Energy, and Government Glossary Bibliography Appendixes: Vendor Information "STAT 101" Techniques Listed by Methodology Phase


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Question: Discriminative Corpus Weight Estimation for Machine Translation

Current statistical machine translation (SMT) systems are trained on sentence-aligned and word-aligned parallel text collected from various sources. Translation model parameters are estimated from the word alignments, and the quality of the translations on a given test set depends on the parameter estimates. There are at least two factors affecting the parameter estimation: domain match and training data quality. This paper describes a novel approach for automatically detecting and down-weighing certain parts of the training corpus by assigning a weight to each sentence in the training bitext so as to optimize a discriminative objective function on a designated tuning set. This way, the proposed method can limit the negative effects of low quality training data, and can adapt the translation model to the domain of interest. It is shown that such discriminative corpus weights can provide significant improvements in Arabic-English translation on various conditions, using a state-of-the-art SMT system.

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Papers on: Genetic Algorithms, Connectionist Models, Case-Based Reasoning, Information Retrieval, Learning, Robotics, Planning and Scheduling, Natural Language Processing and Cognitive Linguistics.


Question: Toward a Comprehension Challenge, Using Crowdsourcing as a Tool

Human readers comprehend vastly more, and in vastly different ways, than any existing comprehension test would suggest. An ideal comprehension test for a story should cover the full range of questions and answers that humans would expect other humans to reasonably learn or infer from a given story. As a step toward these goals we propose a novel test, the Crowdsourced Comprehension Challenge (C3), which is constructed by repeated runs of a three-person game, the Iterative Crowdsourced Comprehension Game (ICCG). ICCG uses structured crowdsourcing to comprehensively generate relevant questions and supported answers for arbitrary stories, whether fiction or nonfiction, presented across a variety of media such as videos, podcasts, and still images.

Contents: Preface. Part I: Complexity Thinking. What Is "Complexity"? What Is "Science"? The Shape of Complexity. The Network of Complexity. Part II: Complexity Thinking, Education, and Educational Inquiry. Descriptive Complexity Research: Qualities of Learning Systems. Descriptive Complexity Research: Level-Jumping. Pragmatic Complexity Research: Conditions of Emergence. Complexity and Education: Vital Simultaneities. Endnotes.


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Question: Developing a human-centric agricultural model in the IoT environment

The prevalent state of agriculture, especially in developing countries, is not efficient and organized enough to address the growing demand for food, a direct result of the increasing human population. Internet of things and cloud computing together have provided a promising opportunity to resolve the challenges posed by this increasing demand worldwide. By employing IoT and cloud services, and through precision farming tactics, the efficiency and quality of agricultural production, storage and transportation can be tremendously improved. In this paper, we present the architecture of a multilayered enabling platform for incorporating IoT technologies in the agricultural sector. This work makes important contributions by proposing a feasible human-centric IoT model for agriculture with special emphasis on developing nations.

Urban strategy, masterplan and building design for a revitalisation of the manufacturing industry in Chicago in times of transition towards advanced manufacturing.


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Question: MetaCluster 4.0: A Novel Binning Algorithm for NGS Reads and Huge Number of Species

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Abstract Next-generation sequencing (NGS) technologies allow the sequencing of microbial communities directly from the environment without prior culturing. The output of environmental DNA sequencing consists of many reads from genomes of different unknown species, making the clustering together reads from the same (or similar) species (also known as binning) a crucial step. The difficulties of the binning problem are due to the following four factors: (1) the lack of reference genomes; (2) uneven abundance ratio of species; (3) short NGS reads; and (4) a large number of species (can be more than a hundred). None of the existing binning tools can handle all four factors. No tools, including both AbundanceBin and MetaCluster 3.0, have demonstrated reasonable performance on a sample with more than 20 species. In this article, we introduce MetaCluster 4.0, an unsupervised binning algorithm that can accurately (with about 80% precision and sensitivity in all cases and at least 90% in some cases) and efficientl...

These invited talks discuss the following: STG Decomposition and Its Correctness; Asynchronous Network-On-Chip and its Potential.


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Question: Sequences, Items And Latent Links: Recommendation With Consumed Item Packs

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Recommenders personalize the web content by typically using collaborative filtering to relate users (or items) based on explicit feedback, e.g., ratings. The difficulty of collecting this feedback has recently motivated to consider implicit feedback (e.g., item consumption along with the corresponding time). ::: In this paper, we introduce the notion of consumed item pack (CIP) which enables to link users (or items) based on their implicit analogous consumption behavior. Our proposal is generic, and we show that it captures three novel implicit recommenders: a user-based (CIP-U), an item-based (CIP-I), and a word embedding-based (DEEPCIP), as well as a state-of-the-art technique using implicit feedback (FISM). We show that our recommenders handle incremental updates incorporating freshly consumed items. We demonstrate that all three recommenders provide a recommendation quality that is competitive with state-of-the-art ones, including one incorporating both explicit and implicit feedback.

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Question: Automatic Composition of Semantic Web Services Based on Fuzzy Predicate Petri Nets

Web service composition is a challenging research issue. This paper presents an automatic Web service composition method that deals with both input/output compatibility and behavioral constraint compatibility of fuzzy semantic services. First, user input and output requirements are modeled as a set of facts and a goal statement in the Horn clauses, respectively. A service composition problem is transformed into a Horn clause logic reasoning problem. Next, a Fuzzy Predicate Petri Net (FPPN) is applied to model the Horn clause set, and T-invariant technique is used to determine the existence of composite services fulfilling the user input/output requirements. Then, two algorithms are presented to obtain the composite service satisfying behavioral constraints, as well as to construct an FPPN model that shows the calling order of the selected services.

Argument-Based Reasoning.- Personality-Based Practical Reasoning.- Argumentation Based Resolution of Conflicts between Desires and Normative Goals.- A Constrained Argumentation System for Practical Reasoning.- An Argumentation Framework Based on Strength for Ontology Mapping.- Contextual Extension with Concept Maps in the Argument Interchange Format.- Argumentation and Dialogue.- Command Dialogues.- Argumentation and Artifact for Dialogue Support.- Co-ordination and Co-operation in Agent Systems: Social Laws and Argumentation.- Annotation and Matching of First-Class Agent Interaction Protocols.- Strategic and Pragmatic Issues.- Argumentation- vs. Proposal-Based Negotiation: An Empirical Case Study on the Basis of Game-Theoretic Solution Concepts.- Argumentation-Based Information Exchange in Prediction Markets.- An Argumentative Approach for Modelling Coalitions Using ATL.- A Dialogue Mechanism for Public Argumentation Using Conversation Policies.


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Question: Towards Robustness for Cyber-Physical Systems

While the importance of robustness in engineering design is well accepted, it is less clear how to design cyber-physical systems (CPS) for robustness. With the objective of developing a robustness theory for CPS, we introduce a notion of robustness for cyber systems inspired by existing notions of input-output stability in control theory. We show that the proposed notion of robustness captures two intuitive goals: bounded disturbances lead to bounded deviations from nominal behavior, and the effect of a sporadic disturbance disappears in finitely many steps. For cyber systems modeled as finite-state transducers, the proposed notion of robustness can be verified in pseudo-polynomial time. The synthesis problem, consisting of designing a controller enforcing robustness, can also be solved in pseudo-polynomial time.

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Question: Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

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Few-shot learning is challenging for learning algorithms that learn each task in isolation and from scratch. In contrast, meta-learning learns from many related tasks a meta-learner that can learn a new task more accurately and faster with fewer examples, where the choice of meta-learners is crucial. In this paper, we develop Meta-SGD, an SGD-like, easily trainable meta-learner that can initialize and adapt any differentiable learner in just one step, on both supervised learning and reinforcement learning. Compared to the popular meta-learner LSTM, Meta-SGD is conceptually simpler, easier to implement, and can be learned more efficiently. Compared to the latest meta-learner MAML, Meta-SGD has a much higher capacity by learning to learn not just the learner initialization, but also the learner update direction and learning rate, all in a single meta-learning process. Meta-SGD shows highly competitive performance for few-shot learning on regression, classification, and reinforcement learning.

This chapter contains sections titled: Introduction System Description and Problem Formulation Sequential Monte Carlo Methods Joint Single-Target Tracking and Classification Multiple-Target Tracking and Classification Sensor Selection Simulation Results Conclusion Appendix: Derivations of (14.38) and (14.40) References


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Question: X-LiSA: Cross-lingual Semantic Annotation

The ever-increasing quantities of structured knowledge on the Web and the impending need of multilinguality and cross-linguality for information access pose new challenges but at the same time open up new opportunities for knowledge extraction research. In this regard, cross-lingual semantic annotation has emerged as a topic of major interest and it is essential to build tools that can link words and phrases in unstructured text in one language to resources in structured knowledge bases in any other language. In this paper, we demonstrate X-LiSA, an infrastructure for cross-lingual semantic annotation, which supports both service-oriented and user-oriented interfaces for annotating text documents and web pages in different languages using resources from Wikipedia and Linked Open Data (LOD).


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Question: Real Time Image Saliency for Black Box Classifiers

In this work we develop a fast saliency detection method that can be applied to any differentiable image classifier. We train a masking model to manipulate the scores of the classifier by masking salient parts of the input image. Our model generalises well to unseen images and requires a single forward pass to perform saliency detection, therefore suitable for use in real-time systems. We test our approach on CIFAR-10 and ImageNet datasets and show that the produced saliency maps are easily interpretable, sharp, and free of artifacts. We suggest a new metric for saliency and test our method on the ImageNet object localisation task. We achieve results outperforming other weakly supervised methods.

Images, Windows and Operators. Methodical Fundamentals. Algorithmic Fundamentals. Coordinate Transformations and Geometrical Operators. Gray Scale Transformations and Point Operators. Window Functions and Local Operators. Global Operators. Glossary. Index.