Miracle-dz/UniFAR-FTUs huggingface dataset:
A Greedy Searching Algorithm for Multiple Object Tracking and Occlusion Handling

This paper presents a novel real-time multiple object tracking algorithm,which contains three parts:region correlation based foreground segmentation,merging-splitting based data association and greedy searching based occluded object localization.The main characteristics of the proposed algorithm are summarized as follows:1) the multiple object tracking and occlusion handling problem is successfully changed into an image classification problem with prior knowledge of object number and feature;2) a highly effcient greedy searching method is presented to meet real-time capability;3) it has good performance in expansibility,and it has no constraints about the number of occluded objects,the occlusion ratio and the object s motion model.Experiment results with hand labeled IBM database demonstrate that the method is effective and effcient.
Serious support for serious gaming

This dissertation focused on evaluating game-based learning in prevocational education. Though many researchers seem convinced of the potential of game-based learning, researchers also acknowledge that the empirical evidence for games as learning tools is ambiguous. Comparison between studies is difficult due to diversity in content, gameplay, and population (among other things). To distinguish between effective and ineffective practices and elements, and to optimize game-based learning, value-added comparison studies are required. Therefore, studies in the current dissertation adopted a value-added approach: a standard version of the game (i.e., an educational math game ‘Zeldenrust’) was compared to versions that were enhanced with different forms of instructional support (i.e., self-explanation prompts, collaboration, and faded worked examples). Three consecutive empirical studies investigated the effect of these forms of instructional support on the in-game performance and knowledge acquisition of prevocational students. It is important to highlight the outcome that learning was significantly enhanced in all three studies. The overall pretest to posttest Cohen’s d effect sizes for studies 1, 2, and 3 were 0.44, 0.46, and 0.46 respectively, which are regarded as medium-sized effects. These findings show that the intervention involving the educational game ‘Zeldenrust’ formed a solid foundation for learning about the domain of proportional reasoning. The results of the three studies show that further improvement of this environment was difficult, but possible. The investigated implementations of self-explanation prompts and collaboration did not result in improved effectiveness, but the addition of faded worked examples did. This seems to suggest that prevocational students benefit from continuous support that contains expert advice. Though game designers show a tendency to hide the instructional component and educational content in educational games, results of the current line of study suggest that a formal representation of the educational content in the game-environment could support students gameplay and learning.
Metal-organic chemical vapor deposition of indium selenide thin films

Indium selenide (InSe) thin films have been grown at 230−420 °C by low-pressure metal−organic chemical vapor deposition (MOCVD) using the single-source precursors [(tBu)2In(μ-SetBu)]2 and [(Me2EtC)In(μ3-Se)]4. Characterization of the films by energy-dispersive X-ray analysis (EDX) showed those grown from [(tBu)2In(μ-SetBu)]2 to be indium rich, while those grown from [(Me2EtC)In(μ3-Se)]4 are stoichiometric InSe. Transmission electron microscopy (TEM) indicates that the film morphology and crystallinity are highly dependent on the precursor and the deposition temperature. At low temperatures <330 °C ball-like morphologies are observed, while deposition at 350−370 °C results in highly crystalline textured films. Use of [(tBu)In(μ3-Se)]4 as the precursor at 320−420 °C results in indium metal films. The relationship between the precursor's structure and the film's morphology and chemical composition are discussed. The efficacy of the single-source precursor approach is considered with respect to elemental comp.
A fast simulator for neural networks on DSPs or FPGAs

The authors present a description of their achievements and current research on the implementation of a fast digital simulator for artificial neural networks. This simulator is mapped either on a parallel digital signal processor (DSP) or on a set of field programmable gate arrays (FPGAs). Powerful tools have been developed that automatically compile a graphical neural network description into executable code for the DSPs, with the flexibility to adjust weights and thresholds at run-time. The next step is to realize similar tools for the FPGAs. <<ETX>>
Compliance modeling of planar flexure-based mechanisms and its application to micro-motion stages

This article presents the compliance modeling method of flexure-based mechanisms. The relationship between deformations and loads of the flexure members and the end effector is analyzed according to the zero virtual work principle. Using the matrix method, concise compliance equations for flexure serial and parallel structures are derived in conjunction with consideration of the compliance calculation errors of flexure hinges. Finally, relationships between the output compliances and geometrical parameters of a 3-revolute-revolute-revolute micro-motion stage are discussed. To validate the proposed compliance modeling method, the output compliances calculated by the empirical and theoretical compliance equations are subsequently compared with values derived from finite element analysis (FEA). The comparisons indicate that the results obtained from the empirical compliance equations are in good agreement with those derived from FEA, whereas the errors calculated by the theoretical equations are much larger, which demonstrate the accuracy of the empirical compliance equations and validate the proposed compliance modeling method.
Tactile Interfaces for Dismounted Soldiers

In military operational environments, an overflow of information along with rapidly changing conditions has the potential to reduce soldiers’ performance and mission success. Performance has been improved by technologies that make use of integrated means of information transfer, but there remains an increase in commanders’ workload and information processing demands. Most of the communication between commanders and their soldiers is handled by audio and visual devices. Less common is the use of tactile interfaces. The tactile modality allows a fast, intuitive, secured, and silent channel of communication. The main goal of the current study was to determine soldiers’ requirements and needs and based on those establish the characteristics of an experimental infrastructure for future experiments to test the efficiency of tactile interfaces in operational settings. To this end, questionnaires were distributed among infantry officers of the Israel Defense Forces (IDF). Analysis of the collected data provided insights into soldiers’ view of the tactile display usage in the battlefield. The results of this study lay the foundations for further research and development of an effective tactile channel to be used by soldiers in operational environments.
Image analysis and platform development for automated phenotyping in cytomics

This thesis is dedicated to the empirical study of image analysis in HT/HC screen study. Often a HT/HC screening produces extensive amounts that cannot be manually analyzed. Thus, an automated image analysis solution is prior to an objective understanding of the raw image data. Compared to general application domain, the efficiency of HT/HC image analysis is highly subjected to image quantity and quality. Accordingly, this thesis will address two major procedures, namely image segmentation and object tracking, in the image analysis step of HT/HC screen study. Moreover, this thesis focuses on expending generic computer science and machine learning theorems into the design of dedicated algorithms for HT/HC image analysis. Additionally, this thesis exemplifies a practical implementation of image analysis and data analysis workflow via empirical case studies with different image modalities and experiment settings. However, the data analysis theorem will be generally illustrated without further expansions. Finally, the thesis will briefly address supplementary infrastructures for end-user interaction and data visualization.
Effective Knowledge Development in Game-Based Learning Environments: Considering Research in Cognitive Processes and Simulation Design

Serious games are, at their core, exploratory learning environments designed around the pedagogy and constraints associated with specific knowledge domains. This focus on instructional content is what separates games designed for entertainment from games designed to educate. As instructional designers and educators, the authors want serious game play to provide learners with a deep understanding of the domain, allowing them to use their knowledge in practice to think through multifaceted problems quickly and efficiently. Attention to the design of serious game affordances is essential to facilitating the development of domain knowledge during game play. As such, the authors contend that serious game designers should take advantage of existing prescriptions found in research on knowledge development in exploratory learning environments and tests of adaptive instructional designs in these environments. It is with this intention that the authors use evidence from research in cognitive processes and simulation design to propose design heuristics for serious game affordances to optimize knowledge development in games.
Study and Development of the Clustering Algorithms of Wireless Sensor Networks

As one of the efficient ways of network topology control,clustering algorithms can reduce the energy consumption of the network and obviously improve the throughput ratio.Based on the architecture of clustering in WSN,the article classifies the representative clustering algorithms.The article analyzes the difficulties and problems in the algorithm design and illustrates some of the results;and further research of this area is foreseen.
Re-Ranking of Web Image Prediction using Multimodal Sparse Code and Voting Strategy

This paper give you the information about the web image search using the Multimodal Sparse code and voting strategy . The performance of Text-based image search which has been improved by using Image re-ranking. Existing re-ranking algorithms have two main drawbacks, first the textual meta-data associated with images is often mismatched with their actual visual content and second the extracted visual features do not accurately describe the semantic similarities between images. Pseudo-relevance feedback (PRF) tool is used in many existing re-ranking system. A critical problem for click-based methods is the lack of click data, so only a small number of web images have actually been clicked on by users. So our aim is to solve this problem by predicting image clicks. Click prediction is effective to improving the performance of prestigious graph-based image re-ranking algorithms is useful in real world data.
Investigação de estratégias para a geração de máquinas de vetores de suporte multiclasses

Several problems involve the classification of data into categories, also called classes. Given a dataset containing data whose classes are known, Machine Learning (ML) algorithms can be employed for the induction of a classifier able to predict the class of new data from the same domain, thus performing the desired discrimination. Among the several ML techniques applied to classification problems, the Support Vector Machines (SVMs) are known by their high generalization ability. They are originally conceived for the solution of problems with only two classes, also named binary problems. However, several problems require the discrimination of examples into more than two categories or classes. This thesis investigates and proposes strategies for the generalization of SVMs to problems with more than two classes, known as multiclass problems. The focus of this work is on strategies that decompose the original multiclass problem into multiple binary subtasks, whose outputs are then combined to obtain the final classification. The proposed strategies aim to investigate the adaptation of the decompositions for each multiclass application considered, using information of the performance obtained for its solution or extracted from its examples. The implemented algorithms were evaluated on general datasets and on real applications from the Bioinformatics domain. The results obtained open possibilities of many future work. Among the benefits observed is the obtainment of simpler decompositions, which require less binary classifiers in the multiclass solution.
An Optimized Semantic Technique for Multi- Document Abstractive Summarization

Background/Objective: Multi-document summarization produces a concise summary from several online topically related documents. A major challenge in this domain is usually the information overlap in documents emanating from various sources. This paper introduces an optimized semantic technique for multi-document abstractive summarization. Methods/Statistical Analysis: Linguistic and semantic approaches are usually employed for abstractive summarization of multiple documents. Linguistic approaches lack semantic representation of source text while semantic approaches mostly rely on human experts to construct domain ontology and rules; which require immense time and effort. The technique in this paper utilizes the benefits of semantic role labeling, clustering and Particle Swarm Optimization (PSO) to rank predicate argument structures (semantic representation) in each cluster using optimized features. Findings: The summary quality is susceptible to the text features i.e., different features have varied importance towards summary generation. Therefore, optimal features weights obtained using PSO integrated in the semantic technique to rank semantic representation improved summarization results. The performance of the technique is evaluated against the benchmark summarization systems using pyramid evaluation measures (mean coverage score, precision and F-measure). A Paired- Samples T-test is carried out to validate the summarization results. Applications/Improvements: Experiment of this research is performed with DUC-2002, a benchmark data set for text summarization. Experimental results confirm that the proposed technique yields better results than other comparison summarization models in terms of mean coverage score and average F-measure.
Error detection and correction technique for Memory applications

In Advanced digital communication, the testing of Memory System is more complicated. As the technology, dimensions and operating voltages of the computer electronics are reduced to satisfy the consumer’s which leads to soft errors. The detection and correction of soft errors in the memory system is more susceptible. To protect memory cells from soft errors, we need more Advanced Errors correction codes. One-step Majority of Logic Decodable codes is suitable for Memory Application to detect and correct large number of errors during communication. One type of Euclidean Geometry Low – Density Parity Check (EG-LPDC) Codes are used for Error correction, because it has fault- secure detector capability. In this paper, an Enhanced Majority Logic Decoder/Detector (MLDD) is proposed to detect silent data errors (SDE) using additional logic and in order to reduce the area of Majority gate, the Sorting Network is designed. Thus, the proposed Method reduces the decoding time, area and power consumption. Hence the proposed Method Simulation Results are Shown as Power saving & Area Utilization compared to existing Method (One-step Majority of Logic Decodable codes).
Multimodale Analyse von Interaktion im fremdsprachlichen Klassenzimmer. Multimodal analysis of interaction in a foreign language class.

The article offers a contribution to the interaction research in the foreign language teaching and learning. It starts with an overview of various research approaches to the foreign language teaching and learning, from the 60s to the present days. A multimodal analysis of an excerpt of videorecorded classroom interaction is then provided. The analysis is focused on several aspects of classroom participation and teaching sequences. Some implications of the present research for teachers' training are finally provided.
High resolution anatomical and functional imaging.

The signal-to-noise ratio available in Magnetic Resonance Imaging (MRI) is determined by the static magnetic field strength, causing a continued drive toward higher fields to enable faster image acquisition at finer spatial resolution. The work in this thesis is primarily concerned with the development of sequences for Ultra High Field Magnetic Resonance Imaging (7T) which allow the acquisition of images with high spatial resolution for study of the structure and function of the brain. The methods developed here for high spatial resolution structural imaging allow the identification of regions of the cortex which exhibit layers of high myelin concentration within the cortical strip. This permits the investigation of the correspondence of functional regions in the visual cortex to their underlying structure in vivo. A robust methodology for high resolution functional mapping over a restricted field of view is presented and the results of fMRI studies demonstrating 1 mm isotropic resolution in the primary somatosensory cortex S1 using this methodology are shown. BOLD responses to vibrotactile digit stimulation were investigated using a travelling wave paradigm to measure the topographic representation of the digits in S1 and an event related paradigm for characterization of the haemodynamic delay. A spin-echo EPI acquisition has been optimized and tested to compare the BOLD response in GE and SE echo planar images by employing visual and motor tasks. The specificity of the BOLD responses of SE and GE data was found to be similar using a travelling wave paradigm.
Local obstacle recognition for a quadruped robot by distance sensors

Legged walking robots are able to not only move on irregular terrain but also change its posture. For example, the robots can pass under above head obstacles with crouching. These advantages are particularly effective in three dimensional complex environment which wheeled robots cannot move on. However, some of the issues of a walking robot include instability and workspace limitation. All of these impact on the realization of applications. We have studied for the improvement of them and deadlock-less gait pattern, called trajectory following gait (TFG), has been proposed. Here, the advantages of walking robots are remarked again. In order to get the most out of these advantages, an environment recognition is absolutely imperative. Applying the vision sensor to the walking robot, however, is difficult because misconception can be occurred easily by the rocking vibration. Although rough surrounding information for global planning should be recognized by the visual sensor, more reliable sensing is desirable by the local one, i.e. steps and above head obstacles. This paper presents the reliable sensing method for a local obstacle recognition using distance sensors. The overcoming locomotion and a posture control method are also proposed. Finally, the local recognition availability is verified by some experiments.
View-Spatial-Temporal Post-Refinement？for View Synthesis in？3D Video Systems

Depth imagebasedrenderingisoneofkeytechniquestorealizeviewsynthesisforthree- dimensionaltelevisionandfree-viewpointtelevision,whichprovidehighqualityand immersiveexperiencestoendviewers.However,artifactsofrenderedimages,including holescausedbyocclusion/disclosureandboundaryartifacts,maydegradethesubjective and objectiveimagequality.Tohandletheseproblemsandimprovethequalityof rendered images,wepresentanovelview-spatial–temporal post-refinementmethod for viewsynthesis,inwhichnewhole-fillingandboundaryartifactremovaltechniquesare proposed. Inaddition,weproposeanoptimalreferenceframeselectionalgorithmfora bettertrade-offbetweenthecomputationalcomplexityandrenderedimagequality. Experimentalresultsshowthattheproposedmethodcanachieveapeaksignal-to-noise ratio gainof0.94dBonaverageformultiviewvideotestsequenceswhencomparedwith the benchmarkviewsynthesisreferencesoftware.Inaddition,thesubjectivequalityof the renderedimageisalsoimproved.
DATA MINING AND KNOWLEDGE DISCOVERY: AN OVERVIEW AND PROSPECT

Discovery knowledge and useful information form large scale databases has been recognized by more and more researchers as a key research topic in many different fields. This paper is to provide a survey of data mining and knowledge discovery on several issues, such as the process, methods, algorithms and applications. Future research work and challenges are also discussed.
Sports The First Five Millennia

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KEPLER INPUT CATALOG: PHOTOMETRIC CALIBRATION AND STELLAR CLASSIFICATION

We describe the photometric calibration and stellar classification methods used by the Stellar Classification Project to produce the Kepler Input Catalog (KIC). The KIC is a catalog containing photometric and physical data for sources in the Kepler mission field of view; it is used by the mission to select optimal targets. Four of the visible-light (g, r, i, z) magnitudes used in the KIC are tied to Sloan Digital Sky Survey magnitudes; the fifth (D51) is an AB magnitude calibrated to be consistent with Castelli & Kurucz (CK) model atmosphere fluxes. We derived atmospheric extinction corrections from hourly observations of secondary standard fields within the Kepler field of view. For these filters and extinction estimates, repeatability of absolute photometry for stars brighter than magnitude 15 is typically 2%. We estimated stellar parameters {Teff, log (g), log (Z), EB − V} using Bayesian posterior probability maximization to match observed colors to CK stellar atmosphere models. We applied Bayesian priors describing the distribution of solar-neighborhood stars in the color–magnitude diagram, in log (Z), and in height above the galactic plane. Several comparisons with samples of stars classified by other means indicate that for 4500 K ⩽Teff ⩽ 6500 K, our classifications are reliable within about ±200 K and 0.4 dex in log (g) for dwarfs, with somewhat larger log (g) uncertainties for giants. It is difficult to assess the reliability of our log (Z) estimates, but there is reason to suspect that it is poor, particularly at extreme Teff. Comparisons between the CK models and observed colors are generally satisfactory with some exceptions, notably for stars cooler than 4500 K. Of great importance for the Kepler mission, for Teff ⩽ 5400 K, comparison with asteroseismic results shows that the distinction between main-sequence stars and giants is reliable with about 98% confidence. Larger errors in log (g) occur for warmer stars, for which our filter set provides inadequate gravity diagnostics. The KIC is available through the MAST data archive.