Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring and Predicting Computer Software Performance: Tools and Approaches

Measurement-based software performance evaluation is essential to computer science and industry alike, yet despite its widespread adoption, the current level of statistical rigor is inadequate, putting published results into question: for example, the majority of publications fail to report any dispersion metric at all. To foster widespread adoption of statistically rigorous performance evaluation, the first part of this thesis proposes the use of formal experiment design and non-parametric analysis techniques, and presents a distributed infrastructure that lowers the cost of rigorous experimentation by automating the experiment design and execution process, while minimizing the variability in computer performance response metrics. Then, to address cases where rigorous performance experimentation is infeasible, either due to infrastructure costs or unavailability of target platforms, the second part of this thesis builds on the previously discussed techniques and infrastructure to introduce two performance prediction techniques: one to predict when code changes will cause performance changes during software development, and another to predict performance metrics on unavailable platforms using benchmark-based statistical models.
Detection and Parameter Estimation of Chirp Signal Based on Time-Frequency Analysis

Based on the relationship between the Radon-Wigner transform and fractional Fourier transform and the time frequency distribution, using the property that Radon-Wigner transform has better performance in time and frequency domain, detection and parameter estimation of Chirp signal have been done by Radon-Wigner transform or fractiona1 Fourier transform. The theoretica1 analysis and simulation prove that two techniques are better than generic time-frequency transform, such as Wigner-Ville transform.
A fuzzy binarization method for complicated background document images

This paper presents a fuzzy binarization method for document images with complicated backgrounds. Our binarization method consists of a preprocessing procedure, a character-line detector, and three binarization processings. The first binarization processing is used to obtain a basic binary image, the second is made by considering edge information, and the third does by considering background information. Experimental results demonstrate that our fuzzy binarization method is efficient for separation of foreground (text) areas and background areas in images with stain areas or complicated backgrounds.
The technologies of in‐cell optical touch panel with novel input functions

Abstract— The technologies behind an in‐cell optical touch‐panel LCD with novel input functions will be described. By improving both the optical and electrical signal‐to‐noise ratio (SNR), a touch‐panel operation robust to ambient conditions can be achieved with a low‐power consumption suitable for integration into mobile products. The high SNR also enables a range of novel input functions making the in‐cell optical touch panel a promising platform for the next‐generation of user interface.
Primal–Dual Type Evolutionary Multiobjective Optimization

Abstract A new, primal-dual type approach for derivation of Pareto front approximations with evolutionary computations is proposed. At present, evolutionary multiobjective optimization algorithms derive a discrete approximation of the Pareto front (the set of objective maps of efficient solutions) by selecting feasible solutions such that their objective maps are close to the Pareto front. As, except of test problems, Pareto fronts are not known, the accuracy of such approximations is known neither. Here we propose to exploit also elements outside feasible sets with the aim to derive pairs of Pareto front approximations such that for each approximation pair the corresponding Pareto front lies, in a certain sense, in-between. Accuracies of Pareto front approximations by such pairs can be measured and controlled with respect to distance between such approximations. A rudimentary algorithm to derive pairs of Pareto front approximations is presented and the viability of the idea is verified on a limited number of test problems.
Routing TCP/IP

I. EXTERIOR GATEWAY PROTOCOLS. 1. Exterior Gateway Protocol. 2. Introduction to BGP-4. 3. BGP Configuration and Troubleshooting. II. ADVANCED IP ROUTING ISSUES. 4. Network Address Translation. 5. Introduction to Multicast Routing. 6. Configuration and Troubleshooting for Multicast Routing. 7. Large-Scale Multicast Routing. 8. IPv6. 9. Router Management. Appendix A: Field Details for the, 'show ip bgp neighbor' Output. Appendix B: A Regular Expression Tutorial. Appendix C: Answers to Review Questions. Appendix D: Answers to Configuration Exercises. Answers to Troubleshooting Exercises.
Partitioning and Scheduling Parallel Programs for Multiprocessing

From the Publisher: This book is one of the first to address the problem of forming useful parallelism from potential parallelism and to provide a general solution. The book presents two approaches to automatic partitioning and scheduling so that the same parallel program can be made to execute efficiently on widely different multiprocessors. The first approach is based on a macro dataflow model in which the program is partitioned into tasks at compile time and the tasks are scheduled on processors at run time. The second approach is based on a compile time scheduling model, where both the partitioning and scheduling are performed at compile time. Both approaches have been implemented in partition programs written in the single assignment language SISAL. The inputs to the partitioning and scheduling algorithms are a graphical representation of the parallel program and a list of parameters describing the target multiprocessor. Execution profile information is used to derive compile-time estimates of execution times and data sizes in the program. Both the macro dataflow and compile-time scheduling problems are expressed as optimization problems and are shown to be NP complete in the strong sense. Efficient approximation algorithms for these problems are presented. Finally, the effectiveness of the partitioning and scheduling algorithms is studied by multiprocessor simulations of various SISAL benchmark programs for different target multiprocessor parameters. Vivek Sarkar is a Member of Research Staff at the IBM T. J. Watson Research Center. Partitioning and Scheduling Parallel Programs for Multiprocessing is included in the series Research Monographs in Parallel and DistributedComputing. Copublished with Pitman Publishing.
Saliency Region Detection via Local and Global Features

Saliency detection plays an important role in many applications such as image segmentation, retrieval, editor and object recognition. Salience detection methods mainly divide into two categories: local contrast method and global contrast method. AC method is one of the typically local contrast methods, and HC method is a classical method base on global contrast. In this paper, we proposed an approach combined with AC and HC method, it not only considers local features but global information. After synthesizing the salience map of the AC and HC methods, firstly, according to the characteristics of human visual system, we pay attention to the salience center of an image, and add a weight value to each pixel, the closer to the center, the higher the weight. Then, we enhance the contrast of the image, receding darker part and increasing bright one. To confirm the proposed approach's effectiveness, we compare AC and HC method with the proposed one. The experimental results show that the new method generates a better salience map. Keywords—salient detection; local contrast; global contrast; weight value
A simple general proof of Kharitonov's generalized stability criterion

In a paper published in 1978 in Russian, Kharitonov proved that the strict Hurwitz property of a set of interval polynomials having complex coefficients may be established from the strict Hurwitz property of eight extreme polynomials. The proof of this significant result is advanced using elementary concepts in circuit theory. The approach not only provides a clear and complete proof of Kharitonov's generalized stability criterion but also has potentialities for generalization to the bivariate and, subsequently, the multivariate polynomial cases.
Synthesis, Editing, and Rendering of Multiscale Textures

The study of textures---images with repeated visual content---has produced a number of useful tools and algorithms for analysis, synthesis, editing, rendering, and a variety of other applications. However, the recent rapid growth in data storage and computational abilities has expanded the notion of what constitutes a texture. Modern textures can often outstrip traditional assumptions on input size by several orders of magnitude. Additionally, these multiscale textures typically contain features at not just one scale but rather across a wide range of scales, further violating existing assumptions. In order to meaningfully capture the large-scale features present in multiscale textures, we introduce a new example-based input representation, the exemplar graph. This representation enables allows us to efficiently define textures spanning a large--or possibly infinite--range of visual scales. We develop a hierarchical, parallelizable algorithm for performing texture synthesis from an input exemplar graph. In addition to automated generation, an increasingly important application of texture synthesis is in interactive tools for guiding texture design. This modality is especially important for multiscale textures, as they offer special perceptual challenges to artists. We examine algorithmic and engineering optimizations to enable real-time analysis and synthesis of multiscale textures, and explore potential implications for editing tools. Finally, we study the issue of display. To accurately view a large image at distance, some filtering operation must be performed. In many cases, such as traditional color images, the filtering operations are well-known. However, other texture representations, such as normal or displacement maps, present special difficulties for filtering. We treat the former case, presenting a principled analysis and algorithms for filtering and display of large normal maps.
Extending Cognitive Architectures with Spatial and Visual Imagery Mechanisms

This research presents a computational synthesis of cognition with spatial and visual imagery processing by extending a symbolic cognitive architecture (Soar) with mechanisms to support reasoning with quantitative spatial and visual depictive representations. Inspired by psychological and neurological evidence of mental imagery, our primary goals are to achieve new functional capability and computational efficiency in a task-independent manner. We describe how our theory and the corresponding architecture derive from behavioral, biological, functional, and computational constraints and demonstrate results from three different domains. Our evaluation reveals that in tasks where reasoning includes many spatial or visual properties, the combination of amodal and perceptual representations provides an agent with additional functional capability and improves its problem-solving quality. We also show that specialized processing units specific to a perceptual representation but independent of task knowledge are likely to be necessary in order to realize computational efficiency in a general manner. The research is significant because past research in cognitive architectures primarily views amodal, symbolic representations as being sufficient for knowledge representation and thought. We expand those ideas with the notion that perceptual-based representations participate directly in the thinking rather than serving simply as a source of sensory information. The new capabilities of the resulting architecture, which includes Soar and its Spatial-Visual Imagery (SVI) component, emerge from its ability to amalgamate symbolic and perceptual representations and use them to inform reasoning. Soar's symbolic memories and processes provide the building blocks necessary for high-level control in the pursuit of goals, learning, and the encoding of amodal, symbolic knowledge for abstract reasoning. SVI encompasses the quantitative spatial and visual depictive representations and processes specialized for efficient construction and extraction of spatial and visual properties.
Cloud computing applications and platforms: a survey

The implementation of cloud computing application takes the higher necessity, especially after suffering from modern problems, such as providing proper funds for social service and purchasing programs. For that, the cloud computing applications propose a promising solution to solve such issues. In this paper, we will discuss the implemention of cloud computing over Smart Grid system; a reliable, cost effective guaranteed and efficient system, it is expected to be Long Term Evolution (LTE): which allows larger pieces of spectrum, or bands to be used furthermore with more coverage and less latency and the third technology is Vehicular network: which is an important research area because of unique features and applications that offers. In this survey, we will present an overview of the smart grid, LTE and the vehicular network when they get integrated with cloud computing, in addition we will highlight the open issues and research direction which faces these technologies with cloud computing implementing in terms of Energy management, Information management for smart grid. In terms of applying cloud computing platforms for 4G Networks to achieve specific criteria and finally in terms of Architectural formation and Privacy and security for Vehicular cloud computing.
Syntactically-informed representation for sentence selection

Sentence Selection consists of identifying the sentences relevant to a particular topic, task, user or linguistic structure. This is a prerequisite step in many document-processing tasks, such as Information Extraction and Text Summarization. Researchers in these areas have typically borrowed ideas and tools from the Automatic Text Categorization (ATC) domain and applied them to their Sentence Selection problems. This is the case with the standard Bag of Words text representation and the machine learning algorithms used. Even though Sentence Selection and ATC are related, not all their characteristics are the same. Because of their differences, some variations to the standard representations and techniques usually used for ATC might be beneficial for Sentence Selection. Consequently, the main contribution of this thesis is the exploration of the benefits of a syntactically and semantically enriched text representation for the Sentence Selection task on technical domains. We further take advantage of the syntactic and semantic relations between words by moving from the propositional learners to a relational model. In particular, we experiment on three documents datasets, two in the Genetics domain and one in the Legal domain. In the first two domains we incorporate semantic knowledge by means of hierarchical dictionaries, while in the third one we use Named Entity Recognition for the same purpose. The syntactic knowledge is obtained from automatic parsing. Bags of words, enriched with the syntactic and semantic features, are given as input to different classifiers induction algorithms. Sentences to be selected constitute the positive class, while the remaining sentences of a document constitute the negative class. We present results with the state of the art algorithms Naive Bayes and Support Vector Machine, as well as the relational learner Aleph. We evaluate the learning performance by comparing on several runs of N-fold cross-validation and time based training/testing splits, and we study the implications of the classification threshold when appropriate. We show the gains of enriching the representation in a syntactic or semantic way, we analyze the cases in which each one is more beneficial, and we explain the particular contributions of each of them.
Frontal and lateral control for unmanned vehicles in urban tracks

This paper presents techniques, related experiments and real results (not simulations) performed with two unmanned vehicles. The test zone is a private circuit with the characteristics of an urban track. The vehicles are common mass-produced cars, provided with automatic actuators operating on the car controls (power-assisted steering and accelerator). These actuators work commanded by a fuzzy logic based control system. The input information to the control system is provided by three elements: high precision GNSS, car tachometer and a wireless network environment.
Switching control in multi-mode Markov decision processes

This paper presents a switching control strategy for multi-mode Markov decision processes. The system to be controlled is modeled as a finite-state controlled Markov chain with a mode that evolves stochastically. Although the system state is observable, the mode is only partially observable in the sense that we know the system mode only when it is in a given set of observable modes. Given a set of controllers for the system, we consider the problem of determining a switching rule that selects the controller to be applied each time the system mode is observable. The objective is to minimize a long-term average cost from the system while satisfying bounds on the long-term average of other given performance measures. We assume the multi-mode model parameters are unknown a priori, so an adaptive switching rule is required. Algorithms are presented for computing approximations to the optimal switching rule based on estimating the model parameters online. The approach is illustrated for an example of dynamic power management of hard disk drives in computer systems.
Rethinking end-user training strategy: applying a hierarchical knowledge-level model

Training in information technology (IT) tools has traditionally been defined in terms of skills. Consequently, training methods and approaches have focused almost exclusively on ensuring that a trainee acquires the skills required to use an IT tool, and in a specific domain. With the advent and increasing use of enterprise-wide IT architectures, such as client/server, integrated processes such as workflow systems and integrated packages such as SAP, this narrow view of training will prove to be inadequate in preparing the workforce of the future. To train such a workforce, we propose a re-conceptualization of training based on a hierarchy of knowledge levels that a trainee will need. We then use this hierarchy to propose an integrated framework that can be used to develop a comprehensive training strategy. We offer directions for research that are needed to use our model to develop effective training strategies.
Ant Colony System Based Approaches to the Air-Express Courier's Routing Problem

Wilson W. LAN Graduate Student Department of Industrial Engineering and Management Yuan Ze University 135 Yuan-Tung Road, Chung-Li, Taiwan 32003, R.O. C. Fax: +866-3-4638907 E-mail: s955424@mail.yzu. edu.tw Ching-Jung TING Associate Professor Department of Industrial Engineering and Management Yuan Ze University 135 Yuan-Tung Road, Chung-Li, Taiwan 32003, R.O. C. Fax: +866-3-4638907 E-mail: ietingcj@saturn.yzu. edu.tw
클라우드 스토리지 시스템에서 데이터 접근빈도와 Erasure Codes를 이용한 데이터 복제 기법

클라우드 스토리지 시스템은 데이터의 저장과 관리를 위해서 분산 파일시스템을 사용한다. 기존 분산 파일시스템은 데이터 디스크의 손실 발생시 이를 복구하기 위해서 3개의 복제본을 만든다. 그러나 데이터 복제 기법은 저장공간을 원본 파일의 복제 횟수만큼 필요로하고 복제과정에서 입출력 발생이 증가하는 문제가 있다. 본 논문에서는 SSD 기반 클라우드 스토리지 시스템에서 저장공간 효율성 향상과 입출력 성능 향상을 위하여 Erasure Codes를 이용한 데이터 복제 기법을 제안한다. 특히, 데이터 접근 빈도에 따라 복제 횟수를 줄이더라도 Erasure Codes를 사용하여 데이터 복구 성능을 동일하게 유지하였다. 실험 결과 제안한 기법이 HDFS 보다 저장공간 효율성은 최대 약40% 향상되었으며, 읽기성능은 약11%, 쓰기성능은 약10% 향상됨을 확인하였다.
Performance of First- and Second-Order Methods for Big Data Optimization

We study the performance of first- and second-order optimization methods for l1-regularized sparse least-squares problems as the conditioning of the problem changes and the dimensions of the problem increase up to one trillion. A rigorously defined generator is presented which allows control of the dimensions, the conditioning and the sparsity of the problem. The generator has very low memory requirements and scales well with the dimensions of the problem.
Application of neural networks for real time control of a ball-beam system

This paper examines the application of neural network techniques to the control of an open loop unstable ball-beam system. Computer simulation and implementation using both conventional pole-placement and neural network methods of control have been undertaken. Two-layer networks have been used in both the simulation and the experimental implementation. The error backpropagation, the temporal-difference, and the reinforcement learning algorithms have been used in the neural network controllers. The results from simulation and also from real time control experiments show that the time required to balance the ball-beam system has been significantly reduced by using neural networks.
RISC: An Improved Costas Estimator-Predictor Filter Bank For Decomposing Multicomponent Signals

We propose an improved version of an estimator-predictor filter bank, originally proposed by Costas [l], for decomposing and tracking multiple, nonstationary sinusoidal components present in a signal. Each component is assigned a signal estimator which is a causal filter, and a predictor. The estimator-predictor combination estimates the next time-sample of its signal component, which is then subtracted from the composite input signal. Ideally, no signal component will then interfere with accurate estimation of the others. However, Costas’s predictor performs poorly when there are components with rapidly changing envelopes. In this paper, we propose an improved predictor that compensates for the group delay introduced in the signal components by the causal filtering, by minimizing a prediction error criterion. With this improved predictor, using a computer synthesized multicomponent signal, we show that we achieve cleaner separation of signal components when compared with Costas’s method. We also show that this method can be used to separate the essentially harmonic partials in voiced speech.
Local Helioseismology

We review the current status of local helioseismology, covering both theoretical and observational results. After a brief introduction to solar oscillations and wave propagation through in-homogeneous media, we describe the main techniques of local helioseismology: Fourier-Hankel decomposition, ring-diagram analysis, time-distance helioseismology, helioseismic holography, and direct modeling. We discuss local helioseismology of large-scale flows, the solar-cycle dependence of these flows, perturbations associated with regions of magnetic activity, and solar supergranulation.