Miracle-dz/UniFAR-FTUs huggingface dataset:
2.5D and 3D I/O designs for energy-efficient memory-logic integration towards thousand-core on-chip

In the past few decades, the design of computers has been primarily driven by improving performance with faster clock frequency of single-core processor using transistor scaling. The transistor scaling towards high performance of fast clock frequency is, however, stuck recently due to the constraint of power density (or thermal reliability). By exploiting parallelism, multi-core processor based design of computers has emerged to scale up performance of throughput under power budget. As such, the scaling paradigm has changed to integrate as many processor cores as possible on one single chip. In the traditional 2D based memory-logic integration, the scalability of many-core integration is limited by the communication between the cores and memory to access via I/O interconnections, which pose stringent requirements for high utilization efficiency of both bandwidth and power. Stacking of one layer (with core or memory blocks) above the other with shortdistance RC interconnects of through-silicon vias (TSVs), termed as 3D integration has become one promising alternative many-core integration technique with high bandwidth. It is however constrained by severe thermal reliability concern due to poor heat dissipation in 3D. The other alternative for many-core integration is to place core and memory blocks on one common substrate, which are further interconnected with the aid of middle-distance transmission lines (underneath the substrate) of through-silicon interposers (TSIs), termed as 2.5D integration. In order to explore the fundamental challenges of I/O interconnections by 3D TSVs and 2.5D TSIs for many-core integration, in this thesis, we have studied various design aspects starting from device level modeling to system level management with the following works done. • Firstly, we have developed physical models of 3D TSV and 2.5D TSI I/Os. 1. We first study 3D TSV I/Os, which are short-distance RC interconnects with inverter buffers. The traditional physical model treats TSV as a resistor with linear electrical-thermal dependence, which is not accurate. With the stacking of layers the heat starts to accumulate in the top layer resulting in
Quantitative Analysis of Voxel Raytracing Acceleration Structures

In this work, we provide a comprehensive analysis of GPU acceleration structures for voxel raytracing. We compare the commonly used octrees to BVH and KD trees, which are the standard in GPU triangle raytracing. Evaluating and analyzing of the behavior is complicated, as modern GPUs provide wide vector units with complex cache hierarchies. Even with sophisticated SIMD simulators, it is increasingly hard to model the hardware with sufﬁcient detail to explain the observed performance. Therefore, instead of relying on SIMD simulation, we use hardware counters to directly measure key metrics like execution coherency on a modern GPU. We provide an extensive analysis comparing different acceleration structures for different raytracing scenarios like primary, diffuse and ambient occlusion rays. For different scenes we show that data structures commonly known for good performance, like KD-trees, are actually not suited for very wide SIMD units. In our work we show that BVH trees are most suitable for GPU raytracing and explain how the acceleration structure affects the execution coherency and ultimately performance, providing crucial information for the future design of GPU acceleration structures.
Robust adaptive spatio-temporal video denoising algorithm based on motion estimation

A robust adaptive spatio-temporal video denoising algorithm based on motion estimation was presented.To adpot the adaptive Wiener filter before the motion estimation promoted the performance of the block matching.The extra judgment based on small block and the adoption of Duncan filter promoted the robustness of the motion estimation.Experimental results demonstrate the effectiveness of this algorithm.
A Reinforcement Learning Approach to Dynamic Norm Generation

This thesis proposes a two-level learning framework for dynamic norm generation. This framework uses the Bayesian reinforcement learning technique to extract behavioral norms and domain-dependent knowledge in a certain environment and later incorporates them into the learning agents in different settings. Reinforcement learning (RL) and norms are mutually beneficial: norms can be extracted through RL, and RL can be improved by incorporating behavioral norms as prior probability distributions into learning agents. An agent should be confident about its beliefs in order to generalize them and use them in future settings. The confidence level is developed by checking two conditions: how familiar the agent is with the current world and its dynamics (including the norm system), and whether it has converged to an optimal policy. A Bayesian dynamic programming technique is implemented and then compared to other methods such as Q-learning and Dyna. It is shown that Bayesian RL outperforms other techniques in finding the best equilibrium
A Testing Method for Web Services Focusing on User Requirements

This paper plans to add user requirement information into test case generations for Web services because of the existed situation,i.e.,Web services only provide interface documents for outsides,and the information for test case generation used by testers(except developers) is not enough.At present,the existed test case generation methods have high redundant and low efficiency to find bugs.This paper firstly set up user requirement model by clustering the existed WSDL files of Web services and selecting the exact Web services with other users' collaborative recommendation,and then generated test cases with domain knowledge and usage scenes(basic scene and optional scenes)by the extension of user requirement model and the selection of paths from the entry node to the exit node;finally,the workflow and case study indicated the efficiency of our method was better than the method which generated test cases based on variables' types in the WSDL files.Therefore,testing combinational Web services focused on user requirements can ensure the selected Web services matching user requirements,and the usage scenes also can enrich the information for testing,thus the test cases for Web services can be heuristically and more efficiently generated combining with the domain knowledge.
THE HISTORY OF MATHEMATICS DURING AN INQUIRY-BASED TEACHING APPROACH

The use of the history of Mathematics in teaching has long been considered as a useful tool in order to enable students to construct conceptually the mathematical concepts. At the same time the inquiry-based teaching approach is proposed to be used in order to improve students’ learning by using their natural tendency to curiosity. The use of the history of mathematical concepts during an inquiry-based teaching approach is expected to multiply the positive effects on students’ learning. The present study examines in-service teachers’ beliefs and knowledge about the use of the history of mathematics in the framework of the inquiry-based teaching approach at the educational system of Cyprus, and the difficulties teachers face in adopting and implementing this specific innovation in primary education. At the first phase of the study a questionnaire was used in order to investigate teachers’ knowledge and beliefs about the use of the history of mathematics in education and mainly in relation to the inquiry-based teaching approach. At the second phase of the study two case studies were examined, where teachers introduced a mathematical concept by using the history of mathematics in order to identify the practices they used and the difficulties they faced. The results indicated that the teachers’ knowledge about the use of the history and mainly the experimental nature of mathematics is significantly related with their positive beliefs about the inquiry-based teaching approach. Teachers’ worries were mainly concentrated on their difficulties to manage the time and the content of the subject and to face efficiently and flexibly their students’ mistakes and difficulties.
Algorithm for Chinese textcategorization based on class feature vector representation

This paper used the approach to Chinese text categorization without word segmentation,expressing text features with bi-gram model.Compared with the classification models with word segmentation,the approach avoided complicated computation of word segmentation.To increase the accuracy of the approach,proposed an algorithm based on the class feature vector representation.And analyzed theoretically and verified experimentally the efficiency of the algorithm.
Separation of Fetal and Maternal ECGs Classification of Heart Diseases

This report describes techniques to extract the fetus signal from the maternal signals in Electrocardiograms (ECGs). The signals are recorded during different stages of a pregnancy from the mother’s abdomen. The main challenge is the detection of fetal heartbeats due to its weakness compared to the mother’s. The methods used are Independent Component Analysis, Peak Detection Analysis and Continuous Wavelet Transform. Additionally, ECG signals are classified from patients’ heartbeats known to be suffering from certain diseases. The classification is based on parameters extracted from the heartbeat shape in the signals. Methods used for classification are a Naive Bayesian Classifier and Support Vector Machines. Chapter
Fundamentals of global positioning system receivers

The Second Edition of this acclaimed publication enables readers to understand and apply the complex operation principles of global positioning system (GPS) receivers. Although GPS receivers are widely used in everyday life to aid in positioning and navigation, this is the only text that is devoted to complete coverage of their operation principles. The author, one of the foremost authorities in the GPS field, presents the material from a software receiver viewpoint, an approach that helps readers better understand operation and that reflects the forecasted integration of GPS receivers into such everyday devices as cellular telephones. Concentrating on civilian C/A code, the book provides the tools and information needed to understand and exploit all aspects of receiver technology as well as relevant navigation schemes: * Overview of GPS basics and the constellation of satellites that comprise the GPS system * Detailed examination of GPS signal structure, acquisition, and tracking * Step-by-step presentation of the mathematical formulas for calculating a user's position * Demonstration of the use of computer programs to run key equations * Instructions for developing hardware to collect digitized data for a software GPS receiver * Complete chapter demonstrating a GPS receiver following a signal flow to determine a user's position
The Basic Theory and Application of BLISS

A new general method is given based on the analyses of bi-level integrated system synthesis(BLISS),which can be used both in the decomposition of the system and in the establishment of the objective function for BLISS.And it is used in the illustration of a gear down equipment optimization to prove its feasibility and effectiveness.It may be used as the complementarity and improvement of BLISS in the sense of practical use.
Outlier detection technique based on cluster analysis and spatial correlation in wireless sensor networks

To the specific features of wireless sensor network localization environment and failed nodes,this paper proposed an outlier detection technique,ODCASC algorithm(outlier detection technique based on cluster analysis and spatial correlation). This algorithm maintained a high outlier detection rate and low false alarm rate with cluster analysis,and also made use of spatial correlation to filter the noises,extracted "interesting" events.Experimental results show that the ODCASC makes up the inadequacy of spatio-temporal correlation technique and improves the detection and distinguish the outliers efficiently.
Incremental Updating Algorithms for Mining Association Rules

Mining association rules is an important data mining problem. There have been many algorithms proposed for efficient discovery of association rules in large databases. However, very little work has been done on maintenance of discovered association rules. When users interactively mine association rules, they may have to continually tune two thresholds, minimum support and minimum confidence, which describe the users' special interestingness. In this paper, two incremental updating algorithms——IUA and PIUA are presented for such efficient maintenance problem of association rules.
Detection and Tracking of Dim Targets Based on Dynamic Programming and Track Matching

The observation of lowSNR moving targets has been a hot spot for a long time. A dynamic programming (DP) and track matching algorithm has been developed for the detection of subpixelsized, lowSNR targets observed by mosaic imaging sensors. The primary advantages of this technique are its sensitivity to weak targets along with its robustness to target maneuvers and sensor instabilities. Enhancement is achieved by performing directional weight operation precede to DP and target data association. Experiments have been conducted and it is shown that lowSNR moving targets with maneuver and maximum speed of 1 pixel/frame can be detected with high probability. 
Partitioning programs for parallel execution : A case study in the Intel iPSC/2 environment

There are two fundamental approaches towards executing programs in parallel. For the first approach, parallel algorithms and languages are normally designed for developing parallel programs to execute on an ensemble of computers with similar or dissimilar configurations. However, because writing and validating parallel algorithms is difficult and not yet understood, a viable alternative is to apply special software, or translation tools, to restructure existing programs for parallel computations. Therefore, for the second approach parallel programming libraries are provided to the user to map sequential programs onto parallel computers. Both approaches emphasize, respectively, the analysis of program loops (or vectorization) and identification of modularity of the underlying program. The methods addressed in this article follow the latter approach, with a focus on modelling and partitioning program statements and procedures into logical parallel program clusters or groups. The methods do not focus on program loops, but rather, are complementary.
Medical image reconstruction algorithm based on the geometric information between sensor detector and ROI

In this paper, we propose a new image reconstruction algorithm considering the geometric information of acoustic sources and senor detector and review the two-step reconstruction algorithm which was previously proposed based on the geometrical information of ROI(region of interest) considering the finite size of acoustic sensor element. In a new image reconstruction algorithm, not only mathematical analysis is very simple but also its software implementation is very easy because we don't need to use the FFT. We verify the effectiveness of the proposed reconstruction algorithm by showing the simulation results by using Matlab k-wave toolkit.
A Review of Registration Methods on Mobile Robots

The task of registering three dimensional data sets with rigid motions is a fundamental problem in many areas as computer vision, medical images, mobile robotic, arising whenever two or more 3D data sets must be aligned in a common coordinate system. In this chapter, the authors review registration methods. Focusing on mobile robots area, this chapter reviews the main registration methods in the literature. A possible classification could be distance-based and feature-based methods. The distance based methods, from which the classical Iterative Closest Point (ICP) is the most representative, have a lot of variations which obtain better results in situations where noise, time, or accuracy conditions are present. Feature based methods try to reduce the great number or points given by the current sensors using a combination of feature detector and descriptor which can be used to compute the final transformation with a method like RANSAC or Genetic Algorithms. Diego Viejo University of Alicante, Spain DOI: 10.4018/978-1-4666-2672-0.ch009
XML-Message Queue Markup Language

This paper introduces a kind of XML based markup language: message queuing markup language(MQML),which could be used to describe e-business,e-government and enterprise integration.MQML is extendables,with flexible structure and can explain itself.It also can be deployed and used in existing application with the help of XML.An application instance is given to describe the value of MQML for e-government.
Study on the structural damage identification method with combined parameters based on RBF neural network

Localized damage in a structure affects its dynamic properties. The change is characterized by changes in the eigenparameters such as natural frequencies, the mode shapes associated with each natural frequency etc. and much work has been undertaken by investigating the single parameter which is assigned as input parameter to neural network to determine the damage location and the damage size. In this paper, a structural damage identification method with combined parameters based on RBF neural network is presented. To overcome the disadvantages of single input parameter, combined parameters, which are obtained by combining natural frequencies, mode shape data and changes in curvature mode shape, are assigned as input parameters to neural network. The simulation results to a lumped-mass system of six degrees show that the method is effective and applicable.
A fast SVM-based feature selection method

Aiming at large computation and slow convergence speed of the traditional feature selection methods,a fast SVM-based feature selection method was proposed.A support vecor machine was employed as the classifier,and particle swarm optimization method was employed as the searching strategy.The proposed method reduced the iterations of training classifiers by taking advantage of the characteristics of linear and polynomial kernel functions,so that it could reduce the complexity of the calculation.Experimental results showed that the method accelerates feature selection in the case of no loss of the classification performances.
Investigating the use of strategic management process in the mining industry

The objective of this study is to investigate the extent to which strategic management process is utilized within the mining industry. Strategic planning is an organizational management activity that is used to set priorities, focus energy and resources, strengthen operations, ascertain that employees and other stakeholders are working toward common goals, establish agreement around intended outcomes, and assess and adjust the organization’s direction in response to a changing environment. A typical strategy management process has the following steps: initial assessment, situation analysis, strategy formulation, strategy implementation, monitoring and evaluation. The other objective is to determine which analytical tools are commonly used for situational, internal and external assessment as input to the strategic management process. A structured questionnaire was used for the study. A total of 300 mines were randomly selected from a research population of mining organizations in South Africa, Africa and globally. The respondents were all part of senior management. A response rate of 64% was achieved. The results indicated that about 20% of the organizations did not institutionalize their strategic planning functions and did not have a good strategic foundation. The results also showed that 60% were not satisfied with their productivity and 30% indicated that their cash flows were not stable at all. There was a significant number of organizations who do not use strategic analytical tools. A statistically and practically significant positive relationship was found between strategic management dimensions and business performance implying that the use of strategic management process can lead to improved business performance.
Word Extraction from Arabic Handwritten Documents Based on Statistical Measures

In Arabic, word extraction is particularly challenging because words are often divided into sub-words, and a few letters do not connect to the following letter. In this paper, we present an efficient method for extracting words from Arabic handwritten documents. The proposed method is based on two groups of spatial measures (the lengths of connected components (CCs) and the gaps between these CCs) which differentiate successive CCs in text lines. Lengths are clustered into three distinct clusters to identify an optimal threshold for separating isolated letters, sub-words, and words. Besides, Gaps are clustered into two clusters, to indicate whether the gap occurs "between-words" or "within-a word". This clustering is implemented using Self-Organizing Map (SOM) algorithm. The efficiency of the proposed method was tested by conducting experiments on 35 ages of handwritten Arabic text, accessed from benchmarking Database for Arabic Handwritten Text Recognition Research (AHDB). Our tests produced very promising results, achieving a correct extraction rate of 86.3%.
A Weibo Topic Tracking System based on K-means

This article studied weibo text representation. For the weibo features such as short, real-time, colloquialism and originality, in the original vector space model, we propose a suitable method for weibo text representation. Make all the content words as feature words after participation. And we proposed T-TFIDF weight calculation method according to the features of weibo. According to the vector space model, we proposed a weibo adaptive topic tracking methods based on K-means clustering. Simulation analysis shows that, the method can by comparing the similarity micro-blog and sub topic vector set, determine whether weibo belonging to the topic.
Research of Attribute Word Extraction Method in Chinese Product Comment

Aiming at solving problems of relatively low precision,rate of coverage when using existing attribute word extraction methods,this paper adopts Baidu Baike and co-occurrence proportion of adjacent words after word segmentation to identify new domain words,decreases impact on recognition of attribute word caused by segmentation errors.This paper designs part of speech sequence templates which contain noun and noun phrase templates,verb and verb phrase templates to obtain attribute word candidates from Chinese product comments,then utilizes statistical technique and natural language processing technique to filter attribute word candidates.Experimental results show that for the 3 623 mobile phone comments,this method obtains 1 732 attribute words,the precision,recall and f-measure reach 0.565,0.726 and 0.636,and it has good extraction performance.
A nearest neighbour classifier based on probabilistically/possibilistically intervals' number for spam filtering

Today, e-mail has become one of the fastest and most economical forms of communication in modern life. However, the increase in e-mail users has resulted in a significant boosting in unsolicited e-mails, widely known as spam, during the past few years. This paper presents an application of Interval's Number KNN (INKNN) for spam filtering. The INKNN algorithm was described lately as a lattice data domain extension of KNN classifier. In our experiment a spam e-mail was presented in the metric space of lattice ordered Interval's Number. Indeed a population of spam e-mails was presented by an Intervals Number. Then INKNN classifier was employed distinguish spam e-mails from non-spam. To investigate the effectiveness of our methods, we conduct extensive experiments on SpamAssassin public mail corpus. Experimental results show that the proposed model is able to achieve higher performance in comparison with those from a number of state-of-the-art machine learning techniques published in the literature.