Miracle-dz/UniFAR-FTUs huggingface dataset:
EDGE DETECTION BY USING LOOKUP TABLE

Edge detection is a fundamental tool used in most iprocessing applications. We proposed a simple, fast and efficient technique to detect the edge for the identifying, locating sh arp discontinuities in an image and boundary of an image. In this paper, we found that proposed method called LookUp Table performs w ell, which requires least computational time as com pared to conventional Edge Detection techniques. And also in this paper we pre sented a comparative performance of various convent ional Edge Detection Techniques.
Reliability computation of multistage interconnection networks

A general algorithm that generates a reliability expression for these measures is presented. The reliability is analyzed under the assumption of statistical independence of failure. Nonetheless, failure dependencies can also be solved. This method is appropriate for analyzing other computer networks. To model networks with switching elements (SEs), the SE is considered as good or bad. The complex fault models are described. The analytic method is implemented by a Pascal program. The advantages of the proposed method are: (1) flexibility-this technique can handle statistically independent and dependent failures within the network, while failure dependencies use the Lam-Li model; this technique is easily extendable for the analysis of M-input N-output multistage interconnection networks; (2) generality-this technique derives the reliability of networks from the points of view of terminal reliability, broadcasting, and network reliability; and (3) cost effectiveness-the necessary system information can be obtained at very little additional cost, once the connection equation of the network has been obtained. >
Thinking-aloud about web navigation

The concurrent think-aloud method is a highly valued technique that is used to understand peoples’ taskbased cognitive processes, within both usability testing and the broader study of human-computer interaction. We investigated whether an explicit explanation-based think-aloud instruction leads to differences in navigation performance over the classic think-aloud method. The results showed an interaction between think-aloud instructions and task difficulty. For the low difficulty tasks, there was no difference in task success or in the number of link traversals between the two think-aloud conditions. However, for the more difficult tasks there were differences: participants in the classic condition completed fewer tasks successfully and engaged in more link traversals than participants in the explicit think-aloud condition. A qualitative analysis of participants’ verbal data showed that both think-aloud conditions were dominated by procedural descriptions. The explicit think-aloud did yield proportionally more explanatory utterances, however the performance differences in respect of task success and link traversal suggest that an explicit think-aloud may threaten test validity and therefore evaluators must exercise caution in using this technique.
Pose Estimation of Web-Shared Landscape Pictures

We propose a robust method for registering high oblique images of landscapes. Typically, an input image can be registered by matching it against a set of registered images of the same location. While this has been shown to work very well for images of popular urban landmarks, registering landscape images remains a very challenging task: For a given place, only a very small amount of registered images is generally already available on photo-sharing platforms. Moreover, the appearance of landscapes can vary drastically depending on the season and the weather conditions. For these two reasons, matching the input images with registered images in a reliable way remains a challenging task. Our contribution is two-fold: first, we show how to estimate the camera orientation for images with GPS data using a novel algorithm for horizon matching based on Dynamic Time Warping. The proposed algorithm exploits an elevation model. Each image is processed independently from the others, there is therefore no need neither for image matching or for a large set of images. This step provides a set of reliable, fully registered images. Second, and in odrer to register new images with no GPS data available, we first ask the user to provide an approximate image localization on a 2D map. Then, we exploit this prior on the camera location to efficiently and robustly constrain and guide the matching process used to register the query image. We apply our method to a case study from the Zermatt area in Southern Switzerland, and show that the method provides registrations, which are accurate enough to map each pixel to an aerial map.
Phonological activation of semantic competitors during lexical access in speech production

Abstract Experimental evidence from picture-naming tasks suggests that lexical access in speech production (lexicalisation) occurs in two non-overlapping stages. Semantic information is used to access an abstract lexical form; only when this stage is complete does phonological realisation begin. Such experimental data are interpreted as evidence against connectionist models of lexicalisation. This paper argues that, counterintuitively, connectionist models are not inconsistent with these data. This proposal is supported by appropriate simulations. Interactive activation models of speech production have the additional advantage of accounting for speech error and other data.
A Study on Movies Recommendation System of Hybrid Filtering-Based

Recommendation system is filtering for users require appropriate information from increasing information. Recommendation system is provides the information based on user information or content that information entered in the original through process of filtering through the algorithm. Recommend system is problems with Cold-start, and Cold-start is not enough information in the occurrences for new users of recommend system in the new information to the user when recommend. Cold-start is should meet to resolve the user of information and item information. In this paper, Suggest for movie recommendation system on collaborative filtering techniques and content-based filtering techniques based to a hybrid of a hybrid filtering techniques to solve problems in cold-start 키워드 : Cold-Start, 내용 기반의 필터링 기법, 추천 시스템, 협업 필터링 기법, 혼합 필터링 기법 Key word : Cold-Start, Collaborative Filtering Technique, Content-Based Filtering Technique, Hybrid Filtering Technique, Recommend System Journal of the Korea Institute of Information and Communication Engineering 한국정보통신학회논문지(J. Korea Inst. Inf. Commun. Eng.) Vol. 19, No. 1 : 113~118 Jan. 2015
Spatial Clustering Analysis of K-Means Algorithm in the Classification of Bank Card Customers

K-means algorithm is a simple and efficient data mining clustering algorithm. For the current status of the bank card customer relationship management, based on data mining technology, design based on K-means clustering algorithm banking customer classification system. Data mining techniques can extract vast amounts of customer information data bank card implicit knowledge and spatial relationship model will represent the bank customers feature set of data objects automatically classified into each composed of clusters of similar objects, bank card customers in the banking system classification. This paper analyzes the existing spatial clustering methods summary and conclusion, based on the combined data bank card customers, according to the volatility of funds used to different customer groups, the use of K-means analysis to study characteristics of client groups, providing appropriate financial services.
Neural Networks for the MS/OR Analyst: An Application Bibliography

From a management scientist's perspective, the neural networks offer three opportunities: statistical methods, optimization methods, and a problem domain in which to apply OR algorithms. This annotated bibliography of application literature should offer a nontechnical fast track to someone getting started in this exciting area. The research published thus far on the applications of neural networks for statistical and optimization problems shows promise for the interface of neural networks and management science.
Parallel Solvers for Finite-Difference Modeling of Large-Scale, High-Resolution Electromagnetic Problems in MRI

With the movement of magnetic resonance imaging (MRI) technology towards higher field (and therefore frequency) systems, the interaction of the fields generated by the system with patients, healthcare workers, and internally within the system is attracting more attention. Due to the complexity of the interactions, computational modeling plays an essential role in the analysis, design, and development of modern MRI systems. As a result of the large computational scale associated with most of the MRI models, numerical schemes that rely on a single computer processing unit often require a significant amount of memory and long computational times, which makes modeling of these problems quite inefficient. This paper presents dedicated message passing interface (MPI), OPENMP parallel computing solvers for finite-difference time-domain (FDTD), and quasistatic finite-difference (QSFD) schemes. The FDTD and QSFD methods have been widely used to model/ analyze the induction of electric fields/ currents in voxel phantoms and MRI system components at high and low frequencies, respectively. The power of the optimized parallel computing architectures is illustrated by distinct, large-scale field calculation problems and shows significant computational advantages over conventional single processing platforms.
A study of integration testing and software regression at the integration level

The authors identify the common errors and faults in combining modules into a working unit. They also make practical recommendations on test selection for integration testing and utilize these recommendations for regression testing at the integration level. With emphasis on reusing the previous test cases and retesting only the parts that are modified, one can reduce the testing expenses. The concept of 'firewall' is proposed to assist the tester in focusing on that part of the system where new errors may have been introduced by a correction or a design change. An experiment is presented in which an application of strategy is compared with the 'retest-all' strategy. By applying the approach for test selection, it is possible to discover all errors found by the retest-all strategy by executing only 35% of the total number of test cases. <<ETX>>
GPU Parallel Computing Algorithm in Target Tracking of Space

Article careful analysis of existing processes and target tracking algorithm and part of the video image processing algorithm that consume large amount of data based CPU. Each algorithm for image pre-processing module and tracking module algorithm designed operational processes. Several studies algorithm for image pre-processing module, and each was carried out in parallel GPU accelerated processing, and were tested in a unified CUDA computing device. Experimental data show that some parts of the algorithm can be improved after the adoption of CUDA target tracking algorithm optimized overall efficiency, has some practical significance.
Program Cache Busy Time Control Method for Reducing Peak Current Consumption of NAND Flash Memory in SSD Applications

In current NAND flash design, one of the most challenging issues is reducing peak current consumption (peak ICC), as it leads to peak power drop, which can cause malfunctions in NAND flash memory. This paper presents an efficient approach for reducing the peak ICC of the cache program in NAND flash memory — namely, a program Cache Busy Time (tPCBSY) control method. The proposed tPCBSY control method is based on the interesting observation that the array program current (ICC2) is mainly decided by the bit‐line bias condition. In the proposed approach, when peak ICC2 becomes larger than a threshold value, which is determined by a cache loop number, cache data cannot be loaded to the cache buffer (CB). On the other hand, when peak ICC2 is smaller than the threshold level, cache data can be loaded to the CB. As a result, the peak ICC of the cache program is reduced by 32% at the least significant bit page and by 15% at the most significant bit page. In addition, the program throughput reaches 20 MB/s in multiplane cache program operation, without restrictions caused by a drop in peak power due to cache program operations in a solid‐state drive.
Real-Geographic-Scenario-Based Virtual Social Environments: Integrating Geography with Social Research

Existing online virtual worlds, or electronic environments, are of great significance to social science research, but are somewhat lacking in rigour. One reason is that users might not participate in those virtual worlds in the way they act in real daily life, communicating with each other in familiar environments and interacting with natural phenomena under the constraints of the human–land relationship. To help solve this problem we propose the real-geographic-scenario-based virtual social environment (RGSBVSE). The aim is to enhance the ability of current virtual worlds in social issues studies by promoting virtual geographic environments that are built with real scenarios in the physical world. In this paper we first discuss the potential shortage of current virtual worlds for serious social research. We then explain how real geographic scenarios can contribute to building a virtual social environment by providing (1) real geographic data, including the time dimension, in terms of data acquisition and organisation; (2) dynamic or real-time natural phenomena and processes for scenario simulation and expression; (3) shared spaces that enhance participants' interaction through a mix of virtuality and reality; and (4) shared hot spots of social phenomena for researchers from multidisciplinary (eg, sociology, psychology) performing collaborative research. Furthermore, two of our projects, the virtual Chinese University of Hong Kong and the Virtual Globe of the Chinese Family Tree, are introduced as case studies, to illustrate how the RGSBVSE can play a significant role in a number of critical social research issues from the local to regional scale.
Specification and testing using generalized machines: a presentation and a case study

Although testing is a major part of software development, it rarely gets the attention it deserves from researchers, partly because its foundations are weak and ill‐understood. The principal purpose of testing is to detect (and then remove) faults in a software system. However, very few of the existing methods allow the tester to make any precise statement about the type or number of faults that remain undetected after testing is completed. In particular, none of the main techniques used by the software industry can give serious guarantees that a system is fault‐free after testing has been completed. This paper advocates the use of a formal method both as a specification language and as the basis of a test data selection strategy. It presents a new method for generating test cases from this type of formal specification that provides a more convincing answer to the problem of detecting all faults in a software system. The method is reductionist in the sense that it guarantees that a system is fault‐free provided that its components are fault‐free; in turn, the same method could be used to test the resulting sub‐systems, so the reduction will continue until the components considered are either known to be correct or are fairly simple pieces of code that can be successfully tested using traditional methods. The formal method used, X‐machines, is a blend of finite state machines, data structures and processing functions and provides a simple and intuitive way of specifying computer systems. The use of X‐machines as a specification tool and the testing method are illustrated with a case study. © 1998 John Wiley & Sons, Ltd.
Blood cell classification utilizing hydrodynamic filtration

Selection or concentration of small particles such as specific cells or microbes is a very important technique in various fields. We have developed a method for separating and concentrating particles or cells according to size, termed hydrodynamic filtration (HDF), using microfluidic devices. In this study, we tried to separate and concentrate blood cells by simply introducing cell suspension into microfluidic devices, without using complicated operations and external devices. © 2010 Wiley Periodicals, Inc. Electron Comm Jpn, 94(1): 1–6, 2011; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ecj.10281
Hybrid predictive control of a DC–DC boost converter in both continuous and discontinuous current modes of operation

Developing efficient and appropriate modeling and control techniques for DC–DC converters is of major importance in power electronics area and has attracted much attention from automatic control theory. Since DC–DC converters have a complex hybrid nature, recently several techniques based on hybrid modeling and control have been introduced. These techniques have shown better results as compared with conventional averaging‐based schemes with limited modeling and control abilities. But the current works in this field have not considered all possible dynamics of the converters in both continuous and discontinuous current modes (CCM, DCM) of operations. These dynamics are results of controlled and uncontrolled switching phenomena in DC–DC converters. This paper proposes a new switching scheme for modeling and controlling a DC–DC boost converter. The converter is represented as a hybrid automaton by considering the all three possible modes. The hybrid automaton is translated into the mixed logical dynamical (MLD) mathematical framework. The switching among these modes is generated by hybrid predictive control method which is calculated by Mixed Integer Quadratic Programming (MIQP). Using the exact model of the converter, the proposed switching algorithm can globally control the converter in all operation regimes, including CCM and DCM operations, considering all constraints in the physical plant, such as maximum inductor current and capacitor voltage limits. The switching algorithm is applied to a real converter circuit, modeled with various parasitic components. Simulation results are provided to show the advantages of the proposed control scheme. Copyright © 2010 John Wiley & Sons, Ltd.
A Model of End Users' Web Threats Information Processing

Abstract Many threats have appeared with an increasingly sophisticated web platform. To cope with these threats, end users have to gather and process relevant information. There are two modes of information processing: systematic processing and heuristic processing. Using the heuristic-systematic model, the study shows the influence of involvement and information insufficiency on the processing mode. The study finds that high involvement is positively related to systematic processing, while low involvement is negatively related to heuristic processing. Information insufficiency has a significant negative relationship with heuristic processing but no significant relationship is found between information insufficiency and systematic processing. The study also shows that systematic processing has a positive relationship with the intention of following recommended protective actions while heuristic processing has a negative relationship with this intention. The results shed light on the role of processing mode related to web threat information.
Multiplex Distribution Interface Analyzer Using Memory Sharing Techniqyes on Ethernet Mode for DRM/DRM+ Systems

본 논문에서는 디지털 라디오 표준인 DRM(Digital Radio Mondiale) 및 DRM+(Digital Radio Mondiale Plus) 시스템을 이더넷모드에서 분석할 수 있는 다중화분산접속(MDI, Multiplex Distribution Interface) 분석기를 설계한다. 제안하는 MDI 분석기는 메모리 공유 기술을 가진 공통 블록 모듈을 사용하여 MDI 패킷들의 부하를 감소시키며, 차세대 디지털 라디오 방송시스템을 위해 수신된 MDI 패킷을 통해 IP(Internet Protocol)와 DRM/DRM+ 시스템의 FAC(Fast Access Channel)/SDC(Service Description Channel) 구성정보를 확인할 수 있다.
Standard library metadata models and structures for the Semantic Web

Purpose – There has been a significant increase in activity over the past few years to integrate library metadata with the Semantic Web. While much of this has involved the development of controlled vocabularies as “linked data”, there have recently been concerted attempts to represent standard library models for bibliographic metadata in forms that are compatible with Semantic Web technologies. This paper aims to give an overview of these initiatives, describing relationships between them in the context of the Semantic Web.Design/methodology/approach – The paper focusses on standards created and maintained by the International Federation of Library Associations and Institutions, including Functional Requirements for Bibliographic Records, Functional Requirements for Authority Data, and International Standard Bibliographic Description. It also covers related standards and models such as RDA – Resource Description and Access, REICAT (the new Italian cataloguing rules) and CIDOC Conceptual Reference Model, .
WIS: Weighted Interesting Sequential Pattern Mining with a Similar Level of Support and/or Weight

Sequential pattern mining has become an essential task with broad applications. Most sequential pattern mining algorithms use a minimum support threshold to prune the combinatorial search space. This strategy provides basic pruning; however, it cannot mine correlated sequential patterns with similar support and/or weight levels. If the minimum support is low, many spurious patterns having items with different support levels are found; if the minimum support is high, meaningful sequential patterns with low support levels may be missed. We present a new algorithm, weighted interesting sequential (WIS) pattern mining based on a pattern growth method in which new measures, sequential s‐confidence and w‐confidence, are suggested. Using these measures, weighted interesting sequential patterns with similar levels of support and/or weight are mined. The WIS algorithm gives a balance between the measures of support and weight, and considers correlation between items within sequential patterns. A performance analysis shows that WIS is efficient and scalable in weighted sequential pattern mining.
Ranking and selecting motor vehicle accident sites by using a hierarchical Bayesian model

Identification, ranking and selecting hazardous traffic accident locations from a group under consideration is a fundamental goal for traffic safety researchers. Few methods exist that can quantitatively, accurately and easily discriminate between sites that commonly have small and variable observation count periods. One method that embodies all these advantages is the hierarchical Bayesian model, the method proposed in this paper. The particular hierarchical Bayesian approach that we use incorporates expert knowledge about accident sites as a group believed a priori to be exchangeable, the Poisson assumption and a conjugate gamma prior. We then propose three natural strategies for ranking and selecting the most hazardous subgroup of accident locations. Also presented is an especially useful procedure that gives the probability that each particular site is worst and by how much it is worst. All proposed strategies are illustrated using previously published fatality accident data from 35 sites in Auckland, New Zealand.
A scheme of improving the quality of speech mixing in multi-media conference system

The factors which influence the voice quality in multi-media conference on IP network, such as delay and the accumulation of delay is analyzed. A scheme which adopts mixing buffer and method of uniform mixing period to increase the quality of mixed speech is proposed. The prototype of multi-media conference system which using this scheme proves that the quality issue of speech mixing can be solved well, and the speech quality is good in the local area network.