Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulink model testing method based on program mutation

In order to solve the current problems( expensive testing cost and low efficiency of test case generation) in mutation test for Simulink models,a mutation testing process and an optimized set of mutation operators were proposed for Simulink models based on program mutation according to the current mutation operators for the Simulink models. Experiments show that this set of mutation operators can effectively reduce the generation number of mutation models without prejudice to the mutation score of testing case set,thus it will effectively save the testing cost. Then a search-based test case generation method for Simulink models mutation testing was described. The test case generation problem was transformed into the objective function minimization problem,and the test cases which can kill the mutation models were ultimately obtained through the optimization of objective function by algorithm of simulated annealing. Finally,the application of a typical case for the method verified the correctness and effectiveness.
New approach on image retrieval based on color information entropy

A new content-based image retrieval method was proposed.Firstly,an image was segmented into several sub-areas,and the block color information was extracted from the image.Secondly,mutual information of color among images was computed using information entropy.Compared with the algorithms of other methods of color description,experiments with different kinds of images indicate that this approach has the advantages of good effect and high stability.
Automatically Generating Hierarchical Summary for Film Video

It is important to properly organize the unstructured video data for content-based video analysis and retrieval. In this paper, we propose a unified approach for film video summarization based on the analysis of video structure and motion attention model. Video shots are firstly grouped into shot clusters. Afterwards, according to the characterization of film video scene, a temporally and spatially integrated strategy is presented to parse shot clusters into semantic scenes in terms of the definition of temporal relationships between two shot clusters. Finally, representative frames and highlight shots are selected from scenes by using motion attention model. The scheme offers an efficient mean for browsing and effectively retrieving film video.
Spatial distribution of knowledge, attitude and practice on leptospirosis prevention and its predictors using stratum risk identification methods among residents in a flood prone area in Kuantan, Pahang, Malaysia

Leptospirosis is a preventable zoonosis of public health importance. Due to seasonal floods and paucity of studies regarding leptospirosis preventive practices in community setting in this country, a study was conducted to determine spatial distribution of knowledge, attitude and practice on leptospirosis preventive practices using stratum risk identification methods among residents in a flood prone area in Kuantan via Geographical Information System (GIS). A cross-sectional study was conducted in Perkampungan Sungai Isap, Kuantan, Pahang, which obtained 568 respondents by stratified proportionate random sampling technique. Data were collected using a validated guided questionnaire. Geographical coordinates were obtained using Garmin gpsmap 62s. Multivariate logistic regression was done using SPSS version 22.0 and spatial mapping using ArcGIS version 9. The response rate was 83.4%. Flood-risk strata were divided into high risk (less than 500 metres), medium risk (500 to 1000 metres) and low risk (more than 1000 metres). Linear measurements were made using ArcMap. Overall, majority (68%) of the respondents had good knowledge. However, only 38% of the respondents had satisfactory attitude and 18% had satisfactory practice. Spatial mapping showed that the lower the stratum risk towards flooding, the poorer the preventive practices. Multivariate logistic regression showed the predictors of satisfactory leptospirosis preventive practices were high-risk stratum (aOR=4.28, 95% CI [1.92, 9.57], P < 0.001), good knowledge (aOR=1.73, 95% CI [1.04, 2.90], P < 0.05) and age group 30 to 39 (aOR=0.30, 95% CI [0.11, 0.83], P < 0.05). In conclusion, GIS is a valuable tool for leptospirosis surveillance by facilitating disease prevention programmes to targeted risk areas. © 2018, Asian Journal of Agriculture and Biology.
Parallel Reactive Power Optimization Based on Differential Evolution and PC-cluster

An approach to on-line optimization of reactive power flow is proposed. It is based on a new branch of evolution algorithm named differential evolution (DE) and parallel computing techniques implemented on a PC-cluster. Case study on the IEEE 118-bus system shows that DE, simple as it is, converges fast to the approximate optimal solution. When DE is parallelized and executed on a PC-cluster of moderate size, it is possible to shorten the computing time of reactive power optimization within an acceptable scope, which is needed for on-line application.
The Research on the Complexity of Progressive Die Edge

Firstly,the present research about defining graphic’s complexity and expressing the complexity mathematically was introduced.In this paper,the research about how to describe and measure a graphic’s complexity was emphasized,two quantificational definition,B and C,were brought forward and the method to calculate C was presented.Combining with the theory about image detection in the pattern recognition,the way to calculate B was presented.According to the experience,the relationship between B and C was deduced.In the end,a foreground about research on the complexity was issued.
Design of Automated Demand Response Information Exchange Interface

Automated demand response information exchange interface, which is one of the key technologies for automated demand response, is entity connecting customer automation systems, demand response service systems in power grid-side and the third-party aggregation service systems for participating in the automated demand response. Information exchange capabilities between demand response systems are realized by the interface. By analyzing demand response business, architecture, hierarchical model and functions of interface were designed in this paper. By integrating function layer, information layer and communication layer entities, efficient, flexible, convenient and secure demand response information exchange can be implemented by the interface that mapped to a physical network reasonable, which is significant for automated demand response.
High Blood pressure and other cardiovascular risk factors in students of the national university of Asunción-Paraguay.

Background High blood pressure is a category of blood pressure, poorly studied in young adults and is associated with other cardiovascular risk factors. Objective: To estimate the prevalence of high blood pressure and other cardiovascular risk factors in university students of the National University of Asunción-Paraguay. Methods Cross-sectional study in first-year university students of careers related to health at the National University of Asunción. High blood pressure was considered at a systolic blood pressure of 120-129 mmHg and / or diastolic blood pressure of ? 80 mmHg. The anthropometric, lipid and metabolic variables were measured and the smoking and alcohol habit were determined. Results 284 university students were included in the analysis (mean age [± SD] 19.4 ± 2.1 years, 73.2% were women). The overall prevalence of high blood pressure was 25.5%; (95% CI: 20.5-30.5%) (Men 40%, Women 29%). High blood pressure was associated with increased abdominal circumference (OR: 1.031, 95% CI: 1.005-1.058, p = 0.024); low HDL-C (OR: 1.355, 95% CI 1.054-1.743, p = 0.018); obesity, OR: 2,124; 95% CI: 1.334-3.941; p = 0.007) and DM2 (OR: 4.431, 95% CI: 3,642-7,963, p = <0.001). The prevalence of other cardiovascular risk factors more frequent were alcohol consumption, overweight and obesity. More than 70% of college students have one or more cardiovascular risk factors. Conclusions high prevalence of high blood pressure and is associated with other cardiovascular risk factors.
Method of Quickly Recognizing Vehicle Plate Based on Pattern Matching and Characteristic Dot Matching

A method which can recognize vehicle plate quickly is discussed. Firstly, the image of vehicle plate must be preproce ssed, and then the image is segmented by the method of vertical projection, fina lly the character of the image is recognized by using matching recognition and c haracteristic dot recognition. By this technique, the vehicle plate can be recog nized more quickly and more correctly.
Nearest Neighbor Method Data Association Algorithm to Support Multi-target Tracking in WSN

Multi-source data association is one of the key technologies of multi-sensor data fusion in wireless sensor network.The joint probability data association algorithm is a data association algorithm of Multi-target tracking,it doesn't need any priori information about targets and clutters,but its computer expense is very large compared with the other related data association algorithms.The joint probability data association algorithm based on the nearest neighbor method integrates the ideology of nearest neighbor data association in the process of the construction of effective matrix,then selects three valid measurements which have the least statistical distance to construct the effective matrix.This method simplifies the effective matrix,thus reduces the computation of original algorithm.
A Hybrid Model Schema Matching Using Constraint-Based and Instance-Based

Schema matching is an important process in the Enterprise Information Integration (EII) which is at the level of the back end to solve the problems due to the schematic heterogeneity. This paper is a summary of preliminary result work of the model development stage as part of research on the development of models and prototype of hybrid schema matching that combines two methods, namely constraint-based and instance-based. The discussion includes a general description of the proposed models and the development of models, start from requirement analysis, data type conversion, matching mechanism, database support, constraints and instance extraction, matching and compute the similarity, preliminary result, user verification, verified result, dataset for testing, as well as the performance measurement. Based on result experiment on 36 datasets of heterogeneous RDBMS, it obtained the highest P value is 100.00% while the lowest is 71.43%; The highest R value is 100.00% while the lowest is 75.00%; and F-Measure highest value is 100.00% while the lowest is 81.48%. Unsuccessful matching on the model still happens, including use of an id attribute with data type as autoincrement; using codes that are defined in the same way but different meanings; and if encountered in common instance with the same definition but different meaning.
A Gray-scale Blind Watermarking Algorithm in DCT Domain Based on Chaotic Encryption

More and more researchers focus on meaningful digital watermarking,especially gray-scale watermarking,because it has much good advantage but is hard to deal with.First,this paper gives a simple but effective chaotic transform encryption algorithm of image,and then presents a gray-scale blind watermarking technique operating on DCT domain,which automatically embeds watermarking information by adjusting the relationship of coefficients of selected DCT block with the mean of corresponding coefficients of DCT blocks around the selected DCT block.The experimental results show that the proposed algorithm is effective and robust to common image processing operations and some geometric distortions such as JPEG compression and cropping.So a conclusion can be made that the proposed technique is practical.
Finding Best Mining Scheme for Development of Multinomial Software Fault Prediction Model

This paper discuss different classification methods toward reliability and quality improvement of software systems by predicting fault-prone module before testing. Classification capability of Data mining techniques and Object-oriented property based knowledge stored in Object-Oriented metrics are used to classify the software module as fault-prone in different error categories or not fault-prone. Three versions of Eclipse, the java-based Open source Integrated Development environment as dataset for training and testing all the classification based data mining techniques are used. First of all, Threshold base feature ranking (i.e. Area under the ROC curve) is used for selecting effective OOmetrics in building prediction model. After that using those subsets of selected attributes, classification models are built with 41 different classifiers for multinomial classification in fault detection systems. Finally, the performance of a classifier is evaluated with respect to the PRC performance metric. Based on the performance results appropriate classifiers (Random Committee, Random Tree, Randomizable Filtered classifier and IBK) which depict a higher performance and accuracy compared to the others are selected. Our results indicate that Random Tree, Random Committee and Randomizable Filtered Classifier have same performance. IBK classifier also has same performance but little bit less and Kstar has less performance compared to previous four selected classifiers.
FGSM: A Framework for Grid Service Mining

With the development of Web Services, Grid Computing and Software Reuse, users want to customize their computing tasks by finding some Grid Services, and use them directly or dynamically compose them to form new services. A framework of Grid Service Mining called FGSM was presented, in which intelligent agents analyze users' service requirements, look up and discovery appropriate Grid Services from sets of service resources, and compose them on users' demand. Grid Service Mining and its overall process were proposed. A key components' implementation of FGSM was described.The usability, reliability and scalability are available in FGSM compared with other SOC framework.
COMPARISON OF VSM, GVSM, AND LSI IN INFORMATION RETRIEVAL FOR INDONESIAN TEXT

Vector space model (VSM) is an Information Retrieval (IR) system model that represents query and documents as n -dimension vector. GVSM is an expansion from VSM that represents the documents base on similarity value between query and minterm vector space of documents collection. Minterm vector is defined by the term in query. Therefore, in retrieving a document can be done base on word meaning inside the query. On the contrary, a document can consist the same information semantically. LSI is a method implemented in IR system to retrieve document base on overall meaning of users’ query input from a document, not based on each word translation. LSI uses a matrix algebra technique namely Singular Value Decomposition (SVD). This study discusses the performance of VSM, GVSM and LSI that are implemented on IR to retrieve Indonesian sentences document of . pdf, . doc and . docx extension type files, by using Nazief and Adriani stemming algorithm. Each method implemented either by thread or no-thread. Thread is implemented in preprocessing process in reading each document from document collection and stemming process either for query or documents. The quality of information retrieval performance is evaluated based-on time response, values of recall, precision, and F-measure were measured. The results show that for each method, the fastest execution time is . docx extension type file followed by . doc and . pdf. For the same document collection, the results show that time response for LSI is more faster, followed by GVSM then VSM. The average of recall value for VSM, GVSM and LSI are 82.86 %, 89.68 % and 84.93 % respectively. The average of precision value for VSM, GVSM and LSI are 64.08 %, 67.51 % and 62.08 % respectively. The average of F-measure value for VSM, GVSM and LSI are 71.95 %, 76.63 % and 71.02 % respectively. Implementation of multithread for preprocessing for VSM, GVSM, and LSI can increase average time response required is about 30.422%, 26.282%, and 31.821% respectively.
Oracle Exalogic Elastic Cloud Handbook

Achieve Extreme Application Performance for a Private Cloud with Oracle Exalogic Elastic Cloud Transition to a private cloud computing infrastructure and achieve unmatched levels of performance with help from this Oracle Press guide. Oracle Exalogic Elastic Cloud Handbook shows you how to plan, implement, and maintain a private cloud. Find out how to set up components, deploy Java applications, maximize redundancy and throughput, and migrate legacy workloads. Examples, screenshots, and diagrams are included throughout this comprehensive resource. Configure Oracle Exalogic Elastic Cloud nodes and racks Optimize Java EE application performance on Oracle WebLogic Server Provision and administer a highly scalable private cloud Build, debug, and manage custom Java applications Secure, monitor, and tune using Oracle Enterprise Manager Leverage capabilities from Oracle Fusion Middleware Consolidate Oracle and third-party application workloads
Visual Data Mining for Collaborative Filtering: A State-of-the-Art Survey

This book chapter provides a state-of-the-art survey of visual data mining techniques used for collaborative filtering. The chapter begins with a discussion on various visual data mining techniques along with an analysis of the state-of-the-art visual data mining techniques used by researchers as well as in the industry. Collaborative filtering approaches are presented along with an analysis of the state-of-the-art collaborative filtering approaches currently in use in the industry. Visual data mining can provide benefit to existing data mining techniques by providing the users with visual exploration and interpretation of data. The users can use these visual interpretations for further data mining. This chapter dealt with state-of-the-art visual data mining technologies that are currently in use apart. The chapter also includes the key section of the discussion on the latest trends in visual data mining for collaborative filtering.
Question Answering and Generation

Automated Question Answering and Asking are two active areas of Natural Language Processing with the former dominating the past decade and the latter most likely to dominate the next one. Due to the vast amounts of information available electronically in the Internet-era, automated Question Answering is needed to fulfill information needs in an efficient and effective manner. Automated Question Answering is the task of providing answers automatically to questions asked in natural language. Typically, the answers are retrieved from large collections of documents. While answering any question is difficult, successful automated solutions to answer some type of questions, so called factoid questions, have been developed recently culminating with the just announced Watson Question Answering system developed by I.B. M. to compete in Jeopardy-like games. The flip process, automated Question Asking or Generation is about generating questions from some form of input such as text, meaning representation, or database. Question Asking/Generation is an important component in learning technologies such as tutoring systems. Advances in Question Asking/Generation will revolutionize learning and dialogue systems. This chapter presents an overview of recent developments in Question Answering and Generation starting with a description of the question landscape.
The Impact of Interactive Environment and Metacognitive Support on Academic Achievement and Transactional Distance in Online Learning

The aim of the present study is to reveal the impact of the interactive environment and metacognitive support (MS) in online learning on academic achievement and transactional distance (TD). The study is designed as 2 × 2 factorial design, and both qualitative and quantitative research techniques are used. The study was carried out on 127 students, and this research design was implemented in an online learning environment and evaluated based on an achievement test, TD scale, and semistructured interviews. The results of the study revealed that synschronous environments had a significant impact in reducing TD perception. In asynschronous environments, on the other hand, providing students with MS was efficient in reducing TD. In terms of academic success, compared with synschronous environments, MS provided in asynschronous environments caused to a significant difference in achievement test scores of the students. Explanations about findings and implications for instructional design are discussed in Conclusion section.
Capability Hardware Enhanced RISC Instructions: CHERI Programmer’s Guide

The CHERI Programmer’s Guide documents the software environment for the Capability Hardware Enhanced RISC Instructions (CHERI) prototype developed by SRI International and the University of Cambridge. The Guide is targeted at hardware and software developers working with capability-enhanced software. It describes how to use the CHERI Clang/LLVM compiler suite and CheriBSD operating system – versions of the off-the-shelf LLVM suite and FreeBSD operating system adapted to use CHERI’s protection features – as well as implementation details of both.
Security Enhanced Linux on CBE

The project creates an implementation of Security Enhanced Linux (SELinux) on the Cell Broadband Engine (CBE) architecture. SELinux is a Linux security feature that provides fine grained control over all system resources through the use of user defined policies. First, an existing SELinux implementation is ported to a CBE based hardware like the Sony Play Station 3. Then the performance overhead of SELinux is measured using automated scripts. The measurement is taken for different parameters that impact the SELinux performance. The results are analyzed to identify the performance bottleneck components in SELinux. The CBE is essentially a multi core architecture targeted towards distributed processing and media rich applications. In order to effectively utilize the multiprocessor architecture of CBE the SELinux library is modified to introduce parallelism in certain operations. The parallelism is implemented using four different approaches. Performance is measured for each of the implementation with different parameters. The results are analyzed and inferences drawn. A set of utilities have been developed to automate the process of SELinux policy creation for specific scenarios. In addition a couple of utilities are created to compare kernel configuration files and to automate the kernel configuration process. Project URL: http://sourceforge.net/projects/selinuxoncbe/ © 2010 Arun K, Bodhisatta, Jansi, Pradeep, Shivam. This material is available under the Creative Commons Attribution-Noncommercial-Share Alike License. See http://creativecommons.org/licenses/by-nc-sa/3.0/ for details.