A test-suite reduction approach to improving fault-localization effectiveness

In order to improve the effectiveness of fault localization, researchers are interested in test-suite reduction to provide suitable test-suite inputs. Different test-suite reduction approaches have been proposed. However, the results are usually not ideal. Reducing the test-suite improperly or excessively can even negatively affect fault-localization effectiveness. In this paper, we propose a two-step test-suite reduction approach to remove the test cases which have little or no effect on fault localization, and improve the distribution evenness of concrete execution paths of test cases. This approach consists of coverage matrix based reduction and path vector based reduction, so it analyzes not only the test cases coverage but also the concrete path information. We design and implement experiments to verify the effect of our approach. The experimental results show that our reduced test-suite can improve fault-localization effectiveness. On average, our approach can reduce the size of a test-suite in 47.87% (for Siemens programs) and 23.03% (for space program). At the same time, on average our approach can improve the fault-localization effectiveness, 2.12 on Siemens programs and 0.13 on space program by Tarantula approach.     

A novel approach to improving search efficiency in unstructured peer-to-peer networks

The decentralized peer-to-peer (P2P) technique has been widely used to implement scalable file sharing systems. It organizes nodes in a system into a structured or unstructured network. The advantages of the unstructured P2P systems are that they have lower maintenance complexity and can better adapt to node heterogeneity as well as network dynamics. However, the search process in unstructured systems is not as efficient as in structured P2P systems because the same search message may go through a node multiple times. To facilitate the complex search and improve the search efficiency, we propose a novel approach of assigning identifications to nodes in an unstructured system. Our method can prevent a node from receiving duplicate search messages and retain the low maintenance overhead for the system. The performance evaluations demonstrate that the proposed approach can improve the search efficiency of unstructured P2P systems while keeping the maintenance overhead at a comparable or even lower level, compared with the traditional unstructured systems.     

Personalized Web search for improving retrieval effectiveness

Current Web search engines are built to serve all users, independent of the special needs of any individual user. Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to learn user profiles from users' search histories. The user profiles are then used to improve retrieval effectiveness in Web search. A user profile and a general profile are learned from the user's search history and a category hierarchy, respectively. These two profiles are combined to map a user query into a set of categories which represent the user's search intention and serve as a context to disambiguate the words in the user's query. Web search is conducted based on both the user query and the set of categories. Several profile learning and category mapping algorithms and a fusion algorithm are provided and evaluated. Experimental results indicate that our technique to personalize Web search is both effective and efficient.     

Modeling signal-based decisions in online search environments: A non-recursive forward-looking approach

Consider a rational decision maker (DM) who must acquire a finite amount of information sequentially online from a set of products. The DM receives signals on the distribution of the product characteristics. Each time an observation is acquired, DMs redefine the probability of improving upon the products observed. The resulting information acquisition process depends on the values of the characteristics observed, the number and potential realizations of the remaining observations, and the type of signal received. We construct two functions determining the information acquisition behavior of DMs and illustrate numerically the importance of the characteristic on which signals are issued.     

A social-cognitive approach to online game cheating

Drawing upon social cognitive theory, the present study constructed a conceptual model to provide an in-depth understanding of the key psychosocial factors shaping the cheating behavior in online multiplayer games. An online survey approach and structural equation modeling were adopted to investigate the hypothesized relationships and to assess the fitness of the model. The findings of this study provided support for the research model and the social cognitive theory perspective on game cheating behavior. The cheating behavior was found to be influenced by both external and self-generated factors. Specifically, individual gamers’ social environment (peer influence), their own attitude towards game cheating, and evaluation of cheating outcomes played an important role in shaping this problematic behavior in online games. Game cheating self-efficacy was interestingly found to be an insignificant factor for the game cheating behavior. The study fills a gap in the current literature on games and cheating, specifically on the social cognitive mechanisms of the cheating behavior in online multiplayer games.     

A reinforcement learning approach to online clustering

A general technique is proposed for embedding online clustering algorithms based on competitive learning in a reinforcement learning framework. The basic idea is that the clustering system can be viewed as a reinforcement learning system that learns through reinforcements to follow the clustering strategy we wish to implement. In this sense, the reinforcement guided competitive learning (RGCL) algorithm is proposed that constitutes a reinforcement-based adaptation of learning vector quantization (LVQ) with enhanced clustering capabilities. In addition, we suggest extensions of RGCL and LVQ that are characterized by the property of sustained exploration and significantly improve the performance of those algorithms, as indicated by experimental tests on well-known data sets.     

A SIMD approach to parallel heuristic search

Serial search algorithms often exhibit exponential run times and may require an exponential amount of storage as well. Thus, the design of parallel search algorithms with limited memory is of obvious interest. This paper presents an efficient SIMD parallel algorithm, called IDPS (for iterative-deepening parallel search). At a broad level IDPS is a parallel version of IDA*. While generically we have called our algorithm an IDPS, performance of four variants of it has been studied through experiments conducted on the well-known test-bed problem for search algorithms, namely the Fifteen Puzzle. During the experiments, data were gathered under two different static load balancing schemes. Under the first scheme, an unnormalized average efficiency of approximately 3/4 was obtained for 4K, 8K, and 16K processors. Under the second scheme, unnormalized average efficiencies of 0.92 and 0.76, and normalized average efficiencies of 0.70 and 0.63 were obtained for 8K and 16K processors, respectively. We show (as shown previously only for MIMD machines) that for admissible search, high average speedup can be obtained for problems of significant size. We believe that this research will enhance AI problem solving using parallel heuristic search algorithms.     

A decision theoretic approach to file search

We investigate the relationship between algorithm construction and optimal decision processes. We provide a sufficient condition, a linear ordering over the experiment set, for when we can efficiently use an optimization approach for selecting a decision strategy. We demonstrate the linear ordering condition within the context of the file search problem; however, any problem whose representation satisfies the linear ordering condition is amenable to the optimization approach.     

A systems theoretical approach to online knowledge building

This article describes the phenomenon of knowledge building in online environments. Knowledge building is a process within a community, which leads to the development of knowledge. In order to analyze this process, we will look into the ways in which individuals interact with the collective as a whole. For this purpose, the psychic and social systems, which are involved here are regarded as meaning-based systems in the sense of Luhmann’s systems theory—open to the environment, but operatively closed. The respective modes of operation of psychic and social systems will be examined, and “structural coupling” between these systems described. Another characteristic of knowledge-building communities is self-organization, which is described here in the sense of autopoiesis. A pivotal task, which knowledge-building communities have to tackle, is to handle complexity and reach common understanding. These aspects will also be described using conceptualizations of Luhmann’s systems theory, illustrated by many examples.     

Intelligent content-based cybercrime detection in online social networks using cuckoo search metaheuristic approach

The subject of content-based cybercrime has put on substantial coverage in recent past. It is the need of the time for web-based social media providers to have the capability to distinguish oppressive substance both precisely and proficiently to secure their clients. Support vector machine (SVM) is usually acknowledged as an efficient supervised learning model for various classification problems. Nevertheless, the success of an SVM model relies upon the ideal selection of its parameters as well as the structure of the data. Thus, this research work aims to concurrently optimize the parameters and feature selection with a target to build the quality of SVM. This paper proposes a novel hybrid model that is the integration of cuckoo search and SVM, for feature selection and parameter optimization for efficiently solving the problem of content-based cybercrime detection. The proposed model is tested on four different datasets obtained from Twitter, ASKfm and FormSpring to identify bully terms using Scikit-Learn library and LIBSVM of Python. The results of the proposed model demonstrate significant improvement in the performance of classification on all the datasets in comparison to recent existing models. The success rate of the SVM classifier with the excellent recall is 0.971 via tenfold cross-validation, which demonstrates the high efficiency and effectiveness of the proposed model.

     

A data-mining approach to improving Polycythemia Vera diagnosis

This paper presents a data-mining approach to the extraction of new decision rules for Polycythemia Vera (PV) diagnosis, based on a reduced and optimized set of lab parameters. Ten laboratory and other clinical findings (eight parameters from the Polycythemia Vera Study Group (PVSG) criteria+sex and hematocrit (HCT)) on 431 PV patients from the original PVSG cohort, and records on 91 patients with other myeloproliferative disorders that can be easily misdiagnosed with PV, were included in this study. Significant differences were not found in the correctness of diagnostic classification of patients using either a trained artificial neural network (98.1%) or a support vector machine (95%) versus using PVSG diagnostic criteria, which are considered as a ‘gold-standard’ for the diagnosis of PV. Reducing the original parameters of our dataset to only four parameters: HCT, PLAT, SPLEEN and WBC, we still have obtained good classification results. New rules for improved differential diagnosis of PV are specified based on these four parameters. These rules may be used as a complement to the standard PVSG criteria, particularly in the differential diagnosis between PV and other myeloproliferative syndromes.     

Studying the effectiveness of an online argumentation model for improving undergraduate students' argumentation ability

This research develops a web-based model, entitled the “intuitive claim, peer-assessment, discussion, and elaborate claim argumentation training” (IPadE) model, and embeds with a Web-based Interactive Argumentation System to enhance undergraduate students' socioscientific argumentation abilities. This research adopts a quasi-experimental research design; the sample comprised 131 undergraduate students from two classes (69 in the experimental group and 62 in the control group). The socioscientific issue discussed were related to global health. This study collected and analysed quantitative and qualitative data, including the pretest and posttest of students' knowledge test scores and argumentation abilities questionnaire. The results generally confirmed the effectiveness of the IPadE model. First, in a comparison of the content knowledge and argumentation skills, the experimental group have statistically significantly improved than the control group. Second, regarding the number of reasoning modes proposed, the experimental group could propose multiple reasoning modes and reasoning levels on rebuttals increased after training.     

A new approach to online generation of association rules

We discuss the problem of online mining of association rules in a large database of sales transactions. The online mining is performed by preprocessing the data effectively in order to make it suitable for repeated online queries. We store the preprocessed data in such a way that online processing may be done by applying a graph theoretic search algorithm whose complexity is proportional to the size of the output. The result is an online algorithm which is independent of the size of the transactional data and the size of the preprocessed data. The algorithm is almost instantaneous in the size of the output. The algorithm also supports techniques for quickly discovering association rules from large itemsets. The algorithm is capable of finding rules with specific items in the antecedent or consequent. These association rules are presented in a compact form, eliminating redundancy. The use of nonredundant association rules helps significantly in the reduction of irrelevant noise in the data mining process     

面向分布式在线学习的共享数据方法

分布式数据流已成为现代数据驱动应用产生数据的主要形式,而局部节点的数据虽然独立存储,但彼此之间是相互关联的,因此如何高效地共享局部节点数据来构建全局学习器是分布式在线学习的关键问题.针对此问题,提出一种分布式在线学习的数据共享解决方案,包括基于指数损失的半监督聚类方法和基于协方差矩阵与均值向量的数据共享方法,并证明重构...     

An online game approach for improving students’ learning performance in web-based problem-solving activities

In this paper, an online game was developed in the form of a competitive board game for conducting web-based problem-solving activities. The participants of the game determined their move by throwing a dice. Each location of the game board corresponds to a gaming task, which could be a web-based information-searching question or a mini-game; the former was used to guide the participants to search for information to answer a series of questions related to the target learning issue, while the latter was used to provide supplementary materials during the gaming process. To evaluate the performance of the proposed approach, an experiment was conducted on an elementary school natural science course. The experimental results showed that the proposed approach not only significantly promoted the flow experience, learning attitudes, learning interest and technology acceptance degree of the students, but also improved their learning achievements in the web-based problem-solving activity.     

A dispositional approach to hostility toward sports commentators in online arenas

A limited amount of studies examined the effects of sports commentary on online readers’ attitudes toward online sports commentators. Approaching disposition effects hypothesis in online arenas, this study examined the effects of the positive/negative sports commentaries and the win/loss of readers’ favorite team on their hostility toward online sports commentators. A two (the win of the favorite team versus the loss of the favorite team) by two (the positive commentaries versus the negative commentaries) within-subjects repeated measures experiment with emotional responses as a covariate was designed to measure readers’ hostility toward online sports commentators. Results showed that disposition effects activated by the win/loss and the positive/negative commentaries significantly led readers to a higher level of hostility toward online sports commentators. Moreover, main effects for the positive/negative commentaries were stronger than main effects for the win/loss of the favorite team. Although readers’ emotional responses could not directly moderate the effects of the positive/negative commentaries on the win/loss of the favorite team, online readers with a more positive level of emotional responses to sports commentaries reported a lower level of hostility toward online sports commentators.     

Assessing the effectiveness of a voluntary online discussion forum on improving students’ course performance

The purpose of this study was to evaluate the effectiveness of voluntary discussion forums in a higher education setting. Specifically, we examined intrinsic forum participation and investigated its relation to course performance across two experiments. In Experiment 1 (N = 1284) an online discussion forum was implemented at the beginning of an undergraduate introductory psychology course, and measures of course performance (i.e., writing assignment grades, exam grades, and extra-credits obtained) were compared with measures of forum participation. In Experiment 2 (N = 1334) an online discussion forum was implemented halfway through a second undergraduate introductory psychology course, after an initial measure of course performance was obtained, to control for the potential confound of student engagement (e.g., students who perform better in the course use the forum more). Overall, the results showed that students who participated in the forum tended to have better performance in the course, and furthermore that participating in the discussion forum, particularly reading posts on the forum, slightly improved exam performance. This study provides empirical support for the theoretical proposition that there is a facilitation effect of discussion forum participation on course performance. The results also suggest that implementation of an online discussion forum is beneficial even if a teacher only invests minimal time on the forum.