Towards a New Semantic Metric for Error Detection Based on Program State Redundancy

Fault tolerance techniques are generally based around a common concept that is redundancy whose measurement is required. A suite of four semantic metrics is proposed to assess program redundancy and reflect their ability to tolerate faults. Literature shows that one of these metrics, namely state redundancy, is limited to compute program redundancy only in their initial and final states and ignores their internal states. Consequently, the authors focus in this paper to overcome this shortcoming by proposing a new redundancy-based semantic metric that computes the redundancy of the different program states including internal ones. The empirical study they perform shows that the proposed metric is a measure of program redundancy in one side and is an error detection indicator in another side. Moreover, they demonstrate that it is more accurate than the basic state redundancy metric in detecting masked errors. It is useful for testers to indicate if a tested program is error-free and to pinpoint the presence of masked errors even if the final states are equal to the expected ones.

ServiceNet

Despite the enormous number of online docking services available, consumers sometimes struggle to discover the services they require from time to time. On the other hand, when finding matching or recommendation platforms from an academic or industry perspective, most of the related work they can find is centralized systems. Unfortunately, the centralized systems often have shortages, such as adv-driven, lack of trust, non-transparency, and unfairness. The authors propose a peer-to-peer (P2P) service network for service discovery and recommendation. ServiceNet is a blockchain-based service ecosystem that promises to provide an open, transparent, self-growing, and self-managing service environment. The article will provide the basic concept, the proto-architecture type's design, and the proto-initial type's implementation and performance assessment.

Homogenization of Japanese Industrial Technology From the Perspective of R&D Expenses

The aim of this paper is to discuss the reasons for the decline in the profitability of Japanese R&D from the perspective of in-house R&D efforts. Focusing on changes in the allocation ratio of in-house R&D expenses by industry from 1972 to 2017, technological structure changes in the Japanese industry during that period are empirically analyzed. Based on the analysis, the technological structure of the Japanese industry has been consistently moving toward homogenization since 1972. Homogenization is mainly directed toward the related technical fields of automobiles and information and communication machinery/equipment/electronic parts, which are the main industries that currently lead the Japanese economy. While the types of technical knowledge possessed by the Japanese industry are decreasing, there is a lack of R&D activities that will create new products and markets and increase the pie of the Japanese industry or greatly promote the metabolism of technology of the entire industry.

Automatic Reel Editing in Chip on Film Quality Control With Computer Vision

Chip on film (COF) is a special packaging technology to pack integrated circuits in a flexible carrier tape. Chips packed with COF are primarily used in the display industry. Reel editing is a critical step in COF quality control to remove sections of congregating NG (not good) chips from a reel. Today, COF manufactures hire workers to count consecutive NG chips in a rolling reel with naked eyes. When the count is greater than a preset number, the corresponding section is removed. A novel method using object detection and object tracking is proposed to solve this problem. Object detection techniques including convolutional neural network (CNN), template matching (TM), and scale invariant feature transform (SIFT) were used to detect NG marks, and object tracking was used to track them with IDs so that congregating NG chips could be counted reliably. Using simulation videos similar to worksite scenes, experiments show that both CNN and TM detectors could solve the reel editing problem, while SIFT detectors failed. Furthermore, TM is better than CNN by yielding a real time solution.

Predicting Bug Priority Using Topic Modelling in Imbalanced Learning Environments

Manual classification of bug reports is time-consuming as the reports are received in large quantities. Alternatively, this project proposed automatic bug prediction models to classify the bug reports. The topics or the candidate keywords are mined from the developer description in bug reports using RAKE algorithm and converted into attributes. These attributes together with the target attribute—priority level—construct the training datasets. Naïve Bayes, logistic regression, and decision tree learner algorithms are trained, and the prediction quality was measured using area under recursive operative characteristics curves (AUC) as AUC does not consider the biasness in datasets. The logistics regression model outperforms the other two models providing the accuracy of 0.86 AUC whereas the naïve Bayes and the decision tree learner recorded 0.79 AUC and 0.81 AUC, respectively. The bugs can be classified without developer involvement and logistic regression is also a potential candidate as naïve Bayes for bug classification.

Understanding Cyber Security

This study provides an understanding of cyber security and further analyses the Cyber Security and Data Protection Bill of Zimbabwe. The internet and other technological developments have provided a variety of platforms for increased and sometimes unrestricted exercise of freedom of expression, association, online transactions, and access to information rights. Zimbabwe has faced increased threats from cyber activities. This has necessitated the country to enact the Cyber Security and Data Protection Bill. The purpose of the Cyber Security and Data Protection Bill is to increase cyber security in order to build confidence and trust in the secure use of information and communication technologies by data controllers, their representatives, and data subjects. This study relied on data gathered through key informant interviews and documentary search. The key informants were drawn from various institutions that deal with cybercrime. Zimbabwe does not have adequate and effective legislative instruments to combat cybercrime.

Deep Neural Network-Based Crime Prediction Using Twitter Data

Crimes have affected the quality of life and economic growth of the country badly. The authors can identify the crime patterns and predict the crimes by detecting and analyzing the historical data. However, some crimes are unregistered and unsolved due to a lack of evidence. Thus, detecting crimes is a still challenging task. Individuals can use social media like Twitter to detect crime-related activities. Because Twitter users sometimes convey messages related to their surrounding environment, this paper proposed a machine learning approach to predict crimes. The proposed framework consists of three modules: data (tweet) collecting, detecting crimes, and predicting crime. Long short-term memory (LSTM) neural network model was used as a proposed approach for crime prediction. Experimental results found that by achieving the highest precision of 82.5%, precision of 86.4%, and recall of 80.4%, the proposed LSTM-based approach worked better than the other approaches.

Managing to Facilitate Cross-Sectoral Inter-Organizational Collaborations

The promotion of new and competitive industries through cross-sectoral inter-organizational collaborations are tackled in many regions globally. This study explores the management of facilitating collaboration with consideration of the planned approach to change based on change management theory. The “initial conditions,” “field,” and “emerging interaction toward the collaborations” are clarified as key elements for management through intervention. It is considered how these interventions are implemented on these elements. A conceptual model for considering comprehensive management of the self-organization process toward collaboration is proposed. In the case study, experiences of the medical technology industry of three German states are examined. The activities of cluster organizations of these states, which provide services to facilitate cross-sectoral collaborations, are scrutinized. The results of the case study are comparatively analyzed, and the modified conceptual framework is depicted by reflecting the findings of the study. The implications are then discussed.

The Effects of R&D Expenditure and Technological Spillover on Industry Revenues in Japan

This research analyzes the effects of research and development (R&D) activities on industries' sales based on accounting information for the Japanese manufacturing sector from 2001–2017, with the aim of accurately grasping the current situation. The analytical model is based on the traditional extended Cobb-Douglas production function. By avoiding the statistical problem of endogeneity and serial correlation, a multiple regression analysis was used to understand the statistical superiority of the effects of R&D expenditure and technological spillovers on sales figures. This study also analyzes the relationship between acceptance and supply of technological spillovers in Japanese industries to understand the current state of the impact of technological spillovers. In conclusion, the effects of recent R&D activities in the Japanese manufacturing sector are low. After the 1997 Asian Currency Crisis, Japan's economic growth has declined; this study could help improve R&D activities that support economic growth in this poor growth climate.

Characteristics and Development Patterns of the Process of Vocational Education for Chinese and Japanese Performing Arts

The main purpose of this research is to contribute to the literature related to management studies and studies on performing arts education by shedding light on the important roles played by formal education at schools for training performers and management talents. The research compares the process of educational modernization for Chinese traditional theater and Japanese Takarazuka Revue to illuminate respective characteristics and developmental patterns. Although Japan and China have very different modernization processes and political systems, both cases of performing arts have been relying on school institutions for human resources development which has evolved from apprenticeship or something alike. By this comparison, the research then clarifies the ways schools are involved in improving skills and inculcating occupational identities of performers and management talents, thereby constantly producing capable young talents. Finally, the research shows how the result can provide a better view on vocational education for future research in the related field.