CODVerif is an approach that aims to verify the data being inserted in a data store continuously. CODVerif leverages the combination of ontology and workflow technologies in order to define workflows that are specific to the domain of “monitoring data insertion.” These domain-specific workflows are constrained on two dimensions: (1) They use a set of workflow elements that are specific to the “monitoring data insertion” domain. (2) The logic that these workflows support is predefined by relying on a set of common data insertion scenarios. Nevertheless, CODVerif is flexible enough to allow users to define continuous data verification workflows with higher complexity logic thanks to workflow operators that can be applied on the “monitoring data insertion domain”-specific workflows. To illustrate the applicability of CODVerif, the authors deploy it in a customer relationship management (CRM) application and show how CODVerif is used to support users to verify the data they populate in the CRM. They have also evaluated the CODVerif approach.
CODVerif is an approach that aims to verify the data being inserted in a data store continuously. CODVerif leverages the combination of ontology and workflow technologies in order to define workflows that are specific to the domain of “monitoring data insertion.” These domain-specific workflows are constrained on two dimensions: (1) They use a set of workflow elements that are specific to the “monitoring data insertion” domain. (2) The logic that these workflows support is predefined by relying on a set of common data insertion scenarios. Nevertheless, CODVerif is flexible enough to allow users to define continuous data verification workflows with higher complexity logic thanks to workflow operators that can be applied on the “monitoring data insertion domain”-specific workflows. To illustrate the applicability of CODVerif, the authors deploy it in a customer relationship management (CRM) application and show how CODVerif is used to support users to verify the data they populate in the CRM. They have also evaluated the CODVerif approach.
Traditional cloud-only architecture faces the challenge of coexistence with the ever-increasing IoT devices and demands for the need of the hybrid cloud computing and fog/IoT architecture to realize better handling of workload/requests. Determining where the user-workload should be assigned depends on the workload itself, and thereby, the workload classification gains the pivotal role. This location and offloading decision to the right resource affects both users and the providers. This work describes various cloud-fog workloads and relates them to their suitable place of execution in such a hybrid environment. The workloads have been classified based on their different parameters and characteristics with the aim to identify appropriate resources for efficient resource provisioning. The workload classification and characterization promises a significant role in the resource management by efficient capacity planning, future resource requirement predictions, workload offloading and an improvement in the Quality of Service (QoS) leading to an improvement in the system performance.
The authors are interested in knowledge engineering for small and medium-sized enterprises (SMEs) suffering from the difficult post-COVID19 business environment. We conducted fixed-point observation using eye tracking in a production site, aiming to improve the work efficiency of industrial production. Results showed that the experts not only paid attention to a large number of points but also spent considerable attention time, moving quickly among points of attention within the constant overall work time. The number of attention points and the attention time were measured over time. The results showed a learning effect, but experts paid more attention to the critical task. Based on these findings, knowledge engineering is discussed in terms of skill transfer.
This research shows how different educational forms such as schooling and apprenticeship are related to the development of artistic creativity from the interdisciplinary perspective based on educational anthropology of performing arts and management studies. This topic has been insufficiently explored by previous studies in both fields. More specifically, by applying the concept of schoolnization, the research compares two contrasting case studies of Chinese Qin opera and Japanese Nohgaku, based on first-hand data acquired from years of ethnographic research, to formulate a hypothesis on the ideal relationship between the enhancement of artistic creativity and process of career development. The hypothesis serves as a practical analytical framework for future research in the field.
With the rapid development of Cloud Computing and Service Oriented Computing, the process of selecting web services which gives the same functionality with different quality of service (QoS) become an important issue. To deal with the large number of Web services candidates, K-representative Skyline is appeared as a Skyline variant to find the short list of the most relevant Web services that represent a summary about the full skyline Web services result. However, it returns generally a conflicting result. To rank-order K-representative Skyline Web Services, we propose an improved Fuzzy AHP which allows to: i) elicit the QoS importance level using linguistic terms based on natural language, asking fewer efforts to users, ii) reduce the number of inputs and generate automatically all pair-wise matrix of Skyline Web services with respect to each attribute. The experimental evaluation conducted on real world dataset illustrates the feasibility and the effectiveness of our framework in comparison with existing works.
When selecting web services, users look for those that meet their requirements, primarily the overall functionality and non-functionality quality of service (QoS). In general, various service providers offer a large number of functionally similar services. That makes it very hard for users to find the best ones that satisfy their needs. Thus, service selection based on QoS has emerged as a challenging problem in service computing. So, the authors propose in this paper a web service selection method based on QoS prediction for clustering and ranking services using auto-encoder and k-means. Experiment results show that the proposed method efficiently improves the services' selection accuracy.
Docker containerization is a newly emerging technology, which brings virtualization to software applications. Specifically, lightweight-ness has brought higher advantage for the Docker containers. Often, the deployment of distributed net centric applications on cloud hosted Docker containers is a new approach for enterprise applications. This emerging approach has been discussed on many online forums, but currently there is no secondary research study to consolidate this approach. This research study aims to identify, systematically compare and taxonomically classify the existing research studies and their practical approaches. The systematic study was conducted of fifty-seven (57) selected research studies. Selected research studies were classified and compared based on the characterization framework. The study brings the results in a discussion of emerging concerns in the Docker based deployments. Furthermore, the study is moving closer to current concerns in distributed, microservices based net centric software applications
This article proposes an approach for transforming UML Statecharts, Sequence Diagrams and activity Diagrams to Time Petri Nets with Action Duration (DTPN) using graph transformation. By this transformation the author aims to bridge the gap between semi-formal models generated by UML and formal models DTPN. UML is considered to be the standardized language for modelling and describing systems behaviors for analysis. In other hand, DTPN models are tools for the specification and performance analysis of distributed and concurrent systems. However, the proposed approach allows to generate automatically a visual modeling tool for DTPN. The cost of building a visual modeling tool from scratch is prohibitive. Meta-modeling approach is useful to deal with this problem since it allows the modeling of the formalisms themselves, by means of graph grammars. The meta-modeling tool AToM3 is used.
The adoption of the Business Process outsourcing strategy has been accentuated with the advent of Cloud computing that brings a new model for consuming and paying resources. Nevertheless, companies hesitate between a high distribution allowing to combine several Cloud offers adapted for process fragments and guarantee a good level of obfuscation or a weak distribution to minimize the data transfer which is charged by most suppliers and which may slow down the execution of processes. Hence, this paper proposes a novel selection method based on a heuristic algorithm that aims to find the right balance by optimizing the cost and performance while ensuring the protection of outsourced processes. To this end, several criteria are considered such as data transfer, task separation constraints and tolerated delay margins. The results obtained through a case study and comparisons with other selection methods show that the configurations selected by the algorithm meet companies’ requirements and are at the same time inexpensive, efficient and secure.
Systems of systems (SoS) are a set of independent systems that are constantly evolving. These systems cooperate in order to achieve a common goal, known as an SoS mission. It is difficult to capture their behaviours while operating in a hierarchical, cooperative and evolutionary structure. Thus, a software architecture based approach is defined in order to model and analyze these systems, reducing their increasing complexity. It ought to describe an SoS on both structural and behavioural levels. In this paper, ArchSoS, an ADL dedicated to describe SoS software architectures, is extended with a syntax-driven formal description, based on Bigraphic Reactive Systems (BRS). SoS defined via ArchSoS have both a graphical and a textual view. For this syntax to be executable, and to verify SoS evolutions, an operational semantic for ArchSoS is defined. It is implemented in Maude language to permit ArchSoS behavioural simulations, and its formal analysis is done through Linear Temporal Logic (LTL). The proposed approach is illustrated through a Crisis Response SoS as a case study.
Automatic Summarization is one of the very important tasks that are performed to improve the searching experience in the internet world. Software Repositories are one of the greatest sources of information for the software development community as it contains varied information like the team behavior, intentions, emotions, the bugs, the project style, project management information, etc. The paper is an extension to the previous work where we have used just the feature-based technique to generate the summary for the Bug Reports. Here in this paper, we have used machine-learning approaches along with the Features to find out how the results vary. For the machine learning approaches, as there are many approaches which are available, we use the very popular approaches KNN, CART, NB and SVM for the observation. We observed that when the machine learning approaches are integrated with the feature-based approach, the results improve.
This paper proposes a framework for career information management and guidance systems (CIMGS) to support career decision-making by providing relevant career information and recommending suitable training courses based on individuals' characteristics. A survey was conducted to collect user requirements and opinions regarding the difficulties in identifying employees' relevant career information. The findings demonstrated that users' subject knowledge at work and age affects their information-seeking behaviors, thus, the system requirements. Further, we identify three underlying factors affecting individuals' career decisions, i.e., opinions from other individuals; abilities, affections, and career plans; and personalities and family influences. The four factors that influence training decisions are information perceived, physical constraints, affections, and resources.
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.
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.
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.
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.
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.
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.
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.
