Opinion spam detection: using multi-iterative graph-based model

The demand to detect opinionated spam, using opinion mining applications to prevent their damaging effects on e-commerce reputations is on the rise in many business sectors globally. The existing spam detection techniques in use nowadays, only consider one or two types of spam entities such as review, reviewer, group of reviewers, and product. Besides, they use a limited number of features related to behaviour, content and the relation of entities which reduces the detection's accuracy. Accordingly, these techniques mostly exploit synthetic datasets to analyse their model and are not able to be applied in the context of the real-world environment. As such, a novel graph-based model called “Multi-iterative Graph-based opinion Spam Detection” (MGSD) in which all various types of entities are considered simultaneously within a unified structure is proposed. Using this approach, the model reveals both implicit (i.e., similar entity's) and explicit (i.e., different entities’) relationships. The MGSD model is able to evaluate the ‘spamicity’ effects of entities more efficiently given it applies a novel multi-iterative algorithm which considers different sets of factors to update the spamicity score of entities. To enhance the accuracy of the MGSD detection model, a higher number of existing weighted features along with the novel proposed features from different categories were selected using a combination of feature fusion techniques and machine learning (ML) algorithms. The MGSD model can also be generalised and applied in various opinionated documents due to employing domain independent features. The output of the MGSD model showed that our feature selection and feature fusion techniques showed a remarkable improvement in detecting spam. The findings of this study showed that MGSD could improve the accuracy of state-of-the-art ML and graph-based techniques by around 5.6% and 4.8%, respectively, also achieving an accuracy of 93% for the detection of spam detection in our synthetic crowdsourced dataset and 95.3% for Ott's crowdsourced dataset

A Quality of Experience-based Recommender System for E-learning Resources

Web services are a rapidly developing and generally acknowledged technology across all areas of management. Independent software systems that can be shared and called from anywhere online. The creation of educational tools (such LMSs, MOOCs, and e-learning) now typically makes use of web services. Having these learning tools readily accessible online is a great method to acquire and disseminate information. The primary objective of this paper is to describe how web services can effectively manage educational resources by leveraging Quality of Experience and to develop an effective E-learning recommender system in the context of web services that help the user choose a course based on his needs in terms of availability, cost, and reputation

A Framework for Improving Intrusion Detection Systems by Combining Artificial Intelligence and Situational Awareness

The vast majority of companies do not have the requisite tools and analysis to make use of the data obtained from security incidents in order to protect themselves from attacks and lower their risk. Intrusion Detection Systems (IDS) are deployed by numerous businesses to lessen the impact of network attacks. This is mostly attributable to the fact that these systems are able to provide a situational picture of network traffic regardless of the method or technology that is used to generate alerts. In this paper, a framework is proposed for improving the performance of contemporary IDSs by incorporating Artificial Intelligence (AI) into multiple layers, presenting the appropriate abstraction and accumulation of information, and generating valuable logs and metrics for security analysts to use in order to make the most informed decisions possible. This is further enabled by including Situational Awareness (SA) at the fundamental levels of the framework. Keywords: Intrusion Detection System, Machine Learning, Deep Learning, Shallow Learning, Security Operation Center, Situational Awarenes

A Framework for Improving Intrusion Detection Systems by Combining Artificial Intelligence and Situational Awareness

The vast majority of companies do not have the requisite tools and analysis to make use of the data obtained from security incidents in order to protect themselves from attacks and lower their risk. Intrusion Detection Systems (IDS) are deployed by numerous businesses to lessen the impact of network attacks. This is mostly attributable to the fact that these systems are able to provide a situational picture of network traffic regardless of the method or technology that is used to generate alerts. In this paper, a framework is proposed for improving the performance of contemporary IDSs by incorporating Artificial Intelligence (AI) into multiple layers, presenting the appropriate abstraction and accumulation of information, and generating valuable logs and metrics for security analysts to use in order to make the most informed decisions possible. This is further enabled by including Situational Awareness (SA) at the fundamental levels of the framework. Keywords: Intrusion Detection System, Machine Learning, Deep Learning, Shallow Learning, Security Operation Center, Situational Awarenes

Sustainable Urban Housing: A Systematic Review of Low-Carbon Design Practices

Low-carbon design practices for sustainable urban housing are essential for mitigating climate change and enhancing urban resilience. However, a comprehensive and consistent overview of this field's state-of-the-art, best practices, challenges, and gaps is lacking. This paper aims to fill this gap by conducting a systematic review of existing literature on low-carbon design practices for sustainable urban housing in Nigeria and other countries. The review followed the PRISMA guidelines and used various analytical methods to synthesize data from 48 articles selected from 219 articles downloaded from Scopus and Web of Science databases, covering a period of 24 years (1997–2021). The results revealed four main themes of low-carbon design practices: energy efficiency, renewable energy, low-carbon materials, and sustainability assessment. The review also identified positive impacts, trade-offs, challenges, opportunities, and trends related to these practices. The paper discusses the implications, limitations, and recommendations for theory, practice, policy, and research on low-carbon design practices for sustainable urban housing. The paper contributes valuable insights for advancing knowledge in this area and guiding the development of sustainable urban housing solutions with low-carbon design practices. Keywords: Sustainable Urban Housing, Low-Carbon Design Practices, Best Practices, Challenges, Practitioners. DOI: 10.7176/ADS/106-03 Publication date:August 31st 2023

A Systematic Review of Scientific Collaboration Studies by Iranian Authors

Scientific collaboration indicates active teamwork between researchers beyond the simple exchange of material or information. This study is a systematic review of the papers published by Iranian researchers, aiming to provide comprehensive indicators, methodologies, and software used for evaluating scientific collaboration. According to guidelines of the Cochrane Handbook, the national and international databases were used for searching by English and Persian keywords without any time limitations. The retrieved articles were managed using EndNote software. By applying the inclusion and exclusion criteria, 201 articles remained for this review. These articles were selected from 93 domestic and foreign journals between 2000 and 2019. The studies used 16 software to extract and analyze scientific collaboration indicators. Systematic review shows that bibliometric and network analysis methods were the main approaches used in scientific collaboration studies among papers published by Iranian researchers (93.5%). More than 25 indicators were extracted from these studies, and they were categorized into patterns of collaboration and co-authorship network analysis. Researchers have revealed an increasing interest in the factors affecting scientific collaboration in recent years. The present study provides comprehensive information on the articles published by Iranian researchers on scientific collaboration. The methodologies and software were identified that are most often used to evaluate scientific collaboration and adapted to direct future research. Still, a variety of indicators situates them in heterogeneous methods of research. This analytical perspective does not locate the evaluation of scientific collaboration at a single spot. Future scientific collaboration studies will continue to evolve to offer more powerful indicators for assessing the knowledge flow status quo.https://dorl.net/dor/20.1001.1.20088302.2022.20.2.13.

NASA commercial technology. Agenda for change

The essence of NASA's new way of doing business to support the agency's commercial technology mission objectives is described. A summary description of the various changes needed to successfully perform this mission is provided

THE USE OF AI IN LANGUAGE LEARNING: WHAT YOU NEED TO KNOW

Kecerdasan Buatan adalah kekuatan transformasional dalam pendidikan, terutama dalam pembelajaran bahasa. Studi ini membahas berbagai alat AI yang digunakan untuk pembelajaran bahasa, seperti terjemahan mesin, teknologi ucapan, chatbot, dan konten yang dihasilkan oleh kecerdasan buatan. Studi ini secara komprehensif menjelajahi potensi dan tantangan yang terkait dengan peran kecerdasan buatan dalam pendidikan bahasa. Di satu sisi, kecerdasan buatan menawarkan manfaat seperti panduan personal, keterlibatan interaktif, dan pelacakan kemajuan. Namun, juga menimbulkan kekhawatiran tentang interaksi manusia yang berkurang, dampak potensial pada otonomi pembelajar, dan peran yang berkembang dari guru bahasa. Oleh karena itu, studi ini menekankan pentingnya menggabungkan prinsip-prinsip etika, transparansi, dan inklusivitas untuk memandu integrasi kecerdasan buatan dalam pendidikan secara bertanggung jawab. Penelitian ini menggunakan metodologi penelitian perpustakaan untuk membangun landasan teoritis yang kuat, menekankan peran penting integrasi kecerdasan buatan yang bertanggung jawab dalam meningkatkan pendidikan bahasa sambil menjaga standar etika yang tinggi

The approaches to quantify web application security scanners quality: A review

The web application security scanner is a computer program that assessed web application security with penetration testing technique. The benefit of automated web application penetration testing is huge, which web application security scanner not only reduced the time, cost, and resource required for web application penetration testing but also eliminate test engineer reliance on human knowledge. Nevertheless, web application security scanners are possessing weaknesses of low test coverage, and the scanners are generating inaccurate test results. Consequently, experimentations are frequently held to quantitatively quantify web application security scanner's quality to investigate the web application security scanner's strengths and limitations. However, there is a discovery that neither a standard methodology nor criterion is available for quantifying the web application security scanner's quality. Hence, in this paper systematic review is conducted and analysed the methodology and criterion used for quantifying web application security scanners' quality. In this survey, the experiment methodologies and criterions that had been used to quantify web application security scanner's quality is classified and review using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol. The objectives are to provide practitioners with the understanding of methodologies and criterions that available for measuring web application security scanners' test coverage, attack coverage, and vulnerability detection rate, while provides the critical hint for development of the next testing framework, model, methodology, or criterions, to measure web application security scanner quality