An effective method for clustering-based web service recommendation

Normally web services are classified by the quality of services; however, the term quality is not absolute and defined relatively. The quality of web services is measured or derived using various parameters like reliability, scalability, flexibility, and availability. The limitation of the methods employing these parameters is that sometimes they are producing similar web services in recommendation lists. To address this research problem, the novel improved clustering-based web service recommendation method is proposed in this paper. This approach is mainly dealing with producing diversity in the results of web service recommendations. In this method, functional interest, quality of service (QoS) preference, and diversity features are combined to produce a unique recommendation list of web services to end-users. To produce the unique recommendation results, we propose a varied web service classification order that is clustering-based on web services’ functional relevance such as non-useful pertinence, recorded client intrigue importance, and potential client intrigue significance. Additionally, to further improve the performance of this approach, we designed web service graph construction, an algorithm of various widths clustering. This approach serves to enhance the exceptional quality, that is, the accuracy of web service recommendation outcomes. The performance of this method was implemented and evaluated against existing systems for precision, and f-score performance metrics, using the research datasets.

Computer-Assisted Cohort Identification in Practice

The standard approach to expert-in-the-loop machine learning is active learning, where, repeatedly, an expert is asked to annotate one or more records and the machine finds a classifier that respects all annotations made until that point. We propose an alternative approach, IQRef , in which the expert iteratively designs a classifier and the machine helps him or her to determine how well it is performing and, importantly, when to stop, by reporting statistics on a fixed, hold-out sample of annotated records. We justify our approach based on prior work giving a theoretical model of how to re-use hold-out data. We compare the two approaches in the context of identifying a cohort of EHRs and examine their strengths and weaknesses through a case study arising from an optometric research problem. We conclude that both approaches are complementary, and we recommend that they both be employed in conjunction to address the problem of cohort identification in health research.

Con&Net: A Cross-Network Anchor Link Discovery Method Based on Embedding Representation

Cross-network anchor link discovery is an important research problem and has many applications in heterogeneous social network. Existing schemes of cross-network anchor link discovery can provide reasonable link discovery results, but the quality of these results depends on the features of the platform. Therefore, there is no theoretical guarantee to the stability. This article employs user embedding feature to model the relationship between cross-platform accounts, that is, the more similar the user embedding features are, the more similar the two accounts are. The similarity of user embedding features is determined by the distance of the user features in the latent space. Based on the user embedding features, this article proposes an embedding representation-based method Con&Net(Content and Network) to solve cross-network anchor link discovery problem. Con&Net combines the user’s profile features, user-generated content (UGC) features, and user’s social structure features to measure the similarity of two user accounts. Con&Net first trains the user’s profile features to get profile embedding. Then it trains the network structure of the nodes to get structure embedding. It connects the two features through vector concatenating, and calculates the cosine similarity of the vector based on the embedding vector. This cosine similarity is used to measure the similarity of the user accounts. Finally, Con&Net predicts the link based on similarity for account pairs across the two networks. A large number of experiments in Sina Weibo and Twitter networks show that the proposed method Con&Net is better than state-of-the-art method. The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve predicted by the anchor link is 11% higher than the baseline method, and Precision@30 is 25% higher than the baseline method.

Graph Neural Collaborative Topic Model for Citation Recommendation

Due to the overload of published scientific articles, citation recommendation has long been a critical research problem for automatically recommending the most relevant citations of given articles. Relational topic models (RTMs) have shown promise on citation prediction via joint modeling of document contents and citations. However, existing RTMs can only capture pairwise or direct (first-order) citation relationships among documents. The indirect (high-order) citation links have been explored in graph neural network–based methods, but these methods suffer from the well-known explainability problem. In this article, we propose a model called Graph Neural Collaborative Topic Model that takes advantage of both relational topic models and graph neural networks to capture high-order citation relationships and to have higher explainability due to the latent topic semantic structure. Experiments on three real-world citation datasets show that our model outperforms several competitive baseline methods on citation recommendation. In addition, we show that our approach can learn better topics than the existing approaches. The recommendation results can be well explained by the underlying topics.

Methodology and Methods of Research on the State and Causes of Academic Failure in Modern Schoolchildren

In this article, the author reveals research methods for working with underperforming students. We conducted an electronic survey (in connection with the pandemic) of students in urban schools and their parents, and each question was analyzed. As diagnostic methods for studying the state and causes of academic failure in modern schoolchildren, the author used various methods: the method of theoretical analysis of scientific, pedagogical, psychological, managerial and methodological literature on the research problem, the method of empirical research, the diagnostic method, the method of pedagogical experiment (ascertaining, forming, control and their description). The study of the reasons for academic failure was carried out in three stages, which differed both substantively and procedurally. The first stage was devoted to a questionnaire survey of students and parents of students in order to identify their interest and participation in general in the upbringing and academic performance of the child. At the second stage, the students' color world analyzer was used. And the final, third stage, contains the application of tests of school anxiety to diagnose the socio-psychological climate. The purpose of the methodology is to identify the level of anxiety in adolescents, localized in three main planes: educational activity, relationships with peers and significance in the eyes of adults and self-image. After all, adolescence is still an insufficiently mature and insufficiently socially matured person; it is a person who is at a special stage in the formation of its most important features and qualities. This stage is the borderline between childhood and adulthood.

Urban voids: identifying and optimizing urban voids potential as a revitalization source in enhancing developing countries’ city income

This study attempts to remedy the issue of urban voids, which are one of the possible choices for extra interactive spaces. As a city with a great civilization history, Egypt is also home to many urban voids, mainly buffer zones. This generates the research problem that urban voids result from managing isolated planning sites irrespective of the context and away from the community. Few studies tackled the impact of public spaces on city life; they were mainly theoretically oriented and focused on piazzas without highlighting other spaces or conducting empirical investigations. The study determines that voids could be a testing ground to establish a framework of how these spaces can be reused. Revitalizing urban voids goal is to reconnect these useless spaces with context, achieve users’ needs, integrate technologies with the space to revitalize the city, and increase its income through combining theoretical findings, empirical study, and questionnaires, which generate a framework that helps the planners and designers in developing urban voids and maximizing its efficiency. Currently, adaptive redesign is a hot topic to discuss, and this may be the moment to realize that following the updated design components, meeting community needs, and using technology will always reinvigorate the void.

Machine Learning-based Orchestration of Containers: A Taxonomy and Future Directions

Containerization is a lightweight application virtualization technology, providing high environmental consistency, operating system distribution portability, and resource isolation. Existing mainstream cloud service providers have prevalently adopted container technologies in their distributed system infrastructures for automated application management. To handle the automation of deployment, maintenance, autoscaling, and networking of containerized applications, container orchestration is proposed as an essential research problem. However, the highly dynamic and diverse feature of cloud workloads and environments considerably raises the complexity of orchestration mechanisms. Machine learning algorithms are accordingly employed by container orchestration systems for behavior modelling and prediction of multi-dimensional performance metrics. Such insights could further improve the quality of resource provisioning decisions in response to the changing workloads under complex environments. In this paper, we present a comprehensive literature review of existing machine learning-based container orchestration approaches. Detailed taxonomies are proposed to classify the current researches by their common features. Moreover, the evolution of machine learning-based container orchestration technologies from the year 2016 to 2021 has been designed based on objectives and metrics. A comparative analysis of the reviewed techniques is conducted according to the proposed taxonomies, with emphasis on their key characteristics. Finally, various open research challenges and potential future directions are highlighted.

The Impact of Savings and Credit Cooperatives (SACCOs) on Sustainable Livelihoods in Rural Communities

This study sought to assess the impact of savings and credit cooperatives (SACCOs) on rural sustainable livelihoods using the case of Nekatambe Ward 13 in Hwange district of Matabeleland North province in Zimbabwe. The study adopted a qualitative approach and a descriptive research design which were consistent with the research problem. Using convenience and purposive sampling, local leaders, non-governmental organisation (NGO) officials and members of the existing SACCOs were selected as respondents. The major findings were that SACCOs played a significant role in sustaining rural livelihoods particularly through enabling members to fend for themselves and their families. In addition, NGOs helped cooperatives through capacitating members with knowledge and technical skills and that SACCOs impacted positively on sustaining rural livelihoods. However, quite a number of challenges are associated with SACCOs in their bit to sustain rural livelihoods; with the major ones being failure to recover loans, competition from more established cooperatives, lack of start-up capital, poor financial and managerial skills and the general national economic meltdown. From the study findings, the researchers concluded that, despite the challenges associated with SACCOs, their existence under members’ resilience, has generally improved the lives of people in rural communities to generate employment, boost food production, send their children to school and empower the marginalized among other positive developments. Accordingly, the researchers recommend that SACCOs should diversify their operations and invest in fixed assets in order to curtail challenges and make lucrative benefits that can sustain their families and communities. On the other hand, the government and other concerned stakeholders should support SACCOs in order to alleviate the possible challenges that cripple them in their bid to promote rural livelihood sustainability.

TinyLFU-Based Semi-Stream Cache Join for Near-Real-Time Data Warehousing

Semi-stream join is an emerging research problem in the domain of near-real-time data warehousing. A semi-stream join is basically a join between a fast stream (S) and a slow disk-based relation (R). In the modern era of technology, huge amounts of data are being generated swiftly on a daily basis which needs to be instantly analyzed for making successful business decisions. Keeping this in mind, a famous algorithm called CACHEJOIN (Cache Join) was proposed. The limitation of the CACHEJOIN algorithm is that it does not deal with the frequently changing trends in a stream data efficiently. To overcome this limitation, in this paper we propose a TinyLFU-CACHEJOIN algorithm, a modified version of the original CACHEJOIN algorithm, which is designed to enhance the performance of a CACHEJOIN algorithm. TinyLFU-CACHEJOIN employs an intelligent strategy which keeps only those records of $R$ in the cache that have a high hit rate in S. This mechanism of TinyLFU-CACHEJOIN allows it to deal with the sudden and abrupt trend changes in S. We developed a cost model for our TinyLFU-CACHEJOIN algorithm and proved it empirically. We also assessed the performance of our proposed TinyLFU-CACHEJOIN algorithm with the existing CACHEJOIN algorithm on a skewed synthetic dataset. The experiments proved that TinyLFU-CACHEJOIN algorithm significantly outperforms the CACHEJOIN algorithm.

relevance of national education goals to the guidance of the Al-Quran and Al-Hadith

The purpose of this study is to gain an in-depth understanding of the issue of the relevance of national education goals to instructions from the Qur'an and hadith as Islamic religious guidelines. To obtain data on the whitening of the research problem and this hypothesis, we conducted a literature search on many international and national communications. The most applied sources we found were scientific journals, educational books, magazines, and literature websites. So that the data can be used, we have first involved an in-depth data evaluation and coding system, finally concluding relevant to answering the study questions with high validity. The search for data was carried out electronically, published between 2010 and 2021, by searching for keywords such as the purpose of education, Islamic instructions, and al-hadith. We review this study qualitatively under a phenomenological approach to seek the broadest possible data to understand seeking answers. Based on the findings and discussion data, there is a profound relevance between the purpose of providing national education and the objectives of religious education mentioned in Qur'an and Al-Hadith, which together prepare the generation of education to become a generation that is devoted to knowledge and skills that can solve life's problems correctly.