A Hybrid Personalized Scientific Paper Recommendation Approach Integrating Public Contextual Metadata

Rapid increase in scholarly publications on the web has posed a new challenge to the researchers in finding highly relevant and important research articles associated with a particular area of interest. Even a highly relevant paper is sometimes missed especially for novice researchers due to lack of knowledge and experience in finding and accessing the most suitable articles. Scholarly recommender system is a very appropriate tool for this purpose that can enable researchers to locate relevant publications easily and quickly. However, the main downside of the existing approaches is that their effectiveness is dependent on priori user profiles and thus, they cannot recommend papers to the new users. Furthermore, the system uses both public and non-public metadata and therefore, the system is unable to find similarities between papers efficiently due to copyright restrictions. Considering the above challenges, in this research work, a novel hybrid approach is proposed that separately combines a Content Based Filtering (CBF) recommender module and a Collaborative Filtering (CF) recommender module. Unlike previous CBF and CF approaches, public contextual metadata and paper-citation relationship information are effectively incorporated into these two approaches separately to enhance the recommendation accuracy. In order to verify the effectiveness of the proposed approach, publicly available datasets were employed. Experimental results demonstrate that the proposed approach outperforms the baseline approaches in terms of standard metrics (precision, recall, F1-measure, mean average precision, and mean reciprocal rank), indicating that the proposed approach is more efficient in recommending scholarly publications

Bad Smells of Gang of Four Design Patterns: A Decade Systematic Literature Review

Gang of Four (GoF) design patterns are widely approved solutions for recurring software design problems, and their benefits to software quality are extensively studied. However, the occurrence of bad smells in design patterns increases the crisis of degenerating design patterns’ structure and behavior. Their occurrences are detrimental to the benefits of design patterns and they influence software sustainability by increasing maintenance costs and energy consumption. Despite the destructive roles of bad smells in such designs, there are an absence of studies systematically reviewing bad smells of GoF design patterns. This study systematically reviews a 10-year state of the art sample, identifying 16 studies investigating this phenomenon. Following a thorough evaluation of the full contents, we observed that the occurrence of bad smells have been investigated in proportion to four granularity levels of analysis: Design level, category level, pattern level, and role level. We identified 28 bad smells, categorized under code smells and grime symptoms, and emphasized their relationship with GoF pattern types and categories. The utilization of design pattern bad smell detection approaches and datasets were also discussed. Consequently, we observed that the research phenomenon is growing intensively, with a prominent focus of studies analyzing code smell occurrences rather than grime occurrences, at various granularity levels. Finally, we uncovered research gaps and areas with significant potentials for future research

A flowchart-based multi-agent system for assisting novice programmers with problem solving activities

In the early stages of learning computer programming, Computer Science (CS) minors share a misconception of what programming is . In order to address th is problem, FMAS, a f lowchar t - based m ulti - a gent s ystem is developed to familiarize students who have no prior k nowledge of programming , with the initial stages in learning programming . The aim is to improve students’ problem solving skills and to introduce the m to the basic programmi ng algorithms prior to surface structure , using an automatic text - to - flowchart conversion approach. Therefore, students can focus less on language and syntax and more on designing solution s through flowchart development. The way text - to - flowchart conversio n as a visualization - based approach is employed in FMAS to engage students in flowchart development for subsequent programming stages is discussed in this paper. Finally, an experimental study is devised to assess the success of FMAS, and positive feedback is achieved . Therefore, using FMAS in practice is supported , as the results indicate considerable gains for the experimental group over the control group. The results also show that an automatic text - to - flowchart conversion approach applied in FMAS succes sfully motivated nearly all participants in problem solving activities. Consequently, the results suggest additional , future development of our proposed approach in the form of an Intelligent Tutoring System (ITS) to mak e the early stages of learning progr amming more encouraging for students