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With the development of the Internet and the informatization construction of universities, the massive data accumulated by "campus big data" presents problems such as discreteness and sparseness. Students with abnormal behaviors have become an urgent problem to be solved in student behavior analysis. This paper proposes an early warning method for abnormal behaviour of college students based on multimodal fusion and an improved decision tree (EWMABCS-MFIDT). First, given the insufficient representation of student behavioral portraits and the problems of timeliness and dynamics in behavioral labels, a student behavioral portrait based on the multimodal fusion method is proposed. Second, aiming at the timeliness and backwardness of abnormal behavior prediction, based on student behavior classification prediction, this paper proposes an improved decision tree-based early warning method for abnormal student behavior. Finally, we design a student behavior analysis and early warning framework under the campus big data environment. Taking the abnormal early warning of students' academic performance as an example, compared with other early warning algorithms, the EWMABCS-MFIDT method can improve the accuracy of early warning and make students' educational work more targeted, personalized, and predictive. [ABSTRACT FROM AUTHOR]

Just-in-time (JIT) bug prediction is an effective quality assurance activity that identifies whether a code commit will introduce bugs into the mobile app, aiming to provide prompt feedback to practitioners for priority review. Since collecting sufficient labeled bug data is not always feasible for some mobile apps, one possible approach is to leverage cross-app models. In this work, we propose a new cross-triplet deep feature embedding method, called CDFE, for cross-app JIT bug prediction task. The CDFE method incorporates a state-of-the-art cross-triplet loss function into a deep neural network to learn high-level feature representation for the cross-app data. This loss function adapts to the cross-app feature learning task and aims to learn a new feature space to shorten the distance of commit instances with the same label and enlarge the distance of commit instances with different labels. In addition, this loss function assigns higher weights to losses caused by cross-app instance pairs than that by intra-app instance pairs, aiming to narrow the discrepancy of cross-app bug data. We evaluate our CDFE method on a benchmark bug dataset from 19 mobile apps with two effort-aware indicators. The experimental results on 342 cross-app pairs show that our proposed CDFE method performs better than 14 baseline methods. [ABSTRACT FROM AUTHOR]

Non‐functional requirements (NFRs) address important issues in software systems, and are vital in successful software. The NFR problems in a system reflect the complexity of that system. Ideally, NFRs are systematically investigated to determine the aspects that may be harmonious or cause conflicts so that conflicts should be detected as early as possible. The work aims to extend the scopes of the NFRs framework and softgoal interdependency graph (SIG) for modelling and analysing NFRs. A formal ontological approach to modelling NFR interactions is proposed, and a core NFR ontology is developed in the ontology web language. The mechanisms involved in NFR interactions with SIG models (through interdependencies in the softgoals) are investigated and formalised. The NFRs are first analyses using a semantic modelling process to provide evaluation criteria. Next, correlation rules for implicit softgoal interdependencies are proposed from knowledge of the domain, and rules for indirect semantic correlations are proposed by extending the delta evaluation method. Reasonable rules in the semantic web environment are defined to allow conflicts between NFRs in SIG instance models to be detected and analysed. A concepts proof is performed to illustrate tool support of the proposed method, and validate the rules for conflict detection. [ABSTRACT FROM AUTHOR]

Abstract: To a certain content, ecosystem function is usually used to evaluate the quality of the ecological environment region and provide scientific reason for environmental protection & ecological construction. The landscape-use changes of Yueqing Bay were studied by Landsat5TM data in 1993 and 2003 with RS and GIS technology. We quantificationally analyzed and identified the change of landscape ecosystem''s service value in Yueqing Bay wetland by computation index of ecosystem. The investigations indicated that the growing trend of ecosystem service value was reduced 4.38% in the whole research landscape area from 1993 to 2003. However, the increased growing trend of landscape was breeding paddy fields and farmlands. It was respectively increased to 31.25% and 1.78%. However, the growing trend of water area, seas, shallows and forests has been declined, and the biggest declination was the forest which reached 10.09%. In the past 10 years, the landscape type in Yueqing Bay wetland has been changed to cause the decline of ecosystem service value for this area. Therefore, the result demonstrated that the ecological environment of Yueqing Bay has been deteriorated, and the protective measures need to be taken in this area as soon as possible. [Copyright &y& Elsevier]