ModuLand plug-in for Cytoscape: determination of hierarchical layers of overlapping network modules and community centrality

Summary: The ModuLand plug-in provides Cytoscape users an algorithm for determining extensively overlapping network modules. Moreover, it identifies several hierarchical layers of modules, where meta-nodes of the higher hierarchical layer represent modules of the lower layer. The tool assigns module cores, which predict the function of the whole module, and determines key nodes bridging two or multiple modules. The plug-in has a detailed JAVA-based graphical interface with various colouring options. The ModuLand tool can run on Windows, Linux, or Mac OS. We demonstrate its use on protein structure and metabolic networks. Availability: The plug-in and its user guide can be downloaded freely from: http://www.linkgroup.hu/modules.php. Contact: [email protected] Supplementary information: Supplementary information is available at Bioinformatics online.Comment: 39 pages, 1 figure and a Supplement with 9 figures and 10 table

Multiple Instance Learning: A Survey of Problem Characteristics and Applications

Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research

Exploring the Confounding Factors of Academic Career Success: An Empirical Study with Deep Predictive Modeling

Understanding determinants of success in academic careers is critically important to both scholars and their employing organizations. While considerable research efforts have been made in this direction, there is still a lack of a quantitative approach to modeling the academic careers of scholars due to the massive confounding factors. To this end, in this paper, we propose to explore the determinants of academic career success through an empirical and predictive modeling perspective, with a focus on two typical academic honors, i.e., IEEE Fellow and ACM Fellow. We analyze the importance of different factors quantitatively, and obtain some insightful findings. Specifically, we analyze the co-author network and find that potential scholars work closely with influential scholars early on and more closely as they grow. Then we compare the academic performance of male and female Fellows. After comparison, we find that to be elected, females need to put in more effort than males. In addition, we also find that being a Fellow could not bring the improvements of citations and productivity growth. We hope these derived factors and findings can help scholars to improve their competitiveness and develop well in their academic careers

Towards a mobile application to aid law enforcement in diagnosing and preventing mobile bully-victim behaviour in Eastern Free State High Schools of South Africa

Mobile bully-victim behaviour is one cyber aggression that is escalating worldwide. Bully-victims are people who bully others but are also victimised by peers. The behaviour of bully-victims therefore swings between that of pure bullies and pure victims, making it difficult to identify and prevent. Prevention measures require the involvement of a number of stakeholders, including communities. However, there has been a lack of whole-community participation in the fight against cyberbullying and the roles of stakeholders are often unclear. We expect the law enforcement in particular, the police, to play a key role in curbing all forms of bullying. This is a challenging task in South Africa as these law enforcement agents often lack the skills and appropriate legislation to address particularly cyber-related bullying. Literature shows that law enforcement agents need to advance their technological skills and also be equipped with digital interventions if they are to diagnose and prevent mobile bully-victim behaviour effectively. This is particularly important in South Africa, where the rate of crime remains one of the highest in the world. The aim of this study was to develop a mobile application that can aid law enforcement in diagnosing and preventing mobile bully-victim behaviour in high schools. As part of requirements to the application development, it identified the impediments to the law enforcement effectiveness in combating mobile bully-victim behaviour. Extensive literature review on the factors influencing mobile bullying and mobile bully-victim behaviour was conducted and an integrative framework for understanding this behaviour and its prevention was developed. In so doing, the dominant behavioural theories were consulted, including the social-ecological theory, social learning theory, social information processing theories, and the theory of planned behaviour, as well as the general strain theory, and the role theory. The conceptual framework developed in this study extended and tailored the “Cyberbullying Continuum of Harm”, enabling inclusive and moderated diagnosis of bullying categories and severity assessment. That is, instead of focusing on mobile bully-victims only, bullies, victims, and those uninvolved were also identified. Also the physical moderation of the identification process by the police helped to minimise dishonest reporting. This framework informed the design, development and evaluation of a mobile application for the law enforcement agents. The Design Science Research (DSR) methodology within pragmatic paradigm and literature guided the development of the mobile application named mobile bullyvictims response system (M-BRS) and its evaluation for utility. The M-BRS features included functions to enable anonymous reporting and confidential assessments of mobile bully-victims effects in school classrooms. Findings from this study confirmed the utility of the M-BRS to identify learners' involvement in mobile bully-victims behaviour through peer nomination and self-nomination. This study also showed that use of the M-BRS has enabled empowerment of marginalised learners, and mitigation of learners' fear to report, providing them with control over mobile bully-victim reporting. In addition, learners using the M-BRS were inclined to report perpetrators through a safe (anonymous and confidential) reporting platform. With the M-BRS, it was much easier to identify categories of bullies, i.e. mobile bully-victims, bullies, victims, and uninvolved. The practical contributions of this study were skills enhancements in reducing the mobile bully-victims behaviour. These included improvement of the police's technical skills to safely identify mobile bully-victims and their characterisation as propagators and retaliators that enabled targeted interventions. This was particularly helpful in response to courts' reluctance to prosecute teenagers for cyberbullying and the South African lack of legislation thereon so that the police are enabled to restoratively address this behaviour in schools. Also, the identification information was helpful to strengthen evidence for reported cases, which was remarkable because sometimes perpetrators cannot be found due to their concealed online identities. Furthermore, this study made possible the surveillance of mobile bully-victims through the M-BRS, which provided the police some control to reducing the mobile bully-victim behaviour. This study provided a practical way for implementing targeted prevention and interventions programmes using relevant resources towards a most efficient solution for mobile bully-victims problem. Since there are not many mobile-based interventions for mobile bully-victim behaviour, this study provided a way in which artefacts' development could be informed by theory, as a new, innovative and practical contribution in research. In so doing, this study contributed to technology applications' ability to modify desired behaviour