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In this study, we suggest combining the monitoring of actual examination time used with grades in order to assess examination time extensions in terms of access provision and expected outcome. Usin...

Security and intelligence agencies around the world invest considerable resources in preventing terrorist attacks, as these may cause strategic damage, national demoralization, infringement of sovereignty, and government instability. Recently, data-mining techniques have evolved to allow identification of patterns and associations in criminal data that were not apparent using traditional analysis. The aim of this paper is to illustrate how to use interpretable classification algorithms to identify subgroups (“patterns”) of terrorist incidents that share common characteristics and that result in mass fatalities. This approach can produce insights far beyond those of conventional macro-level studies that use hypothesis-testing and regression models. In addition to this methodological contribution, from a practical perspective, exploring the characteristics identified in the “patterns” can lead to prevention strategies, such as alteration of the physical or systemic environment. This is in line with situational crime prevention (SCP) theory. We apply our methodology to the Global Terrorism Database (GTD). We present three examples in which terror attacks that are described by a particular pattern (set of characteristics) resulted in a high probability of mass casualties, while attacks that differ in just one of these characteristics (i.e., month of attack, geographical area targeted, or type of attack) resulted in far fewer casualties. We propose exploration of the differentiating characteristic as a means of reducing the probability of mass-fatality terrorist incidents.

Modeling and analysis of students’ performance is a common task that is aimed at identifying important factors that affect the learning process. Typically, the analysis uses one-dimensional input parameters. However, with the advancement of data collections tools, many of the gathered educational datasets have become high-dimensional. Hence, the use of standard statistical methods may be limited in cases that the initial data unit is a vector. This paper proposes to use vector input units, which consist of student performance trajectories, for identifying statistical differences in college performances for several populations of college students. Two kernel based methods named diffusion maps and the kernel two-sample test are utilized. Diffusion maps generates a low-dimensional representation of the data, in which important characteristic factors are identified. The kernel two-sample test is a statistical test for comparing whether high-dimensional samples are drawn from two different probability distributions. The two methods are combined into a unified framework. Two case studies, which are processed similarly, are presented. The first tests for significant distributional differences between students with or without learning disabilities. Our results show that these groups’ performances is significantly different. The second case-study analyzes whether the SAT score impacts students’ performance throughout their 4-year of studies. It was found that significant distribution differences in performance are only present for groups of students having a very high or a very low SAT score. Thus, the SAT score is only weakly correlated to students’ college performance.

We draw on cognitive and behavioural theories and on the artificial intelligence literature in order to propose a framework of future operator – workstation interaction in the ‘Industry 4.0’ era. W...

Currently machine operator interface is mainly focused on providing the operator with easy control over the production processes and easy access to related information. However, myriad of recent technological advances in variety of fields including AI, raise the question of what could be added to the operator-machine interaction capabilities and how. This article explores the possibilities to harness new capabilities in cognitive and behavioral knowledge as well as AI and “Industry 4.0” literature in order to outline the architectural framework and capabilities of future work-station‒operator interaction as a principal component of the human‒machine interaction in the “Industry 4.0” era. The proposed system is named “Workstation‒Operator Interaction 4.0” (WOI 4.0). The equipment’s capabilities allows an adaptive ongoing interaction that aims to improve operator performance, safety, well-being, and satisfaction, as well as production measures. The paper describes the main elements of the proposed WOI 4.0 architecture, and illustrates a case of smart machine‒operator interactions. The contributions, limitations, and implications of the proposed WOI 4.0 system in the “Industry 4.0” arena are discussed, and future research directions are presented.

The importance of sharing mental health issues with supervisors is well established. However, the decision to disclose such intimate information is complex and is influenced by many intrinsic and extrinsic variables. The purpose of this study is to use machine learning algorithms to develop a tool that supervisors may use to enhance disclosure of mental health issues among their employees. Several interpretable machine learning algorithms are established based on a Kaggle dataset of more than 1,400 participants that measures attitudes towards mental health and prevalence of mental health disorders in the tech workplace. The C4.5 algorithm is chosen as the best classifier of willingness to disclose a mental health disorder to supervisors, based on a variety of classification performance measures. Tailored intervention programs are applied and are shown to have the potential to increase the probability of disclosure by between 20% and 60%.

Using a grounded theory approach and real-time data capturing the expressions and behaviors of help givers and recipients in the course of their interactions at work, we generated a typology of interactive and episodic helping behaviors (both help giving and receiving) and applied it to explore if, when, and how the behavioral configurations of the two dyad partners changed from one exchange episode to the next. Our findings suggest that certain configurations of helping behavior often shifted in a rather predictable manner over the course of the helping exchange in response to the behavior of the dyad partner, whereas other configurations were “static,” tending to be repeated by dyad partners once they first appeared. By examining help giving and receiving as a pattern in a stream of dynamic, dyadic, and cross-episodic behaviors, and more importantly, developing a typology allowing scholars to differentiate between different types of dyadic patterns, our study provides important insights into the varying...

This paper describes a new technique that utilizes the typical documentation of complex products to automate the development of the assembly method to be used for production. The technique describes a structured process that gets (as its input) the standard bill of materials (BOM) with specified additional data, and develops a detailed sequential method of assembly operation as its output. This sequential assembly method could be then further automated. The paper also discusses the gap between typical assembly instructions and structured sequential specifications necessary for automating the planning of the assembly method.