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Behavioral pattern mining for intelligent system such as SmEs sensor data are vitally important in many applications and performance optimizations. Sensor pattern mining (SPM) is also dynamic and a hot research issue to pervasive and ubiquitous of smart technologies toward improving human life. However, in large-scale sensor data, exploring and mining pattern, which leads to detect the abnormal behavior is challenging. The paper aims to discuss these issues.

Sensor data are complex and multivariate, for example, which data captured by the sensors, how it is precise, what properties are recorded or measured, are important research issues. Therefore, the method, the authors proposed Sequential Data Mining (SDM) approach to explore pattern behaviors toward detecting abnormal patterns for smart space fault diagnosis and performance optimization in the intelligent world. Sensor data types, modeling, descriptions and SPM techniques are discussed in depth using real sensor data sets.

The outcome of the paper is measured as introducing a novel idea how SDM technique’s scale-up to sensor data pattern mining. In the paper, the approach and technicality of the sensor data pattern analyzed, and finally the pattern behaviors detected or segmented as normal and abnormal patterns.

The paper is focussed on sensor data behavioral patterns for fault diagnosis and performance optimizations. It is other ways of knowledge extraction from the anomaly of sensor data (observation records), which is pertinent to adopt in many intelligent systems applications, including safety and security, efficiency, and other advantages as the consideration of the real-world problems.

Identifying students for intensive intervention (also referred to as Tier 3 supports) is most effective when implemented within a tiered system of support. Effective tiered systems include both academic and behavioral supports for identifying and serving students with varied needs. In this chapter, we review existing research, discuss current practice, and offer guidance for identifying students with intensive academic and/or behavioral needs.

This chapter discusses the benefits, limitations, and challenges in developing research projects that integrate a combination of archival, behavioral, and qualitative research methods. By demonstrating the inherent strengths and weaknesses of using a single method in isolation, this chapter aims to broaden our understanding of why and how research that examines various issues from the different perspectives is richer than employing any single method and enhances our understanding of a given accounting phenomenon. This chapter also discusses how investigating an issue through multiple research methods can help researchers improve the generalizability of findings and present a panoramic view of a particular phenomenon.

In the field of behavioral disabilities, systematic direct observation (SDO) has been an integral tool for describing and explaining relationships between student and teacher behavior in authentic classroom settings. However, this method of measurement can be resource-intensive and presents a series of complex decisions for investigators. The purpose of this chapter is to review a series of critical decisions investigators must make when developing SDO protocols to address their research questions. After describing each decision point and its relevance to the measurement system, we identify trends and special considerations in the field of behavioral disabilities with respect to each decision. We organize content according to deciding what to measure, deciding how to measure it, and critical steps to prevent system breakdowns. Finally, we identify avenues for research to further the impact of SDO in the field of behavioral disabilities.

The purpose of this paper is to present a new paradigm, named BioGames, for the extraction of behavioral biometrics (BB) conveniently and entertainingly. To apply the BioGames paradigm, the authors developed a BB collection tool for mobile devices named BioGames App. The BioGames App collects keystroke dynamics, touch gestures, and motion modalities and is available on GitHub. Interested researchers and practitioners may use it to create their datasets for research purposes.

One major challenge for BB and continuous authentication (CA) research is the lack of actual BB datasets for research purposes. The compilation and refinement of an appropriate set of BB data constitute a challenge and an open problem. The issue is aggravated by the fact that most users are reluctant to participate in long demanding procedures entailed in the collection of research biometric data. As a result, they do not complete the data collection procedure, or they do not complete it correctly. Therefore, the authors propose a new paradigm and introduce a BB collection tool, which they call BioGames, for the extraction of biometric features in a convenient way. The BioGames paradigm proposes a methodology where users play games without participating in an experimental painstaking process. The BioGames App collects keystroke dynamics, touch gestures, and motion modalities.

The authors proposed a new paradigm for the collection of BB on mobile devices and created the BioGames application. The BioGames App is an Android application that collects BB data on mobile devices and sends them to a database. The database design allows multiple users to store their sensor data at any time. Thus, there is no concern about data separation and synchronization. BioGames App is General Data Protection Regulation (GDPR) compliant as it collects and processes only anonymous data.

The BioGames App is a publicly available tool that combines the keystroke dynamics, touch gestures, and motion modalities. In addition, it uses a methodology where users play games without participating in an experimental painstaking process.

For studies in educational data mining or learning Analytics, the prediction of student’s performance or early warning is one of the most popular research topics. However, research gaps indicate a paucity of research using machine learning and deep learning (DL) models in predictive analytics that include both behaviors and text analysis.

This study combined behavioral data and discussion board content to construct early warning models with machine learning and DL algorithms. In total, 680 course sections, 12,869 students and 14,951,368 logs were collected from a K-12 virtual school in the USA. Three rounds of experiments were conducted to demonstrate the effectiveness of the proposed approach.

The DL model performed better than machine learning models and was able to capture 51% of at-risk students in the eighth week with 86.8% overall accuracy. The combination of behavioral and textual data further improved the model’s performance in both recall and accuracy rates. The total word count is a more general indicator than the textual content feature. Successful students showed more words in analytic, and at-risk students showed more words in authentic when text was imported into a linguistic function word analysis tool. The balanced threshold was 0.315, which can capture up to 59% of at-risk students.

The results of this exploratory study indicate that the use of student behaviors and text in a DL approach may improve the predictive power of identifying at-risk learners early enough in the learning process to allow for interventions that can change the course of their trajectory.

Purpose — In this paper we describe a total design data collection method (expanding the definition of the usual “total design” terminology used in typical household travel surveys) to emphasize the need to describe individual and group behaviors embedded within their spatial, temporal, and social contexts.

Methodology/approach — We first offer an overview of recently developed modeling and simulation applications predominantly in North America followed by a summary of the data needs in typical modeling and simulation modules for statewide and regional travel demand forecasting. We then proceed to describe an ideal data collection scheme with core and satellite survey components that can inform current and future model building. Mention is also made to the currently implemented California Household Travel Survey that brings together multiple agencies, modeling goals, and data collection component surveys.

Findings — The preparation of this paper involved reviewing emerging transportation modeling approaches and paradigms, policy questions, and behavioral issues and considerations that are important in the multimodal transportation planning context. It was found that many of the questions being asked of policy makers in the transportation domain require a deep understanding of the interactions and constraints under which individuals make activity-travel choices, the learning processes at play, and the attitudes and perceptions that shape ways in which people adjust their travel behavior in response to policy interventions. Based on the work, it was found that many of the traditional travel survey designs are not able to provide the comprehensive data needed to estimate activity-based model systems that truly capture the full range of behavioral considerations and phenomena of importance.

Originality/value of paper — This paper offers a review of the emerging transportation modeling approaches and behavioral paradigms of importance in activity-based travel demand forecasting. The paper discusses how traditional travel survey designs are inadequate to meet the data needs of emerging modeling approaches. Based on a review of all of the data needs and new data collection methods that are making it possible to observe a full range of human behaviors, the paper offers a total survey data collection design that brings together many different surveys and data collection protocols. The core household travel survey is augmented by a full slate of special purpose surveys that together yield a rich behavioral database for activity-based microsimulation modeling. The paper is a valuable reference for transportation planners and modelers interested in developing data collection enterprises that will feed the next generation of transportation models.

Data security breaches are an increasingly common and costly problem for organizations, yet there are critical gaps in our understanding of the role of stakeholder relationship management and crisis communication in relation to data breaches. In fact, though there have been some studies focusing on data breaches, little is known about what might constitute a “typical” response to data breaches whether those responses are effective at maintaining the stakeholders' relationship with the organization, their commitment to use the organization after the crisis, or the reputational threat of the crisis. Further, even less is known about the factors most influencing response and outcome evaluation during data breaches.

We identify a “typical” response strategy to data breaches and then evaluate the role of this response in comparison to situation, stakeholder demographics and relationships between stakeholders, the issue and the organization using an experimental design. This experiment focuses on a 2 (type of organization) × 2 (prior knowledge of breach risk) with a control group design.

Findings suggest that rather than employing reactive crisis response messaging the role of public relations should focus on proactive relationship building between organizations and key stakeholders.

For the last several decades much of the field of crisis communication has assumed that in the context of a crisis the response strategy itself would materially help the organization. These data suggest that the field crisis communication may have been making the wrong assumption. In fact, these data suggest that reactive crisis response has little-to-no effect once we consider the relationships between organizations, the issue and stakeholders. The findings show that an ongoing program of crisis capacity building is to an organization's strategic advantage when data security breaches occur.