Learning Behavioral Representations of Routines From Large-scale Unlabeled Wearable Time-series Data Streams using Hawkes Point Process

Continuously-worn wearable sensors enable researchers to collect copious amounts of rich bio-behavioral time series recordings of real-life activities of daily living, offering unprecedented opportunities to infer novel human behavior patterns during daily routines. Existing approaches to routine discovery through bio-behavioral data rely either on pre-defined notions of activities or use additional non-behavioral measurements as contexts, such as GPS location or localization within the home, presenting risks to user privacy. In this work, we propose a novel wearable time-series mining framework, Hawkes point process On Time series clusters for ROutine Discovery (HOT-ROD), for uncovering behavioral routines from completely unlabeled wearable recordings. We utilize a covariance-based method to generate time-series clusters and discover routines via the Hawkes point process learning algorithm. We empirically validate our approach for extracting routine behaviors using a completely unlabeled time-series collected continuously from over 100 individuals both in and outside of the workplace during a period of ten weeks. Furthermore, we demonstrate this approach intuitively captures daily transitional relationships between physical activity states without using prior knowledge. We also show that the learned behavioral patterns can assist in illuminating an individual's personality and affect.Comment: 2023 9th ACM SIGKDD International Workshop on Mining and Learning From Time Series (MiLeTS 2023

Predicting individual differences in decision-making process from signature movement styles: an illustrative study of leaders

There has been a surge of interest in examining the utility of methods for capturing individual differences in decision-making style. We illustrate the potential offered by Movement Pattern Analysis (MPA), an observational methodology that has been used in business and by the US Department of Defense to record body movements that provide predictive insight into individual differences in decision-making motivations and actions. Twelve military officers participated in an intensive 2-h interview that permitted detailed and fine-grained observation and coding of signature movements by trained practitioners using MPA. Three months later, these subjects completed four hypothetical decision-making tasks in which the amount of information sought out before coming to a decision, as well as the time spent on the tasks, were under the partial control of the subject. A composite MPA indicator of how a person allocates decision-making actions and motivations to balance both Assertion (exertion of tangible movement effort on the environment to make something occur) and Perspective (through movements that support shaping in the body to perceive and create a suitable viewpoint for action) was highly correlated with the total number of information draws and total response time—individuals high on Assertion reached for less information and had faster response times than those high on Perspective. Discussion focuses on the utility of using movement-based observational measures to capture individual differences in decision-making style and the implications for application in applied settings geared toward investigations of experienced leaders and world statesmen where individuality rules the day

Trusted Artificial Intelligence in Manufacturing; Trusted Artificial Intelligence in Manufacturing

The successful deployment of AI solutions in manufacturing environments hinges on their security, safety and reliability which becomes more challenging in settings where multiple AI systems (e.g., industrial robots, robotic cells, Deep Neural Networks (DNNs)) interact as atomic systems and with humans. To guarantee the safe and reliable operation of AI systems in the shopfloor, there is a need to address many challenges in the scope of complex, heterogeneous, dynamic and unpredictable environments. Specifically, data reliability, human machine interaction, security, transparency and explainability challenges need to be addressed at the same time. Recent advances in AI research (e.g., in deep neural networks security and explainable AI (XAI) systems), coupled with novel research outcomes in the formal specification and verification of AI systems provide a sound basis for safe and reliable AI deployments in production lines. Moreover, the legal and regulatory dimension of safe and reliable AI solutions in production lines must be considered as well. To address some of the above listed challenges, fifteen European Organizations collaborate in the scope of the STAR project, a research initiative funded by the European Commission in the scope of its H2020 program (Grant Agreement Number: 956573). STAR researches, develops, and validates novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project researches and delivers approaches that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. This book is co-authored by the STAR consortium members and provides a review of technologies, techniques and systems for trusted, ethical, and secure AI in manufacturing. The different chapters of the book cover systems and technologies for industrial data reliability, responsible and transparent artificial intelligence systems, human centered manufacturing systems such as human-centred digital twins, cyber-defence in AI systems, simulated reality systems, human robot collaboration systems, as well as automated mobile robots for manufacturing environments. A variety of cutting-edge AI technologies are employed by these systems including deep neural networks, reinforcement learning systems, and explainable artificial intelligence systems. Furthermore, relevant standards and applicable regulations are discussed. Beyond reviewing state of the art standards and technologies, the book illustrates how the STAR research goes beyond the state of the art, towards enabling and showcasing human-centred technologies in production lines. Emphasis is put on dynamic human in the loop scenarios, where ethical, transparent, and trusted AI systems co-exist with human workers. The book is made available as an open access publication, which could make it broadly and freely available to the AI and smart manufacturing communities

SUBJECTIVE WELL-BEING OF TEACHERS IN K-12 CHRISTIAN SCHOOLS

This non-experimental, quantitative study aimed to examine the factors that influence the self-perceived well-being of teachers in K-12 Christian schools. The sample for this study was convenient, non-probable, and purposive and comprised of 81 teachers from one Christian school system in Florida. The measurement tool used in this study is based on Seligman’s (2011) work on well-being. The Workplace PERMAH Profiler is a valid and reliable (α = .94) instrument that measures flourishing in terms of six domains: positive emotion, engagement, relationships, meaning, accomplishment, and health. The internal reliability of study participant responses to survey items associated with the construct of well-being was evaluated using Cronbach’s alpha (α). The internal reliability levels achieved in the study across all 23 survey items associated with the study’s construct of well-being was very good at α = .87. A one-sample t-test was conducted to assess the statistical significance of study participant response to survey items associated with the six dimensions of the study’s overarching construct of well-being. The response effects for all six dimensions of the construct of well-being were statistically significant. In five of the six dimensions of the construct of well-being, the response effects were considered huge (d ≥ 2.0). The response effect for the dimension of health was considered medium (d = .47). The single greatest response effect within the six dimensions of the construct of well-being was reflected in the dimension of meaning (d = 3.94), closely followed by the dimension of accomplishment (d = 3.44)

HRM and Small-Firm Employee Motivation: Before and After the Great Recession

A long-running debate in the small firms’ literature questions the value of formal ‘human resource management’ (HRM) practices which have been linked to high performance in larger firms. We contribute to this literature by exploiting linked employer-employee surveys for 2004 and 2011. Using employees’ intrinsic job satisfaction and organizational commitment as motivational outcomes we find the returns to small firm investments in HRM are U-shaped. Small firms benefit from intrinsically motivating work situations in the absence of HRM practices, find this advantage disturbed when formal HRM practices are initially introduced, but can restore positive motivation when they invest intensively in HRM practices in a way that characterizes ‘high performance work systems’ (HWPS). Although the HPWS effect on employee motivation is modified somewhat by the Great Recession, it remains rather robust and continues to have positive promise for small firms

Enhancing Employability for Autistic Graduates: Using TRIZ Theory to Design Virtual Reality Solutions for Fostering Inclusive Communications in Workplace Environments

The underemployment of post-secondary graduates with autism (AP-PSD) is a critical concern. This issue often stems from difficulties in workplace integration and communication. This research explores the potential of virtual reality (VR) systems to alleviate these challenges by simulating work environments. The methodology, rooted in the TRIZ Theory, a problem-solving algorithm, refines the use of VR systems for enhanced adaptability and efficiency. The primary objectives include enhancing employers\u27 understanding of AP-PSD-related issues and identifying significant workplace challenges faced by AP-PSDs. Through literature reviews, surveys, and focus groups, the study investigates the factors impacting AP-PSDs and identifies key components to develop a more effective VR system to support their workplace integration

Individual adaptability as a predictor of job performance

In the new global economy, organizations frequently have to adjust to meet challenging demands of customers, competitors, or regulatory agencies. These adjustments at the organizational level often cascade down to employees, and they may face changes in their job responsibilities and how work is performed. I-ADAPT theory suggests that individual adaptability (IA) is an individual difference variable that includes both personality and cognitive aspects and has both trait- and state-like properties. As a result, IA may be an acceptable alternative for traditional, stable selection tests for operating within unstable environments. The present paper examined the relationship of individual adaptability, cognitive ability, and personality (conscientiousness) to task performance, citizenship performance, and counterproductive work behaviors. The relationship between an individual\u27s motivational state and IA was also examined. The study was conducted in the form of online surveys, with data being gathered from 313 employees across the United States. As hypothesized, IA was a significant predictor of all three types of performance, and IA was related to state of mind. IA was also a parsimonious predictor of citizenship performance, as stated in the hypotheses. Conscientiousness was found to be related to state of mind. IA was also hypothesized to demonstrate less differential prediction than cognitive ability, but this hypothesis was not supported. Limitations and future research directions are discussed, and practical uses for adaptability tests in the workplace are suggested

Acquisition of focus by adult English learners of Hungarian : evidence of optionality in mature and developing grammars

The process of second language acquisition is usually assumed to be affected by differences between the source language (L 1) and the target language (L2). Within the Minimalist approach (Chomsky 1995) crosslinguistic variation is accounted for in terms of differences in the values of features of functional categories instantiated in specific languages. Mature English differs from Hungarian in that its Tense category does not carry the [+f] feature characteristic of Hungarian focused sentences. Also, English lacks an additional functional projection dominating IP, namely F(ocus)P(hrase), which hosts focused, wh-, and negative operators in Spec,FP and attracts the verb or adjectival predicate into its head in order to satisfy spec-head agreement. It follows that English learners of Hungarian will have . to instantiate a new functional category FP and reset the values of the Tense category in their IL grammar. In this thesis we account for the difficulties faced by adult English learners of Hungarian by adopting the hypothesis that the two main classes of features have distinct learnability properties. It has been suggested that interpretable features (among them phi-features of nouns as well as [+wh] and [+f] features) are acquired easier than non-interpretable features (such as features responsible for V2 word order, resumptive pronouns, verbal inflection and nominal case morphology, as well as verb-movement associated with the Focus Projection in Hungarian). We demonstrate that this effect is also found -in our English-Hungarian interlanguage data. We show that even though L2 learners manage to prepose wh, focus and negative operators, they have continued difficulties with the accompanying verb-movement properties of Hungarian. This is reminiscent of the difficulties we find in child L 1 language acquisition of Hungarian. However, we argue that learnability factors have to be complemented by considerations about the nature of the target language input L2 learners receive. We propose that the nature of the TL input accounts for the differences between child and adult learners of Hungarian. It is well known that robust data (i.e. simple, salient and frequently occurring sentences) are required for the acquisition of correct feature-specifications of a target language. Infrequent data may cause a delay in the process of establishing L2 feature specifications and result in incomplete representations. Ambiguous data, on the other hand, are Iikely to ultimately result in divergent L2 representations at near native level. Testing these predictions in a study of acceptability judgements of adult English-speaking learners of Hungarian, we show that adult English speaking learners of Hungarian have difficulties in acquiring double wh- and double focus constructions as well as focused infinitives, long and partial operator movement in Hungarian. It is demonstrated that in the case of double wh- and double focus constructions native speakers' intuitions are indeterminate/optional, therefore the data L2 learners receive are not robust, leading to optionality in learners' interlanguage grammars. Although enjoying categorical judgements in native grammars, the nature of the input is similarly non-robust in the case of focused infinitives as well as long and partially extracted operator sentences. This is argued to lead to the difficulties L2 learners exhibit with respect to these structures. In the face of non-robust target language data learners are found to fall back on L 1 values and/or to resort to general learning strategies, such as overgeneralization and analogy