Personal Assistants Research Articles

AIS Effect on Human Behavior and Well-Being: An Empirical Analysis

The integration of artificial intelligence (AI) into various facets of our everyday existence has occurred at a rapid pace. This includes the presence of personal assistants on our smartphones as well as the algorithms that serve as the foundation for social media feeds. Artificial intelligence (AI) possesses the capacity to profoundly transform various aspects of human existence and professional endeavors. However, it is imperative to acknowledge that AI is not exempt from certain limitations and disadvantages. An aspect of considerable concern pertains to the potential psychological ramifications associated with the utilization of artificial intelligence (AI) technologies. With the escalating dependence on these technologies, there is a mounting apprehension regarding their potential adverse impact on human behavior and overall well-being. Further investigation is necessary in order to fully grasp the psychological ramifications of artificial intelligence, as these impacts are intricate and multidimensional in their entirety. The majority of tasks performed at the interface between humans and machines necessitate individuals to engage in novel and distinct activities, as well as to approach familiar tasks in alternative ways. The leaders of the future will be those who actively use collaborative intelligence, so revolutionizing their organizational practices, market strategies, industry dynamics, and, significantly, their workforce management. The field of artificial intelligence has witnessed significant advancements in various domains traditionally associated with human expertise, such as disease diagnosis, language translation, and customer service. Moreover, these advancements have been characterized by rapid progress. The emergence of such concerns is rooted in the realistic apprehension that artificial intelligence (AI) has the potential to gradually supplant human labor across all sectors of the economy. However, it is important to note that such a conclusion is not necessarily inevitable, or even the most probable. The current state of digital technologies exhibits an unprecedented level of responsiveness to human users, and conversely, humans have become increasingly attuned to utilizing these tools. The advent of AI will have a profound impact on the nature of work and the individuals involved in it. However, its primary effect will be to enhance and supplement human abilities, rather than displacing them entirely. The main aim to analyse the positive & negative effects of AI on human behaviour & well-being & to understand how AI affects human behavior and well-being and deployment of AI technologies for the benefit of individuals and society.

The Impact of Intelligent Pedagogical Agents’ Interventions on Student Behavior and Performance in Open-Ended Game Design Environments

Research has shown that free-form Game-Design (GD) environments can be very effective in fostering Computational Thinking (CT) skills at a young age. However, some students can still need some guidance during the learning process due to the highly open-ended nature of these environments. Intelligent Pedagogical Agents (IPAs) can be used to provide personalized assistance in real-time to alleviate this challenge. This paper presents our results in evaluating such an agent deployed in a real-word free-form GD learning environment to foster CT in the early K-12 education, Unity-CT. We focus on the effect of repetition by comparing student behaviors between no intervention, 1-shot, and repeated intervention groups for two different errors that are known to be challenging in the online lessons of Unity-CT. Our findings showed that the agent was perceived very positively by the students and the repeated intervention showed promising results in terms of helping students make fewer errors and more correct behaviors, albeit only for one of the two target errors. Building from these results, we provide insights on how to provide IPA interventions in free-form GD environments.

Care recipients’ management of and approaches to receiving personal and intimate care

Summary An interview study was conducted with persons receiving home care services and personal assistance in Sweden (13 individuals, 25 interviews) with the aim to analyze their experiences of managing personal and intimate care. The analytical approach was guided by phenomenologically informed research and Erving Goffman's theoretical work on self-presentation and social life as it differs in frontstage and backstage settings. Findings A reflected approach to the complex challenges associated with becoming and being a person in need of personal and intimate care was revealed. This involved continuous adaptations and attuning to organizational and relational conditions of formal home care. Being a recipient of personal and intimate care does not mean being passive. It entails relating to and sustaining the care relation, where even choosing to accept suboptimal conditions is an act of agency. The recipients’ private homes were hybridized, transformed both into a waiting room, with the recipient on standby and into a workplace. The homes thus partly lost their character as a backstage realm where one could avoid the gaze of others. This also led to a hybridization of the personal sphere, in the form of marginal scope for true privacy, necessitating strategies for protecting one's own space. Applications It is important both to acknowledge the intrusive nature of personal and intimate care, which results in extensive hybridization of the home and personal sphere and to recognize care recipients’ agency in the relationship that care establishes.

Learning domain-heterogeneous speaker recognition systems with personalized continual federated learning

Speaker recognition, the process of automatically identifying a speaker based on individual characteristics in speech signals, presents significant challenges when addressing heterogeneous-domain conditions. Federated learning, a recent development in machine learning methods, has gained traction in privacy-sensitive tasks, such as personal voice assistants in home environments. However, its application in heterogeneous multi-domain scenarios for enhancing system customization remains underexplored. In this paper, we propose the utilization of federated learning in heterogeneous situations to enable adaptation across multiple domains. We also introduce a personalized federated learning algorithm designed to effectively leverage limited domain data, resulting in improved learning outcomes. Furthermore, we present a strategy for implementing the federated learning algorithm in practical, real-world continual learning scenarios, demonstrating promising results. The proposed federated learning method exhibits superior performance across a range of synthesized complex conditions and continual learning settings, compared to conventional training methods.

Bridging the regulatory gaps created by Smart and Connected technologies in South Africa

The prevalence of technology-embedded products, services, and cities, described colloquially as ‘smart’ technologies and ‘smart’ cities, has seen a spate of unprecedented growth in recent years. South Africa (SA) has not been left behind, with smartphones, smart watches, and smart voice-controlled virtual personal assistants such as Amazon’s Alexa now frequently used. But while these technologies hold great promise to revolutionise homes, offices and cities, their adoption poses challenges to individual and collective interests and wellbeing. After demonstrating the legal and ethical difficulties brought about by the introduction of these technologies, this article explores whether SA legislation is sufficiently robust to address these challenges. While the current legislative landscape addresses certain crucial difficulties – such as the safeguarding of personal data by the Protection of Personal Information Act No. 4 of 2013 (‘POPIA’) – it is suggested that the position regulating other aspects of smart technology adoption is, in large part, fragmented and ill-equipped to deal with some of the more pressing legal and ethical questions. Our contention is that, not dissimilar to the issues arising from artificial intelligence-based technological adoption, the extant legislative and regulatory frameworks do not go far enough in addressing the many concerns emerging from recent novel technological design, development, and deployment. Not only do smart technologies give rise to unique challenges, so does their deployment within the Global South and in South Africa, in particular. We suggest that appropriate and effective regulatory reform measures be undertaken in SA to provide better ethical guidance and policy prescriptions buttressed by rigorous regulatory oversight.

A Study on the Service Choice Constraints for Personal Assistance Service - Focusing on the Service Utilization Patterns of Parents of Children with Developmental Disabilities -

A Study on the Service Choice Constraints for Personal Assistance Service - Focusing on the Service Utilization Patterns of Parents of Children with Developmental Disabilities -

The Structural Relationship among User Participation in Personal Assistance Services for Disabled, Encounter Satisfaction and Service Quality

The Structural Relationship among User Participation in Personal Assistance Services for Disabled, Encounter Satisfaction and Service Quality

On the self-adjustment of privacy safeguards for query log streams

Internet-based services process and store numerous search queries around the globe. The use of web search engines, such as Bing and Google, as well as personal assistants (e.g., Alexa and Cortana) and task specific systems (e.g., YouTube, Netflix, Amazon) are relevant examples. The queries associated to such services may be stored and sold out for profit. Before doing so, personal and sensitive information must be sanitized, as requested by current regulations. This can be cumbersome for some organizations. We present an automated solution for anonymizing unstructured data, like the one used within query logs. Our solution uses a light-weight probabilistic k-anonymity approach, which allows verifiable real-time privacy protection. It addresses previous limitations and improves performance. We validate the feasibility of the approach, under some evaluation metrics including data utility, privacy and speed.

A functional near-infrared spectroscopy study on the prefrontal correlates of cognitive offloading via a personal knowledge assistant

The saving of previously encoded information boosts both memory for subsequent information (saving-enhanced memory; SEM) as well as cognitive performance in general (saving-enhanced performance; SEP). These findings have been replicated in a setting that involves the assistance by an intelligent software that automatically structures and saves work content in an interactive sidebar. It is assumed that beneficial effects on cognitive performance due to (automatic) saving are caused by a reduction in current workload by means of cognitive offloading. We tested this assumption by measuring neural activity in the dorsolateral prefrontal cortex (DLPFC) via functional near infrared spectroscopy (fNIRS)—once after saving and once after deleting of previously collected information that had to be recalled later-on. On a behavioral level, there was a brief benefit of saving. However, cognitive offloading became most apparent on a neural level: after saving, participants showed significantly lower activation in the right DLPFC. Also, the more participants benefited from cognitive offloading, the more they were able to re-access previously collected, saved information. Thus, fNIRS results indicated reduced mental load after saving, confirming the assumption that saving triggers cognitive offloading.

Consistency of participation over time among persons aging with physical disability as measured by a tool designed for use among community-based organizations

BackgroundCommunity participation measures for persons aging with disability were developed and evaluated to support community-based organizations (CBOs) with efficient assessment of change in participation and need for supports/services to facilitate participation. ObjectiveTo evaluate a set of nine activity domain measures to broadly assess community participation and change in participation over time. MethodsA community-based sample (N = 323) of persons ages 45–65 responded to a survey with repeated measures three times annually (T1, T2, T3) between 2019 and 2022. Nine activity domain measures were developed based on extant research and evaluated with assistance from community-based support service providers. Statistical analyses employed T-tests and chi-square tests to assess change in participation over time, perceptions of participation satisfaction, and assistance needed to facilitate participation. Participants were asked if they thought changes were attributable to aging, the COVID-19 pandemic, or other factors. ResultsFindings showed varying levels of participation across the nine activity domains, with the lowest participation rate for employment and the highest participation rates for personal leisure and managing medications across T1, T2, and T3. Change in participation over the three-year period was limited; most change was reported as activity reduction. In general, respondents indicated that reduction was due to their aging or the COVID-19 pandemic. Personal assistance, transportation, environmental modifications, and improved health were identified as factors needed to help increase participation levels. ConclusionThe activity domain measures demonstrated efficiency in identifying participation rates and change. CBOs may deem them useful for assessing support and service needs to facilitate participation.

A multiband radio-frequency energy harvester for self-powered biosensor

This paper presents a novel nano-structure of radio frequency (RF) to direct current (DC) converter for the energy control unit in the biosensor. It is based on the Graëtz Bridge supplied with three-phase power (DP3). This circuit can be considered as the result of the proper combination of a common anode assembly and a common cathode assembly. In fact, the same three-phase converter structure was kept. The diodes have been replaced by six NMOS transistors connected in diodes. A number of capacitors have been added for each stage to boost voltage. The benefit of using this type of circuit is to obtain a powerful DC output signal with the smallest number of stages. The proposed architecture also enables MOS transistors to be driven by external 2.45 GHz voluntary signals originating from the implant's personal assistant. This would allow avoiding the use of transistor control circuits which are already power-consuming. For more power, the proposed converter receives two more involuntary global system for mobile signals (GSM) with 900 MHz and 1800 MHz frequencies bands in order to take advantage of the ambient energy. The proposed RF-DC converter efficiency reaches 62.8% for an input power equal to 10 dbm.

RETRACTED ARTICLE: Customization, loneliness, and optimism: drivers of intelligent personal assistant continuance intention during COVID-19

With the growing prominence of artificial intelligence, intelligent personal assistants (IPAs) have become integral to various industries and individuals’ lives. This study investigates the factors influencing the continuance intention of IPA users. The study aims to address the current knowledge gap and provide valuable insights by comprehensively validating factors that can influence IPA users’ behavior in the context of the COVID-19 pandemic. An integrated framework is proposed to identify critical factors impacting IPA usage, and actual IPA users were surveyed. Data from 258 users during the pandemic were collected and analyzed using structural equation modeling. The findings demonstrate that perceived usefulness significantly influences users’ intentions to continue using IPAs, while perceived ease of use has a significant impact on both perceived usefulness and enjoyment. Additionally, customization, feelings of loneliness due to COVID-19, and optimism positively contribute to users’ intention to continue using IPAs. Moreover, the quality of the system and service plays a significant role in user satisfaction. This study sheds light on the crucial determinants of IPA adoption and offers insights for designing and improving IPA systems. The findings can inform the development and enhancement of IPA technologies, enabling service providers to drive user satisfaction, customization, and overall user experience, ultimately fostering further growth and advancement in the IPA industry.

Peningkatan Daya Saing Usaha Mikro melalui Program Pelatihan Sustaining Competitive and Responsible Enterprises

Peningkatan Daya Saing Usaha Mikro melalui Program Pelatihan Sustaining Competitive and Responsible Enterprises

Generative AI and education ecologies

What role can generative AI have an art and design education? Given that we are in a year of change as open-source Open AI systems shift how we teach, learn, and assess in times of question-answering chatbot and personal assistance tools. Applying a post-human approach (Blaikie, et al, 2020) to education might help us rethink pedagogy (Wessels, et al, 2022), knowledge creation and scholarly publication for knowledge sharing. In this SoTEL Symposium presentation/discussion with the ASCILITE MLSIG I propose a move away from a humanist world view that continues to shape our thoughts around the binary of teacher-learner within our walled disciplinary and consider how we might Incorporate generative AI tools in the curriculum to foster interdisciplinary collaborations with the more-than human. What if we shifted teaching and learning to facilitate new ways of being on the planet, so that we prioritised ourselves, one another as well as non-human and more-than-humans in our educational ecologies. Building the digital literacies and computational thinking capabilities (George-Reyes, et al, 2021) to learn with GAI will create opportunities to thinking about the world and all its space and places, as interconnected and entangled.
 In this trendsetter webinar I pose a series of questions and prompts that I had in conversation with Chatty G (ChatGPT) to consider how we might imagine and understand the world in different ways so that we might integrate generative AI and into our education ecologies in higher education.
 Presentation: https://doi.org/10.26188/22281685

Knowledge Graphs Querying

Knowledge graphs (KGs) such as DBpedia, Freebase, YAGO, Wikidata, and NELL were constructed to store large-scale, real-world facts as (subject, predicate, object) triples - that can also be modeled as a graph, where a node (a subject or an object) represents an entity with attributes, and a directed edge (a predicate) is a relationship between two entities. Querying KGs is critical in web search, question answering (QA), semantic search, personal assistants, fact checking, and recommendation. While significant progress has been made on KG construction and curation, thanks to deep learning recently we have seen a surge of research on KG querying and QA. The objectives of our survey are two-fold. First, research on KG querying has been conducted by several communities, such as databases, data mining, semantic web, machine learning, information retrieval, and natural language processing (NLP), with different focus and terminologies; and also in diverse topics ranging from graph databases, query languages, join algorithms, graph patterns matching, to more sophisticated KG embedding and natural language questions (NLQs). We aim at uniting different interdisciplinary topics and concepts that have been developed for KG querying. Second, many recent advances on KG and query embedding, multimodal KG, and KG-QA come from deep learning, IR, NLP, and computer vision domains. We identify important challenges of KG querying that received less attention by graph databases, and by the DB community in general, e.g., incomplete KG, semantic matching, multimodal data, and NLQs. We conclude by discussing interesting opportunities for the data management community, for instance, KG as a unified data model and vector-based query processing.

Reasoning over Public and Private Data in Retrieval-Based Systems

Abstract Users an organizations are generating ever-increasing amounts of private data from a wide range of sources. Incorporating private context is important to personalize open-domain tasks such as question-answering, fact-checking, and personal assistants. State-of-the-art systems for these tasks explicitly retrieve information that is relevant to an input question from a background corpus before producing an answer. While today’s retrieval systems assume relevant corpora are fully (e.g., publicly) accessible, users are often unable or unwilling to expose their private data to entities hosting public data. We define the Split Iterative Retrieval (SPIRAL) problem involving iterative retrieval over multiple privacy scopes. We introduce a foundational benchmark with which to study SPIRAL, as no existing benchmark includes data from a private distribution. Our dataset, ConcurrentQA, includes data from distinct public and private distributions and is the first textual QA benchmark requiring concurrent retrieval over multiple distributions. Finally, we show that existing retrieval approaches face significant performance degradations when applied to our proposed retrieval setting and investigate approaches with which these tradeoffs can be mitigated. We release the new benchmark and code to reproduce the results.1

Development of Transport Management Software

The growth of technology and the increasing competitiveness and opportunity of digital services have meant that the traditional logistic sector has changed dramatically. New technology and globalization affect the need to use solutions known to the IT industry. In recent years, we have seen significant technological growth, from smart devices communicating with each other via the IoT (Internet of Things) to artificial intelligence, and the changes it brings in the future. However, the role of user interface design in enhancing the user experience of mobile applications, such as journey planners, has been overlooked. Given the knowledge of the environmental impact of transport systems, it is important to convince users to use more sustainable modes of transport. Transport systems account for 20% to 25% of global energy consumption and carbon emissions. The aim of the article is to explain the process of creating a user interface for a public transport journey planner application that serves as a personal travel assistant. In addition, a competitive audit and research on a small group of users was also carried out. The conducted research and the audit of the competition were crucial in the ideation process, which became the basis for the first wireframes of the application. This was made possible by using the powerful Figma tool, which allowed for a more detailed design of the application’s appearance and functions. The resulting design of the application interface is a proposed solution to some of the user problems detected during the study. The final prototype needs to be turned into a real product to serve users. For this purpose, a developer must be provided with the prototype, who will bring the application to life using programming tools.

Strategies used by people with systemic sclerosis to perform valued occupations

Introduction: This study explored the strategies used by people with systemic sclerosis to perform valued obligatory (self-care), committed (home management, care of others, productivity), and discretionary (leisure and socialization) activities. Method: This cross-sectional study included a convenience sample of 92 people with systemic sclerosis who completed the Valued Life Activities Scale (VLA). The VLA categorizes activities into three main domains: obligatory, committed, and discretionary. Participants indicated the type of adaptive strategy used for each activity (assistive devices, personal assistance, limited frequency, and taking more time). Participants also completed a demographic questionnaire and measures of pain, fatigue, depression, and daily activity performance. Frequency counts were calculated for strategies used for each activity. One-way analyses of variance compared the mean use of strategies across the three domains (obligatory, committed, and discretionary). Pearson correlation coefficients calculated relationships between strategy use and pain, fatigue, depression, and daily activity performance. Results: Significantly, more strategies were used for committed activities compared to obligatory activities. Limiting the frequency of performing an activity was used significantly more for committed and discretionary activities than for obligatory activities. The least used strategies were assistive devices and assistance from another person. Fatigue and depression were related to strategy use, whereas pain and daily activity performance were not. Conclusion: People with systemic sclerosis use adaptive strategies to perform VLA. However, the strategies, most frequently used, such as taking more time or limiting the frequency of performing some activities, may result in relinquishing other valued activities.

Investigation of Available Datasets and Techniques for Visual Question Answering

Visual Question Answering (VQA) is an emerging AI research problem that combines computer vision, natural language processing, knowledge representation & reasoning (KR). Given image and question related to the image as input, it requires analysis of visual components of the image, type of question, and common sense or general knowledge to predict the right answer. VQA is useful in different real-time applications like blind person assistance, autonomous driving, solving trivial tasks like spotting empty tables in hotels, parks, or picnic places, etc. Since its introduction in 2014, many researchers have worked and applied different techniques for Visual question answering. Also, different datasets have been introduced. This paper presents an overview of available datasets and evaluation metrices used in the VQA area. Further paper presents different techniques used in the VQA domain. Techniques are categorized based on the mechanism used. Based on the detailed discussion and performance comparison we discuss various challenges in the VQA domain and provide directions for future work.

Speaking, but having no voice. Negotiating agency in advertisements for intelligent personal assistants

With the popularisation of intelligent personal assistants (IPAs) like Amazon’s Alexa and the Google Assistant, natural language-based interaction with machines is increasingly becoming a part of everyday life. The conceptualisation of these tools as agentive assistants who help with a variety of tasks in both the household and at work is guided by their marketing: When Apple introduced the Siri-technology at their keynote event in 2011, the system responded to the question ‘Siri, who are you?’ with ‘I am a humble personal assistant’. This claim to a speaking subject position while at the same time locating this subject firmly in a servile social role has become a defining feature of the social place of IPAs: Designed to postulate agency, they do so not in equality with humans but as their servants. This paper offers an interdisciplinary analysis of video advertisements for IPAs, combining sociological and linguistic approaches. We treat agency and actors not as something given but as something that becomes visible through communicative acts, suggesting an understanding of these advertisements as socio-technical visions in which the negotiation of agency in human–machine interaction serves two functions: Firstly, the asymmetrical relationship between the human and the machine promises a symmetrisation of human–human relationships. In an imagined diversified world of equal human rights and relationships, social inequality is reconfigured in the relationship between human and non-human entities. Secondly, the negotiation of agency between humans and machines deflects from questions regarding the increasing agency and power of the companies behind these IPAs and their growing access to and influence on people’s private lives. Our paper will thus provide insights into how agency is ascribed in human–human and human–machine interaction considering social practices of symmetrisation and hierarchisation as well as a critical investigation into the triangular relationships between humans’, machines’, and companies’ agency.