User Tasks Research Articles

FACTORS AFFECTING THE PERFORMANCE OF THE ACCOUNTING INFORMATION SYSTEM AT THE HARAPAN SEHAT CLINIC JEMBER

This study aims to analyze the effect of user participation, user capabilities, information technology sophistication, task complexity and system development formalization on accounting information system performance at Harapan Sehat Jember Clinic. The type of research used is explanatory quantitative research. The data used is primary data with data collection techniques, namely observation, literature and surveys. The number of respondents in this study were 43 people. The data analysis method used is instrument data test, classic assumption test, multiple linear regression, coefficient of determination (R2) and hypothesis testing. The results of the study show that user capabilities and information technology sophistication have a partial effect on the performance of accounting information systems. While User Participation, Task Complexity and System Development Formalization have no partial effect on Accounting Information System Performance. Simultaneously User Participation, User Capability, Information Technology Sophistication, Task Complexity and System Development Formalization affect the Performance of Accounting Information Systems.

Flow-Time Minimization for Timely Data Stream Processing in UAV-Aided Mobile Edge Computing

Unmanned Aerial Vehicle (UAV) has gained increasing attentions by both academic and industrial communities, due to its flexible deployment and efficient line-of-sight communication. Recently, UAVs equipped with base stations have been envisioned as a key technology to provide 5G network services for mobile users. In this paper, we provide timely services on the data streams of mobile users in a UAV-aided Mobile Edge Computing (MEC) network, in which each UAV is equipped with a 5G small-cell base station for communication and data processing. Specifically, we first formulate a flow-time minimization problem by jointly caching services and offloading tasks of mobile users to the UAV-aided MEC with the aim to minimize the flow-time, where the flow-time of a user request is referred to the time duration from the request issuing time point to its completion point, subject to resource and energy capacity on each UAV. We then propose a spatial-temporal learning optimization framework. We also devise an online algorithm with a competitive ratio for the problem based upon the framework, by leveraging the round-robin scheduling and dual fitting techniques. Finally, we evaluate the performance of the proposed algorithms through experimental simulation. The simulation results demonstrated that the proposed algorithms outperform their comparison counterparts, by reducing the flow-time no less than 19% on average.

User Requirements in Developing a Novel Dietary Assessment Tool for Children: Mixed Methods Study.

The prevalence of childhood obesity and comorbidities is rising alarmingly, and diet is an important modifiable determinant. Numerous dietary interventions in children have been developed to reduce childhood obesity and overweight rates, but their long-term effects are unsatisfactory. Stakeholders call for more personalized approaches, which require detailed dietary intake data. In the case of primary school children, caregivers are key to providing such dietary information. However, as school-aged children are not under the full supervision of one specific caregiver anymore, data are likely to be biased. Recent technological advancements provide opportunities for the role of children themselves, which would serve the overall quality of the obtained dietary data. This study aims to conduct a child-centered exploratory sequential mixed methods study to identify user requirements for a dietary assessment tool for children aged 5 to 6 years. Formative, nonsystematic narrative literature research was undertaken to delineate initial user requirements and inform prototype ideation in an expert panel workshop (n=11). This yielded 3 prototype dietary assessment tools: FoodBear (tangible piggy bank), myBear (smartphone or tablet app), and FoodCam (physical camera). All 3 prototypes were tested for usability by means of a usability task (video analyses) and user experience (This or That method) among 14 Dutch children aged 5 to 6 years (n=8, 57% boys and n=6, 43% girls). Most children were able to complete FoodBear's (11/14, 79%), myBear's (10/14, 71%), and FoodCam's (9/14, 64%) usability tasks, but all children required assistance (14/14, 100%) and most of the children encountered usability problems (13/14, 93%). Usability issues were related to food group categorization and recognition, frustrations owing to unsatisfactory functioning of (parts) of the prototypes, recall of food products, and the distinction between eating moments. No short-term differences in product preference between the 3 prototypes were observed, but autonomy, challenge, gaming elements, being tablet based, appearance, social elements, and time frame were identified as determinants of liking the product. Our results suggest that children can play a complementary role in dietary data collection to enhance the data collected by their parents. Incorporation of a training program, auditory or visual prompts, reminders and feedback, a user-friendly and intuitive interaction design, child-friendly food groups or icons, and room for children's autonomy were identified as requirements for the future development of a novel and usable dietary assessment tool for children aged 5 to 6 years. Our findings can serve as valuable guidance for ongoing innovations in the field of children's dietary assessment and the provision of personalized dietary support.

Design and implementation of user task offloading algorithm

After the service provider temporarily selects the required edge nodes based on social and storage capabilities, application execution causes the edge nodes to cache part of the application data. Therefore, offloading part of the application computing tasks to the selected edge nodes can effectively improve application execution performance. However, in cases where the resources of user’s IoT devices are insufficient, tasks can be further offloaded to traditional edge servers or even to the cloud to maximize application execution efficiency. In this paper, the entire uninstall utility is modeled as a weighted sum of task completion time and energy consumption. Under the premise of considering users’ preferences for completion time and energy consumption, a game-based uninstallation algorithm is proposed. The algorithm performs uninstallation by optimizing the uninstallation decision. Based on user preferences, the total system overhead is relatively small. The subsequent simulation experiments show that the algorithm can reduce system overhead on the basis of satisfying user preferences and has relatively good adaptability.

An Improved Gravitational Search Algorithm for Task Offloading in a Mobile Edge Computing Network with Task Priority

Mobile edge computing (MEC) distributes computing and storage resources to the edge of the network closer to the user and significantly reduces user task completion latency and system energy consumption. This paper investigates the problem of computation offloading in a three-tier mobile edge computing network composed of multiple users, multiple edge servers, and a cloud server. In this network, each user’s task can be divided into multiple subtasks with serial and parallel priority relationships existing among these subtasks. An optimization model is established with the objective of minimizing the total user delay and processor cost under constraints such as the available resources of users and servers and the interrelationships among the subtasks. An improved gravitational search algorithm (IGSA) is proposed to solve this optimization model. In contrast with the other gravitational search algorithm, the convergence factor is introduced in the calculation of the resultant force and the crossover operation in a genetic algorithm is performed when generating the new particles during each iteration. The simulation results show that the proposed IGSA greatly improves the system performance compared with the existing algorithms.

Geography-aware Heterogeneous Graph Contrastive Learning for Travel Recommendation

Recommendation system concentrates on quickly matching products to consumer’s needs which plays a major role in improving user experiences and increase conversion rate. Travel recommendation has become a hot topic in both industry and academia with the development of the tourism industry. Nevertheless, the selection of travel products entails careful consideration of various geographical factors, such as departure and destination. Meanwhile, due to the limitation of finance and time, users browse and purchase travel products less frequently than they do for traditional products, which leads to data sparsity problem in representation learning. To solve these challenges, a novel model named GHGCL (short for G eography-aware H eterogeneous G raph C ontrastive L earning) is proposed for recommending travel products. Concretely, we model the travel recommender system as an heterogeneous information network with geographical information, and capture diverse user preferences from local and high-order structures. Especially, we design two kinds of contrastive learning tasks for better user and travel product representation learning. The multi-view contrastive learning aims to bridge the gap between network schema and meta-path view representations. The meta-path contrastive learning focuses on modeling the coarse-grained commonality between different meta-paths from the perspective of different geographical factors, i.e., departure and destination. We assess the performance of GHGCL by performing a series of experiments on a real-world dataset and the results clearly verify its superiority as compared to the baseline methods.

Task Assignment and Path Planning Mechanism Based on Grade-Matching Degree and Task Similarity in Participatory Crowdsensing.

Participatory crowdsensing (PCS) is an innovative data sensing paradigm that leverages the sensors carried in mobile devices to collect large-scale environmental information and personal behavioral data with the user's participation. In PCS, task assignment and path planning pose complex challenges. Previous studies have only focused on the assignment of individual tasks, neglecting or overlooking the associations between tasks. In practice, users often tend to execute similar tasks when choosing assignments. Additionally, users frequently engage in tasks that do not match their abilities, leading to poor task quality or resource wastage. This paper introduces a multi-task assignment and path-planning problem (MTAPP), which defines utility as the ratio of a user's profit to the time spent on task execution. The optimization goal of MATPP is to maximize the utility of all users in the context of task assignment, allocate a set of task locations to a group of workers, and generate execution paths. To solve the MATPP, this study proposes a grade-matching degree and similarity-based mechanism (GSBM) in which the grade-matching degree determines the user's income. It also establishes a mathematical model, based on similarity, to investigate the impact of task similarity on user task completion. Finally, an improved ant colony optimization (IACO) algorithm, combining the ant colony and greedy algorithms, is employed to maximize total utility. The simulation results demonstrate its superior performance in terms of task coverage, average task completion rate, user profits, and task assignment rationality compared to other algorithms.

Integrated Quantitative Evaluation of Spatial Cognition and Motor Function with HoloLens Mixed Reality.

The steady increase in the aging population worldwide is expected to cause a shortage of doctors and therapists for older people. This demographic shift requires more efficient and automated systems for rehabilitation and physical ability evaluations. Rehabilitation using mixed reality (MR) technology has attracted much attention in recent years. MR displays virtual objects on a head-mounted see-through display that overlies the user's field of vision and allows users to manipulate them as if they exist in reality. However, tasks in previous studies applying MR to rehabilitation have been limited to tasks in which the virtual objects are static and do not interact dynamically with the surrounding environment. Therefore, in this study, we developed an application to evaluate cognitive and motor functions with the aim of realizing a rehabilitation system that is dynamic and has interaction with the surrounding environment using MR technology. The developed application enabled effective evaluation of the user's spatial cognitive ability, task skillfulness, motor function, and decision-making ability. The results indicate the usefulness and feasibility of MR technology to quantify motor function and spatial cognition both for static and dynamic tasks in rehabilitation.

Designing Data Visualization System Based on Language-Oriented Approach

The data visualization method based on a language-oriented approach is proposed. An analysis of data visualization tools and their customizability for subject areas based on user needs has been carried out. It is noted that these tools require highly qualified users to customize the data visualization format (users must have programming skills). It is proposed to customize visualization tools to the needs of users and the specifics of the user's tasks being solved by creating domain-specific languages (DSL). A system architecture based on the use of multifaceted ontology is described. The ontology includes descriptions of languages and domains, as well as rules for generating new languages and transforming constructed models. Languages are designed to describe different classes of diagrams. This system includes tools for automatic new DSL generation via mapping domain ontology onto the base language metamodel according to user-specified rules. Different types of diagrams have been classified and the main components of each type diagrams have been identified, which provides the basis for creating an ontology of data visualization languages. A base language is proposed for creating diagrams. The language customizability for specific domains is demonstrated. An example of the created data visualization models is shown.

PREVis: Perceived Readability Evaluation for Visualizations.

We developed and validated an instrument to measure the perceived readability in data visualization: PREVis. Researchers and practitioners can easily use this instrument as part of their evaluations to compare the perceived readability of different visual data representations. Our instrument can complement results from controlled experiments on user task performance or provide additional data during in-depth qualitative work such as design iterations when developing a new technique. Although readability is recognized as an essential quality of data visualizations, so far there has not been a unified definition of the construct in the context of visual representations. As a result, researchers often lack guidance for determining how to ask people to rate their perceived readability of a visualization. To address this issue, we engaged in a rigorous process to develop the first validated instrument targeted at the subjective readability of visual data representations. Our final instrument consists of 11 items across 4 dimensions: understandability, layout clarity, readability of data values, and readability of data patterns. We provide the questionnaire as a document with implementation guidelines on osf.io/9cg8j. Beyond this instrument, we contribute a discussion of how researchers have previously assessed visualization readability, and an analysis of the factors underlying perceived readability in visual data representations.

ПРОБЛЕМИ І РОЛЬ ОНТОЛОГІЧНОЇ ІНЖЕНЕРІЇ У ФОРМУВАННІ ІНДУСТРІЇ ЗНАНЬ З ПОЗИЦІЙ ДОСЛІДНОГО ПРОЄКТУВАННЯ

The article discusses models and mechanisms of transdisciplinary research in the perspective of creating clusters of disciplinary convergence and scientific theories, formal representation of knowledge, and the formation of the knowledge industry using a unified ontology engineering devkit. Special attention is focused on the possibilities of research-related design for the creation of new knowledge and technologies. A conceptual analysis of ontology engineering is conducted, indicating the importance and effectiveness of applying ontology knowledge and mechanisms in solving user tasks. The domain ontology, its ontology graph, and their essential differences and advantages from owl ontologies are examined in depth. An example of the application of task ontology to the design of programmable microchip computing devices is proposed. Keywords: knowledge industry, ontology engineering, transdisciplinary research, convergence clusters, research-related design, new knowledge and technologies, domain ontology, ontology graph, task ontology.

Multicriteria generalized regressive neural federated learning for cloud computing task scheduling and resource allocation

Cloud computing has arisen as a shrewd and well known worldview for people and associations to work with the entrance and use of registering assets through the web.With the rapid growth of cloud computing technology, efficiently running big data applications within minimal time has become a significant challenge. In this dynamic and scalable environment, effective resource allocation and task scheduling of big data applications play pivotal roles in optimizing performance, enhancing efficiency, and ensuring cost-effectiveness. In environments involving remote computing, task scheduling is a crucial consideration. In order to effectively accomplish resource-optimal task scheduling and minimize overall task execution time, a novel technique called Multicriteria Generalized Regressive Neural Federated Learning (MGRNFL) is developed to address the particular issues in cloud systems. Tasks from several users arrive at the cloud server at the start of the procedure. The cloud server's job scheduler then uses Multicriteria Federated Learning to carry out resource-optimal task scheduling. A decentralized machine learning technique called federated learning (FL) enables model training across several tasks that are gathered from cloud computing customers. This decentralized approach primarily focuses on learning from datasets to obtain a global model by aggregating the results of local models. The proposed techniques involve two different steps: local training models and global aggregation models. In the local training model, the task scheduler determines the resource-optimal virtual machine in the cloud server using a Generalized Regression Neural Network (GRNN) based on multicriteria functions of the virtual machine, such as energy, memory, CPU, and bandwidth. Based on these objective functions, resource-efficient virtual machines are determined to schedule multiple user tasks. The locally updated models are then combined and fed into the global aggregation model. Calculated within the global aggregation model is the weighted total of locally updated findings. The algorithm iterates through this process till the maximum number of times. In order to schedule incoming tasks, the resource-optimal virtual machine is found. Various quantitative criteria are used for the experimental evaluation, including makespan, throughput in relation to the number of tasks, and task scheduling efficiency.

Revolutionizing Accessibility: Smart Wheelchair Robot and Mobile Application for Mobility, Assistance, and Home Management

This research aims to advance accessibility and inclusivity for individuals with disabilities. We focus on specific daily challenges facing people with disabilities in communication, mobility, and daily task management and introduce AssistEase, a groundbreaking smart wheelchair solution designed to empower people with disabilities by improving mobility, communication capabilities, and daily task management. AssistEase will contribute to the disabled community around the world by allowing them to manage daily tasks and communicate more easily while ensuring mobility. AssistEase offers control options such as handsfree voice control, traditional manual control, smartphone-based Bluetooth control, or innovative gesture control, designed to cater to different user preferences and needs. This uses technologies such as speech recognition, computer vision, and haptic [92] feedback to help users navigate safely while avoiding obstacles. It integrates technologies like Flutter, TensorFlow, YOLOV8, Global Positioning System (GPS), Bluetooth, and Apple Home Kit, along with hardware components including Arduino and Raspberry PI. Preliminary trials have shown improvements in mobility, communication, and daily tasks for users in need. It achieves 95% precision in guiding wheelchair users while maintaining about 90% accuracy for the robotic arm and 89% for health monitoring and location tracking. Also, it provides a user-friendly app with 90% control accuracy. The communication device has 92% accuracy in facilitating user communication, while hand gesture control achieves 90% accuracy. To advance AssistEase smart wheelchair technology, further research, and development are required to enhance its adaptability for specific disabilities. AssistEase reflects a commitment to creating a more inclusive and thriving society, focusing on innovation and inclusion for individuals of all abilities.

Methods for determining the type of exercise by the sequence of human poses

Over the past decade, international companies and research institutions have devoted considerable resources to the development of neural networks for computer vision that detect human pose sequences from video. Since this data cannot be used directly by humans and requires pre-processing, there is a need for universal methods for processing human pose sequences. The content and structure of the output signal of pose sequence processing depends on the end task of the system and in most cases is not universal. Universal processing methods that can be used for different tasks are especially valuable. This paper describes a method for processing the output signal of a neural network that allows determining the type of physical exercise based on the sequence of human postures. This method is quite versatile and can be used independently or as one of the stages of solving a user task. One example of the method's application is the automatic measurement of the duration of exercise during a sports session. Another example is determining the type of exercise when this intermediate information is needed before applying algorithms for calculating the number of iterations of this exercise.

Testing and Practical Implementation of a User-Friendly Personalized and Long-Term Electronic Informed Consent Prototype in Clinical Research: Mixed Methods Study.

Over the years, there has been increasing interest in electronic informed consent (eIC) in clinical research. The user-friendliness of an eIC application and its acceptance by stakeholders plays a central role in achieving successful implementation. This study aims to identify insights for the design and implementation of a user-friendly, personalized, and long-term eIC application based on a usability study with (potential) research participants and semistructured interviews with stakeholders on the practical integration of such an application into their daily practice. An eIC prototype was evaluated and refined through usability testing among Belgian citizens and iterative redesign. On the basis of a digital literacy questionnaire, a heterogeneous sample of participants was established. Participants needed to complete a series of usability tasks related to personalization and long-term interaction with the research team while using the "think aloud" technique. In addition, usability tests involved completing the System Usability Scale questionnaire and taking part in a semistructured feedback interview. Furthermore, semistructured interviews were conducted with ethics committee members, health care professionals, and pharmaceutical industry representatives active in Belgium and involved in clinical research. Thematic analysis was undertaken using the NVivo software (Lumivero). In total, 3 iterations of usability tests were conducted with 10 participants each. Each cycle involved some participants who reported having low digital skills. The System Usability Scale scores related to the tasks on personalization and long-term interaction increased after each iteration and reached 69.5 (SD 8.35) and 71.3 (SD 16.1) out of 100, respectively, which represents above-average usability. Semistructured interviews conducted with health care professionals (n=4), ethics committee members (n=8), and pharmaceutical industry representatives (n=5) identified the need for an eIC system that can be easily set up. For example, a library could be established enabling stakeholders to easily provide background information about a clinical study, presented in the second layer of the interface. In contrast, some functionalities, such as informing participants about new studies through an eIC system, were not considered useful by stakeholders. This study provides insights for the implementation of a user-friendly personalized and long-term eIC application. The study findings showed that usability testing is key to assessing and increasing the user-friendliness of an eIC application. Although this eIC system has the potential to be usable by a wide audience, participants with low digital literacy may not be able to use it successfully, highlighting the need for additional support for participants or other alternatives to an eIC system. In addition, key lessons emerging from the interviews included ensuring that the application is easy to implement in practice and is interoperable with other established systems.

Evaluating and modeling the effects of brightness on visual attention using multiple object tracking method

This article systematically evaluates and models how brightness affects performance in Multiple Object Tracking (MOT) within screen-based environments. MOT proficiency is essential across various fields, and comprehending the elements that impact MOT performance holds significance for occupational health and safety. While previous studies have scrutinized the influence of brightness on object recognition, its repercussions on MOT performance in screen-based environments remain comparatively less comprehended. This research aims to bridge this gap by delving into the distinct and combined impacts of brightness-related factors on MOT performance. Additionally, it seeks to construct a computational model that can forecast MOT performance across diverse brightness conditions. The outcomes of this study will offer valuable insights into core psychological processes, thereby steering the development of more efficient visual displays to enhance occupational health and safety. Our findings revealed a significant correlation between brightness levels and MOT performance, with optimal tracking observed at medium brightness levels. Additionally, complex object motion patterns were found to exacerbate the challenges of tracking in low brightness settings. These insights have direct implications for screen-based interfaces, suggesting the need for adaptive brightness settings based on the content's complexity and the user's task.

System-centric energy efficient computation offloading and resource allocation in latency-sensitive MEC systems

Mobile edge computing (MEC) enables computation tasks of end users to be offloaded and processed at nearby edge servers, which is a promising technology that can effectively alleviate end users’ computing pressure. However, how to balance two important metrics for MEC computation offloading, i.e., latency and energy efficiency, is still a key challenge. In this paper, we investigate joint computation offloading and resource allocation for latency-sensitive applications in order to maximize system-centric computation energy efficiency (SCEE) in MEC systems. An optimization problem is formulated to maximize the sum computation energy efficiency of all users which contains the energy consumption at both user and edge server sides. Meanwhile, task completion latency constraints for all users are considered. We decompose the original optimization problem into two tractable subproblems. The optimal computation offloading as well as communication and computing resource allocation solutions are obtained by solving these subproblems. Furthermore, a hierarchical system-centric energy efficient computation offloading and resource allocation (SEECR) algorithm is developed. Numerical results show that compared with the benchmark schemes, the proposed algorithm can effectively improve the computation energy efficiency performance while guaranteeing the latency requirements of all users.

Methods for Developing Semantically Oriented Virtual Assistants

Nowadays, various technologies are used to create intelligent assistants, based both on research in the field of neural networks, natural language text analysis tools, and on the use of semantic modeling tools. Each of these approaches allows you to qualitatively solve certain problems. As part of this work, an intelligent assistant is being developed that combines all these approaches. The purpose of the work is to create an intelligent assistant that performs as a virtual consultant on the organization’s work processes. The development is based on the use of the semantic model of the organization and business processes. To recognize user intents, we use homomorphic and generalized user intents. The system allows decomposing user tasks and creating a consistency of their execution based on the user semantic models and the subject area.

Preface

The purpose of the three-day 2023 3rd International Conference on Electronic Communication, Computer Science and Technology (ECCST 2023), held via hybrid form on September 15th to 17th, 2023 in Shanghai, China, was to create a timely forum for multi-disciplinary discussions related to the recent developments on electronic communication, computer science and technology.As usual, the program of the meeting mainly consisted of keynote speeches, oral presentations and poster presentations, with the former including speeches delivered by six sophisticated professors from different countries and regions. The keynote speeches covered six main topics, including the one Network AI: Enabling 6G Pervasive Intelligence given by Prof. Yang Yang (The Hong Kong University of Science and Technology (Guangzhou), China). In this presentation, he first coined the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. Then, he proposed a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions.In the framework of experimental and theoretical approaches, the Conference gathered about 80 delegates from all over the world and addressed a number of highly relevant aspects of electronic communication, computer science and technology. Covering Nonlinear Circuits and Systems, Low Power Design and VLSI Physical Design, Electromagnetic Compatibility, Computer and Data Communication, Computer Programming, etc., the selected contributions of this special issue provide guidance for future interdisciplinary developments. In this way, the multi-scale aspects of electronic communication, computer science and technology should be considered.We received various manuscripts of invited papers and contributions presented at the Conference. All of these have been peer reviewed and are collected in this volume. We would like to thank all the anonymous colleagues who have acted as referees to assess the suitability of the various articles for publication in Journal of Physics: Conference Series. We are confident that the high quality of both invited and contributed papers contained in this Proceedings will be appreciated by electronic and computer communities.We would like to express our thanks to all the authors for their time and genuine efforts, and to the reviewers for their fruitful comments during the preparation of this volume. We also acknowledge the support provided in various ways by Shanghai Pudong New Area Association For Computer and Donghua University.The Committee of ECCST 2023List of Committee Member is available in this Pdf.

Report on the 1st Workshop on Task Focused IR in the Era of Generative AI

Search and recommender systems should prioritize support for user tasks over support for individual queries or actions. To that end, the Information Retrieval (IR) community has spent decades trying to understand users' tasks and their contexts, and how to best assist users in making progress toward completing them. The recent advancements in generative artificial intelligence (AI) have drastically shifted the landscape of task-focused IR. Users can now express not only their queries and questions, but actual information needs, tasks, and goals in natural language to an AI system and receive not just results, but also answers in natural language that are generated specifically for them. This new paradigm raises many interesting questions, opportunities, and challenges. We brought together a group of highly motivated students and scholars in a two-day workshop on the Microsoft campus in Redmond to discuss these issues, learn from each other, and envision a new future for task-focused IR and information access more broadly. Date : 28--29 September 2023. Website : https://ir-ai.github.io.