User Tasks Research Articles

Challenges and Opportunities in Password Management: A Review of Current Solutions

For over six decades, passwords have served as the primary authentication mechanism for almost all modern computer systems. However, password management is a challenging task for most computer users, and that has led users to many malpractices that open the door for most information security breaches over time. Despite many efforts, no alternative solution has ever succeeded in replacing passwords as the primary authentication mechanism. As a result, users are now heavily relying on password managers to alleviate the burden of manual password management. This paper addresses the topic of password management about different types of password managers and their inherent limitations. By evaluating the existing password management approaches and identifying potential improvements, this paper aims to signify an important research gap that exists in the study area; the need for fully automating the process of manual password management.

The Role of Task-Technology Fit on the Design and Use of a Hotel Management System

Hotels have many problems regarding how to improve their service performance by using technologies, especially when the hotels have a bit of understanding the importance of their employees’ tasks and the technology fitness. This study designed a hotel management system based on the tasks that the employees use to provide services. The purpose of this research is to investigate the influence of task-technology fit on the intention to use the designed hotel management system. In addition, statistical testing methods are also carried out on the system by measuring the impact of task-technology fit on the suitability between user task needs and technology, on user perceptions of technology and how these perceptions affect user intentions and behavior in accepting and using technology. The results of this study indicate that task-technology fit has a significant impact in facilitating its use. The results of this study contribute to the academic implementation of task-technology fit theory in the hotel reservation management system.

Developing a Fuzzy Inference Model for Construction Project Risk Management in Iraq

The construction industry is considered a high-risk business. Risk management is one of the most influential methods used in construction project management to increase the chances of delivering the project successfully, Risk Assessment (RA) is necessary to help organizations identify and mitigate risks; therefore, this paper suggests a framework for developing an intelligent RA. There are many Risk Factors (RF) that affect construction projects, and they vary from one country to another. In this paper, a questionnaire of forty-one questions about RF was performed; its evaluation criteria are risk probability and its impact on cost, time, and quality, this questionnaire relied on several experts’ opinions to identify the most common RF affecting Iraqi construction projects. The collected linguistic data were converted into a triangular fuzzy number. Qualitative Risk Analysis was performed to assess the priority of the identified risks; while the Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed as the intelligent model. The training outcome produced three Fuzzy Inference Systems (FISs) models evaluated using the fuzzy designer application and tested using the fuzzy designer app and MATLAB Simulink to evaluate their accuracy and reliability. Finally, a set of corrective actions were suggested to facilitate the task for users.

An Incentive Mechanism for Vehicular Crowdsensing With Security Protection and Data Quality Assurance

With the increase of on-board sensors, as a new paradigm of mobile crowdsensing (MCS), vehicular crowdsensing (VCS) shows great potential in realizing low-cost, large-scale sensing tasks. In order to improve the user engagement and task completion quality of VCS, an appropriate incentive mechanism can promote enough users to participate in the sensing activities and further provide high-quality sensing data. However, due to the contradiction between personal interests and user data security protection, the development of the incentive mechanism is seriously affected. To deal with these challenges, this paper aims to propose a security protection incentive mechanism with data quality assurance (SPIM-DQA) for the VCS system. First, we adopt the blockchain-enabled VCS framework, and propose a series of smart contracts to ensure the automatic execution of the incentive mechanism, which solves the user data security issues existing in the traditional incentive mechanism. Then, based on this framework and these smart contracts, a data quality-aware incentive mechanism is proposed from the perspective of data quality. After selecting low-cost and high-quality users to perform the crowdsensing task, user reputation is updated by evaluating the quality of the provided data. In particular, there is a correlation between user reputation and reward distribution, which incentivizes users to consistently provide high-quality data to increase their rewards. Finally, extensive simulation results show that SPIM-DQA can effectively improve data quality while meeting security requirements.

Chatbot-Based Book Recommender System Using Singular Value Decomposition

In the era of information overload, finding the right book that matches one's preferences and interests has become a challenging task for users as many online book provider service websites such as Amazon, Goodreads, and Gramedia provide books of various types and choices. Recommender systems can be used in addressing such issues, it works by filtering information that provides predictions and suggests the best product or service to the user. Currently, various book recommender systems have been developed, but the systems do not provide interaction between the user and the system. Therefore, we propose a recommender system built with a conversational approach so that it can interact with natural language. Recommender system built using matrix factorization method with Singular Value Decomposition (SVD) algorithm, SVD is proven to have advantages for handling large datasets, extracting features, reducing noise and dimensionality so as to speed up computation. We performed two types of evaluation on the system. First, we tested the prediction accuracy using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) metrics. Second, we use questionnaires to measure user satisfaction levels. The evaluation of the system shows that the results of the prediction accuracy obtain an MAE value of 0.6481 and an RMSE value of 0.8287. Then, the accuracy performance of the system found that 83.2% of users get recommendations according to their interests. The user satisfaction with the whole system is 87.9%. The system built can provide a fairly good recommendation performance, and the chatbot can interact well with users based on the evaluation results obtained.

Voting Classifier for The Interactive Design with Deep Learning for Scene Theory

Tool products play a pivotal role in assisting individuals in various domains, ranging from professional work to everyday tasks. The success of these tools is not solely determined by their functionality but also by the quality of user experience they offer. Designing tool products that effectively engage users, enhance their productivity, and provide a seamless interaction experience has become a critical focus for researchers and practitioners in the field of interaction design. Scene theory proposes that individuals perceive and interpret their surroundings as dynamic "scenes," wherein environmental and situational factors influence their cognitive processes and behavior. This research paper presented a novel approach to the interaction design of tool products by integrating scene theory, flow experience, the Moth Flame optimization (MFO), cooperative game theory (CGT), and voting deep learning. Tool products play a vital role in various domains, and their interaction design significantly influences user satisfaction and task performance. Building upon the principles of scene theory and flow experience, this study proposes an innovative framework that considers the contextual factors and aims to create a seamless and enjoyable user experience. The MFO algorithm, inspired by the behavior of moth flame, is employed to optimize the design parameters and enhance the efficiency of the interaction design process. Furthermore, CGT is integrated to model cooperative relationships between users and tool products, fostering collaborative and engaging experiences. Voting deep learning is employed to analyze user feedback and preferences, enabling personalized and adaptive design recommendations. With the proposed CGT, this paper investigates the impact of the proposed approach on user engagement, task efficiency, and overall satisfaction. The findings contribute to the field of interaction design by providing practical insights for creating tool products that align with users' cognitive processes, environmental constraints, flow-inducing experiences, and cooperative dynamics.

UDL: a cloud task scheduling framework based on multiple deep neural networks

Cloud task scheduling and resource allocation (TSRA) constitute a core issue in cloud computing. Batch submission is a common user task deployment mode in cloud computing systems. In this mode, it has been a challenge for cloud systems to balance the quality of user service and the revenue of cloud service provider (CSP). To this end, with multi-objective optimization (MOO) of minimizing task latency and energy consumption, we propose a cloud TSRA framework based on deep learning (DL). The system solves the TSRA problems of multiple task queues and virtual machine (VM) clusters by uniting multiple deep neural networks (DNNs) as task scheduler of cloud system. The DNNs are divided into exploration part and exploitation part. At each scheduling time step, the model saves the best outputs of all scheduling policies from each DNN to the experienced sample memory pool (SMP), and periodically selects random training samples from SMP to train each DNN of exploitation part. We designed a united deep learning (UDL) algorithm based on this framework. Experimental results show that the UDL algorithm can effectively solve the MOO problem of TSRA for cloud tasks, and performs better than benchmark algorithms such as heterogeneous distributed deep learning (HDDL) in terms of task scheduling performance.

Tying Overdose Data to Action: North Carolina's Opioid and Substance Use Action Plan Data Dashboard.

From 2000 to 2020, more than 28 000 North Carolina (NC) residents died of drug overdose. In response, NC Department of Health and Human Services worked with community partners to develop an Opioid and Substance Use Action Plan (OSUAP), now in its third iteration. The NC OSUAP data dashboard brings together data on 15 public health indicators and 16 local actions across 8 strategies. We share innovations in design, data structures, user tasks, and visual elements over 5 years of dashboard development and maintenance, with a special focus and supplemental material covering the technical details and techniques that dashboard design and implementation teams may benefit from.

Cost‐ and performance‐aware resource selection for parallel software on heterogeneous cloud

SummaryCloud providers offer flexible infrastructures and on‐demand services, including the capability to deploy low cost virtual resources of many different types. However, the diversity of cloud resources followed by the important trade‐off between cost and performance makes the resource selection a challenging task for users in the case of parallel communication‐intensive software. The paper presents cost‐ and performance‐aware resource selection for parallel discrete element method (DEM) software as a service (SaaS) on heterogeneous OpenStack cloud. The developed resource selection uses preliminary application‐specific benchmarks of size smaller than targeted problems and the performance prediction based on speedup of parallel computations to obtain Pareto optimal solutions and to select the best configuration of containers from user's perspective. Hybrid parallelization of DEM software is developed by using OpenCL for shared‐memory multi‐core architectures and MPI for internode communications on distributed‐memory computer clusters. Round up and proportional pricing schemes are examined and compared from a user's perspective. Lower cost of computations obtained by using the proportional pricing scheme is always preferable for users. However, the difference approaches 1.0% of the cost calculated by using proportional pricing scheme, when long lasting computations are performed. The prediction tends to underestimate the execution time of DEM SaaS, but its accuracy is sufficient to obtain the same Pareto optimal solutions by using measured and predicted execution times. Pareto front and linear scalarization propose to select configurations of containers capable of exploiting higher memory bandwidth, which is specific to memory bandwidth bound DEM computations.

Enhanced multi-objective gorilla troops optimizer for real-time multi-user dependent tasks offloading in edge-cloud computing

This research paper investigates Mobile Edge Computing (MEC) networks, which involve numerous wireless devices transferring their computation tasks to various edge servers and a single cloud server. The study considers various real-time dependent computation tasks composing an application running on each connected device. This study aims to determine whether to process each user task locally on the device or to offload and process it on one of the nearby MEC servers or the central cloud server. Despite the continued efforts to address the issue of task offloading, it remains a difficult area of research, especially in the edge-cloud environment. Improving the Quality of Service (QoS) and achieving optimized performance and resource utilization are still necessary goals. New approaches either focus on a single objective, are computationally demanding, depend only on the cloud or MEC for offloading, not both or disregard the interdependence between tasks. This paper proposes an enhanced multi-objective version of the Gorilla Algorithm (EMGA) for offloading interdependent tasks in edge-cloud environments, with three main objectives: 1-reducing the application's execution latency, 2-lowering energy consumption for connected devices, and 3-minimizing imposed cost for using servers' resources. The proposed approach assumes that each MEC and cloud server can support multiple cost levels to increase system flexibility. A customized mutation operation was introduced in EMGA, which extends the functionality of the Standard Gorilla algorithm to improve search strategies. Comparative analyses were conducted between EMGA and other optimizers in the problem domain, and the results indicate the superiority of EMGA.

VOICE BASED VIRTUAL ASSISTANT

The goal of this project is to create a voice-based smart virtual assistant that makes use of cutting-edge machine learning, speech recognition, and other technologies. Through personalized responses and recommendations, the main goal is to improve user experience and streamline tasks. Aside from setting reminders and booking appointments, the virtual assistant will also be able to make calls, send messages, and operate smart home appliances. It will also retrieve current data on the weather, news, sports scores, and other topics. The assistant will offer a seamless and intuitive interface across several languages and maintain contextual information from past talks with the help of context awareness and multilingual assistance. The virtual assistant will gradually enhance its effectiveness and gain a deeper understanding of user preferences by continuously learning from user interactions. An intelligent, hands-free virtual assistant that revolutionizes user interaction and task management is the goal of this project. Keywords— AI, Voice Assistant,

DBSCAN inspired task scheduling algorithm for cloud infrastructure

Cloud computing in today's computing environment plays a vital role, by providing efficient and scalable computation based on pay per use model. To make computing more reliable and efficient, it must be efficient, and high resources utilized. To improve resource utilization and efficiency in cloud, task scheduling and resource allocation plays a critical role. Many researchers have proposed algorithms to maximize the throughput and resource utilization taking into consideration heterogeneous cloud environments. This work proposes an algorithm using DBSCAN (Density-based spatial clustering) for task scheduling to achieve high efficiency. The proposed DBScan-based task scheduling algorithm aims to improve user task quality of service and improve performance in terms of execution time, average start time and finish time. The experiment result shows proposed model outperforms existing ACO and PSO with 13% improvement in execution time, 49% improvement in average start time and average finish time. The experimental results are compared with existing ACO and PSO algorithms for task scheduling.

Usability and acceptability of virtual reality for chronic pain management among diverse patients in a safety-net setting: a qualitative analysis.

The aim of this study was to understand the usability and acceptability of virtual reality (VR) among a racially and ethnically diverse group of patients who experience chronic pain. Using the Technology Acceptance Model theory, we conducted semistructured interviews and direct observation of VR use with English-speaking patients who experience chronic pain treated in a public healthcare system (n = 15), using a commercially available VR technology platform. Interviews included questions about current pain management strategies, technology use, experiences and opinions with VR, and motivators for future use. Before the study, none of the 15 participants had heard about or used VR for pain management. Common motivators for VR use included a previous history of substance use and having exhausted many other options to manage their pain and curiosity. Most participants had a positive experience with VR and 47% found that the VR modules distracted them from their pain. When attempting the navigation-based usability tasks, most participants (73%-92%) were able to complete them independently. VR is a usable tool for diverse patients with chronic pain. Our findings suggest that the usability of VR is not a barrier and perhaps a focus on improving the accessibility of VR in safety-net settings is needed to reduce disparities in health technology use. The usability and acceptability of VR are rarely studied in diverse patient populations. We found that participants had a positive experience using VR, showed interest in future use, and would recommend VR to family and friends.

Priority-Aware Resource Scheduling for UAV-Mounted Mobile Edge Computing Networks

In this paper, we investigate the joint impact of task priority and mobile computing service on the mobile edge computing (MEC) networks, in which one unmanned aerial vehicle (UAV) provides mobile computing service to help compute the tasks from users in multiple hotspots where the task priority is time-varying. For such a system, we firstly measure the system performance by the computing utility of multiples users, where the effect of a wide-range task priority is incorporated. We then analyze the impact of the network wireless bandwidth and UAV computational capability on the system performance, from which we optimize the system through UAV hotspot selection and user task offloading. To solve the optimization problem, we further employ deep Q-learning algorithm to learn an effective solution by continuous interaction between the UAV agent and system environment. Simulations are finally conducted to verify the superiority of the proposed studies in this paper.

Do you agree? Contrasting Google’s Core Web Vitals and the impact of cookie consent banners with actual web QoE

Providing sophisticated web Quality of Experience (QoE) has become paramount for web service providers and network operators alike. Due to advances in web technologies (HTML5, responsive design, etc.), traditional web QoE models focusing mainly on loading times have to be refined and improved. In this work, we relate Google’s Core Web Vitals, a set of metrics for improving user experience, to the loading time aspects of web QoE, and investigate whether the Core Web Vitals and web QoE agree on the perceived experience. To this end, we first perform objective measurements in the web using Google’s Lighthouse. To close the gap between metrics and experience, we complement these objective measurements with subjective assessment by performing multiple crowdsourcing QoE studies. For this purpose, we developed CWeQS, a customized framework to emulate the entire web page loading process, and ask users for their experience while controlling the Core Web Vitals, which is available to the public. To properly configure CWeQS for the planned QoE study and the crowdsourcing setup, we conduct pre-studies, in which we evaluate the importance of the loading strategy of a web page and the importance of the user task. The obtained insights allow us to conduct the desired QoE studies for each of the Core Web Vitals. Furthermore, we assess the impact of cookie consent banners, which have become ubiquitous due to regulatory demands, on the Core Web Vitals and investigate their influence on web QoE. Our results suggest that the Core Web Vitals are much less predictive for web QoE than expected and that page loading times remain the main metric and influence factor in this context. We further observe that unobtrusive and acentric cookie consent banners are preferred by end-users and that additional delays caused by interacting with consent banners in order to agree to or reject cookies should be accounted along with the actual page load time to reduce waiting times and thus to improve web QoE.

Comparing a friction-based uni-directional treadmill and a slip-style omni-directional treadmill on first-time HMD-VR user task performance, cybersickness, postural sway, posture angle, ease of use, enjoyment, and effort

Treadmills allow users to translate their real world walking into movement in virtual worlds. Conventional treadmills used for fitness can be repurposed for users to move around in virtual reality, but permit walking in only one direction (uni-directional). Treadmills that allow walking in any direction (omni-directional) are available, in which users walk-in-place and their feet slip on a low-friction surface. Given that users can experience cybersickness and postural instability in virtual reality, this study explored user performance and side effects of using two different walking platforms: a conventional uni-directional treadmill and a slip-style omni-directional treadmill (Virtuix OMNI).All participants (n = 46) played a game in which they collected items distributed around a virtual world. Participants moved around the virtual world by walking on the conventional uni-directional treadmill (for 10 minutes) and on the omni-directional treadmill (for 10 minutes), whilst in HMD-VR, with a 5-minute break between platforms. Measurements of cybersickness, task achievement, distance covered, posture, postural sway and user experience (ease, enjoyment, and effort) were collected.Participants experienced significantly higher total cybersickness after walking on the slip-style omni-directional treadmill compared to after walking on the uni-directional treadmill (p=.003), and significantly higher Nausea and Oculomotor cybersickness after walking on the slip-style omni-directional treadmill compared to after walking on the uni-directional treadmill (p<.001 and p=.005 respectively). Participants had lowest postural sway after using the uni-directional treadmill compared to after using the omni-directional treadmill (p=.052). Participants completed significantly more of the task (p<.001) and walked further (p<.001) on the uni-directional treadmill compared to the omni-directional treadmill, and their posture angle (back tilt angle) was significantly more forward on the omni-directional treadmill compared to the uni-directional treadmill (p<.001). Significant differences were found with participants reporting walking on the omni-directional treadmill to be more difficult (p<.001), require more effort (p<.001) and be less enjoyable (p=.005), and changing direction to require more effort (p=.010), than compared to the uni-directional treadmill.This study recommends that alternatives to slip-style locomotion treadmills be developed due the non-natural walking action required to use slip-style platforms and the consequent negative psychophysiological impacts of their use with HMD-VR.

Trusted user selection for fusion of multimodal cognition in self-organizing pattern of mobile crowdsensing

Mobile Crowd Sensing is mainly aimed at registered users of the sensing platform, which inevitably has the problem of insufficient users. In addition, the existing methods mostly use single-mode data for user quality assessment. Aiming at the existing limitations, this paper proposes a trusted user selection method that integrates multimodal cognition under the self-organization pattern. Firstly, recruit users based on the group effect and center effect in social networks. Then, Convolutional Neural Network and Doc2Vec are used to extract multimodal information features and calculate the user's task achievement degree. Finally, establish the reputation similarity network by considering the interaction between users under the self-organization pattern, and select trusted users. By selecting high-quality trusted users, we can save platform expenditure and improve sensing quality. Experiments were conducted on the simulation dataset. Under the best conditions, the reputation value of the selected sensing users increased by 12%, the user utility increased by 14%, and the data quality increased by 28%.

EFFECT OF INTERMEDIARY OBJECT USE DURING COLLABORATIVE DESIGN ACTIVITIES OF IMMERSIVE APPLICATIONS: FOCUS ON PROFESSIONAL TRAINING APPLICATION

AbstractIndustry 4.0 introduces innovative ways of professional training thanks to new technologies such as Virtual Reality and Augmented Reality. Despite improvements, there's still a lack of a good user experience and connection between user needs and these applications. To tackle this problem, designers of such applications must work in a collaborative way integrating final users. It's specifically true during design phases such as scenario creation. But the co-creation of scenario is a difficult task for designers and final users who don't have enough expertise with IT. A solution seems to stand in intermediary objects (IOs) which are well known for their characteristics of mediation, transformation, and representation. We have studied the use of different IOs during a project consisting in designing an immersive professional training application for risk hunting for Stäubli company. We used an IO1 made of an excel sheet and an IO2 made of 360 views of the workshop. Two ideation sessions with two groups of designers were conducted to propose a scenario consisting of several risks to spot in the future application. Results show that the scenario was improved using the IO2, and final users and designers were more collaborative.

Multilevel agent negotiation on service bindings for efficient multi-tasking in IoT environments

In Internet-of-Things (IoT) environments, various IoT resources are utilized to provide the services necessary to accomplish users’ tasks. Therefore, IoT resources must be effectively and efficiently allocated so that service providers can successfully generate their service capabilities utilizing IoT resources. Additionally, conflicting resource allocation must be avoided for services supporting multiple user tasks. Furthermore, resource allocation and conflict resolution must be performed dynamically because users perform their tasks spontaneously. To address these issues, we propose an agent-based service-binding approach for efficient resource allocation and conflict resolution through negotiations among resources, service providers, and task agents. Negotiation occurs when a task agent attempts to use a service that is currently occupied by another task agent. The task agent that uses the service is determined in the negotiation process by considering the transfer fee based on the usage price of the service and the availability of a substitute service. Negotiations can occur at the task-service or service-resource levels to support flexible service binding. To evaluate the effectiveness of our approach, we compared its success rate with that of traditional resource allocation methods in terms of allocating resources successfully for multiple services. We also analyze the execution time to measure the overhead incurred by multilevel negotiation, which is an essential component of our approach. The proposed approach showed an average success rate of 79% which is 1.17%p lower than multi-objective optimization algorithms (MOOA), but the running time of the proposed approach was 98.3 s which was 56% of the running time of MOOA.

Joint Optimization of Energy and Task Scheduling in Wireless-Powered IRS-Assisted Mobile-Edge Computing Systems

This paper studies the design of an intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) system, which consists of a mobile user, an IRS equipped with radio-frequency (RF) energy harvesting (EH) circuits, and a hybrid access point (HAP) connected with an MEC server. The IRS is deployed to reflect the users task offloading signals to enhance the received power at HAP, and it needs to harvest energy from the RF signals emitted by the HAP before reflecting signals. To save energy consumption at the user, we first propose a novel MEC protocol, in which the system is enabled to operate in three modes, namely an EH mode, an IRS-assisted task offloading mode, and an IRS-inactive task offloading mode, so that the energy at IRS and the tasks generated at user can be flexibly scheduled within a finite time horizon, depending on channel conditions, IRS energy states and users task queue states. Under the protocol, we optimize the operation mode selection and resource allocation in each mode with a task execution delay constraint. Due to the randomness of wireless channels and task arrivals, the optimization problem is a stochastic programming. To solve this problem, we first transform it into a deterministic one by assuming that noncausal channel state information (CSI) and task state information (TSI) are available, and then derive a practical algorithm where only causal CSI and TSI are required. Simulation results verify that our proposed design can save at most 80% energy consumption as compared with existing baseline schemes.