Individual User Characteristics Research Articles

The Acceptability of Virtual Characters as Social Skills Trainers: Usability Study.

BackgroundSocial skills training by human trainers is a well-established method to provide appropriate social interaction skills and strengthen social self-efficacy. In our previous work, we attempted to automate social skills training by developing a virtual agent that taught social skills through interaction. Previous research has not investigated the visual design of virtual agents for social skills training. Thus, we investigated the effect of virtual agent visual design on automated social skills training.ObjectiveThe 3 main purposes of this research were to investigate the effect of virtual agent appearance on automated social skills training, the relationship between acceptability and other measures (eg, likeability, realism, and familiarity), and the relationship between likeability and individual user characteristics (eg, gender, age, and autistic traits).MethodsWe prepared images and videos of a virtual agent, and 1218 crowdsourced workers rated the virtual agents through a questionnaire. In designing personalized virtual agents, we investigated the acceptability, likeability, and other impressions of the virtual agents and their relationship to individual characteristics.ResultsWe found that there were differences between the virtual agents in all measures (P<.001). A female anime-type virtual agent was rated as the most likeable. We also confirmed that participants’ gender, age, and autistic traits were related to their ratings.ConclusionsWe confirmed the effect of virtual agent design on automated social skills training. Our findings are important in designing the appearance of an agent for use in personalized automated social skills training.

The disinformation warfare: how users use every means possible in the political battlefield on social media

PurposeThe study aims to investigate the predictors of engaging in combat against the spread of misinformation and disinformation online, and of actively sharing disinformation by users. The study advances an understanding of user active engagement with disinformation as political participation, especially linked to violent activism, in alignment with the view of disinformation as political weapon.Design/methodology/approachA survey of 502 Israeli internet users inquired into respondents' political participation, trust and orientation, definitions and perceptions of “Fake News,” and previous engagement in sharing misinformation disinformation items, combating or intention to combat against the spread of disinformation.FindingsIn addition to identifying predictors for each practice, the findings indicate that sharing and combating against disinformation are closely linked. They are also all directly linked to political participation of various kinds. Most interestingly, working for a political party significantly correlates with knowingly sharing disinformation items, and participating in illegal or violent political activities significantly correlates with knowingly sharing and actively participating in combat against disinformation.Originality/valueThe spread of disinformation online and its implications has received much scholarly as well as public attention in recent years. However, the characteristics of individual users who share or combat against the spread of disinformation online, as forms of political participation, have not been examined. This study fills this gap by inquiring into such practices and the behaviors, perceptions and demographics that predict them.

Blockchain adoption: A study of cognitive factors underpinning decision making

The literature so far has been focused on technological sophistication rather than the aspects of blockchain adoption that can hinder or facilitate the use of the technology. To address this gap this paper aims to study the cognitive factors underpinning adoption decision-making moderated by user characteristics. Using a cross-sectional research design, the study recruited 506 respondents to participate and test the relationships hypothesised in the research model. The results of the analysis demonstrated that perceived threat vulnerability, response cost, response efficacy and self-efficacy determine adoption intention. These factors have varying effects on intention depending on users' subjective knowledge, objective knowledge and innovativeness. This evidence contributes to the understanding of users’ perspectives on blockchain adoption, which has been under-researched so far. The findings shed light on the cognitive factors motivating blockchain-based technology use and the individual characteristics of users who are likely to adopt the technology in the context of data privacy and security. In turn, these findings can inform practitioners about the aspects of user behaviour that should be considered while developing and marketing the technology.

How social media effects shape sentiments along the twitter journey?A Bayesian network approach

Consistent with market orientation, marketing activities based on sentiment analysis involve gathering market intelligence, disseminating it firmwide, and responding to customer needs. While it has been applied in various marketing settings, antecedents of consumer sentiments that are rooted in social media platform effects need further examination. Using consumers’ social media journey as conceptual support, the current research investigates how consumer sentiments on social media are shaped by hashtag position, the bandwagon effect, and user anonymity and authority. The conceptual framework presented proposes that individual user characteristics of anonymity and authority influence platform effects including hashtag position and positive and negative bandwagon effects which impact expressed sentiments. The hypotheses are tested by analyzing over half a million tweets for 127 movies using the machine learning methodology of Bayesian Network analysis. Managerial implications for regulating platform effects to achieve the intended sentiment outcome, and limitations and avenues for future research are offered.

Research on Library Personalized Push System Based on Big Data Decision

Big Data is the product of the rapid development and application of information technology, and it is also the inevitable trend of the development and application of intelligent technology. In the environment of big data, by giving full play to the advantages of big data decision-making system and actively constructing library personalized push system, we will break the foundation of Library Service Space and time, fully guarantee the information system and service ability of the library. In the process of operation, libraries should make full use of big data resources and accurately identify the service demand, reading behavior and individual characteristics of library users, innovate the Information Mode and personalized service method, continuously improve the user’s reading experience, improve the user relationship, thus form the accurate, personalized and intelligent book information service system.

The use of antipsychotics in targeted therapy of schizophrenia — prospects and challenges (for example, amisulpride).

Personalized medicine is an innovative approach that takes into account the biological, social and psychological characteristics of people in the development of preventive drugs and treatment of diseases. The goals of personalized medicine in psychiatry are to anticipate a person’s susceptibility to disease, achieve accurate diagnosis, and facilitate a favorable response to treatment. This article analyzes the information presented in the literature on use of antipsychotics in treatment of schizophrenia from the standpoint of personalized medicine with the aim to draw up recommendations for improving the effectiveness of psychotic symptoms therapy and set the vector for further scientific research in this direction. Despite the existence of many international and local guidelines and protocols for antipsychotic therapy, there is still the problem of early determination of which antipsychotic will be effective and safe for a particular patient — before the most effective drug is determined, patients are usually treated with various antipsychotics. In the treatment of schizophrenia, no antipsychotic drug or dosage is universal. Therefore, to maximize the useful effect and minimize the risk of side effects, numerous individual characteristics of each individual user must be considered. In particular, such characteristics as sex, age, clinical features (dominant symptoms, time of onset of symptoms and their intensity), comorbid mental and somatic disorders, presence/absence of bad habits, response to previous therapy (in case of such) should be taken into account. Given the possibility of flexible dosage of amisulpride and its significant effectiveness in reducing various groups of symptoms in various categories of patients, it has the potential for widespread use in personalized psychiatry. Key words personalized medicine, atypical antipsychotics, effectiveness of therapy

Modeling Electric Vehicle Charging Demand with the Effect of Increasing EVSEs: A Discrete Event Simulation-Based Model

Electric vehicle (EV) use is growing at a steady rate globally. Many countries are planning to ban internal combustion engines by 2030. One of the key issues needed to be addressed before the full-scale deployment of EVs is ensuring energy security. Various studies have developed models to simulate and study hourly electricity demand from EV charging. In this study, we present an improved model based on discrete event simulation, which allows for modeling characteristics of individual EV users, including the availability of electric vehicle supply equipment (EVSE) outside homes and the charging threshold of each EV user. The model is illustrated by simulating 1000 random electric vehicles generated using data from an actual survey. The results agree with previous studies that daily charging demands do not significantly vary. However, the results show a significant shift in charging schedule during weekends. Moreover, the simulation demonstrated that the charging peak demand can be reduced by as much as 11% if EVSEs are made more available outside homes. Interestingly, a behavioral solution, such as requiring users to fully utilize their EV’s battery capacity, is more effective in reducing the peak demand (14–17%). Finally, the study concludes by discussing a few potential implications on electric vehicle charging policy.

Prediction of Problematic Smartphone Use: A Machine Learning Approach.

While smartphone addiction is becoming a recent concern with the exponential increase in the number of smartphone users, it is difficult to predict problematic smartphone users based on the usage characteristics of individual smartphone users. This study aimed to explore the possibility of predicting smartphone addiction level with mobile phone log data. By Korea Internet and Security Agency (KISA), 29,712 respondents completed the Smartphone Addiction Scale developed in 2017. Integrating basic personal characteristics and smartphone usage information, the data were analyzed using machine learning techniques (decision tree, random forest, and Xgboost) in addition to hypothesis tests. In total, 27 variables were employed to predict smartphone addiction and the accuracy rate was the highest for the random forest (82.59%) model and the lowest for the decision tree model (74.56%). The results showed that users’ general information, such as age group, job classification, and sex did not contribute much to predicting their smartphone addiction level. The study can provide directions for future work on the detection of smartphone addiction with log-data, which suggests that more detailed smartphone’s log-data will enable more accurate results.

Efficient personalized search over encrypted data for mobile edge-assisted cloud storage

Cloud storage services allow a data owner to share her/his outsourced data with other users, and enable the users to search target data by keywords. To ensure the data confidentiality, data owner always encrypt data using traditional encryption schemes before outsourcing. Whereas, it makes efficiently searching impossible. Symmetric searchable encryption (SSE) is a cryptographic primitive that resolves this tension. However, most existing SSE schemes do not consider the individual characteristics of users during the search, such that they cannot support personalized search services over encrypted data. Meanwhile, security and efficiency issues in the cloud service model have also severely affected the user’s search experience, and the introduction of mobile edge servers can solve these problems to some extent. In this paper, we propose a personalized searchable encryption scheme (PSED) for mobile edge-assisted cloud storage. Our contribution consists of three aspects. First, we incorporate the user’s preference factors into the user’s query which enable users to get accurate personalized search results. Second, the computational overhead of the cloud server is reduced by calculating the relevance scores of the subqueries and subindexes on mobile edge servers. Third, by cutting the index and the query matrix, the encryption efficiency of the index and the query matrix is improved. Security analysis shows that PSED can guarantee the privacy of the data and the user. Experimental results demonstrate that the proposed schemes are highly efficient and accurate.

Feasibility and effectiveness of a novel dynamic arm support in persons with spinal muscular atrophy and duchenne muscular dystrophy

BackgroundNeuromuscular disorders (NMD) commonly affect the upper extremity. Due to muscle weakness, performance of daily activities becomes increasingly difficult, which leads to reduced independence and quality of life. In order to support the performance of upper extremity tasks, dynamic arm supports may be used. The Yumen Arm is a novel dynamic arm support specially developed for people with NMD. The aim of this study is to evaluate the feasibility and effectiveness of the Yumen Arm in persons with Duchenne Muscular Dystrophy (DMD) and persons with Spinal Muscular Atrophy (SMA).MethodsThree persons with DMD and three persons with SMA participated in this study. All participants conducted a set of measures with and without the Yumen Arm. Outcome measures were: active range of motion of the arm and trunk (i.e. Reachable Workspace, Functional Workspace, and trunk movement), fatigue (OMNI-RPE), Performance of Upper Limb (PUL) scale and some additional activities of daily living. User experiences were collected using a questionnaire.ResultsThe Yumen Arm could be used by all participants. Results showed a median increase in active range of motion (4% relative surface area), and a median increase of function ability (> 11% PUL score) when using the Yumen Arm. In addition, three out of four (data from 2 participants was missing) participants indicated that activity performance was less fatiguing when using the Yumen Arm. Four out of five (data from 1 participant was missing) participants indicated that they would like to use the Yumen Arm in their daily lives.ConclusionThis study is one of the first studies describing a range of objective measures to examine the effectiveness of a dynamic arm support. Based on these measurements we can conclude that the Yumen Arm effectively improves arm function in NMD patients, however the effectiveness varies a lot between individual subjects. We provided detailed recommendations for the improvement of the Yumen Arm, and possible also for the development of other dynamic arm supports. This study showed a lot of variability between individual subjects, which emphasizes the importance of tuning dynamic arm supports based on individual user characteristics, such as scoliosis, functional capacity and muscle strength.

Loneliness is negatively related to Facebook network size, but not related to Facebook network structure

High levels of loneliness are associated with poorer outcomes for physical and mental health and a large body of research has examined how using social media sites such as Facebook is associated with loneliness. Time spent on Facebook tends to be associated with higher levels of loneliness, whereas a larger number of Facebook Friends and more active use of Facebook tends to be associated with lower levels of loneliness. However, whilst the network size and structure of ‘offline’ networks have been associated with loneliness, how the network structure on Facebook is associated with loneliness is still unclear. In this study, participants used the Getnet app to directly extract information on network size (number of Facebook Friends), density, number of clusters in the network, and average path length from their Facebook networks, and completed the 20-item UCLA Loneliness questionnaire. In total, 107 participants (36 men, 71 women, Mage = 20.6, SDage = 2.7) took part in the study. Participants with a larger network size reported significantly lower feelings of loneliness. In contrast, network density, number of clusters, and average path length were not significantly related to loneliness. These results suggest that whilst having a larger Facebook network is related to feelings of social connection to others, the structure of the Facebook network may be a less important determinant of loneliness than other factors such as active or passive use of Facebook and individual characteristics of Facebook users.

Would you please like my tweet?! An artificially intelligent, generative probabilistic, and econometric based system design for popularity-driven tweet content generation

An understudied area in the field of social media research is the design of decision support systems that can aid the manager by way of automated message component generation. Recent advances in this form of artificial intelligence has been suggested to allow content creators and managers to transcend their tasks from creation towards editing, thus overcoming a common problem: the tyranny of the blank screen. In this research, we address this topic by proposing a novel system design that will suggest engagement-driven message features as well as automatically generate critical and fully written unique Tweet message components for the goal of maximizing the probability of relatively high engagement levels. Our multi-methods design relies on the use of econometrics, machine learning, and Bayesian statistics, all of which are widely used in the emerging fields of Business and Marketing Analytics. Our system design is intended to analyze Tweet messages for the purpose of generating the most critical components and structure of Tweets. We propose econometric models to judge the quality of written Tweets by way of engagement-level prediction, as well as a generative probability model for the auto-generation of Tweet messages. Testing of our design demonstrates the need to take into account the contextual, semantic, and syntactic features of messages, while controlling for individual user characteristics, so that generated Tweet components and structure maximizes the potential engagement levels.

Modeling of Communication Strategies for Reducing Public Road Accidents Involving Two-Wheel Motorized Vehicles in Urban Areas: Case of Cotonou and Parakou Cities in Benin

<i>Introduction</i>: Road accidents are one of the leading causes of morbidity and mortality in the world. To reduce their scale, some approaches have been favored, such as communication actions. This work aimed to propose an integrated model for the effective reduction of road accidents among motorized two-wheeled vehicle drivers (2WD) in urban areas in Benin. <i>Method</i>: This is a cross-sectional and analytical study of road users (n=1497) in Cotonou and Parakou. The data was collected on a mobile phone using CSPro software and analyzed in three stages: descriptive analysis, multivariate analysis for the selection of variables significantly affecting the variable to be explained, and the development of the integrated model using the Multilayer Perceptron Neural Network. <i>Results</i>: The study showed that communication actions, advocacy, contextual interventions, belief, certain individual characteristics of users, were significantly associated with the reduction of road accidents. The communication model that emerges from this is akin to "persuasive engaging" communication. <i>Conclusion</i>: This study demonstrated the importance of using an integrated national communication program to strengthen road safety. To do so, they need to promote respect for the Highway Code by emphasizing the sanctity of human life must be combined with relevant accompanying measures.

Comfort or Not? Automated Driving Style and User Characteristics Causing Human Discomfort in Automated Driving

ABSTRACT With highly automated vehicles (HAVs), the human role shifts from active driver to mostly passive passenger, which has an impact on the perceived comfort while being driven. A number of factors need to be taken into account when designing an automated driving style (ADS) with the aim to minimize human discomfort. In this paper, the authors build on seven empirical studies to determine first, if a generally comfortable ADS exists and second, whether such an ADS should be designed depending on situational driving dynamics or individual user characteristics such as users’ manual driving styles. The results give an overview of the ADS- and user-related factors causing human discomfort in automated driving and provide potential strategies to overcome potential discomfort. Drawing on the data, upcoming challenges in the field of human discomfort in HAVs for both research and practice are presented.

Formulation of Two-Stage Problem of Structural-Parametric Synthesis of Adaptive Electronic Document Management System

The paper scrutinizers the options of optimization and adaption to the individual characteristics of users of Electronic Document Management Systems (EDMS). The problem solution requires further development of the necessary methods, models and criteria. Therefore, the article considers the problem of the structural-parametric synthesis of adaptive EDMS. On the basis of previously conducted research in this area, a new architecture of EDMS is developed, within which a mathematical model of adaptive EDMS is proposed. It includes the main components of the information system, as well as a set of estimates for a number of indicators: total discounted costs, productivity, software quality and, above all, adaptability to the requirements of the user. Using this mathematical model, a two-stage task of structural-parametric synthesis of an adaptive EDMS was set, at the first stage of which the system is synthesized according to the criterion of economic efficiency, and at the second stage the process happens according to its adaptation to each user. The scientific novelty of the proposed approach consists in dividing the optimization task into two stages such as the formalization of the criteria for adaptive EDMS and development of a new architecture and mathematical model of adaptive EDMS. The results can be used to solve problems of design, modernization and adaptation of various information systems

RETRACTED: Simulation of psychological course satisfaction based on android mobile system and neural network

Adolescents usually show a significant reduction in their inner motivation in their careers. Following self-determination theory, the traditional environment meets young people's three basic psychological needs when they mature their needs, namely autonomy, ability, and relevance. It is assumed that it is not enough. Existing systems are difficult-have a process to identify and computational analysis of psychology course scores. The proposed studies are inconsistent and dissatisfied with the online learning experience, combining a Neural Network analysis course level with a satisfying Android-based mobile phone system, or calculating a latent trait model of mental course scores. Course learning, organization, and evaluation: The findings identified three basic, satisfying components. The purpose of Neural Network is to verify and prepare the relationship between process satisfaction and professional identity and trust this relationship's mediating effect. It shows the strongest relationship of satisfaction, teaching psychological process variables, especially quality and expertise. Based on psychological variables, services, facilities, images, and research activities, Android smart systems are also important. Individual user characteristics, such as outcomes and attitudes, are related to psychological user satisfaction. Neural Network promote user matching of suitable thinking style psychology learning, psychological development course education quality and style, be accurate, and develop positive attitudes such as self-efficacy. For learning assistance, help learners will improve their satisfaction, including Android's intelligent system.

AN ADAPTIVE HESITANT FUZZY SETS BASED GROUP RECOMMENDATION SYSTEM

Accurate group movie recommendation systems are a need in society today. We find that people tend to watch movies in groups rather than by themselves. However, the groups of people that tend to watch movies together are very diverse. In the existing methods, the characteristics of individual users are simply aggregated to obtain the group’s attributes and most of the time useful data is not utilized. This can be improved upon by ensuring the utilization of all the data that we are presented with from the scenario. The method proposed in this paper is termed integrated as we weighed in the individual traits of each user in the group when predicting the group’s rating for a movie. We used the concept of Hesitant Fuzzy Sets (HFS) in order to keep track of the characteristics of each of the users individually. The method we proposed in this paper employs Matrix Factorisation (MF) based Collaborative Filtering (CF) along with hesitant fuzzy sets. The way we performed MF based CF for a group is that we found the factors first and then formed the groups. The ratings were then predicted for these groups. The groups we have considered are of three sizes - 3 users, 5 users, and 10 users.

Cognitive Style and Information Visualization—Modeling Users Through Eye Gaze Data

Information visualizations can be regarded as one of the most powerful cognitive tools to significantly amplify human cognition. However, traditional information visualization systems have been designed in a manner that does not consider individual user differences, even though human cognitive abilities and styles have been shown to differ significantly. In order to address this research gap, novel adaptive systems need to be developed that are able to (1) infer individual user characteristics and (2) provide an adaptation mechanism to personalize the system to the inferred characteristic. This paper presents a first step toward this goal by investigating the extent to which a user's cognitive style can be inferred from their behavior with an information visualization system. In particular, this paper presents a series of experiments that utilize features calculated from user eye gaze data in order to infer a user's cognitive style. Several different data and feature sets are presented, and results overall show that a user's eye gaze data can be used successfully to infer a user's cognitive style during information visualization usage.

Adaptation Algorithm for Application Menus

A software application’s menu is an important part of its user interface. User satisfaction and application usage efficiency depend on how well the menu is designed. Application menus designed by the developer are intended for the average user and do not take into account the needs and characteristics of individual users. A solution to this problem is the creation of user interfaces capable of adapting to the needs of an individual user. The approach to adapting the user menu of an application program proposed in this paper is based on a user model generated by monitoring user actions in the process of working with the program. A mathematical model of the application menu and a model of the application user are constructed, and an algorithm for adaptive modification of the application menu is proposed. The proposed algorithm for creating menu item links at higher hierarchy levels and hiding unused menu items makes it possible to reduce the activation time of menu items, thus increasing the user’s productivity.

Participatory Sensing or Sensing of Participation

Smart devices have become a basic necessity in this technically advanced era. Many smart device applications, when installed, collect personal data and track users' online behavior for marketing or other purposes. This study aims to explore whether users are aware of related privacy issues and whether their knowledge influences the usage of their apps. Cognate-based views of privacy indicate that privacy concern is dynamic and varies depending on an individual's characteristics and the context. Adopting this view creates the attempt to understand the effect of privacy awareness on user's behavior, considering the moderating effects of individual characteristics and application categories. The study was conducted by surveying smart devices' users and analyzing their responses to the privacy awareness treatment across eight groups of applications. Results indicate that privacy awareness impacts smart device user preferences for app usage. This influence varies depending on individual user characteristics and different application categories.