Small Group Of Users Research Articles

Contextual Analysis of the Site

Purpose. The purpose of this article is to describe the method of contextual website research, which is an alternative to traditional benchmark tests. Unlike laboratory tests, where conditions are strictly controlled, contextual research allows you to evaluate the usability of a website in a real context, taking into account the factors that affect the user in his or her usual environment. This means that researchers observe users interacting with a website in their natural environment, such as at home or at work, which allows them to gain a more relevant understanding of the user experience. Methodology. We used the contextual research method, which involves detailed observation and interviewing a small group of users about certain aspects of a website. This helps to get a realistic picture of how the user perceives and uses the site in real conditions, in the context of websites. Researchers can observe how users use the website, what tasks they are trying to accomplish, what difficulties they encounter, and how they interact with other tools and programs. Findings. The study makes it possible to greatly simplify the evaluation of website usability using certain approaches and developed software. Using contextual research, it is possible to identify shortcomings that would not be noticeable during laboratory tests and suggest improvements that increase the usability of a website. Originality. The study is based on key principles such as contextualization, collaborative evaluation, usability as the main indicator, and focus on the subject matter. This allows us to gain a deeper understanding of how users interact with a website in real life, not just in an artificially created laboratory environment. Practical value. The results of the work allow us to use the method of contextual research in a wide range of human activities within websites of different orientations to improve the user experience. This can be especially useful for websites that are developed for certain groups of users, such as people with disabilities or the elderly.

Co-designing restrictive practice elimination: A systems thinking approach with mental health service users and practitioners in rural/regional Australia.

Elimination of restrictive practices (physical/mechanical restraint and seclusion) from adult acute mental health care services has been demanded internationally for many decades. This study aimed to: (1) Identify priority issues in the elimination of and use of alternative approaches to restrictive practices (seclusion and physical/mechanical restraint) in rural/regional acute adult mental healthcare services, as told by mental healthcare service users and practitioners, (2) identify the community-based, system-level feedback loops that enhance or reduce the use of restrictive practices and viable alternatives and, (3) identify potential action areas to improve system structures to increase regional mental healthcare services' ability to eliminate restrictive practices and use alternative approaches. Group model building (GMB) workshops were held with a small group (n = 9) of mental healthcare practitioners and service users with lived experience of restrictive practice use. This participatory approach enables exploration and visual mapping of local structures causing behaviour patterns of practitioner and service user concern over time - in this case, the barriers, and enablers to alternative approaches to restrictive practices in adult acute mental healthcare services within the Geelong-Barwon region. This is the first study that specifically applies GMB in the discussion of the elimination of restrictive practices in mental health in the non-metropolitan regional/rural context. Participants identified four key priorities in relation to eliminating restrictive practices: (1) self-advocacy, (2) continuity of care, (3) early intervention, and (4) safety for all. The study findings were distilled into a novel preliminary set of mental healthcare practitioner and service user action ideas.

JargonFM: A Framework With Multiple Interpretation Modes for Jargon Understanding in Online Communities

Jargon words are commonly used in the communication of online communities. These words are characterized by special and implicit meanings that can only be comprehended by a small group of users, which brings challenges to community regulation and user engagement. For this problem, we present JargonFM, a framework with multiple interpretation modes for jargon understanding in online communities. JargonFM is designed based on the scientific explanation framework and supports three interpretation modes: jargon category prediction based on a jargon classifier, similar word identification based on a jargon synonyms selector, and representative text selection based on an example sentence selector. A jargon interpreter was also implemented to demonstrate the usage and usefulness of the interpretation framework. Automatic and human evaluations suggest that JargonFM can explain jargon words more accurately and more efficiently than the existing interpretation methods, leading to its wide acceptance among the evaluation participants.

Intra- and Inter-group Optimal Transport for User-Oriented Fairness in Recommender Systems

Recommender systems are typically biased toward a small group of users, leading to severe unfairness in recommendation performance, i.e., User-Oriented Fairness (UOF) issue. Existing research on UOF exhibits notable limitations in two phases of recommendation models. In the training phase, current methods fail to tackle the root cause of the UOF issue, which lies in the unfair training process between advantaged and disadvantaged users. In the evaluation phase, the current UOF metric lacks the ability to comprehensively evaluate varying cases of unfairness. In this paper, we aim to address the aforementioned limitations and ensure recommendation models treat user groups of varying activity levels equally. In the training phase, we propose a novel Intra- and Inter-GrOup Optimal Transport framework (II-GOOT) to alleviate the data sparsity problem for disadvantaged users and narrow the training gap between advantaged and disadvantaged users. In the evaluation phase, we introduce a novel metric called ?-UOF, which enables the identification and assessment of various cases of UOF. This helps prevent recommendation models from leading to unfavorable fairness outcomes, where both advantaged and disadvantaged users experience subpar recommendation performance. We conduct extensive experiments on three real-world datasets based on four backbone recommendation models to prove the effectiveness of ?-UOF and the efficiency of our proposed II-GOOT.

Learning-based Relaxation of Completeness Requirements for Data Entry Forms

Data entry forms use completeness requirements to specify the fields that are required or optional to fill for collecting necessary information from different types of users. However, because of the evolving nature of software, some required fields may not be applicable for certain types of users anymore. Nevertheless, they may still be incorrectly marked as required in the form; we call such fields obsolete required fields. Since obsolete required fields usually have “not-null” validation checks before submitting the form, users have to enter meaningless values in such fields to complete the form submission. These meaningless values threaten the quality of the filled data and could negatively affect stakeholders or learning-based tools that use the data. To avoid users filling meaningless values, existing techniques usually rely on manually written rules to identify the obsolete required fields and relax their completeness requirements. However, these techniques are ineffective and costly. In this article, we propose LACQUER, a learning-based automated approach for relaxing the completeness requirements of data entry forms. LACQUER builds Bayesian Network models to automatically learn conditions under which users had to fill meaningless values. To improve its learning ability, LACQUER identifies the cases where a required field is only applicable for a small group of users and uses SMOTE, an oversampling technique, to generate more instances on such fields for effectively mining dependencies on them. During the data entry session, LACQUER predicts the completeness requirement of a target based on the already filled fields and their conditional dependencies in the trained model. Our experimental results show that LACQUER can accurately relax the completeness requirements of required fields in data entry forms with precision values ranging between 0.76 and 0.90 on different datasets. LACQUER can prevent users from filling 20% to 64% of meaningless values, with negative predictive values (i.e., the ability to correctly predict a field as “optional”) between 0.72 and 0.91. Furthermore, LACQUER is efficient; it takes at most 839 ms to predict the completeness requirement of an instance.

Development of Transport Management Software

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

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

It is widely acknowledged that large models have the potential to deliver superior performance across a broad range of domains. Despite the remarkable progress made in the field of machine learning systems research, which has enabled the development and exploration of large models, such abilities remain confined to a small group of advanced users and industry leaders, resulting in an implicit technical barrier for the wider community to access and leverage these technologies. In this paper, we introduce PyTorch Fully Sharded Data Parallel (FSDP) as an industry-grade solution for large model training. FSDP has been closely co-designed with several key PyTorch core components including Tensor implementation, dispatcher system, and CUDA memory caching allocator, to provide non-intrusive user experiences and high training efficiency. Additionally, FSDP natively incorporates a range of techniques and settings to optimize resource utilization across a variety of hardware configurations. The experimental results demonstrate that FSDP is capable of achieving comparable performance to Distributed Data Parallel while providing support for significantly larger models with near-linear scalability in terms of TFLOPS.

Measuring Network Polarization and Political Sectarianism During the 2020 Pandemic

Online social networks are at the limelight of the public debate, where antagonistic groups compete to impose conflicting narratives and polarize the discussions. This article proposes an approach for measuring network polarization and political sectarianism in Twitter based on user interaction networks. Centrality metrics identify a small group of influential users (polarizers and unpolarizers) who influence a larger group of users (polarizees and unpolarizees) according to their ideological stance (left, right, and undefined). This network polarization is computed by the Bayesian probability using typical actions such as following, tweeting, retweeting, and replying. The measurement of political sectarianism also uses Bayesian probability and words extracted from the tweets to quantify the intensity of othering, aversion, and moralization in the debate. We collected Twitter data from 33 conflicted political events in Brazil during 2020, strongly influenced by the COVID-19 pandemic. Based on our methodology and polarization score, our results reveal that the approach based on user interaction networks leads to an increasing understanding of polarized conflicts in Twitter. Also, a small number of polarizers is enough to represent the polarization and sectarianism of Twitter events.

DeFi Lending Platform Liquidity Risk: The Example of Folks Finance

Decentralised finance (DeFi) lending platforms may experience liquidity risk, which occurs when users are unable to withdraw their assets. Researchers and practitioners have found that the concentration of deposits among a small group of users is one of the main drivers of liquidity risk. Typically, lending platforms experience high concentration at the beginning of their operations. As a result, they face a significant liquidity risk that has not been investigated so far. This article closes this gap by investigating liquidity risk from the perspective of a new lending platform, describing the use case of Folks Finance. First, we describe the liquidity risk the lending protocol faces using platform economics. Second, we theoretically assess the efficacy of different liquidity risk measurements. Third, we investigate how a reward mechanism can reduce liquidity risk. We show that the liquidity risk is more pronounced for a new lending platform than for an incumbent protocol. In addition, we find that the Herfindahl–Hirschman index (HHI) outperforms other liquidity risk measurements. Finally, we show that if rewards are sufficient but not too large, a programme that incentivises depositors to lock their assets can reduce liquidity risk and increase liquidity bootstrapping. Several conclusions are drawn from the case study: First, new lending platforms should be particularly cautious regarding liquidity risk. Second, lending protocols should use HHI instead of other concentration measurements when calibrating their parameters. Third, rewards can be used to bootstrap liquidity and incentivise liquidity holdings but should not be overused.

Smart Aided Device for Human Safety Using IoT

Smart Aided Device for Human Safety Using IoT

Dipendenze, empowerment, lavoro: attivazione di un'esperienza di laboratorio ispirata alla metodologia dell'Individual Placement and Support presso un Servizio delle Dipendenze

Addiction also has important consequences on the work sphere. This paper deals with a job search workshop inspired by the Individual Placement Support method. The results obtained in the nine months of experimentation on a small group of drug users appear promising, especially if one considers the proposed activity, which is a low-cost service for the Department of Drug Abuse.

Features requirement elicitation process for designing a chatbot application

AbstractToday, chatbots are being employed across a wide range of businesses. The researchers will present the outcome of the literature exploration on attributes of chatbots that later become a reference to build a new chatbot for banking use. The goal is to use human natural language as an interaction medium to aid humans in doing any prescribed activity. It is important to think about how people engage with chatbots in order to create a viable and high‐quality chatbot solution. The authors aim to help the chatbot community by determining what qualities a chatbot should have and how to build a chatbot for a bank. For the purpose of compiling useful chatbot characteristics, the researchers searched through a group of linked papers. The survey was done on a small group of chatbot end users in order to get a sense of what functionalities are most important to the end users. On the basis of a summary of the literature research and data from a survey, a set of chatbot functional features is proposed. This research intended to provide a quality feature criterion for creating a chatbot application.

The online structure and development of posting behaviour in Dutch anti-vaccination groups on Telegram

Online communities play an important role in spreading public discontent and could contribute to polarization. This study focuses on anti-vaccination views in the Netherlands, which have intensified during the COVID-19 pandemic. We examined the structure and development of five Dutch anti-vaccination Telegram groups and studied their interactivity and posting behaviour. Using group-based trajectory modelling, we examined the development of users’ posting behaviour in these groups. We find four posting trajectories across all five groups. A small group of users contributes the majority of posts. Overall, posting frequency declines over time and our results do not show evidence for a group of users whose posting frequency increases. This is taken to indicate that only a small group of users spread their anti-vaccination views through Telegram groups. While social media can reach a broad audience, most users are not necessarily engaged to also actively contribute to the online anti-vaccination community.

A novel portable sensor to monitor bodily positions and activities in transtibial prosthesis users

BackgroundStep activity monitors provide insight into the amount of physical activity prosthesis users conduct but not how they use their prosthesis. The purpose of this research was to help fill this void by developing and testing a technology to monitor bodily position and type of activity. MethodsThin inductive distance sensors were adhered to the insides of sockets of a small group of transtibial prosthesis users, two at proximal locations and two at distal locations. An in-lab structured protocol and a semi-structured out-of-lab protocol were video recorded, and then participants wore the sensing system for up to 7 days. A data processing algorithm was developed to identify sit, seated shift, stand, standing weight-shift, walk, partial doff, and non-use. Sensed distance data from the structured and semi-structured protocols were compared against the video data to characterize accuracy. Bodily positions and activities during take-home testing were tabulated to characterize participants' use of the prosthesis. FindingsSit and walk detection accuracies were above 95% for all four participants tested. Stand detection accuracy was above 90% for three participants and 62.5% for one participant. The reduced accuracy may have been due to limited stand data from that participant. Step count was not proportional to active use time (sum of stand, walk, and standing weight-shift times). InterpretationStep count may provide an incomplete picture of prosthesis use. Larger studies should be pursued to investigate how bodily position and type of activity may facilitate clinical decision-making and improve the lives of people with lower limb amputation.

History education done different: A collaborative interactive digital storytelling approach for remote learners

Social interaction has been recognized as positively affecting learning, with dialogue–as a common form of social interaction–comprising an integral part of collaborative learning. Interactive storytelling is defined as a branching narrative in which users can experience different story lines with alternative endings, depending on the choices they make at various decision points of the story plot. In this research, we aim to harness the power of dialogic practices by incorporating dialogic activities in the decision points of interactive digital storytelling experiences set in a history education context. Our objective is to explore interactive storytelling as a collaborative learning experience for remote learners, as well as its effect on promoting historical empathy. As a preliminary validation of this concept, we recorded the perspective of 14 educators, who supported the value of the specific conceptual design. Then, we recruited 15 adolescents who participated in our main study in 6 groups. They were called to experience collaboratively an interactive storytelling experience set in the Athens Ancient Agora (Market) wherein we used the story decision/branching points as incentives for dialogue. Our results suggest that this experience design can indeed support small groups of remote users, in-line with special circumstances like those of the COVID-19 pandemic, and confirm the efficacy of the approach to establish engagement and promote affect and reflection on historical content. Our contribution thus lies in proposing and validating the application of interactive digital storytelling as a dialogue-based collaborative learning experience for the education of history.

Online Brand Community User Segments: A Text Mining Approach.

There is a trend that customers increasingly join the online brand community. However, evidence shows that there are nuances between different user segments, and only a small group of users are active. Thus, one key concern marketers face is identifying and targeting specific segments and decreasing user churn rates in an online environment. To this end, this study aims to propose a UGC-based segmentation of online brand community users, identify the characteristics of each segment, and consequently reduce online brand community users' churn rate. We used python to obtain users' post data from a well-known online brand community in China between July 2012 and December 2019, resulting in 912,452 posts and 20,493 users. We then use text mining and clustering methods to segment the users and compare the differences between the segments. Three groups—information-oriented users, entertainment-oriented users, and multi-motivation users—were emerged. Our results imply that entertainment-oriented users were the most active, yet, multi-directional users have the lowest probability of churn, with a churn rate of only 0.607 times than that of users who focus either on information or entertainment. Implications for marketing and future research opportunities are discussed.

Can We Chat . . . Privately? Using Twitter Chats to Facilitate Offline Engagement for Nonprofits

This study examines a health-focused nonprofit organization’s Twitter chats, using qualitative content analysis and social network analysis (SNA) to describe the characteristics of participants and their patterns of interaction. Together, these analyses indicate that Twitter chats do not produce the dialogue that seems inherent to the format. The chats are driven by a small group of users, feature minimal levels of participation from most users, and are heavy on retweets rather than original messages. Nevertheless, these chats can serve to identify committed users and thereby facilitate future offline relationship building rather than being the primary source of relationship building. Offline interactions hold the potential to result in dialogue consistent with theoretical models; however, these interactions may instead serve organizational purposes within an advocacy context. Suggestions that focus on exploring the value of social media for generating offline engagement and relationship building for further research are made.

Few voices, strong echo: Measuring follower homogeneity of politicians’ Twitter accounts

Politicians have discovered Twitter as a tool for political communication. If information provided by politicians is circulated in ideologically segregated user networks, political polarization may be fostered. Using network information on all 1.78 million unique followers of German Members of Parliament by October 2018, follower homogeneity across politicians and parties is measured. While the overall homogeneity is low, politicians of the AfD—a right-wing populist party—stand out with very homogeneous follower networks. These are largely driven by a small group of strongly committed partisans that make up around 7% of the party’s but around 55–75% of the average AfD politician’s followers. The findings add to the literature by showing potentially unequal distributions of network segregation on Twitter. Furthermore, they suggest that small groups of active users can multiply their influence online, which has important implications for future research on echo chambers and other online phenomena.

HOLOGRAPHIC VISIONS FOR ARCHITECTURE IN A PARK

Abstract. The Monza Park, with its more than 7 square meters of green area, divided between lawn and woods, its 110,000 tall trees, its 13 farmhouses, 3 historic villas, 13 m of fences and 90,000 visitors on spring Sundays, represents an irreplaceable source of wellness and sustainability for those who live near it. The pandemic situation of the 20s and 21s by reducing the movements and the possibility of coexistence of a large public in an open space has suggested the possibility of new forms of use and interaction of the same, even remotely, reproducing accurate Virtual Reality experiences. With this paper, the authors intend to illustrate a workflow from Scan to VR applications, taking advantage of the opportunity to explore digital acquisitions and additional materials available and functional to convey the values of open space and historical monuments immersed in them. The VR experiences have been structured for the navigation from the scale of architectural detail to the environmental one, with the goal of using the accurate model results for two different and remote instrumentations: a 7m diameter 360° theatre and a Holographic table, Euclideon Hologram Table©. Both situations, as opposed to hardware tools such as headset, favour the fruition for small groups of users.

A Novel Framework to Monitor and Test the Performance of Mobile Applications

Mobile applications (apps) are being widely used around the world. The long-term success and diffusion of a mobile app depends on it being well-tested, with respect to its performance and its reliability. Mobile app testing is typically carried out in multiple stages. An app is first tested for performance and reliability in a controlled lab-like environment during its development. However, the heterogeneity of in-the-field environments (including many device profiles, network speeds, etc.) is far greater than in a lab setting, with a small group of users. For this reason, a so-called Beta version of the app is typically rolled-out to a limited number of users, to get deeper insights into its performance and reliability in the field. After the Beta test results are analyzed, and refinements are incorporated, the application is released to all of its intended users. However, this work is based on a belief that field testing should be an automatic and ongoing process. Field test data, representing the actual performance and reliability of the app in the field, should be continuously fed back to the developer. To accomplish this, this paper proposes a framework for ongoing monitoring and field testing of mobile apps. This framework includes a library that mobile app developers can use to collect performance and reliability data during ongoing extensive field testing. These data are collected and presented using a web form to inform developers about the performance and reliability of their apps as they are being used by end-users in the field.