User Need Research Articles

Supporting Self-Managed Medication Abortion: Care Needs and Preferences of SMMA Users in Three US States

Supporting Self-Managed Medication Abortion: Care Needs and Preferences of SMMA Users in Three US States

Movie Recommendation System using Machine Learning

There is already enough content available on the movie recommendation system. Showing the movie recommendations is essential so that the user need not waste a lot of time searching for the content which he/she might like. Thus, movie recommendation system plays a vital role to get user personalized movie recommendations. After searching a lot on the internet and referring to a lot of research papers, we got to know that the recommendations made using Content-based Filtering are using a single text to vector conversion technique and a single technique to find the similarity between the vectors. In this research work, we have used multiple text to vector conversion techniques and manipulated the results of the multiple algorithms to get the final recommendation list. You can think of it as a hybrid approach using the Content-based Filtering technique only.

Utilization of Library Collections to Meet User Needs In The Diplomacy Library

Utilization of Library Collections to Meet User Needs In The Diplomacy Library

Framework to Create Inventory Dataset for Disaster Behavior Analysis Using Google Earth Engine: A Case Study in Peninsular Malaysia for Historical Forest Fire Behavior Analysis

This study developed a comprehensive framework using Google Earth Engine to efficiently generate a forest fire inventory dataset, which enhanced data accessibility without specialized knowledge or access to private datasets. The framework is applicable globally, and the datasets generated are freely accessible and shareable. By implementing the framework in Peninsular Malaysia, significant forest fire factors were successfully extracted, including the Keetch–Byram Drought Index (KBDI), soil moisture, temperature, windspeed, land surface temperature (LST), Palmer Drought Severity Index (PDSI), Normalized Vegetation Index (NDVI), landcover, and precipitation, among others. Additionally, this study also adopted large language models, specifically GPT-4 with the Noteable plugin, for preliminary data analysis to assess the dataset’s validity. Although the plugin effectively performed basic statistical analyses and visualizations, it demonstrated limitations, such as selectively dropping or choosing only relevant columns for tests and automatically modifying scales. These behaviors underscore the need for users to perform additional checks on the codes generated to ensure that they accurately reflect the intended analyses. The initial findings indicate that factors such as KBDI, LST, climate water deficit, and precipitation significantly impact forest fire occurrences in Peninsular Malaysia. Future research should explore extending the framework’s application to various regions and further refine it to accommodate a broader range of factors. Embracing and rigorously validating large language model technologies, alongside developing new tools and plugins, are essential for advancing the field of data analysis.

Интернет-комментарий как сетевой жанр: общая характеристика, диалогический потенциал, проблемы классификации

The article deals with current problems of modern communicative linguistics and network genre studies. The author presents general characteristics of such a relatively new network genre as Internet commentary. The research emphasizes dialogical orientation of this genre, which meets the urgent need of Internet users for feedback and represents a media embodiment of the addressee factor that is in demand and meets the spirit of the times. Internet commentary is characterized as a reactive (responsive) Internet genre, the texts of which are thematically, structurally, semantically and pragmatically closely related to the original (root) message (post) and form a kind of dialogic unity with it. The author offers a multi-level classification of Internet comments, taking into account the following characteristics: 1) attribution to the Internet resource; 2) discursive affiliation; 3) the nature of the stimulus that caused the reaction (text or polycode stimulus, video content with verbal accompaniment, video content without verbal accompaniment, etc.); 4) place in the information and commentary block; 5) structural characteristics (linear length; mono-code / poly-code); 6) content and thematic features; 7) the degree of dependence on the original (root) message.

Lipodystrophy in insulin users: a literature review

Diabetes is a metabolic syndrome of multiple origins, resulting from lack of insulin and/or the inability of insulin to adequately exercise its insulin. Since 1921, drug therapy by subcutaneous insulin has helped the diabetic patient. Although there are benefits, it is necessary to report that insulin therapy can lead to complications such as lipodystrophy. This is a literature review aimed at evaluating the characteristics of lipodystrophy in insulin users. The first step was to assess the profile of people who develop lipohypertrophy from using insulin. The authors report that most patients who reach a critical level of LH self-administer insulin, a fact that can lead users to settle for the correct administration of therapy. Some reported that the stains had already appeared for years, but patients felt embarrassed to look for someone else to apply. In many who already had prominences in the subcutaneous tissue, the report was that the site was painless. As an expected result, the absence of rotation at the application sites is the main cause for the appearance of LH. From the moment the patients were diagnosed by the professionals and started to be instructed about the rotation of the application sites, the LH rate dropped to 8% of the people. Therefore, insulin users need

Research on the Integration Innovation of AI and E-commerce and Its Impact on Future Business Models

This paper aims to explore the specific application of Artificial Intelligence (AI) technology in the e-commerce industry and the changes it brings, given the industry's rapid development, intensifying market competition, and evolving user needs. By reviewing the history of e-commerce development, we gain an in-depth understanding of the status of AI application in meeting market demands. The study focuses on the specific application of AI technology in e-commerce operations and its effectiveness, deeply analyzing key areas such as intelligent recommendation systems and data analysis. It reveals how AI can reshape the operational mode and business model of the e-commerce industry by enhancing user experience, optimizing operational efficiency, and deeply exploring data value. Furthermore, the paper explores the potential impact of this integration on the future development of the e-commerce industry and makes predictions on the prospects of AI technology application, providing valuable references for relevant enterprises and researchers. Ultimately, this research concludes that AI technology holds great promise in driving the sustainable and healthy development of the e-commerce industry by enabling more effective market competition response and precise user need satisfaction.

‘rtry’: An R package to support plant trait data preprocessing

AbstractPlant trait data are used to quantify how plants respond to environmental factors and can act as indicators of ecosystem function. Measured trait values are influenced by genetics, trade‐offs, competition, environmental conditions, and phenology. These interacting effects on traits are poorly characterized across taxa, and for many traits, measurement protocols are not standardized. As a result, ancillary information about growth and measurement conditions can be highly variable, requiring a flexible data structure. In 2007, the TRY initiative was founded as an integrated database of plant trait data, including ancillary attributes relevant to understanding and interpreting the trait values. The TRY database now integrates around 700 original and collective datasets and has become a central resource of plant trait data. These data are provided in a generic long‐table format, where a unique identifier links different trait records and ancillary data measured on the same entity. Due to the high number of trait records, plant taxa, and types of traits and ancillary data released from the TRY database, data preprocessing is necessary but not straightforward. Here, we present the ‘rtry’ R package, specifically designed to support plant trait data exploration and filtering. By integrating a subset of existing R functions essential for preprocessing, ‘rtry’ avoids the need for users to navigate the extensive R ecosystem and provides the functions under a consistent syntax. ‘rtry’ is therefore easy to use even for beginners in R. Notably, ‘rtry’ does not support data retrieval or analysis; rather, it focuses on the preprocessing tasks to optimize data quality. While ‘rtry’ primarily targets TRY data, its utility extends to data from other sources, such as the National Ecological Observatory Network (NEON). The ‘rtry’ package is available on the Comprehensive R Archive Network (CRAN; https://cran.r‐project.org/package=rtry) and the GitHub Wiki (https://github.com/MPI‐BGC‐Functional‐Biogeography/rtry/wiki) along with comprehensive documentation and vignettes describing detailed data preprocessing workflows.

Design Thinking in Lighting Design to Meet User Needs

In the age of noticing the adverse effects of our activities on the climate, we pay more and more attention to designing sustainable solutions. Assumptions made during such sustainable designs often do not fulfil user needs. Therefore, we frequently encounter discrepancies, such as higher energy consumption and user modifications in the building systems compared to the design and use stages. To mitigate these problems in the lighting design context, the author describes the proposed lighting design method based on the Design Thinking methodology and concerns the commonly applied basic lighting design process. As an example design, an office room interior and lighting design are presented using the Design Thinking method described by the author. The utilised method allowed us to learn about problems that are only sometimes typical for offices and enter them into the design scope. The article contains a detailed analysis and comparison of normative requirements with users’ needs, collected from four room users and six guests. The discrepancies between the sustainable design and use stages presented in the article can be considered reduced, and users’ interference in the operation of devices will be eliminated due to meeting users’ needs and thus achieving sustainable goals.

WalkingWizard—A Truly Wearable EEG Headset for Everyday Use

Electroencephalography (EEG) provides an opportunity to gain insights to electrocortical activity without the need for invasive technology. While increasingly used in various application areas, EEG headsets tend to be suited only to a laboratory environment due to the long preparation time to don the headset and the need for users to remain stationary. We present our design of a dry, dual-electrodes flexible PCB assembly that realizes accurate sensing in the face of practical motion artifacts. Using it, we present WalkingWizard, our prototype dry-electrode EEG baseball cap that can be used under motion in everyday scenarios. We first evaluated its hardware performance by comparing its electrode-scalp impedance and ability to capture alpha rhythm against both wet EEG and commercially available dry EEG headsets. We then tested WalkingWizard using steady-state visual evoked potential (SSVEP) experiments, achieving high classification accuracy of 87% for walking speeds up to 5.0 km/h, beating state-of-the-art. Expanding on WalkingWizard, we integrated all necessary electronic components into a flexible PCB assembly—realizing WalkingWizard Integrated , in a truly wearable form-factor. Utilizing WalkingWizard Integrated, we demonstrated several applications as proof-of-concept: classification of SSVEP in VR environment while walking, real-time acquisition of emotional state of users while moving around the neighbourhood, and understanding the effect of guided meditation for relaxation.

Evaluation of Conventional and Smart Wheelchair Technologies in the Kingdom of Saudi Arabia

A significant number of people depend on the help of others to facilitate their mobility in wheelchairs. Physical disabilities hinder the mobility of persons with disabilities, making them unable to move around using conventional (manually operated) wheelchairs. Furthermore, people lack the ability to regain an upright posture if the chair falls. This study aims to evaluate conventional and smart wheelchair technologies in the Kingdom of Saudi Arabia. This study was conducted through a quantitative and cross-sectional research methodology to evaluate the effectiveness of wheelchair technologies in the Kingdom of Saudi Arabia. This is done by collecting data from wheelchair users, experts, and specialists in this field, such as medical engineers and others familiar with wheelchairs. The evaluation included the opinions of users and experts on the use of traditional and smart chairs in terms of the technologies used, ease of use, advantages, disadvantages, etc. More than 590 users and experts from all regions of the Kingdom participated in the study, and their answers were characterized by objectivity and realism. About 39% of participants indicated that smart wheelchairs are very effective, and 37.6% confirmed that these chairs can be used on many surfaces, while 23.4% expressed their appreciation that smart wheelchairs are useful for various purposes and terrains. On the other hand, the results showed that smart wheelchairs had high purchase and maintenance costs of 37.4%. This was followed by concerns about more maintenance requirements (23.5%), the need for constant charging (20.4%), and the need for users to be aware of energy use (18.7%). The results show some advantages of using traditional wheelchairs, such as low maintenance, lightweight, and often affordability. This type of chair faces many difficulties, such as crossing slopes and long distances, requiring some strength for use, and users needing assistance to move around. The study revealed significant progress in wheelchair technology, especially in smartphones and navigation. Evaluating existing wheelchair technology in Saudi Arabia has shown significant advances, especially in urban areas and healthcare institutions. Motorized electric wheelchairs are becoming increasingly popular due to their ability to enhance users’ mobility. However, the availability of high-end wheelchair models and their accompanying accessories remains limited.

Balancing Tracking Granularity and Parallelism in Many-Task Systems: The Horizons Approach

Reducing the need for users to manually manage the details of work and data distribution is an important goal of high-level many-task runtime systems. For distributed memory platforms this means that the runtime system has to keep track of both fine-grained task dependencies and data residency meta-information. The amount of such meta-information is proportional to the granularity of parallelism which needs to be managed, introducing a trade-off. More precise tracking of data state allows leveraging more opportunities for compute and transfer parallelism, while also introducing more overhead. As such, the fidelity of the information being tracked needs to be managed carefully, ideally without introducing additional latency, communication or substantial compute overhead. We present the “Horizons” approach, designed to fulfill these goals. Specifically, horizons allow for the effective and efficient management of parallelism and the coalescing of previous fine-grained tracking information while maintaining an easily configurable scheduling window with full information precision. As an additional benefit, they provide consistent cluster-wide decision points without requiring any inter-node communication, and effectively cap the size of state tracking data structures even in the presence of problematic access patterns. Experimental evaluation on microbenchmarks and dry runs demonstrates that horizons are effective in keeping the scheduling complexity constant, while their own overhead is negligible—below 10μs\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10\\, \\upmu {\\rm s}$$\\end{document} per horizon when building a command graph for 512 GPUs. We additionally demonstrate the performance impact of horizons—as well as their low overhead—on a real-world application.

Infusing behavior science into large language models for activity coaching.

Large language models (LLMs) have shown promise for task-oriented dialogue across a range of domains. The use of LLMs in health and fitness coaching is under-explored. Behavior science frameworks such as COM-B, which conceptualizes behavior change in terms of capability (C), Opportunity (O) and Motivation (M), can be used to architect coaching interventions in a way that promotes sustained change. Here we aim to incorporate behavior science principles into an LLM using two knowledge infusion techniques: coach message priming (where exemplar coach responses are provided as context to the LLM), and dialogue re-ranking (where the COM-B category of the LLM output is matched to the inferred user need). Simulated conversations were conducted between the primed or unprimed LLM and a member of the research team, and then evaluated by 8 human raters. Ratings for the primed conversations were significantly higher in terms of empathy and actionability. The same raters also compared a single response generated by the unprimed, primed and re-ranked models, finding a significant uplift in actionability and empathy from the re-ranking technique. This is a proof of concept of how behavior science frameworks can be infused into automated conversational agents for a more principled coaching experience.

DB-ACO: A Deadline-Budget Constrained Ant Colony Optimization for Workflow Scheduling in Clouds

With the development of cloud computing, a growing number of workflows are deployed in cloud platform that can dynamically provide cloud resources on demand for users. In clouds, one basic problem is how to schedule workflow under the deadline constraint and minimize the execution cost. As the capability of cloud resources getting higher, the required cost is also rising. Capability of some resources exceeds the need of users, which leads to higher cost, and the budget of users should be considered. In this paper, a novel scheduling algorithm, named DB-ACO, is proposed to minimize the execution cost for the workflow with deadline and budget constraints. DB-ACO is verified on four typical scientific workflows, and the experiments results show it outperforms four state-of-the-art methods, especially for CyberShake. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Budget and deadline are important requirements for users in cloud computing, which are used as constraints. Extensive works have been devoted to minimize the cost of workflows execution with different scheduling strategies. However, most of them only consider one single constraint and assume the constraint is simple and loose, which is impractical in actual scenarios due to higher requirement of users. This paper investigates a novel scheduling algorithm DB-ACO to optimize cost under budget and deadline. DB-ACO combines heuristic and meta-heuristic, it uses ant colony optimization to optimize the execution cost under the deadline and budget constraints: each ant sorts tasks on the basis of the combination of the pheromone trail and heuristic information, the deadline and budget are distributed fairly to each task by a novel distribution method, then the service selection rules are introduced to build solution.

The information needs of users in the metaverse communities: a grounded theory study

PurposeThis article aims to understand users' information needs in the metaverse communities and to analyze the similarities and differences between their information needs and those of users in Internet communities.Design/methodology/approachThis study conducted semi-structured interviews with users in the metaverse communities to gather raw data. Grounded theory research methods were employed to code and analyze the collected interview data, resulting in the extraction of 40 initial concepts, 15 subcategories and 5 main categories. Based on Maslow’s hierarchy of needs theory, this paper constructs the hierarchical model of users' information needs in the metaverse communities. It compares the differences between users' information needs in the metaverse and Internet fields.FindingsThe user’s information needs in the metaverse communities are divided into two types: deficiency needs and growth needs. Deficiency needs have two levels. The first level is the demand for basic information resources. The second level is the users demand for information assistance. Growth needs have three levels. The first level is the need for information interactions. The second level is the need for community rules. The ownership information in the community rules can provide proof of user status, assets and so on. The third level is the need for users to contribute and share their own created information content.Originality/valueThis article presents the latest research data from in-depth interviews with users in the metaverse communities. It aims to help builders and managers of metaverse communities understand users' information needs and improve the design of virtual communities.

Analysis of users’ perceptions of data privacy on social media networks in Nigeria

This present study aims to classify users' perceptions of data privacy on social media and provide feedback to users on making informed choices. The emergence of social media networks (SMNs) has revolutionized the way people interact and share information online. SMNs have enabled users to connect with friends, family, and colleagues, making communication more efficient and faster. Several studies have highlighted the data privacy challenges without providing a framework for the development of a system for classifying users’ perceptions of these data privacy challenges on SMNs. Furthermore, there is limited evidence-based research that examines the level of awareness about these hidden data privacy issues based on perceived threats by users. However, the widespread adoption of social media technologies has also raised concerns about privacy and security. The study explores and adopts a mixed-methods approach, integrating quantitative surveys and qualitative interviews. The survey collects data using Likert scale questions to measure participants' privacy concerns and awareness of data privacy settings. A total of 468 responses were obtained, representing diverse demographics. The findings indicate that the majority of respondents were males aged 18-25 years, with a high school education. Additionally, participants spent an average of 2-4 hours daily on social media. The study highlights the need for users to read privacy policies before registering information on websites. The results of this research contribute to a better understanding of users' perceptions of data privacy on SMNs, facilitating informed decision-making regarding social media usage.

Customizing Language Model Responses with Contrastive In-Context Learning

Large language models (LLMs) are becoming increasingly important for machine learning applications. However, it can be challenging to align LLMs with our intent, particularly when we want to generate content that is preferable over others or when we want the LLM to respond in a certain style or tone that is hard to describe. To address this challenge, we propose an approach that uses contrastive examples to better describe our intent. This involves providing positive examples that illustrate the true intent, along with negative examples that show what characteristics we want LLMs to avoid. The negative examples can be retrieved from labeled data, written by a human, or generated by the LLM itself. Before generating an answer, we ask the model to analyze the examples to teach itself what to avoid. This reasoning step provides the model with the appropriate articulation of the user's need and guides it towards generting a better answer. We tested our approach on both synthesized and real-world datasets, including StackExchange and Reddit, and found that it significantly improves performance compared to standard few-shot prompting.

Онлайн-ризики в умовах сучасності: проблеми та шляхи подолання

It was emphasized that ensuring the safety of children is one of the priority tasks of the state. Today, in the conditions of rapid digital development, new challenges and threats to the well-being of children appear, which require the search for effective tools to overcome them and provide a comprehensive approach to ensuring the safety of children in cyberspace. Attention is focused on the fact that a comprehensive approach includes, among other things, the appropriate level of training of specialists of entities whose competence includes prevention and response to threats to the safety of children in cyberspace. Timely detection of threats to the safety of children in cyberspace depends on their professionalism, care, and impartiality. It has been proven that information security is more relevant than ever. Growing cyber attacks on public and private enterprises only increase this trend. A separate category of threats concerns citizens, who are increasingly becoming the object of close attention of criminals, therefore, a relatively new concept of the need for users to independently observe basic safety rules is gradually spreading. This approach makes it possible to significantly strengthen the system of collective security of society and the state as a whole. It is noted that cyber hygiene should be treated in the same way as personal hygiene, and when properly integrated into the organization, become a simple daily procedure that will ensure the state of cyber health of the organization in an optimal state. It became clear that violation of the rules of cyber hygiene can lead to harmful consequences not only for an individual person. Quite often, the victim's employer also suffers from the actions of criminals. Even great powers can suffer enormous damage from a careless attitude towards the security requirements of one person. Violators often use an individual to penetrate critical infrastructure facilities, steal government data, and create conditions for coordinated full-scale attacks.

Optimizing Big Data Insights with Serverless Architecture

Big data is the huge amount of data, which can be structured, semi-structured, or unstructured, that is required for current commercial processes. Big Data efforts and technologies are used to analyze large amounts of data in order to gain insights critical for strategic decision-making. Data size is constantly rising, reaching petabytes, exabytes, zettabytes, and even yottabytes, offering substantial management and processing issues. In practice, managing massive amounts of data involves several obstacles, such as server management, storage, clustering, and algorithm deployment. Manual intervention hampers the creation of successful Cloud-based data analysis platforms. Serverless computing provides a solution by offering clients pay-per-use backend services, reducing the need for users to manage server operations. This article describes a serverless architecture for large data analytics, including implementation, maintenance, and governance on Amazon Web Services (AWS). Furthermore, it investigates the differences between traditional and big data analytics in a serverless system

Design of Smartphone Recommendation Application (Phone Finder) with Specifications of Mobile-based User Needs

Design of Smartphone Recommendation Application (Phone Finder) with Specifications of Mobile-based User Needs