Use Of Data Collection Research Articles

How to promote health in indoor living and working spaces: quali-quantitative analysis to evaluate the indoor air quality through users’ perception and low-cost sensors

Abstract It is well-known that humans spend most of their time in indoor spaces: what it is sometimes underestimated; however, is how much the space in which someone is can affect their abilities, feelings and even their mental and physical health. A team developed a methodology to allow the collection of useful data and giving rise to management strategies for guaranteeing healthy spaces, in terms of Indoor Environmental Quality (IEQ). In particular, the methodology is based on a quali-quantitative analysis of Indoor Air Quality (IAQ) supported by a questionnaire on users’ perception and well-being, based on a scoping review, and low-cost sensors for IAQ monitoring air contaminants (carbon dioxide, temperature, relative humidity, volatile organic compounds, etc.). In particular, for the quantitative analysis, the team adopted a method that considers the location of the sensors in different typology of settings with various solar exposures, ventilation systems, human occupancy, type of activity, etc. with the scope of testing the trends in different seasons. As well as the qualitative data collect several information about the users’ perceived comfort and the symptoms (headache, fatigue, etc.) during their stay. The data analysis highlights how the different users’ perceptions affect the conditions of the environmental units, in particular in crowded rooms (4-6 people). Adequate lifestyles, such as door and window-openings, regular air-changes, etc. can highly affect the performances and the productivity, as well as the quality of the spaces. In that sense the use of low-cost sensors with light signals can be helpful as strategy for informing the user of the state of the air and any actions to be implemented. In conclusion, starting from the methodology, the team gives rise to a decalogue of strategies and good habits that permits -through the support of low-cost sensors - to inform the users on how to guarantee healthy living and working spaces.

Weighing user's privacy calculus on personal information disclosure: the moderating effect of social media identification

PurposeWith the widespread collection and utilization of user data, privacy security has become a crucial factor influencing online engagement. In response to the growing concern about privacy security issues on social media, this research aims to examine the key causes of social media users' privacy calculus and how the balance between perceived privacy risks and benefits affects users' privacy concerns and their subsequent willingness to disclose personal information.Design/methodology/approachThe characteristics of the privacy calculus were extracted through partially structured interviews. A research model derived from privacy calculus theory was constructed, and latent variable modeling was employed to validate the proposed hypotheses.FindingsInformation sensitivity, experiences of privacy violations, social influence and the effectiveness of privacy policies influence users' privacy calculus. Privacy risk positively influences privacy concerns. Personal information disclosure willingness is positively influenced by privacy benefits and negatively influenced by privacy concerns, with both paths moderated by social media identification.Originality/valueThis study explores the key antecedents of users' privacy calculus and how these factors influence privacy concerns and subsequent willingness to disclose information on social media. It offers new insights into the privacy paradox observed within social media by validating the moderating role of social media identification on users' information disclosure willingness.

The next-generation of metaverse embodiment interaction devices: A self-powered sensing smart monitoring system

Wearable electronic devices collect user data to provide personalized experiences and real time interactions for the metaverse, thereby driving its development. This paper introduces an innovative self-powered sensing smart monitoring system (SSSMS) designed to harvest human low-frequency inertial energy and achieve gait recognition and fall monitoring (GR-FM) capabilities. The SSSMS enhances energy harvesting efficiency through Halbach electromagnetic enhancement mechanisms, achieving self-sustainability. Additionally, the conical roller triboelectric nanosensor (CR-TENS) accurately captures various human gaits, transmitting these states in the form of electrical signals to a computer via OpenBCI in real time. These signals are integrated with a deep learning-driven diagnostic module to achieve GR-FM. The experimental results demonstrate that under optimal external excitation, the output power is 2.27 mW, with a power density of 32 W/m3. After 100,000 cycles, both the EMG and CR-TENS modules still maintain good performance, sufficiently providing stable and continuous charging for the battery and offering a stable signal for signal processing. Both the EMG and CR-TENS modules exhibit robust feature capture capabilities, effectively sensing different excitation frequencies, amplitudes, and gait patterns. The SSSMS based on the GRU deep learning model achieves recognition accuracies of 96.13 %, 96.60 %, and 92.22 % for frequency, amplitude, and gait, respectively. Deployment experiments demonstrate high accuracy and sensitivity in frequency, amplitude, gait recognition, and fall monitoring, highlighting the advantageous design of EMG and CR-TENS for perceiving subtle motion state changes. The SSSMS demonstrates outstanding capabilities in self-sustainability and real time user embodiment information capture, showcasing its potential as a next-generation interaction device for the metaverse.

Early Identification of Learning and Developmental Disorders in School-Age Children

The aim of developing an online quiz assessment tool is to identify learning disabilities like ‘Dyslexia’, ‘Dyscalculia’, ‘ADHD’ and ‘Anxiety’ as early as possible in children for better educational outcomes. This study applied modern web technologies, such as HTML, CSS, JavaScript, PHP, and FLASK to come up with an easy interface that combines the extensive questionnaire with analytical algorithms. This tool also collects user data as they use it and hence gives information and feedback to a user as well as other stakeholders instantly. By means of user testing, the interface was characterized as easily applicable which contributed to the increase of the attention to the educational needs of the target audience. The main conclusions are that the tool promotes the detection of learning disorders in children as early as possible and encourages minimizing delays in treatment. The presented project is also focused on the application of technologies in the management of development difficulties as well as principles of design that are aimed at making the assessment tools more useful.

Developing web-based interventions for alcohol use screening and alcohol education administered by village health volunteers in the community setting

BackgroundGiven technology’s rising importance in addressing alcohol issues, this study aimed to compare the effectiveness of web-based interventions and that of traditional paper-based methods for alcohol use screening and education administered by village health volunteers (VHVs) in Thailand. MethodsThis cluster randomised controlled trial was conducted in two Thai villages with similar demographics and alcohol-use patterns. The villages were randomly assigned to either a web-based or a paper-based intervention group. The web-based group accessed screenings and interventions through a digital platform designed to facilitate user interaction and data collection, whereas the paper-based group used conventional face-to-face screenings, paper questionnaires, and printed educational materials. ResultsOf the 1,396 participants, those in the web-based group (n = 421) demonstrated a significantly higher likelihood of abstaining from or discontinuing alcohol use by the third session compared to those in the paper group (n = 975) (adjusted odds ratio 1.88 to 8.11). Additionally, satisfaction among VHVs regarding the ease of use and data accuracy was notably higher in the web-based group, with scores averaging 20% higher than those in the control group. ConclusionWeb-based interventions are more effective and efficient than traditional methods and provide a promising tool for expanding alcohol use disorder screening and education. This approach not only enhances participation rates but also improves the accuracy and reliability of health data collection.

A Survey on User Profiling, Data Collection, and Privacy Issues of Internet Services

Users are usually required to share several types of data, including their personal data, as different providers strive to offer high-quality services that are often tailored to end-users’ preferences. However, when it comes to personalizing services, there are several challenges for meeting user’s needs and preferences. For content personalization and delivery of services to end users, services typically create user profiles. When user profiles are created, user data is collected and organized to meet the personalization requirements of the services. In this paper, we provide an overview of current research activities that focus on user profiling and ways to protect user data privacy. The paper presents different types of data that services collect from users on examples of commonly used Internet services. It proposes data categorization as a prerequisite for controlled data sharing between users and Internet services. Furthermore, it discusses how data generalization can be used for anonymization purposes on examples of the proposed data categories. Finally, it gives an overview of the privacy framework being developed and gives guidelines for future work focusing on data generalization methods in order to reduce user privacy risks.

Partisi Ruang Berdasarkan Kebutuhan Pengguna Dengan Konsep Industrial

Along with the development of the era, human desires for housing are increasingly diverse, where houses are not only for carrying out daily activities and carrying on life, but also become a means of inspiration and indicate social levels in the community. The design object to be appointed is the residence of Mr. Antok Suharno which is located in the village of Mulyorejo, Mojosongo District, Boyolali Regency. The residence previously did not have a concept, the Industrial concept became the design theme plan to be implemented. The design objective is to create privacy for the living room and dining room that meets user comfort in accordance with the comfort theory by presenting a partition, functioning as a separator as well as maintaining privacy and presenting decorative value between the living room and dining room. The data collection method is by surveying, measuring and documenting the location of Mr. Antok Suharno's residence, collecting user data for each room and interviewing Mr. Antok Suharno as the owner of the house. Partitions designed to apply materials in accordance with the Industrial concept, namely with black hollow iron material, multiplex and vinyl finishing with wood motifs. The results of the partition design have a length of 180cm, a width of 30cm and a height of 200cm.

The Influence of Artificial Intelligence on Readiness and Acceptance of Technology in E-Commerce

The use of artificial intelligence in e-commerce makes it easier for users to do online shopping. However, user data collection carried out by artificial intelligence in e-commerce can be misused. This is a shift in intention to adopt artificial intelligence in e-commerce. This study aims to identify the factors that impact the adoption of artificial intelligence in the field of e-commerce. The technology readiness model and the technology acceptance model are both utilized in this study. Data was collected from 283 students who have done shopping in e-commerce. The data collected will then be analyzed using SEM-PLS. The findings suggest that optimism, innovativeness, and discomfort have a role in shaping the acceptability of artificial intelligence in e-commerce, through the perceived ease of use and perceived usefulness. However, research findings suggest that there is no correlation between insecurity and the perceived ease of use and usefulness. The findings suggest that the way users view the ease of use, and the utility of artificial intelligence technology directly influences their acceptance of it in e-commerce, which is then through in their intention to use it. The result of this study can be used by online businesses to apply TAM and technology readiness models to maximize the use of AI in e-commerce.

How Users Assess Privacy Risks in the Internet of Things: The Role of Framing, Comparing, and Educating

With the advent of the Internet of Things (IoT), it has become increasingly challenging for users to assess the privacy risks associated with consumer products and the continuous stream of user data needed to operate them. In this study, we propose and test three mechanisms with the potential to help users make more accurate assessments of privacy risks. We refer to these mechanisms as framing (i.e., presenting information on the collection and use of user data with or without direct reference to privacy risks), comparing (i.e., presenting a product and the associated information on data collection and use with or without reference to an alternative product), and educating (i.e., augmenting users’ general privacy literacy). To assess these mechanisms in different IoT contexts, we conducted two scenario-based online experiments with reference to a telematics device ( n = 317) and a fitness tracker ( n = 356). In both studies, we find that actual privacy risks as manipulated in the experiment are only moderately related to the privacy risks perceived by users. However, comparing and educating each helped users make more accurate privacy risk assessments. In Study 2, framing and comparing jointly enabled especially users with low privacy literacy to assess privacy risks more accurately. These findings have meaningful implications for key actors in the IoT ecosystem and those regulating it.

Enhancing TikTok Account Performance with Data Pattern Identification

TikTok is a social media platform widely used to share information through short videos to achieve goals and interests in business, education, politics, government, and personal existence. Every activity on this platform is recorded and presented privately to each account owner. However, this data has not been utilized optimally to improve account performance. This research aims to offer a data analysis concept that integrates statistical and machine learning approaches to identify data patterns in each user's data collection, enabling the improvement of account performance. The approach utilizes Linear Regression, k-means, and Decision Tree methods. The results obtained show that the concept of identifying data patterns in TikTok account data has successfully developed a predictive model for video posts that can potentially increase total viewership, video plays, and audience engagement. This is achieved through optimizing video components such as captions, text, hashtags, sound genre, and video type. The outcome yielded a classification model that can predict capable component content to enhance account performance.

Securing the Digital Landscape: Present Concerns and Hurdles in Digital Rights Management

The landscape of Digital Rights Management (DRM) is witnessing continual evolution, marked by emerging issues and challenges that demand attention and adaptation. As technology advances, DRM faces the complex task of balancing copyright protection with user rights and expectations. One prominent challenge lies in the realm of interoperability, where varying DRM systems can create barriers for users seeking seamless access to content across different platforms. Privacy concerns have also come to the forefront, as DRM systems often involve the collection of user data to enforce licensing and access controls. Striking the right balance between effective content protections and safeguarding user privacy poses a delicate challenge for DRM implementation. Furthermore, the rise of streaming services and the proliferation of diverse digital platforms increasethe demand for standardized DRM solutions. In the absence of universally accepted standards, fragmentation and compatibility issues can arise, affecting both content providers and consumers. As the digital environment undergoes globalization, the significance of legal and regulatory frameworks will grow, playing a crucial role in influencing the future of DRM. Navigating the complexities of international law and dealing with jurisdictional differences are additional barriers to the effective deployment of DRM. Essentially, the emerging issues and challenges in Digital Rights Management highlight the importance of a dynamic and collaborative approach to ensuring intellectual property protection while respecting the constantly changing demands and rights of digital content consumers.

MARLUI: Multi-Agent Reinforcement Learning for Adaptive Point-and-Click UIs

As the number of selectable items increases, point-and-click interfaces rapidly become complex, leading to a decrease in usability. Adaptive user interfaces can reduce this complexity by automatically adjusting an interface to only display the most relevant items. A core challenge for developing adaptive interfaces is to infer user intent and chose adaptations accordingly. Current methods rely on tediously hand-crafted rules or carefully collected user data. Furthermore, heuristics need to be recrafted and data regathered for every new task and interface. To address this issue, we formulate interface adaptation as a multi-agent reinforcement learning problem. Our approach learns adaptation policies without relying on heuristics or real user data, facilitating the development of adaptive interfaces across various tasks with minimal adjustments needed. In our formulation, a user agent mimics a real user and learns to interact with an interface via point-and-click actions. Simultaneously, an interface agent learns interface adaptations, to maximize the user agent's efficiency, by observing the user agent's behavior. For our evaluation, we substituted the simulated user agent with actual users. Our study involved twelve participants and concentrated on automatic toolbar item assignment. The results show that the policies we developed in simulation effectively apply to real users. These users were able to complete tasks with fewer actions and in similar times compared to methods trained with real data. Additionally, we demonstrated our method's efficiency and generalizability across four different interfaces and tasks.

Implementasi Gerakan Literasi Sekolah Untuk Meningkatkan Minat Baca Siswa di Sekolah Dasar 060857 Medan Tembung

Reading literacy is not just an ordinary reading activity but an activity that can build culture itself. Literacy activities do refer to a person's basic ability to read and write. So far, the strategy carried out to improve this ability is to foster interest in reading and writing. Through reading is the same as grasping the whole world, because in reading can access information from all over the world.Increased literacy will lead to higher literacy Optimizing human resources, especially in fulfilling 21st century skills and the formation of good citizens. Unfortunately, the existence of the library today has not been able to inspire students to read even just for visiting. The use of reading corners in the classroom is a very effective solution to improve reading culture and explain the importance of literacy in today's digital era. This is the purpose of this study.This research uses a qualitative approach and a phenomenological type of study research. The method of determining the subject uses purpossive and snowball. User data collection methods using observation, interviews and documentation, and data analysis using interactive models.

Desktop Voice Assistant

Abstract: Desktop voice assistants have become a popular interface for human-computer interaction because they allow users to speak with their computers in natural language. However, modern desktop voice assistants sometimes struggle to understand and respond to complex queries and context. Privacy concerns concerning the collection and use of user data also remain significant barriers. In order to enhance desktop voice assistants, we present a novel personalization and multimodal interaction technique in this research. By combining text, voice, and visual inputs, our approach improves the accuracy and relevance of the responses. By combining several modalities, our system is able to better understand context and user intent, leading to more meaningful interactions. Furthermore, we propose a user-centric personalization strategy whereby the voice assistant progressively learns each user's preferences and usage patterns. Because it depends less on centralized data collection and processing, this customized approach improves user experience while simultaneously addressing privacy concerns. Through a series of experiments and user surveys, we show how effective our approach is in improving the overall performance and user experience of desktop voice assistants. Our results show that the combination of multimodal interaction with personalization produces more accurate and contextually relevant responses, improving the user experience. All things considered, our research advances desktop voice assistant technology by addressing significant interface and personalization issues. Our approach has the potential to significantly improve desktop voice assistant usability and user acceptance, which could result in considerably more intuitive and natural human-computer interaction.

DIET PLANNING AND RECOMMENDATION SYSTEM USING ML AND MERN STACK

The "Diet Planning and Recommendation System Using ML and MERN Stack" project aims to develop an innovative solution to address the challenge of personalized diet planning and recommendation. With the rising awareness of the importance of nutrition in maintaining overall health and wellness, there is a growing demand for tools that can offer tailored dietary guidance to individuals based on their unique needs and preferences. This project leverages the power of Machine Learning (ML) algorithms and the MERN (MongoDB, Express.js, React.js, Node.js) stack to create a comprehensive and user-friendly platform. The system collects user data encompassing demographic information, health metrics, dietary habits, and goals. Using ML techniques such as regression, classification, and clustering, the system analyzes this data to generate personalized diet plans and recommendations. The backend of the system, built on Node.js and Express.js, manages data storage and processing, while the frontend, developed with React.js, provides an intuitive interface for users to interact with the system. MongoDB serves as the database, ensuring scalability and flexibility in data management. The ML models continuously learn and adapt based on user feedback and outcomes, enhancing the accuracy and effectiveness of the recommendations over time. Reinforcement learning techniques are employed to optimize diet plans based on real-world outcomes and user satisfaction. By integrating ML with the MERN stack, this project offers a novel approach to diet planning and recommendation, empowering individuals to make informed dietary choices and improve their overall health and well-being. KeyWords:Diet Planning ,Recommendation System,Machine Learning,(ML),MERNStack,Personalized,Nutrition,Health,,,Metrics,Dietary,Habits,Regression,Classification,Node.js, ,React.js,,MongoDB,User,Feedback,ReinforcementLearni,Real-world Outcomes,User Satisfaction,Wellness,Informed Dietary Choices

Re:Mission study. Evaluating the NHS Low Calorie Diet pilot - an overview of service user data collection methods

Introduction: The National Health Service (NHS) Low Calorie Diet (LCD) pilot programme aimed to support people with type 2 diabetes (T2DM) to lose weight, reduce glycaemia and potentially achieve T2DM remission using total diet replacement alongside behaviour change support. The Re:Mission study seeks to provide an enhanced understanding of the long-term cost-effectiveness of the programme and its implementation, equity, transferability and normalisation across broad and diverse populations. This article presents an overview of the methods used in the Re:Mission study. Methods and analysis: A mixed method approach was used to draw together service user insights from across longitudinal and cross-sectional online surveys and semi-structured interviews supported by a modified photovoice technique. Insights were captured from active service users across the three phases of the programme (total diet replacement, food reintroduction and maintenance) in addition to those discharged prematurely. Survey data were analysed using descriptive statistics and qualitative data were thematically analysed and interpreted through a realist informed lens to understand which aspects of the service work and do not work, for whom, in what context and why. Results: Results from the study are presented elsewhere, with recommendations for policy practice and research.

Research Paper on Role of Data Features and Data Collection Tools in Artificial Intelligence

Abstract: Artificial intelligence (AI) is revolutionizing various industries by enabling machines to learn, reason, and make decisions autonomously. However, the success of AI systems depends heavily on the quality and quantity of data used for training and testing. Therefore, data collection tools have become essential in AI development. In this paper, we will discuss some popular data collection tools in AI that facilitate the process of gathering large volumes of high-quality data for training and testing AI models. Robotics and sensors are increasingly being used to collect data for AI applications in various industries like healthcare, manufacturing, and agriculture. For instance, in healthcare, robots equipped with sensors can collect medical data like vital signs, blood pressure, and heart rate from patients. In agriculture, drones equipped with sensors can collect crop data like moisture levels, temperature, and nutrient content. These tools provide high-quality data that can be used to train AI models for diagnosis, prediction, and decision-making. Mobile apps are increasingly being used to collect user data for AI applications. Apps like Google Maps, Waze, and Uber collect location data that can be used to train AI models for navigation and traffic prediction. Healthcare apps like MyFitnessPal and Fitbit collect user health data that can be used to train AI models for personalized health recommendations. The Internet of Things is enabling the collection of vast amounts of real-time data from various devices like smart homes, smart cities, and smart factories. This data can be used to train AI models for predictive maintenance, energy management, and resource optimization.

Privacy preserving and secure robust federated learning: A survey

SummaryFederated learning (FL) has emerged as a promising solution to address the challenges posed by data silos and the need for global data fusion. It offers a distributed machine learning framework with privacy‐preserving features, allowing model training without the need to collect user data. However, FL also presents significant security and privacy threats that hinder its widespread adoption. The requirements of privacy and security in FL are inherently conflicting. Privacy necessitates the concealment of individual client updates, while security requires the disclosure of client updates to detect anomalies. While most existing research focused on the privacy and security aspects of FL, very few studies have addressed the compatibility of these two demands. In this work, we aim to bridge this gap by proposing a comprehensive defense scheme that ensures privacy, security, and compatibility in FL. We categorize the existing literature into two key directions: privacy defense and security defense. Privacy defense includes methods based on additive masks, differential privacy, homomorphic encryption, and trusted execution environment, whereas security defense encompasses distance‐, performance‐, clustering‐, and similarity‐based anomaly detection techniques and statistical information‐based anomaly update bypassing techniques when the server is trusted and privacy‐compatible anomaly update detection techniques when the server is not trusted. In addition, this article presents decentralized FL solutions based on blockchain. For each direction, we discuss specific technical solutions, their advantages, and disadvantages. By evaluating various defense methods, we identify the most suitable approach to address the primary challenge of “achieving a secure and robust FL system against malicious adversaries while protecting users' privacy.” We then propose a theoretical reference framework for end‐to‐end protection of privacy and security in FL for the key problem, which summarizes the attack surface of FL systems from the client to the server under the security model where the client and server are malicious. Leveraging the strengths and characteristics of existing schemes, our proposed framework integrates multiple techniques to strike a balance between privacy, usability, and efficiency. This framework serves as a valuable reference and provides insights for future work in the field. Finally, we also provide recommendations for future research directions in this field.

Automatic Diagnosis Method for Short Circuit Faults in Power Measurement Instruments Based on CNN

The accuracy of short-circuit fault diagnosis methods for power measuring instruments is still low, which can directly lead to abnormal power data statistics. To address this issue, a convolutional neural network is used to construct an automatic diagnosis model for short circuit faults in power measuring instruments. The model utilizes wavelet packet energy spectrum to extract crucial features from the signal. The model identifies and determines the fault type based on the obtained operation characteristic data and the set short-circuit fault diagnosis criteria. The Sparrow optimization algorithm is used to optimize the model's parameters and enhance its performance. Experimental analysis revealed that after the signal features were extracted by using the wavelet energy spectrum distribution, the error value fluctuated in the range of 0.00 ~ 0.04 with regard to the measured value and was mainly around 0.02. The extracted features achieved high accuracy levels. The designed model exhibited an average diagnostic accuracy of 97.03%, surpassing the other three models by 9.24%, 7.10%, and 4.25%. The presented model can improve the precision and productivity of fault detection, support the safety and reliability of power system operations, and facilitate the collection and analysis of power usage data

Pervasive User Data Collection from Cyberspace: Privacy Concerns and Countermeasures

The increasing use of technologies, particularly computing and communication paradigms, has significantly influenced our daily lives. Interconnecting devices and networks provides convenient platforms for information exchange and facilitates pervasive user data collection. This new environment presents serious privacy challenges. User activities can be continuously monitored in both digital and physical realms. Gathered data can be aggregated and analysed, revealing aspects of user behaviour that may not be apparent from a single data point. The very items that facilitate connectivity simultaneously increase the risk of privacy breaches. The data gathered to provide services can also be used for monitoring and surveillance. This paper discerns three novel categories of privacy concerns relating to pervasive user data collection: privacy and user activity in cyberspace, privacy in personal cyber–physical systems, and privacy in proactive user-driven data collection. We emphasise the primary challenges, ranging from identity tracking in browsing histories to intricate issues in opportunistic networks, situating each within practical, real-world scenarios. Furthermore, we assess the effectiveness of current countermeasures, investigating their strengths and limitations. This paper explores the challenges in preserving privacy in user interactions with dynamic interconnected systems and suggests countermeasures to mitigate identified privacy risks.