Data Collection Issues Research Articles

Methodological issues in technology-mediated qualitative data collection: a mapping of research undertaken in schools during the Covid-19 pandemic

Methodological issues in technology-mediated qualitative data collection: a mapping of research undertaken in schools during the Covid-19 pandemic

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.

Fault diagnosis of drone motors driven by current signal data with few samples

Multi rotor unmanned aerial vehicles (UAVs) are extensively utilized across various domains, and the motor constitutes a pivotal element in the UAV power system. The majority of UAV failures and crashes stem from motor malfunctions, underscoring the imperative need for comprehensive research on fault diagnosis in UAV motors to ensure the stable and reliable execution of flight tasks. This study focuses on quadrotor UAVs as the research subject and devises targeted fault simulation experiments based on the structural features and operational characteristics of the DC brushless motor used in quadrotor UAVs, specifically examining the stator, rotor, and bearings. To address challenges related to the UAV’s own loads, limited space for redundant parts, and the high cost and difficulty associated with installing sensors for traditional fault diagnostic signals such as vibration and temperature, this study opts to use current signals as a substitute. This approach resolves the issue of challenging data collection for UAVs and investigates a current signal based fault diagnosis method for UAV motors. Lastly, in response to the limited training samples available for fault data due to the UAV’s highly sensitive characteristics regarding the health status of its components and flight stability, traditional machine learning and deep learning methods encounter difficulties in identifying representative features with a small number of training samples, leading to the risk of overfitting and reduced model accuracy in fault diagnosis. To overcome this challenge, we propose a hybrid neural network fault diagnosis model that incorporates a width learning system and a convolutional neural network (CNN). The width learning system eliminates temporal characteristics from the original current signal, capturing more comprehensive and representative sample features in the width feature space. Subsequently, the CNN is employed for feature extraction and classification tasks. In empirical small sample fault diagnosis experiments using current signal data for UAV motors, our proposed model outperforms other models used for comparison.

Viral invasion, incubation, and outbreak under the normalized operation of urban systems: a spatial cognition-driven transmission model of infectious diseases

ABSTRACT Large-scale epidemics, such as COVID-19, pose significant threats to human health and social stability due to their rapid and covert transmission, emerging as one of the main challenges to maintaining a well-functioning urban system. During the pandemic, the spatial transmission mechanisms and their influencing factors of infectious diseases have received as a central topic. Scholars concentrated on researching the phase of extensive dissemination and mandatory intervention, utilizing multiple datasets. However, there remains a notable gap in the investigation of virus spatial transmission mechanisms under normalized urban operations due to limitations in detection timeliness and data collection issues. Moreover, compared to various intervention strategies, the influence of human behavioral dynamics and spatial cognition remains understudied. The paper simulates invasion, incubation, and outbreak of the Omicron variant in Zhuhai City in 2022, validating the results against real-world epidemic data. By comparing spatial diffusion patterns under three levels of spatial cognition, it sheds light on the delayed impact of human spatial cognitive abilities on urban pandemic outbreaks and their influence on virus invasion orders. The simulation model and revealed diffusion mechanisms will provide important guidance for the proactive prevention and control of unpredictable large-scale epidemics in future urban systems.

Identifying Student Behavior Patterns Based on LMS Data

In contemporary education, data analysis methods to establish patterns of student behavior for subsequent optimization of the educational process are becoming increasingly relevant. This paper presents research aimed at identifying patterns of student behavior based on the processing of data obtained from Learning Management Systems (LMS). The article examines data collection and analysis issues from LMS, including activity logs, grades, participation in forums, and other interactive elements. Additionally, methods such as statistical analysis and machine learning, applied to identify patterns of student behavior, are discussed. The text describes the identified patterns of student behavior in the electronic educational environment, which are subsequently linked to students' academic performance levels. The paper's concluding section presents the research findings and potential scenarios for their application. Key Words: adaptive learning, machine learning, learning management systems, student behavior patterns.

Exploring Ethical Considerations in AI-driven Autonomous Vehicles: Balancing Safety and Privacy

The deployment of autonomous vehicles (AVs) powered by artificial intelligence (AI) raises profound ethical questions regarding the balance between safety and privacy. While AI-driven AVs promise to revolutionize transportation by potentially reducing accidents and increasing efficiency, concerns regarding data privacy, liability, and decision-making algorithms persist. This paper explores the ethical considerations surrounding AI-driven AVs, focusing particularly on the delicate equilibrium required to ensure both safety and privacy. Drawing upon existing literature and case studies, the paper examines the ethical dilemmas inherent in AV technology, including issues of consent, data collection, and algorithmic bias. Additionally, it delves into the regulatory frameworks and industry standards aimed at addressing these concerns. By highlighting the complexities of navigating safety and privacy in AI-driven AVs, this research contributes to the ongoing discourse on ethical AI development and deployment.

Defining acceptable data collection and reuse standards for queer artificial intelligence research in mental health: protocol for the online PARQAIR-MH Delphi study

IntroductionFor artificial intelligence (AI) to help improve mental healthcare, the design of data-driven technologies needs to be fair, safe, and inclusive. Participatory design can play a critical role in empowering...

Artificial intelligence-assisted repair of peripheral nerve injury: a new research hotspot and associated challenges.

Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury. Specifically, it can be used to analyze and process data regarding peripheral nerve injury and repair, while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms. To investigate advances in the use of artificial intelligence in the diagnosis, rehabilitation, and scientific examination of peripheral nerve injury, we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994-2023. We identified the following research hotspots in peripheral nerve injury and repair: (1) diagnosis, classification, and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques, such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy; (2) motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms, such as wearable devices and assisted wheelchair systems; (3) improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning, such as implantable peripheral nerve interfaces; (4) the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility, enabling them to control devices such as networked hand prostheses; (5) artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation, thereby reducing surgical risk and complications, and facilitating postoperative recovery. Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair, there are some limitations to this technology, such as the consequences of missing or imbalanced data, low data accuracy and reproducibility, and ethical issues (e.g., privacy, data security, research transparency). Future research should address the issue of data collection, as large-scale, high-quality clinical datasets are required to establish effective artificial intelligence models. Multimodal data processing is also necessary, along with interdisciplinary collaboration, medical-industrial integration, and multicenter, large-sample clinical studies.

Multi-Dimensional Wi-Fi Received Signal Strength Indicator Data Augmentation Based on Multi-Output Gaussian Process for Large-Scale Indoor Localization

Location fingerprinting using Received Signal Strength Indicators (RSSIs) has become a popular technique for indoor localization due to its use of existing Wi-Fi infrastructure and Wi-Fi-enabled devices. Artificial intelligence/machine learning techniques such as Deep Neural Networks (DNNs) have been adopted to make location fingerprinting more accurate and reliable for large-scale indoor localization applications. However, the success of DNNs for indoor localization depends on the availability of a large amount of pre-processed and labeled data for training, the collection of which could be time-consuming in large-scale indoor environments and even challenging during a pandemic situation like COVID-19. To address these issues in data collection, we investigate multi-dimensional RSSI data augmentation based on the Multi-Output Gaussian Process (MOGP), which, unlike the Single-Output Gaussian Process (SOGP), can exploit the correlation among the RSSIs from multiple access points in a single floor, neighboring floors, or a single building by collectively processing them. The feasibility of MOGP-based multi-dimensional RSSI data augmentation is demonstrated through experiments using the hierarchical indoor localization model based on a Recurrent Neural Network (RNN)—i.e., one of the state-of-the-art multi-building and multi-floor localization models—and the publicly available UJIIndoorLoc multi-building and multi-floor indoor localization database. The RNN model trained with the UJIIndoorLoc database augmented with the augmentation mode of “by a single building”, where an MOGP model is fitted based on the entire RSSI data of a building, outperforms the other two augmentation modes and results in the three-dimensional localization error of 8.42 m.

The role of AI in individualizing learning and creating personalized programs

This article analyses the technical and methodological aspects of implementing individualised curricula using artificial intelligence in the educational process. The study deals in detail with the issues of technological infrastructure, data collection, and processing, as well as the integration of individualised programmes with existing educational platforms. The methodological aspect of the article includes an analysis of methods for determining the needs and capabilities of each student and the development of a methodology for assessing the success of individualised programmes. The study aims to uncover the potential and benefits associated with the utilization of personalized programs in contemporary education. This is done with the intention of enhancing the overall learning experience and attaining superior outcomes for every individual student and pupil. Future areas of research include further development of technical solutions for individualised programmes, studying methodological approaches to adapting programmes to the needs of different categories of student, and developing ethical standards for protecting personal data in education. This article will be useful for teachers, higher education institutions, researchers, and anyone interested in using artificial intelligence to individualise learning and improve education. It offers important discoveries and practical recommendations for implementing individualised programmes in the educational process.

ПРОДУКТОВЫЙ ПОДХОД ПРИ ПРОВЕДЕНИИ A/B-ТЕСТИРОВАНИЯ ЦИФРОВЫХ СЕРВИСОВ

The methodology of A/B testing has been clarified from the perspective of productbased approach to digital product management. The issue of data collection in the modern product management and its formalization within the concepts of unit economics has been raised. The verification of hypotheses through statistical A/B testing has been studied. To accomplish this, the design of multiple experiments, its mathematical apparatus, the regulation of testing processes, as well as methods of interpreting results using publicly available datasets of user behavior, have been examined.

Prosthodontic Research in the Digital Era: Current Trends and Future Prospects

Prosthodontic Research in the Digital Era: Current Trends and Future Prospects

Analysis of Semi-Rural and Urban Hypertension Prevalence Using WHO Measures

This study investigated the widespread health concern of hypertension and its significant impact on cardiovascular diseases, strokes, and overall health. The research focuses on understanding the prevalence of hypertension in both semi-rural and urban areas, using World Health Organization metrics for a comprehensive evaluation. Despite extensive prior research on hypertension, the distinct frequency gap between semi-rural and urban settings poses a notable challenge. This study addressed issues in data collection, sampling biases, and temporal fluctuations by introducing a novel approach called Hypertension Prevalence Analysis using WHO Measures (HPA-WHOM), which outperforms existing methods. In semi-rural settings, HPA-WHOM estimates a hypertension prevalence of 53.76%, revealing average blood pressure levels, salt intake, physical activity, smoking habits, and alcohol use. These findings underscore HPA-WHOM's effectiveness in understanding hypertension prevalence and associated factors, contributing valuable insights for tailored interventions and global initiatives to combat the worldwide impact of hypertension on public health.

Globalization and Terrorism in Nigeria: A Retrospective Reflection

Nigerian sovereignty for more than a decade was challenged by the onslaught of Sunni Jihadism in the North-East. The Jihadist insurgency took a more frightening dimension with the emergence of Islamic State of West Africa Province, ISWAP in alliance with Jama’atu Ahlis Sunna Lidda Awati Wal-Jihad, otherwise known as Boko Haram in bid to Islamize a secular nation-state. This paper in retrospective sense examined the frightening reality and, argued that this absurdity is facilitated by global spread of Islamic extremism and terrorism. The discourse adopted qualitative research design to explore the relevance of secondary and non-participant observational sources of data collection and content-analyzed issues and enfolding events. The discourse revealed that well-coordinated suicide bombings of Louis Edet House, Nigeria Police Headquarters on June 16, 2011 and United Nations building on August 26, 2011 at Abuja, the Federal Capital, were visible manifestations of global terrorist links of Boko Haram with Al-Qaeda. And, subsequent pledge of allegiance to ISIL in March 2015 watershed the varicosity of global terrorism in Africa most populous country. In credence to these findings, the discourse suggested holistic review of national security strategy and operational

Using a Mobile Messenger Service as a Digital Diary to Capture Patients' Experiences Along Their Interorganizational Treatment Path in Gynecologic Oncology: Lessons Learned.

A digital diary in the form of a mobile messenger service offers a novel method for data collection in cancer research. Little is known about the things to consider when using this data collection method in clinical research for patients with cancer. In this Viewpoint paper, we discuss the lessons we learned from using a qualitative digital diary method via a mobile messenger service for data collection in oncology care. The lessons learned focus on three main topics: (1) data quality, (2) practical aspects, and (3) data protection. We hope to provide useful information to other researchers who consider this method for their research with patients. First, in this paper, we argue that the interactive nature of a digital diary via a messenger service is very well suited for the phenomenological approach and produces high-quality data. Second, we discuss practical issues of data collection with a mobile messenger service, including participant and researcher interaction. Third, we highlight corresponding aspects around technicalities, particularly those regarding data security. Our views on data privacy and information security are summarized in a comprehensive checklist to inform fellow researchers on the selection of a suitable messenger service for different scenarios. In our opinion, a digital diary via a mobile messenger service can provide high-quality data almost in real time and from participants' daily lives. However, some considerations must be made to ensure that patient data are sufficiently protected. The lessons we learned can guide future qualitative research using this relatively novel method for data collection in cancer research.

Transfer Learning-Based Object Detection Model for Steel Structure Bolt Fastening Inspection

As improper inspection of construction works can cause an increase in project costs and a decrease in project quality, construction inspection is considered a critical factor for project success. While traditional inspection tasks are still mainly labor-intensive and time-consuming, computer vision has the potential to revolutionize the construction inspection process by providing more efficient and effective ways to monitor the progress and quality of construction projects. However, previous studies have also indicated that the performance of vision-based site monitoring heavily relies on the volume of training data. To address the issues of challenging data collection at construction sites, this study developed models using transfer learning-based object detection models incorporating data augmentation and transfer learning. The performance of three object detection algorithms was compared based on average precision and inference time for detecting T/S bolt fastening of steel structure. Despite the limited training data available, the model’s performance was improved through data augmentation and transfer learning. The proposed inspection model can increase the efficiency of quality control works for building construction projects and the safety of inspectors.

Federated Learning in Small-Cell Networks: Stochastic Geometry-Based Analysis on the Required Base Station Density.

Recently, federated learning (FL) has been receiving great attention as an effective machine learning method to avoid the security issue in raw data collection, as well as to distribute the computing load to edge devices. However, even though wireless communication is an essential component for implementing FL in edge networks, there have been few works that analyze the effect of wireless networks on FL. In this paper, we investigate FL in small-cell networks where multiple base stations (BSs) and users are located according to a homogeneous Poisson point process (PPP) with different densities. We comprehensively analyze the effects of geographic node deployment on the model aggregation in FL on the basis of stochastic geometry-based analysis. We derive the closed-form expressions of coverage probability with tractable approximations and discuss the minimum required BS density for achieving a target model aggregation rate in small-cell networks. Our analysis and simulation results provide insightful information for understanding the behaviors of FL in small-cell networks; these can be exploited as a guideline for designing the network facilitating wireless FL.

Yoga Programme for Type 2 Diabetes Prevention (YOGA-DP): A Qualitative Study Exploring the Trial Team's Facilitators and Challenges in Conducting a Feasibility Trial in India.

In India, around 77 million people are at high risk of developing type 2 diabetes mellitus (T2DM). Yoga interventions can be effective in preventing T2DM. We conducted a feasibility randomized controlled trial (RCT) in India, and the intervention was the Yoga Programme for T2DM Prevention (YOGA-DP). This study aimed to identify and explore the facilitators and challenges in conducting the feasibility trial in India, and more specifically, to explore the perceptions and experiences of trial staff in relation to running the feasibility trial and Yoga instructors in relation to delivering the intervention. An exploratory qualitative study was conducted at two trial sites in India (Yoga centers in New Delhi and Bengaluru). Semi-structured interviews were conducted with ten participants (six trial staff and four Yoga instructors) to explore their perceptions and experiences related to the study's aim. Data were analyzed using deductive as well as inductive logic and an interpretative phenomenological approach. Feasibility-trial-related facilitators were useful participant recruitment strategies and help and support received from the trial coordination center. Intervention-related facilitators were strengths of the intervention content, structure, and delivery (including materials) and competencies of Yoga instructors. Feasibility-trial-related challenges were lack of awareness about T2DM among potential participants, stigma and fear associated with T2DM among potential participants, difficulties in explaining the research and obtaining written informed consent from potential participants, expectations and demands of potential participants and control-group participants, gender and language issues in participant recruitment, other participant recruitment-related challenges, issues in participant follow-up, and issues in data collection and trial documentation. Intervention-related challenges were the limited interest of participants in Yoga, participants' time constraints on practicing Yoga, participants' health issues hindered Yoga practice, beginners' difficulties with practicing Yoga, participants' demotivation to practice Yoga at home, issues with the Yoga practice venue, confusion regarding the intervention structure, issues with intervention materials, and the incompetence of Yoga instructors. The perceptions and experiences of trial staff and Yoga instructors helped us to understand the facilitators and challenges in running a feasibility trial and delivering the intervention for T2DM prevention, respectively. These findings and their suggestions will be used when designing the definitive RCT for evaluating YOGA-DP's effectiveness, and may be helpful to researchers planning similar trials. India (CTRI) CTRI/2019/05/018893.

Integration of Social Regulations and Zakat Management Regulations as National Efforts to Alleviate Poverty

<p><em>The primary responsibility of the government is to safeguard the best interests of all citizens. In Indonesia, where the majority of the population follows Islam, the principles of poverty eradication through social safety system align with the constitutional guidelines. Among these principles are social assistance and mustahik Zakat. However, the process of obtaining accurate data on beneficiaries of social assistance often encounters challenges, leading to the emergence of inaccurate information. This is in line with Article 28 of Law of the Republic of Indonesia Number 13 of 2011 concerning Handling The Poor, where the government is required to develop a comprehensive plan to aid the impoverished. It also involves establishing a database of those in need and implementing efficient application methods. From this context, the data collection issues hinder the ability of the government to achieve critical objectives, such as poverty reduction and enhancing the overall well-being of the population. Therefore, this study aims to determine a position where Islamic and state law agree on the patterns of helping poor people eradicate poverty toward the coexistence of social aid and mustahik Zakat data, using a normative legal method. The results showed that a single set of data should include both the people obtaining social aid and mustahik Zakat, for poverty reduction to be successful and progressive. For data to be integrated, the rules guiding the agreement of everyone should also be emphasized, to ensure that poor people were obtaining help from social services or Zakat mustahik. This enabled the easy operation of programs, to help affected people eradicate poverty.</em></p>

Unravelling the application of machine learning in cancer biomarker discovery

Machine learning is playing an increasingly important role in the healthcare industry by transforming the way cancer is diagnosed and treated. By analyzing patient data, genomic data, and imaging data, machine learning algorithms can identify molecular signatures that distinguish cancer patients from healthy patients. Biomarkers that can accurately detect and diagnose cancer can be identified through analysis of these data sources. Additionally, personalized cancer therapies can be developed by identifying the most effective treatments based on individual patient characteristics and cancer type. Some of the machine learning techniques used for cancer biomarker discovery include deep learning and support vector machines, which can respectively identify complex patterns in data and classify data to identify relevant biomarkers. The benefits of using machine learning for cancer biomarker discovery are significant, including more precise and personalized treatments, improved patient outcomes, and the potential to transform cancer diagnosis and treatment. However, there are also challenges associated with using machine learning for cancer biomarker discovery, such as data collection and privacy issues, as well as the need for more powerful computational resources. This article explores the potential of machine learning in cancer biomarker discovery and argues that ongoing research in this field has the potential to revolutionize cancer diagnosis and treatment. Future research directions should focus on further developing machine learning algorithms and effective data collection and privacy protocols.