Copies Of Data Research Articles

Best practices for studies using digital data donation

AbstractDigital trace data form a rich, growing source of data for social sciences and humanities. Data donation offers an innovative and ethical approach to collect these digital trace data. In data donation studies, participants request a copy of the digital trace data a data controller (e.g., large digital social media or video platforms) collected about them. The European Union’s General Data Protection Regulation obliges platforms to provide such a copy. Next, the participant can choose to share (part of) this data copy with the researcher. This way, the researcher can obtain the digital trace data of interest with active consent of the participant. Setting up a data donation study involves several steps and considerations. If executed poorly, these steps might threaten a study’s quality. In this paper, we introduce a workflow for setting up a robust data donation study. This workflow is based on error sources identified in the Total Error Framework for data donation by Boeschoten et al. (2022a) as well as on experiences in earlier data donation studies by the authors. The workflow is discussed in detail and linked to challenges and considerations for each step. We aim to provide a starting point with guidelines for researchers seeking to set up and conduct a data donation study.

Deep‐Learning Based Causal Inference: A Feasibility Study Based on Three Years of Tectonic‐Climate Data From Moxa Geodynamic Observatory

AbstractHighly sensitive laser strainmeters at Moxa Geodynamic Observatory (MGO) measure motions of the upper Earth's crust. Since the mountain overburden of the laser strainmeters installed in the gallery of the observatory is relatively low, the recorded time series are strongly influenced by local meteorological phenomena. To estimate the nonlinear effect of the meteorological variables on strain measurements in a non‐stationary environment, advanced methods capable of learning the nonlinearity and discovering causal relationships in the non‐stationary multivariate tectonic‐climate time series are needed. Methods for causal inference generally perform well in identifying linear causal relationships but often struggle to retrieve complex nonlinear causal structures prevalent in real‐world systems. This work presents a novel model invariance‐based causal discovery (CDMI) method that utilizes deep networks to model nonlinearity in a multivariate time series system. We propose to use the theoretically well‐established Knockoffs framework to generate in‐distribution, uncorrelated copies of the original data as interventional variables and test the model invariance for causal discovery. To deal with the non‐stationary behavior of the tectonic‐climate time series recorded at the MGO, we propose a regime identification approach that we apply before causal analysis to generate segments of time series that possess locally consistent statistical properties. First, we evaluate our method on synthetically generated time series by comparing it to other causal analysis methods. We then investigate the hypothesized effect of meteorological variables on strain measurements. Our approach outperforms other causality methods and provides meaningful insights into tectonic‐climate causal interactions.

간호대학생의 공감역량과 회복탄력성, 간호전문직관, 임상실습적응에 관한 연구

Objectives The purpose of this study is to identify differences and correlations between variables such as empathy competency and resilience, nursing professionalism, and clinical practice adaptation according to the general characteristics of nursing students. Methods Structured questionnaires were distributed to 260 students in the 3rd and 4th grades of nursing at four universities, and 252 copies of data were used for the final analysis. For data analysis, descriptive statistics, independent sample t-test, One-way ANOVA, Scheffé post-test, Person's correlation analysis, and the SPSS 25.0 program were used for tool reliability. Results According to the descriptive statistical results of the main variables, the average of empathy competency was 4.11 out of 5, the average of resilience was 3.95 out of 5, the average of nursing professionals was 4.04 out of 5, and the average of clinical practice adaptation was 3.89 out of 5. Looking at the correlation results, resilience was statistically significant with empathy competency (r=.764, p<.001), nursing professionals were statistically significant with empathy competency (r=.606, p<.001), and resilience (r=.591, p<.001). Clinical practice adaptation showed statistically significant positive (+) correlation with empathy competency (r=.650, p<.01), resilience (r=.735, p<.001), and nursing professionals (r=.608, p<.001). Conclusions Since nursing students' empathy competency and resilience have a positive correlation with nursing professions and clinical practice adaptation, it is necessary to apply programs to improve nursing students' empathy competency and resilience. In addition, nursing professionals and clinical practice adaptation have important significance in improving nursing practice and are becoming basic elements of qualitative nursing.

Hercules: Enabling Atomic Durability for Persistent Memory with Transient Persistence Domain

Persistent memory (pmem) products bring the persistence domain up to the memory level. Intel recently introduced the eADR feature that guarantees to flush data buffered in CPU cache to pmem on a power outage, thereby making the CPU cache a transient persistence domain . Researchers have explored how to enable the atomic durability for applications’ in-pmem data. In this article, we exploit the eADR-supported CPU cache to do so. A modified cache line, until written back to pmem, is a natural redo log copy of the in-pmem data. However, a write-back due to cache replacement or eADR on a crash overwrites the original copy. We accordingly developed Hercules, a hardware logging design for the transaction-level atomic durability, with supportive components installed in CPU cache, memory controller (MC), and pmem. When a transaction commits, Hercules commits on-chip its data staying in cache lines. For cache lines evicted before the commit, Hercules asks the MC to redirect and persist them into in-pmem log entries and commits them off-chip upon committing the transaction. Hercules lazily conducts pmem writes only for cache replacements at runtime. On a crash, Hercules saves metadata and data for active transactions into pmem for recovery. Experiments show that, by using CPU cache for both buffering and logging, Hercules yields much higher throughput and incurs significantly fewer pmem writes than state-of-the-art designs.

Certificate-based multi-copy cloud storage auditing supporting data dynamics

With the advent of cloud computing, users are increasingly choosing to store their data on cloud. As a result, data integrity and availability have emerged as key concerns for data owners. Users expect to store multiple copies of their data to cloud and ensure the integrity of these data copies. Currently, numerous multi-copy cloud storage auditing schemes have been proposed. However, most of them depend on public key infrastructure, identity-based cryptography, or certificateless cryptography. These schemes encounter challenges such as complicated certificate management, key escrow, or the necessity for a secure channel for distributing keys, respectively. Furthermore, most of them are not resilient to copy-summation attack. To address the above problems, we propose a certificate-based multi-copy cloud storage auditing scheme supporting data dynamics. We design a novel dynamic structure named Leaves Merkle hash tree (LMHT) to achieve multi-copy dynamic updates. Different from traditional Merkle hash trees, LMHT has significant advantages in data deletion. In addition, the proposed scheme can resist copy-summation attack, in which cloud cannot pass the verification if it only stores summation of all copies without storing data blocks’ all copies. Security analysis and performance evaluation demonstrate that the proposed scheme is secure and efficient.

가상 인플루언서 특성이 신뢰와 만족도를 통해서 관광지의 방문의도에 미치는 영향: 연령집단별 비교를 중심으로

Purpose: This study comprehensively explores the characteristics of virtual influencer by paying attention to the trend of the times, and the purpose of the study was to discuss whether trust and satisfaction with tourists occur due to the characteristics of virtual influencer (attractiveness, similarity, curiosity, personification), and whether tourists' intention to visit were affected. The purpose of this study is to add an age control variable to check how the trust and satisfaction of people by age group affect the effect of intention to visit. Methods: This study was conducted on people over the age of 20 who have watched virtual influencer content via social media platforms. Analyzed a total of 327 copies of data through SPSS and Smart-PLS statistical programs. Results: As a result of the study, virtual influencer characteristics (attractiveness, similarity) positively influenced trust. Virtual influencer (attractiveness, curiosity, personification) had a positive effect on satisfaction. Trust had a positive effect on the intention to visit. Satisfaction had a positive effect on the intention to visit. Looking at the results of the age control test, it was found that satisfaction had a more positive effect on visit intention in the younger age group (β=.381) than in the older age group (β=.021). Conclusion: It can provide academic and practical implications that can be referenced when producing and using a virtual influencer in the field of tourism marketing.

Enhancing security in digitized healthcare system using blockchain technology.

In the modern medical industry, sensors and other medical electronic equipment such as ECG, thermometers, etc. that measure vitals like blood pressure, body temperature, heartbeat, and blood cells are used to record patient data. The information gathered helps to create a patient profile, which in turn helps doctors treat patients appropriately and helps users gain insights into their health. But, the information collected is regionally specific and not worldwide. Thus, this may create problems in transparency, integrity, and much more. The medical details of patients collected are stored in a simple database which appears to be a centralized application that is prone to be modified or hacked by intruders and also suffers from fault tolerance. This paper aims to solve this problem using Blockchain and Data Possession methodologies. Blockchain helps to store multiple copies of data in a decentralized fashion. Thus, a secured model could be implemented using blockchain in which the patient's data can be securely sent to any medical professional, thus promoting transparency, security, and integrity. Provable Data Possession helps the users to verify that their information is intact and has not been tampered. It can be achieved using cryptography which encrypts the information of the patients stored. This paper will be beneficial for the medical department and the public such that it will be beneficial in terms of transparency, scalability, and security.

SimClone: Detecting Tabular Data Clones using Value Similarity

Data clones are defined as multiple copies of the same data among datasets. Presence of data clones between datasets can cause issues such as difficulties in managing data assets and data license violations when using datasets with clones to build AI software. However, detecting data clones is not trivial. Majority of the prior studies in this area rely on structural information to detect data clones (e.g., font size, column header). However, tabular datasets used to build AI software are typically stored without any structural information. In this paper, we propose a novel method called SimClone for data clone detection in tabular datasets without relying on structural information. SimClone method utilizes value similarities for data clone detection. We also propose a visualization approach as a part of our SimClone method to help locate the exact position of the cloned data between a dataset pair. Our results show that our SimClone outperforms the current state-of-the-art method by at least 20% in terms of both F1-score and AUC. In addition, SimClone’s visualization component helps identify the exact location of the data clone in a dataset with a Precision@10 value of 0.80 in the top 20 true positive predictions.

Proof of Inaction for outsourced data with exhaustive audit

In the existing environment of cloud storage and distributed hosting solutions, it is imperative to ensure the long-term preservation of inactive data like keyring backups and essential system logs, as these cannot afford the smallest error or data loss during storage. The current methods of subset sampling for data integrity verification fall short of these critical demands. To solve this problem, we introduce Proof of Inaction (PoI), an innovative framework for verifying the integrity of inactive data through verifiable computation principles. First, during the data owner’s exhaustive audit, the storage provider must maintain complete possession of all data to validate storage integrity, which means failing any integrity challenge if even a single bit is missing. Second, PoI enables the verification of storage integrity for both single and multiple copies of data, supporting an unlimited number of challenges in each scenario. Utilizing the co-CDH assumption, we validate the correctness and soundness of our approach with the random oracle model. Both theoretical analysis and empirical evidence suggest that PoI’s challenge and verification process do not escalate in overhead as the size of the challenged files increases, offering an enhancement over traditional data integrity verification methods.

Pengaturan Motor DC Menggunakan Tuning PID Zigler Nichols Sebagai Media Pembelajaran Interaktif;

DC Motor is a direct current that is often used in everyday life that uses speed regulation, the DC Motor rotation will be given a load so we test the Motor rotation value according to the weight of the motor load that is generated after the Motor is activated. To find out the motor rotation and the resulting value after being given a load, we are here using the Arduino Mega component as a microcontroller, the IBT 2 driver as a DCb motor speed controller that has been given a load, the encoder as a copy of the old motor rotation data to the new motor data, the potentiometer as a sensor to find out DC Motor rotation, LCD to display data that has been taken, Arduino Ide as Software. And the control used is PID (proportional integral differential). This PID control system works by processing calculations based on the control variables Kp, Ki, and Kd to achieve conditions according to the expected setpoint..

Outsourced Privately Verifiable Proofs of Retrievability via Blockchain

Outsourced Proofs of Retrievability (OPoR) with private verification enables a third party verifier to periodically check cloud data on behalf of users. However, such a scheme requires the verifier to keep a copy of the user's data and generate tags for the data like the data owner. In other words, in addition to storing the data and tags from the user, the cloud server also needs to store tags uploaded by the verifier. To overcome this limitation, we propose a concrete construction of outsourced privately verifiable PoR (OPVPoR) without requiring the additional tag storage from the verifier. Furthermore, we extend the OPVPoR scheme to the multi-user setting and build a MOPVPoR scheme, where users storing the same data to the cloud server also share the tag information to further reduce the storage cost. Finally, we implement both schemes to evaluate their performance in practice.

The Case of Unsustainable CPU Affinity

CPU affinity reduces data copies and improves data locality and has become a prevalent technique for high-performance programs in datacenters. This paper explores the tension between CPU affinity and sustainability. In particular, affinity settings can lead to significant uneven aging of cores on a CPU. We observe that infrastructure threads, used in a wide spectrum of network, storage, and virtualization sub-systems, exercise their affinitized cores up to 23× more when compared to typical μ s-scale application threads. In addition, we observe that the affinitized infrastructure threads generate regional heat hot spots and preclude CPUs from being used with the expected lifetime. Finally, we discuss design options to tackle the unbalanced core-aging problem to improve the overall sustainability of CPUs and call for more attention to sustainability-aware affinity and mitigation of such problems.

Effects of Human Service Qualities of Waxing Beauty Professionals on the Perceived Values and Emotional Response of Customer

Owing to the necessities like beauty, hygiene, and health, waxing is settled down as beauty service that could immediately satisfy customers’ interest and needs. Thus, this study could be helpful for establishing high-dimensional service strategies through the perception of customer service, customers’ valuation, and their emotional response for competitive advantage of the waxing industry, by understanding the effects of human service qualities of waxing beauty professionals on the perceived values and emotional response. From January 8, 2024 to February 2, 2024, A total of 332 copies of valid data were collected from adult men and women who had experienced receiving waxing beauty services from experts at waxing beauty shops in Seoul and the metropolitan area. The collected data were analyzed using the SPSS 25.0 program. And Frequency analysis, Exploratory factor analysis, Cronbach’s α, Correlation analysis, Multiple regression analysis were conducted. The results of this study are as follows. First, the human service qualities of waxing beauty professionals had significant effects on the perceived values. Second, the human service qualities of waxing beauty professionals had significant effects on emotional response. This verifies that the emotional, social, and economic values felt by consumers are improved when the reliability, empathy, and tangibles of human service qualities of waxing beauty professionals are higher. And they also have positive effects on emotional response.

Convergent encryption enabled secure data deduplication algorithm for cloud environment

SummaryThe exponential growth of data poses a critical challenge for cloud storage systems. Redundant data consumes valuable storage space and increases infrastructure costs. Data deduplication, a technique for eliminating duplicate data copies, offers a promising solution. However, existing deduplication techniques often compromise data security, especially when dealing with encrypted data. This paper proposes a novel approach that merges convergent encryption (CE) with data deduplication. CE leverages user data itself to generate unique encryption keys, enabling secure deduplication on encrypted data. We analyze existing literature on secure data deduplication and categorize various techniques using UML activity diagrams. We then present our proposed CE‐based deduplication system, outlining its functionalities through UML diagrams. This research contributes to the field of secure data storage by proposing a novel and secure deduplication approach. By demonstrating its efficiency and security benefits, this work paves the way for more efficient and secure cloud storage solutions. Finally, we demonstrate the system's effectiveness through a comparative analysis, highlighting its potential to significantly improve storage efficiency while maintaining data security.

Design and Implementation of IPsec VPN IoT Gateway System in National Secret Algorithm

With the development of Internet of Things technology, the security threats faced by the industrial control field are increasing, and strengthening the security protection capabilities of intelligent systems on IoT highways is becoming increasingly important. IPSec VPN tunneling technology can achieve identity authentication and encrypted data transmission, and is an important means to achieve secure data transmission in intelligent systems on Expressway intelligent tunnel system. The commonly used IPSec VPN gateway uses a traditional Linux protocol stack-based approach for data capture, which requires multiple data copies and context switching, resulting in low efficiency of IPSec services. In addition, the commonly used IPSec VPN security gateway is implemented on the basis of the open-source IPSec framework, using internationally recognized algorithms for encryption and decryption, which poses security risks. This article is based on the IPSec protocol, and studies the high-speed network packet capture framework PFRING technology, the fusion technology of national secret algorithm and IPSec protocol. It designs and implements an IPSec VPN IoT security gateway based on national secret algorithm. After experimental verification, the IPSec VPN gateway system constructed in this article has complete functions and better performance than the common open-source IPSec frameworks OpenSwan and strongSwan, and can meet the application requirements of IoT data encryption transmission.

The impact of health of mothers on health of children delivered by women 35-45 age old after application of auxiliary reproductive technologies

The treatment of women aged 35 years and older with infertility applies assisted reproductive technologies (ART) in every third case. The purpose of the study is to analyze impact of maternal health on health of children who were delivered by women aged 35 years and older after application of ART. The analytical, direct observation, sociological (questioning), and statistical methods were applied. To study health status of children born after application of ART methods in women aged 35-45 years, representative main group of 648 preschool children (4-6 years old) was selected. The second control group included 649 preschool children (4-6 years old), who were as identical as possible to children from the main group, selected according to following characteristics: mother's age at birth of child (35-45 years), age (from 4 to 6 years), all were observed in same medical organization, birth from a singleton, full-term (37 weeks or more) pregnancy. The main and control groups differed from each other only in presence or absence of ART methods. The children born preterm, from egg donor programs and multiple pregnancies were excluded from study. The children health was studied according to medical examinations, medical records, child development history, and mothers questionnaire data on children health. The course of pregnancy and childbirth, morbidity and lifestyle characteristics of mothers were studied according to their questionnaires and copies of data from their outpatient medical records. It was established that there is direct correlation between health of child and health of mother (r = 0.571; p < 0.01, t = 3). At that, it was revealed that differences in level of general morbidity of children after ART and children from spontaneous pregnancy are achieved within account of significant differences in subgroup of children of mothers aged 38-45 years (3353.7‰ and 2341.8‰ control group).

The Effects of Opinions on AI-based Customized Cosmetic Preparation Managers on Potential Demand Values and Development Directions

Currently, the AI-based customized cosmetic market is the time when customized strategies for potential demand and development are needed with the aim of popularization. This study was conducted to investigate the effect of opinions on AI-based customized cosmetic preparation managers on the potential demand values and development directions, and to provide useful data for the AIbased customized cosmetic industry. For the data analysis, the final 827 copies of data collected for women interested in AI-based customized cosmetics in Seoul and Gyeonggi Province were analyzed using SPSS WIN 20.0. The results of the study are as follows: First, the opinions of AI-based customized cosmetic preparation managers influenced the quality value, social ethical value, and economic value factors of the potential demand values of AI-based customized cosmetics and did not affect the brand value factors. Second, it was found that the opinions of AI-Based customized cosmetic preparation managers influenced the development directions of AI-Based customized cosmetics. Therefore, based on this study, it will be able to contribute to expanding the role of AI-based customized cosmetic preparation managers, strengthening their utility through education, and establishing strategic marketing for the AI-based customized cosmetic industry.

Latency and Residual Energy Analysis of MIMO Heterogeneous Wireless Sensor Networks

Energy-constrained Wireless Sensor Networks (WSNs) have garnered significant research interest in recent years. Multiple-Input Multiple-Output (MIMO), or Cooperative MIMO, represents a specialized application of MIMO technology within WSNs. This approach operates effectively, especially in challenging and resource-constrained environments. By facilitating collaboration among sensor nodes, Cooperative MIMO enhances reliability, coverage, and energy efficiency in WSN deployments. Consequently, MIMO finds application in diverse WSN scenarios, spanning environmental monitoring, industrial automation, and healthcare applications. This research paper presents a comparative performance analysis of MIMO wireless sensor networks and traditional wireless sensor networks without MIMO using Network Simulator NS2.35 for analysis of End to End Delay for packet transmission and Residual energy of nodes. The research work shows application of MIMO in Wireless Sensor Networks with considerable improvements in Quality of Service parameters which is achieved through Spatial Multiplexing and Diversity Gain. MIMO enables multiple spatial streams, allowing several data streams to be transmitted simultaneously on the same channel. This increases the overall throughput as multiple sensors can transmit their data concurrently without interference. MIMO systems also provide diversity gain by transmitting multiple copies of the same data over different antennas which helps in mitigating the effects of fading and interference, resulting in a more reliable and higher-throughput communication link as compared to a SISO channel. Another advantage of employing MIMO in WSN is reduction in End-to-End delays in data transmission. Last but not the least, MIMO can be configured to optimize the power consumption of individual sensors by adjusting the number of antennas used and transmission power levels based on channel conditions. Hence, MIMO can help to extend the network's lifetime by conserving energy in resource-constrained sensor nodes by preservation of Residual Energy.

A randomized permutation whole-model test heuristic for Self-Validated Ensemble Models (SVEM)

We introduce a heuristic to test the significance of fit of Self-Validated Ensemble Models (SVEM) against the null hypothesis of a constant response. A SVEM model averages predictions from nBoot fits of a model, applied to fractionally weighted bootstraps of the target dataset. It tunes each fit on a validation copy of the training data, utilizing anti-correlated weights for training and validation. The proposed test computes SVEM predictions centered by the response column mean and normalized by the ensemble variability at each of nPoint points spaced throughout the factor space. A reference distribution is constructed by refitting the SVEM model to nPerm randomized permutations of the response column and recording the corresponding standardized predictions at the nPoint points. A reduced-rank singular value decomposition applied to the centered and scaled nPerm×nPoint reference matrix is used to calculate the Mahalanobis distance for each of the nPerm permutation results as well as the jackknife (holdout) Mahalanobis distance of the original response column. The process is repeated independently for each response in the experiment, producing a joint graphical summary. We present a simulation driven power analysis and discuss limitations of the test relating to model flexibility and design adequacy. The test maintains the nominal Type I error rate even when the base SVEM model contains more parameters than observations.

공립유치원 교사의 발달단계가 승진욕구와 교사헌신에 미치는 영향에 관한 연구

The purpose of this study is to investigate the effect of the developmental stage of public kindergarten teachers on desire for promotion and teacher commitment. The research method collected 233 copies of data collected from teachers of public kindergartens in Gyeonggi-do and statistically analyzed them through SPSS 29.0 by frequency analysis, reliability coefficient calculation, mean and standard deviation, correlation analysis, and regression analysis. As a result of the study, first, among the sub-factors of the teacher development stage of public kindergartens, growth and maturity were found to be high, desire for promotion were found to be at an intermediate level, and teacher commitment was found to be high. Second, looking at the correlation between the teacher development stage and the promotion desire, promotion orientation, growth, and maturity were positively correlated with the promotion desire, and stability, recession, and frustration were negatively correlated. Looking at the correlation between the teacher development stage and teacher commitment, growth, maturity, and promotion orientation were positively correlated, and frustration and survival were negatively correlated.