Data Processing Research Articles

The Structure and Dynamics of Creative Tourism: An ePortfolio Approach

Contributing to limited knowledge of creative tourism product development and operationalization processes in small cities and rural areas, ePortfolios were used as an online fieldnote depository for photographs and texts, which were entered by geographically dispersed researchers within the CREATOUR research-and-application project. The ePortfolios, as a type of research diary, record and examine the ways in which 48 different creative tourism activities were structured, organized, and implemented. The thematic analysis of this empirical data identified five temporally distinct organizational moments that comprise the creative tourism activities and reveal the nuances of active participation, learning, and community engagement as key elements characterizing creative tourism activities. Findings indicate that creative tourism activities are fertile ground for the contemporary tourist trends of wanting to go beyond “gazing” and to experience active involvement, which in these activities involved four different types of active participation. The ePortfolios also highlight the significance of the learning component within creative tourism, illustrating some ways in which learning is integrated into tourism activities. Links to the community are revealed with, for example, elderly women acting as artisan teachers, through which local participation is enabled and communication between visitors and residents is fostered. The use of ePortfolios as an “exploratory” research platform and tool in tourism research is also discussed, for example, in terms of stimulating creative expression and offering researchers freedom to present discovered aspects that may not be captured in “preplanned” data forms and processes.

A multi-constraint representation learning model for identification of ovarian cancer with missing laboratory indicators.

To evaluate the performance of a multi-constraint representation learning classification model for identifying ovarian cancer with missing laboratory indicators. Tabular data with missing laboratory indicators were collected from 393 patients with ovarian cancer and 1951 control patients. The missing ovarian cancer laboratory indicator features were projected to the latent space to obtain a classification model using the representational learning classification model based on discriminative learning and mutual information coupled with feature projection significance score consistency and missing location estimation. The proposed constraint term was ablated experimentally to assess the feasibility and validity of the constraint term by accuracy, area under the ROC curve (AUC), sensitivity, and specificity. Cross-validation methods and accuracy, AUC, sensitivity and specificity were also used to evaluate the discriminative performance of this classification model in comparison with other interpolation methods for processing of the missing data. The results of the ablation experiments showed good compatibility among the constraints, and each constraint had good robustness. The cross-validation experiment showed that for identification of ovarian cancer with missing laboratory indicators, the AUC, accuracy, sensitivity and specificity of the proposed multi-constraints representation-based learning classification model was 0.915, 0.888, 0.774, and 0.910, respectively, and its AUC and sensitivity were superior to those of other interpolation methods. The proposed model has excellent discriminatory ability with better performance than other missing data interpolation methods for identification of ovarian cancer with missing laboratory indicators.

Astronomical Image Denoising Using AttentionGAN

Denoising astronomical images is a significant challenge in the field of astronomical data processing. Image data acquired from astronomical sources typically contains noise from various sources. The study aims to investigate the denoising of astronomical images using an image-to-image translation approach with AttentionGAN method. This method combines attention-guided techniques with a Generative Adversarial Network (GAN) model to improve the quality of noisy astronomical images. Attention-guided technique allows the model to learn the most important features of the image and guide the image generation process. This approach has been tested on several images in different domains, each with varying levels of noise. The results shows that AttentionGAN method produces denoised images with better and sharper quality than several other denoising methods. Two databases, The Panoramic Survey Telescope and Rapid Response System (PAN-STARRS) and the Sloan Digital Sky Survey (SDSS), were used in this research. Images acquired from PAN-STARRS contain noise, while images acquired from SDSS are clean. Overall, this research contributes to improving the quality of astronomical images by demonstrating the effectiveness of the AttentionGAN method in denoising noisy astronomical images. We employed denoising techniques using CycleGAN and AttentionGAN and evaluated them using metrics such as PSNR, SSIM, and FID. The analysis showed that the AttentionGAN model outperformed CycleGAN. We also conducted ablation studies to further investigate the components of the AttentionGAN model. This study provides a foundation for future research in the field of astronomical data processing, which has the potential to enhance image quality.

Tantangan dan Peluang Pengimplementasian Kurikulum Merdeka di SD/MI Sumatera Selatan

This study aims to analyze the implementation process of the Merdeka Curriculum in MI/SD in South Sumatra. Several issues arise during the implementation process, including teacher readiness, limited training, inadequate infrastructure, and community understanding. This research is a qualitative study with a field approach, employing data collection techniques such as interviews, observations, and documentation. The informants in this study consist of 25 individuals, including educators, school principals, and students. The process of data processing and analysis is conducted in three stages: data reduction, data presentation, and conclusion drawing. Data validity is ensured through triangulation methods, which include source, technique, and time triangulation. Based on the results and discussion, the curriculum provides opportunities to enhance learning through a flexible, student-centered approach and the development of character and 21st-century skills. Several schools in South Sumatra have implemented project-based learning, differentiated instruction, and technology to create active and innovative learning environments. Despite the challenges, the Merdeka Curriculum has a positive impact on improving the quality of education and preparing a creative and independent younger generation

Multi-gate neuron-like transistors based on ensembles of aligned nanowires on flexible substrates

The intriguing way the receptors in biological skin encode the tactile data has inspired the development of electronic skins (e-skin) with brain-inspired or neuromorphic computing. Starting with local (near sensor) data processing, there is an inherent mechanism in play that helps to scale down the data. This is particularly attractive when one considers the huge data produced by large number of sensors expected in a large area e-skin such as the whole-body skin of a robot. This underlines the need for biological skin like processing in the e-skin. Herein, we present multi-gate field-effect transistors (v-FET) having capacitively coupled floating gate (FG) to mimic some of the neural functions. The v-FETs are obtained by deterministic assembly of ZnO nanowires on a flexible substrate using contactless dielectrophoresis method, followed metallization using conventional microfabrication steps. The spatial summation of two presynaptic inputs (applied at multiple control gates) of the transistor confirm their neuron-like response. The temporal summation (such as paired-pulse facilitation) by presented v-FETs further confirm their neuron-like mimicking with one presynaptic input. The temporal and spatial summation functions, demonstrated by the v-FET presented here, could open interesting new avenues for development of neuromorphic electronic skin (v-skin) with possibility of biological-skin like distributed computing.

The Concept Of Legal Norms Of Personal Data Protection Related To Data Processing In The Form Of Artificial Intelligence

Issues examined in this study: first, How is the protection of personal data related to data processing in the form of artificial intelligence in Indonesia. Second, what is the concept of the existing legal norms of personal data protection related to data processing in the form of artificial intelligence. The method of research is carried out in literature with the type of normative legal research, according to dogmatic issues related to the emptiness of norms. Findings in this study found the protection of personal data related to data processing in the form of artificial intelligence in Indonesia there are two views. In this regard, the protection of personal data in data processing through artificial intelligence in Indonesia has no legal certainty to be protected. Because legal certainty refers to the application of clear, fixed, consistent and consequent laws whose implementation cannot be influenced by subjective circumstances. The concept of existing legal norms for personal data protection related to data processing in the form of artificial intelligence special arrangements stipulated in Law Number 27 of 2022 can be said to have accommodated personal rights. However, the existing condition of artificial intelligence norm is only legally domiciled as a legal object, because it is needed by the party responsible for the actions of artificial intelligence. Legal liability for the actions of artificial intelligence can be charged or imposed on the organizers of artificial intelligence.

Noise-agnostic quantum error mitigation with data augmented neural models

Quantum error mitigation, a data processing technique for recovering the statistics of target processes from their noisy version, is a crucial task for near-term quantum technologies. Most existing methods require prior knowledge of the noise model or the noise parameters. Deep neural networks have the potential to lift this requirement, but current models require training data produced by ideal processes in the absence of noise. Here we build a neural model that achieves quantum error mitigation without any prior knowledge of the noise and without training on noise-free data. To achieve this feature, we introduce a quantum augmentation technique for error mitigation. Our approach applies to quantum circuits and to the dynamics of many-body and continuous-variable quantum systems, accommodating various types of noise models. We demonstrate its effectiveness by testing it both on simulated noisy circuits and on real quantum hardware.

Designing Face Detection Systems with Gray Wolf Optimization

The main objective of this paper project was to create a state-of-the-art face identification technique that can handle the various difficulties caused by changes in illumination, occlusions, and facial emotions. Face detection is a cornerstone of computer vision, facilitating diverse applications ranging from surveillance systems to human-computer interaction. Throughout this paper, the comprehensive exploration of advancing face detection methodologies has been undertaken, culminating in developing and evaluating a novel approach. The challenges posed by variations in facial expressions, lighting conditions, and occlusions necessitated a multifaceted solution. Our proposed method, which consists of interconnected steps, works quite well to overcome these challenges. Using deep learning architectures to increase feature extraction and discrimination was beneficial in the initial stage of fine-tuning Residual Networks (ResNet-50) to serve as the Region-based Convolutional Neural Network (Faster R-CNN) framework classifier. The process of gradually optimizing thresholds, such as batch size, learning rate, and detection threshold, involved using the Gray Wolf optimization technique (GWO). The conversion process was accelerated and improved overall detection process efficiency and accuracy using a clever fusion of machine learning and metaheuristic optimization techniques. A key component of our methodology is the careful data processing, which was necessary to ensure. The suggested method was carefully examined on a particular dataset, and the 94% training accuracy that was attained together with an identical test dataset accuracy highlights the method's resilience. These findings support the effectiveness of our approach in reducing false positives and negatives, resulting in unmatched recall and precision in the detection system. The discovery has significant significance as it can potentially improve face detection systems' performance and reliability in various real-world applications, such as human-computer interaction and surveillance. Convolutional neural networks, deep learning architectures, and metaheuristic optimization approaches were synergized to produce a new and reliable solution.

Microsoft Azure vs. Google Cloud Platform: An In-Depth Review of Services, Pricing, and Performance

This comparative review paper aims to provide a comprehensive analysis of two leading cloud platforms: Microsoft Azure and Google Cloud Platform (GCP). Cloud computing has become pivotal in modern technology infrastructure, offering scalability, cost-efficiency, and a wide range of services. Both Azure and GCP have established themselves as dominant players in this domain, each with its unique strengths and service offerings. The core services of both platforms, including compute, storage, and networking, are evaluated to understand their performance, flexibility, and ease of use. Azure’s Virtual Machines and Kubernetes Service are compared with GCP’s Compute Engine and Kubernetes Engine, highlighting their capabilities in handling various workloads (Jones, 2020, p. 15). On the storage front, Azure Blob Storage and Data Lake are examined alongside GCP’s Cloud Storage and Filestore for their scalability and integration capabilities (Smith, 2021, p. 25). Specialized services such as AI and machine learning, big data analytics, and database solutions are also compared. Azure’s Machine Learning and Cognitive Services are measured against GCP’s AI Platform and AutoML, showcasing their impact on data-driven decision-making (Miller & Davis, 2022, p. 30). For big data, Azure Synapse Analytics and HDInsight are juxtaposed with GCP’s BigQuery and Dataflow to discuss their efficiency in handling large-scale data processes (Clark, 2019, p. 19). Pricing models and cost management tools are critical for enterprises, making it essential to compare Azure’s Pay-as-you-go and Reserved Instances with GCP’s On-demand and Sustained Use Discounts (Brown, 2021, p. 42). Additionally, security and compliance are paramount, thus the security frameworks and certifications of both platforms are rigorously evaluated (White, 2022, p. 28). Usability factors such as user interfaces, documentation, training resources, and support plans are analyzed to understand the ease of adoption and customer support experiences. Case studies and user feedback provide real-world insights into each platform’s performance and reliability, drawing on enterprise-level and SMB use cases (Green, 2020, p. 37). Finally, the review discusses the innovation trajectories and future directions of both Azure and GCP, highlighting recent feature updates and predicting future trends in the cloud computing landscape (Taylor, 2022, p. 50). The paper concludes with a summary of findings, offering tailored recommendations based on specific use cases, thereby aiding organizations in making informed decisions about their cloud strategy.

A privacy-enhanced framework with deep learning for botnet detection

A botnet is a group of hijacked devices that conduct various cyberattacks, which is one of the most dangerous threats on the internet. Organizations or individuals use network traffic to mine botnet communication behavior features. Network traffic often contains individual users’ private information, such as website passwords, personally identifiable information, and communication content. Among the existing botnet detection methods, whether they extract deterministic traffic interaction features, use DNS traffic, or methods based on raw traffic bytes, these methods focus on the detection performance of the detection model and ignore possible privacy leaks. And most methods are combined with machine learning and deep learning technologies, which require a large amount of training data to obtain high-precision detection models. Therefore, preventing malicious persons from stealing data to infer privacy during the botnet detection process has become an issue worth pondering. Based on this problem, this article proposes a privacy-enhanced framework with deep learning for botnet detection. The goal of this framework is to learn a feature extractor. It can hide the private information that the attack model tries to infer from the intermediate anonymity features, while maximally retaining the interactive behavior features contained in the original traffic for botnet detection. We design a privacy confrontation algorithm based on a mutual information calculation mechanism. This algorithm simulates the game between the attacker trying to infer private information through the attack model and the data processor retaining the original content of the traffic to the maximum extent. In order to further ensure the privacy protection of the feature extractor during the training process, we train the feature extractor in the federated learning training mode. We extensively evaluate our approach, validating it on two public datasets and comparing it with existing methods. The results show that our method can effectively ensure detection accuracy on the basis of removing private information. For the CTU-13 dataset, the detection framework achieves the best detection performance; for the ISCX-2014 dataset, the accuracy of the framework is less than 1% lower than the best effect.

Are psychological attitudes towards vaccination an expression of personality? A cross-sectional study on COVID-19 vaccination in France

BackgroundThe capacity of the 7C model’s psychological antecedents, which include confidence in vaccines, complacency, convenience, calculation, collective responsibility, confidence in the wider system, and social conformism, to explain variance in COVID-19 vaccine intentions and behaviours has been documented. However, it remains unclear whether the attitudes represented by the 7C psychological antecedents are specific to vaccination or if they are, in fact, an expression of underlying personality traits.MethodsFrom February to June 2022, French adults completed self-administered questionnaires assessing COVID-19 vaccination history, the 7C antecedents, and personality traits (“ComCor” and “Cognitiv” studies). Vaccination behaviours were studied through three outcomes: at-least-one-dose vaccination status by 2022 (N = 49,019), up-to-date vaccination status (N = 46,566), and uptake speed of first dose (N = 25,998). Personality traits were evaluated using the French version of the Big Five Inventory (BFI-Fr). Multivariable logistic regressions and Cox models predicting vaccine behaviours were run with the 7C antecedents, both with and without personality traits.ResultsAmong the 49,019 participants, 95.0% reported receipt of at least one dose and 89.8% were up to date with recommendations. All 7C antecedents were significantly associated with the outcomes. The inclusion of personality traits did not substantially alter the effect estimates of the association between the 7C antecedents and vaccination behaviours, with differences between effect sizes of models with and without personality traits being < 5%.ConclusionsOur results suggest that the 7C psychological antecedents of vaccination are not the mere expression of personality and that their impact on vaccine behaviours is independent of personality traits. As such, the 7C antecedents may be modifiable by appropriate information and vaccine promotion.Trial registrationThe “ComCor” study received ethical approval by the Comité de Protection des Personnes Sud Ouest et Outre Mer 1 on 21/09/2020. The addition of the “Cognitiv” questionnaire received ethical approval by the Comité de Protection des Personnes Sud Ouest et Outre Mer 1 on 01/02/2022. The data protection authority Commission Nationale de l’Informatique et des Libertés (CNIL) authorised the processing of data on 21/10/2020. The study is registered with ClinicalTrials.gov under the identifier NCT04607941.

Differential absorption laser spectroscopy at 8 kHz using precompensated current modulation

We present a differential laser absorption spectroscopy (DLAS) system operating at 1550 nm for rapid and sensitive gas concentration measurements. A dual-wavelength toggling mechanism is presented, which significantly reduces data processing, hence supporting a high update rate and data robustness against fast-changing environmental conditions. We showcase the ability to toggle between two wavelengths separated by 90 pm in 14 μs and with minimal chirp (∼1 pm), facilitating sensitive DLAS measurements at 8 kHz update rate. This performance is achieved by driving a 1550 nm diode laser with a modified square-wave current pulse, overcoming the thermal time constant limited wavelength-modulation response of the diode laser. A sensitive feedback mechanism ensures excellent long-term wavelength stability better than 1.4 pm peak-to-peak at 8 kHz toggling over 20 h. As a performance test, we measured the volumetric ratio (VMR) of hydrogen cyanide (HCN) gas in a fiber-coupled gas cell with less than 0.2% peak-to-peak variation over 20 h at 40 Hz. A best sensitivity in VMR of 8×10−6 was achieved at 25 ms integration time. The simplicity and high update rate of our system make it well-suited for gas monitoring in dynamic atmospheric and industrial environments. Further, it offers potential utility in applications requiring precise wavelength control, such as injection seeding of pulsed lasers. A simple analytical model is derived, which, in detail, supports the experimental results, hence offering a tool for future design optimization.

Predicting Risks in Healthcare Claims Using Advanced Data Processing and Machine Learning Techniques

Predicting Risks in Healthcare Claims Using Advanced Data Processing and Machine Learning Techniques

Integrated optical switches based on Kerr symmetry breaking in microresonators

With the rapid development of the Internet of Things and big data, integrated optical switches are gaining prominence for applications in on-chip optical computing, optical memories, and optical communications. Here, we propose a novel approach for on-chip optical switches by utilizing the nonlinear optical Kerr effect induced spontaneous symmetry breaking (SSB), which leads to two distinct states of counterpropagating light in ring resonators. This technique is based on our first experimental observation of on-chip symmetry breaking in a high-Q (9.4×106) silicon nitride resonator with a measured SSB threshold power of approximately 3.9 mW. We further explore the influence of varying pump powers and frequency detunings on the performance of SSB-induced optical switches. Our work provides insights into the development of new types of photonic data processing devices and provides an innovative approach for the future implementation of on-chip optical memories.

Identification of subsurface cavities in urban environment

Gravimetry is the most suitable geophysical method for identifying subsurface cavities in urban or industrial environments, as it is unaffected by nearby electromagnetic disturbances. In this study, we used gravimetric geophysical method to understand the land subsidence, collapses, and fissures observed around the sealed Albian drilling site located in the M’Rara region of Northeast of Algeria. Particularly, we would like study the geological observed phenomena and its potential association with the presence of a cavity within the salt layer. According to the stratigraphic column, a salt layer was located at 936 m depth. To better understand the subsurface conditions, A new gravimetric survey was conducted to image the density distribution and identify anomalous zones associated with subsurface cavities. Various gravity data processing techniques, including polynomial fitting of different orders, vertical and horizontal gradient analysis, and three-dimensional inversion, were applied. These methods aim to evaluate the relationship between detected gravity anomalies and the underlying structures, with a focus on their possible correlation with cavities within the salt layer. The gravimetric evidence suggest that the hydraulic drilling is not the cause of the observed phenomena. These cavities are likely formed by water circulation in the subsoil, originating from the Retem Valley.

AI: Apakah Guru Masih Punya Peran di Masa Depan

The advancement of artificial intelligence (AI) has significantly impacted education, particularly in providing personalized learning and administrative efficiency. However, questions arise about whether the teacher's role remains relevant in this technological era. This study aims to examine the role of teachers in the context of AI development through a literature review method, analyzing previous research on AI integration in education. The findings reveal that while AI excels in data processing and adaptive learning, teachers maintain an irreplaceable role in building emotional connections, instilling moral values, and guiding students' character development. Teachers also act as mentors who provide motivation and facilitators who create interactive learning environments. These findings highlight the importance of collaboration between AI and teachers to create more inclusive and effective education. Therefore, AI should be seen not as a replacement but as a tool to enhance the teacher's role in fostering holistic student development.

Представления пожилых людей о благополучии

&lt;p&gt;The study is aimed at identifying the structure and content of elderly people's ideas about well-being. The study involved 264 people aged 55 to 97 years (M = 69.54; SD = 6.98). Data collection method: free association method in the form of simple limited associations. Participants were asked to give associations to the word &amp;ldquo;well-being&amp;rdquo;. Nine associations were received from each person, three verbs, adjectives, nouns. Data processing was carried out using frequency analysis, rank calculation, z-criterion calculation to compare the frequencies of the identified associative and semantic groups. 2376 associates were received, of which the most frequent ones were identified; the selection boundary was 1% of the total number of associates (24 units). The most frequent ones were combined into 21 associative groups. Then the associative groups were combined into semantic groups (8) according to the principle of semantic enlargement. The obtained results allow us to say that well-being in the ideas of elderly people is health combined with emotions of calmness and happiness. In elderly people, the semantic group &amp;ldquo;emotions&amp;rdquo; belongs to the core ideas, and the associative group &amp;ldquo;health&amp;rdquo; belongs to the middle zone together with the groups &amp;ldquo;employment&amp;rdquo;, &amp;ldquo;material wealth&amp;rdquo;, &amp;ldquo;family&amp;rdquo;. At the same time, this does not indicate a lower significance of the associative group &amp;ldquo;health&amp;rdquo;, since the semantic group &amp;ldquo;emotions&amp;rdquo; collects its high rank only through a set of different emotions. The semantic groups &amp;ldquo;values", &amp;ldquo;existence&amp;rdquo;, &amp;ldquo;success&amp;rdquo; belong to the peripheral zone of ideas. In the semantic group &amp;ldquo;emotions&amp;rdquo; in the structure of ideas about well-being in elderly people, emotions of greater intensity prevail over emotions of lesser intensity. Markers of negative emotions in the structure of ideas about well-being are absent.&lt;/p&gt;

An Embodied Intelligence System for Coal Mine Safety Assessment Based on Multi-Level Large Language Models

Artificial intelligence (AI), particularly through advanced large language model (LLM) technologies, is reshaping coal mine safety assessment methods with its powerful cognitive capabilities. Given the dynamic, multi-source, and heterogeneous characteristics of data in typical mining scenarios, traditional manual assessment methods are limited in their information processing capacity and cost-effectiveness. This study addresses these challenges by proposing an embodied intelligent system for mine safety assessment based on multi-level large language models (LLMs) for multi-source sensor data. The system employs a multi-layer architecture implemented through multiple LLMs, enabling not only rapid and effective processing of multi-source sensor data but also enhanced environmental perception through physical interactions. By leveraging the tool invocation and reasoning capabilities of LLM in conjunction with a coal mine safety knowledge base, the system achieves logical inference, anomalous data detection, and potential safety risk prediction. Furthermore, its memory functionality ensures the learning and utilization of historical experiences, providing a solid foundation for continuous assessment processes. This study established a comprehensive experimental framework integrating numerical simulation, scenario simulation, and real-world testing to evaluate the system through embodied intelligence. Experimental results demonstrate that the system effectively processes sensor data and exhibits rapid, efficient safety assessment capabilities during embodied interactions, offering an innovative solution for coal mine safety.

Zootechnical and Municipal Solid Waste Digestates: Effects on Soil Nitrogen Mineralization and Kinetics

Soil fertilization with fertilizers derived from renewable sources is a topic of great interest in terms of the sustainable management of organic waste. To optimize the management of nitrogen supplied to the soil with digestates, it is necessary to deepen knowledge on the process of mineralization of organic nitrogen over time. In this research, a laboratory incubation system was utilized to study the impact of various digestate sources on nitrogen mineralization processes in soils and nitrogen mineralization kinetics. Six types of digestates of different origins and composition were administered to soil and the soil samples were placed under controlled conditions. The release of N was determined by measuring ammonium-N and nitrate-N concentrations in leachates during a 12-week period of incubation. The nonlinear regression technique was used to fit the cumulative leaching of total N to the Stanford and Smith first-order kinetic model during the incubation period. The results showed that the differences between digestates, nitrogen and organic carbon concentration, and C/N ratio influenced both ammonification and nitrification processes in the soil and the nitrogen mineralization kinetics. The processing of the statistical data highlighted that the potentially mineralizable nitrogen (MPN) followed first-order kinetics.

Hotel digital intelligence capability: dimension exploration and scale development

Purpose This study aims to explore the dimensional structure of hotel digital intelligence capability and develop a measurement scale. Design/methodology/approach This study adopts qualitative and quantitative approaches to conduct an exploratory inquiry into the structural dimensions of hotel digital intelligence capability with the help of grounded theory. Based on this, several questionnaires were developed to test the measurement scale and verify its validity. Findings The results reveal that hotel digital intelligence capability comprises four dimensions: data collection and processing capability, customer service personalization capability, digital intelligence decision support capability and sustainable development capability. The measurement scale consists of four factors and 13 items, with reliability and validity tests demonstrating ideal levels. Originality/value This study not only provides a new perspective to understand hotel digital intelligence capability but also develops a corresponding measurement scale, laying a solid theoretical basis for hotel managers to scientifically evaluate this capability to achieve sustainable competitive advantage.