Reviewerly/exHarmony huggingface dataset:
Resource Allocation Strategies on Cloud Data Center: Survey and Challenges

In the big data era, a strategy to manage the resources for the data center that serves cloud services poses challenges. Big data of various formats, types, volumes, and processes need to manage appropriate resources. It is important to maintain Data Center services in sustainable ways. Data Center infrastructures such as clusters, grids, high-performance computing, and clouds have similar issues on resources capacities and performances. Several strategies for resource allocation have been established, from tendering, auction, and commodity. The research investigates and discovers the development of strategies and the adaptation on big data issues. The research investigates the main strategies for resource allocation, which have several approaches and methods of implementation. It is mostly based on the performance enhancement, but less on the characteristics. The research contribution shows that there are opportunities to manage the resource based on the characteristics, that are as promising as the big data resource allocation strategy.
Applied Fault Topology: Understanding Connectivity and Uncertainty of Fault Systems that Define and Affect Commercial and Environmental Projects

For subsurface commercial ventures or environmental projects that rely on maps of faulted horizons, accurate maps are fundamental. Fault topology provides an ideal tool for analysis of connectivity of fault systems. The data required to undertake the analysis is straightforward to extract from fault maps and can readily be compared to analogue data. In this paper we introduce the concept of fault topology and present existing and new analogue data. To get the most from applying topology the analysis must be coupled with knowledge of the structural history. This includes, the magnitude of faulting, the number of phases of activity and the angle of intersection of successive faulting events. We present a series of case studies that firstly illustrate how topology can capture and define variations in connectivity of fault systems and, secondly, demonstrate how fault topology can be used to identify potential anomalies.
Evaluation of relationships between health anxiety, coronavirus anxiety and healthy eating anxiety among dietitians during COVID-19

Rationale: Health anxiety, which has emotional, perceptual and cognitive dimensions, can lead individuals to exhibit invalid behavior patterns against information about the disease. This may prevent individuals from showing the necessary protective behaviors against diseases. In epidemics such as COVID-19, people's anxiety increases and they give more importance to their nutrition in order to protect their health. Methods: The research was carried out with participants (516 dietitians, 451 women and 65 men), who are members of the Turkish Dietetic Association. An electronic questionnaire was sent to the participants to obtain demographic and health information and anthropometric measurements of the individuals. The Health Anxiety Inventory (HAI), the Coronavirus Anxiety Scale Short Form (CAS) and the Hypersensitive Attitude towards Healthy Eating Scale (ORTO-15) were applied. Results: The median HAI scores of female and male dietitians were 33 and 31, respectively (p=0.038). The median CAS median scores were 2.2 and 1.5 for female and male dietitians, respectively (p<0.001); ORTO-15 scores were 37.55 for female and 38.41 for male dietitians (p=0.164). Dietitians’ gender, education, food supplement use, number of snacks did not make a difference on coronavirus anxiety and orthorexia nervosa status (p>0.05). Orthorexic dietitians consume more main meals (p<0.007). While there was a positive and moderately significant correlation (r=0.747, p<0.001) with the median CAS scores of dietitians with a higher median HAI score, there was a weak negative correlation for ORTO-15 (p<0.001). As HAI scores increased, a 45.7% increase was found in CAS scores. Conclusion: Dietitians with high health anxiety also had high coronavirus anxiety, but they were not orthorexia. Orthorexia nervosa was not determined in dietitians with coronavirus anxiety and health anxiety. Disclosure of Interest: None declared
Research on the Influence Mechanism of School Support on Teaching and Research Satisfaction in Precision Teaching and Research

Precision teaching and research technology can make up for the shortcomings of traditional teacher research activities, strengthen the speed of teachers' self-improvement and self-reflection, so that teachers can quickly and accurately improve their own quality and achieve professional development. The first task to improve the efficiency of teachers' precision teaching and research activities is to improve the key index of the effect of precision teaching and research activities, teaching and research satisfaction. Based on the obstacle theory, this study selected school support and value belief as the key factors affecting the satisfaction of precision training activities. The subjects were 284 elementary school teachers, middle school teachers and high school teachers who had participated in precision teaching and research activities. Structural equation model was used to explore the relationship between school support services, teacher satisfaction and teacher value belief. The results show that: (1) Technical support and peer support have a direct impact on teachers' satisfaction of precision teaching research; (2) Teachers' value belief plays an important mediating role in the process of school administrative support and peer support affecting teachers' satisfaction with precision teaching research. The research conclusion has a good reference value for promoting the implementation of precision teaching and research activities in primary and secondary schools and improving teachers' satisfaction with precision teaching and research activities.
THE ANALISIS KESULITAN BELAJAR SISWA PADA MATA PELAJARAN IPA DI KELAS VIII SMPN 11 SINGKAWANG TIMUR

Based on the results of interviews with science teachers at SMPN 11 Singkawang Timur, that in class VIII students experience learning difficulties indicated by the existence of certain obstacles to achieving learning outcomes. The purpose of the study was to find out how the students' learning difficulties in science subjects in class VIII SMPN 11 Singkawang Timur. The method used in this research is a descriptive qualitative approach. Primary data sources were obtained from students in grades VIII A, VIII B, and VIII C of SMPN 11 Singkawang Timur. Meanwhile, secondary data sources were obtained from science teachers in class VIII and students in grades VIII A, VIII B, and VIII C of SMPN 11 Singkawang Timur. Data collection techniques using indirect communication, interviews and documentation. Data collection tools using a questionnaire sheet, interview guidelines. Data analysis techniques perform data collection, data reduction, data presentation, drawing conclusions. The results showed that students' learning difficulties in science subjects in class VIII of SMPN 11 Singkawang Timur were classically stated as moderate according to the results of the questionnaire scores obtained were interpreted as moderate with a percentage of 60.13%. The conclusion in this study is that when carrying out science learning students have learning difficulties with different percentages of each class, in class VIII A the questionnaire score of 61.23% is interpreted as high which states that learning difficulties in science are low, class VIII B the percentage is 59.13% moderately interpreted and grade VIII C with a percentage of&#x0D; 59.23% interpreted as being in accordance with the scores obtained by class VIII B and VIII C stating that students' learning difficulties in science subjects were moderate.
Enabling the execution of HPC applications on public clouds with <i>HPC@Cloud</i> toolkit

Abstract The advent of cloud computing has made access to computing infrastructure available to millions of users that face resource constraints. In the context of high performance computing (HPC), public cloud resources have emerged as a cost‐effective alternative to expensive on‐premises clusters. However, there are several challenges and limitations in adopting this approach. This paper proposes HPC@Cloud , a provider‐agnostic open‐source software toolkit that facilitates the migration, testing, and execution of HPC applications in public clouds. The toolkit takes advantage of various fault tolerance technologies to enable the use of inexpensive transient cloud infrastructure, commonly known as “spot” instances. Also, it features integration with singularity containers, allowing users to run complex applications on virtual HPC clusters in a portable and reproducible way. Finally, it provides a data‐based empirical approach to estimating cloud infrastructure costs for HPC workloads. The results obtained on two public cloud providers (AWS and Vultr) show that: (i) HPC@Cloud can efficiently build virtual HPC clusters on the cloud; (ii) the new adaptive fault tolerance strategy outperforms other existing strategies based on blocking restoration; (iii) the integration of singularity containers into HPC@Cloud improves the portability of HPC applications to public clouds with negligible performance penalty to the applications; (iv) the proposed cost prediction approach can estimate the cost of running the applications on AWS and Vultr with up to 93% accuracy on average.
Ethnicity Classification: Discriminating Between Iranian and Asian Populations Using Hybrid Deep Learning Algorithm

The rapid advancement of deep learning techniques has opened new avenues for solving complex classification problems in various domains. This research explores the application of Convolutional Neural Networks (CNN), specifically the VGG16 architecture, in conjunction with Support Vector Machines (SVM) for the classification of ethnicity using Iranian and Asian facial images. The classification of ethnicity based on facial features is a challenging task due to the subtle and complex variations within and between different ethnic groups. The proposed methodology involves a two-step process. First, the VGG16 CNN model is utilized to extract high-level features from the facial images. The pre-trained VGG16 model, known for its depth and representational power, is fine-tuned on the dataset to capture relevant ethnic features. The extracted features are then fed into an SVM classifier, which is trained to distinguish between Iranian and Asian facial characteristics. A comprehensive dataset consisting of labeled Iranian and Asian facial images is compiled and preprocessed for training and evaluation. The model's performance is assessed using metrics such as accuracy. Various experiments are conducted to optimize hyperparameters and validate the generalization capability of the proposed model. Additionally, visualization techniques are employed to provide insights into the features learned by the CNN and the decision boundaries established by the SVM. The results indicate that the combined approach of CNN-VGG16 and SVM yields promising accuracy in the classification of ethnicity based on facial images.
Collection Development and Management in the Academic Libraries in the Modern Era: An Overview

Every library collection ought to be created with a specific objective in mind. The collection may have been produced primarily for research and instructional purposes in an academic library setting. Information that is available electronically via the use of computer technology has increasingly complemented traditional media for information, such as books and microfilm, in recent years. This implies that the selection criteria and collection parameters for these new media forms must be included in the current collection development policy for an academic library. In order to match its collection development efforts with the evolving landscape of digital librarianship in the twenty-first century, an academic library must take into account a number of concepts and issues, some of which are examined in this paper.
SECURE BY INTELLIGENCE: ENHANCING PRODUCTS WITH AI-DRIVEN SECURITY MEASURES

In an increasingly interconnected digital landscape, the proliferation of sophisticated cyber threats poses significant challenges to the security and integrity of products and services. As traditional security measures struggle to keep pace with evolving threats, there exists a pressing need for innovative and adaptive approaches to safeguarding digital assets. This abstract introduces the concept of "Secure by Intelligence," a paradigm shift in product security that leverages the power of Artificial Intelligence (AI) to fortify defenses and proactively mitigate risks. This paper explores the integration of AI-driven security measures as a foundational element in enhancing the resilience of various products across industries. It delves into the core principles of AI-powered security, emphasizing the utilization of machine learning, deep learning, natural language processing, and anomaly detection to predict, detect, and respond to potential threats in real time. The key focus areas include Dynamic Threat Detection and Prediction, Behavioral Analysis and Anomaly Detection, and Automated Response and Adaptation: Through AI-based automation, systems can autonomously respond to security incidents, mitigating risks in real-time. Furthermore, adaptive AI systems learn from each encounter, enhancing their ability to preempt future attacks. Privacy-Preserving Solutions, Cross-Industry Applications. This paper illustrates real-world case studies and implementations where AI-driven security measures have significantly bolstered product security and resilience. It highlights the tangible advantages of adopting AI-centric security solutions, including improved threat detection accuracy, reduced response times, and enhanced adaptability to emerging cyber threats.
Adaptive quantum tomography in an indistinct measurement system with superconducting circuits

Adaptive tomography has been widely investigated with the aim of achieving faster state tomography processing of quantum systems. Infidelity of the nearly pure states in a quantum information process generally scales as $O(1/\sqrt{N})$, which requires a large number of statistical ensembles in comparison to the infidelity scaling of $O(1/N)$ for mixed states. One previous report optimized the measurement basis in a photonic qubit system, whose state tomography uses projective measurements, to obtain an infidelity scaling of $O(1/N)$. However, this dramatic improvement cannot be applied to indistinct measurement systems in which two quantum states cannot be distinguished with perfect measurement fidelity. We introduce in this work an optimal measurement basis to achieve fast adaptive quantum state tomography and a minimum magnitude of infidelity in an indistinct measurement system. We expect that the adaptive quantum state tomography protocol can lead to a reduction in the number of required measurements of approximately 33.74% via analytical expectation without changing the $O(1/\sqrt{N})$ scaling. Experimentally, we find a 15.06% measurement number reduction in a superconducting circuit system.
Improving Phishing Website Detection with Machine Learning: Revealing Hidden Patterns for Better Accuracy

Phishing attacks remain a significant threat to internet users globally, leading to substantial financial losses and compromising personal information. This research study investigates various machine learning models for detecting phishing websites, with a primary focus on achieving high accuracy. After an extensive analysis, the Random Forest Classifier emerged as the most suitable choice for this task. Our methodology leveraged machine learning techniques to uncover subtle patterns and relationships in the data, going beyond traditional URL and content-based restrictions. By incorporating diverse website features, including URL and derived attributes, Page source code-based features, HTML JavaScript-based features, and Domain-based features, we achieved impressive results. The proposed approach effectively classified the majority of websites, demonstrating the efficiency of machine learning in addressing the phishing website detection challenge with an accuracy of over 98%, recall exceeding 98%, and a false positive rate of less than 4%. This research offers valuable insights to the field of cyber security, providing internet users with improved protection against phishing attempts.
Consumption Culture in the Metaverse Economy as New Communication Technologies (ChatGPT Analysing)

The intense technological revolution in the field of communication and information technologies has naturally affected economic and social life. As the economy evolves towards Industry 4.0 and the metaverse universe, digital transformation has started to reshape production and consumption relations. Thanks to advanced technology and mass media, the world has shrunk, communication has intensified and access to information is realised with a single click. At the last point, the Metaverse appears as a digital virtual universe. The Metaverse world, which provides a parallel but virtual reality for human beings, can be considered as a twin world where we live with our avatars. However, the virtual twin world changes the known consumption, production and financing patterns of the economic system and creates a commodity universe economy, or in other words, the Metaverse economy. In this context, the aim of this systematic review is to understand the consumption of individuals in the new economic life that comes with the changing world order. Many global, popular and world giant brands include the Metaverse in their production methods. When the Metaverse fully enters our lives, a new economic life will be formed in our daily work, working life and social life thanks to the Metaverse tools we will use. Keeping up with advanced communication and information technologies will harmonise both the production and consumption structure of society with technological change. In this context, the aim of this study is to draw attention to the culture of consumption in the Metaverse economy as new communication technologies. For this reason, the Metaverse economy and ChatGPT analysis method were chosen in the context of the originality of the subject. The research questions of the study are, will the Metaverse economy as a new communication technology increase the consumption culture? Will people shop with their avatars in virtual stores? The originality of the study stems from the fact that there is no study on this subject and ChatGPT analysis method has not been used. Its contribution to the literature will be in this context. A literature review was also conducted. ChatGPT analysis method was applied in the study. With the ChatGPT analysis method, Internet 1.0 consumption culture, social media consumption culture and metaverse period consumption culture change processes were compared.
Object Counting via Group and Graph Attention Network

Object counting, defined as the task of accurately predicting the number of objects in static images or videos, has recently attracted considerable interest. However, the unavoidable presence of background noise prevents counting performance from advancing further. To address this issue, we created a group and graph attention network (GGANet) for dense object counting. GGANet is an encoder-decoder architecture incorporating a group channel attention (GCA) module and a learnable graph attention (LGA) module. The GCA module groups the feature map into several subfeatures, each of which is assigned an attention factor through the identical channel attention. The LGA module views the feature map as a graph structure in which the different channels represent diverse feature vertices, and the responses between channels represent edges. The GCA and LGA modules jointly avoid the interference of irrelevant pixels and suppress the background noise. Experiments are conducted on four crowd-counting datasets, two vehicle-counting datasets, one remote-sensing counting dataset, and one few-shot object-counting dataset. Comparative results prove that the proposed abbr achieves superior counting performance.
A Trust Evaluation Joint Active Detection Method in Video Sharing D2D Networks

The potentially malicious devices in D2D networks may spread forged videos to compromise system reliability. The prevailing passive trust model solutions have limitations, such as insufficient and inaccurate trust evidence, as well as weaker resistance to collusion attacks. This paper presents the <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> rust <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> valuation Joint <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> ctive <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> etection (TEAD) method, which employs content correctness as trust evidence and allows devices to verify the authenticity of received videos via the base station. TEAD incorporates an active detection method to proactively determine the trustworthiness of devices. This promotes interaction among devices, resulting in an increase in trust evidence and an improvement in the accuracy of evaluation. Moreover, TEAD introduces a trust calculation method with a penalty mechanism to strengthen the system's resilience against malicious attacks. Empirical results show that TEAD outperforms state-of-the-art methods by achieving a more accurate trust evaluation and faster trust convergence with low extra energy consumption.
Improving Procedure Text Writing Skills Through Make A Match Learning Model in Junior High School

This Classroom Action Research aims to improve students' ability in writing procedure texts through Make a Match learning model. The subjects in this study were 33 students of class VII A SMP Negeri 36 Semarang. Data were collected in one cycle through tests, classroom observation, and documentation. The tests were conducted in the form of pre-test, post-test of cycle I, and post-test of cycle II. The instruments used were test sheets, observation guidelines, and documentation guidelines. Data were analyzed using qualitative descriptive method. The results showed that students took an active role in their learning activities. The quality of their writing also improved. Their average score increased from 67 (Pre-test), 76 (Cycle I), and 82 (Cycle II). In conclusion, the use of Make a Match learning model is effective to improve students' ability in writing procedure text. The researcher hopes that the use of Make a Match learning model can be used by English teachers in teaching English, especially in teaching writing procedure text.
Research on Practical Teaching System in Network Engineering Speciality

This paper proposes the construction of practical teaching system for network engineering speciality, designs a practical teaching system composed of in-course experiments, on-campus practices and off-campus practices, arranges practical teaching according to the principle of hierarchical and progressive ability cultivation, and proposes a hierarchical and progressive practical teaching system. The practical results show that the implementation plan is conducive to promoting the construction of practical links of the specialty, improving the teaching quality, and cultivating high-quality qualified network engineering technology and network application talents for the society.