Reviewerly/exHarmony huggingface dataset:
Securing the Skies: Cybersecurity Strategies for Smart City Cloud using Various Algorithams

As smart cities continue to evolve, their reliance on cloud computing technologies becomes increasingly apparent, enabling the seamless integration of data-driven services and urban functionalities. However, this transformation also raises concerns about the security of the vast and interconnected cloud infrastructures that underpin these cities' operations. This paper explores the critical intersection of cloud computing and cybersecurity within the context of smart cities.&#x0D; This research is dealing with challenges posed by the rapid expansion of smart city initiatives and their reliance on cloud-based solutions. It investigates the vulnerabilities that emerge from this technological convergence, emphasizing the potential risks to data privacy, urban services, and citizen well-being. The abstract presents a comprehensive overview of the evolving threat landscape that smart cities face in the realm of cloud computing.&#x0D; To address these challenges, the abstract highlights the importance of proactive cybersecurity strategies tailored specifically to the unique needs of smart cities. It underscores the significance of adopting a multi-layered approach that encompasses robust encryption protocols, intrusion detection systems, threat intelligence sharing, and collaborative efforts among stakeholders. Drawing insights from existing research and real-world case studies, the abstract showcases innovative solutions that leverage advanced technologies like artificial intelligence and blockchain to fortify the security posture of smart city cloud infrastructures. It explores the role of data governance, user authentication, and anomaly detection in creating a resilient cybersecurity framework that safeguards critical urban systems.
Organization of practical training of students for the tourism and hospitality industry

Introduction. The study discusses the current issues of practical training in the tourism and hospitality industry in Russia, identifies the main problems of training specialists of Russian specialized universities. The main focus is on building partnerships between universities and employer organizations in order to train highly qualified specialists. The objective of the study is to describe the process of organizing the interaction of the university with the employer organizations in the framework of practical training. Methodology, methods and techniques. As a methodological basis, systematic and structural approaches were used; methods of analysis and synthesis; the study of models of cooperation between universities and employers, methods of monitoring and evaluating the quality of training specialists. Conclusions. In the course of the work, the authors describe the process of practical training of specialists in the tourism and hospitality industry, including the purpose and objectives, process requirements, organization conditions and requirements for the results of the process. The scope of the study. The direction of further research may include modeling the process of practical training and the development of methodological foundations for cooperation between the university and employer organizations in the framework of practical training. Practical application. The results of the study are used in organizing practical training at the Department of Tourism Business and Hospitality of the Ural State University of Economics, and can also be used by other professional educational organizations, which will effectively solve the problem of training personnel for tourism and hospitality. The social consequences of organizing the process of practical training of students will include increasing the competitiveness of graduates in the labor market; improving the quality of tourist services and hotel services; introducing innovations into the tourism and hospitality industry, and developing startups. Originality. The innovativeness of the research lies in the description of the elements of the system of practical training at the university.
Digital and analysis of alarm clock design based on Multisim

With the development of technology and the times, virtual simulation technology represented by Multisim has been widely used, and simulation technology is also an indispensable part of industrial design. This article uses Multisim simulation technology to design and complete a digital electronic clock based on a 74ls160 counter, composed of a decoder, a display, a numerical comparator, and an alarm clock. In this design, digital circuits are used to display and adjust "time", "minute", and "second". By using various integrated digital chips to build circuits to achieve corresponding functions. It successfully realizes the functions of calibrating the time, reporting the time on the hour, and alarming the clock. Compared with traditional electronic clocks, it has the characteristics of accurate travel, intuitive display, and no mechanical transmission, The research in this paper will contribute to the further application of virtual simulation software in the design of small devices, and will not lay a foundation for the future industrial design process.
Improved artificial potential field to solve the problem of local minimum

The artificial potential field approach is a traditional method that is frequently used for local path planning, but in order for it to be successful, it requires more than unreachable target sites, local minimums, and oscillation difficulties. An enhanced method that is based on discretized sampling was presented as a potential solution to these issues as a result of this information. In the first place, a decision must be made regarding whether or not to enter the local minimum mark. In order to free ourselves from the confines of the local minimum, we subsequently devised a motion that was perpendicular to the direction in which the object was being moved. In the end, a regression search is carried out in order to further hone in on the correct path. The results of the simulation show that this method is capable of efficiently handling the local minimum problem and providing appropriate paths, as demonstrated by a comparison between the conventional method and the enhanced method.
Analyzing the Performance of Different Classifier for Detecting Polarity of Customer Reviews

To determine an author's emotional state from their written words is the focus of sentiment analysis, a subfield of NLP. This study focuses on the many techniques used to categorize the text reviews written in natural language according to the viewpoints expressed therein, in order to determine if the widespread behavior is positive, negative, or neutral. Streaming of thoughts and expression of opinion have been facilitated by the proliferation of debate forums, Weblogs, product review sites, e-commerce, and social networking sites. A lot of people's feelings, reviews, and assessments of others' opinions can be found on social media. This research ranks the top classifier for feelings using data derived from online product reviews posted to Twitter. Experimental work on polarity classification with well-known classifiers such as Naive byes, Support vector machine, and Logistic regression for anticipating testimonials was addressed.
An Efficient Information Extraction Mechanism with Page Ranking and a Classification Strategy based on Similarity Learning of Web Text Documents

Users have recently had more access to information thanks to the growth of the www information system. In these situations, search engines have developed into an essential tool for consumers to find information in a big space. The difficulty of handling this wealth of knowledge grows more difficult every day. Although search engines are crucial for information gathering, many of the results they offer are not required by the user because they are ranked according on user string matches. As a result, there were semantic disparities between the terms used in the user inquiry and the importance of catch phrases in the results. The problem of grouping relevant information into categories of related topics hasn't been solved. A Ranking Based Similarity Learning Approach and SVM based classification frame work of web text to estimate the semantic comparison between words to improve extraction of information is proposed in the work. The results of the experiment suggest improvisation in order to obtain better results by retrieving more relevant results.
Algorithm for planning energy-efficient local trajectories of ground-based robotics, considering the global route

In this paper, an algorithm is presented that provides dynamic planning of a local trajectory of movement based on the data from the 3D lidar, considering a predetermined global route on rough terrain. The proposed local planning algorithm considers the features of the LRLHD-A* global energy-efficient trajectory planning algorithm previously developed by the authors. The novelty of the proposed solution lies in the algorithm for generating a graph of the surrounding space from the data obtained from a three-dimensional lidar installed on a mobile robot with O(nlogn) complexity, which reduces the time to build a local trajectory. The developed solution makes it possible to combine the processes of constructing a local and global trajectory of movement by using the energy-efficient algorithm for constructing trajectories LRLHD-A* as the basis. During the experiments, it was revealed that when choosing a step of 0.5 m for a local grid of 10 × 10 m in size, the time for constructing the grid and the trajectory does not exceed 0.1 s. The developed algorithm can be used as a local scheduler in close to real time mode, providing prompt re-sponse to various obstacles on the global trajectory of the robot, considering the features of the terrain and the cur-rent global trajectory.
An Improved Integrity-Based Hybrid Multi-User Data Access Control for Cloud Heterogeneous Supply Chain Databases

Cloud-based supply chain applications play a vital role in the multi-user data security framework for heterogeneous data types. The majority of the existing security models work effectively on small to medium-sized datasets with a homogenous data structure. In contrast, Supply Chain Management (SCM) systems in the real world utilize heterogeneous databases. The heterogeneous databases include a massive quantity of raw SCM data and a scanned image of a purchase quotation. In addition, as the size of the database grows, it becomes more challenging to provide data security on multi-user SCM databases. Multi-user datatypes are heterogeneous in structure, and it is complex to apply integrity and confidentiality models due to high computational time and resources. Traditional multi-user integrity algorithms are difficult to process heterogeneous datatypes due to computational time and variation in hash bit size. Conventional attribute-based encryption models such as "Key-policy attribute-based encryption" (KP-ABE), "Ciphertext-Policy Attribute-Based Encryption" (CP-ABE) etc., are used to provide strong data confidentiality on large textual data. Providing security for heterogeneous databases in a multi-user SCM system requires a significant computational runtime for these conventional models. An enhanced integrity-based multi-user access control security model is created for heterogeneous databases in the cloud infrastructure to address the problems with heterogeneous SCM databases. A non-linear integrity model is developed to provide strong integrity verification in the multi-user communication process. A multi-user based access control model is implemented by integrating the multi-user hash values in the encoding and decoding process. Practical results proved that the multi-user non-linear integrity-based multi-access control framework has better runtime and hash bit variation compared to the conventional models on large cloud-based SCM databases.
Formal Semantic Approach to Detect Smart Contract Vulnerabilities Using KEVM

Smart contracts are self-executing programs that run on blockchain platforms. While smart contracts offer a range of benefits, such as immutability and transparency, they are not immune to vulnerabilities. Malicious actors can exploit smart contract vulnerabilities to execute unintended actions or access sensitive data[1]. One approach to mitigating smart contract vulnerabilities is formal verification. Formal verification is a method of verifying the correctness of software using mathematical techniques. It involves mathematically proving that a program conforms to a set of specifications. Formal verification can help detect and eliminate vulnerabilities in smart contracts before they are deployed on the blockchain. KEVM (K Framework-based EVM) is a framework that allows for formal verification of smart contracts on the Ethereum Virtual Machine (EVM). KEVM uses the K Framework, a formal semantics framework, to specify the behavior of the EVM. With KEVM, smart contract developers can verify the correctness of their contracts before deployment, reducing the risk of vulnerabilities. In this paper, we have studied smart contract vulnerabilities such as Over usage of Gas, Signature Replay attack, and misuse of fallback function. We have also written the formal specification for these vulnerabilities and executed it using KEVM.
Supplementary Materials in Enhancing the Common Learning Competencies of Grade 7 Students in Computer System Servicing

This research uses supplementary materials to enhance the common learning competencies in Computer System Servicing for Grade 7 students. The study explored the potential benefits and challenges of incorporating supplementary material into the curriculum to enhance student's understanding and mastery of the common learning competencies. The research methodology includes a review of the existing literature on supplementary material and its impact on student learning outcomes. Additionally, pretest and posttest data are collected to evaluate respondents' performance levels regarding common learning competencies in Computer System Servicing. The pretest results reveal that the students have basic knowledge and skills, with some competencies from approaching proficiency to developing proficiency. The posttest performance shows that the students achieved proficiency in most competencies, demonstrating a strong understanding and ability in computer system servicing. The significant difference in the pretest and posttest scores indicated that the supplementary material positively impacts students' performance, leading to improved knowledge and skills in computer system servicing. These findings highlight the use of supplementary materials in enhancing student learning outcomes in terms of computer system servicing for Grade 7 TLE students.
Red Deer Optimization with Deep Learning based Robust White Blood Cell Detection and Classification Model

The use of deep learning techniques for White Blood Cell (WBC) classification has garnered significant attention on medical image analysis due to its potential to automate and enhance the accuracy of WBC classification, which is critical for disease diagnosis and infection detection. Convolutional neural networks (CNNs) have revolutionized image analysis tasks, including WBC classification effectively capturing intricate spatial patterns and distinguishing between different cell types. A key advantage of deep learning-based WBC classification is its capability to handle large datasets, enabling models to learn the diverse variations and characteristics of different cell types. This facilitates robust generalization and accurate classification of previously unseen samples. In this paper, a novel approach called Red Deer Optimization with Deep Learning for Robust White Blood Cell Detection and Classification was presented. The proposed model incorporates various components to improve performance and robustness. Image pre-processing involves the utilization of median filtering, while U-Net++ is employed for segmentation, facilitating accurate delineation of WBCs. Feature extraction is performed using the Xception model, which effectively captures informative representations of the WBCs. For classification, BiGRU model is employed, leveraging its ability to model temporal dependencies in the WBC sequences. To optimize the performance of the BiGRU model, the RDO is utilized for parameter tuning, resulting in enhanced accuracy and faster convergence of the deep learning models. The integration of RDO contributes to more reliable detection and classification of WBCs, further improving the overall performance and robustness of the approach. Experimental results demonstrate the superiority of our Red Deer Optimization with deep learning-based approach over traditional methods and standalone deep learning models in achieving robust WBC detection and classification. This research highlights the possibility of combining deep learning techniques with optimization algorithms for improving WBC analysis, offering valuable insights for medical professionals and medical image analysis.
Image based Seen Detection in Real Time Video Interpretation for Surveillance Systems using Support Vectors Machine

People that are perpetually hunting for knowledge will benefit from data acquisition. The early phase in video data acquisition is splitting the video into images. Many images are tiny and don't reveal a lot about the picture's information. Scene boundary identification, or video segmentation into action sequences, enables a more complete comprehension of the image sequence by classifying images based on comparable visual content. The purpose of this article is to discuss video scene recognition, particularly video structure extraction for pattern comparison with significant properties. The article designed and developed a methodology that would include stages for image collection, detecting commonalities among frames, selecting important frames, and detecting the time at where the relevant frame is identified. The pictures are generated by Python's OpenCV and scene classification metrics are used to assess the method. As assessed by numerous parameters, the findings shows that scene identification and accuracy are considerable. Furthermore, we investigated and researched contemporary identification and assessment techniques. Moreover, we have tested extensively our research framework on a variety of publicly available event video databases, and these outperformed several futuristic techniques. The outcomes of this research can be utilized to generate real-time definitional video assessments.
Supplemental Material: Crustal block-controlled contrasts in deformation, uplift, and exhumation in the Santa Cruz Mountains, California, USA, imaged through apatite (U-Th)/He thermochronology and 3-D geological modeling

&lt;p&gt;Thermal models, structural cross sections, and supplementary figures, tables, and text.&lt;/p&gt;
Kim Namjoon’s Speech at the 2018 United Nations General Assembly: The Intentions and Impacts

This study, using a qualitative approach, was conducted to find out the types of illocutionary acts in the speech of Kim Namjoon at the 2018 United Nations General Assembly, including the intentions and the impacts. The speech on “Generation Unlimited”, was aimed to help young people all over the world to help end bullying and violence by sharing kind notes. In conducting the research, the writer used the theory proposed by George Yule (1996) completed by the related theory by O’Keeffe, Clancy, and Adolphs (2011). The findings reveal various intentions through the illocutionary acts and the impacts through the written responses as the perlocution. The intentions through the representative acts were to inform people about his struggles in the past. The intentions through the directive acts were to get the audience to begin speaking up and expressing what they believed. The impact of this speech was that it touched the audience's hearts; they got inspired by the speech to never give up, and they would now continue to love and speak for themselves even if it was hard. In conclusion, Kim Namjoon’s speech at the 2018 United Nations General Assembly resulted in inspiring and motivating impacts on the audience viewed from their written responses.
Design of Multiple Ontology Based Agro Knowledge Mining Model

Farming is regarded as a major industry in India, accounting for 17% of the country's GDP growth. Agriculture employs 60% of the population hence it is considered an important sector in India. The important factors for agriculture are pest management, disease prevention, irrigation management, soil mineral composition, crop management, location, and the season in which the crop is grown. Hence all this information along with the techniques are well known only by the experienced farmers. Hence it is important to create an agro knowledge management system. As a result, this work makes an attempt to develop a multiple ontology-based agro knowledge management system. The designed system consists of agriculture information related to attributes of soil mineral, moisture, season, location, crop type, and temperature. It consists of multiple ontologies such as soil ontology, crop ontology, location ontology, and crop season ontology to provide agronomy knowledge. Soil ontology is premeditated to classify the soil type in a hierarchical order while crop ontology classifies the crop type, location ontology classifies locations suitable for different crop types and finally, crop season ontology classifies the season that is suitable for different crops. A rule base is built to develop the knowledge base and to validate the truthfulness of the knowledge base. Visualization of a knowledge base is carried out for better understanding and decision-making.
Penerapan Metode Design Thinking Pada Perancangan UI/UX Sistem Informasi Penelitian Mahasiswa Berbasis Web

&#x0D; &#x0D; &#x0D; Advances in technology provide opportunities to increase efficiency in various sectors, including the licensing process and the operations of government agencies. The Bantul District Health Office, as a health and research service provider, still relies on a semi-manual research licensing process. To overcome this, a structured research licensing information system is needed on a website basis. The stages of making a website start with UI/UX design. This study designed a UI/UX system using the Design Thinking method. The Empathize stage involves interviews and observations to understand the needs of users, especially the Head of the Health Office Program Subdivision. The Define phase identifies core requirements such as a list of user requirements, business process flowcharts, and the use of Figma for design and prototyping. The Ideate Stage involves brainstorming and wireframes, while the Prototype Stage produces the final design for testing. The Test phase involved Usability Testing, with fixes such as changing the message feature to notifications and adding the option to sign in with a Google account. The results showed that the prototype answered the problems of the Bantul District Health Office in designing the SIPENA UI/UX according to user needs.&#x0D; &#x0D; &#x0D;
Optimizing Robustness and Invisibility in Digital Image Watermarking: A SVM-Based Multi-Level DWT and SVD Approach

This research introduces a new digital image watermarking approach that utilizes discrete wave transformation (DWT), Support vector machine, and singular value decomposition. The method improves robustness under various assault situations by using the SVM classifier during watermark extraction. Multi-level DWT splits the host picture into sub-bands when embedding, and the coefficients are used as input for SVM. After SVD, the scaling factor embeds the watermark. Comparing the proposed approach to existing research under various attacks, the experimental findings demonstrate that it strikes an equilibrium between robustness and invisibility for watermarks of varying sizes. Support Vector Machine is a contemporary category of machine learning techniques that is extensively employed for the purpose of solving classification problems.