Reviewerly/exHarmony huggingface dataset:
Time-Constrained Multi-UAVs Mission Planning Method

Aiming at the mission planning problem of multi-UAVs cooperative operations, a Time-constrained Self-Organizing Allocation Algorithm (TSOAA) based on time constraint rules is designed and proposed, which solves the problem of the limited capacity of multi-UAVs. The problem of task allocation under the control system improves the performance of multi-UAVs tasks. In this paper, the multi-UAVs cooperative mission planning problem is transformed into the classic multi-travelling salesman MTSP problem, and the UAV model, mission model and revenue objective function are designed and proposed, and the multi-UAVs mission planning scheme of typical scenarios is solved according to the algorithm model.. The simulation results show that under the condition of limited bomb load, the TSOAA algorithm can effectively solve the waiting and conflict problems in the multi-UAVs task assignment process, shorten the task execution time and route distance, and ensure the optimal performance of the task execution. It has certain practical value and guiding significance for multi-UAVs cooperative combat mission planning.
Proposal of a System Enabling Adaptive Support Based on Transition of Each Learner's Source Codes

The difficulties that students face in programming education can be broadly classified into two types: difficulty in conceiving a solution and difficulty during the actual coding process. In this study, we developed a machine learning model that enables adaptive instruction by estimating a solution that will be easy for the learner to understand according to the learner's past work on previous tasks. Our evaluation showed that the developed approach could estimate easily understandable code instances for learners with high accuracy (97.5 % AUC).
Collaborative Coverage Strategy for Multiple UAVs Considering Coverage Utility

To complete the monitoring and searching task of large amphibious areas, multiple Unmanned Amphibious Vehicles (UAVs) have been introduced to solve the coverage problem of such areas. However, few studies were conducted on the coverage problem in amphibious environments, which is particularly challenging and urgent. In this case, we propose a collaborative coverage strategy to achieve amphibious regional coverage. Our approach is based on the following parts. Firstly, a centralized area allocation strategy is used to allocate subregion coverage tasks to each agent. Secondly, a distributed path planning algorithm is used to plan the coverage of each UAV in the amphibious environment to minimize the energy loss of the UAVs. This strategy incorporates the importance of the region and the ability of the agent, which shows great adaptability to the amphibious environment. Finally, the proposed strategy is compared with several previous popular methods in the simulations to verify its effective performance.
Reaching Sustainable Development Goals Through Tax Reductions and Incentives within a Trustworthy Decentralized Autonomous Organization

Fulfilling the Sustainable Development Goals (SDGs) relies on the proper implementation of various governmental policies, among which are also financial and taxation policies. The current public administration in Slovenia is based on a fully digitalized data management process. Our goal is to achieve new data integrity methods based on state-of-the-art technologies of the ONTOCHAIN software ecosystem. ONTOCHAIN includes technologies to achieve decentralised knowledge graphs, smart oracles, smart contracts, and tokenomics principles that rely on high integrity and trustworthiness. Consequently, we study the possibility to realize flexible incentives and taxation policies that contribute to the seamless achievement of SDGs.
Response-Time Tail Mitigation in Cloud Systems

Response time is one of the most important metrics in cloud data centers that measure application performance and availability. The response time depends on how the scheduler distributes tasks belonging to a job to various cores. Even with at a maximum level of parallelism, the slowest task (the straggler) dominates the response time, thereby increasing the average and extending the tail of the response time distribution. In this paper, we propose a distributed scheduling scheme where tasks are systematically assigned to randomly selected servers based on the expected response time of each server and the size of the tasks. Consequently, straggling tasks are less likely to become head-of-line blockers and are more likely to finish in a shorter period of time. In light of the bursty nature of task arrivals, the instantaneous response time is not an accurate measure of a server's load. Therefore, we developed a Truncated Exponentially Weighted Moving Average (TEWMA) for the selection of servers. To adapt to the new schemes, we augmented the Sparrow simulation and compared its performance to Sparrow's. Our approach probes fewer servers and selects most suitable ones based on a simple matching algorithm. The results have shown that the tail of response time distribution has been declined considerably, and consequently the average response time has been reduced accordingly.
Key Technology of Distributed Memory File System Based on High-Performance Computer

With the rapid development of computer technology, distributed systems have become an indispensable part of the field of information storage and management. In the process of large-scale data processing, it is an important issue to compress or replay files to ensure their integrity. In order to solve the problem of large-scale computing resources and data storage, the distributed file system emerged as a new system structure. The purpose of this paper to is study the key technologies of the distributed memory file system of high-performance computers is to improve the capability and efficiency of the distributed system. This paper mainly uses the experimental method and the comparative method to analyze the key technology of the distributed memory file system of the high-performance computer. Experimental results show that the maximum bandwidth value of DFMS in file memory processing can reach more than 2000, and the value becomes more stable as the file increases.
A Comparison of IoT Security Threats and their Preventative Methods

The applicationof IoTnetworks in daily life attempts to simplify and minimize the complexity of our lives. However, it has also raised the number of brand-new security risks to our data and information. Our personal lives are also threatened by various kinds of security dangers. As a result, it is now necessary to be aware of many security risks that IoT networks may face. As our daily interactions with Internet of Things (IoT) devices grow, various security and privacy concerns—such as linkability, unauthorized chats, and side-channel attacks—are brought up. These problems could endanger the IoT if they are not addressed. They come about for two basic causes. One is that the limited computational, memory, and bandwidth capabilities of IoT gadgets make it difficult to directly deploy established Internet security approaches. The absence of generally regarded IoT security and privacy principles, as well as suitable implementation methods, is the second factor. Additionally, we identify the IoT stakeholders—including consumers andmanufacturers—who stand to gain the most from these rules. Additionally, we define a set of implementation methods for carrying out such directives and mitigating any threats to the levels stated before. Thirdly, we talk about the difficulties with IoT security and privacy regulations and briefly touch on IoT digital rights management. Finally, through this survey, we identify several unresolved concerns that demand future research. The authors' knowledge indicates that this work is the first survey to address the aforementioned goals. This paper investigates several IoT security risks and how they affect daily living. This paper highlights the strategies used to counter some attacks in an IoT network.
Secure and Anonymous Authentication Scheme for Mobile Edge Computing Environments

The Internet of Things (IoT) is progressively being integrated into everyday life, with cloud computing systems being widely employed to monitor and manage IoT devices. Cloud servers offer centralized high-performance processing of large-scale data utilizing millions of connected sensors and geographically distributed devices. Distributed data processing with low latency is important for autonomous driving, healthcare, virtual reality, and augmented reality in cloud services based on 5G technology. Mobile edge computing (MEC) infrastructure enables cloud services at the edge of the network in a 5G ecosystem, allowing for real-time processing of large amounts of data. MEC involves an open infrastructure that allows for access by heterogeneous devices, increasing the complexity of security challenge. This, in turn, brings the potential risk of attacks that could result in information leakage and compromise the privacy of users. In extreme cases, attacks may even pose a risk to human life. Thus, an effective authentication mechanism is required to prevent attacks and protect privacy in MEC environments. To address this issue, we propose a secure and anonymous authentication scheme that enhances security in MEC environments. In the proposed scheme, the user and MEC server establish a secure session key without the need of a trusted third party. The proposed scheme is designed to be secure from various known attacks attempted by internal and external adversaries. We have conducted both formal and informal analyses to prove the security of the proposed scheme and compared its performance with related schemes to validate its effectiveness and practical application.
A text analysis of operational risk loss descriptions

Financial institutions manage operational risk (OpRisk) by carrying out activities required by regulation, such as collecting loss data, calculating capital requirements and reporting. For this purpose, for each OpRisk event, the loss amounts, dates, event types and descriptions and organizational units involved are recorded in OpRisk databases, and in recent years, OpRisk functions have been required to go beyond their regulatory tasks and to proactively manage OpRisk, preventing or mitigating its impact. As OpRisk databases contain, among other things, event descriptions, one area of opportunity is the extraction of information from such texts. This paper introduces a novel structured workflow for the application of text analysis techniques (one of the main natural language processing tasks) to OpRisk event descriptions in order to identify managerial clusters (which are more granular than regulatory categories) that cause the underlying risks. We complement and enrich the established framework of statistical methods based on quantitative data. Specifically, after delicate tasks such as data cleaning, text vectorization and semantic adjustment, we apply methods of dimensionality reduction and several algorithmic clustering models, and we compare their performance and weaknesses. Our results add to the knowledge of historical loss events and enable the mitigation of future risks.
Managing Work Output for Your New Advocacy Team

The previous chapter discussed how to create a developer advocacy team. But what do you do with your developer advocates (or if you're just starting out, your single advocate) once you've introduced the function into your business. In other words, which work expectations should you set for advocates, how should you structure their tasks, and how do you measure their success?
Phishing Website Detection using Multilayer Perceptron

Phishing attacks pose a significant threat in the cyber world, exploiting unsuspecting users through deceptive emails that lead them to malicious websites. To combat this challenge, various deep learning based anti-phishing techniques have been developed. However, these models often suffer from high false positive rates or lower accuracy. In this study, we evaluate the performance of two neural networks, the Autoencoder and Multilayer Perceptron (MLP), using a publicly available dataset to build an efficient phishing detection model. Feature selection was performed through correlation analysis, and the Autoencoder achieved an accuracy of 94.17%, while the MLP achieved 96%. We used hyperparameters for optimization using the Gridsearch CV, resulting in a False Positive Rate (FPR) of 1.3%, outperforming the Autoencoder’s 4.1% FPR. The MLP model was further deployed to determine the legitimacy of websites based on input URLs, demonstrating its usability in real-world scenarios. This research contributes to the development of effective phishing detection models, emphasizing the importance of optimizing neural network architecture for improved accuracy and reduced false positives
Modeling the Intent to Interact with VR using Physiological Features

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective</b> . Mixed-Reality (XR) technologies promise a user experience (UX) that rivals the interactive experience with the real-world. The key facilitators in the design of such a natural UX are that the interaction has zero lag and that users experience no excess mental load. This is difficult to achieve due to technical constraints such as motion-to-photon latency as well as false-positives during gesture-based interaction.
Introduction

This paper is an attempt for synthetizing 25 years of research on the modeling of context and its use in about twenty real-world project and applications. The topic “Modeling and using context” is found in a number of disciplines either directly (i.e. as context) or indirectly (i.e. like situations, constraints, etc.). Our objective is not to make a thorough study of the literature in all the disciplines, especially in disciplines, where old references (say, 1940) are stable since a long time and remain quoted. Most of the references cited in this paper come from artificial intelligence (AI) and computer science and some related disciplines.
Intellectual property statute in Private International Law

The article highlights the comparative analysis of the conflict-of-laws regulation of intellectual property rights in international, European and Russian law. It is concluded that lex loci protectionis as the main conflict-of laws rule in this area has an ambiguous interpretation, cannot be recognized as lex fori – the law of the country where the competent court is seated, but should be generally understood as the law of the country where the legal protection of the relevant intellectual property object is provided. In addition, in some cases it seems reasonable to apply lex loci originis, lex loci actus and lex loci delicti, as well as lex voluntatis and lex loci solutionis – to contractual relations. In the case of non-contractual infringement of intellectual property rights granted and protected on the territory of several states, including on the Internet, the key issue is to establish the closest connection of the civil legal relationship with a particular state, taking into account various factors (the place of origin or registration of an intellectual property object, the place of committing infringement, the place of occurrence of negative consequences for the intellectual right holder, the orientation of a website to a specific state, the location of a competent court, etc.). It is also argued to enshrine the provisions on the intellectual property statute in Section VI “Private International Law” of the Civil Code of the Russian Federation, including the following issues of its scope: a) authorship; b) types of protected intellectual property objects; c) requirements for the legal protection of intellectual property objects, including registration issues and other formalities to be executed; d) the types, content and effect of intellectual rights arising in relation to intellectual property objects, including the restrictions provided by law and the duration of legal protection; e) the emergence, exercise and termination of intellectual rights, including permissible ways of disposing of them; f) non-contractual protection of intellectual rights.
An efficient human action recognition framework based on hybrid features and enhanced long short term memory

Although the scientific group has given considerable attention, an effective method of detecting human activity in the physical realm remains elusive because of variations in appearance, interactions between objects, and mutual occlusion. So, in this study, an efficient human action recognition (HAR) system based on hybrid features with enhanced long short-term memory (ELSTM) is proposed. Initial key frames are extracted from a sequence of input videos using the structural similarity measure (SSIM). After that, the features namely, coverage factor, space-time interest (STI) points, and shape features are extracted from the key frames. Then, the selected features are fed to the ELSTM classifier to classify a diffident activity of a human. The proposed ELSTM is designed by LSTM with the integration of adaptive golden eagle optimisation (AGEO) in the weight update process to select possible weights. The proposed approach is evaluated in terms of' accuracy, precision, recall, and F-Measure.
A hybrid wrapper approach for optimal feature selection based on a novel multiobjective technique

Recent technological improvements have caused data management challenges. In machine learning and data mining, preprocessing removes noisy, irrelevant, and redundant input. Feature selection is a pre-processing method that increases classifier performance. Researchers have employed single-objective optimisation to improve categorisation. Single-objective optimisation reduces accuracy. In this study, a hybrid wrapper approach feature selection employing recursive feature elimination (RFE) and extreme learning classifier (HWFS-RTE) algorithm is used to anticipate sickness. A multiobjective technique of improved teaching learning-based optimisation (iTLBO) is combined with the RFE method choose categorisation features. iTLBO also performs disease classification with the extreme learning machine (ELM) by tuning ELM's parameters using iTLBO. TLBO is a population-based optimisation strategy, as it requires more rounds to obtain the classification's fitness value. It combines extreme machine learning (ELM) to improve convergence speed and reduce computing complexity. In disease prediction, the hybrid model (HWFS-RTE) applied to the Breast cancer dataset from the UCI repository has a 98.4% accuracy rate.
KRITIK SASTRA FEMINIS DALAM NOVEL THE PERFECT WORLD OF MIWAKO SUMIDA SEBAGAI ALTERNATIF BAHAN AJAR MENGANALISIS ISI SERTA KEBAHASAAN NOVEL PADA KELAS XII SMA

This study aims to determine the content, structure, and linguistic elements in the novel The Perfect World of Miwako Sumida by Clarissa Goenawan; knowing the feminist literary criticism found in the novel The Perfect World of Miwako Sumida by Clarissa Goenawan as an alternative teaching material in learning Indonesian at the high school level. The method used in this research is descriptive qualitative. Data collection techniques used include writing and reading techniques. The research results are: first, the content, structure, and linguistic elements contained in the novel The Perfect World of Miwako Sumida by Clarissa Goenawan have been fulfilled. This can be proven because there are stories, abstract structures, orientations, complications, evaluations, resolutions, and coda. In the novel there are also intrinsic elements in the form of characters, characterizations, plot, setting (place, time, atmosphere), point of view, and message. In the novel there are also linguistic rules in the form of the use of direct sentences, past sentences, material verbs, mental verbs, temporal conjunctions, and adjectives. Second, feminist literary criticisms contained in the novel The Perfect World of Miwako Sumida by Clarissa Goenawan have been fulfilled. This can be proven, because there is gender marginalization, gender subordination, gender stereotypes, gender violence, and gender workload. Third, the feasibility of the results of the analysis to be utilized as teaching materials. Based on these results the researcher concluded that, some of the findings in this analysis can be used and utilized for learning materials because they are in accordance with the demands of an independent curriculum or in accordance with Learning Outcomes.
Nigerian Online English: An Analysis of Its Nominal Group Structure

Online/Internet English is generally considered by scholars in the field of Information Communications Technology (ICT) discourse research to exhibit usage features which mark it as an emergent sub-variety of the English language (Crystal, 2005, 2011; Posteguillo, 2002). Furthermore, Tagliamonte and Denis (2008) observed that the language used on the internet possesses its own grammar, lexicon, graphology and usage conditions. In fact, there have been proposals advanced for ICT discourse research to treat Internet language as a ‘new medium’ and to investigate it as a discipline on its own terms (Crystal, 2003). This study is aimed at analysing the Nominal Group (NG) structure of English used by Nigerians on the Internet. The NG data analyzed in this study were obtained from written productions by Nigerian users of the Internet of four platforms of the social media: Facebook, WhatsApp, Instagram and Twitter. The data were obtained by screenshots of discussion threads in the various media group chats, posts and comments found on the platforms studied. Using insights from the Systemic Functional Grammar (SFG), the isolated NG structures were analysed and found to be mainly syntactic chunks of clauses with expanded structures ‘padded’ with experiential elements which should normally be elements of clause structure. A further syntactic feature of NG constructions which was observed to occur in social media English studied is the tendency to be left-dislocated. It could be concluded that the structural variations observed in the grammar of Internet English derive from constraints of the medium, for instance, character limitation, the creative nature of language and the creative discernment of users of the Internet.
Analysis of Marketing Segmentation and Its Implementation on 7ps Erha’s Treatment Ultimate Acne Cure Using K-Means Clustering

K-Means is a non-hierarchical data grouping method that separates existing data into two or more groups. This method separates existing data into groups so that data with the same character is included in the same group and data with different characters is grouped into other groups. This study aims to produce an analysis that can classify Erha Clinic product/treatment data for March 2022 period using software SPSS 25 IBM to make marketing strategies more targeted. This study divided Erha Clinic product/treatment data with attributes of age group, type of plan, gender, education and total purchase into three clusters (Cluster Metrosexual, Cluster Millenials and Cluster Generation Z). The clustering process is 10 iterations with a minimum distance between clusters of 8,746. The significance value indicates that there is a significant difference between cluster 1, cluster 2 and cluster 3 related to Gender as one of the attributes in the study. The results of clusters show that the marketing target chosen by Erha Clinic is in cluster 3 (Gen-Z Persona) due to acne problems are mostly experienced by young people and although the transaction price is cheap, in the cluster 3 has the most purchased acne products compared to the purchase of the Advance plan and Basic plan bundling in cluster 1 and cluster 2.