Reviewerly/exHarmony huggingface dataset:
The Effect Of Using Audio Visual Media On The Speaking Skills Of Students At SD Negeri 11 Leang-Leang Bantimurung District Kabupaten Maros

This research is a research that uses a quantitative approach with the type ofexperimental research which aims to find out (1) The description of the use of audio-visualmediaonthespeakingskillsofthirdgradestudentsofSDNegeri11Leang-Leang,Bantimurung District, Maros Regency, (2) The description of students' speaking skills afterusing audio-visual media in class III SD Negeri 11 Leang-Leang, Bantimurung District,MarosRegency,(3)Theeffectofthe useofaudio-visual media on the speaking skills of grade III students of SD Negeri 11 Leang-Leang, Bantimurung District, Maros Regency. Theindependent variable in this study is the use of audio-visual media, while the dependentvariable is the result of students' speaking skills. This study uses the type of research OneGroup Pretest-Posttest Design. The population in this study were 23 students of class III SDNegeri 11 Leang-Leang, Bantimurung District, Maros Regency in the academic year 2020-2021. Determination of the sample in this study using non-probability sampling, purposive sampling. Data collection techniques in research are tests, observation and documentation. Data were collected from giving speaking skills test and the analyzed by descriptive statistics and inferential statistics using the paired sample T-Test with the help of SPSS22.0calculations.The results of the research that have been carried out, it can becon cluded that: (1) The description of the use of audio-visual media on students' speaking skills is seen basedon the observation sheet, from the learning stages observed at each meeting the learningprocess seems to increase through the activities of students and teachers, (2) The descriptionof the results of students' speaking skills after using audio-visual media is more improved.Thisis evidenced by the posttest averages core of students in the very good category whilethe pretest scores are in the poor category, (3) There is a significant effect of the use of audio-visual media on students' speaking skills. This is because the probability value oftcount &gt; ttable(55.631&gt; 2.074), with a significance value of 0.000 is smaller than the significance level of0.05 (0.000 &lt;0.05).
DETECTING FAKE JOB POSTINGS USING BIDIRECTIONAL LSTM

Fake job postings have become prevalent in the online job market, posing significant challenges to job seekers and employers. Despite the growing need to address this problem, there is limited research that leverages deep learning techniques for the detection of fraudulent job advertisements. This study aims to fill the gap by employing a Bidirectional Long Short-Term Memory (Bi-LSTM) model to identify fake job advertisements. Our approach considers both numeric and text features, effectively capturing the underlying patterns and relationships within the data. The proposed model demonstrates a superior performance, achieving a 0.91 ROC AUC score and a 98.71% accuracy rate, indicating its potential for practical applications in the online job market. The findings of this research contribute to the development of robust, automated tools that can help combat the proliferation of fake job postings and improve the overall integrity of the job search process. Moreover, we discuss challenges, future research directions, and ethical considerations related to our approach, aiming to inspire further exploration and development of practical solutions to combat online job fraud.
A big data based conflict analysis, control and resolution effect evaluation platform

Focusing on the strategic goal of "Safe China", we will build a technical system for modernizing the theory and governance capacity of a smart society, and solve the problems of lagging theoretical innovation in resolving multiple conflicts and disputes, insufficient systematization of intelligent auxiliary technology for risk control and management, and poor coordination and linkage of governance. Enhance the ability to prevent and resolve major systemic risks, enhance scientific supervision and service capabilities, and provide scientific and technological support to promote the modernization of social governance system and governance capacity and build a smart society. Need to build a platform for analysis and control of conflicts and disputes and evaluation of their resolution effects. The platform aims to address the main problems in the resolution of multiple conflicts, build assessment indexes for the impact of conflicts and disputes, design intervention norms, multiple resolution mechanisms and policy tools synergistic model, build a large- scale dynamic knowledge map in the field of conflicts and disputes integrating the characteristics of the times, build an intelligent platform for the whole process of conflicts and disputes disposal, build a platform for psychological risk assessment and early warning, psychological assistance and automated psychological intervention, build The platform for analysis and control of contradictions and disputes and evaluation of the effectiveness of their resolution will be demonstrated and applied.
Input Method to Support Communication of Intention of Impressions in an Application

An input method that supports users in forming and expressing impressions is proposed. The method focuses on realizing a balance of ease and amount of meaningful content in the recorded impressions. The method is based on a four-step flow to input an impression comprising of an emotion and an intention element to express their responses to an experience. A web application to support users in expressing impressions of museum artifacts is used to demonstrate and evaluate the method. Through an evaluation with prospective users, the user experience in expressing intentions related to impressions was improved by the proposed method. Details of the input method and corresponding application design are presented, and the results of the evaluation are discussed in this paper.
A Hierarchical Watermarking Scheme for PRFs from Standard Lattice Assumptions

A software watermarking scheme is to embed a “mark” or a message into a program in a cryptographic way. It is useful in proving ownership (e.g., in applications to digital rights management) and in authenticating software (e.g., for proving the distributor of the software). A qualified software watermarking scheme should satisfy three requirements: (i) the marked program should not differ from the original program significantly; (ii) the embedded “mark” or message should not be removed without destroying the program dramatically; (iii) forging a marked program without a watermarking secret key is difficult. To the best of our knowledge, existing watermarking schemes for PRFs only deal with a single key, and no scheme supports watermarking the same PRF key for multiple times which is useful for hierarchical organizations. In the paper, we put forward a definition and security requirements for a hierarchical watermarking scheme for PRFs. Under the definition, a hierarchical watermarking scheme for PRFs is constructed to be functionality-preserving, unremovable and unforgeable under standard assumptions, namely, the LWE assumption and the SIS problem. The watermarking scheme is based on a variant translucent constrained PRF with desired security properties.
From Making to Lighting a Candle: On Functions, Meanings, and Bodies in Ritual Design

After a short introduction into the emergent field of ritual design and into current debates in Ritual Studies, an analysis of the ritual body is undertaken, wherein affinities with somaesthetics are detected. Rituals embody meanings through practices of encultured bodies that are expected to be performed attentively. As enactments they presume a non-intellectual knowledge that has to be trained and its reiteration “designs”, in the long run, the living body. At some point, if ritual design were to be integrated into design theory, it should clarify its relation to functionalist and semantic theories of design. In particular, further clarification is necessary with regard to the triangular relation between ritual design, design theory and somaesthetics. The underlying values and worldviews of these related disciplines need to be uncovered so as to illuminate wherein divergences lie – whether these result from their image of the human, their understanding of freedom and the prevalence of tradition versus innovation. In addition to these theoretical challenges, the practice itself of crafting rituals is divided between analytical and intuitive approaches.
Telco Tokenization and Stablecoin Development an Industry Opportunity

Different industries have been evolving to accept and implement blockchain technology for a variety of use cases either for automation purposes, for internal/external process integration amongst multiple entities or for provenance use cases such as in supply chain using blockchains tracking and tracing abilities. The telecommunications industry itself has adopted blockchain technology in areas like wholesale roaming, identity as examples. The focus of this paper is implementation of tokenization, Stablecoins, and digital assets in the telecom industry. The paper provides a brief introduction to tokenization, Stablecoins, digital assets and use case examples from the telco industry in the adoption and deployment of tokens, Stablecoins and overall digital assets. The paper argues the case for tokenization and use of Stablecoins in the telecommunications industry. For instance, can we as an industry imagine incentivizing open-source development via a telco token? The blockchain space involves various stakeholder groups which includes telecom operators, vendors, internet companies, technology partners, regulators etc. and any successful implementation involves managing an eco-system play amongst these requiring strong governance. The authors also propose additional research as next steps particularly for economic modelling of telco token economy and a cybersecurity risk assessment.
Enhancing the Adversarial Transferability with Channel Decomposition

The current adversarial attacks against deep learning models have achieved incredible success in the white-box scenario. However, they often exhibit weak transferability in the black-box scenario, especially when attacking those with defense mechanisms. In this work, we propose a new transfer-based black-box attack called the channel decomposition attack method (CDAM). It can attack multiple black-box models by enhancing the transferability of the adversarial examples. On the one hand, it tunes the gradient and stabilizes the update direction by decomposing the channels of the input example and calculating the aggregate gradient. On the other hand, it helps to escape from local optima by initializing the data point with random noise. Besides, it could combine with other transfer-based attacks flexibly. Extensive experiments on the standard ImageNet dataset show that our method could significantly improve the transferability of adversarial attacks. Compared with the state-of-the-art method, our approach improves the average success rate from 88.2% to 96.6% when attacking three adversarially trained black-box models, demonstrating the remaining shortcomings of existing deep learning models.
COVID-19 Outbreak: A Marketing Perspective on Student’s Satisfaction Towards Online Learning.

This study aims to investigate the factors that may influence students' satisfaction on online learning platforms, the extent to which they are satisfied with their use of online learning platforms, and the effects of switching from traditional classroom instruction to online instruction on undergraduate students. This study employs a quantitative research methodology. Using a survey method, data were collected from voluntarily participating individuals. Using SPSS, the significance of the collected data was then determined. This study discovered that all variables (Internet Accessibility, University Facilities Performance, Discipline, Flexibility, and Teaching) have a positive, albeit weak, correlation with Student Satisfaction. All variables (IA, T, DP, F, and UFP) are positively significant with respect to the dependent variable (SS), but the relationship between the variables is weak. Internet accessibility (IA) and Teaching (T) were discovered to share a moderate amount of variance. Flexibility (F) and Discipline (DP) share a small degree of variance. In contrast, University Facilities Performance (UFP) has a high degree of common variant.
Intelligent Controller Design for Motion Control of Smart Wheelchair

The concept of smart wheelchair system is a very innovative and prolific concept which benefits the people suffering from diseases like cerebral palsy or any other disorder which impairs the muscle control and coordination of the body parts of a patient. Independent mobility is important for every human living on this planet and smart wheelchair system reduces the dependence of such impaired patients on others due to which their self-esteem is improved. Since control and coordination of different components of any system is necessary, so a controller is designed for this purpose. The aim of this research work is to design and evaluate an intelligent controller for the speed control of the wheelchair system. The study focuses on an efficient Fuzzy logic controller designed for the motion control of the smart wheelchair and different results are compared with conventional PID controller. Mathematical modeling of the system is done by using different system equations and the results are verified by using MATLAB software. The results indicate that the fuzzy logic controller is efficient than any other controller like conventional PID controller.
Application Research of Mind Mapping in Aerospace Military Project Management

Mind mapping is an important graphic thinking tool at present, which can show the objective intuitively and has strong logicality.The rational application of mind map in aerospace military project management can also show strong value.This paper focuses on the effective application of mind map in aerospace military project management, which can be used for the future project management level.
Analysis of electricity service evaluation based on multi-mode heterogeneous data fusion

The electricity Internet of Things (IoT) generates a large amount of heterogeneous data under the edge cloud topology. The rational use of the heterogeneous data of the electricity Internet of Things can effectively improve the benefits of electricity enterprises. User service evaluation is a key indicator reflecting the good or bad service quality of the enterprise, and accurate identification of user sentiment is an important reference for subsequent service adjustment of the enterprise. In this paper, a distributed cloud computing-based data collection framework is proposed, which has the advantages of low cost and fast collection, and provides a physical basis for effective integration of multi-source heterogeneous data resources. In addition, this paper also proposes an emotion recognition method based on user evaluation of audio and text multimodal data fusion, constructs BiLSTM-Transformer and CNN-BiLSTM models for text and audio emotion analysis respectively, then uses self-assignment fusion strategy to realize decision fusion and verifies its efficient performance on the dataset.
An Algorithm Transform DNA Sequences to Improve Accuracy in Similarity Search

Similarity search of DNA sequences is a fundamental problem in the bioinformatics, serving as the basis for many other problems.In this, the calculation of the similarity value between sequences is the most important, with the Edit distance (ED) commonly used due to its high accuracy, but slow speed.With the advantage of transforming the original DNA sequences into numerical vector form that retaining unique features based on properties.The calculation processing on these transformed data will be much faster, many times faster than a direct comparison on the original sequence.Additionally, from a long DNA sequence, after transformation, it typically has a lower storage capacity, making it have good data compression.The challenge of this job is to develop algorithms based on features that maintain biological significance while ensuring search accuracy, which is also the problem to be solved.Previous methods often used pure mathematical statistics such as frequency statistics and matrix transformations to construct features.In this paper, an improved algorithm is proposed based on both biological significances and mathematical statistics to transforming gene data into numerical vectors for ease of storage and to improve accuracy in similarity search between DNA sequences.Based on the experimental results, the new algorithm improves the accuracy of similarity calculations while maintaining good performance.
Method for Inferring the Optimal Number of Clusters with Subsequent Automatic Data Labeling based on Standard Deviation

Machine learning is a suitable pattern recognition technique for detecting correlations between data.In the case of unsupervised learning, the groups formed from these correlations can receive a label, which consists of describing them in terms of their most relevant attributes and their respective ranges of values so that they are understood automatically.In this research work, this process is called labeling.However, a challenge for researchers is establishing the optimal number of clusters that best represent the underlying structure of the data subjected to clustering.This optimal number may vary depending on the data set and the grouping method used and influences the data clustering process and, consequently, the interpretability of the generated groups.Therefore, this research aims to provide an inference approach to the number of clusters to be used in the grouping based on the range of attribute values, followed by automatic data labeling based on the standard deviation to maximize the understanding of the groups obtained.This methodology was applied to four databases.The results show that it contributes to the interpretation of the groups since it generates more accurate labels without any overlap between ranges of values, considering the same attribute in different groups.
Hybrid Approach Used to Analyze the Sentiments of Romanized Text (Sindhi)

Sentiment analysis is an important part of natural language processing (NLP).This study evaluated the sentiment of Romanized Sindhi Text (RST) using a hybrid approach and ground truth values.The methodology of sentiment analysis involves three major steps: input data, process on tool, analysis of data and evaluation of results.One hundred RST sentences were used in this study's sentiment analysis, which can be positive, neutral, or negative.The statements in the corpus of this study are simple to understand and are used in everyday life.This research used an online Python tool to process a text and get results in the form of outcomes.The results showed that 86% of the sentences have neutral sentiments, 9% of the total results of sentiment analysis have negative sentiments, and only 5% of sentences of Romanized Sindhi Text have positive sentiments.The accuracy of the RST was measured on an online calculator and the value was 87.02% on the basis of ground truth values.An error ratio of 12.98% was calculated on the basis accuracy found on the online calculator of confusion matrix.
An Autonomous Multi-Agent Framework using Quality of Service to prevent Service Level Agreement Violations in Cloud Environment

Cloud is a specialized computing technology accommodating several million users to provide seamless services via the internet.The extension of this reverenced technology is growing abruptly with the increase in the number of users.One of the major issues with the cloud is that it receives a huge volume of workloads requesting resources to complete their executions.While executing these workloads, the cloud suffers from the issue of service level agreement (SLA) violations which impacts the performance and reputation of the cloud.Therefore, there is a requirement for an effective design that supports faster and optimal execution of workloads without any violation of SLA.To fill this gap, this article proposes an automatic multiagent framework that ensures the minimization of the SLA violation rate in workload execution.The proposed framework includes seven major agents such as user agent, system agent, negotiator agent, coordinator agent, monitoring agent, arbitrator agent and the history agent.All these agents work cooperatively to enable the effective execution of workloads irrespective of their dynamic nature.With effective execution of workloads, the proposed model also resulted in an advantage of minimized energy consumption in data centres.The inclusion of a history agent within the framework enabled the model to predict future requirements based on the records of resource utilization.The proposed model followed the Poisson distribution to generate random numbers that are further used for evaluation purposes.The simulations of the model proved that model is more reliable in reducing SLA violations compared to the existing works.The proposed method resulted in an average SLA violation rate of 55.71% for 1200 workloads and resulted in an average energy consumption of 47.84kWh for 1500 workloads.
Improved ciphertext-policy time using short elliptic curve Diffie–Hellman

&lt;span lang="EN-US"&gt;Ciphertext-policy attribute-based encryption (CP-ABE) is a suitable solution for the protection of data privacy and security in cloud storage services. In a CP-ABE scheme which provides an access structure with a set of attributes, users can decrypt messages only if they receive a key with the desired attributes. As the number of attributes increases, the security measures are strengthened proportionately, and they can be applied to longer messages as well. The decryption of these ciphertexts also requires a large decryption key which may increase the decryption time. In this paper, we proposed a new method for improving the access time to the CP using a new elliptic curve that enables a short key size to be distributed to the users that allows them to use the defined attributes for encryption and decryption. Each user has a specially created key which uses the defined attributes for encryption and decryption based on the Diffie-Hellman method. After the implement, the results show that this system saves nearly half of the execution time for encryption and decryption compared to previous methods. This proposed system provides guaranteed security by means of the elliptic curve discrete logarithmic problem.&lt;/span&gt;
Implementation of recurrent neural network for the forecasting of USD buy rate against IDR

&lt;span lang="EN-US"&gt;This study implements a recurrent neural network (RNN) by comparing two RNN network structures, namely Elman and Jordan using the backpropagation through time (BPTT) programming algorithm in the training and forecasting process in foreign exchange forecasting cases. The activation functions used are the linear transfer function, the tan-sigmoid transfer function (Tansig), and the log-sigmoid transfer function (Logsig), which are applied to the hidden and output layers. The application of the activation function results in the log-sigmoid transfer function being the most appropriate activation function for the hidden layer, while the linear transfer function is the most appropriate activation function for the output layer. Based on the results of training and forecasting the USD against IDR currency, the Elman BPTT method is better than the Jordan BPTT method, with the best iteration being the 4000&lt;sup&gt;th&lt;/sup&gt; iteration for both. The lowest root mean square error (RMSE) values for training and forecasting produced by Elman BPTT were 0.073477 and 122.15 the following day, while the Jordan backpropagation RNN method yielded 0.130317 and 222.96 also the following day.&lt;/span&gt;&lt;p&gt; &lt;/p&gt;