Reviewerly/exHarmony huggingface dataset:
Upgraded Very Fast Decision Tree: Energy Conservative Algorithm for Data Stream Classification

Traditional machine learning (ML) techniques model knowledge using static datasets.With the increased use of the Internet in today's digital world, a massive amount of data is generated at an accelerated rate that must be handled.This data must be handled as soon as it arrives because it is continuous, and cannot be kept for a long period of time.Various methods exist for mining data from streams.When developing methods like these, the machine learning community put accuracy and execution time first.Numerous sorts of studies take energy consumption into consideration while evaluating data mining methods.However, this work concentrates on Very Fast Decision Tree, which is the most often used technique in data flow classification, despite the fact that it wastes a huge amount of energy on trivial calculations.The research presents a proposed mechanism for upgrading the algorithm's energy usage and restricts computational resources, without compromising the algorithm's efficiency.The mechanism has two stages: the first is to eliminate a set of bad features that increase computational complexity and waste energy, and the second is to group the good features into a candidate group that will be used instead of using all of the attributes in the next iteration.Experiments were conducted on real-world benchmark and synthetic datasets to compare the proposed method to state-of-the-art algorithms in previous works.The proposed algorithm works considerably better and faster with less energy while maintaining accuracy.
Development of E-Comic as a Physics Learning Media for Class X Students on Momentum and Impulse Materials

This study aims to produce an interesting and valid learning media so that it is suitable for use in physics learning in the form of an e-comic for class X students on Momentum and Impulse material. The research method used is R&amp;D (Research and Development) with the ADDIE development model (Analyze, Design, Development, Implementation, and Evaluation). The validity of the e-comic learning media was tested through aspects of material competence, aspects of material suitability, language aspects, display aspects, and usage aspects. The results of the validator's assessment of the material competence aspect are 4.33, the material suitability aspect is 4.33, the language aspect is 4.27, the display aspect is 4.43, and the usage aspect is 4.25. The validity of physics learning media in the form of e-comic in terms of all aspects is 4.32 with a very valid category. Thus, e-comic as a medium for learning physics for class X students on Momentum and Impulse material is declared valid and feasible be used as a medium for learning physics
Efficient and Anonymous Password-Hardened Encryption Services

Password-based authentication and encryption schemes are commonly used to ensure data privacy on the Internet. Nevertheless, these schemes are vulnerable to brute-force attacks, as the user passwords always have low-entropy. The password-hardened encryption (PHE) was proposed to resist brute-force attacks by introducing an external crypto service to perform rate limiting for user requests. However, existing PHE schemes all need to apply computationally expensive zero-knowledge proofs or exponentiations and fail to provide anonymity for end users. In this paper, we propose the cross-epoch anonymity in PHE and use Intel SGX to design a PHE scheme to enjoy this property. Based on the trusted execution environment provided with Intel SGX, our scheme provides the cross-epoch anonymity for end users. Besides, our scheme achieves both the soundness and strong soundness without using zero-knowledge proofs since sensitive operations are performed in the trusted execution environment, which can greatly reduce the latencies for encryption and decryption. We evaluate the performance of our scheme. The results show that our scheme outperforms the existing PHE scheme with more than 8 times lower latencies for encryption and decryption.
Using Inconsistency Reduction Algorithms in Comparison Matrices to Improve the Performance of Generating Random Comparison Matrices with a Given Inconsistency Coefficient Range

The aim of this paper is to present a new method for generating random pairwise comparison matrices with a given inconsistency ratio (CR) interval using inconsistency reduction algorithms.Pairwise comparison (PC) is a popular technique for multi-criteria decision-making, its purpose is to assign weights to the compared entities, thus ranking them from best to worst.The presented method combines the traditional random generation of comparison matrices supported by inconsistency reduction algorithms: the "Xu and Wei" algorithm and the "Szybowski" algorithm.This paper presents research that shows an increase in performance when generating such matrices relative to the standard random comparison matrix generation procedure using the "Szybowski" algorithm.The other algorithms also improve the process, but to a lesser extent, making the "Szybowski" supporting algorithm the preferred solution for the new process.As a result of the research, a free online tool "PC MATRICES GENERATOR" has also been made available to efficiently generate a large number of comparison matrices with a given CR factor range, any matrix size, and any number of matrices, enabling much more efficient and less time-consuming research in many fields that use comparison matrices, as the analytic hierarchy/network process (AHP/ANP), ELECTREE, PAPRIKA, PROMETHE, VIKOR or the Best-Worst method (BWM).
Anti-Egalitarian Values Predict Opposition to Hate Speech Restrictions

Why do people oppose hate speech laws? Policymakers and tech platforms face demands to restrict hate speech, yet people across the political spectrum disagree on introducing hate speech restrictions. One account suggests that opposition to regulating hate speech is motivated by group-based dominance attitudes. In this account, hate speech is a tool to derogate minority or historically oppressed groups. Yet another account suggests that people oppose hate speech restrictions because of conservative ideology, including preferences for anti-egalitarian values such as individualism, limited government and a free market of ideas. I test these two competing accounts using observational data and conjoint experiments in Denmark and the United States. I show that anti-egalitarian values are a consistent and overall stronger predictor of restricting free speech and hate speech than than group-based dominance. Thus, people who hold strong anti-egalitarian values are less supportive of regulating hate speech in general, regardless of whom it targets. These findings suggest that support for free speech and opposition towards hate speech laws are shaped by preferences for anti-egalitarian values, rather than group-based dominance motivations.
Implementation of Flood Emergency Response System with Face Analytics

Disaster management system is developed to monitor flood, tsunami and earthquake, which is effectively preparing for and responding to the disaster.In fact, Malaysia is building the emergency response system for managing the flood disaster since it is highly occurred.However, the current flood emergency response system has limitations, which, has no integration that data entered into spreadsheets and transferred to different storages, and, data analytics that assist in data collection and decision-making.Even though flood emergency response system have been improved, which introducing sirens and loudspeakers to reach flood victims but still difficult to access the basic facilities and receive flood responses.Therefore, this study implements a flood emergency response system with face analytics to assist data acquisition, which analyze flood victim's faces using CCTV infrastructure with HOG algorithm that incorporated with a dashboard.The dashboard categorizes the number of flood occurrence, maximum flood period (days), the number of displaced flood victims and loss assessment.Findings have shown that the dashboard helps the enforcement agencies to implement the real-time information about flood victims.Based on the number flood frequency occurrence in Malaysia from 2017 to 2021, the percentage produced was 27%, 19%, 12%, 19% and 23%.Moreover, the duration of floods has been decreased from 30% to 17% in 5 years that shows the flood emergency response helps the Malaysia government to improve the infrastructure.The significant of this study beneficial to the local enforcement unit and evacuation centers in identifying the flood victims. Keywords-Disaster management system; flood emergency response system; flood emergency response system with face analytics; flood emergency response system with face analytics using HOG algorithm; flood emergency response system dashboard
Analysis of Educational Needs for College Students’ Career Adaptability Program Using the IPA

Objectives The purposes of this study were to examnin college students’ educational needs for the development of career adaptability program.&#x0D; Methods To accomplish this purpose, a survey was conducted with 149 students at D university. We were analysed by the difference between impoartance and performance and were confirmed their educational needs using IPA.&#x0D; Results As a result for the study: first, we were confirmed the educational needs of career adaptability because of the importance of career adaptability was higher than the performance of career adaptability. Second, According to the IPA, the career adaptability factors with the highest priority in the eucational needs were career preparation behavior and social resource utilization ability. Third, Factors in the maintained and strengthened area were career goal adjustment ability and social responsible behavior. Fourth, Factors career adaptability with relatively low educational needs were found self-understanding, work environment exploration, and positive career beliefs.&#x0D; Conclusions Those results will suggest basic directions to development of the career adaptability program at college.
Integrated Assessment of Teaching Efficacy: A Natural Language Processing Approach

The most significant component in the education domain is evaluation.Apart from student evaluation, teacher evaluation plays a vital role in the colleges or universities.The implementation of a scientific and appropriate assessment method for enhancing teaching standards in educational institutions is absolutely essential.Conventional teacher assessment techniques have always been bounded to bias and injustice for single dimensional assessment criteria, biased scoring, and ineffective integration.In this regard,it is crucial to develop a specialized teacher evaluation assistant (TEA) system that integrates with some computational intelligence algorithms.This research concentrates on using Natural language processing(NLP) based techniques for empirically analysing teaching effectiveness.We develop a model in which a teacher is evaluated based on the content he delivers during a lecture.Two techniques are employed to evaluate teacher effectiveness using topic modelling and text clustering.By the application of topic modelling, an accuracy of 75% is achieved and text clustering achieved an accuracy of 80%.Thus, the method can effectively be deployed to assess and predict the effectiveness of a teacher's teaching.
PENGARUH SISTEM INFORMASI AKUNTANSI DAN PELATIHAN KERJA TERHADAP KINERJA KARYAWAN PT. BANK MANDIRI (PERSERO) Tbk AREA PLUIT SELATAN

This study aims to analyze the accounting information system and job training on employee performance either partially or simultaneously. This research was conducted at Pt Bank Mandiri (Persero) Tbk South Pluit Area. The type in this research is quantitative research. This sampling uses a saturated sample technique in which all the population is sampled in this study, amounting to 57 respondents, and data collection in this study includes observation, questionnaires and literature study. Data analysis techniques in this study are validity, reliability, classical assumption test, descriptive analysis, multiple linear analysis and hypothesis testing. by using the SPSS Version 25 application program. The results of this study indicate that simultaneously accounting information systems and job training affect employee performance, partially accounting information system affects employee performance and job training affects employee performance.
PHONEMIC ANALYSIS BETWEEN ENGLISH AND INDONESIAN IN “HEAVEN” SONG

This study compares and contrasts the phonetic systems of English and Indonesian in the lyrics to the song "Heaven." In order to teach students how to pronounce English with Indonesian phonemes, the research aims to compare and contrast the phonemes in song lyrics. Ten English consonant phonemes and ten Indonesian consonant phonemes are the focus of this study. A qualitative technique is used in the descriptive-contextual research methodology. Based on the types of phonemes, the technique design compares the phonemes in song lyrics written in English and Indonesian. The "Heaven" song lyrics, which are made up of 10 consonant phonemes, were discovered to share commonalities between the English and Indonesian phonemic systems. Ten consonants in the lyrics to the song "Heaven" represent the distinctions between the English and Indonesian phonetic systems.
Enhancing Collaborative Interaction with the Augmentation of Sign Language for the Vocally Challenged

As per Census 2011, in India, there were 26.8 million differently abled people, out of which more than 25% of the people faced difficulty in vocal communication.They use Indian Sign Language (ISL) to communicate with others.The proposed solution is developing a sensor-based Hand Gesture Recognition (HGR) wearable device capable of translating and conveying messages from the vocally challenged community.The proposed method involves designing the hand glove by integrating flex and Inertial Measurement Unit (IMU) sensors within the HGR wearable device, wherein the hand and finger movements are captured as gestures.They are mapped to the ISL dictionary using machine learning techniques that learn the spatio-temporal variations in the gestures for classification.The novelty of the work is to enhance the capacity of HGR by extracting the spatio-temporal variations of the individual's gestures and adapt it to their dynamics with aging and context factors by proposing Dynamic Spatio-temporal Warping (DSTW) technique along with long short term memory based learning model.Using the sequence of identified gestures along with their ISL mapping, grammatically correct sentences are constructed using transformer-based Natural Language Processing (NLP) models.Later, the sentences are conveyed to the user through a suitable communicable media, such as, text-to-voice, text-image, etc. Implementation of the proposed HGR device along with the Bidirectional Long-Short Memory (BiLSTM) and DSTW techniques is carried out to evaluate the performance with respect to accuracy, precision and reliability for gesture recognition.Experiments were carried out to capture the varied gestures and their recognition, and an accuracy of 98.91% was observed.
Assessing User Interest in Web API Recommendation using Deep Learning Probabilistic Matrix Factorization

Internet 2.0 Things connected to the Internet not only manage data supply through devices but also control the commands that flow through it.The communication technology created by the desired sensor is used by a new computing model so that the collected data appears in Web 2.0 for management.In addition to enhancing Sense efficiency through the simple IoT computing process, it is used in many cases for example video surveillance, and improved and intelligent manufacturing.Every fragment of the system is carefully continued and supervised in this process by software collection using a large number of recurs.An important process for this is to access web APIs from various public platforms in an efficient way.The use of different APIs by developers for the integration of different IoT devices and the deployment process required for this is unnecessary.Obtaining configured target APIs makes it easy to know where and how to get started with the workflow approach.Rapid industrial development can be achieved through this powerful API approach.But finding adequately powerful APIs from a large number of APIs has become a great challenge.However, due to the massive spike in the count of APIs, combining the two APIs has now become a major challenge.In this paper, for the time being, only the relationships between users and the API are considered.In this case, they had to face difficulties in extracting contextual value from their interpretation.So better accuracy could not be obtained due to this.The consequence of the user's time aspect on the cryptographic properties concerning the information collected from the API contextual description can be enhanced by the Deep Learning Probabilistic Matrix Factorization (DL-PMF) method, which improves the accuracy of the API recommendation in considering the cryptographic features of the user in the API recommendation.In this paper, we have used CNN (Convulsive Neural Network) for web elements such as APIs, and LSTM (Long-Term and Short-Term Memory) Network, which works with a diligent mechanism to find hidden features, to find hidden features that suit the tastes of the users.In conclusion, the combination of PMF (Probabilistic Matrix Factorization) evaluation of the recommended results was obtained as described above.The combination of DL-PMF method experimental results was found to be better than previous PMF, ConvMF, and other methods, thus improving the recommended accuracy.
Hybrid Resource Allocation Technique for Cloud Computing

The scheduling and distribution of resources are the two main factors in cloud computing that affect the overall quality of services. Since allocating resources inside a single cloud environment is such a complicated procedure, cloud computing necessitates the deployment of a resource allocation module that is both effective and efficient. While the complexity of the techniques used for allocation is further increased by the allocation of resources in a setting with many clouds. In the past, the multi-cloud environment's resources were allocated based on the demands of various workloads. It is vital to do research on the existing availability of resources and their skills before allocating resources to the activities that have been requested. As a result, the goal of this study is to offer an optimised hybrid resource allocation model that takes into account the job requirements, bandwidth needs, resource status, and distance. To distribute the resource, this model employs the particle swarm optimization technique and the bat optimization algorithm. The effectiveness of the suggested model is evaluated by simulation, and the outcomes are contrasted with those of conventional optimization techniques. With a minimal energy consumption of 168 kWh, the approach that has been provided can distribute the available resources across 400 activities in around 48 seconds. The performance of the suggested model is much superior than that of traditional methods in terms of the number of missed deadlines for assignments, the amount of resources required, the amount of energy utilized, and the amount of time provided.
Implementation Of Citizen Data Updating Managent In Order To Educate The Community

Community service is one of the necessities for Higher Education. The next implementation of the Tri Dharma of Higher Education is to carry out Teaching and Research. Implementation of community service by Pertiwi College of Economics Bekasi. Providing understanding and implementation of management and governance of population data for community members in RT 22/08 CKM Bangle Village, Majalaya District, Karawang Regency, West Java. The purpose of implementing population data updating management for community members is to strengthen the duties and functions for community servants entrusted to management, so that valid data is available as a servant of the surrounding community. The art of population data updating is a concept developed using applications to facilitate the collection of population in Bangle Village. The stages of implementing this activity first conduct a survey and needs analysis in order to map the needs and problems that occur in environment, then explore the existing potential and design activities for the need to implement population data management applications. The results of this community service activity are the creation of population data application management with the output of valid and complete citizen profiles and accurate numbering for residents of RT 22/08 Bangle Village.
State Estimation of Complex-Valued Neural Networks with Leakage Delay: A Dynamic Event-Triggered Approach

In this paper, the problem of state estimation is investigated for a class of discrete-time complex-valued neural networks (CVNNs) with both leakage delay and discrete timevarying delays. The signal transmission from output sensors to state estimator is implemented via a shared wireless network with limited communication resources. For the aim of reducing the consumption of limited communication resources, the transmission strategy based on dynamic event-triggering is introduced to determine when the updating of the output measurement should be carried out. By taking use of some properties of Hermitian matrix and constructing an appropriate LyapunovKrasovskii functional, a sufficient criterion is derived for ensuring the asymptotical stability of the estimation error system without separating the CVNN to its real-part system and imagination one is derived, which is quite different from those approach used in exiting literature. The gain matrix for estimator is designed by resorting to a set of feasible solutions of linear matrix inequalities (LMIs) with complex-valued variables. A numerical example and its simulation results are given to illustrate the validity of the theoretical result.