Reviewerly/exHarmony huggingface dataset:
Penerapan Model Pembelajaran Cooperative Learning Tipe Snowball Throwing untuk Meningkatkan Sikap Percaya Diri Dan Tanggung Jawab

This study aims to describe the application of the Snowball Throwing type Cooperative Learning model and increase the self-confidence and responsible attitudes of third-grade students of SD Negeri 02 Kabawetan. This research is classroom action research. The subjects of this study were 16 students consisting of 7 male students and 9 female students. The research procedure is in cyclic form. Each cycle consists of four stages including Planning, implementing actions, observing and reflecting. This research data collection technique using observation techniques. The research instrument was a self-confidence and responsibility observation sheet. The data analysis technique used is the observation sheet using a formula that is processed descriptively, namely quantitative analysis and the range of values for each criterion. The results of this study indicate that the application of the Snowball Throwing learning model has increased from cycle one to cycle two. This can be seen from the increase in students' self-confidence and responsibility. The results of the observation of the assessment of students' self-confidence in the first cycle obtained that 55.30% was included in the sufficient category. Then the increase in the results of the observation of the second cycle was obtained that 75.93% was included in the Good category, while the results of the observation on the assessment of the responsibility of students' attitudes were obtained, namely, 57.98% included in the sufficient category Then the increase in the observation results of the second cycle was obtained that 77.70% was included in the good category. Thus, the application of the Snowball Throwing model can increase the self-confidence and responsibility of third-grade students of Elementary School 02 Kabawetan.
Optimalisasi Pengelolaan Arsip melalui Sistem Informasi Kearsipan Digital (SIKENDI) di Fakultas Ekonomi dan Bisnis Universitas Padjadjaran

This study aims to determine the process of digital archive management at the Faculty of Economics and Business, Universitas Padjadjaran through the Sikendi website-based application. This research was also conducted to find out the background of Sikendi's creation, obstacles in its use, and changes that occur in manual archive management with digital management. The method used was descriptive qualitative with data collection techniques including observation, interviews, literature review, and documentation. The data was then analyzed using the Miles and Huberman data analysis model. The research informants were 2 archivists at the Faculty of Economics and Business, Padjadjaran University. The results showed that archive digitization makes management more efficient and effective. The digital system used, Sikendi, consists of 5 stages, namely creation, categorization, filing, classification coding, and digital media transfer. The obstacles encountered in managing with a digital system are in the aspects of lack of human resources, lack of facilities, and systems that experience errors.
MPPT Design for Hybrid PV-TEG System under Partial Shading Condition via Manta Ray Foraging Optimization

The highly efficient Manta Ray Foraging optimization (MRFO) algorithm is utilized to achieve maximum power point tracking (MPPT) in a centralized hybrid photovoltaic-thermoelectric generator (PV-TEG) system under partial shading conditions (PSC). This approach enhances the power generation efficiency and energy utilization of the hybrid system. Given the existence of multiple local maximum power points (LMPPs) in the centralized hybrid PV-TEG system under PSC, MRFO is leveraged to effectively identify the global maximum power point (GMPP) and overcome the challenge of multiple LMPPs, a challenge that is causing traditional MPPT algorithms to fall into local optimization, leading to tracking failures and reducing the total output power of the hybrid system. To validate the effectiveness of the MRFO-based MPPT approach, two case studies are conducted, including start-up test and step variation test. Simulation results demonstrate that compared to other methods such as the Whale optimization Algorithm (WOA), Firefly Algorithm (FA), Perturbation Observation (P&O), and Incremental Conductance (INC) methods, the MRFO-based MPPT approach achieves higher power collection efficiency in a shorter time and delivers minimal power fluctuations.
Digital 3D Intelligent Forward Design for Data Centres Based on Improved Ant Colony Algorithm

Around the three-dimensional intelligent layout of assets within data centre space of different sizes, based on the function and data centre design standard GB50174-2017, the equipment is divided into multiple asset combination variables, and the vertical and horizontal coordinate position of each variable is used as the layout variable to establish the mathematical model of occupied volume, and under the conditions of satisfying each other's power consumption density, cabinet occupancy ratio, battery room occupancy ratio, power room occupancy ratio, air conditioning room occupancy ratio The mathematical model of multi-objective optimization is established by solving the single-objective optimization result values under the conditions of mutual power consumption density, cabinet occupancy ratio, battery room occupancy ratio, power supply room occupancy ratio, air conditioning room occupancy ratio, adjacent distance of area, net height, required air conditioning cooling capacity evaluation index system, deployment environment limitation and basic size, respectively. A population-based graph structure description is established and an improved ant colony algorithm is introduced to use the search direction as the solution space for the transfer probability, combined with the pheromone volatility of the deployment criteria, and solved in multiple iterations. Lightweighting and assisted computing techniques are used to achieve rapid generation of digital 3D intelligent layout solutions for data centres. Experiments show that the proposed method reduces the design workload by 95%, achieves the rapid generation of 10 optimal forward design solutions in 5 minutes, and rapidly exports 3D design models, greatly improving the design efficiency of the data centre.
Text Mining of Future Dividend Policy Sentences from Annual Securities Reports

The Japanese annual securities reports provide a wide range of information, including corporate performance, business risks, ESG activities, as well as dividend policies that cover both current and future dividend payout ratios. It is widely known that future dividend increases can significantly impact stock prices, but securities analysts are limited in their ability to analyze small and mid-sized companies due to resource constraints. This study proposes data augmentation methods that utilize a topic model and BERT model to extract relevant information on dividend policies from annual securities reports, improving the accuracy of future dividend predictions. Experimental results show that our proposed method accurately extracts information about future dividends, indicating the potential for the approach to generate new investment strategies and criteria.
Proposal of a Learning Process Visualization Method for Learning Tasks with Toggles of Card Operation-Based Programming Learning Support System

In order to support the proper management of cognitive load in programming learning. We have been developing a card-operated programming learning system that focuses on thinking between meaningful parts. In this study, we proposed a new analysis method based on the distance from the goal by using the logs of card operations including toggling and confirmed its usefulness. The proposed method enables us to visualize more detailed features according to the learner.
A Social Capital-Based NFT Collection Recommendation Mechanism

The non-fungible token (NFT) market has seen rapid growth, making it challenging for investors to select valuable NFT collections. Moreover, the prevalence of wash trading in NFT transactions has distorted market activity and misled investors. To address these issues, we propose a novel NFT collection recommendation mechanism based on social capital theory to support the healthy development of the NFT market. We filter wash trading and predict the trends of NFT collections. This study offers a promising approach to enhancing user experience in the NFT market and mitigating the harmful effects of wash trading on market activity.
Using digital educational courses for online learning students

The article demonstrates the possibilities of using digital educational courses for online training of students. The question is being updated an effective combination of traditional classroom and virtual interactions that can form a comprehensive paradigm of quality education that is competitive in the market environment.
A Graph-Cut Guided ROI Segmentation Algorithm with Lightweight Deep Learning Framework for Cervical Cancer Classification

Cervical cancer classification has witnessed numerous advancements through deep learning methods; however, existing approaches often rely on multiple models for segmentation and classification, leading to heightened computational demands and prolonged training times. In this research, a lightweight deep learning framework for cervical cancer classification is presented. The framework comprises three primary components: a Graph-Cut Guided Region of Interest (ROI) segmentation algorithm, a streamlined DenseNet architecture, and a Multi-Class Logistic Regression classifier. The Graph-Cut Guided ROI segmentation algorithm is used to accurately isolate nuclei regions within multicellular Pap smear images. This is a lightweight algorithm that is able to achieve high segmentation accuracy with minimal computational overhead. The streamlined DenseNet architecture is used to efficiently extract salient features from the segmented images. This architecture is specifically designed to reduce feature redundancy and eliminate incongruous feature maps. The Multi-Class Logistic Regression classifier is used to classify the segmented images into different cell types and stages of cervical cancer. Experimental results show the proposed method is able to achieve high classification accuracy with minimal training time. The framework was trained and evaluated on a dataset of 963 Pap smear images. The proposed framework achieved a 98% cell type classification accuracy in precision, recall, and F1-score for classifying multi-cell Pap smear images. The training loss was also very low. The average training time was 21 minutes for different sets of training images, and the average testing time was 0.50 seconds for different sizes of testing images, which is much lower than the existing methods.
Network Oral English Teaching System Based on Speech Recognition Technology and Deep Neural Network

With the development of computer technology, computer-aided instruction is being used more and more widely in the field of education. Based on speech recognition technology and deep neural network, this paper proposes an online oral English teaching system. Firstly, the speech recognition technology is introduced and its feature extraction is elaborated in detail. Then, three basic problems and three basic algorithms that need to be solved in speech recognition system using Markov model are discussed. The application of HMM technology in speech recognition system is studied, and some algorithms are optimized. The logarithmic processing of Viterbi algorithm, compared with the traditional algorithm, greatly reduces the amount of computation and solves the overflow problem in the operation process. By combining deep network with HMM, continuous speech signal modeling is realized. According to the characteristics of the DNN-HMM model, it is proposed that the model cannot model the long-term dependence of speech signals and train complex problems. Based on Kaldi, the model training comparison experiments of monophonon model, triphonon model and adding feature transformation technology are carried out to continuously improve the model performance. Finally, through simulation experiments, it is found that the recognition rate of the optimized DNN-HMM mixed model proposed in this paper is the highest, reaching 97.5%, followed by the HMM model, which is 95.4%, and the lowest recognition rate is the PNN model, which is 90.1%.
Hybrid Approach with VADER and Multinomial Logistic Regression for Multiclass Sentiment Analysis in Online Customer Review

Sentiment analysis is crucial for businesses to understand customer reviews and assess sentiment polarity. A hybrid technique combining VADER and Multinomial Logistic Regression was used to analyze customer sentiment in online customer review data. VADER is a lexicon-based approach that labels reviews with sentiment using a predefined lexicon, whereas Multinomial Logistic Regression can determine the polarity of sentiment using VADER data. This study employed multiclass classification using TF-IDF vectorization to categorize sentiment as a positive, negative, or neutral class. Correctly managing neutral sentiments can assist businesses in identifying improvement opportunities. The utilization of the VADER lexicon and Multinomial Logistic Regression has been shown to significantly improve the performance of sentiment analysis in the context of multiclass classification problems. With a 75.213% accuracy rate, the VADER lexicon accurately recognizes neutral sentiment and is appropriate to adapt in categorizing sentiment related to customer reviews. Combined with Multinomial Logistic Regression, accuracy increases to 92.778%. In conclusion, the hybrid approach with VADER and Multinomial Logistic Regression can leverage the accuracy and reliability of multiclass customer sentiment analysis.
Efficient Video QoE Prediction in Intelligent O-RAN

Open Radio Access Network (O-RAN) is a platform developed by a collaboration between wireless operators, infrastructure vendors, and service providers for deploying mobile fronthaul and midhaul networks, built entirely on cloud-native principles. The vision of O-RAN lies in the virtualization of traditional wireless infrastructure components, like Central Units (CU), Radio Units (RU), and Distributed Units (DU). O-RAN decouples the above-mentioned wireless infrastructure components into open-source elements, operating consistently with other elements of different vendors in the network. Quality of Experience (QoE) deals with a user's subjective measure of satisfaction. RAN Intelligent Controller (RIC) in O-RAN provides flexibility to intelligently program and control RAN functions using AI/ML-based models. We argue that various QoE parameters can be measured and operated by the RIC in O-RAN. We propose to improve the efficiency of O-RAN's radio resources by creating a RIC xApp that estimates the QoE measured using Video Mean Opinion of Score (MOS), and accurately optimizes the usage of radio resources across multiple network slices. We use predictive AI/ML-based models to accurately predict the QoE parameters in the network after which we can optimize the usage of network compo-nents leading to an enhanced user experience. Simulation results on 3 simulated data sets show that our proposed approach can achieve up to 95% QoE prediction accuracy.
Identifying Research Topics in Explainable Artificial Intelligence (xAI): the Value Add of Natural Language Processing Techniques

Explainable AI (xAI) refers to the concept and set of techniques that aim to make artificial intelligence (AI) systems more transparent and understandable to humans. The increased use of AI models in complex tasks and critical domains drives the current focus on xAI to enable humans to comprehend and trust the decisions made by AI systems. The fast-growing literature base on xAI means researchers need support in identifying the core research areas and current research patterns effectively, efficiently and with provenance. Our research design includes a systematic literature review (SLR) of xAI publications whereafter the same dataset was analyzed using two NLP techniques, namely Latent Derelict Allocation (LDA) and Encoder Representations from Transformers (BERT). The wordlists of possible xAI topics created from LDA and BERT were independently labelled by three xAI researchers and an xAI expert selected the most appropriate label. These results were then triangulated with the results from the SLR to gain new insights into xAI research topics and trends.
A Machine Learning Based Framework for Collecting and Using Social Media for Real-time Terrorist Attacks Prediction

Terrorism has become a global plague causing insecurity and jeopardizing the development of many countries. In the past few years, terrorism has exploded in Burkina Faso, affecting education, national security, health, and the economy. There is a great need for solutions to detect and stop terrorist attacks before they occur. This research project seeks to use Artificial Intelligence (AI) to mine social media and detect probable future terrorist attacks. This article describes the design of a framework, its partial implementation, and an experiment to validate the technique. The system consists of five steps: taking social media as input, converting it to text, validating it, extracting essential information, predicting its class, and storing it in a dataset. The modest size of the manually produced dataset utilized in the original experiment is a key drawback of the work discussed in this research. The modest size proved inconvenient for Deep Learning algorithms, which operate best with massive datasets. When we complete the entire system, inserting increased data from social media into the dataset will resolve this limitation. The other limitation is the partial implementation of the framework, which does not provide a comprehensive picture of the proposed approach. Our future works will address the remainder parts of the proposed framework.
CRYPTOCURRENCY VOLATILITY: BEFORE, DURING AND AFTER COVID-19

The World Health Organization (WHO) announced the Covid-19 pandemic in March 2020, which had a negative impact on economic activities and financial markets. Cryptocurrencies with blockchain technology, whose history is not old, took off in the Covid-19 period thanks to digital transformation and became popular in the financial markets. However, the fact that cryptocurrencies lose blood after the pandemic period. This study examines the volatility of cryptocurrencies before, during and after the pandemic Covid-19 using data from 4 cryptocurrencies (Bitcoin, Ethereum, Binance and Litecoin) and the CCI30 index, using autoregressive conditional variance models with two dummy variables. According to the results, the volatility of cryptocurrencies decreases throughout the pandemic period, moreover, decreases more after the pandemic compared to the pre-pandemic period. Investors should be cautious about investing in these risky instruments, which may become popular again in the future, just in case.
TIB: A Dataset for Abstractive Summarization of Long Multimodal Videoconference Records

Large language models and multimodal language-vision models give impressive results on current available summarization benchmarks, but are not designed to handle long multimodal documents. Most summarization datasets are composed of either mono-modal documents or short multimodal documents. In order to develop models designed for understanding and summarizing real-world videoconference records that are typically around 1 hour long, we propose a dataset of 9,103 videoconference records extracted from the German National Library of Science and Technology (TIB) archive, along with their abstract. Additionally, we process the content using automatic tools in order to provide the transcripts and key frames. Finally, we present experiments for abstractive summarization, to serve as baseline for future research work in multimodal approaches.
Understanding of Scales of Measurements and its Consideration in Clinical Research

In the present digital world, regardless of scale of measurements, often collected data are likely to be inappropriately considered as numeric. It might be mainly because even data on qualitative cum categorical variables are collected using certain numeric codes which look like quantitative instead of qualitative variables. Such inappropriate understanding about scales of measurements often leads to consideration of inappropriate analytical methods providing inaccurate results. Hence, such practice results to not only inaccurate interpretations but sometimes disastrous implications. Therefore, understandings of scales of measurements go in a long way to appropriately plan &amp; execute study; collect accurate data, analyze appropriately, interpret results accurately and derive policy driven implications.
DESAIN KONSEP PURWARUPA MESIN INJECTION MOLDING TIPE VERTIKAL UNTUK DAUR ULANG LIMBAH PLASTIK SKALA RUMAH TANGGA

Plastic waste is the main cause of environmental pollution. It is necessary to research the design of the Injection Molding machine to process plastic waste. This study describes the design concept of a vertical type Injection Molding machine prototype by considering the factors to be utilized by small-scale plastic recycling businesses. The design stage starts from the identification of component requirements to the design of 3D drawings. The results of this study obtained a prototype design of a Vertical Injection Molding Machine. The embodiment of the design is not only determined directly by the design team and the manufacturing team, but is obtained from customer requests described in the technical specifications. At the concept design stage, there are 5 sub-functions and each function has 3 to 4 options. From the combination of these sub-functions and options, 3 assembly sketches were obtained so that 3 alternative design concepts were obtained. From the results of the concept assessment, design concept C was chosen from several alternative concepts.
Unsupervised KeyPhrase Extraction Based on Multi-granular Semantics Feature Fusion

In Unsupervised Keyphrase Extraction (UKE) tasks, candidate phrases are ranked based on their similarity to the document embedding. However, This method assumes that every document focuses on only one topic. As a result, it can be difficult to distinguish the significance of potential keyphrases among different topics. Hence, it is necessary to discover a method for acquiring diversified topic information to obtain accurate key phrases. In this paper, we propose a new unsupervised key phrase extraction method (MSFFUKE) that utilizes multi-granularity semantic feature fusion. We first cluster phrases into different clusters through granulation, calculate the semantic similarity between phrases and each cluster, and take the mean to obtain the semantic features of topic granularity. Then, we obtain semantic features of phrase granularity based on the degree centrality of candidate phrases in the graph structure. Finally, we integrate semantic features of different granularity to sort candidate phrases. Three public benchmarks (Inspec, DUC 2001, SemEval 2010) are used to evaluate our model and compared it to the most advanced models currently available. The results demonstrate that our model performs better than most models and can generalize well when processing input documents from various domains and of different lengths. Another ablation study indicates that both topic granularity semantic features and phrase granularity semantic features are crucial for unsupervised keyphrase extraction tasks.