Reviewerly/exHarmony huggingface dataset:
A Hybrid Approach for Sentiment Analysis Using Game Theory in Word Sense Disambiguation

This study explores the use of Evolutionary Game Theory (EGT) for the task of sentiment analysis. The proposed approach involves the use of EGT concepts to disambiguate the particular sense of a word and analyze the context in which it is used. Methods involving Evolutionary Game Theory are employed to learn the associations between different words and synsets. Each word is treated as a player and its synset space as its strategy space. The model aims to find the Nash Equilibria to correctly disambiguate all tokens. The SentiWordNet lexicon to identify the sentiment of each sentence. The effectiveness of the proposed approach is evaluated using labeled twitter datasets in which WSD (EGT)-wup-lesk-word2vec variation showed an accuracy of 80.6%. The results demonstrate the efficacy of using pre-trained word embeddings and the potential of WSD for sentiment analysis.
Religious Hegemony in The Serat Menak Lare by Yasadipura I and Its Relevance as a Material for Indonesian Reading Literacy

Serat Menak Lare tells the story of the figure of Amir Hamzah. This study aims to describe (1) beliefs and religious hegemony in Serat Menak Lare; (2) the relevance of Serat Menak Lare as a material for reading literacy of Indonesian language. The research method used is an anthropological literary
Novels of Children's Literature in Vorstenlanden 1910-1940

The study of children's literature in Indonesia needs to be more extensive. The history of children's literature in Indonesia is deadlocked and lacks references. The curation of original Indonesian children's literature still needs to be improved, so studies on Indonesian classic children's literatu
Multimedia-based Picture Story Design as an Effort to Develop Reading Ability in Early Grades of State Elementary Schools in Ternate City

This research aims to develop a multimedia-based picture story tool to help with the reading skills of early-grade students in State Elementary School at Ternate City. This is a developmental research with data collected from 35 students of State Elementary School in Ternate City. Based on the data
Software Change Prediction Model Using Ensemble Learning

This research study has devised an ensemble learning strategy that employs a range of deep learning and machine learning classifiers to minimize the amount of time that developers spend detecting software defects. The process of collecting data initiates this empirical study. A dataset was created using the Understand Tool and will be subject to data under-sampling techniques and outlier detection. The dataset has a binary classification output, and the count of instances of positive and negative classes will remain balanced after applying these techniques. After this, the selected classifiers will be trained using the training dataset. Next, the built model will compare envisaged outcomes generated by these classifiers with actual outcomes of training data. Classifiers that accurately anticipate the outcomes would be assigned a data point. After this, each classifier is used to train a neural network with training independent variables as input and the saved data on whether a classifier accurately predicted an outcome at a given point or not as output variables. The predicting network will determine if a classifier will accurately classify a specific data point. Now for prediction of software change of recent points in dataset, it will be provided as an input for the prediction network and the selected classifier’s aggregated result that correctly predicts the outcome would be recorded.
Early Reading Skill Development Early Children Through Letters Board Games

Early childhood reading skills can be developed through play activities. One game that has an important role in developing early childhood reading skills is a letter board game. For this reason, this study aims to find and describe the development of early reading skills at an early age by playing l
Efforts In Improving The Ability Of Observation Report Text Writing Through Discovery Learning Model Containing Banyumas Culture Of Batiks Making

This study aimed to determine improvement in the ability of observation report text writing through discovery learning model containing Banyumas culture of making batiks. This classroom action study was conducted in 2 cycles. The objects of this study were the result of observation report text wr
Bitcoin and Cryptocurrency Exchange Market Prediction and Analysis Using Big Data and Machine Learning Algorithms

Due to economic uncertainty and the financial crisis of 2008, a desire for an unregulated currency arose, leading to the invention of Bitcoin. Using a pseudonym called Satoshi Nakamoto, Bitcoin was created in 2009, anonymously or by a group of unknown individuals. Since Bitcoin has been the most valuable cryptocurrency in recent years, its prices have fluctuated dramatically, making it difficult to predict their prices. Investors, businesses, risk managers, and market analysts can all benefit from being able to predict Bitcoin prices. By using the Bitcoin transaction data obtained from the Bitstamp website in this study, several different Machine Learning models are employed to determine the most accurate model for predicting Bitcoin prices. These models are based on 1-minute interval exchange rates in USD from January 1, 2012, to January 8, 2022. Analysis was performed primarily with Python, but it was also used and Hadoop, a distributed data storage and processing framework that uses the map-reduce programming model to allow efficient parallel processing of Big Data. Based on the results of our research, comprising three experiments, autoregres-sive-integrated moving average (ARIMA) makes the most accurate prediction of Bitcoin prices, with a 95.98% success rate.
VT-MCNet: High-Accuracy Automatic Modulation Classification Model based on Vision Transformer

Cognitive radio networks’ evolution hinges significantly on the use of automatic modulation classification (AMC). However, existing research reveals limitations in attaining high AMC accuracy due to ineffective feature extraction from signals. To counter this, we propose a vision-centric approach employing diverse kernel sizes to augment signal extraction. In addition, we refine the transformer architecture by incorporating a dual-branch multi-layer perceptron network, enabling diverse pattern learning and enhancing the model’s running speed. Specifically, our architecture allows the system to focus on relevant portions of the input sequence, thus, it improves classification accuracy for both high and low signal-to-noise regimes. By utilizing the widely recognized DeepSig dataset, our pioneering deep model, termed as VT-MCNet, outshines prior leading-edge deep networks in terms of classification accuracy and computational costs. Notably, VT-MCNet reaches an exceptional cumulative classification rate of up to 99.24%, while the state-of-the-art method, even with higher computational complexity, can only achieve 99.06%.
Multi-Class Counterfactual Explanations using Support Vector Data Description

Explainability has become crucial in Artificial Intelligence studies and, as the complexity of the model increases, so does the complexity of its explanation. However, the higher the complexity of the problem, the higher the amount of information it may provide, and this information can be exploited to generate a more precise explanation of how the model works. One of the most valuable ways to recover such input-output relation is to extract counterfactual explanations that allow us to find minimal changes from an observation to another one belonging to a different class. In this article, we propose a novel methodology to extract multiple counterfactual explanations (MUCH, MUlti Counterfactual via Halton sampling) from an original Multi-Class Support Vector Data Description algorithm. To evaluate the performance of the proposed method, we extracted a set of counterfactual explanations from three state-of-the-art datasets achieving satisfactory results that pave the way to a range of real-world applications.
Safe Model-Based Reinforcement Learning With an Uncertainty-Aware Reachability Certificate

Safe reinforcement learning (RL) that solves constraint-satisfactory policies provides a promising way to the broader safety-critical applications of RL in real-world problems such as robotics. Among all safe RL approaches, model-based methods reduce training time violations further due to their high sample efficiency. However, lacking safety robustness against the model uncertainties remains an issue in safe model-based RL, especially in training time safety. In this paper, we propose a distributional reachability certificate (DRC) and its Bellman equation to address model uncertainties and characterize robust persistently safe states. Furthermore, we build a safe RL framework to resolve constraints required by the DRC and its corresponding shield policy. We also devise a line search method to maintain safety and reach higher returns simultaneously while leveraging the shield policy. Comprehensive experiments on classical benchmarks such as constrained tracking and navigation indicate that the proposed algorithm achieves comparable returns with much fewer constraint violations during training. Our code is available at https://github.com/ManUtdMoon/Distributional-Reachability-Policy-Optimization. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Although it has been proven that RL can be applied in complex robotics control tasks, the training process of an RL control policy induces frequent failures because the agent needs to learn safety through constraint violations. This issue hinders the promotion of RL because a large amount of failure of robots is too expensive to afford. This paper aims to reduce the training-time violations of RL-based control methods, enabling RL to be leveraged in a broader application area. To achieve the goal, we first introduce a safety quantity describing the distribution of potential constraint violations in the long term. By imposing constraints on the quantile of the safety distribution, we can realize safety robust to the model uncertainty, which is necessary for real-world robot learning with environment uncertainty. Second, we further devise a shield policy aiming to minimize the constraint violation. The policy will intervene when the agent is about to violate state constraints, further enhancing exploration safety. Third, we implement a line search method to find an action pursuing near-optimal performance when fulfilling safety requirements strictly. Our experimental results indicate that the proposed algorithm reduces training-time violations significantly while maintaining competitive task performance. We make a step towards applying RL safely in real-world tasks. Our future work includes conducting physical verification on real robots to evaluate the algorithm and improving safety further by starting from an initially safe control policy that comes from domain knowledge.
Characterization of some minihypers in PG(4,3)

In this paper we characterize the minihypers with parameters $(66,21)$ in the geometry PG(4,3). These parameters are important because they are instrumental in solving the problem of the existence of several hypothetical ternary Griesmer codes of dimension $6$. This classification gives also insight into the classification problem for $(v_r+2v_{r-1},v_{r-1}+2v_{r-2})$-mimihypers in PG$(r,q)$ for any $r\ge3$ and any prime power $q\ge4$.
Performance Analysis of Block Chaining Message Authentication Code (CBC MAC) and its Variants

The cryptographic algorithms employed in internet security must be able to handle packets which may vary in size over a large range. The majority of cryptography algorithms divide messages into large blocks to process them. due to this fact the messages must be prepared by padding the desired amount of zero bits to induce an integer number of blocks. This process contributes a considerable overhead when the short messages are more dominant within the message stream. In this paper, analyze is targeted on the performance of varied message authentication code generator algorithm supported cipher block. These all variants of cipherbased must share symmetric key before creating message authentication code. All variants of CBC MAC are implemented in JAVA. The results of empirical performance shows that two variants namely TMAC perform better for AES Encryption algorithm in larger size otherwise EMAC show the higher result with the Triple DES symmetric algorithm. The result shows that, when consider only on the performance aspect. Cycle/byte is calculated for comparing different variants of CBC MAC. Cycle/byte is decreased when input size of message is increased. Advanced Encryption Standard (AES) algorithm shows good performance than TDES and its better safety features than DES. CBC-MAC is maybe visiting be standardized as an AES mode of operation.
Object Detection and Tracking in Real Time Video

Object tracking is the process of locating moving objects over time using the camera in video sequences. The objective of object tracking is to associate target objects in consecutive video frames. Object tracking requires the location and shape or features of objects in the video frames. So, object detection and object classification are the preceding steps of object tracking in computer vision applications. To detect or locate the moving object in the frame, Object detection is the first stage in tracking. It is a challenging or difficult task in image processing to track the objects into consecutive frames. Various challenges can arise due to complex object motion, irregular shape of object, occlusion of object to object and object to the scene, and real-time processing requirements. This paper presents the various techniques of object tracking in video sequences.
Preserving and Nurturing Tausug Language: The Bahasa Sug Mobile Learning Application Tool for Enhancing Mother Tongue Development for Toddlers

In the fast-paced modern world, English and other commonly spoken languages have become prevalent even in Tausug households. This shift towards these languages presents difficulties in preserving and advancing the Tausug language. Therefore, it is essential to discover effective methods to cultivate language foundations and encourage the development of the mother tongue among Tausug toddlers. One hundred and sixty toddlers and 160 parents from eight Barangay in Jolo, Sulu, participated in a study. It was to investigate the effect between Bahasa Sug Mobile Learning Applications and traditional teaching methods in enhancing mother tongue development for toddlers. The results indicated high significance in favor of the Bahasa Sug Mobile Learning Application over the traditional teaching method, as shown by a t-test with a p-value of 0.05. The parents noted Bahasa Sug Mobile Apps' significant value in increasing their children's learning. Bahasa Sug, mobile learning applications, have become valuable tools for nurturing and preserving mother tongues in toddlers. These applications provide engaging and interactive experiences that align with children's natural learning processes. They can create an immersive and interactive environment for toddlers to develop and preserve their mother tongue. By embracing this mobile learning application, it can ensure the continuity and vitality of native languages, allowing children to maintain a strong connection with their cultural heritage and linguistic identity.
Societal Risks of Using Cyber Metaverse Technology

AI's widespread use poses challenges, especially accountability and legal readiness for metaverse's unique traits. This study addresses AI's current hurdles, focusing on regulating AI-related crimes, including metaverse. It aims to define responsibilities and penalties. Metaverse's societal, reputational, and security implications, along with tech firms' oversight, can escalate cybercrime. Complex evolving cybercrime patterns hinder guilt proof. Criminalizing tech use in terrorism planning and financing is crucial. Social media users face risks like deepfakes, fostering doubt and inciting terrorism. Tech-security agency partnership is vital. Growing digital interconnectedness heightens cybersecurity issues. Metaverse's growth and internet use call for strengthened anti-terrorism and IT crime laws.
PENGEMBANGAN MEDIA BOOKLET UNTUK MENINGKATKAN PENYESUAIAN DIRI SISWA KELAS VII SMP PGRI LEMBANG

To help students adjust to their new school, the author of the book "Development of a Media booklet to Improve Self-adjustment for Class VII Students of SMP PGRI Lembang" uses a Research and Development (R&amp;D) approach to create a booklet designed to help students adapt. In addition, a customized questionnaire consisting of a total of 42 statement items was used in this investigation. Class VII students of SMP PGRI Lembang were the subjects of this study. Out of a total population of 27, only 8 were randomly selected to participate. In this study, we used descriptive percentages to analyze our data (including 27 participants). This study uses expert validation tests to determine whether or not the media to be tested for spaciousness is appropriate. This test involves media experts, materials, and practitioners. The results of the research on class VII A students before receiving guidance services for self-adjustment booklet media were very low with a percentage score of 43% which could be categorized as very low, and after students were given group guidance services with self-adjustment booklet media, the percentage score increased to 76%. The results of this research were carried out to increase students' understanding of the importance of self-adjustment so that students can get success both academically and non-academically.
Pengembangan Sistem Informasi Bank Sampah untuk Optimalisasi Pengelolaan Data

Bank Sampah Sumber Rejeki in Cilacap City provides an opportunity for customers to deposit waste and receive monetary rewards. Unfortunately, current data management is still manual through unstructured notebooks, potentially resulting in data loss due to notebook damage, difficulty in finding customer information, and delays in making performance reports. This research aims to develop a website-based management information system that is tailored to the needs of the sampah bank. This system includes customer data management, recording waste deposit transactions, recording customer balance withdrawals, and improving waste bank data security. Data was collected through direct interviews with the manager of Sumber Rejeki Waste Bank. The development method used is Feature Driven Development (FDD) using the PHP programming language and MySQL database. In addition, testing this system also involves the black box testing method to ensure the functionality is as expected. The result of this research is a web-based system that is significant in improving the efficiency of managing customer data, transactions, and waste bank performance reports. The implementation of this system reduces the risk of errors and data loss due to manual recording and provides easier access for customers to view transaction history in the waste bank.
Antichains in the Bruhat Order for the Classes $$\mathcal {A}(n,k)$$

Abstract Let $$\varvec{\mathcal {A}(n,k)}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>A</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>,</mml:mo> <mml:mi>k</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> represent the collection of all $$\varvec{n\times n}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>×</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> zero-and-one matrices, with the sum of all rows and columns equalling $$\varvec{k}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> </mml:math> . This set can be ordered by an extension of the classical Bruhat order for permutations, seen as permutation matrices. The Bruhat order on $$\varvec{\mathcal {A}(n,k)}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>A</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>,</mml:mo> <mml:mi>k</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> differs from the Bruhat order on permutations matrices not being, in general, graded, which results in some intriguing issues. In this paper, we focus on the maximum length of antichains in $$\varvec{\mathcal {A}(n,k)}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>A</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>,</mml:mo> <mml:mi>k</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> with the Bruhat order. The crucial fact that allows us to obtain our main results is that two distinct matrices in $$\varvec{\mathcal {A}(n,k)}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>A</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>,</mml:mo> <mml:mi>k</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> with an identical number of inversions cannot be compared using the Bruhat order. We construct sets of matrices in $$\varvec{\mathcal {A}(n,k)}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>A</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>,</mml:mo> <mml:mi>k</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> so that each set consists of matrices with the same number of inversions. These sets are hence antichains in $$\varvec{\mathcal {A}(n,k)}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>A</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>,</mml:mo> <mml:mi>k</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> . We use these sets to deduce lower bounds for the maximum length of antichains in these partially ordered sets.
Analysis of Misuse of Eyd in Elementary School Students of SD Negeri Sangiang Jaya

Education is a process, namely the process of maturing students. This process is carried out by educators consciously and responsibly. Children who have difficulty in one or more of these difficulties usually have low achievement and grades in certain subjects. Research Focus, determining what factors cause students to have difficulty writing EYD, especially the use of capital letters, periods and commas, students' problems in using EYD (capital letters, periods, commas), problem formulation based on the background of the problem So the formulation of the problem in the research is, what difficulties do students have in using EYD, how many mistakes do students make in writing narrative essays in accordance with EYD, research objectives In accordance with the problem formulation that has been determined, the objectives to be achieved in this research namely, describing what factors cause students to have difficulty writing EYD, describing what mistakes students make in using EYD.
Reform of the Teaching Model of Korean Intensive Reading Courses in Vocational High Schools in the Era of All-Media

The use of multimedia technology in the teaching process is the current mainstream trend in teaching development. The arrival of the all-media era is not only a huge challenge to the traditional teaching model, but also a huge opportunity for the reform of the teaching model. Combining traditional education models with modern scientific and technological means, and learning from each other’s strengths can stimulate students’ subjective initiative in learning. On the other hand, it is also an important way for teachers to improve their teaching abilities. This article analyzes the opportunities brought by the all-media era to the reform of education models based on the teaching status and teaching difficulties of Korean intensive reading courses in vocational high schools, and proposes a teaching model reform strategy for Korean intensive reading courses in vocational high schools in the all-media era.