Reviewerly/exHarmony huggingface dataset:
Reinforcement Learning-Based Data Scheduling for MPTCP in Streaming Media Delivery

In this paper, an MPTCP scheduling method based on DQN is proposed, which aims to solve the dynamic adaptive problem in the process of video streaming media transmission by introducing the chunk manager. To address the problem of performance degradation caused by targeting only a single performance metric in the DASH environment, a reward function is proposed that uses two metrics, throughput and chunk download time, and aims to help the intelligence to better learn the video chunk delivery policy. The proposed use of a chunk manager helps the DQN to understand the chunk information, which is used to accurately detect the transmission status of video chunks and pass the chunk information to the scheduler. Finally the use of online training and offline decision making can make the DQN more efficient and flexible, enabling it to better adapt to complex environments and tasks. It is experimentally verified that the DQN-based data scheduling algorithm proposed in this paper has some performance improvement over the classical MPTCP data scheduling algorithm, and the DQN-based scheduling algorithm keeps the download time distribution of chunks at a low and stable level.
Information-Based Word Extraction Modeling

In Wordle, the words used in the game are used to train and evaluate the model used for recognizing the solution word and identifying the attributes associated with each category of the given words associated with each category of the given words. Meanwhile, the words are used to create training and testing datasets, which are used to train and evaluate the model. The training data consists of a set of words that are labeled with their solution words and the categories associated with the given words. The model is then trained on this data to learn how to recognize the solution word and identify the associated categories. During the training process, the model is exposed to a wide range of words and categories, allowing it to learn patterns and associations between words and categories. The model is trained using various machine learning algorithms, including deep learning techniques, which can learn complex The model is trained using various machine learning algorithms, including deep learning techniques, which can learn complex relationships between words and categories. Once the model is trained, it is tested using a separate set of data that was not used in the training process. The testing data consists of words that the model has not seen before, and the model's accuracy in recognizing the solution word and identifying the categories associated with the given words is evaluated. The testing data consists of words that the model has not seen before, and the model's accuracy in recognizing the solution word and identifying the categories associated with the given words is evaluated.
A Safety Motion Planning Algorithm for Mobile Manipulator Based on Improved LSTM Neural Network and Capability Map

Currently, indoor narrow environments, such as assembly in large aircraft cabins, suffer from the inefficiency and compromised quality of manual operations. The existing mobile manipulators for human-machine collaboration still have certain deficiencies, including high redundancy, insufficient constraints, and weak active safety protection capabilities. In light of these challenges, we present a safety motion planning algorithm for mobile manipulator based on improved LSTM neural network and Capability Map (CM). We have modified the robot’s capability index (CI) based on the safety potential field function. This provides the spatial representation of robot operation ability for motion planning. In addition, we have added a security gate mechanism to LSTM, enabling more accurate prediction of personnel movements in high-risk areas. The experimental results demonstrate that this algorithm holds great potential in human-machine collaboration. By doing so, the algorithm enables more accurate prediction of the movement trajectory of personnel and facilitates the avoidance of motion conflicts between robots and personnel.
Research on Multilingual Speech Recognition Based on Streaming End-to-End Speech-Language Integrated Modeling Algorithm

End-to-end Automatic Speech Recognition (ASR) is a technology that directly converts speech into text and has received extensive attention and research in recent years. From an industrial perspective, this paper provides an overview of recent advances in end-to-end models in the current ASR space and techniques to address challenges in real-world applications. First, three common end-to-end models, CTC, AED, and RNN-T are presented, then their strengths, weaknesses, and applicable scenarios are analyzed. Two learning strategies are then presented: multitask learning and self-supervised learning, which shows how the performance and generalization of the end-to-end model improve. Then, cross-language modeling and adaptive technology are introduced, and how they solve the problem of multilingual speech and personalized recognition is explained. Finally, some new models such as Transformer, Conformer, and UniSpeech are introduced. It shows how they use attention mechanisms and self-supervised learning to improve recognition accuracy and efficiency. This paper is intended to provide a comprehensive and useful reference for ASR researchers and engineers.
PENGARUH MODEL PEMBELAJARAN DISCOVERY LEARNING TERHADAP HASIL BELAJAR IPA SISWA KELAS III SD 3 TENGGELES

This study aims to identify that there is influence and improvement in the Discovery Learning Learning Model on Science Learning Outcomes for class III SD 3 Tenggeles.The problem in this research is motivated by the lack of understanding of students in receiving learning material and the lack of understanding of students in a reading, this is because students' reading literacy is still low. This of course can affect learning outcomes in grade 3 students in science content. This study aims to identify the effect of the discovery learning model assisted by concrete media on the science learning outcomes of third grade students at SD 3 Tenggeles. This research method is a quantitative pre-experimental design. The population of this research were all class III students, totaling 22 students.
Interactive Inspirations: The Case for Incorporating Joy and Play in Open Social Scholarship

Inspired by the way that hands-on science centres playfully introduce children and adults alike to the curiosities and complexities of our biological, chemical, and physical world, the *Virtual Realizations, Playable Archives* project, emerging from the INKE SSHRC partnership’s *Community* cluster, is exploring strategies to interactively engage broader publics with the wonders and challenges of humanities knowledge, research, and research communication. This paper’s focus on *Chowdr*, one prototype example from the *Virtual Realizations* project, is intended to initiate larger critical conversations about play, community, and open social scholarship values by using a food-based social networking app. This prototype is designed to create meaningful engagement among users by having them share their personal recipes for seafood chowder, and in doing so, playfully connect with their community and their food culture.
LaenNet: Learning robust GCNs by propagating labels

Graph Convolutional Networks (GCNs) can be acknowledged as one of the most significant methodologies for graph representation learning, and the family of GCNs has recently achieved great success in the community. However, in real-world scenarios, the graph data may be imperfect, e.g., with noisy and sparse features or labels, which poses a great challenge to the robustness of GCNs. To meet this challenge, we propose a simple-yet-effective LAbel-ENhanced Networks (LaenNet) architecture for GCNs, where the basic spirit is to propagate labels together with features. Specifically, we add an extra LaenNet module at one hidden layer of GCNs, which propagates labels along the graph and then integrates them with the hidden representations as the inputs to the deeper layer. The proposed LaenNet can be directly generalized to the variants of GCNs. We conduct extensive experiments to verify LaenNet on semi-supervised node classification tasks under four noisy and sparse graph data scenarios, including the graphs with noisy features, sparse features, noisy labels, and sparse labels. Empirical results indicate the superiority and robustness of LaenNet compared to the state-of-the-art baseline models. The implementation code is available to ease reproducibility1.
Petrology and geochemistry of sandstone from Lower Cretaceous Kikhla Formation, NW Libya: Implications for provenance and depositional setting.

This study aims to determine and investigate the provenance of the source rocks and the depositional setting of the Kikhla Formation, northwestern Libya. It is mainly composed of mature quartzitic sandstone alternating with conglomerate and clay. The study proved that the sandstone is yellow-white in color and commonly carries quartz granular and small fragments of silicified wood. The environmental deposition is under differing fluvial conditions and by a large braided river. Texturally, the Kikhla sandstone is poorly cemented, coarse to very coarse grained and strongly unimodal and is classified as litharenite and subarkose by the modal composition, which is supported by geochemical studies. According to petrography and geochemistry, the Kikhla sandstone was deposited in a passive continental margin basin, mainly from granitic sources. On the basis of major elements ratios, and petrographic interpretation, the source rocks are most likely recognized as Proterozoic granites that were exposed via rifting. The CIA values (73.28-93.97) indicate a high degree of chemical weathering, which could be attributed to the arid climate conditions in the source area and mainly controlled by the source-rock provenance, hydraulic sorting during transport and deposition, diagenesis and depositional environment. According to chemical investigations, sandstone exhibits significant concentrations of SiO2, Na2O&gt; K2O, and Fe2O3, which is compatible with the modal data.
Theoretical Study of The Determination of Optimal Tilt Angle of a Solar Collector Case Study: Mataram – Nusa Tenggara Barat

The performance of solar energy based apparatus in various locations is inseparable from the determination of the orientation direction and tilt angle. This study aims to determine an optimal tilt angle in Mataram city area. Photovoltaic Geographical Information System from European Commission (online) was used as the database to obtain global radiation and monthly average diffusion radiation on the horizontal surface with locus coordinates at Latitude/Longitude: -8.583°/116,107° South Latitude. That data was then processed mathematically using Microsoft Excel software. As the result, for the application of solar-based apparatus with a fixed structural configuration throughout the year, the optimal tilt angle (β) was 15° with the orientation facing north, and it was estimated that total solar radiation received throughout the year on average was 115,195 MJ/m2.
Deep Normalization Cross-Modal Retrieval for Trajectory and Image Matching

In response to the current state of urban traffic, this work introduces a brand-new challenge termed cross-modal retrieval of traffic trajectory and picture, which aims to achieve cross-modal retrieval between trajectory coordinates and traffic images. It is difficult to effectively capture the temporal-spatial properties of trajectory data, and there is still a challenge in narrowing the heterogeneity gap between the hash representations of these two modalities. Thus, we provide a brand-new end-to-end cross-modal retrieval technique for trajectory data that extracts features from both the picture and the trajectory and computes similarity. In order to confirm the efficacy of the suggested strategy, we also produced a traffic multimodal dataset with more than 20,000 traffic photos and trajectory data. The experimental findings on our own cross-modal dataset show that our model works better than other standard techniques.
THE LECTURERS’ PERCEPTION OF THE IMPLEMENTATION OF ICT FOR CONDUCTING ONLINE LEARNING DURING COVID-19 PANDEMIC

During the Covid-19 pandemic, integrating information and communication technology (ICT) in the education sector is inevitable. The use of technology was expected to replace face-to-face instruction with online learning. However, not all educators were ready to use ICT for their instruction. Thus, this study aimed to find out the lecturers' perception towards the implementation of online learning and the problems they faced during the Covid-19 pandemic. The subjects of the research were the lecturers at STKIP Agama Hindu Singaraja. There were 40 lecturers involved in this study. They were selected using the saturated sampling technique. The researcher collected the data through a questionnaire. The questionnaire results were analyzed quantitatively using descriptive statistics. Then, the total score of the questionnaire result was classified using a category based on the theoretical ideal reference assessment formula. The finding showed that, in general, the lecturers had a positive perception of the use of ICT for online learning during the Covid-19. Besides, this study also found some problems faced by the lecturers in implementing online learning. Thus, this study concluded that the lecturers believed that using ICT for online learning was the best option they had during the Covid-19 pandemic, even though they should face various challenges in delivering the instruction through online learning.
An Improved Particle Swarm Algorithm for UAV Path Planning

Optimizing UAV flight paths is a critical challenge for modern unmanned aircraft systems. Conventional manual methods are inefficient and error-prone. To address this, we propose an improved 3D path planning algorithm using particle swarm optimization. Our method employs a multi-objective evolutionary genetic algorithm to determine optimal waypoints while considering terrain obstacles, distances, and performance constraints. An adaptive linear coefficient of variation with constant acceleration and maximum velocity enhances optimization. We use a multi-objective genetic algorithm to tackle nonlinear planning, optimizing convergence by replacing undesirable particles. For complex environments, we combine genetic and particle swarm algorithms with simulated annealing for global optimization. Experimental results show outstanding performance, surpassing other algorithms in accuracy and stability. Our approach promises significant advancements in UAV path planning, aiding UAV technology development and operation.
SELF-ASSESSMENT TOOL FOR IMPROVING STUDENT’S SKILLS REGARDING THE EXAMS IN HIGHER EDUCATION

Preparing for exams in higher education can be challenging, and students often need help to assess their skills and competencies. This paper presents a self-assessment tool to improve students’ exam preparation and performance. The study involved collecting data from students before and after using the tool, which asked questions related to exam preparation strategies such as understanding the exam format, imagining potential test questions, and following directions carefully. The results were compared to assess the tool's effectiveness in improving students' exam skills. The findings showed a significant improvement in the student’s self-assessment and exam performance after using the tool. This paper provides an overview and comparison of existing e-learning management systems and self-assessment tools to highlight the unique features of the proposed tool. The self-assessment tool was designed and tested, with a pre-feedback and post-feedback evaluation of students' skills. Comparing pre and post results showed a statistically significant improvement in students' skills, leading to a notable shift in the percentage of responses. The "never" category experienced a considerable decrease of 27.91%, while the "always" category witnessed a substantial increase of 53.46%. These findings underscore the positive impact of the self-assessment tool on students' behaviors and attitudes toward exam preparation. Overall, this study showcases how self-assessment tools can effectively improve test readiness and outcomes in higher education.
Movie Recommender System Using Decision Tree Method

In this modern era, many things that can be done online, one of which is watching movies. When the number of movies increases, people often find it difficult to decide which movie to watch next. To solve this problem, a useful recommendation system was developed to find movies that one might like based on movies that have been watched before. This research develops a movie recommendation system using Collaborative Filtering (CF) with the Decision Tree algorithm. In this study, the data used were movie data and ratings obtained from the grouplens.org website. Then the movielens dataset is filtered and only saves movies with a rating of more than 50 that are used in the recommendation system. In this study, Mean Absolute Error (MAE) is used as a method to assess the accuracy of the movie recommendation system. Based on the research that has been done, Decision Tree gets better results with an MAE value of 0,942 compared to Collaborative Filtering with an MAE value of 1,242.
COMPARATIVE ANALYSIS OF PROFESSIONAL MASS MEDIA AND TELEGRAM CHANNELS INFORMATION CONTENT NEWS PRODUCTION (ON THE CASE OF DZERKALO TYZHNYA AND UKRAYINA SEJCHAS)

A comparative analysis of journalistic texts in the so-called new media (Telegram channel “Ukrayina Sejchas”) and professional media (online publication “Dzerkalo tyzhnya”) is conducted in the paper. The similarities and differences of the content are clarified (by topic, the most popular personalities who become the heroes of the news, compliance with the standards of accuracy and separation of facts from comments, or the neutrality of the news). The research method is computer analysis of texts using Python modules and Spacy natural language processing library. It was found that the most popular topic for both media was Russia’s full-scale war against Ukraine; Zelenskyi, the General Staff, the Armed Forces, and Putin were mentioned more often. However, at the same time, the Telegram channel bets on hot news from the regions closest to the front line, while the professional mass media takes care of the international agenda. Also, the content of the US is more about personalities, while the DT prefers institutions, some of which are other mass media. Therefore, the online mass media also accumulates exclusive messages from other professional mass media. However, the telegram channel’s attempt to report news as quickly as possible also leads to a violation of standards, in particular accuracy: the telegram channel can report an explosion or missile attack almost immediately as it happened, without having enough facts. The same applies to photos and videos, published as some evidence. Another difference between the Telegram channel and the professional media is that they do not always adhere to the standard of separating facts from comments. Therefore the channel not only report news, but also comment, ironize, etc. The performed analysis allows us to talk about a certain distribution of functions of new and professional media. Telegram channels better adhere to the rule of convergence of interests, and also produce content that is easier to perceive. However, without professional mass media, which will publish the so-called hard news – socially significant messages coming from various institutions and international organizations, as well as check the hot news reported by telegram channels, one cannot talk about an effective media system.
Teknik Memanggil Kaum Milenial (TMKM) untuk Modul Optimasi menggunakan Linear Programing dengan Excel Solver

Optimization is the search for the optimal value of a problem with the help of one or more objective functions. Optimization problems are often found in everyday life and become important in the operation of a production system. The simplest optimization problem in the industrial world is solved using linear programming. Linear programming equations are made to solve optimization problems in the industrial world. In optimizing a problem, computational tools are needed to produce the optimal solution. Many computational tools are used, one of which is Excel Solver. Excel Solver is operated because many Microsoft Office users have this computing tool. Where everyone commonly uses Excel. The world of industrial Engineering needs someone who can solve optimization problems, especially since the current industrial era is getting more advanced. More skills are necessary for dealing with the Industrial Revolution 4.0 and Society 5.0, and it becomes a mandatory ability for an industrial engineer to solve this problem. Therefore, this community service activity is proposed with the topic: "Technique for Calling Millennials (TMKM) for Optimization Modules using Linear Programming with Excel Solver" to introduce optimization in the industrial world using existing optimization techniques and simple tools needed as applications in industrial engineering science. This community service activity was carried out in collaboration with Citra Blessing Tangerang High School. This activity is given to all students and teachers at SMA Citra Blessing Tangerang. The target audience for this activity is 50 participants. It is hoped that this activity can provide knowledge in the field of optimization and also provide an overview of industrial developments in the Industrial Revolution 4.0 era and in Society 5.0. Besides that, it also opens students' insights about the field of Industrial Engineering, especially opening students' insights about career prospects in the Industrial Engineering field.&#x0D;
Utilization of School Facilities for Literacy and Numeracy Displays

The Sorek Dua Village KKN student activity program with a qualitative descriptive method was carried out at SDN 006 Sorek Dua. The literacy level of Indonesian students is relatively low. On this basis, reading and writing activities are needed. Students know the unit of length and unit of weight. This certainly increases student learning motivation. Therefore, suggestions for the future are that it is hoped that there will be more recent improvements so that students can know the units of length and units of weight. From the activities carried out at SDN 006 Sorek Dua, it can be concluded that students' ability to understand the concepts of reading, writing and arithmetic lies not only in the school environment but also in the surrounding environment. seem more eager to learn. This certainly increases student learning motivation.