Reviewerly/exHarmony huggingface dataset:
The Anaysis of Using Verbs in Descriptive Paragraph in Semester II STBA JIA in Academic Year 2022/2023

This research aimed to describe how the student’s constructing sentences in their writing descriptive paragraph. The sample uses are the students of semester II class A, B and evening class with the total number of students are 41 students. The instrument uses in this research are test and interview. The researcher limits the sentences constructing are simple, compound and complex sentences. It assumed that the students are able to construct and use simple sentences, compound sentences and complex sentence in developing descriptive writing. The result show that from the first test, the average is 79. In the second test, from 41 students, there are 12 students do all correct sentences. There are 9 students made mistakes in simple sentence, with 10 mistakes. For compound sentence, there are 18 students who made 35 mistakes. and complex sentences, there are 18 students who made 41 mistakes.
The Development of Instructional Model Based on Gardner’s Multiple Intelligence Theory to Improve the Mathematics Achievements of Second Grade Primary School Students

This research aimed to 1) study the factors affecting the development of mathematics achievement of second-grade students in Qinhuang Town Primary School, 2) develop an instructional model based on Gardner’s Multiple Intelligence Theory, 3) compare the mathematics achievement of second-grade students before and after using an instructional model based on Gardner’s Multiple Intelligence Theory. The sample group was 30 second-grade students in Qinhuang Town Primary School. The research Instruments were 1) a questionnaire about the factors affecting elementary school students' mathematics achievement 2) an interview form about the factors affecting elementary school students' mathematics achievement 3) lesson plans 4) a mathematics achievement test 5) students' behavioral observation form 6) interview form about the opinions on teaching and learning. This study was conducted in three steps: a study of the factors affecting the development of second-grade students' mathematics achievement, the development of an instructional model based on Gardner’s Multiple Intelligence Theory, and an experimental and improvement process. Data were statistically analyzed, Qualitative data were analyzed through content analysis, and Quantitative data were analyzed through descriptive statistics, frequency, percentage, means, and standard deviation, and the different scores of mathematics achievement before and after using the instructional model were analyzed through t-test for dependent samples. The results of the study show that: 1) the factors affecting elementary school students' mathematics achievement include four aspects: 1) students 2) teachers 3) the learning process and the environment. 2) the instructional model consisted of four components: Principle, Objective, Learning Process, and Result. 3) the implementation of the instructional model based on the multiple intelligences students have after mathematics achievement was significantly higher than that before class with statistical significance at the level .01
IMPROVING LISTENING COMPREHENSION BY USING ENGLISH SONGS FOR THE TENTH-GRADE STUDENTS AT SMAN 10 GOWA

Listening is one of the four most essential English learning skills students have in learning. The research objective was to improve students' listening skills through the use of English songs. This study used a quantitative type of experimental method. There were two classes, namely the experimental class with English songs and the control class without English songs. The research sample was grade X students consisting of 50 students in which 25 students in each experimental class and control class. Data collection techniques in this study used a pre-test and post-test. The results showed that the pre-test for the experimental class had a mean value of 57.88 and the pre-test for the control class had a mean value of 55.04. Then, after treatment, the research results showed that the experimental class post-test had a mean value of 86.08 and the control class post-test had a mean value of 73.00. The results of the data were analyzed by using an independent T-test showing that Sig. (2-tailed) is 0.000 which is lower than 0.05 so that (Ha) is accepted and (Ho) is rejected so that there is an influence on listening results of using English songs. Based on the research results, it can be concluded that English songs can improve listening skills for grade X of SMAN 10 Gowa.
Path Planning of Automatic Parking System by a Point-Based Genetic Algorithm

The path planning module in automatic parking systems plays a crucial role in enhancing performance and user experience. To tackle this vital aspect, we propose a path planning algorithm that utilizes a point-based genetic algorithm (POGA) in automatic parking systems. Initially, we generate some control points randomly within the parking range and then establish trajectories between each pair of control points using a cubic spline curve. In addition, we incorporate a penalty term to increase the distance between the vehicle and obstacles along the collision path. By implementing this technique, we successfully obtain the initial shortest path that ensures safety, along with the corresponding control points. Finally, POGA is proposed to conduct an optimal search for control points, in which each control points is an individual. This algorithm combines the well-established Dijkstra algorithm with a genetic algorithm, thereby improving stability and convergence. Through comprehensive comparisons with a path-based genetic algorithm, our experimental results demonstrate the superior stability and faster convergence rate exhibited by POGA across various parking scenarios.
Technocratic utopias in art: transformation of physicality as the future of man

The article is devoted to the presentation of the image of the future man in modern utopias of technological art. The author sees the peculiarity of the modern situation in Utopian Studies in the reorientation of utopian creativity from projects of global transformation of society to projects of human improvement, and in the latter case, from the socialization of the individual to changing his physicality. The “individualization” of utopia was complementary to the emergence and development of the transhumanism movement, which connects the possibilities of limitless personal development with scientific and technological progress in the field of bio- and nanotechnology. The emergence of transhumanism was a consequence of the convergence of two trends in new European culture: the atropomorphization of machines and the mechanization of humans (Tishchenko). Many representatives of technological art identified themselves with transhumanism and became conductors of its ideas into artistic culture.
Classification of handwritten drawing shape in 3D Direct Drawing System using Machine Learning

The 3D direct drawing system can create the point, the segment, the polygonal and curved surface, and the solid model. However, creating the model is difficult for a user because the command is multiple and complicated. In this paper, the drawing trends and trajectories are investigated. After that, they are predicted and converted to the desired shape using the k-NN method and Neural Network method of machine learning, and the accuracy rates of the two methods are compared.
Differences in nonverbal behavior in online communication

Pandemic of COVID-19 accelerated usage of various online communication tools including video conference software, and Metaverse applications. But it is not clear that what are characteristics of communication performed in each online communication environment. Especially, research of comparing nonverbal communication between different environments is not enough. It is supposed that adapting nonverbal behaviors to the online communication environment can enhance facilitate effective communication among collaborative employers, such as engaging engineering works. This paper aims to reveal characteristics of communication in video conference and metaverse environment by comparing them. The experiment was conducted to observe how subjects perform gestures using card game which is playable on the web browser. Subjects are assigned to two groups, video conference and metaverse environment. Video conference environment group used Zoom as communication tool, and metaverse group were in 3D virtual reality space created by Unity, wearing Meta Quest 2. Parameters to measure the characteristics of communication are number of the gestures per second, percentage of gesture time per second, and average duration of gestures, for hands and head respectively. In addition, gestures of hand are used as information transmission gesture, and gestures of head is used as responding gesture. The result shows that comparing two environment groups, the video conference environment group had more responding gestures, while the metaverse environment group had more information transmitting gestures.
Integration of Artificial Intelligence Technology in Distance Learning in Higher Education

Background. Higher education in this digital era is faced with significant changes, especially with the development of artificial intelligence (AI) technology. Purpose. This research aims to explore the potential and limitations of integrating AI technology in improving the quality of distance learning and present findings that can guide the development of AI-based pedagogy. Method. This research method adopts a quantitative survey approach to detail the integration of artificial intelligence (AI) technology in the context of distance learning in higher education. A total of 20 students were randomly selected as respondents, with sample selection using the purposive sampling method. This process ensures maximum representation of students who have significant experience with the integration of AI technology in their learning. Data was collected through questionnaires focused on effectiveness, adaptability of material, and level of interactivity during learning. Next, descriptive and inferential statistical analysis will analyze patterns and relationships between variables to explore the effectiveness of AI technology, the factors that influence it, and its impact on student learning experiences. Results. Survey results show that the majority of students actively use AI technology, especially several times a week, and express a high level of satisfaction with the use of AI technology in distance learning. Virtual Reality or Augmented Reality learning experiences were considered to benefit the most, even though all respondents experienced challenges or obstacles in using AI technology. Conclusion. The conclusions of this research emphasize the need to address these challenges to maximize the benefits of integrating AI technology in increasing the effectiveness and efficiency of distance learning in higher education.
A Fast Attention-Guided Hierarchical Decoding Network for Real-Time Semantic Segmentation

Semantic segmentation provides accurate scene understanding and decision support for many applications. However, many models strive for high accuracy by adopting complex structures, decreasing the inference speed, and making it challenging to meet real-time requirements. Therefore, a fast attention-guided hierarchical decoding network for real-time semantic segmentation (FAHDNet), which is an asymmetric U-shaped structure, is proposed to address this issue. In the encoder, we design a multi-scale bottleneck residual unit (MBRU), which combines the attention mechanism and decomposition convolution to design a parallel structure for aggregating multi-scale information, making the network perform better at processing information at different scales. In addition, we propose a spatial information compensation (SIC) module that effectively uses the original input to make up for the spatial texture information lost during downsampling. In the decoder, the global attention (GA) module is used to process the feature map of the encoder, enhance the feature interaction in the channel and spatial dimensions, and enhance the ability to mine feature information. At the same time, the lightweight hierarchical decoder integrates multi-scale features to better adapt to different scale targets and accurately segment objects of different sizes. Through experiments, FAHDNet performs outstandingly on two public datasets, Cityscapes and Camvid. Specifically, the network achieves 70.6% mean intersection over union (mIoU) at 135 frames per second (FPS) on Cityscapes and 67.2% mIoU at 335 FPS on Camvid. Compared to the existing networks, our model maintains accuracy while achieving faster inference speeds, thus enhancing its practical usability.
A Safe Heuristic Path-Planning Method Based on a Search Strategy

In industrial production, it is very difficult to make a robot plan a safe, collision-free, smooth path with few inflection points. Therefore, this paper presents a safe heuristic path-planning method based on a search strategy. This method first expands the scope of the search node, then calculates the node state based on the search strategy, including whether it is a normal or dangerous state, and calculates the danger coefficient of the corresponding point to select the path with a lower danger coefficient. At the same time, the optimal boundary is obtained by incorporating the environmental facilities, and the optimal path between the starting point, the optimal boundary point and the end point is obtained. Compared to the traditional A-star algorithm, this method achieved significant improvements in various aspects such as path length, execution time, and path smoothness. Specifically, it reduced path length by 2.89%, decreased execution time by 13.98%, and enhanced path smoothness by 93.17%. The resulting paths are more secure and reliable, enabling robots to complete their respective tasks with reduced power consumption, thereby mitigating the drain on robot batteries.
Recommendation System for Mobile Applications Tour Guide and Travel Services using Demographic Filtering and Content-Based Filtering Methods based on Android

This research aims to build a tourism service recommendation system by providing recommendations for tour guides and tourism destinations using machine learning recommendation system methods. The recommendation system method in this research uses the demographic filtering method to recommend tour guides using a model with three stages, namely filtering, scoring and sorting the tour guide data category. At the scoring stage, it is carried out using a decision-making system method using the Simple Additive Weighting method to weight the tour guide value with the weighting value of each criterion in the tour guide data. This research also applies a content-based filtering method using a model with two stages, namely training and recommendation. The training stage uses tf-idf weighting and the recommendation stage uses cosine similarity to recommend tourism spots based on the similarity of metadata for each item. In using the recommendation system, the data will focus on collecting tour guide data and data on tourist attractions selected by the user. This data will be processed by a recommendation system using demographic filtering and content-based filtering methods. Through the Simple Additive Weighting method in demographic filtering and tf-idf weighting with consistent similarity in content-based filtering, this research found personalized recommendation results so that the level of accuracy of the recommendation results becomes more personal and accurate. The results of this research provide a recommendation system for tourist guides and tourist attractions which is implemented into an Android-based mobile application that can be used to meet tourists' needs.
TEXT GENERATION SERVICES AS AN OBJECT OF RESEARCH: METHODOLOGY OF COMPARATIVE ANALYSIS

The relevance of natural language generation algorithms is primarily due to the rapid development of digital technologies, widespread use of the Internet and an increase in the activity of users of the global network. The authors of this study propose a text generation system based on the Python programming language. At the same time, there is a well-known text generation service called ChatGPT. In this article, the authors propose an original methodology for creating a text generation service based on the Python programming language. This approach allows you to better understand the human perception of the results of the neural network, as well as problem areas and growth points of developments in this area.
Menghitung Kecepatan Membaca Arab Melayu Dalam Cerita “Orang Batak Menyetop Taksi” Kelas 2021 A

This research aims to assess the reading speed levels of students. The sample for this study consists of students from the 2021 A class of the Indonesian Language and Literature Education Program, Faculty of Education, Universitas Riau. The data analysis technique employed is the test method, with quantitative data analysis techniques used to analyze the results of a fast reading test in Arabic Malay texts. The data analysis procedure involves measuring the reading speed of students when reading Arabic Malay texts. The research findings indicate that the reading speed of the 2021 A class students is approximately 54.08 words per minute (wpm). This figure suggests that the students' reading speed is below the established standard for reading speed in the context of Arabic Malay texts, which is 200 wpm. The conclusion drawn from this research is that students tend to read at a slower pace, possibly as an effort to reduce reading errors, resulting in their reading speed falling below the expected standard.
Prediksi Sifat Mekanik Aluminium Berdasarkan Unsur Kimia Paduan Menggunakan Model Machine Learning

This study designs and compares optimal machine learning models for predicting the mechanical properties of alloyed aluminum with code 1050 (A91050) based on the percentage composition of chemical elements using the software RapidMiner with decision tree and Random Forest algorithms. The aim is to develop a data-driven predictive model with high accuracy to minimize the need for physical testing on aluminum with various compositional variations. The machine learning modeling in this study involves nine input variables, comprising chemical elements such as Al, Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and two output or target variables, YS and TS (yield strength and tensile strength). Additionally, a Heatmap correlation is employed to observe the correlations between the chemical elements and the mechanical properties of the alloyed aluminum. The comparison of these algorithms reveals that Random Forest (RF) outperforms other algorithms in predicting YS with a Mean Absolute Error (MAE) of 7.157, Root Mean Square Error (RMSE) of 11.248, and a coefficient of determination (SC) of 0.977. On the other hand, Random Forest (RF) also exhibits better performance in predicting TS with an MAE of 29.296, RMSE of 42.382, and SC value of 0.443.
Learning Causal Chain Graph Structure via Alternate Learning and Double Pruning

Causal chain graphs model the dependency structure between individuals when the assumption of individual independence in causal inference is violated. However, causal chain graphs are often unknown in practice and require learning from data. Existing learning algorithms have certain limitations. Specifically, learning local information requires multiple subset searches, building the skeleton requires additional conditional independence testing, and directing the edges requires obtaining local information from the skeleton again. To remedy these problems, we propose a novel algorithm for learning causal chain graph structure. The algorithm alternately learns the adjacencies and spouses of each variable as local information and doubly prunes them to obtain more accurate local information, which reduces subset searches, improves its accuracy, and facilitates subsequent learning. It then directly constructs the chain graphs skeleton using the learned adjacencies without conditional independence testing. Finally, it directs the edges of complexes using the learned adjacencies and spouses to learn chain graphs without reacquiring local information, further improving its efficiency. We conduct theoretical analysis to prove the correctness of our algorithm and compare it with the state-of-the-art algorithms on synthetic and real-world datasets. The experimental results demonstrate our algorithm is more reliable than its rivals.
A New Strategy for Combining Nonlinear Kalman Filters with Smooth Variable Structure Filters

Bayesian filters exemplified by the celebrated Kalman Filter (KF), and its non-linear variants rely on a fairly accurate state-space model of the system under study. To address the issue of modelling uncertainty in state estimation, the smooth variable structure filter (SVSF) was proposed in 2007. Since then, several SVSF variants have been proposed to extend its domain of applicability. In some of these algorithms, SVSF has been viewed as a complementary approach alongside the well-established nonlinear Kalman Filters. This paper seeks a general framework for SVSF formulation to unify some of the recent developments in SVSF literature under one umbrella. In this way, the SVSF variants are revisited as special cases of the proposed framework. This paper proposes a new strategy to combine SVSF filters with other nonlinear filters and puts existing SVSF filters under one umbrella. Six filters are formulated based on the proposed method of combining filters. The proposed filters relax limitations of existing SVSF variants, making the proposed filters more universal. In simulations, the new filters outperform state-of-the-art nonlinear KFs and some existing SVSF filters. To demonstrate the merits of the proposed framework, the new filters are applied to target tracking and are comparatively evaluated.
RL-based Cache Replacement: A Modern Interpretation of Belady’s Algorithm with Bypass Mechanism and Access Type Analysis

Belady’s algorithm is widely known as an optimal cache replacement policy. It has been the foundation of numerous recent studies on cache replacement policies, and most studies assume this as an upper limit. Despite its widespread adoption, we discovered opportunities to unleash the headroom by addressing cache access types and implementing cache bypass. In this study, we propose Stormbird, a cache replacement policy that synergistically integrates the extensions of Belady’s algorithm and the power of reinforcement learning. Reinforcement learning is well-suited for cache replacement policy problems owing to its ability to interact dynamically with the environment, adapt to changing access patterns, and optimize the maximum cumulative rewards. Stormbird utilizes several selected features from the reinforcement learning model to enhance the instructions per cycle efficiency while maintaining a low hardware overhead. Furthermore, it considers cache access types and integrates dynamic set dueling techniques to improve the cache performance. For 2 MB last-level cache per core, Stormbird achieves an average instructions per cycle improvement of 0.13% over the previous state-of-the-art on a single-core system and 0.02% on a four-core system while simultaneously reducing hardware overhead by 62.5%. Stormbird incurs a low hardware overhead of only 10.5 KB for 2 MB last-level cache and can be implemented without using program counter values.