Reviewerly/exHarmony huggingface dataset:
Research on Path Planning of Soccer Robot Based on Potential Field Improved Ant Colony Algorithm

As an important branch of the current robot field, soccer robots have a wide range of application prospects. The robot soccer game is an effective means to test robot technology. In a robot soccer game, the robot is in a complex environment with real-time confrontation, and the real-time cooperation between robots is high. Aiming at the complexity of the traditional robot path planning algorithm, the artificial potential field method and ant colony optimization algorithm are studied in depth, integrating the advantages of the two, combined with the characteristics of the soccer robot system, an improved ant colony algorithm based on artificial potential field is proposed, and through computer simulation analysis and physical robot experiments, the results show that the algorithm can obtain the optimal path, which meets the real-time, feasibility and effectiveness requirements of soccer robot path planning. It is also possible to effectively improve the speed of route planning.
A mask-wearing detection algorithm based on improved YOLOv7

Aiming at the problem that the accuracy of mask-wearing detection is poor and it is difficult to deploy in low-cost equipment, this paper has proposed a mask-wearing detection algorithm based on improved YOLOv7. By adding an attention mechanism to the backbone network and introducing the partial convolution method, the algorithm enhances the perception ability of the network to small targets, improves the accuracy of mask-wearing detection, reduces the parameters and calculation of the network, and improves the efficiency of feature extraction of the network. By integrating Distribution Shift Convolution in the head network, the required memory and equipment requirements are reduced while the operation speed is also improved. The experimental results show that, compared to the original model, the mean average precision of the improved YOLOv7 network improved by 0.5%, the parameters are reduced by 20%, the computation only accounts for 51% while the model size is lower to 63%and the reasoning speed remains basically unchanged. These improvements can be applied in the current mask-wearing monitoring task and provide a useful reference for future object detection tasks.
Communication Signal Modulation Recognition Based on Inception-V3 Transfer Learning

Deep learning-based modulation recognition algorithms for communication signals have poor recognition performance when only a small number of labeled signal samples are available. A modulation recognition method based on Inception-V3 transfer learning is proposed to solve this problem. This method first converts eight types of modulated signal samples from 0 to 20 dB into time-frequency diagrams as input and fine-tunes the pre-trained network Inception-V3 for transfer learning, conducts test experiments on three datasets with finitely labeled samples, and finally visualizes the feature learning of the training process to ensure that the features of the signals are correctly learned. The experimental results show that the proposed method can achieve the highest recognition accuracy of 94.07% at 20 dB with insufficient training samples, and still achieve 92.50% and 91.67% recognition accuracy at 20 dB with only 4400 and 1760 samples, respectively.
A Sample Detection Model Applying IoT Technology

This article first analyzes the development trend of IoT technology to determine its process framework in the industry. Then, through sample analysis, the application value and direction of intelligent instruments in sample analysis are identified. Finally, by integrating intelligent instruments in sample analysis with IoT technology, the application of intelligent instruments in sample analysis under IoT technology is studied. This article is based on the application of Internet of Things technology to intelligent instruments, obtaining a new type of intelligent instrument and exploring its application in blood cells. It mainly conducts comparative experiments on the counting and classification of blood cells. The experimental results of the comparison between the model algorithm and instrument method in this article indicate that the algorithm of this model has a certain degree of accuracy and effectiveness, and the intelligent analysis in this article further improves the research on samples.
Multi-scale Feature Fusion Algorithm Based on Generative Adversarial Network for Motion Deblurring

The engine vibration of the gasoline-powered unmanned helicopter induces motion blur in the images acquired by the onboard camera, degrading the performance of visual tasks such as target identification and visual navigation. To address this issue, this paper proposes a multi-scale feature fusion deblurring algorithm based on a generative adversarial network (GAN). The algorithm utilizes different scales of image features to enhance both semantic and detailed information in the images. Additionally, a frequency selection method is employed to extract useful frequency information from the blurred images, further improving the deblurring effect. Experimental validation is conducted on the publicly available GoPro dataset, where the results demonstrate that the algorithm achieves a PSNR (Peak Signal-to-Noise Ratio) of 32.24dB and an SSIM (Structural Similarity Index) of 0.934. Furthermore, the algorithm is tested on a dataset captured by the gasoline-powered unmanned helicopter, showcasing its effectiveness in enhancing target identification performance for motion-blurred images. This finding provides strong support for the visual task capabilities of gasoline-powered unmanned helicopters.
A Fast and Secure Diffusion Key-Based Colour Image Encryptions Using One-Dimensional Chaotic Maps

Images are a highly valuable medium for communicating data, as they possess the unique capability to visually represent information and convey a multitude of messages. Unfortunately, there is currently a deficiency in the development and research of rapid encryption methods specifically designed for colour images. This paper proposes a rapid and reliable scheme for encrypting colour images through the utilisation of one-dimensional (1D) chaotic maps in diffusion key-based encryption. The proposed technique involves an initial step of separating the three parts, namely blue, green, and red. Following this, the three components are horizontally concatenated. Chaotic key-based diffusion operations are performed on individual pixels that have been selected by indexing sequences based on chaotic maps. This process is repeated for each selected pixel. The diffused output is partitioned into three constituents and merged to produce the cipher images. The proposed method is deemed to be efficient due to its utilisation of a straightforward and effective colour image encryption procedure, coupled with the repeated use of a single 1D chaotic map. This approach results in a notably fast encryption process. The security of the proposed method is attributed to the utilisation of individual pixel diffusion operations based on chaotic keys. Based on the computer simulations and analysis on security, it has been determined that the suggested scheme is both efficient and secure in its ability to encrypt colour images of varying dimensions.
Learning Media for Introduction of Traditional Weapons in Indonesia Using Augmented Reality Technology

Augmented Reality (AR) is a technology that can visualize 3D objects to make them more realistic with the help of interactive digital images. This technology can be combined with various kinds of digital information so that it becomes more interesting. The Pusakaku application is an AR-based application that was created to introduce traditional weapons in Indonesia. This application is developed using the ADDIE framework, which consists of the Analysis, Design, Development, Implementation, and Evaluation stages. This framework helps in creating applications in accordance with the appropriate flow to produce an application that can be used in general. With this AR technology, it is expected to attract the attention of young people to learn various kinds of Indonesian cultural wealth in the field of traditional weapons. The application was tested before based on a phone and tablet. The result questionnaire of this application display is fairly clear and good, accompanied by traditional songs and combined with digital information containing the history of traditional weapons, which makes this AR traditional weapon more interesting.
“Intelligent Heuristics Are the Future of Computing”

Back in 1988, the partial game trees explored by computer chess programs were among the largest search structures in real-world computing. Because the game tree is too large to be fully evaluated, chess programs must make heuristic strategic decisions based on partial information, making it an illustrative subject for teaching AI search. In one of his lectures that year on AI search for games and puzzles, Professor Hans Berliner—a pioneer of computer chess programs 1 —stated: As a student in the field of the theory of computation, I was naturally perplexed but fascinated by this perspective. I had been trained to believe that “Algorithms and computational complexity theory are the foundation of computer science.” However, as it happens, my attempts to understand heuristics in computing have subsequently played a significant role in my career as a theoretical computer scientist. I have come to realize that Berliner’s postulation is a far-reaching worldview, particularly in the age of big, rich, complex, and multifaceted data and models, when computing has ubiquitous interactions with science, engineering, humanity, and society. In this article, 2 I will share some of my experiences on the subject of heuristics in computing, presenting examples of theoretical attempts to understand the behavior of heuristics on real data, as well as efforts to design practical heuristics with desirable theoretical characterizations. My hope is that these theoretical insights from past heuristics—such as spectral partitioning, multilevel methods, evolutionary algorithms, and simplex methods—can shed light on and further inspire a deeper understanding of the current and future techniques in AI and data mining.
TEACHERS’ STRATEGIES IN TEACHING READING COMPREHENSION AT SENIOR HIGH SCHOOLS

Reading strategy is an important role in teaching reading comprehension to achieve the goals of reading. The teacher must choose a strategy that supports the development of students' skills in analyzing the content of the text. The objectives of this research were to investigate EFL teachers’ strategies in teaching reading comprehension and the most frequent one applied by them. The subjects of this research consisted of 30 EFL teachers. This research was a survey design that used a quantitative approach. The instrument was a questionnaire using Vacca and Vacca’s theory and it was administrated to the respondents through Google Forms. The data were analyzed quantitatively using the Microsoft Excel software. The results of the research showed that the EFL teachers applied five strategies in teaching reading comprehension at Senior High Schools in Bengkulu City, namely the SQ3R, Reciprocal Teaching, Think-Aloud, QARs, and Scaffolding strategies. The most frequent strategy applied by them was the SQ3R which was as always category while other strategies were as often category. In conclusion, EFL teachers have applied various strategies in teaching reading comprehension at Senior High Schools. This research recommended that EFL teachers to apply all strategies in teaching reading comprehension.
A Comprehensive Study of Cryptocurrency Trend Analysis Based on a Novel Machine Learning Technique

Cryptocurrencies are digital blockchain technology-based money that mainly got into the trend in 2015. In the present manuscript, we are finding a Machine Learning-based technique that would be best optimized to predict the prices of cryptocurrencies. We have applied Linear Regression and Bayesian models on five cryptocurrency datasets gathered from Kaggle. First, the datasets are split into testing and training data and then the model is run over all the cryptocurrency datasets. Training of every model on the training dataset is done and later it is tested and individually analyzed by checking its precision and accuracy. We then tried to obtain a comparative analysis of the models. Then we have created a tabular representation of the comparative analysis so that it can be used to determine which model is working best.
Key-Insulated Aggregate Proxy Signature

The proxy signature is one among significant cryptographic primitives that enables proxy signer sign on given message in support of original signer. Aggregate signature is another such primitive, which is a single compressed signature attained on combining different n (signatures; signers; messages). The former possesses applications involving distributed computing in which the allocation of rights is relatively common and the latter greatly reduces the communication bandwidth in multicast, group oriented communications. To keep the exposure of private keys minimum in signature schemes; keeping the benefits of both proxy and aggregate schemes, we construct the first efficient aggregate proxy signature scheme using key-insulated mechanism in the Identity-based framework. The constructed scheme, delivers tight secure reductions to Computational Difie-Hellman problem, since it is obtained without the use of Forking lemma. Also, the constructed scheme is efficient both computational as well communicational view point.
An Interview with Gail Murphy - 2023 SIGSOFT Awardee

Gail Murphy received the 2023 SIGSOFT Outstanding Research Award for pioneering contributions to recommenders for software engineering and program comprehension that have impacted both theory and practice. She received a Ph.D. in computer science from the University of Washington in 1996 (USA), and she is now a Professor in the Department of Computer Science and Vice-President of Research &amp; Innovation at the University of British Columbia (Canada). Her research interests are in improving the productivity of software developers and knowledge workers by giving them tools to identify, manage and coordinate the information that really matters for their work.
Vicarious Calibration activity over southern Israel for the new family of orbital hyperspectral sensors

The challenge of maintaining hyperspectral orbital sensors has become more relevant in recent years. We established two test sites in Israel for vicarious calibration/validation of the sensor's performance. The study was conducted in cooperation with NASA, DLR, and ASI, and the sites are ready to serve as operational for any Hyperspectral sensor.
Analisis Sifat Fisis dan Mekanik Biodegradable Foam Berbahan Dasar Selulosa Jerami Padi dan Polivinyl Alcohol

Research has been carried out on analyzing the physical and mechanical properties of biodegradable foam made from rice straw cellulose with a polyvinyl alcohol binding matrix. This study aims to produce a substitute for styrofoam, which is considered not environmentally friendly and harmful to health. The composition of straw fiber and polyvinyl alcohol was varied by the mass ratio of the mixture of rice straw fiber and PVA (%) 40:20%, 30:30%, 20:40%, 50:10%, with the size of the fiber passing through the 80 mesh sieve. The results of the characterization using FT-IR showed that the highest content of functional groups was carbon (C), which corresponds to the content of conventional biofoam, which consists of the elements carbon (C) and hydrogen (H). The highest biofoam tensile strength value is in the comparison fraction (20:40%) of 18.11 MPa. The highest percentage of biofoam water absorption was obtained in the fraction ratio (50:10%) of 0.8928% in the treated sample and 1.1928% in the untreated sample. The percentage of biofoam water content closest to conventional biofoam water content is obtained at a fraction ratio (40:20%) of 0.78%. The highest degradation rate was obtained in samples with a comparative fraction (50:10%) of 0.04237%/day in treated and 0.06403%/day in untreated samples. The results of this study stated that the resulting biofoam did not contain harmful substances. After identifying the functional groups using the FT-IR test, the tensile strength of biofoam still did not meet the SNI 7188.7: 2016 bioplastic standard.
Quantum spatial superpositions and the possibility of superluminal signaling

A recently proposed gedanken experiment involving the (gravitational or electromagnetic) interaction between two objects---one placed in a state of quantum superposition of two locations---seems to allow for faster-than-light communication. However, it has been argued that, if the mediating fields are endowed with quantum properties, then the possibility for superluminal signaling is fully avoided. Moreover, in the gravitational case, this conclusion has been used to argue for the view that the gravitational field must be quantized. In this work, we clarify and complement some aspects of the discussion. In particular, by focusing on the way in which entanglement spreads across the components of the system, we offer some insights into the fundamental quantum features behind the impossibility of superluminal signaling and we provide a more general proof of such an impossibility in this and related protocols.
Can the intensity of playing online games affect interest in learning?

The different levels of student interest in learning and also the influencing factors, including of playing online games. How often the students spend their time playing online games and whether or not they have high interest in learning. This research aims to determine the relationship between the intensity of playing online games and students interest in learning at MI Darul Fikri Wonoayu. The hypothesis of this research is that there is a relationship between the intensity of playing online games and students interest in learning at MI Darul Fikri Wonoayu. This research uses a quantitative approach with product moment correlation approach. The sample in this research was 31 students and the data was taken by using purposive sampling technique. This research consists of 35 scale items uses an intensity scale for playing online games and a scale for the students learning interest. The results of the research show that there is a significant relationship between the intensity of playing online games and students learning interest in MI DArul Fikri Wonoayu, with a coefficient value of 0,3044. Thus, the hhigher the intensity of the students plaing online games, the lower the students interest in learning. In contrast, the lowerthe intensity of playing online games, the higher the students interest in learning. Tingkatan minat belajar siswa yang berbeda-beda dan juga factor-faktor yang mempengaruhiinya, sebagai contoh intensitas bermain game online. Seberapa sering siswa ini menghabiskan waktunya untuk bermain game online dan seberapa tinggi minat belajar yang dimiliki siswa. Penelitian ini emiliki tujua untuk mengetahui adanya hubungan antara intensitas bermain game online terhadap minat belajar siswa di MI Darul Fikri Wonoayu. Hipotesis penelitian yaitu adanya hubungan antara intensitas bermain game online dengan minat belajar siswa di MI Darul Fikri Wonoayu. Penelitian ini menggunakan pendekatan kuantitatif, dengan korelasi product moment. Sampel dalam penelitian ini berjumlah 31 orang siswa, dengan teknik pengambilan sampel yaitu purposive sampling. Skala yang digunakan dalam penelitian ini yaitu model skala likert, dengan jumlah 35 butir soal yang menggunakan skala intensitas bermain game online dan skala minat belajar siswa. Hasil penelitian ini menunjukkan adanya hubngan signifikan antara intensitas bermain game online dengan minat belajar siswa di MI Darul Fikri Wonoayu, dengan nilai koefisien 0,3044. Maka semakin tinggi intensitas bermain game online maka semakin rendah minat belajar siswa, begitupun sebaliknya semakin rendah intensitas bermain game online maka semakin tinggi minat belajar siswa di MI Darul Fikri Wonoayu.
Peculiarities of Distance Learning Organization in the Professional Training of Information, Librarianship, and Archives (European Experience)

The article’s aim is to analyze the distance learning peculiarities in the professional training of the specialty “Information, library, and archive management” based on the European experience. Methodology. For the revelation of the problematic the systematic method, comparative analysis, abstraction, and dialectical method were used. The results evaluated the experience of European universities in the implementation of relevant educational programs, namely the British, French, Spanish practices were analyzed. Since 2007 the European Union has been funding research to explore the best models for organizing distance education. Many universities have integrated library, information, and archival specialties, inspired by the British experience. Training in these fields focuses on equipping students with competencies that are applicable across these professions. While the UK's digitalization and information infrastructure are advanced, other countries can still benefit from adopting their experience, formalized in a national standard. France and Spain have also given significant attention to the issue of distance learning. Spain, for instance, has been diligently preparing for the introduction of distance learning since 2010. The practical significance and novelty of the research lies in its potential to offer valuable insights into the best practices and approaches adopted by EU in organizing distance learning for these professions. Other nations and regions can learn from these experiences. In conclusions it is shown, that across all European Union countries, there has been a common emphasis on ensuring both teachers and students possess digital competence, a critical element in organizing training for information, archiving, and librarianship.
Construction and Implementation of Curriculum Knowledge Bases by Integrating New Educational Resources

With the development of information technology (IT), the educational field has gradually entered a modernization stage. In this context, it is important to effectively integrate various educational resources to further improve teaching quality. However, existing methods of constructing curriculum knowledge bases face many challenges in several aspects, such as data fusion, knowledge reasoning, and so on. To address these issues, this study conducted in-depth research and practice on the construction of curriculum knowledge bases in the educational modernization field. The main content of this study includes the data fusion and knowledge reasoning of curriculum knowledge bases and the intelligent Q&amp;A framework design of the bases in the educational modernization field. In the data fusion part, an entity alignment method based on semantic similarity was adopted, which effectively solved the problem of semantic relationship fusion between entities. In the knowledge reasoning part, the option-aware network based on attention mechanisms was used to significantly improve the accuracy and efficiency of reasoning. As part of the intelligent Q&amp;A framework design, a sequence-to-sequence machine translation model was adopted, which intelligently queried the knowledge bases successfully and provided customized teaching content, thereby optimizing the learning process. This study provides effective theoretical and practical support for promoting educational modernization.