Reviewerly/exHarmony huggingface dataset:
Hubungan literasi informasi dengan self-directed learning mahasiswa Fakultas Ilmu Komunikasi

The creation of a thesis, which is the outcome of student research, is one of the requirements for graduation in higher education. In the thesis completion process, students are required to have adequate information literacy and self-directed learning skills. Students of Fikom Unpad, class of 2016, were introduced to the concept of information literacy during the new student orientation and in the TPB (Preparation Stage for Learning) program in the KBLI (Information Literacy Learning Activities) course. This research aims to identify the relationship between information literacy and self-directed learning among Fikom Unpad students of the 2016 class in the thesis preparation process. The research method used is quantitative, utilizing Chi-square and Pearson Product Moment analysis. The theoretical foundation refers to the ACRL (Association of College, Research, and Library) standards of 2000, covering aspects of information needs, information search strategies, information evaluation, information usage, and ethics and law in information use. This study involved 209 students of Fikom Unpad, class of 2016, with a sample size of 136 individuals. The results show a significant relationship between information literacy (X) and self-directed learning (Y), with a high correlation between sub-variable X (information needs, information search strategies, and information usage) and variable Y. Meanwhile, other sub-variables of X (information evaluation, ethics, and the law of information use) have a moderate relationship with variable Y. It was found that students' motivation and self-directed learning ability can increase with improved information literacy skills, considering the interrelation between the indicators of information literacy and self-directed learning. The higher the level of students' information literacy, the better their self-directed learning abilities.
Efektivitas Metode AMT (Achievement Motivation Training) guna Meningkatkan Perilaku Asertif Siswa SMP Muhammadiyah 04 Singosari

Passive behavior in learning occurs a lot among Indonesian students, based on the results of pre-research conducted by researchers by interviewing teachers and also students of Junior High School Muhammadiyah 04 Singosari during the pre-research showed that 80% of students were less assertive. The purpose of this research is to find out whether the AMT (Achievement Motivation Training) method can increase students' assertive behavior. This research is included in the experimental research with pretest-posttest control group design experimental design. The population in this study was grade 9 Junior High School Muhammadiyah 04 Singosari, and the samples taken in this study totaled 37 students and used a sampling technique with saturated samples. Analysis of the results of the study using the normality test and T-test using SPSS. Based on the results of data collection, it was found that the normality test was 0.200 &gt; 0.05 so the pre-test and post-test values were normally distributed. The results of the T-test were 0.31 &gt; 0.05, which means that there were differences in the post-test results of the two groups that were given treatment and were not given treatment. So the AMT method is effective in increasing assertive behavior.
Proses digitalisasi karya ilmiah lama di Perpustakaan Fakultas Pertanian Universitas Padjadjaran

Because the Padjadjaran University Faculty of Agriculture Library has so many scholarly books, there is a mountain of accumulation inside of it. Science work is damp, moldy, and dirty. Furthermore, it is very challenging to find scientific articles later due to the mountains of them. The University of Padjadjaran Library's digitalization initiatives are predicated on this understanding. Examining the process of digitizing historical scientific articles (published prior to 2016) in the University of Padjadjaran's Faculty of Agriculture Library is the aim of this study. The research methodology used is a case study methodology using a qualitative approach. In order to gather data, observations and interviews are conducted. Interviews were conducted with the coordinator of digitization activities at the Library of the Faculty of Agriculture, University of Padjadjaran, digitization staff, and students who had done digitalization internships. Data analysis uses data reduction techniques, data presentation, and verification. The study's findings indicated that this digitizing process had previously been done, halted, and then continued till the present. The first step involves scanning; the second involves editing the results of the scanning process, such as adding a watermark or a password, but this step has not yet been completed; the three uploading procedures are uploaded to the Unpad Faculty of Agriculture Library's Google Drive. The last phase involves scanning and destroying the physical scientific works; nevertheless, official protocols have not been followed in this process. The study's findings demonstrate that while the digitization efforts undertaken are generally excellent, crucial information pertaining to particular SOPs at every stage of the process is still lacking.
Kegiatan preservasi preventif dan kuratif koleksi di Perpustakaan Universitas Komputer Indonesia

Collection preservation is a crucial aspect of a library system. A higher education library called the University of Computer Indonesia (Unikom) Library uses preservation to make sure that users can access collections in the future. This study aims to examine the collection preservation activities at the Unikom Library, focusing on the analysis of preventive and curative preservation activities. This research uses a qualitative descriptive method. The findings show that preventive preservation activities at the Unikom Library include educating users, controlling room temperature and humidity, keeping collections away from direct sunlight, installing naphthalene balls on bookshelves, and setting up CCTV, flap barriers, fire extinguishers, smoke detectors, and sprinklers. Meanwhile, curative activities include covering library materials, fumigation by third parties, regular cleaning of rooms and collections, binding library materials, replacing damaged covers or pages of books by third parties, and media conversion (digitization) of thesis collections done using a scanner through the scanning process. This study concludes that both preventive and curative preservation activities at the Unikom Library have been carried out effectively.
Partisipasi Pemuda pada Perjuangan PRRI dalam Novel Bergolak: Derita Anak Negeri karya Armini Arbain dan Ronidin

This research examines social reflection in the novel Bergolak: Derita Anak Negeri by Armini Arbain and Ronidin, focusing on the forms and motives of youth participation in the PRRI events (1958-1961). The descriptive qualitative method and the Sociology of Literature approach are used to analyze the text of the novel. Data were analyzed by data reduction, classification of forms and motives of participation, and review of social reality with Ningrum's theory. The results showed that the youth and students, represented by Bahar, Sarman, and Karman, joined PRRI for various reasons. Their forms of participation included becoming spies, soldiers, and guerrillas. This participation comes without coercion, and the motives vary between characters. The social reality depicted in the novel is in line with the PRRI events written in historical documents. In conclusion, the novel Bergolak: Derita Anak Negeri presents a strong social reflection of the PRRI event through the forms and motives of youth participation.
Industry 4.0 Implementation Drivers in Manufacturing Companies in Algeria: A Structural Analysis via ISM Method

The new industrial era is characterised by an incredible speed of development, in addition to fierce competition, which prompts companies to quickly adopt Industry 4.0 to maintain their position in the future industrial market. This paper aims to identify the main drivers that facilitate the implementation of Industry 4.0 in manufacturing companies in Algeria. The Interpretative Structural Modelling (ISM) method and the Cross-Impact Matrix Multiplication Applied to a Ranking (MICMAC) method were used. The ISM-MICMAC method aims to determine interrelationships and levels of influence between different factors. This allows the identification of the key drivers for the success of Industry 4.0 implementation in manufacturing companies. The results showed that awareness of the concept of Industry 4.0, research and development, support and vision of senior management, and society acceptance of the technology, in addition to cooperation and openness, are the most important drivers that contribute to the success of the implementation of Industry 4.0. Moreover, improving production, maintenance efficiency, and improving mass customisation also play a crucial role in the adoption of Industry 4.0 in manufacturing companies.
Hyperconnectivity and Its Discontents. By Rogers Brubaker. Cambridge: Polity Press, 2022. 288p. $69.95 cloth, $24.95 paper. - Political Theory of the Digital Age: Where Artificial Intelligence Might Take Us. By Mathias Risse. Cambridge: Cambridge University Press, 2023. 400p. $39.99 paper.

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Online presentation of the digital reconstruction process of a megalithic tomb : “3Duewelsteene”

A recommendation of: Tharandt, Louise 3Duewelsteene - A website for the 3D visualization of the megalithic passage grave Düwelsteene near Heiden in Westphalia, Germany https://doi.org/10.5281/zenodo.7948379
Train Distance Estimation for Virtual Coupling Based on Monocular Vision

By precisely controlling the distance between two train sets, virtual coupling (VC) enables flexible coupling and decoupling in urban rail transit. However, relying on train-to-train communication for obtaining the train distance can pose a safety risk in case of communication malfunctions. In this paper, a distance-estimation framework based on monocular vision is proposed. First, key structure features of the target train are extracted by an object-detection neural network, whose strategies include an additional detection head in the feature pyramid, labeling of object neighbor areas, and semantic filtering, which are utilized to improve the detection performance for small objects. Then, an optimization process based on multiple key structure features is implemented to estimate the distance between the two train sets in VC. For the validation and evaluation of the proposed framework, experiments were implemented on Beijing Subway Line 11. The results show that for train sets with distances between 20 m and 100 m, the proposed framework can achieve a distance estimation with an absolute error that is lower than 1 m and a relative error that is lower than 1.5%, which can be a reliable backup for communication-based VC operations.
Immovable Cultural Relics Preservation Through 3D Reconstruction Using NeRF

Urban and rural development poses significant threats to immovable cultural relics, leading to their degradation and potential extinction. To address this issue, 3D reconstruction has emerged as an effective preservation method. However, traditional radar-based techniques require close scanning and extensive manual intervention, limiting their efficiency. This paper explores the application of neural radiance fields (NeRF) for visually reconstructing immovable cultural relics. NeRF employs a fully connected non-convolutional neural network to generate high-quality 3D models from 2D images. The study discusses NeRF’s sampling strategy, positional encoding, field architecture, and rendering process. Experimental investigations evaluate the impact of Fourier parameters on reconstruction quality. A case study reconstructing Tianhou Temple using NeRF is presented, comparing it with ContextCapture software. All results demonstrate that NeRF offers an efficient, and cost-saving solution for reconstructing and preserving immovable cultural relics, providing accurate and realistic models with superior structural integrity.
Research on Automatic Recognition of Rhetorical Questions’ Types in Modern Chinese Texts

Effective social dialogue patterns must understand rhetorical means of expression in a broad sense. Rhetorical questioning is a commonly used rhetorical expression that typically employs an interrogative form to convey a negative function. As the analysis progresses, we find that there is a continuum from interrogative to rhetorical question, with the degree of questioning decreasing and the degree of negation increasing. The rhetorical question allows for a response, and the form of the response reflects the addressee’s understanding of the degree of negation. The article manually marked 1002 rhetorical questions by experts and divided them into four categories: Awakening, Questioning, Discovering, and Constantly Changing. By comparing CNN, RNN, Transformer, FastText, Baidu PaddlePaddle model, and CNN with FastText composite model, it was found that CNN alone can achieve an accuracy of 53.12%, with good prediction performance, and performs well in various types of rhetorical questions. Therefore, CNN can be applied in natural language processing of rhetorical questions in fields such as sentiment analysis, translation, and automatic writing.
Research and Application of Data Mining Algorithms in Power Marketing

With the continuous advancement of smart grid construction and the integration of more and more distributed power sources for new energy generation into the grid, higher requirements have been put forward for power marketing information management. This article takes the typical application of big data technology in power marketing as an example, analyzes the reasons for the problems faced by specific power enterprises in marketing, and with the aim of solving the problems, refines the problems into multiple information needs of power marketing. Finally, compression testing and response testing were conducted, and the system experienced no more than 1.1 interruptions and no more than 0.5 seconds of delay during the testing process.
A New Method for Improving the Accuracy of Word Segmentation in Modern Chinese Texts

Chinese does not adopt the form of word segmentation in writing like English. The role of words in NLP is enormous and can have a significant impact on subsequent tasks. Presently, NLP research in China mainly focuses on modern Chinese, leaving a gap in studying the pre modern Chinese stage during the late Qing Dynasty to early Republic. Many texts from this time were in traditional paper books with varying preservation conditions, making it hard to obtain a representative dataset, limiting in-depth research. Most texts haven’t undergone digital processing, lacking segmentation or annotation. This poses great challenges for researchers. Researchers must design word segmentation algorithms from scratch, increasing difficulty and workload. By using N-gram to extract bigram and trigram fragments, and combining with 100 Year Chinese New Words and Phrases Dictionary and other books, this paper constructs a Chinese wordlist at the end of the Qing Dynasty to the beginning of the Republic of China. 10000 sentences were randomly selected from the corpus constructed in this article and manually segmented and annotated. The experimental results show that adding the wordlist constructed in this article has indeed improved the accuracy of word segmentation for most of these 10000 sentences. Specifically, the three software (CoreNLP, FudanNLP, and Jieba) show a certain degree of performance improvement when using the wordlist. For HanLP, the use of wordlist in this set of data did not bring significant performance improvement, or even a slight decline to some extent.
PathoGraph: An Attention-Based Graph Neural Network Capable of Prognostication Based on CD276 Labelling of Malignant Glioma Cells

Computerized methods have been developed that allow quantitative morphological analyses of whole slide images (WSIs), e.g., of immunohistochemical stains. The latter are attractive because they can provide high-resolution data on the distribution of proteins in tissue. However, many immunohistochemical results are complex because the protein of interest occurs in multiple locations (in different cells and also extracellularly). We have recently established an artificial intelligence framework, PathoFusion which utilises a bifocal convolutional neural network (BCNN) model for detecting and counting arbitrarily definable morphological structures. We have now complemented this model by adding an attention-based graph neural network (abGCN) for the advanced analysis and automated interpretation of such data. Classical convolutional neural network (CNN) models suffer from limitations when handling global information. In contrast, our abGCN is capable of creating a graph representation of cellular detail from entire WSIs. This abGCN method combines attention learning with visualisation techniques that pinpoint the location of informative cells and highlight cell-cell interactions. We have analysed cellular labelling for CD276, a protein of great interest in cancer immunology and a potential marker of malignant glioma cells/putative glioma stem cells (GSCs). We are especially interested in the relationship between CD276 expression and prognosis. The graphs permit predicting individual patient survival on the basis of GSC community features. Our experiments lay a foundation for the use of the BCNN-abGCN tool chain in automated diagnostic prognostication using immunohistochemically labelled histological slides, but the method is essentially generic and potentially a widely usable tool in medical research and AI based healthcare applications.
Enhancing Privacy in the Internet of Vehicles via Hyperelliptic Curve Cryptography

The Internet of Things (IoT) is a technological paradigm that has gained significant momentum the last decade and, among other features, enables the development of intelligent and interoperable device networks. In this respect, it has triggered the creation and evolution of vehicular ad-hoc networks (VANETs), which are initially implemented in order to guarantee the safety of drivers and the avoidance of traffic accidents. The drawback is that this fast evolution comes with serious concerns in terms of the privacy of users, while the population of attackers or entities that try to eavesdrop and intercept information has significantly increased. This imposes a serious risk for drivers moving across a Smart City. The research presented in this paper aims to evaluate privacy protection mechanisms in VANET environments, based on the efficiency and security level they ensure, considering the fact that VANETs provide limited resources to users/drivers. Moreover, the usage of elliptic curve cryptography in reduced resources environments is discussed. Finally, this paper compares the performance of three cryptographic algorithms, elliptic curve cryptography (ECC), hyperelliptic curve cryptography genus 2 (HECC-2) and HECC genus 3 (HECC-3), employed for an efficient authentication and safe message transmission mechanism in VANETs, aimed at reaching conclusions related to the implementation of each cryptographic scheme in this specific application area. The evaluation results indicate that ECC supersedes HECC-2 and HECC-3 in most metrics. However, HECC-2 and HECC-3 demonstrate better responses than ECC does in selected energy metrics. Overall, it is observed that HECC algorithms are not yet mature enough to compete with ECC. This is due to the fact that the research community has not sufficiently progressed toward the optimization of HECC, and moreover, HECC builds on quite complex mathematics. There are indications, however, that once HECC curves are indeed optimized, HECC will outperform ECC in speed as well as in other metrics, sinceHECC-2 and HECC-3 use a significantly smaller key size with the same level of security as that of ECC.
Evaluation and Forecasting Models for GGDP

The evaluation and prediction of GGDP has important implications for global development. We derived positive and negative factors affecting climate change through indicator analysis. We then also developed a grey prediction model and a regression analysis model to predict global climate change. The analysis of the results shows that GGDP benefits from a reduction in carbon emissions of 1791.67665767 Mt C02/year. In addition, we used a quadratic non-linear regression model to analyze the environmental impact of the specific use of GGDP. The model proposed in this paper facilitates the evaluation and prediction of GGDP.
Detection of Suspicious Front Yard-View Burglary Aided with Moving Service

This work is devoted to the implementation and evaluation of a front-yard human abnormal/crime activity detection system. This system identifies abnormal/crime activities (burglary emptying site with moving service) in the front yard and gives an early warning message in time. In this way, the system realizes the maximum protection of the owner’s life and property. The system combines video surveillance systems and computer vision technology to detect abnormal human behavior in videos. Based on the human action understanding of burglary emptying sites with moving services in the real world, this paper designs an abnormal behavior detection system in the front yard scene. The system mainly uses YOLO5 and OpenCV for computer vision-related design. First, YOLO is used to identify the objects in the video scene, and then the OpenCV color comparison algorithm is used to track the relevant people in the scene. The activity relationship between people, vehicles, and buildings in the scene is combined to evaluate whether abnormal behavior occurs.
A Weakly Supervised Hybrid Lightweight Network for Efficient Crowd Counting

Crowd-counting networks have become the mainstream method to deploy crowd-counting techniques on resource-constrained devices. Significant progress has been made in this field, with many outstanding lightweight models being proposed successively. However, challenges like scare-variation, global feature extraction, and fine-grained head annotation requirements still exist in relevant tasks, necessitating further improvement. In this article, we propose a weakly supervised hybrid lightweight crowd-counting network that integrates the initial layers of GhostNet as the backbone to efficiently extract local features and enrich intermediate features. The incorporation of a modified Swin-Transformer block addresses the need for effective global context information. A Pyramid Pooling Aggregation Module handles the inherent scale variation problem in crowd-counting tasks in a more computation-efficient way. This module, along with the cross-attention module, serves as bridges to promote the feature information flow between local features and global context information. Finally, a simplified regressor module is designed to allow the proposed model with weakly supervised guidance for training to avoid precise location-level annotations, noting that the omission of density map generation makes the proposed network more lightweight. Our results on the UCF-QNRF dataset indicate our model is 8.73% and 12.17% more accurate on MAE and MSE metrics, respectively, than the second-best ARNet, in which the parameters decrease by 4.52%. On the ShanghaiTech A dataset, MAE and MSE drop 1.5% and 3.2%, respectively, compared to the second-best PDDNet. The experimental results for accuracy and inference speed evaluation on some mainstream datasets validate the effective design principle of our model.