Reviewerly/exHarmony huggingface dataset:
KESIAPAN SUMBER DAYA MANUSIA DINAS PERPUSTAKAAN DAN KEARSIPAN KOTA PALEMBANG DALAM MENGHADAPI TRANSFORMASI DIGITAL

This research aims to test the readiness of human resources in facing digital transformation. The method used is quantitative, the object of research is employees of the Palembang City Library and Archives Service. The respondents in this research were 94 employees of the Palembang City Library and Archives Service, collecting data using a questionnaire. Sampling in this study used a saturated sampling technique, namely sampling carried out as a whole from the total population. The analysis technique used in this research is logistic regression, which is a type of regression analysis used to describe the relationship between independent variables and dependent variables, with the dependent variable (Y) being assumed to be binary qualitative/having two categories. The data analysis technique was carried out using SPSS version 20. Based on the results of the binary logistic regression analysis, it was found that significant factors in the readiness of human resources in facing digital transformation were age, length of work, personality and skills. Meanwhile, factors that were not found to be significant in the readiness of human resources in facing digital transformation were innovation &amp; optimism. It is hoped that this research can add information and reading references, thereby increasing knowledge about the digital transformation process.
Facial Expression Recognition Based on Deep Learning: A Review

This review paper provides a comprehensive analysis of recent advancements in Facial Expression Recognition (FER) through various deep learning models. Seven state-of-the-art models are scrutinized, each offering unique contributions to the field. The MBCC-CNN model demonstrates improved recognition rates on diverse datasets, addressing the challenges of facial expression recognition through multiple branches and cross-connected convolutional neural networks. The Deep Graph Fusion model introduces a novel approach for predicting viewer expressions from videos, showcasing superior performance on the EEV database. Multimodal emotion recognition is explored in the EEG and facial expression fusion model, achieving high accuracy on the DEAP dataset. The Spark-based LDSP-TOP descriptor, coupled with a 1-D CNN and LSTM Autoencoder, excels in capturing temporal dynamics for facial expression understanding. Vision transformers for micro-expression recognition exhibit outstanding accuracy on datasets like CASMEI, CASME-II, and SAMM. Additionally, a hierarchical deep learning model is proposed for evaluating teaching states based on facial expressions. Lastly, a visionary transformer model achieves remarkable recognition accuracy of 100% on SAMM dataset, showcasing the potential of combining convolutional and transformer architectures. This review synthesizes key findings, highlights model performances, and outlines directions for future research in FER.
Clustering Research on Islamic Empowerment Model using R

This bibliometric research was conducted to provide a more in-depth picture of the "Islamic Empowerment Model" by analyzing a number of relevant articles that have been published in the Dimensions database. A total of 233 imlia publication documents in the period 2022-2023 became the object of this research. Using bibliometric analysis methods with the help of Biblioshiny-R software, this study seeks to reveal publication trends over time, identify the most prolific authors in this field, explore journals that have a significant impact in related literature, and reveal the main themes that consistently appear in these scientific works. The results show that the number of publications has increased significantly since 2016, reaching a maximum of 52 publications in 2021 and 2022. 2011 was the year with the highest average number of citations throughout the observation period. The Journal of Social Science Research, is the top publishing journal. There are several authors associated with this journal, such as Huda N, Alhifni A, and Ahwarumi B. The next important finding is that there are 3 research clusters including, Islamic empowerment models through Islamic financial instruments, women's empowerment models in Islam, and Islamic empowerment models through pesantren education. The results of this study are expected to provide valuable insights for various parties, including researchers who want to better understand the development of Islamic empowerment literature, practitioners involved in this field, and academics who want to explore this topic in more depth.
Perception on Bitcoin: A Senti-Strength Application

Since 2008, the number of new virtual currencies, as well as the consumer base and frequency of transactions, have all increased dramatically. However, policymakers, economists and entrepreneurs face significant difficulties and opportunities as a result of monetary market turmoil. The purpose of this study is to analyze the sentiment generated from researchers' opinions through published articles related to bitcoin. The data analyzed consisted of 99 research articles indexed by Scopus in 2022. The method used is a qualitative approach with descriptive statistics from the study of bitcoin-themed literature. The results showed that neutral sentiment was the highest result with a percentage of 48% followed by negative sentiment of 23%, positive sentiment 22%, high positive sentiment 4%, and high negative sentiment 3%. Bitcoin has no centralized authority to distribute coins or keep track of who owns coins by design. As a result, creating currency and verifying transactions is much more complicated than in traditional bookkeeping systems. The diversity of sentiment results is obtained because of the pros and cons of bitcoin's existence.
Upaya Meningkatkan Hasil Belajar dengan Model Cooperative Learning Tipe Student Teams Achievement Division Kelas VII SMP Negeri 6 Rokan IV Koto

The problem in this research is the low learning outcomes of class VII students at SMP Negeri 6 Rokan IV Koto in Mathematics subjects. To improve student learning outcomes, researchers applied the Student Teams Achievement Divisions (STAD) Cooperative Learning Model in Mathematics learning in class VII of SMP Negeri 6 Rokan IV Koto. This research is classroom action research, carried out in two cycles with a total of 23 students, each cycle consisting of four stages, namely planning action implementation, observation and reflection. From the results of the first cycle of action, 16 students got a score of more than 65 or 69.6% of the 23 students with an average score of 67.3, while 7 students got a score of less than 65 or 30.4% of the first cycle. 23 students. The results of the second cycle of action showed that 21 students got a score of more than 65 or 91.1% of 23 students with an average score of 83.3, while 2 students got a score of less than 65 or 8.9% of 23 students. student. Thus it can be concluded that the application of the Student Teams Achievement Divisions (STAD) Cooperative Learning Model can improve the learning outcomes of class VII students at SMP Negeri 6 Rokan IV Koto.
Detection of sarcasm in tweets using hybrid machine learning method

&lt;p&gt;When one wants to express the contrary of what they mean, especially when insulting someone, sarcasm is used. One of the challenges associated with the control and management of content on social networking sites such as Twitter and other social media sites, is the recognition of sarcasm. Because sarcasm is purposefully conveyed through ambiguous word choice, even for humans it can be challenging to identify. Existing methods for automatically detecting sarcasm rely heavily on lexical as well as linguistic clues the majority of the time. On the other hand, these methods have not yielded a large or even a minor enhancement in terms of the correctness of the mood. The purpose of this study is to increase the accuracy of sentiment analysis by proposing a system that is both reliable and effective at identifying sarcasm. In this investigation, three different types of features are focussed: lexical, sarcastic, and context features. These feature sets are utilised in the process of categorising tweets as either sarcastic or not sarcastic. This research presents a sarcastic feature set coupled with an efficient hybrid machine learning approach, which ultimately results in improved accuracy. The results of the experiments reveal that the recommended hybrid machine learning method achieves 97.3% accuracy for sarcastic feature sets which is better when compared to existing machine learning techniques namely k-nearest neighbor, random forest, support vector machine and decision tree for the selection of the appropriate features.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;
THE INFLUENCE OF CORPORATE SOCIAL RESPONSIBILITY, GOOD CORPORATE GOVERNACE AND ENVIRONMENTAL PERFORMANCE ON FINANCIAL PERFORMANCE

The purpose of this study was to identify the influence of corporate social responsibility, good corporate governance and environmental performance on financial performance partially or simultaneously.This research uses quantitative research methods. The population in this research relies on manufacturing companies in the industrial sector listed on the Indonesia Stock Exchange for the 2017-2021 period. This study used a sampling technique that is purposive sampling in order to obtain a sample of 6 companies. Methods of data analysis using multiple linear regression analysis method. The results of this study indicate that; (1) CSR partially has no effect on financial performance, (2) Audit Committee partially has no effect on financial performance, (3) Board of Commissioners partially has no effect on financial performance, (4) Managerial ownership partially affects company financial performance, (5) Environmental Performance simultaneously influences financial performance, (6) CSR, Managerial Ownership, Audit Committee, Board of Commissioners and Environmental Performance simultaneously affect Financial Performance.
Detecting Spam E-mails with Content and Weight-based Binomial Logistic Model

Spam e-mails are continuously increasing and are a serious threats to a network and its users. Several efficient methods are available regarding this context, but still, it is evolving randomly. Considering this, the proposed approach addresses the problem of spam detection by combining traditional content-matching criteria with the modified version of the binomial logistic algorithm. The work generates seven categories for content-matching, which begins from three basic categories, namely: special words, adult content, and specific symbols and digits. The remaining four categories are derived from various possible combinations of these basic categories. The words selected for each category are carefully curated based on the human psychology of action and reaction. Then, a weight is assigned to each of the categories to signify their importance and a threshold criterion is deployed before implementing the binomial logistic algorithm, which not only increases the efficiency of the proposed algorithm but also reduces the rate of misclassification. The proposed model is tested on six separate datasets of Enron Spam Corpus, where 98.31% and 92.575% are the maximum and minimum accuracies achieved, respectively, in spam e-mail classification. The AUC_ROC scores for the entire Spam Corpus range between 0.927 and 0.983. A comparison is also carried out between the proposed algorithm and the other methods of spam detection that have logistic regression. Finally, the suggested method can adequately handle a large sample size without compromising the efficacy, which is measured using accuracy, precision, recall, F-measure, and AUC_ROC score.
Fine-grained Sentiment-enhanced Collaborative Filtering-based Hybrid Recommender System

Developing online educational platforms necessitates the incorporation of new intelligent procedures in order to improve long-term student experience. Presently, e-learning recommender systems rely on deep learning methods to recommend appropriate e-learning materials to the students based on their learner profiles. Fine-grained sentiment analysis (FSA) can be leveraged to enrich the recommender system. User-posted reviews and rating data are vital in accurately directing the student to the appropriate e-learning resources based on posted comments by comparable learners. In this work, a new e-learning recommendation system is proposed based on individualization and FSA. A hybrid framework is provided by integrating alternating least square (ALS) based collaborative filtering (CF) with FSA to generate an effective e-content recommendation named HCFSAR. ALS attempts to capture the learner’s latent factors based on their selections of interest to build the learner profile. Three FSA models based on attention mechanisms and bidirectional long short-term memory (bi-LSTM) are suggested and used to train twelve models in order to predict new ratings from learner-posted book reviews based on the extracted learner profile. HCFSAR used multiplication word embeddings for stronger corpus representation that were trained on a dataset generated for an educational context and showed a better accuracy of 93.39% for the best model entitled MHAM based ABHR-2 with multiplication (MHAAM), which performed better than other models. A tailored dataset that has been created by scraping reviews of different e-learning resources is leveraged to train different proposed models and validate against public datasets.
Extended Reality Platform for Metaverse Exhibition

This paper proposes a web-based extended reality (XR) platform integrating exhibitions based on augmented reality (AR) and virtual reality (VR). The proposed platform includes an XR cloud for managing and sharing data in XR spaces, a VR client for users, and an AR client for real-world users. The XR cloud is a web-based VR service responsible for managing objects and spatial information related to real and virtual spaces while simultaneously facilitating real-time information sharing and synchronization between AR and VR users. The VR client offers a virtual exhibition environment based on the virtual space and information for VR users. The AR client overlays virtual information over objects in a real space, allowing for intuitive interaction with them. The proposed XR platform was implemented with Oculus Quest 2 for VR and HoloLens 2 for AR and was evaluated in a small exhibition environment. In this experimental environment, VR and AR users were able to participate in the same exhibition from different spaces and share their experiences in real time.
Extreme Learning Machine Classification Method based on Twin Strategy Salp Swarm Algorithm

Extreme Learning Machines (ELM) are a type of Single Hidden Layer Feedforward Neural Network (SLFN). In recent years, they have attracted attention for their powerful approximation ability and fast learning speed. Compared with traditional neural network algorithms, ELM have the advantages of simple structure, fast learning speed, and good generalization performance. However, since the input weights and biases of ELM are randomly generated, there may be some suboptimal or unnecessary input weights and biases. In addition, ELM may require more hidden nodes, which may slow down its response to unknown test data. To address these issues, a twin strategy Salp Swarm Algorithm (TSSA) is proposed to increase the population diversity to improve the convergence speed of the algorithm through chaotic initialization, while shock inertia weights and learning paradigms are introduced into the follower position updating to enhance the stochasticity of the particles. Classification tests are conducted on six datasets using the proposed TSSA, and the experimental results show that the proposed TSSA-ELM has higher accuracy and better generalization performance than some existing ELM variants.
Entropy Error-based Convolutional Neural Network Sparse Optimization and Application

Convolutional Neural Networks (CNNs), as quintessential representatives of deep neural networks, have found widespread applications in numerous domains such as image recognition, object detection, and image generation, owing to their robust nonlinear mapping capabilities. However, the practical deployment of CNNs faces challenges such as low learning efficiency and subpar accuracy due to the intricacies in the network structure. This paper introduces the smoothing L1 regularization term atop the conventional entropy loss function, effectively enhancing both the learning efficiency and algorithmic precision of Convolutional Neural Networks. The efficacy of the improved algorithm is substantiated through numerical simulations.
PPBRFL: Privacy-Preserving Byzantine-Robust Federated Learning

Federated learning is a distributed machine learning approach that allows the neural network to be trained without exposing private user data. Despite its advantages, federated learning schemes still face two critical security challenges: user privacy disclosure and Byzantine robustness. The adversary may try to infer the private data from the trained local gradients or compromise the global model update. To tackle the above challenges, we propose PPBRFL, a privacy-preserving Byzantine-robust federated learning scheme. To resist Byzantine attacks, we design a novel Byzantine-robust aggregation method based on&#x0D; cosine similarity, which can guarantee the global model update and improve the model’s classification accuracy. Furthermore, we introduce a reward and penalty mechanism that considers users’ behavior to mitigate the impact of Byzantine users on the global model. To protect user privacy, we utilize symmetric homomorphic encryption to encrypt the users’ trained local models, which requires low computation cost while maintaining model accuracy. We conduct the experimental assessment of the performance of PPBRFL. The experimental results show that PPBRFL maintains model classification accuracy while ensuring privacy preservation and Byzantine robustness compared to traditional federated learning scheme.
ES Data Aggregation Scheme based on Personalized Local Differential Privacy

In modern society, with the rapid development of technology, research on Epidemiological Survey Data (ESD) has become increasingly crucial. As various diseases continue to evolve and spread, there is a growing need for a profound understanding of the patterns and trends of disease transmission. Epidemiological investigation, as a key method, provides fundamental data support for devising effective prevention and control strategies by tracking cases, contacts, and potential infectees. However, accompanying this progress is the issue of dealing with a large amount of privacy-sensitive data. Once these data are compromised, it may pose severe harm to individuals and society. To address this challenge, we propose a uOUE-based epidemiological survey data aggregation scheme for the collection and processing of ESD, aiming to enhance the efficiency and accuracy of data coding. We ensure the secure, efficient, and accurate aggregation processing of ESD. Through rigorous validation and comparative analysis, our algorithm complies with the requirements of local differential privacy and unbiased estimation. It demonstrates good practicality and accuracy in ESD collection, providing users with a reliable privacy protection effect.
Digital Death

This paper reflexively unpicks digital ethnographic methods employed during ongoing online fieldwork on ‘digital deaths’. To do so, this research delves into the digital afterlife, exploring the fate of online traces and social media profiles after death, and how social media has changed our relationship with death and grieving. Anthropological studies of online death and grief faced new challenges even before COVID-19 moved research projects online. These include shared vulnerabilities and the ethnographer’s position, online field sites, omnipresent online traces and posthumous personhood, and ethical algorithms and duty to the dead. By transparently detailing my research methods whilst conducting research with Facebook and Instagram users navigating loss, this article contributes an honest and extensive debate on processes, challenges, ethics, and research collaboration. Guided by visual and media anthropology, I advocate for a set of methods rooted in shared anthropology (Rouch 1995) which fosters ongoing dialogue with participants. Thus, this article offers a new perspective on digital death, rooted in collaborative storytelling and reflexive methodologies, facilitating discussions on a still-contentious subject in certain societies. Leveraging the benefits of digital ethnography’s multi-sited nature, the research widens its geographical reach and comments on the sociocultural impacts of digital death. Keywords: digital afterlife, digital death, digital ethnography, social media, reflexive ethnography, shared anthropology, grief, methodology
Correlation Avoidance in Single-Photon Detecting Quantum Random Number Generators by Dead Time Overestimation

Abstract In the case of quantum random number generators based on single photon arrivals, the physical properties of single-photon detectors, such as time-tagger clocks and dead time, influence the stochastic properties of the generated random numbers. This can lead to unwanted correlations among consecutive samples. We present a method based on extending the insensitive periods after photon detections. This method eliminates the unwanted stochastic effects at the cost of reduced generation speed. We calculate performance measures for our presented method and verify its correctness with computer simulations and measurements conducted on an experimental setup. Our algorithm has low complexity, making it convenient to implement in QRNG schemes, where the benefits of having uncorrelated output intervals exceed the disadvantages of the decreased rate.
Learning Guarantee of SDP Relaxation on Directed Stochastic Block Models

Community detection in networks has been a focal point in various scientific domains, but the study of directed networks remains relatively under-explored despite their prevalence and importance in capturing real-world systems. This work addresses this research gap by focusing on Directed Stochastic Block Models (DSBMs), a natural extension of traditional Stochastic Block Models (SBMs) to directed graphs. The inherent complexity of directionality in DSBMs makes them challenging to analyze, requiring new mathematical frameworks and computational approaches. We introduce an augmented matrix to encapsulate the directional relationships within these networks, providing a nuanced perspective for further analysis. In this work, we prove the information-theoretical threshold for exact recovery in the DSBMs and propose an SDP relaxation that can achieve this threshold, thereby contributing to the theoretical understanding of community detection in the realm of directed graphs.