Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN KOMIK DIGITAL BERBASIS CANVA TEMA INDAHNYA KEBERSAMAAN SUBTEMA KEBERSAMAAN DALAM KEBERAGAMAN

This study aims to determine the development of canva-based digital comics on the theme of the beauty of togetherness, the sub-theme of togetherness in class IV at SDN Karadenan 01 Bogor Regency and to determine the feasibility of canva-based digital comics for the learning process. The method used in this study is the ADDIE method (Analysis, Design, Development, Implementation, Evaluation). In this study, validation tests were carried on experts in the media, language, and material fields to develop and perfect Canva-based digital comis products for students. The result of the data from the validation of material experts are 80%, which means it is very feasible to implement. Media experts are obtained 86% results, which means that they were stated to be very feasible to implement and the result from linguists obtained 100% result which meant that they were very feasible to implement, as well as limited trials conducted on class IV students as many as 23 stundents obtained proper scores for used by 89,82%, which means that this canva-based digital comics product is feasible to use.
Physical reservoir computing using vertically aligned graphene/diamond photomemristors

Abstract Reservoir computing is one of the most promising machine learning architectures and could allow highly efficient, high-speed processing of time-series data. Physical reservoir computing based on various physical phenomena that exhibit complicated dynamics has been widely investigated in recent years. The present work demonstrates vertically aligned graphene/diamond junctions (photomemristors) could be employed for physical reservoir computing involving image recognition of single digits. Exceptional image recognition performance of 92% was obtained due to their complex photoconducting behaviors. This work is expected to assist in the realization of novel visual information processing systems using photomemristors that mimic human brain functions.
A Lightweight Detection Method for Remote Sensing Images and Its Energy-Efficient Accelerator on Edge Devices

Convolutional neural networks (CNNs) have been extensively employed in remote sensing image detection and have exhibited impressive performance over the past few years. However, the abovementioned networks are generally limited by their complex structures, which make them difficult to deploy with power-sensitive and resource-constrained remote sensing edge devices. To tackle this problem, this study proposes a lightweight remote sensing detection network suitable for edge devices and an energy-efficient CNN accelerator based on field-programmable gate arrays (FPGAs). First, a series of network weight reduction and optimization methods are proposed to reduce the size of the network and the difficulty of hardware deployment. Second, a high-energy-efficiency CNN accelerator is developed. The accelerator employs a reconfigurable and efficient convolutional processing engine to perform CNN computations, and hardware optimization was performed for the proposed network structure. The experimental results obtained with the Xilinx ZYNQ Z7020 show that the network achieved higher accuracy with a smaller size, and the CNN accelerator for the proposed network exhibited a throughput of 29.53 GOPS and power consumption of only 2.98 W while consuming only 113 DSPs. In comparison with relevant work, DSP efficiency at an identical level of energy consumption was increased by 1.1–2.5 times, confirming the superiority of the proposed solution and its potential for deployment with remote sensing edge devices.
Towards a More User-Friendly and Easy-to-Use Benchmark Library for Recommender Systems

In recent years, the reproducibility of recommendation models has become a severe concern in recommender systems. In light of this challenge, we have previously released a unified, comprehensive and efficient recommendation library called RecBole, attracting much attention from the research community. With the increasing number of users, we have received a number of suggestions and update requests. This motivates us to make further improvements on our library, so as to meet the user requirements and contribute to the research community. In this paper, we present a significant update of RecBole, making it more user-friendly and easy-to-use as a comprehensive benchmark library for recommendation. More specifically, the highlights of this update are summarized as: (1) we include more benchmark models and datasets, improve the benchmark framework in terms of data processing, training and evaluation, and release reproducible configurations to benchmark the recommendation models; (2) we upgrade the user friendliness of our library by providing more detailed documentation and well-organized frequently asked questions, and (3) we propose several development guidelines for the open-source library developers. These extensions make it much easier to reproduce the benchmark results and stay up-to-date with the recent advances on recommender systems. Our update is released at the link: https://github.com/RUCAIBox/RecBole.
Retracted: The Application of Nurse Stratified Management in Nursing Management

[This retracts the article DOI: 10.1155/2022/6368765.].
Klasifikasi Sentimen Masyarakat di Twitter Terhadap Ancaman Resesi Ekonomi 2023 dengan Metode Naïve Bayes Classifier

Economic recession is a condition in which the economic turnover of a country changes to slow or bad that can last for years as a result of the growth of the Gross Domestic Product (GDP) a country decreases over two decades significantly. Early warnings of the emergence of a global recession become a concern for all countries in the world, even global recessions also have a major impact on Indonesia. Such as declining public spending due to decreasing incomes, increasing unemployment, increasing poverty, and many of whom have to accept PHK or salary cuts. Economic strengthening will be important in minimizing these threats, this research needs to be done to see the response of the public to the threat of economic recession. Twitter provides a container to users to comment on the problem of the economy recession 2023 which can be used as sentiment classification information to know positive and negative comments. This research uses the naive bayes classifier algorithm. In this study there are seven main processes, namely data collection, manual labelling, processing, feature weighing (tf-idf), tresholding, naive bayes method classification, testing. From the 1408 comments data on Twitter about the threat of a 2023 economic recession. Based on the results of the classification, using 2 testing models namely data balance and non-balance data obtained the best balance data test results with the highest accuracy result with the process of classification using algortima naïve bayes classifier resulted in accurateness of 78% obtainable by using a comparison of 90% training data and 10% test data.
Penerapan Metode Naïve Bayes Classifier Pada Klasifikasi Sentimen Terhadap Anies Baswedan Sebagai Bakal Calon Presiden 2024

Anies Baswedan is a political figure who has been declared as a 2024 presidential candidate. Public opinion is a valuable source of information to analyze sentiment towards Anies Baswedan as a 2024 presidential candidate. Limited human power, emotional instability, and the length of time required are difficulties in analyzing sentiment on large amounts of data manually. Machine learning is utilized to provide convenience in sentiment classification. This research applies the Naïve Bayes Classifier method in the classification of sentiment towards Anies Baswedan as a 2024 presidential candidate. This study aims to determine the performance of the Naïve Bayes Classifier method in the classification of sentiment towards Anies Baswedan as a 2024 presidential candidate. The dataset used was 3,400 which were labeled by crowdsourcing resulting in 2,130 positive (62.65%) and 1,270 negative (37.35%). Tests were conducted using the 10-fold cross-validation and 5-fold cross-validation methods, each consisting of two experimental scenarios, namely using an unbalanced dataset and using a balanced dataset.The Naive Bayes Classifier method produces the best model in the 10-fold cross-validation test with an accuracy of 89.76%, precision of 89.92%, recall of 89.76%, and f1-score of 89.75% on the sixth fold by determining a threshold value of 13 in an experiment using a balanced dataset consisting of 1,270 positives and 1,270 negatives with an average accuracy rate of 79.88%.
Klasifikasi Sentimen Masyarakat di Twitter Terhadap Kenaikan Harga BBM dengan Metode Support Vector Machine

The increase in the price of fuel oil (BBM) in Indonesia has always been a controversy which can be seen from online media such as Twitter which has an effect on the Indonesian economy, with this problem it has a change in the impact of cost instability due to an increase in fuel prices which will also affect the rate of increase in transportation costs and the rate of inflation. The effect of these changes leads to many different public opinions so as to produce pros and cons of these changes, with the existence of the problems above, the classification process is needed. This study uses 3000 tweet data obtained from the crawling process. This study obtains an accuracy of 85% at a ratio of 90:10, for a precision value of 85%, 99% recall and 91% f1-score for negative sentiment, while 83% precision value, 19% recall, 30% f1-score for positive sentiment. Then in the 80:20 comparison experiment, an accuracy of 83% was obtained, for a precision value of 83%, a recall of 99% and an f1-score of 91% for negative sentiment, while a precision value of 82%, a recall of 16%, an f1-score of 26% for positive sentiment.
Reconstruction from Multi-view Sketches: an Inverse Rendering Approach

Reconstruction from real images has evolved very differently from reconstruction from sketches. Even though both present similarities, the latter aims to surpass the subjectivity that drawings present, increasing the task"s uncertainty and complexity. In this work, we draw inspiration from reconstruction over real multi-view images and adapt existing methods in this area to work over sketches. We leverage inverse rendering as a refinement process for 3D colored meshes while proposing modifications for the domain of drawings. Compared to previous reconstructions from sketches, our proposal recovers not only shape but color, offering an optimization system that does not require previous training. Through the results, we evaluate how different quality factors in sketches affect the reconstruction quality and report how our proposal adapts to them compared to directly applying existing inverse rendering systems for real images.
The Optimal Equations with Chinese Remainder Theorem for RSA’s Decryption Process

This research was designed to provide an idea for choosing the best two equations that can be used to finish the RSA decryption process. In general, the four strategies suggested to accelerate this procedure are competitors. Chinese Remainder Theorem (CRT) is among four rivals. The remains are improved algorithms that have been adjusted from CRT. In truth, the primary building block of these algorithms is CRT, but the sub exponent of CRT is substituted with the new value. Assuming the modulus is obtained by multiplying two prime numbers, two modular exponentiations must be performed prior to combining the results. Three factors are chosen to determine the optimal equation: modular multiplications, modular squares, and modular inverses. In general, the proposed method is always the winner since the optimal equation is selected from among four methods. The testing findings show that the proposed technique is consistently 10-30% faster than CRT.
Pemodelan Klasifikasi Untuk Menentukan Penyakit Diabetes dengan Faktor Penyebab Menggunakan Decision Tree C4.5 Pada Wanita

Diabetes is closely related to the pancreas, where the pancreas produces the natural hormone insulin, but its function is problematic which causes an increase in blood sugar levels in the body. Rising blood pressure can affect organ function in damaging the function of organs in a person's body such as the kidneys, heart and brain. Where makes a person have a history of diabetes. Diabetes that attacks adults can be prevented through exercise and a regular and healthy diet. According to the International Diabetes Federation (IDF) organization, it is estimated that at least 19.5 million Indonesian people between the ages of 20 and 79 will suffer from diabetes in 2021. China is in first place with diabetes with 140.9 million people. India is next in line with the number of people with diabetes of 74.2 million people. Therefore, early diagnosis is very important because it aims to reduce diabetes and diabetes complications in the future. It is necessary to collect data on patients with diabetes who are expected to be able to do prevention. Therefore applying classification techniques with data mining with the C4.5 algorithm. Where the classification can achieve better accuracy. Algorithm C4.5 is generally used in determining the nodes of a decision tree. Based on the test results, the accuracy is 76.67 percent, the precision is 72 percent, and the recall is 41.67 percent.
Detection of Handwriting Characteristics using Convolutional Neural Networks

This paper presents an application based on image processing techniques that use deep neural networks in the field of psychology. The approach proposed in the paper automates the well-known methods of determining the main personality traits using graphological analysis (handwriting analysis). The study is based on three characteristics of handwriting: baseline, word slant and writing pressure. To achieve this goal, three VGG-16 Convolutional Neural Networks were trained using a database of handwriting samples. The results obtained are promising, with an accuracy between 78% and 92%. Also, the image processing speed (after training the network) is very high with values around 60 sec.
The Equivalence Problems Produced by Machine Translation on A Literary Text: A Study on The Indonesian Translation of Harry Potter: The Order of Phoenix

Google Translate (GT) is a machine translator (MT) this is powered by neural machine technology (NMT) which can produce generally fluent textual translation in more than 100 languages with 60% accuracy. Studies have been done to measure GT ability in translating texts and the quality of its translation products. The results showed that GT’s products are generally satisfying but they have inadequacies at some level of translation aspects. This study aims at finding the problems of equivalence that resulted from the process translating a Harry Potter novel, a literary work that is rich with cultural words and complex sentences, using GT from the English language into the Indonesian language. Using a descriptive qualitative method, the current study examines the problem of equivalence based on Mona Baker’s theory to categorize the translation errors found in the GT output text. This study showed that literary texts are still problematic for GT in terms of words that two languages do not share and sentences that contain several points of view. This paper suggest that GT has to update its database of Indonesian lexicons and any MT output has to go post-editing process in order to ensure its readability and naturalness to its target readers. The implication of this study emphasizes the need to concentrate on translation training programs in the post-editing work.
SIGNAL DISCRIMINATION IN CZTS GAMMA DETECTORS USING SELF-SUPERVISED DEEP LEARNING MODELS

on a gradient-descent learning. The other algorithm maximizes a criterion which represents a kind of ratio of the likelihood of a positive example to the likelihood of the total example, using a multiplicative type of parameter updating which is more efficient in actual computation time than the additive type one. We compare our two methods with two conventional methods on a type of cross-validation of actual motif classification experiments. Experimental results show that in terms of the average number of classification errors, our two methods out-perform the two conventional algorithms. 14 refs., 4 figs., 1 tab.
Optical transmittance based store and forward routing in satellite networks

Quantum computing will play a crucial part in our security infrastructure for the coming years. Quantum networks can consist of direct optical fiber or free-space links. With the use of satellite channels, we can create a quantum network with higher coverage than using optical fibers where the distances are limited due to the properties of the fiber. One of the highest drivers of cost for satellite networks, apart from the cost of the technology needed for such systems, are the costs of launching and maintaining said satellites. By minimizing the satellites needed for a well-functioning quantum network, we can decrease said network’s cost, thus enabling a cheaper quantum internet. In this paper, we present an optical transmittance-based routing algorithm with which it is possible to conduct successful quantum entanglement transfer between terrestrial nodes.
Corporate Social Responsibility and Financial Performance of Deposit Money Banks in Nigeria

This study examines the effect of CSR on financial performance in the Nigerian banking industry. Thirteen banks were studied. Annual reports and financial statements of the banks for the period of ten years after recapitalisation (2005-2014) were analysed. The banks’ financial performance indices—ROA, ROE, ROD—and PAT as well as CSR expenditure were measured and analysed. The data were analysed using regression analyses of STATA version 13. The results of the overall model of the multiple regression, indicating the simultaneous effect of Banks’ financial performance on CSR expenditure, the findings indicated that previous financial performance had significant positive effect on current CSR expenditure at p-value=0.0000. However, when the financial performance indices were individually analysed, the results were mixed. For example, previous ROA and ROD reveals negative effect at -4.0535(0.080) -3.655622(0.090) and -2.990002(0.169) and -9.7102(0.000), - 7.648551(0.000) and -8.77916(0.001), respectively. However, previous ROE and PAT revealed regression coefficients and p-values of 0.083(0.022), 0.062(0.070) and 0.038(0.281) and 0.258(0.000), 0.213(0.000), and 0.162(0.004) respectively, both indicated significant positive effect on CSR expenditure. In the second model of simple linear regression, the results revealed the existence of strong positive effects between the investigated variables, the p-values were thus, 0.002, 0.000, 0.023 and 0.000 for ROA, ROE, ROD and PAT, respectively, indicating that previous CSR expenditure has a significant positive effect on current financial performance in the Nigerian banking sector. Based on these results, the study, therefore, concluded that there is positive relationship between banks’ CSR activities and their financial performance. Thus, banks should be more committed to CSR for better financial performance and for social benefit of the society and; should diversify CSR programs.
Psychophysiological effects of evaluative language use on Twitter complaints and compliments

Abstract This article explores the role of evaluative language in the identification of emotions in–and psychophysiological responses to–Twitter complaints and compliments by the readers of these messages. Three hypotheses were tested in this research. First, in line with recent experimental work in French, we expected the presence of negative evaluative language in complaints to increase perceived dissatisfaction, impoliteness, and offensiveness by the reader. Second, assuming the negativity bias hypothesis, stronger psychophysiological responses should be found in complaints compared to compliments. Third, readers’ psychophysiological responses should be stronger for complaints and compliments including evaluative language. To test these hypotheses, we used a reading task involving cardiovascular reactivity measurements and a questionnaire. We found that perceived customer dissatisfaction, impoliteness and offensiveness were higher in complaints with vs. without evaluative language. We did not find an effect of the negativity bias on cardiovascular reactivity. Rather, compliments with evaluative language elicited larger cardiac slowing compared to complaints (with or without evaluative language) and compliments without evaluative language. As the stimuli is our study concern a railway company (which is mostly the target of criticism and complaints on Twitter), participants may have reacted more to the sort of feedback they would not expect the company to receive. Future research will be necessary to establish whether our findings still hold in the case of companies that achieve a better balance between negative and positive feedback.
Quantum-Inspired Vehicle Routing Scheme for Rebalancing in Bike Sharing Systems

Bike sharing systems (BSSs) are gaining popularity worldwide, and with the rise of global warming, they have become even more critical for reducing greenhouse gas emissions in cities. One of the key operations of a BSS is rebalancing, which involves maintaining the number of bikes at each station to a target value by arranging vehicle loading and unloading operations. This paper proposes a quantum-inspired vehicle routing scheme to solve the rebalancing problem in BSSs. The problem is modeled as a variant of the Traveling Salesman Problem (TSP), which is an NP-hard problem that is computationally difficult to solve. The proposed approach formulates the problem as a Quadratic Unconstrained Binary Optimization (QUBO) model, which can be solved using a digital annealer. The proposed scheme’s performance is evaluated on the Fujitsu digital annealer and compared with the proposed greedy algorithm. The experimental results show that the proposed approach outperforms the greedy algorithm in finding a better feasible solution.
The Housing Recommendation System Uses Multi-Criteria Decision-Making Methods

Economic and population growth, increasing urbanization, changing habits, new welfare requirements, and lower interest rates have led to increased demand for housing in cities. However, housing conditions in many cities are slightly alarming, while housing is a primary need for the community. Selecting housing for low-income people (LIP) that meets the criteria required by LIP is not an easy task. Because most of the decisions people made did not utilize detailed information. Therefore, a recommendation system for LIP is required. This study aims to develop the housing selection recommendation system for LIP that best suits their wishes. This study integrated two multi-criteria decision-making (MCDM) methods: the Best Worst (BW) method, which has fewer pairwise comparisons compared to other MCDM methods for selecting criteria and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method for determining housing recommendations for LIP according to their wishes. Based on the analysis results, ten criteria dominate the housing selection for LIP sequentially: Location, Land Size, Down Payment, Public Facilities, Price, Booking Fee, Home Design, House Specifications, House Quality, and Home Ownership Credit. Furthermore, the sensitivity analysis results showed that the robustness score of this approach was high. The model could recommend housing for LIP that best suits their wishes.