Reviewerly/exHarmony huggingface dataset:
Strategi Guru Mengelola Kelas dalam Meningkatkan Keaktifan Belajar Siswa Kelas VIII pada Mata Pelajaran Bahasa Indonesia di SMP Islam Integral Luqman Al-Hakim Batam

This thesis discusses the teacher’s strategy for managing the classroom in increasing the learning activities of class VIII students in Indonesian subjects at the Luqman Al-Hakim Batam Integral Islamic Junior High School. This type of research is qualitative research, data collection is carried out using the methods of observasion, interviews, and documentation. This study found that the implementation of the teacher’s strategy in managing the classroom in increasing student learning activity was quite good, namely with activities that could motivate students to learn by arranging student seats, clean and tidy classroom. Meanwhile, the obstacles faced by teachers are the condition of students who are sleepy during class hours and the lask of adequate facilities and infrastructure such as books, learning tools and the state of student desks and chairs. The result of this study suggest that teachers are able to be examples and role models for students to achieve their goals, and always provide motivation to students..
Solar Hotspot Analysis using Image Processing in MATLAB

Abstract: Solar energy has proven to be an undisputed frontrunner among renewable energy sources: it is clean, environmentally responsible, and cost-effective. The utilization of solar energy as a sustainable and renewable source of power has gained significant attention in recent years. The rapid growth of solar farms and solar roofs of residential buildings has led to an increase in demand for solar modules in the global market. Therefore, the maintenance and monitoring of solar modules must rely on an effective technique for predicting and evaluating future performance. Solar hotspots, also known as solar irradiance anomalies, are areas on solar photovoltaic (PV) panels or arrays that experience excessive heating, resulting in decreased energy output and potential damage to the PV cells.
Short- and long-run relationship between financial development and economic competitiveness in WAEMU: does institutional quality matter?

Purpose This paper aims to assess the short- and long-run effects of the interaction between institutional quality and financial development (FD) on the competitiveness of the WAEMU economy over the period 2007–2018. Design/methodology/approach The methodology consisted of cross-referencing a synthetic indicator of FD with indicators of institutional quality and then estimating an auto regressive distributed lag model. Findings The results of the pooled mean group and dynamic fixed effect estimation show a positive and significant impact of this interaction on the competitiveness of the economy in the long run. In the short run, the results are quite similar to those in the long run for the direct effects but different for the crosses. Also, the analysis of country specificity shows that the results are similar to those in the short run since the interaction between FD and institutional quality (political stability and government effectiveness) negatively affects the competitiveness of Burkina Faso, Ivory Coast and Mali, and positively affects the competitiveness of Benin and Senegal. Social implications These results suggest the need for effective policies to improve the quality of institutions to enhance the mobilization of financial resources through FD to ensure the competitiveness of economies. Improving the quality of the political and institutional environment is a prerequisite for economic competitiveness. Originality/value The paper is in line with the New Institutional Economics that developed in the 1970s. This referential framework is a heterogeneous body of work that encompasses works whose common point is the determination of the role of institutions in economic coordination. Unlike previous studies, which have focused on the contribution of the interaction between institutional quality variables and FD on economic growth, this paper analyzes the effects of this interaction on economic competitiveness. It, therefore, constitutes a contribution to this literature and aims primarily to fill this gap.
An Interaction Is Worth a Thousand Words [Editor's Remarks]

One year ago, CIM published the inaugural AI-eXplained (AI-X) immersive article on IEEE Xplore. It is thrilled to witness that our call for the new form of content representation has been responded to with tremendous passion and a surge of submissions. With great efforts, CIM offers a platform for delivering Al/CI concepts, designs, and applications via a vivid and innovative means of knowledge distribution. This success cannot be achieved without the solid support of CIS and IEEE staff teams, especially CIS President Jim Keller, who is the bellwether in foreseeing the potential of immersive articles and promoting their publication. At this point of his sign-off as President, my sincere gratitude goes to Jim for his leadership and contribution to the realization of immersive articles. Besides, he has accompanied CIM readers to pull through the global difficult times with his witty and insightful President's Messages, and CIS has been continuously growing under his guidance. I heartily thank Jim and wish him the very best in his future endeavors.
An information-theoretically secure quantum multiparty private set intersection

In the current age of increasing data, Private Set Intersection (PSI) is one of the most prominent and important cryptographic building block. It enables two organizations to determine the intersection of their input sets in such a way that no other information is leaked. In real life, there are situations where more than two parties desire to compute the intersection of their private sets. Herein, we propose a generalized version of PSI, called Multi-party private set intersection (MPSI), where number of involved parties is not restricted to two. Nearly all of the existing MPSI protocols are relying on the hardness problems like integer factorization and discrete logarithm. However, these hard problems are unable to withstand the threat possessed by large scale quantum computers. Thus, there is a need of MPSI protocols which are secure from the quantum attacks and also provide long-term security. Quantum cryptography (QC) is one such tool that addresses this issue. In this work, we develop the first MPSI based on QC. The scheme put-forwarded by us attains long-term security and remains secure against quantum attacks.
MVL-Tra: Multi-view LFM signal source classification using Transformer

Classifying the sources of linear frequency modulation (LFM) signals has practical significance, as these signals are widely applied in various scenarios. However, the signals emitted by the same configured sources exhibit insignificant differences and may suffer severe degradation in low signal-to-noise ratio (SNR) conditions. Previous research failed to achieve satisfactory classification results in these two conditions. To address such challenges, we propose a signal source classification method for LFM signals using multi-view representations, based on the Transformer network, named MVL-Tra. Multi-view learning improves robustness by utilizing multiple feature sets and multi-dimensional analysis on these features. Transformer, a emerging deep neural network, enhances the distinctiveness of features between signals through capturing long-range dependencies when dealing with large-scale time-series data. In the experiment, we conducted classification on a dataset comprising 15 sets of low-SNR LFM signals. Notably, Even under the challenging condition of SNR = −5, our method achieved an accuracy of 93.78%. Th results confirm the effectiveness of using multi-view representations and Transformer for LFM signal source classification.
MAP-Elites for Genetic Programming-Based Ensemble Learning: An Interactive Approach [AI-eXplained]

Evolutionary ensemble learning is an emerging research area, and designing an appropriate quality-diversity optimization algorithm to obtain a set of effective and complementary base learners is important. However, how to maintain such a set of learners remains an open issue. This paper proposes using cosine similarity-based dimensionality reduction methods to maintain a set of effective and complementary base learners within the MAP-Elites framework for evolutionary ensemble learning. Additionally, this paper proposes a reference point synthesis strategy to address the issue of individuals being unevenly distributed in semantic space. The experimental results show that the ensemble model induced by the cosine similarity-based dimensionality reduction method outperforms the models induced by the other seven dimensionality reduction methods in both interactive examples and large-scale experiments. Moreover, reference points are shown to be helpful in improving the algorithm’s effectiveness. The main contribution of this paper is to provide an interactive approach to explore the methods and results, which is detailed in the full paper presented in IEEE Xplore.
Photovoltaic potential prediction and techno-economic analysis of China railway stations

As an infrastructure, the railway stations' roof and platform canopy have considerable space potential for deploying photovoltaic power generation systems. In order to study the feasibility of installing PV systems in railway stations, this paper analyzes the PV potential and techno-economic characteristics of China's high-grade railroad stations by combining a three-dimensional digital earth system (LSV) and PV plant calculation methods. The results show that the total installed PV capacity of Chinese high-grade railway stations, which are mainly used for passenger transportation, can reach 820 MW, and the total annual PV power generation capacity can reach 1111GWh. The self-sufficiency rate of each station is affected by the scale of the station building, the number of canopies on the passenger platform and the architectural design factors. The smaller the scale of the station building, the larger the area of the platform canopy, the smaller the overlap between the station building and the station canopy, the easier the railway station to achieve self-sustaining. The payback period is the time it takes for the total return on PV power generation to equal the investment in PV construction. 63% of the stations can realize full payback of their investment within 4–7 years under the operation strategy of full self-consumption of power generation. In addition the photovoltaic systems in railroad stations can generate considerable ecological benefits, reducing carbon dioxide emissions by 820,000 tons per year, which is about the carbon emissions of 304,000 cars in one year.
Development of a Work Environment Monitoring System for Improving HSE and Production Information Management Within a Shipyard Based on Wireless Communication

As the Fourth Industrial Revolution accelerating, countries worldwide are developing technologies to digitize and automate various industrial sectors. Building smart factories not only reduces costs through improved process productivity but also allows for preemptive identification and removal of risk factors through the practice of Health, Safety, and Environment (HSE) management, thereby reducing industrial accident risks. In this study, we visualized pressure, temperature, power, and wind speed data measured in real-time via a monitoring GUI, enabling field managers and workers to easily access related information. Through the work environment monitoring system developed in this study, it is possible to conduct economic analysis on per-unit basis, based on the digitization of production management elements and the tracking of required resources. By implementing HSE in shipyards, potential risk factors can be improved, and gas and electrical leaks can be identified, which are expected to reduce production costs.
Factors affecting accounting students’ misuse of chatgpt: an application of the fraud triangle theory

Purpose This study aims to explore the factors that contribute to student academic dishonesty through an examination of the misuse of AI language models. Using the fraud triangle theory, which posits that opportunity, rationalization and pressure are key factors for fraudulent behavior, this study investigates how these elements interact and contribute to academic dishonesty among students. Design/methodology/approach In this study, data on how accounting students used ChatGPT to cheat was acquired from 279 accounting students in Jordanian public universities over the course of two months, from January 2023 to March 2023, through previously tested and validated questionnaires. The main tool for gathering data was a questionnaire distributed online using Microsoft Forms. Findings The results show that all of the fraud triangle factors are significant determinants of student academic dishonesty and student misuse of ChatGPT. The findings of this research can be used to guide the development of technology-based preventative measures. Originality/value This study provides valuable insights into the motivations and factors that drive students to engage in academic dishonesty and sheds light on the broader issue of technology-assisted academic dishonesty and its impact on the educational system. This study’s contribution is significant, as it sheds light on a pressing issue in education and provides valuable information for educators and policymakers to address the problem and improve academic standards.
Feature Extraction Network SFPN for Edge Computing Scenarios

Aiming at the problems that the existing networks do not adapt to the image characteristics of edge scenes, do not effectively process the detail information in images and have insufficient feature fusion, this thesis proposes a feature extraction network model SFPN that can better utilize the different depths and detail semantic information of the network. Firstly, the S-soft unit is designed to process the detail information of the feature map. Secondly, this thesis proposes a new secondary fusion strategy that combines the features of the middle layer are fused with the lower and higher layers respectively, and with this, SFPN Block is proposed as an auxiliary classifier, which participates in the final prediction by weighting, fully considering the prediction information of different layers of the network. Finally, a joint loss function is introduced for training, so that the feature extraction ability of the network for targets with large scale changes and small sizes is improved, and it is more adapted to the edge computing scenario of detection requirements and improve the accuracy of feature extraction.
Accelerating the Deep Learning Recommendation System Model Inference on X86 Processors

Deep learning recommendation systems have commonly been regarded as an indispensable part of E-commerce. The exponential growth of model size requires a growing demand for hardware. In this paper, the overview of the recommendation system and three significant hardware features - Advanced Vector Extensions (AVX), Brain Floating Point (BFloat16), and Advanced Matrix Extensions (AMX) were respectively introduced. Then this paper introduces multiple effective and generic optimization methods for accelerating deep learning recommendation system models' inference performance on Intel® Xeon Scalable processors which take full advantage of the capabilities of the hardware. Developers could leverage these methods on their own recommendation systems and get significant improvements in performance and productivity.
Digital Forensic Analysis of Online Dating Applications on Android Using the Digital Forensic Research Workshop 2001 Investigation Model

One kind of internet dating service used to find a partner is Tantan and Omi. Tantan and Omi application users are required to enter and display personal data in the form of name, age, photo, origin, and gender to provide information to other users about the partner being sought. This provides an opportunity for criminals to utilize the Tantan and Omi applications to launch their crimes. Crimes can be uncovered with digital evidence obtained by conducting digital forensics on the device used. The research was performed using the Digital Forensic Research Workshop (DFRWS) investigation model with the implementation of three research scenarios. The forensic process is assisted by using the MOBILedit Forensic tool to identify and find digital evidence. The results of the digital evidence found have an accuracy of 85.7% in scenario 1 Tantan, 57.1% in scenario 2 Tantan, 0% in scenario 3 Tantan, and 100% in scenario 1 Omi, 100% scenario 2 Omi, and 0% scenario 3 Omi.
Single Image HDR Reconstruction via A Feature Modulation Network

Reconstructing HDR images from a single LDR image with noise and saturated regions is a highly challenging problem. Recent approaches have utilized cascaded network structures to address this challenge in image space. However, this approach increases the difficulty of network training and may lead to cumulative errors. To this end, we propose a feature modulation network (FMN) for image denoising and HDR generation in feature space. Our FMN introduces a simple yet effective Feature Modulation Block (FMB) that combines an Enhanced Attention Module (EAM) and a Dilated Residual Dense Block (DRDB). The EAM effectively eliminates image noise using multiple residual blocks, while the DRDB learns the details lost in saturated regions of the input image with a large receptive field. Moreover, we employ a Space Feature Transform (SFT) layer to perform spatial-wise feature modulation. Extensive experiments are conducted on publicly available datasets, and the results demonstrate that our method achieves competitive performance compared to state-of-the-art methods.
Algebraic Attack on 2-Round SMALLPRESENT-[2] Algorithm

Block cipher is a symmetric key algorithm by mapping plaintext blocks into ciphertext blocks. Block ciphers can be classified into several of structures, such as Feistel and SPN. The SMALLPRESENT-[2] algorithm is a simplified version of the PRESENT-80 algorithm using 8 bits of key and 8 bits of message input with SPN structure. In this paper, the algebraic attack is performed on the 2-round SMALLPRESENT-[2]. The attack is conducted to determine the algebraic representation of the algorithm in 8 multivariate polynomial equations. The algebraic representation of the algorithm is obtained from the s-Box used in the SMALLPRESENT-[2] algorithm. The equations are devided by implementing the encryption process using a randomly chosen plaintext under unknown key. The results showed that the higher order of the multivariate polynomial is 9 with 24 unknown variables to be solved. Due to the large number of unknown variables, the key recovery can be conducted using XL algorithm or Grobner Basis.
Performance Analysis for a Recommending System Based on Partially Observed Markov Decision Processes

Recommender systems have become an essential tool for online platforms to provide personalized recommendations to their users. Among various recommendation techniques, deep learning-based approaches have attracted significant attention due to their ability to capture complex user-item interactions and provide accurate recommendations. In this paper, we propose a new recommendation method that combines the Deep Deterministic Policy Gradient (DDPG) algorithm and Long Short-Term Memory (GRU and RNN) networks. The goal is to improve the recommendation performance in the partially observable. Markov decision process (POMDP) scenario, where user preferences and interactions with items evolve over time.
Local Search and the Evolution of World Models

An open question regarding how people develop their models of the world is how new candidates are generated for consideration out of infinitely many possibilities. We discuss the role that evolutionary mechanisms play in this process. Specifically, we argue that when it comes to developing a global world model, innovation is necessarily incremental, involving the generation and selection among random local mutations and recombinations of (parts of) one's current model. We argue that, by narrowing and guiding exploration, this feature of cognitive search is what allows human learners to discover better theories, without ever grappling directly with the problem of finding a "global optimum," or best possible world model. We suggest this aspect of cognitive processing works analogously to how blind variation and selection mechanisms drive biological evolution. We propose algorithms developed for program synthesis provide candidate mechanisms for how human minds might achieve this. We discuss objections and implications of this perspective, finally suggesting that a better process-level understanding of how humans incrementally explore compositional theory spaces can shed light on how we think, and provide explanatory traction on fundamental cognitive biases, including anchoring, probability matching, and confirmation bias.
Gabor-DCTnet: a Simple and Efficiency Method for Image Classification

To address the problem of image classification in which the intra-class variation and inter-class similarity are large due to external interference, thus increasing the difficulty of image classification, a lightweight convolutional network Gabor-DCTnet based on Gabor and DCT filters is proposed in this paper. Gabor-DCTnet cascades predefined filter sets FB <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Gabor-DCT</inf> , nonlinear and average pooling operations to make intra-class deformation and inter-class similarity more stable. First, a complex filter FB <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Gabor-DCT</inf> consisting of Gabor filters and DCT bases is constructed in the filtering layer to extract rich image features; second, the nonlinear layer performs binarized hash coding and chunked histogram operations on the filter coefficients to obtain robustness in illumination, rotation, and occlusion; finally, the chunked histogram is averaged pooling and wPCA dimensionality reduction in the pooling layer and input to the nearest neighbor classifier based on cosine distance. The experimental results show that the proposed network can effectively classify images on FERET_I, FERET_II, AR and KTH_TIPS, CUReT datasets, and the classification results are significantly better than those of lightweight networks such as PCANet, DCTNet and M-FFC.
A Proven Sentiment Annotation Guideline for Indonesian Twitter Data

This study presents a validated sentiment annotation guideline for Indonesian Twitter data, collaboratively developed with 18 experienced annotators. The guideline’s effectiveness was assessed by its application to biodiversity-related tweets, iteratively refining it based on feedback. The conclusive testing demonstrated a strong inter-annotator agreement level, evidenced by a Fleiss’ Kappa value surpassing 0.67. To assess the guideline’s effectiveness in constructing a robust sentiment analysis model, annotators labeled a dataset containing 15,000 tweets. Three classifiers trained on this dataset achieved F1-scores exceeding 0.7, highlighting the guideline’s suitability for developing sentiment analysis models. This guideline consistently provides sentiment annotations, particularly pertinent to biodiversity and Indonesian Twitter contexts, due to the similarity in sentence characteristics.