Reviewerly/exHarmony huggingface dataset:
Nature-Guided Cognitive Evolution for Predicting Dissolved Oxygen  Concentrations in North Temperate Lakes

Authors:
raw_author_name: Yu, Runlong
raw_author_name: Zhu, Peijun
raw_author_name: Hanson, Paul C.
raw_author_name: Xie, Yiqun
raw_author_name: Jia, Xiaowei

Abstract: Predicting dissolved oxygen (DO) concentrations in north temperate lakes requires a comprehensive study of phenological patterns across various ecosystems, which highlights the significance of selecting phenological features and feature interactions. Process-based models are limited by partial process knowledge or oversimplified feature representations, while machine learning models face challenges in efficiently selecting relevant feature interactions for different lake types and tasks, especially under the infrequent nature of DO data collection. In this paper, we propose a Nature-Guided Cognitive Evolution (NGCE) strategy, which represents a multi-level fusion of adaptive learning with natural processes. Specifically, we utilize metabolic process-based models to generate simulated DO labels. Using these simulated labels, we implement a multi-population cognitive evolutionary search, where models, mirroring natural organisms, adaptively evolve to select relevant feature interactions within populations for different lake types and tasks. These models are not only capable of undergoing crossover and mutation mechanisms within intra-populations but also, albeit infrequently, engage in inter-population crossover. The second stage involves refining these models by retraining them with real observed labels. We have tested the performance of our NGCE strategy in predicting daily DO concentrations across a wide range of lakes in the Midwest, USA. These lakes, varying in size, depth, and trophic status, represent a broad spectrum of north temperate lakes. Our findings demonstrate that NGCE not only produces accurate predictions with few observed labels but also, through gene maps of models, reveals sophisticated phenological patterns of different lakes.
Enhancing Efficiency in Sparse Models with Sparser Selection

Authors:
raw_author_name: Qi, Shiyi
raw_author_name: Gu, Wenchao
raw_author_name: Wang, Chaozheng
raw_author_name: Gao, Cuiyun
raw_author_name: Xu, Zenglin

Abstract: Sparse models, including sparse Mixture-of-Experts (MoE) models, have emerged as an effective approach for scaling Transformer models. However, they often suffer from computational inefficiency since a significant number of parameters are unnecessarily involved in computations via multiplying values by zero or low activation values. To address this issue, we present \tool, a novel MoE designed to enhance both the efficacy and efficiency of sparse MoE models. \tool leverages small experts and a threshold-based router to enable tokens to selectively engage only essential parameters. Our extensive experiments on language modeling and machine translation tasks demonstrate that \tool can enhance model performance while decreasing the computation load at MoE layers by over 50\% without sacrificing performance. Furthermore, we present the versatility of \tool by applying it to dense models, enabling sparse computation during inference. We provide a comprehensive analysis and make our code available at https://anonymous.4open.science/r/XMoE.
Pembuatan Website untuk Jendela Informasi dan Publikasi Pemerintahan Desa Cijengkol – Serangpanjang

Authors:
raw_author_name: Sulistiawati Sulistiawati
raw_affiliation_string: Universitas Bhayangkara Jakarta Raya

Abstract: The public service information system is a variety of storage operations and management of communication information mechanisms from organizers to public addresses and vice versa as well as in direct word-of-mouth communication. The study in this research uses the theory of CMC (Computer Mediated Communication), as a format that is applied in the form of reading-based communication similar to document messages. The application method in community service is contained in the creation of the Cijengkol Village Website by utilizing the Wix platform as the basis for building the Cijengkol Village government Website with various stages associated with the Waterfall method. In addition, the initial phase of making this Website is by conducting discussions, creating details of the content, collecting databases, and designing the Website concept. The result of this research is that the Website has been handed over to the Cijengkol Village Government. This update can be seen from the profile photos of all village officials, surveys and documentation of some locations in Cijengkol Village, to ensure the completeness of administrative data. This handover is accompanied by knowledge transfer, especially to operators that will managing the Website.
Pengenalan dan Pembelajaran Literasi Berbasis Media Digital Mengenai Beretika Media Sosial di SDN Pasir Luhur

Authors:
raw_author_name: Sutrisno Sutrisno
raw_affiliation_string: Universitas Bhayangkara Jakarta Raya

Abstract: In all-digital era like this, digital literacy is something that must be done in order to keep abreast of the fast technological movements but also to avoid all things that are not desirable in the vast digital media. This journal article aims to be able to find out and provide an understanding of digital literacy regarding the use of Microsoft Word and how to be ethical on social media aimed at elementary school children who are auspices of SDN Pasirluhur to be able to avoid the threat of crime that is rife in the digital world, For example, the spread of fake news on social media and many others. The implementation method used in this article is the stage of monitoring and situation analysis, survey and location determination, learning and socialization with material presentation, as well as the evaluation stage. In activities regarding digital literacy there are several main discussions such as the meaning of social media, the functions and uses of the positive and negative impacts of social media, as well as how to be ethical and deal with fake news on social media.
Hybrid Deep Learning Model for Earthquake Time Prediction

Authors:
raw_author_name: Anıl Utku
raw_affiliation_string: MUNZUR ÜNİVERSİTESİ
raw_author_name: M. Ali Akcayol
raw_affiliation_string: GAZI UNIVERSITY

Abstract: Earthquakes are one of the most dangerous natural disasters that have constantly threatened humanity in the last decade. Therefore, it is extremely important to take preventive measures against earthquakes. Time estimation in these dangerous events is becoming more specific, especially in order to minimize the damage caused by earthquakes. In this study, a hybrid deep learning model is proposed to predict the time of the next earthquake to potentially occur. The developed CNN+GRU model was compared with RF, ARIMA, CNN and GRU. These models were tested using an earthquake dataset. Experimental results show that the CNN+GRU model performs better than others according to MSE, RMSE, MAE and MAPE metrics. This study highlights the importance of predicting earthquakes, providing a way to help take more effective precautions against earthquakes and potentially minimize loss of life and material damage. This study should be considered an important step in the methods used to predict future earthquakes and supports efforts to reduce earthquake risks.
Analysis of Consistency in Students' Understanding of Multi-Representations in the Context of Determining One-Dimensional Kinematics Distance

Authors:
raw_author_name: Muhammad Reyza Arief Taqwa
raw_affiliation_string: Universitas Negeri Malang

Abstract: A structured and comprehensive understanding of physics concepts is crucial to physics learning. Such proficiency is characterized by the consistency in students' thought processes across multiple representations. This research aims to ascertain the consistency in the thought processes of first-year students when determining distance in multiple representations. This study employed a descriptive research design with a quantitative-qualitative approach, involving 77 students as research subjects. The participants are divided into classes: Class H with 41 students and Class I with 36 students. Data analysis techniques included quantitative descriptive analysis and Huberman and Miles' analysis. The research instrument consisted of 28 reasoned multiple-choice items that have undergone validation. The findings indicate that only 8 (10.39%) students possess a precise and consistent understanding of the definition of distance, applying it across various representations. A small proportion (12.99%) experiences misconceptions. These research findings corroborate previous studies, emphasizing that, in general, first-year students are novices in physics.
Improving Customer Data Quality on Pension Bank Loans in Palembang-South Sumatra

Authors:
raw_author_name: Herlina Herlina
raw_affiliation_string: Prodi Ekonomi Syariah, Fakultas Ekonomi dan Bisnis Islam, Institut Agama Islam Alquran Al Ittifaqiah, Indralaya, Sumatera Selatan, Indonesia, Indonesia
raw_author_name: Muyasaroh Muyasaroh
raw_affiliation_string: Prodi Pendidikan Agama Islam, Fakultas Tarbiyah, Institut Agama Islam Alquran Al Ittifaqiah, Indralaya, Sumatera Selatan, Indonesia, Indonesia

Abstract: The bank has transferred the retirement loan application service to the cooperative as one of its subsidiaries. The phenomenon that often occurs is misinformation from cooperative officers when explaining the number of credit applications that should be made. The purpose of this research is to find the right pattern and form for applying the latest data technology so that it can provide the right information in the credit application process. Measurement and analysis of research data are qualitative, with a descriptive explanation level. Viewed by place and classified as field research. Types and research strategies, including Case Studies. Retrieval, collection, and analysis techniques were carried out repeatedly and simultaneously until the data was saturated and credible. The research yields findings about internal customer data connection patterns that are accurate and up-to-date. The form of credit data management by providing recommendations and internal references to customer performance, so that process and procedure connectivity is guaranteed between Bank Financial Institutions (LKB) and Non-Bank Financial Institutions (LKBB). The conclusion from the research is that the track records or data records of retired customers at the bank must be properly and up-to-date so that when applying for the same credit at a subsidiary of the institution, there is no misunderstanding of information and guaranteed correct and correct data.
İHA Tarafından Elde Edilen Termal Görüntüler Kullanılarak Fotovoltaik Sistemde Derin Öğrenme Tabanlı Arıza Tespiti ve Teşhisi

Authors:
raw_author_name: Batıkan Erdem DEMİR
raw_affiliation_string: Karabük Üniversitesi

Abstract: Solar power is one of the largest renewable energy sources in the world. With photovoltaic systems, electrical energy can be generated wherever the sun is located. To prevent efficiency losses in photovoltaic systems, these systems should be tested at regular intervals. In this study, it is discussed to detect cell, module and panel faults in panels using thermal images obtained from solar panels. Within the scope of the study, a four-rotor unmanned aerial vehicle (drone) was designed and a thermal camera was placed on the vehicle. Thus, thermal images of the solar panels on the roof of Karabuk University buildings were taken. A thermal data set with cell fault, module fault and panel fault were created using the resulting thermal images. The YOLOv3 deep learning-based convolutional neural network was trained with the created dataset. This training was conducted on Nvidia Jetson TX2, an embedded AI (Artificial Intelligence) computing device. After the completion of the training of the YOLOv3 network, it was concluded that the faults mentioned in the tests were successfully detected.
Understanding the deterrence effect of punishment for marine information security policies non-compliance

Authors:
raw_author_name: Xiaolong Wang
raw_affiliation_string: School of Economics and Management, Binzhou University, Binzhou, 256600, China
raw_author_name: Changlin Wang
raw_affiliation_string: School of Economics and Management, Binzhou University, Binzhou, 256600, China
raw_author_name: Wenli Li
raw_affiliation_string: School of Economics and Management, Dalian University of Technology, Dalian, 116024, China

Abstract: In the organizational setting of marine engineering, a significant number of information security incidents have been arised from the employees’ failure to comply with the information security policies (ISPs). This may be treated as a principal-agent problem with moral hazard between the employer and the employee for the practical compliance effort of an employee is not observable without high cost-. On the other hand, according to the deterrence theory, the employer and the employee are inherently self-interested beings.It is worth examining to what extent the employee is self-interested in the marine ISPs compliance context. Moreover, it is important to clarify the proper degree of severity of punishment in terms of the deterrent effect. In this study, a marine ISPs compliance game model has been proposed to evaluate the deterrence effect of punishment on the non-compliance behavior of employee individuals. It is found that in a non-punishment contract, the employee will decline to comply with the marine ISPs; but in a punishment contract, appropriate punishment will lead her to select the marine ISPs compliance effort level expected by the employer, and cause no potential backfire effect.
EneX: An Energy-Aware Execution Scheduler for Serverless Computing

Authors:
raw_author_name: Hossein Shafiei
raw_affiliation_string: Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran
raw_author_name: Ahmad Khonsari
raw_affiliation_string: School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract: The emerging serverless computing paradigm has recently attracted huge attention from both academia and industry. It brings benefits such as less operational complexity, high scalability, and availability, and lower costs. Serverless applications are usually partitioned into several chains of functions. The serverless provider should schedule functions for execution per customers' requests considering their chained nature. Also, the existing scheduling mechanisms for serverless platforms pay little attention to the reduction of energy consumption during functions' execution. To fill this gap, we present an energy-aware execution scheduler for serverless service providers named EneX. To do so, we formulate the minimization of energy consumption for executing the incoming chains of functions with specified computational loads and deadlines. Due to the intractability of the problem, we introduce a linear programming reformulation based on which, we propose an online scheduler. Finally, our experiments demonstrate the significant improvement of EneX in terms of energy efficiency.
ECC + +: An algorithm family based on ensemble of classifier chains for classifying imbalanced multi-label data

Authors:
raw_author_name: Jicong Duan
raw_affiliation_string: School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, PR China
raw_author_name: Yan Gu
raw_affiliation_string: School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, PR China
raw_author_name: Hualong Yu
raw_affiliation_string: School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, PR China
raw_author_name: Xibei Yang
raw_affiliation_string: School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, PR China
raw_author_name: Shang Gao
raw_affiliation_string: School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, PR China

Abstract: Multi-label learning has attracted a great deal of research interests as it has a wide range of real-world applications. Although many multi-label learning methods have been proposed, very few of them have addressed the problem of class imbalance distribution in multi-label data. Moreover, most of the existing class imbalance multi-label learning algorithms only focus on solving the class imbalance problem, without taking into account the correlations among labels. To address these issues simultaneously, we propose to combine the well-known ensemble of classifier chain (ECC) algorithm with various binary-class imbalance learning techniques such as sampling, cost-sensitive learning, and threshold moving. This approach creates a new algorithm family called ECC++, designed specifically for class imbalance multi-label learning. ECC is already an excellent ensemble high-order binary relevance multi-label learning algorithm that is well-suited to exploiting correlations among labels. Combining it with binary-class imbalance learning techniques enables each link in a classifier chain (CC) to overcome the negative effect of skewed data distribution. ECC++ is a dynamic algorithm family that can be extended arbitrarily by applying any new binary-class imbalance learning techniques. To demonstrate the effectiveness and superiority of the proposed ECC++ algorithm family, we developed several ECC++ family members using some popular binary-class imbalance learning techniques. We then compared them with several state-of-the-art class imbalance multi-label learning algorithms on twelve benchmark and four real-world multi-label datasets. Our experimental results showed the effectiveness and superiority of the proposed ECC++ algorithm family over existing class imbalance multi-label learning algorithms. In conclusion, the proposed ECC++ algorithm family combines the strengths of the well-established ECC algorithm and binary-class imbalance learning techniques, resulting in a superior methodology for class imbalance multi-label learning.
Scanning interferometric phase-calculation formula for simultaneous topographic profiling of thickness and surface of optical flats

Authors:
raw_author_name: Wonjun Bae
raw_affiliation_string: School of Mechanical Engineering, Pusan National University, 2, Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan 46241, South Korea
raw_author_name: Yangjin Kim
raw_affiliation_string: School of Mechanical Engineering, Pusan National University, 2, Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan 46241, South Korea
raw_author_name: Seokyoung Ahn
raw_affiliation_string: School of Mechanical Engineering, Pusan National University, 2, Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan 46241, South Korea
raw_author_name: Naohiko Sugita
raw_affiliation_string: Department of Mechanical Engineering, The University of Tokyo, 1-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

Abstract: With the rapid development of industrial technologies, both the thickness irregularity and surface shape of transparent optical flats should be profiled precisely to maintain the performance of industrial equipment. When measuring the optical flat thickness using an interferometer, the profiling uncertainty can deteriorate owing to a constant phase error. In this study, a new 3N phase-extraction formula that can compensate for the Zernike piston error was developed for the simultaneous measurement of thickness and surface. Using Fourier representation, the characteristics of the 3N formula were shown in the frequency domain. Subsequently, the combined and Zernike piston error suppression ability of the 3N formula was verified by comparison with the other formulas. Finally, the thickness and surface of the optical flats were measured using the 3N formula and a wavelength-scanning Fizeau interferometer. The standard deviations of the thickness and surface profiling measurements were 4.435 nm and 9.654 nm, respectively, which were much smaller than those calculated using other phase-calculation formulas.
Quantised and Simulated Max–min Fairness in Blockchain Ecosystems

Authors:
raw_author_name: Serdar Metin
raw_affiliation_string: Boğaziçi University, Bebek, Istanbul, Turkey
raw_author_name: Can Özturan
raw_affiliation_string: Boğaziçi University, Bebek, Istanbul, Turkey

Abstract: Blockchain systems heavily rely on user incentive and participation for the success and scope of their operation. For this end, fairness is a key precondition to attract users to join the ecosystem. In order to assure users of the fairness of the ecosystem, the distribution of shared resources must be done according to a fair and transparent policy, and it must be handled by a robust mechanism that implements this policy. The present study addresses this problem and contributes four decentralised and autonomous algorithms that may serve for the fair distribution of shared intrinsic resources. These are the adaptations of Max–min Fairness (MF), a distribution scheme which is well established in the computer science literature as fair, and its weighted version (WMF). Blockchain faucets are shared resource allocation mechanisms which accomplish this task by providing users with fixed amounts of free cryptocurrency. Faucets are employed in academic networks such as Bloxberg, and also in test networks such as Ropsten and Rinkeby We implemented both MF and WMF as blockchain faucets for the demonstration of their operation and measurement of their performance. The first algorithm, we call quantised Max–min Fairness (QMF), operates under the restriction on demand volumes but is scalable to large numbers of users, and the second, we call Simulated Max–min Fairness (SMF), operates under the restriction on the number of users but allows for various weighting policies. The other two are the weighted versions of these algorithms.
Video-Based Depth Estimation Autoencoder With Weighted Temporal Feature and Spatial Edge Guided Modules

Authors:
raw_author_name: Wei-Jong Yang
raw_affiliation_string: Department of Artificial Intelligence and Computer Engineering, National Chin-Yi University of Technology, Taichung, Taiwan
raw_author_name: Pau-Choo Chung
raw_affiliation_string: Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan

Abstract: Convolutional neural networks with encoder and decoder structures, generally referred to as autoencoders, are used in many pixel- wise transformation, detection, segmentation and estimation applications, for example, which can be applied for face swapping, lane detection, semantic segmentation, and depth estimation, respectively. However, traditional autoencoders, which are based on single-frame inputs, ignore the temporal consistency between consecutive frames, and may hence produce unsatisfactory results. Accordingly, in this paper, a video-based depth estimation (VDE) autoencoder is proposed to improve the quality of depth estimation through the inclusion of two weighted temporal feature (WTF) modules in the encoder and a single spatial edge guided (SEG) module in the decoder. The WTF modules designed with channel weighted block (CWB) submodule effectively extract the temporal similarities in consecutive frames, while the SEG module provides spatial edge guidance of the object contours. Through the collaboration of the proposed modules, the accuracy of the depth estimation is greatly improved. The experimental results confirm that the proposed VDE autoencoder achieves a better monocular depth estimation performance than the existing autoencoders with only a slight increase in the computational cost.