Reviewerly/exHarmony huggingface dataset:
Resampling Techniques Study on Class Imbalance Problem in Credit Risk Prediction

Authors:
raw_author_name: Tianxiang Cui
raw_affiliation_string: School of Computer Science, University of Nottingham Ningbo China, Ningbo 315100, China
raw_author_name: Shusheng Ding
raw_affiliation_string: School of Business, Ningbo University, 818 Fenghua Road Ningbo, Ningbo 315211, China
raw_author_name: Jiawei Li
raw_affiliation_string: School of Computer Science, University of Nottingham Ningbo China, Ningbo 315100, China
raw_author_name: Anthony Graham Bellotti
raw_affiliation_string: School of Computer Science, University of Nottingham Ningbo China, Ningbo 315100, China

Abstract: Credit risk prediction heavily relies on historical data provided by financial institutions. The goal is to identify commonalities among defaulting users based on existing information. However, data on defaulters is often limited, leading to a concentration of credit data where positive samples (defaults) are significantly fewer than negative samples (nondefaults). It poses a serious challenge known as the class imbalance problem, which can substantially impact data quality and predictive model effectiveness. To address the problem, various resampling techniques have been proposed and studied extensively. However, despite ongoing research, there is no consensus on the most effective technique. The choice of resampling technique is closely related to the dataset size and imbalance ratio, and its effectiveness varies across different classifiers. Moreover, there is a notable gap in research concerning suitable techniques for extremely imbalanced datasets. Therefore, this study aims to compare popular resampling techniques across different datasets and classifiers while also proposing a novel hybrid sampling method tailored for extremely imbalanced datasets. Our experimental results demonstrate that this new technique significantly enhances classifier predictive performance, shedding light on effective strategies for managing the class imbalance problem in credit risk prediction.
Smartphones enabled up to 58 s strong-shaking warning in the M7.8 Türkiye earthquake

Authors:
raw_author_name: Francesco Finazzi
raw_affiliation_string: Department of Economics, University of Bergamo, Bergamo, Italy
raw_author_name: Rémy Bossu
raw_affiliation_string: European-Mediterranean Seismological Centre, Arpajon, France
raw_author_name: Fabrice Cotton
raw_affiliation_string: GFZ German Research Centre for Geosciences, Potsdam, Germany

Abstract: Public earthquake early warning systems (PEEWSs) have the potential to save lives by warning people of incoming seismic waves up to tens of seconds in advance. Given the scale and geographical extent of their impact, this potential is greatest for destructive earthquakes, such as the M7.8 Pazarcik (Türkiye) event of 6 February 2023, which killed almost 60,000 people. However, warning people of imminent strong shaking is particularly difficult for large-magnitude earthquakes because the warning must be given before the earthquake has reached its final size. Here, we show that the Earthquake Network (EQN), the first operational smartphone-based PEEWS and apparently the only one operating during this earthquake, issued a cross-border alert within 12 s of the beginning of the rupture. A comparison with accelerometer and macroseismic data reveals that, owing to the EQN alerting strategy, Turkish and Syrian EQN users exposed to intensity IX and above benefitted from a warning time of up to 58 s before the onset of strong ground shaking. If the alert had been extended to the entire population, approximately 2.7 million Turkish and Syrian people exposed to a life-threatening earthquake would have received a warning ranging from 30 to 66 s in advance.
A Survey on Adaptive Cameras

Authors:
raw_author_name: Julien Ducrocq
raw_affiliation_string: MIS - Modélisation, Information et Systèmes - UR UPJV 4290 (Université de Picardie Jules Verne - UFR des Sciences - 33, rue Saint Leu - 
80039 AMIENS CEDEX 1 - France)
raw_author_name: Guillaume Caron
raw_affiliation_string: CNRS-AIST JRL  - Joint Robotics Laboratory (National Institute of Advanced Industrial Science and Technology (AIST)
Tsukuba Central 1, 1-1-1 Umezono, 
Tsukuba, Ibaraki 305-8560 
Japon - Japan); MIS - Modélisation, Information et Systèmes - UR UPJV 4290 (Université de Picardie Jules Verne - UFR des Sciences - 33, rue Saint Leu - 
80039 AMIENS CEDEX 1 - France)

Abstract: This paper surveys adaptive cameras, i.e. any camera device able to change its geometric settings. We consider their classification in four categories: lensless, dioptric, catadioptric and polydioptric cameras. In each category, we report and describe all the existing adaptive cameras. Then, the known applications of these devices are summarized. Finally, we discuss open research lines for new adaptations of cameras, and their promising uses.
Global biasing using a hardware-based artificial Zeeman term in spinwave Ising machines

Authors:
raw_author_name: Victor H. González
raw_affiliation_string: Physics Department, University of Gothenburg 1 , 412 96 Gothenburg, Sweden
raw_author_name: Artem Litvinenko
raw_affiliation_string: Physics Department, University of Gothenburg 1 , 412 96 Gothenburg, Sweden
raw_author_name: Roman Khymyn
raw_affiliation_string: Physics Department, University of Gothenburg 1 , 412 96 Gothenburg, Sweden
raw_author_name: Johan Åkerman
raw_affiliation_string: Center for Science and Innovation in Spintronics, Tohoku University 2 , 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan; Physics Department, University of Gothenburg 1 , 412 96 Gothenburg, Sweden

Abstract: A spinwave Ising machine (SWIM) is a recently proposed type of time-multiplexed hardware solver for combinatorial optimization that employs feedback coupling and phase sensitive amplification to map an Ising Hamiltonian into phase-binarized propagating spinwave RF pulses in an Yttrium-Iron-Garnet film. In this work, we increase the mathematical complexity of the SWIM by adding a global Zeeman term to a 4-spin nearest neighbor Hamiltonian using a continuous external electrical signal with the same frequency as the spin pulses and phase locked with one of the two possible states. We are able to induce ferromagnetic ordering in both directions of the spin states despite antiferromagnetic pairwise coupling. Embedding a planar antiferromagnetic spin system in a magnetic field has been proven to increase the complexity of the graph associated with its Hamiltonian, and, thus, this straightforward implementation helps explore higher degrees of complexity in this evolving solver.
Controlling the charge-transfer dynamics of two-level systems around avoided crossings

Authors:
raw_author_name: Agostino Migliore
raw_affiliation_string: Department of Chemical Sciences, University of Padova 1 , Via Marzolo 1, 35131 Padova, Italy
raw_author_name: Antonino Messina
raw_affiliation_string: Dipartimento di Matematica e Informatica, Università degli Studi di Palermo 2 , Via Archirafi, 34, I-90123 Palermo, Italy

Abstract: Two-level quantum systems are fundamental physical models that continue to attract growing interest due to their crucial role as a building block of quantum technologies. The exact analytical solution of the dynamics of these systems is central to control theory and its applications, such as that to quantum computing. In this study, we reconsider the two-state charge transfer problem by extending and using a methodology developed to study (pseudo)spin systems in quantum electrodynamics contexts. This approach allows us to build a time evolution operator for the charge transfer system and to show new opportunities for the coherent control of the system dynamics, with a particular emphasis on the critical dynamic region around the transition state coordinate, where the avoided crossing of the energy levels occurs. We identify and propose possible experimental implementations of a class of rotations of the charge donor (or acceptor) that endow the electronic coupling matrix element with a time-dependent phase that can be employed to realize controllable coherent dynamics of the system across the avoided level crossing. The analogy of these rotations to reference frame rotations in generalized semiclassical Rabi models is discussed. We also show that the physical rotations in the charge-transfer systems can be performed so as to implement quantum gates relevant to quantum computing. From an exquisitely physical–mathematical viewpoint, our approach brings to light situations in which the time-dependent state of the system can be obtained without resorting to the special functions appearing in the Landau–Zener approach.
Time Series Feature Selection Method Based on Mutual Information

Authors:
raw_author_name: Lin Huang
raw_affiliation_string: Ship Comprehensive Test and Training Base, Naval University of Engineering, Wuhan 430033, China
raw_author_name: Lianhui Shi
raw_affiliation_string: Ship Comprehensive Test and Training Base, Naval University of Engineering, Wuhan 430033, China
raw_author_name: Li Gong
raw_affiliation_string: Ship Comprehensive Test and Training Base, Naval University of Engineering, Wuhan 430033, China

Abstract: Time series data have characteristics such as high dimensionality, excessive noise, data imbalance, etc. In the data preprocessing process, feature selection plays an important role in the quantitative analysis of multidimensional time series data. Aiming at the problem of feature selection of multidimensional time series data, a feature selection method for time series based on mutual information (MI) is proposed. One of the difficulties of traditional MI methods is in searching for a suitable target variable. To address this issue, the main innovation of this paper is the hybridization of principal component analysis (PCA) and kernel regression (KR) methods based on MI. Firstly, based on historical operational data, quantifiable system operability is constructed using PCA and KR. The next step is to use the constructed system operability as the target variable for MI analysis to extract the most useful features for the system data analysis. In order to verify the effectiveness of the method, an experiment is conducted on the CMAPSS engine dataset, and the effectiveness of condition recognition is tested based on the extracted features. The results indicate that the proposed method can effectively achieve feature extraction of high-dimensional monitoring data.
Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video

Authors:
raw_author_name: Arkadiusz Biernacki
raw_affiliation_string: Department of Computer Networks and Systems, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland

Abstract: Predicting throughput is essential to reduce latency in time-critical services like video streaming, which constitutes a significant portion of mobile network traffic. The video player continuously monitors network throughput during playback and adjusts the video quality according to the network conditions. This means that the quality of the video depends on the player’s ability to predict network throughput accurately, which can be challenging in the unpredictable environment of mobile networks. To improve the prediction accuracy, we grouped the throughput trace into clusters taking into account the similarity of their mean and variance. Once we distinguished the similar trace fragments, we built a separate LSTM predictive model for each cluster. For the experiment, we used traffic captured from 5G networks generated by individual user equipment (UE) in fixed and mobile scenarios. Our results show that the prior grouping of the network traces improved the prediction compared to the global model operating on the whole trace.
A neural machine translation method based on split graph convolutional self-attention encoding

Authors:
raw_author_name: Ping Li
raw_affiliation_string: School of Information Engineering, Fuyang Normal University, Fuyang, Anhui, China

Abstract: With the continuous advancement of deep learning technologies, neural machine translation (NMT) has emerged as a powerful tool for enhancing communication efficiency among the members of cross-language collaborative teams. Among the various available approaches, leveraging syntactic dependency relations to achieve enhanced translation performance has become a pivotal research direction. However, current studies often lack in-depth considerations of non-Euclidean spaces when exploring interword correlations and fail to effectively address the model complexity arising from dependency relation encoding. To address these issues, we propose a novel approach based on split graph convolutional self-attention encoding (SGSE), aiming to more comprehensively utilize syntactic dependency relationships while reducing model complexity. Specifically, we initially extract syntactic dependency relations from the source language and construct a syntax dependency graph in a non-Euclidean space. Subsequently, we devise split self-attention networks and syntactic semantic self-attention networks, integrating them into a unified model. Through experiments conducted on multiple standard datasets as well as datasets encompassing scenarios related to team collaboration and enterprise management, the proposed method significantly enhances the translation performance of the utilized model while effectively mitigating model complexity. This approach has the potential to effectively enhance communication among cross-language team members, thereby ameliorating collaborative efficiency.
A novel automatic annotation method for whole slide pathological images combined clustering and edge detection technique

Authors:
raw_author_name: Wei‐long Ding
raw_affiliation_string: College of Computer Science &amp; Technology Zhejiang University of Technology Hangzhou China
raw_author_name: Xiao‐jie Zhu
raw_affiliation_string: College of Computer Science &amp; Technology Zhejiang University of Technology Hangzhou China

Abstract: Abstract Pixel‐level labeling of regions of interest in an image is a key step in building a labeled training dataset for supervised deep learning networks of images. However, traditional manual labeling of cancerous regions in digital pathological images by doctors is time‐consuming and inefficient. To address this issue, this paper proposes an automatic labeling method for whole slide images, which combines clustering and edge detection techniques. The proposed method utilizes the multi‐level feature fusion model and the Long‐Short Term Memory network to discriminate the cancerous nature of the whole slide images, thereby improving the classification accuracy of the whole slide images. Subsequently, the automatic labeling of cancerous regions is achieved by integrating a density‐based clustering algorithm and an edge point extraction algorithm, both based on the discriminated results of the cancerous properties of whole slide images. The experimental results demonstrate the effectiveness of the proposed method, which offers an efficient and accurate solution to the challenging task of cancerous region labeling in digital pathological images.
FL-GUARD: A Holistic Framework for Run-Time Detection and Recovery of Negative Federated Learning

Authors:
raw_author_name: Lidan Shou
raw_affiliation_string: The State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, 310027, Zhejiang, China
raw_author_name: Ke Chen
raw_affiliation_string: The State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, 310027, Zhejiang, China
raw_author_name: Gang Chen
raw_affiliation_string: The State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, 310027, Zhejiang, China
raw_author_name: Sai Wu
raw_affiliation_string: The State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, 310027, Zhejiang, China

Abstract: Abstract Federated learning (FL) is a promising approach for learning a model from data distributed on massive clients without exposing data privacy. It works effectively in the ideal federation where clients share homogeneous data distribution and learning behavior. However, FL may fail to function appropriately when the federation is not ideal, amid an unhealthy state called Negative Federated Learning (NFL), in which most clients gain no benefit from participating in FL. Many studies have tried to address NFL. However, their solutions either (1) predetermine to prevent NFL in the entire learning life-cycle or (2) tackle NFL in the aftermath of numerous learning rounds. Thus, they either (1) indiscriminately incur extra costs even if FL can perform well without such costs or (2) waste numerous learning rounds. Additionally, none of the previous work takes into account the clients who may be unwilling/unable to follow the proposed NFL solutions when using those solutions to upgrade an FL system in use. This paper introduces FL-GUARD, a holistic framework that can be employed on any FL system for tackling NFL in a run-time paradigm. That is, to dynamically detect NFL at the early stage (tens of rounds) of learning and then to activate recovery measures when necessary. Specifically, we devise a cost-effective NFL detection mechanism, which relies on an estimation of performance gain on clients. Only when NFL is detected, we activate the NFL recovery process, in which each client learns in parallel an adapted model when training the global model. Extensive experiment results confirm the effectiveness of FL-GUARD in detecting NFL and recovering from NFL to a healthy learning state. We also show that FL-GUARD is compatible with previous NFL solutions and robust against clients unwilling/unable to take any recovery measures.
Enhancing blockchain scalability with snake optimization algorithm: a novel approach

Authors:
raw_author_name: Shimal S. H. Taher
raw_affiliation_string: Department of Computer Science, Iraq
raw_author_name: Siddeeq Y. Ameen
raw_affiliation_string: Quality Assurance Directorate, Iraq
raw_author_name: Jihan A. Ahmed
raw_affiliation_string: Department of Computer Science, Iraq

Abstract: Scalability remains a critical challenge for blockchain technology, limiting its potential for widespread adoption in high-demand transactional systems. This paper proposes an innovative solution to this challenge by applying the Snake Optimization Algorithm (SOA) to a blockchain framework, aimed at enhancing transaction throughput and reducing latency. A thorough literature review contextualizes our work within the current state of blockchain scalability efforts. We introduce a methodology that integrates SOA into the transaction validation process of a blockchain network. The effectiveness of this approach is empirically evaluated by comparing transaction processing times before and after the implementation of SOA. The results show a substantial reduction in latency, with the optimized system achieving lower average transaction times across various transaction volumes. Notably, the latency for processing batches of 10 and 100 transactions decreased from 30.29 ms to 155.66 ms–0.42 ms and 0.37 ms, respectively, post optimization. These findings indicate that SOA is exceptionally efficient in batch transaction scenarios, presenting an inverse scalability behavior that defies typical system performance degradation with increased load. Our research contributes a significant advancement in blockchain scalability, with implications for the development of more efficient and adaptable blockchain systems suitable for high throughput enterprise applications.
Automated hearing impairment diagnosis using machine‐learning: An open‐source software development undergraduate research project

Authors:
raw_author_name: Waseem Sheikh
raw_affiliation_string: Department of Engineering Utah Valley University Orem Utah USA

Abstract: Abstract Approximately 700 million people will have disabling hearing loss by 2050. Underdeveloped and developing countries, which encompass a considerable proportion of people with disabling hearing impairment, have a sparse number of audiologists and otolaryngologists. The lack of specialists leaves most hearing impairments undiagnosed for a long time, resulting in negative societal and economic impacts. In this article, we propose an automated hearing impairment diagnosis software—based on machine learning—to support audiologists and otolaryngologists in accurately and efficiently diagnosing and classifying hearing loss. We present the design, implementation, and performance analysis of an open‐source automated hearing impairment diagnosis software, which consists of two modules: a hearing test data‐generation module and a machine‐learning model. The data‐generation module produces a diverse and exhaustive data set for training and evaluating the machine‐learning model. By employing multiclass and ultilabel classification techniques to learn from the hearing test data, the model can swiftly predict the type, degree, and configuration of hearing loss with high reliability. Our proposed machine‐learning model demonstrates promising results with a prediction time of 634 ms, a log‐loss reduction rate of 0.9848 and accuracy, precision, recall, and f1‐score of 1.0000—showing the model's applicability to assist audiologists and otolaryngologists in rapidly and accurately classifying the type, degree, and configuration of hearing loss. In addition to the technical contributions, this article also highlights the importance of involving undergraduate students in open‐source software development projects which have a direct impact on improving the quality of human life.
Recent advances in earthquake seismology using machine learning

Authors:
raw_author_name: Hisahiko Kubo
raw_affiliation_string: National Research Institute for Earth Science and Disaster Resilience, 3-1 Tennodai, Tsukuba, 305-0006, Japan
raw_author_name: Masayuki Kano
raw_affiliation_string: Graduate School of Science, Tohoku University, 6-3, Aramaki Aza-Aoba, Aoba-Ku, Sendai, 980-8578, Japan

Abstract: Abstract Given the recent developments in machine-learning technology, its application has rapidly progressed in various fields of earthquake seismology, achieving great success. Here, we review the recent advances, focusing on catalog development, seismicity analysis, ground-motion prediction, and crustal deformation analysis. First, we explore studies on the development of earthquake catalogs, including their elemental processes such as event detection/classification, arrival time picking, similar waveform searching, focal mechanism analysis, and paleoseismic record analysis. We then introduce studies related to earthquake risk evaluation and seismicity analysis. Additionally, we review studies on ground-motion prediction, which are categorized into four groups depending on whether the output is ground-motion intensity or ground-motion time series and the input is features (individual measurable properties) or time series. We discuss the effect of imbalanced ground-motion data on machine-learning models and the approaches taken to address the problem. Finally, we summarize the analysis of geodetic data related to crustal deformation, focusing on clustering analysis and detection of geodetic signals caused by seismic/aseismic phenomena. Graphical Abstract