Reviewerly/exHarmony huggingface dataset:
MoMENt: Marked Point Processes with Memory-Enhanced Neural Networks for User Activity Modeling

Authors:
raw_author_name: Mengfan Yao
raw_affiliation_string: State University of New York at Albany, Albany, USA
raw_author_name: Siqian Zhao
raw_affiliation_string: State University of New York at Albany, Albany, USA

Abstract: Marked temporal point process models (MTPPs) aim to model event sequences and event markers (associated features) in continuous time. These models have been applied to various application domains where capturing event dynamics in continuous time is beneficial, such as education systems, social networks, and recommender systems. However, current MTPPs suffer from two major limitations, i.e., inefficient representation of event dynamic’s influence on marker distribution and losing fine-grained representation of historical marker distributions in the modeling. Motivated by these limitations, we propose a novel model called M arked P o int Processes with M emory- E nhanced N eural Ne t works (MoMENt) that can capture the bidirectional interrelations between markers and event dynamics while providing fine-grained marker representations. Specifically, MoMENt is constructed of two concurrent networks: Recurrent Activity Updater (RAU) to capture model event dynamics and Memory-Enhanced Marker Updater (MEMU) to represent markers. Both RAU and MEMU components are designed to update each other at every step to model the bidirectional influence of markers and event dynamics. To obtain a fine-grained representation of maker distributions, MEMU is devised with external memories that model detailed marker-level features with latent component vectors. Our extensive experiments on six real-world user interaction datasets demonstrate that MoMENt can accurately represent users’ activity dynamics, boosting time, type, and marker predictions, as well as recommendation performance up to \(76.5\% \) , \(65.6\% \) , \(77.2\% \) , and \(57.7\% \) , respectively, compared to baseline approaches. Furthermore, our case studies show the effectiveness of MoMENt in providing meaningful and fine-grained interpretations of user-system relations over time, e.g., how user choices influence their future preferences in the recommendation domain.
Assessing Sensor Integrity for Nuclear Waste Monitoring Using Graph Neural Networks

Authors:
raw_author_name: Francisco Chinesta
raw_affiliation_string: PIMM Laboratory, Arts et Métiers Institute of Technology, Centre National de la Recherche Scientifique (CNRS), 151 Boulevard de l’Hôpital, 75013 Paris, France

Abstract: A deep geological repository for radioactive waste, such as Andra's Cigéo project, requires long-term (persistent) monitoring. To achieve this goal, data from a network of sensors are acquired. This network is subject to deterioration over time due to environmental effects (radioactivity, mechanical deterioration of the cell, etc.), and it is paramount to assess each sensor's integrity and ensure data consistency to enable the precise monitoring of the facilities. Graph neural networks (GNNs) are suitable for detecting faulty sensors in complex networks because they accurately depict physical phenomena that occur in a system and take the sensor network's local structure into consideration in the predictions. In this work, we leveraged the availability of the experimental data acquired in Andra's Underground Research Laboratory (URL) to train a graph neural network for the assessment of data integrity. The experiment considered in this work emulated the thermal loading of a high-level waste (HLW) demonstrator cell (i.e., the heating of the containment cell by nuclear waste). Using real experiment data acquired in Andra's URL in a deep geological layer was one of the novelties of this work. The used model was a GNN that inputted the temperature field from the sensors (at the current and past steps) and returned the state of each individual sensor, i.e., faulty or not. The other novelty of this work lay in the application of the GraphSAGE model which was modified with elements of the Graph Net framework to detect faulty sensors, with up to half of the sensors in the network being faulty at once. This proportion of faulty sensors was explained by the use of distributed sensors (optic fiber) and the environmental effects on the cell. The GNNs trained on the experimental data were ultimately compared against other standard classification methods (thresholding, artificial neural networks, etc.), which demonstrated their effectiveness in the assessment of data integrity.
Toward Verification of DAG-Based Distributed Ledger Technologies through Discrete-Event Simulation

Authors:
raw_author_name: Misbah Khan
raw_affiliation_string: Canbbera Campus, University of New South Wales Canberra at the Australian Defence Force Academy, Campbell, ACT 2612, Australia
raw_author_name: Frank den Hartog
raw_affiliation_string: Canbbera Campus, University of New South Wales Canberra at the Australian Defence Force Academy, Campbell, ACT 2612, Australia
raw_author_name: Jiankun Hu
raw_affiliation_string: Canbbera Campus, University of New South Wales Canberra at the Australian Defence Force Academy, Campbell, ACT 2612, Australia

Abstract: As the potential of directed acyclic graph (DAG)-based distributed ledgers in IoT systems unfolds, a need arises to understand their intricate dynamics in real-world scenarios. It is well known that discrete event simulations can provide high-fidelity evaluations of protocols. However, there is a lack of public discrete event simulators capable of assessing DAG-based distributed ledgers. In this paper, a discrete-event-based distributed ledger simulator is introduced, with which we investigate a custom Python-based implementation of IOTA's Tangle DAG protocol. The study reveals the dynamics of Tangle (particularly Poisson processes in transaction dynamics), the efficiency and intricacies of the random walk in Tangle, and the quantitative assessment of node convergence. Furthermore, the research underscores the significance of weight updates without depth limitations and provides insights into the role, challenges, and implications of the coordinator/validator in DAG architectures. The results are striking, and although the findings are reported only for Tangle, they demonstrate the need for adaptable and versatile discrete event simulators for DAG architectures and tip selection methodologies in general.
MTS-PRO2SAT: Hybrid Mutation Tabu Search Algorithm in Optimizing Probabilistic 2 Satisfiability in Discrete Hopfield Neural Network

Authors:
raw_author_name: Ju Chen
raw_affiliation_string: School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu 610000, China; School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia
raw_author_name: Yuan Gao
raw_affiliation_string: School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia; School of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu 610000, China
raw_author_name: Mohd Shareduwan Mohd Kasihmuddin
raw_affiliation_string: School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia
raw_author_name: Chengfeng Zheng
raw_affiliation_string: School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia
raw_author_name: Nurul Atiqah Romli
raw_affiliation_string: School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia
raw_author_name: Mohd. Asyraf Mansor
raw_affiliation_string: School of Distance Education, Universiti Sains Malaysia, Penang 11800, Malaysia
raw_author_name: Nur Ezlin Zamri
raw_affiliation_string: School of Distance Education, Universiti Sains Malaysia, Penang 11800, Malaysia

Abstract: The primary objective of introducing metaheuristic algorithms into traditional systematic logic is to minimize the cost function. However, there is a lack of research on the impact of introducing metaheuristic algorithms on the cost function under different proportions of positive literals. In order to fill in this gap and improve the efficiency of the metaheuristic algorithm in systematic logic, we proposed a metaheuristic algorithm based on mutation tabu search and embedded it in probabilistic satisfiability logic in discrete Hopfield neural networks. Based on the traditional tabu search algorithm, the mutation operators of the genetic algorithm were combined to improve its global search ability during the learning phase and ensure that the cost function of the systematic logic converged to zero at different proportions of positive literals. Additionally, further optimization was carried out in the retrieval phase to enhance the diversity of solutions. Compared with nine other metaheuristic algorithms and exhaustive search algorithms, the proposed algorithm was superior to other algorithms in terms of time complexity and global convergence, and showed higher efficiency in the search solutions at the binary search space, consolidated the efficiency of systematic logic in the learning phase, and significantly improved the diversity of the global solution in the retrieval phase of systematic logic.
Searchable Blockchain-Based Healthcare Information Exchange System to Enhance Privacy Preserving and Data Usability

Authors:
raw_author_name: Sejong Lee
raw_affiliation_string: Department of Computer Science and Engineering, Major in Bio-Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea
raw_author_name: Yushin Kim
raw_affiliation_string: Department of Computer Science and Engineering, Major in Bio-Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea
raw_author_name: Sunghyun Cho
raw_affiliation_string: Department of Computer Science and Engineering, Hanyang University ERICA, Ansan 15588, Republic of Korea

Abstract: Ensuring the security and usability of electronic health records (EHRs) is important in health information exchange (HIE) systems that handle healthcare records. This study addressed the need to balance privacy preserving and data usability in blockchain-based HIE systems. We propose a searchable blockchain-based HIE system that enhances privacy preserving while improving data usability. The proposed methodology includes users collecting healthcare information (HI) from various Internet of Medical Things (IoMT) devices and compiling this information into EHR blocks for sharing on a blockchain network. This approach allows participants to search and utilize specific health data within the blockchain effectively. The results demonstrate that the proposed system mitigates the issues of traditional HIE systems by providing secure and user-friendly access to EHRs. The proposed searchable blockchain-based HIE system resolves the trade-off dilemma in HIE by achieving a balance between security and the data usability of EHRs.
A Lightweight Remote Sensing Aircraft Object Detection Network Based on Improved YOLOv5n

Authors:
raw_author_name: Jiale Wang
raw_affiliation_string: University of Chinese Academy of Sciences, Beijing 100049, China; Xi’an Institute of Optics and Precision Mechanics of CAS, Xi’an 710119, China
raw_author_name: Ximing Zhang
raw_affiliation_string: Xi’an Institute of Optics and Precision Mechanics of CAS, Xi’an 710119, China
raw_author_name: Yuehong Qiu
raw_affiliation_string: Xi’an Institute of Optics and Precision Mechanics of CAS, Xi’an 710119, China

Abstract: Due to the issues of remote sensing object detection algorithms based on deep learning, such as a high number of network parameters, large model size, and high computational requirements, it is challenging to deploy them on small mobile devices. This paper proposes an extremely lightweight remote sensing aircraft object detection network based on the improved YOLOv5n. This network combines Shufflenet v2 and YOLOv5n, significantly reducing the network size while ensuring high detection accuracy. It substitutes the original CIoU and convolution with EIoU and deformable convolution, optimizing for the small-scale characteristics of aircraft objects and further accelerating convergence and improving regression accuracy. Additionally, a coordinate attention (CA) mechanism is introduced at the end of the backbone to focus on orientation perception and positional information. We conducted a series of experiments, comparing our method with networks like GhostNet, PP-LCNet, MobileNetV3, and MobileNetV3s, and performed detailed ablation studies. The experimental results on the Mar20 public dataset indicate that, compared to the original YOLOv5n network, our lightweight network has only about one-fifth of its parameter count, with only a slight decrease of 2.7% in mAP@0.5. At the same time, compared with other lightweight networks of the same magnitude, our network achieves an effective balance between detection accuracy and resource consumption such as memory and computing power, providing a novel solution for the implementation and hardware deployment of lightweight remote sensing object detection networks.
TOPSIS-ACO based feature selection for multi-label classification

Authors:
raw_author_name: Gurudatta Verma
raw_affiliation_string: Department of Information Technology, National Institute of Technology Raipur, Raipur, India
raw_author_name: Tirath Prasad Sahu
raw_affiliation_string: Department of Information Technology, National Institute of Technology Raipur, Raipur, India

Abstract: This paper introduces an approach for optimal feature selection (FS) in multi-label (ML) data, where each sample can be associated with multiple class labels. The complexity of the feature space is significantly increased in comparison to single-label data, making decision-making challenging. Therefore, FS plays a crucial role in ML classification. The proposed method, called ML-TOPSIS-ACO, leverages the ‘Technique of Order Preference by Similarity to Ideal Solution’ (TOPSIS) as a Multi-Criteria Decision Making (MCDM) technique and ‘Ant Colony Optimization’ (ACO) as a meta-heuristic technique. The ML-TOPSIS-ACO method operates in two phases. In the first phase, Modified TOPSIS (MTOPSIS) is used to perform feature ranking and select the most important features. These selected features are then passed to Modified-ACO for feature re-ranking, aiming to identify the best subset of features for ML classification. To evaluate the effectiveness of ML-TOPSIS-ACO, experiments were conducted on nine benchmark datasets. The experimental results demonstrate that ML-TOPSIS-ACO outperforms other existing methods, achieving a winning percentage of 83%.
Effects of caring climate on career commitment for preschool teachers: the mediating roles of interpersonal trust and cooperative learning

Authors:
raw_author_name: I-Hsiung Chang
raw_affiliation_string: Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei, Taiwan, ROC

Abstract: Preschool teachers in Taiwan face pedagogical and emotionalchallenges that shape their job satisfaction, commitment, and socialidentity. Understanding their views on professional developmentand organizational commitment in early childhood education is critical. This study aims to examine the relationship between caring climate and career commitment and test the multiple mediating effects of interpersonal trust and cooperative learning in this relationship. A survey was conducted in Taiwan to test preschool teachers’ attitudes toward career commitment and other related factors. The questionnaire consisted of four latent factors: caring climate, career commitment, interpersonal trust and cooperative learning. We used partial least squares to analyze the sample data and examine the reliability and validity in the multiple mediation model. The results of the multiple mediating analysis indicated that interpersonal trust and cooperative learning fully positively mediated the association between caring climate and career commitment. Preschool teachers’ career commitment is influenced by their perceptions of caring climate in workplaces. Their positive attitudes toward kind interactions and teamwork with colleagues in preschools actively support sustainable development of their career paths.
Analog spin simulators: How to keep the amplitude homogeneous

Authors:
raw_author_name: W. Verstraelen
raw_affiliation_string: Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
raw_author_name: P. Deuar
raw_affiliation_string: Institute of Physics, Polish Academy of Sciences, Al. Lotników 32/46, Warsaw PL-02-668, Poland
raw_author_name: M. Matuszewski
raw_affiliation_string: Institute of Physics, Polish Academy of Sciences, Al. Lotników 32/46, Warsaw PL-02-668, Poland
raw_author_name: T.C.H. Liew
raw_affiliation_string: Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore

Abstract: A setup that simulates ground states of spin graphs would allow one to solve computationally hard optimization problems efficiently. Current optical setups to this goal have difficulties decoupling the amplitude and phase degrees of freedom of each effective spin, which risks that the resultant mapping will be invalid, a problem known as amplitude heterogeneity. Here, we propose a setup with coupled active optical cavity modes, where this problem is eliminated through their particular geometric arrangement. Acting as an effective Monte Carlo solver, the ground state can be found exactly. By tuning a parameter, the setup solves $XY$ or Ising problems.
Decomposing large unitaries into multimode devices of arbitrary size

Authors:
raw_author_name: Jasmin Meinecke
raw_affiliation_string: Department für Physik, Ludwig-Maximilians-Universität, 80539 München, Germany; Institute of Solid State Physics, Technische Universität Berlin, 10623 Berlin, Germany; Max-Planck-Institut für Quantenoptik, 85748 Garching, Germany; Munich Center for Quantum Science and Technology (MCQST), 80799 München, Germany
raw_author_name: Sonja Barkhofen
raw_affiliation_string: Department of Physics, Universität Paderborn, Warburger Strasse 100, 33098 Paderborn, Germany; PhoQS, Universität Paderborn, Warburger Strasse 100, 33098 Paderborn, Germany
raw_author_name: Tim J. Bartley
raw_affiliation_string: Department of Physics, Universität Paderborn, Warburger Strasse 100, 33098 Paderborn, Germany; PhoQS, Universität Paderborn, Warburger Strasse 100, 33098 Paderborn, Germany

Abstract: In the quest for the realization of larger and larger quantum networks, the established decomposition in 2 \ifmmode\times\else\texttimes\fi{} 2 subunitaries, easily implemented by beam splitters and phase shifters, comes up against borders if the network becomes too big. A new mathematical decomposition algorithm to decompose large network unitaries into subunitaries of arbitrary size takes heed of technological advances to build larger physical components, realizing larger subunitary sizes in order to increase the achievable size of high-quality networks.
Visual analysis method for unmanned pumping stations on dynamic platforms based on data fusion technology

Authors:
raw_author_name: Zhen Liu
raw_affiliation_string: Guangdong GDH Pearl River Delta Water Supply Co., Ltd., Guangzhou, 511458, Guangdong, China
raw_author_name: Zhaobo Zhang
raw_affiliation_string: Guangdong GDH Pearl River Delta Water Supply Co., Ltd., Guangzhou, 511458, Guangdong, China
raw_author_name: Jiahao Qin
raw_affiliation_string: Guangdong GDH Pearl River Delta Water Supply Co., Ltd., Guangzhou, 511458, Guangdong, China

Abstract: Abstract As the scale of water conservancy projects continues to expand, the amount and complexity of analytical data have also correspondingly increased. At present, it is difficult to realize project management decision support based on a single data source, and most manual analysis methods not only have high labor costs, but also are prone to the risk of misjudgment, resulting in huge property losses. Based on this problem, this paper proposes visual analysis method for unmanned pumping stations on dynamic platforms based on data fusion technology. First, the method uses the transfer learning method to enable ResNet18 obtain generalization ability. Secondly, the method uses ResNet18 to extract image features, and outputs fixed length sequence data as the input of long short-term memory (LSTM). Finally, the method uses LSTM outputs the classification results. The experimental results demonstrate that the algorithm model can achieve an impressive accuracy of 99.032%, outperforming the combination of traditional feature extraction and machine learning methods. This model effectively recognizes and classifies images of pumping stations, significantly reducing the risk of accidents in these facilities.
High-frequency channel attention and contrastive learning for image super-resolution

Authors:
raw_author_name: Hujun Yin
raw_affiliation_string: Department of Electrical and Electronic Engineering, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK

Abstract: Abstract Over the last decade, convolutional neural networks (CNNs) have allowed remarkable advances in single image super-resolution (SISR). In general, recovering high-frequency features is crucial for high-performance models. High-frequency features suffer more serious damages than low-frequency features during downscaling, making it hard to recover edges and textures. In this paper, we attempt to guide the network to focus more on high-frequency features in restoration from both channel and spatial perspectives. Specifically, we propose a high-frequency channel attention (HFCA) module and a frequency contrastive learning (FCL) loss to aid the process. For the channel-wise perspective, the HFCA module rescales channels by predicting statistical similarity metrics of the feature maps and their high-frequency components. For the spatial perspective, the FCL loss introduces contrastive learning to train a spatial mask that adaptively assigns high-frequency areas with large scaling factors. We incorporate the proposed HFCA module and FCL loss into an EDSR baseline model to construct the proposed lightweight high-frequency channel contrastive network (HFCCN). Extensive experimental results show that it can yield markedly improved or competitive performances compared to the state-of-the-art networks of similar model parameters.