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

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

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

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

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

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

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.
Visualizing road connectivity under traffic influence: a CiteSpace-based method

The connectivity of roads under the influence of traffic is determined by the topological connections established by the road network and the transport connections formed by moving vehicles between two roads simultaneously. A thorough examination of this connectivity is crucial for comprehending traffic patterns and providing guidance to address transportation issues such as traffic congestion. However, existing methods ignore the intuitive representation and explanation of this connectivity, limiting the understanding of actual traffic patterns. This study aimed to intuitively visualize the connectivity of roads under the influence of traffic using CiteSpace, a bibliometric tool for analyzing the relationship between literature keywords. Here, the literature was replaced by trajectory, and keywords were replaced by roads. We then presented the connectivity of roads under the influence of traffic in a network, where nodes represented roads and edges represented the topological and transport connections between roads. A case study was conducted in Beijing, demonstrating the dynamic connectivity of roads under the influence of traffic in the visualized network. By comparing the visualized network with the topological road network, we identified three types of road connectivity under the influence of traffic: roads with topological connections but lacking transport connections, roads with transport connections but lacking topological connections, and roads with both topological and transport connections. This finding further revealed the traffic relationship between roads. Additionally, we simultaneously presented the multidimensional traffic characteristics in the visualized network, aiding in predicting traffic states.
PEDAGOGY OF THE FUTURE: ECONOMIC TRENDS IN THE EDUCATION OF THE NEW GENERATION

The paper examines the influence of economic factors on the development of education in the context of global changes and the requirements of modern society. The author analyzes current economic trends and their impact on the formation of educational strategies, methods and approaches. Special attention is paid to the transition to the digital economy, the growing importance of information skills and technological literacy. The issues of financing education, accessibility of quality education for all segments of the population, as well as the impact of global competition on educational programs and innovations are also considered.
TOPSIS-ACO based feature selection for multi-label classification

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%.
Text Mining Approaches to Language Use in Social Media: The Case of Portuguese Bué

This study describes processes of language change in Angolan Portuguese focusing on the use of intensifiers. Previous studies have shown that intensifiers are a relevant category for the study of language change due to their rapid change and variable meaning. It has been noted that intensifiers are particularly prone to renewal, suggesting speakers’ desire to innovate. Informed by a Digital Humanities approach, we collect and analyze data from Twitter (now X), focusing on the multi-functional intensifier bué, ‘very’, in Angolan Portuguese (AP). In this paper, we (1) provide an overview of the word’s distribution in AP, (2) consider the processes of change involved in bué’s variation, and (3) discuss the role of linguistic borrowing in language change and grammaticalization, shedding light on some of the cultural aspects that play a role in this word’s development, such as the influence of the media and the contact situation between Angolan and European Portuguese.
Effects of caring climate on career commitment for preschool teachers: the mediating roles of interpersonal trust and cooperative learning

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

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

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.
MANAGEMENT OF DISASTER THROUGH KNOWLEDGE MANAGEMENT SYSTEM: A CASE STUDY ON SAMBALPUR DISTRICT, ODISHA

Odisha is one of the hazard-prone state and suffers both natural and man-made hazards extensively threatening millions of lives and causing large-scale losses. Disaster management in India also saw a paradigm shift after 2002 from-the humanitarian relief and rehabilitation of victims to the holistic management of disasters viewpoint. The present research addressed this absence of information related to hazards and disasters affecting the district-level Sambalpur. This research aimed to find out some aid in the management of disasters at the district level by collecting and synthesizing all related information about disasters that occur in Sambalpur using an integrated approach. The research used publicly available tools as well as data from credible sources (e.g., government reports). To full-fill the aim, the research compiled information about disasters in the Sambalpur district and prepares a spatial database. Based on socio-economic factors-population density and working population, exposure of the blocks was calculated. Risk and exposure to it assisted in making a vulnerability map of the district at the block level. Based on vulnerability, blocks were prioritized for the policy-makers to ease their management in pre-during and post-disaster situations. The result of this paper would assist policy makers to take proper and quick decisions for the management of the blocks making their management efficient and effective and it could be replicated for other districts of Odisha and India.
Identifying families of multipartite states with non-trivial local entanglement transformations

The study of state transformations by spatially separated parties with local operations assisted by classical communication (LOCC) plays a crucial role in entanglement theory and its applications in quantum information processing. Transformations of this type among pure bipartite states were characterized long ago and have a revealing theoretical structure. However, it turns out that generic fully entangled pure multipartite states cannot be obtained from nor transformed to any inequivalent fully entangled state under LOCC. States with this property are referred to as isolated. Nevertheless, multipartite states are classified into families, the so-called SLOCC classes, which possess very different properties. Thus, the above result does not forbid the existence of particular SLOCC classes that are free of isolation, and therefore, display a rich structure regarding LOCC convertibility. In fact, it is known that the celebrated <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>-qubit GHZ and W states give particular examples of such classes and in this work, we investigate this question in general. One of our main results is to show that the SLOCC class of the 3-qutrit totally antisymmetric state is isolation-free as well. Actually, all states in this class can be converted to inequivalent states by LOCC protocols with just one round of classical communication (as in the GHZ and W cases). Thus, we consider next whether there are other classes with this property and we find a large set of negative answers. Indeed, we prove weak isolation (i.e., states that cannot be obtained with finite-round LOCC nor transformed by one-round LOCC) for very general classes, including all SLOCC families with compact stabilizers and many with non-compact stabilizers, such as the classes corresponding to the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>-qunit totally antisymmetric states for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi><mml:mo>&amp;#x2265;</mml:mo><mml:mn>4</mml:mn></mml:math>. Finally, given the pleasant feature found in the family corresponding to the 3-qutrit totally antisymmetric state, we explore in more detail the structure induced by LOCC and the entanglement properties within this class.
Visual analysis method for unmanned pumping stations on dynamic platforms based on data fusion technology

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.