Reviewerly/exHarmony huggingface dataset:
Dynamic optimization based on quantum computation-A comprehensive review

Authors:
raw_author_name: Yaowen Zhang
raw_affiliation_string: School of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China
raw_author_name: Heow Pueh Lee
raw_affiliation_string: Department of Mechanical Engineering, National University of Singapore, Singapore 117576, Singapore

Abstract: Solving dynamic optimization problems (DOPs) induced by time-varying optimization objective functions and constraints is challenging. Quantum computation has received considerable attention to accelerate the solution of such optimization problems in recent years, and quantum optimization algorithms have been developed. Yet, there are no comprehensive review papers on quantum optimization algorithms for DOPs. This paper presents a critical review of dynamic optimization based on quantum computing. A brief overview of dynamic optimization problems is first given. The existing quantum optimization algorithms are introduced in detail, including QPSO, QEA, QAA, QGA, QNN, etc. The principles of different quantum algorithms and their improved forms are presented and discussed. The optimization results obtained by different quantum optimization algorithms prove the superiority of quantum algorithms over traditional optimization algorithms for DOPs. In the near future, quantum computing applied to traditional intelligent algorithms can still be the platform for innovating new algorithms. The dynamic optimization based on quantum learning algorithms needs further research. The ultimate combination of quantum algorithms and quantum computers is still lacking. Developments in this area may show powerful capabilities to solve dynamic optimization problems.
HSB based reversible data hiding using sorting and pairwise expansion

Authors:
raw_author_name: Samayveer Singh
raw_affiliation_string: Department of Computer Science & Engineering, Dr B R Ambedkar National Institute of Technology Jalandhar, Punjab, India

Abstract: Reversible data hiding (RDH) continues to advance in its applicability across diverse fields, including medical imaging, military applications, and cloud computing. In recent times, there has been a proliferation of RDH methods that focus on embedding in the higher significant bit (HSB) plane, as opposed to the least significant bit (LSB), in order to leverage the greater correlation, thereby achieving a larger embedding capacity (EC). This paper introduces a novel and efficient RDH approach that utilizes sorting and pairwise expansion. The proposed method initiates by partitioning the original image into HSB and LSB planes and then converting the HSB plane into a chessboard pattern. Subsequently, pixels are organized in ascending order based on their predicted values, forming groups of 1 × 3 pixels. The embedding process occurs in two passes: the first employs a difference expansion-based RDH, while the second pass employs pairwise expansion to minimize distortion. This method accomplishes an expanded EC while preserving the quality of the resulting image. Experimental results also demonstrate that the proposed approach maintains a significant lead of approximately 0.5 dB over the best-performing existing methods, underscoring its superior embedding efficiency.
Investigating FAIR data principles compliance in horizon 2020 funded Agri-food and rural development multi-actor projects

Authors:
raw_author_name: Parveen Kumar
raw_affiliation_string: Institute of Data Science, Maastricht University, Maastricht, the Netherlands; Wageningen Environmental Research, Wageningen University & Research, the Netherlands
raw_author_name: Hercules Panoutsopoulos
raw_affiliation_string: Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, Athens, Greece; Institute of Data Science, Maastricht University, Maastricht, the Netherlands
raw_author_name: Christopher Brewster
raw_affiliation_string: Institute of Data Science, Maastricht University, Maastricht, the Netherlands; Netherlands Organisation for Applied Scientific Research (TNO), The Hague, the Netherlands

Abstract: The agri-food and rural development sector in Europe is undergoing digitalization, and therefore becoming increasingly data-dependent. However, limited experience in implementing Findable, Accessible, Interoperable, and Reusable (FAIR) data principles has restricted knowledge sharing and reusability. This study aims to investigate the existing FAIR Data Management practices in the agri-food and rural development sector by assessing the FAIRness of project outputs from recent H2020-funded Multi-Actor Projects (MAPs). We conducted a FAIRness assessment of project outputs using both semi-automatic and manual framework, and we also carried out a comprehensive review of the data sharing practices among selected MAPs. Of the investigated MAPs, <10% have achieved FAIR compliance and applied FAIR data management practices. The measured FAIRness of project products, including journal articles, conference papers, and books, is higher than that of other product types such as videos, audios, and presentations. The study highlights the critical need for standardizing the adoption of FAIR data principles and data management practices across both the agri-food and rural development sector but also across bureaucratic data management throughout Europe more widely. Such a change would facilitate broader utilization and re-use of MAPs outputs and results, enhancing decision-making and agri-food and rural development practices.
GST-based polarization approach to picking P-and S-waves arrival time for high-speed rail early warning system

Authors:
raw_author_name: Minjia Tan
raw_affiliation_string: School of Automation, Nanjing University of Science and Technology, Nanjing, 210094, China
raw_author_name: Qizhou Hu
raw_affiliation_string: School of Automation, Nanjing University of Science and Technology, Nanjing, 210094, China
raw_author_name: Yikai Wu
raw_affiliation_string: School of Automation, Nanjing University of Science and Technology, Nanjing, 210094, China

Abstract: The first arrival time of seismic P- and S- waves affects the determination of the beginning of an earthquake by the Earthquake Early Warning System for High-speed Rail (HSREEWs). Accordingly, we propose an approach to picking P- and S-waves arrival time for HSREEWs. This approach can treat the continuous seismic signals in real-time without prior knowledge of whether an earthquake occurs. Firstly, we replace the fixed time window of the S transform (ST) with the dynamic window of the generalized S transform (GST) to make the necessary time of signal processing more flexible. Meanwhile, the GST extends the sequential signal to the time-frequency domain (TF), and combines the TF polarization to construct four three-component polarization filters. Accordingly, the P-wave and S-wave can be separated from the seismic waveform by those filters, which effectively avoids the phenomenon that the S-wave phase is difficult to determine because of the interference of S-wave overlapping in the coda region. Secondly, based on the instantaneous frequency of seismic signal, we construct a new STA/LTA (GSPA-STA/LTA) with the characteristic function (CF) of constrained TF spectrum to pick the first arrivals of the P- and S-waves, and provide the corresponding automatic picking algorithm. Finally, we analyze 504 seismic events larger than Ms40 near the Japanese Tohoku Shinkansen to comprehensively verify the effectiveness of our approach. Since the P-wave and S-wave are first separated, the subsequent process just has to identify the first arrival times of their respective components. The approach has a good practical value in maintaining the safe operation of high-speed rail under earthquake, which meets the industry demands for speed, scale and accuracy in HSREEWs.
Zero-order fuzzy neural network with adaptive fuzzy partition and its applications on high-dimensional problems

Authors:
raw_author_name: Bingjie Zhang
raw_affiliation_string: School of Mathematical Sciences, Dalian University of Technology, Dalian, 116024, China
raw_author_name: Jian Wang
raw_affiliation_string: College of Science, China University of Petroleum (East China), Qingdao, 266580, China
raw_author_name: Chao Zhang
raw_affiliation_string: School of Mathematical Sciences, Dalian University of Technology, Dalian, 116024, China
raw_author_name: Jie Yang
raw_affiliation_string: Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province, Dalian, 116024, China; School of Mathematical Sciences, Dalian University of Technology, Dalian, 116024, China
raw_author_name: Tufan Kumbasar
raw_affiliation_string: Control and Automation Engineering Department, Istanbul Technical University, 34469 Istanbul, Turkey
raw_author_name: Wei Wu
raw_affiliation_string: School of Mathematical Sciences, Dalian University of Technology, Dalian, 116024, China

Abstract: The number of fuzzy rules in a fuzzy neural network usually depends on the grid-type partition or clustering-based partition. However, the number of grids or clusters needs to be set in advance and the initial number of fuzzy sets on each dimension is usually the same. This usually does not fit the real situation (for example, when forecasting the weather, various features such as temperature, humidity, wind direction, wind speed, etc., may have different numbers of fuzzy sets). In this paper, a novel zero-order fuzzy neural network with an adaptive fuzzy partition (AdFPFNN) is proposed, which does not need to preset the number of fuzzy sets and owns various numbers of fuzzy sets on different dimensions. The trick is to use the mean shift algorithm to perform adaptive clustering for each feature individually. For common fuzzy neural networks, dealing with high-dimensional problems is challenging work on account of the “fuzzy rule curse” and “computation underflow”. To overcome these issues, an improved version of AdFPFNN is devised to tackle high-dimensional problems, called PaCoAMF-based AdFPFNN. It adopts two techniques to overcome the “fuzzy rule curse” and “computation underflow”. One is the construction of a partially combined fuzzy rule base, which can generate rules that are adequate but do not grow exponentially with the number of features. Another is the proposal of an adaptive membership function that can guarantee the elimination of numerical underflow due to the product T-norm. Without feature selection or other dimensional reduction methods, the proposed PaCoAMF-based AdFPFNN can be directly used to solve high-dimensional problems. Simulation results on one constructed dataset and 13 real-world datasets (including 5 high-dimensional problems) confirm the effectiveness of the proposed models.
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing

Authors:
raw_author_name: Nicolò Dal Fabbro
raw_affiliation_string: Department of Information Engineering, University of Padova, Italy
raw_author_name: Subhrakanti Dey
raw_affiliation_string: Department of Electrical Engineering, Uppsala University, Sweden
raw_author_name: Michele Rossi
raw_affiliation_string: Department of Information Engineering, University of Padova, Italy; Department of Mathematics “Tullio Levi-Civita”, University of Padova, Italy
raw_author_name: Luca Schenato
raw_affiliation_string: Department of Information Engineering, University of Padova, Italy

Abstract: There is a growing interest in the distributed optimization framework that goes under the name of Federated Learning (FL). In particular, much attention is being turned to FL scenarios where the network is strongly heterogeneous in terms of communication resources (e.g., bandwidth) and data distribution. In these cases, communication between local machines (agents) and the central server (Master) is a main consideration. In this work, we present SHED, an original communication-constrained Newton-type (NT) algorithm designed to accelerate FL in such scenarios. SHED is by design robust to non independent identically distributed (non i.i.d.) data distributions, handles heterogeneity of agents’ communication resources (CRs), only requires sporadic Hessian computations, and achieves global asymptotic super-linear convergence. This is possible thanks to an incremental strategy, based on eigendecomposition of the local Hessian matrices, which exploits (possibly) outdated second-order information. SHED is thoroughly validated on real datasets by assessing (i) the number of communication rounds required for convergence, (ii) the overall amount of data transmitted and (iii) the number of local Hessian computations. For all these metrics, SHED shows superior performance against state-of-the art techniques like BFGS, GIANT and FedNL.
Dealing with Drift of Adaptation Spaces in Learning-based Self-Adaptive Systems Using Lifelong Self-Adaptation

Authors:
raw_author_name: Omid Gheibi
raw_affiliation_string: Katholieke Universiteit Leuven, Belgium
raw_author_name: Danny Weyns
raw_affiliation_string: Katholieke Universiteit Leuven, Belgium, Linnaeus University, Sweden

Abstract: Recently, machine learning (ML) has become a popular approach to support self-adaptation. ML has been used to deal with several problems in self-adaptation, such as maintaining an up-to-date runtime model under uncertainty and scalable decision-making. Yet, exploiting ML comes with inherent challenges. In this article, we focus on a particularly important challenge for learning-based self-adaptive systems: drift in adaptation spaces. With adaptation space, we refer to the set of adaptation options a self-adaptive system can select from to adapt at a given time based on the estimated quality properties of the adaptation options. A drift of adaptation spaces originates from uncertainties, affecting the quality properties of the adaptation options. Such drift may imply that the quality of the system may deteriorate, eventually, no adaptation option may satisfy the initial set of adaptation goals, or adaptation options may emerge that allow enhancing the adaptation goals. In ML, such a shift corresponds to a novel class appearance, a type of concept drift in target data that common ML techniques have problems dealing with. To tackle this problem, we present a novel approach to self-adaptation that enhances learning-based self-adaptive systems with a lifelong ML layer. We refer to this approach as lifelong self-adaptation . The lifelong ML layer tracks the system and its environment, associates this knowledge with the current learning tasks, identifies new tasks based on differences, and updates the learning models of the self-adaptive system accordingly. A human stakeholder may be involved to support the learning process and adjust the learning and goal models. We present a general architecture for lifelong self-adaptation and apply it to the case of drift of adaptation spaces that affects the decision-making in self-adaptation. We validate the approach for a series of scenarios with a drift of adaptation spaces using the DeltaIoT exemplar.
Two-stage GNN-based fraud detection with camouflage identification and enhanced semantics aggregation

Authors:
raw_author_name: Jun Zhang
raw_affiliation_string: State Key Laboratory of Public Big Data, Guizhou University, Guiyang, Guizhou, China
raw_author_name: Xianghong Tang
raw_affiliation_string: State Key Laboratory of Public Big Data, Guizhou University, Guiyang, Guizhou, China

Abstract: Graph neural network (GNN) has got lots of attention recently in the fraud detection task due to its message-passing mechanism, which can aggregate neighbors feature in the information network. While promising, currently most GNN-based fraud detectors fail to identify camouflage caused by fraud with graph structure information and embed entity in deep layer with rich semantics effectively. To solve these problems, we propose a two-stage GNN-based approach with camouflage identification and enhanced semantics aggregation (CIES-GNN) for fraud detection. In the proposed approach, camouflage is identified by reconstructing subgraphs with both node feature and structure information. Detailedly, hidden edges between fraudsters are found by reconstructing a dense subgraph of fraudster nodes, and redundant connections between benign nodes and fraudster nodes are eliminated by reconstructing subgraphs in a label-balance distribution. Moreover, to embed entity in deep layer without semantics obfuscation, the node information is embedded in an enhanced semantics aggregation module, which fuses higher-order information in intra-relation and aggregates semantics in inter-relation respectively. Experiments on two real-world datasets demonstrate that the proposed CIES-GNN outperforms state-of-the-art baselines.
Entity recognition method for airborne products metrological traceability knowledge graph construction

Authors:
raw_author_name: Shengjie Kong
raw_affiliation_string: College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
raw_author_name: Xiang Huang
raw_affiliation_string: College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
raw_author_name: Mingye Yang
raw_affiliation_string: College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Abstract: The airborne system, as one of the complex and extensive subsystems of an aircraft, primarily performs critical flight assurance functions. The quality of its components has a direct impact on the aircraft's safety and reliability. Metrology documents comprehensively document the performance parameters throughout the entire product life cycle. The Metrological Traceability Knowledge Graph (MTKG) for airborne products offers decision support to engineers engaged in metrological tasks, ensuring the continuous high quality of the products. This paper introduces an entity recognition method for airborne product metrological traceability knowledge graph construction. First, the ontology for MTKG is developed. Next, a fine-tuned multi-network model is proposed. Named entities in the field of metrology are recognized through three stages: word vector representation, sentence feature extraction, and optimal label assignment. Meanwhile, active learning methods are incorporated to reduce the expense of data annotation. The proposed model is validated using an actual metrology corpus, and the experimental results demonstrate its superior performance compared to the other four baseline methods. Finally, the MTKG is developed using this approach, offering engineers intelligent applications, including metrological traceability analysis and traceability path reasoning within the process of product metrology. This enhances the metrology capabilities of airborne products and demonstrates the extensive potential of knowledge graphs in metrology.
Safe multi-agent drone control using control barrier functions and acceleration fields

Authors:
raw_author_name: Vinicius Mariano Gonçalves
raw_affiliation_string: Center for Artificial Intelligence and Robotics (CAIR), New York University Abu Dhabi, United Arab Emirates
raw_author_name: Dimitris Chaikalis
raw_affiliation_string: New York University, Electrical & Computer Engineering Department, Brooklyn, NY 11201, USA
raw_author_name: Anthony Tzes
raw_affiliation_string: Center for Artificial Intelligence and Robotics (CAIR), New York University Abu Dhabi, United Arab Emirates; New York University Abu Dhabi, Electrical Engineering, Abu Dhabi 129188, United Arab Emirates
raw_author_name: Farshad Khorrami
raw_affiliation_string: Center for Artificial Intelligence and Robotics (CAIR), New York University Abu Dhabi, United Arab Emirates; New York University, Electrical & Computer Engineering Department, Brooklyn, NY 11201, USA

Abstract: The objective of this article is to develop real-time controllers for positioning multi-agent autonomous platforms (specifically drones) without any collisions. The framework is divided in two controllers. The first, based on control barrier functions and quadratic programming, is an acceleration field, that computes an acceleration for each position and velocity of the system that guides the agents towards the goal while avoiding obstacles and inter-collisions. The second controls the agents’ total thrusts and angular velocities to track the previously-defined acceleration profiles. We show formal guarantees for both controllers, and simulation and experimental studies are used to show the efficacy of the proposed approach.
Interpretable hierarchical belief rule base expert system for complex system modeling

Authors:
raw_author_name: You Cao
raw_affiliation_string: High-tech Institute, Qing Zhou, Shandong 262500, PR China
raw_author_name: Shuaiwen Tang
raw_affiliation_string: College of Systems Engineering, National University of Defense Technology, Changsha, Hunan 410073, PR China
raw_author_name: Leilei Chang
raw_affiliation_string: School of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, PR China

Abstract: Belief rule base (BRB) expert system has been widely used in complex system modeling. Readability and consistency are the primary characteristics and advantages of BRB. However, these two characteristics may be lost in BRB establishment and optimization. Aiming at this problem, a novel hierarchical BRB (H-BRB) expert system is proposed. To quantify the interpretability of BRB, the definition of interpretability of BRB is proposed from the readability and the consistency. Two strategies are proposed to construct the H-BRB expert system with good readability. A new optimization method is proposed to guarantee the consistency of H-BRB, including a new optimization mechanism, a new objective function with interpretability constraints, and a modified optimization algorithm. With all the above, an H-BRB expert system with good readability and consistency can be established. A case study of the health state assessment of the aerospace relay is implemented to verify the effectiveness of the proposed method.