Reviewerly/exHarmony huggingface dataset:
BDIDA-IoT: A Blockchain-Based Decentralized Identity Architecture Enhances the Efficiency of IoT Data Flow

Authors:
raw_author_name: Zequan Yang
raw_affiliation_string: College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China
raw_author_name: Yumeng Liu
raw_affiliation_string: College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China
raw_author_name: Xiaopeng Jin
raw_affiliation_string: College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China
raw_author_name: Xiaoling Luo
raw_affiliation_string: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518061, China
raw_author_name: Yuan Xu
raw_affiliation_string: College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China
raw_author_name: Meng Li
raw_affiliation_string: College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China
raw_author_name: Peng Chen
raw_affiliation_string: College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China
raw_author_name: Bixia Tang
raw_affiliation_string: College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China

Abstract: With the booming development of the Internet of Things (IoT) industry, millions of data are generated every day. How to use and manage these data safely and efficiently has become a hot issue of concern to people. Due to the accumulation of IoT data, the isolated data island phenomenon makes it difficult to connect and interact with each data owner, and the security and privacy of IoT data also become a challenge. Blockchain is a decentralized database technology that uses distributed accounting to ensure reliable data transmission and access, along with smart contracts that can be executed automatically to program and manipulate data. At the same time, blockchain techniques have stronger security and privacy, which can better meet the needs of users. In this paper, we analyze the current management mode and challenges of IoT data and propose an IoT data platform based on blockchain. The proposed platform takes into account important factors including IoT data traceability, IoT data trusted transactions, etc. Our IoT data platform achieves the trusted management and transaction of IoT data. We also propose follow-up optimization solutions to expand the application scope of our platform and achieve more efficient management of IoT data.
Iterative Camera Calibration Method Based on Concentric Circle Grids

Authors:
raw_author_name: Liang Wei
raw_affiliation_string: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
raw_author_name: Ju Huo
raw_affiliation_string: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
raw_author_name: Lin Yue
raw_affiliation_string: Signal and Communication Research Institute, China Academy of Railway Sciences, Beijing 100081, China

Abstract: A concentric circle target is commonly used in the vision measurement system for its detection accuracy and robustness. To enhance the camera calibration accuracy, this paper proposes an improved calibration method that utilizes concentric circle grids as the calibration target. The method involves accurately locating the imaged center and optimizing camera parameters. The imaged concentric circle center obtained by cross-ratio invariance is not affected by perspective projection, which ensures the location accuracy of the feature point. Subsequently, the impact of lens distortion on camera calibration is comprehensively investigated. The sub-pixel coordinates of imaged centers are taken into the iterative calibration method, and camera parameters are updated. Through simulations and real experiments, the proposed method effectively reduces the residual error and improves the accuracy of camera parameters.
Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example

Authors:
raw_author_name: Xiaoke Huang
raw_affiliation_string: Control Science and Engineering with the State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
raw_author_name: Chunjie Yang
raw_affiliation_string: Control Science and Engineering with the State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China

Abstract: Industrial knowledge graphs (IKGs) have received widespread attention from researchers in recent years; they are intuitive to humans and can be understood and processed by machines. However, how to update the entity triples in the graph based on the continuous production data to cover as much knowledge as possible, while applying a KG to meet the needs of different industrial tasks, are two difficulties. This paper proposes a two-stage model construction strategy to benefit both knowledge graph completion and industrial tasks. Firstly, this paper summarizes the specific forms of multi-source data in industry and provides processing methods for each type of data. The core is to vectorize the data and align it conceptually, thereby achieving the fusion modeling of multi-source data. Secondly, this paper defines two interrelated subtasks to construct a pretrained language–knowledge graph model based on multi-task learning. At the same time, considering the dynamic characteristics of the production process, a dynamic expert network structure is adopted for different tasks combined with the pretrained model. In the knowledge completion task, the proposed model achieved an accuracy of 91.25%, while in the self-healing control task of a blast furnace, the proposed model reduced the incorrect actions rate to 0 and completed self-healing control for low stockline fault in 278 min. The proposed framework has achieved satisfactory results in experiments, which verifies the effectiveness of introducing knowledge into industry.
Micro-FL: A Fault-Tolerant Scalable Microservice-Based Platform for Federated Learning

Authors:
raw_author_name: Mikael Sabuhi
raw_affiliation_string: Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
raw_author_name: Petr Musilek
raw_affiliation_string: Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
raw_author_name: Cor-Paul Bezemer
raw_affiliation_string: Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada

Abstract: As the number of machine learning applications increases, growing concerns about data privacy expose the limitations of traditional cloud-based machine learning methods that rely on centralized data collection and processing. Federated learning emerges as a promising alternative, offering a novel approach to training machine learning models that safeguards data privacy. Federated learning facilitates collaborative model training across various entities. In this approach, each user trains models locally and shares only the local model parameters with a central server, which then generates a global model based on these individual updates. This approach ensures data privacy since the training data itself is never directly shared with a central entity. However, existing federated machine learning frameworks are not without challenges. In terms of server design, these frameworks exhibit limited scalability with an increasing number of clients and are highly vulnerable to system faults, particularly as the central server becomes a single point of failure. This paper introduces Micro-FL, a federated learning framework that uses a microservices architecture to implement the federated learning system. It demonstrates that the framework is fault-tolerant and scalable, showing its ability to handle an increasing number of clients. A comprehensive performance evaluation confirms that Micro-FL proficiently handles component faults, enabling a smooth and uninterrupted operation.
Dynamic Screening Strategy Based on Feature Graphs for UAV Object and Group Re-Identification

Authors:
raw_author_name: Guoqing Zhang
raw_affiliation_string: Jiangsu Key Laboratory of Image and Video Understanding for Social Safety, Nanjing University of Science and Technology, Nanjing 210094, China; School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
raw_author_name: Tianqi Liu
raw_affiliation_string: School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
raw_author_name: Zhonglin Ye
raw_affiliation_string: The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Qinghai Normal University, Xining 810008, China

Abstract: In contemporary times, owing to the swift advancement of Unmanned Aerial Vehicles (UAVs), there is enormous potential for the use of UAVs to ensure public safety. Most research on capturing images by UAVs mainly focuses on object detection and tracking tasks, but few studies have focused on the UAV object re-identification task. In addition, in the real-world scenarios, objects frequently get together in groups. Therefore, re-identifying UAV objects and groups poses a significant challenge. In this paper, a novel dynamic screening strategy based on feature graphs framework is proposed for UAV object and group re-identification. Specifically, the graph-based feature matching module presented aims to enhance the transmission of group contextual information by using adjacent feature nodes. Additionally, a dynamic screening strategy designed attempts to prune the feature nodes that are not identified as the same group to reduce the impact of noise (other group members but not belonging to this group). Extensive experiments have been conducted on the Road Group, DukeMTMC Group and CUHK-SYSU-Group datasets to validate our framework, revealing superior performance compared to most methods. The Rank-1 on CUHK-SYSU-Group, Road Group and DukeMTMC Group datasets reaches 71.8%, 86.4% and 57.8%, respectively. Meanwhile, our method performance is explored on the UAV datasets of PRAI-1581 and Aerial Image, the infrared datasets of SYSU-MM01 and CM-Group and the NIR dataset of RBG-NIR Scene dataset; the unexpected findings demonstrate the robustness and wide applicability of our method.
A Transfer Learning-Enhanced Generative Adversarial Network for Downscaling Sea Surface Height through Heterogeneous Data Fusion

Authors:
raw_author_name: Hong Zheng
raw_affiliation_string: School of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China

Abstract: In recent decades, satellites have played a pivotal role in observing ocean dynamics, providing diverse datasets with varying spatial resolutions. Notably, within these datasets, sea surface height (SSH) data typically exhibit low resolution, while sea surface temperature (SST) data have significantly higher resolution. This study introduces a Transfer Learning-enhanced Generative Adversarial Network (TLGAN) for reconstructing high-resolution SSH fields through the fusion of heterogeneous SST data. In contrast to alternative deep learning approaches that involve directly stacking SSH and SST data as input channels in neural networks, our methodology utilizes bifurcated blocks comprising Residual Dense Module and Residual Feature Distillation Module to extract features from SSH and SST data, respectively. A pixelshuffle module-based upscaling block is then concatenated to map these features into a common latent space. Employing a hybrid strategy involving adversarial training and transfer learning, we overcome the limitation that SST and SSH data should share the same time dimension and achieve significant resolution enhancement in SSH reconstruction. Experimental results demonstrate that, when compared to interpolation method, TLGAN effectively reduces reconstruction errors and fusing SST data could significantly enhance in generating more realistic and physically plausible results.
Exploring Semantic Prompts in the Segment Anything Model for Domain Adaptation

Authors:
raw_author_name: Ziquan Wang
raw_affiliation_string: School of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China
raw_author_name: Yongsheng Zhang
raw_affiliation_string: School of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China
raw_author_name: Zhenchao Zhang
raw_affiliation_string: School of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China
raw_author_name: Zhipeng Jiang
raw_affiliation_string: School of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China
raw_author_name: Ying Yu
raw_affiliation_string: School of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China
raw_author_name: Li Li
raw_affiliation_string: School of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China
raw_author_name: Lei Li
raw_affiliation_string: School of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China

Abstract: Robust segmentation in adverse weather conditions is crucial for autonomous driving. However, these scenes struggle with recognition and make annotations expensive, resulting in poor performance. As a result, the Segment Anything Model (SAM) was recently proposed to finely segment the spatial structure of scenes and to provide powerful prior spatial information, thus showing great promise in resolving these problems. However, SAM cannot be applied directly for different geographic scales and non-semantic outputs. To address these issues, we propose SAM-EDA, which integrates SAM into an unsupervised domain adaptation mean-teacher segmentation framework. In this method, we use a “teacher-assistant” model to provide semantic pseudo-labels, which will fill in the holes in the fine spatial structure given by SAM and generate pseudo-labels close to the ground truth, which then guide the student model for learning. Here, the “teacher-assistant” model helps to distill knowledge. During testing, only the student model is used, thus greatly improving efficiency. We tested SAM-EDA on mainstream segmentation benchmarks in adverse weather conditions and obtained a more-robust segmentation model.
DMCVS: Decomposed motion compensation‐based video stabilization

Authors:
raw_author_name: Qian Huang
raw_affiliation_string: Key Laboratory of Water Resources Big Data Ministry of Water Resources Hohai University Nanjing China
raw_author_name: Jiwen Liu
raw_affiliation_string: Key Laboratory of Water Resources Big Data Ministry of Water Resources Hohai University Nanjing China
raw_author_name: Chuanxu Jiang
raw_affiliation_string: Key Laboratory of Water Resources Big Data Ministry of Water Resources Hohai University Nanjing China
raw_author_name: Yiming Wang
raw_affiliation_string: Key Laboratory of Water Resources Big Data Ministry of Water Resources Hohai University Nanjing China

Abstract: Abstract With the popularity of handheld devices, video stabilization is becoming increasingly important. In previous studies, many methods have been proposed to stabilize shaky videos. However, these methods fail to balance between image content integrity and stability. Some methods sacrifice image content for better stability. Other methods ignore the subtle jitters, which leads to poor stability. This work innovatively proposes a video stabilization method based on decomposed motion compensation. First, a grid‐based motion statistics method is adopted for motion estimation, which obtains more accurate motion vectors according to matched likelihood estimates. Then, the motion compensation is inherently decomposed into two parts: linear motion compensation and auxiliary motion compensation. Linear motion compensation removes complex jitter by constructing linear path constraints to obtain a more stable camera path. Auxiliary motion compensation uses a moving average filter to remove the high‐frequency jitter as a supplement and preserve more image content. The two components are combined with individual weights to derive the final transform matrix and warp the original frames. Experimental results show that our method outperforms the previous methods on NUS and DeepStab datasets qualitatively and quantitatively.
Coin dimensionality as a resource in quantum metrology involving discrete-time quantum walks

Authors:
raw_author_name: Simone Cavazzoni
raw_affiliation_string: Dipartimento di Scienze Fisiche, Informatiche e Matematiche, Università di Modena e Reggio Emilia, I-41125 Modena, Italy
raw_author_name: Luca Razzoli
raw_affiliation_string: Center for Nonlinear and Complex Systems, Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell'Insubria, I-22100 Como, Italy; Istituto Nazionale di Fisica Nucleare, Sezione di Milano, I-20133 Milano, Italy
raw_author_name: Giovanni Ragazzi
raw_affiliation_string: Dipartimento di Scienze Fisiche, Informatiche e Matematiche, Università di Modena e Reggio Emilia, I-41125 Modena, Italy
raw_author_name: Paolo Bordone
raw_affiliation_string: Centro S3, CNR–Istituto di Nanoscienze, I-41125 Modena, Italy; Dipartimento di Scienze Fisiche, Informatiche e Matematiche, Università di Modena e Reggio Emilia, I-41125 Modena, Italy
raw_author_name: Matteo G. A. Paris
raw_affiliation_string: Istituto Nazionale di Fisica Nucleare, Sezione di Milano, I-20133 Milano, Italy; Quantum Technology Lab, Dipartimento di Fisica Aldo Pontremoli, Università degli Studi di Milano, I-20133 Milano, Italy

Abstract: We address metrological problems where the parameter of interest is encoded in the internal degree of freedom of a discrete-time quantum walker, and provide evidence that coin dimensionality is a potential resource to enhance precision. In particular, we consider estimation problems where the coin parameter governs rotations around a given axis and show that the corresponding quantum Fisher information (QFI) may increase with the dimension of the coin. We determine the optimal initial state of the walker to maximize the QFI and discuss whether, and to what extent, precision enhancement may be achieved by measuring only the position of the walker. Finally, we consider Grover-like encoding of the parameter and compare results with those obtained from rotation encoding.
Learning from artificial intelligence researchers about international business implications

Authors:
raw_author_name: Vanessa Ratten
raw_affiliation_string: La Trobe Business School La Trobe University  Melbourne Australia
raw_author_name: Rakibul Hasan
raw_affiliation_string: School of Marketing and Communication, International Business University of Vaasa  Vaasa Finland
raw_author_name: Deepak Kumar
raw_affiliation_string: Department of Industrial and Management Engineering, Department of Computer Science and Information Technology Indian Institute of Technology Kanpur, La Trobe University  Melbourne, Kanpur Australia, India
raw_author_name: Arto Ojala
raw_affiliation_string: School of Marketing and Communication, International Business University of Vaasa  Vaasa Finland
raw_author_name: Yashar Salamzadeh
raw_affiliation_string: University of Sunderland Business School University of Sunderland  Sunderland UK

Abstract: Abstract Artificial intelligence is a dynamic and emerging form of technological innovation that has numerous ramifications for international business managers. The aim of this article is to obtain commentary from researchers about the role artificial intelligence will play in the global arena. This includes asking questions about how it will affect internationalization processes and whether it will lead to more international collaboration. Well‐known researchers provide advice on what international business managers should do in terms of staying competitive but also how they can integrate learning from artificial intelligence into their business operations. Lastly, suggestions for future research regarding the interplay between international business and artificial intelligence are provided.
Conflict-free joint decision by lag and zero-lag synchronization in laser network

Authors:
raw_author_name: Takatomo Mihana
raw_affiliation_string: Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan
raw_author_name: Ryoichi Horisaki
raw_affiliation_string: Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan
raw_author_name: Makoto Naruse
raw_affiliation_string: Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan

Abstract: Abstract With the end of Moore’s Law and the increasing demand for computing, photonic accelerators are garnering considerable attention. This is due to the physical characteristics of light, such as high bandwidth and multiplicity, and the various synchronization phenomena that emerge in the realm of laser physics. These factors come into play as computer performance approaches its limits. In this study, we explore the application of a laser network, acting as a photonic accelerator, to the competitive multi-armed bandit problem. In this context, conflict avoidance is key to maximizing environmental rewards. We experimentally demonstrate cooperative decision-making using zero-lag and lag synchronization within a network of four semiconductor lasers. Lag synchronization of chaos realizes effective decision-making and zero-lag synchronization is responsible for the realization of the collision avoidance function. We experimentally verified a low collision rate and high reward in a fundamental 2-player, 2-slot scenario, and showed the scalability of this system. This system architecture opens up new possibilities for intelligent functionalities in laser dynamics.
Fast elementary gates for universal quantum computation with Kerr parametric oscillator qubits

Authors:
raw_author_name: Taro Kanao
raw_affiliation_string: Frontier Research Laboratory, Corporate Research & Development Center, Toshiba Corporation, 1 Komukai-Toshiba-cho, Saiwai-ku, Kawasaki 212-8582, Japan
raw_author_name: Hayato Goto
raw_affiliation_string: Frontier Research Laboratory, Corporate Research & Development Center, Toshiba Corporation, 1 Komukai-Toshiba-cho, Saiwai-ku, Kawasaki 212-8582, Japan

Abstract: Kerr parametric oscillators (KPOs) can stabilize the superpositions of coherent states, which can be utilized as qubits, and are promising candidates for realizing hardware-efficient quantum computers. Although elementary gates for universal quantum computation with KPO qubits have been proposed, these gates are usually based on adiabatic operations and thus need long gate times, which result in errors caused by photon loss in KPOs realized by, e.g., superconducting circuits. In this work we accelerate the elementary gates by experimentally feasible control methods, which are based on numerical optimization of pulse shapes for shortcuts to adiabaticity. By numerical simulations, we show that the proposed methods can achieve speedups compared to adiabatic ones by up to six times with high gate fidelities of 99.9%. These methods are thus expected to be useful for quantum computers with KPOs.