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arXiv 2025年

作者： Li, Haiyang Liao, Hao Wang, Ping Faculty of Arts and Sciences Beijing Normal University Zhuhai519087 China National Engineering Laboratory on Big Data System Computing Technology Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

Hyper-polarization of nuclear spins is crucial for advancing nuclear magnetic resonance (NMR) and quantum information technologies, as nuclear spins typically exhibit extremely low polarization at room temperature due to their small gyro-magnetic ratios. A promising approach to achieving high nuclear spin polarization is transferring the polarization of electron to nuclear spin. The nitrogen-vacancy (NV) center in diamond has emerged as a highly effective medium for this purpose, and various hyper-polarization protocols have been developed. Among these, the pulsed polarization (PulsePol) method has been extensively studied due to its robustness against static energy shifts of the electron spin. In this study, we introduce a sequential polarization protocol and identify a series of magic and digital sequences for hyper-polarizing nuclear spins. Notably, we demonstrate that some of these magic sequences exhibit significantly greater robustness compared to the PulsePol protocol in the presence of finite half π pulse duration of the protocol. This enhanced robustness positions our protocol as a more suitable candidate for hyper-polarizing nuclear spins species with large gyromagnetic ratios and also ensures better compatibility with high-efficiency readout techniques at high magnetic fields. Additionally, the generality of our protocol allows for its direct application to other solid-state quantum systems beyond the NV center. © 2025, CC BY.

关键词： Chemical shift

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DAC-MD: A privacy preserving drone-access control scheme for last mile delivery

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Transactions on Emerging Telecommunications Technologies 2024年第3期35卷

作者： Irshad, Azeem Farooq, Muhammad Mahmood, Khalid Mallah, Ghulam Ali Chaudhry, Shehzad Ashraf Department of Computer Science and Software Engineering International Islamic University Islamabad Pakistan Department of Mathematics and Statistics College of Arts and Sciences Abu Dhabi University Abu Dhabi United Arab Emirates Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Douliou Taiwan Department of Computer Science Shah Abdul Latif University Sindh Khairpur Pakistan Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates Department of Software Engineering Faculty of Engineering and Architecture Nisantasi University Istanbul Turkey

The service delivery has lately witnessed strides in the form of paradigm shifts from conventional logistics to drone-oriented supply chain to conserve ecosystem. The use of drones or unmanned aerial vehicles (UAVs) for package deliveries offer enhanced accessibility, flexibility, efficiency, speed, safety, and even few environmental gains. However, the drones can come across various security hazards while communicating with ground installations on open channels including physical capture attack, privacy infringement, and so on. Thus, earnest endeavors are needed to fix those security challenges in applications related to drone communications. To address these problems, an efficient authenticated key agreement technique, drone access control scheme for last mile delivery (DAC-MD) is suggested for user-drone interactions in IoD environment that help the participating entities in developing a mutually agreed session key for securing subsequent communication interactions. The proposed scheme is proved using automated ProVerif tool as well as analyzed by using formal analysis based on RoR random oracle model. The comparative analysis against recently presented known studies reveals that our scheme not only carries comparable properties in terms of security and privacy, but also low computational and communication costs. © 2024 John Wiley & Sons, Ltd.

关键词： Drones

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Contrastive Independent Subspace Analysis Network for Multi-View Spatial Information Extraction

SSRN

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SSRN 2024年

作者： Zhang, Tengyu Zeng, Deyu Liu, Wei Wu, Zongze Ding, Chris Zhong, Xiaopin College of Mechatronics and Control Engineering Shenzhen University Guangdong Shenzhen China College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen China School of Software Engineering Xi’an Jiaotong University Shaanxi Xian China Shenzhen University Guangdong Shenzhen China School of Computer Science Wuhan University Hubei Wuhan China School of Data Science The Chinese University of Hong Kong Guangdong Shenzhen China

Multi-view classification optimally integrates various features from different views to improve classification tasks. Most of the existing works typically utilize semantic information to achieve view fusion but neglect the spatial information of data itself, which accommodates data representation with correlation information and is proven to be an essential aspect. Thus we present a novel contrastive independent subspace analysis framework for multi-view classification (MvCISA). The core idea is utilizing robust Independent Subspace Analysis (RISA) Network to extract the latent spatial information of multi-view data with subspace bases, which is further optimized from spatial perspective with proposed multi-view contrastive feature-subspace fusion learning (MvCF) to find a nonoverlapping data representation. Extensive experiments on several public datasets demonstrate that the subspace representation generated by our method is rational and effective for classification, and is promising for other downstream tasks. Our code is available at https://***/raRn0y/MvCISA. © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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An Efficient Security Scheme For Charging Piles 5

An Efficient Security Scheme For Charging Piles

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5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021

作者： Sun, Xiaoxin Huang, Jiajia Li, Jianshuang Yin, Haotian Zhang, Xin Kan, Jia Huang, Xin Department of Taiyuan University of Technology College of Data Science Taiyuan China Department of Computer Science and Software Engineering Xi 'an Jiaotong-Livepool University Suzhou China

ISBN: (纸本)9781665434256

Electric bicycles are popular among citizens because of their green, environmentally friendly and convenient features. However, due to the limited battery capacity of electric bicycles, charging problems have become the main factor limiting its further development. This paper proposes a secure payment scheme for charging piles based on unbalanced asymmetric authenticated key exchange (AKE). To ensure the security of the charging and billing process, the user needs to authenticate to the charging pile for subsequent communication. The scheme can allow users to effectively charge while ensuring their payment security. © 2021 IEEE

关键词： Piles

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CEGRL-TKGR: A Causal Enhanced Graph Representation Learning Framework for Temporal Knowledge Graph Reasoning

arXiv

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arXiv 2024年

作者： Sun, Jinze Sheng, Yongpan He, Lirong Qin, Yongbin Liu, Ming Jia, Tao College of Computer and Information Science Southwest University Chongqing400715 China School of Big Data & Software Engineering Chongqing University Chongqing401331 China School of Information Science and Engineering Chongqing Jiaotong University Chongqing400074 China College of Computer Science and Technology The State Key Laboratory of Public Big Data Guizhou University China Deakin University VIC3125 Australia

Temporal knowledge graph reasoning (TKGR) is increasingly gaining attention for its ability to extrapolate new events from historical data, thereby enriching the inherently incomplete temporal knowledge graphs. Existing graph-based representation learning frameworks have made significant strides in developing evolving representations for both entities and relational embeddings. Despite these achievements, there’s a notable tendency in these models to inadvertently learn biased data representations and mine spurious correlations, consequently failing to discern the causal relationships between events. This often leads to incorrect predictions based on these false correlations. To address this, we propose an innovative Causal Enhanced Graph Representation Learning framework for TKGR (named CEGRL-TKGR). This framework introduces causal structures in graph-based representation learning to unveil the essential causal relationships between events, ultimately enhancing the performance of the TKGR task. Specifically, we first disentangle the evolutionary representations of entities and relations in a temporal knowledge graph sequence into two distinct components, namely causal representations and confounding representations. Then, drawing on causal intervention theory, we advocate the utilization of causal representations for predictions, aiming to mitigate the effects of erroneous correlations caused by confounding features, thus achieving more robust and accurate predictions. Finally, extensive experimental results on six benchmark datasets demonstrate the superior performance of our model in the link prediction task. © 2024, CC BY.

关键词： Spatio-temporal data

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G2Pxy: Generative Open-Set Node Classification on Graphs with Proxy Unknowns

arXiv

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arXiv 2023年

作者： Zhang, Qin Shi, Zelin Zhang, Xiaolin Chen, Xiaojun Fournier-Viger, Philippe Pan, Shirui Big Data Institute College of Computer Science and Software Engineering Shenzhen University China School of Information and Communication Technology Griffith University Queensland Australia

Node classification is the task of predicting the labels of unlabeled nodes in a graph. State-of-the-art methods based on graph neural networks achieve excellent performance when all labels are available during training. But in real-life, models are often applied on data with new classes, which can lead to massive misclassification and thus significantly degrade performance. Hence, developing open-set classification methods is crucial to determine if a given sample belongs to a known class. Existing methods for open-set node classification generally use transductive learning with part or all of the features of real unseen class nodes to help with open-set classification. In this paper, we propose a novel generative open-set node classification method, i.e., G2Pxy, which follows a stricter inductive learning setting where no information about unknown classes is available during training and validation. Two kinds of proxy unknown nodes, inter-class unknown proxies and external unknown proxies are generated via mixup to efficiently anticipate the distribution of novel classes. Using the generated proxies, a closed-set classifier can be transformed into an open-set one, by augmenting it with an extra proxy classifier. Under the constraints of both cross entropy loss and complement entropy loss, G2Pxy achieves superior effectiveness for unknown class detection and known class classification, which is validated by experiments on benchmark graph datasets. Moreover, G2Pxy does not have specific requirement on the GNN architecture and shows good generalizations. Copyright © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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Multimodal Hybrid Approach for Fine-Grained Classification of Diverse Dermatological Conditions

Multimodal Hybrid Approach for Fine-Grained Classification o...

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Intelligent Perception and Computer Vision (CIPCV), International Conference on

作者： Kai Kang Jianyu Xu Shilong An Jilin Chen Renfang Wang Han Wang Faculty of Information Science and Engineering Ocean University of China Qingdao China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China Qingdao Municipal Center For Disease Control Prevention Qingdao China

ISBN: (数字)9798350360097

ISBN: (纸本)9798350360103

Among the spectrum of dermatological conditions, melanoma stands as the most lethal, emanating from melanocytes-cells responsible for producing melanin, the skin’s pigment. Distinctively more aggressive than its counterparts, melanoma possesses a higher propensity to metastasize, rendering it one of the most formidable types of skin cancer. Given its rapid invasion and propensity for widespread metastasis, prompt and accurate diagnosis, followed by immediate intervention, is imperative. Fine-grained classification tasks are particularly pertinent in this context, aiming to discern between closely resembling subclasses, a challenge akin to differentiating subtle visual nuances among similar-appearing skin pathologies. Consequently, fine-grained classification emerges as a critical tool in the nuanced diagnosis, early detection, and ongoing surveillance of skin diseases. In response, we have developed a innovative network, termed Multimodal Hybrid Approach for Fine-Grained Classification of Diverse Dermatological Conditions (MultiFG), adept at handling a variety of skin conditions. This model proficiently extracts and processes metadata while concurrently capturing visual details through a Convolutional Neural Network (CNN). This is followed by the integration of visual and metadata insights within a Transformer architecture, facilitating enhanced fusion and refined fine-grained analysis. Our proposed methodology’s efficacy is substantiated through rigorous testing on two distinct datasets, where it has demonstrated promising results.

关键词： Visualization Computer vision Melanoma Metadata Transformers Feature extraction Skin

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Personalized Federated Learning with Enhanced Implicit Generalization

Personalized Federated Learning with Enhanced Implicit Gener...

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International Joint Conference on Neural Networks (IJCNN)

作者： Heping Liu Songbai Liu Junkai Ji Qiuzhen Lin Jianyong Chen Kay Chen Tan College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computing The Hong Kong Polytechnic University

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Integrating personalization into federated learning is crucial for addressing data heterogeneity and surpassing the limitations of a single aggregated model. Personalized federated learning excels at capturing inter-client similarities and meeting diverse client needs through custom-made models. However, even with personalized approaches, it’s essential to aggregate knowledge among clients to ensure universal benefits. This paper proposes Federated Dual Objectives and Dual Models (FedDodm), a novel approach that employs two independent models to separately address explicit personalization and implicit generalization objectives in personalized federated learning. By treating these objectives as distinct loss functions and training models accordingly, we achieve a balance between the two through a fusion method. Extensive experiments across various models and learning tasks demonstrate that FedDodm outperforms state-of-the-art federated learning approaches, marking a significant advancement in effectively integrating personalized and generalized knowledge.

关键词： Training Federated learning Aggregates Neural networks data models

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Multi-Agent Enhanced Complex Decision-Making Support Framework: An Urban Emergency Case Study

Multi-Agent Enhanced Complex Decision-Making Support Framewo...

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Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Lyulong He Hongyuan Zhang Kunxiao Liu Xi Wu Joint Operations College National Defense University Beijing China Software Engineering. Northwestern Polytechnical University Suzhou Key Lab of Aerospace Industry Software and Data Science Yangtze River Delta Research Institute of NPU Taicang China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

With the rapid development of large language model (LLM) technology, its advantages in text generation and experimental design are gradually emerging, such as powerful semantic understanding and generation capabilities, as well as high flexibility and adaptability. Complex decision-making problem experiment design is a field that requires high flexibility and complex design, where traditional methods often struggle to cope with this complexity, resulting in low accuracy, poor consistency, and inefficiency. This paper presents a MA-DMS framework, a multi-agent enhanced design framework for the formulation of experiments on complex decision-making problems based on large language models. Through multi-round collaboration among multiple intelligent roles, this framework transforms broad and complex strategic challenges into experiment-ready problems with clear conditions and well-defined objectives. The experimental results show that the MA-DMS system performs excellently in the transformation process of complex decision-making problem, significantly enhancing the efficiency and accuracy of the problem formulation phase.

关键词： Analytical models Accuracy Large language models Computational modeling Decision making Semantics Collaboration Transforms Risk management Resource management

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Bilinear-Experts Network with Self-Adaptive Sampler for Long-Tailed Visual Recognition

SSRN

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SSRN 2024年

作者： Wang, Qin Wang, Xizhao Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

Long-tail distributed data hinders the practical application of state-of-the-art deep models in computer vision. Consequently, exclusive methodologies for handling the long-tailed problem are proposed, focusing on different hierarchies. For embedding hierarchy, existing works manually augment the diversity of tail-class features for specific datasets. However, prior knowledge about datasets is not always available for practical use, which brings unsatisfactory generalization ability in human fine-turned augmentation under such circumstances. To figure out this problem, we introduce a novel model named Bilinear-Experts Network (BENet) with Self-Adaptive Sampler (SAS). This model leverages model-driven perturbations to tail-class embeddings while preserving generalization capability on head classes through a designed bilinear experts system. The designed perturbations adaptively augment tail-class space and shift the class boundary away from the tail-class centers. Moreover, we surprisingly find that SAS automatically assigns more significant perturbations to specific tail classes rather than fewer-sample classes, which is against the common assumption that fewer-sample classes deserve greater attention. Also, experiments conducted across various long-tailed benchmarks validate the comparable efficacy of proposed BENet. © 2024, The Authors. All rights reserved.

关键词： Embeddings

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