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arXiv 2023年

作者： Zhao, Kun Yang, Bohao Lin, Chenghua Rong, Wenge Villavicencio, Aline Cui, Xiaohui Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China Department of Computer Science The University of Sheffield United Kingdom State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Engineering Beihang University China

The long-standing one-to-many issue of the open-domain dialogues poses significant challenges for automatic evaluation methods, i.e., there may be multiple suitable responses which differ in semantics for a given conversational context. To tackle this challenge, we propose a novel learning-based automatic evaluation metric (CMN), which can robustly evaluate open-domain dialogues by augmenting Conditional Variational Autoencoders (CVAEs) with a Next Sentence Prediction (NSP) objective and employing Mutual Information (MI) to model the semantic similarity of text in the latent space. Experimental results on two open-domain dialogue datasets demonstrate the superiority of our method compared with a wide range of baselines, especially in handling responses which are distant to the golden reference responses in semantics. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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A Study on the Construction and Practical Application of the Smart Education Data Platform at Guizhou Traffic Technician and Transportation College

A Study on the Construction and Practical Application of the...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Hong Li Hong Zhang Shaoxun Deng Bo Li Research and Development Department Guizhou Bit Software Co. Ltd. Guiyang China Department of Management Engineering Guizhou Traffic Technician and Transportation College Guiyang China School of Computer Science and Technology Guizhou University Guiyang China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

This paper investigates the digital transformation at Guizhou Traffic Technician and Transportation College, aligned with the 'China Education Modernization 2035' agenda. It introduces a microservices-based data platform aimed at upgrading educational IT systems, enhancing instructional quality, and informing decisions through smarter data use. The approach integrates microservices, cloud, big data, and AI to achieve efficient data handling and analytics, leading to improved management and decision-making processes. The study documents the platform's architecture, security, challenges, and solutions, along with successes like data visualization and automated reporting. Findings emphasize the project's relevance for vocational education's digital transition, pointing out future challenges, including data security and technology updates. The research concludes with key takeaways for institutions undergoing digitalization, underscoring the paramountcy of data security and tech optimization.

关键词： Data security Digital transformation Data handling Decision making Microservice architectures Transportation Data visualization Educational technology Information technology Optimization

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Unsupervised Domain Adaptive Detection with Network Stability Analysis

Unsupervised Domain Adaptive Detection with Network Stabilit...

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International Conference on computer Vision (ICCV)

作者： Wenzhang Zhou Heng Fan Tiejian Luo Libo Zhang Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering University of North Texas Denton USA

Domain adaptive detection aims to improve the generality of a detector, learned from the labeled source domain, on the unlabeled target domain. In this work, drawing inspiration from the concept of stability from the control theory that a robust system requires to remain consistent both externally and internally regardless of disturbances, we propose a novel framework that achieves unsupervised domain adaptive detection through stability analysis. In specific, we treat discrepancies between images and regions from different domains as disturbances, and introduce a novel simple but effective Network Stability Analysis (NSA) framework that considers various disturbances for domain adaptation. Particularly, we explore three types of perturbations including heavy and light image-level disturbances and instance-level disturbance. For each type, NSA performs external consistency analysis on the outputs from raw and perturbed images and/or internal consistency analysis on their features, using teacher-student models. By integrating NSA into Faster R-CNN, we immediately achieve state-of-the-art results. In particular, we set a new record of 52.7% mAP on Cityscapes-to-FoggyCityscapes, showing the potential of NSA for domain adaptive detection. It is worth noticing, our NSA is designed for general purpose, and thus applicable to one-stage detection model (e.g., FCOS) besides the adopted one, as shown by experiments. Code is released at https://***/tiankongzhang/NSA.

关键词：

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Precoding Optimization for Rate Splitting Enabled Internet of Underwater Things over Optical Wireless Underwater Turbulent Channels

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IEEE Internet of Things Journal 2025年

作者： Xing, Fangyuan Tong, Fei Liu, Xiaonan Wang, Zhenduo Leung, Victor C. M. Southeast University School of Cyber Science and Engineering Nanjing211189 China School of Natural and Computing Science University of Aberdeen AberdeenAB24 3FX United Kingdom Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T1Z4 Canada

With the booming development of the underwater Internet of Things, underwater devices and data volume are explosively increasing. Thanks to the advantages of high efficiency and low complexity, the emerging rate splitting multiple access (RSMA) can be integrated with underwater optical wireless communication (UOWC), which can help improve resource utilization and system performance. However, the oceanic characteristics are distinctive and pose great challenges for RSMA-enabled UOWC networks. Precoding design is crucial for benefitting from RSMA-enabled systems, and thus, we explore the precoding optimization for RSMA-enabled UOWC networks over underwater turbulent channels. Specifically, the RSMA-enabled UOWC network is modeled, where a combined turbulent channel involving the effects of absorption, scattering, and underwater turbulence is considered, and a message transmission scheme with rate splitting design is analyzed. A precoding problem is formulated to maximize the ergodic sum rate (ESR) under the constraint of total transmitted power. Since the accurate expression of ESR is a non-closed form and intractable, we first propose a consecutive Fenton-Wilkinson (CFW)-based ergodic rate approximation approach to solve this formulation problem. Fenton-Wilkinson moment matching is consecutively employed to achieve a closed-form and low-complexity expression of the ergodic rate, as only Fenton-Wilkinson moment matching method can offer closed-form solutions for underlying parameters of the approximating log-normal distributions. For further solving the non-convex precoding problem, the successive convex approximation (SCA) approach is adopted, which is particularly applicable for resource-limited underwater environments. The effectiveness of the proposed CFW approach and precoding strategy is evaluated by extensive simulation results for various user deployments and different network loads. © 2025 IEEE.

关键词： Normal distribution

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An efficient and secure arbitrary N-party quantum key agreement protocol using Bell states

arXiv

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arXiv 2023年

作者： Liu, Wen-Jie Xu, Yong Yang, Ching-Nung Gao, Pei-Pei Yu, Wen-Bin Nanjing210044 China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science & Technology Nanjing210044 China Department of Computer Science and Information Engineering National Dong Hwa University Hualien974 Taiwan

Two quantum key agreement protocols using Bell states and Bell measurement were recently proposed by Shukla et al. [Quantum Inf. Process. 13, 2391(2014)]. However, Zhu et al. pointed out that there are some security flaws and proposed an improved version[Quantum Inf. Process. 14, 4245(2015)]. In this study, we will show Zhu et al.’s improvement still exists some security problems, and its efficiency is not high enough. For solving these problems, we utilize four Pauli operations {I, Z, X, Y } to encode two bits instead of the original two operations {I, X} to encode one bit, and then propose an efficient and secure arbitrary N-party quantum key agreement protocol. In the protocol, the channel checking with decoy single photons is introduced to avoid the eavesdropper’s flip attack, and a post-measurement mechanism is used to prevent against the collusion attack. The security analysis shows the present protocol can guarantee the correctness, security, privacy and fairness of quantum key agreement. © 2023, CC BY.

关键词： Photons

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Pretrained Model with CNN for Lung Cancer Identification and Classification

Pretrained Model with CNN for Lung Cancer Identification and...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Shaista Kanwal Sajid Ali Tahani Jaser Alahmadi Muhammad Assam Ghulam Jillani Ansari Uzair Aslam Bhatti Department of Information Sciences University of Education Lahore Pakistan Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Software Engineering University of Science and Technology Bannu KPK Pakistan School of information and communication engineering Hainan University

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

Lung cancer impacts both genders, and its early detection is key to lowering death rates. Current deep learning techniques for automated identification and classification of lung carcinoma face issues with interpretability, data variability handling, and broader applicability. In our research, we propose a novel method that merges a convolutional neural network (CNN) with a streamlined RESNET18 framework. This approach is designed for lung cancer detection and categorization from CT scans, involving image preprocessing like resizing and rescaling. It effectively differentiates between healthy and cancerous tissues, further classifying them as either Normal, Benign, or Malignant, including variants such as adenocarcinoma and squamous cell carcinoma. Our CNN+RESNET18 model boasts an impressive 99% accuracy, supporting radiologists in the intricate task of image analysis, reducing risks of incorrect or delayed diagnosis, and fostering prompt treatment for improved patient outcomes.

关键词： Accuracy Squamous cell carcinoma Computed tomography Scalability Lung cancer Mortality Streaming media Feature extraction Convolutional neural networks Artificial intelligence

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Depth Based Region Proposal: Towards Robust Two-Stage Real-Time Object Detection

SSRN

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SSRN 2022年

作者： Ayman, Shehab Eldeen Hussein, Walid Karam, Omar H. Department of Software Engineering Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt Department of Computer Science Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt Department of Information Systems Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt

The majority of real-time object recognition systems operate on two-dimensional images, degrading the influence of the involved objects' third-dimensional (i.e., depth) information. The depth information of a captured scene provides a thorough understanding of an object in full-dimensional space. During the last decade, several region proposal techniques have been integrated into object detection. scenes’ objects are then localized and classified but only in a two-dimensional space. Such techniques exist under the umbrella of two-dimensional object detection models such as YOLO and SSD. However, these techniques have the issue of being uncertain that an object's boundaries are properly specified into the particular scene. This paper proposes a unique region proposal and object detection strategy based on retrieving depth information for localization and segmentation of the scenes’ objects in real-time manner. The obtained results on different datasets show superior accuracy in comparison to the commonly implemented techniques with regards to not only detection but also a pixel-by-pixel accurate localization of objects. © 2022, The Authors. All rights reserved.

关键词： Object recognition

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Learning the Relation between Similarity Loss and Clustering Loss in Self-Supervised Learning

arXiv

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arXiv 2023年

作者： Ge, Jidong Liu, Yuxiang Gui, Jie Fang, Lanting Lin, Ming Kwok, James Tin-Yau Huang, LiGuo Luo, Bin State Key Laboratory for Novel Software Technology Software Institute Nanjing University Nanjing210093 China The School of Cyber Science and Engineering Southeast University Purple Mountain Laboratories Nanjing210000 China *** L.L.C. United States Alibaba Group China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology 999077 Hong Kong The Department of Computer Science Southern Methodist University DallasTX75205 United States

Self-supervised learning enables networks to learn discriminative features from massive data itself. Most state-of-the-art methods maximize the similarity between two augmentations of one image based on contrastive learning. By utilizing the consistency of two augmentations, the burden of manual annotations can be freed. Contrastive learning exploits instance-level information to learn robust features. However, the learned information is probably confined to different views of the same instance. In this paper, we attempt to leverage the similarity between two distinct images to boost representation in self-supervised learning. In contrast to instance-level information, the similarity between two distinct images may provide more useful information. Besides, we analyze the relation between similarity loss and feature-level cross-entropy loss. These two losses are essential for most deep learning methods. However, the relation between these two losses is not clear. Similarity loss helps obtain instance-level representation, while feature-level cross-entropy loss helps mine the similarity between two distinct images. We provide theoretical analyses and experiments to show that a suitable combination of these two losses can get state-of-the-art results. Code is available at https://***/guijiejie/ICCL. Copyright © 2023, The Authors. All rights reserved.

关键词： Image classification

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Dual-Modes Grasping of SMA-Based Variable-Stiffness Soft Gripper

Dual-Modes Grasping of SMA-Based Variable-Stiffness Soft Gri...

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IEEE International Conference on Robotics and Biomimetics

作者： Lei Zhao Hanyuan Zhao Zhiyu Feng Sicheng Wang Huaping Liu Fuchun Sun Bin Fang Jianqin Yin Department of Computer Science and Technology Tsinghua University Beijing China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China School of Software Beijing China Tandon School of Engineering New York University

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

In this article, we have designed a novel variable-stiffness soft gripper that combines the advantages of both flexible and rigid grippers. It is capable of performing two different tasks: pinching and enveloping. The soft gripper comprises spring steel sheets, memory alloy sheets, tendons, and servo motors, offering the advantages of low cost and simple structure. This variable-stiffness soft gripper can freely switch between the pinching and enveloping by heating the memory alloy sheets and controlling the tendons with servo motors. Finally, to validate the effectiveness of the soft gripper and the algorithm, we constructed experimental platforms for autonomous grasping in virtual and real environments. Experimental results demonstrate that our variable-stiffness soft gripper exhibits excellent grasping performance, stably pinching, and enveloping objects of various shapes and materials, and the achieved grasping success rate is 90%, surpassing the performance of using only precision pinching or enveloping.

关键词： Strips Shape memory alloys Grasping Switches Steel Grippers Servomotors Springs Robots Tendons

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Dynamic Searchable Scheme with Forward Privacy for Encrypted Document Similarity

Dynamic Searchable Scheme with Forward Privacy for Encrypted...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Mustafa A. Al Sibahee Chengwen Luo Jin Zhang Yijing Huang Zaid Ameen Abduljabbar National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen PR China College of Computer Science and Software Engineering Shenzhen University Shenzhen PR China Computer Technology Engineering Department Iraq University College Basrah Iraq Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah Iraq Shenzhen Institute Huazhong University of Science and Technology Shenzhen PR China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Document retrieval plays an essential role in many real-world applications especially when the data storage is outsourced. Due to the great advantages offered by cloud computing, clients tend to outsource their personal data to remote servers maintained by external service providers. This raises serious privacy concerns about outsourced data because such providers are usually considered untrusted entities. The majority of previous schemes of document similarity search share the same limitation: they focus mainly on static collections. Dynamic searchable schemes (DSE) allow adding or removing documents at the expense of more leakage than static schemes. To thwart certain attacks, DSE schemes should support forward privacy property, which ensures that newly added documents cannot be related to previously issued search queries. We design and implement dynamic secure similarity search schemes with forward privacy for textual documents utilizing simhash method for hamming similarity. Our scheme provides an efficient search time and a sufficient level of privacy. To show the practicality of our proposed scheme, we performed excremental results with large document collections.

关键词： Privacy Data privacy Cloud computing Memory Search problems Cryptography Servers

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