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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Hongbo Wang Rong Fei Zilong Wang Aimin Li Yajun Liu Fuyu Zhu School of Computer Science and Engineering Xi'an University of Technology Xi’an China Shannxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi’an China

ISBN: (数字)9798331517090

ISBN: (纸本)9798331517106

This paper constructs a scenic tour task scheduling model (hereinafter referred to as the model) based on heuristic intelligent search algorithm, puts forward the heuristic intelligent search solution scheme under single-task and multi-task scenarios, aims to unify the planning of tour routes according to the scenic spot flow situation, prevents the formation of attraction passenger flow peaks, and can effectively solve the problem of the traditional scheduling model's dynamic scheduling ability lack. In particular, the performance of different scheduling algorithms is compared and simulation tests are designed to verify that the proposed model and solution method are effective for solving the optimal path problem of scenic tour, which is of practical significance for realizing the intelligent management of scenic spots and promoting the intelligent development of tourism.

关键词： Scheduling algorithms Heuristic algorithms Dynamic scheduling Search problems Multitasking Planning Artificial intelligence

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Enhanced Vgg16 for Medical Image Zero Watermarking: Integrating Attention, Skip Connections, Depth-Separated Convolution, and Dual-Logistic Encryption

SSRN

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SSRN 2024年

作者： Li, Jingyou Rongle, Wei Xi, Xiaotian Zhang, Guangda Yang, Zixin Zhang, Fengshan 1. School of Computer and Control Engineering Qiqihar University 2. Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar University

The paper proposes an enhanced VGG16 network((referred to as DS-ResVGG16 with CBAM)) that incorporates the attention mechanism CBAM, skip connections, and depth-separable convolution to solve the medical image content protection problem. The three innovative improvements of the enhanced VGG16 network are reflected in the following: first, replacing the regular convolution with depth-separable convolution substantially improves the computational efficiency and parameter utilization of the network;second, the introduction of the CBAM after the 2nd, 3rd, 4th, and 5th max-pooling layers further strengthens the network's capability to recognize and respond to crucial features.;and lastly, by adding a skip connection after the 2nd, 3rd, and 4th CBAM modules followed by the addition of skip connections, the transfer and integration of features were enhanced, improving the learning ability and generalization of the model. The improved network extracts Deep features from the original medical images, and binary feature vectors are generated using the mean hash algorithm. To further strengthen content protection security, this study innovatively adopts an encryption technique based on dual-logistics mapping and Joseph's ring for high-strength encryption of watermarks. During the generation of the zero watermark, the encrypted watermark and the feature vectors of the medical image are subjected to an iso-orthogonal operation, which ensures the concealment and security of the embedding. The validity and reasonableness of the modules and locations added by the scheme are verified and compared through experiments. The experimental results show that the scheme is robust enough to handle typical and geometric attacks. The medical image content protection problem is realized. © 2024, The Authors. All rights reserved.

关键词： Convolution

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RDGFuzz: A directed greybox fuzzing optimization method based on Rich-Branch nodes 22

RDGFuzz: A directed greybox fuzzing optimization method base...

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Proceedings of the 2022 6th International Conference on Electronic Information Technology and computer engineering

作者： Zejun Wu Li Lu Qiong Jia Zhihao Chen State Key Laboratory of Mathematical Engineering and advanced Computing China National Local Joint Engineering Laboratory of Network Space Security Technology China Beijing Institute of Computer Technology and Application China

ISBN: (纸本)9781450397148

Directed fuzzing technology is one of the key technologies to quickly reach a specific location of software, and to conduct targeted testing or bug recurrence. However, directed fuzzing technology has some problems, such as unreasonable seed energy allocation, low code coverage and incomplete testing. To solve the above problems, this paper proposes an optimization method of directed fuzzing based on Rich-Branch nodes. In this method, the concept of Rich-Branch nodes is defined and the algorithm of extracting Rich-Branch nodes is given. The optimization method collects the coverage information of the target program in the running process, calculates the weights of covered functions and nodes in real time by combining CG and CFG of the target program, and generates a list of Rich-Branch nodes. According to the weights of Rich-Branch nodes, the seed energy allocation algorithm of AFLGo is optimized and improved. Compared with AFLGo, this optimization method improves the average code coverage of each targeted point by 56.79%, and has the same target reaching ability as AFLGo.

关键词： Rich-Branch nodes Directed fuzzing Code coverage rate Vulnerability discovery

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Zero Watermarking Algorithms for Medical Images Based on Cnn and Transformer with Dual-Branch network

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SSRN 2024年

作者： Li, Jingyou Wei, Rongle Xi, Xiaotian Zhang, Guangda Yang, Zixin Zhang, Fengshan 1. School of Computer and Control Engineering Qiqihar University 2. Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar University

The current research proposes a new zero-watermarking approach that aims to protect medical images from tampering by utilizing the power of deep learning. In this paper, we construct an innovative dual-branch network to capture subtle features of pictures and use them efficiently. First, one of the branches of the dual-branch network is to analyze the local details of the medical images by an enhanced convolutional neural network (specifically the Skip-connected VGG16, i.e., Res-VGG16). In contrast, the other branch employs the state-of-the-art Swin transformer v2 structure to parse the global context of the medical images. Second, adding a spatial transform network and a Gaussian filter as preprocessing before the two-branch network can further overcome the low robustness of medical images to geometric and noise attacks. Thirdly, we use a feature fusion method with a maximum voting mechanism to merge the features extracted from the two-branch network. We generate feature vectors of medical images using the DCT method and achieve watermark encryption by utilizing logistic mapping and a hash function. Finally, we generate a zero watermark by applying logical operations to the encrypted watermark and the medical image feature vectors. The implementation results show that the algorithm can effectively overcome the tampering problem of medical images and can recover the original data from the encrypted watermark with good robustness under conventional and geometric attacks. © 2024, The Authors. All rights reserved.

关键词： Hash functions

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Causal Prompting: Debiasing Large Language Model Prompting Based on Front-Door Adjustment 39

Causal Prompting: Debiasing Large Language Model Prompting B...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Congzhi Zhang, Linhai Wu, Jialong He, Yulan Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King’s College London United Kingdom The Alan Turing Institute United Kingdom

ISBN: (纸本)157735897X

Despite the notable advancements of existing prompting methods, such as In-Context Learning and Chain-of-Thought for Large Language Models (LLMs), they still face challenges related to various biases. Traditional debiasing methods primarily focus on the model training stage, including approaches based on data augmentation and reweighting, yet they struggle with the complex biases inherent in LLMs. To address such limitations, the causal relationship behind the prompting methods is uncovered using a structural causal model, and a novel causal prompting method based on front-door adjustment is proposed to effectively mitigate LLMs biases. In specific, causal intervention is achieved by designing the prompts without accessing the parameters and logits of LLMs. The chain-of-thought generated by LLM is employed as the mediator variable and the causal effect between input prompts and output answers is calculated through front-door adjustment to mitigate model biases. Moreover, to accurately represent the chain-of-thoughts and estimate the causal effects, contrastive learning is used to fine-tune the encoder of chain-of-thought by aligning its space with that of the LLM. Experimental results show that the proposed causal prompting approach achieves excellent performance across seven natural language processing datasets on both open-source and closed-source LLMs. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Natural Human Skin‑Inspired Wearable and Breathable Nanofiber‑based Sensors with Excellent Thermal Management Functionality

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Advanced Fiber Materials 2024年第6期6卷 1955-1968页

作者： Peng Wang Xiaodan Li Guifen Sun Guoqing Wang Qing Han Chuizhou Meng Zhonghe Wei Yang Li School of Mechanical Engineering University of JinanJinan 250022China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China State Key Laboratory for Reliability and Intelligence of Electrical Equipment Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection TechnologyHebei Key Laboratory of Smart Sensing and Human‑Robot InteractionSchool of Mechanical EngineeringHebei University of TechnologyTianjin 300401China Shandong Provincial Key Laboratory of Network‑Based Intelligent Computing School of Information Science and EngineeringUniversity of JinanJinan 250022China Network Information Center Department of Computer ScienceQilu University of Technology(Shandong Academy of Sciences)Jinan 250353China School of Integrated Circuits Shandong UniversityJinan 250101China

Wearable sensors have been rapidly developed for application in various human monitoring ***,the wearing comfort and thermal properties of these devices have been largely ignored,and these characteristics urgently need to be ***,we develop a wearable and breathable nanofiber-based sensor with excellent thermal management functionality based on passive heat preservation and active Joule heating *** multifunctional device consists of a micropatterned carbon nanotube(CNT)/thermoplastic polyurethane(TPU)nanofiber electrode,a microporous ionic aerogel electrolyte and a microstructured Ag/TPU nanofiber *** to the presence of a supercapacitive sensing mechanism and the appli-cation of microstructuration,the sensor shows excellent sensing performance,with a sensitivity of 24.62 ***,due to the overall porous structure and hydrophobicity of TPU,the sensor shows good breathability(62 mm/s)and water repellency,with a water contact angle of 151.2°.In addition,effective passive heat preservation is achieved by combining CNTs with high solar absorption rates(85%)as the top layer facing the outside,aerogel with a low thermal conductivity(0.063 W m-1 k-1)as the middle layer for thermal insulation,and Ag with a high infrared reflectance rate as the bottom layer facing the *** warming,this material yields a higher temperature than ***,the active Joule heat-ing effect is realized by applying current through the bottom resistive electrode,which can quickly increase the temperature to supply controlled warming on *** proposed wearable and breathable sensor with tunable thermal properties is promising for monitoring and heat therapy applications in cold environments.

关键词： Wearable sensor Breathable Personal thermal management Electrospun nanofibers Joule heating

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MTGA: Multi-View Temporal Granularity Aligned Aggregation for Event-Based Lip-Reading 39

MTGA: Multi-View Temporal Granularity Aligned Aggregation fo...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Wenhao Wang, Jun Luo, Yong Yu, Lei Yu, Wei He, Zheng Shen, Jialie School of Computer Science National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China School of Electronics and Information Wuhan University China Department of Computer Science City University of London United Kingdom

ISBN: (纸本)157735897X

Lip-reading is to utilize the visual information of the speaker's lip movements to recognize words and sentences. Existing event-based lip-reading solutions integrate different frame rate branches to learn spatio-temporal features of varying granularities. However, aggregating events into event frames inevitably leads to the loss of fine-grained temporal information within frames. To remedy this drawback, we propose a novel framework termed Multi-view Temporal Granularity aligned Aggregation (MTGA). Specifically, we first present a novel event representation method, namely time-segmented voxel graph list, where the most significant local voxels are temporally connected into a graph list. Then we design a spatio-temporal fusion module based on temporal granularity alignment, where the global spatial features extracted from event frames, together with the local relative spatial and temporal features contained in voxel graph list are effectively aligned and integrated. Finally, we design a temporal aggregation module that incorporates positional encoding, which enables the capture of local absolute spatial and global temporal information. Experiments demonstrate that our method outperforms both the event-based and video-based lip-reading counterparts. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Rerouting in Cognitive Optical networks

Rerouting in Cognitive Optical Networks

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IEEE International Conference on Communications (ICC)

作者： Anny Xijia Zheng Vincent W.S. Chan Department of Electrical Engineering and Computer Science Claude E. Shannon Communication and Network Group Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

We investigate the design of an efficient rerouting scheme for dynamic bursty high granularity traffic as a part of an agile network management and control scheme in cognitive optical networks. An efficient rerouting scheme allows more efficient use of wavelengths resources. The design includes the fast reconfiguration of wavelengths in the existing lit and spatially routed fibers, and the setting up and lighting of new fibers. Rerouting is necessary to maintain users' quality of service when the network load exceeds the load limit of the primary path. Our design is to trigger reconfiguration when a load threshold is crossed, and the threshold is determined by requirements on both the delay and the blocking (i.e. no available lightpath) probability. The delay is dependent on the probability of exhausting all wavelengths on the primary route, the availability of secondary routes, the holder (buffer) size, and the topology of the network. A dynamic rerouting scheme implemented with a shortest-path algorithm with immediate wavelength availability is proposed to find the alternate paths. It is desirable that the alternate paths for rerouting have small numbers of hops and are disjoint with other busy paths. In addition, a good candidate network topology should have high edge-connectivity. Wavelength reservation for rerouted traffic does not maximize wavelength utilization. Traffic with different sizes should be broken up into multiclasses with dedicated partitioned resources and will have different maximum load thresholds for triggering rerouting to meet the performance requirements.

关键词： Integrated optics network topology Heuristic algorithms Wavelength assignment Lighting Quality of service Optical fiber networks

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Fair and Verifiable Identity-Based Broadcast Proxy Re-Encryption With Designated Sender Feasible for Medical Internet of Things

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IEEE Transactions on Information Forensics and Security 2025年 20卷 6033-6045页

作者： Liqing Chen Shiyu Chen Hao Zhang Jian Weng Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai’an Jiangsu China Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou Fujian China College of Information Science and Technology Jinan University Guangzhou Guangdong China

With the development of big data, cloud computing is widely used by individual and enterprise users due to its powerful computing and storage capabilities. How to share the encrypted data stored in cloud servers flexibly to a group of users and provide effective access control to the users is a hot topic in current research. For that, this paper proposes fair and verifiable identity-based broadcast proxy re-encryption with designated sender (FV-IBBPRE-DS) for Medical Internet of Things (MIoT), which gives the system model, formal definition, and security model of FV-IBBPRE-DS, and constructs a concrete FV-IBBPRE-DS scheme. The data owner in the FV-IBBPRE-DS scheme is able to designate a sender, which is given the decryption privilege of the encrypted data to a set of receivers. The scheme ensures that the participating entities perform the process of data sharing fairly and are able to verify the consistency of the ciphertexts. The FV-IBBPRE-DS scheme achieves chosen ciphertext attack security. The performance analysis demonstrates that the scheme in this paper has significant advantages in re-encrypted ciphertext generation and decryption phase, and can be applied to resource-constrained MIoT devices.

关键词： Receivers Encryption Hospitals Cryptography Cloud computing Genetics Servers Access control Sun Software engineering

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IQ-VFI: Implicit Quadratic Motion Estimation for Video Frame Interpolation

IQ-VFI: Implicit Quadratic Motion Estimation for Video Frame...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Mengshun Hu Kui Jiang Zhihang Zhong Zheng Wang Yinqiang Zheng National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Harbin Institute of Technology Shanghai Artificial Intelligence Laboratory The University of Tokyo

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Advanced video frame interpolation (VFI) algorithms approximate intermediate motions between two input frames to synthesize intermediate frame. However, they struggle to handle complex scenarios with curvilinear motions since they overlook the latent acceleration information between the input frames. Moreover, the supervision of predicted motions is tricky because ground-truth motions are not available. To this end, we propose a novel frame-work for implicit quadratic video frame interpolation (IQ-VFI), which explores latent acceleration information and accurate intermediate motions via knowledge distillation. Specifically, the proposed IQ-VFI consists of an implicit acceleration estimation network (IANet) and a VFI back-bone, the former fully leverages spatio-temporal information to explore latent acceleration priors between two input frames, which is then used to progressively modulate linear motions from the latter into quadratic motions in coarse-to-fine manner. Furthermore, to encourage both components to distill more acceleration and motion cues oriented towards VFI, we propose a knowledge distillation strategy in which implicit acceleration distillation loss and implicit motion distillation loss are employed to adaptively guide latent acceleration priors and intermediate motions learning, respectively. Extensive experiments show that our proposed IQ-VFI can achieve state-of-the-art performances on various benchmark datasets.

关键词： Interpolation computer vision Accuracy Motion estimation Computational modeling Estimation Benchmark testing

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