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Fine-grained Access Control Scheme Supporting Cloud-assisted Write Permission Control in Cloud-aided E-Health System

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International Journal of Network Security 2022年第3期24卷 457-468页

作者： He, Kai Wang, Ziqi Shi, Jiaoli Deng, Anyuan Lv, Shunlin School of Computer Science and Artificial Intelligence Wuhan Textile University China School of Computer and Big Data Science Jiujiang University China Hubei Clothing Information Engineering Technology Research Center China

We consider a multi-reader-multi-writer scene in cloudaided E-Health systems. data is produced on all kinds of medical devices and encrypted to ciphertexts for security. These pieces of ciphertext would be aggregated as an electronic medical records file on controllers and uploaded onto the cloud. Doctors then download and decrypt the encrypted file, make diagnoses and treatment plans, and write them in the encrypted file. Nurses implement real-time treatment plans and record progress in the same file. This paper proposes a Fine-grained Access Control Scheme supporting Cloud-assisted Write Permission Control. In this scheme, multiple authorized users can read the same file but cannot write files until they are appropriate. We represent Users’ statuses as a matrix and use a Viète formula to match them with a write access policy on the cloud to judge whether the user can modify the file or not. © 2022. International Journal of Network *** Rights Reserved

关键词： Access control

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Hyperbolic Neural Network Based Preselection for Expensive Multi-Objective Optimization

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Li, Bingdong Yang, Yanting Hong, Wenjing Yang, Peng Zhou, Aimin Shanghai Institute of AI for Education the School of Computer Science and Technology and the Shanghai Frontiers Science Center of Molecule Intelligent Syntheses East China Normal University Shanghai China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

A series of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed for expensive multi-objective optimization problems (EMOPs), building cheap surrogate models to replace the expensive real function evaluations. However, the search efficiency of these SAEAs is not yet satisfactory. More efforts are needed to further exploit useful information from the real function evaluations in order to better guide the search process. Facing this challenge, this paper proposes a Hyperbolic Neural Network (HNN) based preselection operator to accelerate the optimization process based on limited evaluated solutions. First, the preselection task is modeled as a multi-label classification problem where solutions are classified into different layers (ordinal categories) through -relaxed objective aggregation. Second, in order to resemble the hierarchical structure of candidate solutions, a hyperbolic neural network is applied to tackle the multi-label classification problem. The reason for using HNN is that hyperbolic spaces more closely resemble hierarchical structures than Euclidean spaces. Moreover, to alleviate the data deficiency issue, a data augmentation strategy is employed for training the HNN. In order to evaluate its performance, the proposed HNN-based preselection operator is embedded into two surrogate-assisted evolutionary algorithms. Experimental results on two benchmark test suites and three real-world problems with up to 11 objectives and 150 decision variables involving seven state-of-the-art algorithms demonstrate the effectiveness of the proposed method. IEEE

关键词： Multiobjective optimization

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Real-Time Speech Emotion Recognition Using Hybrid Transformer Networks for Mental Health Monitoring

Real-Time Speech Emotion Recognition Using Hybrid Transforme...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： P. Janani Durga C. H. Hussaian Basha Nelson Kennedy Babu Christopher S. Senthilkumar N. Rathina Kumar M. Kavitha Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Electrical and Electronics Engineering SR University Hanumakonda Telangana India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science SNS College of Engineering Coimbatore Tamilnadu India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

This study proposes a hybrid method to multi dialect speech recognition using the transfer learning with transformer-based architecture. The primary objective of this method is to enhance the automatic speech recognition (ASR) across the different dialectal variations of specific language. The transfer learning methods are utilized to modify the base model that was trained on high resource language to manage the multiple dialects with limited labelled data for each dialect. This is achieved by using pre trained transformer methods such as Wav2Vec2.0 and Whisper. An approach which employs dialect specific embeddings is presented. It enables the method to capture data and differentiate between prosodic elements and phonetic that are distinctive to a variety of dialects. data augmentation technique are utilized in order to overcome the limited availability of labelled data for low resource dialects. These methods are utilized to increase the efficiency of training set and enhance the robustness of the method. Evaluation of the method is carried out using multi dialect speech datasets which include Arabic MGB-3 and common voice. These datasets represent a variety of dialects that are spoken in various linguistic regions. The result of this study indicates that the transformer-based method is effective in managing difficulties of dialectal variances while the reliability is reduced on different datasets. Different real-world applications are provided by the proposed method for improving voice enabled applications. Furthermore, this method leads the way for the development of ASR method that are more accurate and independent in locations that have a significant linguistic variation.

关键词： Measurement Training Emotion recognition Accuracy Virtual assistants Transfer learning Mental health Transformers Real-time systems Monitoring

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Feature Fusion Network Based on Academic Content Detection

Feature Fusion Network Based on Academic Content Detection

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

作者： Shuai Han Zengzhen Shao Jianxin Xiao Xu Zhang School of Information Science and Engineering Shandong Normal University Jinan China School of Data Science and Computer Science Shandong Women’s University Jinan China

At a time when academic achievements are being protected with emphasis the testing of academic contents has become a hot topic of much attention at present. In order to avoid the phenomenon of "multiple use of one image" and "image plagiarism" in the academic world, in view of the defects of insufficient feature fusion and low efficiency in the current target detection model, we propose a more powerful model method AIDPaper based on Faster R-CNN. AIDPaper adds PAFPN and DIoU Loss to the Faster R-CNN model. PAFPN is a bottom-up path added on top of FPN to greatly shorten the information propagation path and avoid information loss to a large extent. Subsequently, adaptive feature pooling is added to perform feature fusion for each regional proposal projected onto the feature layer to further enhance the fusion information of the features. DIoU loss provides the prediction direction for the prediction box making the regression more accurate not only speeding up the convergence but also reducing the detection time of the model. We perform experimental comparisons on the PaperImg dataset. The experimental results show that our proposed AIDPaper performs better and the mAP of AIDPaper is much higher than that of the baseline model Faster R-CNN at 3.2% ahead of Libra R-CNN at 6.1%. It has been shown experimentally that our model is more competitive compared to current detection models.

关键词： Adaptation models Computational modeling Plagiarism Object detection Predictive models Feature extraction Propagation losses

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Time-Slotted On-Demand Predictive Routing for UAV Networks

Time-Slotted On-Demand Predictive Routing for UAV Networks

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IEEE Conference on Wireless Communications and Networking

作者： Houze Feng Jingjing Wang Jianrui Chen Xiangwang Hou Jiacheng Wang Geng Sun Dusit Niyato School of Cyber Science and Technology Beihang University Beijing China Department of Electronic Engineering Tsinghua University Beijing China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Technology Jilin University Changchun China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Flying ad hoc networks (FANETs) composed of small unmanned aerial vehicles (UAVs) are flexible, inexpensive, and fast to deploy, which have been used in an increasing number of mission scenarios. However, unstable link quality and frequently changing network topology pose significant challenges for adopting existing routing protocols in mobile ad hoc networks (MANETs). In this paper, we propose a time-slotted on-demand predictive (TSDP) routing protocol designed specifically for UAV networks. The TSDP protocol introduces a novel approach to route selection by incorporating multiple criteria, including delivery ratio, adjacent degree, and mobility prediction factor, to ensure reliable and efficient data transmission. By addressing high latency in route discovery and excessive broadcast overhead, TSDP employs a time-slotted communication mechanism that reduces packet drop rates and enhances route stability. Simulation results demonstrate that TSDP consistently outperforms ad hoc on-demand distance vector (AODV) and dynamic source routing (DSR) protocols in terms of throughput, packet delivery ratio, end-to-end delay, and overhead, particularly in highly dynamic network environments.

关键词： Simulation Stability criteria Autonomous aerial vehicles Throughput Routing Routing protocols Vectors Delays Vehicle dynamics AODV

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CoMT: A Novel Benchmark for Chain of Multi-modal Thought on Large Vision-Language Models 39

CoMT: A Novel Benchmark for Chain of Multi-modal Thought on ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Cheng, Zihui Chen, Qiguang Zhang, Jin Fei, Hao Feng, Xiaocheng Che, Wanxiang Li, Min Qin, Libo School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for SCIR Harbin Institute of Technology Harbin China National University of Singapore Singapore

ISBN: (纸本)157735897X

Large Vision-Language Models (LVLMs) have recently demonstrated amazing success in multi-modal tasks, including advancements in Multi-modal Chain-of-Thought (MCoT) reasoning. Despite these successes, current benchmarks still follow a traditional paradigm with multi-modal input and text-modal output, which leads to significant drawbacks such as missing visual operations and vague expressions. Motivated by this, we introduce a novel Chain of Multi-modal Thought (CoMT) benchmark to address these limitations. Different from the traditional MCoT benchmark, CoMT requires both multi-modal input and multi-modal reasoning output, aiming to mimic human-like reasoning that inherently integrates visual operations. Specifically, CoMT consists of four categories: (1) Visual Creation, (2) Visual Deletion, (3) Visual Update, and (4) Visual Selection to comprehensively explore complex visual operations and concise expression in real scenarios. We evaluate various LVLMs and strategies on CoMT, revealing some key insights into the capabilities and limitations of the current approaches. We hope that CoMT can inspire more breakthroughs on introducing multi-modal generation into the reasoning process. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Modal analysis

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Towards Event Extraction with Massive Types: LLM-based Collaborative Annotation and Partitioning Extraction

arXiv

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arXiv 2025年

作者： Liu, Wenxuan Li, Zixuan Bai, Long Zuo, Yuxin Xu, Daozhu Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China State Key Laboratory of Geo-Information Engineering Shaanxi Xi’an China

Developing a general-purpose extraction system that can extract events with massive types is a long-standing target in Event Extraction (EE). In doing so, the challenge comes from two aspects: 1) The absence of an efficient and effective annotation method. 2) The absence of a powerful extraction method can handle massive types. For the first challenge, we propose a collaborative annotation method based on Large Language Models (LLMs). Through collaboration among multiple LLMs, it first refines annotations of trigger words from distant supervision and then carries out argument annotation. Next, a voting phase consolidates the annotation preferences across different LLMs. Finally, we create the EEMT dataset, the largest EE dataset to date, featuring over 200,000 samples, 3,465 event types, and 6,297 role types. For the second challenge, we propose an LLM-based Partitioning EE method called LLM-PEE. To overcome the limited context length of LLMs, LLM-PEE first recalls candidate event types and then splits them into multiple partitions for LLMs to extract events. The results in the supervised setting show that LLM-PEE outperforms the state-of-the-art methods by 5.4% in event detection and 6.1% in argument extraction. In the zero-shot setting, LLM-PEE achieves up to 12.9% improvement compared to mainstream LLMs, demonstrating its strong generalization capabilities. Copyright © 2025, The Authors. All rights reserved.

关键词： Large datasets

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`A real-time classification model based on joint sparse-collaborative representation

`A real-time classification model based on joint sparse-coll...

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作者： Li, Yanting Jin, Junwei Chen, C. L. Philip College of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou450000 China College of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450000 China School of Computer Science and Engineering South China University of Technology Guangzhou510641 China

Due to the low computational complexity, the two-phase test sample representation method shows outstanding advantages in the field of real-time face recognition. However, its first phase does not fully consider the imbalance of the determined K-nearest training samples. This defect is not well reflected and will seriously affect the recognition accuracy of the second phase. In this paper, we explore the above issues and propose to incorporate the unselected training samples into the modeling process. The proposed method not only allows the unselected samples to perform the final representation but also makes the selected nearest training samples play a more significant role than the unselected ones in classification. It is efficiently solved with an analytical solution. The rationales and probability interpretation of the proposed method are presented to further guarantee its rationality and effectiveness. Extensive experiments on diverse face databases are conducted to demonstrate the superior recognition performance in comparison with other competing classifiers. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Sampling

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data Insights as data: Quick Overview and Exploration of Automated data Insights

arXiv

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arXiv 2025年

作者： Wu, Shangxuan Luan, Wendi Wang, Yong Zeng, Dan Shen, Qiaomu Tang, Bo Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen China College of Computing and Data Science Nanyang Technological University Singapore Singapore Singapore School of Artificial Intelligence Sun Yat-sen University Guangdong Zhuhai China

Automated data insight mining and visualization have been widely used in various business intelligence applications (e.g., market analysis and product promotion). However, automated insight mining techniques often output the same mining results to different analysts without considering their personal preferences, while interactive insight discovery requires significant manual effort. This paper fills the gap by integrating automated insight mining with interactive data visualization and striking a proper balance between them to facilitate insight discovery and exploration. Specifically, we regard data insights as a special type of data and further present InsightMap, a novel visualization approach that uses the map metaphor to provide a quick overview and in-depth exploration of different data insights, where a metric is proposed to measure the similarity between different insights. The effectiveness and usability of InsightMap are demonstrated through extensive case studies and in-depth user interviews. Copyright © 2025, The Authors. All rights reserved.

关键词： data assimilation

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A New NVM Storage System with High Throughput Device Driver

A New NVM Storage System with High Throughput Device Driver

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IEEE International Conference on Big data and Cloud Computing (BdCloud)

作者： Tai Cai Jianfei Da Deiiao Niu Lei Li Jianchong Shi Ranzhuo Li School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Department of Statistics and Data Science School of Science National University of Singapore Singapore Singapore

NVM device offers an effective way to solve the storage wall of the computer system, but its high read/write speed poses a significant challenge to the existing I/O software stack in Linux. This paper designs an effective BIO management strategy for NVM device driver and builds a high throughput NVM storage system to improve the concurrency and reduce BIO management overhead. First, a two-phase dynamic BIO management strategy module is proposed to separate BIO reception and execution, thereby improving the concurrency of BIO management in the NVM device driver. A lightweight BIO migration strategy is then designed to reduce the access number to NVM devices and improve the throughput of NVM device driver. Finally, based on Intel's open-source NVM device driver named PMEM, a high throughput NVM device driver named SVTNVM is implemented to build the new NVM storage system prototype. Some tests and analysis were carried out by using Filebench and Fio to simulate application workloads for evaluation. The results show that SVTNVM can improve IOPS by 29.3% and I/O bandwidth by 23.3% compared with current NVM storage system with PMEM.

关键词： Concurrent computing Linux Prototypes Full stack Bandwidth Throughput Device drivers

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