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arXiv 2024年

作者： Zhang, Qian Chen, Panfeng Li, Jiali Feng, Linkun Liu, Shuyu Zhao, Heng Chen, Mei Li, Hui Wang, Yanhao State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Guizhou Engineering Laboratory for Advanced Computing and Medical Information Services Guiyang China College of Big Data and Internet Shenzhen Technology University Shenzhen China School of Data Science and Engineering East China Normal University Shanghai China

The emergence of Large Language Models (LLMs) in the medical domain has stressed a compelling need for standard datasets to evaluate their question-answering (QA) performance. Although there have been several benchmark datasets for medical QA, they either cover common knowledge across different departments or are specific to another department rather than pediatrics. Moreover, some of them are limited to objective questions and do not measure the generation capacity of LLMs. Therefore, they cannot comprehensively assess the QA ability of LLMs in pediatrics. To fill this gap, we construct PediaBench, the first Chinese pediatric dataset for LLM evaluation. Specifically, it contains 4,117 objective questions and 1,632 subjective questions spanning 12 pediatric disease groups. It adopts an integrated scoring criterion based on different difficulty levels to thoroughly assess the proficiency of an LLM in instruction following, knowledge understanding, clinical case analysis, etc. Finally, we validate the effectiveness of PediaBench with extensive experiments on 20 open-source and commercial LLMs. Through an in-depth analysis of experimental results, we offer insights into the ability of LLMs to answer pediatric questions in the Chinese context, highlighting their limitations for further improvements. Our code and data are published anonymously at https://***/ACMISLab/PediaBench. © 2024, CC BY-SA.

关键词： Question answering

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BLDTS: Blockchain-based Lightweight Data Trusted Sharing Scheme for Internet of Vehicles

BLDTS: Blockchain-based Lightweight Data Trusted Sharing Sch...

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International Conference on computer Supported Cooperative Work in Design

作者： Haoran Lin Zhongyuan Yu Guijuan Wang Anming Dong Xiang Tian School of Computer Science Qufu Normal University Rizhao China School of Information Science and Engineering Lanzhou University Lanzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Ensuring safe and reliable data sharing is crucial for the development of Internet of Vehicles (IoV) technology. To provide a trusted data environment for IoV and enable traditional consensus algorithms to meet the high dynamic requirements of the IoV. In this article, we propose a blockchain-based data sharing scheme for IoV (BLDTS) to achieve secure and trusted sharing. First, we design a false information identification strategy that utilizes a bayesian inference model to determine the authenticity of shared data with the assistance of reputation value. Second, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to construct a novel lightweight consensus mechanism based on vehicle reputation values and traffic environment factors, and the nodes with high scores were selected as participants in the consensus, which can reduce the computational overhead. Finally, experimental results show that our scheme has advantages in improving the accuracy of false message identification and consensus efficiency.

关键词： Data privacy Federated learning Heuristic algorithms Bayes methods Consensus protocol Reliability Vehicle dynamics Protection Fake news Internet of Vehicles

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Balance Multi-Head Attention based on Software and Hardware Co-design 9

Balance Multi-Head Attention based on Software and Hardware ...

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9th IEEE International Conference on Cyber Security and Cloud computing and 8th IEEE International Conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2022

作者： Xu, Dian Hu, Wei Liu, Fang Fan, Zimeng Shi, Qingsong Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science Wuhan China Wuhan University Department of Information Engineering Wuhan Institute of City School of Computer Science Wuhan China Zhejiang University College of Computer Science Hangzhou China

ISBN: (数字)9781665480666

ISBN: (纸本)9781665480666

Recently, the Transformer-based models have achieved leading results in many research areas such as natural language processing and computer vision. However, since Transformer-based models have a huge computational complexities, optimization for Transformer is the focus of current research. the core of Transformer models is the attention module, and in this paper we propose a software and hardware co-designed attention module, which reduces the computational units of the multi-head attention module by balancing the The inference time of the attention module is reduced by balancing the computational units of multiple attention modules. At the same time, we design the corresponding structure on FPGA, which accelerate 14.6x inference time in comparison with the original attention module implementation on GPU. We applied this attention module to multiple visual Transformer models and tested the accuracy difference between the balanced multi-head attention module-based model and the original model on CIFAR-10, and obtained a result of 80.46%, which lower from the original version by 0.18%. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Complementarity Analysis of Cloud API Ecosystems: An Empirical Study on ProgrammableWeb and Huawei AppGallery Datasets

Complementarity Analysis of Cloud API Ecosystems: An Empiric...

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IEEE International Conference on Web services (ICWS)

作者： Xiaowei Liu Mengmeng Sun Wenhui Chen Dianlong You Limin Shen Zhen Chen School of Information Science and Engineering Yanshan University Qinhuangdao China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

With the proliferation of the digital economy, cloud API has become the best carrier for data transmission and capability replication in software development. In the service-oriented software development paradigm, developers select appropriate cloud APIs for mashup creation based on their practical needs, where complementarity is an important consideration. Complementarity is reflected in various implicit relationships within mashups and cloud APIs, making complementarity analysis of cloud API ecosystems a critical issue. However, existing studies have overlooked the complementarity, and the lack of application variety in the primary dataset ProgrammableWeb has added a threat to the validity and generalization. In this paper, we first build a dataset of two cloud API ecosystems: ProgrammableWeb centering on mashup creation and Huawei AppGallery centering on mobile app development. Then, we conduct statistical and graph analysis to compare the two ecosystems. Finally, we propose a modeling approach to quantify complementarity and perform an analysis to investigate the role of complementarity in cloud API ecosystems. The results reveal differences between mashups and mobile apps in their preferences for cloud APIs. It also highlights the potential of complementarity in addressing the long-tail problem in cloud API ecosystems and improving cloud API recommendations.

关键词： Analytical models Mashups Ecosystems Mobile applications Data communication Software development management

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Analysis of Eavesdropping Region in Hybrid mmWave-Microwave Wireless systems

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Wireless Communications and Mobile computing 2023年第1期2023卷

作者： Qu, Qianyue Zhang, Yuanyu Kasahara, Shoji Graduate School of Information Science Nara Institute of Science and Technology Ikoma Japan School of Computer Science and Technology Xidian University Xi’an China Shannxi Key Laboratory of Network and System Security Beijing Sunwise Information Technology Ltd Beijing China

Hybrid communication systems, where millimeter-wave (mmWave) links coexist with microwave links, have been an essential component in the fifth-generation (5G) wireless networks. Nevertheless, the open feature of the wireless medium makes hybrid systems vulnerable to eavesdropping attacks. Eavesdroppers in hybrid communication systems can enhance their attack performance by opportunistically eavesdropping on mmWave or microwave links. This paper, therefore, aims to answer a natural question: in which region do eavesdroppers prefer the mmWave links? To this end, we first formulate this question as an eavesdropping region characterization problem from the physical layer security perspective, where eavesdroppers select the link to eavesdrop based on the ratio between the security performances of the mmWave and microwave links. To model the security performances of both the mmWave and microwave links, we derive closed-form expressions for the secrecy outage probabilities and lower bounds/exact expressions for the secrecy rates of both links. Finally, we provide numerical results to validate our theoretical analysis and also illustrate the mmWave eavesdropping region under various network parameter settings. Copyright © 2023 Qianyue Qu et al.

关键词： Millimeter waves

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MambaRF: A Bi-directional Mamba Structure for Radio Frequency Signal Classification of Unmanned Aerial Vehicle

MambaRF: A Bi-directional Mamba Structure for Radio Frequenc...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mufeng Yao Chao Liu Lexu Xie Mingmin Chi School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Shanghai China School of Computer Science Fudan University Zhengzhou Zhongke Institute of Integrated Circuit and System Application Shanghai China Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The rapid development of Unmanned Aerial Vehicle (UAV) technology has facilitated the widespread use of UAVs in daily life, this advancement brings huge regulatory demand for UAVs. Automatic identification of UAVs using radio frequency (RF) signals can effectively reduce regulatory costs. Previous studies first extracts spectrogram features from raw RF signals using short-time Fourier transform, and then use neural networks to classify the spectrograms. These studies are mainly based on local convolution, ignoring the long-range dependence of the spectrograms, and thus are limited in terms of UAV classification accuracy. In addition, these works also focus only on classifying UAV types without identifying flight states (e.g., hovering, flying, and switch on). To this end, we propose MambaRF, which employs a bi-directional selective scanning mechanism to capture long-range information of spectrograms from two different directions. MambaRF also jointly classifies UAV types and flight states by using two decoupled classification heads, which has not been addressed in previous studies. Experiments on two publicly available datasets show that our proposed MambaRF outperforms VMamba and other previous studies in both type classification and flight state classification.

关键词： Accuracy Convolution RF signals Bidirectional control Switches Autonomous aerial vehicles Feature extraction Speech processing Spectrogram Radiofrequency identification

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Integrated Sensing and Communication for 6G Holographic Digital Twins

arXiv

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arXiv 2025年

作者： Zhang, Haijun Zhang, Ziyang Liu, Xiangnan Li, Wei Li, Haojin Sun, Chen Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing100083 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden Sony China Research Laboratory Beijing100027 China

With the advent of 6G networks, offering ultra-high bandwidth and ultra-low latency, coupled with the enhancement of terminal device resolutions, holographic communication is gradually becoming a reality. Holographic digital twin (HDT) is considered one of key applications of holographic communication, capable of creating virtual replicas for real-time mapping and prediction of physical entity states, and performing three-dimensional reproduction of spatial information. In this context, integrated sensing and communication (ISAC) is expected to be a crucial pathway for providing data sources to HDT. This paper proposes a four-layer architecture assisted by ISAC for HDT, integrating emerging paradigms and key technologies to achieve low-cost, high-precision environmental data collection for constructing HDT. Specifically, to enhance sensing resolution, we explore super-resolution techniques from the perspectives of parameter estimation and point cloud construction. Additionally, we focus on multi-point collaborative sensing for constructing HDT, and provide a comprehensive review of four key techniques: node selection, multi-band collaboration, cooperative beamforming, and data fusion. Finally, we highlight several interesting research directions to guide and inspire future work. Copyright © 2025, The Authors. All rights reserved.

关键词： Data fusion

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Improving Multiple Sclerosis Disease Prediction Using Hybrid Deep Learning Model

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computers, Materials & Continua 2024年第10期81卷 643-661页

作者： Stephen Ojo Moez Krichen Meznah A.Alamro Alaeddine Mihoub Gabriel Avelino Sampedro Jaroslava Kniezova Department of Electrical and Computer Engineering College of EngineeringAnderson UniversityAndersonSC 29621USA Faculty of Computing and Information Al-Baha UniversityAl-Baha65528Saudi Arabia ReDCAD Laboratory University of SfaxSfax3038Tunisia Department of Information Technology College of Computer&Information SciencePrincess Nourah Bint Abdul Rahman UniversityRiyadh11564Saudi Arabia Department of Management Information Systems College of Business and EconomicsQassim UniversityP.O.Box 6640Buraidah51452Saudi Arabia School of Management and Information Technology De La Salle-College of Saint BenildeManila1004Philippines Department of Information Management and Business Systems Faculty of ManagementComenius University BratislavaBratislava 2582005Slovakia

Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis(MS),a chronic autoimmune neurological *** disrupts signals between the brain and body,causing symptoms including tiredness,muscle weakness,and difficulty with memory and *** methods for detecting MS are less precise and time-consuming,which is a major gap in addressing this *** gap has motivated the investigation of new methods to improve MS detection consistency and *** paper proposed a novel approach named FAD consisting of Deep Neural Network(DNN)fused with an Artificial Neural Network(ANN)to detect MS with more efficiency and accuracy,utilizing regularization and combat *** use gene expression data for MS research in the GEO GSE17048 *** dataset is preprocessed by performing encoding,standardization using min-max-scaler,and feature selection using Recursive Feature Elimination with Cross-Validation(RFECV)to optimize and refine the ***,for experimenting with the dataset,another deep-learning hybrid model is integrated with different ML models,including Random Forest(RF),Gradient Boosting(GB),XGBoost(XGB),K-Nearest Neighbors(KNN)and Decision Tree(DT).Results reveal that FAD performed exceptionally well on the dataset,which was evident with an accuracy of 96.55%and an F1-score of 96.71%.The use of the proposed FAD approach helps in achieving remarkable results with better accuracy than previous studies.

关键词： Multi Sclerosis(MS) machine learning deep learning artificial neural network healthcare

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MIME: Fast and Accurate Flow Information Compression for Multi-Spread Estimation

MIME: Fast and Accurate Flow Information Compression for Mul...

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International Conference on Network Protocols

作者： Hanwen Zhang He Huang Yu-E Sun Zhaojie Wang School of Computer Science & Technology Soochow University Suzhou China School of Rail Transportation Soochow University Suzhou China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

Spread estimation is an essential issue in high-speed networks with wide applications, such as network billing, quality of service, anomaly detection, etc. As a promising technique, sketch can efficiently estimate per-flow spread with only a small memory cost. Many studies focus on improving the performance of sketches. However, these works primarily focus on optimizing the counter level or sketch level without considering the scenario of multi-spread estimation, which is crucial for improving memory utilization and detecting anomalies. In this paper, we propose an efficient flow information compression algorithm based on the on-chip/off-chip hybrid framework to estimate multiple flow spreads. In the on-chip memory, we filter out non-duplicates and sample them to the off-chip space for recording. In the off-chip memory, we compress each sampled non-duplicate to a carefully designed bit-cube. When the measurement is finished, we separate corresponding KV-flows from a specific flow based on the user query. Then, we rebuild this flow to a subset group based on the duplicate number of each KV-flow. Finally, according to the Multi-set theory, we derive an accurate multi-spread estimate formula to solve this issue with a high throughput and small on-chip memory usage. Furthermore, we evaluate the performance of our proposed estimator using real Internet traffic traces downloaded from CAIDA. Experiments show that, compared to the state-of-the-art, our proposal achieves a 97.2% lower average relative error in per-destination source flow spread estimation with a tight on-chip memory, e.g., 320KB. And our proposed method achieves 31.86 higher processing throughput.

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OpenDrift: Online Evolving Fraud Detection for Open-Category and Concept-Drift Transactions

OpenDrift: Online Evolving Fraud Detection for Open-Category...

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IEEE International Conference on Web services (ICWS)

作者： Cheng Wang Songyao Chai Hangyu Zhu Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education China Shanghai Artificial Intelligence Laboratory Shanghai China

The rapid growth of electronic commerce brings convenience to modern life but comes with security risks by various cybercrimes in online payment services. Most existing security methods for fraud detection depend on the static learning paradigm, which trains a model over a static training dataset and deploys the trained model for inference with the frozen model parameters under the i.i.d. assumption. Unfortunately, this paradigm becomes incommensurate with the increasingly complicated and varying fraud patterns due to the untimely and delayed responses in the offline environment. Without sensing the evolution of fraud timely, it is challenging to train and deploy targeted countermeasures. The emerging means of fraud are not only reflected in the openness of their category, but also in the drift of their superimposed risk features. The interweaving of open-category and concept drift accelerates the process of existing methods becoming powerless. In this paper, we propose EvoFD, an online evolving fraud detection framework to enable continual learning to cope with undercurrent surges of evolving fraud. The core idea of EvoFD is to weaken the bias caused by the anchoring effect on the learned information. It learns in an online streaming fashion by using instructive representations as anchors. Specially, we maintain the progressively updatable class anchors and optimize the representation network to embed features and class anchors into a unified normalized space, where the training and predicting can be conducted simultaneously or independently. In the framework, we preserve the balanced replay memory for each class to accumulate knowledge and avoid forgetting. The advantages of our method are validated by extensive experiments over the real-world dataset from a prestigious bank.

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