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International Conference on Computer and Communications (ICCC)

作者： Renxu Wang Jihao Fan School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China Laboratory for Advanced Computing and Intelligence Engineering Wuxi China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Quantum error-correction codes leverage encoding and redundancy to effectively detect and correct errors, thereby enhancing the reliability and accuracy of signal detection. Based on the scheme of entanglement-assisted concatenated quantum codes (EACQCs), we construct both new binary and non-binary EACQCs. Several of the new EACQCs achieve better parameters than the best-known standard entanglement-assisted quantum error-correction codes while maintaining the same code lengths and net transmissions. The quantum Gilbert-Varshamov (GV) bound serves as a crucial benchmark for evaluating quantum code parameters. As the code length increases, we construct binary and non-binary EACQCs that exceed the quantum GV bound. Index Terms-entanglement-assisted quantum error-correction code, entanglement-assisted concatenated quantum codes, quan-tum Gilbert-Varshamov bound

关键词： Quantum computing Accuracy Quantum entanglement Redundancy Benchmark testing Encoding Error correction codes Reliability Standards Signal detection

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Genetic and phenotypic relationships between coronary atherosclerotic heart disease and electrocardiographic traits

Genetic and phenotypic relationships between coronary athero...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Wang, Xinfeng Zhang, Haoyang Xiu, Xuehao Qi, Mengling Yang, Yuedong Zhao, Huiying Sun Yat-sen University Key Laboratory of Machine Intelligence and Advanced Computing of Moe School of Computer Science and Engineering Guangzhou China Sun Yat-sen Memorial Hospital Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Department of Medical Research Center Guangzhou China

ISBN: (纸本)9781665468190

Observational studies have revealed that Coronary Atherosclerotic Heart Disease (CAHD) is associated with abnormal electrocardiogram (ECG) traits. However, it remains unclear whether there are genetic correlations between ECG and CAHD. Here, we explored genetic correlations and putative causal relationships between CAHD and ECG by performing Mendelian randomization (MR) and Polygenic risk score (PRS) analyses on the summary statistics from a large-scale genome-wide association study (GWAS) for CAHD (FinnGen: Ncase 23363, Ncontrol 187840) and ECG traits (UK Biobank: Ncase=1137, Ncontrol=40823). Results showed a causal genetic relationship between CAHD and six ECG traits in the lead V6. These ECG traits combining with age and gender have predicted CAHD risk with an AUC of 0.76. Further summary data-based Mendelian randomization (SMR) analysis identified 11 risk genes associated with the causality between CAHD and ECG. Thus, the revealed putative causal effects of CAHD on ECG traits provide genetic evidence to support the importance of monitoring CAHD risk through the ECG. © 2022 IEEE.

关键词： Electrocardiograms

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LLMUZZ: LLM-based seed optimization for black-box device fuzzing

LLMUZZ: LLM-based seed optimization for black-box device fuz...

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

作者： Guangming Gao Shuitao Gan Xiaofeng Wang Shengkai Zhu School of Artificial Intelligence and Computer Science Jiangnan University China Labortory for Advanced Computing and Intelligence Engineering Wuxi China Pengcheng Laboratory Shenzhen China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

As an increasing number of Internet of Things (IoT) devices are being deployed, the threat from vulnerabilities inside these devices is growing. Fuzzing is a primary method used for discovering vulnerabilities in IoT devices. The quality of the initial seeds and the seed mutation strategy are two crucial components of fuzzing that largely determine the effectiveness of the fuzzing process. However, owing to the diversity of IoT devices and the highly structured nature of inputs, designing universal seed generation and mutation strategies is extremely challenging. In this paper, we propose LLMUZZ, which is a large language model (LLM)-based black-box fuzzing approach for IoT devices. Specifically, we employ prompt engineering techniques in few-shot learning, using HTML form data from frontend files and an example HTTP request as inputs to LLMs to generate initial seeds. Then, we input the requests to be mutated into LLMs to identify the fields requiring mutation, thereby assisting in the seed mutation process. This approach ensures that the mutated seeds remain valid. Additionally, static analysis methods are utilized to discover hidden keywords within the firmware, thereby further expanding the initial seeds. In the experiments, we implement a prototype of LLMUZZ and evaluate it on 8 different IoT devices. A total of 16 previously unknown vulnerabilities are found, for which we have received 4 CVEs; the remaining vulnerabilities still under review, demonstrating that LLMUZZ has a strong capacity for vulnerability discovery.

关键词： Reviews Large language models Closed box Prototypes Static analysis Fuzzing Internet of Things Security Prompt engineering Microprogramming

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Labrador: Response Guided Directed Fuzzing for Black-box IoT Devices

Labrador: Response Guided Directed Fuzzing for Black-box IoT...

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IEEE Symposium on Security and Privacy

作者： Hangtian Liu Shuitao Gan Chao Zhang Zicong Gao Hongqi Zhang Xiangzhi Wang Guangming Gao Information Engineering University Tsinghua University Laboratory for Advanced Computing and Intelligence Engineering Zhongguancun Laboratory Henan Key Laboratory of Information Security University of Electronic Science and Technology of China

ISBN: (数字)9798350331301

ISBN: (纸本)9798350331318

Fuzzing is a popular solution to finding vulnerabilities in software including IoT firmware. However, due to the challenges of emulating or rehosting firmware, some IoT devices (e.g., enterprise-level devices) can only be fuzzed in a black-box manner, which makes fuzzers blind and inefficient due to missing feedbacks (e.g., code coverage or distance). In this paper, we present a novel response guided directed fuzzing solution Labrador, able to test black-box IoT devices efficiently. Specifically, we leverage the network response to infer the execution trace of firmware and deduce the code coverage of testing. Second, we leverage the test case (i.e., request) and its response to estimate the distance to the target sensitive code (i.e., sink). Lastly, we further leverage the distance to guide test case mutation, which efficiently drives directed fuzzing toward candidate vulnerable code. We have implemented a prototype of Labrador and evaluated it on 14 different enterprise-level IoT devices. Results showed that Labrador significantly outperforms state-of-the-art (SOTA) solutions. It finds 44X more vulnerabilities than SNIPUZZ, BOOFUZZ and FIRM-AFL and 8.57X more vulnerabilities than SaTC. In total, it discovered 79 unknown vulnerabilities, of which 61 were assigned with CVEs.

关键词： Privacy Codes Closed box Prototypes Fuzzing Drives Internet of Things

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Deep knn for medical image classification 23rd

Deep knn for medical image classification

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23rd International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2020

作者： Zhuang, Jiaxin Cai, Jiabin Wang, Ruixuan Zhang, Jianguo Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University Guangzhou China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Pazhou Lab Guangzhou China

ISBN: (纸本)9783030597092

Human-level diagnostic performance from intelligent systems often depends on large set of training data. However, the amount of available data for model training may be limited for part of diseases, which would cause the widely adopted deep learning models not generalizing well. One alternative simple approach to small class prediction is the traditional k-nearest neighbor (kNN). However, due to the non-parametric characteristics of kNN, it is difficult to combine the kNN classification into the learning of feature extractor. This paper proposes an end-to-end learning strategy to unify the kNN classification and the feature extraction procedure. The basic idea is to enforce that each training sample and its K nearest neighbors belong to the same class during learning the feature extractor. Experiments on multiple small-class and class-imbalanced medical image datasets showed that the proposed deep kNN outperforms both kNN and other strong classifiers. © Springer Nature Switzerland AG 2020.

关键词： Nearest neighbor search

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A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection

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Plant Phenomics 2023年第2期5卷 209-225页

作者： Xiaojun Xie Fei Xia Yufeng Wu Shouyang Liu Ke Yan Huanliang Xu Zhiwei Ji College of Artificial Intelligence Nanjing Agricultural UniversityNanjingJiangsu 210095China Center for Data Science and Intelligent Computing Nanjing Agricultural UniversityNanjingJiangsu 210095China State Key Laboratory for Crop Genetics and Germplasm Enhancement Bioinformatics CenterAcademy for Advanced Interdisciplinary StudiesNanjing Agricultural UniversityNanjingJiangsu 210095China Academy for Advanced Interdisciplinary Studies Nanjing Agricultural UniversityNanjingJiangsu 210095China Department of the Built Environment College of Design and EngineeringNational University of Singapore4 Architecture DriveSingapore 117566Singapore

Deep learning has been widely used for plant disease recognition in smart agriculture and has proven to be a powerful tool for image classification and pattern ***,it has limited interpretability for deep *** the transfer of expert knowledge,handcrafted features provide a new way for personalized diagnosis of plant ***,irrelevant and redundant features lead to high *** this study,we proposed a swarm intelligence algorithm for feature selection[salp swarm algorithm for feature selection(SSAFS)]in image-based plant disease *** is employed to determine the ideal combination of handcrafted features to maximize classification success while minimizing the number of *** verify the effectiveness of the developed SSAFS algorithm,we conducted experimental studies using SSAFS and 5 metaheuristic *** evaluation metrics were used to evaluate and analyze the performance of these methods on 4 datasets from the UCI machine learning repository and 6 plant phenomics datasets from *** results and statistical analyses validated the outstanding performance of SSAFS compared to existing state-of-the-art algorithms,confirming the superiority of SSAFS in exploring the feature space and identifying the most valuable features for diseased plant image *** computational tool will allow us to explore an optimal combination of handcrafted features to improve plant disease recognition accuracy and processing time.

关键词： Plant image redundant

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Renumber coevolutionary multiswarm particle swarm optimization for multi-objective workflow scheduling on cloud computing environment 15

Renumber coevolutionary multiswarm particle swarm optimizati...

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17th Genetic and Evolutionary Computation Conference, GECCO 2015

作者： Li, Hai-Hao Chen, Zong-Gan Zhan, Zhi-Hui Du, Ke-Jing Zhang, Jun Department of Computer Science Sun Yat-sen University Guangzhou510275 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education P.R. China Collaborative Innovation Center of High Performance Computing National University of Defense Technology Changsha 410073 China Engineering Research Center of Supercomputing Engineering Software Ministry of Education P.R. China

ISBN: (纸本)9781450334884

Resources scheduling is a significant research topic in cloud computing, which is often modeled as a cost-minimization and deadline-constrained workflow scheduling model. This is a constrained single objective problem that to minimize the overall workflow execution cost while meeting deadline constraints. In this paper, we offer a new horizon to convert this single-objective problem to a multi-objective problem and present coevolutionary multiswarm particle swarm optimization (CMPSO) to find the non-dominated solutions with different execute cost and time. Meanwhile, the renumber strategy is adopted in CMPSO to make the learning efficient. CMPSO is compared with a renumber PSO (RNPSO) by setting the execute time in the CMPSO's nondominated solutions as the deadline constraint of RNPSO. Results show that CMPSO not only offers many non-dominated solutions with different prices and execute time, but also obtains better solution than RNPSO under a same deadline. Copyright is held by the owner/author(s).

关键词： Cloud computing

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Circuit Simulation and Optimization of Quantum Search Algorithm 22

Circuit Simulation and Optimization of Quantum Search Algori...

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

作者： Xiaonan Liu Chenyan Zhao Haoshan Xie Zhengyu Liu State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University China School of Computer and Artificial Intelligence ZhengZhou University China

ISBN: (纸本)9781450397148

At present, the scale of quantum computers in the real sense is still small, and quantum simulation has become one of the important ways of quantum theory research, grover quantum search algorithm is suitable for the search problem of disordered database. Firstly, according to the implementation principle of Grover algorithm and Boolean logic relationship, the design idea of multi-objective Oracle is analyzed, based on IBMQ quantum cloud platform, the quantum circuit of Grover algorithm with multi-objective items is simulated. Based on the characteristics of Grover algorithm and the simulation process of quantum gate, the action of multiple identical quantum gates is combined to reduce the update times of probability amplitude and improve the simulation efficiency. The libquantum quantum simulator is used for experiments and the target item is successfully searched, which proves the feasibility of the optimization method and provides reference for the simulation and optimization of other quantum algorithms.

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Preparation and Simulation of Quantum States of Matrices 22

Preparation and Simulation of Quantum States of Matrices

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

作者： Zhengyu Liu Xiaonan Liu Haoshan Xie Chenyan Zhao School of Computer and Artificial Intelligence of Zhengzhou University China State Key Laboratory of Mathematical Engineering and advanced computing Information Engineering University China

ISBN: (纸本)9781450397148

A basic problem of quantum computing is how to effectively express classical data in quantum systems. This problem is called state preparation problem, and the process of preparing quantum states is called coding. In this paper, the classical data of matrix is transformed into quantum states in the form of amplitude coding and simulated. This paper first introduces the detailed process of matrix transformation into the linear combination of quantum ground state, then designs a quantum circuit model to realize amplitude coding according to the idea of binary tree model and quantum random walk algorithm, and finally simulates the three matrices on IBM quantum cloud platform, and analyzes and introduces many situations. The experimental results verify the feasibility of converting the matrix into quantum states by amplitude coding, analyze the causes of errors in the experimental process, and put forward the corresponding solutions. This paper introduces in detail the whole process of matrix conversion to quantum states and the construction of quantum circuits for simulation. Compared with the existing experiments, the quantum circuits constructed in this paper are shorter in length, less in number of quantum gates, and more versatile.

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Continual Learning with Bayesian Model Based on a Fixed Pre-Trained Feature Extractor

SSRN

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SSRN 2022年

作者： Yang, Yang Cui, Zhiying Xu, Junjie Zhong, Changhong Zheng, Wei-Shi Wang, Ruixuan School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing MOE

Deep learning has shown its human-level performance in various applications. However, current deep learning models are characterised by catastrophic forgetting of old knowledge when learning new classes. This poses a challenge particularly in intelligent diagnosis systems where initially only training data of a limited number of diseases are available. In this case, updating the intelligent system with data of new diseases would inevitably downgrade its performance on previously learned diseases. Inspired by the process of learning new knowledge in human brains, we propose a Bayesian generative model for continual learning built on a fixed pre-trained feature extractor. In this model, knowledge of each old class can be compactly represented by a collection of statistical distributions, e.g. with Gaussian mixture models, and naturally kept from forgetting in continual learning over time. Unlike existing class-incremental learning methods, the proposed approach is not sensitive to the continual learning process and can be additionally well applied to the data-incremental learning scenario. Experiments on multiple medical and natural image classification tasks showed that the proposed approach outperforms state-of-the-art approaches which even keep some images of old classes during continual learning of new classes. © 2022, The Authors. All rights reserved.

关键词： Intelligent systems

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