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检索条件"机构=Computer Software and Engineering"

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A Deep Intrusion Detection System in Lambda Architecture Based on Edge Cloud Computing for IoT 4

A Deep Intrusion Detection System in Lambda Architecture Bas...

引用

4th International Conference on Artificial Intelligence and Big Data, ICAIBD 2021

作者： Alghamdi, Rubayyi Bellaiche, Martine Ecole Polytechnique de Montreal Computer and Software Engineering Department Montreal Canada

ISBN: (纸本)9780738131702

IoT devices enable a massive amount of data to be aggregated and analyzed for anomaly detection. The nature of heterogeneous devices introduces the challenge of collecting and handling these massive datasets to perform data analyses to discover cyber attacks in near real-time. However, the traditional IDS cannot deal with such a problem due to scalability limitations and insufficient storage and processing capabilities. Moreover, issues such as network bandwidth, real-time support, and security limit the power of cloud server-based IDSs. This paper presents an edge-cloud deep IDS model in Lambda architecture for IoT security to address these challenges. Our system decreases the training phase's time compared to traditional machine learning algorithms and increases the accuracy of true positive detected attacks. Furthermore, neural network layers lead deep learning to achieve better performance and flexibility compared to conventional machine learning. Our solution enables the detection of suspicious activities in real-time and allows to classify them by analyzing historical data in a batch process. © 2021 IEEE.

关键词： Network security

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Brain-Brake: A Hybrid Brain-Operated computer Vision-Enabled System for Collision Avoidance

Brain-Brake: A Hybrid Brain-Operated Computer Vision-Enabled...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Omar Hatem Omar Sheta Abdallah Abdelaziz Ali Ashraf Hashem El Maleeh Mohamed Ali Mariam Gadallah Muhammad Habash Ahmed Moro Seif Eldawlatly Computer Science and Engineering Department The American University in Cairo Cairo Egypt Siemens Digital Industries Software Cairo Egypt Computer and Systems Engineering Department Ain Shams University Cairo Egypt

ISBN: (数字)9798331530143

ISBN: (纸本)9798331530150

A major challenge in daily automotive use is the high incidence of car accidents, primarily attributed to human errors. This paper introduces Brain-Brake, an innovative brain-operated advanced driver-assistance system (ADAS) designed for collision avoidance. Utilizing electroencephalography (EEG) signals, Brain-Brake detects a driver's braking intention faster than traditional methods, addressing critical reaction time delays. It integrates a computer vision module that measures the proximity of objects to the vehicle, complementing the EEG- based intention detection for enhanced reliability. Implemented on embedded chips using the AUTOSAR framework, preliminary results indicate a significant reduction in reaction times during emergency braking scenarios, highlighting the system's potential to enhance road safety significantly.

关键词： computer vision Semiconductor device measurement Protocols Embedded systems Delay effects Force Electroencephalography Road safety Reliability Collision avoidance

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Using time-correlated noise to encourage exploration and improve autonomous agents performance in Reinforcement Learning 18

Using time-correlated noise to encourage exploration and imp...

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18th International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2021, The 16th International Conference on Future Networks and Communications, FNC 2021 and the 11th International Conference on Sustainable Energy Information Technology, SEIT 2021

作者： Peixoto, Maria J.P. Azim, Akramul Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

Intelligent autonomous agents need to know how to carry out actions based on reasoning, perception, and analysis. Therefore, reinforcement learning algorithms guide the agent to reach this goal by performing steps that guarantee the most significant reward. The problem with this approach is that when the agent finds an optimal action with a considerable premium, it tends to stop exploring the environment to guarantee only that great reward. In this way, the agent stops making a great exploration to find new ways and learn alternatives that could generate a bigger bonus in the face of a change in its context. To alleviate this problem, some techniques, such as Soft Actor-Critic (SAC) and Asynchronous Advantage Actor-Critic (A3C), use entropy regulation to improve policy optimization in reinforcement learning. Thus, the greater the entropy, the lower the probability of the agent's certainty in a given action. Despite entropy, these algorithms are not immune to local optimal and require additional exploration mechanisms. Therefore, this work proposes an approach that helps the agent's exploitation policy during reinforcement learning training and improves its performance during the test time. We use the latest state-of-the-art (SOTA) approaches, that is, Asynchronous Advantage Actor-Critic (A3C), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC) in this work. We created a time-correlated noise in the agent's policy network from the use of a random disturbance generated with the Ornstein-Uhlenbeck process. According to the experiments carried out and the results obtained, we can see that our proposal allowed the agent to explore the environment more during training and improve its performance during the testing time, increasing the reward received in different learning contexts. © 2021 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

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Music Genre Classification using Deep Neural Networks

Music Genre Classification using Deep Neural Networks

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第35届中国控制与决策会议

作者： Mekonen Hiwot Yimer Yongbin Yu Kwabena Adu Ekong Favour Sinishaw Melikamu Liyih Rutherford Agbeshi Patamia Information and Software Engineering University of Electronic Science and Technology of China Computer Science and Engineering Central South University

Classifying music to its genre is one of the most challenging tasks in Music Information Retrieval(MIR).Music genre classification has been a critical activity in recent years due to the increasing development of online and offline music *** make these tracks more accessible,they need to be indexed *** paper reviews the current state-of-theart methods in music genre classification and proposes a new approach using the Deep Convolution Neural Network(DCNN)*** extract feature vectors and classify music into their respective genres,two models were designed,implemented,and evaluated on the Mel Frequency Cepstral Coefficients(MFCCs)of the songs:a 16-layered Convolutional Neural Network(CNN)named Music Genre Convolutional Neural Network(MG-CNN)and a pre-trained Deep Neural Network(DNN) VGG16 named Music Genre VGG16(MG-VGG16).The experimental results demonstrated that the MG-CNN model achieved an accuracy of89.48%,while the MG-VGG16 model achieved an accuracy of78.93%.Compared to the state-of-the-art methods,the proposed method can significantly improve and facilitate music genre classification tasks.

关键词： Music genre Mel Frequency Cepstral Coefficients classification Convolutional Neural Networks

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AI-Driven Drought Management System: A Turkish Case Study

AI-Driven Drought Management System: A Turkish Case Study

引用

2023 4th International Informatics and software engineering Conference (IISEC)

作者： Milad Sabamehr Cansu Çiğdem Ekin Department of Software Engineering Atilim University Ankara Turkey Department of Computer Engineering Atilim University Ankara Turkey

Nowadays, drought is one of the trending topics in the world that has turned into a challenge for the world. By developing countries and cities worldwide, especially in the economic aspect, governments started to damage the environment such as through the use of fossil fuels, pollution of the seas, unregulated use of fresh water also deforestation for personal purposes. The presented research aims to change the format of drought mitigation strategies from traditional ways into the up to date treats. Leveraging AI technologies, including machine learning algorithms and data analytics, a comprehensive AI-driven drought management system is designed and implemented. In this system, inconsistent data have been obtained from the Ministry of Agriculture and Forestry organization and transformed into insightful data and analyzed in real-time style to provide the status of agricultural products in Turkey. This research contributes to the fields of environmental science and agriculture by innovatively augmenting traditional approaches with AI-driven solutions. Ultimately, our research offers a means to monitor weather conditions in different regions of Turkey, moving beyond manual drought prediction and guesswork that were prevalent in previous systems. Additionally, it facilitates the evaluation of vegetation health by considering precipitation and temperature averages in each area.

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A Method Based on Genetic Programming to Automatically Construct Factors for Annual Report Scenarios

A Method Based on Genetic Programming to Automatically Const...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Yan Ma Changsheng Zhang Yan Gao Ying Guo School of Computer Science and Engineering Northeastern University Shenyang China Software College Northeastern University Shenyang China College of Computer Science and Engineering Ningxia Institute of Science and Technology Shizuishan China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

Factors have always played an important role in stock analysis, but they are only effective for specific problems in specific scenarios. Therefore, constructing factors timely and quickly for different scenarios is an urgent problem to be solved. Although some experts have constructed factors, they need to manually construct factors for each scenario, and the construction process consumes time and effort. The annual report is an important and common scenario. It is an important form of information disclosure and financial reporting. Therefore, this paper proposes a method for automatically constructing factors based on genetic programming that combines expert experience for this scenario. It incorporates the knowledge and insights of experts in the field of stock analysis into the process of automati-cally constructing factors, and continuously adjusts and improves the combination of factors through genetic programming to adapt to the needs of the scenario. The effectiveness of this method is verified through empirical analysis and its advantages in specific scenarios are demonstrated.

关键词： Industries Genetic programming Prediction algorithms Knowledge discovery Boosting Decision trees Stock markets Random forests Fuzzy systems

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XMODOS: An Explainable Model for Denial of Service Attack Detection

XMODOS: An Explainable Model for Denial of Service Attack De...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Molokwu, Reginald C. Molokwu, Bonaventure C. Molokwu, Victor C. Canadian Institute for Cybersecurity University of New Brunswick Canada Software Engineering Department of Computer Science Concordia University Canada School of Energy Geoscience Infrastructure and Society Hariot-Watt University Edinburgh United Kingdom

ISBN: (纸本)9798350337020

This paper proposes a Machine Learning-based approach for detecting Denial-of-Service (DoS) attacks using different datasets for training and testing. The study evaluates the performance of the proposed approach on a unique dataset;thereafter, compares it to existing approaches in a bid to demonstrate its superiority in terms of accuracy, false-positive rate, and computational efficiency. Furthermore, the impacts of different Machine Learning algorithms and hypertuning configurations on the performance of the proposed approach are investigated. Also, via Feature Importance Analysis, this study examines the influence of each feature (present in the dataset) on our proposed model. The contributions of our work herein demonstrate the potentials of the proposed approach in detecting DoS attacks;and our research highlights the importance of employing Machine Learning in this domain. Future research directions have been suggested based on insights acquired from the investigations of different algorithms and hyperparameters. © 2023 IEEE.

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Test Generation for Mutation Testing by Symbolic Execution 23

Test Generation for Mutation Testing by Symbolic Execution

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23rd IEEE International Conference on software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Su, Rui Zhang, Zhiyi Zhou, Yuqian Yao, Yongming Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Nanjing University of Aeronautics and Astronautics Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing China Army Engineering of PLA Nanjing China

ISBN: (纸本)9798350359398

Mutation testing is a crucial technique to assess the effectiveness of test suites, but it can be computationally expensive. Symbolic execution is a formal technique that generates high-coverage test cases, but its scalability is limited. In this paper, we present a novel approach to mutation testing that utilizes symbolic execution, which explores only program regions impacted by mutations, prioritizes paths containing mutant branches, and generates test cases aimed at mutations. We implemented our approach in MutSym and evaluated it on real-world programs, demonstrating its effectiveness and efficiency. Our approach enhances the quality and reliability of software systems by detecting and eliminating faults early in the development process. © 2023 IEEE.

关键词： software testing

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Emotional Synchronization for Audio-Driven Talking-Head Generation

Emotional Synchronization for Audio-Driven Talking-Head Gene...

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IEEE International Conference on Data Mining (ICDM)

作者： Zhao Zhang Yan Luo Zhichao Zuo Richang Hong Yi Yang Meng Wang School of Computer Science and Information Engineering University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunan China School of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Audio-driven talking-head synthesis has become a significant focus in the field of virtual human applications. However, existing methodologies face challenges in effectively synchronizing audio and video, especially in maintaining emotional consistency. Additionally, there is a notable inefficiency in leveraging emotional prompts to guide expression generation. To address these limitations, this paper introduces an Emotion Synchronized audio-driven Talking-head synthesis (EST) approach. The EST approach aims to enhance the emotion-agnostic talking-head models by enabling emotion control, and it incorporates a diffusion module to learn diverse latent rep-resentations. Furthermore, EST utilizes null-text embedding to align the latent code with emotional prompts. Additionally, a novel Sync Attention Block (SAB) is developed to broaden the spatial perceptual field, thus preventing the loss of critical information. Extensive experiments demonstrate the effectiveness of the EST method, showcasing state-of-the-art performance across widely-adopted datasets. Moreover, the EST approach exhibits exceptional generalization capabilities, even in scenarios where emotional training videos are unavailable.

关键词： Training Codes Accuracy Digital humans Synchronization Data mining Faces Testing

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Blockchain-Based Anonymous Authentication Mechanism with Semi-TTP for VANETs 6

Blockchain-Based Anonymous Authentication Mechanism with Sem...

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6th IEEE International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2021

作者： Jiang, Yuzhang He, Mingxing Zhang, Xinhai Xiong, Ling School of Computer and Software Engineering Xihua University Cheng'du610039 China

ISBN: (纸本)9780738105338

With the rapid development of the vehicular ad hoc networks (VANETs), vehicle services are increasingly diversified. it will inevitably lead to authentication across data centers, privacy and security are crucial issues due to the unique characteristics of VANETs. The traditional cross-datacenter authentication mechanisms are mostly not anonymous or rely on a completely trusted third party (TTP) to guarantee the conditional anonymity of the user while TTP can easily obtain users' privacy. In this article, we introduce an lightweight anonymous authentication mechanism based on the combination of ring signature and blockchain technologies for VANETs. Our mechanism can achieve the following advantages: 1) achieving condition anonymity without TTP;2) realizing a flexible anonymous authentication, in which a vehicle can decides whether to reauthentication or increases the intensity of anonymous according to the situation;3)our mechanism is lightweight by recording signed pseudonym on blockchain to reduce the time consumption of anonymous authentication in different data centers, user only prove that pseudonym is recorded on the blockchain rather execute ring signature again;Our mechanism achieves these advantages by leveraging conditionally anonymous ring signature and extended blockchain technologies. These security features are demonstrated by carrying out security analysis. Meanwhile, extensive simulations are conducted to validate the efficiency and practicality of our mechanism. © 2021 IEEE.

关键词： Authentication

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