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40th Annual computer Security Applications Conference Workshops, ACSACW 2024

作者： Hasani, Afshin Nia, Mehran Alidoost Atani, Reza Ebrahimi University of Guilan Dept. of Computer Engineering Rasht Iran Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran

ISBN: (纸本)9798331532819

In the era of autonomous vehicles (AVs), ensuring the safety and security of the system is an ongoing challenge, particularly when faced with increasingly cyber-Attacks such as GPS spoofing and man-in-The-middle. This paper presents a novel reinforcement learning (RL)-based framework that prioritizes safety over security concerns in AVs, ensuring that life-critical tasks such as collision avoidance and speed control are always maintained, even in the presence of security threats. Our system leverages the MAPE-K loop to dynamically adapt to changing conditions, using RL in the Analyzing phase to make real-Time decisions that maximize cumulative safety rewards while addressing security risks that directly affect operational integrity. We demonstrate how our RL-based self-Adaptive system mitigates security threats while maintaining safe driving behaviors through various simulations and experiments. The proposed system shows significant improvements in collision avoidance and threat detection, ultimately ensuring that the AV's safety is preserved in even the most challenging scenarios. © 2024 IEEE.

关键词： Adversarial machine learning

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An Edge AI and Adaptive Embedded System Design for Agricultural Robotics Applications 25

An Edge AI and Adaptive Embedded System Design for Agricultu...

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30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Huang, Chun-Hsian Chen, Zhi-Rui Hsu, Huai-Shu Dept. Electrical Engineering National Changhua University of Education Changhua Taiwan Dept. Computer Science and Information Engineering National Taitung University Taitung Taiwan

ISBN: (纸本)9798400706356

This work presents the AgrBot, an agricultural robot designed to intelligently estimate and predict crop pest and disease severity (PDS). The AgrBot incorporates two binarized neural network (BNN) hardware modules for recognizing target crops and estimating their PDS. In a resource-constrained FPGA-based design, these BNN hardware modules can be configured on-demand, showcasing system adaptivity. Furthermore, a multimodal model that integrates crop images, sensor data, and time features is presented for predicting PDS. Employing edge artificial intelligence (AI) through the BNN hardware modules and the multimodal model enables the AgrBot to determine if biological agents are applied to protect crops from pests and diseases, creating a comprehensive agricultural cyber-physical system (CPS). Experimental results demonstrate accuracies of 76.3% for recognizing target crops, 65.3% for estimating PDS, and 67% for predicting PDS. In comparison to existing microprocessor-based design methods, the AgrBot's BNN hardware modules improve frames per second (FPS) by a factor of 790, while the multimodal model reduces processing time by up to 50.9%. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Digital storage

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Optimal Scheduling in Multiserver Queues

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Performance Evaluation Review 2024年第3期51卷 29-32页

作者： Grosof, Isaac Carnegie Mellon University Computer Science Department United States Northwestern University Dept. of Industrial Engineering & Management Sciences United States

Scheduling theory is a key tool for reducing latency (i.e. response time) in queueing systems. Scheduling, i.e. choosing the order in which to serve jobs, can reduce response time by an order of magnitude with no additional resources. Scheduling theory is well-developed in single-server systems, where one job is processed at a time. However, little is known about scheduling in multiserver systems, where many jobs are processed at once. Results are especially limited in stochastic multiserver scheduling theory. Today’s data-centers have thousands of servers, and scheduling theory is unable to analyze such systems. My thesis proves the first optimality results and first closed-form bounds on mean response time for scheduling policies in stochastic multiserver models which reflect the behavior of modern computing systems. My thesis proves novel optimality results in each of three areas: I start by studying one-server-per-job multiserver models, and prove the first results on optimal scheduling in that setting. Next, I study the multiserver-job (MSJ) model, where different jobs require different amounts of resources to be served. I prove the first characterization of mean response time for any scheduling policy in the MSJ model, as well as the first optimality results. Finally, I study the effects of scheduling on the tail of response time, rather than mean response time. I invent a novel scheduling policy, Nudge, which I prove to be the first policy to outperform FCFS’s asymptotic tail of response time. This extended abstract briefly outlines the main contributions of my thesis, describing my novel performance bounds and optimality results in each of these three areas. © 2024 Association for Computing Machinery. All rights reserved.

关键词： Stochastic systems

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An IoT-Based Health Care Solution: A Smart Medical Box 4

An IoT-Based Health Care Solution: A Smart Medical Box

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4th Asia Service sciences and software engineering Conference, ASSE 2023

作者： Khan, Adib Mahamud Kabir, Raihan Sudha, Nigar Sultana Ahmed, Nadeem Watanobe, Yutaka Islam, Md Rashedul Dept. of Computer Science and Engineering University of Asia Pacific Dhaka1216 Bangladesh Dept. of Computer Science and Engineering University of Aizu Aizuwakamatsu Fukushima Japan

ISBN: (纸本)9798400708534

IoT in healthcare is intended to keep people safe and healthy, with the primary goal of lowering healthcare costs and reducing the involvement of caregivers in the coming years. This research proposes a smart IoT-based health- care system, which includes an intelligent medical box connected to a mobile app for continuous health monitoring. Patients can get regular health care with the help of this medical box with a dedicated app and web server. The proposed system helps the patients to check their heart rate, temperature, and oxygen level, all using just one device. It provides an easy way for the user to do this checkup by themselves without any help from caregivers or even doctors. Moreover, the proposed system assists the patients in taking the proper medicinal drugs in due time with an SMS and buzzer alarm. It additionally creates a clean verbal exchange between medical doctors and patients rather than bodily contact. The proposed system can be accessed through an app that stores all the detailed health reports about the patient. Doctors can easily check the patient's report and can change the prescriptions based on the condition of the reports, if necessary. This box also consists of an emergency button that the user can use in case of any emergency to notify their respective caregivers immediately. © 2023 ACM.

关键词： Electrocardiograms

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Balance Relation-Aware Attention Embedding Model for Knowledge Graph Completion 6

Balance Relation-Aware Attention Embedding Model for Knowled...

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6th IEEE Pune Section International Conference, PuneCon 2023

作者： Khobragade, Anish Ghumbre, Shashikant Pachghare, Vinod Coep Technological University Dept. of Computer Science and Engineering Pune India Government College of Engineering and Research Avasari Khurd Dept. of Computer Science and Engineering Pune India

ISBN: (纸本)9798350324204

Predicting missing links has great significance in knowledge graph completion because it helps improve the accuracy and completeness of the information in the graph. The recent models employ an attention mechanism to capture the graph network. Previous attention-based models treat similar relation semantics for associated pairs of entities, but relations might have similar names but represent different semantics. Thus, models fail to capture the semantic dissimilarity of relations involved within the triplet entity pair. This paper proposes a new graph attention model capable of effectively learning embeddings by combining relationship features with triplet entity embeddings to balance relationship contributions while capturing meaningful graph structure. The model incorporates relational compositional embeddings to determine distinct attention for neighboring nodes during computation and update the entity embedding. This novel approach outperforms the previously established models on the benchmark dataset. © 2023 IEEE.

关键词： Knowledge graph

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Stock Market Prediction Using Genetic Algorithm Assisted LSTM-CNN Hybrid Model 14

Stock Market Prediction Using Genetic Algorithm Assisted LST...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Nikhil, Shreyas Sah, Rahul Kumar Kumar Parki, Santosh Tamang, Til Bikram Somashekhara Reddy, D. Mahesh, T.R. Faculty of Engineering and Technology Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335095

Stock price prediction has always been a tough task for all the stakeholders involved. This paper focusses on four different models, namely LSTM, CNN, LSTM-CNN, and Genetic Algorithm-Assisted LSTM-CNN (GA-LSTM-CNN) for stock price prediction. The day-to-day stock prices of a certain company for a period of more than 10 years make up the dataset utilized in this study. Root mean square error (RMSE) has been utilized as performance metric for each model. Outcomes of our experiment show that the GA-LSTM-CNN model performed better than other models with an RMSE value of 0.0064 upon training and an RMSE value of 0.03 upon testing. The GA-LSTM-CNN model combines the strengths of LSTM as well as CNN models with the help of a genetic algorithm, which helps in optimizing the hyperparameters. According to the results, the GA-LSTM-CNN model can be very helpful for the stakeholders in predicting future stock values. © 2023 IEEE.

关键词： Genetic algorithms

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Semi-Asynchronous Online Federated Crowdsourcing 40

Semi-Asynchronous Online Federated Crowdsourcing

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Kang, Xiangping Yu, Guoxian Li, Qingzhong Wang, Jun Li, Hui Domeniconi, Carlotta School of Software Shandong University Jinan China George Mason University Dept. of Computer Science VA United States

ISBN: (纸本)9798350317152

Crowdsourcing is a promising human-in-the-loop paradigm for processing computer hard tasks by harnessing crowd intelligence. However, canonical crowdsourcing systems mostly need to aggregate/transmit worker data and may lead to privacy-leakage. To tackle this problem, we propose a novel approach, called FedCS (Federated CrowdSourcing), to achieve privacy protection while ensuring quality. FedCS aggregates model parameters from clients to build a shared server model while keeping the training data locally on worker devices to protect data privacy. To mitigate the staleness of stragglers and boost efficiency, we introduce a semi-asynchronous federated crowdsourcing mechanism, where the parameter server performs global aggregation periodically. Moreover, due to the different frequencies of workers participating in asynchronous update, FedCS uses a staleness-aware grouping and weighted aggregation heuristic to balance the training process. To speed up the convergence rate and improve the training accuracy, FedCS deploys adaptive learning step size for worker devices by their participation frequency. We further present a task assignment algorithm to help workers choose worthy and suitable tasks for annotations and to save the budget. Extensive experiments on benchmark datasets and a real-world crowdsourcing project show that FedCS can complete secure crowdsourcing projects with high quality and low budget. © 2024 IEEE.

关键词： Crowdsourcing

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Comparative Study on Extractive Summarization Using Sentence Ranking Algorithm and Text Ranking Algorithm

Comparative Study on Extractive Summarization Using Sentence...

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2023 International Conference on Power, Instrumentation, Control and Computing, PICC 2023

作者： Majeed, Mansoora Kala, M.T. Government Engineering College Dept. of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350334463

It is possible to create an accurate and succinct summary using a method called automatic text summarising. Before creating the necessary summary sentences, the machine learning algorithms may be trained to understand documents and recognise the portions that transmit crucial facts and information. Abstractive text summa- rization and Extractive text summarization are its two methods. An extractive text summarization refers to the process of removing significant text from a text document or source manuscript. Deep learning techniques are used to train an abstractive text summarizer, which then generates new phrases and keywords based on the content of the original text. The process of acquiring and assimilation of the knowledge from numerous sources takes time. Automatic text summary is used to speed up the process of finding and consuming pertinent information. An extractive text summarising technique chooses crucial phrases, sentences, and other parts of the original text and concatenates them into a compressed form. The extractive text summarization produces grammatically correct sentences. The project is to develop a model that demonstrates extractive text summarization using sentence ranking and text rank algorithm. It shows that the text rank algorithm does a remarkable performance to extractive text summarization. Using a sentence ranking algorithm, extractive summaries are created that rate sentences according to their weights. In order to create a high-quality summary of the input document and store the summary as audio, the input document's highly scored sentences are extracted. A non-supervised, extractive text summarising method is called TextRank. While calculating the value of words and phrases in a document, the TextRank algorithm looks at how they relate to one another. According on how important the words are in each phrase, it then assigns a score to each one. The summary is formed using the algorithm's choice of the most crucial phrases. © 2023 IE

关键词： Deep learning

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Leveraging Network Vulnerability Detection using Improved Import Vector Machine and Cuckoo Search based Grey Wolf Optimizer 1

Leveraging Network Vulnerability Detection using Improved Im...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Preethi, P. Vasudevan, I. Saravanan, S. Krishna Prakash, R. Devendhiran, A. Kongunadu College of Engineering and Technology Dept. of Computer Science and Engineering Trichy India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India Kl University Dept. of Computer Science and Engineering Vijayawada India Sona College of Technology Dept. of Information Technology Salem India Vivekanandha College of Engineering for Women Dept. of Computer Science and Technology Namakkal India

ISBN: (纸本)9798350328042

In the current interconnected and susceptible digital environment, the demand for reliable network attack prediction systems has reached a critical level. This research paper presents a novel approach for network intrusion detection utilizing the Improved Import Vector Machine (IVM) classification algorithm combined with feature selection using Cuckoo Search based Grey Wolf Optimization (CS-GWO). The aim is to improve the accuracy and effectiveness of intrusion detection systems by selecting the most relevant features and employing a powerful classification algorithm. The proposed methodology begins with data preprocessing, ensuring the dataset is appropriately cleaned and normalized. Subsequently, the CS-GWO algorithm is applied for feature selection, leveraging the strengths of both Cuckoo Search and Grey Wolf Optimization. This hybrid approach efficiently explores the search space to identify the optimal feature subset that contributes to accurate intrusion detection. Once the feature selection process is completed, the IVM classification algorithm is employed to classify network instances into intrusion or normal categories. The IVM algorithm is chosen for its ability to handle high-dimensional data and its robustness in dealing with imbalanced datasets commonly encountered in network intrusion detection. To evaluate the performance of the proposed approach, extensive experiments are conducted using benchmark datasets. Various evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of the system in detecting network intrusions. The results demonstrate that the combination of IVM classification and CS-GWO feature selection significantly improves the accuracy and efficiency of network intrusion detection. The selected features obtained through CS-GWO contribute to enhanced classification performance, enabling the system to accurately identify and respond to potential intrusions in real-time scenarios. © 2023 IE

关键词： Network security

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Hypertesting of Programs: Theoretical Foundation and Automated Test Generation 24

Hypertesting of Programs: Theoretical Foundation and Automat...

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44th ACM/IEEE International Conference on software engineering, ICSE 2024

作者： Pasqua, Michele Ceccato, Mariano Tonella, Paolo University of Verona Dept. of Computer Science Verona Italy Software Institute Universitá della Svizzera Italiana Lugano Switzerland

ISBN: (纸本)9798400702174

Hyperproperties are used to define correctness requirements that involve relations between multiple program executions. This allows, for instance, to model security and concurrency requirements, which cannot be expressed by means of trace properties. In this paper, we propose a novel systematic approach for auto-mated testing of hyperproperties. Our contribution is both foundational and practical. On the foundational side, we define a hyper-testing framework, which includes a novel hypercoverage adequacy criterion designed to guide the synthesis of test cases for hyper-properties. On the practical side, we instantiate such framework by implementing HyperFuzz and HyperEvo, two test generators targeting the Non-Interference security requirement, that rely respectively on fuzzing and search algorithms. Experimental results show that the proposed hypercoverage ad-equacy criterion correlates with the capability of a hypertest to expose hyperproperty violations and that both HyperFuzz and Hy-perEvo achieve high hypercoverage and high vulnerability exposure with no false alarms (by construction). While they both outperform the state-of-the-art dynamic taint analysis tool Phosphor, HyperEvo is more effective than HyperFuzz on some benchmark programs. © 2024 ACM.

关键词： software testing

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