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Contextual Decision-Making with Knapsacks Beyond the Worst Case 38

Contextual Decision-Making with Knapsacks Beyond the Worst C...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Zhaohua Ai, Rui Yang, Mingwei Pan, Yuqi Wang, Chang Deng, Xiaotie School of Computer Science Peking University Haidian Beijing China IDSS LIDS Massachusetts Institute of Technology CambridgeMA02139 United States Dept. of Management Science and Engineering Stanford University StanfordCA94305 United States School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Dept. of Computer Science Northwestern University EvanstonIL60208 United States School of Computer Science Institute for Artificial Intelligence Peking University Haidian Beijing China

We study the framework of a dynamic decision-making scenario with resource constraints. In this framework, an agent, whose target is to maximize the total reward under the initial inventory, selects an action in each round upon observing a random request, leading to a reward and resource consumptions that are further associated with an unknown random external factor. While previous research has already established an Oe(√T) worst-case regret for this problem, this work offers two results that go beyond the worst-case perspective: one for the worst-case gap between benchmarks and another for logarithmic regret rates. We first show that an Ω(√T) distance between the commonly used fluid benchmark and the online optimum is unavoidable when the former has a degenerate optimal solution. On the algorithmic side, we merge the re-solving heuristic with distribution estimation skills and propose an algorithm that achieves an Oe(1) regret as long as the fluid LP has a unique and non-degenerate solution. Furthermore, we prove that our algorithm maintains a near-optimal Oe(√T) regret even in the worst cases and extend these results to the setting where the request and external factor are continuous. Regarding information structure, our regret results are obtained under two feedback models, respectively, where the algorithm accesses the external factor at the end of each round and at the end of a round only when a non-null action is executed. © 2024 Neural information processing systems foundation. All rights reserved.

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Machine Learning for Medical Image Feature Extraction

Machine Learning for Medical Image Feature Extraction

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Interdisciplinary Approaches in Technology and management for Social Innovation (IATMSI), IEEE Conference on

作者： Saurabh Pratap Singh Rathore Ali Guma Sakshi Chamoli Rayappan Lotus Yogendra Kumar Shailendra Singh Sikarwar Dept. of Management & Research ICAPSR New Delhi India Dept. of Computer & Information Science Muni University Kampala Uganda Dept. of Management Uttaranchal University Dehradun India Dept. of Computer Sci. & Engineering Vel Tech Rangarajan University Chennai India Dept. of Computer Eng. & Applications GLA University Mathura India Dept. of CS Jiwaji University Gwalior India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Feature extraction from medical images is crucial for harnessing the vast information they contain, aiding in diagnosis, treatment planning, and disease monitoring. Traditional feature extraction methods often struggle to capture the complex patterns and subtle variations in medical images. Recently, machine learning techniques have become powerful tools for automatically extracting discriminative features, enabling more accurate and efficient analysis. This paper provides a comprehensive review of advanced machine learning approaches for medical image feature extraction. It covers various methods, including deep learning architectures, convolutional neural networks (CNNs), and feature learning techniques, highlighting their applications across different medical imaging modalities such as MRI, CT, and X-ray. Our CNN model achieves an average classification accuracy of approximately 94%, outperforming the pneumonia detection accuracy of KNN (91%) and SVM (92%).

关键词： Support vector machines Pneumonia Accuracy Tuberculosis Nearest neighbor methods Feature extraction Planning Convolutional neural networks X-ray imaging Medical diagnostic imaging

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Design and Development of Free-Space Optical Transmission Systems and Applications 3

Design and Development of Free-Space Optical Transmission Sy...

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3rd International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2022

作者： Khodayer Al-Dulaimi, Aymen Mohammed Khodayer Al-Dulaimi, Omer Mohammed Hassan Al-Dulaimi, Mohammed Khodayer Osiceanu Alexandra, Maiduc Al-Farahidi University College Dept. of Communication Technical Engineering Baghdad Iraq University Politehnica of Bucharest Dept. of Telecommunication Engineering Bucharest Romania Al-Rafidain University College Dept. of Computer Engineering Baghdad Iraq University Politehnica of Bucharest Dept. Director For Management of Scientific Research Bucharest Romania

ISBN: (数字)9781665491495

ISBN: (纸本)9781665491495

In recent years have seen several significant advances in technology, particularly in the field of wireless communication, and demand has been growing at an exponential rate. This has led to problems including interference, lack of suitable space, and bandwidth restrictions. Conventionally, radio waves have been utilized to interact with one another;as a result of this channel's inherent inefficiency. There is an increasing requirement to search for other technologies. As a result, contact through the use of light is an extremely compelling vision of wireless communication that will solve the majority of channel ambiguity. The most up-to-date method of contact is known as LiFi (Light Fidelity). The Visible Light Communication system solved the issues of illumination as well as communication issues concurrently. Within the scope of this article, a Visible Light Communication system idea was conceived and put into action. © 2022 IEEE.

关键词： Visible light communication

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Understanding Statistical Correlation of Application Security Vulnerability Data from Detection and Monitoring Tools 33

Understanding Statistical Correlation of Application Securit...

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33rd International Telecommunication Networks and Applications Conference, ITNAC 2023

作者： Kasturi, Santanam Li, Xiaolong Pickard, John Li, Peng Indiana State University Technology Management Terre Haute United States Indiana State University Dept. of Electronics and Computer Engineering Terre Haute United States East Carolina University Dept. of Technology Sys Greenville United States East Carolina University Dept. of Technology Systems Greenville United States

ISBN: (纸本)9798350317138

Vulnerability data gathered from multiple detection and monitoring capabilities at different layers of an application using a time-series analysis will provide value and insights by doing a statistical correlation with attack requests observed using a Web Application Firewall (WAF) monitoring solution. Static Analysis Security Testing, Software Composition Analysis, Dynamic Analysis Security Testing (DAST), Application Ethical Hack, Application Programming Interface (API) testing are the tests / scans that have been used to gather vulnerability data for this study. Correlations can further help track abnormal transaction paths if we follow specific ones pointed out by the statistical analysis for those requests that are not blocked by the rules and are allowed as valid transactions to pass through. This provides a narrowed down focus on the convergence of observability and security, critical to realizing a near-real time rapid action. Observations must continue for many days as a time series ensuring consistency and reliability in data collections and analysis. Multiple applications must be observed in a similar manner for ensuring validity of the process for analysis. Also, gathering sufficient data that is large enough to represent a reasonable population of web applications within an organization is a significant factor in achieving reliable correlation. Applying Pearson Correlation (or Spearman Correlation for distributions that are not normal) technique provides insight into Significance (two-tailed) as to whether a correlation is present over large number of data points. Results of analysis show evidence of correlations among specific attack requests monitored by the WAF and corresponding vulnerabilities in applications, detected using one or more methods. This is significant to looking for more insights into how these correlations can further explored into predicting attack patterns based on existing vulnerabilities. © 2023 IEEE.

关键词： Time series analysis

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Deep Learning Approaches for Natural Language Understanding

Deep Learning Approaches for Natural Language Understanding

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Interdisciplinary Approaches in Technology and management for Social Innovation (IATMSI), IEEE Conference on

作者： Saurabh Pratap Singh Rathore Shristi Chamoli Roy T P Ajith Sundaram Neeraj Varshney Vinod Sharma Dept. of Management & Research ICAPSR New Delhi India Dept. of Management Uttaranchal University Dehradun India Dept. of Computer Science & Eng. TKM College of Engineering Kerala India Dept. of Business Amrita Vishwa Vidyapeetham Kochi India Dept. of Computer Eng. & Applications GLA University Mathura India Dept. of Computer Science Jiwaji University Gwalior India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Deep learning models have really revolutionized the domain of NLU, enabling robots to understand and process human language with high accuracy. These models, which include neural networks like RNNs, CNNs, and Transformers, are quite efficient in extracting complex verbal patterns and contextual information. Recurrent Neural Networks, including LSTM and GRU, are specially proficient in modeling sequences, sentiment analysis, and language translation. Conversely, CNNs are applicable for sentence classification and the extraction of key phrases. The Transformer architecture, especially with models like BERT and GPT, set new benchmarks for NLU by leveraging self-attention mechanisms to improve context understanding and handle long-range dependencies. Deep learning has really advanced tasks such as text classification, question answering, machine translation, among others, which in fact has pushed the boundaries of what machines can achieve in understanding language.

关键词： Deep learning Training Sentiment analysis Analytical models Technological innovation Accuracy Translation Text categorization Transformers Context modeling

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Em-RIPE: Runtime Intrusion Prevention Evaluator for ARM Microcontroller Systems

Em-RIPE: Runtime Intrusion Prevention Evaluator for ARM Micr...

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2022 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2022

作者： Roascio, Gianluca Serra, Gabriele Eftekhari Moghadam, Vahid Politecnico di Torino Dept. of Control and Computer Engineering Turin Italy Scuola Superiore sant'Anna TECIP Department Pisa Italy

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Although they have been known for some time, the security implications of buffer overflows (BOF) continue to rouse great attention among software experts in the academic and commercial sectors. Recently, there has been particular interest in discussing how to mitigate risks deriving from BOF on embedded and loT devices, which have lower computational capabilities given their low-cost and low-power requirements. Although the literature is rich of solutions for these devices as well, authors often fail to quantitatively compare their techniques with related work from a security perspective, and mostly rely on qualitative analysis. Existing evaluator benchmarks (such as the famous RIPE, introduced in 2011) are designed to be used only on general-purpose systems, e.g., with a rich Linux OS and Intel architecture. This paper presents Em-RIPE, a prototype evaluation tool written for assessing protections applied to real-time embedded systems, such as microcontrollers equipped with ARM processors. This first version of the tool supports 105 different possible attack combinations, on which the resilience level of the platform under test can be measured. As experimental data, the obtained pro-tection coverage for major compiler-based firmware protections is reported. © 2022 IEEE.

关键词： Firmware

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PhishGuard: A Convolutional Neural Network-Based Model for Detecting Phishing URLs with Explainability Analysis

PhishGuard: A Convolutional Neural Network-Based Model for D...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： Md Robiul Islam Md Mahamodul Islam Mst. Suraiya Afrin Anika Antara Nujhat Tabassum Al Amin Dept. of Computer Science and Engineering Uttara University Dhaka Bangladesh Dept. of Informatics University of Oslo Oslo Norway Dept. of Building Engineering and Construction Management Khulna University of Engineering and Technology (KUET) Khulna Bangladesh Dept. of Electrical and Electronics Engineering BRAC University Dhaka Bangladesh Dept. of Electrical and Computer Engineering North South University Dhaka Bangladesh Dept. of Computer Science American International University Bangladesh (AIUB) Dhaka Bangladesh

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Abstract-Cybersecurity is one of the global issues because of the extensive dependence on cyber systems of individuals, industries, and organizations. Among the cyber attacks, phishing is increasing tremendously and affecting the global economy. Therefore, this phenomenon highlights the vital need for enhancing user awareness and robust support at both individual and organizational levels. Phishing URL identification is the best way to address the problem. Various machine learning and deep learning methods have been proposed to automate the detection of phishing URLs. However, these approaches often need more convincing accuracy and rely on datasets consisting of limited samples. Furthermore, these black-box intelligent models’ decision to detect Suspicious URLs needs proper explanation to understand the features affecting the output. To address the issues, we propose a $1 D$ Convolutional Neural Network (CNN) and trained the model with extensive features and a substantial amount of data. The proposed model outperforms existing works by attaining an accuracy of 99.85 %. Additionally, our explainability analysis highlights certain features that significantly contribute to identifying the phishing URL.

关键词： Uniform resource locators Deep learning Analytical models Accuracy Phishing Organizations Feature extraction

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Improvisation of QoS in SDN-Frame Work for UAV Networks Using Dijkstra Shortest Path Routing Algorithm 1

Improvisation of QoS in SDN-Frame Work for UAV Networks Usin...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Mathapati, Mahantesh Nandihal, Priya Mishra, Priti Kotagi, Veeranna Dept.of Cse Bengaluru India Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Engineering Bangalore India Information Science and Engineering Atria Institute of Technology Bengaluru India Nitte Meenakshi Institute of Technology Dept. of Ise Bengaluru India

ISBN: (纸本)9798350316469

This work presents a unique SDN-based Dijkstra shortest path routing algorithm for QoS improvisation in UAV Networks. It discusses the various problems pertaining to the networking of unmanned aerial vehicles in disaster rescue operations and unique networking algorithmic solutions to the problems. Involving the SDN technology in networks provides faster service delivery than the traditional technology, as it isolates the control plane and data plane. This SDN technology has the centralised governing management service on the distributed network that affords improved security. But, SDN with Wireless Sensor Network (WSN) does not provide enhanced Quality of Services (QoS) in sophisticated cloud computing (CC) applications. Across the research community, end-users seek to attain a QoS while accessing the cloud-based services depending on their knowledge in using today's technology. Therefore, this research study is aimed to prioritize the cloud application data without any delay from the normal data and also to provide the fastest communication using the SDN framework applying shortest path routing. The traditional networking infrastructure's speed, scalability, and agility are increased by the key functionality of SDN, which is used to intellectual the network information from the applications. Moreover, massive size data are managed and customized by using OpenContrail, which is a more responsive and scalable network. The QoS provided by the SDN with cloud-based services using shortest path, stimulates this research to apply the integrated technology in UAV. UAV can transfer the data through cloud service using SDN, in order to maintain a communication with remote experts. The proposed Shortest Path (SP) direction-finding algorithm provides appropriate deployment & connectivity to the modules of group of UAVs, also decreases time delay within the network. The simulation results reveal that the proposed technique is suitable and more effective to set up an emergency

关键词： Cloud Computing OpenStack Quality of Service Shortest Path Algorithm Software-Defined Networking UAV

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Intelligent Operational Decision Support System for Sugar Factory Resource Efficiency 5

Intelligent Operational Decision Support System for Sugar Fa...

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5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Lutska, Nataliia Zaiets, Nataliia Vlasenko, Lidiia Junge, Stefan National University of Food Technologies Dept. of Automation and Computer Technologies of Control Systems Kyiv Ukraine National University of Life and Environmental Sciences of Ukraine Dept. of Automation and Robotic Systems Kyiv Ukraine State University of Trade and Economics Dept. of Software Engineering and Cyber Security Kyiv Ukraine Berlin University of Applied Sciences Dept. of Packaging Technology Berlin Germany

ISBN: (纸本)9798350359787

With the economic crisis that is looming over the world due to changes in the energy sector and a reduction in the supply of certain types of products, energy and resource efficiency issues are becoming more relevant for the food industry. Also important is the preservation of the environment and the implementation of Cleaner Production trends. Most enterprises follow the concept of Industry 4.0 and increase efficiency through the implementation of intelligent methods, in particular machine learning. The paper proposes a neural network model of sugar production. The model makes it possible to predict the main estimates of resource efficiency used to correct the technological modes of production by the operator-technologist. The structure of the operational decision support system has been developed and is used by personnel to model and predict changes in resource efficiency estimates and KPIs calculated on their basis. Its feature is the integration of methods for achieving resource efficiency, improving environmental performance and ensuring KPIs. To do this, at the initial stage, an analysis of the main flows that affect resource efficiency and an assessment of their importance was carried out. Ensuring Production Performance is confirmed by the simulation and the adequacy of the created neural network accuracy of more than 95%. © 2023 IEEE.

关键词： Decision support systems

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An Innovative Approach for Delivering Food and Grocery

An Innovative Approach for Delivering Food and Grocery

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Global Conference for Advancement in Technology (GCAT)

作者： Chidananada C. K. Srinivasa P Panirama Prsad Dept. of Computer Science & Engineering Ballari Institute of Technology and Management Ballari

“ShopNest” is a mobile application which connects local customers to local retailers. Due to massive supermarkets most of the minute retail shops have taken a bad hit. This idea provides a great opportunity for small retail shops to increase their business and also a quick delivery saves time for customers. As we see, most parents and elder people depend on the caretaker of their family to get their essentials. So, the concept is to help the dependent, to reach out for their essentials. It was also observed that some people are emotionally connected with some specific shop(s) and people usually write down a list of their essentials on a piece of paper which has no use in online ordering. In this article a novel approach is proposed which provides the liberty to the people in the way they order, providing value to that piece of paper while placing the order and to connect people to their favorite shop(s) through the application and service and deliver the ordered products.

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