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Enhancing Movie Recommendations through Multi-Modal Analysis and Contextual Dynamics 5

Enhancing Movie Recommendations through Multi-Modal Analysis...

引用

5th international conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Jaiswal, Priyansh Mowade, Kaustubh Nandlal Anbarasu, V. Department of Networking and Communications SRM Institute of Science and Technology SRM Nagar Chengalpattu Tamil Nadu Kattankulathur603203 India Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Chengalpattu Tamil NaduKattankulathur Chennai603203 India

ISBN: (纸本)9798400709418

A fascinating new era marked by the convergence of multi-modal analysis and contextual dynamics is now upon us in the search of individualized and compelling movie suggestions. This abstract explores the transformational potential of "Enhancing Movie Recommendations through Multi-Modal Analysis and Contextual Dynamics."This innovative strategy creates an unmatched theatrical experience by seamlessly fusing the complex fabric of user choices, multidimensional movie qualities, and real-time environmental clues. This methodology fundamentally differs from conventional recommendation systems, which sometimes rely on imprecise choices based on genre or actors. Instead, it focuses extensively on the nuances of personal preferences and constantly changing cinematic interests. It carefully tailors recommendations in real-time by carefully examining a user's viewing history, interaction patterns, and feedback to make sure each movie suggestion fits harmoniously with the viewer's individual cinematic journey. Additionally, it breaks down movies into their various layers, delving into things like story structure, cinematography, aesthetic, and emotional nuance. The actual novelty is the use of real-time contextual information, such as the viewer's mood, location, time, and social environment, to make sure that the suggested cinematic experience is not only customized but also precisely in sync with the current situation. © 2023 ACM.

关键词： Modal analysis

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Towards Federated Learning on Fresh Datasets 20

Towards Federated Learning on Fresh Datasets

引用

20th IEEE international conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Wu, Chen Xiao, Mingjun Wu, Jie Xu, Yin Zhou, Jinrui Sun, He University of Science and Technology of China School of Data Science China University of Science and Technology of China School of Computer Science and Technology & Suzhou Institute for Advanced Research China Temple University Department of Computer and Information Sciences United States

ISBN: (纸本)9798350324334

Federated Learning (FL) is an emerging privacy-preserving distributed computing paradigm that enables numerous clients to collaboratively train machine learning models without the need for transmitting the private datasets of clients to the FL server. Unlike most existing research where the local datasets of clients are considered to be unchanging over time during the whole FL process, we consider such scenarios in this paper where the local datasets of clients need to be updated periodically, and the server can stimulate clients to use as fresh as possible datasets to train their local models. Our objective is to determine a client selection strategy to minimize the loss of global model for FL with a limited budget. To this end, we leverage the concept of Age of Information (AoI) to quantify the freshness of local datasets and theoretically analyze the convergence bound of our AoI-aware FL system. Based on the convergence bound, we formalize our problem as a restless multi-armed bandit problem. Then, we devise a Whittle's-Index-based Client Selection algorithm, called WICS, to tackle the client selection problem. Extensive simulations show that the proposed algorithm can reduce the training loss and improve learning accuracy compared to state-of-the-art algorithms. © 2023 IEEE.

关键词： Budget control

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Communication-Efficient Federated Learning Based on Chained Secure Multiparty computing and Compressed Sensing 24

Communication-Efficient Federated Learning Based on Chained ...

引用

24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Xiao, Di Jiang, Pengcen Li, Min College of Computer Science Chongqing University Chongqing China

ISBN: (纸本)9798350319934

Federal learning (FL), as a new technology with privacy protection capabilities, has been widely used. However, the protection capabilities of FL are limited and a lot of communication overhead is required, which is often limited by communication environments. In order to improve the privacy protection and reduce the communication burden, we combine secure multi-party computing(SMC) and compressed sensing(CS) to propose a new chained federated learning scheme called Chain-FL via CS (Chain-FL-CS). We first design a heuristic compression transmission strategy for the model based on CS so that the model can be compressed with a lower loss. Then we use a chained-SMC to perform lossless and secure aggregation of the calculation results. This technology mainly uses masking mechanism for communication. Compared with other schemes, ours has lower complexity. We have conducted experiments using a Deep Neural Network Model (DNN). Experimental results show that our scheme can guarantee the convergence of model under the premise of high transmission efficiency, and it has certain privacy protection capability. © 2022 IEEE.

关键词： Compressed sensing

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Usage of Basic Geometrical Ideas and Principles of Symmetry for Machine Learning

Usage of Basic Geometrical Ideas and Principles of Symmetry ...

引用

IEEE international conference on System, Computation, Automation and Networking (ICSCAN)

作者： Vatsal Srivastava Sakthitharan Subramanian Department of Computational Intelligence School of Computing SRM Institute of Technology Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

The paper discusses the usage of geometric principles and theories in order to manipulate the position of decision boundary itself to classify data points belonging to two different classes. Moving away from the close relation between statistical approaches at classification, we will attempt to create a classifier that establishes a more direct connection, and works with the decision boundary. We explore binary classification problems using the dataset, compare convergence conditions and evaluate our results against some popular classifiers.

关键词： Machine learning algorithms Automation Computational modeling Machine learning Mathematical models Classification algorithms Reliability convergence

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ON THE EFFECT OF BATCH SIZE IN BYZANTINE-ROBUST DISTRIBUTED LEARNING 12

ON THE EFFECT OF BATCH SIZE IN BYZANTINE-ROBUST DISTRIBUTED ...

引用

12th international conference on Learning Representations, ICLR 2024

作者： Yang, Yi-Rui Shi, Chang-Wei Li, Wu-Jun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

Byzantine-robust distributed learning (BRDL), in which computing devices are likely to behave abnormally due to accidental failures or malicious attacks, has recently become a hot research topic. However, even in the independent and identically distributed (i.i.d.) case, existing BRDL methods will suffer a significant drop on model accuracy due to the large variance of stochastic gradients. Increasing batch size is a simple yet effective way to reduce the variance. However, when the total number of gradient computation is fixed, a too-large batch size will lead to a too-small iteration number (update number), which may also degrade the model accuracy. In view of this challenge, we mainly study the effect of batch size when the total number of gradient computation is fixed in this work. In particular, we show that when the total number of gradient computation is fixed, the optimal batch size corresponding to the tightest theoretical upper bound in BRDL increases with the fraction of Byzantine workers. Therefore, compared to the case without attacks, a larger batch size is preferred when under Byzantine attacks. Motivated by the theoretical finding, we propose a novel method called Byzantine-robust stochastic gradient descent with normalized momentum (ByzSGDnm) in order to further increase model accuracy in BRDL. We theoretically prove the convergence of ByzSGDnm for general non-convex cases under Byzantine attacks. Empirical results show that when under Byzantine attacks, using a relatively large batch size can significantly increase the model accuracy, which is consistent with our theoretical results. Moreover, ByzSGDnm can achieve higher model accuracy than existing BRDL methods when under deliberately crafted attacks. In addition, we empirically show that increasing batch size has the bonus of training acceleration. © 2024 12th international conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Stochastic systems

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Poisoning Attack against Online Regression Learning with Maximum Loss for Edge Intelligence

Poisoning Attack against Online Regression Learning with Max...

引用

2022 international conference on computing, Communication, Perception and Quantum technology, CCPQT 2022

作者： Zhu, Yanxu Wen, Hong Zhang, Peng Han, Wen Sun, Fan Jia, Jia School of Aeronautics and Astronautics University of Electronic Science and Technology Chengdu China Beijing Special Engineering and Design Institute Beijing China

ISBN: (数字)9781665470209

ISBN: (纸本)9781665470209

Recent trends in the convergence of edge computing and artificial intelligence (AI) have led to a new paradigm of 'edge intelligence', which are more vulnerable to attack such as data and model poisoning and evasion of attacks. This paper proposes a white-box poisoning attack against online regression model for edge intelligence environment, which aim to prepare the protection methods in the future. Firstly, the new method selects data points from original stream with maximum loss by two selection strategies;Secondly, it pollutes these points with gradient ascent strategy. At last, it injects polluted points into original stream being sent to target model to complete the attack process. We extensively evaluate our proposed attack on open dataset, the results of which demonstrate the effectiveness of the novel attack method and the real implications of poisoning attack in a case study electric energy prediction application. © 2022 IEEE.

关键词： Regression analysis

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A QoE-based Optimization Approach to Computation Offloading in Vehicle-assisted Multi-access Edge computing 12

A QoE-based Optimization Approach to Computation Offloading ...

引用

12th international conference on ICT convergence (ICTC) - Beyond the Pandemic Era with ICT convergence Innovation

作者： Pham, Xuan-Qui Thien Huynh-The Kim, Dong-Seong Kumoh Natl Inst Technol ICT Convergence Res Ctr Gumi South Korea

ISBN: (纸本)9781665423830

In recent years, multi-access edge computing (MEC) has been proposed to provide low-latency computing services for mobile devices (MDs) by enabling computation task offloading from MDs to nearby MEC servers. However, the computing resources of a MEC server is usually limited and unable to satisfy the ever-increasing demand of task requests. To tackle this challenge, in this paper, we investigate a vehicle-assisted MEC (vMEC) paradigm, which expands the current available resources of a MEC server by integrating it with the resources of vehicular nodes (VNs) for task offloading from MDs. We propose a new utility optimization approach that factors in quality of experience (QoE) metrics to address the problem of computation offloading in vMEC. Simulation results validate the advantage of our proposal.

关键词： multi-access edge computing vehicular network task offloading QoE

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Quantum computing in Cybersecurity and LLM

Quantum Computing in Cybersecurity and LLM

引用

international conference on Web Research (ICWR)

作者： Fazel Mohammad Ali Pour Mohammad Hossein Arastoo Faculty of Technology and Engineering Behbahan Khatam-Alanbia University of Technology Khuzestan Iran Faculty of Technology and Engineering Azad University of Kashan Kashan Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

Quantum computing is fundamentally transforming the cybersecurity landscape by simultaneously introducing robust defense mechanisms and sophisticated attack vectors. This paper explores the dual nature of quantum computing in cybersecurity, examining how it enhances artificial intelligence-powered malware detection systems and strengthens encryption protocols, while also enabling the development of adaptive quantum-enhanced threats such as polymorphic malware and quantum key distribution attacks. Additionally, the paper analyzes the convergence of quantum computing with Large Language Models (LLMs) and neural networks, highlighting implications for threat intelligence across social media platforms, cybersecurity operations, and open-source intelligence. Through comprehensive analysis and the proposition of quantum-resilient security frameworks, this research provides cybersecurity professionals with actionable strategies for navigating the emerging quantum computing era.

关键词： Quantum computing Protocols Social networking (online) Navigation Neural networks Vectors Encryption Quantum key distribution Ransomware Computer security

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Optimization Study of BP Neural Network Based on Genetic Algorithm

Optimization Study of BP Neural Network Based on Genetic Alg...

引用

2023 IEEE international conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Cai, Yongheng Zhou, Zhuye Li, Zehui School of Computing Zhuhai College of Science and Technology Zhuhai China School of Data Sciences Zhejiang University of Finance & Economics Hangzhou China School of Automation and Software Engineering Shanxi University Taiyuan China

ISBN: (纸本)9798350309614

In today's era of big data, neural networks play a pivotal role in fields such as image recognition and natural language processing. However, traditional Backpropagation (BP) neural networks often encounter challenges when dealing with complex problems, including slow convergence and getting stuck in local optima. To address these issues, this study employs a genetic algorithm as an optimization technique to optimize the weights and thresholds of BP neural networks. Training is conducted on a set of highly nonlinear mapping datasets to obtain the optimal weight and threshold configurations for the GA-BP model. Experimental results demonstrate that compared to traditional BP neural networks, the GA-BP model exhibits a 19.8% improvement in prediction accuracy, an approximately 9.20% increase in the coefficient of determination (R2), a reduction of approximately 62.50% in mean squared error (MSE), and a decrease of about 38.76% in root mean squared error (RMSE). Furthermore, the GA-BP model displays significantly accelerated convergence. The contribution of this research lies in proposing an optimization method that combines genetic algorithms with BP neural networks. This model not only enhances prediction accuracy compared to the standalone BP neural network but also significantly strengthens its generalization capabilities. As a result, it demonstrates substantial practical application potential across diverse datasets and application scenarios. © 2023 IEEE.

关键词： BP neural network genetic algorithm optimization algorithm

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Neural Network Model Transformation Framework for On-Devices 14

Neural Network Model Transformation Framework for On-Devices

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14th international conference on Information and Communication technology convergence, ICTC 2023

作者： Lee, Kyung Hee Kwak, Ji Young Park, Jaebok Kim, Hong Soog Kim, Seon-Tae Cho, Chang Sik Etri Ai Computing System Sw Research Section Daejeon Korea Republic of

ISBN: (纸本)9798350313277

On-device systems generally have limitations on computational resources, and there are various types of hardware and neural network engines supporting them. Due to the limitations of computational resources, on-device systems often only execute inference using a model trained in a personal computer or server system. Therefore, in order to use the trained model in various on-device environments, it is necessary to provide functions that support transformation of the neural network model, optimization of the neural network model, and execution of inference for each neural network engine to suit the on-device environment using the neural network model. In this paper, we implemented an on-device neural network model transformation framework that receives the trained neural network model and the environment information of the target on-device and creates a neural network model that can be performed on-device. This framework improved transformation efficiency by using a standard neural network model format based on an open source project called ONNX, and supported neural network inference engines such as TensorRT, TVM, and PyTorch. In the future, this framework will be supplemented with functions such as automatic generation of neural networks, linkage with neural network learning frameworks, and expansion of support for various inference engines. © 2023 IEEE.

关键词： Model Transformation Neural Network ONNX

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