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Deep Learning Approaches for Malware Detection in the Industrial Internet of Things: A Comprehensive Analysis 25

Deep Learning Approaches for Malware Detection in the Indust...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Samara, Ghassan Fadda, Mohammad Abu Al-Mohtaseb, Abeer Alazaidah, Raed Alrefai Mohamed, N. Odeh, Mahmoud Aljaidi, Mohammad Shuaib, Mohammed Mahmod Kanan, Mohammad Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of Science Department of Mathematics Zarqa Jordan University of Business and Technology Department of Industrial Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

This survey aims to identify hazards associated with the Internet of Things (IoT), focusing on malware that can infiltrate various devices, applications, and systems within the Industrial Internet of Things (IIoT). Such malware enables malicious actors to manipulate these systems. This research utilizes deep learning as a method for detecting malicious programs. Additionally, it provides hypotheses and background information on deep learning and the IIoT, along with an overview of the types of malware that can compromise IoT applications. Finally, the study presents findings related to the research hypotheses and relevant topics. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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Turnstile p leverage score sampling with applications 41

Turnstile p leverage score sampling with applications

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41st International Conference on Machine Learning, ICML 2024

作者： Munteanu, Alexander Omlor, Simon Dortmund Data Science Center Faculties of Statistics and Computer Science TU Dortmund University Dortmund Germany Faculty of Statistics TU Dortmund University Dortmund Germany Lamarr-Institute for Machine Learning and Artificial Intelligence Dortmund Germany

The turnstile data stream model offers the most flexible framework where data can be manipulated dynamically, i.e., rows, columns, and even single entries of an input matrix can be added, deleted, or updated multiple times in a data stream. We develop a novel algorithm for sampling rows ai of a matrix A ∈ Rn×d, proportional to their p norm, when A is presented in a turnstile data stream. Our algorithm not only returns the set of sampled row indexes, it also returns slightly perturbed rows ãi ≈ ai, and approximates their sampling probabilities up to Ε relative error. When combined with preconditioning techniques, our algorithm extends to p leverage score sampling over turnstile data streams. With these properties in place, it allows us to simulate subsampling constructions of coresets for important regression problems to operate over turnstile data streams with very little overhead compared to their respective off-line subsampling algorithms. For logistic regression, our framework yields the first algorithm that achieves a (1 + Ε) approximation and works in a turnstile data stream using polynomial sketch/subsample size, improving over O(1) approximations, or exp(1/Ε) sketch size of previous work. We compare experimentally to plain oblivious sketching and plain leverage score sampling algorithms for p and logistic regression. Copyright 2024 by the author(s)

关键词： data streams

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Optimal bounds for p sensitivity sampling via 2 augmentation 41

Optimal bounds for p sensitivity sampling via 2 augmentation

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41st International Conference on Machine Learning, ICML 2024

data subsampling is one of the most natural methods to approximate a massively large data set by a small representative proxy. In particular, sensitivity sampling received a lot of attention, which samples points proportional to an individual importance measure called sensitivity. This framework reduces in very general settings the size of data to roughly the VC dimension d times the total sensitivity S while providing strong (1 ± Ε) guarantees on the quality of approximation. The recent work of Woodruff & Yasuda (2023c) improved substantially over the general Õ(Ε−2Sd) bound for the important problem of p subspace embeddings to Õ(Ε−2S2/p) for p ∈ [1, 2]. Their result was subsumed by an earlier Õ(Ε−2Sd1−p/2) bound which was implicitly given in the work of Chen & Derezinski (2021). We show that their result is tight when sampling according to plain p sensitivities. We observe that by augmenting the p sensitivities by 2 sensitivities, we obtain better bounds improving over the aforementioned results to optimal linear Õ(Ε−2(S+d)) = Õ(Ε−2d) sampling complexity for all p ∈ [1, 2]. In particular, this resolves an open question of Woodruff & Yasuda (2023c) in the affirmative for p ∈ [1, 2] and brings sensitivity subsampling into the regime that was previously only known to be possible using Lewis weights (Cohen & Peng, 2015). As an application of our main result, we also obtain an Õ(Ε−2µd) sensitivity sampling bound for logistic regression, where µ is a natural complexity measure for this problem. This improves over the previous Õ(Ε−2µ2d) bound of Mai et al. (2021) which was based on Lewis weights subsampling. Copyright 2024 by the author(s)

关键词： Importance sampling

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A Critical Review of AI-Driven AR Glasses for Realtime Multilingual Communication

A Critical Review of AI-Driven AR Glasses for Realtime Multi...

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2024 Advances in science and Engineering Technology International Conferences, ASET 2024

作者： Rasheed, Zainab Ghwanmeh, Sameh Almahadin, Aziz Emirates Aviation University Dubai Faculty of Mathematics & Data Science United Arab Emirates College of Computer Information Technology American University in the Emirates Dubai United Arab Emirates Higher Colleges of Technology Faculty of Engineering Technology & Science Dubai United Arab Emirates

ISBN: (纸本)9798350344134

This research paper examines the integration of Artificial Intelligence-driven Augmented Reality glasses within wired and wireless systems to facilitate real-time multilingual communication. The synergy between AI and AR technologies presents a transformative potential in overcoming language barriers, enabling seamless communication among diverse linguistic communities. By harnessing the power of AI, AR glasses offer instant translation and interpretation services, enhancing interconnectivity in multicultural settings. The paper discusses the technical underpinnings of these AR systems, exploring their architecture, functionalities, and the algorithms driving real-time multilingual translation. Furthermore, this study investigates the implications and challenges of embedding AI-powered AR glasses into wired and wireless communication networks. It assesses the system's effectiveness in translating spoken languages and analyzing their potential impact on communication infrastructure. The review of existing literature, coupled with case studies, offers insights into the successful applications of these integrated systems. This comprehensive analysis advocates for the utilization of AI-driven AR glasses as an instrumental tool in bridging language gaps, emphasizing their significance in fostering inclusive and efficient communication ecosystems within wired and wireless networks. © 2024 IEEE.

关键词： Translation (languages)

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Entrodeduper: data Deduplication Using Entropy and Secure Hash Algorithm

Entrodeduper: Data Deduplication Using Entropy and Secure Ha...

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2023 IEEE Conference on computer Applications, ICCA 2023

作者： Thwel, Tin Thein Maung, Hsu Mon University of Computer Studies Faculty of Information Science Yangon Myanmar Myanmar Institute of Information Technology Data Center Department Mandalay Myanmar

ISBN: (纸本)9781665435994

Being a data science era, any bulk of data concerned with specific fields need to maintain in data centers with the help of rack server. For reducing storage for data center, the need to implement the data deduplication mechanism and keeping backup of those data in data center is becoming more important. In the case of reducing redundant data from the various servers in data center, the huge amount of data become the biggest challenge. As the data deduplication mechanism not only it can reduce the size of data to be stored or to be transmit but also it can give the backup and recovery features. This paper proposed the efficient data deduplication mechanism called EntroDeduper, which is the hybrid of entropy, secure hash algorithm and indexing. The input files are divided into blocks using file separator and the redundant block is removed by applying the entropy gain theory, which is used to get not only the significant reduction rate but also the full block coverage for reconstruction. © 2023 IEEE.

关键词： Entropy

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Cloud Cost Prediction with LSTM on Parametric data 16th

Cloud Cost Prediction with LSTM on Parametric Data

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： San, Khin Pyae Phyo Thein, Nwe Nwe Myint Data Distribution Management Lab University of Information Technology Parami Road Hlaing Campus Yangon Myanmar Faculty of Computer Science Data Distribution Management Lab University of Information Technology Parami Road Hlaing Campus Yangon Myanmar

ISBN: (纸本)9789819615308

In the distributed computing paradigm, cloud computing provides a wide range of software, platforms and computing infrastructure. It has ability sharable elastic resource pools in dynamic. In this way, cloud computing offers many benefits. It reduces the cost of deployment in applications. It makes easy to increase the capacity of local infrastructures. And it can reduce power consumption by sharing resources. There are challenges over the benefits. The virtual instances needed to be shared equally distributed across different availability zones per demand. That can be categorized into operational and business challenges. In this paper, we implement the cloud prediction system using historical billing data and study the measurement of the model accuracy using Mean Absolute Error and Root Mean Squared Error. The trained model evaluates by the predicted and targeted value graph in this study. By leveraging historical financial data, this method aims to aid organizations in better foreseeing and managing AWS expenditures, leading to improved cost control and financial planning for cloud infrastructure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Comments on "A Lightweight Privacy Preserving Authentication Protocol for VANETs" 9

Comments on "A Lightweight Privacy Preserving Authentication...

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9th International Conference on computer and Communications, ICCC 2023

作者： Awais, Syed Muhammad Yucheng, Wu Mahmood, Khalid Kharel, Rupak Chongqing University School of Microelectronics and Communication Engineering Chongqing China University of Central Lancashire School of Psychology and Computer Science Graduate School of Intelligent Data Science Preston United Kingdom National Yunlin University of Science and Technology School of Psychology and Computer Science Graduate School of Intelligent Data Science Yunlin64002 Taiwan University of Central Lancashire School of Psychology and Computer Science Faculty of Electrical and Electronics Engineering Preston United Kingdom Ton Duc Thang University School of Psychology and Computer Science Faculty of Electrical and Electronics Engineering Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350317251

Vehicle use and the concept of a "smart city"are developing quickly. As a result of this progression, the Vehicular Ad-Hoc Network (VANET) is a popular network for inter-vehicular communication. The data gathering about the state of the road, the whereabouts of vehicles, their speeds, and the description of traffic congestion are obtained using the VANET. The public nature of information flow on the VANET creates serious security risks. data security is typically one of the most important duties of VANET. As a result, it becomes one of the researchers' top priorities. Li et al. [1] introduced a Lightweight Privacy Preserving Authentication Protocol for VANETs. They stated that several attacks could be resisted using their secure authentication devised protocol. However, after analyzing we discovered that their authentication protocol is susceptible to roadside impersonation attack and it does not provide vehicle anonymity. In the end, we have provided a few suggestions to cope with Li et al.'s devised protocol current shortcomings. © 2023 IEEE.

关键词： Vehicular ad hoc networks

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Essentia: Boosting Artifact Removal from EEG through Semantic Guidance Utilizing Diffusion Model

Essentia: Boosting Artifact Removal from EEG through Semanti...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Haoran Zhang, Zhibo Li, Yuchen Gong, Xiaoli Zhang, Jin Lu, Tingjuan Zhou, Jin Sun, Zhe Cichocki, Andrzej College of Computer Science Nankai University Tianjin China 903rd Hospital of PLA Hangzhou China Tianjin Chest Hospital Tianjin China Faculty of Medicine Faculty of Health Data Science Juntendo University Japan Systems Research Institute Polish Academy of Science Warsaw Poland

ISBN: (纸本)9798350368741

Electroencephalography (EEG) is a time-series signal containing semantic information that can be used to determine human brain activities. Artifacts within EEG data can interfere with the intrinsic distribution of this semantic information, so removing artifacts is crucial for improving EEG analysis performance on downstream tasks. In this paper, we redefine the efficacy of the artifact removal model by evaluating the performance of the noisy EEG data in downstream tasks before and after artifact removal. Currently, most artifact removal models fail to ensure semantic consistency, rendering them ineffective. To solve it, we propose an artifact removal model based on the 1-dimensional diffusion model utilizing the U-Net, referred to as Essentia. Moreover, we find that the skip-connection layer in U-Net contains mid-to-high-frequency information that interferes with the semantic representation. We introduce a semantic guidance module (SGM) that leverages contrastive learning to generate semantic distribution weights, boosting semantic representation. We evaluate Essentia on three datasets with six solutions. The accuracy of downstream tasks from the denoised EEG data increased by 4% compared with the DeepSeparetor. The code and models are available at https://***/NKUEmbeddedSystem/Essentia. © 2025 IEEE.

关键词： contrastive learning diffusion model EEG artifact removal semantic consistency

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Evaluating Pipeline Architecture with Apache Kafka and Apache Flink: data-Driven Architecture 16th

Evaluating Pipeline Architecture with Apache Kafka and Apach...

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Kyaw, Chan Min Thein, Nwe Nwe Myint Data Distribution Management Lab University of Information Technology Parami Road Hlaing Campus Yangon Myanmar Faculty of Computer Science Data Distribution Management Lab University of Information Technology Parami Road Hlaing Campus Yangon Myanmar

ISBN: (纸本)9789819615308

Nowadays, the ability to process, generate and collect various data in real-time has become more relevant for faster reactions to important events in technical enterprises. The numerous distributed stream processing engines is developed to provide the advance large streaming applications. Apache Kafka and Flink also become more popular in the processing and analyzing of large amount of data. Apache Flink is best suited for data analytics, event-driven and data pipeline applications. Applying Apache Kafka to Flink provides low idle time and low latency, and the most demanding stream processing applications in the world. This paper investigates the effective pipeline architecture for developing data driven analytics. Confirming the experimental results, the system proves to optimize the maximum latency, average latency, and idle time of the pipeline architecture. Additionally, further experiments were performed by implementing different levels of parallelism while streaming data, allowing for a comprehensive analysis of maximum latency and average latency of the pipeline. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： data streams

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A Novel 3D Medical Image Segmentation Model Using Improved SAM

A Novel 3D Medical Image Segmentation Model Using Improved S...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Kuang, Yuansen Ma, Xitong Zhao, Jing Wang, Guangchen Zeng, Yijie Liu, Song Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

3D medical image segmentation is an essential task in the medical image field, which aims to segment organs or tumours into different labels. A number of issues exist with the current 3D medical image segmentation task: existing models cannot simultaneously obtain the space correlation and depth correlation of 3D slices;previous models suffer from local detail loss of positional embedding in 3D images;previous approaches often have blurring of boundaries in segmenting 3D images. To solve these shortcomings, we propose a 3D medical image segmentation model named TPM-SAM. In our model, we design a twinchannel image encoder to simultaneously capture the space correlation and depth correlation of 3D slices through a multi-head attention mechanism and improved adapters. Furthermore, we design a prompt encoding generator, which divides the volumetric image into small blocks and better captures the local detail information. In addition, we introduce a multi-layer aggregation decoder by employing U-Net with multi-level skip connection to solve the blurring of boundaries in processing 3D images. Finally, we experimented and evaluated our model on KiTS21 and LiTS17 datasets to compare with other baseline models. © 2024 IEEE.

关键词： Image segmentation

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