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Artificial Intelligence And Machine Learning

Artificial Intelligence And Machine Learning

引用

2022 IEEE Future Networks World Forum, FNWF 2022

作者： Kataria, Deepak Walid, Anwar Daneshmand, Mahmoud Dutta, Ashutosh Enright, Michael A. Gu, Rentao Lackpour, Alex Ramachandran, Prakash Wang, Honggang Chen, Chi-Ming Chng, Baw Darema, Frederica Ip Junction United States Nokia Bell Labs United States Stevens Institute of Technology United States Johns Hopkins University Applied Physics Lab United States Quantum Dimension Inc. United States Bupt China Drexel University United States Dell Technologies United States EOTF India UMass Dartmouth United States United States Bawman Llc United States Info Symbiotic Systems Society United States

ISBN: (纸本)9781665462501

In the evolution of artificial Intelligence (AI) and machine learning (ML), reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects have been widely used. These features enable the creation of intelligent mechanisms for decision support to overcome the limits of human knowledge processing. In addition, ML algorithms enable applications to draw conclusions and make predictions based on existing data without human supervision, leading to quick, near-optimal solutions even in problems with high dimensionality. Hence, autonomy is a key aspect of current and future AI/ML algorithms. This chapter focuses on the development and implementation of AI/ML technologies for 5G and future networks. The objective is to illustrate how these technologies can be smoothly migrated into 5G systems to increase their performance and to decrease their cost. To that end, this chapter presents the Drivers, Needs, Challenges, Enablers, and Potential Solutions identified for the AI/ML field as applicable to future networks over three-, five-, and ten-year horizons. AI/ML applications for 5G are wide and diverse. In this document, some of the key areas are described which includes networking, securing, cloud computing and others. Over time, this white paper will evolve to encompass even more areas where AI/ML technologies can improve future network performance objectives. © 2022 IEEE.

关键词： Cloud computing

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Social Media Governance and Fake News Detection Integrated with Artificial Intelligence Governance

Social Media Governance and Fake News Detection Integrated w...

引用

IEEE International conference on Information Reuse and Integration (IRI)

作者： Bhavani Thuraisingham Teena Thomas Erik Jonsson School of Engineering and Computer Science The University of Texas at Dallas

ISBN: (数字)9798350351187

ISBN: (纸本)9798350351194

Social Media Systems such as Facebook, Instagram, and Twitter (i.e., X) are exploding. These systems need proper governance so that the users are safe and post accurate information. This paper focuses on social media governance with an emphasis on Artificial Intelligence. First, we discuss various aspects of governance of such policies, procedures and risk and then address a key topic which is detecting fake news on social media. In order for the users to be safe using social media we have to ensure that the governance aspects also include fake news detection. Many of the fake news detection techniques utilize Machine Learning (ML) and Artificial Intelligence (AI) and more recently Generative AI (GenAI) techniques. Therefore, the AI systems that implement the various techniques have to be trustworthy. That means these systems have to be secure as well as ensure fairness, privacy and integrity. Therefore, the paper will also discuss AI Governance as an integral part of Social Media Governance. Finally, to support the various applications and frameworks, both data and the cloud are critical. Large amounts of data are stored and managed by the social media systems as well as used to train the AI models. Furthermore, we need massive amounts of computing power that can be provided by the cloud. Therefore, we will also discuss data and cloud governance that provides the infrastructure for social media and AI governance.

关键词： Privacy Social networking (online) Generative AI Machine learning Data science Rough surfaces Web sites multimedia communication Artificial intelligence Fake news

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Arabic fake news detection in social media Based on AraBERT

Arabic fake news detection in social media Based on AraBERT

引用

IEEE International conference on Cognitive Informatics

作者： Sghaira Ben Ali Zied Kechaou Ali Wali Computer science department Higher Institute of Computer Science and Multimedia of Sfax sfax Tunisia RGIM-lab Research Groups on Intelligent Machines university of sfax national engineering school of sfax (ENIS) sfax Tunisia Higher School of commerce of Sfax sfax Tunisia Higher Institute of Computer Science and Multimedia of Sfax sfax Tunisia

Social media has become a source of information for many people because of its freedom of use. As a result, fake news spread quickly and easily, regardless of its credibility, especially over the past decade. The vast amount of information being shared has fraudulent practices that negatively affect readers' cognitive abilities and mental health. In this study, we aim to introduce a new Arabic COVID-19 dataset for fake news related to COVID-19 from Twitter and Facebook. Afterward, we applied two pre-trained models of classification AraBERT and BERT base Arabic. As a result, AraBERT models obtained better accuracy than BERT base Arabic in two datasets.

关键词： COVID-19 Deep learning Social networking (online) Computational modeling Bit error rate Mental health Information filters

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Efficient Dual Batch Size Deep Learning for Distributed Parameter Server Systems 46

Efficient Dual Batch Size Deep Learning for Distributed Para...

引用

46th Annual IEEE-Computer-Society International Computers, Software, and Applications conference (COMPSAC) - Computers, Software, and Applications in an Uncertain World

作者： Lu, Kuan-Wei Liu, Pangfeng Hong, Ding-Yong Wu, Jan-Jan Natl Taiwan Univ Grad Inst Networking & Multimedia Taipei Taiwan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan Acad Sinica Inst Informat Sci Taipei Taiwan

ISBN: (纸本)9781665488105

Distributed machine learning is essential for applying deep learning models with many data and parameters. Current researches on distributed machine learning focus on using more hardware devices powerful computing units for fast training. Consequently, the model training prefers a larger batch size to accelerate the training speed. However, the large batch training often suffers from poor accuracy due to poor generalization ability. Researchers have come up with many sophisticated methods to address this accuracy issue due to large batch sizes. These methods usually have complex mechanisms, thus making training more difficult. In addition, powerful training hardware for large batch sizes is expensive, and not all researchers can afford it. We propose a dual batch size learning scheme to address the batch size issue. We use the maximum batch size of our hardware for maximum training efficiency we can afford. In addition, we introduce a smaller batch size during the training to improve the model generalization ability. Using two different batch sizes in the same training simultaneously will reduce the testing loss and obtain a good generalization ability, with only a slight increase in the training time. We implement our dual batch size learning scheme and conduct experiments. By increasing 5% of the training time, we can reduce the loss from 1.429 to 1.246 in some cases. In addition, by appropriately adjusting the percentage of large and small batch sizes, we can increase the accuracy by 2.8% in some cases. With the additional 10% increase in training time, we can reduce the loss from 1.429 to 1.193. And after moderately adjusting the number of large batches and small batches used by GPUs, the accuracy can increase by 2.9%. Using two different batch sizes in the same training introduces two complications. First, the data processing speeds for two different batch sizes are different, so we must assign the data proportionally to maximize the overall processing speed. I

关键词： deep neural networks batch size distributed learning parameter server

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Video-assisted Overtaking System Enabled by C-V2X Mode 4 Communications 16

Video-assisted Overtaking System Enabled by C-V2X Mode 4 Com...

引用

16th International conference on Wireless and Mobile computing, networking and Communications (IEEE WiMob)

作者： Magalhaes, Pedro D'Orey, Pedro M. Andrade, Maria Teresa Castro, Francisco Bosch Car Multimedia Portugal SA Braga Portugal Inst Telecomunicacoes Porto Portugal Univ Porto Porto Portugal

ISBN: (纸本)9781728197227

The overtaking maneuver can be often dangerous and risky, especially when driving in rural roads, with a significant probability of leading to fatalities and serious injuries. Vehicle communication emerges as one of the leading technologies to improve the driving experience and road safety. This article presents a system designed to augment road safety in an Overtaking Maneuver, providing the ahead vehicle's Line-of-Sight by real-time video-streaming. It uses the C-V2X Transmission Mode 4 that, building upon cellular technology, has evolved as one of the main standards for vehicle communication. Results show an effective application of the system in real overtaking scenarios, enhancing the safety and proving its usefulness. Whilst having some impact on the latency, the solution incurred in low packet losses and provided good video quality.

关键词： Accident prevention

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A New Load Balance Scheme for Heterogeneous Entities in Cloud Network Convergence 14th

A New Load Balance Scheme for Heterogeneous Entities in Clou...

引用

14th EAI International conference on Mobile multimedia Communications, Mobimedia 2021

作者： Liu, Jiaji Zhang, Zhiwei Xu, Wangzhe Zhu, Xinghui Dong, Xuewen School of Computer Science and Technology Xidian University Xi’an Shaanxi710071 China

ISBN: (纸本)9783030898137

For future Internet and next-generation network, the cloud networking convergence is one of the most popular research directions, and it has attracted widespread attention from academia as well as industry. Network adapting cloud and network cloudification are two dimensions in cloud network convergence that can break the closeness and independence between cloud and network. However, the techniques related to the network adapting cloud and network cloudification unavoidably introduce more heterogeneous devices, services and users. That disables the existing load balance schemes which are almost proposed for data centers in cloud computing environments, where the entities are typically standard hardware and software modules. As a result, the overhead and cost of load balance shcemes would be raised significantly in the progress of cloud network convergence. Therefore, in this paper, to make the most usage of heterogeneous entities and encourage the development of future Internet as well as next-generation networking, we propose a model and the requirements of load balance for heterogeneous entities in the convergence of cloud and network, then we present a concrete load balance scheme. Finally, we discuss the abilities and applications of our proposed model and scheme. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Cloud computing

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Detecting Fake Profile in Online Social Networks using EnsemStack Classification Algorithm

Detecting Fake Profile in Online Social Networks using Ensem...

引用

International conference on computing Communication Control and Automation (ICCUBEA)

作者： Aayush Sunil Chamria Abhishek Dinesh Mane Prithvi Vadiraj Dambal Smita Bharne Ramrao Adik Institute of Technology D Y Patil Deemed to be University Navi Mumbai India

ISBN: (纸本)9781665484527

Online Social Media (OSN) Platforms are the most dominating in the world amongst all age groups. These platforms are termed to be dynamic because they have increasing users day by day. The main benefit of social networking over the internet is to connect with people and communicate better. Users are sharing their data, photos and personal information on OSN platform like Facebook, Instagram, LinkedIn, and Twitter. Criminals are also used OSN platforms for new attack methods such as fake identities, disinformation, and many more. According to a recent study, the number of accounts that exist on social networks far outweighs the number of users who actually use them. Hence the no. of fake profile on OSN platforms are increasing over the last decade. The proposed model based on EnsemStack classification mode has the potential to identify the OSN account as real or fake with more accuracy. Our model hence takes the input of the from Instagram user account from and extracts all the required features the account as real or fake. The primary objective of proposed system is to absolute the existence of these fake profiles in these OSNs using machine learning. Eliminating these fake profiles will help to curb all the malpractices.

关键词： Automation Social networking (online) multimedia Web sites Blogs Support vector machine classification Media Feature extraction

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Emotion Recognition Using Bayesian Learning from a Multi-Label Data Corpus

Emotion Recognition Using Bayesian Learning from a Multi-Lab...

引用

computing, networking, Telecommunications & Engineering Sciences Applications (CoNTESA), International conference on

作者： Bandi Swathi Madapuri Rudra Kumar Mohammad Riyaz Belgaum Zainab Alansari Computer Science and Engineering G. Pulla Reddy Engineering College Kurnool India Computer Science and Engineering G Pullaiah College of Engineering and Technology Kurnool India Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia Computer System and Information Technology University of Malaya Kuala Lumpur Malaysia Information Technology University of Technology and Applied Science (UTAS) Muscat Oman

ISBN: (纸本)9798350398021

In the digital era of information systems, emotion detection from audio signals is crucial for forensic services and operator or driver emotion monitoring in large-scale companies’ safety and security. Speech is a uniquely human trait used to express and communicate one’s point of view to others. Emotion recognition from speech audio signals is obtaining a presenter’s emotions from the presenter’s audio signal. Machine learning is used to develop emotion recognition systems, a critical research goal in current engineering research. The three main stages in speech emotion recognition are extraction of features, feature engineering, and classification. Even though powerful machine learning-based emotion identification algorithms for speech audio signals exist, the detection rate with maximum specificity and sensitivity is not scalable using most modern methods. A significant research objective is feature optimization for emotion recognition from speech audio signals. This article introduced a unique technique called Supervised Bayes Learning on Digital Features (SBL-DF). To scale its performance, the trials compared the suggested approach’s performance to an earlier model depicted against a similar goal. Experiment results indicate that the proposed method for optimizing features could be used on a large scale. The reference classifiers’ performance in making accurate detections with few false positives.

关键词： Training Emotion recognition Sensitivity Hidden Markov models Speech recognition Benchmark testing Feature extraction

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Localization of Image Splicing Under Segment Anything Model With Integrated Compression and Edge Artifacts

Localization of Image Splicing Under Segment Anything Model ...

引用

IEEE International conference on Image Processing

作者： Ruhao Zhao Xian Zhong Liang Liao Wenxuan Liu Wenxin Huang Zheng Wang School of Computer Science and Artificial Intelligence Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology School of Electrical and Electronic Engineering Rapid-Rich Object Search Lab Nanyang Technological University College of Computing and Data Science Nanyang Technological University School of Computer Science and Information Engineering Hubei University School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

The localization of image splicing involves identifying pixels in an image that have been spliced from other images, necessitating the discernment of splicing features. Despite significant advancements driven by the rise of social media and deep learning, existing methods exhibit limitations, often neglecting the integration of coarse and precise features and lacking the ability to understand objects. This leads to erroneous predictions in identifying spliced regions. This paper proposes Segment Anything Model with Integrated Compression and Edge artifacts (SAM-ICE) for the localization of image splicing, addressing these limitations by fusing forged edge features and compression artifact features. Leveraging SAM’s object understanding ability, our method identifies spliced regions using the fused features as guidance. Specifically, we employ Edge Artifact Extractor (EAE) to extract fine high-frequency edge splicing features and Compression Artifact Extractor (CAE) to extract coarse compression artifact features. By combining these features, our method utilizes coarse-fine features to accurately pinpoint the spliced portions of the image. Experimental results demonstrate the superior accuracy, robustness, and generalizability of our method compared to the state-of-the-arts.

关键词： Location awareness Image segmentation Image coding Social networking (online) Image edge detection Splicing Feature extraction

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AI-Based Child Care Parental Control System

AI-Based Child Care Parental Control System

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Advancements in computing (ICAC), International conference on

作者： Udara Jayasekara Hansindu Maniyangama Kalhan Vithana Tharana Weerasinghe Janaka Wijekoon Rrubaa Panchendrarajan Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Sri Lanka Department of Computer System Engineering Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (纸本)9798350398106

Due to the prevalence of the COVID-19 epidemic around the globe, children were compelled to engage in remote learning through online platforms, hence mobile phone has become one of their predominant devices. Mobile device with Internet access offers a major outlet for education, entertainment, and social connection, but this combination can lead to several significant bad sequences such as online exploitation, harmful addictions, and other negative impacts of online social networking. To address harmful effects, parental controls are becoming more crucial, yet Sri Lankan parents are less aware of this. Consequently, this study proposes a parental control system to monitor their child’s activities. Android, Microsoft Azure, Java, Python, OpenCV, MySQL, and FastAPI are among the most prominent technologies utilized in the proposed application’s development. The suggested approach focuses primarily on the Sri Lankan context and aims to enhance parental digital literacy while safeguarding children from cyber threats. Yielded results showed the proposed mobile application for the identification of toxic words, drugs & alcohol content, game character images, and Instagram Sinhala comments severity as 94%, 95%, 97%, and 55% respectively in controlled experiments.

关键词： Pediatrics Social networking (online) multimedia Web sites Process control Control systems Real-time systems Mobile applications

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