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Does Artificial Intelligence Help Reduce Audit Risks? 13

Does Artificial Intelligence Help Reduce Audit Risks?

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13th international conference on Advanced Computer Information Technologies, ACIT 2023

作者： Adamyk, Oksana Benson, Vladlena Adamyk, Bogdan Al-Khateeb, Haider Chinnaswamy, Anitha Loughborough University Loughborough Business School Accounting and Finance Group Loughborough United Kingdom Aston University Operations and Information Management Department Birmingham United Kingdom

This article aims to discover how AI-powered systems facilitate auditing, what risks emerge for AI-assisted audits and how to deal with these new risks. The paper studies the impact of cognitive computing on audit risk. AI-powered software is capable of self-learn so that it can identify patterns in data and codify them in predictions, rules and decisions. This self-learning ability can become both a benefit and, at the same time, insecurity. Although AI-self-learning helps make the process more efficient and calculations more accurate by improving the algorithm, eliminating errors and reducing risks, it creates new previously unknown threats. We discovered inherent limitations of cognitive-based technologies and risks for the audit process associated with using AI systems. We also proposed a complex security model that can reduce the uncertainty of AI-enabled audit and provides insight into future research opportunities. © 2023 ieee.

关键词： cognitive systems

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Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments 6

Deep Reinforcement Learning-based Obstacle Avoidance for Rob...

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6th ieee international conference on Civil Aviation Safety and Information Technology, ICCASIT 2024

作者： Li, Keqin Chen, Jiajing Yu, Dezhi Dajun, Tao Qiu, Xinyu Lian, Jieting Ji, Ryan Zhang, Shengyuan Wan, Zhenyu Sun, Baiwei Hong, Bo Ni, Fanghao Han, Jiaqi Ama University Department of Computer Science Quezon Philippines Courant Institute New York University 251 Mercer St New YorkNY10012 United States University of California Berkeley University Avenue and Oxford St School of Information BerkeleyCA94720 United States Carnegie Mellon University Master of Science in Mechanical Engineering School of Engineering 5000 Forbes Avenue PittsburghPA United States Northeastern University College of Engineering Seattle United States School of Professional Studies New York University Master of Science in Database Technology 50 West 4th Street New YorkNY United States Cornell University College of Engineering 616 Thurston Ave IthacaNY14853 United States Cornell University Master of Professional Science in Applied Statistics-Data Science College of Computing and Information Science 616 Thurston Ave IthacaNY United States Independent Researcher Beijing China University of California Irvine Master of Computer Science Donald Bren School of Information and Computer Sciences 510 Aldrich Hall IrvineCA United States Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ United States Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9798350389418

This paper proposes a deep reinforcement learning based on the warehouse environment, the mobile robot obstacle avoidance Algorithm. Firstly, the value function network is improved based on the pedestrian interaction. We can learn to obtain the relative importance of the current state and the historical trajectory state as well as the joint impact on the robot's obstacle avoidance strategy. Secondly, the reward function of reinforcement learning is designed based on the spatial behaviour of pedestrians, and the robot is punished for the state where the angle changes too much, so as to achieve the requirement of comfortable obstacle avoidance. Finally, the feasibility and effectiveness of the deep reinforcement learning-based mobile robot obstacle avoidance algorithm in the warehouse environment in the complex environment of the warehouse are verified through simulation experiments. © 2024 ieee.

关键词： Deep reinforcement learning

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Pre-Segmentation: A data Augmentation-Based Strategy for Thinning ViT Models 10

Pre-Segmentation: A Data Augmentation-Based Strategy for Thi...

引用

10th international conference on Big data computing and Communications, BIGCOM 2024

作者： Guo, Houbin Guo, Xing Du, Haohua Anhui University School of Computer Science and Technology HeFei China USTC-DEQING Alpha Innovation Institute China Beihang University School of Cyber Science and Technology Beijing China

ISBN: (纸本)9798331509538

In the basic Vision transformer (ViT) model, the way to process the image is to cut the image into a certain number of patches as labeled (location coded) tokens, which are inputted to the multi-attention part of the model to extract the information, and then passed through the full connectivity layer to a classifier to classify the image. If all the Tokens are utilized for interaction, the complex ViT structure brings a huge amount of computation, which is a major drawback of the model. The ViT model itself is characterized by global interaction, and Attention is extremely sensitive to global information, so the over-pursuit of interaction with all the Tokens will bring about a sharp increase in complexity and a multiplication of the pressure on the hardware facilities, such as the reduction in the number of Tokens, which will bring about a reduction in the amount of information. If the number of Tokens is reduced, it brings about loss of information and unsatisfactory accuracy. In this study, we take the lightweight ViT model as a starting point, and preprocess the images: the images are chunked and then entered into the coding layer for information extraction respectively, and finally the respective cls labels are processed to get the classification results, which can solve the problem of over-attention to global attention of ViT as well as avoiding the loss of information due to the reduction of Tokens, and ultimately realizing the model's lightweighting. In conclusion, our model (PS-ViT) starts from two aspects, firstly, data enhancement is used to improve the generalization ability of the model;secondly, in the processing of input images, pre-blocking can both change the range of model information interaction and reduce the computation of the model in order to achieve efficient reasoning. Finally, a 40% reduction in computation can be achieved while maintaining accuracy. © 2024 ieee.

关键词： Image segmentation

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Attribute-Based Re-Encryption for data Cloud Secure Storage and Access in End-Edge-Cloud Networks 3

Attribute-Based Re-Encryption for Data Cloud Secure Storage ...

引用

3rd international conference on Intelligent computing and Next Generation Networks, ICNGN 2024

作者： Kou, Tingting Dong, Zhicheng Yang, Jing Zhao, Jing Cai, Donghong College of Information Science Technology Tibet University Lhasa850000 China College of Cyber Security Jinan University Guangzhou510632 China School of Network and Communication Engineering Chengdu Technological University Chengdu China

ISBN: (纸本)9798331529222

Cloud storage can enhance the efficiency of the data storage and sharing, while it is challenge for the security of the data transmission. Attribute-Based Encryption (ABE) improves security of data sharing, but the main issues are key management and user-side computational efficiency. In this paper, an Online-Offline Re-Encryption (OORE) algorithm is proposed to improve the secure storage and access of data in cloud storage. To resolve the key management problem, we ensure that the Attribute Authority doesn’t possess the user’s secret key through key computation techniques. To enhance system security, we implement distinct access control strategies for users and edge routers, employing re-encryption computations. Additionally, to address the issue of prolonged pairing computation times in ABE algorithms, the proposed OORE algorithm combines online and offline encryption/decryption operations. Most complex computations are performed offline, requiring only a single pairing and a few divisions and multiplications during the online phase, thus alleviating the computational burden on users. Security analysis demonstrates that the proposed OORE algorithm can withstand chosen ciphertext attacks, affirming its security. Experimental results indicate that our algorithm effectively reduces the computational load on users while keeping ciphertext and key storage overhead within reasonable limits. ©2024 ieee.

关键词： Storage efficiency

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A Reverse Method of data Augmentation for High Quality PET Image Synthesis 25

A Reverse Method of Data Augmentation for High Quality PET I...

引用

25th international conference on Digital Image computing: Techniques and Applications, DICTA 2024

作者： Tan, Boyuan Xue, Yuxin Bi, Lei Kim, Jinman The School of Computer Science The University of Sydney Sydney Australia Institute of Translational Medicine National Center for Translational Medicine Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350379037

Positron Emission Tomography (PET) is crucial in oncology for cancer diagnosis and management but involves radiation exposure that has health concerns. An alternative is to use low-dose PET (LPET) instead of standard-dose PET (SPET) where reduced LPET administrates lower radiation dosage fot the scan. However, low-dose PET (LPET) images typically have low signal-to-noise ratio (SNR) and poorer image quality when compared to SPET images, which inadvertently affect diagnostic accuracy. Therefore, using generative artificial intelligence (AI) methods to synthesize (via image generation) SPET images from LPET images has become an active research. Methods based on using convolutional neural networks (CNNs) and Generative Adversarial Networks (GANs) have shown promising performance in SPET synthesis from LPET. However, the performances of these models are highly reliant on the number of paired training samples to learn the complex mappings between LPET and SPET images, e.g., LPET and SPET of the same patient. Without sufficient number of paired training samples to cover all the possible variations, e.g., patient-specific tumour structures, complex anatomical structures, and variety of diseases, existing methods tend tosuffer in generating accurate synthetic SPET. In this study, we propose a new method to boost the number of training samples for LPET to SPET synthesis, where we introduce a new 'reverse' data augmentationfor SPET synthesis where we exploit large amounts of SPET images that are acquried as part of a routine imaging. In our approach, we do not need a paired training data, and instead, we convert the SPET images into their LPET image counterparts;these converted images are then used to augment the existing training datasets. Our experiments with the Ultra-low Dose PET Imaging Challenge benchmark datasets show that our method was able to enhance the state-of-the-art methods for producing SPET images, especially for the challenging studies. © 2024 ieee.

关键词： Positron emission tomography

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A PARAMETRIC APPROACH OF computing TAGUCHI SIGNAL-TO-NOISE RATIO FROM INCOMPLETE LIFE data 14

A PARAMETRIC APPROACH OF COMPUTING TAGUCHI SIGNAL-TO-NOISE R...

引用

14th international conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2024

作者： Jiang, Renyan Xue, Wei Cao, Yu China International Science & Technology Cooperation Base for Laser Processing Robotics Wenzhou University Wenzhou China Zhejiang Provincial Innovation Center of Laser Intelligent Equipment Technology Wenzhou China

ISBN: (纸本)9781837242672

The Taguchi method for experimental design is widely utilized to improve product reliability through the systematic arrangement of multiple runs or treatments, each representing a unique combination of factor levels. In such experiments, lifetime data of sample units are gathered via life tests, which are typically monitored periodically and terminated at a pre-determined endpoint. The resultant data, characterized by small sample sizes, high censorship, and heterogeneity, preclude direct computation of the Taguchi signal-to-noise ratio (SNR), a crucial measure of robustness used to evaluate the performance of a run. This paper addresses this issue by introducing a parametric approach that begins by constructing a life distribution model for each run through a three-step parameter estimation procedure. It then calculates the SNR from the fitted distribution using SNR expressions derived from typical life distributions discussed herein. The effectiveness and superiority of the proposed method are demonstrated through two real-world examples, underscoring its applicability and potential benefits in enhancing the reliability analysis of complex products. © The Institution of Engineering & Technology 2024.

关键词： Taguchi methods

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A Study on Viability of Cryptocurrency Adoption in Qatar: A Thematic Coding Approach of Fifty Reviews

A Study on Viability of Cryptocurrency Adoption in Qatar: A ...

引用

2024 international conference on Computer and Applications, ICCA 2024

作者： Al-Nassr, Asma Nasser M. N. Alazab, Moutaz Al-Siddiqi, Essa H. M. Oryx Universal College in Partnership with Liverpool John Moores University in Qatar Dept. of Computing and Data Science Doha Qatar

ISBN: (纸本)9798350367560

This study investigates the factors driving cryptocurrency adoption in Qatar, with a focus on creating a comprehensive taxonomy to guide policymakers and stakeholders in this emerging financial landscape. Given Qatar's pivotal role in the Gulf Cooperation Council (GCC) and the broader Middle East and North Africa (MENA) region, the paper is particularly significant. By examining cryptocurrency adoption through the lens of Islamic finance principles and regulatory considerations, the study offers valuable insights into this evolving financial domain. The research employs a thematic coding analysis of 50 literature sources, covering aspects such as regulatory frameworks, Sharia compliance, socio-cultural factors, and technological advancements, ensuring a well-rounded understanding of the topic. The findings reveal a complex interplay of factors influencing cryptocurrency adoption in Qatar, including the need for Sharia-compliant models, robust regulatory frameworks, and the impact of cultural and religious perspectives on financial innovation. Additionally, the study highlights the importance of addressing privacy and security concerns in this context. The research emphasizes the necessity of a balanced approach that tackles regulatory challenges, ensures Sharia compliance, and leverages technological advancements to facilitate successful cryptocurrency adoption. This study significantly contributes to the understanding of cryptocurrency adoption in the Islamic world and provides a strong foundation for future research and policy development in Qatar and similar economies. © 2024 ieee.

关键词： Decentralized finance

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Drone enabled Smart Air-Agent for 6G Network

Drone enabled Smart Air-Agent for 6G Network

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ieee international conference on Communications (ICC)

作者： Miao, Yiming Xu, Jinfeng Chen, Min Hwang, Kai Chinese Univ Hong Kong Sch Data Sci Shenzhen CUHK SZ Shenzhen Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

The future ubiquitous network, which is mainly characterized by full coverage communication, air-ground integration, multidimensional fusion, network reconfiguration and sensing-communication-computing integration, has become the development trend of 6G technology. The realization of ubiquitous coverage and perceptive fusion of IoT-UAV-Edge is an urgent problem to be solved for complex fusion services. Therefore, this paper proposes a drone-enabled smart air agent in 6G edge fusion system. Firstly, the energy efficient dynamic routing strategy based on joint air-ground control optimization is designed to improve the fusion sensing performance and prolong the service time of drone swarm. Then, the system integration of user-IoT-UAV-Edge is realized to achieve the functionalities of perception, transmission, computing and analysis. Finally, an airborne data fusion mechanism based on multi-source sensing is designed to solve the associated cognitive optimization problem for multi-modal information. The experimental results invalidate the effectiveness and practicability of our system on autonomous path planning, effective computing offloading and accurate airborne fusion.

关键词： 6G computing Control Drone Fusion

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Research on Sentiment Analysis Method of Fan Culture Based on CNN-LSTM

Research on Sentiment Analysis Method of Fan Culture Based o...

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cognitive computing and complex data (ICCD), international conference on the

作者： Aiting Pan Yuangan Wang Junqi Luo Qingqing Wang School of Electric and Information Engineering Beibu Gulf University Qinzhou China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Fan culture is a popular youth subculture phenomenon in recent years, and analyzing and monitoring the deviant behaviors within fan culture plays an important role in social governance. This paper proposes a sentiment analysis method for Weibo comments based on Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) by combining the adaptive feature extraction capability of CNN with the long-distance information processing characteristics of LSTM. A training set is constructed by crawling online comments on recent hot fan culture topics, and the text is then preprocessed into vectors. Comparative experiments show that the proposed model achieves an emotion prediction accuracy rate of over 85%, indicating that this method can effectively analyze Weibo comments.

关键词： Fans Sentiment analysis Analytical models Accuracy Blogs Predictive models Vectors data models Convolutional neural networks Long short term memory

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Autonomous Robot Navigation in Unknown Terrains using Generative Adversarial Networks 4

Autonomous Robot Navigation in Unknown Terrains using Genera...

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4th international conference on Soft computing for Security Applications, ICSCSA 2024

作者： Chitra, P. Sridar, K. Govindan, Sudha Britto Raj, S. Akshya, J. Choudhry, Mani Deepak Sundarrajan, M. St Joseph's Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Veerammal Engineering College Department of Computer Science and Engineering Dindigul India Sona College of Technology Department of Computer Science and Engineering Salem India RRASE College of Engineering Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computational Intelligence Chennai India SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798331515720

Autonomous Robot Navigation in unknown and dynamic environments is considered one of the grand challenges in robotics. These motions that guide it span from rescue operations to planetary exploration. Classical methods for path planning, including A*, Dijkstra, and RRT, were unable to balance the efficiency and adaptability of motion planning over complex terrain. This may have a powerful solution using Generative Adversarial Networks (GANs) with advanced machine learning techniques to improve the ability of robotic navigation. This study proposes a GAN-based approach for designing an effective self-adaptive path planning and obstacle avoidance system for autonomous robots. Despite performing well in structured environments, traditional techniques normally fail in analysing the unstructured terrains with real-time adjustments and dynamic changes due to the high computational costs and lack of flexibility involved. In this regard, the proposed GAN-based system resolves these issues by rating navigation paths concerning safety and efficiency. Quantitative analysis demonstrates that within a forest, desert, urban, or off-road environment, the computational times by using the developed strategy turn out to be 0.12 seconds, 0.10 seconds, 0.14 seconds, and 0.13 seconds, respectively, behaving much lower than what can be achieved with other algorithms. This approach proves much superior in terms of computational efficiency path smoothness and adaptability under different environmental conditions in applications related to autonomous robot navigation systems. © 2024 ieee.

关键词： Generative adversarial networks

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