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Towards intelligent Auditing: Exploring the Future of Artificial Intelligence in Auditing 11th

Towards Intelligent Auditing: Exploring the Future of Artifi...

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11th international conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Huang, Ling Liu, Dongbing School of Management Wuhan Technology and Business University Hubei Wuhan China College of Mathematics and Computer Panzhihua University Sichuan Panzhihua China

Recent years have witnessed an increasingly broad application of artificial intelligence (AI) technologies such as speech recognition, computer vision, natural language processing, machine learning, algorithmic framework, cognitive computing, deep learning and neural networks in the field of auditing, producing a far-reaching impact on traditional audit work. However, the application of AI technologies in auditing practices is still in its infancy stage and further exploration and development are needed. Based on an in-depth investigation of AI-powered auditing practices, this paper proposes four innovative paths towards intelligent auditing in response to the key problems and challenges in practices, namely, audit procedure design, audit data processing, audit approach transformation and audit model exploration, with a view of achieving full coverage of intelligent auditing and making it standardized, normalized, popularized and practically effective. these innovations will effectively advance the improvement in auditing competencies and promote the high-quality development of audit work. © 2024 the Authors. Published by Elsevier B.V.

关键词： Audit Approach Transformation Audit data Processing Audit Model Exploration Audit Procedure Design

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Towards a Benchmark for Fog data Processing 11

Towards a Benchmark for Fog Data Processing

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11th IEEE international conference on Cloud engineering (IC2E)

作者： Pfandzelter, Tobias Bermbach, David Tech Univ Berlin Berlin Germany Einstein Ctr Digital Future Mobile Cloud Comp Res Grp Berlin Germany

ISBN: (纸本)9798350343946

Fog data processing systems provide key abstractions to manage data and event processing in the geo-distributed and heterogeneous fog environment. the lack of standardized benchmarks for such systems, however, hinders their development and deployment, as different approaches cannot be compared quantitatively. Existing cloud data benchmarks are inadequate for fog computing, as their focus on workload specification ignores the tight integration of application and infrastructure inherent in fog computing. In this paper, we outline an approach to a fog-native data processing benchmark that combines workload specifications with infrastructure specifications. this holistic approach allows researchers and engineers to quantify how a software approach performs for a given workload on given infrastructure. Further, by basing our benchmark in a realistic IoT sensor network scenario, we can combine paradigms such as low-latency event processing, machine learning inference, and offline data analytics, and analyze the performance impact of their interplay in a fog data processing system.

关键词： fog computing internet of things performance benchmarking

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TURNING thE CURSE OF HETEROGENEITY IN FEDERATED learning INTO A BLESSING FOR OUT-OF-DISTRIBUTION DETECTION 11

TURNING THE CURSE OF HETEROGENEITY IN FEDERATED LEARNING INT...

引用

11th international conference on learning Representations, ICLR 2023

作者： Yu, Shuyang Hong, Junyuan Wang, Haotao Wang, Zhangyang Zhou, Jiayu Department of Computer Science and Engineering Michigan State University United States Department of Electrical and Computer Engineering University of Texas Austin United States

Deep neural networks have witnessed huge successes in many challenging prediction tasks and yet they often suffer from out-of-distribution (OoD) samples, misclassifying them with high confidence. Recent advances show promising OoD detection performance for centralized training, and however, OoD detection in federated learning (FL) is largely overlooked, even though many security sensitive applications such as autonomous driving and voice recognition authorization are commonly trained using FL for data privacy concerns. the main challenge that prevents previous state-of-the-art OoD detection methods from being incorporated to FL is that they require large amount of real OoD samples. However, in real-world scenarios, such large-scale OoD training data can be costly or even infeasible to obtain, especially for resource-limited local devices. On the other hand, a notorious challenge in FL is data heterogeneity where each client collects non-identically and independently distributed (non-iid) data. We propose to take advantage of such heterogeneity and turn the curse into a blessing that facilitates OoD detection in FL. the key is that for each client, non-iid data from other clients (unseen external classes) can serve as an alternative to real OoD samples. Specifically, we propose a novel Federated Out-of-Distribution Synthesizer (FOSTER), which learns a class-conditional generator to synthesize virtual external-class OoD samples, and maintains data confidentiality and communication efficiency required by FL. Experimental results show that our method outperforms the state-of-the-art for OoD tasks by 2.49%, 2.88%, 1.42% AUROC, and 0.01%, 0.89%, 1.74% ID accuracy, on CIFAR-10, CIFAR-100, and STL10, respectively. Codes are available: https://***/illidanlab/FOSTER. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： data privacy

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Practical Application of Distributed Photovoltaic Orderly Control Based on Platform intelligent Fusion Terminal 11

Practical Application of Distributed Photovoltaic Orderly Co...

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11th China international conference on Electricity Distribution, CICED 2024

作者： Zhu, Jiran Ren, Qi Ren, Lei Ding, Shijing Kang, Tong Zhang, Di Distribution network technology center Electric Power Research Institute State Grid Hunan Electric Power Co. Ltd. Changsha China School of Electrical and Control Engineering Henan University of Urban Construction Changsha China

ISBN: (纸本)9798350368345

this study proposes an innovative distributed photovoltaic access distribution system architecture based on the intelligent integration terminal in the context of the challenges of grid security power supply and user reliable electricity consumption resulting from the large-scale integration of distributed photovoltaic and other new energy sources into distribution networks. this architecture introduces an innovative method of orderly control for distributed photovoltaics by analyzing real-time grid conditions and dynamic data of distributed photovoltaics. By leveraging the edge computing capabilities of the intelligent integration terminal at the distribution transformer substation, autonomous control strategies are generated to ensure the orderly connection and control of distributed photovoltaics, thereby achieving the safe and stable operation of the distribution network upon the large-scale integration of distributed photovoltaics. this approach has been field-tested in the distribution network in Hunan Province, demonstrating a significant improvement in the reliability of power supply to distributed photovoltaic transformer substations. © 2024 IEEE.

关键词： Distribution transformers

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MINIMAX OPTIMAL KERNEL OPERATOR learning VIA MULTILEVEL TRAINING 11

MINIMAX OPTIMAL KERNEL OPERATOR LEARNING VIA MULTILEVEL TRAI...

引用

11th international conference on learning Representations, ICLR 2023

作者： Jin, Jikai Lu, Yiping Blanchet, Jose Ying, Lexing School of Mathematical Sciences Peking University Beijing China Institute for Computational & Mathematical Engineering Stanford University StanfordCA United States Stanford University StanfordCA United States Department of Mathematics Stanford University StanfordCA United States

learning mappings between infinite-dimensional function spaces have achieved empirical success in many disciplines of machine learning, including generative modeling, functional data analysis, causal inference, and multi-agent reinforcement learning. In this paper, we study the statistical limit of learning a Hilbert-Schmidt operator between two infinite-dimensional Sobolev reproducing kernel Hilbert spaces (RKHSs). We establish the information-theoretic lower bound in terms of the Sobolev Hilbert-Schmidt norm and show that a regularization that learns the spectral components below the bias contour and ignores the ones above the variance contour can achieve the optimal learning rate. At the same time, the spectral components between the bias and variance contours give us flexibility in designing computationally feasible machine learning algorithms. Based on this observation, we develop a multilevel kernel operator learning algorithm that is optimal when learning linear operators between infinite-dimensional function spaces. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： learning algorithms

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Deep learning Analysis of Electrophysiological Series for Continuous Emotional State Detection 11

Deep Learning Analysis of Electrophysiological Series for Co...

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11th international conference on Affective Computing and intelligent Interaction (ACIIW)

作者： Pinzon-Arenas, Javier O. Mercado-Diaz, Luis Tejada, Julian Marmolejo-Ramos, Fernando Barrera-Causil, Carlos Ivan Padilla, Jorge Ospina, Raydonal Posada-Quintero, Hugo Univ Mil Nueva Granada Fac Engn Bogota Colombia Univ Connecticut Dept Biomed Engn Storrs CT USA Univ Fed Sergipe Dept Psicol Sao Cristovao Brazil Univ South Australia Ctr Change & Complex Learning Adelaide SA Australia Inst Tecnol Metropolitano Fac Exact & Appl Sci Medellin Colombia Inst Tecnol Metropolitano Dept Qual & Prod Medellin Colombia Univ Fed Bahia Dept Estat Salvador BA Brazil

ISBN: (纸本)9798350327458

the detection of emotions has a wide range of applications in psychology, marketing, and human-computer interaction. the detection of emotional states using biomedical signals is attractive because it is non-invasive and there is a large body of knowledge in digital signal processing that can be applied to the analysis of these signals. Additionally, artificial intelligence can be used to improve the accuracy of emotion detection by using machine learning algorithms to analyze large amounts of data. In this study, we explored the application of a parallel hybrid architecture called the parallel TCN-SBU-LSTM, which combines a temporal convolutional network and a stacked bi- and uni-directional LSTM. For the EPiC 2023 Challenge, we set out to estimate the arousal and valence states of different subjects in four different scenarios, using a continuous analysis of five physiological signals. To determine the best hyperparameters for each scenario and which signals to use for estimation, we used a methodology involving grid search and 5-fold cross validation. the models obtained an average root mean square error of 1.585 across scenarios, demonstrating the suitability of the parallel TCN-SBU-LSTM network to estimate the emotions of the subjects in different scenarios with consistent performance.

关键词： biomedical signals temporal convolutional network LSTM arousal valence emotional state detection

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ON thE EFFECTIVENESS OF OUT-OF-DISTRIBUTION data IN SELF-SUPERVISED LONG-TAIL learning 11

ON THE EFFECTIVENESS OF OUT-OF-DISTRIBUTION DATA IN SELF-SUP...

引用

11th international conference on learning Representations, ICLR 2023

作者： Bai, Jianhong Liu, Zuozhu Wang, Hualiang Hao, Jin Feng, Yang Chu, Huanpeng Hu, Haoji Zhejiang University China The Hong Kong University of Science and Technology Hong Kong Harvard University United States Angelalign Technology China

though Self-supervised learning (SSL) has been widely studied as a promising technique for representation learning, it doesn't generalize well on long-tailed datasets due to the majority classes dominating the feature space. Recent work shows that the long-tailed learning performance could be boosted by sampling extra in-domain (ID) data for self-supervised training, however, large-scale ID data which can rebalance the minority classes are expensive to collect. In this paper, we propose an alternative but easy-to-use and effective solution, Contrastive with Out-of-distribution (OOD) data for Long-Tail learning (COLT), which can effectively exploit OOD data to dynamically re-balance the feature space. We empirically identify the counter-intuitive usefulness of OOD samples in SSL long-tailed learning and principally design a novel SSL method. Concretely, we first localize the 'head' and 'tail' samples by assigning a tailness score to each OOD sample based on its neighborhoods in the feature space. then, we propose an online OOD sampling strategy to dynamically re-balance the feature space. Finally, we enforce the model to be capable of distinguishing ID and OOD samples by a distribution-level supervised contrastive loss. Extensive experiments are conducted on various datasets and several state-of-the-art SSL frameworks to verify the effectiveness of the proposed method. the results show that our method significantly improves the performance of SSL on long-tailed datasets by a large margin, and even outperforms previous work which uses external ID data. Our code is available at https://***/JianhongBai/COLT. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Large datasets

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Revolutionizing Vehicle Safety: IOT-Enabled Inter Vehicular Communication for Real-time data Transfer and Emergency Response Via Wi-Fi 11

Revolutionizing Vehicle Safety: IOT-Enabled Inter Vehicular ...

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Rana, Kaplileswar Vaz, Aldrin Claytus Hegde, Navya thirumaleshwar Manipal Institute of Technology Manipal Academy of Higher Education Department of Aeronautical and Automobile Engineering Karnataka Manipal576104 India St Jospeh Engineering College Visvesvaraya Technological University Department of Electronics and Communiaction Engineering Karnataka Mangaluru575028 India

ISBN: (纸本)9798350350357

the integration of Internet technology has led to the seamless incorporation of cloud computing into automotive systems, resulting in the development of autonomous vehicles known for enhanced efficiency and robust security. through the utilization of Wireless Fidelity (Wi-Fi) technology, intervehicular communication has been established, employing internet proto- cols as the conduit for the real-time transmission of diverse sensor data between two vehicles. this exchange forms the basis for intelligent decision-making processes, with Node Microcontroller unit (NodeMCU) chipsets serving as integral components in the intricate framework. Operative within the 2.4GHz frequency spectrum, these chipsets facilitate the expeditious transfer and real-time processing of data, demonstrating the mechanisms that yield visual outputs through seamless integration with cloud infrastructure. the outcomes yielded were both efficacious and precise, evincing a seamless data transfer devoid of any latency. the two chipsets demonstrated facile data transmission through a cloud-based repository, amenable to simultaneous access by a tertiary chipset. Real-time inputs and outputs conformed meticulously to external environmental conditions, validating the anticipated efficacy of the proposed model. Leveraging Wi-Fi technology provided the system with an encrypted channel for data transmission, thereby enhancing speed and resulting in rapid and accurate inter-vehicular responses. this strengthens security and expedites Cellular Vehicle-to-Everything (C- V2X) communication. © 2024 IEEE.

关键词： data transfer

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HARNESSING OUT-OF-DISTRIBUTION EXAMPLES VIA AUGMENTING CONTENT AND STYLE 11

HARNESSING OUT-OF-DISTRIBUTION EXAMPLES VIA AUGMENTING CONTE...

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11th international conference on learning Representations, ICLR 2023

作者： Huang, Zhuo Xia, Xiaobo Shen, Li Han, Bo Gong, Mingming Gong, Chen Liu, Tongliang Syndey AI Centre The University of Sydney Australia JD Explore Academy Department of Computer Science Hong Kong Baptist University Hong Kong The University of Melbourne Australia Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China

Machine learning models are vulnerable to Out-Of-Distribution (OOD) examples, and such a problem has drawn much attention. However, current methods lack a full understanding of different types of OOD data: there are benign OOD data that can be properly adapted to enhance the learning performance, while other malign OOD data would severely degenerate the classification result. To Harness OOD data, this paper proposes a HOOD method that can leverage the content and style from each image instance to identify benign and malign OOD data. Particularly, we design a variational inference framework to causally disentangle content and style features by constructing a structural causal model. Subsequently, we augment the content and style through an intervention process to produce malign and benign OOD data, respectively. the benign OOD data contain novel styles but hold our interested contents, and they can be leveraged to help train a style-invariant model. In contrast, the malign OOD data inherit unknown contents but carry familiar styles, by detecting them can improve model robustness against deceiving anomalies. thanks to the proposed novel disentanglement and data augmentation techniques, HOOD can effectively deal with OOD examples in unknown and open environments, whose effectiveness is empirically validated in three typical OOD applications including OOD detection, open-set semi-supervised learning, and open-set domain adaptation. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： learning systems

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SecureMed: Federated learning empowered approach for securing medical data 11

SecureMed: Federated learning empowered approach for securin...

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11th international conference on Soft Computing and Machine Intelligence, ISCMI 2024

作者： Makkar, Aaisha University of Derby College of Science and Engineering United Kingdom

ISBN: (纸本)9798331518127

In today's era, the world is suffering from an infectious disease COVID-19 which proved to be health predicament. the COVID-19 testing data is being produced by the healthcare center at preeminent scale. the efficient handling of such data by World Health Organization (WHO) team and medical practitioners is the considerable challenge. the researchers have introduced many deep learning approaches to detect COVID-19, but security remained challenging. In this paper, we present federated learning based approach 'SecureMed,' which allows to securely process the data. In this approach, two parameters, 'client selection' and 'aggregation method' are considered. the proposed scheme inherits Markov model for designing the these two parameters. the approach is been validated by using publicly available COVID-19 dataset of size 771, 634 Genomes (source: NCBI, U.S. National Library of Medicine). Taking into account the probability computations, the proposed approach succeeds in client local training by 50%. © 2024 IEEE.

关键词： COVID-19

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