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5th International Conference on Frontiers Technology of Information and computer, ICFTIC 2023

作者： Wang, Ximiao Hu, Qinghui Dai, Xisheng Li, Peijun Chen, Yuan Guangxi University of Science and Technology Institute of Intelligent Systems and Control Guangxi Province Liuzhou City545006 China Guilin University of Aerospace Technology School of Computer Science and Engineering Guangxi Province Guilin City541004 China Jiangmen Central Hospital Department of Radiology Guangdong Province Jiangmen City529000 China Jiangmen Central Hospital Department of Gynecology Guangdong Province Jiangmen City529000 China

ISBN: (纸本)9798350309034

For the prediction of deep myometrial invasion (DMI) in endometrial cancer (EC), this study proposes an ensemble learning method which combines deep learning (DL) and improved Bayesian extreme learning machine (BELM). Firstly, the MRI images of endometrial cancer are preprocessed to meet the requirements. Secondly, the deep features relevant to the task are extracted from the images by using convolutional kernels. Finally, the bootstrap resampling method is employed to repeatedly sample multiple training subsets from the training set. The improved Bayesian extreme learning machine is used as the base classifier to train multiple independent sub-models. An ensemble learning classifier is constructed using an ensemble strategy to improve the prediction accuracy and stability of the model. Experimental results show that the proposed method achieves an AUC of 0.758 on the internal validation set and 0.740 on the external validation set, which demonstrat good generalization ability and stability of the proposed method. © 2023 IEEE.

关键词： Bayesian Extreme Learning Machine Deep Learning Deep Myometrial Invasion Endometrial Cancer Ensemble Learning

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CoChain: High Concurrency Blockchain Sharding via Consensus on Consensus

CoChain: High Concurrency Blockchain Sharding via Consensus ...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Mingzhe Li You Lin Jin Zhang Wei Wang Computer Science and Engineering Department Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Computer Science and Engineering Department Hong Kong University of Science and Technology Technology and Research Institute of High Performance Computing and Agency for Science Singapore

Sharding is an effective technique to improve the scalability of blockchain. It splits nodes into multiple groups so that they can process transactions in parallel. To achieve higher parallelism and concurrency at large scales, it is desirable to maintain a large number of small shards. However, simply configuring small shards easily results in a higher fraction of malicious nodes inside shards, causing shard corruption and compromising system security. Existing sharding techniques hence demand large shards, at the expense of limited concurrency. To address this limitation, we propose CoChain: a blockchain sharding system that can securely configure small shards for enhanced concurrency. CoChain allows some shards to be corrupted. For security, each shard is monitored by multiple other shards. The latter reach a cross-shard Consensus on the Consensus results of their monitored shard. Once a corrupted shard is found, its subsequent consensus will be taken over by another shard, hence recovering the system. Via Consensus on Consensus, CoChain allows the existence of shards with more fraction of malicious nodes (<2/3) while securing the system, thus reducing the shard size safely. We implement CoChain based on Harmony and conduct extensive experiments. Compared with Harmony, CoChain achieves 35x throughput gain with 6,000+ nodes.

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AuDimo: A Musical Companion Robot to Switching Audio Tracks by Recognizing the Users Engagement 22nd

AuDimo: A Musical Companion Robot to Switching Audio Tracks ...

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22nd International Conference on Human computer Interaction,HCII 2020

作者： Hansika, W.K.N. Nanayakkara, Lakindu Yasassri Gammanpila, Adhisha de Silva, Ravindra Robotics and Intelligent Systems Laboratory Department of Computer Science University of Sri Jayewardenepura Nugegoda Sri Lanka

ISBN: (纸本)9783030601164

Music has become an integral part that enriches human lives. It serves as a method of entertainment in social gatherings. Consequently Different playlists of music are played to energize, uplift, and comfort listeners in social gatherings. If most in that group of people don’t like the songs playing energy in between them can be lost. In this study, a musical companion robot was developed to interact with the audience through its rhythmic behaviors and through the song selections done by the robot based on the moods of the audience using Neural Networks and emotion recognition through facial recognition. The study demonstrated high levels of user acceptance based on the feedback received from a sample set of 15 volunteers within the age range of 21–25 years. Though The robot’s interaction through its rhythmic behaviors was a success, the song selection algorithm was below the level of acceptance at the startup of the robot. However, as the data demonstrates, the robot was able to accomplish the task of enhancing the audience through its appearance and behaviors. © 2020, Springer Nature Switzerland AG.

关键词： Music

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Trust in Human Autonomy Teaming: A Recent Review on Subjective and Objective Assessment of Trust

SSRN

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SSRN 2024年

作者： Jui, Julakha Jahan Hettiarachchi, Imali Bhatti, Asim Farghaly, Mohamed Ragab Mahmoud Molla, M.M. Imran Institute for Intelligent Systems Research and Innovation Deakin University 75 Pigdons Road Waurn Ponds VIC3216 Australia Department of Computer Science and Engineering Pabna University of Science and Technology Pabna Hwy Rajapur Pabna6600 Bangladesh

The advancement of artificial intelligence (AI) potential in autonomous systems and robotics has moved researchers towards understanding the distinctive dynamics of human autonomy teaming (HAT) interaction. This explosion in advanced technologies aims to speed up the processes of decision-making, improve situational awareness, foster mutual understanding, and improve interpersonal relationships, ultimately minimising risks associated with collaborative endeavours. However, HAT is only as good as the fine-tuning of team trust, particularly when we transcend traditional dyadic partnerships. We require a precise understanding of trust dynamics to plan interactions in high-risk operations. This review undertakes a comprehensive exploration and evaluation of trust measurement within the domain of human and autonomy interactions. Commencing with an overview of HAT and trust, including the Mayer trust model for trust, the paper underscores the pivotal role of trust in facilitating seamless collaboration in HAT scenarios. Emphasising the significance of trust measurement in optimising team performance, the review scrutinises existing empirical works to categorise trust measurement methodologies into subjective (e.g., self-report, questionnaires, surveys) and objective (e.g., behavioural, physiological) indices. Drawing insights from recent literature spanning from 2019 to 2024, the paper explores the complexities of trust measurement techniques, addressing methodologies employed by researchers and synthesising their findings. The paper provides a thorough knowledge of the mechanisms driving trust evaluation in HAT situations by outlining the details of self-report, behavioural, and physiological assessments. The study also suggests directions for further investigation into improving trust assessment techniques and developing theoretical models to better suit the changing context of human-autonomy collaboration. The review highlights the significance of trust calibration in

关键词： Human robot interaction

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Applying Pre-trained Multilingual BERT in Embeddings for Improved Malicious Prompt Injection Attacks Detection

Applying Pre-trained Multilingual BERT in Embeddings for Imp...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Md Abdur Rahman Hossain Shahriar Fan Wu Alfredo Cuzzocrea Dept. of Intelligent Systems and Robotics University of West Florida Pensacola FL USA Center for Cybersecurity University of West Florida Pensacola FL USA Department of Computer Science Tuskegee University Tuskegee AL USA iDEA Lab University of Calabria Rende Italy

ISBN: (数字)9798331529529

ISBN: (纸本)9798331529536

Large language models (LLMs) are renowned for their exceptional capabilities, and applying to a wide range of applications. However, this widespread use brings significant vulnerabilities. Also, it is well observed that there are huge gap which lies in the need for effective detection and mitigation strategies against malicious prompt injection attacks in large language models, as current approaches may not adequately address the complexity and evolving nature of these vulnerabilities in real-world applications. Therefore, this work focuses the impact of malicious prompt injection attacks which is one of most dangerous vulnerability on real LLMs applications. It examines to apply various BERT (Bidirectional Encoder Representations from Transformers) like multilingual BERT, DistilBert for classifying malicious prompts from legitimate prompts. Also, we observed how tokenizing the prompt texts and generating embeddings using multilingual BERT contributes to improve the performance of various machine learning methods: Gaussian Naive Bayes, Random Forest, Support Vector Machine, and Logistic Regression. The performance of each model is rigorously analyzed with various parameters to improve the binary classification to discover malicious prompts. Multilingual BERT approach to embed the prompts significantly improved and outperformed the existing works and achieves an outstanding accuracy of 96.55% by Logistic regression. Additionally, we investigated the incorrect predictions of the model to gain insights into its limitations. The findings can guide researchers in tuning various BERT for finding the most suitable model for diverse LLMs vulnerabilities.

关键词： Support vector machines Logistic regression Accuracy Large language models Bidirectional control Transformers Encoding Multilingual Tuning Random forests

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Performance Analysis of Distributed Filtering under Mismatched Noise Covariances

arXiv

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arXiv 2024年

作者： Lyu, Xiaoxu Wen, Guanghui Shi, Ling Duan, Peihu Duan, Zhisheng The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The Laboratory of Security Operation and Control for Intelligent Autonomous Systems Department of Systems Science School of Mathematics Southeast University Nanjing211189 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China

This paper systematically investigates the performance of consensus-based distributed filtering under mismatched noise covariances. First, we introduce three performance evaluation indices for such filtering problems, namely the standard performance evaluation index, the nominal performance evaluation index, and the estimation error covariance. We derive difference expressions among these indices and establish one-step relations among them under various mismatched noise covariance scenarios. We particularly reveal the effect of the consensus fusion on these relations. Furthermore, the recursive relations are introduced by extending the results of the one-step relations. Subsequently, we demonstrate the convergence of these indices under the collective observability condition, and show this convergence condition of the nominal performance evaluation index can guarantee the convergence of the estimation error covariance. Additionally, we prove that the estimation error covariance of the consensus-based distributed filter under mismatched noise covariances can be bounded by the Frobenius norms of the noise covariance deviations and the trace of the nominal performance evaluation index. Finally, the effectiveness of the theoretical results is verified by numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Wiener filtering

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A Metamodel and Framework for Artificial General Intelligence From Theory to Practice

Journal of Artificial Intelligence and Consciousness

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Journal of Artificial Intelligence and Consciousness 2021年第2期8卷 205-227页

作者： Latapie, Hugo Kilic, Ozkan Liu, Gaowen Kompella, Ramana Lawrence, Adam Sun, Yuhong Srinivasa, Jayanth Yan, Yan Wang, Pei Thórisson, Kristinn R. Cisco Systems Inc. San Francisco CA United States Department of Computer Science Illinois Institute of Technology Chicago IL United States Department of Computer Science Temple University Philadelphia PA United States Reykjavik University and Icelandic Institute for Intelligent Machines Reykjavik Iceland

This paper introduces a new metamodel-based knowledge representation that significantly improves autonomous learning and adaptation. While interest in hybrid machine learning/symbolic AI systems leveraging, for example, reasoning and knowledge graphs, is gaining popularity, we find there remains a need for both a clear definition of knowledge and a metamodel to guide the creation and manipulation of knowledge. Some of the benefits of the metamodel we introduce in this paper include a solution to the symbol grounding problem, cumulative learning and federated learning. We have applied the metamodel to problems ranging from time series analysis, computer vision and natural language understanding and have found that the metamodel enables a wide variety of learning mechanisms ranging from machine learning, to graph network analysis and learning by reasoning engines to interoperate in a highly synergistic way. Our metamodel-based projects have consistently exhibited unprecedented accuracy, performance, and ability to generalize. This paper is inspired by the state-of-the-art approaches to AGI, recent AGI-aspiring work, the granular computing community, as well as Alfred Korzybski's general semantics. One surprising consequence of the metamodel is that it not only enables a new level of autonomous learning and optimal functioning for machine intelligences, but may also shed light on a path to better understanding how to improve human cognition. © 2021 World Scientific Publishing Company.

关键词： AGI AI Artificial Intelligence Cognitive Architecture General Semantics Levels of Abstraction Neurosymbolic

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End-to-end Semantic Segmentation Network for Low-Light Scenes

End-to-end Semantic Segmentation Network for Low-Light Scene...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Hongmin Mu Gang Zhang MengChu Zhou Zhengcai Cao College of Information Science and Technology Beijing University of Chemical Technology Beijing China Department of Macao Institute of Systems Engineering Macau University of Science and Technology Macao China Helen and John C. Hartmann Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In the fields of robotic perception and computer vision, achieving accurate semantic segmentation of low-light or nighttime scenes is challenging. This is primarily due to the limited visibility of objects and the reduced texture and color contrasts among them. To address the issue of limited visibility, we propose a hierarchical gated convolution unit, which simultaneously expands the receptive field and restores edge texture. To address the issue of reduced texture among objects, we propose a dual closed-loop bipartite matching algorithm to establish a total loss function consisting of the unsupervised illumination enhancement loss and supervised intersection-over-union loss, thus enabling the joint minimization of both losses via the Hungarian algorithm. We thus achieve end-to-end training for a semantic segmentation network especially suitable for handling low-light scenes. Experimental results demonstrate that the proposed network surpasses existing methods on the Cityscapes dataset and notably outperforms state-of-the-art methods on both Dark Zurich and Nighttime Driving datasets.

关键词： Training computer vision Accuracy Convolution Semantic segmentation Lighting Logic gates

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Blockchain in Environmental Sustainability Measures: a Survey

arXiv

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arXiv 2024年

作者： Vladucu, Maria-Victoria Wu, Hailun Medina, Jorge Salehin, Khondaker M. Dong, Ziqian Rojas-Cessa, Roberto The Network and Innovation Laboratory Department of Electrical and Computer Engineering New York Institute of Technology New YorkNY10023 United States Department of Computer Science University of Portland PortlandOR97203 United States The Networking and Intelligent Systems Laboratory Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Real and effective regulation of contributions to greenhouse gas emissions and pollutants requires unbiased and truthful monitoring. Blockchain has emerged not only as an approach that provides verifiable economical interactions but also as a mechanism to keep the measurement, monitoring, incentivation of environmental conservationist practices and enforcement of policy. Here, we present a survey of areas in what blockchain has been considered as a response to concerns on keeping an accurate recording of environmental practices to monitor levels of pollution and management of environmental practices. We classify the applications of blockchain into different segments of concerns, such as greenhouse gas emissions, solid waste, water, plastics, food waste, and circular economy, and show the objectives for the addressed concerns. We also classify the different blockchains and the explored and designed properties as identified for the proposed solutions. At the end, we provide a discussion about the niches and challenges that remain for future research. © 2024, CC BY-NC-ND.

关键词： Circular economy

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SDEs for Minimax Optimization

arXiv

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arXiv 2024年

作者： Compagnoni, Enea Monzio Orvieto, Antonio Kersting, Hans Proske, Frank Norbert Lucchi, Aurelien Department of Mathematics and Computer Science University of Basel Switzerland ELLIS Institute Tübingen MPI for Intelligent Systems Tübingen AI Center Germany Yahoo! Research United States Department of Mathematics University of Oslo Norway

Minimax optimization problems have attracted a lot of attention over the past few years, with applications ranging from economics to machine learning. While advanced optimization methods exist for such problems, characterizing their dynamics in stochastic scenarios remains notably challenging. In this paper, we pioneer the use of stochastic differential equations (SDEs) to analyze and compare Minimax optimizers. Our SDE models for Stochastic Gradient Descent-Ascent, Stochastic Extragradient, and Stochastic Hamiltonian Gradient Descent are provable approximations of their algorithmic counterparts, clearly showcasing the interplay between hyperparameters, implicit regularization, and implicit curvature-induced noise. This perspective also allows for a unified and simplified analysis strategy based on the principles of Itô calculus. Finally, our approach facilitates the derivation of convergence conditions and closed-form solutions for the dynamics in simplified settings, unveiling further insights into the behavior of different optimizers. © 2024, CC BY.

关键词： Stochastic systems

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