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Information Fusion-2-Text: Explainable Aggregation via Linguistic Protoforms 18th

Information Fusion-2-Text: Explainable Aggregation via Lingu...

18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based systems, IPMU 2020

作者： Murray, Bryce J. Anderson, Derek T. Havens, Timothy C. Wilkin, Tim Wilbik, Anna Electrical Engineering and Computer Science Department University of Missouri ColumbiaMO United States College of Computing and Department of Electrical and Computer Engineering Michigan Technological University HoughtonMI United States School of Information Technology Deakin University GeelongVIC Australia Information Systems Group of the Department of Industrial Engineering and Innovation Sciences Eindhoven University of Technology Eindhoven Netherlands

ISBN: (纸本)9783030501525

Recent advancements and applications in artificial intelligence (AI) and machine learning (ML) have highlighted the need for explainable, interpretable, and actionable AI-ML. Most work is focused on explaining deep artificial neural networks, e.g., visual and image captioning. In recent work, we established a set of indices and processes for explainable AI (XAI) relative to information fusion. While informative, the result is information overload and domain expertise is required to understand the results. Herein, we explore the extraction of a reduced set of higher-level linguistic summaries to inform and improve communication with non-fusion experts. Our contribution is a proposed structure of a fusion summary and method to extract this information from a given set of indices. In order to demonstrate the usefulness of the proposed methodology, we provide a case study for using the fuzzy integral to combine a heterogeneous set of deep learners in remote sensing for object detection and land cover classification. This case study shows the potential of our approach to inform users about important trends and anomalies in the models, data and fusion results. This information is critical with respect to transparency, trustworthiness, and identifying limitations of fusion techniques, which may motivate future research and innovation. © 2020, Springer Nature Switzerland AG.

关键词： Information fusion

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On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical systems

arXiv

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arXiv 2021年

作者： Muehlebach, Michael Jordan, Michael I. Learning and Dynamical Systems Group Max Planck Institute for Intelligent Systems Tübingen72076 Germany Department of Electrical Engineering and Computer Sciences Department of Statistics University of California BerkeleyCA94720 United States

We introduce a class of first-order methods for smooth constrained optimization that are based on an analogy to non-smooth dynamical systems. Two distinctive features of our approach are that (i) projections or optimizations over the entire feasible set are avoided, in stark contrast to projected gradient methods or the Frank-Wolfe method, and (ii) iterates are allowed to become infeasible, which differs from active set or feasible direction methods, where the descent motion stops as soon as a new constraint is encountered. The resulting algorithmic procedure is simple to implement even when constraints are nonlinear, and is suitable for large-scale constrained optimization problems in which the feasible set fails to have a simple structure. The key underlying idea is that constraints are expressed in terms of velocities instead of positions, which has the algorithmic consequence that optimizations over feasible sets at each iteration are replaced with optimizations over local, sparse convex approximations. In particular, this means that at each iteration only constraints that are violated are taken into account. The result is a simplified suite of algorithms and an expanded range of possible applications in machine learning. © 2021, CC BY.

关键词： Convex optimization

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NTIRE 2023 Quality Assessment of Video Enhancement Challenge

NTIRE 2023 Quality Assessment of Video Enhancement Challenge

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Liu, Xiaohong Min, Xiongkuo Sun, Wei Zhang, Yulun Zhang, Kai Timofte, Radu Zhai, Guangtao Gao, Yixuan Cao, Yuqin Kou, Tengchuan Dong, Yunlong Jia, Ziheng Li, Yilin Wu, Wei Hu, Shuming Deng, Sibin Xiao, Pengxiang Chen, Ying Li, Kai Zhao, Kai Yuan, Kun Sun, Ming Cong, Heng Wang, Hao Fu, Lingzhi Zhang, Yusheng Zhang, Rongyu Shi, Hang Xu, Qihang Xiao, Longan Ma, Zhiliang Agarla, Mirko Celona, Luigi Rota, Claudio Schettini, Raimondo Huang, Zhiwei Li, Ya'nan Wang, Xiaotao Lei, Lei Liu, Hongye Hong, Wei Chuang, Ironhead Lin, Allen Guan, Drake Chen, Iris Lou, Kae Huang, Willy Tasi, Yachun Kao, Yvonne Fan, Haotian Kong, Fangyuan Zhou, Shiqi Liu, Hao Lai, Yu Chen, Shanshan Wang, Wenqi Wu, Haoning Chen, Chaofeng Zhu, Chunzheng Guo, Zekun Zhao, Shiling Yin, Haibing Wang, Hongkui Meftah, Hanene Brachemi Fezza, Sid Ahmed Hamidouche, Wassim Déforges, Olivier Shi, Tengfei Mansouri, Azadeh Motamednia, Hossein Bakhtiari, Amir Hossein Aznaveh, Ahmad Mahmoudi Shanghai Jiao Tong University China Eth Zürich Switzerland University of Würzburg Germany Department of Tao Technology Alibaba Group China Kuaishou Technology China Interactive Entertainment Group of Netease Inc Guangzhou China Transsion China Hefei University of Technology China Department of Informatics Systems and Communication University of Milano - Bicocca Italy Xiaomi Inc. China China Ji Liang University China FreeTech Advanced Research Center KKCompany Taiwan ByteDance China Mgtv China Fuzhou University China Shopee Information Technology Co. Ltd. S-Lab Nanyang Technological University Singapore Hunan University China Hangzhou Dianzi University China Insa Rennes Cnrs Ietr - Umr 6164 Rennes France National Higher School of Telecommunications and Ict Oran Algeria Buaa Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran

ISBN: (纸本)9798350302493

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance. © 2023 IEEE.

关键词： Image enhancement

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A Video-based Study on Perceived Intelligence, Subjective Performance and Trust under Variation of Prior Information given to Users in Autonomous Driving

A Video-based Study on Perceived Intelligence, Subjective Pe...

引用

IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO

作者： Kolja Kü hnlenz Ute Schmid Barbara Kü hnlenz Department of Electrical Engineering and Computer Science Robotics Research Lab Coburg University of Applied Sciences and Arts Coburg Germany Cognitive Systems Group University of Bamberg Bamberg Germany Academic Center for Sciences and Humanities Coburg University of Applied Sciences and Arts Coburg Germany

This paper presents results from a video-based study on the impact of prior information on the user experience dimensions perceived intelligence, subjective performance, and trust in autonomous driving. A simulated autonomous driving situation is presented to test participants, while they are given different prior information in terms of descriptions of the autonomous vehicle and its integration in a network of intelligent vehicles. Significant differences of the tested user experience dimensions are revealed underlining the importance of information provided to the potential users and towards the criticality of information regarding the introduction of intelligent technology in general.

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Augmented input estimation in multiple maneuvering target tracking

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Journal of systems engineering and Electronics 2019年第5期30卷 841-851页

作者： HADAEGH Mahmoudreza KHALOOZADEH Hamid BEHESHTI Mohammadtaghi Department of Electrical and Computer Engineering Science and Research BranchIslamic Azad UniversityTehran ***Iran Department of Systems and Control K.N.Toosi University of TechnologyTehran ***Iran Department of Electrical Engineering Control GroupTarbiat Modares UniversityTehran 14115111Iran

This paper presents augmented input estimation(AIE)for multiple maneuvering target ***-target tracking(MTT)is based on two main parts,data association and *** data association(DA),the best observations are assigned to the considered *** real conditions,the number of observations is more than targets and also locations of observations are often so scattered that the association between targets and observations cannot be done *** this case,for general MTT problems with unknown numbers of targets,we present a Markov chain Monte-Carlo DA(MCMCDA)algorithm that approximates the optimal Bayesian filter with low complexity in *** DA,estimation and tracking should be *** in general cases,many targets can have maneuvering motions,then AIE is proposed to cover both the non-maneuvering and maneuvering parts of motion and the maneuver detection procedure is *** model with an input estimation(IE)approach is a special augmentation in the state space model which considers both the state vector and the unknown input vector as a new augmented state *** comparisons based on the Monte-Carlo simulations are also made to evaluate the performances of the proposed method and other older methods in MTT.

关键词： multi-target tracking (MTT) Markov chain Monte-Carlodata association (MCMCDA) data association (DA) augmentedinput estimation (AIE)

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Frequency response data based LPV controller synthesis applied to a control moment gyroscope

arXiv

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arXiv 2021年

作者： Bloemers, Tom Tóth, Roland Oomen, Tom The Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Control Systems Technology Department of Mechanical Engineering Eindhoven University of Technology The Systems and Control Laboratory Institute for Computer Science and Control Kende u. 13-17 BudapestH-1111 Hungary

Control of systems with operating condition-dependent dynamics, including control moment gyroscopes, often requires operating condition-dependent controllers to achieve high control performance. The aim of this paper is to develop a frequency response data-driven linear parameter-varying control design approach for single-input single-output systems, which allows improved performance for a control moment gyroscope. A stability theory using closed-loop frequency response function data is developed, which is subsequently used in a synthesis procedure that guarantees local stability and performance. Experimental results on a control moment gyroscope demonstrate the performance improvements. Copyright © 2021, The Authors. All rights reserved.

关键词： Controllers

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AADNet: An End-to-End Deep Learning Model for Auditory Attention Decoding

arXiv

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arXiv 2024年

作者： Nguyen, Nhan Duc Thanh Phan, Huy Geirnaert, Simon Mikkelsen, Kaare Kidmose, Preben Center for Ear-EEG Department of Electrical and Computer Engineering Aarhus University Aarhus N8200 Denmark Amazon CambridgeMA02142 United States Stadius Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven and Leuven.AI - KU Leuven institute for AI Kasteelpark Arenberg 10 LeuvenB-3001 Belgium The Department of Neurosciences Research Group ExpORL Herestraat 49 box 721 LeuvenB-3000 Belgium

Auditory attention decoding (AAD) is the process of identifying the attended speech in a multi-talker environment using brain signals, typically recorded through electroencephalography (EEG). Over the past decade, AAD has undergone continuous development, driven by its promising application in neuro-steered hearing devices. Most AAD algorithms are relying on the increase in neural entrainment to the envelope of attended speech, as compared to unattended speech, typically using a two-step approach. First, the algorithm predicts representations of the attended speech signal envelopes;second, it identifies the attended speech by finding the highest correlation between the predictions and the representations of the actual speech signals. In this study, we proposed a novel end-to-end neural network architecture, named AADNet, which combines these two stages into a direct approach to address the AAD problem. We compare the proposed network against the traditional approaches, including linear stimulus reconstruction, canonical correlation analysis, and an alternative non-linear stimulus reconstruction using two different datasets. AADNet shows a significant performance improvement for both subject-specific and subject-independent models. Notably, the average subject-independent classification accuracies from 56.1% to 82.7% with analysis window lengths ranging from 1 to 40 seconds, respectively, show a significantly improved ability to generalize to data from unseen subjects. These results highlight the potential of deep learning models for advancing AAD, with promising implications for future hearing aids, assistive devices, and clinical assessments. © 2024, CC BY-NC-SA.

关键词： Hearing aids

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Improving HLS Generated Accelerators Through Relaxed Memory Access Scheduling

Improving HLS Generated Accelerators Through Relaxed Memory ...

引用

IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Johanna Rohde Karsten Müller Christian Hochberger Department for Electrical Engineering and Information Technology Computer Systems Group TU Darmstadt

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

High-Level-Synthesis can be used to generate hardware accelerators for compute intense software parts (so called kernels). For meaningful acceleration, such kernels should be able to autonomously access the memory. Unfortunately, such memory accesses can constitute dependences (e.g. writing an array before reading from it) leading to bottlenecks. The analysis of potential conflicts of memory accesses is often difficult and in many cases not even possible. In order to improve the scheduling of memory accesses, we propose a novel methodology to fully automatically place bypasses and squashes into the data flow graph that is used to generate the hardware accelerator. Evaluating our approach with the Powerstone benchmark suite, we can show that execution time is reduced on average by 6.5%.

关键词： Tools Hardware Runtime Kernel Benchmark testing Schedules Pipeline processing

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Implicit Multi-Scale Swin Transformer Network for Image Denoising

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhang, Qi Ding, Yuwei Zhang, Weiqi Zhu, Yian Zhang, Bob Lin, Jerry Chun-Wei Harbin Institute of Technology at Weihai School of Economics and Management Weihai China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Northwestern Polytechnical University School of Computer Science Xi’an China Northwestern Polytechnical University Yangtze River Delta Research Institute Taicang China University of Macau PAMI Research Group Department of Computer and Information Science Taipa China Department of Distributed Systems and IT Devices Electrical Engineering and Mathematical Sciences Silesian university of technology Poland

Image denoising has been used in various edge computing scenarios such as consumer electronics to improve the image quality and user experience. Existing image denoising methods based on Convolutional Neural Networks (CNNs) and vision Transformers achieve good performance by empirically utilizing residual neural network (ResNet) as basic component. However, ResNet lacks interpretability in network design and also long-term memory of features, potentially restricting the performance of denoising networks. In this paper, we propose an Implicit Multi-scale Swin Transformer Network (IMSNet) for image denoising, which introduces the implicit Euler scheme from the feature memory perspective. Specifically, densely connected implicit feature extraction blocks (IFEBs) are designed to learn the residual mapping between noisy and clean images. The IFEB reformulates the initial skip connection of ResNet based on the implicit Euler discretization, providing both network interpretability and long-term memory. In IFEB, the multi-scale swin Transformer block (MSB) is designed as the implicit layer to capture spatial details and non-local contextual information at different scales. Additionally, a cross-layer feature fusion block (CLFF) is proposed to further improve feature reuse capabilities. Compared to existing denoising networks, the extensive experiments demonstrate the superior performance of our IMSNet in various image denoising tasks, and the flexibility in the practical applications with proposed light model on resource-restricted platforms such as consumer electronic devices. © 1975-2011 IEEE.

关键词： Image denoising

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A soft-aided staircase decoder using three-level channel reliabilities

arXiv

引用

arXiv 2021年

作者： Lei, Yi Chen, Bin Liga, Gabriele Balatsoukas-Stimming, Alexios Sun, Kaixuan Alvarado, Alex The School of Computer Science and Information Engineering Hefei University of Technology China The Electronic Systems group Department of Electrical Engineering Eindhoven University of Technology Netherlands State Key Laboratory of Information of Photonics and Optical Communications Beijing University of Posts and Telecommunications China Group Department of Electrical Engineering Eindhoven University of Technology Netherlands

The soft-aided bit-marking (SABM) algorithm is based on the idea of marking bits as highly reliable bits (HRBs), highly unreliable bits (HUBs), and uncertain bits to improve the performance of hard-decision (HD) decoders. The HRBs and HUBs are used to assist the HD decoders to prevent miscorrections and to decode those originally uncorrectable cases via bit flipping (BF), respectively. In this paper, an improved SABM algorithm (called iSABM) is proposed for staircase codes (SCCs). Similar to the SABM, iSABM marks bits with the help of channel reliabilities, i.e., using the absolute values of the log-likelihood ratios. The improvements offered by iSABM include: (i) HUBs being classified using a reliability threshold, (ii) BF randomly selecting HUBs, and (iii) soft-aided decoding over multiple SCC blocks. The decoding complexity of iSABM is comparable of that of SABM. This is due to the fact that on the one hand no sorting is required (lower complexity) because of the use of a threshold for HUBs, while on the other hand multiple SCC blocks use soft information (higher complexity). Additional gains of up to 0.53 dB with respect to SABM and 0.91 dB with respect to standard SCC decoding at a bit error rate of 10−6 are reported. Furthermore, it is shown that using 1-bit reliability marking, i.e., only having HRBs and HUBs, only causes a gain penalty of up to 0.25 dB with a significantly reduced memory requirement. © 2021, CC BY.

关键词： Stairs

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