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Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond

Journal of Machine Learning Research 2023年第1期24卷 1-11页

作者： Hedström, Anna Weber, Leander Bareeva, Dilyara Krakowczyk, Daniel Motzkus, Franz Samek, Wojciech Lapuschkin, Sebastian Höhne, Marina M.-C. Understandable Machine Intelligence Lab TU Berlin Berlin10587 Germany Department of Electrical Engineering and Computer Science TU Berlin Berlin10587 Germany Department of Artificial Intelligence Fraunhofer Heinrich-Hertz-Institute Berlin10587 Germany Department of Computer Science University of Potsdam Potsdam14476 Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

The evaluation of explanation methods is a research topic that has not yet been explored deeply, however, since explainability is supposed to strengthen trust in artificial intelligence, it is necessary to systematically review and compare explanation methods in order to confirm their correctness. Until now, no tool with focus on XAI evaluation exists that exhaustively and speedily allows researchers to evaluate the performance of explanations of neural network predictions. To increase transparency and reproducibility in the field, we therefore built Quantus—a comprehensive, evaluation toolkit in Python that includes a growing, wellorganised collection of evaluation metrics and tutorials for evaluating explainable methods. The toolkit has been thoroughly tested and is available under an open-source license on PyPi (or on https://***/understandable-machine-intelligence-lab/Quantus/). © 2023 Anna Hedström, Leander Weber, Dilyara Bareeva, Daniel Krakowczyk, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, and Marina M.-C. Höhne.

关键词： Open systems

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Multiobjective Sequential Transfer Optimization: Benchmark Problems and Preliminary Results 13

Multiobjective Sequential Transfer Optimization: Benchmark P...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Xue, Xiaoming Feng, Liang Yang, Cuie Liu, Songbai Song, Linqi Tan, Kay Chen City University of Hong Kong Department of Computer Science Hong Kong College of Computer Science Chongqing University Chongqing China Northeastern University State Key Lab of Synthetic Automation for Process Industries Shenyang China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9798350308365

In cases of frequent problem-solving of multiobjective optimization tasks from a domain due to changing conditions or problem features, a growing number of individual tasks will be solved and stored in a database, providing an opportunity for a target task at hand to achieve better optimization performance through knowledge transfer from the previously-solved tasks, which is also known as sequential transfer optimization. Despite a variety of transfer algorithms that have been developed over the years, the research on the design of benchmark problems for evaluating such algorithms received far less attention. Oftentimes, the source and target tasks in existing test problems are manually assembled or extended from specific practical problems, limiting their ability to represent the diverse yet complex source-target similarity relationships in real-world problems. In light of this, we propose design methods to generate multiobjective sequential transfer optimization problems (MSTOPs) systematically in this work, wherein the Pareto manifolds of individual tasks and the manifold-based similarity between the tasks can be customized with ease, enabling a broad spectrum of representation of the diverse similarity relationships between the source-target Pareto manifolds of MSTOPs. Lastly, a benchmark suite with 12 test problems is developed using the proposed methods, which would serve as an arena for electing superior multiobjective sequential transfer optimization algorithms. The source code is available at https://***/XmingHsueh/MSTOP. © 2024 IEEE.

关键词： Optimization algorithms

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27th International Conference on Intelligent User Interfaces (IUI)

作者： Ike, Chinaemere Polsley, Seth Hammond, Tracy Sketch Recognition Lab Department of Computer Science and Engineering Texas A&M University United States

ISBN: (纸本)9781450391450

Voice-enabled technology has become increasingly common in homes, businesses, and other parts of everyday life. The benefits of smart speakers, hands-free controllers, and digital assistants should be equally accessible to everyone, yet voice recognition performance can be frustratingly low for speakers with accents. In this work, we examine algorithmic bias in several voice recognition systems by measuring standard accuracy metrics on under-represented English accents.

关键词： accent recognition smart home word error rate

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Online crowdsourced truck delivery using historical information

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第2期301卷 486-501页

作者： Zhang, Huili Luo, Kelin Xu, Yao Xu, Yinfeng Tong, Weitian [a]School of Management Xi’an Jiaotong University and State Key Lab for Manufacturing Systems Engineering Xi’an 710049 China [b]Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven the Netherlands [c]Department of Computer Science Georgia Southern University Statesboro GA 30458 USA

Various crowdsourced logistics platforms are forming rapidly along with the booming mobile Internet. Motivated by modern crowdsourced truck logistics platforms, we introduce the online crowdsourced truck delivery (OCTD) problem and reformulate it as the online bipartite hyper-matching problem. We then explore the possibility of accommodating historical information to design efficient online algorithms to serve online orders. To the best of our knowledge, it is the first work on incorporating historical information to solve the online bipartite hyper-matching problem. Depending on whether orders can be partially served, we investigate two practical situations, i.e. separable and inseparable cases. For the inseparable case, we propose a randomized online algorithm, named HYPER-MATCHING , whose competitive ratio is a non-decreasing function of the amount of historical information. For the separable case, we modify HYPER-MATCHING to present another randomized online algorithm, named SEPARABLE-HYPER-MATCHING . It is worth noting that the competitive ratios of HYPER-MATCHING and SEPARABLE-HYPER-MATCHING either beat or match the current best online algorithms when no historical information is considered. We then present four computationally efficient heuristic algorithms, including a greedy variant and a batch processing variant for each of the inseparable and separable cases. We perform a sequence of experiments using synthetic and real-world datasets, with an emphasis on the influence that historical information has on algorithm performance. The experiment results demonstrate the effectiveness of our algorithms and particularly the positive influence of historical information on our algorithms.(c) 2021 Elsevier B.V. All rights reserved.

关键词： Transportation Online crowdsourced truck delivery Online bipartite hyper-matching Random order model Historical information

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An Indoor Environment Sensing and Localization System via mmWave Phased Array

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Journal of Communications and Information Networks 2022年第4期7卷 383-393页

作者： Yifei Sun Jie Li Tong Zhang Rui Wang Xiaohui Peng Xiao Han Haisheng Tan Department of Electronic and Electrical Engineering Southern University of Science and TechnologyShenzhen 518055China Department of Computer Science the University of Hong KongHong Kong 999077China Huawei Technologies Co. Ltd.Shenzhen 518129China LINKE Lab School of Computer Science and Technologythe University of Science and Technology of ChinaHefei 230026China

In this paper,an indoor layout sensing and localization system with testbed in the 60-GHz millimeter wave(mmWave)band,named mmReality,is *** mmReality system consists of one transmitter and one mobile receiver,both with a phased array and a single radio frequency(RF)*** reconstruct the room layout,the pilot signal is delivered from the transmitter to the receiver via different pairs of transmission and receiving beams,so that multipath signals in all directions can be *** spatial smoothing and the two-dimensional multiple signal classification(MUSIC)algorithm are applied to detect the angle-of-departures(AoDs)and angle-of-arrivals(AoAs)of propagation ***,the technique of multi-carrier ranging is adopted to measure the path ***,with the measurements of the receiver in different locations of the room,the receiver and virtual transmitters can be pinpointed to reconstruct the room *** show that the reconstructed room layout can be utilized to localize a mobile device via the AoA spectrum.

关键词： millimeter wave indoor sensing and localization MUSIC algorithm multi-input multi output(MIMO) room layout integrated sensing and communication(ISAC) environment sensing orthogonal frequency division multiplexing(OFDM)

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Current Challenges and Advancements of Aerial Thermography for Outdoor Structural Health Monitoring: A Review

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IEEE Sensors Journal 2025年第12期25卷 21000-21016页

作者： Ma, Yue Zeng, Zhi Luo, Zipei Tao, Ning Deng, Lei Tian, Yibin Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Chongqing Normal University College of Computer and Information Science Chongqing401331 China Capital Normal University Department of Physics Key Lab of Terahertz Optoelectronics of the Ministry of Education Beijing100048 China

In the rapid development of outdoor structural health monitoring, infrared thermal imaging and unmanned aerial vehicles (UAVs) have emerged as transformative tools, significantly enhancing maintenance efficiency in expansive outdoor structures. This review highlights the advancements in UAV-assisted thermography, focusing on its impact on the monitoring capabilities of outdoor engineering structures. It introduces UAV-assisted thermographic methods tailored for outdoor environments, explorees current challenges influencing their efficacy, and thoroughly examines their integration with machine learning techniques, demonstrating their applications in identifying structural flaws and deformations. Furthermore, the review emphasizes the transformative potential of 3D thermographic modelling to localize and characterize defects effectively. It contributes actionable insights to optimize UAV-assisted thermographic practices. The findings underscore the importance of integrating UAVs and thermography to overcome existing limitations in outdoor structural health monitoring inspections, setting the stage for a paradigm shift in large-scale outdoor structural condition assessments. © 2001-2012 IEEE.

关键词： Thermography (imaging)

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Determination of 24-Hour EV Hosting Capacity of Qatar's Utility Grid Considering Existing Load Patterns and Different Charging Speeds 60

Determination of 24-Hour EV Hosting Capacity of Qatar's Util...

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60th IEEE/IAS Industrial and Commercial Power Systems Technical Conference, I and CPS 2024

作者： Rahman, Syed Khan, Irfan Ayaz, Muhammad Ashar Iqbal, Atif Su, Chun-Lien Texas A&m University Electrical & Computer Engineering College Station United States Lab Galveston United States Qatar University Department of Electrical Engineering Doha Qatar National Kaohsiung University of Science & Technology Department of Electrical Engineering Taiwan

ISBN: (纸本)9798350345308

The widespread adoption of electric vehicles (EVs) has induced profound transformations in the resulting power demand and voltage profile of the low-voltage utility grids. The impact of this paradigm shift is contingent upon subtle factors, including the power rating of EV chargers, point of installation, prevailing power demand profiles, and driving patterns (dictating kWh energy of EV load). The introduction of EV loads without considering these factors and the subsequent comprehensive analysis will result in grid congestion, heightened peak demand, and augmented system losses. To address this, this paper presents a comprehensive case study of determining the EV hosting capacity of Qatar's utility grid, considering real-world demand profile data, existing load types, and their corresponding variation pattern. Secondly, considering the energy and instantaneous power requirement of EV load, the EV hosting is estimated using a 24-hour profile considering all EV charger types (DC fast chargers, level-2 chargers, and domestic chargers). A 2-step optimization algorithm is proposed to maximize the hosting capacity by optimally distributing the charging load among the residential nodes, without any events of grid congestion. Detailed simulation results show that EV charging load up to 11.4% of the distribution system rating can be accommodated without grid congestions. © 2024 IEEE.

关键词： Electric load flow

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Attack Named Entity Recognition by Entity Boundary Interference 30

Attack Named Entity Recognition by Entity Boundary Interfere...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Yang, Yifei Wu, Hongqiu Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

ISBN: (纸本)9782493814104

Named Entity Recognition (NER) is a cornerstone natural language processing task while its robustness has been given little attention. This paper rethinks the principles of the conventional text attack, as they can easily violate the label consistency between the original and adversarial NER samples. This is due to the fine-grained nature of NER, as even minor word changes in the sentence can result in the emergence or mutation of any entity, producing invalid adversarial samples. To this end, we propose a novel one-word modification NER attack based on a key insight, NER models are always vulnerable to the boundary position of an entity to make their decision. We thus strategically insert a new boundary into the sentence and trigger the victim model to make a wrong recognition either on this boundary word or on other words in the sentence. We call this attack Virtual Boundary Attack (ViBA), which is shown to be remarkably effective when attacking both English and Chinese models with a 70%-90% attack success rate on state-of-the-art language models, and also significantly faster than previous methods. We share the code in https://***/yangyifei729/ViBA. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Natural language processing systems

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Video quality enhancement using different enhancement and dehazing techniques

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16607-16635页

作者： Ayoub, Abeer Naeem, Ensherah A. El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. El-Rabaie, El-Sayed M. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Electrical Department Faculty of Technology and Education Suez University Suez43527 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Video in an outdoor environment suffers from the haze problem due to smoke, dust, and other particles in the atmosphere. The quality of video under these atmospheric conditions is clearly/dramatically degraded, leading to poor contrast and information leakage. The proposed algorithm in this paper depends on pre-processing of video frames prior to the dehazing process. Different enhancement techniques are used to remove noise and control the dynamic range, because each frame has some noise due to sensor measurement errors. This noise might be magnified during the haze removal process, if totally neglected. Previous dehazing algorithms enhance the video contrast and reduce frame degradation. We use optimized dehazing algorithms, namely a dual-transmission-map dehazing algorithm, and a recursive Deep Residual Learning (DRL) network, as dehazing tools. We modify the previous dehazing algorithms to generate new algorithms to be suitable not only for visible frames but also for Near Infrared (NIR) frames. In the optimized dehazing algorithms, the haze effect is reduced, according to the hazing model. In the dual-transmission-map dehazing algorithm, the effect of attenuation parameter R and attenuation weight U on the dehazed frames is investigated. A dual-transmission-map algorithm, depending on the Dark Channel Prior (DCP) technique, is studied. We study the effect of a regularization parameter Ω on the visible and NIR dehazed frames without and with enhancement techniques. For the recursive deep residual learning algorithm, we increase the number of iterations in the DRL network from three iterations in the traditional DRL algorithm without denoising techniques to nine iterations, in order to explore the impact of increasing the number of iterations on the output dehazed frames. Since elapsed time grows with the number of iterations, we terminate the operation after the ninth iteration. A comparison between traditional algorithms and the proposed dehazing algorithms is

关键词： Deep learning

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Diverse and Vivid Sound Generation from Text Descriptions 48

Diverse and Vivid Sound Generation from Text Descriptions

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Li, Guangwei Xu, Xuenan Dai, Lingfeng Wu, Mengyue Yu, Kai Shanghai Jiao Tong University X-Lance Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai China

ISBN: (纸本)9781728163277

Previous audio generation mainly focuses on specified sound classes such as speech or music, whose form and content are greatly restricted. In this paper, we go beyond specific audio generation by using natural language description as a clue to generate broad sounds. Unlike visual information, a text description is concise by its nature but has rich hidden meanings beneath, which poses a higher possibility and complexity on the audio to be generated. A Variation-Quantized GAN is used to train a codebook learning discrete representations of spectrograms. For a given text description, its pre-trained embedding is fed to a Transformer to sample codebook indices to decode a spectrogram to be further transformed into waveform by a melgan vocoder. The generated waveform has high quality and fidelity while excellently corresponding to the given text. Experiments show that our proposed method is capable of generating natural, vivid audios, achieving superb quantitative and qualitative results. © 2023 IEEE.

关键词： Music

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