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International Conference on Military Communications and Information systems (ICMCIS)

作者： J. DeMarchi R. Rijken J. Melrose B. Madahar G. Fumera F. Roli E. Ledda M. Aktaş F. Kurth P. Baggenstoss B. Pelzer L. Kanestad Collaborative Engineering Systems & Aerospace Systems Information Supremacy Royal Netherlands Aerospace Centre NLR Amsterdam NLD Cyber & Information Systems Division Defence Science and Technology Laboratory Portondown GBR Department of Electrical and Electronic Engineering University of Cagliari Cagliari ITA Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa ITA Department of Computer Control and Management Engineering Sapienza University of Rome Rome ITA Defence Systems Technologies Division ASELSAN Ankara TUR Fraunhofer Institute for Communication Information Processing and Ergonomics Bonn DEU Swedish Defence Research Agency Cyber Defence and C2 Technology Division Stockholm SWE

In this paper we explore some of the potential applications of robustness criteria for machine learning (ML) systems by way of tangible “demonstrator” scenarios. In each demonstrator, ML robustness metrics are applied to real-world scenarios with military relevance, indicating how they might be used to help detect and handle possible adversarial attacks on ML systems. We conclude by sketching promising future avenues of research in order to: (1) help establish useful verification methodologies to facilitate ML robustness compliance assessment; (2) support development of ML accountability mechanisms; and (3) reliably detect, repel, and mitigate adversarial attack.

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Few-shot learning based histopathological image classification of colorectal cancer

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Intelligent Medicine 2024年第4期4卷 256-267页

作者： Rui Li Xiaoyan Li Hongzan Sun Jinzhu Yang Md Rahaman Marcin Grzegozek Tao Jiang Xinyu Huang Chen Li Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyangLiaoning 110167China Cancer Hospital China Medical UniversityShenyangLiaoning 110122China Shengjing Hospital China Medical UniversityShenyang.Liaoning 110000China Institute for Medical Informatics University of LuebeckGermany Department of Knowledge Engineering University of Economics in KatowicePoland School of Intelligent Medicine Chengdu University of Traditional Chinese MedicineChengduSichuan 610075China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information TechnologyChengduSichuan 610225China School of Computer Science and Engineering University of New South WalesSydneyNSW 2052Australia

Background Colorectal cancer is a prevalent and deadly disease worldwide,posing significant diagnostic *** histopathologic image classification is often inefficient and *** some histopathologists use computer-aided diagnosis to improve efficiency,these methods depend heavily on exten-sive data and specific annotations,limiting their *** address these challenges,this paper proposes a method based on few-shot *** This study introduced a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant *** model comprises modules for feature extraction,dimensionality reduction,and classification,trained using a combi-nation of contrast loss and cross-entropy *** this paper,we detailed the setup of hyperparameters:n-way,κ-shot,β,and the creation of support,query,and test *** Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per *** documented the model’s loss,accuracy,and the confusion matrix of the ***,we employed the t-SNE algorithm to analyze and assess the model’s classification *** The proposed model may demonstrate significant advantages in accuracy and minimal data depen-dency,performing robustly across all tested n-way,κ-shot *** consistently achieved over 93% accuracy on comprehensive test datasets,including 1916 samples,confirming its high classification accuracy and strong generalization *** research could advance the use of few-shot learning in medical diagnostics and also lays the groundwork for extending it to deal with rare,difficult-to-diagnose cases.

关键词： Colorectal cancer Few-shot learning Transfer learning Contrastive learning Histopathological images Benign and malignant categories

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Mobile Manipulation Leveraging Multiple Views

arXiv

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arXiv 2021年

作者： Watkins-Valls, David Allen, Peter K. Maia, Henrique Seshadri, Madhavan Sanabria, Jonathan Waytowich, Nicholas Varley, Jacob Department of Computer Science Columbia University New YorkNY United States U.S. Army Research Laboratory BaltimoreMD United States Robotics at Google United States

While both navigation and manipulation are challenging topics in isolation, many tasks require the ability to both navigate and manipulate in concert. To this end, we propose a mobile manipulation system that leverages novel navigation and shape completion methods to manipulate an object with a mobile robot. Our system utilizes uncertainty in the initial estimation of a manipulation target to calculate a predicted next-best-view. Without the need of localization, the robot then uses the predicted panoramic view at the next-best-view location to navigate to the desired location, capture a second view of the object, create a new model that predicts the shape of object more accurately than a single image alone, and uses this model for grasp planning. We show that the system is highly effective for mobile manipulation tasks through simulation experiments using real world data, as well as ablations on each component of our system. © 2021, CC BY-NC-ND.

关键词： Navigation

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Predicting Grinding Mill Power Consumption in Mining: A Comparative Study

Predicting Grinding Mill Power Consumption in Mining: A Comp...

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International Conference on Information science and Technology (ICIST)

作者： Chaimae Loudari Moha Cherkaoui Rachid Bennani Imad El Harraki Omar Fares Mohamed El Adnani El Hassan Abdelwahed Intissar Benzakour François Bourzeix Karim Baina dept. LMAID Laboratory National School of Mines of Rabat (ENSMR) Rabat Morocco dept. Digitalization and Microelectronic Smart Devices Moroccan Foundation for Advanced Science Innovation and Research (MAScIR) Rabat Morocco dept. EIA Reminex Research Center MANAGEM Group Casablanca Morocco dept. LISI Laboratory University of Cadi Ayyad (UCA) Marrakech Morocco dept. Embedded Systems and Artificial Intelligence Department Moroccan Foundation for Advanced Science Innovation and Research (MAScIR) Rabat Morocco dept. Al-Qualsadi Research & Development Team National Higher School for Computer Science and System analysis (ENSIAS) Rabat Morocco

The mining industry deals with complexity and high energy consumption in mineral production. Artificial intelligence technology has been integrated to manage and optimize energy usage in mining equipment. A special focus is given to grinding mills, which are essential but difficult to model due to their energy-intensive nature. Data-driven machine learning approaches have emerged as effective solutions, enabling energy consumption prediction and optimization. These methods offer valuable insights to improve the overall energy efficiency of grinding mills in mining. In this article, a comparative study is conducted to investigate the use of machine learning models for predicting the power consumption of grinding mills. The study starts by providing a detailed description of the dataset used and the predictive models employed in the research. Notably, the results of power prediction for grinding mills are compared, with the Random Forest model emerging as the top performer, boasting an impressive R2 value of 0.94. The article underscores the significance of this predictive capability in helping mining experts make informed decisions to optimize energy consumption in grinding mills. Finally, the article concludes by offering insightful recommendations and identifying promising avenues for future research in this field. Overall, this study sheds light on the potential of machine learning to enhance energy efficiency in the mining industry.

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When Vision Meets Touch: A Contemporary Review for Visuotactile Sensors from the Signal Processing Perspective

arXiv

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arXiv 2024年

作者： Li, Shoujie Wang, Zihan Wu, Changsheng Li, Xiang Luo, Shan Fang, Bin Sun, Fuchun Zhang, Xiao-Ping Ding, Wenbo Tsinghua Shenzhen International Graduate School Shenzhen518055 China RISC-V International Open Source Laboratory Shenzhen518055 China Department of Materials Science and Engineering National University of Singapore Singapore117575 Singapore Centre for Robotics Research King’s College London LondonWC2R 2LS United Kingdom Department of Automation Tsinghua University Beijing100084 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

Tactile sensors, which provide information about the physical properties of objects, are an essential component of robotic systems. The visuotactile sensing technology with the merits of high resolution and low cost has facilitated the development of robotics from environment exploration to dexterous operation. Over the years, several reviews on visuotactile sensors for robots have been presented, but few of them discussed the significance of signal processing methods to visuotactile sensors. Apart from ingenious hardware design, the full potential of the sensory system toward designated tasks can only be released with the appropriate signal processing methods. Therefore, this paper provides a comprehensive review of visuotactile sensors from the perspective of signal processing methods and outlooks possible future research directions for visuotactile sensors. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal processing

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EFaR 2023: Efficient Face Recognition Competition

EFaR 2023: Efficient Face Recognition Competition

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Jan Niklas Kolf Fadi Boutros Jurek Elliesen Markus Theuerkauf Naser Damer Mohamad Alansari Oussama Abdul Hay Sara Alansari Sajid Javed Naoufel Werghi Klemen Grm Vitomir Štruc Fernando Alonso-Fernandez Kevin Hernandez Diaz Josef Bigun Anjith George Christophe Ecabert Hatef Otroshi Shahreza Ketan Kotwal Sébastien Marcel Iurii Medvedev Bo Jin Diogo Nunes Ahmad Hassanpour Pankaj Khatiwada Aafan Ahmad Toor Bian Yang Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates Laboratory for Machine Intelligence Faculty of Electrical Engineering University of Ljubljana Slovenia Halmstad University Sweden Idiap Research Institute Martigny Switzerland École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Université de Lausanne (UNIL) Lausanne Switzerland Institute of Systems and Robotics University of Coimbra Coimbra Portugal Department of Information Security and Communication Technology eHealth and Welfare Security Group Norwegian University of Science and Technology Norway

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well.

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Distributed Energy Optimization for Mobile Networks Using Potential Games

Distributed Energy Optimization for Mobile Networks Using Po...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor systems (MASS)

作者： Zhizongkai Wang Hanfei Wang Zhongji Wang Yilin Xiao Yunzhi Zhao Xiaowen Li Xufeng Chen Lin Gao Fen Hou Jianwei Huang School of Electronics and Information Engineering and the Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Harbin Institute of Technology Shenzhen Guangdong P.R. China School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network and CSIJRI Joint Research Centre on Smart Energy Storage The Chinese University of Hong Kong Shenzhen Guangdong P.R. China Department of Electrical and Computer Engineering State Key Laboratory of IoT for Smart City University of Macau Macau SAR P.R. China Huawei Technologies P.R. China

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

The rapid advancement of information and communication technology (ICT) has made the industry a significant contributor to global carbon emissions. As the foundation of ICT, next-generation mobile communication networks aim to be more powerful and energy-efficient. However, optimizing energy efficiency in real networks is challenging due to large-scale, multi-layer control variables and the dynamic environment. This paper addresses the energy efficiency optimization problem from a network perspective by controlling cross-layer variables including both cell activation status and cell priority, to minimize overall network energy consumption while ensuring user quality-of-experience, which poses an NP-hard mixed-integer nonlinear programming problem. To tackle this, we propose a non-cooperative game where each cell acts as a player, optimizing its activation status and reference signal transmission power (determining its priority). We show that the game is a potential game, guaranteeing the existence of Nash equilibrium and the convergence of simple distributed algorithms towards Nash equilibrium. We further show that the Nash equilibrium points of the game can (but not always) reach the global optimal energy efficiency. Simulation results show that our proposed method can reduce the total system cost (including both energy consumption cost and user experience loss) by up to 28% compared to existing methods in the literature. Moreover, the performance loss of our proposed method, compared to the global optimal solution, is less than 13.7%. In summary, this work offers a realistic network model, introduces a novel game-based method, and provides extensive performance evaluation, making a significant contribution to both industry and academia.

关键词： Industries Energy consumption Costs Simulation Games Nash equilibrium Energy efficiency Information and communication technology Distributed algorithms Optimization

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A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition systems

A Novel Duo-Stage driven Deep Neural Network Approach for Mi...

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IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Frank Kulwa Oluwarotimi Williams Samuel Mojisola Grace Asogbon Tolulope Tofunmi Oyemakinde Olumide Olayinka Obe Guanglin Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Institute of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) Shenzhen Guangdong China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen Guangdong China School of Computing and Engineering University of Derby Derby United Kingdom Department of Computer Science Federal University of Technology Akure Nigeria

A major barrier to the commercialization of pattern recognition (PR)-based myoelectric prostheses is the lack of robustness to confounding factors such as electrode shift which has been lingering for years. To overcome this challenge, a novel Duo-Stage Convolutional Neural Network (DS-CNN) is proposed. The DS-CNN is comprised of two cascaded stages in which the first stage deciphers the occurrence of a particular kind of shift upon which a requisite CNN model is triggered in the second stage for accurate decoding of individual motion intent, which is necessary for initiating robust control of the prostheses. The proposed scheme works on raw EMG signals as input which reduces the preprocessing time that would be required in conventional machine learning-based PR schemes, to effectively mitigate both transverse and longitudinal shifts using the same network architecture. This approach was validated for four distinct electrode shift conditions (with shifts in the range of 7.50mm-10.05mm) in a dataset obtained from 18 able-bodied subjects that performed 8 classes of targeted hand gestures. The experimental results show that the proposed dual-stage driven deep neural network model can adequately resolve the effects of electrode shift with classification accuracy near the No-shift scenario (< 1.70% difference between shift mitigation and No shift scenarios). These outcomes suggest that our method can provide a practical solution for adaptation to electrode shift, thus improving the robustness of the EMG pattern recognition systems in both clinical and commercial settings.

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Automatic Organ and Pan-cancer Segmentation in Abdomen CT: the FLARE 2023 Challenge

arXiv

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arXiv 2024年

作者： Ma, Jun Zhang, Yao Gu, Song Ge, Cheng Wang, Ershuai Zhou, Qin Huang, Ziyan Lyu, Pengju He, Jian Wang, Bo The Department of Laboratory Medicine and Pathobiology University of Toronto Peter Munk Cardiac Center UHN AI Hub University Health Network Vector Institute Toronto Canada Lenovo Research Beijing China The Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Ocean University of China Qingdao China Department of Research and Development ShenZhen Yorktal DMIT Co. LTD. Shenzhen China Institute of Medical Robotics School of Biomedical Engineering Shanghai Jiao Tong University Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Jiao Tong University Shanghai AI Laboratory Shanghai China City University of Macau China Hanglok-Tech Co. Ltd. Hengqin China The Department of Nuclear Medicine Nanjing Drum Tower Hospital Nanjing China The Peter Munk Cardiac Center University Health Network Department of Laboratory Medicine and Pathobiology Department of Computer Science University of Toronto Vector Institute UHN AI Hub University Health Network Toronto Canada

Organ and cancer segmentation in abdomen Computed Tomography (CT) scans is the prerequisite for precise cancer diagnosis and treatment. Most existing benchmarks and algorithms are tailored to specific cancer types, limiting their ability to provide comprehensive cancer analysis. This work presents the first international competition on abdominal organ and pan-cancer segmentation by providing a large-scale and diverse dataset, including 4650 CT scans with various cancer types from over 40 medical centers. The winning team established a new state-of-the-art with a deep learning-based cascaded framework, achieving average Dice Similarity Coefficient (DSC) scores of 92.3% for organs and 64.9% for lesions on the hidden multi-national testing set. The dataset and code of top teams are publicly available, offering a benchmark platform to drive further innovations https://***/competitions/12239. © 2024, CC BY-NC-ND.

关键词： Lung cancer

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Non-invasive brain stimulation-based sleep stage classification using transcranial infrared based electrocardiogram

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Neuroscience Informatics 2025年第2期5卷 100197-100197页

作者： Ramesh, Janjhyam Venkata Naga Quraishi, Aadam Aoudni, Yassine Mudhafar, Mustafa Nimma, Divya Bansal, Monika Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh 522302 India M.D. Research Intervention Treatment Institute Houston TX United States Computer & Embedded Systems Laboratory National School of Engineers of Sfax (ENIS) University of Sfax BP 1173 Sfax 3038 Tunisia Department of Medical Physics Faculty of Medical Applied Sciences University of Kerbala Karbala 56001 Iraq Department of Anesthesia Techniques and Intensive Care Al-Taff university college Kerbala 56001 Iraq Data Analyst in UMMC United States Department of Computer Science SSD Women's Institute of Technology Bathinda India

Non-invasive brain stimulation (NIBS) techniques, such as transcranial infrared (tNIR) stimulation, offer promising advancements in sleep monitoring and regulation. To enhance sleep stage classification without relying on traditional polysomnography (PSG) systems, we propose a novel approach integrating single-channel electrocardiogram (ECG) signals, heart rate variability (HRV) features, and tNIR stimulation. The maximal overlap discrete wavelet transform (MODWT) is applied for multi-resolution analysis of ECG signals, followed by peak information extraction. Based on the first-order deviation of peak positions, multi-dimensional HRV features are extracted. To identify HRV features strongly associated with different sleep stages, we introduce a feature selection method combining the ReliefF algorithm and Gini index. The selected features are then processed using the INFO-ABC Logit Boost method to establish correlations between HRV dynamics and sleep stages. Experimental results on publicly available datasets demonstrate that the proposed model achieves an overall accuracy of 83.67%, a precision of 82.59%, a Kappa coefficient of 77.94%, and an F1-score of 82.97%. Compared with conventional sleep staging methods, our approach enhances sleep quality assessment and facilitates real-time, non-invasive monitoring in home and mobile healthcare settings, leveraging the potential of tNIR-based NIBS for sleep modulation. © 2025 The Author(s)

关键词： ECG Healthcare HRV Non-invasive brain stimulation tNIR

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