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SSRN 2024年

作者： Ramesh, Dhanush Babu Sridhar, Rishika Iytha Upadhyaya, Pulakesh Kamaleswaran, Rishikesan Department of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States Department of Biomedical Informatics Emory University School of Medicine AtlantaGA United States

Chest radiography is a commonly utilized imaging technique for acquiring Chest X-Ray (CXR) images due to its cost-effectiveness and its role in diagnosing lung-related disorders. Nevertheless, interpreting CXR images can be challenging, and the process of separating the lung field from CXR images can be a valuable tool for assessing and diagnosing lung diseases. While various segmentation methods exist, this study primarily focuses on META’s latest Segment Anything Model (SAM). SAM is an Artificial Intelligence (AI) model designed to segment objects within an image. This research aims to harness SAM’s capabilities for segmenting CXR images. Additionally, we explore the potential of another novel model called Grounding DINO. Grounding DINO is a zero-shot object detection model that utilizes a Swin (Shifted Windows) transformer for extracting image features and BERT (Bidirectional Encoder Representations from Transformers) for extracting textual information. It is primarily employed to detect objects in an image based on a provided text prompt, creating bounding boxes around the objects when certain text and box thresholds are met. These bounding boxes are then used as prompts for SAM to generate segmentation masks. The proposed framework has been assessed on CXRs obtained from patients at Emory Hospital in Atlanta, Georgia, USA and further evaluated using NIH clinical center’s CXR image dataset. © 2024, The Authors. All rights reserved.

关键词： Object detection

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Addressing imperfect symmetry: A novel symmetry-learning actor-critic extension

arXiv

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arXiv 2023年

作者： Abreu, Miguel Reis, Luís Paulo Lau, Nuno LIACC/LASI/FEUP Artificial Intelligence and Computer Science Laboratory Faculty of Engineering University of Porto Porto Portugal IEETA/LASI/DETI Institute of Electronics and Informatics Engineering of Aveiro Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal

Symmetry, a fundamental concept to understand our environment, often oversimplifies reality from a mathematical perspective. Humans are a prime example, deviating from perfect symmetry in terms of appearance and cognitive biases (e.g. having a dominant hand). Nevertheless, our brain can easily overcome these imperfections and efficiently adapt to symmetrical tasks. The driving motivation behind this work lies in capturing this ability through reinforcement learning. To this end, we introduce Adaptive Symmetry Learning (ASL) — a model-minimization actor-critic extension that addresses incomplete or inexact symmetry descriptions by adapting itself during the learning process. ASL consists of a symmetry fitting component and a modular loss function that enforces a common symmetric relation across all states while adapting to the learned policy. The performance of ASL is compared to existing symmetry-enhanced methods in a case study involving a four-legged ant model for multidirectional locomotion tasks. The results show that ASL can recover from large perturbations and generalize knowledge to hidden symmetric states. It achieves comparable or better performance than alternative methods in most scenarios, making it a valuable approach for leveraging model symmetry while compensating for inherent perturbations. © 2023, CC BY.

关键词： Contrastive Learning

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Dermoscopic Image Classification Using Attention Mechanism and Ensemble Learning Approaches

Dermoscopic Image Classification Using Attention Mechanism a...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Huang, Shanchuan Lei, Hongwei Jin, Liuhan Yang, Jinzhu Jiang, Tao Yao, Yudong Grzegorzek, Marcin Li, Chen Northeastern University Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Liaoning Shenyang China Chengdu University of Traditional Chinese Medicine School of Intelligent Medicine Chengdu China Stevens Institute of Technology Department of Electrical and Computer Engineering United States Institute of Medical Informatics University of Luebeck Luebeck Germany

ISBN: (纸本)9798350324457

Background and purpose: Skin tumours have become one of the most common diseases worldwide. While benign ones are not usually a threat to human health, malignant ones can develop into skin cancer and become life-threatening if left untreated. Early detection of the disease is important for the treatment of patients with skin tumours and dermoscopy is the most effective means of diagnosing skin tumours. However, the complexity of skin tumour cells makes the diagnosis somewhat erroneous for doctors. Therefore, a dermoscopic classification network based on deep learning and computer-aided diagnostic techniques is needed to obtain a high diagnostic accuracy rate for skin tumours. Methods: In this paper, Deep-skin, a model for dermoscopic image classification is proposed, which is based on both attention mechanism and ensemble learning. Considering the characteristics of dermoscopic images, embedding different attention mechanisms on top of Inception-V3 has been suggested to obtain more potential features. We then improve the classification performance by late fusion of the different models. To demonstrate the effectiveness of Deep-skin, experiments and evaluations are performed on the publicly available dataset Skin Cancer: Malignant vs. Benign and compare the performance of Deep-skin with other classification models. Results: The experimental results indicate that Deep-skin performs well on the dataset in comparison to other models, achieving a maximum accuracy of 87.8%.Conclusion: In this paper, the Deep-skin model is proposed for the classification of dermoscopic images and has shown better performance. In the future, we intend to investigate better classification models for automatic diagnosis of skin tumours. Such models can potentially assist physicians and patients in clinical settings. © 2023 IEEE.

关键词： Attention mechanism Ensemble learning Image classification Late fusion Skin Cancer

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Analysis of Virtual Reality Based on the Internet of Things on Human Psychology ‘Internet of Thoughts’ (IoThs) for Rich Content Extraction Applied Natural Language Processing and Deep Learning 1

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20th International Conference on Smart Living and Public Health, ICOST 2023

作者： Mah, Pascal Muam Skalna, Iwona Pelech-Pilichowski, Tomasz Derlecki, Tomasz Nasr, Mahmoud Munyeshuri, Eric Amuzang, Gilly Njoh Itoe, Micheal Blake Somaah Tah, Ning Frida Department of Technical Informatics and Telecommunications AGH University of Science and Technology Krakow Poland Department of Business Informatics and Management Engineering AGH University of Science and Technology Krakow Poland Institute of Computer Science AGH University of Science and Technology Krakow Poland Department of Economics and Management Industry AGH University of Science and Technology Krakow Poland Department of Biomedical Engineering AGH University of Science and Technology Krakow Poland Department of Engineering Mathematics and Physics Faculty of Engineering and Technology Future University Cairo Egypt Department of Production Engineering and Logistics Enterprise Silesian University of Technology Gliwice Poland Department of Logistics Management Silesian University of Technology Gliwice Poland

ISBN: (数字)9783031439506

ISBN: (纸本)9783031439490

In the past decade, a lot of challenges to access, assess, and to acquire the needed technological opportunities to teach computers what naturally comes from the human brain and to understand how we naturally react when we rely on technology. The ability to document human thoughts, reactions and behavior to computers has led to the coming of NLP, AI, Dl, & ML. Aim to understand the influence of IoT on humans with the use of DL to achieve content correctness and accuracy with virtual technology. Studies show that the way we think, react, and do the things we think "Internet of thoughts" reflect our personality. The way we think determines the way we react and the way we do things are based on how we think. Technology advancement has reinforced a lot of changes in humans which makes humans vulnerable to personal content exposure misappropriation due to the continuously changing nature of humanity and language. The study uses NLP, DL and behavior-oriented drive and influential function and results show that IoT based on VR influences human psychology "Internet of Thoughts". © 2023, The Author(s).

关键词： Internet of things

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Asynchronous Deep Reinforcement Learning for Collaborative Task Computing and On-Demand Resource Allocation in Vehicular Edge Computing

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IEEE Transactions on Intelligent Transportation Systems 2023年第12期24卷 15513-15526页

作者： Liu, Lei Feng, Jie Mu, Xuanyu Pei, Qingqi Lan, Dapeng Xiao, Ming Xidian University Guangzhou Institute of Technology Guangzhou510555 China Ministry of Education Shanghai201804 China Xidian University State Key Laboratory of Integrated Services Networks School of Telecommunication Engineering Shaanxi Xi'an710071 China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Shaanxi Xi'an710121 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China University of Oslo Department of Informatics Oslo0373 Norway Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm10044 Sweden

Vehicular Edge Computing (VEC) is enjoying a surge in research interest due to the remarkable potential to reduce response delay and alleviate bandwidth pressure. Facing the ever-growing service applications in VEC, how to effectively aggregate and flexibly schedule ubiquitous network resources for implementing diverse tasks and meeting differentiated demands from numerous vehicular users remains haunting. Toward this end, we investigate collaborative task computing and on-demand resource allocation. The collaborative computing framework in VEC is provided to support deep collaboration and intelligent management of heterogeneous resources widely distributed in vehicles, edge servers and cloud. Based on this framework, the joint optimization problem of distributed task offloading and multi-resource management is formulated with the aim to maximize the system utility by making the optimal task and resource scheduling policy, the novelty of which lies in the exploration of available vehicle resources and the consideration of service migration. In view of the dynamics, randomness and time-variant of vehicular networks, the asynchronous deep reinforcement algorithm is leveraged to find the optimal solution. Extensive simulation experiments are implemented to demonstrate the superiority of our proposed algorithm in terms of response latency compared with full offloading and random offloading. © 2000-2011 IEEE.

关键词： Reinforcement learning

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MassSpecGym: A benchmark for the discovery and identification of molecules 38

MassSpecGym: A benchmark for the discovery and identificatio...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Bushuiev, Roman Bushuiev, Anton de Jonge, Niek F. Young, Adamo Kretschmer, Fleming Samusevich, Raman Heirman, Janne Wang, Fei Zhang, Luke Dührkop, Kai Ludwig, Marcus Haupt, Nils A. Kalia, Apurva Brungs, Corinna Schmid, Robin Greiner, Russell Wang, Bo Wishart, David S. Liu, Li-Ping Rousu, Juho Bittremieux, Wout Rost, Hannes Mak, Tytus D. Hassoun, Soha Huber, Florian van der Hooft, Justin J.J. Stravs, Michael A. Böcker, Sebastian Sivic, Josef Pluskal, Tomáš Institute of Organic Chemistry and Biochemistry The Czech Academy of Sciences Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Republic Bioinformatics Group Wageningen University & Research Netherlands Department of Computer Science University of Toronto Canada Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Germany Department of Computer Science University of Antwerp Belgium Department of computing science University of Alberta Canada Alberta Machine Intelligence Institute Canada Department of Molecular Genetics University of Toronto Canada Bright Giant GmbH Department of Computer Science Tufts University United States Department of Biological Sciences University of Alberta Canada Department of Computer Science Aalto University Finland Mass Spectrometry Data Center National Institute of Standards and Technology United States Department of Chemical and Biological Engineering Tufts University United States Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Germany Department of Biochemistry University of Johannesburg South Africa Eawag Swiss Federal Institute of Aquatic Science and Technology Switzerland

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym. © 2024 Neural information processing systems foundation. All rights reserved.

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Annealing-Based Optimization for Selecting Training Space in Ensemble Learning

Annealing-Based Optimization for Selecting Training Space in...

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International Conference of Advanced informatics: Concept, Theory and Application (ICAICTA)

作者： Lenny Putri Yulianti Agung Trisetyarso Judhi Santoso Kridanto Surendro School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Department of Mathematics School of Computer Science Bina Nusantara University Jakarta Indonesia

Ensemble learning integrates many learners to achieve better performance, however, it can increase the complexity dramatically. Redundant learners should be pruned to reduce ensemble complexity while allowing for increased accuracy. There are two common ways to achieve this, which include selecting the optimal training space and the optimal trained learners. This study contributes to reducing redundant learners by selecting the optimal training space using annealing-based optimization. The optimal training space was defined by 1) measuring the similarity between training spaces (clusters) and 2) searching for the best set of training spaces by minimizing the similarity between the selected training spaces. The proposed method was tested on 10 datasets from the UCI repository and compared to five state-of-the-art random subspace methods. The results showed that the proposed method could reduce the size of the ensemble while significantly increasing its accuracy.

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Regularized and smooth double core tensor factorization for heterogeneous data

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 13162-13210页

作者： Davoud Ataee Tarzanagh George Michailidis Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI and University of Florida Informatics Institute Gainesville FL Department of Statistics University of Florida Gainesville FL and University of Florida Informatics Institute Gainesville FL

We introduce a general tensor model suitable for data analytic tasks for heterogeneous datasets, wherein there are joint low-rank structures within groups of observations, but also discriminative structures across different groups. To capture such complex structures, a double core tensor (DCOT) factorization model is introduced together with a family of smoothing loss functions. By leveraging the proposed smoothing function, the model accurately estimates the model factors, even in the presence of missing entries. A linearized ADMM method is employed to solve regularized versions of DCOT factorizations, that avoid large tensor operations and large memory storage requirements. Further, we establish theoretically its global convergence, together with consistency of the estimates of the model parameters. The effectiveness of the DCOT model is illustrated on several real-world examples including image completion, recommender systems, subspace clustering, and detecting modules in heterogeneous Omics multi-modal data, since it provides more insightful decompositions than conventional tensor methods.

关键词： double core tensor factorization heterogeneity smoothing loss functions regularization ADMM

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Hybrid-RIS Empowered UAV-Aided ISAC Systems

Hybrid-RIS Empowered UAV-Aided ISAC Systems

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IEEE Conference on Vehicular Technology (VTC)

作者： Prajwalita Saikia Anand Jee Keshav Singh Theodoros A. Tsiftsis Alexandros-Apostolos A. Boulogeorgos Institute of Communications Engineering National Sun Yat-sen University Kaohsiung Taiwan Department of Electrical Engineering Indian Institute of Technology Delhi New Delhi India Department of Informatics & Telecommunications University of Thessaly Lamia Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

In this paper, we consider a novel hybrid reconfigurable intelligent surface (HRIS) consisting of active as well as passive reflecting elements mounted on unmanned aerial vehicle (UAV). The aim is to improve air-to-ground communication by assisting multiple users, while detecting several moving targets. We formulate an optimization model that account for statistical channel estimation errors (SCEEs) to concurrently fine-tune both active and passive phase-shift matrices, UAV trajectory and transmit beamformer for integrated sensing and communication (ISAC), while aiming to maximize the overall achievable sum-rate. Subsequently, we introduce an alternating optimization algorithm that employs a repetitive method to address the combinatorial nonconvex optimization problem and ultimately yielding a solution that is nearly optimal. Using Monte Carlo simulations, we showcase the superiority of the suggested approach in comparison to baseline schemes.

关键词： Vehicular and wireless technologies Monte Carlo methods Optimization models Human-robot interaction Channel estimation Reconfigurable intelligent surfaces Autonomous aerial vehicles Integrated sensing and communication Trajectory Optimization

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Cyber-Physical Steganography in Robotic Motion Control

arXiv

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arXiv 2025年

作者： Chang, Ching-Chun Lin, Yijie Echizen, Isao The Information and Society Research Division National Institute of Informatics Japan The Department of Information Engineering and Computer Science Feng Chia University Taiwan The Graduate School of Information Science and Technology University of Tokyo Japan

Steganography, the art of information hiding, has continually evolved across visual, auditory and linguistic domains, adapting to the ceaseless interplay between steganographic concealment and steganalytic revelation. This study seeks to extend the horizons of what constitutes a viable steganographic medium by introducing a steganographic paradigm in robotic motion control. Based on the observation of the robot's inherent sensitivity to changes in its environment, we propose a methodology to encode messages as environmental stimuli influencing the motions of the robotic agent and to decode messages from the resulting motion trajectory. The constraints of maximal robot integrity and minimal motion deviation are established as fundamental principles underlying secrecy. As a proof of concept, we conduct experiments in simulated environments across various manipulation tasks, incorporating robotic embodiments equipped with generalist multimodal policies. © 2025, CC BY.

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