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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Lakshadeep Naik Sinan Kalkan Sune L. Sørensen Mikkel B. Kjærgaard Norbert Krüger SDU Robotics Mærsk Mc-Kinney Møller Institute (MMMI) Faculty of Engineering University of Southern Denmark Odense M Denmark Department of Computer Engineering and ROMER Robotics Center Middle East Technical University (METU) Ankara Turkey SDU Software Engineering Mærsk Mc-Kinney Møller Institute (MMMI) Faculty of Engineering University of Southern Denmark Odense M Denmark Danish Institute for Advanced Studies (DIAS) Odense M Denmark

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In many applications, a mobile manipulator robot is required to grasp a set of objects distributed in space. This may not be feasible from a single base pose and the robot must plan the sequence of base poses for grasping all objects, minimizing the total navigation and grasping time. This is a Combinatorial Optimization problem that can be solved using exact methods, which provide optimal solutions but are computationally expensive, or approximate methods, which offer computationally efficient but sub-optimal solutions. Recent studies have shown that learning-based methods can solve Combinatorial Optimization problems, providing near-optimal and computationally efficient *** this work, we present BaSeNet - a learning-based approach to plan the sequence of base poses for the robot to grasp all the objects in the scene. We propose a Reinforcement Learning based solution that learns the base poses for grasping individual objects and the sequence in which the objects should be grasped to minimize the total navigation and grasping costs using Layered Learning. As the problem has a varying number of states and actions, we represent states and actions as a graph and use Graph Neural Networks for learning. We show that the proposed method can produce comparable solutions to exact and approximate methods with significantly less computation time. The code and Reinforcement Learning environments will be made available on the project webpage * .

关键词： Learning systems Navigation Grasping Reinforcement learning Manipulators Graph neural networks Computational efficiency Planning Optimization Intelligent robots

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Playing the video games during COVID-19 pandemic and its effects on player's well-being

Playing the video games during COVID-19 pandemic and its eff...

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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Samar Billi Noaman Amer Ibrahim Liaqat Ali M. W. Iqbal Asma Ashraf Usama Haseeb Salman Muneer Rasah Almajed K. Hamid Faculty of Management Canadian University Dubai Dubai UAE College of Computer and Information Technology American University in the Emirates Dubai United Arab Emirates College of Engineering and Technology University of Science and Technology of Fujairah Fujairah UAE Department of Software Engineering Superior University Lahore Pakistan Department of Software Engineering Superior University Lahore Pakistan Department of Computer Science Superior University Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan College of Computer and Information Technology American University in the Dubai United Arab Emirates

COVID-19 affected our lives intensly. That state of affairs made humans helpless. They had been depressed and experienced loneliness. At that time many human beings were determined to play video games just to loosen up their minds. Many games changed into an additional source of revenue wherein during COVID-19 they were playing while earning money. With these advantages, there were also some poor effects was accrued. A number of players remained playing video games post COVID-19. The carried out survey is based on the social video games results on players’ well-being and additionally on the effects of gamers’ health and their sensible lifestyles. We are going to investigate the behavior of gamers engaged with video games during the COVID-19 lockdown and the video games affects on their well-being, the time they served in playing video games, and the consequential effect on their behavior and social and mental well-being. The results provide a start line for empirically grounded discussions on video games at some stage in the pandemic, their use, and potential outcomes. Different agegroups of players have been investigated. Most players are between 18 and 30 years. A number of the gamers during lock down played a few hours but most of players were males who spent most of their day playing video games. However, now the ratio of playing video games is reduced as examined with past circumstances. Roughly we can say that the condition as a whole is better, the reason why players enforced video games in their post COVID-19 practical life 1 .

关键词： COVID-19 Video games Pandemics Games Machine learning Behavioral sciences Security

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PID Controller Machine Learning Algorithm Applied to the Mathematical Model of Quadrotor Lateral Motion 6

PID Controller Machine Learning Algorithm Applied to the Mat...

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6th IEEE International Conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2021

作者： Kucherov, Dmytro Kozub, Andrei Tkachenko, Valerii Rosinska, Galina Poshyvailo, Olexii Faculty of Cybersecurity Computer and Software Engineering National Aviation University Department of Computerized Control Systems Kyiv Ukraine National Space Facilities Control and Test Center State Space Agency of Ukraine Kyiv Ukraine

ISBN: (纸本)9781665438223

The paper discusses the problem of automatic tuning of the PID controller. The auto-tuning algorithm of the PID controller based on one machine learning method, which is equivalent to the steepest descent, is proposed. A feature of the proposed approach is the vector adjustment of the PID controller parameters, where the angular direction of descent is taken into account at first, and the step sizes are made if taking into account the increased accuracy of the approximation near the optimum point. Modeling performed in the Matlab environment confirms the effectiveness of the proposed approach. © 2021 IEEE.

关键词： Three term control systems

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SLIDE-BASED GRAPH COLLABORATIVE TRAINING FOR HISTOPATHOLOGY WHOLE SLIDE IMAGE ANALYSIS

arXiv

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arXiv 2024年

作者： Shi, Jun Shu, Tong Jiang, Zhiguo Wang, Wei Wu, Haibo Zheng, Yushan School of Software Hefei University of Technology Hefei230601 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Image Processing Center School of Astronautics Beihang University Beijing102206 China Department of Pathology The First Affiliated Hospital of USTC University of Science and Technology of China Hefei230036 China School of Engineering Medicine Beijing Advanced Innovation Center on Biomedical Engineering Beihang University Beijing100191 China

The development of computational pathology lies in the consensus that pathological characteristics of tumors are significant guidance for cancer diagnostics. Most existing research focuses on the inner-contextual information within each WSI yet ignores the possible inter-correlations between slides. As the development of tumors is a continuous process involving a series of histological, morphological, and genetic changes that accumulate over time, the similarities and differences between WSIs across various stages, grades, locations and patients should potentially contribute to the representation of WSIs and deserve to be taken into account in WSI modeling. To verify the advancement of introducing the slide inter-correlations into the representation learning of WSIs, we proposed a generic WSI analysis pipeline SlideGCD that can be adapted to any existing Multiple Instance Learning (MIL) frameworks and improve their performance. With the new paradigm, the prior knowledge of cancer development can participate in the end-to-end workflow, which concurrently initializes and refines the slide representation, as a guide for message passing in the slide-based graph. Extensive comparisons and experiments are conducted to validate the effectiveness and robustness of the proposed pipeline across 4 different tasks, including cancer subtyping, cancer staging, survival prediction, and gene mutation prediction, with 7 representative SOTA WSI analysis frameworks as backbones. Copyright © 2024, The Authors. All rights reserved.

关键词： WSI circuits

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Incentive Mechanism for Uncertain Tasks under Differential Privacy

arXiv

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arXiv 2023年

作者： Jiang, Xikun Ying, Chenhao Li, Lei Düdder, Boris Wu, Haiqin Jin, Haiming Luo, Yuan Department of Computer Science Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science University of Copenhagen Copenhagen2100 Denmark Blockchain Advanced Research Center Jiangsu214101 China Department of Computer Science University of Copenhagen Copenhagen2100 Denmark Department of Software Engineering Institute East China Normal University Shanghai200062 China

Mobile crowd sensing (MCS) has emerged as an increasingly popular sensing paradigm due to its cost-effectiveness. This approach relies on platforms to outsource tasks to participating workers when prompted by task publishers. Although incentive mechanisms have been devised to foster widespread participation in MCS, most of them focus only on static tasks (i.e., tasks for which the timing and type are known in advance) and do not protect the privacy of worker bids. In a dynamic and resource-constrained environment, tasks are often uncertain (i.e., the platform lacks a priori knowledge about the tasks) and worker bids may be vulnerable to inference attacks. This paper presents an incentive mechanism HERALD*, that takes into account the uncertainty and hidden bids of tasks without real-time constraints. Theoretical analysis reveals that HERALD* satisfies a range of critical criteria, including truthfulness, individual rationality, differential privacy, low computational complexity, and low social cost. These properties are then corroborated through a series of evaluations. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost effectiveness

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Text-to-Image Diffusion Models can be Easily Backdoored through Multimodal Data Poisoning

arXiv

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arXiv 2023年

作者： Zhai, Shengfang Dong, Yinpeng Shen, Qingni Pu, Shi Fang, Yuejian Su, Hang School of Software and Microelectronics Peking University China Department of Computer Science & Technology Tsinghua University China ShengShu Beijing China School of Software and Microelectronics National Engineering Research Center for Software Engineering PKU-OCTA Laboratory for Blockchain and Privacy Computing Peking University Beijing100871 China Dept. of Comp. Sci. and Tech. Institute for AI Tsinghua-Bosch Joint ML Center THBI Lab BNRist Center Tsinghua University Beijing100084 China

With the help of conditioning mechanisms, the state-of-the-art diffusion models have achieved tremendous success in guided image generation, particularly in text-to-image synthesis. To gain a better understanding of the training process and potential risks of text-to-image synthesis, we perform a systematic investigation of backdoor attack on text-to-image diffusion models and propose BadT2I, a general multimodal backdoor attack framework that tampers with image synthesis in diverse semantic levels. Specifically, we perform backdoor attacks on three levels of the vision semantics: Pixel-Backdoor, Object-Backdoor and Style-Backdoor. By utilizing a regularization loss, our methods efficiently inject backdoors into a large-scale text-to-image diffusion model while preserving its utility with benign inputs. We conduct empirical experiments on Stable Diffusion, the widely-used text-to-image diffusion model, demonstrating that the large-scale diffusion model can be easily backdoored within a few fine-tuning steps. We conduct additional experiments to explore the impact of different types of textual triggers, as well as the backdoor persistence during further training, providing insights for the development of backdoor defense methods. Besides, our investigation may contribute to the copyright protection of text-to-image models in the future. Our Code: https://***/sf-zhai/BadT2I. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Detection and Replay of Distributed Denial of Service Attacks In Smart Cities Using a Hybrid Deep Learning Approach

Detection and Replay of Distributed Denial of Service Attack...

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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Muhammad Waseem Iqbal Ghassan F. Issa Muhammad Yousif Muhammad Atif Department of Software Engineering Superior University Lahore Pakistan School of Information Technology Skyline University College Sharjah UAE Department of Computer Science NCBAE Lahore. Pakistan Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science NCBAE Lahore Pakistan

Infrastructure for smart cities is presently being built, mostly because of the (IoT) platform, which connects a wide variety of things by using the website's underlying infrastructure. As a result, it is possible to employ a uniform platform to automate the services that are delivered. Smart city infrastructure, on the other hand, is susceptible to cyberattacks owing to security flaws in IoT networks. Distributing denial of service (DDoS) and replay assaults, for example, violate the requirements for certification in smart cities. The lack of citations in this part backs up many of the statements it makes. Who needs a citation? Both assaults have the potential to substantially damage smart city infrastructure, which might also cost money and even result in human fatalities. This paper covers the creation of a blended deep- learning algorithm for replay and DDoS intrusion prevention on a growth-oriented smart city platform. This is made possible by combining proven machine-learning approaches with more recent advances in artificial intelligence. We simulate scattered denial of service and replay attacks on three datasets acquired from real-world smart cities to assess the efficacy of the proposed hybrid strategy on environmental, smart river, and smart soil dataset. The suggested model has shown excellent rates of accuracy. The ecosystem collection has an accuracy level of 98.37%, the smart riverbed dataset must have an accuracy of 98.13%, and the smart mud dataset must have an accuracy of 99.51%. The findings demonstrated that the proposed model outperformed past instances of evolutionary computation and machine learning techniques employed in the corpus of academic literature.

关键词： Analytical models Machine learning algorithms Smart cities Computational modeling Soil Probability distribution Real-time systems

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OpenRANet: Neuralized Spectrum Access by Joint Subcarrier and Power Allocation with Optimization-based Deep Learning

arXiv

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arXiv 2024年

作者： Chen, Siya Tan, Chee Wei Zhai, Xiangping Poor, H. Vincent Department of Computer Science City University of Hong Kong Hong Kong Nanyang Technological University Nanyang Ave. Singapore Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing210023 China Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

The next-generation radio access network (RAN), known as Open RAN, is poised to feature an AI-native interface for wireless cellular networks, including emerging satellite-terrestrial systems, making deep learning integral to its operation. In this paper, we address the nonconvex optimization challenge of joint subcarrier and power allocation in Open RAN, with the objective of minimizing the total power consumption while ensuring users meet their transmission data rate requirements. We propose OpenRANet, an optimization-based deep-learning model that integrates machine-learning techniques with iterative optimization algorithms. We start by transforming the original nonconvex problem into convex subproblems through decoupling, variable transformation, and relaxation techniques. These subproblems are then efficiently solved using iterative methods within the standard interference function framework, enabling the derivation of primal-dual solutions. These solutions integrate seamlessly as a convex optimization layer within OpenRANet, enhancing constraint adherence, solution accuracy, and computational efficiency by combining machine learning with convex analysis, as shown in numerical experiments. OpenRANet also serves as a foundation for designing resource-constrained AI-native wireless optimization strategies for broader scenarios like multi-cell systems, satellite-terrestrial networks, and future Open RAN deployments with complex power consumption requirements. Copyright © 2024, The Authors. All rights reserved.

关键词： Radio access networks

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Weighting Assessment of the Effect of Chinese State-Changing Words on Emotions

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Journal of Psycholinguistic Research 2023年第6期52卷 2545-2566页

作者： Chang, Chia-Yueh Tsai, Meng-Ning Sung, Yao-Ting Cho, Shu-Ling Chen, Hsueh-Chih Department of Educational Psychology and Counseling National Taiwan Normal University Education College Building Room 612 No. 162 Sec. 1 Heping E. Rd. Da-an District Taipei City 10610 Taiwan Chinese Language and Technology Center National Taiwan Normal University Taipei Taiwan Institute for Research Excellence in Learning Sciences National Taiwan Normal University Taipei Taiwan Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Graduate Institute of Information and Computer Education National Taiwan Normal University Taipei Taiwan Department of Clinical Psychology Fu-Jen Catholic University Taipei Taiwan NSTC AI Biomedical Research Center Taipei Taiwan

Past studies of sentiment analysis have mainly applied algorithms based on vocabulary categories and emotional characteristics to detect the emotionality of text. However, the collocation of state-changing words and emotional vocabulary affects emotions. For example, adverbs of degree strengthen emotions, and negative adverbs reverse emotions. This study investigated the weighted effect of state-changing words on emotion. The research material comprised 73 state-changing words that were collocated with four emotions: happiness, sadness, fear, and anger. A total of 84 participants participated in the vocabulary assessment. The results revealed that state-changing words could be classified into four types: intensifying, weakening, neutralizing, and reversing. In a comparison of the weighting factors among emotions, the weighting effect of the same state-changing word in the positive emotion category was particularly evident. The results could serve as a reference for follow-up studies on detecting emotions in text. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Emotion Language intensity Negativity effect Pollyanna principle State-changing word

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Modeling and simulation of the“IL-36 cytokine”and CAR-T cells interplay in cancer onset

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International Journal of Modeling, Simulation, and Scientific Computing 2022年第3期13卷 207-222页

作者： Khaled A.A-Utaibi Alessandro Nutini Ayesha Sohail Robia Arif Sumeyye Tunc Sadiq M.Sait Computer Science and Software Engineering Department University of Ha’il Ha’ilSaudi Arabia Biology and Biomechanics Department Centro Studi Attivita Motorie via di Tiglio 94 LuccaItaly Department of Mathematics Comsats University Islamabad Lahore 54000Pakistan Vocational School of Sciences Physiotherapy Programme Medipol University UnkapanıAtat¨urk BulvarıNo.27Halic Campus34083 FatihIstanbulTurkey Center for Communications and IT Research Research Institute King Fahd University of Petroleum&Minerals Dhahran 31261Saudi Arabia

Background:CAR-T cells are chimeric antigen receptor(CAR)-T cells;they are targetspecific engineered cells on tumor cells and produce T cell-mediated antitumor ***-T cell therapy is the“first-line”therapy in immunotherapy for the treatment of highly clonal neoplasms such as lymphoma and *** adoptive therapy is currently being studied and tested even in the case of solid tumors such as osteosarcoma since,precisely for this type of tumor,the use of immune checkpoint inhibitors remained *** CAR-T is a promising therapeutic technique,there are therapeutic limits linked to the persistence of these cells and to the tumor’s immune ***-T cell engineering techniques are allowed to express interleukin IL-36,and seem to be much more efficient in antitumoral ***-36 is involved in the long-term antitumor action,allowing CAR-T cells to be more efficient in their antitumor action due to a“cross-talk”action between the“IL-36/dendritic cells”axis and the adaptive ***:This analysis makes the model useful for evaluating cell dynamics in the case of tumor relapses or specific understanding of the action of CAR-T cells in certain types of *** model proposed here seeks to quantify the action and interaction between the three fundamental elements of this antitumor activity induced by this type of adoptive immunotherapy:IL-36,“armored”CAR-T cells(i.e.,engineered to produce IL-36)and the tumor cell population,focusing exclusively on the action of this interleukin and on the antitumor consequences of the so modified CAR-T *** model was developed and numerical simulations were carried out during this *** development of the model with stability analysis by conditions of Routh–Hurwitz shows how IL-36 makes CAR-T cells more efficient and persistent over time and more effective in the antitumoral treatment,making therapy more effective against the“solid tumor”.Findings:Primary malignant bone tumors are

关键词： IL-36 bone remodeling mathematical model BMUs CAR-T cells

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