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arXiv 2022年

作者： Tuli, Shreshth Bansal, Rajas Paul, Rohan Mausam Department of Computing Imperial College London United Kingdom Department of Computer Science Stanford University United States Department of Computer Science and Engineering Yardi School of Artificial Intelligence Indian Institute of Technology Delhi India

Robots assisting us in environments such as factories or homes must learn to make use of objects as tools to perform tasks, for instance using a tray to carry objects. We consider the problem of learning commonsense knowledge of when a tool may be useful and how its use may be composed with other tools to accomplish a high-level task instructed by a human. Specifically, we introduce a novel neural model, termed TOOLTANGO, that first predicts the next tool to be used, and then uses this information to predict the next action. We show that this joint model can inform learning of a fine-grained policy enabling the robot to use a particular tool in sequence and adds a significant value in making the model more accurate. TOOLTANGO encodes the world state, comprising objects and symbolic relationships between them, using a graph neural network and is trained using demonstrations from human teachers instructing a virtual robot in a physics simulator. The model learns to attend over the scene using knowledge of the goal and the action history, finally decoding the symbolic action to execute. Crucially, we address generalization to unseen environments where some known tools are missing, but alternative unseen tools are present. We show that by augmenting the representation of the environment with pre-trained embeddings derived from a knowledge-base, the model can generalize effectively to novel environments. Experimental results show at least 48.8-58.1% absolute improvement over the baselines in predicting successful symbolic plans for a simulated mobile manipulator in novel environments with unseen objects. This work takes a step in the direction of enabling robots to rapidly synthesize robust plans for complex tasks, particularly in novel settings. © 2022, CC BY.

关键词： Forecasting

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Realizing Emotional Interactions to Learn User Experience and Guide Energy Optimization for Mobile Architectures 22

Realizing Emotional Interactions to Learn User Experience an...

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Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Xueliang Li Zhuobin Shi Junyang Chen Yepang Liu National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China Research Institute of Trustworthy Autonomous Systems Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation and Department of Computer Science and Engineering Southern University of Science and Technology China

ISBN: (纸本)9781665462723

In the age of AI, mobile architectures such as smartphones are still "cold machines"; machines do not feel. If the architecture is able to feel users' feelings and runtime user experience (UX), it will accordingly adapt performance/energy to find the optimal system-operating state that consumes the least energy to satisfy users. In this paper, we will utilize users' facial expressions (FEs) to learn their runtime UX. We know that FEs are the natural and direct way for humans to convey their emotions and feelings. Our study reveals that FEs also reflect UX. Our research for the first time quantifies the link between FEs and UX. Leveraging this link, the architecture will be able to use the front camera to see FEs and feel users' UX. Based on UX, the architecture can appropriately provision computing resources. We propose Vi-energy system to realize the above idea. Our evaluation shows that Vi-energy reduces energy consumption by 52.9% at maximum and secures UX.

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LOG-LIO2: A LiDAR-Inertial Odometry with Efficient Uncertainty Analysis

arXiv

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arXiv 2024年

作者： Huang, Kai Zhao, Junqiao Lin, Jiaye Zhu, Zhongyang Song, Shuangfu Ye, Chen Feng, Tiantian The School of Surveying and Geo-Informatics Tongji University Shanghai China Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China

Uncertainty in LiDAR measurements, stemming from factors such as range sensing, is crucial for LIO (LiDAR-Inertial Odometry) systems as it affects the accurate weighting in the loss function. While recent LIO systems address uncertainty related to range sensing, the impact of incident angle on uncertainty is often overlooked by the community. Moreover, the existing uncertainty propagation methods suffer from computational inefficiency. This paper proposes a comprehensive point uncertainty model that accounts for both the uncertainties from LiDAR measurements and surface characteristics, along with an efficient local uncertainty analytical method for LiDAR-based state estimation problem. We employ a projection operator that separates the uncertainty into the ray direction and its orthogonal plane. Then, we derive incremental Jacobian matrices of eigenvalues and eigenvectors w.r.t. points, which enables a fast approximation of uncertainty propagation. This approach eliminates the requirement for redundant traversal of points, significantly reducing the time complexity of uncertainty propagation from O(n) to O(1) when a new point is added. Simulations and experiments on public datasets are conducted to validate the accuracy and efficiency of our formulations. The proposed methods have been integrated into a LIO system, which is available at https://***/tiev-tongji/LOG-LIO2. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical radar

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Federated Learning Model for Early Detection of Dementia Using Blood Biosamples

Federated Learning Model for Early Detection of Dementia Usi...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Mohamed Elsersy Ahmed Sherif Ahmad Abdel-Aliem Imam Mohammad M. R. Khan Mamun Kasem Khalil Mohamed Haitham Computer and Information Systems Dept. Higher Colleges of Technology Abu Dhabi UAE School of Computing Sciences and Computer Engineering University of Southern Mississippi MS USA College of Osteopathic Medicine William Carey University Mississippi MS USA Electrical and Computer Engineering Department Tennessee Technological University TN USA Electrical and Computer Engineering Department University of Mississippi MS USA University of Prince Edward Island (UPEI) Cairo Egypt

Alzheimer’s disease (AD) is a serious, long-term health problem that causes much pain and loss for the person with it and their family. Its early and accurate detection might result in a substantial reduction of the disease outcomes and consequences. Blood biosamples are a simple and inexpensive technique in medical testing. This paper proposes diagnostic models for blood biosamples based on federated learning (FL) and its modifications to detect AD early. Our experiments used blood biosample data sets from the ADNI website to evaluate our models. Our performance analysis indicates that our algorithms are more accurate and achieve an accuracy of 87% for early detection.

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Human-computer Interaction Lab(HCIL)in Seoul National University

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Visual informatics 2020年第4期4卷 35-39页

作者： GuHyun Han Jaemin Jo Han Joo Chae Jinwook Seo Department of Computer Science and Engineering Seoul National UniversityRepublic of Korea College of Computing Sungkyunkwan UniversityRepublic of Korea ROKIT Healthcare Republic of Korea

This article introduces Human–computer Interaction Laboratory(HCIL)established at Seoul National University,Korea,in *** first summarized the history of foundation,achievement,and collaboration for the last 10 ***,we delineated our current research directions related to information ***,we presented our facilities and equipment to adequately support the research.

关键词： Lab introduction Information visualization Visual analytics Progressive visualization Machine learning for visualization MR and AR

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Oral Cancer Detection and Classification Using Deep Learning with DenseNet121-CatBoost Classifier

Oral Cancer Detection and Classification Using Deep Learning...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： D Anitha Thota Soujanya Subhra Chakraborty Ahmad Alkhayyat R Revathi Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur India School of Agricultural Sciences SR University Warangal India Department of ECE Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

Oral cancer is a growing universal health challenge, which is rising in the current situation. Early detection is vital for effective treatment and better patient results. However, current diagnostic methods, notably manual histopathological analysis, suffer from subjectivity and time limitations due to classifying the images according to their type. The dependence on manual analysis can lead to delays in accurately diagnosing oral cancer. The proposed method DenseNet121-CatBoost classifier addresses this challenge by using the Histopathological Images dataset. The DenseNet121 for the extracting process and the CatBoost classifier for classifying the images according to their classes. DenseNet121 is utilized to extract features and patterns from images and the CatBoost classifier in the classification process helps to classify the dataset according to its classes using the extracted information. The performance of the DenseNet121-CatBoost method is analyzed using accuracy, specificity, sensitivity, f1score, precision, and Area Under Curve (AUC). This paper shows that the proposed DenseNet121-CatBoost outperforms the existing methods such as ResNet50+CatBoost, DenseNet201+SVM (Support Vector Machine), and GoogLeNet+XGBoost in terms of accuracy of 97.62%, specificity of 98.96%, sensitivity of 94.55%, f1-score of 93.87%, precision of 98.65%, and AUC of 95.26%.

关键词： Support vector machines Accuracy Sensitivity Image color analysis Manuals Feature extraction Cancer detection Gabor filters Data mining Cancer

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Gsqas: Graph Self-Supervised Quantum Architecture Search

SSRN

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SSRN 2023年

作者： He, Zhimin Deng, Maijie Zheng, Shenggen Li, Lvzhou Situ, Haozhen School of Electronic and Information Engineering Foshan University Foshan528000 China School of Mechatronic Engineering and Automation Foshan University Foshan528000 China Peng Cheng Laboratory Shenzhen518055 China Institute of Quantum Computing and Computer Theory School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China

Quantum Architecture Search (QAS) is a promising approach for designing quantum circuits specifically tailored for variational quantum algorithms (VQAs). However, existing QAS algorithms require calculating the ground-truth performances of a substantial number of quantum circuits during the search process, rendering them computationally demanding and limiting their applicability to large-scale quantum circuits. Recently, a predictor-based QAS has been proposed to address this challenge by estimating circuit performance directly based on their structures using a predictor trained on a set of labeled quantum circuits. However, the predictor is trained by purely supervised learning, which suffers from poor generalization ability when labeled training circuits are scarce. It is highly time-consuming to obtain a substantial number of labeled quantum circuits because the gate parameters of quantum circuits need to be optimized until convergence to obtain their ground-truth performances. To overcome these limitations, we propose GSQAS, a graph self-supervised QAS that trains a predictor by self-supervised learning. Specifically, we first pre-train a graph encoder using a well-designed pretext task on a large number of unlabeled quantum circuits, aiming to generate meaningful representations of quantum circuits. Subsequently, the downstream predictor is trained on a small set of quantum circuits' representations and their corresponding labels. Once the encoder is trained, it becomes applicable to various downstream tasks. To effectively encode spatial topology information and avoid huge dimensions of feature vectors for large-scale quantum circuits, we propose a graph-based encoding scheme for quantum circuits. Simulation results on QAS for variational quantum eigensolver and quantum state classification demonstrate that GSQAS outperforms the state-of-the-art predictor-based QAS, yielding superior performance with fewer labeled circuits. © 2023, The Authors. All rights rese

关键词： Timing circuits

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Stochastic cluster head selection model for energy balancing in IoT enabled heterogeneous WSN

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Measurement: Sensors 2024年 35卷

作者： Anto Pravin, R. Murugan, K. Thiripurasundari, C. Ranjith Christodoss, Prasanna Puviarasi, R. Abdul Lathif, Syed Ismail Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Tamil Nadu India Department of Electronics and Communication Engineering KSK College of Engineering and Technology Tamil Nadu India Department of Computing Mathematics and Physics Messiah University Mechanicsburg PA United States Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu India

Energy dissipation is the most important design limitation for Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs). In order to prolong the life of WSNs, the energy of nodes must be used in an effective way. Clustering is a strategy that may effectively use the energy of the sensors, extending the life and scalability by managing the network load balance. The energy usage for network operation is reduced by using an evolutionary algorithm called Genetic Algorithm (GA). The Stochastic Cluster Head Selection Model (SCHSM) is described in the proposed protocol by taking the factors such as distance, node energy, density and capacity of nodes for developing the fitness function. The proposed protocol is designed for multiple movable sink nodes and this greatly improves the energy balancing factor in the network. For minimizing the communication gap among sensors and sinks, movable sinks can be placed carefully. Simulation results are analyzed for the system effectiveness. © 2024 The Authors

关键词： Internet of things

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Adaptive System for Human Scenario Forecasting in Intelligent Homes

Adaptive System for Human Scenario Forecasting in Intelligen...

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Communication, computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： P. Kalyani K. V. Prashanth R. Manivannan Raksha Puthran Shruti Jalapur Dhananjaya Babu K AI&ML Department Sree Venkateswara College of Engineering Nellore Andhra Pradesh India Department of Electronics & Communication Engineering Koneru Lakshmaiah Education Foundation Hyderabad Telangana India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology(Deemed to be University) Chennai India Department of MCA NMAM Institute of Technology NITTE (Deemed to be University) Udupi India Department of Computer Science and Engineering Christ University Bangalore India Department of Electrical and Electronics Engineering Annamacharya University Rajampet Andhra Pradesh India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Due to the rise in energy usage in recent years, there has been a worldwide push to reduce energy use, particularly in the housing sector. When the concept of a "smart house" first emerged, it brought together automation and control via communication network technologies, as well as warmth from many heat source mechanisms and the terminology "process controllers" stated earlier. The Internet of Things, or IoT, is a multibillion dollar industry that shows how overlapping technologies span industries. It does this by utilizing the internet as an objective standard for sharing information that IoT services manage, supervise, or *** home automation systems, big data, and machine learning, together with the Internet of Things, to increase energy efficiency is another exciting topic. This article introduces HEMS-IoT, a comprehensive energy management system designed for the modern home. It leverages big data and machine learning to provide security and convenience in a comprehensive, eco-efficient setting. Weka API and the J48 neural network technology were used to extract user behavior and energy consumption trends. This approach was used to categorize loads depending on *** Mahout with Rule-based language (RuleML) in a smart house for user comfort and security This is because these technologies are able to predict what consumers would require for energy action and supply it during this consultation because to the aggregation of user preferences. XtraPrinting As they demonstrated how they would monitor the instance, the article offers an experimental way for monitoring intelligent buildings for comfort and safety with the least amount of energy use. Therefore, their approach, which explains the demonstration success rate, is responsible for the test’s high performance.

关键词： Industries Energy consumption Machine learning Big Data Energy efficiency Safety Behavioral sciences Internet of Things Security Monitoring

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Dynamic real-time risk analytics of uncontrollable states in complex internet of things systems: cyber risk at the edge

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Environment Systems and Decisions 2021年第2期41卷 236-247页

作者： Radanliev, Petar De Roure, David Van Kleek, Max Ani, Uchenna Burnap, Pete Anthi, Eirini Nurse, Jason R. C. Santos, Omar Montalvo, Rafael Mantilla Maddox, La’Treall Oxford e-Research Centre Department of Engineering Sciences University of Oxford Oxford United Kingdom STEaPP Faculty of Engineering Science University College London London United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom School of Computing University of Kent Canterbury United Kingdom Cisco Research Centre Research Triangle Park Durham NC United States

The Internet of Things (IoT) triggers new types of cyber risks. Therefore, the integration of new IoT devices and services requires a self-assessment of IoT cyber security posture. By security posture this article refers to the cybersecurity strength of an organisation to predict, prevent and respond to cyberthreats. At present, there is a gap in the state of the art, because there are no self-assessment methods for quantifying IoT cyber risk posture. To address this gap, an empirical analysis is performed of 12 cyber risk assessment approaches. The results and the main findings from the analysis is presented as the current and a target risk state for IoT systems, followed by conclusions and recommendations on a transformation roadmap, describing how IoT systems can achieve the target state with a new goal-oriented dependency model. By target state, we refer to the cyber security target that matches the generic security requirements of an organisation. The research paper studies and adapts four alternatives for IoT risk assessment and identifies the goal-oriented dependency modelling as a dominant approach among the risk assessment models studied. The new goal-oriented dependency model in this article enables the assessment of uncontrollable risk states in complex IoT systems and can be used for a quantitative self-assessment of IoT cyber risk posture. © 2020, The Author(s).

关键词： Cyber risk regulations Cyber risk self-assessment Cyber risk target state Empirical analysis Functional dependency Goal-oriented approach Internet of things Micro-mort model Network-based linear dependency modelling Transformation roadmap

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