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arXiv 2024年

作者： Qin, Jason Ban, Shikun Zhu, Wentao Wang, Yizhou Samaras, Dimitris Department of Computer Science Stony Brook University United States Center on Frontiers of Computing Studies Peking University China

Developing robots that can assist humans efficiently, safely, and adaptively is crucial for real-world applications such as healthcare. While previous work often assumes a centralized system for co-optimizing human-robot interactions, we argue that real-world scenarios are much more complicated, as humans have individual preferences regarding how tasks are performed. Robots typically lack direct access to these implicit preferences. However, to provide effective assistance, robots must still be able to recognize and adapt to the individual needs and preferences of different users. To address these challenges, we propose a novel framework in which robots infer human intentions and reason about human utilities through interaction. Our approach features two critical modules: the anticipation module is a motion predictor that captures the spatial-temporal relationship between the robot agent and user agent, which contributes to predicting human behavior;the utility module infers the underlying human utility functions through progressive task demonstration sampling. Extensive experiments across various robot types and assistive tasks demonstrate that the proposed framework not only enhances task success and efficiency but also significantly improves user satisfaction, paving the way for more personalized and adaptive assistive robotic systems. Code and demos are available at https: //***/Human-Aware-Assistance/. Copyright © 2024, The Authors. All rights reserved.

关键词： Human robot interaction

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Scientific Research Transformation Needed for Comprehensive Artificial Intelligence Implementation 17

Scientific Research Transformation Needed for Comprehensive ...

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17th International Conference on Management of Large-Scale System Development, MLSD 2024

作者： Medennikov, Viktor Bogatyreva, Larisa Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Information and Computing Systems Department Moscow Russia V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences Laboratory of Modular Data Processing and Control Systems Moscow Russia

ISBN: (纸本)9798350375718

The paper deals with complementary changes in scientific research and real economy (exemplified by the farming industry) taking place when using the holistic approach to the industry's digital transformation resulting in the appearance of precise agrarian technologies that require a comprehensive, system-based combination of research and production resources that are able to ensure increased productivity in the industry. We demonstrate that among these new technologies, the artificial intelligence methods have a special significance. They have to, however, go through integration transformations to become standards for the proposed common digital farming management platform. © 2024 IEEE.

关键词： Agricultural economics

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Detecting entanglement of quantum channels

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Communications in Theoretical Physics 2021年第11期73卷 77-84页

作者： Chaojian Li Bang-Hai Wang Bujiao Wu Xiao Yuan School of Computer Science and Technology Guangdong University of TechnologyGuangzhou 510006China Advanced Institute of Information Technology Peking UniversityChina Center on Frontiers of Computing Studies Department of Computer SciencePeking UniversityBeijing 100871China

Entanglement is the crucial resource for different quantum information processing *** conventional studies focus on the entanglement of bipartite or multipartite quantum states,recent works have extended the scenario to the entanglement of quantum channels,an operational quantification of the channel entanglement manipulation *** on the recently proposed channel entanglement resource framework,here we study a further task of resource detection—witnessing entanglement of quantum *** first introduce the general framework and show how channel entanglement detection is related to the Choi state of the channel,enabling channel entanglement detection via conventional state entanglement detection *** also consider entanglement of multipartite quantum channels and use the stabilizer formalism to construct entanglement witnesses for circuits consisting of controlled-Z *** study the effectiveness of the proposed detection methods and compare their performance for several typical *** work paves the way for systematic theoretical studies of channel entanglement and practical benchmarking of noisy intermediate scaled quantum devices.

关键词： quantum entanglement quantum channel entanglement detection entanglement witness

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MAD-ANET:Malware Detection Using Attention-Based Deep Neural Networks

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computer Modeling in Engineering & sciences 2025年第4期143卷 1009-1027页

作者： Waleed Khalid Al-Ghanem Emad Ul Haq Qazi Tanveer Zia Muhammad Hamza Faheem Muhammad Imran Iftikhar Ahmad Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11362Saudi Arabia Centre of Artificial Intelligence Naif Arab University for Security SciencesRiyadh14812Saudi Arabia School of Arts and Sciences The University of Notre DameSydneyNSW 2007Australia Center for Smart Analytics Institute of InnovationScience and SustainabilityFederation University AustraliaBerwickVIC 3806Australia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

In the current digital era,new technologies are becoming an essential part of our ***,the number ofmalicious software ormalware attacks is rapidly *** is no doubt,themajority ofmalware attacks can be detected by most antivirus ***,such types of antivirus programs are one step behind malicious *** to these dilemmas,deep learning become popular in the detection and classification of malicious ***,researchers have significantly focused on finding solutions for malware attacks by analyzing malicious samples with the help of different techniques and *** this research,we presented a lightweight attention-based novel deep Convolutional Neural Network(DNN-CNN)model for binary and multi-class malware classification,including benign,trojan horse,ransomware,and *** applied the Principal Component Analysis(PCA)technique for feature extraction for binary *** used the Synthetic Minority Oversampling Technique(SMOTE)to handle the imbalanced data during multi-class *** proposed attention-based malware detectionmodel is trained on the benchmarkmalware memory dataset named *** indicate that our model obtained high accuracy for binary and multi-class classification,99.5% and 97.9%,respectively.

关键词： Attention-based CNN malware detection machine learning deep learning classification

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Classification of Cacao Pods Ripeness Using Convolutional Neural Networks 16

Classification of Cacao Pods Ripeness Using Convolutional Ne...

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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Borromeo, Karl O. Bonotan, Melbert R. Llagas, Archie P. Juan, Joana Marie O. Sanchez, Philip Donald C. Berro, Nathan Kit A. Department of Computer Science College of Computing and Information Sciences Caraga State University Butuan City Philippines Department of Information Technology College of Computing and Information Sciences Caraga State University Butuan City Philippines Center for Resource Assessment Analytics and Emerging Technologies College of Engineering and Geo-Sciences Caraga State University Butuan City Philippines Department of Information Systems College of Computing and Information Sciences Caraga State University Butuan City Philippines

This paper presents methods for the development of Ripeness Identification of Cacao Pods using Convolutional Neural Network (CNN) and precision agriculture employed in a Mobile Application. This research explores if there is a correlation between the color dynamics of cacao pods and its physiological characteristics, and the possibility of using this correlation to develop Artificial Intelligence (AI) model for ripeness and variety identification, which conventional eye estimation resulted to unripe and overripe harvesting of Cacao Pods. Data analysis and correlation was developed, integrating detection and classification models based on the Single Shot Detection (SSD) MobileNet and visual Geometry Group (VGG) 16 or VGG16 layers network architectures, respectively. The mobile application utilizes the TensorFlow template for object detection and incorporates image preprocessing techniques for enhanced accuracy. The successful integration of the model into a mobile application demonstrates its potential for practical applications in the cacao industry. © 2025 Elsevier B.V.. All rights reserved.

关键词： Convolutional neural networks

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Insect Identification in Pulsed Lidar Images Using Changepoint Detection Algorithms

Insect Identification in Pulsed Lidar Images Using Changepoi...

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2023 Annual Intermountain Engineering, Technology and computing, IETC 2023

作者： Sweeney, Nathaniel Xu, Caroline Shaw, Joseph A. Hocking, Toby D. Whitaker, Bradley M. Montana State University Electrical and Computer Engineering Department United States University of Michigan Electrical Engineering and Computer Science Department United States Montana State University Optical Technology Center United States School of Informatics Computing and Cyber Systems Northern Arizona University United States

ISBN: (纸本)9798350335903

Noninvasive entomological insect monitoring often utilizes a variety of tools such as LiDAR to gather information without interfering with the insects in their habitat. These collection methods often result in large amounts of data that can be te-dious and lengthy to interpret and analyze. Machine learning has been previously used in the past in order to analyze Li-DAR images to detect insects, but often suffers from pitfalls such as long training times and large computational power requirements. In an attempt to offer an alternative that takes little to no training on the data and much less computational power, this paper looks at the use of changepoint detection algorithms to analyze LiDAR images containing insects. By analyzing the rows or columns of a LiDAR image, the algorithms should be able to detect abrupt changes in the image that would represent the insects. While not as accurate, the changepoint detection algorithms give comparable results to a machine learning algorithm tested on the same dataset without the need for supervised training. © 2023 IEEE.

关键词： Anomaly detection

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Heuristic Elastic Scaling for Kubernetes Heterogeneous Microservices 21

Heuristic Elastic Scaling for Kubernetes Heterogeneous Micro...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Wang, Song He, Sheng Ning, Yishuang Gao, Xiang Ding, Zhijun The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China The Department of Computer Science and Technology Tongji University Shanghai201804 China Kingdee Research Kingdee International Software Group Co. Ltd Shenzhen518057 China Intelligent Computing Facility Innovation Center ZhejiangLab Hangzhou30012 China

ISBN: (纸本)9798350365221

Kubernetes has become the basic platform for building cloud native applications. However, existing horizontal scaling methods based on Kubernetes have problems with resource redundancy. Furthermore, the combined horizontal and vertical scaling based on Kubernetes takes a long time. Regarding the issue above, first, we describe the execution processes of three scaling techniques and compare them in different dimensions;second, we construct a formal elastic scaling model for expansion scenarios, with the goal of minimizing the total cost;and third, we introduce a heuristic expansion algorithm after balancing resource redundancy and execution time as the heuristic information. The experiment results show that the elastic scaling algorithm proposed in this paper achieves better performance than other algorithms. © 2024 IEEE.

关键词： Cloud platforms

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Optimizing Healthcare Delivery through CloudBased Clinical Decision Support Systems

Optimizing Healthcare Delivery through CloudBased Clinical D...

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2024 OPJU International Technology Conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Ebenezar, U. Samson Jenila Vennila, G. Balakrishnan, T. Suresh Krishnan, Prabhakar Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Mohan Babu University School of Computing Department of Ai & Ml Andhra Pradesh Tirupati India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-campus India

ISBN: (纸本)9798350373783

The experiment aims at evaluating how four machine learning algorithms work: Decision Tree, Random Forest, SVM, and Neural Network, in a task, of binary classification. Models were evaluated using comprehensive metrics such as accuracy, precision, recall, F-score and AUC that revealed a good diagnostic performance. Models selected for the experiment and parameters tested on real datasets show steady changes in performance. The Neural Network model remained on top, achieving accuracy, precision, recall, F-score and AUC-ROC of 91.8%, 92.1%, 91.3%, 91.7% and 0.93 respectively. Random Forest displayed a competitive accordance along with the accuracy of 88.6%, precision of 89.2%, recall of 88.7%, and the F-score of 89.0%, and the AUC of 0.91. From the results, Support Vector Machine and Decision Tree models posted lower accuracy, recall, and fscores compared to the Neural Network and Random Forest models in most cases. These reveal the need for the improvement of the suitable machine learning model that comes from specific task needs as well as the consideration of each one of factors including accuracy, interpretability, and computational efficiency. Extensive and development of the model may result in more comprehensive understanding of how the model leads classifying of real-world applications such as medical diagnoses, fraud detection and target marketing. © 2024 IEEE.

关键词： Random forests

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LEARNING DOMAIN-AGNOSTIC REPRESENTATION FOR DISEASE DIAGNOSIS 11

LEARNING DOMAIN-AGNOSTIC REPRESENTATION FOR DISEASE DIAGNOSI...

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11th International Conference on Learning Representations, ICLR 2023

作者： Wang, Churan Li, Jing Sun, Xinwei Zhang, Fandong Yu, Yizhou Wang, Yizhou School of Computer Science Peking University China CFCS School of CS Inst. for AI Peking University China China Nat'l Eng. Research Center of Visual Technology China AI lab Deepwise Beijing China Department of Computer Science The University of Hong Kong Hong Kong School of Data Science Fudan University China

In clinical environments, image-based diagnosis is desired to achieve robustness on multi-center samples. Toward this goal, a natural way is to capture only clinically disease-related features. However, such disease-related features are often entangled with center-effect, disabling robust transferring to unseen centers/domains. To disentangle disease-related features, we first leverage structural causal modeling to explicitly model disease-related and center-effects that are provable to be disentangled from each other. Guided by this, we propose a novel Domain Agnostic Representation Model (DarMo) based on variational Auto-Encoder. To facilitate disentanglement, we design domain-agnostic and domain-aware encoders to respectively capture disease-related features and varied center effects by incorporating a domain-aware batch normalization layer. Besides, we constrain the disease-related features to well predict the disease label as well as clinical attributes, by leveraging Graph Convolutional Network (GCN) into our decoder. The effectiveness and utility of our method are demonstrated by the superior performance over others on both public datasets and in-house datasets. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Diagnosis

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Mental Health Stigma: Strategies for Destigmatization in Healthcare Settings

Mental Health Stigma: Strategies for Destigmatization in Hea...

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2024 International Conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Verma, Aishwarya Panda, Krishna Tanaya Sharma, Vandana Tripathy, Hrudaya Kumar Al-Khasawneh, Mahmoud Ahmad Alkhayyat, Ahmed Kalinga Institute of Industrial Technology India CHRIST University Computer Science Department India Skyline University College University City Sharjah School of Computing Sharjah1797 United Arab Emirates Applied Science Private University Applied Science Research Center Amman Jordan Jadara University Research Center Jadara University Jordan The Islamic University College of Technical Engineering Najaf Iraq

ISBN: (纸本)9798350378092

Mental illness is one of the most common disabilities in the world. The term "mental illness stigma"describes harmful practices and misconceptions that lead to a detrimental effect on the mental health, motivation, and self-worth of those who suffer from mental illnesses. Health care services are important for treating and reducing the negative stigma of mental health, as they are areas where patients seek relief and support. The study aims to investigate the causes and how to reduce them. Explores ways to disrupt the health care environment, specifically the RESHAPE program, which focuses on the concept of "critical". This review paper looks at 8-10 papers on mental health and stigma and how stigma will be reduced. The results show that a large number of doctors and students are stigmatized, negatively affecting the lives of people affected by mental illness. RESHAPE, KAP, and IBH therapies are also effective ways to minimize mental health stigma. This intervention aims to educate public health workers, promote social cohesion, and integrate treatment into primary health care, improving treatment into primary health care, improving treatment quality and patient outcomes. The study draws attention to the importance of stigma reduction efforts in the long term in health education and practice emphasis. © 2024 IEEE.

关键词： Electronic health record

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