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Toward parallel intelligence: An interdisciplinary solution for complex systems

The Innovation 2023年第6期4卷 152-164页

作者： Yong Zhao Zhengqiu Zhu Bin Chen Sihang Qiu Jincai Huang Xin Lu Weiyi Yang Chuan Ai Kuihua Huang Cheng He Yucheng Jin Zhong Liu Fei-Yue Wang College of Systems Engineering National University of Defense TechnologyChangsha 410073China Hunan Institute of Advanced Technology Changsha 410073China Shanghai Institute of Infectious Disease and Biosecurity Shanghai 200032China Institute of Epidemiology Helmholtz Zentrum Minchen-German Research Center for Environmental Health(GmbH)NeuherbergGermany Department of Computer Science Hong Kong Baptist UniversityHong KongChina State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

The growing complexity of real-world systems necessitates interdisciplinary solutions to confront myriad challenges in modeling,analysis,management,and *** meet these demands,the parallel systems method rooted in the artificial systems,computational experiments,and parallel execution(ACP)approach has been *** method cultivates a cycle termed parallel intelligence,which iteratively creates data,acquires knowledge,and refines the actual *** the past two decades,the parallel systems method has continuously woven advanced knowledge and technologies from various disciplines,offering vers atile interdisciplinary solutions for complex systems across diverse *** review explores the origins and fundamental concepts of the parallel systems method,showcasing its accomplishments as a diverse array of parallel technologies and applica-tions while also prognosticating potential *** posit that this method will considerably augment sustainable development while enhancing interdisciplinary communication and cooperation.

关键词： execution artificial offering

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Impact of Robotic Process Automation on Enterprise Resource Planning systems

Impact of Robotic Process Automation on Enterprise Resource ...

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2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management, IC-RVITM 2023

作者： Yendluri, Dileep Kumar Ponnala, Jainath Thatikonda, Ramya Kempanna, M. Tatikonda, Reshmi Bhuvanesh, A. University of the Cumberlands Masters in Computer Science United States Jawaharlal Technological University Bachelors in Ece Hyderbad India University of the Cumberlands PhD in Information Technology Williamsburg United States Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India University of the Central Missouri Masters in Computer Information Systems Warrensburg United States Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350306088

ERP systems are merging their powers to generate a dynamic collaboration. By examining the integration between RPA (Robotic Process Automation) and ERP (Enterprise Resource Planning) systems, we can identify their shared potential for enhancing process flow. Combining automation and ERP integration makes RPA a powerful tool for process management across multiple industries. The paper examines the basics of RPA and its ERP integration, highlighting the advantages of enhanced productivity and fewer errors. Addressing security, compliance, and change management issues simultaneously. In addition to top-down and bottom-up methods, process analysis and selecting suitable processes are covered. Practical examples offer insightful observations on this integration's success. The union of RPA and ERP creates a powerful combination that can elevate operational effectiveness, facilitate strategic insights, and reshape the workplace. © 2023 IEEE.

关键词： Integration

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Chatbots and Customer Service: Enhancing Marketing Management with AI Technologies

Chatbots and Customer Service: Enhancing Marketing Managemen...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Rajchandar, K. Hari Krishna, Somanchi Khalil Wani, Showkat Pathak, Supriya Jain, Amit Kumar Naveen Kumar, C.M. Sr University School of Cs & Ai Telangana Warangal India Vignana Bharathi Institute of Technology Department of Business Management Telangana State 501301 India Saudi Electronic University College of Administrative and Financial Sciences Department of Business Administration Riyadh Saudi Arabia Oriental University Faculty of Management and Commerce Madhya Pradesh Indore India Poornima University Department of Electrical & Electronics Engineering Rajasthan JaipurIS-2027-2031 India Malnad College of Engineering Department of Computer Science & Business Systems Karnataka Hassan573202 India

ISBN: (纸本)9798331508685

By increasing engagement, expediting client contacts, and boosting satisfaction through AI-driven solutions, chatbot integration in customer service has revolutionised marketing management. With an emphasis on how model assessment techniques - such as accuracy, precision, recall, and F1 score - can be applied to enhance chatbot efficacy in customer service scenarios, this paper investigates the role of machine learning models in optimising chatbot performance. In order to appropriately meet a variety of consumer demands, this research offers perspectives into the selection, training, and evaluation of chatbot models by analysing a range of AI technologies, such as natural language processing (NLP) and predictive analytics. The influence of chatbots on customer loyalty and retention is highlighted in this article, which also explores the benefits and limitations of utilising AI in real-time consumer interactions. According to research, well-considered ML models can produce more precise and sympathetic answers, which enhances brand perception and opens up new channels for CRM in marketing plans. © 2025 IEEE.

关键词： Sales

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SOUND ADVERSARIAL AUDIO-VISUAL NAVIGATION 10

SOUND ADVERSARIAL AUDIO-VISUAL NAVIGATION

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10th International Conference on Learning Representations, ICLR 2022

作者： Yu, Yinfeng Huang, Wenbing Sun, Fuchun Chen, Changan Wang, Yikai Liu, Xiaohong State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China Tsinghua University China College of Information Science and Engineering Xinjiang University China UT Austin United States JD Explore Academy ***

Audio-visual navigation task requires an agent to find a sound source in a realistic, unmapped 3D environment by utilizing egocentric audio-visual observations. Existing audio-visual navigation works assume a clean environment that solely contains the target sound, which, however, would not be suitable in most real-world applications due to the unexpected sound noise or intentional interference. In this work, we design an acoustically complex environment in which, besides the target sound, there exists a sound attacker playing a zero-sum game with the agent. More specifically, the attacker can move and change the volume and category of the sound to make the agent suffer from finding the sounding object, while the agent tries to dodge the attack and navigate to the goal under the intervention. Under certain constraints to the attacker, we can improve the robustness of the agent towards unexpected sound attacks in audio-visual navigation. For better convergence, we develop a joint training mechanism by employing the property of a centralized critic with decentralized actors. Experiments on two real-world 3D scan datasets (Replica and Matterport3D) verify the effectiveness and the robustness of the agent trained under our designed environment when transferred to the clean environment or the one containing sound attackers with random policy. Project: https://***/SAAVN. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Navigation

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Leveraging Fine-Tuned Retrieval-Augmented Generation with Long-Context Support: For 3GPP Standards

arXiv

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arXiv 2024年

作者： Erak, Omar Alabbasi, Nouf Alhussein, Omar Lotfi, Ismail Hussein, Amr Muhaidat, Sami Debbah, Merouane KU 6G Research Centre Department of Computer Science Khalifa University Abu Dhabi United Arab Emirates KU 6G Research Centre Department of Computer and Information Engineering Khalifa University Abu Dhabi United Arab Emirates Department of Systems and Computer Engineering Carleton University Ottawa Canada

Recent studies show that large language models (LLMs) struggle with technical standards in telecommunications. We propose a fine-tuned retrieval-augmented generation (RAG) system based on the Phi-2 small language model (SLM) to serve as an oracle for communication networks. Our developed system leverages forward-looking semantic chunking to adaptively determine parsing breakpoints based on embedding similarity, enabling effective processing of diverse document formats. To handle the challenge of multiple similar contexts in technical standards, we employ a re-ranking algorithm to prioritize the most relevant retrieved chunks. Recognizing the limitations of Phi-2’s small context window, we implemented a recent technique, namely SelfExtend, to expand the context window during inference, which not only boosts performance but also accommodates a wider range of user queries and design requirements from customers to specialized technicians. For fine-tuning, we utilize the low-rank adaptation (LoRA) technique to enhance computational efficiency during training and enable effective fine-tuning on small datasets. Our comprehensive experiments demonstrate substantial improvements over existing question-answering approaches in the telecom domain, achieving performance that exceeds larger language models such as GPT-4 (which is about 880 times larger in size). This work presents a novel approach to leveraging SLMs for communication networks, offering a balance of efficiency and performance. This work can serve as a foundation towards agentic language models for networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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GAA-Enhanced UNet++ Model for Brain Tumor Segmentation

GAA-Enhanced UNet++ Model for Brain Tumor Segmentation

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Networking and Communications (ICNWC), International Conference on

作者： S.K. Manigandan Ramya D A. Vishnukumar J. Velmurugan V. Muthu K. Vigneshwaran Department of Information Technology Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of Information Technology Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of Computer Science and Business Systems Rajalakshmi Engineering College Chennai Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences (SIMATS) Chennai Department of AIML Panimalar Engineering College Chennai

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

Advanced techniques in medical image segmentation often leverage deep learning methodologies. UNet and its variants plays a major role in image segmentation of medical images. UNet based networks have so many limitations that there needs an improvement in the segmentation performance. The proposed UNet++ architecture integrates dense skip connections and nested convolutions to enhance feature representation and improve accuracy of segmentation. Its superior performance and versatility make it a precious tool for assisting clinicians in detection of diseases and planning for treatment, ultimately improving patient outcomes in neuro-oncology. In this paper, the new method called GAAUNET++ (Gated Axial Attention UNet++) have been proposed with enhancements like 1. The inclusion of the Axial Attention gate aims to enhance performance while also capturing global features of the image. 2. Different metrics like precision, dice coefficient, recall and IoU have been used. The results shows that the proposed approach achieves finer performance in segmentation and accuracy also has been improved.

关键词： Measurement Image segmentation Semantic segmentation Gallium arsenide Logic gates Planning Task analysis

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Joint UAV Deployment and Resource Allocation in THz-Assisted MEC-Enabled Integrated Space-Air-Ground Networks

arXiv

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arXiv 2024年

作者： Tun, Yan Kyaw Dán, György Park, Yu Min Hong, Choong Seon The Department of Electronic Systems Aalborg University A. C. Meyers Vænge 15 København2450 Denmark The Division of Network and Systems Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Brinellvägen 8 Stockholm114 28 Sweden The Department of Computer Science and Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of

Multi-access edge computing (MEC)-enabled integrated space-air-ground (SAG) networks have drawn much attention recently, as they can provide communication and computing services to wireless devices in areas that lack terrestrial base stations (TBSs). Leveraging the ample bandwidth in the terahertz (THz) spectrum, in this paper, we propose MEC-enabled integrated SAG networks with collaboration among unmanned aerial vehicles (UAVs). We then formulate the problem of minimizing the energy consumption of devices and UAVs in the proposed MEC-enabled integrated SAG networks by optimizing tasks offloading decisions, THz sub-bands assignment, transmit power control, and UAVs deployment. The formulated problem is a mixed-integer nonlinear programming (MILP) problem with a non-convex structure, which is challenging to solve. We thus propose a block coordinate descent (BCD) approach to decompose the problem into four sub-problems: 1) device task offloading decision problem, 2) THz sub-band assignment and power control problem, 3) UAV deployment problem, and 4) UAV task offloading decision problem. We then propose to use a matching game, concave-convex procedure (CCP) method, successive convex approximation (SCA), and block successive upper-bound minimization (BSUM) approaches for solving the individual subproblems. Finally, extensive simulations are performed to demonstrate the effectiveness of our proposed algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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Task-Driven Privacy-Preserving Data-Sharing Framework for the Industrial Internet

Task-Driven Privacy-Preserving Data-Sharing Framework for th...

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IEEE International Conference on Big Data

作者： Parshin Shojaee Yingyan Zeng Muntasir Wahed Avi Seth Ran Jin Ismini Lourentzou Grado Department of Industrial and Systems Engineering Virginia Tech Department of Computer Science Virginia Tech

ISBN: (纸本)9781665480468

Industrial Internet provides a collaborative computational platform for participating enterprises, allowing the collection of big data for machine learning tasks. Despite the promise of training and deployment acceleration, and the potential to optimize decision-making processes through data-sharing, the adoption of such technologies is impacted by the increasing concerns about information privacy. As enterprises prefer to keep data private, this limits interoperability. While prior work has largely explored privacy-preserving mechanisms, the proposed methods naively average or randomly sample data shared from all participants instead of selecting the most well-suited subsets for a particular downstream learning task. Motivated by the lack of effective data-sharing mechanisms for heterogeneous machine learning tasks in Industrial Internet, we propose PriED, a task-driven data-sharing framework that selectively fuses shared data and local data from participants to improve supervised learning performance. PriED utilizes privacy-preserving data distillation to facilitate data exchange, and dynamic data selection to optimize downstream machine learning tasks. We demonstrate performance improvements on a real semiconductor manufacturing case study.

关键词： Training Data privacy Fuses Supervised learning Machine learning Big Data Semiconductor device manufacture

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Troi: Towards Understanding Users Perspectives to Mobile Automatic Emotion Recognition System in Their Natural Setting

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Proceedings of the ACM on Human-computer Interaction 2022年第MHCI期6卷 1–22页

作者： Dissanayake, Vipula Tang, Vanessa Elvitigala, Don Samitha Wen, Elliott Wu, Michelle Nanayakkara, Suranga The University of Auckland New Zealand Augmented Human Lab ABI The University of Auckland New Zealand HCI BoDi Lab School of Computer Science and Engineering University of New South Wales Australia Augmented Human Lab Department of Information Systems & Analytics School of Computing National University of Singapore Singapore

Emotional Self-Awareness (ESA) plays a vital role in physical and mental well-being. Recent advancements in artificial intelligence technologies have shown promising emotion recognition results, opening new opportunities to build systems to support ESA. However, little research has been done to understand users’ perspectives on artificial-intelligence-based emotion recognition systems. We introduce Troi, an automatic emotion recognition mobile app using wearable signals. With Troi, we ran a multi-day user study with 12 users to understand user preference parameters, such as perceived accuracy, confidence, preferred emotion representations, effect of self-awareness of emotions, and real-time use cases. Further, we extend our study to evaluate the machine learning model in-the-wild to understand behaviours in-the-wild. We found that users perceived accuracy of the emotion recognition model is higher than the actual model prediction accuracy;there was no strong preference for one specific emotion representation, and users’ self-awareness of emotions improved over time. © 2022 Association for Computing Machinery.

关键词： Emotion Recognition

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Iot Based Smart Framework to Predict Air Quality in Congested Traffic Areas Using Sv-Cnn Ensemble and Knn Imputation Model

SSRN

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SSRN 2024年

作者： Cascone, Lucia Alnowaiser, Khaled Alarfaj, Aisha Ahmed Alabdulqader, Ebtisam Abdullah Umer, Muhammad Alankar, Bhavya Department of Computer Engineering College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Technology College of Computer and Information Sciences King Saud University Saudi Arabia Department of Computer Science & Information Technology The Islamia University of Bahawalpur Bahawalpur63100 Pakistan Department of Computer Science University of Salerno Fisciano Italy Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi110062 India

Addressing air pollution presents a significant environmental challenge in the context of smart city environments. Real-time monitoring of pollution data empowers local authorities to assess current traffic conditions and make informed decisions accordingly. The integration of Internet of Things (IoT) sensors has revolutionized air quality prediction, with human activities being the primary contributors to air pollution, posing threats to all forms of life. Gases such as SO2, PM10, O3, CO, NO2, among others, are key pollutants. Exposure to air pollution can lead to severe health issues and fatalities, underscoring the criticality of air quality monitoring. Distinguishing between breathable and non-breathable air quality further enhances the value of air quality monitoring. However, existing techniques face challenges in achieving high accuracy, exacerbated by missing values in datasets, which can significantly impact machine learning model performance. In response to these challenges, this research introduces an IoT-based automated system designed to classify air quality. This system is adept at managing missing data and attaining high accuracy levels. Central to this approach is a stacked ensemble voting classifier model, which combines two machine learning models. Additionally, the system incorporates the KNN Imputer to address missing values effectively. The work assesses the system's performance against seven alternative machine learning algorithms across two scenarios: one with missing values removed and another with KNN imputation applied. Notably, the proposed strategy achieves remarkable metrics, including 99.17% accuracy, 97.75% precision, 95.24% recall, and a 96.52% F1 score, when leveraging the KNN imputer. These results underscore the effectiveness of the proposed model compared to current state-of-the-art methodologies. © 2024, The Authors. All rights reserved.

关键词： Air quality

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