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arXiv 2023年

作者： Rashid, Tarik A. Hassan, Bryar A. Alsadoon, Abeer Qader, Shko Vimal, S. Chhabra, Amit Yaseen, Zaher Mundher Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler Kurdistan Region Erbil Iraq Department of Information Technology Kurdistan Institution for Strategic Studies and Scientifc Research KRI Sulaymaniyah46001 Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia School of Computing Engineering and Mathematics Western Sydney University Sydney City Campus Sydney Australia Sydney Australia Information Technology Department Kent Institute Australia Sydney Australia Department of Information Technology University College of Goizha Kurdistan Region Sulaimani Iraq Data Analytics Lab Department of Artifcial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Civil and Environmental Engineering Department King Fahd University of Petroleum & Minerals Dhahran31621 Saudi Arabia Department of Computer Science College of Science Charmo University KRI Chamchamal46023 Iraq Department of Information Technology Computer Science Institute Sulaimani Polytechnic University KRI Sulaymaniyah46001 Iraq

Self-adaptive software can assess and modify its behavior when the assessment indicates that the program is not performing as intended or when improved functionality or performance is available. Since the mid-1960s, the subject of system adaptivity has been extensively researched, and during the last decade, many application areas and technologies involving self-adaptation have gained prominence. All of these efforts have in common the introduction of self-adaptability through software. Thus, it is essential to investigate systematic software engineering methods to create self-adaptive systems that may be used across different domains. The primary objective of this research is to summarize current advances in awareness requirements for adaptive strategies based on an examination of state-of-the-art methods described in the literature. This paper presents a review of self-adaptive systems in the context of requirement awareness and summarizes the most common methodologies applied. At first glance, it gives a review of the previous surveys and works about self-adaptive systems. Afterward, it classifies the current self-adaptive systems based on six criteria. Then, it presents and evaluates the most common self-adaptive approaches. Lastly, an evaluation among the self-adaptive models is conducted based on four concepts (requirements description, monitoring, relationship, dependency/impact, and tools). © 2023, CC BY-NC-ND.

关键词： Adaptive systems

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Qpplab: A Generally Applicable Software Package for Detecting, Analyzing, and Visualizing Large-Scale Quasiperiodic Spatiotemporal Patterns (Qpps) of Brain Activity

SSRN

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SSRN 2024年

作者： Xu, Nan Yousefi, Behnaz Anumba, Nmachi LaGrow, Theodore J. Zhang, Xiaodi Keilholz, Shella Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA United States Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University AtlantaGA United States Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Georgia Institute of Technology United States

One prominent brain dynamic process detected in functional neuroimaging data is large-scale quasi-periodic patterns (QPPs) which display spatiotemporal propagations along brain cortical gradients. QPP associates with the infraslow neural activity related to attention and arousal fluctuations and has been identified in both resting and task-evoked brains across various species. Several QPP detection and analysis tools were developed for distinct applications with study-specific parameters methods. This MATLAB package provides a simplified and user-friendly generally applicable toolbox for detecting, analyzing, and visualizing QPPs from fMRI timeseries of the brain. This paper describes the software functions and presents its ease of use on any brain datasets. © 2024, The Authors. All rights reserved.

关键词： Brain

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Cutting-Edge Robotic Arm Design: Harnessing Flex Sensors and Novel Controller Framework to Meet Real-World Innovations

Cutting-Edge Robotic Arm Design: Harnessing Flex Sensors and...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： V. Syambabu K. Kalaiselvi S. Derisha Mahil Hemalatha S Badi Alekhya N. Kanagavalli Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of CSE(AIML) S.A.Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of AIML R.M.D Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

This research paper introduces a sophisticated arm movement tracking system, leveraging a multi-sensor fusion approach for enhanced precision and versatility. The comprehensive system comprises various components, including a dedicated cabinet, six Leap Motion sensors, six microcomputers, a Kinect depth camera, a KVM switcher, a router, and flex sensors. In addition to the aforementioned components, the integration of flex sensors enhances the system's capability to capture nuanced movements and gestures of the human arm. Flex sensors contribute valuable data to the multi-sensor fusion, further refining the arm movement tracking and providing a comprehensive understanding of the intricacies involved. By quantifying the Root Mean Square Error (RMSE) for each filtering technique, including the data from flex sensors, we gained valuable insights into the accuracy and precision achieved by Particle Filtering in refining data from the specified angles. This approach ensures a thorough validation of Particle Filtering's superior performance when compared to Existing filtering Methods. The systematic evaluation of RMSE serves as a robust method for assessing the effectiveness of Particle Filtering, affirming its potential as a high-performing solution for precise angle estimation and tracking within the context of the presented arm movement tracking system that incorporates flex sensors.

关键词： Flexible printed circuits Systematics Filtering Tracking Refining Switches Sensor fusion

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A Novel Framework for the Development of Quality-Aware Autonomic IoT Applications

A Novel Framework for the Development of Quality-Aware Auton...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Ankur Singh Bisht Manjunatha R M. P. Karnik A. Amudha Nimesh Raj Shalini M Computer Science and Engineering Graphic Era Hill University Bhimtal Graphic Era Deemed to be University Dehradun Uttarakhand India School of Sciences Data Analytics and Mathematical Sciences JAIN (Deemed to be University) Bangalore Karnataka Computer Engineering Vishwakarma Institute of Technology Pune India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

A tremendous quantity of data is being generated via the Internet of Things (IoT). While our knowledge of how to assess the quality of this data at the network and device levels is expanding, there are still significant obstacles to overcome when it comes to interpreting and assessing the quality at the informational and application levels. If you want to know what aspects impact the overall system quality, this post will go over some of the problems with IoT systems and how to fix them at various OSI levels. Using two digital health apps made possible by the internet of things (IoT), the authors look at how semantics may be used to measure the system’s data quality and how to integrate multimodal information to clinical decision support. Important data quality issues arise from the fact that the Internet of Things (IoT) ecosystem generates massive amounts of data collected from a variety of heterogeneous sources. The reason for this is that the whole operation of the internet of things system is badly affected by low data quality standards. data quality has been the subject of much research outside of the Internet of Things (IoT) domain, and the idea is backed by massive standardization efforts. However, data quality management inside the IoT has received little attention. An essential idea with many existing applications for issue solving, data quality (DQ) is used in the creation and execution of information, handling data, decision making, as well as engineering systems. But the ways DQ is now integrated into data management pipelines aren’t flexible enough to accommodate all kinds of uses, which limits its use in the IoT space. A generic framework is introduced in this research that allows for the implementation of DQ assessment in a wide range of Internet of Things applications. A knowledge graph serves as the foundation of the framework, making it easy to incorporate into new uses. The study’s findings highlight many gaps in the current state of IoT quality a

关键词： Training Support vector machines Accuracy data integrity Semantics Vectors Classification algorithms Internet of Things Cultural differences Standards

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Seeing biodiversity: Perspectives in machine learning for wildlife conservation

arXiv

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arXiv 2021年

作者： Tuia, Devis Kellenberger, Benjamin Beery, Sara Costelloe, Blair R. Zuffi, Silvia Risse, Benjamin Mathis, Alexander Mathis, Mackenzie Weygandt van Langevelde, Frank Burghardt, Tilo Kays, Roland Klinck, Holger Wikelski, Martin Couzin, Iain D. van Horn, Grant Crofoot, Margaret C. Stewart, Charles V. Berger-Wolf, Tanya Switzerland United States Max Planck Institute of Animal Behavior Germany Centre for the Advanced Study of Collective Behaviour University of Konstanz Germany Department of Biology University of Konstanz Germany Institute for Applied Mathematics and Information Technologies IMATI-CNR Italy Computer Science Department University of Münster Germany Switzerland Environmental Sciences Group Wageningen University Netherlands Computer Science Department University of Bristol United Kingdom Department of Forestry and Environmental Resources North Carolina State University United States North Carolina Museum of Natural Sciences United States Cornell Lab of Ornithology Cornell University United States Department of Computer Science Rensselaer Polytechnic Institute United States Translational Data Analytics Institute Ohio State University United States

data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices. These new technologies and the data they generate hold great potential for large-scale environmental monitoring and understanding, but are limited by current data processing approaches which are inefficient in how they ingest, digest, and distill data into relevant information. We argue that machine learning, and especially deep learning approaches, can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species. Incorporating machine learning into ecological workflows could improve inputs for population and behavior models and eventually lead to integrated hybrid modeling tools, with ecological models acting as constraints for machine learning models and the latter providing data-supported insights. In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies in order to reliably estimate population abundances, study animal behavior and mitigate human/wildlife conflicts. To succeed, this approach will require close collaboration and cross-disciplinary education between the computer science and animal ecology communities in order to ensure the quality of machine learning approaches and train a new generation of data scientists in ecology and conservation. © 2021, CC BY-NC-SA.

关键词： Ecology

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Visualizing Functional Network Connectivity Difference between Healthy Control and Major Depressive Disorder Using an Explainable Machine-learning Method

Visualizing Functional Network Connectivity Difference betwe...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ji Ye Chun Mohammad S. E. Sendi Jing Sui Dongmei Zhi Vince D. Calhoun Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta Georgia Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University Atlanta Georgia Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences and University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Major depressive disorder (MDD) is a complex mental disorder characterized by a persistent sad feeling and depressed mood. Recent studies reported differences between healthy control (HC) and MDD by looking to brain networks including default mode and cognitive control networks. More recently there has been interest in studying the brain using advanced machine learning-based classification approaches. However, interpreting the model used in the classification between MDD and HC has not been explored yet. In the current study, we classified MDD from HC by estimating whole-brain connectivity using several classification methods including support vector machine, random forest, XGBoost, and convolutional neural network. In addition, we leveraged the SHapley Additive exPlanations (SHAP) approach as a feature learning method to model the difference between these two groups. We found a consistent result among all classification method in regard of the classification accuracy and feature learning. Also, we highlighted the role of other brain networks particularly visual and sensory motor network in the classification between MDD and HC subjects.

关键词： Radio frequency Support vector machines Visualization Functional magnetic resonance imaging Machine learning Hospitals Brain modeling

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MODULAR SENSOR SYSTEM (MSS) FOR URBAN AIR POLLUTION MONITORING

MODULAR SENSOR SYSTEM (MSS) FOR URBAN AIR POLLUTION MONITORI...

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IEEE Sensors

作者： Wei-Ying Yi Kwong-Sak Leung Yee Leung Mei-Ling Meng Terrence Mak Institute of Future Cities Department of Computer Science and Engineering Stanley Ho Big Data Decision Analytics Research Center The Chinese University of Hong Kong China Department of Systems Engineering and Engineering Management Department of Electronics and Computer Science University of Southampton UK

Air pollution attracts extensive attention globally due to its critical impacts on human life. Monitoring systems providing real-time micro-level pollution information have been developed to provide authorities with data to mitigate these impacts. However, current systems are usually application-specific with fixed hardware and software configurations. They are inconvenient in maintenance, infeasible in reconfiguration, and unexpandable in sensing capabilities. This paper proposes a novel Modular Sensor System (MSS), which aims at tackling these issues by adopting the proposed Universal Sensor Interface (USI) and modular design in a sensor node. A compact MSS senor node with expandable plug-and-play sensor modules and multiple Wireless Sensor Networks (WSNs) compatibility is implemented and evaluated. Results indicate that MSS sensor node can be deployed in different scenarios while dynamically adapting to reconfigurations and monitoring air pollution at low concentration levels with high energy efficiency. We anticipate that MSS is able to relax the efforts on system maintenance, adaptation, and evolution in real-life large-scale deployment situation.

关键词： plug-and-play air pollution monitoring wireless sensor network modular sensor node

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Cooperative Multi-source data Trading

Cooperative Multi-source Data Trading

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jin Cheng Ningning Ding John C. S. Lui Jianwei Huang School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen Data Science and Analytics Thrust Information Hub Hong Kong University of Science and Technology Guangzhou Department of Computer Science and Engineering The Chinese University of Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network and CSIJRI Joint Research Centre on Smart Energy Storage The Chinese University of Hong Kong Shenzhen

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In the era of big data, data trading significantly enhances data-driven technologies by facilitating data sharing. Despite the clear advantages often experienced by data users when incorporating multiple sources, the topic of multi-source data trading remains largely unexplored. This paper designs a novel data trading framework, which enables multi-source data trading through multi-source cooperation. The proposed framework aims to improve data usage efficiency and increase seller revenue. In particular, we model data sellers’ cooperative decisions through the Nash bargaining framework and systematically outline the interactions between sellers and buyers as a two-stage Stackelberg game. A key contribution of this work is the consideration of coupling among diverse data products, which is essential but often overlooked in prior studies. We properly classify data’s utility into endogenous and relational categories to disentangle the coupling. Despite the inherent non-convex nature of the optimization problem, we methodically derive the closed-form optimal solutions by decomposing the problem into several subproblems. Interestingly, we reveal that, under our proposed framework, sellers’ revenue initially remains steady with the increase of product coupling level, but begins to rise once the level exceeds a certain threshold due to the substitute effect. Finally, experimental results show that our proposed framework can improve the seller’s profit by up to 46.32% compared to traditional data trading methods in the current data market.

关键词： Couplings Games Big data data models Global communication Optimization

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Discrepancy and Thematic Bibliometric Analyses of the Remaining Limitations in Artificial Intelligence

Discrepancy and Thematic Bibliometric Analyses of the Remain...

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Computing (ICOCO), IEEE International Conference on

作者： Mahayaudin M. Mansor Nurain Ibrahim Noor Fadhilah Ahmad Radi Nadirah Abdul Rahim Syarul Heiry Yahaya Mohd Aftar Abu Bakar Roslinazairimah Zakaria School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA (Penang Campus) Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability.

关键词： Visualization Technological innovation Ethics Emotion recognition Bibliometrics Decision making Focusing Electronic healthcare Object recognition Artificial intelligence

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Energetic Content based Image Retrieval Scheme using Improved Deep Learning Strategy with Image Analysis Technique

Energetic Content based Image Retrieval Scheme using Improve...

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International Conference on Smart Structures and Systems (ICSSS)

作者： S. Karkuzhali P. Malathi A Thenmozhi Velu Aiyyasamy Keerthika A G.S. Uthayakumar Department of General Engineering R.M.K. Engineering College Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Department of Electronics and Communication Engineering Kings Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Meenakshi Sundararajan Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of ECE St. Joseph’s Institute of Technology Chennai Tamil Nadu India

One popular approach to retrieving images from huge, unprocessed datasets is content-based image retrieval, or CBIR. The conventional ways of data retrieval, however, are not meeting the needs of consumers. The number of consumer-accessible photographs also keeps growing, and there are an abundance of digital platforms for development and dissemination. Consequently, digital image processing is both ubiquitous and permanent in many fields. Consequently, effective strategies are required for both the speedy access to these massive picture databases and the recovery of comparable photos from this massive number of images based on a specific picture (query). The computation of depiction of features and resemblance is crucial to the efficiency of a CBIR system. We introduce LAIRS, a cutting-edge deep learning method that integrates the extraction of features and aims to do this with classification to facilitate rapid picture retrieval. For a transparent evaluation of the proposed scheme’s efficiency, the suggested logic is cross-validated using Convolutional Neural Networks (CNNs), a classic deep learning paradigm. Several in-depth observational experiments for various CBIR tasks employing an image database have shown encouraging results, teaching us important lessons for making CBIR system more efficient. With the use of CBIR structures, a different collection can find similar pictures to the query image. There is little doubt that Google’s search per photo tool is perhaps the most widely used CBIR approach.

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