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2022 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2022

作者： Sharma, Himanshu Pulkit Sharma, Gitika Kumar, Neeraj Thapar Institute of Engineering and Technology Computer Science and Engineering Department Punjab Patiala India MAQ Software Hyderabad India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9781665473408

Picocells can enhance the capacity and spectral efficiency of heterogeneous wireless networks (HWNs) and reduce user equipment (UE) power consumption. Also, the wireless backhaul technology has emerged as an efficient way to boost the data rate in dense 5G HWNs. However, due to the dynamic and distributed nature of 5G HWNs, pico edge users (PEUs) cannot resist the eavesdropping attempts. Moreover, the PEUs are located far away from the PBS and receive considerably low signal-to-interference-plus-noise ratio (SINR) values. Thus, it is critical to improve the achievable secrecy rate (SR) of PEUs for reliable and secure transmission of confidential data in the existence of eavesdroppers. Aiming to enhance the SR of PEUs, this paper formulates a joint optimization problem of beamforming and power allocation of millimeter-wave (mmWave)enabled pico base station (PBS) having backhaul wireless links with macro base station (MBS). As the system is diverse and complex, addressing this non-convex optimization problem directly is challenging. Thus, we have used the Markov decision process (MDP) to transform this optimization problem into a multi-agent reinforcement learning (MARL) problem. Then, to determine the optimal policy for MARL problem of beamforming and power allocation, we propose a Quantum reinforcement learning (QRL) approach. It uses quantum parallelism and state superposition principles to maximize the SR of PEUs. Simulation results indicate that the proposed scheme achieves 28.12% and 7.9% better secrecy rate than MAQL and MADQL schemes, respectively. © 2022 IEEE.

关键词： Markov processes

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European Perceptions of Artificial Intelligence and Their Social Variability. An Exploratory Study 24

European Perceptions of Artificial Intelligence and Their So...

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24th International Conference on Control Systems and computer science, CSCS 2023

作者： Budeanu, Andrea-Mariana Turcanu, Dinu Rosner, Daniel Tech Lounge Association Bucharest Romania Technical University of Moldova Department of Software Engineering and Automatics Chisinǎu Moldova University Politehnica of Bucharest Computer Science Department Bucharest Romania

ISBN: (纸本)9798350313390

We are currently living in societies that are profoundly concerned about the impact of current and potential technologies on our present and future lives. How is the future of Artificial Intelligence (AI) impact perceived in the general population of the European Union? How are its effects evaluated as regards the job markets? These are just two questions that require, from the general public, an exercise of information and also an exercise of the imagination. A survey is a useful instrument to collect these public perceptions for in-depth analysis of the respective societies. This paper is based on a secondary study of a 2021 Eurobarometer survey that provides insights into the perceived future impact of AI and the social sources of its variability. It reveals evolving aspects about AI's perception that help us better understand ourselves. The identified trends, relationships, as well as the lack of some expected correlations, offer useful information for various stakeholders interested in the social life of AI. © 2023 IEEE.

关键词： Artificial intelligence

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Joint enhancement of automatic chest X-ray diagnosis and radiological gaze prediction with multi-stage cooperative learning

arXiv

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arXiv 2024年

作者： Qiu, Zirui Rivaz, Hassan Xiao, Yiming Department of Computer Science and Software Engineering Concordia University Montreal Canada Department of Electrical and Computer Engineering Concordia University Montreal Canada

Background: As visual inspection is an inherent process during radiological screening, the associated eye gaze data can provide valuable insights into relevant clinical decision processes and facilitate computer-assisted diagnosis. However, the relevant techniques are still under-explored. Purpose: With deep learning becoming the state-of-the-art for computer-assisted diagnosis, integrating human behavior, such as eye gaze data, into these systems is instrumental to help guide machine predictions with clinical diagnostic criteria, thus enhancing the quality of automatic radiological diagnosis. In addition, the ability to predict a radiologist’s gaze saliency from a clinical scan along with the automatic diagnostic result could be instrumental for the end users. Methods: We propose a novel deep learning framework for joint disease diagnosis and prediction of corresponding radiological gaze saliency maps for chest X-ray scans. Specifically, we introduce a new dual-encoder multi-task UNet, which leverages both a DenseNet201 backbone and a Residual and Squeeze-and-Excitation block-based encoder to extract diverse features for visual saliency map prediction, and a multi-scale feature-fusion classifier to perform disease classification. To tackle the issue of asynchronous training schedules of individual tasks in multi-task learning, we propose a multi-stage cooperative learning strategy, with contrastive learning for feature encoder pretraining to boost performance. Results: Our proposed method is shown to significantly outperform existing techniques for chest radiography diagnosis (AUC = 0.93) and the quality of visual saliency map prediction (correlation coefficient = 0.58). Conclusion: Benefiting from the proposed multi-task multi-stage cooperative learning, our technique demonstrates the benefit of integrating clinicians’ eye gaze into radiological AI systems to boost performance and potentially explainability. © 2024, CC BY-NC-SA.

关键词： Contrastive Learning

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Automated Surface Classification System Using Vibration Patterns - A Case Study With Wheelchairs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 884-895页

作者： Yu, Haoxiang Raychoudhury, Vaskar Saha, Snehanshu Edinger, Janick Smith, Roger O. Gani, Md Osman The University of Texas at Austin Department of Electrical and Computer Engineering AustinTX78712 United States Miami University Department of Computer Science & Software Engineering OxfordOH45056 United States Bits Pilani K.K. Birla Goa Campus Department of Computer Science & Information Systems Appcair Zuarinagar403726 India University of Hamburg Department of Informatics & Distributed Operating Systems Hamburg20146 Germany University of Wisconsin-Milwaukee Department of Rehabilitation Sciences & Technology MilwaukeeWI53706 United States University of Maryland Baltimore County Department of Information Systems BaltimoreMD21250 United States

Finding a barrier-free accessible path through the built environment is necessary for wheelchair users. Researchers have identified the effect of surface vibration on the health of wheelchair users and proposed various solutions to identify and annotate mobility barriers. However, the effects of surface-induced vibration on accessibility are yet to be investigated. In order to address this problem, we present the WheelShare system, which uses machine learning to identify surfaces and curbs from vibration captured by accelerometer and gyroscope sensors (both smartphone and wearable device). WheelShare allows users to share their collected data via crowdsourcing. Based on this surface-specific vibration, we recognize 32 different surfaces found across the built environment (in Austria, China, France, Germany, India, and the USA for 32 human subjects) with an accuracy of 97.5%. The recognized surfaces are then grouped into different categories based on their characteristics. Our novel contributions in this article include the following. 1) We have developed a machine-learning-enabled surface recognition system that uses sensor data to capture surface-induced vibration patterns. 2) We have tested the robustness of the system for devices with only accelerometer data (such as low-end smartphones) and achieved an accuracy of 91.6%. 3) We have deployed our prototype WheelShare system for real-time surface classification in new sites that are unseen in training and testing and successfully recognized the surface characteristics. © 2020 IEEE.

关键词： Accelerometers

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Efficiency-First Fault-Tolerant Replica Scheduling Strategy for Reliability Constrained Cloud Application 18th

Efficiency-First Fault-Tolerant Replica Scheduling Strategy ...

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18th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2021

作者： Zhang, Yingxue Fan, Guisheng Yu, Huiqun Chen, Xingpeng Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9783030935702

Reliability requirement assurance is an important prerequisite for application execution in the cloud. Although copy management can improve the reliability of applications, it also brings a series of resource waste and overhead issues. Therefore, the efficiency-first fault-tolerant algorithm (EFFT) with minimum execution cost in the cloud application is proposed. This algorithm minimizes the execution cost of the application under the constraints of reliability, and solves the problem of excessive overhead caused by too many copies. The EFFT algorithm is divided into two stages: initial allocation and dynamic adjustment. On the initial allocation of EFFT algorithm, a sorting rule is defined to determine the priority of tasks and instances. During the adjustment phase, by defining an actual efficiency ratio indicator to measure the cost-effectiveness of an instance, the EFFT algorithm makes a good trade-off between cost and reliability in order to minimize execution costs. Run our algorithm on randomly generated parallel applications of different scales and compare the experimental results with four advanced algorithms. The experiments show that the performance of the algorithm we proposed is better than the other algorithms in terms of execution cost and fault tolerance. © 2022, IFIP International Federation for Information Processing.

关键词： Fault tolerance

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A Gamified Virtual Learning Environment to Enhance Online Teaching and Learning Experience 7

A Gamified Virtual Learning Environment to Enhance Online Te...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Tharaka, W.C.M.K. Dilanka, R.M.T. Perera, H.D.D.S. Rathnayake, R.H.C.S. Kodagoda, Nuwan Suriyawansa, Kushnara Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Sri Lanka

ISBN: (纸本)9781665421683

In-class teaching not only concentrates on lecture content delivery but also on maintaining strong mutuality between lecturer-students and student-student. Online lectures are gaining popularity due to the Covid-19 pandemic. However, the learning-teaching process has become ineffective because existing video conferencing solutions are not intended for academic purposes. This research was conducted to identify the pain points of online education and develop an enhanced software solution. A user survey confirmed that an isolated environment tends to diminish attentiveness during online lectures. Also, it is difficult for teachers to observe the attentiveness of all the students. As a solution, student attentiveness was measured using their facial expressions and collected data shown to lecturers through a virtual student behavioural environment. The physical separation causes students to feel isolated during lectures, which can negatively affect their academic development and social and psychological development. The developed application also provides a virtual group study environment as a feature. According to the results gathered in the user acceptance testing phase, it was found that the attention detection feature helps students keep their attention at a significant level. Further, 9 out of 10 teachers who participated in the testing acknowledged that the simulated student behavioural view could provide a more immersive experience. 71% of students preferred the new collaborative virtual environment, and students further elaborated that the virtual environment was more likely the physical group studies. 72% of students mentioned that the collaborative group study tool helped eliminate isolation during group studies. © 2022 IEEE.

关键词： E-learning

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Sentiment Analysis Based on Performance of Linear Support Vector Machine and Multinomial Naïve Bayes Using Movie Reviews with Baseline Techniques

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Journal on Big Data 2023年第1期5卷 1-18页

作者： Mian Muhammad Danyal Sarwar Shah Khan Muzammil Khan Muhammad Bilal Ghaffar Bilal Khan Muhammad Arshad Department of Computer Science City University of Science&TechnologyPeshawar25000Pakistan Department of Computer Science and Software Technology University of SwatSwat19200Pakistan Department of Computer Software Engineering University of Engineering&Technology MardanMardan23200Pakistan Department of Computer Science IQRA National UniversitySwat19200Pakistan

Movies are the better source of *** year,a great percentage of movies are *** comment on movies in the form of reviews after watching *** it is difficult to read all of the reviews for a movie,summarizing all of the reviews will help make this decision without wasting time in reading all of the *** mining also known as sentiment analysis is the process of extracting subjective information from textual *** mining involves identifying and extracting the opinions of individuals,which can be positive,neutral,or *** task of opinion mining also called sentiment analysis is performed to understand people’s emotions and attitudes in movie *** reviews are an important source of opinion data because they provide insight into the general public’s opinions about a particular *** summary of all reviews can give a general idea about the *** study compares baseline techniques,Logistic Regression,Random Forest Classifier,Decision Tree,K-Nearest Neighbor,Gradient Boosting Classifier,and Passive Aggressive Classifier with Linear Support Vector Machines and Multinomial Naïve Bayes on the IMDB Dataset of 50K reviews and Sentiment Polarity Dataset Version *** applying these classifiers,in pre-processing both datasets are cleaned,duplicate data is dropped and chat words are treated for better *** the IMDB Dataset of 50K reviews,Linear Support Vector Machines achieve the highest accuracy of 89.48%,and after hyperparameter tuning,the Passive Aggressive Classifier achieves the highest accuracy of 90.27%,while Multinomial Nave Bayes achieves the highest accuracy of 70.69%and 71.04%after hyperparameter tuning on the Sentiment Polarity Dataset Version *** study highlights the importance of sentiment analysis as a tool for understanding the emotions and attitudes in movie reviews and predicts the performance of a movie based on the average sentiment of all the reviews.

关键词： Opinion mining machine learning movie reviews IMDB Dataset of 50K reviews Sentiment Polarity Dataset Version 2.0

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ProtoKD: Learning from Extremely Scarce Data for Parasite Ova Recognition 22

ProtoKD: Learning from Extremely Scarce Data for Parasite Ov...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Trehan, Shubham Ramachandran, Udhav Scimeca, Ruth Aakur, Sathyanarayanan N. Auburn University Department of Computer Science and Software Engineering AuburnAL36849 United States Oklahoma State University Department of Veterinary Pathobiology StillwaterOK74078 United States

ISBN: (纸本)9798350345346

Developing reliable computational frameworks for early parasite detection, particularly at the ova (or egg) stage, is crucial for advancing healthcare and effectively managing potential public health crises. While deep learning has significantly assisted human workers in various tasks, its application in diagnostics has been constrained by the need for extensive datasets. The ability to learn from an extremely scarce training dataset, i.e., when fewer than 5 examples per class are present, is essential for scaling deep learning models in biomedical applications where large-scale data collection and annotation can be expensive or not possible (in case of novel or unknown infectious agents). In this study, we introduce ProtoKD, one of the first approaches to tackle the problem of multi-class parasitic ova recognition using extremely scarce data. Combining the principles of prototypical networks and self-distillation, we can learn robust representations from only one sample per class. Furthermore, we establish a new benchmark to drive research in this critical direction and validate that the proposed ProtoKD framework achieves state-of-the-art performance. Additionally, we evaluate the framework's generalizability to other downstream tasks by assessing its performance on a large-scale taxonomic profiling task based on metagenomes sequenced from real-world clinical data. © 2023 IEEE.

关键词： Learning from Extremely Scarce Data Microscope Image Analysis Ova Detection

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An software Defined Networking (SDN) Enhanced Edge Computing Framework for Internet of Healthcare Things (IoHT)

An Software Defined Networking (SDN) Enhanced Edge Computing...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Anh, Dang Van Chehri, Abdellah Quy, Nguyen Minh Hue, Chu Thi Minh Nguyen, Dinh C. Quy, Vu Khanh Hung Yen University of Technology and Education Faculty of Information Technology Hungyen Viet Nam Royal Military College of Canada Department of Mathematics and Computer Science Kingston Canada Fpt University Faculty of Software Engineering Hanoi Viet Nam The University of Alabama Department of Electrical and Computer Engineering HuntsvilleAL United States

ISBN: (纸本)9798350351255

The rapid proliferation of intelligent Internet of Health Things (IoHT) applications within the context of the COVID-19 pandemic has exerted significant strain on the backhaul network infrastructure. This paper aims to introduce a novel framework that leverages software-defined networking (SDN) to enhance edge computing capabilities. This framework will expect to facilitate dynamic and adaptable communication between edge and cloud servers, specifically designed to support real-time Internet of Health Things applications. Through the establishment of a connection between servers and the software-Defined Networking controller, the system is expected to facilitate load balancing, network optimization, and the utilization of resources in an efficient manner. This, in turn, enables the provision of real-time healthcare services. Ultimately, the efficacy of the suggested framework is assessed by analyzing its impact on service response time. The research results indicate that the proposed framework significantly benefits IoHT systems' service response times across various workloads and traffic. © 2024 IEEE.

关键词： Electronic health record

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Hybrid Machine Learning Model for Face Recognition Using SVM

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computers, Materials & Continua 2022年第8期72卷 2697-2712页

作者： Anil Kumar Yadav R.K.Pateriya Nirmal Kumar Gupta Punit Gupta Dinesh Kumar Saini Mohammad Alahmadi IES College of Technology Bhopal462044India Maulana Azad National Institute of Technology Bhopal462003India Department of Information Technology Manipal University JaipurJaipur303007India Department of Computer and Communication Engineering Manipal University JaipurJaipur303007India Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah23218Saudi Arabia

Face recognition systems have enhanced human-computer interactions in the last ten ***,the literature reveals that current techniques used for identifying or verifying faces are not immune to *** Component Analysis-Support Vector Machine(PCA-SVM)and Principal Component Analysis-Artificial Neural Network(PCA-ANN)are among the relatively recent and powerful face analysis *** to PCA-ANN,PCA-SVM has demonstrated generalization capabilities in many tasks,including the ability to recognize objects with small or large data *** from requiring a minimal number of parameters in face detection,PCA-SVM minimizes generalization errors and avoids overfitting problems better than ***-SVM,however,is ineffective and inefficient in detecting human faces in cases in which there is poor lighting,long hair,or items covering the subject’s *** study proposes a novel PCASVM-based model to overcome the recognition problem of PCA-ANN and enhance face *** experimental results indicate that the proposed model provides a better face recognition outcome than PCA-SVM.

关键词： Face recognition system(FRS) face identification SVM discrete cosine transform(DCT) artificial neural network(ANN) machine learning

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