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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Nguyen, Tan M. Nguyen, Tam Bui, Long Do, Hai Nguyen, Duy Khuong Le, Dung D. Tran-The, Hung Ho, Nhat Osher, Stan J. Baraniuk, Richard G. University of California Department of Mathematics Los Angeles United States Rice University Department of Electrical and Computer Engineering Houston United States Fpt Software Ai Center Ha Noi Viet Nam Vin University College of Engineering and Computer Science Viet Nam Deakin University Applied Artificial Intelligence Institute Geelong Australia The University of Texas Department of Statistics and Data Sciences Austin United States

ISBN: (纸本)9781728163277

Pairwise dot product-based self-attention is key to the success of transformers which achieve state-of-the-art performance across a variety of applications in language and vision, but are costly to compute. It has been shown that most attention scores and keys in transformers are redundant and can be removed without loss of accuracy. In this paper, we develop a novel probabilistic framework for pruning attention scores and keys in transformers. We first formulate an admixture model of attention keys whose input data to be clustered are attention queries. We show that attention scores in self-attention correspond to the posterior distribution of this model when attention keys admit a uniform prior distribution. We then relax this uniform prior constraint and let the model learn these priors from data, resulting in a new Finite Admixture of Keys (FiAK). The learned priors are used for pruning away redundant attention scores and keys in the baseline transformers, improving the diversity of attention patterns that the models capture. We corroborate the efficiency of transformers pruned with FiAK on the ImageNet object classification and WikiText-103 language modeling tasks. Our experiments demonstrate that transformers pruned with FiAK yield similar or better accuracy than the baseline dense transformers while being much more efficient in terms of memory and computational cost. © 2023 IEEE.

关键词： admixture models pruning Transformers

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Usability Evaluation of Kids' Learning Apps 2

Usability Evaluation of Kids' Learning Apps

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Ibrahim, Amer Al-Rajab, Murad Hamid, Khalid Aqeel, Muhamamd Muneer, Salman Parveen, Mehvish Saleem, Maryam American University in the Emirates College of Computer and Information Technology Dubai United Arab Emirates Abu Dhabi University Computer Scienc and IT College of Engineering United Arab Emirates Superior University Department of Computer Science Lahore Pakistan Superior University Department of Software Engineering Lahore Pakistan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan NCBAE School of Computer Science Lahore Pakistan

ISBN: (纸本)9798350335644

The main objective of learning applications is to use modern electronic technologies to impart knowledge, communicate knowledge, and Direct learning models with their learning activities in a timely and effective manner. The preponderance of the apps were designed to up-skill the kids in the fundamentals of letters and numbers. Generally, they were grounded and practice skills in nature, based on a lack of abilities in critical thinking, favoring parrot-fashion skills, and contributing to a deep conceptual comprehension of more specific concepts are unable. By using appropriate evaluation methodologies, the purposed study observes essential characteristics and aspects that affect the usability of interfaces. An evaluation of the app's features will figure out to ensure user contentment. When a kid interacts with a learning interface, the ease of use provided by the interface is validated. Because a large number of the apps were designed to train kids' fundamentals of alphabetic and numeric. The focus of this research is to see whether self-proclaimed learning apps for kids are built according to developmentally appropriate standards to help kids in formal and informal learning settings develop socially, emotionally, and academically. © 2023 IEEE.

关键词： Learning systems

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Detection of False Information in Digital Media Using Blockchain Technology

Detection of False Information in Digital Media Using Blockc...

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Electrical Power engineering, Communication and Computing Technology(ODICON), Odisha International Conference on

作者： Aswini Kilaru L V Narasimha Prasad G S Pradeep Ghantasala U Ananthanagu Suresh Kallam Rajesh Sharma R Department of CSE Institute of Aeronautical Engineering Hyderabad Telangana Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Center of Excellence in Immersive Technologies Alliance University Bengaluru India School of Computer Science and Engineering Jain Deemed to be University Bengaluru India

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

The spreading of false information over the internet, highly impacts society and causes social and economic loss to the nation. One of the most important factors for the increase in the number of misleading information is that all the users of any particular digital platform usually do not verify the authenticity of the information. The principal impartial of this proposed work is to afford a solution to society to reduce the social and economic losses caused by spreading false information circulated digitally. Blockchain is a disseminated, decentralized network that conducts peer-to-peer transactions, meaning that it does not have a central entity to operate. Here we present a literature survey on various approaches towards this problem statement and investigate a Blockchain-based solution that focuses on all types of digital content by providing the end user to validate the information along with the evidence for data that was collected at each step

关键词： Surveys Economics Power engineering Neural networks Machine learning Blockchains Peer-to-peer computing Security Reliability Fake news

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Clustered Single-Board Devices with Docker Container Big Stream Processing Architecture

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computers, Materials & Continua 2022年第12期73卷 5349-5365页

作者： N.Penchalaiah Abeer S.Al-Humaimeedy Mashael Maashi J.Chinna Babu Osamah Ibrahim Khalaf Theyazn H.H.Aldhyani Department of CSE AITSRajampet516126Andhra PradeshIndia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityP.O.Box 103786Riyadh11616Saudi Arabia Software Engineering Department King Saud UniversityRiyadh11543Saudi Arabia Department of Electronics and Communication Engineering Annamacharya Institute of Technology and SciencesRajampet516126Andhra PradeshIndia Al-Nahrain University Al-Nahrain Nanorenewable Energy Research CenterBaghdad10072Iraq Applied College in Abqaiq King Faisal UniversityP.O.Box 400Al-Ahsa31982Saudi Arabia

The expanding amounts of information created by Internet of Things(IoT)devices places a strain on cloud computing,which is often used for data analysis and *** paper investigates a different approach based on edge cloud applications,which involves data filtering and processing before being delivered to a backup cloud *** Paper suggest designing and implementing a low cost,low power cluster of Single Board computers(SBC)for this purpose,reducing the amount of data that must be transmitted elsewhere,using Big Data ideas and *** Apache Hadoop and Spark Cluster that was used to run a test application was containerized and deployed using a Raspberry Pi cluster and *** obtain system data and analyze the setup’s performance a Prometheusbased stack monitoring and alerting solution in the cloud based market is *** Paper assesses the system’s complexity and demonstrates how containerization can improve fault tolerance and maintenance ease,allowing the suggested solution to be used in *** evaluation of the overall performance is presented to highlight the capabilities and limitations of the suggested architecture,taking into consideration the suggested solution’s resource use in respect to device restrictions.

关键词： Big data edge cloud cluster architecture performance engineering Raspberry pi dockers warm container technology data streaming

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Incremental Learning with Concept Drift Detection and Prototype-based Embeddings for Graph Stream Classification

Incremental Learning with Concept Drift Detection and Protot...

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International Joint Conference on Neural Networks (IJCNN)

作者： Kleanthis Malialis Jin Li Christos G. Panayiotou Marios M. Polycarpou KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Data stream mining aims at extracting meaningful knowledge from continually evolving data streams, addressing the challenges posed by nonstationary environments, particularly, concept drift which refers to a change in the underlying data distribution over time. Graph structures offer a powerful modelling tool to represent complex systems, such as, critical infrastructure systems and social networks. Learning from graph streams becomes a necessity to understand the dynamics of graph structures and to facilitate informed decision-making. This work introduces a novel method for graph stream classification which operates under the general setting where a data generating process produces graphs with varying nodes and edges over time. The method uses incremental learning for continual model adaptation, selecting representative graphs (prototypes) for each class, and creating graph embeddings. Additionally, it incorporates a loss-based concept drift detection mechanism to recalculate graph prototypes when drift is detected.

关键词： Incremental learning Social networking (online) Image edge detection Concept drift Neural networks Decision making Prototypes

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WPT Enabled SP3T Switch for Broadband Antenna Switching Applications

WPT Enabled SP3T Switch for Broadband Antenna Switching Appl...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

作者： Quddious, Abdul Bilal, Asif Darvazehban, Amin Rezaeieh, Sasan Ahdi Iezekiel, Stavros Antoniades, Marco A. University of Cyprus KIOS Research Center for Excellence Nicosia Cyprus University of Cyprus EMPHASIS Research Centre Nicosia Cyprus University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus The University of Queensland School of Information Technology and Electrical Engineering Brisbane Australia Ryerson University Department of Electrical Computer & Biomedical Engineering TorontoON Canada

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

This paper presents a battery-less single pole triple throw (SP3T) switch enabled through wireless power transfer (WPT) for a frequency range of 0.5-6 GHz. A set of three rectifying circuits, each connected to their independent antenna elements (rectennas) were designed. These rectennas were designed to operate at 2.3 GHz, 2.45 GHz, and 2.6 GHz, respectively. Different frequencies were used to avoid any interference between received signals. The three RF output ports of the switch were biased using each rectenna. A minimum of 1.8 V is required to actuate each RF port that was rectified when the input power ranges from 0-2 dBm with RF-to-DC power conversion efficiency (PCE) which is around 50% for each rectifier. The proposed battery-less SP3T switch paves the way to reconfigure microwave devices and antennas for a broad range of applications. © 2022 IEEE.

关键词： Rectennas

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Genetic-Algorithm-Based Road Traffic Network Division with Improved Refining

Genetic-Algorithm-Based Road Traffic Network Division with I...

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Conference on Human System Interactions, HSI

作者： Tomas Potuzak Department of Computer Science and Engineering NTIS - European Center of Excellence Faculty of Applied Sciences University of West Bohemia Plzen Czech Republic

ISBN: (数字)9798350362916

ISBN: (纸本)9798350362923

In this paper, a method for road traffic network division for distributed and/or parallel road traffic simulation is described. Similarly to its former version, it employs multi-level graph partitioning scheme with graph coarsening, where a genetic algorithm is used for initial partitioning. The version of the method described in this paper (Improved Dividing Genetic Algorithm with Graph Coarsening and Improved Refining - IDGA-GC-IR) incorporates improved refining phase. In order to investigate its performance, it was compared to a third-party division method using testing on five road traffic networks. The tests showed that the IDGA-GC-IR method outperforms the compared third-party method in some important aspects of the division (e.g., number of divided traffic lanes), but is significantly slower.

关键词： Refining Road traffic Genetic algorithms Testing

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Real-Time Relative Positioning System Implementation Employing Signals of Opportunity, Inertial, and Optical Flow Modalities

Real-Time Relative Positioning System Implementation Employi...

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2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021

作者： Souli, Nicolas Makrigiorgis, Rafael Kolios, Panayiotis Ellinas, Georgios Kios Research and Innovation Center of Excellence University of Cyprus Department of Electrical and Computer Engineering Cyprus

ISBN: (纸本)9780738131153

Current navigation technologies are relying on global navigation satellite system (GNSS) information. As in terms of reliability and precision next-generation autonomous vehicle requirements cannot be fully satisfied by GNSS, a sensor information fusion must be employed, leading to the exploration of new positioning methods. In this work, a reliable relative positioning solution in GNSS-challenged areas is investigated, using a combination of signals of opportunity (SOPs), inertial, and optical flow data. The proposed real-time relative positioning system exploits the fused data in the absence of GNSS signals for localization, employing a tracking algorithm to estimate the agent's trajectory in space and time. Extensive outdoor experiments employing an Unmanned Aerial Vehicle (UAV) are carried out to demonstrate the applicability of the proposed technique, validating its performance against various positioning approaches, including GNSS. © 2021 IEEE.

关键词： computer vision

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Cardiac Ischemia Detection Using Self-Organizing Map Feature Selection Method

Cardiac Ischemia Detection Using Self-Organizing Map Feature...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Ramin Aghili Karampour Amir Arshia Bakhtiar Kiarash Hajizamani Alireza Fallahi Hamid Soltanian Zadeh Biomedical Engineering Department Hamedan University of Technology Hamedan Iran Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331529710

ISBN: (纸本)9798331529727

Cardiac ischemia, a prevalent cause of heart failure, remains the leading cause of death in Iran. Early diagnosis of this condition is crucial, and electrocardiogram (ECG) signal processing techniques offer valuable insights. By analyzing ECG signals from ischemic patients and comparing them to those of normal individuals, we can extract relevant features for classification. Our study extracted some features from ECG signals, including statistical, time-based, frequency, and time-frequency features. We employed the self-organizing maps method (SOM) to identify the most informative features. Subsequently, we evaluated the classification accuracy using several machine learning algorithms, including support vector machines (SVM), Naive Bayes, Linear Discriminant Analysis (LDA), and Logistic Regression. The results demonstrated the superiority of the SOM method in selecting the best features compared to PCA, with accuracies of $\mathbf{9 1. 2 \%}, \mathbf{8 4. 3 \%}, \mathbf{7 2. 5 \%}$, and $88.9 \%$ for SVM, LDA, NB, and LR methods, respectively. Our findings contribute to the early detection and management of cardiac ischemia, potentially improving patient outcomes.

关键词： Self-organizing feature maps Support vector machines Time-frequency analysis Logistic regression Accuracy Machine learning algorithms Electrocardiography Signal processing Feature extraction Principal component analysis

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A Multiform Many-objective Evolutionary Algorithm for Multi-label Feature Selection in Classification

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IEEE Transactions on Evolutionary Computation 2024年

作者： Hancer, Emrah Xue, Bing Zhang, Mengjie Victoria University of Wellington Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Burdur Mehmet Akif Ersoy University Department of Software Engineering Burdur15039 Turkey

Multi-label classification (MLC) deals with instances associated with multiple labels simultaneously and often includes high dimensional data with noisy, irrelevant, and redundant features. Feature selection for MLC is crucial for achieving successful classification performance. However, no unified metric exists for evaluating learning performance in MLC tasks. Instead, multiple metrics exist, each assessing a different aspect of the classification process, and these metrics are often inconsistent with one another. Consequently, multi-label feature selection (MLFS) becomes a many-objective optimization problem (MaOP) when optimizing three or more classification metrics and the number of selected features simultaneously. Evolutionary computation (EC) techniques have shown great promise in addressing many-objective tasks due to their ability to effectively explore large and complex search spaces. EC techniques can handle multiple objectives concurrently, making them well-suited for the challenges posed by MLFS. Despite this potential, research on EC-based MLFS methods remains limited, with few studies treating it as a MaOP. To address this gap, this paper proposes a new many-objective evolutionary algorithm within a multiform framework. The proposed algorithm leverages distinct sub-populations to address specific MLFS tasks, incorporates a strategy to exchange information between MLFS tasks, employs a local density-based selection mechanism to maintain diverse high-quality solutions, and uses an adaptive parameter scheme inspired by stochastic gradient descent to guide the search towards new regions in the objective space. Experimental results demonstrate the superiority of the proposed algorithm across a diverse range of high-dimensional datasets, outperforming both recently introduced many-objective and conventional MLFS algorithms. © 1997-2012 IEEE.

关键词： Optimization algorithms

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