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Analyzing Different Protocols of Information Granularity Distribution to Improve Consistency of Fuzzy Preference Relations in Decision-Making

Analyzing Different Protocols of Information Granularity Dis...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Juan Carlos González-Quesada Ignacio Javier Pérez Juan Antonio Morente-Molinera Sergio Alonso Enrique Herrera-Viedma Francisco Javier Cabrerizo Dept. of Computer Science and AI University of Granada Granada Spain Dept. of Software Engineering University of Granada Granada Spain

A fundamental principle of Granular Computing is that of an information granularity distribution and its optimization process. It has been used in system modelling to elevate a numerical model to its granular counterpart, which is more in rapport with reality. For example, in decision-making with fuzzy preference relations, it has been applied to enhance the existing numerical consistency improvement procedures. However, even though different protocols of information granularity distribution have been proposed, only the one based on a uniform and symmetric distribution and the one based on a symmetric but non-uniform distribution have been considered. Given that there exist others, this study aims to analyze how we can take advantage of all of them to improve the consistency of the fuzzy preference relations. Some numerical experiments are also completed to show the performance of these protocols.

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EdgStr: Automating Client-Cloud to Client-Edge-Cloud Transformation

EdgStr: Automating Client-Cloud to Client-Edge-Cloud Transfo...

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International Conference on Distributed Computing Systems

作者： Kijin An Eli Tilevich Software Engineering Team Samsung Research Seoul South Korea Dept. of Computer Science Software Innovations Lab Virginia Tech Blacksburg VA United States

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

To harness the potential of edge resources, two-tier client-cloud applications require transformation into three-tier client-edge-cloud applications. Such transformations are hard for programmers to perform correctly by hand. Many cloud services maintain a runtime state that needs to be replicated at the edge. Once replicated, this state must then be synchronized efficiently and correctly. To facilitate the transition to edge computing, we present a framework that automatically transforms client-cloud apps to their client-edge-cloud versions. Our framework, EdgStr, automatically replicates cloud-based services at the edge. EdgStr synchronizes the replicated service state by relying on a third-party Conflict-Free Replicated Data Type (CRDT). It generates code that connects service state changes to CRDT update operations, thus ensuring that the state changes at each replica eventually converge to the same replicated state. As an evaluation, we applied EdgStr to transform representative distributed mobile apps for deployment in dissimilar network and device setups. EdgStr correctly replicates cloud services (targeting the important domain of ***), deploying the resulting replicas on an ad-hoc edge cluster, hosted by Raspberry PI devices. As long as eventual consistency is congruent with the functionality of a cloud service, EdgStr can automatically replicate this service and deploy the replicas at the edge, thus offering the performance benefits of edge-based execution, without the high costs of manual program transformation.

关键词： Performance evaluation Costs Runtime Codes Transforms Manuals Mobile applications

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Decentralized Drug Transaction Tracking: A Novel Hybrid Blockchain-IPFS Platform

Decentralized Drug Transaction Tracking: A Novel Hybrid Bloc...

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Public Safety Technology (WFPST), IEEE World Forum on

作者： Mouhamadou Mouctar Gueye Wael Jaafar Fatoumata Baldé Dept. Computer Science Université Alioune Diop Bambey Sénégal Dept. Software and IT Engineering École de Technologie Superieure (ÉTS) Montreal Canada

ISBN: (数字)9798350329148

ISBN: (纸本)9798350329155

Counterfeit drugs are a serious global problem that poses threats to public health. It occurs when falsified or counterfeit drugs are intentionally manufactured and introduced into the pharmaceutical supply chain, masquerading as genuine products. This is due to the complexity of the supply chain that involves independent entities. As a result, a drug transits through several stages before reaching the patient. Current drug management systems are centralized, thus creating data transparency and authenticity risks within the supply chain. To bypass this issue, we propose in this paper a novel hybrid and distributed drug transaction tracking platform that leverages a combination of the Interplanetary File System (IPFS) protocol and Ethereum blockchain. Relying on IPFS guarantees the integrity of drug traceability data, while the blockchain ensures the authenticity of this data. We developed our platform and, through experiments, we prove that our method is more efficient, in terms of process costs, than the conventional blockchain-based drug tracking system.

关键词： Drugs Costs Supply chains Smart contracts Public security Blockchains InterPlanetary File System

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A Web Based Four-Tier Architecture using Reduced Feature Based Neural Network Approach for Prediction of Student Performance 2

A Web Based Four-Tier Architecture using Reduced Feature Bas...

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2nd International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2021

作者： Hossen, Anwar Alamgir, Rakib Bin Alam, Arman Ul Siddika, Fatema Hossain, Shah Fahad Arman, Md. Shohel Daffodil International University Dept. of Software Engineering Dhaka Bangladesh Jagannath University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665415767

Enhancing student's performance is a significant part of developing quality education in any educational institute. It is very difficult to get promising student performance without student categorization according to their academic performance as there are different standardized students. In this paper, our aim is to determine the performance of the students. For this purpose, a survey has been conducted on students in our university in order to collect data and to analyze and predict the student category based on their performance. Apart from this, another purpose of this study is to examine the effect of the reduced features on the classification model using state-of-art machine learning algorithms. Here, we propose a workflow of web-based four-tier architecture for the student performance prediction that will define the student's category in order to help them exactly pinpoint their learning capabilities. Hence, we used multiple supervised learning-based machine learning techniques for the prediction of student performance. Each of the student category categorized by considering on the top features. The analysis results indicate that we got the highest performance that is 88.00% by using the Artificial Neural Network (ANN) among the classifiers by showing its superiority to the existing model. © 2021 IEEE.

关键词： Students

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Effectiveness of Performance Visualizations for Declarative Model Transformations

Effectiveness of Performance Visualizations for Declarative ...

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IEEE International Workshop on Visualizing software for Understanding and Analysis (VISSOFT)

作者： Raffaela Groner Matthias Tichy Dept. of Computer Science and Engineering Chalmers | University of Gothenburg Gothenburg Sweden Institute of Software Engineering and Programming Languages Ulm University Ulm Germany

ISBN: (数字)9798331528485

ISBN: (纸本)9798331528492

Several profilers for general-purpose languages like Java offer visualizations to support users in understanding the execution of a program and identifying the causes of a performance issue. Unfortunately, these performance visualizations are difficult to reuse in profilers for declarative model transformations since they cannot display transformation-specific information. For example, a profiler for a declarative model transformation language must provide information on the traversal of the input model since it impacts the performance but is hidden from the developer. Moreover, the respective visualization must scale for input models that consist of several thousand model elements. Hence, we developed performance visualizations for the declarative model transformation language Henshin that provide insights into the transformation execution. Subsequently, we performed a mixed methods study with 18 Henshin novices to evaluate the effectiveness of our visualizations. In our study, the participants tried to improve the execution performance of four transformations by performing a root cause analysis using our visualizations. The results of our study show that depending on the task, between 16 and 18 participants understood the execution of a transformation correctly based on our visualizations. Moreover, between 12 and 18 participants proposed effective optimizations using our visualizations.

关键词： Visualization Root cause analysis Java Color software Encoding Data mining Optimization

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The YOLOv8 Edge: Harnessing Custom Datasets for Superior Real-Time Detection 18

The YOLOv8 Edge: Harnessing Custom Datasets for Superior Rea...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Ahmed, Tafreed Maaz, Ahmad Mahmood, Danyaal Abideen, Zain Ul Arshad, Usama Ali, Raja Hashim BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. Developing a custom object detection solution that can detect specific objects in real-time video streams has the potential to revolutionize various fields and has been the subject of extensive research. Although there have been advances in object detection, there is still a gap in the research for real-time detection of custom objects with high accuracy and speed. This research addresses this gap by training a YOLOv8 detector on a custom dataset of objects and evaluating its performance on real-time video streams which is by far the latest model and thus is faster and more accurate. Our experimental results demonstrate that our custom-trained YOLOv8 detector achieves high accuracy and real-time performance on a custom dataset of objects. The detector achieved an overall mAP50 of 0.864 and a mAP50-95 of 0.758, with individual class results ranging from 0.47 to 0.995. These findings show that custom training data and YOLOv8 are effective in real-time object detection, which has practical applications in various fields. The significance of the results and our contribution lies in demonstrating the effectiveness of custom training data for improving object detection accuracy and speed using YOLO, which has implications for a wide range of real-world applications. © 2023 IEEE.

关键词： Object detection

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RFSNU: Reliable Forwarding Strategy in NDN based UWSN

RFSNU: Reliable Forwarding Strategy in NDN based UWSN

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： Ahmed Khalid Rana Asif Rehman Byung-Seo Kim Department of Computer Science National University of Computer & Emerging Sciences Pakistan Dept. of Software and Communications Engineering Hongik University Sejong-si South Korea

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

Underwater wireless sensor network (UWSN) is a communication paradigm that works in underwater environment such as in oceans, rivers, lakes, and seas. Multiple sensors are deployed in underwater that collect information according to user demand and forwards the data towards the Underwater Autonomous Vehicle (UAV) that send the data towards the ship or user. UWSN is based on Internet Protocol (IP). There are several limitations in existing IP based architecture such as data redundancy, location dependency, mobility. Therefore, there is need for a new network that enhance the network efficiently. Named Data Network (NDN) is a new communication paradigm that provides several built-in benefits such as in-network caching, mobility, avoid data duplication, and fast content retrieval time. In case of NDN based UWSN (NUWSN), network traffic increases due to bandwidth and large propagation delay. Due to dynamic nature of water the ratio of traffic load, packet duplication, packet retransmission increases. When delay is large then more packets can loss and more number of packet retrans-mission, and data duplication occurs. Therefore, there is need for efficient forwarding mechanism in NUWSN that minimize data duplication and packet retransmission. In this paper, we proposed a forwarding mechanism named as Reliable Forwarding Strategy in NDN based UWSN (RFSNU). RFSNU improves the forwarding process in underwater environment using dept. based forwarding. Our scheme improves packets retransmission rate, and packet duplication that are main problems in NUWSN.

关键词： Wireless communication Wireless sensor networks Protocols Redundancy Telecommunication traffic Sensors Rivers

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Using Deep Learning dept. Maps to Improve Monocular SLAM

Using Deep Learning Depth Maps to Improve Monocular SLAM

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International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： Simon Hollows Richard Green Dept. Mechatronics Engineering University of Canterbury Christchurch New Zealand Dept. Computer Science and Software Engineering University of Canterbury Christchurch New Zealand

This paper proposes the use of a neural network to generate dept. maps from 2D frames, offering an alternative to dept. cameras. The goal is to address the accuracy limitations of monocular SLAM by gaining more accurate dept. data through deep learning. This approach combines the simplicity and cost-effectiveness of a single camera setup with the enhanced accuracy of RGB-D ***-SLAM running with RGB-D input was used as the SLAM framework for this method. This allowed for accurate comparisons between dept. sensor dept. maps in test datasets and the generated dept. maps. The MiDaS neural network was used to generate estimated dept. maps. These were then scaled to create consistency over the entire test sequence. The TUM RGB-D dataset was used for *** generated dept. maps were able to compare with the TUM RGB-D data in accuracy. On one dataset, the TUM dept. maps had a root mean square error (RMSE) or 0.24 m while the generated dept. maps had a RMSE of 0.18 m. In general, the proposed method lacks consistency and the lack of good fusion between dept. maps causes this approach to fail in slow-moving scenes.

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A Task Scheduler for Mobile Edge Computing Using Priority-based Reinforcement Learning

A Task Scheduler for Mobile Edge Computing Using Priority-ba...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Amin Avan Farnaz Kheiri Qusay H. Mahmoud Akramul Azim Masoud Makrehchi Shahryar Rahnamayan Dept. of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada Dept. of Engineering Brock University St. Catharines ON Canada

Edge computing offers cloud-like services closer to users and IoT devices, providing high speed and accessibility for network users. Edge computing, often called Mobile Edge Computing (MEC), is a distributed paradigm that utilizes heterogeneous computational and storage resources with well-provisioned capabilities rather than relying on the ample resources of the cloud. In addition, edge users usually refer to portable and mobile devices that connect to and disconnect from the network at will. Therefore, scheduling tasks at the appropriate time and allocating the right resources can be modeled as a multi-objective optimization problem in MEC. Moreover, each task has specific requirements, further adding to the complexity of the optimization problem. In this study, we formulate the scheduling problem as a Markov Decision Process (MDP) to schedule the tasks. The learning time of the task scheduler is minimized when it faces new users and edge servers. Subsequently, we employ the Q-learning (QL) algorithm from the Reinforcement Learning (RL) paradigm to address the optimization problem and effectively adapt the proposed scheduler to the dynamic nature of MEC. Accordingly, we designed the valid state space, action space, and reward function with appropriate conditions and proper rewards for the proposed QL-based technique. We conducted comprehensive experiments to validate the results of the proposed solution, taking into account the inherent randomness of the QL-based technique. The experimental results demonstrate that the proposed technique achieves the lowest learning time compared to Deep learning-based and Deep RL-based approaches. Furthermore, on average, the proposed technique obtains a 72% faster runtime compared to previous works, using 58% fewer computation cycles and 50% less memory. These improvements make the proposed approach an efficient and lightweight task scheduler for MEC.

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A New Rhythm in A1: Convolutional Neural Networks for Music Genre Classification 18

A New Rhythm in A1: Convolutional Neural Networks for Music ...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Javed, Huzaifa Arshad, Usama Abideen, Zain Ul Ali, Raja Hashim BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Despite the significance of music genre classification in audio identification, it remains under-explored within AI research. This tool is crucial for personalized music recommendations and similar music detection. We have developed an efficient AI model that leverages Convolutional Neural Networks (CNNs), offering high-precision genre identification when integrated into a graphical user interface. The model effectively extracts audio features like Mel Frequency Cepstral Coefficients (MFCCs), zero-crossing rate, and tempo. Testing results reveal strong performance in genre prediction across diverse tracks, affirming the model's ability to discern unique characteristics of various music genres. This performance not only attests to the model's capability in discerning the unique characteristics inherent to different music genres but also suggests that it can effectively generalize to novel, unseen data. Our model lays the groundwork for future enhancements and demonstrates the potential of AI in transforming the music industry - from personalized music playlists to exploratory recommendation systems. The success of this model paves the way for more intricate applications of AI within music analysis. © 2023 IEEE.

关键词： Convolution

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