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5th International Conference on Advancements in Computing, ICAC 2023

作者： Basnayaka, A.I. Epitakaduwa, E.K.G.D. Jayawardena, C.P.U. Zoysa, Ashan Dharmakeerthi, Uditha Jayawardena, Sanath Sri Lanka Institute of Information Technology Dept of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept of Computer Science and Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept of Computer Science and Network Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

The term special needs mean the special educational needs that the students who suffer from learning disabilities must have due to several reasons. Learning impairment is a condition of the neurological system that impairs one's capacity to efficiently acquire and process knowledge. Failures in diagnosing the people with learning disabilities have been repeatedly highlighted and remain unpopular among people in the developing countries like Sir Lanka. Dyscalculia (difficulties in math), dysgraphia (difficulties in writing) and dyslexia (difficulties in reading) can be considered as some of the major learning disability issues. The 'mobile application based' solution 'Sankalpa Math Master' has been created to offer a game based all-in-one solution including screening, and intervention to identify one of the above-mentioned learning disabilities called 'Dyscalculia'. Dyscalculia is generally distinguished by challenges in learning math, including organizing problems, multi-step calculations, transposing numbers, distinguishing directions, and understanding mathematical signs, leading to confusion with basic operations and facts. The provided solution has been accurate with the use of machine learning and deep learning techniques. In the process of screening with the use of Support Vector Machine (SVM) machine learning algorithm 99.77% of accuracy level has been obtained. The skill level prediction process has acquired an accuracy level of 97% with the use of Random Forest machine learning algorithm. Sri Lankan traditional education system will be vastly benefited by the high level of accuracy in screening, skill predicting, and other game and voice chat-based activities. The students with special needs will gain more effective and personalized learning experience which will improve their learning abilities and help to close the technology gap in education. © 2023 IEEE.

关键词： Forecasting

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Object Recognition and Tracking System to Assist Visually Impaired: A Neural network-Based Deep RBM Technique 1

Object Recognition and Tracking System to Assist Visually Im...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Patil, Sonal Ramagundam, Shashishekhar Sukumar, P. Khan, Mohiuddin Ali Patil, Nisha Deepak Nishant, Neerav G H Raisoni Institute of Engineering and Business Management HoD of CSE-IT Department Jalgaon India Software and Dev Engg4- SDET/ IT COMCAST MonroviaMD United States Department of Computer Science and Engineering Tamilnadu Erode India Jazan University Department of Computer & Network Engineering Jazan Saudi Arabia GCOERC Computer Engineering Nashik India School of Engineering Babu Banarasi Das University Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350371802

Being able to see is fundamental to almost every aspect of our everyday lives, thus those who are visually impaired confront enormous obstacles. Thanks to recent developments in computer vision and computing, a system that can provide auditory input to visually impaired individuals regarding objects and their environments has been designed. Sequencing is crucial for preprocessing, feature selection, and model training. Although extremely accurate data input is still required for object detection, preparation is getting computationally cheaper. We were partially successful in reducing the image size by employing Gaussian Blur, which imparts a smoother appearance to the photos. We use color descriptors and Fourier transforms for feature extraction. Deep RBM training necessitates precise control over the attributes. In comparison to this method, current RBM and DNN algorithms look antiquated. The results demonstrated a notable enhancement in accuracy, reaching 98.31%. © 2024 IEEE.

关键词： Object detection

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The Reliability of OKRidge Method in Solving Sparse Ridge Regression Problems 38

The Reliability of OKRidge Method in Solving Sparse Ridge Re...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Li, Xiyuan Wang, Youjun Liu, Weiwei School of Computer Science Wuhan University National Engineering Research Center for Multimedia Software Wuhan University Institute of Artificial Intelligence Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University

Sparse ridge regression problems play a significant role across various domains. To solve sparse ridge regression, [1] recently proposes an advanced algorithm, Scalable Optimal K-Sparse Ridge Regression (OKRidge), which is both faster and more accurate than existing approaches. However, the absence of theoretical analysis on the error of OKRidge impedes its large-scale applications. In this paper, we reframe the estimation error of OKRidge as a Primary Optimization (PO) problem and employ the Convex Gaussian min-max theorem (CGMT) to simplify the PO problem into an Auxiliary Optimization (AO) problem. Subsequently, we provide a theoretical error analysis for OKRidge based on the AO problem. This error analysis improves the theoretical reliability of OKRidge. We also conduct experiments to verify our theorems and the results are in excellent agreement with our theoretical findings. © 2024 Neural information processing systems foundation. All rights reserved.

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A Boosting-Type Convergence Result for *** with Factorized Multi-Class Classifiers 38

A Boosting-Type Convergence Result for *** with Factorized M...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zou, Xin Zhou, Zhengyu Xu, Jingyuan Liu, Weiwei School of Computer Science Wuhan University National Engineering Research Center for Multimedia Software Wuhan University Institute of Artificial Intelligence Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University

ADABOOST is a well-known algorithm in boosting. Schapire and Singer propose, an extension of ADABOOST, named ***, for multi-class classification problems. Kégl shows empirically that *** works better when the classical one-against-all base classifiers are replaced by factorized base classifiers containing a binary classifier and a vote (or code) vector. However, the factorization makes it much more difficult to provide a convergence result for the factorized version of ***. Then, Kégl raises an open problem in COLT 2014 to look for a convergence result for the factorized ***. In this work, we resolve this open problem by presenting a convergence result for *** with factorized multi-class classifiers. © 2024 Neural information processing systems foundation. All rights reserved.

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Deploying AI-Based Applications with Serverless Computing in 6G networks: An Experimental Study

Deploying AI-Based Applications with Serverless Computing in...

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IEEE Conference on Vehicular Technology (VTC)

作者： Marc Michalke Chukwuemeka Muonagor Admela Jukan Institute of Computer and Network Engineering Technische Universität Braunschweig Germany

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Future 6G networks are expected to heavily utilize machine learning capabilities in a wide variety of applications with features and benefits for both, the end user and the provider. While the options for utilizing these technologies are almost endless, from the perspective of network architecture and standardized service, the deployment decisions on where to execute the AI-tasks are critical, especially when considering the dynamic and heterogeneous nature of processing and connectivity capability of 6G networks. On the other hand, conceptual and standardization work is still in its infancy, as to how to categorize ML applications in 6G landscapes; some of them are part of network management functions, some target the inference itself, while many others emphasize model training. It is likely that future mobile services may all be in the AI domain, or combined with AI. This work makes a case for the serverless computing paradigm to be used to this end. We first provide an overview of different machine learning applications that are expected to be relevant in 6G networks. We then create a set of general requirements for software engineering solutions executing these workloads from them and propose and implement a high-level edge-focused architecture to execute such tasks. We then map the ML-serverless paradigm to the case study of 6G architecture and test the resulting performance experimentally for a machine learning application against a setup created in a more traditional, cloud-based manner. Our results show that, while there is a tradeoff in predictability of the response times and the accuracy, the achieved median accuracy in a 6G setup remains the same, while the median response time decreases by around 25% compared to the cloud setup.

关键词： 6G mobile communication Training Vehicular and wireless technologies Accuracy Serverless computing Machine learning computer architecture Standardization network architecture Time factors

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Fast Vehicular TSN network Reconfiguration with Application Aware network Synchronization

Fast Vehicular TSN Network Reconfiguration with Application ...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Dominik Stöhrmann Rolf Ernst Institute of Computer and Network Engineering Technische Universität Braunschweig Germany

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

In-Vehicle networks (IVN) are under intense pressure to be both resource efficient at low cost and flexible enough for the future. The deployment of a large number of new sensors for autonomous driving and self-awareness significantly increases the demand for network resources. In addition, statically designed networks lack flexibility or have large over-provisioning of resources due to the dynamics of network traffic. Application-aware resource management addresses both of these issues. It allows the network to be reconfigured while controlling application requirements, increasing resource utilization, and providing flexibility for future demands. Our timestamp-based reconfiguration protocol ensures fast, lossless reconfiguration of high bandwidth data connections. Based on global time synchronization, it enables precise reconfiguration without increasing the risk of data loss and reduces the risk of inconsistent network configurations by exploiting the object slack created by large object transfers consisting of hundreds of individual packets. Using software-based Linux switches as the network, we demonstrate the use of the reconfiguration protocol under real-world conditions.

关键词： Protocols Linux Operating systems Telecommunication traffic Reliability engineering Sensors Synchronization Resource management Vehicle dynamics Monitoring

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Deploying AI-Based Applications with Serverless Computing in 6G networks: An Experimental Study

arXiv

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arXiv 2024年

作者： Michalke, Marc Muonagor, Chukwuemeka Jukan, Admela Institute of Computer and Network Engineering Technische Universität Braunschweig Germany

Future 6G networks are expected to heavily utilize machine learning capabilities in a wide variety of applications with features and benefits for both, the end user and the provider. While the options for utilizing these technologies are almost endless, from the perspective of network architecture and standardized service, the deployment decisions on where to execute the AI-tasks are critical, especially when considering the dynamic and heterogeneous nature of processing and connectivity capability of 6G networks. On the other hand, conceptual and standardization work is still in its infancy, as to how to categorizes ML applications in 6G landscapes;some of them are part of network management functions, some target the inference itself, while many others emphasize model training. It is likely that future mobile services may all be in the AI domain, or combined with AI. This work makes a case for the serverless computing paradigm to be used to this end. We first provide an overview of different machine learning applications that are expected to be relevant in 6G networks. We then create a set of general requirements for software engineering solutions executing these workloads from them and propose and implement a high-level edge-focused architecture to execute such tasks. We then map the ML-serverless paradigm to the case study of 6G architecture and test the resulting performance experimentally for a machine learning application against a setup created in a more traditional, cloud-based manner. Our results show that, while there is a tradeoff in predictability of the response times and the accuracy, the achieved median accuracy in a 6G setup remains the same, while the median response time decreases by around 25% compared to the cloud setup. © 2024, CC BY.

关键词： network architecture

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Wireless Large Object Transmission Under Safety Constraints (LOTUS)

arXiv

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arXiv 2024年

作者： Bendrick, Alex Tappe, Daniel Ernst, Rolf Institute of Computer and Network Engineering Technische Universität Braunschweig Germany

Future autonomous mobile systems will greatly benefit from cooperation and real-time sensor data exchange using V2X communication. In such applications, wireless communication has to cope with stringent real-time and safety constraints, a huge challenge given the inherently lossy wireless communication with highly dynamic channel and error conditions. To meet the safety and real-time goals, the use of state-of-the-art (5G and 802.11) V2X technologies focuses on reliable exchange of small data objects, as in URLLC. In contrast, reliable low-latency exchange of large data, such as camera frames, has received little attention, despite its predicted benefits in safe perception and cooperation. The LOTUS project, outlined in this paper, exploits the specific properties of large application data objects to develop novel, application-aware mechanisms for low-latency reliable large data exchange on top of existing and future V2X technologies. Evaluation with statistical analysis, simulation, and physical prototypes demonstrates the feasibility of low-latency large data object exchange at unprecedented levels of reliability. © 2024, CC BY-SA.

关键词： Reliability analysis

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Continuous multi-access communication for high-resolution low-latency V2X sensor streaming

Continuous multi-access communication for high-resolution lo...

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IEEE Symposium on Intelligent Vehicle

作者： Daniel Tappe Alex Bendrick Rolf Ernst Institute of Computer and Network Engineering Technische Universität Braunschweig Germany

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Future automated mobility is expected to rely on Cooperative Perception (CP) applications to achieve high degrees of reliable autonomy. CP applications are characterized by the exchange of large sensor data objects, such as camera frames or LIDAR point clouds. The high mobility of nodes in combination with the safety-critical nature of CP data, demands architectures enabling continuous connectivity for reliable transmission of large data objects. This work presents a user-centric architecture combining a cell-free Radio-Access-network (RAN) with a centralized backbone management. By monitoring multiple available connections through an ultra-lightweight heartbeat-based protocol, the handover procedure can be reduced to a low-latency backbone reconfiguration. Thus, the need for redundant data transmission to achieve loss-free and continuous data transmissions during handover is avoided.

关键词： Protocols Bandwidth Handover Ultra reliable low latency communication Robot sensing systems Radio links Timing

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Ultra Reliable Hard Real-Time V2X Streaming with Shared Slack Budgeting

Ultra Reliable Hard Real-Time V2X Streaming with Shared Slac...

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IEEE Symposium on Intelligent Vehicle

作者： Alex Bendrick Daniel Tappe Rolf Ernst Institute of Computer and Network Engineering Technische Universität Braunschweig Germany

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

While autonomous driving roadmaps envision large data to be exchanged in V2X scenarios, current V2X communication standards only focus on reliable exchange of small objects. The Wireless Reliable Real-Time Protocol (W2RP) addresses this challenge, however, cannot properly handle scenarios where multiple applications share the channel especially as there are predetermined periodic and dynamic retransmissions phases for each data object. If transient error spikes occur, applications will compete for dynamic channel resources, potentially creating critical overload situations that can lead to safety violations. Furthermore, the channel is shared by multiple applications with different criticality, properties and constraints. We propose a two-level hierarchical approach that assigns a fixed budget to each node, with periodic and dynamic transmission parts of a node’s applications being scheduled by the node itself. Prioritization is used to differentiate applications based on their criticality, thereby enabling reliable data exchange for safety-critical applications. The concept was evaluated using an OMNeT++-based simulation of an Automated Valet Parking use case and proved highly effective in enabling safe sample exchange even under varying channel conditions, while offering significantly more efficient resource reservation compared to a static configuration.

关键词： Wireless communication Protocols Bandwidth Dynamic scheduling Cameras Real-time systems Reliability

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