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Regional Correlation Aided Mobile Traffic Prediction with Spatiotemporal Deep Learning

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arXiv 2023年

作者： Park, JeongJun Mwasinga, Lusungu J. Yang, Huigyu Raza, Syed M. Le, Duc-Tai Kim, Moonseong Chung, Min Young Choo, Hyunseung Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of Department of Superintelligence Engineering Sungkyunkwan University Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Department of IT Convergence Software Seoul Theological University Bucheon Korea Republic of

Mobile traffic data in urban regions shows differentiated patterns during different hours of the day. The exploitation of these patterns enables highly accurate mobile traffic prediction for proactive network management. However, recent Deep Learning (DL) driven studies have only exploited spatiotemporal features and have ignored the geographical correlations, causing high complexity and erroneous mobile traffic predictions. This paper addresses these limitations by proposing an enhanced mobile traffic prediction scheme that combines the clustering strategy of daily mobile traffic peak time and novel multi Temporal Convolutional Network with a Long Short Term Memory (multi TCN-LSTM) model. The mobile network cells that exhibit peak traffic during the same hour of the day are clustered together. Our experiments on large-scale real-world mobile traffic data show up to 28% performance improvement compared to state-of-the-art studies, which confirms the efficacy and viability of the proposed approach. © 2023, CC BY.

关键词： Long short-term memory

Intelligent Gen: An Efficient On-Demand Access Protocol in MANETs using Network Simulator - 2

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Intelligent Gen: An Efficient On-Demand Access Protocol in M...

International Conference on Computing for Sustainable Global Development (INDIACom)

作者： T. Ganesan N. Murali S. Palani Murugan M. Chandrakumar Peter P. Anand Raj S. Manikandan Department of Computer Science & Engineering E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Artificial Intelligence & Data Science E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Software Engineering Periyar Maniammai Institute of Science & Technology Thanjavur Tamil Nadu India Department of Computer Science and Business Systems E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Information Technology E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India

Mobile Ad-Hoc network is a self-configuring and multi-hop collection of mobile devices for making successful communication. The wireless and mobile communication process are encountered based on bandwidth, process control and energy conception. Energy conception is considered and dynamically changed to rely on life time of batteries and power. The life time of battery get exhausted very quickly the nodes are moved and changed rapidly across the network. This paper noticed the problem of life time of power, batteries and energy consumption problem in various routing stages. The proposed Intelligent Gen Algorithm is used to get optimal solution for ensuring high robustness, flexibility and Energy consumption factor. It presents a common framework to balance various routing requirements in MANETs dynamically. The following are the main contributions includes, i. introducing Intelligent GEN Algorithms to classify the throughout bandwidth, delay, data ratio. ii. Various routing performance are evaluated and viewed by speed, packetize and simulation time. The comparison evaluated by select routing algorithm using NS-2. The simulation results are recorded and the performance is monitored by using on demand access protocl. The result clearly demonstrated the proposed Intelligent Gen algorithm used in self-organized manner by wireless channel.

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Detecting Unexpected Behaviors in Scenario-Based Specification: Asystematic Literature Review

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SSRN

SSRN 2022年

作者： Jahan, Munima Shakeri Hossein Abad, Zahra Far, Behrouz Department of Electrical and Software Engineering University of Calgary Canada Department of Computer Science University of Calgary Canada

Scenarios are effective means for requirement elicitation. However, scenarios represent a partial story of a system and combining multiple scenarios for the complete system behavior may introduce unexpected behaviors within the system. Detecting and fixing these unwanted flaws in the early design phase can substantially reduce the deployment cost as well as any run-time hazards. The goal of this paper is to summarize the state-of-the-art of detection approaches for unexpected behaviours in scenario-based specifications. We provide an overview of the research area by searching for and categorizing relevant studies, along with discussing gaps and trends of the field. We investigated different types of scenario specifications, approaches used for behavioral modeling and types of unexpected behaviors identified by the researchers. We performed a systematic literature review (SLR) using the PRISMA framework. We studied 64 primary studies from multiple sources and found that the area of research is dominated by solution proposals. Most of the studies use MSC and UML SD as specification types. The automata based behavioral modeling are prevailing in the studies while new technologies like probabilistic modelsand ML models are introduced recently. Moreover, tool support for detecting EB is not matured enough for industry level acceptance. © 2022, The Authors. All rights reserved.

关键词： Specifications

Enhancing Predictive Maintenance in Industrial IoT through Machine Learning Models

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Enhancing Predictive Maintenance in Industrial IoT through M...

Innovation in Technology (ASIANCON), Asian Conference on

作者： A. Jaya Mabel Rani V. Prithivirajan S Arumai Shiney Ch.Babaiah Hemanth Swamy Sivagamidevi. G Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering S A Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India Senior Software Engineer Motorola Solutions India Department of AI & DS Dhanalakshmi College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Significant production losses and increased maintenance costs can arise from unplanned downtime in industrial settings caused by machine faults. In order to identify issues early and prevent significant breakdowns, predictive maintenance approaches make use of data collected from Internet of Things (IoT)-enabled devices installed in operating equipment. Here, we present a predictive maintenance system that employs machine learning methods, namely AdaBoost, to categorize various types of machine pauses in real-time, with a focus on knitting machines. Six different types of machine stops were classified using machine learning: idle stop, Lycra stop, full roll stop, gate stop, feeder stop, and needle stop. We did this by preprocessing and inputting machine learning model data, which included machine speeds and steps. After a rigorous training and optimization process that combines cross-validation and hyperparameter tuning, the model achieves an impressive accuracy rate of 92% on the test dataset. These results highlight how our suggested solution might help the textile sector become more productive and efficient overall by precisely detecting machine stoppage and enabling timely maintenance interventions.

关键词： Training Technological innovation Machine learning Production Predictive models Real-time systems Maintenance Tuning Predictive maintenance Textiles

Learning Unnormalized Statistical Models via Compositional Optimization

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arXiv 2023年

作者： Jiang, Wei Qin, Jiayu Wu, Lingyu Chen, Changyou Yang, Tianbao Zhang, Lijun National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Engineering University at Buffalo New York United States Department of Computer Science and Engineering Texas A&m University College Station United States

Learning unnormalized statistical models (e.g., energy-based models) is computationally challenging due to the complexity of handling the partition function. To eschew this complexity, noise-contrastive estimation (NCE) has been proposed by formulating the objective as the logistic loss of the real data and the artificial noise. However, as found in previous works, NCE may perform poorly in many tasks due to its flat loss landscape and slow convergence. In this paper, we study a direct approach for optimizing the negative log-likelihood of unnormalized models from the perspective of compositional optimization. To tackle the partition function, a noise distribution is introduced such that the log partition function can be written as a compositional function whose inner function can be estimated with stochastic samples. Hence, the objective can be optimized by stochastic compositional optimization algorithms. Despite being a simple method, we demonstrate that it is more favorable than NCE by (1) establishing a fast convergence rate and quantifying its dependence on the noise distribution through the variance of stochastic estimators;(2) developing better results for one-dimensional Gaussian mean estimation by showing our objective has a much favorable loss landscape and hence our method enjoys faster convergence;(3) demonstrating better performance on multiple applications, including density estimation, out-of-distribution detection, and real image generation. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

A Lightweight CNN-Based Pothole Detection Model for Embedded Systems Using Knowledge Distillation 21

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A Lightweight CNN-Based Pothole Detection Model for Embedded...

21st International Conference on New Trends in Intelligent software Methodologies, Tools and Techniques, SoMeT 2022

作者： Musa, Aminu Hassan, Mohammed Hamada, Mohamed Kakudi, Habeebah Adamu Amin, Md. Faizul Ibne Watanobe, Yutaka Department of Computer Science Federal University Dutse Nigeria Faculty of Computer Sci. & Info. Technology Bayero University Kano Nigeria Software Engineering Lab University of Aizu Japan Graduate School of Computer Science University of Aizu Japan Division of Information System University of Aizu Japan

ISBN: (纸本)9781643683164

Recent breakthroughs in computer vision have led to the invention of several intelligent systems in different sectors. In transportation, this advancement led to the possibility of proposing autonomous vehicles. This recent technology relies heavily on wireless sensors and Deep learning. For an autonomous vehicle to navigate safely on highways, the vehicle needs equipment to aid with detecting road anomalies such as potholes ahead of time. The massive improvement in computer vision models such as Deep Convolutional Neural networks (DCNN) or vision transformers (ViT) resulted in many success stories and tremendous breakthroughs in object detection tasks;this enabled the use of such models in different application areas. But many of the reported results are theoretical and unrealistic in real-life. Usually, the nature of these models is extensive;they are trained on High-performance computers or cloud computing environments with GPUs, which challenge their usage on edge devices. However, to come up with a light model that can fit into embedded devices, the model size has to be reduced significantly so that the performance will not be affected. Therefore, this paper proposes a lightweight model of pothole detection for an embedded device. The model achieved a state-of-the-art accuracy of 98%, with the number of parameters reduced to more than 70% compared with a deep CNN model;the model can be trained and deployed on embedded devices such as smartphones efficiently. © 2022 The authors and IOS Press. All rights reserved.

关键词： Autonomous vehicles

Learning Activation Functions for Sparse Neural Networks

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arXiv 2023年

作者： Loni, Mohammad Mohan, Aditya Asadi, Mehdi Lindauer, Marius Division of Computer Science and Software Engineering Mälardalen University Sweden Institute of Artificial Intelligence Leibniz University Hannover Germany Department of Electrical Engineering Tarbiat Modares University Tehran Iran

Sparse Neural Networks (SNNs) can potentially demonstrate similar performance to their dense counterparts while saving significant energy and memory at inference. However, the accuracy drop incurred by SNNs, especially at high pruning ratios, can be an issue in critical deployment conditions. While recent works mitigate this issue through sophisticated pruning techniques, we shift our focus to an overlooked factor: hyperparameters and activation functions. Our analyses have shown that the accuracy drop can additionally be attributed to (i) Using ReLU as the default choice for activation functions unanimously, and (ii) Fine-tuning SNNs with the same hyperparameters as dense counterparts. Thus, we focus on learning a novel way to tune activation functions for sparse networks and combining these with a separate hyperparameter optimization (HPO) regime for sparse networks. By conducting experiments on popular DNN models (LeNet-5, VGG-16, ResNet-18, and EfficientNet-B0) trained on MNIST, CIFAR-10, and ImageNet-16 datasets, we show that the novel combination of these two approaches, dubbed Sparse Activation Function Search, short: SAFS, results in up to 15.53%, 8.88%, and 6.33% absolute improvement in the accuracy for LeNet-5, VGG-16, and ResNet-18 over the default training protocols, especially at high pruning ratios.1 © 2023, CC BY.

关键词： Drops

Defining and Detecting the Defects of the Large Language Model-based Autonomous Agents

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arXiv 2024年

作者： Ning, Kaiwen Chen, Jiachi Zhang, Jingwen Li, Wei Wang, Zexu Feng, Yuming Zhang, Weizhe Zheng, Zibin The School of Software Engineering Sun Yat-Sen University Peng Cheng Laboratory China The School of Software Engineering Sun Yat-Sen University China The School of Computer Science and Technology Harbin Institute of Technology China Peng Cheng Laboratory China The Department of New Networks Peng Cheng Laboratory China

Artificial intelligence (AI) agents are systems capable of perceiving their environment, autonomously planning and executing tasks. Recent advancements in Large Language Models (LLMs) have introduced a transformative paradigm for AI agents, enabling them to interact with external resources and tools through prompt techniques. This advancement has significantly extended the capabilities of LLMs, positioning LLM-based AI Agents as an important research area. In such agents, the workflow integrates developer-written code, which manages framework construction and logic control, with LLM-generated natural language that enhances dynamic decision-making and interaction. However, discrepancies between developer-implemented logic and the dynamically generated content of LLMs in terms of behavior and expected outcomes can lead to defects, such as tool invocation failures and task execution errors. These issues introduce specific risks, leading to various defects in LLM-based AI Agents, including service interruptions and incorrect output. Despite the importance of these issues, there is a lack of systematic work that focuses on analyzing LLM-based AI Agents to uncover defects in their code. To address this gap, we present the first study focused on identifying and detecting defects in LLM Agents. We collected and analyzed 6, 854 relevant posts from StackOverflow to define and classify eight types of agent defects. For each defect type, we provided detailed descriptions and illustrated with an example. Then, we designed a static analysis tool, named Agentable, to detect the defined defects. Agentable leverages Code Property Graphs (CPGs) and LLMs to analyze Agent workflows by efficiently identifying specific code patterns and analyzing natural language descriptions. To evaluate Agentable, we constructed two datasets: AgentSet, which consists of 84 real-world Agent projects, and AgentTest, which contains 78 Agent projects specifically designed to include various types of defect

关键词： Autonomous agents

Location-Based Service Recommendation for Cold-Start in Mobile Edge Computing 17th

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Location-Based Service Recommendation for Cold-Start in Mobi...

17th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2020

作者： Yu, Mengshan Fan, Guisheng Yu, Huiqun Chen, Liang 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)9783030794774

With the rapid development of the 5G and Internet of Things (IoT), mobile edge computing has gained considerable popularity in academic and industrial field, which provides physical resources closer to end users. Service recommendation in such a distributed environment is a hot issue. However, the cold-start problem for service recommendation in mobile edge computing is still urgent to be solved. In this paper, we propose a service recommendation method based on collaborative filtering (CF) and user location, by comprehensively considering the characteristic of services at the edge and mobility of users. In detail, we first synthesize the service characteristics of each dimension through multidimensional weighting method. We further introduce the idea of Inverse_CF_Rec to the traditional CF and then predict the lost QoS value to solve the problem of sparse data. Finally, a recommendation algorithm based on predicted QoS value and user geographic location is proposed to recommend appropriate services to users. The experimental results show that our multidimensional inverse similarity recommendation algorithm based on collaborative filtering (MDICF) outperforms Inverse_CF_Rec in terms of the accuracy of recommendation. © 2021, IFIP International Federation for Information Processing.

关键词： Location based services