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2024 CHI Conference on Human Factors in Computing Sytems, CHI 2024

作者： Bai, Yunpeng Ikkala, Aleksi Oulasvirta, Antti Zhao, Shengdong Wang, Lucia J. Yang, Pengzhi Xu, Peisen Dept. of Computer Science National University of Singapore Singapore Dept. of Computer Science Aalto University Espoo Finland Dept. of Information and Communications Engineering Aalto University Helsinki Finland School of Creative Media Dept. of Computer Science City University of Hong Kong Hong Kong Hong Kong Dept. of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge United States Dept. of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Dept. of Computer Science IPAL National University of Singapore Singapore

ISBN: (纸本)9798400703300

Optical Head-Mounted Displays (OHMDs) allow users to read digital content while walking. A better understanding of how users allocate attention between these two tasks is crucial for improving OHMD interfaces. This paper introduces a computational model for simulating users' attention switches between reading and walking. We model users' decision to deploy visual attention as a hierarchical reinforcement learning problem, wherein a supervisory controller optimizes attention allocation while considering both reading activity and walking safety. Our model simulates the control of eye movements and locomotion as an adaptation to the given task priority, design of digital content, and walking speed. The model replicates key multitasking behaviors during OHMD reading while walking, including attention switches, changes in reading and walking speeds, and reading resumptions. © 2024 Copyright held by the owner/author(s)

关键词： Reinforcement learning

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Energy Efficient Coverage Optimization in Mobile Wireless Sensor Network Using Grey Wolf Algorithm 14

Energy Efficient Coverage Optimization in Mobile Wireless Se...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Gupta, Ankit Mamatha, K.M. Kiran, M. National Institute of Technology Karnataka Dept. of Information Technology Surathkal India Viseveshvaraya Technological University Dept. of Computer Science and Engineering Belgaum India

ISBN: (纸本)9798350335095

The issue of decreased coverage rate in Mobile Wireless Sensor Networks (MWSNs), caused by mobile sensor nodes being randomly placed inside a monitoring area. Additionally, it becomes extremely important to utilise a sensor node's energy very effectively due to the finite energy of sensor nodes. Hence, to provide optimised positions for the sensor nodes while using the energy of sensor nodes adept.y authors propose an energy efficient coverage algorithm. Initially, article focus on optimal placement of the sensor nodes within a area to achieve the maximum coverage and later authors have focused on improvising the network lifetime. Article presents a combination of Grey Wolf Optimization and Virtual Force algorithm for optimization of coverage in MWSN. Further, to improve the network lifetime, a GWO-based clustering algorithm is presented using distance and energy as a parameter. The algorithms are implemented and simulated on Matlab. The efficiency of the presented algorithm is observed comparing with other Swarm Intelligence (SI) based optimization algorithms, like GWO, VFA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant-lion Optimization (ALO) and the results of the GWO-based clustering is compared with the traditional LEACH algorithm and energy-balanced clustering based on PSO. Simulation results demonstrate that the presented algorithms outperform the considered algorithms. © 2023 IEEE.

关键词： Sensor nodes

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Design of a Hybrid Framework for Detecting Attacks in Fog-based IoT Architecture using Deep Learning 15

Design of a Hybrid Framework for Detecting Attacks in Fog-ba...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Pokale, Navnath B. Sharma, Pooja Mane, Deepak T. Dept. of Computer Engineering School of Engineering and Technology D. Y. Patil University Ambi Pune410507 India Dept. of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798331300579

IoT-Fog computing offers a broad variety of services for IoT-based end-systems. End IoT devices communicate with cloud nodes and fog nodes to administer client tasks. During the data collection process between the fog and cloud layers, IoT endpoints are more likely to be compromised by critical attacks such as DDoS and other security threats. It is necessary to detect these network vulnerabilities early on. By extracting features and placing the danger in the network, DL is crucial in predicting end-user behavior. Due to the limited processing power and less storage, Deep Learning algorithms cannot be directly implemented on IoT devices. In this article, we present a three stage Deep Hybrid Detection Model for Attack Detection in IoT-Fog Architecture. Enhanced Z-score normalization based data preparation will be carried out in the beginning. On the basis of preprocessed data, features such as IG, raw data, entropy, and enhanced MI are extracted in the second step. The collected characteristics are used as an input to hybrid classifiers called DBN and optimized DeepMaxout in the third stage to classify the assaults based on the input dataset. A hybrid optimization model known as the BMJFO (Blue Monkey Jelly Fish Optimization) algorithm is presented for the best DeepMaxout training. The suggested model produced increased accuracy, precision, sensitivity, and specificity. The suggested model produced superior accuracy, precision, sensitivity, and specificity of 95.80%, 94.94%, 96.38%, and 97.94%, respectively. © Grenze Scientific Society, 2024.

关键词： Fog computing

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Employing Convolutional Neural Network for Real-time Detection and Localization of Physical Contact in a Redundant Manipulator Body 12

Employing Convolutional Neural Network for Real-time Detecti...

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12th RSI International Conference on Robotics and Mechatronics, ICRoM 2024

作者： Jafari, Amirhossein Tafti, Ali Abolhasani Karami, Abbas Isfahan University of Technology Dept. of Electrical and Computer Engineering Isfahan Iran Isfahan University of Technology Dept. of Mechanical Engineering Isfahan Iran Shiraz University of Technology Dept. of Mechanical Engineering Shiraz Iran

ISBN: (纸本)9798331529734

In the developing field of human-robot collaboration, robots increasingly share their workspace with humans, which becomes necessary to achieve seamless and safe interactions for manual guidance of robots in physical cooperation scenarios. The present study proposes a new method based on a convolutional neural network (CNN) for safe human-robot cooperation in the presence of intentional physical interactions, which is able to detect the occurrence and localization of contact on the robot arm in real-time. In this approach, contact detection and its localization are done only using the online external torque vector obtained by the residual-based observer of the robot, which eliminates any need to use additional force and vision sensors. Unlike traditional methods that depend on predefined thresholds or model-based techniques, our presented architecture with one-dimensional CNN can automatically learn the patterns from collected raw torque data and, by analyzing them, can properly predict the contact position. The performance of the proposed approach during manipulation has been validated through multiple online experiments in static and dynamic states on a KUKA LBR iiwa robot. © 2024 IEEE.

关键词： Redundant manipulators

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Block-Hash Signature (BHS) for Transaction Validation in Smart Contracts for Security and Privacy using Blockchain

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Journal of Mobile Multimedia 2023年第4期19卷 935-962页

作者： Bhatnagar, Sonika Dayal, Mohit Singh, Deepti Upreti, Shitiz Upreti, Kamal Kumar, Jitender Dept. of Computer Science & Engineering Government Polytechnic Baheri Bareilly India Dept. of Applied Science Bharati Vidyapeeth’s College of Engineering New Delhi India Dept. of Information Technology ABES Institute of Technology Ghaziabad India Dept.of Information Technology Asian Education Group (AEG) Noida India Dept. of Computer Science & Engineering Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Dept. of Computer Science & Engineering J.C Bose University of Science & Technology YMCA Haryana Faridabad India

Some of the well-known signature techniques like Winternitz and Lamport are not considered to be very appropriate for the usage of hashing or smart contracts in Blockchains security because of their size O(n²), which is prominently too high. Although in Blockchain, the security concern is on the top priority because of its distributed P2P design still, the security enhancement is required to sign and verify the documents forwarded to the peers, especially in Hyperledger Fabric. Here, this paper presents a new signature technique “Block-Hash” to enhance Blockchain security by using it in smart contracts as well as hashing with size 3Xn bits (n = 256, generally for SHA-256 Hashing) and which can score 112 bits security. The proposed signature can be used appropriately for signing a smart contract by the endorser or committer node. Also, it can be used with a hash algorithm in forming a Merkle tree. Apart from the description and implementation of Block-Hash Signature, this paper has covered the analysis of its security and correctness measures with a table for result comparison. © 2023 River Publishers.

关键词： blockchain security Digital signature hyperledger fabric smart contract

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Cost-balanced Adaptive Replica Server Cache Management under Time-Varying User Request Density in CCDNs 14

Cost-balanced Adaptive Replica Server Cache Management under...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Gupta, Rohit Kumar Mazumdar, Arka Prokash Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Manipal University Jaipur India Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Jaipur India

ISBN: (纸本)9798331531935

In today’s growing Internet, cost-effective on-demand provisioning of caching resources in Cloud-based Content Delivery Networks (CCDNs) is essential to preserve the cache hit ratio while reducing storage requirements. However, during the high and fluctuating request rates at different locations, the sudden increase in cache to mitigate the miss rate requires a balance between storage cost (from dynamic cache provisioning) and miss cost (from latency in retrieving content from the Origin Server (OS) or other Replica Servers (RSs)). In such an environment, some RS instances remain underutilized while others are overburdened across different locations and time intervals, which requires effective RS management to activate only sufficient RSs by adapting to the user demands. To overcome these challenges, a cost-balanced adaptive cache management approach is proposed for dynamic cache provisioning in CCDN. This method aims to dynamically manage the activation of RS to optimize resource utilization, and cache instance provisioning and content caching operations in these RSs are managed by introducing an Adaptive Cache Manager that retrieves content from the OS or other RSs by effectively balancing costs in different time intervals. The experimental results indicate that the proposed method reduces the number of active replica servers in CCDN across different time intervals while decreasing the average total cost by about 56% relative to the LRU caching policy on fixed cache and about 10% compared to the dynamic selective caching policy on RCW cache. © 2025 IEEE.

关键词： Cache memory

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Sub Event Relation Classification for Amharic Texts Using Deep Learning Approach

Sub Event Relation Classification for Amharic Texts Using De...

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2024 International Conference on information and Communication technology for Development for Africa, ICT4DA 2024

作者： Desta, Abinet Bizuayehu Abetw, Abebe Alemu Werku, Kebad Nigussie Asfaw, Nega Agmas Belachew, Habtamu Molla Alemu, Wubie Belay Debark University Dept. Information Technology Debark Ethiopia University of Gondar Dept. Computer Science Gondar Ethiopia Woldia University Dept. Computer Science Woldia Ethiopia

ISBN: (纸本)9798331527792

This research focuses on the challenge of Sub-event Relation Classification (SERC) in Amharic text, with the objective of identifying relationships between event triggers or mentions in sentences. The aim is to predict sub-event relationships in Amharic sentences, benefiting applications such as information extraction, question answering, and document summarization. Amharic text requires a lot of effort to manually identify event relationships, and there isn't yet a model that specializes in extracting event relationships from Amharic language. Deep learning techniques, particularly LSTM and BiLSTM, are used in this work to categorize the relation type between event pairs in unstructured Amharic text in order to address this difficulty. The models are trained and assessed using a dataset of 6622 manually annotated Amharic texts. Experimental results showcase the effectiveness of the BiLSTM model, achieving a high accuracy of 93% for Amharic sub-event relation classification. The findings of this research hold significant implications for natural language processing applications in Amharic and other languages. Moreover, the developed model can be extended to other languages, making it a valuable contribution to the field of natural language processing. © 2024 IEEE.

关键词： Amharic Sub-event Relation Classification (A-SERC) Bidirectional Long Short-Term Memory (BiLSTM) Deep Learning (DL) Long Short-Term Memory (LSTM) Natural Language Processing (NLP) Word2Vec

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Inflation Prediction: A Comparative Study of ARIMA and LSTM Models Across Different Temporal Resolutions 3

Inflation Prediction: A Comparative Study of ARIMA and LSTM ...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Lakshmi Narayanaa, T. Skandarsini, R.R. Ida, S. Jhansi Sabapathy, S. Rathana Nanthitha, P. RMK Engineering College Dept. of Computer Science and Business Systems Thiruvallur India Panimalar Engineering College Dept. of Information Technology Chennai India

ISBN: (纸本)9798350343632

This research study focuses on the prediction of inflation rates using two distinct models, Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM), tailored to accommodate the temporal resolutions of monthly and quarterly inflation data. While a comprehensive evaluation across varying time horizons was not conducted, the results suggest that the ARIMA model excels in short-term forecasting, as evidenced by its impressive performance on a 2021 holdout dataset. Conversely, the LSTM model displays potential for medium-term predictions. This study underscores the significance of aligning forecasting models with data characteristics, emphasizes the importance of temporal resolution, and discusses potential avenues for improvement, such as multivariate modeling or alternative techniques like Prophet. The findings have broad implications for economists, policymakers, and analysts seeking precise inflation forecasts, shedding light on the nuanced interplay between models and data in the domain of inflation prediction. © 2023 IEEE.

关键词： ARIMA Model Inflation Prediction LSTM Model Short-Term Forecasting Temporal Resolution Time Series Forecasting

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Framework for Cloud-Based Data Management in Autonomous Vehicles

Framework for Cloud-Based Data Management in Autonomous Vehi...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Pallapothu, Harshith Gopi Krishna, G. Sunil, Digvijay Teja Yadav, S. Nagasundari, S. Pes University Dept. of Computer Science & Engineering Bengaluru India Research Center for Information Security Forensics and Cyber Resilience Pes University Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331529833

The increasing adoption of autonomous vehicles has driven the need for robust data management solutions that support real-time operations and ensure vehicle safety and efficiency. This work introduces a cloud-based framework for management of sensor data from autonomous vehicles, focusing on optimizing payload transmission rates. The framework leverages AWS IoT Core and AWS IoT Analytics to ensure efficient data flow from vehicle sensors to cloud storage. This framework demonstrates its potential for scalable deployment in real-world autonomous vehicle networks, contributing to the evolution of connected vehicle technologies and intelligent transport systems. This framework ensures reliable data transmission up to 250 payloads per second, beyond which data loss occurs. © 2025 IEEE.

关键词： Cloud platforms

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Enhancing Workload Predictions Using Service Interactions in Cloud-Native Microservices 13

Enhancing Workload Predictions Using Service Interactions in...

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13th IEEE International Conference on Cloud Networking, CloudNet 2024

作者： Mahajan, Isham Nadig, Deepak Purdue University Dept. of Computer and Information Technology West LafayetteIN47907 United States

ISBN: (纸本)9798350376562

Container orchestration systems, such as Kuber-netes, often rely on manual resource allocation to manage re-sources, which can be inefficient and inflexible due to the frequent over-provisioning or underprovisioning of resources. Kubernetes horizontal pod autoscaler (HPA), vertical pod autoscaler (VPA), and Google Kubernetes Engine (GKE) Autopilot are primarily threshold-based, making them reactive rather than proactive as they adjust resources after exceeding utilization thresholds, leading to temporary degradation in quality of service (QoS). While some solutions utilize calls per minute (CPM) counts for requests to microservices to estimate resource consumption dynamically, they do not fully exploit distributed traces or associated microservices' interdependencies. We hypothesize that we can gain deeper insights into future workload patterns by exploiting microservices' interaction and the CPM counts for each pair of communicating microservices. In this paper, we propose a comprehensive machine learning workflow to assess whether factoring in the interdependencies between microservices results in improved workload prediction. Our findings indicate that an LSTM model performs well, with average mean absolute error (MAE) and root mean square error (RMSE) values of 7.02 and 10.54, respectively. The highest R2 score observed was 0.07. This suggests that although incorporating distributed traces and inter-microservice CPM counts provides valuable insights, the models fail to capture the full complexity of workload dynamics. These results highlight the potential for enhancing workload prediction accuracy and underscore the need to refine these methods further to achieve more proactive and efficient resource allocation in container orchestration systems. © 2024 IEEE.

关键词： Quality of service

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