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4th International Conference on Power, Control and computing Technologies, ICPC2T 2025

作者： Galhotra, Parth Singh, Kumar Aniruddh Hasan, Zafarul Safa, M. Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM IST SRM Nagar Kattankulathur TN Chennai603203 India

ISBN: (纸本)9798331530402

This study will introduce the use of Docker for simple application packaging to investigate the impact of packaging on application deployment and management. Containerization is a technological method for packaging and consuming applications in a self-contained environment that is more efficient than virtualization. Docker is a well-known containerization platform that enables efficient creation, deployment, and management of applications by focusing and efficiently utilizing resources across multiple locations. Delivering programs and investigating their similarities in terms of consistency, efficiency, and effectiveness. It also solves security issues and multi-container issues. The approach will be to package a simple web application, deploy it via Docker, and then measure how performance and manageability are improved compared to traditional deployments. Using Docker for application deployment helps you better understand how containerization can improve and simplify deployment and management in today's software development. © 2025 IEEE.

关键词： Software design

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Anti-seizure Medication Classification using EEG signals via Attention-based CNN

Anti-seizure Medication Classification using EEG signals via...

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Annual IEEE Symposium on Computer-Based Medical Systems

作者： Hrishikesh Tiwary Arnav Bhavsar School of Computing and Electrical Engineering Indian Institute of Technology Mandi (IIT Mandi) Mandi India

Anti-Seizure medications (also known as anticonvulsants or anti-epileptic medications) are a broad class of medicines mostly used to treat epileptic seizures. Objectively relating anticonvulsant identification with electroencephalogram (EEG) signals of the subject having a history of taking medication(s), can result in evaluating the effectiveness of the course of treatments for seizure-related illness, which is helpful in patient care. In this work, we propose a deep learning based method to discriminate the EEG recordings of the subjects who have used anti-epileptic medicines in the past from those who have not. Specifically, EEG recordings taken from the subjects are used to distinguish between the presence of the two common anticonvulsants, namely- keppra & dilantin; and the presence of no anticonvulsants at all. We extracted frequency domain features from the EEG recordings using the Fourier transform, and we then used the information as input to the proposed model. Frequency domain features are extracted since the frequency domain representation of EEG signals has some notable differences across the medication paradigms. We then suggest a model based on a convolutional neural network framework with attention to solve the three class (keppra, dilantin, and no medication) classification problem. The attention network aids in giving the features differential weights. The model's classification performance is shown to increase as a result of differential weighting, which makes it possible to focus more on the pertinent aspects needed for classification. We demonstrate that our approach yields improvements over the state-of-the-art results using the standard publicly available Temple University Hospital EEG Corpus (TUEG).

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Multifunctional silicene/CeO_(2)heterojunctions:Desirable electronic material and promising water-splitting photocatalyst

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Chinese Chemical Letters 2022年第8期33卷 3947-3950页

作者： Liang Xu Jian Zeng Quan Li Xin Luo Tong Chen Jingjing Liu Ling-Ling Wang Energy Materials Computing Center School of Energy and Mechanical EngineeringJiangxi University of Science and TechnologyNanchang 330013China Department of Applied Physics School of ScienceEast China Jiaotong UniversityNanchang 330013China Key Laboratory for Micro-Nano Optoelectronic Devices of Ministry of Education School of Physics and ElectronicsHunan UniversityChangsha 410082China

The first-principles calculations demonstrate that covalently bonded(cb)heterojunction and van der Waals(vd W)heterojunction can coexist in silicene/CeO_(2) heterojunctions,due to the different stacking ***,the cb heterojunction with band gap of 1.97 e V,forms a type-II heterojunction,exhibits good redox performance and has high-effective optical absorption spectra,thus it is a promising photocatalyst for overall water ***,for the vd W heterojunction,the Dirac cone of silicene is well kept on CeO_(2) semiconducting substrate,with a considerable energy gap of 0.43 e V,which can be an ideal material in building silicene-based electronic *** results may open a new gateway in both of nanoelectronic device and energy conversion for silicene/Ce O2 nanocomposites.

关键词： Silicene CeO_(2) Photocatalyst Interfacial interaction Dirac cone

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OSM: Leveraging model checking for observing dynamic 1 behaviors in aspect-oriented applications

arXiv

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arXiv 2024年

作者： AlSobeh, Anas Mohammad Ramadan Department of Information Systems Faculty of Information Technology and Computer Science Yarmouk University Irbid Jordan Program of Information Technology School of Computing College of Engineering Computing Technology and Mathematics Southern Illinois University CarbondaleIL United States

In the intricate domain of software systems verification, dynamically model checking multifaceted system characteristics remains paramount, yet challenging. This research proposes the advanced observe-based statistical model-checking (OSM) framework, devised to craft executable formal models directly from foundational system code. Leveraging model checking predicates, the framework melds seamlessly with aspect-oriented programming paradigms, yielding a potent method for the analytical verification of varied behavioral attributes. Exploiting the transformative capacity of OSM framework, primary system code undergoes a systematic metamorphosis into multifaceted analysis constructs. This not only simplifies the model verification process but also orchestrates feature interactions using an innovative observing join point abstraction mechanism. Within this framework, components encompassing parsing, formal verification, computational analytics, and rigorous validation are intrinsically interwoven. Marrying the principles of model checking with aspect-oriented (AO) modularization, OSM framework stands as a paragon, proficiently scrutinizing and affirming system specifications. This ensures the unyielding performance of electronic health record systems amidst shifting preconditions. OSM framework offers runtime verification of both object-oriented and AO deployments, positioning itself as an indispensable open-source resource, poised to automate the enhancement of system performance and scalability. Copyright © 2024, The Authors. All rights reserved.

关键词： Aspect oriented programming

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POPoS: Improving Efficient and Robust Facial Landmark Detection with Parallel Optimal Position Search 39

POPoS: Improving Efficient and Robust Facial Landmark Detect...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xiang, Chong-Yang He, Jun-Yan Cheng, Zhi-Qi Wu, Xiao Hua, Xian-Sheng School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Institute for Intelligent Computing Alibaba Group Shenzhen China School of Engineering and Technology University of Washington TacomaWA United States College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)157735897X

Achieving a balance between accuracy and efficiency is a critical challenge in facial landmark detection (FLD). This paper introduces Parallel Optimal Position Search (POPoS), a high-precision encoding-decoding framework designed to address the limitations of traditional FLD methods. POPoS employs three key contributions: (1) Pseudo-range multilateration is utilized to correct heatmap errors, improving landmark localization accuracy. By integrating multiple anchor points, it reduces the impact of individual heatmap inaccuracies, leading to robust overall positioning. (2) To enhance the pseudo-range accuracy of selected anchor points, a new loss function, named multilateration anchor loss, is proposed. This loss function enhances the accuracy of the distance map, mitigates the risk of local optima, and ensures optimal solutions. (3) A single-step parallel computation algorithm is introduced, boosting computational efficiency and reducing processing time. Extensive evaluations across five benchmark datasets demonstrate that POPoS consistently outperforms existing methods, particularly excelling in low-resolution heatmaps scenarios with minimal computational overhead. These advantages make POPoS as a highly efficient and accurate tool for FLD, with broad applicability in real-world scenarios. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Attribute expansion relation extraction approach for smart engineering decision-making in edge environments

Attribute expansion relation extraction approach for smart e...

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作者： Cui, Mengmeng Zhang, Yuan Hu, Zhichen Bi, Nan Du, Tao Luo, Kangrong Liu, Juntong School of Software Nanjing University of Information Science and Technology Nanjing China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing China School of Competitive Sports Shandong Sport University Jinan China School of Continuing Education and Training Shandong Sport University Jinan China Department of Physical Education Chengdu Technological University Chengdu China Sports Department Chengdu University of Information Technology Chengdu China

In sedimentology, the integration of intelligent engineering decision-making with edge computing environments aims to furnish engineers and decision-makers with precise, real-time insights into sediment-related issues. This approach markedly reduces data transfer time and response latency by harnessing the computational power of edge computing, thereby bolstering the decision-making process. Concurrently, the establishment of a sediment knowledge graph serves as a pivotal conduit for disseminating sediment-related knowledge in the realm of intelligent engineering decision-making. Moreover, it facilitates a comprehensive exploration of the intricate evolutionary and transformative processes inherent in sediment materials. By unveiling the evolutionary trajectory of life on Earth, the sediment knowledge graph catalyzes a deeper understanding of our planet's history and dynamics. Relationship extraction, as a key step in knowledge graph construction, implements automatic extraction and establishment of associations between entities from a large amount of sedimentary literature data. However, sedimentological literature presents multi-source heterogeneous features, which leads to a weak representation of hidden relationships, thus decreasing the accuracy of relationship extraction. In this article, we propose an attribute-extended relation extraction approach (AERE), which is specifically designed for sedimentary relation extraction scenarios. First, context statements containing sediment entities are obtained from the literature. Then, a cohesive hierarchical clustering algorithm is used to extend the relationship attributes between sediments. Finally, mine the relationships between entities based on AERE. The experimental results show that the proposed model can effectively extract the hidden relations and exhibits strong robustness in dealing with redundant noise before and after sentences, which in turn improves the completeness of the relations between deposits. Af

关键词： Knowledge graph

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Mulseplayer: A Multi-Sensorial Media Content Delivery Solution to Enhance End-User Quality of Experience

Mulseplayer: A Multi-Sensorial Media Content Delivery Soluti...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Cristina Hava Muntean Irina Tal Diana Bogusevschi Marilena Bratu Ting Bi Gabriel-Miro Muntean School of Computing National College of Ireland Dublin Ireland Lero School of Computing Dublin City University Dublin Ireland Department of Visual and Human-Centred Computing Dundalk Institute of Technology Ireland Faculty of Psychology and Educational Sciences University of Bucharest Bucharest Romania School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Electronic Engineering Dublin City University Dublin Ireland

ISBN: (数字)9798350364262

ISBN: (纸本)9798350364279

abstract-The rapid evolution of technology and its integration into educational settings has paved the way for innovative approaches to content delivery. This research work delves into the cutting-edge domain of multi-sensorial media content (mulsemedia) delivery applied in the education domain, where traditional pedagogical methods are reimagined to engage learners and increase their Quality of Experience through a myriad of sensory experiences. A novel Mulsemedia Content Delivery solution (Mulseplayer) is presented in this paper that was developed as part of a large-scale European project. Results from a pilot conducted with 64 participants indicate that the Mulsemedia-based teaching approach enhanced students’ Quality of Experience and improved the overall learning process.

关键词： Electronic learning Interactive systems Education Europe Games Media Sensor systems

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Improved EEG Decoding Method Based on Spiking Neural Network for SSVEPs' Classification

Improved EEG Decoding Method Based on Spiking Neural Network...

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2023 International Conference on Neuromorphic computing, ICNC 2023

作者： He, Yufan Huang, Yong Li, Yan Chu, Haoming Zheng, Lirong Ma, Ning Huan, Yuxiang Southeast University State Key Laboratory of Digital Medical Engineering Jiangsu Key Laboratory for Biomaterials and Devices School of Biological Science and Medical Engineering Nanjing210096 China Guangdong Institute of Intelligence Science and Technology Brain-Inspired Computing Laboratory Zhuhai519031 China

ISBN: (纸本)9798350316889

This paper presents an electroencephalogram (EEG) signals decoding method based on an event-driven spiking neural network (SNN) model design for steady-state visual evoked potentials (SSVEP) tasks' classification and recognition. Adopting the approach of data-driven ensemble empirical mode decomposition (EEMD) of little additional input parameters, the original signals is enhanced to obtain relatively accurate EEG data with valid SSVEP characteristics, which strengthen the signal-to-noise (SNR) at the stimulation frequency. A canonical correlation analysis (CCA) method is considered to be applied to extract frequency characteristics from the multi-lead EEG data, declining the amount of data and training overhead. The training samples with correlation coefficient vectors are sampled into sparse spike sequences through the level crossing (LC) coding and input directly to a resembled excitation-inhibition (E-I) balanced SNN model in the way of event-driven processing. With the sparse data representation, the SNN model adopts the approaches of sparse training and neuromorphic computing to perform pattern recognition for the SNN-compatible spikes. On the Wearable SSVEP-BCI dataset and BETA database provided by Tsinghua University for 12-target and 40-target EEG classification, this SNN method can achieve 96.95% and 89.72% classification accuracy, with 99.66 bits/min and 130.26 bits/min information transfer rate (ITR), respectively. © 2023 IEEE.

关键词： Biomedical signal processing

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Concatenated Residual Attention U-Net with DenseNet: A Hybrid Deep Learning Framework for Pavement Condition Evaluation and Maintenance Planning 2

Concatenated Residual Attention U-Net with DenseNet: A Hybri...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Devi, S. Rukmani Alabdeli, Haideer M. Abbas, Haider Mohammed Hindumathy, N. Sivasubramanian, S. Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Artificial intelligence School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Information Technology New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (纸本)9798331504960

Recent days, pavement management systems are important for country’s economic well-being as road authorities strive to protect their valuable road assets and minimize the maintenance costs which optimize resource allocation. Traditional approaches for accurate pavement condition evaluation faced significant challenges which include infrequent evaluations lead to delayed maintenance by causing further deterioration, insufficient funding lead to inadequate maintenance. Therefore, this paper proposes a Deep Learning (DL) based Concatenated Residual Attention U-Net with DenseNet (CRAUNet-DenseNet) for Accurate Pavement Condition Evaluation and Maintenance Planning. Initially, data is collected from pavement surface images by using two images. Then, preprocessing is done by using data augmentation to improve training process. After that, image segmentation is done by CRAUNet where, it has encoder, decoder, and skip connection parts to make image segments as well as restore feature maps to its original size. The image features were extracted by utilizing Spatial Pyramid Pooling Layer (SPPL) which allows arbitrary scales of any sizes of image. At last, DenseNet is used for determining and classify condition of road from pavement surface images. The proposed CRAUNet-DenseNet model generated outperforming results in precision 97.91%, recall 98.36%, and F1 score 99.10% when compared with existing Convolutional Neural Network (CNN). © 2024 IEEE.

关键词： Resource allocation

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Maximizing Profit of the Service Providers through Efficient Content Placement and Retrieval in an IoT-enabled Edge-Cloud Platform

Maximizing Profit of the Service Providers through Efficient...

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International Symposium on Advanced Networks and Telecommunication Systems (ANTS)

作者： Preet Gandhi Abhishriya Mahanta Manojit Ghose Aryabartta Sahu Department of Computer Science and Engineering Indian Institute of Technology Guwahati India School of Computing and Electrical Engineering Indian Institute of Technology Mandi India Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

In today’s 5G era, the amount of data generated by the Internet of Things (IoT) devices is enormous. Data is processed and stored in the cloud under a traditional cloud computing architecture, and real-time processing and retrieval of these data is time-consuming. Therefore, an edge-cloud framework comes in handy where the content generated is placed on the edge nodes of the network, and it can reach the endusers in a time-bound manner. A majority of the previous research works on content placement under the edge-cloud architecture primarily aimed at improving the latency of the system. However, the approach proposed in this paper aims to maximize the profit of the service provider by effectively placing the content in a hierarchically organized edge-cloud network. We propose two strategies and compare them with random and greedy approaches for both synthetic and real data sets. The experimental results demonstrate the effectiveness of the proposed strategies, and the profit of the service providers increases by 38.81% (maximum) and 10.51% (on average) for the synthetic dataset and by 65.74% (maximum) and 22.83% (on average) for the real-world dataset when compared with the greedy approach.

关键词： Cloud computing 5G mobile communication Computer architecture Real-time systems Telecommunications Internet of Things Synthetic data

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