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5th International Conference on Advancements in Computing, ICAC 2023

作者： Rathnayake, R.M.K.G. Kalana, K.A.V. Dharmakeerthi, Uditha Senarathne, Amila Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Software Defined Networking (SDN) promises to introduce flexibility and programmability into networks by offering developers a northbound interface (NBI) to create SDN applications. However, current designs and implementations have drawbacks, including the lack of Proper Standardization, Controller-Lock-In Situation, and Loss of Compatibility with SDN applications. Using Google Remote Procedure Call (gRPC) Application Programming Interface (API), the SDN controller can offer network resources, such as statistics, flow information, or topology data, to registered SDN applications. Furthermore, the gRPC enables controller-agnostic and language-agnostic development to enable proactive and reactive applications and services to interact with controller services. Transport Layer Security (TLS) enables confidentiality with encryption, authenticity with mutual authentication, and integrity with Message Authentication Code (MAC). Finally, Attribute-based Access Control (ABAC) and Logging capability for controller services allow fine-grained access control and accountability enforcement to ensure the security goals of the NBI. This research paper provides a complete architecture to converge different Northbound API streams into one secure, language-agnostic, controller-agnostic API stream with the help of the gRPC framework. © 2023 IEEE.

关键词： Controllers

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An asynchronous P system for solving the minimum Steiner tree with branch and bound 11

An asynchronous P system for solving the minimum Steiner tre...

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11th International Symposium on Computing and Networking Workshops, CANDARW 2023

作者： Ueno, Reo Fujiwara, Akihiro Kyushu Institute of Technology Iizuka Graduate School of Computer Science and Systems Engineering Fukuoka820-8502 Japan

ISBN: (纸本)9798350306941

Membrane computing, which is also known as a P system, is a computational model inspired by the activity of living cells. Several P systems, which work in a polynomial number of steps, have been proposed for solving computationally hard problems. However, most of the proposed algorithms use an exponential number of membranes, and reduction of the number of membranes must be considered in order to make a P system a more realistic *** the present paper, we propose an asynchronous P system using branch and bound for solving the minimum Steiner tree. The proposed P system solves the minimum Steiner tree with n vertices and m edges in O(n2) parallel steps or O(2mn2) sequential *** evaluate the number of membranes used in the proposed P system using experimental simulations. Our experimental results show validity and efficiency of the proposed P system. © 2023 IEEE.

关键词： Membranes

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Leveraging UAV-assisted communications to improve secrecy for URLLC in 6G systems

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Digital Communications and Networks 2023年第6期9卷 1458-1464页

作者： Hemant Kumar Narsani Ali Ranjha Kapal Dev Fida Hussain Memon Nawab Muhammad Faseeh Qureshi Faculty of Engineering and Technology University of SindhPakistan Department of Electrical Engineering Ecole de Technologie Superieure Montreal QCCanada Department of Institute of Intelligent Systems University of JohannesburgSouth Africa Department of Electrical Engineering Sukkur IBA UniversityPakistan Department of Computer Education Sungkyunkwan UniversitySeoulRepublic of Korea

Unmanned Aerial Vehicles(UAVs)will be essential to support mission-critical applications of Ultra Reliable Low Latency Communication(URLLC)in futuristic Sixth-Generation(6G)***,several security vulnerabilities and attacks have plagued previous generations of communication systems;thus,physical layer security,especially against eavesdroppers,is vital,especially for upcoming 6G *** this regard,UAVs have appeared as a winning candidate to mitigate security *** this paper,we leverage UAVs to propose two *** first method utilizes a UAV as Decode-and-Forward(DF)relay,whereas the second method utilizes a UAV as a jammer to mitigate eavesdropping attacks for URLLC between transmitter and receiver ***,we present a low-complexity algorithm that outlines the two aforementioned methods of mitigating interception,*** secrecy rate,and we compare them with the benchmark null method in which there is a direct communication link between transmitter and receiver without the UAV DF ***,simulation results show the effectiveness of such methods by improving the secrecy rate and its dependency on UAV height,blocklength,decoding error probability and transmitter-receiver separation ***,we recommend the best method to enhance the secrecy rate in the presence of an eavesdropper based on our simulations.

关键词： URLLC UAV-Assisted communications 6G systems Secrecy rate

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Post-operative Smart Healthcare Kit

Post-operative Smart Healthcare Kit

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International Conference on Smart systems and Wireless Communication, SSWC 2024

作者： Kanchana Devi, V. Umamaheswari, E. Gao, Xiao-Ghi Rathinavel, Nirupama School of Computer Science and Engineering Vellore Institute of Technology Chennai India Centre for Cyber Physical Systems Vellore Institute of Technology Chennai India University of Eastern Finland Kuopio Finland

ISBN: (纸本)9789819613472

The Internet of Things is becoming an increasingly important tool for the healthcare sector. It is utilized in common healthcare settings, such as smart watches and oxygen monitors, as well as in hospitals for anything from routine checks to critical care units. One of the most crucial hospital departments is post-operative care, where patients are monitored to make sure they are getting better. Here, a constant watch is kept on the patient’s vital signs and the associated organs to detect any anomalies that might develop following the treatment. Utilizing IoT in post-operative treatment will guarantee patient contentment. This is ensured by the Post-Operative Smart Healthcare Kit (POSHK), presented in this paper, furnished with several sensors, including heartbeat, oxygen and carbon dioxide sensors, blood pressure, room, and patient temperature sensors, and more. During discharge, data is gathered and analyzed to look at possible future recovery trends. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electrotherapeutics

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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within computer Science and engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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Robot Raconteur®: Updates on an Open Source Interoperable Middleware for Robotics 19

Robot Raconteur®: Updates on an Open Source Interoperable M...

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19th IEEE International Conference on Automation Science and engineering, CASE 2023

作者： Wason, John D. Wen, John T. Wason Technology Llc United States Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute United States

ISBN: (纸本)9798350320695

Robot Raconteur® (RR) is a powerful communication framework for robotics, automation, building control, and the 'Internet of Things'. RR provides unique plug-and-play, standardized, augmented object-oriented capabilities that greatly accelerate the development of systems and provide stable production performance. The project became open-source in 2018, and is available on GitHub under the Apache 2.0 license. This paper provides updates on the development of RR, the standardization of RR, the development of an ecosystem of standardized drivers, and example use cases for the framework. The RR Core library is approaching a 1.0 release with ROS Quality Level 2. © 2023 IEEE.

关键词： Middleware

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Deployment Architectures of MQTT Brokers in Event-Driven Industrial Internet of Things 50

Deployment Architectures of MQTT Brokers in Event-Driven Ind...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Amiri, Amirali Just, Valentin Steindl, Gernot Nastic, Stefan Kastner, Wolfgang Gorton, Ian Institute of Computer Engineering TU Wien Vienna Austria Distributed Systems Group TU Wien Vienna Austria Khoury College of Computer Sciences Northeastern University Seattle United States

ISBN: (纸本)9781665464543

The Industrial Internet of Things (IIoT) involves various standards, protocols, and tools, requiring extensive expertise for system design. The traditional automation pyramid limits scalability due to tightly-coupled components. Integrating IIoT data in the cloud through event-driven communication, like MQTT brokers, provides loose coupling. This integration also aids in resource monitoring and planning, enhancing IIoT application performance. Despite many IIoT architecture studies, there is a lack of empirical comparisons of MQTT broker deployment strategies. This paper examines an edge-cloud aggregation scenario, where IIoT device requests are aggregated at the edge and fog services before being transmitted to the cloud. We study four MQTT deployment architectures and compare the results empirically. We use an Arduino Opta as an IIoT device. Also, we use software-based load generation to support replicability of our experiment and reproducibility of our results. Findings indicate that a central broker on a dedicated virtual machine gives 13.8% improvements of the mean response time compared to the shared deployment of MQTT broker and device gateways. Our results offer insights into effective deployment strategies. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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Semantic2Graph: graph-based multi-modal feature fusion for action segmentation in videos

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Applied Intelligence 2024年第2期54卷 2084-2099页

作者： Zhang, Junbin Tsai, Pei-Hsuan Tsai, Meng-Hsun Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan Institute of Manufacturing Information and Systems National Cheng Kung University Tainan701 Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan

Video action segmentation have been widely applied in many fields. Most previous studies employed video-based vision models for this purpose. However, they often rely on a large receptive field, LSTM or Transformer methods to capture long-term dependencies within videos, leading to significant computational resource requirements. To address this challenge, graph-based model was proposed. However, previous graph-based models are less accurate. Hence, this study introduces a graph-structured approach named Semantic2Graph, to model long-term dependencies in videos, thereby reducing computational costs and raise the accuracy. We construct a graph structure of video at the frame-level. Temporal edges are utilized to model the temporal relations and action order within videos. Additionally, we have designed positive and negative semantic edges, accompanied by corresponding edge weights, to capture both long-term and short-term semantic relationships in video actions. Node attributes encompass a rich set of multi-modal features extracted from video content, graph structures, and label text, encompassing visual, structural, and semantic cues. To synthesize this multi-modal information effectively, we employ a graph neural network (GNN) model to fuse multi-modal features for node action label classification. Experimental results demonstrate that Semantic2Graph outperforms state-of-the-art methods in terms of performance, particularly on benchmark datasets such as GTEA and 50Salads. Multiple ablation experiments further validate the effectiveness of semantic features in enhancing model performance. Notably, the inclusion of semantic edges in Semantic2Graph allows for the cost-effective capture of long-term dependencies, affirming its utility in addressing the challenges posed by computational resource constraints in video-based vision models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

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Deep Learning with a Novel Concoction Loss Function for Identification of Ophthalmic Disease

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computers, Materials & Continua 2023年第9期76卷 3763-3781页

作者： Sayyid Kamran Hussain Ali Haider Khan Malek Alrashidi Sajid Iqbal Qazi Mudassar Ilyas Kamran Shah Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering Faculty of Computer ScienceLahore Garrison UniversityLahore54000Pakistan Department of Computer Science Applied CollegeUniversity of TabukTabukSaudi Arabia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl Hofuf31982Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl HofufSaudi Arabia

As ocular computer-aided diagnostic(CAD)tools become more widely accessible,many researchers are developing deep learning(DL)methods to aid in ocular disease(OHD)*** eye diseases like cataracts(CATR),glaucoma(GLU),and age-related macular degeneration(AMD)are the focus of this study,which uses DL to examine their *** imbalance and outliers are widespread in fundus images,which can make it difficult to apply manyDL algorithms to accomplish this analytical *** creation of efficient and reliable DL algorithms is seen to be the key to further enhancing detection *** the analysis of images of the color of the retinal fundus,this study offers a DL model that is combined with a one-of-a-kind concoction loss function(CLF)for the automated identification of *** study presents a combination of focal loss(FL)and correntropy-induced loss functions(CILF)in the proposed DL model to improve the recognition performance of classifiers for biomedical *** is done because of the good generalization and robustness of these two types of losses in addressing complex datasets with class imbalance and *** classification performance of the DL model with our proposed loss function is compared to that of the baseline models using accuracy(ACU),recall(REC),specificity(SPF),Kappa,and area under the receiver operating characteristic curve(AUC)as the evaluation *** testing shows that the method is reliable and efficient.

关键词： Deep learning multi-classification focal loss CNN eye disease

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Comparative Analysis of Word Embedding and Machine Learning Techniques for Classification of Software Developer Communications on Gitter 18

Comparative Analysis of Word Embedding and Machine Learning ...

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18th Conference on computer Science and Intelligence systems, FedCSIS 2023

作者： Akshar, Tumu Kumar, Lov Yogita Murthy, Lalita Bhanu Department of Computer Science & Information Systems BITS Pilani Hyderabad Campus India National Institute of Technology Department of Computer Engineering Kurukshetra India

ISBN: (纸本)9788396744784

In recent times, software developers widely use instant messaging and collaboration platforms, as these plat-forms aid them in exploring new technologies, raising different development-related issues, and seeking solutions from their peers virtually. Gitter is one such platform that has a heavy userbase. It generates a tremendous volume of data, analysis of which is helpful to gain insights about trends in open-source software development and the developers' inclination toward various technologies. Analyzing these trends helps these platforms better cater to the needs of the developers, in turn increasing the usage of these platforms and promoting collaborations between more developers. The classification techniques can be deployed for this purpose. The selection of an apt word embedding for a given dataset of text messages plays a vital role in determining the performance of classification techniques. In the present work, the comparative analysis of nine-word embeddings in combination with seventeen classification techniques with onevsone and onevs-rest has been performed on the GitterCom dataset for categorizing text messages into one of the pre-determined classes based on their purpose. Further, two feature selection methods have been applied. The SMOTE technique has been used for handling data imbalance. It resulted in a total of 1836 classification pipelines for analysis. The objective is to analyze their performances to recommend efficient pipelines for the classification task at hand. The experimental results show that word2vect, GLOVE with 300 vector size, and GLOVE with 100 vector size are three top-performing word embeddings having performance values taken across different classification techniques. The models trained using ANOVA features performed similarly to those models trained using all features. Finally, using the SMOTE technique helps models to get a better prpediction ability. © 2023 Polish Information Processing Society.

关键词： Classification (of information)

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