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Packet Too Big Detection and its Integration into QUIC 16

Packet Too Big Detection and its Integration into QUIC

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16th International Conference on Signal Processing and Communication System, ICSPCS 2023

作者： Völker, Timo Tuxen, Michael FH Münster University of Applied Sciences Dep. of Electrical Engineering and Computer Science Steinfurt Germany

ISBN: (纸本)9798350333510

A communication over an Internet Protocol (IP) based network fails if an endpoint sends packets that are too big to reach their destination and if the sender is unable to detect that. The node on the path that drops these packets should respond with a Packet Too Big (PTB) message. However, multiple scenarios exist in which the sender will not receive a PTB message. Even if it does, it refrains from using the information in case it suspects that a potential attacker forged the message. In particular, we are not aware of any implementation of the secure transport protocol QUIC (e.g., used by HTTP/3) that processes PTB messages. In this paper, we present a novel parameterizable PTB detection algorithm for reliable transport protocols that does not dep.nd on PTB messages. We further describe how to integrate our algorithm into QUIC, present results from an evaluation using the algorithm within a QUIC simulation model and, based on these results, suggest concrete parameter values. © 2023 IEEE.

关键词： HTTP

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An Ethereum Oracle-based solution for P2P Energy Trading Market

An Ethereum Oracle-based solution for P2P Energy Trading Mar...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Matteo Vaccargiu Roberto Tonelli Dep. of Mathematics and Computer Science University of Cagliari Italy Dep. of Mathematics and Computer Science University of Camerino Italy

ISBN: (数字)9798350351637

ISBN: (纸本)9798350351644

The increasing demand for renewable energy and sustainable development has led to the evolution of peer-to-peer (P2P) energy exchange models, transitioning from traditional centralized distribution to decentralized marketplaces. This paper presents an overview of the P2P energy market and then analyzes the potential of blockchain technology, highlighting how Ethereum smart contracts and oracles can provide significant support in energy trading and certification processes. By integrating data from smart meters through Ethereum oracles, we propose a basic starting theoretical framework for verifying renewable energy production, make transactions within the P2P market and use renewable energy credits within the real community. This approach provides transparency and trust among participants and addresses regulatory and technical challenges by providing a verifiable and decentralized ledger of energy transactions. Our proposal emphasizes the feasibility of a blockchain-based P2P energy trading system that empowers consumers as active participants, promoting a more resilient and sustainable energy landscape. The results of this paper are to show a theoretical blockchain architecture that facilitates renewable energy certification and decentralized trading, aligning with global sustainability goals and providing a basis for the creation of decentralized local markets and energy communities.

关键词： Renewable energy sources Social computing Energy exchange Smart contracts Production Smart meters Peer-to-peer computing Proposals Sustainable development Certification

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Colex2Lang: Language Embeddings from Semantic Typology 24

Colex2Lang: Language Embeddings from Semantic Typology

引用

24th Nordic Conference on Computational Linguistics, NoDaLiDa 2023

作者： Chen, Yiyi Biswas, Russa Bjerva, Johannes Dep. of Computer Science Aalborg University Copenhagen Denmark FIZ Karlsruhe AIFB Karlsruhe Inst. of Technology Karlsruhe Germany

ISBN: (纸本)9789916219997

In semantic typology, colexification refers to words with multiple meanings, either related (polysemy) or unrelated (homophony). Studies of cross-linguistic colexification have yielded insights into, e.g., psychology, historical linguistics and cognitive science (Xu et al., 2020;Brochhagen and Boleda, 2022;Schapper and Koptjevskaja-Tamm, 2022;Karjus et al., 2021;François, 2022). While NLP research up until now has mainly focused on integrating syntactic typology (Naseem et al., 2012;Täckström et al., 2013;Zhang and Barzilay, 2015;Daiber et al., 2016;de Lhoneux et al., 2018;Ponti et al., 2019;Chaudhary et al., 2019;Üstün et al., 2020;Oncevay et al., 2020;Yu et al., 2021;Ansell et al., 2021;Zhao et al., 2021;Oncevay et al., 2022), we here investigate the potential of incorporating semantic typology, of which colexification is an example. We propose a framework for constructing a large-scale synset graph and learning language representations with node embedding algorithms. We demonstrate that cross-lingual colexification patterns provide a distinct signal for modelling language similarity and predicting typological features. Our representations achieve a 9.97% performance gain in predicting lexico-semantic typological features and expectantly contain a weaker syntactic signal. This study is the first attempt to learn language representations and model language similarities using semantic typology at a large scale, setting a new direction for multilingual NLP, especially for low-resource languages. © 2023 Association for Computational Linguistics.

关键词： Computational linguistics

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UAV Detection Method Using Convolution Neural Networks Optimized by Hyper Parameters Tuning 5

UAV Detection Method Using Convolution Neural Networks Optim...

引用

5th International Conference on Engineering technology and its Applications, IICETA 2022

作者： Abdulmunem, Farah Adil Mahmood, Sawsen Abdulhadi Mustansiriyah University Computer Science Dep. College of Education Iraq

ISBN: (纸本)9781665472159

Recently, a great advantageous of autonomous unmanned air vehicles (UAVs) have increased widely due to device low cost and smaller size. Unmanned aerial vehicles (UAV) are commonly exploited to provide abundant helpfully services such as package delivery and surveillance systems. However, the possible threat to public security and personals live occasioned by drones intrusion should be addressed as a critical issue needs to resolve. In this paper, a new deep learning model for UAV detection using a customized convolution neural network CNN named IC-CNN is designed and introduced. The main objective of this research is to detect the drone object appearing at variant locations in the acquired images with different resolutions. Meanwhile, birds' objects may possibly present in the image foreground. An optimal configuration of hyper-parameters adopted in this work is conducted to get higher accuracy of drones' detection. The proposed deep learning model adopts forward and backward training workflow with customized layers for detecting drone object in the acquired images and distinguishing drone object from bird object. An optimization process is achieved to select accurate hyper-parameters that effected mainly in the classification accuracy. IC-CNN deep learning model is constructed by stacking sequence of convolution;maximum pooling and fully connected layers integrated in light weight network architecture. The proposed deep learning model based UAV detection system is evaluated using three types of drones' image datasets along with common evaluation metrics. The obtained results achieved 88%, 100% and 100% detection accuracy on the three datasets respectively. © 2022 IEEE.

关键词： Drones

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A New Implementation of SHA-3 Hash Function Using Seed Value 5

A New Implementation of SHA-3 Hash Function Using Seed Value

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5th International Conference on information technology, Applied Mathematics and Statistics, ICITAMS 2023

作者： Kashmar, Ali H. Shihab, Ammar I. Jawad, Wafaa S. University Of Baghdad Dept. Of Computer Science College Of Science Baghdad Iraq Islamic University Of Lebanon Faculty Of Engineering Dep. Of Computer And Communication Beirut Lebanon

ISBN: (纸本)9798350335118

This paper aimed to suggest a new Secured Hash Algorithm 3 (SHA-3) with seed value implementation. Seed worth ensuring that there are fewer expectations collisions, despite an attacker selecting the data. Furthermore, attackers who combine seed values with hash functions are prevented from exploiting the properties of a hash function to construct many security attacks, such as denial of service attacks and dictionary attacks. A novel approach to enhanced SHA-3 diffusion was proposed here by XORing the seed value with θ step result after transforming the seed bits into a 5 × 5 × 64 matrix and discarding the rest. © 2023 IEEE.

关键词： Hash functions

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Machine Learning Approach for Complaint Prediction in the Telecom Industry 2

Machine Learning Approach for Complaint Prediction in the Te...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Gewaly, Mohamed Mahmoud Saeed, Mahmoud Samy Ammar, Ammar Mohammed FGSSR Cairo University Dept. of Data Science Cairo Egypt Telecom Egypt Cairo Egypt FGSSR Cairo University Dep. of Computer Science Cairo Egypt

ISBN: (纸本)9798350362633

The quality of service of the individual user of telecommunication services is one of the most critical criteria in the telecom industry that affects customer satisfaction. This paper aims to predict customer complaints before escalation and validate current escalated complaints in fixed broadband from telecommunication companies via historical and real-time customer data, including service logs and previous complaints, to optimize resource allocation and maximize customer satisfaction as the challenges are related to the volume of physical complains and churn. The dataset is real-world data provided by a single Egyptian mobile network operator and consists of 12 features pertaining to 100,000 customers. We represent 12 predictive models developed using machine learning algorithms via advanced techniques such as classification algorithms like Random Forest Classifier (RFC), AdaBoost Classifier with a Decision Tree (DT), Logistic Regression (LR), -Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), Decision Tree Classifier (DTC), Support Vector Machine (SVM) to analyze various features and patterns within the data. The results show that the AdaBoost with a Decision Tree model did well using a variety of assessment indicators, including Accuracy, Precision, Recall, and F1-score;the model aims to classify customers, indicating their likelihood of raising complaints in the future. The results and insights obtained from this study can provide telecom service providers with valuable guidance for implementing data-driven strategies to improve customer experience and take proactive maintenance that will reduce cost and churn. © 2024 IEEE.

关键词： Customer satisfaction

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Deep Learning Technique for Real-Time Forest Fire Detection from Video Streams 11

Deep Learning Technique for Real-Time Forest Fire Detection ...

引用

11th International Conference on Intelligent Systems and Embedded Design, ISED 2023

作者： Gupta, Nidhi Chakravarty, Sujata Dash, Satyabrata Centurion University of Technology & Management Dept. of Computer Science & Engineering Odisha India Gandhi Institute of Technology & Management Gitam Deemed to Be University Dep. of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798350318159

Forest fires are currently the most common issue that we are dealing with. The resources that have been lost are irreplaceable. Forest fires are uncontrollable fires that occur in nature as a result of human involvement or any other natural event. Forest fires can be so large that putting them out takes a long time. Contrasting with previous years, there has been a striking surge in the frequency of forest fires. Changes in climatic conditions are the key factor affecting the scope of forest fires. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. The incorporation of a Video Fire Detection model significantly enhances the efficiency of fire detection systems, particularly in large spaces. Establishing a video fire detection system involves focusing on spatial, spectral, and temporal indicators, as well as flame picture segmentation, recognition, tracking, and prediction. Analytical techniques, specifically utilizing ResNet50 and VGG16 models, can be applied to identify pool fire images through these segmentation processes, contributing to more robust fire detection capabilities. © 2023 IEEE.

关键词： Fires

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Maximize Leader Election Algorithm Performance in Bi-Directional Ring Networks With Both Links Down Between Two Nodes 25

Maximize Leader Election Algorithm Performance in Bi-Directi...

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25th International Arab Conference on information technology, ACIT 2024

作者： Al Refai, Mohammed N. Fawareh, Hamed Aljawawdeh, Hamzeh Alshraiedeh, Fuad Sameh Al-Quraan, Mohammad Al-Odat, Hisham Ali Otoom, Ahmed Jamhawi, Zeyad M. Kanan, Mohammad Zarqa University Software Engineering Dep. Zarqa Jordan Zarqa University Zarqa Jordan Applied Science Private University Department of Cyber Security Amman Jordan Applied Science Private University Department of Computer Science Amman Jordan University of Business and Technelogy Dep. of Industry Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

The process of selecting a leader to avoid instability in distributed system network after leader failure is called Leader Election Algorithm (LEA). This process becomes more complicated when it faces intermittent or complete links disconnected. the network becomes a one-way behavior if both lines of communication between two points are disrupted and thus the network becomes very slow, especially in systems where time is a critical issue, and the process of communication between points becomes much slower if the network leader is next to the fault or close to it, so the criteria will be established to ensures that the system leader in the Bidirectional ring communication network is in a place that allows next leader to communicate with all points as quickly as possible, and this will be during the election *** research presents a solution for leader failure problem in bidirectional ring network. This research takes into account the existence of intermittent or disconnection links between nodes. It will produce three algorithms to determine a new leader in the network with maximum network performance. It uses the least probability of time steps and the number of messages needed to elect the new leader in bidirectional ring network with N communicated nodes. so that the system remains able to work and communicate with its best capacity during this problem with the number of message complexity O(N) in the worst case. The third scenario minimizes the value of proper leader ID IDCurrent/IDCurrent-idStepCount', and maximizes the value of proper leader place N - (N - nStepCount). © 2024 IEEE.

关键词： Artificial intelligence

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The Correlation Between Architecture Smells, Package Stability and Reusability: A Case Study 4

The Correlation Between Architecture Smells, Package Stabili...

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4th IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2024

作者： Benidris, Mrwan Elfallah, Ehab A. Omar Cheema, Marwa Aslam Alsma, Mohammed Salah Addin Kadiki, Tareq Ziad Elbarsha, Abdelfattah University of Benghazi Faculty of Information Technology Dep. of Computer Science Libya Libyan International Medical University Faculty of Information Technology Dept. of Software Engineering Libya University of Benghazi Faculty of Public Health Health Informatics Department Libya

ISBN: (纸本)9798350372632

the design decisions made in the architecture of a software system are essential to its maintainability, and thus its quality is of great importance. Architecture smells (ASs) can be used to identify any quality issues within the system. To further explore the relationships between ASs, stability, and reusability, Spearman correlation coefficients will be calculated. This paper seeks to examine the connection between architecture smells, software stability, and reusability. To do this, we utilized a dataset created by other researchers and conducted an empirical study of the associations between five types of architecture smells and software stability and reusability. This research is necessary as there is a lack of in-dep.h research on architecture smells and their relationship to software stability and reusability. We found a relationship between the presence of Feature Concentration in package and the stability and reusability of the package. Also, package's stability is affected by both Cyclic dep.ndency and Unstable dep.ndency. A slight positive correlation has been found between God Class and stability, as well as a very weak negative correlation between Unstable dep.ndency and reusability. Finally, we found a slight negative correlation between package stability and reusability. © 2024 IEEE.

关键词： Reusability

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Enhancing Freezing of Gait Detection in Parkinson’s Through Fine-Tuned Deep Learning Models

Enhancing Freezing of Gait Detection in Parkinson’s Through...

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Digital Health (ICDH), IEEE International Conference on

作者： Michele Tebaldi Graziano Pravadelli Florenc Demrozi Rosalba Giugno Cristian Turetta Dep. of Computer Science University of Verona Italy Dep. of Engineering for Innovation Medicine University of Verona Italy Dep. of Electrical Engineering and Computer Science University of Stavanger Norway

ISBN: (数字)9798350368574

ISBN: (纸本)9798350368581

Freezing of Gait (FoG) is a common and disabling symptom in Parkinson’s Disease (PD), characterized by a sudden and temporary inability to initiate or continue walking. FoG arises from various factors such as environmental triggers, or physiological status of people with Parkinson’s. Traditional methods for preventing or alleviating FoG have limitations, prompting exploration into new technologies, such as the combination of sensing technologies and Deep Learning (DL) and Machine Learning (ML) algorithms. However, recognizing FoG with sensors and ML/DL poses challenges, such as the generalizability of the FoG recognition models over different individuals. Moreover, current approaches often require extensive time and effort to personalize the FoG recognition models. To mitigate these challenges, we propose a system that reduces the workload for creating personalized models through a fine-tuning approach. Our methodology has undergone rigorous testing in a subject-indep.ndent setup on a self-collected dataset of 22 subjects. Through the fine-tuning phase, we observed a remarkable average increase of up to $20.9 \%$ in F1-score performance compared to the training and testing approach without fine-tuning.

关键词： Training Deep learning Radio frequency Measurement Machine learning algorithms Refining Physiology

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