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检索条件"任意字段=12th International Symposium on From Data Models and Back, DataMod 2024"

共 49 条记录，以下是11-20 订阅

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A Dense Network Model of Traffic Classification Based on Autoencoder and Attention Mechanism 12

A Dense Network Model of Traffic Classification Based on Aut...

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12th IEEE international Conference on Computer Science and Network Technology, ICCSNT 2024

作者： Zhou, Yansen Wang, Jing Zhou, Songyan University of International Relations School of Cyber Science and Engineering Beijing China University of International Relations Graduate Department Beijing China

ISBN: (纸本)9798350350791

To address the problems of poor effect of feature dimensionality reduction, gradient disappearance and inadequate extraction of features from different layers in existing models of network traffic classification, a dense network model of traffic classification based on auto-encoder combined with attention mechanism is proposed. the proposed model firstly adopts auto-encoder to better achieve dimensionality reduction of data feature. Secondly, the model introduces the attention mechanism to increase the weight of features that have an important impact on the classification performance. Finally, it achieves multi-layer feature reuse by introducing DenseNet, a dense connection network, and then improves feature learning capability by shortening the connection between the front and back layers. the proposed classification model is trained and validated using the CIC2018 dataset. the experimental results show that the proposed model achieves a certain magnitude of improvement in traffic classification performance relative to the existing classification model. © 2024 IEEE.

关键词： Dimensionality reduction

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Arabic Text Detection from Natural Images Using Transformers 12

Arabic Text Detection From Natural Images Using Transformers

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12th IEEE international symposium on Signal, Image, Video, and Communications, ISIVC 2024

作者： Abdessamad, Ayad Habib, Ayad Abdellah, Adib Hassan Ii University of Casablanca Team Data Science & Artificial Intelligence Mathematics Computer Science & Applications Laboratory Faculty of Sciences and Technologies Mohammedia Morocco

ISBN: (纸本)9798350385267

Text detection in the wild is an important stage for recognizing and interpreting text present in images or videos captured in uncontrolled environments in the wild. It helps in various applications such as scene understanding, robot navigation, augmented reality, and automatic captioning. However, it remains a difficult task, due to the vast differences in lighting, backgrounds, textures, and fonts. the research, in this topic, has predominantly concentrated on languages utilizing Latin characters, neglecting other languages with distinct features, such as the Arabic language. this work specifically addresses the topic of Arabic scene text detection. We propose a model for Arabic text detection from natural images based on transformers. We experiment two variations of this model: the first focuses on Arabic text, whereas the second is bilingual and handles both Latin and Arabic languages. We evaluate the performance of both models on existing datasets namely, the Everyday Arabic-English Scene Text dataset EvarEst [1] and the Arabic-Latin Scene Text Localization in Highway Traffic Panels dataset ASAYAR [2]. © 2024 IEEE.

关键词： Augmented reality

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Fuel injection fault diagnosis using structural analysis and data-driven residuals 12

Fuel injection fault diagnosis using structural analysis and...

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12th IFAC symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)

作者： Allansson, Niklas Mohammadi, Arman Jung, Daniel Krysander, Mattias Linkoping Univ Div Vehicular Syst Linkoping Sweden

A data-driven diagnosis system is developed for fault diagnosis of a fuel injection system in a heavy-duty diesel truck. Physical insights and standard component modeling are used to derive a structural model of the fuel injection system. Based on structural analysis, a set of data-driven residual generator candidates is derived, both linear and nonlinear models, and trained using nominal training data from a truck. Evaluations of different fault scenarios show that the proposed models can distinguish between different faults and show the potential of utilizing basic physical insights in data-driven fault diagnosis design. Copyright (c) 2024 the Authors. this is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： data-driven fault diagnosis structural analysis automotive system identification

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Breaking SHA-1 through QUBO models 12

Breaking SHA-1 through QUBO models

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12th international symposium on Computing and Networking Workshops

作者： Tsukiyama, Shunsuke Nakano, Koji Ito, Yasuaki Kato, Takumi Kawamata, Yuya Hiroshima Univ Grad Sch Adv Sci & Engn Kagamiyama 1-4-1 Higashihiroshima Hiroshima 7398527 Japan NTT Data Grp Corp Res & Dev Headquarters Toyosu Ctr BldgAnnex 3-9Toyosu 3 ChomeKoto Ku Tokyo 1358671 Japan

ISBN: (纸本)9798331505356;9798331505349

A Quadratic Unconstrained Binary Optimization (QUBO) model is defined by a quadratic objective function comprising binary variables, each taking values of 0 or 1. the objective of the QUBO problem is to determine the optimal assignment of these binary variables to minimize the QUBO model. QUBO models are instrumental in solving various combinatorial optimization problems, leading to extensive research on solver development, as many combinatorial optimization problems can be reformulated as QUBO problems. Notably, D-Wave Systems, a company specializing in quantum technologies, has developed quantum annealers which are programmable QUBO solvers that utilize quantum mechanics. SHA-1 is a cryptographic hash function that generates a 160-bit hash value from a message of an arbitrary length. this hash value makes it difficult to compute the original message within a practical timeframe. A pre-image attack, which aims to compute the original message from a hash value, is important in cryptographic hash functions. Previous research has shown that a QUBO can be designed to simulate SHA-1, facilitating the execution of a pre-image attack. In this paper, we present a novel QUBO model with fewer bits, linear terms, and quadratic terms than previous QUBO models for simulating hash values and pre-image calculations in SHA-1. While the previous QUBO model requires 34,016 bits, 32,584 linear terms, and 168,188 quadratic terms, our new model requires only 32,096 bits, 26,600 linear terms, and 157,308 quadratic terms. this approach can efficiently execute pre-image attacks using a quantum annealer, providing valuable insights for evaluating the robustness of cryptographic security systems.

关键词： Combinatorial optimization

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Construction and Application of a Mathematical Model for Air Quality data Calibration Based on Time Series Analysis Method 12

Construction and Application of a Mathematical Model for Air...

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12th international symposium on Project Management, ISPM 2024

作者： Liu, Xiaoyu Hu, Zhengkun Chen, Zhansheng College of Applied Science and Technology Beijing Union University Beijing100012 China

ISBN: (纸本)9798331302344

Air pollution is a great hazard to the ecosystems, climate and human health. therefore, it is of great research significance to fully utilize the accuracy of " national monitor stations " and the economy of " locality control points " to monitor the concentration of "two dust and four gases" (PM2.5, PM10, CO, NO2, SO2, O3) in real time. Firstly, SPSS was used to perform correlation analysis on the parameters, and it was found that PM2.5 and PM10 have a strong correlation (0.816), while PM2.5, PM10, and CO have a strong correlation (0.662/0.582). Secondly, taking the collection time of national monitor stations as a reference, the data of locality control points was first screened and organized, and then the time offset processing of the self-built points data was conducted, and the correlation between national monitor stations and locality control points data was established with time as a link. thirdly, based on the periodic characteristics of the data difference between the national monitor stations and the locality control points and the continuity of the data collected from the locality control points, the locality control points data was calibrated by using SPSS tools and ARIMA, a time series analysis method. According to the results of several models, over 83% of R2>0.75, with a maximum value of 0.976, indicating a high degree of fit. this indicated that the model was reasonable and can achieve good calibration results. © 2024 ISPM. All Rights Reserved.

关键词： Time series analysis

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12th international symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2024

12th International Symposium on Leveraging Applications of F...

引用

12th international symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2024

ISBN: (纸本)9783031753794

the proceedings contain 25 papers. the special focus in this conference is on Leveraging Applications of Formal Methods, Verification and Validation. the topics include: Klaim in the Making;formal Approaches for Modeling and Analysis of Business Process Collaborations;an Abstract Account of Up-to Techniques for Inductive Behavioural Relations;language Equivalence from Nondeterministic to Weighted Automata—and back;a Process Algebraic View of In/Out Prisoners;towards a Formal Testing theory for Quantum Processes;testing Quantum Processes;the ProbInG Project: Advancing Automatic Analysis of Probabilistic Loops;towards a Probabilistic Programming Approach to Analyse Collective Adaptive Systems;Can AI Help with the Formalization of Railway Cybersecurity Requirements?;white-Box Validation of Collective Adaptive Systems by Statistical Model Checking and Process Mining;analysing Collective Adaptive Systems by Proving theorems;engineering Ethical-Aware Collective Adaptive Systems;emerging Synchrony in Applauding Audiences: Formal Analysis and Specification;flocks of Birds: A Quantitative Evaluation;strategies in Spatio-Temporal Logics for Multi-agent Systems;function-as-a-Service Allocation Policies Made Formal;asynchronous Multiparty Sessions with Internal Delegation: Dedicated to Rocco De Nicola on the Occasion of his 70th Birthday;code-Centric Code Generation;attack Tree Generation via Process Mining;local Spaces in Soft Concurrent Constraint Programming Oriented to Security;Riding the data Storms: Specifying and Analysing IoT Security Requirements with SURFING;preventing Out-of-Gas Exceptions by Typing.

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MindWave app: Leveraging AI for Mental Health Support in English and Arabic 12

MindWave app: Leveraging AI for Mental Health Support in Eng...

引用

12th IEEE international symposium on Signal, Image, Video, and Communications, ISIVC 2024

作者： Bensalah, Nouhaila Ayad, Habib Adib, Abdellah Farouk, Abdelhamid Ibn El University of Hassan II Casablanca Team: Data Science & Artificial Intelligence Casablanca20000 Morocco Teaching Languages and Cultures Laboratory Mohammedia Morocco

ISBN: (纸本)9798350385267

the global concern regarding mental health, especially amidst crises like stress, burnout or depression, necessitates accessible platforms catering to diverse populations, including Arabic speakers. this paper introduces 'MindWave,' a pioneering web application for English and Arabic-speaking communities. It is an innovative web application designed to offer support and early detection for individuals experiencing or at risk of mental health. the app uses advanced Natural Language Processing (NLP) and Sentiment Analysis (SA) to analyze user-generated text to detect signs of mental health, providing personalized insights, resources, and support for users in need. Its innovation lies in seamlessly integrating text and voice to comprehensively assess emotional well-being. the app fosters a supportive community where users can connect, share experiences, and provide mutual support. Users can engage in discussions, join support groups, or share coping strategies in a safe and anonymous environment. To address the absence of Arabic sentiment datasets for mental health, this study explores the effectiveness of fine-tuning various pre-trained Machine Translation (MT) models from English to Arabic using our parallel dataset of 945,383 sentences. Furthermore, the study delves into training different variants of Bidirectional Encoder Representations from Transformers (BERT) models on two distinct datasets: the original English dataset and the translated Arabic dataset. the classifiers' performance trained on the source English dataset is juxtaposed with the results achieved when trained on the translated Arabic dataset. Moreover, we conduct a comparative evaluation of diverse tokenization techniques for Arabic, including Stochastic tokenizer, Disjoint-letter tokenizer, and Morphological tokenizer. © 2024 IEEE.

关键词： Sentiment analysis

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the scenario approach for data-driven prognostics 12

The scenario approach for data-driven prognostics

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12th IFAC symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)

作者： Cesani, D. Mazzoleni, M. Previdi, F. Univ Bergamo Dept Management Informat & Prod Engn Via Marconi 5 I-24044 Dalmine BG Italy

Prognostics is the process of forecasting the time-to-failure or the time-to-alarm of an industrial item using degradation models. data-driven approaches to prognostics employ regression models fit on condition indicators computed from raw run-to-failure data to extrapolate the degradation behaviour of the item. the development of a reliable data-driven degradation model typically requires many run-to-failure acquisitions to understand the degrading behavior. Such experimental tests are destructive and expensive for items manufacturers. thus, decreasing the number of run-to-failure experiments is key in reducing predictive maintenance costs. In this work, focusing on time-to-alarm prediction to anticipate items breakdown, we propose a data-driven method based on the scenario approach to characterise the degradation behaviour of an industrial item in certain operative conditions using only one run-to-failure experiment, updating the time-to-alarm prediction only when needed. the scenario approach gives probabilistic guarantees on the time-to-alarm predictions. Copyright (c) 2024 the Authors.

关键词： Prognostics Scenario approach

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12th international symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2024