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2021 international conference on Network Communication and Information Security, ICNCIS 2021

作者： Chunyan, Zhou Hao, Cao Qiyun, Li Shanghai Maritime University China

ISBN: (数字)9781510653283

ISBN: (纸本)9781510653276

It is difficult to improve underwater acoustic communication [2] due to the long delay extension of underwater acoustic channel and serious selective fading in frequency domain. At the same time, there is residual redundancy in the actual source of underwater acoustic communication, which is difficult to be utilized by traditional methods, resulting in a certain broadband waste. In order to improve the reliability of underwater acoustic communication, a joint coding method based on Polar code is proposed in this paper. [6] According to the characteristics of source coding and channel coding, this method uses redundancy in source coding to resist channel errors. In addition, the unequal error protection mechanism is added, which can improve transmission efficiency by adopting the code with [5] good error correction ability for important code streams and adopting the code with poor error correction ability or not for non-important code streams according to the different importance of the output code streams of the source. In this way, a joint encoder responsible for both source compression and channel error protection is realized. © 2022 SPIE

关键词： Acoustics Forward error correction Polarization Telecommunications Data compression computer programming Orthogonal frequency division multiplexing Reliability Receivers systems modeling

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Short-term speed prediction of urban roads based on multi-source feature fusion 23

Short-term speed prediction of urban roads based on multi-so...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on Data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Liu, Silin Liao, Zhuhua Liu, Yizhi Yi, Aiping School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory of Service Computing and New Software Service Technology Xiangtan China

ISBN: (纸本)9781665494571

Short-term traffic forecasting is an important means to prevent urban congestion. In this paper, the urban traffic operation time series data sets are used for modeling, combining with various characteristic variables of weather conditions, air quality index (AQI) and travel time index (TTI). A bidirectional long and short-term memory network framework (MF-BiLSTM) based on multi-source feature fusion was established to predict the average road speed. The comparison experiment selects three benchmark models: ARIMA, LSTM and CNN, and a bidirectional long short-term memory network (BiLSTM) model using only road speed time series data sets, and compares the prediction accuracy of these models under three prediction durations. The results show that MF-BiLSTM has the best prediction performance in terms of prediction error measurement, the prediction accuracy is 2.93%, 1.95%, 2.87%, 0.89% higher than that of ARIMA, LSTM, CNN, and BiLSTM, respectively. In addition, comparing the prediction accuracy of each model under different weather conditions and air pollution levels, MF-BiLSTM has the smallest deviation. © 2021 IEEE.

关键词： Correlation Roads Atmospheric modeling Time series analysis Weather forecasting Predictive models Air pollution

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The Adaptive Search Strategy Selection Based on the Guide of Two Learning Mechanisms for High-Dimensional Expensive Problems 5

The Adaptive Search Strategy Selection Based on the Guide of...

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5th international conference on Data-Driven Optimization of Complex systems, DOCS 2023

作者： Gong, Yiyun Kang, Li Qiao, Gangzhu Guo, Dongpeng Yu, Haibo Zeng, Jianchao School of Electrical and Control Engineering North University of China Taiyuan China School of Environment and Safety Engineering North University of China Taiyuan China School of Computer Science and Technology North University of China Taiyuan China School of Environment and Resources Taiyuan University of Science and Technology Taiyuan China

ISBN: (纸本)9798350393521

To enhance the efficiency of the surrogate-assisted evolutionary algorithm (SAEA) for solving high-dimensional expensive optimization problems with multiple local optima and multivariate coupling, an SAEA incorporating two machine learning methods is proposed, named the adaptive search strategy selection based on the guide of two learning mechanisms (ASSS-GTLM). The proposal is based on three heterogeneous surrogate modeling techniques integrating four search strategies with different merit search orientations. In ASSS-GTLM, reinforcement learning is adopted to adaptively adjust the selection of the search strategies according to the online feedback information of the optimal solution during optimization so as to achieve a balance between global exploration and local exploitation in the solution space. In addition, an Autoencoder-based subspace surrogate-assisted search strategy is designed to reconstruct the original space features to incrementally search the space for potentially optimal sample information. Experimental results show that the ASSS-GTLM algorithm performs significantly better than others in high-dimensional benchmark problems up to 300 dimensions and has stronger convergence performance and optimization efficiency. © 2023 IEEE.

关键词： Reinforcement learning

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Real-time inverse kinematics function generation using (GANs) and advanced computer vision for Robotics joints

Real-time inverse kinematics function generation using (GANs...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER international conference on

作者： Aakash Palliwar Aman Khapre Aarya Kasare Samiksha Nikhare Department of Artificial Intelligence Gh Raisoni College of Engineering Nagpur India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

This research presents a novel approach for real-time inverse kinematic function generation for robotic joints using Generative Adversarial Networks (GANs) integrated with advanced computer vision techniques. This project includes an automated [15] model that moves based on human hand movements. The system uses Modern computer vision techniques to precisely interpret human hand motions, which enables a robot to imitate hand movements in real-time, mainly in the X and Y axes. The proposed technique leverages GANs for modeling complex, high-dimensional kinematic transformations, allowing for accurate and effortless computation of joint configurations determined by end-effector positions. Thus, if a human hand performs a wave motion, the robotic arm will move from left to right, replicating the wave action. Using sensors, inverse kinematics, and AI/ML algorithms, the outcome shows significant improvements in motion accuracy and computational efficiency over usual inverse kinematic solutions, showcasing the model’s potential in human-robot interactions.

关键词： Hands computer vision Accuracy Service robots Heuristic algorithms Computational modeling Kinematics Arms Robot sensing systems Real-time systems

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NIPVS-FL: A Non-interactive Publicly Verifiable Secure Federated-Learning Scheme against Malicious Servers 3

NIPVS-FL: A Non-interactive Publicly Verifiable Secure Feder...

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3rd international conference on computer Communication and Network Security, CCNS 2022

作者： Wang, Yizhuo Cao, Zhenfu Dong, Xiaolei Shen, Jiachen School of Software Engineering East China Normal University Shanghai200062 China

ISBN: (纸本)9781510660113

Federated learning (FL) enables decentralized data sources like mobile phones to joint training a neural network model without sharing the original data. However, shared local gradients make the privacy of local data in FL vulnerable. The aggregation server also may return incorrect results to clients due to unexpected error or the deliberately attack. In this work, we explore how to design a non-interactive and publicly verifiable aggregation scheme. The existing verifiable schemes are under semi-honest adversary model, in which the server is honest-but-curious but with additional power to counterfeit the aggregation result. We propose a scheme under stronger security model against malicious servers. The proposed scheme guarantees that as long as the two servers are non-colluding, even a malicious server cannot obtain input privacy of client. The malicious server will be detected by honest clients when it tries to tamper the result. © 2022 SPIE.

关键词： computer simulations Data modeling systems modeling Information security Java Machine learning Neural networks Receivers Resistance Software engineering

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6th international joint conference on Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM), APWeb-WAIM 2022

6th International Joint Conference on Asia-Pacific Web (APWe...

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6th international joint conference on Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM), APWeb-WAIM 2022

ISBN: (纸本)9783031252006

The proceedings contain 125 papers. The special focus in this conference is on Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM). The topics include: Neighborhood Constraints Based Bayesian Personalized Ranking for Explainable Recommendation;hierarchical Aggregation Based Knowledge Graph Embedding for Multi-task Recommendation;mixed-Order Heterogeneous Graph Pre-training for Cold-Start Recommendation;MISRec: Multi-Intention Sequential Recommendation;MARS: A Multi-task Ranking Model for Recommending Micro-videos;how to Share Medical Data Belonging to Multiple Owners in a Secure Manner;LAP-BFT: Lightweight Asynchronous Provable Byzantine Fault-Tolerant Consensus Mechanism for UAV Network Trusted systems;a Secure Order-Preserving Encryption Scheme Based on Encrypted Index;computing Online Average Happiness Maximization Sets over Data Streams;A Deep Reinforcement Learning-Based Approach for Android GUI Testing;RoFL: A Robust Federated Learning Scheme Against Malicious Attacks;FDP-LDA: Inherent Privacy Amplification of Collapsed Gibbs Sampling via Group Subsampling;multi-modal Fake News Detection Use Event-Categorizing Neural Networks;unified Proof of Work: Delegating and Solving Customized Computationally Bounded Problems in a Privacy-Preserving Way;TSD3: A Novel Time-Series-Based Solution for DDoS Attack Detection;executing Efficient Retrieval Over Blockchain Medical Data Based on Exponential Skip Bloom Filter;IMPGA: An Effective and Imperceptible Black-Box Attack Against Automatic Speech Recognition systems;FD-Leaks: Membership Inference Attacks Against Federated Distillation Learning;when Self-attention and Topological Structure Make a Difference: Trajectory modeling in Road Networks;A Penetration Path Planning Algorithm for UAV Based on Laguerre Diagram;block-Join: A Partition-Based Method for Processing Spatio-Temporal Joins;RN-Cluster: A Novel Density-Based Clustering Approach for Road Network Partition;fine-Grained Urban Flow Inferring via Condit

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On Predictive Big Data Quality

On Predictive Big Data Quality

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international IEEE conference on Intelligent systems, IS

作者： Ismael Rhemat Panagiotis Chountas Francois Roubert School of Computer Science & Engineering University of Westminster London Mellitah Oil & Gas Co. Tripoli-Libya Tripoli Libya

ISBN: (数字)9798350350982

ISBN: (纸本)9798350350999

Businesses process use data to produce services and or goods, and most importantly for developing business strategies as part of data driven change management approach. The goodness of the data used is an important factor regarding process lead time, customer satisfaction and service quality. The main contribution of this work is to define a big data quality conceptual framework related to business process performance as part of an online analytical, predictive mining framework. Here we consider the domain of Mellitah Oil & Gas (MOG), in Libya.

关键词： measurement Biological system modeling Data integrity Unified modeling language Big Data Predictive models Data models Open wireless architecture Data mining Business

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6th international joint conference on Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM), APWeb-WAIM 2022

6th International Joint Conference on Asia-Pacific Web (APWe...

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6th international joint conference on Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM), APWeb-WAIM 2022

ISBN: (纸本)9783031251573

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A Knowledge Graph-Based Deep Learning Framework for Efficient Content Similarity Search of Sustainable Development Goals Data

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Data Intelligence 2023年第3期5卷 663-684页

作者： Irene Kilanioti George A.Papadopoulos School of Electrical and Computer Engineering National Technical University of Athens9 Heroon Polytechneiou St.Zografou Campus Athens15780Greece Department of Computer Science University of Cyprus1 University AvenueAglantziaCY-2109NicosiaCyprus

Sustainable development denotes the enhancement ofliving standards in the present without compromising future generations'*** Development Goals(SDGs)quantify the accomplishment of sustainable development and pave the way for a world worth living in for future *** can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data,as intended by this *** propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves(HSFCs)in order to semantically cluster new uncategorised SDG data and novel indicators,and efficiently place them in the environment of a distributed knowledge graph ***,a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment,for retrieval of indicators and loadbalancing along with an approach for data classification of entrant-indicators is ***,a thorough case study in a distributed knowledge graph environment experimentally evaluates our *** results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data,including intergovernmental organizations,government agencies and social welfare *** approach empowers SDG knowledge graphs for causal analysis,inference,and manifold interpretations of the societal implications of SDG-related actions,as data are accessed in reduced retrieval *** facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching,as semantic cohesion of data is preserved.

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System Engineering-Based Threat modeling for Heavy-Duty Vehicles

System Engineering-Based Threat Modeling for Heavy-Duty Vehi...

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Annual IEEE systems conference

作者： Narges Rahimi Beth-Anne Schuelke-Leech Mitra Mirhassani Mechanical Automotive and Materials Engineering University of Windsor Windsor Canada Electrical and Computer Engineering University of Windsor Windsor Canada

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

With rapid advancements in connected vehicle technologies, modern vehicles have become increasingly vulnerable to cyber-attacks. While concerns about automotive cybersecurity are growing, research specifically addressing the cybersecurity of heavy-duty vehicles, such as freight trucks, remains limited. Given that disruptions in the operation of these vehicles can have significant impacts on the economy, supply chains, and transportation systems, it is urgent to explore the cybersecurity of such vehicles. This study addresses this gap by performing a cybersecurity threat analysis of a freight truck. As highlighted in ISO/SAE 21434, threat analysis and risk assessment are fundamental steps in implementing cybersecurity. This study focuses on the interfaces of heavy-duty vehicles, using the STRIDE framework. The process begins by identifying critical assets and analyzing potential attack vectors. Following a systems engineering approach, the truck's system is decomposed into subsystems to identify potential cybersecurity vulnerabilities. After constructing a data flow diagram based on the system engineering results, the STRIDE framework is then applied using the Microsoft Threat modeling Tool to systematically identify and classify possible attack scenarios. The study also outlines the features of extracted threats related to heavy-duty vehicle interfaces. This work presents a structured approach to threat modeling, focusing on truck interfaces, and contributes to a deeper understanding of cybersecurity risks in connected heavyduty vehicles.

关键词： Threat modeling measurement Prevention and mitigation Supply chains Transportation Feature extraction Telematics Vectors systems thinking computer security

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