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10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021

作者： Zeng, Ximu Wang, Yixiong Deng, Xin Wang, Jin Key Laboratory of Data Engineering and Visual Computing College of Computer Science and Technology Chongqing University of Posts and Telecommunication Chongqing400065 China

ISBN: (纸本)9781665424233

In order to solve the problem of traffic congestion, many city governments have begun to develop intelligent transportation systems. As a research hotspot in the field of intelligent transportation, the short-term traffic flow prediction is of great significance to traffic diversion and route planning. In the recent big data era, the machine learning (ML) algorithms have been applied to mining deep information in the data. However, the performance of a single ML model is usually not good. The ensemble learning can improve accuracy in most cases. In this paper, we propose a new short-term traffic flow prediction model. This paper regards the traffic prediction problem as a regression prediction problem, rather than a time series forecasting problem. The proposed model combines the prediction results of the XGBoost, LightGBM and CatBoost models through the ensemble machine learning strategy. With the experiments on real traffic data in Xi'an city, this paper has obtained the comparison of predictive performance of the models. Compared with single models such as the ARIMA, LSTM, XGBoost, LightGBM and CatBoost models, the results show the proposed model is more accurate and more suitable for short-time traffic status prediction. Key Words: traffic prediction, ensemble learning, XGBoost, LightGBM, CatBoost © 2021 IEEE.

关键词： Traffic congestion

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Privacy-Preserving Information Fusion Technique for Device to Server-Enabled Communication in the Internet of Things:A Hybrid Approach

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computers, Materials & Continua 2024年第7期80卷 1305-1323页

作者： Amal Al-Rasheed Rahim Khan Tahani Alsaed Mahwish Kundi Mohamad Hanif Md.Saad Mahidur R.Sarker Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science AbdulWali Khan University MardanMardan23200Pakistan Faculty of Computing and Informatics University Malaysia SabahKota KinabaluSabah88400Malaysia Applied College Taibah UniversityMadinah41477Saudi Arabia Maynooth International Engineering College Maynooth UniversityCo KildareW23 X021Irland Department of Mechanical Engineering Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia Institute of Visual Informatics Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia University of Design Innovation and Technology(UDIT)Av.Alfonso XIII97Madrid28016Spain

Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare *** addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,***,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled *** this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT *** window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission ***,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s *** contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of ***,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the *** proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information *** and analytical results ha

关键词： Internet of things information fusion differential privacy dynamic programming Laplace function

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An Effective Deployment Scheme for Elimination of Phase Cancellation in Backscatter-based WPCN

An Effective Deployment Scheme for Elimination of Phase Canc...

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IEEE Conference on Wireless Communications and Networking

作者： Qiang Wang Yuzhuo Ma Chenglong Zhang Tang Liu Jilin Yang Dié Wu College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Lab Sichuan Normal University Chengdu China

Without the need for batteries, backscatter-based Wireless Powered Communication Network (WPCN) has been envisioned as a promising alternative to conventional wireless networks. Unfortunately, the unique phase cancellation problem in backscatter-based WPCN is essentially a phenomenon that severely affects connectivity and reliability of the network. Many arts have tried to tackle this issue either by using multiple antennas to employ the signal diversity, which increases the size and is not cost-efficient, or by making a repetition of the same information with different load impedances, which significantly decreases the throughput of network. In our paper, we propose an effective deployment scheme, aiming to fundamentally eliminate the phase cancellation problem. Specifically, we first build a practical communication model seeking the blind areas caused by phase cancellation. Then, a greedy algorithm and a minimum-weight graph based algorithm are proposed to elaborate topology of the network to ensure the connectivity. Finally, extensive experiments are carried out to evaluate the performance.

关键词： Greedy algorithms Network topology Wireless networks Throughput Topology Reliability Communication networks

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Enhanced Multi-Style Transfer Method based on VGG Network

Enhanced Multi-Style Transfer Method based on VGG Network

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Cloud computing, Big Data Applications and Software Engineering (CBASE), International Conference on

作者： Yong Zheng Juanni Jiao Fange Ye Wei Li College of Computer Science Sichuan Normal University Chengdu Sichuan China Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University

In the domain of conventional image style transfer, remarkable progress has been achieved; nonetheless, certain issues persist. A large grayscale image with only one channel of brightness information lacks diverse color and texture features. This limits the variety in style transformation, resulting in unsatisfactory diversity of effects. To overcome this challenge, the present study introduces an enhanced multi-style transfer approach founded upon the VGG network. To achieve a greater variety of style transfers for single-channel grayscale images. This is accomplished by introducing a dual VGG network architecture coupled with the incorporation of the Gram matrix concept. Additionally, a multi-style fusion module is introduced to merge various style transfers for single-channel grayscale images. The experimental outcomes unequivocally demonstrate that the proposed approach effectively accomplishes manifold style transfers within single-channel grayscale images. Furthermore, apart from retaining the inherent content attributes, it bestows images with fresh visual allure. In comparison to conventional methods, the approach showcased in this study achieves significant advancements in both the quality and diversity of style transfer. Consequently, this innovative approach not only expands the scope of image style transfer applications but also paves the way for a novel potential in the realm of style transformation for single-channel grayscale images.

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An Optimization Method of Primer Design Based on Attention-BiLSTM 2

An Optimization Method of Primer Design Based on Attention-B...

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2nd International Conference on Robotics, Artificial Intelligence and Intelligent Control, RAIIC 2023

作者： Bai, Binhao Long, Jinyu Yang, Zhibo Li, Junli Wei, Ping Sichuan Normal University College of Computer Science Chengdu China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Chengdu China Sichuan Center of Translational Medicine Sichuan Key Laboratory of Translational Medicine of Traditional Chinese Medicine Sichuan Academy of Traditional Chinese Medicine Chengdu China

ISBN: (纸本)9798350328004

In this paper, we propose a method to predict the success of primer amplification based on the relationship existing between the sequence of primer and template, which can optimize the primer design and select the primer with better amplification from the candidate primer set. The double-stranded structure between primer and template nucleotide sequences is represented here by a number of words, each consisting of five characters that form sentences, as the dataset for the experiment, which is learned using an attention-based mechanism of bidirectional long short-term memory neural network model (Attention-BiLSTM), and then predicts primer amplification. The model predicted the results of polymerase chain reaction (PCR) involving specific primers and specific DNA templates with 82% accuracy, an improvement of about 2% over the performance of the LSTM with more stable value. These results show that the model can be used to effectively predict the results of PCR. This is the first paper to optimize primer design by screening the candidate primer set with a neural network model. © 2023 IEEE.

关键词： Neural network models

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Perceptual Evaluation Based on WOA-BP Neural Network

Perceptual Evaluation Based on WOA-BP Neural Network

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Artificial Intelligence and Intelligent Information Processing (AIIIP), International Conference on

作者： Jiao Juanni Zheng Yong Li Wei College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu China

In modern society, various patterns play an indispensable role. With the help of computer technology, the efficiency of designers in the field of pattern design has been significantly improved. However, in the process of pattern design, we often only rely on the subjective experience of designers, while ignoring the personal preferences of consumers. In order to explore the relationship between different pattern designs and consumers' perceptual evaluation, the Kansei engineering theory is first employed to capture consumers' perceptual evaluation of the patterns. Subsequently, various pattern designs and consumers' perceptual evaluation were utilized as inputs and outputs for the WOA-BP neural network, thereby constructing an intelligent model capable of predicting consumers' perceptual evaluation of different pattern designs. The experimental results demonstrate that the model has a highly accurate prediction level. With this model, designers can quickly adjust the pattern design to better meet the aesthetic needs of today's consumers.

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A MODIFIED DE CASTELJAU SUBDIVISION THAT SUPPORTS SMOOTH STITCHING WITH HIERARCHICALLY ORGANIZED BICUBIC BÉZIER PATCHES

arXiv

引用

arXiv 2024年

作者： Zarrinmehr, Saied Akleman, Ergun Chen, Jianer Computer Science and Engineering Department Texas A&M University College StationTX77831 United States Visual Computing & Computational Media Texas A&M University College StationTX77831 United States Computer Science and Engineering College of Engineering Texas A&M University College StationTX77831 United States

One of the theoretically intriguing problems in computer-aided geometric modeling comes from the stitching of the tensor product Bézier patches. When they share an extraordinary vertex, it is not possible to obtain continuity C1 or G1 along the edges emanating from that extraordinary vertex. Unfortunately, this stitching problem cannot be solved by using higher degree or rational polynomials. In this paper, we present a modified de Casteljau subdivision algorithm that can provide a solution to this problem. The main advantage of the modified subdivision is that the continuity C1 on a given boundary edge does not depend on the positions of the control points on other boundary edges. The modified subdivision allows us to obtain the desired C1 continuity along the edges emanating from the extraordinary vertices along with the desired G1 continuity in the extraordinary vertices (see Figures 2 and 3). Our modified de Casteljau subdivision, when combined with topological modeling, provides a framework for interactive real-time modeling of piecewise smooth manifold meshes with arbitrary topology. Based on this approach, we have extended an interactive modeling system to topologically update a smooth surface in real-time by opening or closing holes, creating handles, and combining and disconnecting surfaces (see Figure 1 for an example). The smooth surfaces resulting from any given orientable two-manifold polynomial mesh have the following properties: (1) These surfaces consist of hierarchically organized C2 continuous bicubic Bézier patches. (2) They provide visual smoothness by providing geometric G1 continuity in the extraordinary vertices and C1 continuity along the edges emanating from the extraordinary vertices. (3) They can be updated interactively in real time by directly evaluating the underlying Bézier patches. © 2024, CC BY.

关键词： Topology

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StegoFL: Using Steganography and Federated Learning to Transmit Malware

StegoFL: Using Steganography and Federated Learning to Trans...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Rong Wang Junchuan Liang Haiting Jiang Chaosheng Feng Chin-Chen Chang College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu China Graduate School of Science and Engineering Hosei University Tokyo Japan Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Federated learning (FL) has gained increasing popularity in today’s privacy-focused world due to its ability to break data silos. Although federated learning can keep all the training data private at each site, it results in clients not being effectively monitored, which may pose security risks to the learning process. Previous studies mainly focus on backdoor and poisoning attacks against federated learning. In this paper, we propose a new stegomalware attack in FL, where an attacker, disguised as a benign client, hides malware into his local model and distributes the malware to other clients through the FL process. Conducting such an attack faces two challenges. First, embedding data into a local model may degrade its model performance, making it easier for a server in FL to detect this anomaly. Second, the local model with embedded malware is recalculated after model aggregation, which unavoidably alters the malware’s carrier and hinders the success of malware extraction. To address these challenges, we propose a method called StegoFL to incorporate steganography into federated learning to transmit malware. Specifically, we split the malware to be transmitted into segments and randomly select a few model parameters as carriers. Each malware segment is concealed within these carriers over several training rounds. We also propose a simple value-mapping method to extract the embedded data by comparing the aggregated carrier values with a threshold. Experimental results demonstrate that StegoFL can circumvent server-side detection mechanisms, i.e., accuracy tests and parametric distribution comparisons. In addition, it increases transmission capacity by at least 50 times compared to state-of-the-art covert communication methods in federated learning.

关键词： Training Steganography Federated learning Training data Malware Data models Security Servers Monitoring Faces

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PROJECTIVE HOLDER-MINKOWSKI COLORS: A GENERALIZED SET OF COMMUTATIVE áASSOCIATIVE OPERATIONS WITH INVERSE ELEMENTS FOR REPRESENTING AND MANIPULATING COLORS A PREPRINT

arXiv

引用

arXiv 2024年

作者： Akleman, Ergun Oh, Somyung Wang, Youyou Akgun, Bekir Tevfik Chen, Jianer Visual Computing áComputational Media School of PVFA Texas A&M University College StationTX77831 United States Computer Science and Engineering College of Engineering Texas A&M University College StationTX77831 United States Computer Science and Engineering Texas A&M University College StationTX77831 United States College of Computer and Information Sciences Yeditepe University Istanbul Turkey

One of the key problems in dealing with color in rendering, shading, compositing, or image manipulation is that we do not have algebraic structures that support operations over colors. In this paper, we present an all-encompassing framework that can support a set of algebraic structures with associativity, commutativity, and inverse properties. To provide these three properties, we build our algebraic structures on an extension of projective space by allowing for negative and complex numbers. These properties are important for (1) manipulating colors as periodic functions, (2) solving inverse problems dealing with colors, and (3) being consistent with the wave representation of the color. Allowance of negative and complex numbers is not a problem for practical applications, since we can always convert the results into desired range for display purposes as we do in High Dynamic Range imaging. This set of algebraic structures can be considered as a generalization of the Minkowski norm Lp in projective space. These structures also provide a new version of the generalized Holder average with associativity property. Our structures provide inverses of any operation by allowing for negative and complex numbers. These structures provide all properties of generalized Holder average by providing a continuous bridge between the classical weighted average, harmonic mean, maximum, and minimum operations using a single parameter p. The parameter p = 1 corresponds to a classical weighted average operation, and p = -1 provides a harmonic mean. The p → ∞ produces the maximum operator and the p → -∞ minimum operator. Our main contribution is the demonstration of the associative property in a projective space. We have also shown that these operations are scale invariant. Our version is slightly different than the classical Holder average, but it still guarantees that the results of the operations are always between the maximum and minimum numbers. We also show that these operations ar

关键词： Color

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Virtual Network Embedding: Literature Assessment, Recent Advancements, Opportunities, and Challenges

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IEEE Communications Surveys and Tutorials 2025年

作者： Satpathy, Anurag Sahoo, Manmath Narayan Swain, Chittaranjan Bellavista, Paolo Guizani, Mohsen Muhammad, Khan Bakshi, Sambit Missouri University of Science and Technology Department of Computer Science RollaMO United States National Institute of Technology Visual Surveillance Laboratory Department of Computer Science and Engineering Odisha Rourkela769008 India Atal Bihari Vajpayee Indian Institute of Information Technology and Management Department of Computer Science and Engineering Gwalior India Department of Computer Science and Engineering Italy Machine Learning Department Abu Dhabi United Arab Emirates Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Seoul03063 Korea Republic of

Network virtualization (NV) allows service providers (SPs) to instantiate logically isolated entities called virtual networks (VNs) on top of a substrate network (SN). Though VNs bring about multiple benefits, particularly in terms of economic costs and elasticity, they also force various technical challenges to be addressed. The primary one is the issue of optimally allocating resources to VNs, also termed virtual network embedding (VNE). This paper presents an exhaustive survey of VNE by extensively covering the state-of-the-art research field in this very active field and focusing on the emerging research trends in industry and academia over the last decade. In addition, this survey originally contributes to the literature by proposing a novel taxonomy for existing VNE solutions and providing a thorough comparative study of their strategies. © 1998-2012 IEEE.

关键词： Virtual machine

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