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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Patsch, Constantin Steinbach, Eckehard Technical University of Munich School of Computation Information and Technology Germany Chair of Media Technology Department of Computer Engineering Germany

ISBN: (纸本)9781728163277

Segmenting activities in untrimmed videos remains a critical challenge to fully understand complex human activity sequences. A correct representation of temporal action relations is key for improving incorrect segmentations. We propose a self-attention-based model that refines initial segmentations by separately considering intra-as well as inter-segment relations between predicted action segments. Furthermore, in order to enhance the training process, we use a similarity-guided regularization technique that ensures intra-segment similarity and the validity of action transitions between adjacent segments. In an extensive evaluation on three public datasets -Georgia Tech Egocentric Activities, 50Salads, and Breakfast -our proposed architecture enhances the backbone model by 6.1% on GTEA, 3.8% on 50Salads, and 3.9% on Breakfast with regard to the F 1@50 metric. © 2023 IEEE.

关键词： computer vision

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EDU-GAN:Edge Enhancement Generative Adversarial Networks with Dual-Domain Discriminators for Inscription Images Denoising

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computers, Materials & Continua 2024年第7期80卷 1633-1653页

作者： Yunjing Liu Erhu Zhang Jingjing Wang Guangfeng Lin Jinghong Duan School of Mechanical and Precision Instrument Engineering Xi’an University of TechnologyXi’an710048China Department of Information Science Xi’an University of TechnologyXi’an710054China School of Faculty of Painting Packaging Engineering and Digital MediaXi’an University of TechnologyXi’an710048China School of Computer Science and Engineering Xi’an University of TechnologyXi’an710048China

Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research *** fromnatural images,character images pay more attention to stroke ***,existingmodelsmainly consider pixel-level informationwhile ignoring structural information of the character,such as its edge and glyph,resulting in reconstructed images with mottled local structure and character *** solve these problems,we propose a novel generative adversarial network(GAN)framework based on an edge-guided generator and a discriminator constructed by a dual-domain U-Net framework,i.e.,*** existing frameworks,the generator introduces the edge extractionmodule,guiding it into the denoising process through the attention mechanism,which maintains the edge detail of the restored inscription ***,a dual-domain U-Net-based discriminator is proposed to learn the global and local discrepancy between the denoised and the label images in both image and morphological domains,which is helpful to blind denoising *** proposed dual-domain discriminator and generator for adversarial training can reduce local artifacts and keep the denoised character structure *** to the lack of a real-inscription image,we built the real-inscription dataset to provide an effective benchmark for studying inscription image *** experimental results show the superiority of our method both in the synthetic and real-inscription datasets.

关键词： Dual-domain discriminators inscription images denoising edge-guided generator

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R3 - Rescale, Rotate, and Randomize: A Novel Image Cryptosystem Utilizing Chaotic and Hyper-Chaotic Systems

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IEEE Access 2023年 11卷 119284-119312页

作者： Gabr, Mohamed Korayem, Yousef Chen, Yen-Lin Yee, Por Lip Ku, Chin Soon Alexan, Wassim Faculty of Media Engineering and Technology Computer Science Department Cairo11835 Egypt National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia Faculty of Information Engineering and Technology Communications Department Cairo11835 Egypt New Administrative Capital Mathematics Department Cairo13507 Egypt

This work proposes a novel image encryption algorithm that integrates unique image transformation techniques with the principles of chaotic and hyper-chaotic systems. By harnessing the unpredictable behavior of the Chua system and the hyper-chaotic nature of the Chen system, the algorithm carries out rescaling, rotation, and randomization on the target image. The intrinsic unpredictability and sensitivity to initial conditions of these chaotic systems endow the encryption algorithm with an expansive key space of 25208. This feature not only bolsters its resilience against brute-force attacks but also magnifies its overall security profile. The algorithm's efficiency is evident in its rapid computational speed and lean resource consumption, making it suitable for real-time applications. To gauge its robustness, a battery of rigorous tests and analyses, spanning differential attacks, statistical attacks, and brute-force assaults, were employed. The results validate its ability to resist a diverse spectrum of threats. With its expansive key space, exceptional efficiency, and sturdy defenses, the proposed algorithm emerges as a potential cornerstone for safeguarding digital images in arenas like secure communication, data storage, and multimedia transmission. In sum, this research pushes the boundaries of high-security image encryption methodologies, catering to the burgeoning demands of the digital age. © 2013 IEEE.

关键词： Cryptography

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RSSI-Based 3D Wireless Sensor Node Localization Using Hybrid T Cell Immune and Lotus Optimization

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computers, Materials & Continua 2024年第12期81卷 4833-4851页

作者： Weiwei Hu Kiran Sree Pokkuluri Rajesh Arunachalam Bander A.Jabr Yasser A.Ali Preethi Palanisamy School of Information Engineering Technology&MediaUniversity of HenanKaifeng475004China Department of Computer Science and Engineering Shri Vishnu Engineering College forWomenBhimavaram534202Andhra PradheshIndia Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalam602105ChennaiIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalam602105ChennaiIndia

Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding *** sensor nodes are responsible for accumulating and exchanging ***,node local-ization is the process of identifying the target node’s *** this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization ***,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D *** position of the anchor nodes is fixed for detecting the location of the ***,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node *** the regular and irregular surfaces,this hybrid algorithm effectively performs the localization *** suggested hybrid algorithm converges very fast in the three-dimensional(3D)*** accuracy of the proposed node localization process is 94.25%.

关键词： Sensor node localization received signal strength indicator 3D wireless sensor network deep neural network average localization error and hybrid T cell immune with lotus effect optimization algorithm

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Development and Investigation of Vision System for a Small-Sized Mobile Humanoid Robot in a Smart Environment

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International Journal of Crowd Science 2025年第1期9卷 29-43页

作者： Abu-Jassar, Amer Tahseen Attar, Hani Amer, Ayman Lyashenko, Vyacheslav Yevsieiev, Vladyslav Solyman, Ahmed Department of Computer Science College of Information Technology Amman Arab University Amman11937 Jordan Faculty of Engineering Zarqa University Zarqa2000 Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Department of Media Systems and Technology Kharkiv National University of Radio Electronics Kharkiv61166 Ukraine Department of Electrical and Electronics Engineering Faculty of Engineering and Architecture Nişantaşı University Istanbul34398 Turkey

The conducted research aims to develop a computer vision system for a small-sized mobile humanoid robot. The decentralization of the servomotor control and the computer vision systems is investigated based on the hardware solution point of view, moreover, the required software level to achieve an efficient matched design is obtained. A computer vision system using the upgraded tiny-You Only Look Once (YOLO) network model is developed to allow recognizing and identifying objects and making decisions on interacting with them, which is recommended for crowd environment. During the research, a concept of a computer vision system was developed, which describes the interaction between the main elements, on the basis of which hardware modules were selected to implement the task. A structure of information interaction between hardware modules is proposed, and a connection scheme is developed, on the basis of which a model of a computer vision system is assembled for research, with the required algorithmic and software for solving the problem. To ensure the high speed of the computer vision system based on the ESP32-CAM module, the neural network was improved by replacing the Visual Geometry Group 16 (VGG-16) network as the base network for extracting the functions of the Single Shot Detector (SSD) network model with the tiny-YOLO lightweight network model, which made it possible to preserve the multidimensional structure of the network model feature graph, resulting in increasing the detection accuracy, while significantly reducing the amount of calculations generated by the network operation, thereby significantly increasing the detection speed, due to a limited set of objects. Finally, a number of experiments were carried out, both in static and dynamic environments, which showed a high accuracy of identifications. © The author(s) 2025.

关键词： Anthropomorphic robots

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An Intelligent Diagnostic Framework Based on Digital Twins and Partial Transfer Learning: Methodology and Industrial Application

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 1-13页

作者： Azari, Mehdi Saman Ricci, Liana Santini, Stefania Flammini, Francesco Linnaeus University Department of Computer Science and Media Technology Växjo352 52 Sweden GE Power Sweden Steam Power Unit Norrköping602 23 Sweden University of Naples Federico II Department of Electrical Engineering and Information Technology Naples80125 Italy Mälardalen University School of Innovation Design and Engineering Eskilstuna632 17 Sweden Linnaeus University Department of Computer Science and Media Technology Växjö352 52 Sweden

In Industry 4.0, efficient fault diagnosis is crucial for predictive maintenance but is often hindered by significant domain shifts between training and testing domains and lack of training datasets, limiting the effectiveness of machine learning in practice. Transfer learning has been used to address these challenges by utilizing knowledge from similar source domains. However, the scarcity of faulty data from real machines and the difficulty of obtaining labeled datasets from lab machines as source domain pose significant obstacles. This paper presents a novel diagnostic framework that integrates digital twins and transfer learning to overcome these limitations. Digital twins generate training datasets, while a model update strategy based on parameter sensitivity analysis improves adaptability. The framework also incorporates a partial transfer diagnostic model with a double attention mechanism to handle data distribution discrepancies and label inconsistencies between digital twins and real machines. Validated on an industrial rotating machine case study using real data, the proposed approach improves diagnostic accuracy by over 10% compared to state-of-the-art methods. © 2023 IEEE.

关键词： Predictive maintenance

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Explainable Classification Model for Android Malware Analysis Using API and Permission-Based Features

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computers, Materials & Continua 2023年第9期76卷 3167-3188页

作者： Nida Aslam Irfan Ullah Khan Salma Abdulrahman Bader Aisha Alansari Lama Abdullah Alaqeel Razan Mohammed Khormy Zahra Abdultawab AlKubaish Tariq Hussain SAUDI ARAMCO Cybersecurity Chair Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Computer Engineering Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhouChina

One of the most widely used smartphone operating systems,Android,is vulnerable to cutting-edge malware that employs sophisticated *** malware attacks could lead to the execution of unauthorized acts on the victims’devices,stealing personal information and causing hardware *** previous studies,machine learning(ML)has shown its efficacy in detecting malware events and classifying their ***,attackers are continuously developing more sophisticated methods to bypass ***,up-to-date datasets must be utilized to implement proactive models for detecting malware events in Android mobile ***,this study employed ML algorithms to classify Android applications into malware or goodware using permission and application programming interface(API)-based features from a recent *** overcome the dataset imbalance issue,RandomOverSampler,synthetic minority oversampling with tomek links(SMOTETomek),and RandomUnderSampler were applied to the Dataset in different *** results indicated that the extra tree(ET)classifier achieved the highest accuracy of 99.53%within an elapsed time of 0.0198 s in the experiment that utilized the RandomOverSampler ***,the explainable Artificial Intelligence(EAI)technique has been applied to add transparency to the high-performance ET *** global explanation using the Shapely values indicated that the top three features contributing to the goodware class are:Ljava/net/URL;->openConnection,Landroid/location/LocationManager;->getLastKgoodwarewnLocation,and *** the other hand,the top three features contributing to themalware class are Receive_Boot_Completed,Get_Tasks,and Kill_Background_*** is believed that the proposedmodel can contribute to proactively detectingmalware events in Android devices to reduce the number of victims and increase users’trust.

关键词： Android malware machine learning malware detection explainable artificial intelligence cyber security

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Conformational Search with Implicit Solvation Model for Prediction of Ligand-Bound Conformations 10

Conformational Search with Implicit Solvation Model for Pred...

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10th International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2023

作者： Tachibana, Naoto Ikabata, Yasuhiro Goto, Hitoshi Toyohashi University of Technology Department of Computer Science and Engineering Toyohashi Japan Toyohashi University of Technology Information and Media Center Toyohashi Japan

ISBN: (纸本)9798350329919

Accurate prediction of ligand-bound conformations in protein-ligand complexes only from ligand information is expected to be useful in drug design. In this study, we compared the conformations obtained by a conformational search algorithm and the geometry optimization based on quantum chemical calculations with the ligand-bound conformations obtained by the constrained geometry optimization. Conformational searches for ligands of 25 atoms or less in the Platinum Diverse Dataset were performed using the reservoir algorithm and MMFF94s force field. The geometries of the obtained conformations were optimized using quantum chemical calculations at the level of ωB97X-D/6-31+G (d,p). The ligand-bound conformations were obtained by the geometry optimization at ωB97X-D/6-31+G(d,p) with fixed dihedral angles. The environment around the ligand was considered by implicit solvation models, and better results were obtained when aqueous solvents were specified than in the case of no solvent effects or octanol solvent. The RMSD means of the most similar conformation and the ligand-bound conformation were 0.534, 0.449, and 0.427 Å for no solvent effect, octanol, and water, respectively. The result indicates the importance of incorporating the effect of the surrounding environment in the prediction of the ligand-bound conformation. © 2023 IEEE.

关键词： Ligands

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Mcrood: Multi-Class Radar Out-Of-Distribution Detection 48

Mcrood: Multi-Class Radar Out-Of-Distribution Detection

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Kahya, Sabri Mustafa Sami Yavuz, Muhammet Steinbach, Eckehard Technical University of Munich School of Computation Information and Technology Department of Computer Engineering Media Technology Germany Infineon Technologies Ag Germany

ISBN: (纸本)9781728163277

Out-of-distribution (OOD) detection has recently received special attention due to its critical role in safely deploying modern deep learning (DL) architectures. This work proposes a reconstruction-based multi-class OOD detector that operates on radar range doppler images (RDIs). The detector aims to classify any moving object other than a person sitting, standing, or walking as OOD. We also provide a simple yet effective pre-processing technique to detect minor human body movements like breathing. The simple idea is called respiration detector (RESPD) and eases the OOD detection, especially for human sitting and standing classes. On our dataset collected by 60GHz short-range FMCW Radar, we achieve AUROCs of 97.45%, 92.13%, and 96.58% for sitting, standing, and walking classes, respectively. We perform extensive experiments and show that our method outperforms state-of-the-art (SOTA) OOD detection methods. Also, our pipeline performs 24 times faster than the second-best method and is very suitable for real-time processing. © 2023 IEEE.

关键词： 60GHz FMCW radar deep neural networks Out-of-distribution detection

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Network bandwidth variation-adapted state transfer for geo-replicated state machines and its application to dynamic replica replacement

Network bandwidth variation-adapted state transfer for geo-r...

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作者： Chiba, Tairi Ohmura, Ren Nakamura, Junya Department of Computer Science and Engineering Toyohashi University of Technology Aichi Japan Information and Media Center Toyohashi University of Technology Aichi Japan

This paper proposes a new state transfer method for geographic state machine replication (SMR) that dynamically allocates the state to be transferred among replicas according to changes in communication bandwidths. SMR improves fault tolerance by replicating a service to multiple replicas. When a replica is newly added or recovered from a failure, the other replicas transfer the current state of the service to it. However, in geographic SMR, the communication bandwidths of replicas are different and constantly changing. Therefore, existing state transfer methods cannot fully utilize the available bandwidth, and their state transfer time increases. To overcome this problem, our method divides the state into multiple chunks and assigns them to replicas based on each replica's bandwidth so that the broader a replica's bandwidth is, the more chunks it transfers. The proposed method also updates the chunk assignment of each replica dynamically based on the currently estimated bandwidth. The performance evaluation on Amazon EC2 shows that the proposed method reduces the state transfer time by up to 47% compared to the existing one. In addition, we apply the proposed method to dynamic replacement of replicas, which can mitigate latency degradation caused by network trouble, and evaluate how fast the method can relocate a replica. © 2022 John Wiley & Sons, Ltd.

关键词： Bandwidth

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