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DeepCNN:Spectro-temporal feature representation for speech emotion recognition

CAAI Transactions on Intelligence Technology 2023年第2期8卷 401-417页

作者： Nasir Saleem Jiechao Gao Rizwana Irfan Ahmad Almadhor Hafiz Tayyab Rauf Yudong Zhang Seifedine Kadry Department of Electrical Engineering Faculty of Engineering and TechnologyGomal UniversityD.I.KhanPakistan Department of Computer Science University of VirginiaCharlottesvilleVirginiaUSA Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySkaka AljoufSaudi Arabia Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke‐on‐TrentUK School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Speech emotion recognition(SER)is an important research problem in human-computer interaction *** representation and extraction of features are significant challenges in SER *** the promising results of recent studies,they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive *** mitigate this problem,this article proposes DeepCNN,which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks(CNNs)and a convolution layer-based *** parallel CNNs are applied to extract the spectral features(2D-CNN)and temporal features(1D-CNN)representations.A 2D-convolution layer-based transformer module extracts spectro-temporal features and concatenates them with features from parallel *** learnt low-level concatenated features are then applied to a deep framework of convolutional blocks,which retrieves high-level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification *** fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature *** Berlin database of Emotional Speech(EMO-BD)and Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets are used in experiments to recognise distinct speech *** efficient spectral and temporal feature representation,the proposed SER model achieves 94.2%accuracy for different emotions on the EMO-BD and 81.1%accuracy on the IEMOCAP dataset *** proposed SER system,DeepCNN,outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO-BD and IEMOCAP datasets.

关键词： decision making deep learning

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Automatic Unusual Activities Recognition Using Deep Learning in Academia

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computers, Materials & Continua 2022年第1期70卷 1829-1844页

作者： Muhammad Ramzan Adnan Abid Shahid Mahmood Awan School of Systems and Technology University of Management and TechnologyLahore54782Pakistan Department of Computer Science and Information Technology University of SargodhaSargodha40100Pakistan School of Electronics Computing and MathematicsUniversity of DerbyDerbyUnited Kingdom

In the current era,automatic surveillance has become an active research problem due to its vast real-world applications,particularly for maintaining law and order.A continuous manual monitoring of human activities is a tedious *** use of cameras and automatic detection of unusual surveillance activity has been growing exponentially over the last few *** computer vision techniques have been applied for observation and surveillance of real-world *** research study focuses on detecting and recognizing unusual activities in an academic situation such as examinationhalls,which may help the invigilators observe and restrict the students from cheating or using *** the best of our knowledge,this is the first research work in this area that develops a dataset for unusual activities in the examination and proposes a deep learning model to detect those unusual *** proposed model has been named Automatic Unusual Activity Recognition(AUAR),which employsmotion-based frame extraction approaches to extract key-frames and then applies advanced deep learning Convolutional Neural Network algorithm with diverse *** evaluation using standard performancemeasures confirmthat theAUARmodel outperforms the already proposed approaches for unusual activity *** from evaluating the proposed model on the examination dataset,we also apply AUAR on Violent and Movies datasets,widely used in the relevant literature to detect suspicious *** results reveal that AUAR performs well on various data sets compared to existing state-of-the-art models.

关键词： Deep learning unusual activities examination CNN surveillance human activity recognition

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Sanitizable Cross-domain Access Control with Policy-driven Dynamic Authorization

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Sun, Jianfei Xu, Guowen Li, Hongwei Zhang, Tianwei Wu, Cong Yang, Xuehuan Deng, Robert H. School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering Nanyang Technological University Singapore School of Computing and Information Systems Singapore Management University Singapore

The increasing demand for secure and efficient data sharing has underscored the importance of developing robust cryptographic schemes. However, many existing endeavors have overlooked the following critical issues: (1) unauthorized access resulting from malicious information leakage by senders;(2) absence of constraints on write and read permissions for participants;(3) and inflexibility of strategies to dynamically designate ciphertexts to multiple recipients. In this paper, we present SCPA, a cross-domain access control scheme imbued with sanitization features and propelled by policy-driven dynamic authorization, tailored for cloud-based data sharing. This scheme not only facilitates access controls, including regulations for no-read and no-write stipulations, governing the data permissible for senders to transmit and recipients to acquire but also enables the dynamic sharing of a data ciphertext subset with additional recipients beyond the originally sanctioned ones. We also provide comprehensive security proofs rigorously indicating the security of the invented SCPA. Moreover, to assess the efficacy of our SCPA, we undertake thorough theoretical and experimental analyses, showcasing its feasibility and superior performance. © 2004-2012 IEEE All rights reserved.

关键词： Ciphertext

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DarkSAM: Fooling Segment Anything Model to Segment Nothing 38

DarkSAM: Fooling Segment Anything Model to Segment Nothing

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Zhou, Ziqi Song, Yufei Li, Minghui Hu, Shengshan Wang, Xianlong Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM. © 2024 Neural information processing systems foundation. All rights reserved.

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Incentivizing Crowdsensing for DT-Enabled Metaverse 20th

Incentivizing Crowdsensing for DT-Enabled Metaverse

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20th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2024

作者： Ye, Dongdong Huang, Xumin Wu, Yuan Kang, Jiawen Zhong, Weifeng Niyato, Dusit School of Automation Guangdong University of Technology Guangzhou China State Key Laboratory of Internet of Things for Smart City University of Macau Taipa China Department of Computer and Information Science University of Macau Taipa China Collge of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9789819628292

Digital Twin (DT) has been proposed as a key enabling technology to produce a digital clone of the physical world and facilitate the convergence of the physical and virtual worlds for building a real-world Metaverse. For physical-virtual synchronization, crowdsensing is applied to employ Internet of Things (IoT) devices to sense physical objects and collect status information for timely updating the associated DTs. However, an incentive mechanism is necessitated to stimulate the devices to perform sensing tasks while meeting task requirements such as Age of information (AoI) requirements. Thus, we adopt a contract theoretic approach to design an incentive mechanism for the devices that are classified into several types and the type parameters are unknown to others. The contract consists of a set of contract items with each contract item representing a device type, which specifies the required data updating frequency and monetary reward. We formulate an optimal contract design problem subject to feasible constraints including the average AoI requirement for each device type. Furthermore, we solve the problem by transforming it into a convex optimization problem that can be directly tackled by the existing methods. Finally, we provide numerical results to demonstrate the effectiveness and efficiency of our scheme. © IFIP International Federation for information Processing 2025.

关键词： Convex optimization

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DualOpt: A Dual Divide-and-Optimize Algorithm for the Large-scale Traveling Salesman Problem

arXiv

引用

arXiv 2025年

作者： Zhou, Shipei Ding, Yuandong Zhang, Chi Cao, Zhiguang Jin, Yan School of Computer Science Huazhong University of Science and Technology China School of Computing and Information Systems Singapore Management University Singapore

This paper proposes a dual divide-and-optimize algorithm (DualOpt) for solving the large-scale traveling salesman problem (TSP). DualOpt combines two complementary strategies to improve both solution quality and computational efficiency. The first strategy is a grid-based divide-and-conquer procedure that partitions the TSP into smaller sub-problems, solving them in parallel and iteratively refining the solution by merging nodes and partial routes. The process continues until only one grid remains, yielding a high-quality initial solution. The second strategy involves a path-based divide-and-optimize procedure that further optimizes the solution by dividing it into sub-paths, optimizing each using a neural solver, and merging them back to progressively improve the overall solution. Extensive experiments conducted on two groups of TSP benchmark instances, including randomly generated instances with up to 100,000 nodes and real-world datasets from TSPLIB, demonstrate the effectiveness of DualOpt. The proposed DualOpt achieves highly competitive results compared to 10 state-of-the-art algorithms in the literature. In particular, DualOpt achieves an improvement gap up to 1.40% for the largest instance TSP100K with a remarkable 104x speed-up over the leading heuristic solver LKH3. Additionally, DualOpt demonstrates strong generalization on TSPLIB benchmarks, confirming its capability to tackle diverse real-world TSP applications. © 2025, CC0.

关键词： Traveling salesman problem

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Model of Visualization and Analytics for Open data (Case: Election Voters & Kids Disability Category) 1

Model of Visualization and Analytics for Open Data (Case: El...

引用

1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Wibowo, Ripto Mukti Fakieh, Bahjat Ramzan, Muhammad Sher Alzahrani, Ahmed Saeed Siddiqui, Muazzam Alzahrani, Bander King Abdulaziz University Faculty of Computing and Information Technology Department of Information Systems Saudi Arabia King Abdulaziz University Faculty of Computing and Information Technology Department of Computer Science Saudi Arabia Indiana University Luddy School of Informatics Computing and Engineering Bloomington United States

ISBN: (纸本)9781665472753

Several regional head elections had to be postponed due to the pandemic, including in Indonesia because of the COVID-19 pandemic. Several big cities in Indonesia are of concern because of their large population and GDP. This study conducts analysis and testing of datasets taken from Open data in a city in Indonesia. In addition to conducting research on regional head elections, we also present information on voters from the category of kids with disabilities. The steps used in this research are using regional mapping data of the city of Surabaya in the Election of the Regional Head. Download the data or dataset for the Regional Head Election ampersand Categories of kids with disabilities. Based on the dataset voters from the category of children with disabilities are more than 5 *** this research, we use Python to process our datasets & Big data technology. data cleaning or cleansing, Exploratory data Analysis, and Empirical Cumulative Distribution Functions (ECDF) in python are also needed. Result from ECDF chart with steady increase (increment of 0.1). The highest variance value is in Electoral District 5 = 6.090 and the lowest value is in Electoral District 4 = 0.90. The result of Open data is graphical data visualization and candidate scores to help as an alternative for the 2024 Regional Head Election and the Category of kids with disabilities. © 2023 IEEE.

关键词： Open data

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Efficient GPU Offloading with OpenMP for a Hyperbolic Finite Volume Solver on Dynamically Adaptive Meshes 38th

Efficient GPU Offloading with OpenMP for a Hyperbolic Fin...

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38th International Conference on High Performance computing, ISC High Performance 2023

作者： Wille, Mario Weinzierl, Tobias Brito Gadeschi, Gonzalo Bader, Michael TUM School of Computation Information and Technology Technical University of Munich Garching Germany Department of Computer Science Institute for Data Science—Large-scale Computing Durham University Durham United Kingdom NVIDIA Munich Germany

ISBN: (纸本)9783031320408

We identify and show how to overcome an OpenMP bottleneck in the administration of GPU memory. It arises for a wave equation solver on dynamically adaptive block-structured Cartesian meshes, which keeps all CPU threads busy and allows all of them to offload sets of patches to the GPU. Our studies show that multithreaded, concurrent, non-deterministic access to the GPU leads to performance breakdowns, since the GPU memory bookkeeping as offered through OpenMP’s map clause, i.e., the allocation and freeing, becomes another runtime challenge besides expensive data transfer and actual computation. We, therefore, propose to retain the memory management responsibility on the host: A caching mechanism acquires memory on the accelerator for all CPU threads, keeps hold of this memory and hands it out to the offloading threads upon demand. We show that this user-managed, CPU-based memory administration helps us to overcome the GPU memory bookkeeping bottleneck and speeds up the time-to-solution of Finite Volume kernels by more than an order of magnitude. © 2023, The Author(s).

关键词： Graphics processing unit

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COVID-19 Lung CT Image Segmentation: A Comparison of Various U-Net Variants 2nd

COVID-19 Lung CT Image Segmentation: A Comparison of Various...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Alebiosu, David Olayemi Folayan, Adeola Chen, Wei Tolulope, Abejide Ajibade, Samuel-Soma M. School of Engineering and Technology Department of Computing and Information Systems Sunway University Petaling Jaya Malaysia School of Medical and Health Science Monash University Petaling Jaya Malaysia School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China College of Science and Engineering University of Derby Derby United Kingdom Department of Computing and Information Systems Sunway University Petaling Jaya Malaysia Department of Computer Engineering Istanbul Ticaret Universitesi Istanbul Turkey

ISBN: (纸本)9789819639489

Lung segmentation has become a bedrock in the effective diagnosis, and classification of coronavirus (COVID-19) from radiological images such as computed tomography (CT) and X-ray images. Since the coronavirus (COVID-19) discovery, several methods have been employed to segment the COVID-19-infected areas from lung CT images. One of the most popular segmentation methods is the U-Net model. U-Net is a convolutional neural network used for medical image segmentation. U-Net and its variants have become a more reliable architecture used for medical image segmentation. U-Net models have produced outstanding results in segmenting diseases such as COVID-19 from lung CT images. The exceptional results produced by the U-Net model have inspired various researchers to explore the potential of U-Net for various segmentation tasks. This study compares the performances of recently used state-of-the-art U-Net models on lung CT images for tuberculosis segmentation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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UAV-Assisted data Synchronization for Digital-Twin-Enabled Vehicular Networks

UAV-Assisted Data Synchronization for Digital-Twin-Enabled V...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Wu, Guoquan Nie, Jiangtian Tang, Jianhang Zhang, Yang Xiao, Jiang Xiong, Zehui Yanshan University School of Information Science and Engineering China Nanyang Technological University School of Computer Science and Engineering Singapore Guizhou University State Key Laboratory of Public Big Data China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China Huazhong University of Science and Technology School of Computer Science and Technology China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore

ISBN: (纸本)9798350345384

Digital twin (DT) is becoming a promising solution for vehicular networks to improve the interoperability of distributed autonomous driving systems. Mobile edge computing (MEC) has been introduced to provide low-latency services for DT-enabled vehicular networks at the edge of the network. However, it is hard to obtain the dynamic network topology for moving vehicles by the ground-based MEC system, which may deteriorate the service quality for DT synchronization. In this paper, we propose a novel unmanned aerial vehicle (UAV)-assisted synchronization framework for DT-enabled vehicular networks. With the proposed framework, an intelligent resource allocation algorithm is developed to improve UAV resource utilization and maximize the synchronization completion ratio. By leveraging an advantage actor-critic (A2C) algorithm, the synchronization decisions are obtained with low time complexity. Experiment results demonstrate that the proposed algorithm can reduce the synchronization latency and improve the synchronization completion ratio effectively. © 2023 IEEE.

关键词： Resource allocation

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