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DCT-DiffPose: A Lightweight Diffusion Model With Multi-Hypothesis for 3D Human Pose Estimation

IEEE Access 2025年 13卷 73319-73331页

作者： Zhong, Linzhan Chen, Fangni Zheng, Bolun Feng, Rui Zhang, Lei Wan, Jian Zhejiang University of Science and Technology College of Information and Electronic Engineering Hangzhou310023 China Zhejiang Key Laboratory of Biomedical Intelligent Computing Technology Hangzhou310023 China Hangzhou Dianzi University School of Automation Hangzhou310018 China

3D human pose estimation is a crucial task in computer vision with extensive applications, yet it remains challenging due to depth ambiguity and constraints on computational efficiency. In this paper, we propose DCT-DiffPose, a novel framework that integrates a diffusion model with Confidence and Consistency-based Multi-Hypothesis Aggregation (CCMA). Moreover, it incorporate the Discrete Cosine Transform (DCT) for frequency-domain feature extraction. Specifically, the diffusion model generates diverse and plausible hypotheses, and CCMA aggregates them based on confidence and consistency, effectively addressing depth ambiguity. Additionally, we incorporate DCT into the self-attention mechanism to transform input data into the frequency domain, thereby enhancing feature extraction while significantly reducing computational complexity. To validate DCT-DiffPose, we conducted extensive experiments on the Human3.6M and MPI-INF-3DHP datasets. Our method achieves a 19% lower Mean Per Joint Position Error (MPJPE) and a 55% reduction in FLOPs compared to D3DP. The results demonstrate its excellent trade-off between accuracy and complexity. © 2013 IEEE.

关键词： Discrete cosine transforms

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A Design Thinking Approach to the Development of Digitalized Hotel Valet Services

A Design Thinking Approach to the Development of Digitalized...

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2025 International Conference on Advancement in Data Science, E-learning and information System, ICADEIS 2025

作者： Makky, Muhammad Al Raharjo, Tito Pandu Rahman, Sheina Fathur Yasirandi, Rahmat School of Computing Telkom University Bandung Indonesia School of Applied Science Telkom University Bandung Indonesia School of Information Technology King's Mongkut Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (纸本)9798331513320

Technological advancements in Indonesia have brought numerous benefits, including to hospitality industries like valet parking. However, traditional valet services still rely on manual processes, leading to inefficiencies such as long wait times, miscommunication, and security concerns. This study focuses on developing a digital valet system using the Design Thinking Approach to address these challenges. The proposed prototype demonstrates strong acceptance, addressing user expectations and improving overall service efficiency. During the testing phase, the System Usability Scale (SUS) was employed with 50 participants who had experience in valet services, resulting in a score of 77.7. This score places the prototype in the 'Good' grade and 'Acceptable' category, indicating that the proposed system successfully addresses the functionality and requirements of its intended users. By modernizing valet operations, this study highlights the potential for broader adoption of digital solutions in the hospitality sector, ultimately improving guest experiences and operational effectiveness. © 2025 IEEE.

关键词： Efficiency

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Constructive Interference Precoding for IRS-NOMA Networks

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IEEE Transactions on Wireless Communications 2025年第5期24卷 3964-3978页

作者： Cui, Ke Wang, Wei Dong, Chao Zhao, Nan Wu, Qihui Niyato, Dusit Dalian University of Technology School of Information and Communication Dalian116024 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing210016 China Nanyang Technological University College of Computing and Data Science Jurong West 639798 Singapore

Owing to the ability of reconfiguring wireless channels, intelligent reflecting surface (IRS) can help non-orthogonal multiple access (NOMA) to release its tremendous potential. However, the inter-user interference becomes the bottleneck of IRS-NOMA networks. To tackle this challenge, we propose two constructive interference precoding (CIP) based countermeasures in this paper for interference exploitation in IRS-NOMA networks. Specifically, the first scheme makes the residual interference from higher-order users (HUs) be constructive to lower-order users (LUs), so that the interference-free decoding can be achieved. While the second scheme directly utilizes the interference from LUs for the signal reception of HUs to avoid successive interference cancellation (SIC). The transmit power is minimized by jointly optimizing the BS active beamforming and the IRS passive beamforming for the two schemes, subject to the signal-to-interference-plus-noise ratio (SINR) requirement of each user, SIC decoding constraints, constructive condition and IRS unit-modulus constraint. Due to the coupled variables and non-convex constraints, we first decompose each problem into two subproblems, and then apply successive convex approximation (SCA) to convert them into convex ones. Finally, an alternating optimization (AO) based algorithm is proposed to solve the two convex subproblems for each scheme iteratively. Simulation results are presented to show the superiority and applicability of the proposed schemes compared to benchmarks. © 2002-2012 IEEE.

关键词： Signal to noise ratio

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AI-Enabled STAR-RIS Aided MISO ISAC Secure Communications

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清华大学学报自然科学版（英文版） 2025年第3期30卷 998-1011页

作者： Zhengyu Zhu Mengfei Gong Gangcan Sun Peijia Liu De Mi School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China National Mobile Communications Research Laboratory Southeast UniversityNanjing 210019China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China School of Computing and Digital Technology Birmingham City UniversityBirmingham B55JUUK

A simultaneous transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)aided integrated sensing and communication(ISAC)dual-secure communication system is studied in this *** sensed target and legitimate users(LUs)are situated on the opposite sides of the STAR-RIS,and the energy splitting and time switching protocols are applied in the STAR-RIS,*** long-term average security rate for LUs is maximized by the joint design of the base station(BS)transmit beamforming and receive filter,along with the STAR-RIS transmitting and reflecting coefficients,under guarantying the echo signal-to-noise ratio thresholds and rate constraints for the *** the channel information changes over time,conventional convex optimization techniques cannot provide the optimal performance for the system,and result in excessively high computational complexity in the exploration of the long-term gains for the *** continuity control decisions into account,the deep deterministic policy gradient and soft actor-critic algorithms based on off-policy are applied to address the complex non-convex *** results comprehensively evaluate the performance of the proposed two reinforcement learning algorithms and demonstrate that STAR-RIS is remarkably better than the two benchmarks in the ISAC system.

关键词： secure communications STAR-RIS ISAC reinforcement learning(RL)

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Performance Analysis of Text Classification Algorithms for Dhivehi Language Documents 5

Performance Analysis of Text Classification Algorithms for D...

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5th International Conference on Advanced Research in computing, ICARC 2025

作者： Mohamed, Fathimath Rashma Haddela, Prasanna S. Mohamed Jamaaludhin School Maldives Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka

ISBN: (纸本)9798331530983

This study examines the effectiveness of various machine learning algorithms in classifying text written in 'Dhivehi,' the official language of the Maldives. As a low-resource language with limited research in text analytics, 'Dhivehi' poses unique challenges due to its distinctive linguistic properties. To address these challenges, this research evaluates the performance of algorithms, including Support Vector Machines, Naive Bayes, Decision Trees, Neural Networks, XGBoost, and Random Forest, leveraging a newly curated 'Dhivehi' language dataset. The evaluation highlights that K-Neighbors achieved the highest performance, with an accuracy of 64.7% and F1 scores (macro: 0.640, weighted: 0.642), demonstrating a strong balance between precision and recall. Support Vector Machines (accuracy: 63.9%) and XGBoost (accuracy: 62.8%) also showed competitive results, with SVM slightly outperforming XGBoost in F1 metrics. Decision Tree exhibited the lowest performance across all metrics. The findings provide critical insights into improving text classification for low-resource languages and contribute to developing natural language processing tools adapted explicitly for 'Dhivehi.' Furthermore, the dataset is publicly available on Mendeley data under the name 'Dhivehi Categories data set' to foster future research and innovation in this domain. © 2025 IEEE.

关键词： Support vector machines

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ABL-SMOTE: A Novel Resampling Method by Handling Noisy and Borderline Challenge for Imbalanced Dataset for Software Defect Prediction

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IEEE Access 2025年 13卷 78781-78794页

作者： Bashir, Kamal Abdelwahab Ghorashi, Sara Ahmed, Ali Khader, Abdolraheem Karary University College of Computer Science and Information Technology Department of Information Technology Omdurman12304 Sudan Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences P.O. Box 84428 Riyadh11671 Saudi Arabia King Abdulaziz University Faculty of Computing and Information Technology Jeddah21589 Saudi Arabia Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

Machine learning algorithms face important implementation difficulties due to imbalanced learning since the Synthetic Minority Oversampling Technique (SMOTE) helps improve performance through the creation of new minority class examples in feature space before preprocessing. The underlying problem leading to performance deterioration emerges from noise and boundary instances found in the minority class rather than an excessive imbalance ratio. Noise and marginal samples become problematic during oversampling operations since they reduce the performance of classification models. This paper proposes an advanced version of Borderline-SMOTE (BL-SMOTE), called ABL-SMOT approach that utilizes INFFC for noise filtering to optimize data quality in the dataset. This improvement method for minority class sample placement and noise filtering ensures higher classifier performance through ABL-SMOTE. The proposed technique receives assessment through the usage of software defect datasets along with multiple testing classifiers against conventional data sampling procedures. The experimental outcomes show that ABL-SMOTE performs better than alternative approaches in most tested datasets because it is an effective preprocessing tool for imbalanced classification problems. © 2013 IEEE.

关键词： Software testing

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A cyber-resilient and explainable intrusion detection system for Internet of Things networks

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Cluster computing 2025年第5期28卷 1-14页

作者： Bilal, Muhammad Zeng, Rongfei Muhammad, Owais Adil, Muhammad Shoukat, Shifa Software College Northeastern University Shenyang110169 China School of Information Engineering Southwest University of Science and Technology Mianyang621010 China College of Intelligence and Computing Tianjin University Tianjin300350 China

This study presents an advanced deep learning-based Intrusion Detection System (IDS) designed to enhance security in Internet of Things (IoT) networks, where traditional security mechanisms often fall short. The proposed IDS utilizes a bidirectional gated recurrent unit (Bi-GRU) architecture combined with an attention mechanism to effectively capture both short-term and long-term dependencies and emphasize critical features relevant to intrusion detection. The attention mechanism enhances the model’s ability to focus on the most impactful features in real-time data streams, improving detection accuracy. To address the interpretability issue commonly associated with deep learning models, we integrate the SHapley Additive exPlanations (SHAP) method, which provides a comprehensive breakdown of feature importance, allowing security analysts to gain deeper insights into the decision-making process of the IDS. We rigorously evaluate the model on the CICIDS2017 dataset, achieving accuracies of 99.57% for binary classification and 99.44% for multi-class classification, outperforming current state-of-the-art solutions. These results demonstrate that the proposed IDS not only improves detection accuracy but also provides transparency in model decisions, making it more suitable for practical deployment in complex IoT environments prone to evolving cyber threats. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Network intrusion

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Data Augmentation for Cognitive Behavioral Therapy: Leveraging ERNIE Language Models using Artificial Intelligence 3

Data Augmentation for Cognitive Behavioral Therapy: Leveragi...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sambana, Bosubabu Archana, Kondreddygari Reddy, Suram Indhra Sena Basha, Shaik Meethaigar Jameer Karishma, Shaik School of Computing Mohan Babu University Tirupati India Department of Information Technology Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331527549

Cognitive Behavioral Therapy (CBT) is a proven approach for addressing the irrational thought patterns associated with mental health disorders, but its effectiveness relies on accurately identifying cognitive pathways to provide targeted treatment. In today's digital age, individuals often express negative emotions on social media, where they may reveal cognitive distortions, and in severe cases, exhibit suicidal tendencies. However, there is a significant gap in methodologies designed to analyze these cognitive pathways, which could be critical for psychotherapists aiming to deliver timely and effective interventions in online environments. Cognitive Behavioral Therapy (CBT) framework leveraging acceptance, commitment and data augmentation to categorize and address both textual and visual content as positive or negative. Specifically, the system employs BERT, RoBERTa for Sentiment Analysis and T5, PEGASUS for Text Summarization, mT5 for Text Translation in Multiple Languages focusing on detecting negative emotions and cognitive distortions within social media data. While existing models are primarily designed to identify negative thoughts, the proposed system goes beyond this by predicting additional negative side effects and other potential mental health disorders likes Phobias, Eating Disorders. This enhancement allows for a more comprehensive understanding and intervention strategy, offering psychotherapists a powerful tool for early detection and treatment of various psychological issues. © 2025 IEEE.

关键词： Acceptance Cognitive Behavioral Therapy (CBT) Commitment Therapy Data Augmentation Phobias Sentiment Analysis Text Classification

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Revenue Forecast for an Agritech Platform using Time Series 3

Revenue Forecast for an Agritech Platform using Time Series

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Naveen Kumar, S. Simha, J.B. Kiran Kumar, K.V. School of Computing and Information Technology REVA University Bengaluru India REVA Academy for Corporate Excellence REVA University Bengaluru India

ISBN: (纸本)9798331527549

Revenue forecasting is a critical component of strategic planning and financial stability for Agritech platforms, particularly in a dynamic and often unpredictable market. Given the intricate nature of agricultural data and the volatility of market conditions, Traditional methods often struggle to capture the seasonal trends and complex market behaviors unique to the agricultural industry. The objective is to enhance revenue forecasting capabilities by applying advanced statistical techniques and machine learning methodologies, specifically leveraging Seasonal Autoregressive Integrated Moving Average (SARIMA) models. The CRISP-DM methodology is employed to define and structure the problem effectively. This approach is tailored to capture seasonal fluctuations and nonlinear dynamics present in revenue data. Upper and lower confidence intervals are computed for the forecasts, ensuring robust predictions. The use of advanced forecasting techniques leads to an average accuracy of 95% with MAPE less than 0.05. © 2025 IEEE.

关键词： Agribusiness

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More Than Just a Random Number Generator! Unveiling the Security and Privacy Risks of Mobile OTP Authenticator Apps 25th

More Than Just a Random Number Generator! Unveiling the Se...

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25th International Conference on Web information Systems Engineering, WISE 2024

作者： Ikram, Muhammad Sentana, I. Wayan Budi Asghar, Hassan Kaafar, Mohamed Ali Kepkowski, Michal School of Computing Macquarie University SydneyNSW2109 Australia Department of Information Technology Politeknik Negeri Bali Jimbaran Indonesia

ISBN: (纸本)9789819605750

One-Time Passwords (OTPs) are a crucial component of multi-factor authentication (MFA) systems, providing additional security by requiring users to supply a dynamically generated code for authenticating to web services. The growth in smartphone usage has resulted in a shift from hardware tokens to mobile app-based OTP authenticators;however, these apps also present potential security and privacy threats. In this paper, we present a comprehensive analysis of 182 publicly available OTP apps on Google Play. Our analysis entails an array of passive and active measurements meticulously designed to assess the security and privacy attributes inherent to each OTP application. We investigate the presence of suspicious libraries, usage of binary protections, access to root privileges, secure backup and cryptographic mechanisms, and protection against traffic interception. Our experiments highlight several security and privacy weaknesses in instances of OTP apps. We observe that 28% of the analyzed apps are signed using a vulnerable version of the Android application signing mechanism. Over 40% of the OTP apps include third-party libraries leading to user information leakage to third-parties. 31.9% of the OTP applications are vulnerable to network interception, and only 13.2% possess the capability to detect devices that have been Jailbroken or rooted, which poses a significant concern. Our study highlights the need for better security and privacy guarantees in OTP apps and the importance of user awareness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Random number generation

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