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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Das, Jiban Krisna Elegbe, Ifeoluwa Coffie, Lord Khadka, Rojeena Chen, Lei Ji, Yiming Georgia Southern University Dept. of Information Technology StatesboroGA United States College of Computing and Software Engineering Kennesaw State University United States

ISBN: (纸本)9798331504847

The integration of Artificial Intelligence (AI) into project management has significantly transformed traditional methodologies, particularly within the Information Technology (IT) sector. This study investigates the effectiveness of AI-powered tools, specifically Asana, Jira, and *** to improve project management efficiency through automation, resource optimization, and decision-making processes. Using a systematic literature review and quantitative analysis, the research highlights the capabilities and limitations of these tools in the management of complex IT projects. The study findings reveal that AI-powered platforms facilitate real-time tracking, predictive analytics and dynamic resource allocation, which collectively contribute to reduced project timelines and improved outcomes. However, challenges such as data quality, integration complexities, and the need for skilled personnel remain prevalent. The study was summarized with strategic recommendations for organizations to incorporate automation, leverage predictive analytics, and improve data integrity, thus maximizing the benefits of AI in project management. This research underscores the critical role of AI in modernizing project management practices, which ultimately leads to increased productivity and project success rates in the IT domain. © 2025 IEEE.

关键词： Project management

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Image Tampering Detection With Frequency-Aware Attention and Multiview Fusion

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 614-625页

作者： Xu, Xu Chen, Junxin Lv, Wenrui Wang, Wei Zhang, Yushu Dalian University of Technology School of Software Dalian116621 China Northeastern University School of Computer Science and Engineering Shenyang110004 China Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen518172 China Jiangxi University of Finance and Economics School of Computing and Artificial Intelligence Nanchang330013 China

Manipulated images are flooding our daily lives, which poses a threat to social security. Recently, many studies have focused on image tampering detection. However, they have poor performance on independent validation due to differences in image scenes and tampering methods. The key question is how to design a network that is able to adaptively enhance the tampering information and suppress the generalization features during training. To this end, we propose a dual-branch network with a frequency adaptation paradigm and a feature fusion module for robust tampering image detection. First, this paradigm is designed to adaptively highlight tampering features through frequency conversion and learnable weight. Second, a feature fusion module is developed to filter redundant features and dynamically fuse two-branch features. Experiments on eight typical datasets demonstrate that our model has advantages over state-of-the-art algorithms, and our paradigm can well empower semantic segmentation networks for tampering detection. © 2024 IEEE.

关键词： Semantic Segmentation

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EagerLog: Active Learning Enhanced Retrieval Augmented Generation for Log-based Anomaly Detection

EagerLog: Active Learning Enhanced Retrieval Augmented Gener...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Duan, Chiming Jia, Tong Yang, Yong Liu, Guiyang Liu, Jinbu Zhang, Huxing Zhou, Qi Li, Ying Huang, Gang School of Software and Microelectronics Peking University Beijing China Institute for Artificial Intelligence Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China National Key Laboratory of Data Space Technology and System Beijing China Alibaba Cloud Computing Company Hangzhou China

ISBN: (纸本)9798350368741

Logs record essential information about system operations and serve as a critical source for anomaly detection, which has generated growing research interest. Utilizing large language models (LLMs) within a retrieval-augmented generation (RAG) framework for log-based anomaly detection is an effective approach due to its strong generalization capabilities and efficient few-shot performance. However, the effectiveness of this method hinges on the quality of the knowledge source, which can be impacted by noise and changes within the software systems. Facing these problems, in this paper, we propose a novel log-based anomaly detection method named EagerLog, employing active learning to choose the logs for humans to label, thereby adding them to the knowledge source, thus enhancing the knowledge source and maintaining its quality. Our experiments on three open datasets (BGL, Thunderbird, Zookeeper) and one industrial dataset demonstrate that EagerLog can achieve 93.65% F1 score with approximately 10 labeled log sequences, surpassing existing methods by 15.32%. © 2025 IEEE.

关键词： Active Learning Anomaly Detection Log Analysis Retrieval Augmented Generation

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A Lightweight and High-Fidelity Model for Generalized Audio-Driven 3D Talking Face Synthesis 27th

A Lightweight and High-Fidelity Model for Generalized Audi...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Liu, Shunce Zhong, Yuwei Wang, Huixuan Peng, Jingliang Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China School of Information Science and Engineering University of Jinan Jinan China School of Software and Microelectronics Peking University Beijing China School of Software Shandong University Jinan China

ISBN: (纸本)9783031781247

We present LW-GeneFace, a lightweight and high-fidelity model for generalized audio-driven facial animation, in this paper. We develop this model by reducing the size while maintaining the synthetic quality of GeneFace, an audio-driven facial animation model known for its high fidelity and generalization capabilities. Specifically, we compress the first and the third stages of GeneFace as they dominate the model size. In the first stage, we propose a lightweight version of the WaveNet-based network inspired by MobileNetV3 and DP-block. It utilizes depthwise separable convolution and dual-path feature extraction to compress the network while maintaining effective feature extraction. The shared network structure in the dual-path feature extraction further reduces model complexity and improves training efficiency. In the third stage, we generate realistic 3D renderings at reduced model size by introducing novelties in RAD-NeRF. Technically, we reduce the hash table sizes in the grid-based encoding modules, as well as present a lightweight bottleneck MLP architecture to increase the non-linearity of the model. Experimental results demonstrate that LW-GeneFace achieves state-of-the-art performance with both model size and synthetic quality considered. The source code of LW-GeneFace will be released after acceptance of this paper. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Animation

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COSIS: An AI-Enabled Digital Transformation Framework Integrating Large Language Models and Key Performance Indicators 21st

COSIS: An AI-Enabled Digital Transformation Framework Integr...

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21st International Conference on Services computing, SCC 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Zhang, Liang-Jie Chen, Huan He, Sheng Li, Changhu Chen, Junyang Zhang, Haodi Du, Wenfeng Center for AI Services Computing College of Computer Science and Software Engineering Shenzhen University Shenzhen China SF Technology Co. Ltd. Shenzhen China Kingdee Research Kingdee International Software Group Co. Ltd. Shenzhen China

ISBN: (纸本)9783031769993

The rapid advancement of Artificial Intelligence (AI) and Large Language Models (LLMs) has significantly transformed various sectors of society, compelling enterprises to undertake comprehensive digital transformations to remain competitive in the AI era. This paper introduces COSIS, an AI-enabled digital trans-formation framework comprising five key dimensions: Culture, Operation, Strategy, Innovation, and Service. The COSIS framework provides a structured approach for enterprises to navigate the complexities of digital transformation by integrating AI and LLM technologies into their core functions. For each dimension, the paper identifies specific key performance indicators (KPIs) to measure the effectiveness of AI-enabled transformations. These KPIs enable enterprises to quantitatively assess progress and outcomes, ensuring alignment with strategic objectives and facilitating continuous improvement. Additionally, the paper discusses the implications of AI and LLM technologies on enterprise management team building in the AI era. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Human resource management

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STBA: Towards Evaluating the Robustness of DNNs for Query-Limited Black-Box Scenario

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IEEE Transactions on Multimedia 2025年 27卷 2666-2681页

作者： Liu, Renyang Lam, Kwok-Yan Zhou, Wei Wu, Sixing Zhao, Jun Hu, Dongting Gong, Mingming Yunnan University School of Information Science and Engineering Kunming650500 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Yunnan University School of Software Engineering Research Center of Cyberspace Kunming650500 China University of Melbourne Melbourne Centre for Data Science School of Mathematics and Statistics ParkvilleVIC3010 Australia

Extensive studies have revealed that deep neural networks (DNNs) are vulnerable to adversarial attacks, especially black-box ones, which can heavily threaten the DNNs deployed in the real world. Many attack techniques have been proposed to explore the vulnerability of DNNs and further help to improve their robustness. Despite the significant progress made recently, existing black-box attack methods still suffer from unsatisfactory performance due to the vast number of queries needed to optimize desired perturbations. Besides, the other critical challenge is that adversarial examples built in a noise-adding manner are abnormal and struggle to successfully attack robust models, whose robustness is enhanced by adversarial training against small perturbations. There is no doubt that these two issues mentioned above will significantly increase the risk of exposure and result in a failure to dig deeply into the vulnerability of DNNs. Hence, it is necessary to evaluate DNNs' fragility sufficiently under query-limited settings in a non-additional way. In this paper, we propose the Spatial Transform Black-box Attack (STBA), a novel framework to craft formidable adversarial examples in the query-limited scenario. Specifically, STBA introduces a flow field to the high-frequency part of clean images to generate adversarial examples and adopts the following two processes to enhance their naturalness and significantly improve the query efficiency: a) we apply an estimated flow field to the high-frequency part of clean images to generate adversarial examples instead of introducing external noise to the benign image, and b) we leverage an efficient gradient estimation method based on a batch of samples to optimize such an ideal flow field under query-limited settings. Compared to existing score-based black-box baselines, extensive experiments indicated that STBA could effectively improve the imperceptibility of the adversarial examples and remarkably boost the attack success rate u

关键词： Structured Query Language

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FreqSense: Universal and Low-Latency Adversarial Example Detection for Speaker Recognition with Interpretability in Frequency Domain

FreqSense: Universal and Low-Latency Adversarial Example Det...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Yihuan Li, Yuanzhe Ren, Yanzhen Tu, Weiping Yang, Yuhong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education China School of Cyber Science and Engineering Wuhan University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China

ISBN: (纸本)9798350368741

Speaker recognition (SR) systems are particularly vulnerable to adversarial example (AE) attacks. To mitigate these attacks, AE detection systems are typically integrated into SR systems. To overcome the limitations of low detection accuracy, poor generalization, and high latency in existing schemes, this paper proposes FreqSense, an AE detection scheme based on frequency distribution features. FreqSense detects a variety of unknown AE attacks with low latency, and provides interpretability in its detection process. The basic idea of FreqSense is that AE typically introduce carefully designed noise in specific frequency bands that are associated with highly distinctive speaker identities. Therefore, leveraging the distributional variations in these frequency bands can effectively distinguish between AE and benign audio. FreqSense models frequency distribution features by integrating time-frequency transformation technology with a self-attention mechanism and employs a neural network-based classifier to distinguish between AE and benign audio. Experimental results show that FreqSense achieves an overall detection accuracy of 99.2%, surpassing state-of-the-art (SOTA) schemes by 27.2%. When confronting unknown AE attacks, FreqSense achieves a detection accuracy of 98.3% with a latency of just 0.0014 seconds. © 2025 IEEE.

关键词： Adversarial Attack Adversarial Example Detection Speaker Recognition

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Deep Learning Algorithm for Person Re-Identification Based on Dual Network Architecture

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Computers, Materials & Continua 2025年第5期83卷 2889-2905页

作者： Meng Zhu Xingyue Wang Honge Ren Abeer Hakeem Linda Mohaisen College of Information Engineering Harbin UniversityHarbin150086China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin UniversityHarbin150086China College of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China Heilongjiang Forestry Intelligent Equipment Engineering Research Center Northeast Forestry UniversityHarbin150040China Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

Changing a person’s posture and low resolution are the key challenges for person re-identification(ReID)in various deep learning *** this paper,we introduce an innovative architecture using a dual attention network that includes an attentionmodule and a joint measurement module of spatial-temporal *** proposed approach can be classified into two main ***,the spatial attention feature map is formed by aggregating features in the spatial ***,the same operation is carried out on the channel dimension to formchannel attention ***,the receptive field size is adjusted adaptively tomitigate the changing person posture ***,we use a joint measurement method for the spatial-temporal information to fully harness the data,and it can also naturally integrate the information into the visual features of supervised ReID and hence overcome the low resolution *** experimental results indicate that our proposed algorithm markedly improves the accuracy in addressing changing human postures and low-resolution issues compared with contemporary leading *** proposed method shows superior outcomes on widely recognized benchmarks,which are the Market-1501,MSMT17,and DukeMTMC-reID ***,the proposed algorithmattains a Rank-1 accuracy of 97.4% and 94.9% mAP(mean Average Precision)on the Market-1501 ***,it achieves a 94.2% Rank-1 accuracy and 91.8% mAP on the DukeMTMC-reID dataset.

关键词： Person reidentification ReID computer vision self-attention spatial-temporal information

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The Robustness of Spiking Neural Networks in Federated Learning with Compression Against Non-omniscient Byzantine Attacks

arXiv

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arXiv 2025年

作者： Nguyen, Manh V. Zhao, Liang Deng, Bobin Wu, Shaoen College of Computing and Software Engineering Kennesaw State University Georgia United States

Spiking Neural Networks (SNNs), which offer exceptional energy efficiency for inference, and Federated Learning (FL), which offers privacy-preserving distributed training, is a rising area of interest that highly beneficial towards Internet of Things (IoT) devices. Despite this, research that tackles Byzantine attacks and bandwidth limitation in FL-SNNs, both poses significant threats on model convergence and training times, still remains largely unexplored. Going beyond proposing a solution for both of these problems, in this work we highlight the dual benefits of FL-SNNs - against non-omniscient Byzantine adversaries (ones that restrict attackers access to local clients' datasets), and greater communication efficiency - over FL-ANNs. Specifically, we discovered that a simple integration of Top-κ sparsification into the FL apparatus can help leverage the advantages of the SNN models in both greatly reducing bandwidth usage and significantly boosting the robustness of FL training against non-omniscient Byzantine adversaries. Most notably, we saw a massive improvement of ∼40% accuracy gain in FL-SNNs training under the lethal MinMax attack. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Unified and interoperable digital twin models for emergency evacuation using building simulation identity cards

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Automation in Construction 2025年 175卷

作者： Jahangir, Muhammad Farhan Kamari, Aliakbar Schultz, Carl Peter Leslie DIGIT Department of Civil and Architectural Engineering Aarhus University Denmark DIGIT Department of Software Engineering & Computing Aarhus University Denmark

Digital Twin (DT) technology is crucial for effective emergency evacuation planning in buildings, requiring specialized simulation models, from earthquake propagation to evacuation route planning. However, the lack of a unified vocabulary and standardized framework for integrating these models hinders seamless collaboration and limits holistic simulations. This fragmentation poses a significant challenge, as no single simulator can accurately capture the complex dynamics in emergency scenarios. To address this gap, this paper proposes the Building Simulation Identity Card (BSIC), a framework designed to standardize, characterize, and integrate the properties of building simulation models. BSIC facilitates the enhancement of traceability, reliability, and interoperability between DT systems in the building industry. This paper presents the application of BSIC in a case study focused on earthquake-induced evacuation in a multi-story office building. Findings demonstrate BSIC's efficacy in aligning discrete-event (DE) and continuous-time (CT) simulations, enabling synchronized data flows, accurate event handling, and minimized integration errors. © 2025 The Authors

关键词： Construction industry

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