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Events and findings of the Purdue University Calumet computer information Technology & Graphics (CIT & G) department teaching partnership with the college of software engineering of Southeast University in Nanjing China 09

Events and findings of the Purdue University Calumet Compute...

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10th ACM Special Interest Group for information Technology Education, SIGITE 2009

作者： Winer, Charles Wu, Hanqian Department of Computer Information Technology and Graphics Purdue University Calumet Hammond IN United States College of Software Engineering Southeast University Nanjing China

ISBN: (纸本)9781605587653

In this poster session we are reporting on the results of two, three-week summer graduate teaching experiences that took place in Nanjing, China over a two-year period. A faculty exchange program was entered into between Southeast University of Nanjing China and Purdue University Calumet of Hammond, Indiana, USA. One of the goals of the exchange program was to expose Chinese students to the instructional methods employed by United States Universities. By understanding the cultural differences and utilizing various teaching methodologies employed by American teachers, the faculty and students involved in these three-week classroom intensive training courses were able to adapt and successfully complete the graduate level material that was presented.

关键词： Teaching

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Adaptive Multi-strategy Rabbit Optimizer for Large-scale Optimization

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Journal of Bionic engineering 2025年第1期22卷 398-416页

作者： Baowei Xiang Yixin Xiang Department of Computing School of Electronic and Information Engineering Taizhou UniversityTaizhou318000China Department of Software Engineering College of Software Nankai UniversityTianjin300350China

As optimization problems continue to grow in complexity,the need for effective metaheuristic algorithms becomes increasingly ***,the challenge lies in identifying the right parameters and strategies for these *** this paper,we introduce the adaptive multi-strategy Rabbit Algorithm(RA).RA is inspired by the social interactions of rabbits,incorporating elements such as exploration,exploitation,and adaptation to address optimization *** employs three distinct subgroups,comprising male,female,and child rabbits,to execute a multi-strategy *** parameters,including distance factor,balance factor,and learning factor,strike a balance between precision and computational *** offer practical recommendations for fine-tuning five essential RA parameters,making them versatile and *** is capable of autonomously selecting adaptive parameter settings and mutation strategies,enabling it to successfully tackle a range of 17 CEC05 benchmark functions with dimensions scaling up to *** results underscore RA’s superior performance in large-scale optimization tasks,surpassing other state-of-the-art metaheuristics in convergence speed,computational precision,and ***,RA has demonstrated its proficiency in solving complicated optimization problems in real-world engineering by completing 10 problems in CEC2020.

关键词： Adaptive parameter Large scale optimization Rabbit algorithm Swarm intelligence engineering optimization

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VAT: Visibility Aware Transformer for Fine-Grained Clothed Human Reconstruction

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IEEE Transactions on Visualization and computer Graphics 2025年第5期31卷 2277-2287页

作者： Zhang, Xiaoyan Zhu, Zibin Xie, Hong Ren, Sisi Shenzhen University College of Computer Science and Software Engineering Shenzhen518000 China

In order to reconstruct 3D clothed human with accurate fine-grained details from sparse views, we propose a deep cooperating two-level global to fine-grained reconstruction framework that constructs robust global geometry to guide fine-grained geometry learning. The core of the framework is a novel visibility aware Transformer VAT, which bridges the two-level reconstruction architecture by connecting its global encoder and fine-grained decoder with two pixel-aligned implicit functions, respectively. The global encoder fuses semantic features of multiple views to integrate global geometric features. In the fine-grained decoder, visibility aware attention mechanism is designed to efficiently fuse multi-view and multi-scale features for mining fine-grained geometric features. The global encoder and fine-grained decoder are connected by a global embeding module to form a deep cooperation in the two-level framework, which provides global geometric embedding as a query guidance for calculating visibility aware attention in the fine-grained decoder. In addition, to extract highly aligned multi-scale features for the two-level reconstruction architecture, we design an image feature extractor MSUNet, which establishes strong semantic connections between different scales at minimal cost. Our proposed framework is end-to-end trainable, with all modules jointly optimized. We validate the effectiveness of our framework on public benchmarks, and experimental results demonstrate that our method has significant advantages over state-of-the-art methods in terms of both fine-grained performance and generalization. © 2025 IEEE.

关键词： Decoding

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Intelligent integrated sensing and communication:a survey

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Science China(information Sciences) 2025年第3期68卷 5-46页

作者： Jifa ZHANG Weidang LU Chengwen XING Nan ZHAO Naofal AL-DHAHIR George K.KARAGIANNIDIS Xiaoniu YANG School of Information and Communication Engineering Dalian University of Technology College of Information Engineering Zhejiang University of Technology School of Information and Electronics Beijing Institute of Technology Department of Electrical and Computer Engineering The University of Texas at Dallas Department of Electrical and Computer Engineering Aristotle University of Thessaloniki

Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

关键词： artificial intelligence deep learning deep reinforcement learning federated learning generative artificial intelligence integrated sensing and communication machine learning transfer learning

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Coarse-to-fine lightweight meta-embedding for ID-based recommendations

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Science China(information Sciences) 2025年第4期68卷 82-97页

作者： Yang WANG Haipeng LIU Zeqian YI Biao QIAN Meng WANG School of Computer Science and Information Engineering Hefei University of Technology College of Information and Intelligence Hunan Agricultural University

State-of-the-art recommender systems are increasingly focused on optimizing implementation efficiency, such as enabling on-device recommendations under memory constraints. Current methods commonly use lightweight embeddings for users and items or employ compact embeddings to enhance reusability and reduce memory usage. However, these approaches consider only the coarse-grained aspects of embeddings, overlooking subtle semantic nuances. This limitation results in an adversarial degradation of meta-embedding performance, impeding the system's ability to capture intricate relationships between users and items, leading to suboptimal recommendations. To address this, we propose a novel approach to efficiently learn meta-embeddings with varying grained and apply fine-grained meta-embeddings to strengthen the representation of their coarse-grained counterparts. Specifically, we introduce a recommender system based on a graph neural network, where each user and item is represented as a node. These nodes are directly connected to coarse-grained virtual nodes and indirectly linked to fine-grained virtual nodes, facilitating learning of multi-grained semantics. Fine-grained semantics are captured through sparse meta-embeddings, which dynamically balance embedding uniqueness and memory constraints. To ensure their sparseness, we rely on initialization methods such as sparse principal component analysis combined with a soft thresholding activation function. Moreover, we propose a weight-bridging update strategy that aligns coarse-grained meta-embedding with several fine-grained meta-embeddings based on the underlying semantic properties of users and items. Comprehensive experiments demonstrate that our method outperforms existing baselines. The code of our proposal is available at https://***/htyjers/C2F-MetaEmbed.

关键词： lightweight meta-embedding coarse-to-fine learning ID-based recommendations

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Chinese Medical Named Entity Recognition Using Pre-Trained Language Models and an Efficient Global Pointer Mechanism

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第19期49卷 97-114页

作者： Zhang, Xu Li, Feihong Li, Chenlong Yu, Xufeng College of Software Engineering Xiamen University of Technology Xiamen361000 China College of Computer and Information Engineering Xiamen University of Technology Xiamen361000 China

Medical Named Entity Recognition (MNER) is a critical task in medical text mining, serving as a foundation for intelligent diagnosis, disease prediction, and related tasks. However, Chinese medical texts present unique challenges, such as diverse entity classifications, varying entity lengths, and complex entity nesting, which hinder NER performance. To address these issues, we propose a Chinese MNER method leveraging pre-trained models and the efficient global pointer (EGP). Our approach incorporates three key innovations: (1) data augmentation techniques, including easy data augmentation and remote supervision with a medical entity dictionary, to address imbalanced entity types distribution and varying entity lengths;(2) a character-word fusion strategy that integrates RoBERTa-generated character vectors and Word2Vec-generated word vectors, enhancing semantic representation;and (3) an improved decoding layer based on EGP, enabling efficient recognition of both nested and flat entities while reducing computational costs. Experimentals show that our method achieves F1 scores of 75.87% and 92.77% on the CMeEE-V2 and CCKS2020 datasets, respectively, outperforming the RoBERTa-BiLSTM-CRF baseline by 3.06% and 4.38%, respectively. © 2025 Slovene Society Informatika. All rights reserved.

关键词： data augmentation EGP MNER RoBERTa Word2Vec

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Attribute grouping-based naive Bayesian classifier

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Science China(information Sciences) 2025年第3期68卷 125-149页

作者： Yulin HE Guiliang OU Philippe FOURNIER-VIGER Joshua Zhexue HUANG Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) College of Computer Science & Software Engineering Shenzhen University

The naive Bayesian classifier(NBC) is a supervised machine learning algorithm having a simple model structure and good theoretical interpretability. However, the generalization performance of NBC is limited to a large extent by the assumption of attribute independence. To address this issue, this paper proposes a novel attribute grouping-based NBC(AG-NBC), which is a variant of the classical NBC trained with different attribute groups. AG-NBC first applies a novel effective objective function to automatically identify optimal dependent attribute groups(DAGs). Condition attributes in the same DAG are strongly dependent on the class attribute, whereas attributes in different DAGs are independent of one another. Then,for each DAG, a random vector functional link network with a SoftMax layer is trained to output posterior probabilities in the form of joint probability density estimation. The NBC is trained using the grouping attributes that correspond to the original condition attributes. Extensive experiments were conducted to validate the rationality, feasibility, and effectiveness of AG-NBC. Our findings showed that the attribute groups chosen for NBC can accurately represent attribute dependencies and reduce overlaps between different posterior probability densities. In addition, the comparative results with NBC, flexible NBC(FNBC), tree augmented Bayes network(TAN), gain ratio-based attribute weighted naive Bayes(GRAWNB), averaged one-dependence estimators(AODE), weighted AODE(WAODE), independent component analysis-based NBC(ICA-NBC), hidden naive Bayesian(HNB) classifier, and correlation-based feature weighting filter for naive Bayes(CFW) show that AG-NBC obtains statistically better testing accuracies, higher area under the receiver operating characteristic curves(AUCs), and fewer probability mean square errors(PMSEs) than other Bayesian classifiers. The experimental results demonstrate that AG-NBC is a valid and efficient approach for alleviating the attribute i

关键词： naive Bayesian classifier attribute independence assumption attribute grouping dependent attribute group posterior probability class-conditional probability

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Adaptive ROI Optimization Pyramid Network: Lane Detection for FSD under Data Uncertainty

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engineering Letters 2025年第2期33卷 282-291页

作者： Cao, Xu Liu, Weisheng Wang, Zhijian College of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China College of Computer Science and Software Engineering University of Science and Technology Liaoning Co Anshan114051 China

To mitigate the challenges posed by data uncertainty in Full-Self Driving (FSD) systems. This paper proposes a novel feature extraction learning model called Adaptive Region of Interest Optimized Pyramid Network (ARO). Specifically, ARO introduces a novel cross-layer fusion attention mechanism that dynamically assigns weighted attention to feature maps across different levels, facilitating deep feature fusion and enabling the effective extraction and utilization of salient feature information. Furthermore, ARO incorporates a feature replication layer to duplicate and refine feature maps at multiple levels, thereby enhancing its capability to capture fine-grained details and generate richer feature representations. Additionally, a multi-path upsampling strategy preserves fine-grained features during upsampling. Extensive experimental evaluations conducted on the benchmark CuLane and Tusimple datasets demonstrate that ARO achieves an F1 score of 80.60% on the CuLane dataset, outperforming state-of-the-art methods, showcasing the effectiveness of the proposed approach in handling data uncertainty for robust lane detection in autonomous driving. Our code are available at https://***/caoxu0109/lane detect ldfr. © 2025, International Association of Engineers. All rights reserved.

关键词： Attention mechanisms Culane Lane detection Multi-scale fusion network Tusimple

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LoRaDIP: Low-Rank Adaptation With Deep Image Prior for Generative Low-Light Image Enhancement

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 909-920页

作者： Zhao, Zunjin Shi, Daming Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

This article presents LoRaDIP, a novel low-light image enhancement (LLIE) model based on deep image priors (DIPs). While DIP-based enhancement models are known for their zero-shot learning, their expensive computational cost remains a challenge. In addressing this issue, our proposed LoRaDIP introduces a low-rank adaptation technique, significantly reducing computational expenses without compromising performance. The contributions of this work are threefold. First, we eliminate the need for estimating initial illumination and reflectance, opting instead to directly estimate the illumination map from the observed image in a generative fashion. The illumination is parameterized by a DIP network. Second, considering the overparameterization of DIP networks, we introduce a low-rank adaptation technique to decrease the number of trainable parameters, thereby reducing computational demands. Third, differing from the existing DIP-based models that rely on a preset fixed number of iterations to halt the optimization process of Retinex decomposition, we propose an automatic stopping criterion based on stable rank, preventing unnecessary iterations. LoRaDIP not only inherits the advantage of requiring only the single input image but also exhibits reduced computational costs while maintaining or even surpassing the performance of state-of-the-art models. © 2020 IEEE.

关键词： Zero-shot learning

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Enhancing User Experience in AI-Powered Human-computer Communication with Vocal Emotions Identification Using a Novel Deep Learning Method

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computers, Materials & Continua 2025年第2期82卷 2909-2929页

作者： Ahmed Alhussen Arshiya Sajid Ansari Mohammad Sajid Mohammadi Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science College of Engineering and Information TechnologyOnaizah CollegesQassim51911Saudi Arabia

Voice, motion, and mimicry are naturalistic control modalities that have replaced text or display-driven control in human-computer communication (HCC). Specifically, the vocals contain a lot of knowledge, revealing details about the speaker’s goals and desires, as well as their internal condition. Certain vocal characteristics reveal the speaker’s mood, intention, and motivation, while word study assists the speaker’s demand to be understood. Voice emotion recognition has become an essential component of modern HCC networks. Integrating findings from the various disciplines involved in identifying vocal emotions is also challenging. Many sound analysis techniques were developed in the past. Learning about the development of artificial intelligence (AI), and especially Deep Learning (DL) technology, research incorporating real data is becoming increasingly common these days. Thus, this research presents a novel selfish herd optimization-tuned long/short-term memory (SHO-LSTM) strategy to identify vocal emotions in human communication. The RAVDESS public dataset is used to train the suggested SHO-LSTM technique. Mel-frequency cepstral coefficient (MFCC) and wiener filter (WF) techniques are used, respectively, to remove noise and extract features from the data. LSTM and SHO are applied to the extracted data to optimize the LSTM network’s parameters for effective emotion recognition. Python software was used to execute our proposed framework. In the finding assessment phase, Numerous metrics are used to evaluate the proposed model’s detection capability, Such as F1-score (95%), precision (95%), recall (96%), and accuracy (97%). The suggested approach is tested on a Python platform, and the SHO-LSTM’s outcomes are contrasted with those of other previously conducted research. Based on comparative assessments, our suggested approach outperforms the current approaches in vocal emotion recognition.

关键词： Human-computer communication(HCC) vocal emotions live vocal artificial intelligence(AI) deep learning(DL) selfish herd optimization-tuned long/short K term memory(SHO-LSTM)

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