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Knowledge-Empowered,Collaborative,and Co-Evolving AI Models:The Post-LLM Roadmap

工程（英文） 2025年第1期44卷 87-100页

作者： Fei Wu Tao Shen Thomas Bäck Jingyuan Chen Gang Huang Yaochu Jin Kun Kuang Mengze Li Cewu Lu Jiaxu Miao Yongwei Wang Ying Wei Fan Wu Junchi Yan Hongxia Yang Yi Yang Shengyu Zhang Zhou Zhao Yueting Zhuang Yunhe Pan College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Leiden Institute of Advanced Computer Science Leiden UniversityLeiden 2333 CCNetherlands College of Electrical Engineering Zhejiang UniversityHangzhou 310027China School of Engineering Westlake UniversityHangzhou 310024China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Cyber Science and Technology Sun Yat-Sen UniversityShenzhen 518107China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China

Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple *** these achieve-ments,LLMs have inherent limitations including outdated information,hallucinations,inefficiency,lack of interpretability,and challenges in domain-specific *** address these issues,this survey explores three promising directions in the post-LLM era:knowledge empowerment,model collaboration,and model ***,we examine methods of integrating external knowledge into LLMs to enhance factual accuracy,reasoning capabilities,and interpretability,including incorporating knowledge into training objectives,instruction tuning,retrieval-augmented inference,and knowledge ***,we discuss model collaboration strategies that leverage the complementary strengths of LLMs and smaller models to improve efficiency and domain-specific performance through techniques such as model merging,functional model collaboration,and knowledge ***,we delve into model co-evolution,in which multiple models collaboratively evolve by sharing knowledge,parameters,and learning strategies to adapt to dynamic environments and tasks,thereby enhancing their adaptability and continual *** illustrate how the integration of these techniques advances AI capabilities in science,engineering,and society—particularly in hypothesis development,problem formulation,problem-solving,and interpretability across various *** conclude by outlining future pathways for further advancement and applications.

关键词： Artificial intelligence Large language models Knowledge empowerment Model collaboration Model co-evolution

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On Designs of Decentralized Reputation Management for Permissioned Blockchain Networks

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computer Modeling in engineering & sciences 2024年第5期139卷 1755-1773页

作者： Jinyu Chen Long Shi Qisheng Huang Taotao Wang Daojing He School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing210094China School of Mechanical Engineering and Automation Harbin Institute of TechnologyShenzhen518055China College of Electronics and Information Engineering Shenzhen UniversityShenzhen518060China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

In permissioned blockchain networks,the Proof of Authority(PoA)consensus,which uses the election of authorized nodes to validate transactions and blocks,has beenwidely advocated thanks to its high transaction throughput and fault ***,PoA suffers from the drawback of centralization dominated by a limited number of authorized nodes and the lack of anonymity due to the round-robin block proposal *** a result,traditional PoA is vulnerable to a single point of failure that compromises the security of the blockchain *** address these issues,we propose a novel decentralized reputation management mechanism for permissioned blockchain networks to enhance security,promote liveness,and mitigate centralization while retaining the same throughput as traditional *** paper aims to design an off-chain reputation evaluation and an on-chain reputation-aided ***,we evaluate the nodes’reputation in the context of the blockchain networks and make the reputation globally verifiable through smart ***,building upon traditional PoA,we propose a reputation-aided PoA(rPoA)consensus to enhance securitywithout sacrificing *** particular,rPoA can incentivize nodes to autonomously form committees based on reputation authority,which prevents block generation from being tracked through the randomness of reputation ***,we develop a reputation-aided fork-choice rule for rPoA to promote the network’s ***,experimental results show that the proposed rPoA achieves higher security performance while retaining transaction throughput compared to traditional PoA.

关键词： Blockchain reputation management PoA throughput security decentralization

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Extraction of road using semi-supervised deep learning method 4th

Extraction of road using semi-supervised deep learning metho...

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4th International Conference on Computational Methods in science and technology, ICCMST 2024

作者： Mishra, Jalaj Khan, Mohd Jawed Negi, Himanshi Alam, Jitendra Alam, Junaid Singh, Pankaj Pratap Department of Computer Science & Engineering Greater Noida Institute of Technology Greater Noida India Department of Computer Science & Engineering Central Institute of Technology Assam Kokrajhar India

ISBN: (纸本)9781032911571

Detection of road networks using high-resolution aerial or remote sensing imagery constitutes a significant focus within modern research efforts. Currently, deep learning models demonstrate efficiency to a certain degree, they are constrained by a notable reliance on annotated datasets, a practice divergent from optimal methodologies due to the scarcity of extensive, high-quality labeled data. The precision of road prediction is additionally influenced by the latent information embedded within pixels exhibiting diminished confidence values. These pixels play a crucial role in discriminating between road and non-road regions within the images, yet their utilization by deep learning models is suboptimal. To handle these two different issues, opted U-Net as our base model with a sole generator paired with twin dis-criminators and presented this combined setup of U-Net on the Generative Adversarial Network (GAN). At the base of the U-Net architecture, a bottleneck module is incorporated to augment the functionalities of the proposed model. This proposed model attains 98% accuracy on DeepGlobe Road Dataset. Comparative and quantitative assessment demonstrates the enhanced efficiency of our model compared to similar methodologies. © 2025 the Author(s).

关键词： Generative adversarial networks

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A comparative study of recommender systems in different domains 4th

A comparative study of recommender systems in different doma...

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4th International Conference on Computational Methods in science and technology, ICCMST 2024

作者： Rohit Sharma, Aditi Kumar, Aditya Singh, Prabal Singh, Ashutosh Kumar Department of Computer Science and Engineering Institute of Engineering and Rural Technology Prayagraj India

ISBN: (纸本)9781032911571

Recommender systems are techniques designed to enhance user experience in various domains. They suggest relevant items to users based on their behavior and preferences (Linyuan et al. Feb 2012). These systems are being extensively utilized in domains such as e-commerce, social media, and video streaming platforms. A recommender system aims to provide personalized recommendations to users based on their past behavior, preferences, and other similar data. The recommender system receives input data from users. Several recommender systems are available nowadays, making it challenging to select one. It would be beneficial to conduct a comparative analysis of various recommendation systems. The research paper attempts to conclude the performance of various recommender systems by using a variety of evaluation metrics. Evaluation metrics provide insights into the effectiveness of the recommendation algorithms. This guides the selection of the most appropriate model for a given application (Oona et al. 2024). The comparative study aims to examine and analyze different recommender systems across various domains. We evaluate the performance of these systems in improving user experiences and increasing user interaction. By studying the performance of recommender systems in different domains, we gain insights into the strengths and limitations of each approach. © 2025 the Author(s).

关键词： collaborative content-based evaluation metric hybrid recommendation recommender

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Unveiling Sentiments: Analyzing Learner's Experience Using VADER and RoBERTa Models

Unveiling Sentiments: Analyzing Learner's Experience Using V...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Navaneetan, M. Tharagesh, G. Sabitha, A. Sai Hindustan Institute Of Technology & Science Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

Educational Institutions receive a large amount of data, which includes student review. The text-based interactions, reveal patterns in student sentiments and emotions and enhance the overall educational experience. The text analytics allows institutions to perform analysis on these feedbacks, that gives insights to the institution on student satisfaction, identifying areas of improvement, and analyzing the students' response. The Sentiment analysis is performed on reviews to help institutions understand the student needs and their engagement in courses in a MOOC environment. The sentiment analysis models are used to classify positive or negative reviews or to determine emotions such as happy or sad. In this research work two approaches, lexicon and transformer based algorithms were used to understand the review of Coursera data, The analysis further shows sentiment of learners based on different domains of study. © 2024 IEEE.

关键词： Sentiment analysis

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Enhancing Cardiovascular Risk Prediction through Age-Based and Behavioral Health Indicator Machine Learning Models

Enhancing Cardiovascular Risk Prediction through Age-Based a...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Gupta, Deepal Tyagi, Praveen Kumar Poornima Institute of Engineering Technology Computer Science and Engineering Rajasthan Jaipur India

ISBN: (纸本)9798331529833

Cardiovascular diseases (CVDs) are among the leading causes of death globally, causing around 17.9 million deaths every year, as reported by the WHO. Patient outcomes would also be maximally improved if predictions and management were to be made available early, especially in regions that are resource-scarce. The prevailing conventional diagnosis has mainly relied on relatively costly tests, such as angiography and electrocardiograms (ECG). However, the developing field of machine learning now provides a cost-effective and efficient way of analyzing health data to predict the risk of heart disease. This paper introduces a new approach that integrates, in the heart disease predictive models, factors concerning psychological states, such as stress, hours worked, and sleep patterns. Using the Cleveland Heart Disease dataset enriched with synthetic features representing mental health, we track the amount each of these factors contributes to predictions. We also show personalized modeling based directly on training within demographic subgroups- age under 40 versus 40 years of age or older, as well as gender - for determining whether such personal models are better than global models. We use many ML classifiers, namely Random Forest, XGBoost, KNN, SVM, Logistic Regression, Decision Tree, and Naive Bayes, to develop and compare the prediction models. All the models are assessed both on the original dataset as well as the augmented dataset using metrics such as accuracy, precision, recall, F1 score. Incorporating mental health indicators significantly improves the power of the models and lends importance to the balanced monitoring of health in cardiovascular care. When specific groups of patients are considered for the demographic-based models, the performance is good, and it underlines possible applications in personalized healthcare solutions. © 2025 IEEE.

关键词： Electronic health record

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The Evolution of Labor Market: A Blockchain Based Decentralized Hiring System for Efficient Labor Acquisition 11

The Evolution of Labor Market: A Blockchain Based Decentrali...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Prasanna Kumar, Kuna Sai Manoj Kumar, Kodisana Sai Yogitha, R. Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798350350357

The significant impact that technological advancement has had on the evolution of the labour market has led to the emergence of innovative and imaginative approaches to hiring more workers more effectively. In order to address the issues with the current job market, this essay suggests a novel approach to hiring that is decentralised and blockchain-based. The proposed method increases labour procurement's efficacy and efficiency by leveraging blockchain technology's decentralisation, transparency, and immutability. Rather than using middlemen like traditional job boards or recruiting agencies, this approach eliminates middlemen by connecting companies and job seekers directly through blockchain technology. Smart contract automation of the hiring process guarantees the safe implementation of employment terms and conditions. The de centralised nature of the system ensures that individuals from all around the world can find work, and employers can locate talent from all over the world. Furthermore, the system ensures that both companies and job seekers have credible profiles by incorporating reputation management into a de centralised identification system. This helps to reduce problems caused by dishonesty or misrepresentation of qualifications. Furthermore, the entire process incorporates a grading and feedback system, allowing participants to assess one another's trustworthiness and performance, resulting in better hiring decisions. © 2024 IEEE.

关键词： Blockchain

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High Utility Periodic Frequent Pattern Mining in Multiple Sequences

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computer Modeling in engineering & sciences 2023年第10期137卷 733-759页

作者： Chien-Ming Chen Zhenzhou Zhang Jimmy Ming-Tai Wu Kuruva Lakshmanna College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Technology Vellore Institute of TechnologyVellore632014India

Periodic patternmining has become a popular research subject in recent years;this approach involves the discoveryof frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic patternmining have ignored the utility (profit, value) of patterns. Additionally, these algorithms only identify periodicpatterns in a single sequence. However, identifying patterns of high utility that are common to a set of sequencesis more valuable. In several fields, identifying high-utility periodic frequent patterns in multiple sequences isimportant. In this study, an efficient algorithm called MHUPFPS was proposed to identify such patterns. To addressexisting problems, three new measures are defined: the utility, high support, and high-utility period sequenceratios. Further, a new upper bound, upSeqRa, and two new pruning properties were proposed. MHUPFPS usesa newly defined HUPFPS-list structure to significantly accelerate the reduction of the search space and improvethe overall performance of the algorithm. Furthermore, the proposed algorithmis evaluated using several *** experimental results indicate that the algorithm is accurate and effective in filtering several non-high-utilityperiodic frequent patterns.

关键词： Decision making frequent periodic pattern multi-sequence database sequential rules utility mining

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A Construction of Object Detection Model for Acute Myeloid Leukemia

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Intelligent Automation & Soft Computing 2023年第4期36卷 543-560页

作者： K.Venkatesh S.Pasupathy S.P.Raja Department of Computer Science and Engineering Annamalai UniversityChidambaram608002India School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India

The evolution of bone marrow morphology is necessary in Acute Mye-loid Leukemia(AML)*** takes an enormous number of times to ana-lyze with the standardization and inter-observer ***,we proposed a novel AML detection model using a Deep Convolutional Neural Network(D-CNN).The proposed Faster R-CNN(Faster Region-Based CNN)models are trained with Morphological *** proposed Faster R-CNN model is trained using the augmented *** overcoming the Imbalanced Data problem,data augmentation techniques are *** Faster R-CNN performance was com-pared with existing transfer learning *** results show that the Faster R-CNN performance was signiﬁcant than other *** number of images in each class is *** example,the Neutrophil(segmented)class consists of 8,486 images,and Lymphocyte(atypical)class consists of eleven *** dataset is used to train the CNN for single-cell morphology classiﬁ*** proposed work implies the high-class performance server called Nvidia Tesla V100 GPU(Graphics processing unit).

关键词： Acute myeloid leukemia(AML) convolutional neural network(CNN) and nvidia tesla v100 gpu

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Secure and Efficient Deduplication for Multimedia Sharing in Privacy-Sensitive Cloud Ecosystems 3

Secure and Efficient Deduplication for Multimedia Sharing in...

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

作者： Suresh, A. Murali, Erasappa Anjali Sri, V. Sumana, E. Sathwik, Gopaluni Venkata Sai Devendra Reddy, P. Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Puttur India

ISBN: (纸本)9798331527549

Current cloud-based systems face the challenge of managing storage and eliminating redundant data associated with the exponentially growing multimedia content, where numerous semantically similar but nonidentical files are included. In this paper we introduce a novel secure deduplication framework for encrypted cloud storage. This proposed system employs similarity-preserving hashing, fuzzy PoW protocol, and attribute-based encryption to perform efficient deduplication without compromising security and providing ownership verification. Security and deduplication rate scalability are improved over brute-force and ownership-faking attacks by dynamically varying thresholds based on file characteristics. Experimental evaluation shows that a 48% average deduplication rate and resistance to common attack vectors make it suitable for real-world cloud storage scenarios. The framework fills critical gaps in existing approaches and provides an innovative solution for dealing with similarity-aware data deduplication in privacy-sensitive environments. © 2025 IEEE.

关键词： Cloud storage

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