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arXiv 2025年

作者： Liu, Jian Shi, Xiongtao Nguyen, Thai Duy Zhang, Haitian Zhang, Tianxiang Sun, Wei Li, Yanjie Vasilakos, Athanasios V. Iacca, Giovanni Khan, Arshad Ali Kumar, Arvind Cho, Jae Won Mian, Ajmal Xie, Lihua Cambria, Erik Wang, Lin School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Robotics Hunan University China School of Intelligence Science and Engineering Harbin Institute of Technology Shenzhen China Department of Information and Communication Technology University of Agder Norway Department of Information Engineering and Computer Science University of Trento Italy Elm Company London United Kingdom Division of Computational Science and Technology KTH Royal Institute of Technology Sweden School of Artificial Intelligence and Data Science Sejong University Korea Republic of Department of Computer Science the University of Western Australia Australia College of Computing and Data Science Nanyang Technological University Singapore

The rapid evolution of artificial intelligence (AI) has shifted from static, data-driven models to dynamic systems capable of perceiving and interacting with real-world environments. Despite advancements in pattern recognition and symbolic reasoning, current AI systems, such as large language models, remain disembodied, unable to physically engage with the world. This limitation has driven the rise of embodied AI, where autonomous agents, such as humanoid robots, must navigate and manipulate unstructured environments with human-like adaptability. At the core of this challenge lies the concept of Neural Brain, a central intelligence system designed to drive embodied agents with human-like adaptability. A Neural Brain must seamlessly integrate multimodal sensing and perception with cognitive capabilities. Achieving this also requires an adaptive memory system and energy-efficient hardware-software co-design, enabling real-time action in dynamic environments. This paper introduces a unified framework for the Neural Brain of embodied agents, addressing two fundamental challenges: (1) defining the core components of Neural Brain and (2) bridging the gap between static AI models and the dynamic adaptability required for real-world deployment. To this end, we propose a biologically inspired architecture that integrates multimodal active sensing, perception-cognition-action function, neuroplasticity-based memory storage and updating, and neuromorphic hardware/software optimization. Furthermore, we also review the latest research on embodied agents across these four aspects and analyze the gap between current AI systems and human intelligence. By synthesizing insights from neuroscience, we outline a roadmap towards the development of generalizable, autonomous agents capable of human-level intelligence in real-world scenarios. Our project page is at Neural-Brain-for-Embodied-Agents. Copyright © 2025, The Authors. All rights reserved.

关键词： Anthropomorphic robots

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A Tent Lévy Flying Sparrow Search Algorithm for Feature Selection: A COVID-19 Case Study

arXiv

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arXiv 2022年

作者： Yang, Qinwen Gao, Yuelin Song, Yanjie School of Computer Science and Engineering North Minzu University Yinchuan750021 China School of Mathematics and Information Science North Minzu University Yinchuan750021 China Ningxia Key Laboratory of Intelligent Information and Big Data Processing Yinchuan750021 China Ningxia Province Cooperative Innovation Center of Scientific Computing and Intelligent in Formation Processing North Minzu University Yinchuan750021 China College of Systems Engineering National University of Defense Technology Changsha410073 China

The "Curse of Dimensionality" induced by the rapid development of information science, might have a negative impact when dealing with big datasets. In this paper, we propose a variant of the sparrow search algorithm (SSA), called Tent Lévy flying sparrow search algorithm (TFSSA), and use it to select the best subset of features in the packing pattern for classification purposes. SSA is a recently proposed algorithm that has not been systematically applied to feature selection problems. After verification by the CEC2020 benchmark function, TFSSA is used to select the best feature combination to maximize classification accuracy and minimize the number of selected features. The proposed TFSSA is compared with nine algorithms in the literature. Nine evaluation metrics are used to properly evaluate and compare the performance of these algorithms on twenty-one datasets from the UCI repository. Furthermore, the approach is applied to the coronavirus disease (COVID-19) dataset, yielding the best average classification accuracy and the average number of feature selections, respectively, of 93.47% and 2.1. Experimental results confirm the advantages of the proposed algorithm in improving classification accuracy and reducing the number of selected features compared to other wrapper-based algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： COVID-19

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FedPHE: A Secure and Efficient Federated Learning via Packed Homomorphic Encryption

引用

IEEE Transactions on Dependable and Secure computing 2025年

作者： Li, Yuqing Yan, Nan Chen, Jing Wang, Xiong Hong, Jianan He, Kun Wang, Wei Li, Bo Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University RiZhao Information Technology Institute Rizhao276800 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China Shanghai Jiao Tong University School of Cyber Science and Engineering Shanghai200240 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

Cross-silo federated learning (FL) enables multiple institutions (clients) to collaboratively build a global model without sharing private data. To prevent privacy leakage during aggregation, homomorphic encryption (HE) is widely used to encrypt model updates, yet incurs high computation and communication overheads. To reduce these overheads, packed HE (PHE) has been proposed to encrypt multiple plaintexts into a single ciphertext. However, the original design of PHE assumes all clients share a single private key, making the system vulnerable to security threats of ciphertexts being intercepted and decrypted by honest-but-curious clients. Also, it does not consider the heterogeneity among different clients, resulting in undermined training efficiency with slow convergence and stragglers. To address these challenges, we propose FedPHE, a secure and efficient FL framework with PHE by jointly exploiting contribution-aware secure aggregation and straggler-resistant client selection. Using CKKS with sparsification and blinding, FedPHE achieves efficient secure aggregation that allows clients to only provide obscured encrypted updates while the server can perform aggregation by accounting for contributions of local updates. To mitigate the straggler effect, we devise a perturbed sketch-based selection to cherry-pick representative clients with heterogeneous models and computing capabilities in a communication-efficient and privacy-preserving manner. We show, through rigorous security analysis and extensive experiments, that FedPHE can efficiently safeguard clients' privacy, achieve 2.45-6.56× training speedup, cut the communication overhead by 1.32-24.85×, and reduce straggler effects by 1.89-2.78×. © 2004-2012 IEEE.

关键词： Privacy by design

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Reflections from the Workshop on AI-Assisted Decision Making for Conservation

arXiv

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arXiv 2023年

作者： Xu, Lily Rolf, Esther Beery, Sara Bennett, Joseph R. Berger-Wolf, Tanya Birch, Tanya Bondi-Kelly, Elizabeth Brashares, Justin Chapman, Melissa Corso, Anthony Davies, Andrew Garg, Nikhil Gaylard, Angela Heilmayr, Robert Kerner, Hannah Klemmer, Konstantin Kumar, Vipin MacKey, Lester Monteleoni, Claire Moorcroft, Paul Palmer, Jonathan Perrault, Andrew Thau, David Tambe, Milind School of Engineering and Applied Sciences Harvard University United States Harvard University United States United States Department of Computer Science University of Colorado Boulder United States Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Department of Biology Carleton University Canada Department of Computer Science and Engineering The Ohio State University United States Department of Electrical and Computer Engineering The Ohio State University United States Department of Evolution Ecology and Organismal Biology The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States Google United States Electrical Engineering and Computer Science University of Michigan United States Department of Environmental Science Policy and Management University of California Berkeley United States Department of Aeronautics and Astronautics Stanford University United States Department of Organismic and Evolutionary Biology Harvard University United States Operations Research and Information Engineering Cornell Tech United States African Parks South Africa Bren School of Environmental Science and Management University of California Santa Barbara United States School of Computing and Augmented Intelligence Arizona State University United States Microsoft Research United States Department of Computer Science and Engineering University of Minnesota United States Inria Paris France United States Wwf Global Science United States

In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022. We identify key open research questions in resource allocation, planning, and interventions for biodiversity conservation, highlighting conservation challenges that not only require AI solutions, but also require novel methodological advances. In addition to providing a summary of the workshop talks and discussions, we hope this document serves as a call-to-action to orient the expansion of algorithmic decision-making approaches to prioritize real-world conservation challenges, through collaborative efforts of ecologists, conservation decision-makers, and AI researchers. Copyright © 2023, The Authors. All rights reserved.

关键词： Conservation

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Dynamic Ensemble Transfer Learning with Multi-View Ultrasonography for Improving Thyroid Cancer Diagnostic Reliability

SSRN

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SSRN 2024年

作者： Zhang, Xinyu Liu, Feng Lee, Vincent Cheng Siong Jassal, Karishma Muzio, Bruno Di Lee, James C. Northwestern Polytechnical University School of Electronics and Information Shaanxi Xi’an710072 China Sichuan University West China Hospital Sichuan Chengdu610041 China Monash University Faculty of IT Department of Data Science & AI MelbourneVIC3800 Australia Southeast University Monash Suzhou Campus Jiangsu Suzhou215123 China Alfred Hospital Monash University Endocrine Surgery Unit MelbourneVIC3004 Australia Monash University School of Translational Medicine Department of Surgery MelbourneVIC3004 Australia Alfred Hospital Department of Radiology MelbourneVIC3004 Australia Monash University Australia School of Computing & Information Technology Alfred Health Australia

Diagnostic decision-making requires the integration of relevant facts and clinician experience. Incorporating the clinical experience from diverse backgrounds is beneficial in multi-disciplinary model to mitigate uncertainties aroused by incomplete mastery of knowledge. However, current computer-aided diagnostic systems are generally designed using unitary datasets and are challenging to adapt to diverse institutions, leading to the limited reliability of the generated decisions. Accordingly, this study proposes a dynamic ensemble transfer learning-based system that simulates such diversity in its training and structure by integrating knowledge and data. The approach consists of a self-directed model selection scheme, a dynamic weighting mechanism, and a unified weighted ensemble averaging model, tailored for reliable diagnostic decision-making. This study adopts the most rapidly rising malignancy worldwide, thyroid cancer, for evaluation. Two multi-view thyroid ultrasonography datasets with matching tissue diagnosis from over 700 cross-national patients are used to pre-train the individual networks. The learnt knowledge is then transferred to the weighted ensemble averaging model through the dynamic weighting mechanism. The fine-tuned ensemble model is evaluated using an external set of thyroid nodules with radiological risk of malignancy based on the Thyroid Imaging Reporting and data System. Further, we alter the datasets through up-sampling and down-sampling to evaluate the ensemble model's generalisation. Extensive experiments demonstrate the proposed ensemble model yields promising performance with an area under curve value between 0.87 and 0.93 under diversified strategies. Benchmarking results show the proposed approach surpasses existing studies and improves diagnostic reliability in thyroid cancer care while guiding subsequent management options. © 2024, The Authors. All rights reserved.

关键词： Diseases

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SDCLR: Scalable Dual Control-Layer Routing for continuous-flow microfluidic biochips with minimized control ports

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Integration 2025年 104卷

作者： Zhisheng Chen Hongjin Su Bohan Dong Genggeng Liu Xing Huang School of Informatics Xiamen University Xiamen 361005 China College of Computer and Data Science Fuzhou University Fuzhou 350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou 350116 China Lyle School Of Engineering Southern Methodist University Dallas 75275 United States School of Computer Science Northwestern Polytechnical University Xi’an 710072 China Key Laboratory of Data Science and Intelligent Computing Fuzhou University of International Studies and Trade Fuzhou 350202 China

Recent advancements in continuous-flow microfluidic biochips have led to highly integrated lab-on-a-chip systems with complex functionality and minimized control ports. Despite these advancements, achieving efficient control-layer routing under strict valve synchronization remains a significant challenge. Clustering more valves into the same group can reduce the usage of control ports but necessitates building a larger routing tree to meet the length-matching requirements of synchronized valve groups, significantly reducing the routing resources available for other valves and diminishing routing feasibility. To strike an optimized balance between control port usage and routing feasibility, this paper proposes a scalable dual-control layer routing method called SDCLR, aimed at optimizing routing feasibility while minimizing the number of control ports. The main features of SDCLR include a DBSCAN-based valve readdressing to optimize routing area allocation, a conflict-aware layer assignment to minimize resource contention, and a synchronized-driven routing framework to ensure precise synchronized valve operations. Experimental results show that SDCLR performs excellently in terms of routability, control port number, and timing requirements.

关键词：

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Why Do Developers Remove Lambda Expressions in Java? 21

Why Do Developers Remove Lambda Expressions in Java?

引用

IEEE International Conference on Automated Software Engineering (ASE)

作者： Mingwei Zheng Jun Yang Ming Wen Hengcheng Zhu Yepang Liu Hai Jin Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan China School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China The Hong Kong University of Science and Technology Hong Kong China Southern University of Science and Technology Shenzhen China Cluster and Grid Computing Lab School of Computer Science and Technology HUST Wuhan China

ISBN: (纸本)9781665447843

Java 8 has introduced lambda expressions, a core feature of functional programming. Since its introduction, there is an increasing trend of lambda adoptions in Java projects. Developers often adopt lambda expressions to simplify code, avoid code duplication or simulate other functional features. However, we observe that lambda expressions can also incur different types of side effects (i.e., performance issues and memory leakages) or even severe bugs, and developers also frequently remove lambda expressions in their implementations. Consequently, the advantages of utilizing lambda expressions can be significantly compromised by the collateral side effects. In this study, we present the first large-scale, quantitative and qualitative empirical study to characterize and understand inappropriate usages of lambda expressions. Particularly, we summarized seven main reasons for the removal of lambdas as well as seven common migration patterns. For instance, we observe that lambdas using customized functional interfaces are more likely to be removed by developers. Moreover, from a complementary perspective, we performed a user study over 30 developers to seek the underlying reasons why they remove lambda expressions in practice. Finally, based on our empirical results, we made suggestions on scenarios to avoid lambda usages for Java developers and also pointed out future directions for researchers.

关键词： Java Codes computer bugs Market research Functional programming Open source software Software engineering

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MORAL: A Multimodal Reinforcement Learning Framework for Decision Making in Autonomous Laboratories

arXiv

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arXiv 2025年

作者： Tirabassi, Natalie Kumar, Sathish A.P. Jha, Sumit Ramanathan, Arvind Department of Computer Science Cleveland State University ClevelandOH United States School of Computing and Information Sciences Florida International University MiamiFL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States

We propose MORAL (a multimodal reinforcement learning framework for decision making in autonomous laboratories) that enhances sequential decision-making in autonomous robotic laboratories through the integration of visual and textual inputs. Using the Bridgedata V2 dataset, we generate fine-tuned image captions with a pretrained BLIP-2 vision-language model and combine them with visual features through an early fusion strategy. The fused representations are processed using Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) agents. Experimental results demonstrate that multimodal agents achieve a 20% improvement in task completion rates and significantly outperform visual-only and textual-only baselines after sufficient training. Compared to transformer-based and recurrent multimodal RL models, our approach achieves superior performance in cumulative reward and caption quality metrics (BLEU, METEOR, ROUGE-L). These results highlight the impact of semantically aligned language cues in enhancing agent learning efficiency and generalization. The proposed framework contributes to the advancement of multimodal reinforcement learning and embodied AI systems in dynamic, real-world environments. © 2025, CC BY.

关键词： Reinforcement learning

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ACE-RLHF: Automated Code Evaluation and Socratic Feedback Generation Tool using Large Language Models and Reinforcement Learning with Human Feedback

arXiv

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arXiv 2025年

作者： Rahman, Tasnia Kumar, Sathish A.P. Jha, Sumit Ramanathan, Arvind Department of Computer Science Cleveland State University ClevelandOH United States School of Computing and Information Sciences Florida International University MiamiFL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States

Automated Program Repair tools are developed for generating feedback and suggesting a repair method for erroneous code. State of the art (SOTA) code repair methods rely on data-driven approaches and often fail to deliver solution for complicated programming questions. To interpret the natural language of unprecedented programming problems, using Large Language Models (LLMs) for code-feedback generation is crucial. LLMs generate more comprehensible feedback than compiler-generated error messages, and Reinforcement Learning with Human Feedback (RLHF) further enhances quality by integrating human-in-the-loop which helps novice students to lean programming from scratch interactively. We are applying RLHF fine-tuning technique for an expected Socratic response such as a question with hint to solve the programming issue. We are proposing code feedback generation tool by fine-tuning LLM with RLHF, Automated Code Evaluation with RLHF (ACE-RLHF), combining two open-source LLM models with two different SOTA optimization techniques. The quality of feedback is evaluated on two benchmark datasets containing basic and competition-level programming questions where the later is proposed by us. We achieved 2-5% higher accuracy than RL-free SOTA techniques using Llama-3-7B-Proximal-policy optimization in automated evaluation and similar or slightly higher accuracy compared to reward model-free RL with AI Feedback (RLAIF). We achieved almost 40% higher accuracy with GPT-3.5 Best-of-n optimization while performing manual evaluation. © 2025, CC BY.

关键词： Reinforcement learning

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Enhanced Penalty-based Bidirectional Reinforcement Learning Algorithms

arXiv

引用

arXiv 2025年

作者： Pula, Gana Sandeep Kumar, Sathish A.P. Jha, Sumit Ramanathan, Arvind Department of Computer Science Cleveland State University ClevelandOH United States School of Computing and Information Sciences Florida International University MiamiFL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States

This research focuses on enhancing reinforcement learning (RL) algorithms by integrating penalty functions to guide agents in avoiding unwanted actions while optimizing rewards. The goal is to improve the learning process by ensuring that agents learn not only suitable actions but also which actions to avoid. Additionally, we reintroduce a bidirectional learning approach that enables agents to learn from both initial and terminal states, thereby improving speed and robustness in complex environments. Our proposed Penalty-Based Bidirectional methodology is tested against Mani skill benchmark environments, demonstrating an optimality improvement of success rate of approximately 4% compared to existing RL implementations. The findings indicate that this integrated strategy enhances policy learning, adaptability, and overall performance in challenging scenarios. © 2025, CC BY.

关键词： Learning algorithms

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