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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Joao Buzzatto Junbang Liang Mojtaba Shahmohammadi Saori Matsunaga Rintaro Haraguchi Toshisada Mariyama Bruce A. MacDonald Minas Liarokapis Department of Mechanical and Mechatronics Engineering New Dexterity research group University of Auckland New Zealand Information Technology R&D Center Mitsubishi Electric Corporation Japan Advanced Technology R&D Center Mitsubishi Electric Corporation Japan Department of Electrical Computer and Software Engineering Centre for Automation and Robotic Engineering Science University of Auckland New Zealand

Over the last decade, a plethora of soft robotic devices have been proposed for the execution of complex grasping and dexterous manipulation tasks. Tasks requiring such increased dexterity are typically executed using fully-actuated, rigid end-effectors equipped with sophisticated sensing and controlled with complex control laws. The new class of soft robotic devices offers an alternative to the traditional end-effectors and facilitates the development of robotic grasping and manipulation solutions that are lightweight, safe to interact with, affordable, and easy to use and control. Within the class of soft robotic grippers and hands, promising recent developments were made in ultra-affordable, even disposable mechanisms based on origami and kirigami structures. This paper proposes a new kirigami-inspired robotic gripper geometry employing compression-based actuation. The compression actuation fundamentally differentiates this new design class from previous kirigami grippers, resulting in more compact robotic grippers with superior grasping capabilities. In particular, we investigate how the shapes of the internal cuts of the kirigami geometries can affect the gripper performance in terms of force exertion and grasping capabilities. A series of experiments are conducted to understand better the working principles behind this new type of kirigami grippers and experimentally validate their efficacy in the execution of complex, everyday life tasks. Further demonstrations of the gripper's capabilities include the pick-and-placing of human hair, egg yolk, and even liquids.

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Fixed-point iterative computation of Gaussian barycenters for some dissimilarity measures

Fixed-point iterative computation of Gaussian barycenters fo...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Alessandro D'Ortenzio Costanzo Manes Umut Orguner Department of Information Engineering Computer Science and Mathematics University of L’ Aquila Center of Excellence EX-EMERGE University of L’ Aquila Department of Electrical and Electronics Engineering Middle East Technical University Turkey

In practical contexts like sensor fusion or computer vision, it is not unusual to deal with a large number of Gaussian densities that encode the available information. In such cases, if the computational capabilities are limited, a data compression is required, often done by finding the barycenter of the set of Gaussians. However, such computation strongly depends on the chosen loss function (dissimilarity measure) to be minimized, and most often it must be performed by means of numerical methods, since the barycenter can rarely be computed analytically. Some constraints, like the covariance matrix symmetry and positive definiteness can make nontrivial the numerical computation of the Gaussian barycenter. In this work, a set of Fixed-Point Iteration algorithms are presented in order to allow for the agile computation of Gaussian barycenters according to several dissimilarity measures.

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Better Language Models of Code through Self-Improvement

arXiv

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arXiv 2023年

作者： To, Hung Quoc Bui, Nghi D.Q. Guo, Jin Nguyen, Tien N. FPT Software AI Center Department of Computer Science Fulbright University Viet Nam Viet Nam School of Computer Science McGill University Canada Mila - Quebec AI Institute Canada School of Engineering and Computer Science The University of Texas Dallas United States

Pre-trained language models for code (PLMCs) have gained attention in recent research. These models are pre-trained on large-scale datasets using multi-modal objectives. However, fine-tuning them requires extensive supervision and is limited by the size of the dataset provided. We aim to improve this issue by proposing a data augmentation framework using knowledge distillation. Our framework utilizes knowledge gained during the pre-training and fine-tuning stage to generate pseudo data, which is then used as training data for the next step. We incorporate this framework into the state-of-the-art language models, such as CodeT5, CodeBERT, and UnixCoder. The results show that our framework significantly improves PLMCs’ performance in sequence-generation tasks, such as code summarization and code generation in the CodeXGLUE benchmark. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey

arXiv

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arXiv 2024年

作者： Ye, Mang Shen, Wei Du, Bo Snezhko, Eduard Kovalev, Vassili Yuen, Pong C. The National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China United Institute of Informatics Problems of Belarus National Academy of Sciences National Academy of Sciences of Belarus Minsk Belarus Department of Computer Science Hong Kong Baptist University Hong Kong

Vertical Federated Learning (VFL) is a privacy-preserving distributed learning paradigm where different parties collaboratively learn models using partitioned features of shared samples, without leaking private data. Recent research has shown promising results addressing various challenges in VFL, highlighting its potential for practical applications in cross-domain collaboration. However, the corresponding research is scattered and lacks organization. To advance VFL research, this survey offers a systematic overview of recent developments. First, we provide a history and background introduction, along with a summary of the general training protocol of VFL. We then revisit the taxonomy in recent reviews and analyze limitations in-depth. For a comprehensive and structured discussion, we synthesize recent research from three fundamental perspectives: effectiveness, security, and applicability. Finally, we discuss several critical future research directions in VFL, which will facilitate the developments in this field. We provide a collection of research lists and periodically update them at https://***/shentt67/VFL_Survey. Copyright © 2024, The Authors. All rights reserved.

关键词： Privacy-preserving techniques

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Unleashing the Power of Machine Learning: Advancing Early Prediction and Analysis of Diabetes Mellitus

Unleashing the Power of Machine Learning: Advancing Early Pr...

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2023 IEEE World Conference on Communication and Computing, WCONF 2023

作者： Nancy Sil, Sagarika Rani, Ritu Bisla, Nidhi Jaiswal, Garima Sharma, Arun Indira Gandhi Delhi Technical University of Women Department of Information Technology Delhi India Indira Gandhi Delhi Technical University of Women Department of Electronics & Communication Engineering Delhi India University for Women Center of Excellence-Artificial Intelligence Indira Gandhi Delhi Technical Delhi India OP Jindal Global University Jindal Global Business School Sonipat India Bennet University Computer Science Deptt. Noida India

ISBN: (纸本)9798350311204

Diabetes is a condition which makes it difficult to regulate blood sugar/glucose. Diabetes is a leading cause of heart failure, stroke, renal failure, disability, blindness, and kidney failure. As per the figures given by International Diabetes Federation, there are 382 million diabetics worldwide. By 2035, there is a possibility that this will increase to 592 million. There are multiple forms of diabetes, and each one is treated differently. The most crucial step, however, is a precise diagnosis of the level of diabetes. Diabetes risk and severity can be significantly reduced if reliable early prognosis is possible. Here, we are proposing a model to predict diabetes using variables like glucose, body mass index (BMI), age, insulin, etc. using Johndasilva dataset. Several machine learning algorithms, including Random Forest(RF), Support Vector Machine (SVM), and Decision Tree (DT), are employed. The model is then chosen among those with the maximum accuracy. Random Forest gave the greatest accuracy of 99.5%. © 2023 IEEE.

关键词： Forecasting

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R2 Indicator and Deep Reinforcement Learning Enhanced Adaptive Multi-Objective Evolutionary Algorithm

arXiv

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arXiv 2024年

作者： Tahernezhad-Javazm, Farajollah Rankin, Debbie Bois, Naomi Du Smith, Alice E. Coyle, Damien Intelligent Systems Research Center School of Computing Engineering and Intelligent Systems Ulster University LondonderryBT48 7JL United Kingdom The Bath Institute for Augmented Human University of Bath BathBA2 7AY United Kingdom The Department of Industrial and Systems Engineering Department of Computer Science and Software Engineering Auburn University AuburnAL36849 United States

Choosing an appropriate optimization algorithm is essential to achieving success in optimization challenges. Here we present a new evolutionary algorithm structure that utilizes a reinforcement learning-based agent aimed at addressing these issues. The agent employs a double deep q-network to choose a specific evolutionary operator based on feedback it receives from the environment during optimization. The algorithm’s structure contains five single-objective evolutionary algorithm operators. This single-objective structure is transformed into a multi-objective one using the R2 indicator. This indicator serves two purposes within our structure: first, it renders the algorithm multi-objective, and second, provides a means to evaluate each algorithm’s performance in each generation to facilitate constructing the reinforcement learning-based reward function. The proposed R2-reinforcement learning multi-objective evolutionary algorithm (R2-RLMOEA) is compared with six other multiobjective algorithms that are based on R2 indicators. These six algorithms include the operators used in R2-RLMOEA as well as an R2 indicator-based algorithm that randomly selects operators during optimization. We benchmark performance using the CEC09 functions, with performance measured by inverted generational distance and spacing. The R2-RLMOEA algorithm outperforms all other algorithms with strong statistical significance (p © 2024, CC BY.

关键词： Reinforcement learning

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PIG: Prompt Images Guidance for Night-Time Scene Parsing

arXiv

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arXiv 2024年

作者： Xie, Zhifeng Qiu, Rui Wang, Sen Tan, Xin Xie, Yuan Ma, Lizhuang Department of Film and Television Engineering Shanghai University Shanghai200072 China Shanghai Key Laboratory of Computer Software Testing and Evaluating China Hanghai Engineering Research Center of Motion Picture Special Effects Shanghai200072 China School of Computer Science and Technology China Normal University Shanghai200062 China Chongqing Institute of East China Normal University Chongqing401120 China School of Computer Science and Technology East China Normal University Shanghai200062 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

Night-time scene parsing aims to extract pixel-level semantic information in night images, aiding downstream tasks in understanding scene object distribution. Due to limited labeled night image datasets, unsupervised domain adaptation (UDA) has become the predominant method for studying night scenes. UDA typically relies on paired day-night image pairs to guide adaptation, but this approach hampers dataset construction and restricts generalization across night scenes in different datasets. Moreover, UDA, focusing on network architecture and training strategies, faces difficulties in handling classes with few domain similarities. In this paper, we leverage Prompt Images Guidance (PIG) to enhance UDA with supplementary night knowledge. We propose a Night-Focused Network (NFNet) to learn night-specific features from both target domain images and prompt images. To generate high-quality pseudo-labels, we propose Pseudo-label Fusion via Domain Similarity Guidance (FDSG). Classes with fewer domain similarities are predicted by NFNet, which excels in parsing night features, while classes with more domain similarities are predicted by UDA, which has rich labeled semantics. Additionally, we propose two data augmentation strategies: the Prompt Mixture Strategy (PMS) and the Alternate Mask Strategy (AMS), aimed at mitigating the overfitting of the NFNet to a few prompt images. We conduct extensive experiments on four night-time datasets: NightCity, NightCity+, Dark Zurich, and ACDC. The results indicate that utilizing PIG can enhance the parsing accuracy of UDA. The code is available at https://***/qiurui4shu/PIG. Copyright © 2024, The Authors. All rights reserved.

关键词： Network architecture

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Amplifying the Music Listening Experience through Song Comments on Music Streaming Platforms

arXiv

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arXiv 2023年

作者： Chen, Longfei Liu, Qianyu Zhang, Chenyang Huang, Yangkun Peng, Zhenhui Zeng, Haipeng Sun, Zhida Ma, Xiaojuan Li, Quan The School of Information Science and Technology ShanghaiTech University Shanghai Engineering Research Center of Intelligent Vision and Imaging China The Department of Computer Science University of Illinois Urbana-Champaign United States Tandon School of Engineering New York University United States The School of Artificial Intelligence Sun Yat-Sen University China The School of Intelligent Systems Engineering Sun Yat-Sen University China The College of Computer Science and Software Engineering Shenzhen University China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Music streaming services are increasingly popular among younger generations who seek social experiences through personal expression and sharing of subjective feelings in comments. However, such emotional aspects are often ignored by current platforms, which affects the listeners' ability to find music that triggers specific personal feelings. To address this gap, this study proposes a novel approach that leverages deep learning methods to capture contextual keywords, sentiments, and induced mechanisms from song comments. The study augments a current music app with two features, including the presentation of tags that best represent song comments and a novel map metaphor that reorganizes song comments based on chronological order, content, and sentiment. The effectiveness of the proposed approach is validated through a usage scenario and a user study that demonstrate its capability to improve the user experience of exploring songs and browsing comments of interest. This study contributes to the advancement of music streaming services by providing a more personalized and emotionally rich music experience for younger generations. © 2023, CC BY.

关键词： Music

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A blockchain-based computation offloading method for edge computing in 5G networks

A blockchain-based computation offloading method for edge co...

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作者： Xu, Xiaolong Chen, Yi Zhang, Xuyun Liu, Qingxiang Liu, Xihua Qi, Lianyong School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Electrical and Computer Engineering University of Auckland Auckland New Zealand School of Information Science and Engineering Qufu Normal University Qufu China

Edge computing (EC) emerges as a novel computing paradigm to offload computing tasks from user equipments (UEs) to edge notes (ENs) in fifth-generation networks, which definitely breaks the resource limitation of UEs to a certain degree. However, it is troublesome to guarantee the overall operating performance of ENs due to the uneven distributed resource demands of UEs, the resulting transmission delay and the data loss for computation offloading between the covered EN and the deployed destination EN. In view of this challenge, a blockchain-based computation offloading method, named BCO, is proposed in this paper. Technically, since blockchain is a promising technique for the decentralized system, a blockchain-based EC framework is designed to degrade the data loss possibility by integrating blockchain and EC. Then, the nondominated sorting genetic algorithm, the third version (NSGA-III), is leveraged to acquire the balanced offloading strategies. Furthermore, by taking advantage of Simple Additive Weighting and Multiple Criteria Decision Making, the optimal offloading strategy is identified. Finally, systematic experiments and analyses on the comparative experiment are conducted to verify the efficiency of our proposed method BCO. © 2019 John Wiley & Sons, Ltd.

关键词： Blockchain

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Refining the Unseen: Self-supervised Two-stream Feature Extraction for Image Quality Assessment

Refining the Unseen: Self-supervised Two-stream Feature Extr...

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IEEE International Conference on Data Mining (ICDM)

作者： Yiwei Lou Yanyuan Chen Dexuan Xu Doudou Zhou Yongzhi Cao Hanpin Wang Yu Huang Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of Education School of Computer Science Peking University Beijing China School of Software & Microelectronics Peking University Beijing China Department of Biostatistics Harvard T.H. Chan School of Public Health Boston USA National Engineering Research Center for Software Engineering Peking University Beijing China

The inadequacy of labeled datasets for image quality assessment has led to the development and popularity of self-supervised approaches. However, most existing self-supervised methods primarily focus on content and fidelity features extracted with convolutional neural networks, overlooking the crucial importance of structural features in quality assessment. To address this problem, we present a novel self-supervised two-stream feature extraction and representation approach. In our approach, the first stream leverages a contrastive learning framework to extract image fidelity features, while the second stream emphasizes structural features by incorporating an attention mechanism. This innovative combination results in a comprehensive feature representation for quality assessment. Moreover, our proposed method facilitates transfer learning, allowing the pre-trained two-stream model in the source domain to be seamlessly applied to target domains for quality regression. This compatibility with transfer learning enhances the adaptability and generalization of the model. Extensive experiments are carried out on three synthetic distortion datasets to validate the effectiveness of our approach. The results demonstrate that our work not only competes with state-of-the-art self-supervised methods but also outperforms some supervised approaches.

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