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Automatic piano performance interaction system based on greedy algorithm for dexterous manipulator

虚拟现实与智能硬件（中英文） 2024年第6期6卷 473-485页

作者： Yufei WANG Junfeng YAO Yalan ZHOU Zefeng WANG Center for Digital Media Computing School of FilmXiamen UniversityXiamen 361005China Institute of Artificial Intelligence Xiamen UniversityXiamen 361005China School of Informatics Xiamen UniversityXiamen 361005China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen 361005China

With continuous advancements in artificial intelligence(AI), automatic piano-playing robots have become subjects of cross-disciplinary interest. However, in most studies, these robots served merely as objects of observation with limited user engagement or interaction. To address this issue, we propose a user-friendly and innovative interaction system based on the principles of greedy algorithms. This system features three modules: score management, performance control, and keyboard interactions. Upon importing a custom score or playing a note via an external device, the system performs on a virtual piano in line with user inputs. This system has been successfully integrated into our dexterous manipulator-based piano-playing device, which significantly enhances user interactions.

关键词： Human-robot interaction Piano-playing robot Greedy algorithm Score parsing

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E2C: A Visual Simulator to Reinforce Education of Heterogeneous computing Systems

E2C: A Visual Simulator to Reinforce Education of Heterogene...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Ali Mokhtari Drake Rawls Tony Huynh Jeremiah Green Mohsen Amini Salehi High Performance Cloud Computing (HPCC) Laboratory School of Computing and Informatics University of Louisiana at Lafayette LA USA

Heterogeneity has been an indispensable aspect of distributed computing throughout the history of these systems. ln particular, with the increasing popularity of accelerator technologies (eg., GPUs and TPUs) and the emergence of domain-specific computing via ASlCs and FPGA, the matter of heterogeneity and understanding its ramifications on the system performance has become more critical than ever before. However, it is challenging to effectively educate students about the potential impacts of heterogeneity on: (a) the performance of distributed systems; and (b) the logic of resource allocation methods to efficiently utilize the resources. Making use of the real infrastructure (such as those offered by the public cloud providers) for benchmarking the performance of heterogeneous machines, for different applications, with respect to different obiectives, and under various workload intensities is cost- and time-prohibitive. Moreover, not all students (globally and nationally) have access or can afford such real infrastructure. To reinforce the quality of learning about various dimensions of heterogeneity, and to decrease the widening gap in education, we develop an open-source simulation tool, called E2C, that can help students researchers and practitioners to study any type of heterogeneous (or homogeneous) computing system and measure its performance under various system configurations. To make the learning curve shallow, E2C is equipped with an intuitive graphical user interface (GU1) that enables its users to easily examine system-level solutions (scheduling, load balancing, scalability, etc.) in a controlled environment within a short time and at no cost. In particular, E2C is a discrete event simulator that offers the following features: (i) simulating a heterogeneous computing system; (ii) implementing a newly developed scheduling method and plugging it into the system, (iii) measuring energy consumption and other output-related metrics; and (iv) powerful visual

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An Adaptive Image Watermarking based on Bellman-Ford Algorithm 23

An Adaptive Image Watermarking based on Bellman-Ford Algorit...

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Proceedings of the 7th International Conference on Future Networks and Distributed Systems

作者： Brahim Ferik Lakhdar Laimeche Abdallah Meraoumia Abdelkader Laouid Muath Alshaikh Khaled Chait Laboratory of Mathematics Informatics and Systems University of Larbi Tebessi Algeria LIAP Laboratory University of El Oued Algeria College of Computing and Informatics Saudi Electronic University Kingdom of Saudi Arabia

ISBN: (纸本)9798400709036

Ensuring the integrity and authenticity of images is a paramount task in the digital multimedia context. The emergence of digital technologies necessitates the development of robust techniques to safeguard medical images against tampering or unauthorized alterations. One such crucial approach is watermarking, which involves embedding imperceptible and secure data within the images to facilitate subsequent verification and authentication. This paper proposes a novel medical image watermarking technique utilizing fingerprint data and a hybrid standard deviation-Bellman Ford algorithm for enhanced security and minimal distortion. The host image is divided into blocks, and the standard deviation quantifies texture variations. The Bellman-Ford algorithm then identifies optimal paths in the image graph corresponding to regions suitable for embedding. Our methodology diverges from existing techniques by enabling adaptive watermark embedding within textured domains while circumventing smooth regions. This study concentrates on the targeted objects within the image to uphold their integrity by inserting the adaptive watermark in the regions of interest. Extensive evaluation of various modalities proves the efficacy of the proposed method with an average Peak Signal Noise Ratio of 60.67dB and Structural Similarity Index of 0.9999, demonstrating marked improvements in imperceptibility over state-of-the-art techniques. The approach is efficient, with embedding and extraction times of 0.0094s and 0.0083s, respectively.

关键词： Adaptive Watermarking

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An Adaptive Image Watermarking based on Bellman-Ford Algorithm 7

An Adaptive Image Watermarking based on Bellman-Ford Algorit...

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7th International Conference on Future Networks and Distributed Systems, ICFNDS 2023

作者： Ferik, Brahim Laimeche, Lakhdar Meraoumia, Abdallah Laouid, Abdelkader Alshaikh, Muath Chait, Khaled University Of Larbi Tebessi Tebessa Algeria Liap Laboratory University Of El Oued El Oued Algeria Department Of Computer Science College Of Computing And Informatics Saudi Electronic University Riyadh Saudi Arabia

ISBN: (纸本)9798400709036

关键词： Embeddings

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Functional signatures: new definition and constructions

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Science China(Information Sciences) 2021年第12期64卷 189-201页

作者： Qingwen GUO Qiong HUANG Sha MA Meiyan XIAO Guomin YANG Willy SUSILO College of Mathematics and Informatics South China Agricultural University Guangzhou Key Laboratory of Intelligent Agriculture South China Agricultural University School of Computing and Information Technology University of Wollongong

Functional signatures(FS) enable a master authority to delegate its signing privilege to an assistant. Concretely, the master authority uses its secret key skFto issue a signing key skffor a designated function f ∈ FFSand sends both f and skfto the assistant E, which is then able to compute a signature σf with respect to pkFfor a message y in the range of f. In this paper, we modify the syntax of FS slightly to support the application scenario where a certificate of authorization is necessary. Compared with the original FS, our definition requires that FFSis an injective function family and for any f0, f1 ∈ FFS there does not exist an intersection between range(f0) and range(f1). Accordingly, we redefine the security of FS and introduce two additional security notions, called unlinkability and accountability. Signatures σfin our definition do not expose the intention of the master authority. We propose two constructions of FS. The first one is a generic construction based on signatures with perfectly re-randomizable keys, non-interactive zero-knowledge proof(NIZK) and traditional digital signatures, and the other is based on RSA(RivestShamir-Adleman) signatures with full domain hash and NIZK. We prove that both schemes are secure under the given security models.

关键词： cloud computation security digital signature functional signature non-interactive zero-knowledge proof e-commerce

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Quality Control Based on Species Classification of Herbal Medicines Using Hyperspectral Imaging and Machine Learning 13

Quality Control Based on Species Classification of Herbal Me...

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13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2023

作者： Filho, Arlindo Rodrigues Galvao Provensi, Laryssa Rosset Rosseto, Lucimar Pinheiro Coelho, Clarimar Jose Federal University of Goiás Institute of Informatics Brazil Evangelical University of Goiás Society Technology and Environment Department Brazil Pontifical Catholic University of Goiás Scientific Computing Laboratory Brazil

ISBN: (纸本)9798350395570

The utilization of herbal medicines for primary healthcare needs is prevalent among the majority of the global population, estimated to be around 80%. There is a mandatory requirement to establish quality assurance parameters for individual raw drugs and combination formulations: the proper classification of the herbs/plants to be used. This work proposes a new methodology for quality control in terms of classification of plants from the Brazilian cerrado. Hyperspectral images of powdered leaves of plants of the Acanthaceae family were obtained. Two species identification is done using principal components analysis (PCA), partial least squares and discriminant analysis (PLS-DA). Qualitatively, the results obtained from the PCA showed the separation of the clusters, highlighting the spectral differences between the classes. The classification obtained using PLS-DA identified the spectra between J. pectoralis and J. thunbergioides above 95% for all samples tested. © 2023 IEEE.

关键词： Principal component analysis

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Combating Label Noise by Uncertainty-Aware Neighborhood Sample Selection

SSRN

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SSRN 2024年

作者： Zhang, Yiliang Lu, Yang Wang, Hanzi Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen361005 China

Existing deep learning methods often require a large amount of high-quality labeled data. Yet, the presence of noisy labels in the real-world training data seriously affects the generalization ability of the model. Sample selection techniques, the current dominant approach to mitigating the effects of noisy labels on models, use the consistency of sample predictions and observed labels to make clean selections. However, these methods still inevitably suffer from incorrect sample prediction. Furthermore, such methods do not take into account the prediction bias introduced by epistemic uncertainty, which further reduces their performance in sample selection. To address these issues, we propose an uncertainty-aware neighborhood sample selection method. Especially, it calibrates for sample prediction by neighbor prediction and reassigns model attention to the selected samples based on sample uncertainty. By alleviating the influence of prediction bias on sample selection and avoiding the occurrence of prediction bias, our proposed method achieves excellent performance in extensive experiments. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Mining High-Value Patents Leveraging Massive Patent Data 22nd

Mining High-Value Patents Leveraging Massive Patent Data

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Luo, Ruixiang Weng, Lijuan Ji, Junxiang Chen, Longbiao Zhang, Longhui Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Fujian Xiamen361021 China ZFusion Technology Co. Ltd. Fujian Xiamen361021 China

ISBN: (纸本)9783031226762

A patent is an inventor’s way of protecting its intellectual property. In recent years, the trend of patent filings has become more prevalent than ever as commercial competition among firms has intensified. While patents have promoted our society forward, only a few patents have made significant contributions, and they are often referred to as high-value patents. However, there is no clear definition of high-value patents, so traditional mining methods often rely on expert reviews in related fields, which is usually labor-intensive and time-consuming. Although existing literatures have resorted to human-designed statistical features to identify high-value patents, they ignore potentially valuable text and images in patents. In this work, we propose a two-phase framework to effectively extract heterogeneous features from the multi-modal text and image to mine high-value patents among the massive patents. In feature extraction phase, features are divided into three categories: statistical features, visual features, and textual features. Among them, statistical features are obtained according to a pretrained graph, textual features are extracted by a BERT-like language model, and a DenseNet-based network is used to extract visual features. In the multi-view learning phase, we use heterogeneous features to train views, then concatenate their features to the final several layers to evaluate the value of a patent. The evaluation result shows that our method outperforms the baseline methods. © 2023, Springer Nature Switzerland AG.

关键词： Competition

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FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning

arXiv

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arXiv 2024年

作者： Wang, Zihui Wang, Zheng Lyu, Lingjuan Peng, Pengzhao Yang, Zhicheng Wen, Chenglu Yu, Rongshan Wang, Cheng Fan, Xiaoliang Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Xiamen University China Sony AI

Collaborative fairness stands as an essential element in federated learning to encourage client participation by equitably distributing rewards based on individual contributions. Existing methods primarily focus on adjusting gradient allocations among clients to achieve collaborative fairness. However, they frequently overlook crucial factors such as maintaining consistency across local models and catering to the diverse requirements of high-contributing clients. This oversight inevitably decreases both fairness and model accuracy in practice. To address these issues, we propose FedSAC, a novel Federated learning framework with dynamic Submodel Allocation for Collaborative fairness, backed by a theoretical convergence guarantee. First, we present the concept of "bounded collaborative fairness (BCF)", which ensures fairness by tailoring rewards to individual clients based on their contributions. Second, to implement the BCF, we design a submodel allocation module with a theoretical guarantee of fairness. This module incentivizes high-contributing clients with high-performance submodels containing a diverse range of crucial neurons, thereby preserving consistency across local models. Third, we further develop a dynamic aggregation module to adaptively aggregate submodels, ensuring the equitable treatment of low-frequency neurons and consequently enhancing overall model accuracy. Extensive experiments conducted on three public benchmarks demonstrate that FedSAC outperforms all baseline methods in both fairness and model accuracy. We see this work as a significant step towards incentivizing broader client participation in federated learning. The source code is available at https://***/wangzihuixmu/FedSAC. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Visual-Linguistic Representation Learning with Deep Cross-Modality Fusion for Referring Multi-Object Tracking

Visual-Linguistic Representation Learning with Deep Cross-Mo...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wenyan He Yajun Jian Yang Lu Hanzi Wang Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen University Xiamen China

Referring multi-object tracking is a new rising research topic that aims at detecting and tracking the referred objects in a video sequence based on a natural language expression. Compared with traditional multi-object tracking, this setting guides object tracking with high-level semantic information, which may bring more flexible and robust tracking performance in practical scenarios. However, existing methods perform cross-modal fusion in only one phase. The limitation of visual-linguistic representation is prone to causing visionlanguage mismatching and producing poor tracking results. To effectively fuse vision and language modalities, we propose DeepRMOT with deep cross-modality fusion, including an enhanced early-fusion module, a bidirectional crossmodality encoder, and a cross-modality decoder. Therefore, DeepRMOT can boost object detection and data association by the enhanced visual-linguistic representation. Extensive experiments on the Refer-KITTI demonstrate the effectiveness of our method.

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