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arXiv 2023年

作者： Lau, Nuno Reis, Luís Paulo Simões, David Kasaei, Mohammadreza Abreu, Miguel Silva, Tiago Resende, Francisco DETI/UA Electronics Telecommunications and Informatics Dep. University of Aveiro Portugal DEI/FEUP Informatics Engineering Dep. Faculty of Engineering The University of Porto Portugal IEETA Institute of Electronics and Telematics Engineering of Aveiro Portugal LIACC Artificial Intelligence and Computer Science Lab. University of Porto Portugal

The FC Portugal 3D team is developed upon the structure of our previous Simulation league 2D/3D teams and our standard platform league team. Our research concerning the robot low-level skills is focused on developing behaviors that may be applied on real robots with minimal adaptation using model-based approaches. Our research on high-level soccer coordination methodologies and team playing is mainly focused on the adaptation of previously developed methodologies from our 2D soccer teams to the 3D humanoid environment and on creating new coordination methodologies based on the previously developed ones. The research-oriented development of our team has been pushing it to be one of the most competitive over the years (World champion in 2000 and Coach Champion in 2002, European champion in 2000 and 2001, Coach 2nd place in 2003 and 2004, European champion in Rescue Simulation and Simulation 3D in 2006, World Champion in Simulation 3D in Bremen 2006 and European champion in 2007, 2012, 2013, 2014 and 2015). This paper describes some of the main innovations of our 3D simulation league team during the last years. A new generic framework for reinforcement learning tasks has also been developed. This paper also includes general information related to the design of our agent architecture. The current research is focused on improving the above-mentioned framework by developing new learning algorithms to optimize low-level skills, such as running and sprinting. We are also trying to increase student contact by providing reinforcement learning assignments to be completed using our new framework, which exposes a simple interface without sharing low-level implementation details. © 2023, CC BY-NC-ND.

关键词： Reinforcement learning

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DISENTANGLING THE PROSODY AND SEMANTIC INFORMATION WITH PRE-TRAINED MODEL FOR IN-CONTEXT LEARNING BASED ZERO-SHOT VOICE CONVERSION

arXiv

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arXiv 2024年

作者： Chen, Zhengyang Wang, Shuai Zhang, Mingyang Liu, Xuechen Yamagishi, Junichi Qian, Yanmin Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shenzhen Research Institute of Big Data Shenzhen China Chinese University of Hong Kong Shenzhen China National Institute of Informatics Tokyo Japan

Voice conversion (VC) aims to modify the speaker's timbre while retaining speech content. Previous approaches have tokenized the outputs from self-supervised into semantic tokens, facilitating disentanglement of speech content information. Recently, in-context learning (ICL) has emerged in text-to-speech (TTS) systems for effectively modeling specific characteristics such as timbre through context conditioning. This paper proposes an ICL capability enhanced VC system (ICL-VC) employing a mask and reconstruction training strategy based on flow-matching generative models. Augmented with semantic tokens, our experiments on the LibriTTS dataset demonstrate that ICL-VC improves speaker similarity. Additionally, we find that k-means is a versatile tokenization method applicable to various pre-trained models. However, the ICL-VC system faces challenges in preserving the prosody of the source speech. To mitigate this issue, we propose incorporating prosody embeddings extracted from a pre-trained emotion recognition model into our system. Integration of prosody embeddings notably enhances the system's capability to preserve source speech prosody, as validated on the Emotional Speech Database. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Mpfc-Net: A Skin Lesion Segmentation Algorithm with Multi-Path Fusion Convolution

SSRN

引用

SSRN 2024年

作者： Cao, Jiaxuan Li, Yang Yan, Junfeng Zou, Beiji Zhao, Guoying Liu, Qingping School of Informatics Hunan University of Chinese Medicine Changsha410208 China AI TCM Lab Hunan Changsha410699 China School of Computer Science and Engineering Central South University Changsha410083 China Center for Machine Vision and Signal Analysis University of Oulu Finland

Skin lesion segmentation plays a crucial role in the early detection and accurate skin cancer diagnosis. The complex network architectures in existing studies fail to meet the requirements for rapid segmentation in practical applications and cannot extract effective features from different depth feature layers, leading to poor segmentation results. To address the above issues, this paper proposes a skin cancer lesion segmentation algorithm based on multi-path fusion convolution, named MPFC-Net. A shallow feature acquisition module is developed to reduce computational costs and retain more contour features. A new downsampling module with a dual-branch structure is designed to compensate for feature loss. A multi-path fusion convolution module is proposed to enable the network to fully learn contextual information and better adapt to complex situations such as varying lesion morphology and blurred edges. Experiments are conducted on the ISIC-2018 public dataset to segment skin cancer lesions under noisy interference background, achieving accuracy rates of 96.07%, mIoU indices of 81.87%, and Dice indices of 88.57%. Compared with other state-of-the-art methods, the performance of MPFC-Net obtains excellent results in all metrics, validating the effectiveness and feasibility of MPFC-Net. © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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NeRF-Texture: Synthesizing Neural Radiance Field Textures

arXiv

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arXiv 2024年

作者： Huang, Yi-Hua Cao, Yan-Pei Lai, Yu-Kun Shan, Ying Gao, Lin Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences Beijing China VAST ARC Lab Tencent PCG China School of Computer Science & Informatics Cardiff University United Kingdom

Texture synthesis is a fundamental problem in computer graphics that would benefit various applications. Existing methods are effective in handling 2D image textures. In contrast, many real-world textures contain meso-structure in the 3D geometry space, such as grass, leaves, and fabrics, which cannot be effectively modeled using only 2D image textures. We propose a novel texture synthesis method with Neural Radiance Fields (NeRF) to capture and synthesize textures from given multi-view images. In the proposed NeRF texture representation, a scene with fine geometric details is disentangled into the meso-structure textures and the underlying base shape. This allows textures with meso-structure to be effectively learned as latent features situated on the base shape, which are fed into a NeRF decoder trained simultaneously to represent the rich view-dependent appearance. Using this implicit representation, we can synthesize NeRF-based textures through patch matching of latent features. However, inconsistencies between the metrics of the reconstructed content space and the latent feature space may compromise the synthesis quality. To enhance matching performance, we further regularize the distribution of latent features by incorporating a clustering constraint. In addition to generating NeRF textures over a planar domain, our method can also synthesize NeRF textures over curved surfaces, which are practically useful. Experimental results and evaluations demonstrate the effectiveness of our approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Image texture

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Confidence-oriented Contrastive Graph Clustering

Confidence-oriented Contrastive Graph Clustering

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Yan-Di Huang Guang-Yu Zhang Dong Huang Chang-Dong Wang Yang Liu Enbo Huang College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-Sen University Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Nanning Normal University Nanning China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Contrastive clustering has recently been an emerging topic in deep unsupervised learning. Nevertheless, the previous works mostly adopt the stochastic data augmentations, which easily leads to the semantic drift problem by limited transformations. Moreover, these approaches ignore the data distribution information when generating the positive and negative pair-wise samples. In light of this, this paper proposes a simple yet effective unsupervised clustering network termed Confidence-oriented Contrastive Graph Clustering (CoCGC). Particularly, we design an end-to-end network paradigm with un-shared weights, among which a hybrid graph filter is utilized to generate two views of reliable augmentations. Guided by the non-dominated sorting theory, we further construct a confidence-oriented sample set from the latent data distribution perspective. By considering the local density and cluster distribution of the embedding representations, the discriminative sample pairs can be derived from the confidence-oriented sets in a two-view contrastive manner. Finally, a cross-view neighbor contrastive loss is devised for better exploiting the self-supervised network signals. Extensive experimental results on five benchmark datasets demonstrate the effectiveness of our method against the existing state-of-the-art deep graph clustering methods.

关键词： Network topology Semantics Neural networks Reliability theory Reliability engineering Information filters Filtering theory

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Disentangling The Prosody And Semantic Information With Pre-Trained Model For In-Context Learning Based Zero-Shot Voice Conversion

Disentangling The Prosody And Semantic Information With Pre-...

引用

IEEE Spoken Language Technology Workshop

作者： Zhengyang Chen Shuai Wang Mingyang Zhang Xuechen Liu Junichi Yamagishi Yanmin Qian Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Shenzhen Research Institute of Big Data Shenzhen China Chinese University of Hong Kong Shenzhen China National Institute of Informatics Tokyo Japan

ISBN: (数字)9798350392258

ISBN: (纸本)9798350392265

Voice conversion (VC) aims to modify the speaker’s timbre while retaining speech content. Previous approaches have tokenized the outputs from self-supervised into semantic tokens, facilitating disentanglement of speech content information. Recently, in-context learning (ICL) has emerged in text-to-speech (TTS) systems for effectively modeling specific characteristics such as timbre through context conditioning. This paper proposes an ICL capability enhanced VC system (ICL-VC) employing a mask and reconstruction training strategy based on flow-matching generative models. Augmented with semantic tokens, our experiments on the LibriTTS dataset demonstrate that ICL-VC improves speaker similarity. Additionally, we find that k-means is a versatile tokenization method applicable to various pre-trained models. However, the ICL-VC system faces challenges in preserving the prosody of the source speech. To mitigate this issue, we propose incorporating prosody embeddings extracted from a pre-trained emotion recognition model into our system. Integration of prosody embeddings notably enhances the system’s capability to preserve source speech prosody, as validated on the Emotional Speech Database.

关键词： Training Emotion recognition Databases Semantics Self-supervised learning Speech enhancement Tokenization Text to speech Multilingual Timbre

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Exploring Relationships Between Cryptocurrency News Outlets and Influencers’ Twitter Activity and Market Prices

arXiv

引用

arXiv 2024年

作者： Alizadeh, Meysam Asgari, Yasaman Samei, Zeynab Yari, Sara Dehghani, Shirin Kubli, Mael Zare, Darya Bermeo, Juan Diego Batzdorfer, Veronika Gilardi, Fabrizio Digital Democracy Lab University of Zurich Zurich Switzerland Digital Society Initiative Department of Mathematical Modeling and Machine learning University of Zurich Zurich Switzerland Department of Computer Science IPM Tehran Iran Department of Computer Engineering Allameh Tabataba’i University Tehran Iran Department of Informatics University of Zurich Zurich Switzerland Institute for Sociology and Computational Social Science Karlsruhe Institute of Technology Karlsruhe Germany

Academics increasingly acknowledge the predictive power of social media for a wide variety of events and, more specifically, for financial markets. Anecdotal and empirical findings show that cryptocurrencies are among the financial assets that have been affected by news and influencers’ activities on Twitter. However, the extent to which Twitter crypto influencer’s posts about trading signals and their effect on market prices is mostly unexplored. In this paper, we use LLMs to uncover buy and not-buy signals from influencers and news outlets’ Twitter posts and use a VAR analysis with Granger Causality tests and cross-correlation analysis to understand how these trading signals are temporally correlated with the top nine major cryptocurrencies’ prices. Overall, the results show a mixed pattern across cryptocurrencies and temporal periods. However, we found that for the top three cryptocurrencies with the highest presence within news and influencer posts, their aggregated LLM-detected trading signal over the preceding 24 hours granger-causes fluctuations in their market prices, exhibiting a lag of at least 6 hours. In addition, the results reveal fundamental differences in how influencers and news outlets cover cryptocurrencies. © 2024, CC BY.

关键词： Tweets

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Semi-markov Resource Flow as a Bit-Level Model of Traffic 24th

Semi-markov Resource Flow as a Bit-Level Model of Traffic

引用

24th International Conference on Distributed computer and Communication Networks, DCCN 2021

作者： Nazarov, Anatoly Moiseev, Alexander Lapatin, Ivan Paul, Svetlana Lizyura, Olga Pristupa, Pavel Peng, Xi Chen, Li Bai, Bo Institute of Applied Mathematics and Computer Science National Research Tomsk State University 36 Lenina Avenue Tomsk634050 Russia Theory Lab Central Research Center 2012 Labs Huawei Tech. Investment Co. Ltd 8/F Bio-informatics Center No. 2 Science Park West Avenue Hong Kong Science Park Pak Shek Kok Shatin N.T. Hong Kong

ISBN: (纸本)9783030971090

In this paper, we consider semi-Markov flow as a bit-level model of traffic. Each request of the flow brings some arbitrary distributed amount of information to the system. The current paper aims to investigate the amount of information received in semi-Markov flow during time unit. We use the asymptotic analysis method under the limit condition of growing time of observation to derive the limiting probability distribution of the amount of information received in the flow and build the approximation of its prelimit distribution function. © 2022, Springer Nature Switzerland AG.

关键词： Asymptotic analysis

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

Classification of Breast Thermal Images into Healthy/Cancer ...

引用

2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022

作者： Kadry, Seifedine Crespo, Rubén González Herrera-Viedma, Enrique Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja Andalusia26006 Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores. © 2023 The Authors. Published by Elsevier B.V.

关键词： Classification (of information)

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Bi-level Alignment for Cross-Domain Crowd Counting

arXiv

引用

arXiv 2022年

作者： Gong, Shenjian Zhang, Shanshan Yang, Jian Dai, Dengxin Schiele, Bernt PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China MPI Informatics Germany

Recently, crowd density estimation has received increasing attention. The main challenge for this task is to achieve high-quality manual annotations on a large amount of training data. To avoid reliance on such annotations, previous works apply unsupervised domain adaptation (UDA) techniques by transferring knowledge learned from easily accessible synthetic data to real-world datasets. However, current state-of-the-art methods either rely on external data for training an auxiliary task or apply an expensive coarse-to-fine estimation. In this work, we aim to develop a new adversarial learning based method, which is simple and efficient to apply. To reduce the domain gap between the synthetic and real data, we design a bi-level alignment framework (BLA) consisting of (1) task-driven data alignment and (2) fine-grained feature alignment. In contrast to previous domain augmentation methods, we introduce AutoML to search for an optimal transform on source, which well serves for the downstream task. On the other hand, we do fine-grained alignment for foreground and background separately to alleviate the alignment difficulty. We evaluate our approach on five real-world crowd counting benchmarks, where we outperform existing approaches by a large margin. Also, our approach is simple, easy to implement and efficient to apply. The code is publicly available at https://***/Yankeegsj/BLA. © 2022, CC BY.

关键词： Alignment

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