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28th International Conference on Knowledge Based and intelligent information and Engineering systems, KES 2024

作者： Wieckowski, Jakub Gajewski, Pawel Swaldek, Krzysztof Salabun, Wojciech National Institute of Telecommunications ul. Szachowa 1 Warsaw04-894 Poland Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Zolnierska 49 Szczecin71-210 Poland Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland

This paper provides a comparative analysis of three selected Multi-Criteria Decision Analysis (MCDA) methods in a practical decision problem to examine the robustness of the obtained results. The Complex Proportional Assessment (COPRAS), Preference Ranking Organization METHod for Enrichment of Evaluations (PROMEHTEE) II, and Evaluation based on Distance from Average Solution (EDAS) methods were utilized for the evaluation purposes, while the Ranking Comparison (RANCOM) method was applied to determine criteria weights. The application was directed toward sustainable development management in selected European countries considering different energy sources in the consumption share per capita. The obtained results showed, that depending on the used MCDA method, the ranking of countries differed, thus showing that the evaluation process is sensitive to changing assessment methods. © 2024 The Authors.

关键词： Sustainable development goals

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Comparison of Monocular ROS-Based Visual SLAM Methods 1

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7th International Conference on Interactive Collaborative robotics, ICR 2022

作者： Safarova, Liaisan Abbyasov, Bulat Tsoy, Tatyana Li, Hongbing Magid, Evgeni Laboratory of Intelligent Robotic Systems Intelligent Robotics Department Institute of Information Technology and Intelligent Systems Kazan Federal University Kremlyovskaya Str. 35 Kazan420111 Russia Department of Instrument Science and Engineering Shanghai Jiao Tong University Shanghai China Tikhonov Moscow Institute of Electronics and Mathematics HSE University Tallinn Str 34 Moscow123592 Russia

ISBN: (数字)9783031236099

ISBN: (纸本)9783031236082

Simultaneous Localization and Mapping (SLAM) is a robot navigation approach used to estimate a movement of a sensor in an unknown environment. SLAM application examples include urban search and rescue operations in high-risk environments, visual surveillance and service robotics. Compared to laser-range finders, visual sensors are more light weight, have less power consumption and provide more vast amount of environmental information. Visual SLAM methods are based on visual information only and employ monocular, stereo or RGD-D cameras as input sensors. Monocular SLAM methods are often preferred for a robotic platform when cost, energy and system weight requirements are limited. Calculation of errors and testing on ready-made datasets allow to check applicability of an algorithm in real-world scenarios with less efforts than experiments with a real robot. Verification of a sensor position and robot trajectory estimation accuracy is achieved by measuring Absolute Trajectory Error (ATE), Relative Pose Error (RPE) and Root Mean Square Error (RMSE). This article presents analysis and comparison of the most widely used Visual SLAM monocular algorithms built for Robot Operating System (ROS): ORB-SLAM2, ORB-SLAM3, DSO and LDSO. The KITTI and Euroc MAV datasets were selected to evaluate the algorithms’ performance. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Robot Operating System

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Decomposition and Reorganization of Phonetic Information for Speaker Embedding Learning

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IEEE/ACM Transactions on Audio Speech and Language Processing 2023年 31卷 1745-1757页

作者： Hong, Qian-Bei Wu, Chung-Hsien Wang, Hsin-Min National Cheng Kung University and Academia Sinica Graduate Program of Multimedia Systems and Intelligent Computing Tainan701 Taiwan Academia Sinica Taipei115 Taiwan National Cheng Kung University Department of Computer Science and Information Engineering Tainan701401 Taiwan Academia Sinica Institute of Information Science Taipei115 Taiwan

Speech content is closely related to the stability of speaker embeddings in speaker verification tasks. In this paper, we propose a novel architecture based on self-constraint learning (SCL) and reconstruction task (RT) to remove the influence of phonetic information on speaker embedding generation. First, SCL is used to reduce the divergence of frame-level features, which can avoid ambiguity between the resulting embeddings of the two utterances being compared. Second, RT is used to further remove phonetic information in frame-level layers, focusing on speaker-discriminative feature transformation. In our experiments, the speaker embedding models were trained on the VoxCeleb2 dataset and evaluated on the VoxCeleb1, Librispeech, SITW and VoxMovies datasets. Experimental results on VoxCeleb1 show that the proposed DROP-TDNN system reduced the EER by 7.5%, compared to the state-of-the-art ECAPA-TDNN system. Furthermore, the proposed DROP-TDNN system also outperformed the ECAPA-TDNN system in the experiments on SITW, Librispeech and VoxMovies under cross-dataset conditions. In the experiments on SITW, the proposed system reduced the EER by 3.4% compared to the ECAPA-TDNN system. In the experiments on Librispeech, the proposed system demonstrated the advantage of removing phonetic information under the clean speech condition, with a significant reduction of 25.5% in EER compared to the ECAPA-TDNN system. In the experiments on VoxMovies, the proposed system reduced the EER by up to 7.9% compared to the ECAPA-TDNN system under different pronunciation and background conditions. © 2014 IEEE.

关键词： Speech recognition

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Joint Radar-Communication systems by Optimizing Radar Performance and Quality of Service for Communication Users

IEEE Transactions on Radar Systems

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IEEE Transactions on Radar systems 2024年 2卷 778-790页

作者： Tsinos, Christos G. Arora, Aakash Tsiftsis, Theodoros A. National and Kapodistrian University of Athens Department of Digital Industry Technologies Evripus Campus Euboea 34400 Greece University of Twente Radio Systems Group Faculty of Electrical Engineering Mathematics and Computer Science AE Enschede7500 Netherlands Indian Institute of Technology Delhi Centre for Applied Research in Electronics New Delhi110016 India University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece Jinan University Zhuhai Campus School of Intelligent Systems Science and Engineering Zhuhai519070 China

In this article, the problem of linear precoding and radar receive beamforming design for joint radar-communication (JRC) systems is studied. A multiple antenna base station (BS) that serves multiple single-antenna user terminals on the downlink is assumed. Furthermore, the BS employs a simultaneous radar function in the form of point-like target detection from the reflected return signals in a signal-dependent interference environment. In this work, we jointly design the JRC linear precoder and the radar receive beamformer, thus aiming to optimize the performance of the radar part while maintaining a desired quality of service (QoS) for the communication one subject to a total transmit power constraint. To that end, we formulate a challenging fractional nonconvex optimization problem via which the optimal precoder and radar receive beamformer are derived. Then, we develop algorithmic solutions based on the majorization-minimization (MM) principle and the semidefinite relaxation (SDR) methodology for the formulated optimization problem. The performance of both the proposed solutions is examined and compared to the one of a system that supports only the radar functionality via numerical results. © 2023 IEEE.

关键词： Signal to noise ratio

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Value-Compressed Sparse Column (VCSC): Sparse Matrix Storage for Single-cell Omics Data

Value-Compressed Sparse Column (VCSC): Sparse Matrix Storage...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Wolfgang, Seth Ruiter, Skyler Tunnell, Marc Triche, Timothy Carrier, Erin Debruine, Zachary University of Indiana Department of Intelligent Systems Engineering BloomingtonIN United States Purdue University Department of Computer Science West LafayetteIN United States Van Andel Institute Department of Epigenetics Grand RapidsMI United States Grand Valley State University College of Computing AllendaleMI United States

ISBN: (纸本)9798350362480

Genomics datasets, such as single-cell transcriptomics, are often very large and highly sparse, posing significant challenges for both storage and computation. As the scale of data generation accelerates, efficiently compressing these datasets becomes crucial. Current compression methods, like the popular Compressed Sparse Column (CSC) format, capitalize only on sparsity but overlook other properties like redundancy, which can offer additional opportunities for compression. Genomics data, especially single-cell assays, often exhibit high redundancy within columns, making traditional sparse formats inefficient for in-core computation. In this paper, we present two extensions to CSC: (1) Value-Compressed Sparse Column (VCSC) and (2) Index-and Value-Compressed Sparse Column (IVCSC). VCSC takes advantage of high redundancy within a column to further compress data up 1.9-fold over CSC on real data, without significant negative impact to performance characteristics. IVCSC extends VCSC by compressing index arrays through delta encoding and byte-packing, achieving up to a 4.4-fold decrease in memory usage over CSC on real data. Our benchmarks show that VCSC and IVCSC can be used in compressed form with little added computational cost. These formats represent a step forward in balancing the growing demands of data storage and processing in the era of large-scale genomics. © 2024 IEEE.

关键词： data redundancy matrix compression sparse data

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Recurrent neural networks: vanishing and exploding gradients are not the end of the story 24

Recurrent neural networks: vanishing and exploding gradients...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Nicolas Zucchet Antonio Orvieto Department of Computer Science ETH Zürich ELLIS Institute Tübingen MPI for Intelligent Systems Tübingen AI Center

ISBN: (纸本)9798331314385

Recurrent neural networks (RNNs) notoriously struggle to learn long-term memories, primarily due to vanishing and exploding gradients. The recent success of deep state-space models (SSMs), a subclass of RNNs, to overcome such difficulties challenges our theoretical understanding. In this paper, we delve into the optimization challenges of RNNs and discover that, as the memory of a network increases, changes in its parameters result in increasingly large output variations, making gradient-based learning highly sensitive, even without exploding gradients. Our analysis further reveals the importance of the element-wise recurrence design pattern combined with careful parametrizations in mitigating this effect. This feature is present in deep SSMs, as well as in other architectures, such as LSTMs. Overall, our insights provide a new explanation for some of the difficulties in gradient-based learning of RNNs and why some architectures perform better than others.

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HOOD: Hierarchical Graphs for Generalized Modelling of Clothing Dynamics

HOOD: Hierarchical Graphs for Generalized Modelling of Cloth...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Artur Grigorev Michael J. Black Otmar Hilliges Department of Computer Science ETH Zurich Max Planck Institute for Intelligent Systems Tübingen

We propose a method that leverages graph neural networks, multi-level message passing, and unsupervised training to enable efficient prediction of realistic clothing dynamics. Whereas existing methods based on linear blend skinning must be trained for specific garments, our method, called HOOD, is agnostic to body shape and applies to tight-fitting garments as well as loose, free-flowing clothing. Furthermore, HOOD handles changes in topology (e.g., garments with buttons or zippers) and material properties at inference time. As one key contribution, we propose a hierarchical message-passing scheme that efficiently propagates stiff stretching modes while preserving local detail. We empirically show that HOOD outperforms strong baselines quantitatively and that its results are perceived as more realistic than state-of-the-art methods.

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Cyberbullying Classification Using a Natural Language Processing and Machine Learning Techniques

Cyberbullying Classification Using a Natural Language Proces...

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2024 International Conference on Advances in Computing Research on science Engineering and Technology, ACROSET 2024

作者： Mankar, Nikhilesh Pramod Mahure, Tushar Joshi, Anushka A. Pande, Sagar Dhanraj Khamparia, Aditya Nathe, Ankita Shrikrushna Turjman, Fadi Al Pune India Independent Researcher Amravati India Babasaheb Bhimrao Ambedkar University Department of Computer Science Amethi India Kavikulguru Institute of Technology and Science Department of Information Technology Nagpur India Ai and Robotics Institute Near East University Artificial Intelligence Software Information Systems Engineering Departments Nicosia Cyprus University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey

ISBN: (纸本)9798350388800

The usage of online entertainment has increased dramatically after some time with the enhancement of the Internet and has turned into the most compelling systems administration stage in this century. Notwithstanding, the upgrade of a social network regularly makes adverse consequences on society that add to two or three terrible peculiarities, for example, online maltreatment, badgering cyberbullying, cybercrime, and internet savaging. Cyberbullying habitually prompts genuine mental and actual misery, especially for ladies and kids, and even occasionally make them to use self-destruction methods. Multiple number of episodes have as of late happened worldwide because of online badgering, like sharing private talks, bits of hearsay, and sexual comments. In this way, the distinguishing proof of harassing messages or messages via online entertainment has gained a significant measure for development for consideration with in scientists. The ground or motive for this exploration is to create a full-fledged way or a method to differentiate online harmful and tormenting drafts by combining various methods and techniques in the domain of natural language processing and AI © 2024 IEEE.

关键词： Adversarial machine learning

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Hierarchical Inference at the Edge: A Batch Processing Approach

Hierarchical Inference at the Edge: A Batch Processing Appro...

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IEEE/ACM Symposium on Edge Computing (SEC)

作者： Afroditi Letsiou Vishnu Narayanan Moothedath Adarsh Prasad Behera Jaya Prakash Champati James Gross Department of Intelligent Systems KTH Royal Institute of Technology Stockholm Sweden Computer Science University of Victoria British Columbia Canada

ISBN: (数字)9798350378283

ISBN: (纸本)9798350378290

Deep learning (DL) applications have rapidly evolved to address increasingly complex tasks by leveraging large-scale, resource-intensive models. However, deploying such models on low-power devices is not practical or economically scalable. While cloud-centric solutions satisfy these computational demands, they present challenges in terms of communication costs and latencies for real-time applications when every computation task is offloaded. To mitigate these concerns, hierarchical inference (HI) frameworks have been proposed, enabling edge devices equipped with small ML models to collaborate with edge servers by selectively offloading complex tasks. Existing HI approaches depend on immediate offloading of data upon selection, which can lead to inefficiencies due to frequent communication, especially in time-varying wireless environments. In this work, we introduce Batch HI, an approach that offloads samples in batches, thereby reducing communication overhead and improving system efficiency while achieving similar performance as existing HI methods. Additionally, we find the optimal batch size that attains a crucial balance between responsiveness and system time, tailored to specific user requirements. Numerical results confirm the effectiveness of our approach, highlighting the scenarios where batching is particularly beneficial.

关键词： Wireless communication Costs Computational modeling Biological system modeling Probabilistic logic Linear programming Real-time systems Time factors Servers Optimization

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Cloth2Tex: A Customized Cloth Texture Generation Pipeline for 3D Virtual Try-On

Cloth2Tex: A Customized Cloth Texture Generation Pipeline fo...

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International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： Daiheng Gao Xu Chen Xindi Zhang Qi Wang Ke Sun Bang Zhang Liefeng Bo Qixing Huang Alibaba XR Lab Department of Computer Science ETH Zurich Max Planck Institute for Intelligent Systems The University of Texas at Austin

ISBN: (数字)9798350362459

ISBN: (纸本)9798350362466

Fabricating and designing 3D garments has become extremely demanding with the increasing need for synthesizing realistic dressed persons for a variety of applications, e.g. 3D virtual try-on, digitalization of 2D clothes into 3D apparel, and cloth animation. It thus necessitates a simple and straightforward pipeline to obtain high-quality texture from simple input, such as 2D reference images. Since traditional warping-based texture generation methods require a significant number of control points to be manually selected for each type of garment, which can be a time-consuming and tedious process. We propose a novel method, called Cloth2Tex, which eliminates the human burden in this process. Cloth2Tex is a self-supervised method that generates texture maps with reasonable layout and structural consistency. Another key feature of Cloth2Tex is that it can be used to support high-fidelity texture inpainting. This is done by combining Cloth2Tex with a prevailing latent diffusion model. We evaluate our approach both qualitatively and quantitatively and demonstrate that Cloth2Tex can generate high-quality texture maps and achieve the best visual effects in comparison to other methods. For more details and animated results, please see https://tomguluson92. ***/projects/cloth2tex/.

关键词： Training Three-dimensional displays Pipelines Clothing Layout Process control Manuals

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