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arXiv 2025年

作者： Xia, Mengfei Zhou, Yu Yi, Ran Liu, Yong-Jin Wang, Wenping MOE-Key Laboratory of Pervasive Computing Department of Computer Science and Technology Tsinghua University Beijing100084 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science and Computer Engineering Texas A&M University TX United States

Applying diffusion models to image-to-image translation (I2I) has recently received increasing attention due to its practical applications. Previous attempts inject information from the source image into each denoising step for an iterative refinement, thus resulting in a time-consuming implementation. We propose an efficient method that equips a diffusion model with a lightweight translator, dubbed a Diffusion Model Translator (DMT), to accomplish I2I. Specifically, we first offer theoretical justification that in employing the pioneering DDPM work for the I2I task, it is both feasible and sufficient to transfer the distribution from one domain to another only at some intermediate step. We further observe that the translation performance highly depends on the chosen timestep for domain transfer, and therefore propose a practical strategy to automatically select an appropriate timestep for a given task. We evaluate our approach on a range of I2I applications, including image stylization, image colorization, segmentation to image, and sketch to image, to validate its efficacy and general utility. The comparisons show that our DMT surpasses existing methods in both quality and efficiency. Code will be made publicly available. Copyright © 2025, The Authors. All rights reserved.

关键词： Image denoising

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Facesight: Enabling hand-to-face gesture interaction on ar glasses with a downward-facing camera vision 21

Facesight: Enabling hand-to-face gesture interaction on ar g...

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2021 CHI Conference on Human Factors in computing Systems: Making Waves, Combining Strengths, CHI 2021

作者： Weng, Yueting Yu, Chun Shi, Yingtian Department of Computer Science and Technology Tsinghua University Key Laboratory of Pervasive Computing Ministry of Education Beijing China

ISBN: (纸本)9781450380966

We present FaceSight, a computer vision-based hand-to-face gesture sensing technique for AR glasses. FaceSight fxes an infrared camera onto the bridge of AR glasses to provide extra sensing capability of the lower face and hand *** obtained 21 hand-to-face gestures and demonstrated the potential interaction benefts through fve AR applications. We designed and implemented an algorithm pipeline that segments facial regions, detects hand-face contact (f1 score: 98.36%), and trains convolutional neural network (CNN) models to classify the hand-to-face gestures. The input features include gesture recognition, nose deformation estimation, and continuous fngertip movement. Our algorithm achieves classifcation accuracy of all gestures at 83.06%, proved by the data of 10 users. Due to the compact form factor and rich gestures, we recognize FaceSight as a practical solution to augment input capability of AR glasses in the future. © 2021 ACM.

关键词： Glass

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Investigation on Analog and Digital Modulations Recognition Using Machine Learning Algorithms

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World Journal of Engineering and Technology 2024年第4期12卷 867-884页

作者： Jean Ndoumbe Ivan Basile Kabeina Gaelle Patricia Talotsing Soubiel-Noël Nkomo Biloo Laboratory of Computer Engineering Data Science and Artificial Intelligence National Higher Polytechnic School of Douala Douala Cameroon Mobile Computing and Networking Research Laboratory Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada

In the field of radiocommunication, modulation type identification is one of the most important characteristics in signal processing. This study aims to implement a modulation recognition system on two approaches to machine learning techniques, the K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN). From a statistical and spectral analysis of signals, nine key differentiation features are extracted and used as input vectors for each trained model. The feature extraction is performed by using the Hilbert transform, the forward and inverse Fourier transforms. The experiments with the AMC Master dataset classify ten (10) types of analog and digital modulations. AM_DSB_FC, AM_DSB_SC, AM_USB, AM_LSB, FM, MPSK, 2PSK, MASK, 2ASK, MQAM are put forward in this article. For the simulation of the chosen model, signals are polluted by the Additive White Gaussian Noise (AWGN). The simulation results show that the best identification rate is the MLP neuronal method with 90.5% of accuracy after 10 dB signal-to-noise ratio value, with a shift of more than 15% from the k-nearest neighbors’ algorithm.

关键词： Automatic Recognition Artificial Neural Networks K-Nearest Neighbors Machine Learning Analog Modulations Digital Modulations

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A Belief Rule-Based Expert System to Assess Multiple Human Reaction in the Context of Facebook Posts under Uncertainty

A Belief Rule-Based Expert System to Assess Multiple Human R...

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2021 International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2021

作者： Mazumder, Shazzad Hossain Hossain, Mohammad Shahadat Andersson, Karl University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Pervasive and Mobile Computing Laboratory Luleå University of Technology Skellefteå Sweden

ISBN: (纸本)9781665414609

Human may have multiple reactions at a time. Social media is a real-life example where people can express their reactions or opinions. For example, Facebook has become a widely used social media where users express their opinion on different posts such as status or photo post. Therefore, user opinions can easily be achieved through social media and then analyzed and applied in different practical fields. Reaction assessment of Social media can be an excellent source of information. Therefore, an accurate assessment of human reaction is a must. Facebook provides six emoticons for each of the posts of its users. The six emoticons define six types of reactions. The user, who wants to react to any post, have to choose only one of the six emoticons. There is no scope for a user to express multiple reactions at a time. Moreover, if a user selects an emoticon, then the system takes that input as 100% of that corresponding reaction, or 0% if not selected, thus the reaction assessment system becomes a Boolean system. Therefore, the assessment of multiple reactions of the user cannot be measured with 100% certainty due to the existence of various types of uncertainties such as vagueness, imprecision, randomness, ignorance, incompleteness, and ambiguity in the system. Therefore, to assess multiple human reactions, an expert system is needed to handle all these uncertainties. The system design, development process, and applications of an expert system to assess multiple human reactions are described in this paper. For the development of the expert system, the Belief Rule-Based Inference Methodology using the Evidential Reasoning (RIMER) approach has been used and the system is named as a Belief Rule-Based Expert System (BRBES). The developed BRBES can mitigate all the uncertainties mentioned above. © 2021 IEEE.

关键词： Expert systems

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Cross-domain depression detection via harvesting social media

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Physical Review Letters 2021年 1611-1617页

作者： Shen, Tiancheng Jia, Jia Shen, Guangyao Feng, Fuli He, Xiangnan Luan, Huanbo Tang, Jie Tiropanis, Thanassis Chua, Tat-Seng Hall, Wendy Department of Computer Science and Technology Tsinghua University Beijing100084 China Key Laboratory of Pervasive Computing Ministry of Education Beijing National Research Center for Information Science and Technology China School of Computing National University of Singapore Singapore Electronics and Computer Science University of Southampton United Kingdom

Depression detection is a significant issue for human well-being. In previous studies, online detection has proven effective in Twitter, enabling proactive care for depressed users. Owing to cultural differences, replicating the method to other social media platforms, such as Chinese Weibo, however, might lead to poor performance because of insufficient available labeled (self-reported depression) data for model training. In this paper, we study an interesting but challenging problem of enhancing detection in a certain target domain (e.g. Weibo) with ample Twitter data as the source domain. We first systematically analyze the depressionrelated feature patterns across domains and summarize two major detection challenges, namely isomerism and *** further propose a crossdomain Deep Neural Network model with Feature Adaptive Transformation & Combination strategy (DNN-FATC) that transfers the relevant information across heterogeneous domains. Experiments demonstrate improved performance compared to existing heterogeneous transfer methods or training directly in the target domain (over 3.4% improvement in F1), indicating the potential of our model to enable depression detection via social media for more countries with different cultural settings. © 2018 American Physical Society. All rights reserved.

关键词： Neural network models

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A Revisit of Shape Editing Techniques:From the Geometric to the Neural Viewpoint

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Journal of computer science & Technology 2021年第3期36卷 520-554页

作者： Yu-Jie Yuan Yu-Kun Lai Tong Wu Lin Gao Ligang Liu Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China School of Computer Science and Informatics Cardiff UniversityCardiff CF243AAU.K. School of Mathematical Sciences University of Science and Technology of ChinaHefei 230026China

3D shape editing is widely used in a range of applications such as movie production,computer games and computer aided *** is also a popular research topic in computer graphics and computer *** past decades,researchers have developed a series of editing methods to make the editing process faster,more robust,and more ***,the deformed shape is determined by the optimal transformation and weights for an energy *** increasing availability of 3D shapes on the Internet,data-driven methods were proposed to improve the editing *** recently as the deep neural networks became popular,many deep learning based editing methods have been developed in this field,which are naturally *** mainly survey recent research studies from the geometric viewpoint to those emerging neural deformation techniques and categorize them into organic shape editing methods and man-made model editing *** traditional methods and recent neural network based methods are reviewed.

关键词： mesh deformation man-made model editing deformation representation optimization deep learning

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MMPI: a Flexible Radiance Field Representation by Multiple Multi-plane Images Blending

MMPI: a Flexible Radiance Field Representation by Multiple M...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yuze He Peng Wang Yubin Hu Wang Zhao Ran Yi Yong-Jin Liu Wenping Wang Department of Computer Science and Technology MOE-Key Laboratory of Pervasive Computing Tsinghua University China Department of Computer Science The University of Hong Kong Hong Kong Department of Computer Science and Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Texas A&M University USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper presents a flexible representation of neural radiance fields based on multi-plane images (MPI), for high-quality view synthesis of complex scenes. MPI with Normalized Device Coordinate (NDC) parameterization is widely used in NeRF learning for its simple definition, easy calculation, and powerful ability to represent unbounded scenes. However, existing NeRF works that adopt MPI representation for novel view synthesis can only handle simple forward-facing unbounded scenes (e.g., the scenes in the LLFF dataset), where the input cameras are all observing in similar directions with small relative translations. Hence, extending these MPIbased methods to more complex scenes like large-range or even 360-degree scenes is very challenging. In this paper, we explore the potential of MPI and show that MPI can synthesize high-quality novel views of complex scenes with diverse camera distributions and view directions, which are not only limited to simple forward-facing scenes. Our key idea is to encode the neural radiance field with multiple MPIs facing different directions and blend them with an adaptive blending operation. For each region of the scene, the blending operation gives larger blending weights to those advantaged MPIs with stronger local representation abilities while giving lower weights to those with weaker representation abilities. Such blending operation automatically modulates the multiple MPIs to appropriately represent the diverse local density and color information. Experiments on the KITTI dataset and ScanNet dataset demonstrate that our proposed MMPI synthesizes high-quality images from diverse camera pose distributions and is fast to train, outperforming the previous fast-training NeRF methods for novel view synthesis. Moreover, we show that MMPI can encode extremely long trajectories and produce novel view renderings, demonstrating its potential in applications like autonomous driving. Our demo video is available at https://***/watch?v

关键词： Image color analysis Neural radiance field Cameras Rendering (computer graphics) Reliability engineering Trajectory Robotics and automation

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Skinned Motion Retargeting with Dense Geometric Interaction Perception

arXiv

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arXiv 2024年

作者： Ye, Zijie Liu, Jia-Wei Jia, Jia Sun, Shikun Shou, Mike Zheng Department of Computer Science and Technology BNRist Tsinghua University China Key Laboratory of Pervasive Computing Ministry of Education China Show Lab National University of Singapore Singapore

Capturing and maintaining geometric interactions among different body parts is crucial for successful motion retargeting in skinned characters. Existing approaches often overlook body geometries or add a geometry correction stage after skeletal motion retargeting. This results in conflicts between skeleton interaction and geometry correction, leading to issues such as jittery, interpenetration, and contact mismatches. To address these challenges, we introduce a new retargeting framework, MeshRet, which directly models the dense geometric interactions in motion retargeting. Initially, we establish dense mesh correspondences between characters using semantically consistent sensors (SCS), effective across diverse mesh topologies. Subsequently, we develop a novel spatio-temporal representation called the dense mesh interaction (DMI) field. This field, a collection of interacting SCS feature vectors, skillfully captures both contact and non-contact interactions between body geometries. By aligning the DMI field during retargeting, MeshRet not only preserves motion semantics but also prevents self-interpenetration and ensures contact preservation. Extensive experiments on the public Mixamo dataset and our newly-collected ScanRet dataset demonstrate that MeshRet achieves state-of-the-art performance. Code available at https://***/abcyzj/MeshRet. Copyright © 2024, The Authors. All rights reserved.

关键词： Mesh generation

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Exploring BERT and ELMo for Bangla Spam SMS Dataset Creation and Detection

Exploring BERT and ELMo for Bangla Spam SMS Dataset Creation...

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International Conference on computer and Information Technology (ICCIT)

作者： Fariba Khan Rashed Mustafa Farzana Tasnim Tanjim Mahmud Mohammad Shahadat Hossain Karl Andersson Department of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong Bangladesh Department of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Pervasive and Mobile Computing Laboratory Luleå University of Technology Luleå Sweden

The proliferation of digital communication and social media platforms has led to an upsurge in spam messages across various languages, including Bangla. These intrusive messages not only cause user annoyance but also elevate the risk of important messages going unnoticed. To address this concern, we harnessed the power of natural language processing (NLP) techniques in conjunction with machine-learning models. This study delves into the efficacy of diverse machine-learning and deep-learning models in detecting spam messages in the Bangla language. Particularly, deep-learning methodologies such as ELMo and BERT have exhibited promising results in spam detection across other languages. However, the realm of Bangla spam detection remains underexplored, with a dearth of deep-learning approaches. To bridge this gap, we constructed a unique Bangla SMS dataset, given the scarcity of available Bangla Spam SMS datasets, and proceeded to evaluate the performance of ELMo, BERT, and conventional machine learning models. The findings from our experiments underscore the superior performance of deep learning-based models over traditional methods, with Elmo surpassing BERT in Bangla spam detection, achieving an impressive accuracy of approximately 94%. This endeavor bears the potential to not only enhance SMS utilization efficiency but also bolster cybersecurity measures against phishing attacks.

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A novel deep learning approach to predict air quality index 2nd

A novel deep learning approach to predict air quality index

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2nd International Conference on Trends in Computational and Cognitive Engineering, TCCE 2020

作者： Hossain, Emam Shariff, Mohd Arafath Uddin Hossain, Mohammad Shahadat Andersson, Karl Department of Computer Science and Engineering University of Chittagong Chattogram Bangladesh Department of Computer Science and Engineering Port City International University Chattogram Bangladesh Pervasive and Mobile Computing Laboratory Luleå University of Technology Skellefteå931 87 Sweden

ISBN: (纸本)9789813346727

In accordance with the World Health Organization’s instruction, the air quality in Bangladesh is considered perilous. A productive and precise air quality index (AQI) is a must and one of the obligatory conditions for helping the society to be viable in lieu of the consequences of air contamination. If we know the index of air quality in advance, then it would be of a great help saving our health from air contamination. This study introduces an air quality index prediction model for two mostly polluted cities in Bangladesh: Dhaka and Chattogram. Gated recurrent unit (GRU), long short-term memory (LSTM) are the two robust variation of recurrent neural network (RNN). This model combines these two together. We have used GRU as first hidden layer and LSTM as the second hidden layer of the model, followed by two dense layers. After collecting and processing the data, the model was trained on 80% of the data and then validated against the remaining data. We have evaluated the performance of the model considering MSE, RMSE, and MAE to see how much error does the model produce. Results reflect that our model can follow the actual AQI trends for both cities. At last, we have juxtaposed the performance of our proposed hybrid model against a standalone GRU model and a standalone LSTM model. Results also show that combining these two models improves the overall model’s performance. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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