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arXiv 2024年

作者： Luo, Yang Chen, Zhineng Zhou, Peng Wu, Zuxuan Gao, Xieping Jiang, Yu-Gang School of Computer Science Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University China University of Maryland College Park United States College of Information Science and Engineering Hunan Normal University China

Images suffer from heavy spatial redundancy because pixels in neighboring regions are spatially correlated. Existing approaches strive to overcome this limitation by reducing less meaningful image regions. However, current leading methods rely on supervisory signals. They may compel models to preserve content that aligns with labeled categories and discard content belonging to unlabeled categories. This categorical inductive bias makes these methods less effective in real-world scenarios. To address this issue, we propose a self-supervised framework for image redundancy reduction called Learning to Rank Patches (LTRP). We observe that image reconstruction of masked image modeling models is sensitive to the removal of visible patches when the masking ratio is high (e.g., 90%). Building upon it, we implement LTRP via two steps: inferring the semantic density score of each patch by quantifying variation between reconstructions with and without this patch, and learning to rank the patches with the pseudo score. The entire process is self-supervised, thus getting out of the dilemma of categorical inductive bias. We design extensive experiments on different datasets and tasks. The results demonstrate that LTRP outperforms both supervised and other self-supervised methods due to the fair assessment of image content. Code is available at https://***/irsLu/ltrp. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Utilizing the Neglected Back Lobe for Mobile Charging

Utilizing the Neglected Back Lobe for Mobile Charging

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Meixuan Ren Dié Wu Jing Xue Wenzheng Xu Jian Peng Tang Liu College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Lab Sichuan Normal University Chengdu China College of Computer Science Sichuan University Chengdu China

Benefitting from the breakthrough of wireless power transfer technology, the lifetime of Wireless Sensor Networks (WSNs) can be significantly prolonged by scheduling a mobile charger (MC) to charge sensors. Compared with omnidirectional charging, the MC equipped with directional antenna can concentrate energy in the intended direction, making charging more efficient. However, all prior arts ignore the considerable energy leakage behind the directional antenna (i.e., back lobe), resulting in energy wasted in vain. To address this issue, we study a fundamental problem of how to utilize the neglected back lobe and schedule the directional MC efficiently. Towards this end, we first build and verify a directional charging model considering both main and back lobes. Then, we focus on jointly optimizing the number of dead sensors and energy usage effectiveness. We achieve these by introducing a scheduling scheme that utilizes both main and back lobes to charge multiple sensors simultaneously. Finally, extensive simulations and field experiments demonstrate that our scheme reduces the number of dead sensors by 49.5% and increases the energy usage effectiveness by 10.2% on average as compared with existing algorithms.

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Concurrent Charging with Wave Interference

Concurrent Charging with Wave Interference

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Yuzhuo Ma Dié Wu Meixuan Ren Jian Peng Jilin Yang Tang Liu College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Lab Sichuan Normal University Chengdu China College of Computer Science Sichuan University Chengdu China

To improve the charging performance, employing multiple wireless chargers to charge sensors concurrently is an effective way. In such charging scenarios, the radio waves radiated from multiple chargers will interfere with each other. Though a few work have realized the wave interference, they do not fully utilize the high power caused by constructive interference while avoiding the negative impacts brought by the destructive interference. In this paper, we aim to investigate the power distribution regularity of concurrent charging and take full advantage of the high power to enhance the charging efficiency. Specifically, we formulate a concurrent charGing utility mAxImizatioN (GAIN) problem and build a practical charging model with wave interference. Further, we propose a concurrent charging scheme, which not only can improve the power of interference enhanced regions by deploying chargers, but also find a set of points with the highest power to locate sensors. Finally, we conduct both simulations and field experiments to evaluate the proposed scheme. The results demonstrate that our scheme outperforms the comparison algorithms by 40.48% on average.

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ON THE POWER OF SUBTLE EXPRESSIVE CUES IN THE PERCEPTION OF HUMAN AFFECTS

arXiv

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arXiv 2024年

作者： Dede, Ezgi Agilonu, Kamile Asli Akleman, Ergun Sezgin, Metin College of Engineering Koc University Istanbul Turkey Visual Computing & Computational Media School of PVFA Computer Science and Engineering College of Engineering Texas A&M University College StationTX77831 United States

In this study, we introduce a sketch-based method for testing how subtle expressive cues influence the perception of affect in illustrations of human figures. We specifically study the impact of human posture and gaze direction, implicitly specified through nose orientation, on perceived emotions and mood. Through a series of user studies using sketchy illustrations of a running figure, where a professional illustrator manipulated gaze direction through adjustments on the nose orientation, we found that this simple change resulted in a diverse range of perceived affects, spanning from fear to concern and wonder. These findings shed light on the importance of fine details in defining context for context-aware system designs and underscore the importance of recognizing and expressing affect. Understanding minor expressive cues is crucial to developing emotionally intelligent systems capable of expressing affect. © 2024, CC BY.

关键词： Intelligent systems

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Deploying Hybrid MAELM Approach for Human Emotion Detection Through Speech and Facial Expressions 3

Deploying Hybrid MAELM Approach for Human Emotion Detection ...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Senthil Kumar, K. Rukmani Devi, S. Ranjan, Nidhi Rath, Gitika Indira, G. Nishant, Neerav SRM Institute of Science and Technology School of Computing Department of Networking and Communications College of Engineering and Technology Chennai India SIMATS Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Science Chennai India Computer Engineering Vasantdada Patil Pratishthan's College of Engineering and Visual Arts Mumbai India Marri Laxman Reddy Institute of Technology and Management Department of CSE Hyderabad India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai India Babu Banarasi das University School of Engineering Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350343632

Emotion recognition is one of the most fascinating emerging fields of research. It's useful in a lot of different contexts. Some of the most exciting applications of this technology involve robots' ability to see and communicate with one another. Human emotions can be recognized through both visual and audible cues. Facial expressions are one of the most accurate indicators of a person's emotional state. Data preprocessing, feature extraction, and model training are the first three steps of the proposed methodology. Image resizing, a median filter, noise removal, and histogram equalization are all components of preprocessing. Gaussian mixture models and the gray level co-occurrence matrix are being used for feature extraction. After the extraction of features, models with those features are trained using MA-ELM. The proposed method outperforms PCA and ELM, two of the most popular alternatives. © 2023 IEEE.

关键词： Gaussian Mixture Models(GMM) Gray Level Co-Occurrence Matrix (GLCM) Principal Component Analysis (PCA)

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Struct2Hair: A hair shape descriptor for hairstyle modeling

Struct2Hair: A hair shape descriptor for hairstyle modeling

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作者： Zhang, Wenshu Nie, Yinyu Guo, Shihui Chang, Jian Zhang, Jianjun Tong, Ruofeng Faculty of Arts and Media Norwich University of the Arts Norfolk United Kingdom The Visual Computing & Artificial Intelligence Lab Technical University of Munich München Germany School of Informatics Xiamen University Fujian China National Centre for Computer Animation Bournemouth University Dorset United Kingdom College of Computer Science and Technology Zhejiang University Zhejiang China

In recent years, it becomes possible to extract hair information for hair reconstruction from multiple cameras or monocular camera. Using a single image as the input avoids the high cost setups and complex calibration compared to multiviewed reconstruction. Taking advantage of an extendible hairstyle database, this paper introduced Struct2Hair, a novel single-viewed hair modelling approach by extracting hair shape descriptor (HSD). The HSD is defined as the fundamental structure-aware feature, which is a combination of critical shapes in a hairstyle. A complete dataset of critical hair shapes is constructed from a known database of three-dimensional (3D) hair models. We first analyze the input two-dimensional (2D) image to extract the orientation information and 2D hair sketch automatically. The extracted information is then used to retrieve the corresponding critical shapes with optimization to build the robust HSD. Finally, the HSD constructs a weighted 3D hair orientation field to guide full-head hair model generation. Our method can preserve local geometric features of hair and retain the whole shape of the hairstyle globally owing to the HSD, which will benefit further hair editing and stylization. © 2022 John Wiley & Sons Ltd.

关键词： Cameras

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Fitweet: Arduino-Based Smartwatch for Early Anticipatory Anxiety Notification System 9th

Fitweet: Arduino-Based Smartwatch for Early Anticipatory Anx...

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9th International Conference on Computational science and Technology, ICCST 2022

作者： Abdulmanab, Muhammad Rahiman Nor’Azman, Nur Faidz Hazirah Meon, Hasni Razak, Chempaka Seri Abdul Ab Hamid, Siti Hafizah Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Communication Faculty of Communication Visual Art and Computing Universiti Selangor Bestari Jaya Malaysia School of Pre-University Studies Taylor’s College Lakeside Campus Subang Jaya Malaysia

ISBN: (纸本)9789811984051

As anxiety becomes increasingly prevalent among youths especially university students, early prevention via anxiety disorder profiling is crucial. Nevertheless, most screening tools to date are not automated, labour-intensive and time-consuming. Mental health detection in online social networks (OSNs) using artificial intelligence (AI) can provide means for capturing overlooked behavioural attributes, but few are centred around anxiety detection. Internet of Things (IoT)’s increasing popularity enables bio-signals monitoring to aid in mental health care. However, minimum research has been devoted to IoT-based assistive technologies for anxiety, which currently are only used by individuals already diagnosed and actively monitored. This study proposes a novel approach to anticipatory anxiety detection for Malaysian university students aged between 18 and 25 inclusive as an early notification system using AI and IoT for bilingual Malay and English tweets and vital signs analysis respectively. Our result shows that Logistic Regression with Bag-of-Words (BoW) is the overall best performing model with 81.06% accuracy. The Arduino smartwatch sensor readings from the MAX30102 pulse oximeter sensor are used to determine different body conditions using a rule-based approach. The comparison test result between the prototype and conventional measuring devices shows the prototype’s ability to accurately distinguish vital signs with a relatively low Mean Absolute Percent Error (MAPE) rate of 10.92% and 1.51% for pulse rate and temperature respectively. Together, our proof-of-concept results indicate the potential of AI and IoT in deriving new measures of anxiety, by analysing social data combined with bio-signals monitoring to alert users regarding their anxiety state. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Fast Collection of Target Tags Information for Large-Scale RFID Systems 20

Fast Collection of Target Tags Information for Large-Scale R...

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20th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Chu, Chu Fu, Ning Xu, Rui College of Computer Science Sichuan Normal University Chengdu610101 China The Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu610101 China School of Network and Communication Engineering Chengdu Technological University Chengdu611730 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798350318982

Radio Frequency Identification (RFID) technology is greatly supporting a variety of life and industry. Enhanced by sensors, RFID tags can monitor real-time information about attached objects and the environment, making efficient collection of tag information an important task. However, most works focus on collecting all tags but lack effective mechanisms to avoid interfering from irrelevant tags when the application only cares about a part of the tags in some cases. Besides, in more common scenarios with multiple RFID readers, existing works fail to effectively determine the surrounding tags for each reader, increasing the execution time. To conquer these defects, we propose a new solution called the efficient Vector-based target tag Information Collection (VIC) protocol to accelerate the collection rate. First, we introduce a composite vector to silence irrelevant tags and determine the local tags. During this process, the expected empty slots are skipped and the useless information transmitted by tags is reduced. Second, we use an allocation vector to collect tag information by resolving tag collision, thus improving the time efficiency. Finally, we provide theoretical analyses to optimize the parameter settings. Extensive experiments prove that VIC outperforms existing solutions by up to 32% increase in time efficiency. © 2023 IEEE.

关键词： Efficiency

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The Classification of Motor Imagery EEG Signals Based on the Time-Frequency-Spatial Feature 10

The Classification of Motor Imagery EEG Signals Based on the...

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10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021

作者： Deng, Xin Zhang, Boxian Liu, Ke Wang, Jin Yang, Pengfei Hu, Chengxin Key Laboratory of Data Engineering and Visual Computing College of Computer Science and Technology Chongqing University of Posts and Telecommunication Chongqing400065 China

ISBN: (纸本)9781665424233

The effective features of the motor imagery (MI) electroencephalogram (EEG) signals plays a significant role to improve the classification accuracy for the brain-computer interface (BCI) system. Some traditional methods usually extract the frequency or spatial features without considering the related information between different channels that would affect the classification performance. This paper proposes a new method for feature extraction of EEG signals based on the fusion of time-frequency and spatial features. At the beginning, the common spatial pattern (CSP) algorithm is adopted to extract the spatial features. Then the discrete wavelet transform (DWT) and the wavelet packet decomposition (WPD) are used to extract the μ rhythm of the motor imagery EEG signals as the time-frequency features. After that, by combining the spatial and time-frequency features, the time-frequency-spatial feature is formed. Based on different kinds of features, the experimental data are classified by using the support vector machine (SVM), as well as the sparse representation classification (SRC) algorithm with the elastomeric network (EN) and L1 norm, respectively. The experimental results show that the SRC with EN has a better performance on either the time-frequency feature or spatial feature than the SRC with L1 norm does. In contrast, the SVM and the SRC with Ll norm perform better than the SRC with EN based on the time-frequency-spatial feature. The study concludes that the time-frequency-spatial feature cooperating with the certain classifiers can achieve the good classification effect for the MI EEG signals, which not only reduces the operation time but also improves the classification accuracy. © 2021 IEEE.

关键词： Support vector machines

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Short-Term Traffic Flow Prediction Based on Ensemble Machine Learning Strategies 10

Short-Term Traffic Flow Prediction Based on Ensemble Machine...

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10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021

作者： Zeng, Ximu Wang, Yixiong Deng, Xin Wang, Jin Key Laboratory of Data Engineering and Visual Computing College of Computer Science and Technology Chongqing University of Posts and Telecommunication Chongqing400065 China

ISBN: (纸本)9781665424233

In order to solve the problem of traffic congestion, many city governments have begun to develop intelligent transportation systems. As a research hotspot in the field of intelligent transportation, the short-term traffic flow prediction is of great significance to traffic diversion and route planning. In the recent big data era, the machine learning (ML) algorithms have been applied to mining deep information in the data. However, the performance of a single ML model is usually not good. The ensemble learning can improve accuracy in most cases. In this paper, we propose a new short-term traffic flow prediction model. This paper regards the traffic prediction problem as a regression prediction problem, rather than a time series forecasting problem. The proposed model combines the prediction results of the XGBoost, LightGBM and CatBoost models through the ensemble machine learning strategy. With the experiments on real traffic data in Xi'an city, this paper has obtained the comparison of predictive performance of the models. Compared with single models such as the ARIMA, LSTM, XGBoost, LightGBM and CatBoost models, the results show the proposed model is more accurate and more suitable for short-time traffic status prediction. Key Words: traffic prediction, ensemble learning, XGBoost, LightGBM, CatBoost © 2021 IEEE.

关键词： Traffic congestion

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