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SSRN 2024年

作者： Hao, Yutao Sun, Yanshuo Wen, Jing Gao, Xiaobo Wang, Yutong Zhu, Zhiyuan Wang, Zhong Lin Chen, Baodong Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of Sciences Beijing101400 China School of Nanoscience and Engineering University of Chinese Academy of Sciences Beijing100049 China Research Institute of Technology of Shougang Group Beijing100043 China The Key Laboratory of High Temperature Electromagnetic Materials and Structure of MOE Wuhan University of Science and Technology Wuhan430081 China Georgia Institute of Technology AtlantaGA30332-0245 United States

The sustainable development of the ocean requires sensors capable of detecting objects in underwater or high humidity conditions. However, traditional sensors struggle in complex underwater environments due to signal attenuation, biofouling, and flow interference, which seriously affect the performance and reliability. Inspired by the tactile system of octopus suckers, we developed a tactile sensor that mimics the structure of octopus tentacle suckers, ingeniously harnessing triboelectric tactile receptors (TTRs) to emulate the mechanism of cephalopod-specific chemoreceptors (CRs), aiming to address the challenging problem of underwater object recognition. Additionally, the superhydrophobic treatment increases the microstructure of the sensor surface, effectively mitigating environmental interference and improved underwater performance, resulting in a 67% increase in voltage output, a sensitivity of 0.195 V kPa-1, and a remarkable response time of only 85 ms. Most importantly, we have constructed an underwater material identification system (UMIS) to achieve 98% accuracy by integrating machine learning, which enables precise identification and quantitative sensing of underwater objects, offering novel insights and directions for the intelligence and autonomy of underwater robots. © 2024, The Authors. All rights reserved.

关键词： Shellfish

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Short-term Portfolio Optimization using Doubly Regularized Exponential Growth Rate

Short-term Portfolio Optimization using Doubly Regularized E...

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International Conference on Computer Supported Cooperative Work in Design

作者： Quanlong Guan Jinneng He Zhao-Rong Lai Yuyu Zhou Quming Jiang Ziliang Chen College of Information Science and Technology Jinan University Guangzhou China Guangdong Institute of Smart Education Jinan University Guangzhou China Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou China Guangdong-Macao Advanced Intelligent Computing Joint Laboratory Zhuhai China Chutian Dragon Co. Ltd Dongguan China School of Intelligent Systems Science and Engineering Jinan University China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In the realm of short-term portfolio optimization, the integration of machine learning with exponential growth rate techniques is gaining prominence. This paper introduces a novel approach for short-term portfolio optimization, termed Short-term Portfolio Optimization using Doubly Regularized EGR (SPODR), to address the challenges posed by limited data availability. SPODR utilizes radial basis functions for the effective identification of market trends, enabling improved stock market forecasts. The approach uniquely combines ℓ 1 and ℓ 2 -regularization, adhering to empirical financial principles, to strike a balance between risk and return in short-term portfolios. A key aspect of SPODR is addressing the complexity of its ElasticNet-like objective, which poses a challenge for traditional methods due to its online learning nature. To overcome this, we have developed an algorithm based on the log barrier interior-point method. This algorithm is adept at efficiently optimizing portfolio allocation, taking into account the specific constraints inherent in our approach. Extensive comparative experiments across five benchmark datasets demonstrate that SPODR significantly outperforms existing short-term portfolio optimization models. It achieves a right balance between return and risk. Furthermore, SPODR showcases efficient computational speed, enhancing its applicability in real-world financial settings.

关键词： Computational modeling Benchmark testing Market research Robustness Regulation Computational efficiency Resource management

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Long-Tailed Multi-Label Classification with Noisy Label of Thoracic Diseases from Chest X-Ray

Long-Tailed Multi-Label Classification with Noisy Label of T...

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IEEE International Symposium on Biomedical Imaging

作者： Haoran Lai Qingsong Yao Zhiyang He Xiaodong Tao S. Kevin Zhou School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Hefei P.R. China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou P.R. China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China Medical Business Department iFlytek Co. Ltd Hefei China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Chest X-rays (CXR) often reveal rare diseases, demanding precise diagnosis. However, current computer-aided diagnosis (CAD) methods focus on common diseases, leading to inadequate detection of rare conditions due to the absence of comprehensive datasets. To overcome this, we present a novel benchmark for long-tailed multi-label classification in CXRs, encapsulating both common and rare thoracic diseases. Our approach includes developing the "LTML-MIMIC-CXR" dataset, an augmentation of MIMIC-CXR with 26 additional rare diseases. We propose a baseline method for this classification challenge, integrating adaptive negative regularization to address negative logits' over-suppression in tail classes, and a large loss reconsideration strategy for correcting noisy labels from automated annotations. Our evaluation on LTML- MIMIC-CXR demonstrates significant advancements in rare disease detection. This work establishes a foundation for robust CAD methods, achieving a balance in identifying a spectrum of thoracic diseases in CXRs. Access to our code and dataset is provided at: https://***/laihaoran/LTML-MIMIC-CXR.

关键词： Design automation Annotations MIMICs Pipelines Tail Computer aided diagnosis Noise measurement

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Static Gesture Recognition and Distance Estimation Using a Capacitive Proximity Sensor Array

SSRN

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SSRN 2023年

作者： Ye, Yong Liu, Yuting Du, Chunhui Deng, Jiahao Qiu, Chenguang School of Electronics and Information Engineering The Information Materials and Intelligent Sensing Laboratory of Anhui Province The Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education The Key Laboratory of Electromagnetic Environmental Sensing of Anhui Higher Education Institutes Anhui University No. 111 Jiulong Road Anhui Province Hefei230601 China Institute of high energy physics Chinese academy of sciences The School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China Tai Yuan institute Co. Ltd. of China coal technology and engineering group Shanxi Province Taiyuan030006 China East China Institute of Photo-Electronic IC Jiangsu Province Suzhou215163 China

In this paper, we present a capacitive proximity sensor (CPS) array for static gesture recognition and distance estimation. A 4×4 electrode matrix is employed as the sensing element that is configured to 14 electrode operation patterns to obtain 14 capacitive sensors to percept objects within their sensing region. These 14 capacitances are fused into one feature vector which is used as the input of machine learning (ML) classifier for gesture recognition and neural network or regression model for distance estimation. In our experiments, we selected 10 static gestures to verify the proposed method. We compared the performance of 8 ML classifiers and their variations with training data size. The results show that the Bagging achieves the highest average accuracy of 93.2% and the KNN shows a good performance even with a small training data size. The results in distance estimation experiment demonstrate that the RBF network performs a high accuracy. © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Attribute-Guided Pedestrian Retrieval: Bridging Person Re-ID with Internal Attribute Variability

Attribute-Guided Pedestrian Retrieval: Bridging Person Re-ID...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yan Huang Zhang Zhang Qiang Wu Yi Zhong Liang Wang Center for Research on Intelligent Perception and Computing (CRIPAC) State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS) Chinese Academy of Sciences Institute of Automation Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences (UCAS). School of Electrical and Data Engineering University of Technology Sydney Australia School of Information and Electronics Beijing Institute of Technology China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In various domains such as surveillance and smart retail, pedestrian retrieval, centering on person re-identification (Re-ID), plays a pivotal role. Existing Re-ID methodologies often overlook subtle internal attribute variations, which are crucial for accurately identifying individuals with changing appearances. In response, our paper introduces the Attribute-Guided Pedestrian Retrieval (AGPR) task, focusing on integrating specified attributes with query images to refine retrieval results. Although there has been progress in attribute-driven image retrieval, there remains a notable gap in effectively blending robust Re-ID models with intra-class attribute variations. To bridge this gap, we present the Attribute-Guided Transformer-based Pedestrian Retrieval (ATPR) framework. ATPR adeptly merges global ID recognition with local attribute learning, ensuring a co-hesive linkage between the two. Furthermore, to effectively handle the complexity of attribute interconnectivity, ATPR organizes attributes into distinct groups and applies both inter-group correlation and intra-group decorrelation regularizations. Our extensive experiments on a newly estab-lished benchmark using the RAP dataset [32] demonstrate the effectiveness of ATPR within the AGPR paradigm.

关键词： Couplings Pedestrians Correlation Surveillance Image retrieval Focusing Benchmark testing

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FoodLogoDet-1500: A dataset for large-scale food logo detection via multi-scale feature decoupling network

arXiv

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arXiv 2021年

作者： Hou, Qiang Min, Weiqing Wang, Jing Hou, Sujuan Zheng, Yuanjie Jiang, Shuqiang School of Information Science and Engineering Shandong Normal University Shandong China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Food logo detection plays an important role in the multimedia for its wide real-world applications, such as food recommendation of the self-service shop and infringement detection on e-commerce platforms. A large-scale food logo dataset is urgently needed for developing advanced food logo detection algorithms. However, there are no available food logo datasets with food brand information. To support efforts towards food logo detection, we introduce the dataset FoodLogoDet-1500, a newlarge-scale publicly available food logo dataset, which has 1,500 categories, about 100,000 images and about 150,000 manually annotated food logo objects. We describe the collection and annotation process of FoodLogoDet-1500, analyze its scale and diversity, and compare it with other logo datasets. To the best of our knowledge, FoodLogoDet-1500 is the first largest publicly available high-quality dataset for food logo detection. The challenge of food logo detection lies in the large-scale categories and similarities between food logo categories. For that, we propose a novel food logo detection method Multi-scale Feature Decoupling Network (MFDNet), which decouples classification and regression into two branches and focuses on the classification branch to solve the problem of distinguishing multiple food logo categories. Specifically, we introduce the feature offset module, which utilizes the deformation-learning for optimal classification offset and can effectively obtain the most representative features of classification in detection. In addition, we adopt a balanced feature pyramid in MFDNet, which pays attention to global information, balances the multi-scale feature maps, and enhances feature extraction capability. Comprehensive experiments on FoodLogoDet-1500 and other two popular benchmark logo datasets demonstrate the effectiveness of the proposed method. The code and FoodLogoDet-1500 can be found at https://***/hq03/FoodLogoDet-1500-Dataset. © 2021, CC BY.

关键词： Feature extraction

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The structure design and process analysis for a metrorological monitoring device based on GIS 4

The structure design and process analysis for a metrorologic...

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2020 4th International Conference on Material Science and technology, ICMST 2020

作者： Wang, Ying Jiangxi Province Key Laboratory for Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang330099 China

A kind of meteorological monitoring device based on geographic information system (GIS) is studied in this paper, which belongs to the field of meteorological monitoring technology. The device include base, turntable, themometer shelter, roof, bracket and processor. Specifically, batteries and turntables motors are fixed on the left side and middle on the top respectively, and motors are fixed on the middle of the bottom. The device adopts a technological design of modularity design to ensure its scientific, rigorous and flexible. Its configuration can not only meet functional requirements of meteorological monitoring and the overall performance, but also its scalability can be achieved by adjusting the hardware configuration or adding a server to achieve a smooth upgrade of the device and expanding the overall performance of the device. © 2020 institute of Physics Publishing. All rights reserved.

关键词： Phonographs

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Multi-View Mutual Learning Network for Multimodal Fake News Detection

SSRN

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SSRN 2023年

作者： Cui, Wei Zhang, Xuerui Shang, Mingsheng College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China School of Electronic Information and Communication Engineering Chongqing Aerospace Polytechnic Chongqing400021 China

Multimodal fake news is more deceptive than unimodal content and often has adverse social and economic impacts. However, most existing methods learn modal features from a single perspective, without considering simultaneously learning and sharing knowledge across modalities from different perspectives. Our work presents a novel Multi-View Mutual Learning (MVML) network for multimodal fake news detection, which explores the semantic relationship between words and scenes, as well as words and objects, and investigates the semantic connection that exists between global concepts and local objects from multiple perspectives. We first construct text-scenes and text-objects graphs respectively, and perform intra-graph inference to obtain multi-view features. Then, the multi-view fusion layer based on the cross-view attention mechanism interactively models the cross-view dependencies between the image and text. The extracted features before feeding into the final classifier are further processed via a feed-forward attention module, which adaptively reweights and aggregates the features for redundancy reduction. In addition, to exploit the respective advantages of two multi-view classifiers, we propose a mutual learning mechanism that allows them to perform knowledge distillation and align the learning targets. The proposed MVML is thoroughly assessed on four publicly available benchmark datasets, and the findings demonstrate that it outperforms the existing standard approaches. © 2023, The Authors. All rights reserved.

关键词： Semantics

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ASM: Adaptive Sample Mining for In-The-Wild Facial Expression Recognition

arXiv

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arXiv 2023年

作者： Zhang, Ziyang Sun, Xiao An, Liuwei Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Heifei China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machines Hefei University of Technology Heifei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Heifei China

Given the similarity between facial expression categories, the presence of compound facial expressions, and the subjectivity of annotators, facial expression recognition (FER) datasets often suffer from ambiguity and noisy labels. Ambiguous expressions are challenging to differentiate from expressions with noisy labels, which hurt the robustness of FER models. Furthermore, the difficulty of recognition varies across different expression categories, rendering a uniform approach unfair for all expressions. In this paper, we introduce a novel approach called Adaptive Sample Mining (ASM) to dynamically address ambiguity and noise within each expression category. First, the Adaptive Threshold Learning module generates two thresholds, namely the clean and noisy thresholds, for each category. These thresholds are based on the mean class probabilities at each training epoch. Next, the Sample Mining module partitions the dataset into three subsets: clean, ambiguity, and noise, by comparing the sample confidence with the clean and noisy thresholds. Finally, the Tri-Regularization module employs a mutual learning strategy for the ambiguity subset to enhance discrimination ability, and an unsupervised learning strategy for the noise subset to mitigate the impact of noisy labels. Extensive experiments prove that our method can effectively mine both ambiguity and noise, and outperform SOTA methods on both synthetic noisy and original datasets. The supplement material is available at https://***/zzzzzzyang/ASM. Copyright © 2023, The Authors. All rights reserved.

关键词： Face recognition

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Forensicability Assessment of Questioned Images in Recapturing Detection

arXiv

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arXiv 2022年

作者： Chen, Changsheng Zhao, Lin Cai, Rizhao Yu, Zitong Huang, Jiwu Kot, Alex C. The Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology College of Electronics and Information Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China The Department of Electrical and Electrics Engineering Nanyang Technological University Singapore639798 Singapore

Recapture detection of face and document images is an important forensic task. With deep learning, the performances of face anti-spoofing (FAS) and recaptured document detection have been improved significantly. However, the performances are not yet satisfactory on samples with weak forensic cues. The amount of forensic cues can be quantified to allow a reliable forensic result. In this work, we propose a forensicability assessment network to quantify the forensicability of the questioned samples. The low-forensicability samples are rejected before the actual recapturing detection process to improve the efficiency of recapturing detection systems. We first extract forensicability features related to both image quality assessment and forensic tasks. By exploiting domain knowledge of the forensic application in image quality and forensic features, we define three task-specific forensicability classes and the initialized locations in the feature space. Based on the extracted features and the defined centers, we train the proposed forensic assessment network (FANet) with cross-entropy loss and update the centers with a momentum-based update method. We integrate the trained FANet with practical recapturing detection schemes in face anti-spoofing (FAS) and recaptured document detection tasks. Experimental results show that, for a generic CNN-based FAS scheme, FANet reduces the EERs from 33.75% to 19.23% under ROSE to IDIAP protocol by rejecting samples with the lowest 30% forensicability scores. The performance of FAS schemes is poor in the rejected samples, with EER as high as 56.48%. Similar performances in rejecting low-forensicability samples have been observed for the state-of-the-art approaches in FAS and recaptured document detection tasks. To the best of our knowledge, this is the first work that assesses the forensicability of recaptured document images and improves the system efficiency. Copyright © 2022, The Authors. All rights reserved.

关键词： Image quality

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