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joint Multi-Scale Contextual and Noise Suppression for Group Emotion Recognition

Joint Multi-Scale Contextual and Noise Suppression for Group...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wangdong Guo Qing Zhu Qirong Mao School of Computer Science and Communication Engineering Jiangsu University Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Group Emotion Recognition (GER) seeks to identify emotional states within multi-person groups. The complexity of the environment and the reliance on a single group emotion label often result in noisy individuals with inconsistent emotional expressions, hampering accurate emotion classification. Mainstream GER networks attempt to downweight noisy individuals, but when their numbers are high and over-relying on a single group label, the negative impact of noise on individuals remains significant, further complicating classification and diminishing accuracy. To address these limitations, we propose the Multi-Scale Contextual and Noise Suppression Model (MCon-NSM), a novel framework that enhances GER by capturing fine-grained interaction contexts and collaboratively suppressing noise at both the individual and group label levels. Extensive experiments on the GAF series datasets demonstrate that our method achieves results comparable to state-of-the-art techniques, validating its effectiveness in mitigating noise in GER.

关键词： Emotion recognition Analytical models Accuracy Noise Noise reduction Speech recognition Complexity theory Noise measurement Speech processing Context modeling

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Deep Ordinal Focus Assessment for Whole Slide Images 18

Deep Ordinal Focus Assessment for Whole Slide Images

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18th IEEE/CVF international conference on computer Vision (ICCV)

作者： Albuquerque, Tome Moreira, Ana Cardoso, Jaime S. INESC TEC Porto Portugal FEUP Porto Portugal

ISBN: (纸本)9781665401913

Medical image quality assessment plays an important role not only in the design and manufacturing processes of image acquisition but also in the optimization of decision support systems. This work introduces a new deep ordinal learning approach for focus assessment in whole slide images. From the blurred image to the focused image there is an ordinal progression that contains relevant knowledge for more robust learning of the models. With this new method, it is possible to infer quality without losing ordinal information about focus since instead of using the nominal cross-entropy loss for training, ordinal losses were used. Our proposed model is contrasted against other state-of-the-art methods present in the literature. A first conclusion is a benefit of using data-driven methods instead of knowledge-based methods. Additionally, the proposed model is found to be the top-performer in several metrics. The best performing model scores an accuracy of 94.4% for a 12 classes classification problem in the FocusPath database.

关键词： Training measurement Image quality Decision support systems Manufacturing processes Databases Computational modeling

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Chaotic Fuzzy Logic for Enhanced Educational Q&A System

Chaotic Fuzzy Logic for Enhanced Educational Q&A System

引用

IEEE international conference on Power, Intelligent Computing and systems (ICPICS)

作者： Haoyuan Chen Nuobei Shi Ling Chen Raymond Lee Department of Computer Science BNU-HKBU United International College Zhuhai China School of Applied Science and Civil Engineering Beijing Institute of Technology Zhuhai China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

Online question-and-answer (Q&A) platforms are replete with extensive human resources support. This paper proposes a novel methodology of a customized Large Language Model (LLM) called Chaotic LLMs-based Educational Q&A System (CHAQS) to navigate the complexities associated with intelligent Q&A systems for educational sector. It uses an expansive dataset comprising over 383,000 educational data pairs, an intricated fine-tuning process encompassing p-tuning v2, Low Rank Adaptation (LRA), and strategies for parameter freezing at an open-source Large Language Model ChatGLM as baseline model. Also, Fuzzy Logic is implemented to regulate parameters, system's adaptability with Lee-Oscillator to refine model's response variability and precision. Experiment results showed that 5.12% improvement in Precision score, 11% increase in Recall metric, and 8% improvement in F1 score as compared to other models.

关键词： Fuzzy logic measurement Adaptation models Navigation Large language models Computational modeling Coherence Data models Complexity theory Prompt engineering

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Application of Edge Computing Based on MultiScale Convolutional Neural Networks in Gear Fault Diagnosis

Application of Edge Computing Based on MultiScale Convolutio...

引用

Sensing, measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), international conference on

作者： Tianming Chen Manyi Wang Daye Wang School of Mechanical Engineering Nanjing University of Science and Technology Nanjng China Qiqihar Heping Heavy Industries Group Co.Ltd Qiqihar China

ISBN: (数字)9798331529192

ISBN: (纸本)9798331529208

Gear fault diagnosis plays a crucial role in detecting gear faults promptly, enabling timely repairs or replacements to minimize potential losses. While deep learning-based methods have been widely used for gear fault diagnosis, their effective application in real-world industrial environments remains a challenge. In this paper, we introduce a novel approach that combines a multi-scale convolutional neural network with multiknowledge distillation (MSCNN-MKD). This approach is deployed on edge computing nodes for online gear fault diagnosis. Experiments results validate the effectiveness of our proposed methodology. The proposed method achieves an accuracy of 98.76% in recognizing 7 fault categories. Additionally, the number of parameters is 18.54K, the floating-point operations are 41.54M, and the average inference time is 1.07ms. The methodology has great potential for practical industrial applications in gear faults diagnosis.

关键词： Fault diagnosis Learning systems Accuracy Data analysis Gears Computational modeling computer architecture Maintenance engineering Convolutional neural networks Edge computing

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Evaluation of the joint of superconducting tapes for railway feeder cable 34

Evaluation of the joint of superconducting tapes for railway...

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34th international Symposium on Superconductivity, ISS 2021

作者： Akasaka, T. Ishihara, A. Onji, T. Yamamoto, H. Tomita, M. Zhong, Y. Otabe, E.S. Applied Superconductivity Laboratory Maglev Systems Technology Division Railway Technical Research Institute 2-8-38 Hikari-cho Kokubunji-shi Tokyo185-8540 Japan Department of Computer Science and Electronics Kyushu Institute of Technology 680-4 Kawazu Fukuoka820-8502 Japan

Developing superconducting cable for railway power transmission should lead to increased regeneration efficiency, reduced power loss, equalization of load between substations. In order to realize a kilometer-class superconducting feeder cable, on-site joint technique between superconducting feeder cables is required. For joining superconducting cables at railway construction sites, we conducted solder joining of superconducting tapes and its evaluation in the laboratory. We performed electromagnetic analysis of the joints by the finite element method in order to obtain a prospect of the optimum conditions of the solder joining. The target value of the joint resistance to 10-7 ω class will be achieved by the solder joining regardless of the solder thickness and the joint length. Referring to the analysis results, mechanical and electrical properties measurement of the joined superconducting tape was conducted. From the results of the tensile and bending test, it is found that the solder joined superconducting tape with the lap length of more than 20 mm achieve the mechanical and electrical targets. These results are being utilized for cable joining at railway construction sites. © Published under licence by IOP Publishing Ltd.

关键词： Joining

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CACam: Consecutive Angular measurements with Camera on Smartphone for Distance Estimation

CACam: Consecutive Angular Measurements with Camera on Smart...

引用

international conference on Parallel and Distributed systems (ICPADS)

作者： Yifan Zhu Xiaojun Zhu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory of Novel Software Technology Nanjing University Nanjing China

To estimate the distance between a user and the target object for personal interest or for subsequent actions, the commonly used method is to search the target in the map application. However this is limited by GPS signal status under different environments and incomplete information on the map application of smartphones. This paper introduces a ranging method, CACam, which utilizes consecutive angle measurement based on camera of smartphone. Geometric model and ranging process are proposed to calculate the distance. When the user aligns the target from different position points, we record readings from the gyroscope sensor during every rotation in the procedure. The camera is used for aligning the target to build geometric model. Vector mapping of angular readings and data processing of intermediate points are used to get more accurate angles, which is suitable for the situation that the smartphone could not be strictly rotated in perpendicular to the plane of the gravity. The effectiveness of our ranging method is verified through experiments in outdoor environments.

关键词： Geometric modeling Cameras Search problems Distance measurement Hardware Gyroscopes Smart phones

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Delta-γ modeling of Wireless System Resilience in Multipath Fading and Interference Environments

Delta-γ Modeling of Wireless System Resilience in Multipath...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Deepak Upadhyay Abhay Upadhyay Kunj Bihari Sharma Mridul Gupta Nookala Venu Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

In this paper, we pursue the resilience of wireless communication systems in multipath fading and interference environments utilizing Delta- $\gamma$ modelling. Furthermore, fading phenomena such as Rayleigh/Rician/Nakagami channels can be simulated to examine the impact on important performance metrics like SNR, BER and throughput. Different types of interference scenarios, covering co-channel and adjacent channel interferences are also studied to understand impact on system capacity as well as reliability. This article investigates the use of adaptive modulation techniques, multiple-input multiple-output (MIMO) strategies and advanced interference management schemes by extensive *** results illustrate the importance of adaptive strategies in overcoming fading and interference which leads to an increase in system resilience as well as a robust communication link. Metrics for the outage probability and mean time to failure (MTTF) can inform how reliable systems are across operational scenarios. This work reconciles theory with practice to provide a systematic way of controlling the operational parameters in wireless networks under varying conditions. These lessons will be crucial in steering the design of forthcoming wireless systems that must more reliably satisfy a broader set of application requirements.

关键词： Fading channels measurement Adaptive systems Wireless networks Interference Reliability theory Probability Throughput Resilience Signal to noise ratio

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Logical Relation modeling and Mining in Hyperbolic Space for Recommendation

Logical Relation Modeling and Mining in Hyperbolic Space for...

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international conference on Data Engineering

作者： Yanchao Tan Hang Lv Zihao Zhou Wenzhong Guo Bo Xiong Weiming Liu Chaochao Chen Shiping Wang Carl Yang College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Department of Computer Science Emory University Atlanta United States

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity- based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable.

关键词： measurement Accuracy Taxonomy Semantics Predictive models Performance gain Linguistics

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Self-Supervised Uncertainty-Guided Refinement for Robust joint Optical Flow and Depth Estimation

Self-Supervised Uncertainty-Guided Refinement for Robust Joi...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Rokia Abdein Wei Li Chenghao Li Xiangping Zheng Rahul Yadav College of Computer Science and Technology Harbin Engineering University Harbin China Modeling and Emulation in E-Government National Engineering Laboratory Harbin Engineering University Harbin China Qingdao Innovation and Development Base of Harbin Engineering University Qingdao China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

jointly estimating the optical flow and depth tasks in real-world scenes presents considerable hurdles due to some phenomena, such as occlusion, ambiguous textures, and illumination variation. The lack of guidance from the labeled data makes these challenges harder to overcome. This paper presents a novel approach to learning the regions with high uncertainties in a self-supervised manner. Our method allows the network to learn these regions by leveraging its predictions’ confidence. The uncertainties are then utilized in the estimation and refinement processes for optical flow and depth. We also introduce a novel uncertainty-guided smoothness regularization technique that leverages the uncertainty map to increase the robustness through concentrating the smoothness on regions with low confidence scores. Our results in the KITTI real-world dataset demonstrate the effectiveness of our approach for enhancing the predictions, particularly in the challenging scenes, showcasing the potential of our approach for real-world applications.

关键词： Uncertainty Smoothing methods Depth measurement Estimation Lighting Signal processing Robustness Acoustics Speech processing Optical flow

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Quantifying the Odds in Real World Attack Scenarios

Quantifying the Odds in Real World Attack Scenarios

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Cyber Security and Resilience (CSR), IEEE international conference on

作者： Paul Tavolato Robert Luh Sebastian Eresheim Simon Gmeiner Sebastian Schrittwieser Faculty of Computer Science University of Vienna Vienna Austria Department of Computer Science University of Applied Sciences St. Pölten Austria

ISBN: (数字)9798350375367

ISBN: (纸本)9798350375374

In cyber security an important part of risk analysis for IT systems is threat analysis. Threat analysis is an indispensable prerequisite for the planning and budgeting of efficient defense measures. This paper describes a strictly formal method for modeling realistic cyber-attack scenarios. These scenarios are modeled as Discrete Time Markov Decision Processes, opening the opportunity for the application of formal methods to calculate quantitative success probabilities of cyber threats depending on the attacker's skill level and the victim's infrastructure and defense measures. Techniques and tools of probabilistic model checking are applied to find quantitative answers to important questions relevant in threat analysis, such as the attacker's minimum and maximum success probabilities in the victim's IT environment. This provides valuable decision support for security managers when they are forced to assess different security measures in the course of deciding which measure to implement under given budget constraints. To guarantee the practical relevance of the method, a list of 159 attack actions and a list of 118 defense actions are compiled, where the information is gained from several proven tactical and technical knowledge bases. An example - stealing confidential data - shows the application of the method. For calculating probabilities, the model checking tool PRISM is used.

关键词： Force measurement Markov decision processes Probability Model checking Probabilistic logic Time measurement Planning

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