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A Self-Attention Based Method for Facial Expression recognition 2021

A Self-Attention Based Method for Facial Expression Recognit...

引用

7th international conference on computing and Artificial Intelligence, ICCAI 2021

作者： Ling, Xufeng Liang, Jingxin Yang, Jie Shanghai Normal University Tianhua College Ai School No. 1661 North Sheng Xin Road China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China

ISBN: (纸本)9781450389501

We present a self-attention-based method termed as Vision Transformer (ViT) to efficiently classify the human facial expressions. Our work can be divided into two contributions. First, the facial expression image is divided to N∗N patches, each of which corresponds to word, and the whole image data is used as a paragraph that composed of n words. Second, we design a learnable module, the ViT, with sequence length of L, latent dimension, and 12 attention layers which are integrated together as a unified framework. We also train the proposed model on the normalized and augmented version of FER2013plusdataset. We show empirically that ViT has superior performance compared to alternative approaches. © 2021 ACM.

关键词： Face recognition

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Query Processing And Optimization Method of Ethnic Music Data Based on Deep Belief Network

Query Processing And Optimization Method of Ethnic Music Dat...

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Data Analytics, computing and Artificial Intelligence (ICDACAI), international conference on

作者： Sixian He Siyuan He Xin Gao College of Music and Dance North Minzu University School Yinchuan China College of Music Ningxia University Yinchuan China Faculty of Music and Performing Arts Universiti Pendidikan Sultan Idris Tanjung Malim Perak Malaysia School of Materials Science & Engineering North Minzu University Yinchuan China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

With the development of the Internet and the large-scale digital music industry, the acquisition and listening of music are presented to users in a more convenient way. How to find the music that users like to hear from the massive digital music data on the Internet has become a key issue in the field of ethnic music information retrieval. Key issues and main goals addressed. There is a complex relationship between ethnic music data, which is suitable for modeling and query processing with data structure. However, the existing query processing algorithms only focus on structural features, and are not optimized for folk music metadata and music content data, and the query efficiency is not high. Currently, the amount of data in various media is exploding, and digital music is everywhere and increasing day by day. At the same time, various music applications have also greatly contributed to the advancement of information technology, such as music sharing, which made the ancient peer-to-peer technology mainstream at the end of the 20th century. The method used in this paper is a deep belief network. Compared with other classification algorithms, its error convergence speed is faster, and the final recognition error for the data set is relatively smaller.

关键词： Query processing Refining Signal processing algorithms Optimization methods Music Prototypes Signal processing Internet Peer-to-peer computing pattern matching

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Constraint Pushing Multi-threshold Framework for High Utility Time Interval Sequential pattern Mining 3rd

Constraint Pushing Multi-threshold Framework for High Utili...

引用

3rd international conference on soft computing and its Engineering Applications, icsoftComp 2021

作者： Saleti, Sumalatha Naga Sahithya, N. Rasagna, K. Hemalatha, K. Sai Charan, B. Karthik Upendra, P.V. SRM University AP Andhra Pradesh Amaravati India

ISBN: (纸本)9783031057663

This paper aims to detect high utility sequential patterns including time intervals and multiple utility thresholds. There are many algorithms that mine sequential patterns considering utility factor, these can find the order between the items purchased but they exclude the time interval among items. Further, they consider only the same utility threshold for each item present in the dataset, which is not convincing to assign equal importance for all the items. Time interval of items plays a vital role to forecast the most valuable real-world situations like retail sector, market basket data analysis etc. Recently, UIPrefixSpan algorithm has been introduced to mine the sequential patterns including utility and time intervals. Nevertheless, it considers only a single minimum utility threshold assuming the same unit profit for each item. Hence, to solve the aforementioned issues, in the current work, we proposed UIPrefixSpan-MMU algorithm by utilizing a pattern growth approach and four time constraints. The experiments done on real datasets prove that UIPrefixSpan-MMU is more efficient and linearly scalable for generating the time interval sequences with high utility. © 2022, Springer Nature Switzerland AG.

关键词： Multiple utility thresholds Sequential patterns Time intervals Utility mining

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Multi-Scale Features Integration for Handwritten Mathematical Expression recognition 22

Multi-Scale Features Integration for Handwritten Mathematica...

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Proceedings of the 2022 11th international conference on computing and pattern recognition

作者： Xianghao Liu Da-Han Wang Shunzhi Zhu Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology China

ISBN: (纸本)9781450397056

Handwritten mathematical expression recognition (HMER) is a challenging task due to the complex two-dimensional structure of mathematical expressions and the similarity of handwritten texts. Most existing methods for HMER only consider single-scale features while ignoring multi-scale features that are very important to HMER. Few works have explored the fusion of multi-scale features in HMER, but exhibited an extra branch that brings more parameters and computation. In this paper, we propose an end-to-end method to integrate multi-scale features using a unified model. Specifically, we customized the Dense Atrous Spatial Pyramid Pooling (DenseASPP) to our backbone network to capture the multi-scale features of the input image meanwhile expanding the receptive fields. Moreover, we added a symbol classifier using focal loss to better discriminate and recognize similar symbols, to further improve the performance of HMER. Experiments on the Competition on recognition of Online Handwritten Mathematical Expressions (CROHME) 2014, 2016 and 2019 shows that the proposed method achieves superior performance to most state-of-the-art methods, demonstrating the effectiveness of the proposed method.

关键词： Multi-Scale Handwritten Mathematical Expression recognition, Dense Atrous Spatial Pyramid Pooling, Symbol Classifier

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Research on Face recognition Algorithm Based on D-S Evidence Theory and Local Domain pattern

Research on Face Recognition Algorithm Based on D-S Evidence...

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2021 international conference on Intelligent computing, Automation and Applications, ICAA 2021

作者： Wang, Xuyang Wang, Tongyan Lanzhou University of Technology School of Computer and Communication Technology Lanzhou730050 China

ISBN: (纸本)9781665437301

Local Binary pattern is a kind of description of the texture within the scope of gray level, but under the influence of illumination and noise, the performance of classification decline rapidly. Therefore, a local feature extraction method is proposed, that is, Local Neighborhood patterns (LNP). First of all, the image is divided into some local block. Then, treat every pixel and the pixel within the scope of neighborhood as a vector, calculate the distance of each local regional center vector and other pixels vector. Then take the ring extraction features in each local block, it means, using the center of the coded local image as the center of the different radius circle, from the center of the external circular in a certain sequence (clockwise or counterclockwise) extraction characteristics. Make the characteristics of the local image connect one by one as a whole image characteristic vector. At last use the nearest neighbor classifier. This paper also combines data fusion with the LNP, using D-S evidence theory for decision fusion. First of all, it used the gaussian filtering illumination pretreatment method to reduce the influence of the extreme imaging conditions on the face image. Secondly, the image convolution with sobel operator, then get horizontal and vertical edge image. Thirdly, extraction feature vector with LNP and calculate the distance between the test sample and all the classes, by means of the constructor function to realize the conversion of the Euclidean distance and the objective evidence. In the end, to make optimal decision Using D-S evidence theory to objective evidence for fusion. The result in the Extended Yale B database shown that this method can not only get high recognition rate, but also can effectively improve the robustness of illumination, posture, facial expression change. © 2021 IEEE.

关键词： Pixels

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One pattern recognition Algorithm Based on Centroids of Fuzzy Hyper-Pyramid Numbers 15th

One Pattern Recognition Algorithm Based on Centroids of Fuzz...

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15th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th international conference on Harmony Search, soft computing and Applications, ICHSA 2019

作者： Xu, Yifeng Wang, Guixiang Shen, Chenjie Institute of Operations Research and Cybernetics Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9783030324551

In this paper, a pattern recognition algorithm is given based on centroids of fuzzy hyper-pyramid numbers which are special type fuzzy n-cell numbers. The specific calculation formula (which can be easy calculated by computer program in applications) is obtained for fuzzy hyper-pyramid numbers. Using the results obtained by us, we propose a algorithm of pattern recognition. At last, a practical example is given to demonstrate the application of the algorithm given by us in recognizing uncertain or imprecise multidimensional digital information. © Springer Nature Switzerland AG, 2020.

关键词： pattern recognition

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Hyperdimensional Consumer pattern Analysis with Quantum Neural Architectures using Non-Hermitian Operators 23

Hyperdimensional Consumer Pattern Analysis with Quantum Neur...

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Proceedings of the 5th international conference on Information Management & Machine Intelligence

作者： Shivam Goyal Sunil K. Singh Sudhakar Kumar Saket Sarin Brij B. Gupta Varsha Arya Kwok Tai Chui CSE Chandigarh College of Engineering and Technology India Department of Computer Science and Information Engineering (CSIE) Asia University Taichung Taiwan Taiwan Department of Business Administration Asia University Taiwan CSE Hong Kong Metropolitan University China

ISBN: (纸本)9798400709418

In an era inundated with high-dimensional consumer data, the need for advanced hyper-dimensional pattern analysis poses a significant computational challenge. This research pioneers the use of quantum computing to revolutionize consumer technology. Modern data streams, including images, audio, sensor data, and more, require an agile solution to overcome dimensionality challenges that traditional machine learning and classical computing struggle with. Our work combines Quantum Neural Architectures (QNAs) with Non-Hermitian Operators (NHOs), harnessing NHOs’ unique non-unitary properties for quantum speedup in feature extraction, dimensionality reduction, and pattern recognition. This approach allows for simultaneous processing of large datasets through quantum parallelism, demonstrating substantial gains in efficiency and accuracy compared to classical and Hermitian quantum methods. We also explore quantum cryptography, offering insights into quantum-safe encryption and cryptographic primitives. This multidisciplinary effort represents a paradigm shift in quantum technology’s application across consumer domains. This paper presents a quantum-inspired framework for hyperdimensional consumer pattern analysis, supported by mathematical rigor and empirical validation. The fusion of NHOs and QNAs heralds a new era in consumer technology, marked by exceptional computational power, robust security, and transformative potential.

关键词： Consumer Applications

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Development of Marker-Based Web Augmented Reality Educational Board Games for Learning Process Support in Computer Science

Development of Marker-Based Web Augmented Reality Educationa...

引用

Experiment@ international conference (exp.at)

作者： Oleksandr Blazhko Natalia Shtefan Department of Information Systems Odessa Polytechnic National University Odesa Ukraine Department of Information Technology Odessa State Environmental University Odesa Ukraine

ISBN: (数字)9798350314403

ISBN: (纸本)9798350314410

This article demonstrates the results of the implementation of the strategy while students are studying the topics of the discipline “Operating systems” based on the development of educational board games with augmented reality technology. Relationships between the development of education augmented reality board games and soft skills in computer science are shown. Recommendations on the use of play-components of game mechanics (random, select, manage, move, shoot, write) for board game elements (dice, coin, card, token) with paper markers are given.

关键词： Computer science Operating systems Education Games Teamwork pattern recognition Problem-solving

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MQTT equipped Intrusion detection system for IoT devices based Machine Learning Algorithms

MQTT equipped Intrusion detection system for IoT devices bas...

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international conference on computing, Communication, and Intelligent Systems (ICCCIS)

作者： Priya Sharma Sanjay Kumar Sharma Diksha Dani University School of Information & Communication Technology Gautam Buddha University Greater Noida Indraprastha Engineering College Ghaziabad India

ISBN: (纸本)9781665462013

In the era of COVID19, the world has shifted to an online presence and is now forced to embrace the usage of digital technology in their daily lives. With the meteoric rise of internet-based devices, there is a requirement for a protocol for secure communication between these devices. Message Queueing Telemetry Transport (MQTT) is the standard protocol for IoT devices. The MQTT implementation with IDS have very prominent usability and has huge potential for increasing efficiency. Therefore, in this paper, an IDS has been proposed with MQTT as a protocol for IoT devices, using machine learning to improve the pattern recognition of the IDS. The proposed system has been tested with three machine learning algorithms, namely, and the results show that they are adequate for the MQTT protocol

关键词： Protocols Machine learning algorithms Machine learning Real-time systems pattern recognition Internet of Things Telemetry

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Multimodal Emotion Cause Pair Extraction in Conversations Using Knowledge Distillation and Large Language Models

Multimodal Emotion Cause Pair Extraction in Conversations Us...

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Computational Intelligence and Network Systems (CINS), international conference on

作者： Aruna Gladys A Vetriselvi V Rajasekar S K Department of Computer Science and Engineering College of Engineering Guindy Anna University Chennai Tamil Nadu India

ISBN: (数字)9798331504106

ISBN: (纸本)9798331504113

This paper presents a novel approach to Multimodal Emotion Cause Pair Extraction in Conversations (ECPEC), by addressing the complexities of emotion recognition by integrating features from different data sources. The method consists of a two-stage process. In the first context modeling stage, context-rich summaries of conversations filled with emotion cues are generated using a teacher-student knowledge distillation approach, lever-aging a Large Language Model (LLM) as the teacher and fine-tuning smaller LLM to mimic its summarizing capability. The generated summaries serve as input for the second stage, where the linguistic, acoustic, and visual features are fused to extract both emotions and emotion-cause pairs. To enhance the learning process, Direct Preference Optimization (DPO) is employed to fine-tune the compact LLM model by creating a preference dataset using a Ground Truth Augmented Reasoning technique. The responses from the fine-tuned LLM are converted into structured outputs using Mistral 7B model facilitating objective evaluation through the traditional performance metrics used in classification tasks. The findings demonstrate the critical role of multimodal data in capturing emotions, by enhancing the role of each modality through the reasoning capabilities of the LLM models. This work establishes a robust framework for ECPEC, demonstrating the effectiveness of integrating multimodal cues and advanced LLM fine-tuning strategies in affect computing tasks.

关键词： Measurement Emotion recognition Visualization Large language models Computational modeling soft sensors Feature extraction Cognition Optimization Network systems

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