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Improving Intention Recognition Efficiency: A Study on Skeletal data Dimensionality Reduction and Neural Architectures

Improving Intention Recognition Efficiency: A Study on Skele...

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17th international conference on Social Computing and Social Media, SCSM 2025, held as part of the 27th HCI international conference, HCII 2025

作者： Orsag, Luka Stipancic, Tomislav Koren, Leon Zidaric, Matija Faculty of Mechanical Engineering and Naval Architecture University of Zagreb Zagreb Croatia Visage Technologies AB Linköping Sweden

ISBN: (纸本)9783031935381

Understanding human intent is crucial for human-computer interaction (HCI) and human-robot collaboration (HRC) in industrial environments. However, existing intention recognition models suffer from poor generalization, high computational costs, and latency issues, particularly when processing high-dimensional skeletal motion data. This study explores dimensionality reduction techniques, including Principal Component Analysis (PCA), Kernel PCA (kPCA), and Uniform Manifold Approximation and Projection (UMAP), to optimize feature selection while maintaining relevant motion features. A multimodal system using RGB, depth, and embedded sensors is developed to classify human intentions. The proposed system integrates neural network-based models to analyze spatial and temporal motion patterns, enabling adaptive robotic behavior through short-term pose prediction, collision sensing, and path correction. The OAK-D AI platform is employed for real-time depth perception and edge AI processing. While the applied dimensionality reduction techniques lead to reduced model accuracy, they significantly enhance computational efficiency, lower processing latency, and reduce power consumption. These trade-offs make the system more suitable for real-time, resource-constrained industrial applications, ensuring faster but slightly less accurate decision-making in AI-driven industrial automation and workplace safety monitoring. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Industrial robots

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Neural Network as a Preferred Method for Microarray data Classification 7

Neural Network as a Preferred Method for Microarray Data Cla...

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7th international conference on Software Engineering and Computer Systems and 4th international conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021

作者： Ramadhani, Putri Tsatsabila Nasution, Benny Benyamin University of North Sumatera Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9781665414074

According to a WHO on World Cancer Report published in February 2020, one of six cancer survior died each year. The cancer can be known and detected early have possibilty to get better treatment, so do minimized death rates affected by cancer. A person can be detected with cancer by performing an accurate analysis and classsiflcation of microarray data. Several methods and algorithms have been used in various related studies, one of them is Neural Network. In this paper, presents recent works proposing Neural Network models as classifier for cancer predection purposes used gene expression datasets to know how capable Neural Network is in classifying microarray data. © 2021 IEEE.

关键词： Classification (of information)

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NUCLEAR DENSITY DISTRIBUTION FEATURE FOR IMPROVING CERVICAL HISTOPATHOLOGICAL IMAGES RECOGNITION

NUCLEAR DENSITY DISTRIBUTION FEATURE FOR IMPROVING CERVICAL ...

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IEEE international conference on Image processing (ICIP)

作者： Wang, Zhuangzhuang Yang, Mengning Lyu, Yangfan Chen, Kairun Tang, Qicheng Chongqing Univ Sch Big Data & Software Engn Chongqing Peoples R China Army Med Univ Affiliated Hosp 2 Dept Pathol Chongqing Peoples R China

ISBN: (纸本)9781665441155

Cervical carcinoma is a common type of cancer in the female reproductive system. Early detection and diagnosis can facilitate immediate treatment and prevent progression of the disease. However, in order to achieve better performance, DL-based algorithms just stack various layers with low interpretability. In this paper, a robust and reliable Nuclear Density Distribution Feature (NDDF) based on priors of the pathologists to promote the Cervical Histopathological Image Classification (CHIC) is proposed. Our proposed method combines the nucleus mask segmented by U-Net with the segmentation grid-lines generated from pathology images utilizing SLIC to obtain the NDDF map, which contains information about the morphology, size, number, and spatial distribution of nuclei. The result shows that the proposed model trained with NDDF maps has better performance and accuracy than that trained on RGB images (patch-level histopathological images). More significantly, the accuracy of the two-stream network trained with RGB images and NDDF maps is steadily improved over the corresponding baselines of different complexity.

关键词： Nuclear Density Distribution Superpixel Deep Learning Histopathological Image Classification

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SpZip: Architectural Support for Effective data Compression In Irregular Applications 21

SpZip: Architectural Support for Effective Data Compression ...

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ACM/IEEE 48th Annual international Symposium on Computer Architecture (ISCA)

作者： Yang, Yifan Emer, Joel S. Sanchez, Daniel MIT Cambridge MA 02139 USA NVIDIA Santa Clara CA USA

ISBN: (纸本)9781665433334

Irregular applications, such as graph analytics and sparse linear algebra, exhibit frequent indirect, data-dependent accesses to single or short sequences of elements that cause high main memory traffic and limit performance. data compression is a promising way to accelerate irregular applications by reducing memory traffic. However, software compression adds substantial overheads, and prior hardware compression techniques work poorly on the complex access patterns of irregular applications. We present SpZip, an architectural approach that makes data compression practical for irregular algorithms. SpZip accelerates the traversal, decompression, and compression of the data structures used by irregular applications. In addition, these activities run in a decoupled fashion, hiding both memory access and decompression latencies. To support the wide range of access patterns in these applications, SpZip is programmable, and uses a novel dataflow Configuration Language to specify programs that traverse and generate compressed data. Our SpZip implementation leverages dataflow execution and time-multiplexing to implement programmability cheaply. We evaluate SpZip on a simulated multicore system running a broad set of graph and linear algebra algorithms. SpZip outperforms prior state-of-the art software-only (hardware-accelerated) systems by gmean 3.0 x (1.5 x) and reduces memory traffic by 1.7 x (1.4 x). These benefits stem from both reducing data movement due to compression, and offloading expensive traversal and (de)compression operations.

关键词： Art Multicore processing Software algorithms data compression Linear algebra Performance gain data structures

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PRE-TRAINING TRANSFORMER DECODER FOR END-TO-END ASR MODEL WITH UNPAIRED TEXT data

PRE-TRAINING TRANSFORMER DECODER FOR END-TO-END ASR MODEL WI...

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IEEE international conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Gao, Changfeng Cheng, Gaofeng Yang, Runyan Zhu, Han Zhang, Pengyuan Yan, Yonghong Inst Acoust Key Lab Speech Acoust & Content Understanding Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781728176055

This paper presents a method to pre-train transformer-based encoder-decoder automatic speech recognition (ASR) models using sufficient target-domain text. During pre-training, we train the transformer decoder as a conditional language model with empty or artifical states, rather than the real encoder states. By this pre-training strategy, the decoder can learn how to generate grammatical text sequence before learning how to generate correct transcriptions. Contrast to other methods which utilize text only data to improve the ASR performance, our method does not change the network architecture of the ASR model or introduce extra component like text-to-speech (TTS) or text-to-encoder (TTE). Experimental results on LibriSpeech corpus show that the proposed method can relatively reduce the word error rate over 10%, using 960 hours transcriptions.

关键词： Speech recognition pre-training end-to-end unpaired data transformer

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Contemporary trends in privacy-preserving data pattern recognition 11th

Contemporary trends in privacy-preserving data pattern recog...

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2020 Annual international conference on Brain-Inspired Cognitive Architectures for Artificial Intelligence: Eleventh Annual Meeting of the BICA Society, BICA*AI 2020

作者： Zapechnikov, Sergey Institute of Cyber Intelligence Systems National Research Nuclear University Moscow Engineering Physics Institute Kashirskoye shosse 31 Moscow115409 Russia

The article is devoted to the recent scientific problem of privacy-preserving data pattern recognition. The purposes of the work are to systematize the security models for such tasks, to identify algorithmic tools that can be used to ensure the privacy of the data processing, and application of models and to analyze the privacy-preserving data pattern recognition systems. The article presents the main concepts and some definitions related to privacy-preserving machine learning, gives a systematization of related problems, and notes modern and promising areas of development of machine learning. Special cryptographic methods and protocols are correlated to the solved problems. A brief description of the known privacy-preserving data pattern recognition systems is given. Unsolved problems in the field of privacy-preserving data pattern recognition are considered. © 2020 Elsevier B.V.. All rights reserved.

关键词： data mining

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Analysis of Papers Based on Sentiment Analysis Applications on E-Commerce data 11th

Analysis of Papers Based on Sentiment Analysis Applications ...

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11th international conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2020 and 10th World Congress on Information and Communication Technologies, WICT 2020

作者： Kapadia, Beena Jain, Amita Career Point University Kota India Vidyalankar School of Information Technology Wadala Mumbai India

ISBN: (纸本)9783030736026

Sentiment Analysis is the computational treatment of opinions, sentiments and subjectivity of text and use them for the benefit of the business operations. This survey paper tackles a comprehensive overview of various sentiment analysis applications related to E-commerce data and includes analysis of related papers from 2008 to 2020. This paper gives overall idea about various data pre-processing techniques, Sentiment Analysis algorithms, its accuracy, further improvements and other related details of each referred applications used, as literature survey in the area of E-commerce. The main contributions of this paper include comprehensive analysis of many relevant E-commerce articles, illustration of data pre-processing techniques and the illustration of the recent trend of research in the sentimental Analysis and related areas. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation algorithms 21

RecBole: Towards a Unified, Comprehensive and Efficient Fram...

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30th ACM international conference on Information and Knowledge Management (CIKM)

作者： Zhao, Wayne Xin Mu, Shanlei Hou, Yupeng Lin, Zihan Chen, Yushuo Pan, Xingyu Li, Kaiyuan Lu, Yujie Wang, Hui Tian, Changxin Min, Yingqian Feng, Zhichao Fan, Xinyan Chen, Xu Wang, Pengfei Ji, Wendi Li, Yaliang Wang, Xiaoling Wen, Ji-Rong Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Renmin Univ China Sch Information Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China East China Normal Univ Shanghai Peoples R China Alibaba Hangzhou Peoples R China Liaoning Univ Shenyang Peoples R China

ISBN: (纸本)9781450384469

In recent years, there are a large number of recommendation algorithms proposed in the literature, from traditional collaborative filtering to deep learning algorithms. However, the concerns about how to standardize open source implementation of recommendation algorithms continually increase in the research community. In the light of this challenge, we propose a unified, comprehensive and efficient recommender system library called RecBole (pronounced as [rEk'boUl@r]), which provides a unified framework to develop and reproduce recommendation algorithms for research purpose. In this library, we implement 73 recommendation models on 28 benchmark datasets, covering the categories of general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation. We implement the RecBole library based on PyTorch, which is one of the most popular deep learning frameworks. Our library is featured in many aspects, including general and extensible data structures, comprehensive benchmark models and datasets, efficient GPU-accelerated execution, and extensive and standard evaluation protocols. We provide a series of auxiliary functions, tools, and scripts to facilitate the use of this library, such as automatic parameter tuning and break-point resume. Such a framework is useful to standardize the implementation and evaluation of recommender systems. The project and documents are released at https://***/.

关键词： recommendation toolkit collaborative filtering recommendation framework

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A Systematic Review of data Science and Deep Learning Applications in Extracting Biological data

A Systematic Review of Data Science and Deep Learning Applic...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Murala Praveena J. Paramesh K. Bala School of Computing Dept. of CSE Bharath Institute of Higher Education and Research Chennai India

This article provides a thorough analysis of data sciences and deep learning(DL) characters in data science emphasizing which one is needed out of DL for finer prediction has been as well addressed. This study gives a detailed correlation of disparate DL approaches employed in the biological sector (BS). Because of the appearance of dominant computers, advanced DL algorithms, and broad data production out of disparate industrial tools, a promising tomorrow in establishing solutions to the intricate issues in the BS, which remained formerly besides the hold of analytical solutions (AS) or numerical simulations (NS) is noticed. DL tools could include each particular within the log data and each data linked to the target data. Despite their constraints, these remain unlimited by constraining presumptions of AS or by specific data and/or power processing requisites of NS. This exhaustive work could provide an exceptional reference for DL implementations in this sector. Centered upon the review performed, it has been observed that DL approaches provide immense power in resolving issues in the BS’ major fields incorporating prediction, classification, and clustering.

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Big data processing Technology in Film and Television Post Production

Big Data Processing Technology in Film and Television Post P...

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2020 international conference on Artificial Intelligence, Computer Networks and Communications, AICNC 2020

作者： Xu, Qianqian School of Software Engineering Jiangxi University of Software Professional Technology Nanchang China

Nowadays, the wave of digitization, networking and informatization is sweeping the world, which makes the visual image become an important way of communication and transmission of global culture. The purpose of this paper is to analyze the application of big data processing technology in film and television post production. The distributed computing power of MapReduce is used to extract the content features of video images, construct corresponding indexes, and store and retrieve them in HBase database according to the indexes to form a big data processing platform for video images. This paper deeply discusses the framework related theory as the support of big data processing technology in film and television post production, analyzes the common algorithms in nonlinear editing system, discusses the mirror effect monorail and double track special effects in video special effects, and makes the realization effect of each kind of special effects in the space domain, and makes some special effects among them, The implementation of compressed domain is discussed. In this paper, through the application of experimental video big data processing technology to the film and television post production of various possibilities, and draw the following conclusions: in extreme cases, the stunt tree becomes a one-dimensional linear structure, using traditional parallel topology sorting, the device parallelism is 0, and using pipeline scheduling, because of breaking the sequence between frames, can play a relatively large parallelism. © Published under licence by IOP Publishing Ltd.

关键词： Big data

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