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International Conference on Electronics Information and Emergency Communication

作者： Zan Yang Dan Li Wei Nai Faculty of Science Tongji Zhejiang College Jiaxing Zhejiang Province China Department of Electronic and Information Engineering Tongji Zhejiang College Jiaxing Zhejiang Province China

t-distributed stochastic neighbor embedding (t-SNE), as a type of dimensionality reduction algorithm, is usually used to visualize and explore high-dimensional data. By comparing with other dimensionality reduction algorithms, t-SNE has the best effect for data visualization. If it is applied to n-dimensional data, intelligent mapping can be successfully done from n-dimensional data to 3-dimensional data, or even 2-dimensional data, and the probability distribution of the original data can be properly kept even after the dimensionality reduction process. t-SNE is not a linear dimensionality reduction technique, and it is good at processing nonlinear structural data, thus, it is easy for t-SNE algorithm to obtain complex manifold structures from high-dimensional data. Usually, t-SNE does not have a unique optimal solution, and the number of iterations and the convergence of its objective function are the main factors which can affect its visualization effect. Generally, gradient descent (GD) method is used to solve the problem of minimum Kullback-Leiber (KL) divergence of the objective function. However, due to the disadvantage of GD method that can easily fall into local optimal value, in this paper, Halton sequence initialized butterfly optimization algorithm (HSBOA) has been combined with traditional t-SNE algorithm so as to improve the optimization accuracy and convergence speed, and to effectively help t-SNE jump out of the local optimal value trap during the optimization process.

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The NPU-ASLP System for Audio-Visual Speech Recognition in MISP 2022 Challenge

The NPU-ASLP System for Audio-Visual Speech Recognition in M...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Pengcheng Guo He Wang Bingshen Mu Ao Zhang Peikun Chen Audio Speech and Language Processing Group (ASLP@NPU) School of Computer Science Northwestern Polytechnical University Xian China

This paper describes our NPU-ASLP system for the Audio-Visual Diarization and Recognition (AVDR) task in the Multi-modal Information based Speech Processing (MISP) 2022 Challenge. Specifically, the weighted prediction error (WPE) and guided source separation (GSS) techniques are used to reduce reverberation and generate clean signals for each single speaker first. Then, we explore the effectiveness of Branchformer and E-Branchformer based ASR systems. To better make use of the visual modality, a cross-attention based multi-modal fusion module is proposed, which explicitly learns the contextual relationship between different modalities. Experiments show that our system achieves a concatenated minimum-permutation character error rate (cpCER) of 28.13% and 31.21% on the Dev and Eval set, and obtains a second place in the challenge.

关键词： visualization Source separation Error analysis Speech recognition data processing data models Reverberation

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A Host Kernel-Based Approach for Tracing and Analyzing vCPUs in Virtual Machines

A Host Kernel-Based Approach for Tracing and Analyzing vCPUs...

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ieee Conference on Global Communications (GLOBECOM)

作者： Ravjot Singh Prakhar Gupta Naman Jain Neetesh Kumar Pravendra Singh Computer Science and Engineering Indian Institute of Technology Roorkee IIT Roorkee

Virtual machines (VMs) are a crucial technology for cloud computing, permitting multiple operating systems to run on a single physical host. Here, we propose a method for analyzing the performance of virtual machines in a cloud computing environment. The current approach of tracing both the host and virtual machines can be inefficient and may not be feasible for various reasons, such as security and privacy issues. We propose a host-only kernel tracing technique that uses only trace data generated on the host system. The trace data is then analyzed using the TraceCompass tool and two algorithms: Virtual CPU State Finder (VSF) and State Time Analysis (STA). The VSF algorithm generates a graphical view of the state of each thread running inside virtual machines for the identification of latency and performance degradation. The STA algorithm calculates the amount of time each virtual CPU spends in different states, providing important insights into the resource utilization and performance of virtual resources. In addition, our usage of the Trace Compass tool and EASE scripting allows more manageable analysis and visualization of the tracing data, making it accessible to a broader range of users. This method provides cloud providers with valuable information to maintain QoS and SLA parameters and improve the overall performance of running VMs.

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The DKU Post-Challenge Audio-Visual Wake Word Spotting System for the 2021 MISP Challenge: Deep Analysis

The DKU Post-Challenge Audio-Visual Wake Word Spotting Syste...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haoxu Wang Ming Cheng Qiang Fu Ming Li School of Computer Science Wuhan University Wuhan China Data Science Research Center Duke Kunshan University Kunshan China Alibaba Group China

This paper further explores our previous wake word spotting system ranked 2-nd in Track 1 of the MISP Challenge 2021. First, we investigate a robust unimodal approach based on 3D and 2D convolution and adopt the simple attention module (SimAM) for our system to improve performance. Second, we explore different combinations of data augmentation methods for better performance. Finally, we study the fusion strategies, including score-level, cascaded and neural fusion. Our proposed multimodal system leverages multimodal features and uses the complementary visual information to mitigate the performance degradation of audio-only systems in complex acoustic scenarios. Our system obtains a false reject rate of 2.15% and a false alarm rate of 3.44% in the evaluation set of the competition database, which achieves the new state-of-the-art performance by 21% relative improvement compared to previous systems. Related resource can be found at: https://***/Mashiro009/DKU_WWS_MISP.

关键词： Degradation visualization Solid modeling Three-dimensional displays Convolution databases Semantics

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Research on Digital Mining Model Algorithm of Computer 3D science Knowledge Graph visualization Technology

Research on Digital Mining Model Algorithm of Computer 3D Sc...

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International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Nan Zhang Zhen Sun Yingnan Shang State Grid Electric Power Research Institute NARI Group Co. Ltd Nanjing China

Through the visibility of knowledge map and the use of map visibility to express the correlation between different types of information. Firstly, the paper mines the multimodal data related to the field of computer science and establishes a more systematic multimodal knowledge base. Combined with association extraction rules, association extraction was carried out. A multimodal computer domain knowledge map which can be automatically extracted from relational ternary associations is constructed. The simulation results show that this method can be used to integrate academic resources in a deep level. It provides advanced knowledge services to the online community.

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Pragmatic Domestic Electrical Load Disaggregation

Pragmatic Domestic Electrical Load Disaggregation

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ieee Annual Computing and Communication Workshop and Conference (CCWC)

作者： Sudarshan S. Chawathe School of Computing and Information Science & Climate Change Institute University of Maine Orono Maine USA

This paper studies methods for determining the major electrical loads that contribute to aggregate electric energy consumption for a household or similar unit. It provides an alternate formulation of a well studied general problem and a framework and prototype implementation to address it. The focus is on methods that do not require any instrumentation or data beyond hourly (or similar low frequency) records of aggregate energy consumption, as is often easily available from power utility companies due to the increasing prevalence of smart meters. As well, the focus is on pragmatic approaches that are likely to provide useful information for typical household electricity consumption in contrast to methods more suited to industrial environments. Another notable feature is that disaggregation is performed not centrally at the utility company or similar entity with data from a large number of households but instead in a distributed and independent manner at each household. This feature provides two key benefits: (1) It permits the injection of information known to a household but not (easily) by others in order to simplify the problem. (2) It provides better privacy protections for such data.

关键词： Energy consumption data privacy Aggregates data visualization Prototypes Companies Smart meters

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Crime Analysis Framework for Predicting Criminal Behavioral Patterns with Machine Learning

Crime Analysis Framework for Predicting Criminal Behavioral ...

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Renewable Energy and Sustainable E-Mobility Conference (RESEM), ieee

作者： Sham Venkat S Jeberson Retna Raj Arjun A Senduru Srinivasulu Gowri Jabez Department of Information Technology School of Computing Sathyabama Institute of Science and Technology Chennai India

data analytics is critical for businesses and organizations to understand their data's behaviour, patterns and insights. Crime analysis is an important component of controlling crime rates in cities and regions based on historical data. This paper proposes a crime analysis methodology supported by machine learning techniques to predict and analyze criminal behaviour patterns. Machine learning algorithms such as Random Forest, Decision Tree, SVM, XGBoost and Multilayer Perceptron are used on the crime dataset for detailed analysis. The framework consists of three phases: data preprocessing, model training, and prediction and analysis. By analyzing crime data using Kaggle's open-source crime records, our project aims to understand crime patterns, determine the crime's severity, and identify where and when it occurred. We use the abovementioned algorithms to classify crime patterns and compare the performance accurately. Our proposed framework uses various visualization techniques to reveal crime trends and predict crimes using machine learning algorithms. Input features used in the algorithms include time, state, district, year, murder, rape, theft and dowry deaths.

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Octave: An End-User Programming Environment for Analysis of Spatiotemporal data for Construction Students

Octave: An End-User Programming Environment for Analysis of ...

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ieee Symposium on Visual Languages and Human Centric Computing (VL/HCC)

作者： Daniel Manesh Andy Luu Mohammad Khalid Jiangyue Li Chinedu Okonkwo Abiola Akanmu Ibukun Awolusi Homero Murzi Sang Won Lee Department of Computer Science Virginia Tech Blacksburg Virginia Myers-Lawson School of Construction Virginia Tech Blacksburg Virginia School of Civil & Environmental Engineering and Construction Management UTSA San Antonio Texas Department of Engineering Education Virginia Tech Blacksburg Virginia

The construction industry is a new avenue for big data and data science with sensors and cyber-physical systems deployed in the field. Construction students need to develop computational thinking skills to help make sense of this data, but existing data science environments designed with textual programming languages create a significant barrier to entry. To bridge this gap, we introduce Octave, an end-user programming environment designed to help non-expert programmers analyze spatiotemporal data (e.g., as gathered by a GPS sensor) in an interactive graphical user interface. To aid exploration and understanding, Octave's design incorporates a high degree of liveness, highlighting the interconnection between data, computation, and visualization. We share the underlying design principles behind Octave and details about the system design and implementation. To evaluate Octave, we conducted a usability study with students studying construction. The results show that non-programmer construction students were able to learn Octave easily and were able to effectively use it to solve domain-specific problems from construction education. The participants appreciated Octave's liveness and felt they could easily connect it to real-life problems in their field. Our work informs the design of future accessible end-user programming environments for data analysis targeting non-experts.

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PubExplorer:An interactive analytical system for visualizing publication data

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Visual Informatics 2023年第3期7卷 65-74页

作者： Minzhu Yu Yang Wang Xiaomin Yu Guihua Shan Zhong Jin Computer Network Information Center Chinese Academy of SciencesChina University of Chinese Academy of Sciences China

With the intersection and convergence of multiple disciplines and technologies,more and more researchers are actively exploring interdisciplinary cooperation outside their main research *** a new research field,researchers often hope to quickly learn what is being studied in this field,which research points are receiving high attention,which researchers are studying these research points,and then consider the possibility of collaborating with core researchers on these research *** addition,students who are preparing for academic further education usually conduct research on mentors and mentors’research platforms,including academic connections,employment opportunities,*** order to satisfy these requirements,we(1)design a research point state map based on a science map to help researchers and students understand the development state of a new research field;(2)design a bar-link author-affiliation information graph to help researchers and students clarify academic networks of scholars and find suitable collaborators or mentors;(3)designs citation pattern histogram to quickly discover research achievements with high research value,such as the Sleeping Beauty papers,recently hot papers,classic papers and so ***,an interactive analytical system named PubExplorer was implemented with ieee VIS publication data,and its effectiveness is verified through case studies.

关键词： Scientific literature Research points analysis Visual analytics system Publication analysis Text visualization

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An Automated Pipeline to Create an Atlas of in Situ Hybridization Gene Expression data in the Adult Marmoset Brain

An Automated Pipeline to Create an Atlas of in Situ Hybridiz...

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ieee International Symposium on Biomedical Imaging

作者： Charissa Poon Muhammad Febrian Rachmadi Michal Byra Matthias Schlachter Binbin Xu Tomomi Shimogori Henrik Skibbe Brain Image Analysis Unit RIKEN Center for Brain Science Wako Japan Institute of Fundamental Technological Research Polish Academy of Sciences Warsaw Poland EuroMov Digital Health in Motion Univ Montpellier Alès France Lab for Molecular Mechanisms of Brain Development RIKEN Center for Brain Science Wako Japan

We present the first automated pipeline to create an atlas of in situ hybridization gene expression in the adult marmoset brain in the same stereotaxic space. The pipeline consists of segmentation of gene expression from microscopy images and registration of images to a standard space. Automation of this pipeline is necessary to analyze the large volume of data in the genome-wide whole-brain dataset, and to process images that have varying intensity profiles and expression patterns with minimal human bias. To reduce the number of labelled images required for training, we develop a semi-supervised segmentation model. We further develop an iterative algorithm to register images to a standard space, enabling comparative analysis between genes and concurrent visualization with other datasets, thereby facilitating a more holistic understanding of primate brain structure and function.

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