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Information Classification and Extraction on Official Web Pages of Organizations

computers, Materials & Continua 2020年第9期64卷 2057-2073页

作者： Jinlin Wang Xing Wang Hongli Zhang Binxing Fang Yuchen Yang Jianan Liu School of Computer Science and Technology Harbin Institute of TechnologyHarbin150006China China Electronic Equipment System Engineering Company Beijing100039China

As a real-time and authoritative source,the official Web pages of organizations contain a large amount of *** diversity of Web content and format makes it essential for pre-processing to get the unified attributed data,which has the value of organizational analysis and *** existing research on dealing with multiple Web scenarios and accuracy performance is *** paper aims to propose a method to transform organizational official Web pages into the data with *** locating the active blocks in the Web pages,the structural and content features are proposed to classify information with the specific *** extraction methods based on trigger lexicon and LSTM(Long Short-Term Memory)are proposed,which efficiently process the classified information and extract data that matches the ***,an accurate and efficient method to classify and extract information from organizational official Web pages is *** results show that our approach improves the performing indicators and exceeds the level of state of the art on real data set from organizational official Web pages.

关键词： Web pre-process feature classification data extraction trigger lexicon LSTM

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An Evolutionary Network Architecture Search Framework with Adaptive Multimodal Fusion for Hand Gesture Recognition

SSRN

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SSRN 2024年

作者： Xia, Yizhang Xu, Junwen Hou, Zhanglu Song, Shihao Zou, Juan Liu, Yuan Yang, Shengxiang School of Computer Science Xiangtan University Hunan Province Xiangtan411105 China Hunan Engineering Research Center of Intelligent System Optimization and Security Xiangtan University Hunan Province Xiangtan411105 China School of Computer Science and Informatics De Montfort University Leicester LeicesterLE1 9BH United Kingdom

Hand gesture recognition (HGR) based on multimodal data has attracted considerable attention due to its potential in various practical applications. Although manually designed multimodal deep networks for multimodal HGR (MHGR) have shown promising results, they often require extensive expertise and time-consuming manual adjustments. To overcome these challenges, we propose an evolutionary network architecture search framework with adaptive multimodal fusion (AMF-ENAS), which automates the design process of multimodal deep networks. Our framework incorporates a novel multimodal fusion strategy that optimizes both the positioning of fusion nodes and the fusion ratios between different branches of the network. It effectively captures the characteristics of data at shallow and deep layers while also considering the varying importance of different data streams. Furthermore, we introduce an encoding strategy for multimodal data adaptation, structuring the encoding space into three functional components: fusion points, fusion ratios, and block selection. This encoding strategy enables flexible customization of the network architecture, with the evolutionary algorithm iteratively searching for optimal configurations across different multimodal datasets. To our knowledge, this is the first application of ENAS in MHGR to directly address the challenges of determining fusion positions and ratios in multimodal data. Experimental results confirm that AMF-ENAS achieves state-of-the-art performance on the Ninapro DB2, DB3, and DB7 datasets. © 2024, The Authors. All rights reserved.

关键词： Gesture recognition

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GrabDAE: An Innovative Framework for Unsupervised Domain Adaptation Utilizing Grab-Mask and Denoise Auto-Encoder

arXiv

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arXiv 2024年

作者： Chen, Junzhou Wen, Xuan Zhang, Ronghui Ren, Bingtao Wu, Di Xu, Zhigang Wang, Danwei The Guangdong Provincial Key Laboratory of Intelligent Transport System School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510275 China The School of Transportation Science and Engineering Beihang University State Key Lab of Intelligent Transportation System Beijing100191 China The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China The Guangdong Key Laboratory of Big Data Analysis and Processing Guangdong510006 China The School of Information Engineering Chang’an University Shaanxi Xi’an710064 China The School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore

Unsupervised Domain Adaptation (UDA) aims to adapt a model trained on a labeled source domain to an unlabeled target domain by addressing the domain shift. Existing Unsupervised Domain Adaptation (UDA) methods often fall short in fully leveraging contextual information from the target domain, leading to suboptimal decision boundary separation during source and target domain alignment. To address this, we introduce GrabDAE, an innovative UDA framework designed to tackle domain shift in visual classification tasks. GrabDAE incorporates two key innovations: the Grab-Mask module, which blurs background information in target domain images, enabling the model to focus on essential, domain-relevant features through contrastive learning;and the Denoising Auto-Encoder (DAE), which enhances feature alignment by reconstructing features and filtering noise, ensuring a more robust adaptation to the target domain. These components empower GrabDAE to effectively handle unlabeled target domain data, significantly improving both classification accuracy and robustness. Extensive experiments on benchmark datasets, including VisDA-2017, OfficeHome, and Office31, demonstrate that GrabDAE consistently surpasses state-of-the-art UDA methods, setting new performance benchmarks. By tackling UDA’s critical challenges with its novel feature masking and denoising approach, GrabDAE offers both significant theoretical and practical advancements in domain adaptation. © 2024, CC BY-NC-SA.

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Parallelism or Fairness? How to Be Friendly for SSDs in Cloud Environments

Parallelism or Fairness? How to Be Friendly for SSDs in Clou...

引用

IEEE International Conference on Cluster Computing

作者： Yang Zhou Fang Wang Zhan Shi Dan Feng Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of data storage systems and Technology Ministry of Education of China Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350358711

ISBN: (纸本)9798350358728

Modern SSDs achieve low latency and high throughput by utilizing multiple levels of SSD parallelism. Fairness is also a critical design consideration in cloud environments and has inspired great interest in recent years to study it. However, we find that excessive striving for SSD parallelism can inadvertently harm the SSD fairness due to the striped data layout. We observe from our experimental analysis that some common access patterns in cloud loads (e.g., high-intensity I/O) are not addressed by SSD parallelism, which can affect both SSD parallelism and fairness. In this paper, we analyze two address mapping policies with orthogonal characteristics from SSD parallelism and fairness perspectives. Based on extensive experiments and observations, we schedule a hybrid address mapping policy to achieve fairness of SSDs in cloud environments by distinguishing different I/O access modes and cold/hot data, while ensuring SSD performance. Experimental results on the latest block-level I/O traces show that our approach significantly improves performance and fairness by up to 77.3% and 62.8%, respectively, over the previous schemes with negligible cost. Meanwhile, it can also reduce the write amplification by 23.5% to 33.5%.

关键词： Schedules Layout Interference Parallel processing Predictive models Throughput Data models Resource management Decision trees Low latency communication

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion

arXiv

引用

arXiv 2024年

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024, CC BY.

关键词： Color image processing

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Improving Vision Transformers with Nested Multi-head Attentions

Improving Vision Transformers with Nested Multi-head Attenti...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Jiquan Peng Chaozhuo Li Yi Zhao Yuting Lin Xiaohan Fang Jibing Gong School of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Microsoft Research Asia Beijing China

Vision transformers have significantly advanced the field of computer vision in recent years. The cornerstone of these transformers is the multi-head attention mechanism, which models interactions between visual elements within a feature map. However, the vanilla multi-head attention paradigm independently learns parameters for each head, which ignores crucial interactions across different attention heads and may result in redundancy and under-utilization of the model’s capacity. To enhance model expressiveness, we propose a novel nested attention mechanism, Ne-Att, that explicitly models cross-head interactions via a hierarchical variational distribution. We conducted extensive experiments on image classification, and the results demonstrate the superiority of Ne-Att.

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Video key concept extraction using Convolution Neural Network

Video key concept extraction using Convolution Neural Networ...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Tanvir H Sardar Ruhul Amin Hazarika Bishwajeet Pandey Guru Prasad M S Sk Mahmudul Hassan Radhakrishna Dodmane Hardik Gohel Dept. of Computer Science & Engg School of Technology (GST) GITAM University Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru India Department of Intelligent System and Cyber Security Astana IT University Kazakhstan Dept. of Computer Science & Engg. Graphic Era (Deemed to be University) Dehradun India School of Computer Science and Engineering VIT-AP India Department of Computer Science & Engg. NMAM Institute of Technology Nitte India Department of CSE University of Houston Victoria USA

Objectives: This work aims to develop an automated video summarising methodology and timestamping that uses natural language processing (NLP) tools to extract significant video ***: The methodology comprises extracting the audio from the video, splitting it into chunks by the size of the pauses, and transcribing the audio using Google's speech recognition. The transcribed text is tokenised to create a summary, sentence and word frequencies are calculated, and the most relevant sentences are selected. The summary quality is assessed using ROUGE criteria, and the most important keywords are extracted from the transcript using ***: Our proposed method successfully extracts key points from video lectures and creates text summaries. Timestamping these key points provides valuable context and facilitates navigation within the lecture. Our method combines video-to-text conversion and text summarisation with timestamping key concepts, offering a novel approach to video lecture analysis. Existing video analysis methods focus on keyword extraction or summarisation, while our method offers a more comprehensive approach. Our timestamped key points provide a unique feature compared to other methods. Our method enhances existing video reports by (i) providing concise summaries of key concepts and (ii) enabling quick access to specific information through timestamps. (iii) Facilitating information retrieval through a searchable index. Further research directions: (i) Improve the accuracy of the multi-stage processing pipeline. (ii) Develop techniques to handle diverse accents and pronunciations. (iii) Explore applications of the proposed method to other video genres and ***/Improvements: This approach is practical in giving accurate video summaries, saving viewers time and effort when comprehending the main concepts presented in a video.

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Research on high-precision silicon-based integrated optical temperature sensor

Research on high-precision silicon-based integrated optical ...

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2021 Applied Optics and Photonics China: Optoelectronics and Nanophotonics, AOPC 2021

作者： Wang, Yimeng Shu, Haowen Han, Xiuyou School of Optoelectronic Engineering and Instrumentation Science Dalian University of Technology Dalian116024 China State Key Laboratory of Advanced Optical Communications System and Networks Department of Electronics School of Electronics Engineering and Computer Science Peking University Beijing100871 China

ISBN: (纸本)9781510649996

Integrated optical temperature sensor nowadays has attracted extensive interests thanks to its merits of high accuracy, electromagnetic immunity, and compact size compared with the traditional solutions. So far, most of the state-of-art integrated optical temperature sensors work by spectrum detection of a resonant cavity, which has certain limitations in terms of response time and cost, and it is difficult to achieve high level integration of the overall system. To solve the above problems, in this paper we use silicon-based integrated micro-ring array technology for high-precision real-time temperature sensing. By constructing a monotonic relationship between the output light intensity of the micro-ring array with the variation of the temperature, rapid and high-precision temperature measurement could be realized. Depending on different practical scenarios, the system can be customized to adapt specific temperature measurement range and resolution. Through the optimized design, the system accuracy could be better than 60mK covering temperature range of 297K∼415K, with response time better than 20μs. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Temperature measurement

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General Impedance Modeling for Modular Multilevel Converter with Grid-forming and Grid-following Control

arXiv

引用

arXiv 2024年

作者： Sun, Chu Zhang, Fei Xiao, Huafeng Wang, Na Chen, Jikai The School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden The School of Electrical Engineering Southeast University Nanjing210096 China The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China

Modular multilevel converter (MMC) has complex topology, control architecture and broadband harmonic spectrum. For this, linear-time-periodic (LTP) theory, covering multi-harmonic coupling relations, has been adopted for MMC impedance modeling recently. However, the existing MMC impedance models usually lack explicit expressions and general modeling procedure for different control strategies. To this end, this paper proposes a general impedance modeling procedure applicable to various power converters with grid-forming and grid-following control strategies. The modeling is based on a unified representation of MMC circuit as the input and output relation between the voltage or current on the AC side and the exerted modulation index, while the control part vice versa, thereby interconnected as closed-loop feedback. With each part expressed as transfer functions, the final impedance model keeps the explicit form of harmonic transfer function matrix, making it convenient to directly observe and analyze the influence of each part individually. Thereby the submodule capacitance is found as the main cause of difference between MMC impedance compared to two-level converter, which will get closer as the capacitance increases. Effectiveness and generality of the impedance modeling method is demonstrated through comprehensive comparison with impedance scanning using electromagnetic transient simulation. Copyright © 2024, The Authors. All rights reserved.

关键词： Harmonic analysis

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Formal Verification of Interrupt Isolation for the TrustZone-based TEE

Formal Verification of Interrupt Isolation for the TrustZone...

引用

Asia-Pacific Conference on Software engineering

作者： Leping Zhang Qianying Zhang Xinyue Wang Ximeng Li Guohui Wang Zhiping Shi Yong Guan College of Information Engineering Capital Normal University Beijing China School of Computer Science and Engineering Beihang University Beijing China Beijing Engineering Research Center of High Reliable Embedded System Beijing China Beijing Key Laboratory of Electronic System Reliability Technology Beijing China

ARM TrustZone is a hardware security technology commonly used to implement the Trusted Execution Environment (TEE), which is a hardware-based isolated execution environment in which security-critical software can execute without interference and is widely applied to embedded systems. Isolation of interrupts in the two security domains of TrustZone is an important security mechanism that plays a vital role in protecting the execution of the TEE. This paper introduces a formal modeling and verification approach for the interrupt isolation mechanism of the TEE based on TrustZone. We propose a model formalizing the TrustZone-based TEE system at the instruction level, which focuses on modeling system behaviors of interrupt handling and captures crucial instructions related to interrupt isolation. We formally define three types of properties for the model: correctness, safety, and information flow security. The verification results in the theorem prover Isabelle/HOL show that the model satisfies the above properties, which indicates that the interrupt isolation mechanism of TrustZone correctly enforces the isolation of interrupts and protects the TEE from being interfered with non-secure interrupts.

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