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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Su, Jian Liao, Zhenlong Mao, Qiankun Sheng, Zhengguo Liu, Alex X. Nanjing University of Information Science and Technology The School of Software Nanjing China Zhejiang Wanli University The Hwamei College of Life and Health Sciences Ningbo China Nanjing University of Information Science and Technology The School of Computer Nanjing China University of Sussex The Department of Engineering and Design Brighton United Kingdom Michigan State University The Department of Computer Science and Engineering East Lansing United States

ISBN: (纸本)9798350330717

With the rapid development of the Internet of Things (IoT) related technologies, the application of digital twins (DT) in industry and healthcare becomes possible. Human activity recognition (HAR) is emerging as a hot research area with great potential in healthcare. Activity recognition systems combined with DT will make it easier to monitor human health conditions to improve the quality of life and happiness with individualized healthcare. In this paper, we design an effective HAR system, called HAR-Net, which uses WiFi time series data collected by sensors to train a deep learning network. Deep learning's great learning ability is utilized to extract features of various human activities for activity recognition. We built the DT system with Unity, which is combined with the HAR system. In the DT system, real-world physical activities are mapped onto human models. The results of activity prediction can be evaluated in real-time in DT, and warnings can be issued quickly when dangerous activities occur. To make our human activity recognition system more adaptive, we propose a one-shot recognition method based on meta-learning. Specifically, we design a Bi-path basic network that extracts features in the time-domain and frequency-domain, and a meta-learning framework with a classification module and a WiFi metric module. Using datasets from different environments, we conducted various experiments on HAR-Net, and the results proved that our presented method was superior to the baseline network. © 2023 IEEE.

关键词： Digital Twin Healthcare Human Activity Recognition Meta-Learning WiFi

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A Document-level Event Extraction Method Based on Mogrifier LSTM 4

A Document-level Event Extraction Method Based on Mogrifier ...

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4th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2022

作者： Ge, Junwei Qin, Zhixiang Fang, Yiqiu School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781450398343

Element dispersion is a difficulty for document-level event extraction. While classic LSTM lacks the capability to interact between input and context while collecting long sequence features, previous document-level event extraction utilizes the entire document as input, leaving the sequence features of the document devoid of deeper contextual information. This paper suggests a document-level event extraction strategy based on Mogrifier LSTM to solve this issue. We divide the text into multiple paragraphs and then input each paragraph individually into the Mogrifier LSTM. To increase the context modeling capability of lengthy sequence text, the upgraded LSTM will allow the input of the present moment and the output of the preceding moment to be computed several times initially. Then an attention mechanism is introduced to capture the internal correlation of each paragraph and integrate the contextual semantics of each paragraph. Finally, sequence annotation is used to extract dispersed event elements and match event types. According to the experimental results on Chinese financial dataset, the method in this paper can effectively solve the problems of loss of depth information and scattered theoretical elements of long sequence features of documents, and improve the effectiveness of document-level event extraction. © 2022 ACM.

关键词： Semantics

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Face Age Estimation Based on CSLBP and Lightweight Convolutional Neural Network

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computers, Materials & Continua 2021年第11期69卷 2203-2216页

作者： Yang Wang Ying Tian Ou Tian School of Computer Science and Software Engineering University of Science&Technology LiaoningAnshan114051China Medical and Health Sciences University of WollongongNSW2522Australia

As the use of facial attributes continues to expand,research into facial age estimation is also *** face images are easily affected by factors including illumination and occlusion,the age estimation of faces is a challenging *** paper proposes a face age estimation algorithm based on lightweight convolutional neural network in view of the complexity of the environment and the limitations of device computing *** face age estimation based on Soft Stagewise Regression Network(SSR-Net)and facial images,this paper employs the Center Symmetric Local Binary Pattern(CSLBP)method to obtain the feature image and then combines the face image and the feature image as network input *** feature images to the convolutional neural network can improve the accuracy as well as increase the network model *** experimental results on IMDB-WIKI and MORPH 2 datasets show that the lightweight convolutional neural network method proposed in this paper reduces model complexity and increases the accuracy of face age estimations.

关键词： Face age estimation lightweight convolutional neural network CSLBP SSR-Net

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Adaptive information fusion network for multi-modal personality recognition

Adaptive information fusion network for multi-modal personal...

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作者： Bao, Yongtang Liu, Xiang Qi, Yue Liu, Ruijun Li, Haojie School of Computer Science and Engineering Shandong University of Science and Technology Qingdao China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Virtual Reality Research Institute Beihang University Qingdao Research Institute Qingdao China School of Software Beihang University Beijing China

Personality recognition is of great significance in deepening the understanding of social relations. While personality recognition methods have made significant strides in recent years, the challenge of heterogeneity between modalities during feature fusion still needs to be solved. This paper introduces an adaptive multi-modal information fusion network (AMIF-Net) capable of concurrently processing video, audio, and text data. First, utilizing the AMIF-Net encoder, we process the extracted audio and video features separately, effectively capturing long-term data relationships. Then, adding adaptive elements in the fusion network can alleviate the problem of heterogeneity between modes. Lastly, we concatenate audio-video and text features into a regression network to obtain Big Five personality trait scores. Furthermore, we introduce a novel loss function to address the problem of training inaccuracies, taking advantage of its unique property of exhibiting a peak at the critical mean. Our tests on the ChaLearn First Impressions V2 multi-modal dataset show partial performance surpassing state-of-the-art networks. © 2024 John Wiley & Sons Ltd.

关键词： Information fusion

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Emotional Synchronization for Audio-Driven Talking-Head Generation

Emotional Synchronization for Audio-Driven Talking-Head Gene...

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IEEE International Conference on Data Mining (ICDM)

作者： Zhao Zhang Yan Luo Zhichao Zuo Richang Hong Yi Yang Meng Wang School of Computer Science and Information Engineering University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunan China School of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Audio-driven talking-head synthesis has become a significant focus in the field of virtual human applications. However, existing methodologies face challenges in effectively synchronizing audio and video, especially in maintaining emotional consistency. Additionally, there is a notable inefficiency in leveraging emotional prompts to guide expression generation. To address these limitations, this paper introduces an Emotion Synchronized audio-driven Talking-head synthesis (EST) approach. The EST approach aims to enhance the emotion-agnostic talking-head models by enabling emotion control, and it incorporates a diffusion module to learn diverse latent rep-resentations. Furthermore, EST utilizes null-text embedding to align the latent code with emotional prompts. Additionally, a novel Sync Attention Block (SAB) is developed to broaden the spatial perceptual field, thus preventing the loss of critical information. Extensive experiments demonstrate the effectiveness of the EST method, showcasing state-of-the-art performance across widely-adopted datasets. Moreover, the EST approach exhibits exceptional generalization capabilities, even in scenarios where emotional training videos are unavailable.

关键词： Training Codes Accuracy Digital humans Synchronization Data mining Faces Testing

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XSema: A Novel Framework for Semantic Extraction of Cross-chain Transactions

arXiv

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arXiv 2024年

作者： Zheng, Ziye Wu, Jiajing Lin, Dan Li, Quanzhong Ruan, Na School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Software Engineering Sun Yat-sen University Zhuhai China School of Computer Science and Engineering Shanghai Jiaotong University Shanghai China

As the number of blockchain platforms continues to grow, the independence of these networks poses challenges for transferring assets and information across chains. Cross-chain bridge technology has emerged to address this issue, establishing communication protocols to facilitate cross-chain interaction of assets and information, thereby enhancing user experience. However, the complexity of cross-chain transactions increases the difficulty of security regulation, rendering traditional single-chain detection methods inadequate for cross-chain scenarios. Therefore, understanding cross-chain transaction semantics is crucial, as it forms the foundation for cross-chain security detection tasks. Although there are existing methods for extracting transaction semantics specifically for single chains, these approaches often overlook the unique characteristics of cross-chain scenarios, limiting their applicability. This paper introduces XSema, a novel cross-chain semantic extraction framework grounded in asset transfer and message-passing, designed specifically for cross-chain contexts. Experimental results demonstrate that XSema effectively distinguishes between cross-chain and non-cross-chain transactions, surpassing existing methods by over 9% for the generality metric and over 10% for the generalization metric. Furthermore, we analyze the underlying asset transfer patterns and message-passing event logs associated with cross-chain transactions. We offer new insights into the coexistence of multiple blockchains and the cross-chain ecosystem. Copyright © 2024, The Authors. All rights reserved.

关键词： Blockchain

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Dina: Deformable Interaction Analogy

SSRN

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SSRN 2024年

作者： Huang, Zeyu Dai, Sisi Xu, Kai Zhang, Hao Huang, Hui Hu, Ruizhen College of Computer Science and Software Engineering Shenzhen University China School of Computer Science National University of Defense Technology China School of Computing Science Simon Fraser University Canada

We introduce Deformable Interaction Analogy (DINA) to create similar interactions between 3D objects. Our goal is to generate multiple analogous 3D interactions between an anchor object and different target objects based on a single demo interaction. To achieve this, we optimize the anchor object's pose or shape to mimic the demo interaction with new target objects. We propose using an Interaction Interface (ITF) as a descriptive and robust representation of interactions. For interaction analogy, we transform the ITF to guide feature matching during the anchor object's repositioning and deformation. Qualitative and quantitative experiments demonstrate the effectiveness of our ITF-guided deformable interaction analogy, even when compared to state-of-the-art methods that utilize more complex interaction representations and feature learning from large datasets. © 2024, The Authors. All rights reserved.

关键词： Large datasets

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An optimal container update method for edge-cloud collaboration

An optimal container update method for edge-cloud collaborat...

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作者： Zhang, Haotong Lin, Weiwei Xie, Rong Li, Shenghai Dai, Zhiyan Wang, James Z. School of Software Engineering South China University of Technology Guangzhou China School of Computer Science & Engineering South China University of Technology Guangzhou China Department of New Network Technologies Peng Cheng Laboratory Shenzhen China School of Information Guangdong University of Finance & Economics Guangzhou China School of Computing Clemson University ClemsonSC United States

Emerging computing paradigms provide field-level service responses for users, for example, edge computing, fog computing, and MEC. Edge virtualization technologies represented by Docker can provide a platform-independent, low-resource-consumption operating environment for edge service. The image-pulling time of Docker is a crucial factor affecting the start-up speed of edge services. The layer reuse mechanism of native Docker cannot fully utilize the duplicate data of node local images. In this paper, we propose a chunk reuse mechanism (CRM), which effectively targets node-local duplicate data during container updates and reduces the volume of data transmission required for image building. We orchestrate the CRM process for cloud and remote-cloud nodes to ensure that the resource overhead from container update data preparation and image reconstruction is within an acceptable range. The experimental results show that the CRM proposed in this paper can effectively utilize the node local duplicate data in the synchronous update of containers in multiple nodes, reduce the volume of data transmission, and significantly improve container update efficiency. © 2023 John Wiley & Sons, Ltd.

关键词： Containers

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Akane: Perplexity-Guided Time Series Data Cleaning

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Proceedings of the ACM on Management of Data 2024年第3期2卷 1-26页

作者： Xiaoyu Han Haoran Xiong Zhenying He Peng Wang Chen Wang X. Sean Wang School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China National Engineering Research Center for Big Data Software EIRI Tsinghua University Beijing China

Dirty data are prevalent in time series, such as energy consumption or stock data. Existing data cleaning algorithms present shortcomings in dirty data identification and unsatisfactory cleaning decisions. To handle these drawbacks, we leverage inherent recurrent patterns in time series, analogize them as fixed combinations in textual data, and incorporate the concept of perplexity. The cleaning problem is thus transformed to minimize the perplexity of the time series under a given cleaning cost, and we design a four-phase algorithmic framework to tackle this problem. To ensure the framework's feasibility, we also conduct a brief analysis of the impact of dirty data and devise an automatic budget selection strategy. Moreover, to make it more generic, we additionally introduce advanced solutions, including an ameliorative probability calculation method grounded in the homomorphic pattern aggregation and a greedy-based heuristic algorithm for resource savings. Experiments on 12 real-world datasets demonstrate the superiority of our methods.

关键词： data cleaning perplexity recurrent patterns time series

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Bias Unveiled: Investigating Social Bias in LLM-Generated Code

arXiv

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arXiv 2024年

作者： Ling, Lin Rabbi, Fazle Wang, Song Yang, Jinqiu Computer Science and Software Engineering Concordia University Montreal Canada Lassonde School of Engineering York University Toronto Canada

Large language models (LLMs) have significantly advanced the field of automated code generation. However, a notable research gap exists in evaluating social biases that may be present in the code produced by LLMs. To solve this issue, we propose a novel fairness framework, i.e., Solar, to assess and mitigate the social biases of LLM-generated code. Specifically, Solar can automatically generate test cases for quantitatively uncovering social biases of the auto-generated code by LLMs. To quantify the severity of social biases in generated code, we develop a dataset that covers a diverse set of social problems. We applied Solar and the crafted dataset to four state-of-the-art LLMs for code generation. Our evaluation reveals severe bias in the LLM-generated code from all the subject LLMs. Furthermore, we explore several prompting strategies for mitigating bias, including Chain-of-Thought (CoT) prompting, combining positive role-playing with CoT prompting and dialogue with Solar. Our experiments show that dialogue with Solar can effectively reduce social bias in LLM-generated code by up to 90%. Last, we make the code and data publicly available is highly extensible to evaluate new social problems. Copyright © 2024, The Authors. All rights reserved.

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