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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Wang, Jiaan Meng, Fandong Lu, Ziyao Zheng, Duo Li, Zhixu Qu, Jianfeng Zhou, Jie Pattern Recognition Center WeChat AI Tencent Inc China School of Computer Science and Technology Soochow University Suzhou China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Beijing University of Posts and Telecommunications Beijing China

We present CLIDSUM, a benchmark dataset towards building cross-lingual summarization systems on dialogue documents. It consists of 67k+ dialogue documents and 112k+ annotated summaries in different target languages. Based on the proposed CLIDSUM, we introduce two benchmark settings for supervised and semi-supervised scenarios, respectively. We then build various baseline systems in different paradigms (pipeline and end-to-end) and conduct extensive experiments on CLIDSUM to provide deeper analyses. Furthermore, we propose mDIALBART which extends mBART via further pre-training, where the multiple objectives help the pre-trained model capture the structural characteristics as well as key content in dialogues and the transformation from source to the target language. Experimental results show the superiority of mDIALBART, as an end-to-end model, outperforms strong pipeline models on CLIDSUM. Finally, we discuss specific challenges that current approaches faced with this task and give multiple promising directions for future research. We have released the dataset and code at https://***/krystalan/ClidSum. © 2022 Association for Computational Linguistics.

关键词： Pipelines

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Deep semantic segmentation and skin disease classification fromdermoscopic images based on a modernized deep learning network with multi-feature extraction

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Australian Journal of Electrical and Electronics Engineering 2024年

作者： Prabu, M. MuthuKumar, S. Sudhahar, T.N. Chakaravarthi, S. Shanmugapriya, N. Mahajan, Rashima Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Tamil Nadu Trichy India Department of Computer Science and Engineering Manav Rachna International Institute of Research and Studies Faridabad India

In this research, an advanced deep learning model is proposed to segment and classify skin diseases. The skin images required for classification purposes are collected from public databases. Skin lesion segmentation is initially performed over collected images using Multi-head Attention-based MobileUNet, which helps locate lesions in dermoscopic images. The segmented images are fed to the feature extraction stage to get more representative features from the images to improve the classification performance. Features like texture, shape, and colour are extracted in this stage. The extracted features are given to the Adaptive Deep Capsule Network to classify the images. The segmented images are directly given to the Adaptive MobileNet to provide classification results. The final decision is made by averaging the classified outcome using the Adaptive Hybrid Network. The performance of classification is further enhanced by tuning the parameters from DCNet and MNet using the Enhanced Secretary Bird Optimisation. The performance validation finally shows the accurate performance in the skin disease classification model. ©, Engineers Australia.

关键词： Semantic Segmentation

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A Novel Framework for Detection of Digital Face Video Manipulation using Deep Learning 3

A Novel Framework for Detection of Digital Face Video Manipu...

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3rd International Conference on Computing and Information Technology, ICCIT 2023

作者： Chandra, Sanskriti Saxena, Shivam Kumar, Santosh Chaube, Mithilesh Kumar Srinivas, K.G. Alsamhi, Saeed Hamood Curry, Edward Saif, Abdu IIIT-Naya Raipur Department of Data Science and Artificial Intelligence Chhattisgarh India IIIT-Naya Raipur Department of Computer Science and Engineering Chhattisgarh India IIIT-Naya Raipur Department of Mathematical Sciences Chhattisgarh India University of Galway Insight Sfi Research Centre for Data Analytics Galway Ireland Faculty of Engineering & Information Technology Taiz Yemen

ISBN: (纸本)9798350321487

Digital face manipulation and classification have recently attracted the attention of academia and industry worldwide. Researchers have developed deep learning and computer vision techniques for detecting face manipulations, and it has become a challenging task to differentiate between authentic and manipulated face images manually. The challenge results in the decline of authenticity in digital media content. In this paper, we propose a framework for the classification of manipulating face images using the EfficientNet learning model. The proposed framework takes four digital facial forgeries: Face-Swap, Face-2-Face, DeepFakes, and neural textures. Multiple manipulation techniques are used to process manipulated faces, such as the Blaze-face tracking method, to determine the locations of the face images and pixel coordinates. The proposed framework is used first to identify the type of face manipulation and then to perform detection of the tampered regions in the face images. The proposed framework provided an automated benchmark that considers all four modification techniques in a realistic situation. The results show that the proposed framework outperforms existing approaches regarding accuracy and efficiency. Furthermore, the proposed framework is suitable for detecting digital face video manipulation in various applications, including forensics and security. © 2023 IEEE.

关键词： Textures

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MTMD: Multi-Scale Temporal Memory Learning and Efficient Debiasing Framework for Stock Trend Forecasting

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IEEE Transactions on Emerging Topics in Computational Intelligence 2025年第3期9卷 2151-2163页

作者： Wang, Mingjie Tian, Juanxi Zhang, Mingze Guo, Jianxiong Jia, Weijia BNU-HKBU United International College Guangdong Key Lab of Ai and Multi-Modal Data Processing Department of Computer Science Zhuhai519087 China Gonzaga University School of Engineering and Applied Sciences SpokaneWA99258 United States Beijing Normal University Advanced Institute of Natural Sciences Zhuhai519087 China BNU-HKBU United International College Guangdong Key Lab of Ai and Multi-Modal Data Processing Zhuhai519087 China

The endeavor of stock trend forecasting is principally focused on predicting the future trajectory of the stock market, utilizing either manual or technical methodologies to optimize profitability. Recent advancements in machine learning technologies have showcased their efficacy in discerning authentic profit signals within the realm of stock trend forecasting, predominantly employing temporal data derived from historical stock price patterns. Nevertheless, the inherently volatile and dynamic characteristics of the stock market render the learning and capture of multi-scale temporal dependencies and stable trading opportunities a formidable challenge. This predicament is primarily attributed to the difficulty in distinguishing real profit signal patterns amidst a plethora of mixed, noisy data. In response to these complexities, we propose a Multi-Scale Temporal Memory Learning and Efficient Debiasing (MTMD) model. This innovative approach encompasses the creation of a learnable embedding coupled with external attention, serving as a memory module through self-similarity. It aims to mitigate noise interference and bolster temporal consistency within the model. The MTMD model adeptly amalgamates comprehensive local data at each timestamp while concurrently focusing on salient historical patterns on a global scale. Furthermore, the incorporation of a graph network, tailored to assimilate global and local information, facilitates the adaptive fusion of heterogeneous multi-scale data. Rigorous ablation studies and experimental evaluations affirm that the MTMD model surpasses contemporary state-of-the-art methodologies by a substantial margin in benchmark datasets. © 2025 IEEE.

关键词： Profitability

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Talk With Human-like Agents: Empathetic Dialogue Through Perceptible Acoustic Reception and Reaction

arXiv

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arXiv 2024年

作者： Yan, Haoqiu Zhu, Yongxin Zheng, Kai Liu, Bing Cao, Haoyu Jiang, Deqiang Xu, Linli School of Computer Science and Technology University of Science and Technology China School of Data Science University of Science and Technology China State Key Laboratory of Cognitive Intelligence China Tencent Youtu Lab China

Large Language Model (LLM)-enhanced agents become increasingly prevalent in Human-AI communication, offering vast potential from entertainment to professional domains. However, current multi-modal dialogue systems overlook the acoustic information present in speech, which is crucial for understanding human communication nuances. This oversight can lead to misinterpretations of speakers' intentions, resulting in inconsistent or even contradictory responses within dialogues. To bridge this gap, in this paper, we propose PerceptiveAgent, an empathetic multi-modal dialogue system designed to discern deeper or more subtle meanings beyond the literal interpretations of words through the integration of speech modality perception. Employing LLMs as a cognitive core, PerceptiveAgent perceives acoustic information from input speech and generates empathetic responses based on speaking styles described in natural language. Experimental results indicate that PerceptiveAgent excels in contextual understanding by accurately discerning the speakers' true intentions in scenarios where the linguistic meaning is either contrary to or inconsistent with the speaker's true feelings, producing more nuanced and expressive spoken dialogues. Code is publicly available at: https://***/Haoqiu-Yan/PerceptiveAgent. Copyright © 2024, The Authors. All rights reserved.

关键词： Speech communication

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UniSleepPos: Sleep Posture Identification System Utilizing Millimeter-wave Radar

UniSleepPos: Sleep Posture Identification System Utilizing M...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Kun Li, Min He, Chu Tian, Xu Mao, Junbin Zeng, Min Liu, Jin Xinjiang University Xinjiang Engineering Research Center of Big Data and Intelligent Software School of Software Urumqi China Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350386226

Sleep posture identification is crucial for accurately assessing sleep quality and diagnosing related diseases. In the realm of non-intrusive sleep monitoring, non-contact technologies are becoming increasingly mainstream. Millimeter-wave radar is frequently utilized in sleep posture identification due to its high resolution, strong penetration, and excellent sensitivity. However, traditional radar-based methods for sleep posture identification often struggle with reliability when dealing with diverse individuals and complex sleep environments. To address these challenges, we propose UniSleepPos, which designs a novel dual-view fusion mechanism to integrate depression and elevation angle signals obtained from radar, thus accurately capturing the posture information of the monitored subject in three-dimensional space. Furthermore, we combine sleep posture identification with individual characteristics, utilizing existing individual labels as prior knowledge to assist in sleep posture identification. The integration of prior knowledge provides a valuable information source for the model, helping to enhance its understanding of the data and improve its performance. We collected sleep posture data from eight volunteers using millimeter-wave radar devices under various environmental conditions. Leave-one-subject-out experiments were conducted to validate the effectiveness of UniSleepPos. The results indicated that UniSleepPos significantly outperforms existing methods, demonstrating its potential for practical applications. © 2024 IEEE.

关键词： Sleep research

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Fine-Grained Access Control Proxy Re-encryption with HRA Security from Lattice 18th

Fine-Grained Access Control Proxy Re-encryption with HRA Se...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Hou, Jinqiu Peng, Changgen Tan, Weijie Zhong, Chongyi Niu, Kun Li, Hu State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Key Laboratory of Advanced Manufacturing Technology Ministry of Education Guizhou University Guiyang550025 China School of Mathematics and Big data Guizhou Education University Guiyang550025 China

ISBN: (纸本)9789819998951

With the gradual formation of the cloud computing ecosystem, the value of cloud computing is becoming increasingly evident, and the accompanying information security issues have become core elements. The challenge lies in securely transmitting, computing, and sharing data in a cloud computing environment while keeping privacy. In this paper, we propose an access control proxy re-encryption scheme that supports inner product operations based on lattices in the standard model. First, the ciphertext is linked to the attributes, and condition for transition is associated with the re-encryption key. The proxy may re-encrypt ciphertext that meets attribute conditions instead of all ciphertext, thereby limiting the proxy’s conversion permissions. Furthermore, the user outputs the inner product value after decryption. Finally, honestly re-encryption attacks (HRA) security with increased CPA security is employed to better capture the target of proxy re-encryption. In addition, we propose a new method of hiding access policy, which uses differential privacy technology to perturb the attribute values to better safeguard the privacy of users. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Access control

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A Tiny Efficient U-Net with Gated Linear Attention for Medical Image Segmentation

A Tiny Efficient U-Net with Gated Linear Attention for Medic...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Sibo Ju Zhaozhen Chen Xiangwen Liao Yiqing Shen Junjun He Yanzhou Su College of Computer and Data Science Fuzhou University Fuzhou China Department of Computer Science Johns Hopkins University Baltimore USA Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Medical image segmentation is crucial for diagnosis and treatment planning. While recent advancements in deep learning, particularly UNet variants, have improved segmentation performance, they often result in increased model complexity, which limits their real-time applicability on resource-constrained devices in clinical settings. To address this challenge, we present the Tiny Efficient U-Net (TE-UNet), a novel lightweight model balancing efficiency and accuracy. TE-UNet uses a U-shaped encoder-decoder framework with a gated linear attention mechanism to process low-level and high-level features, preserving details and reducing complexity. It employs depth-wise separable convolutions for higher-level processing, enhancing efficiency without losing performance. Additionally, skip connections improve multi-scale feature extraction and information flow. Experiments on two public datasets across different modalities demonstrate that TE-UNet outperforms ten state-of-the-art methods, maintaining a parameter size under 40KB and low computational cost. TE-UNet makes real-time segmentation more accessible for various clinical applications.

关键词： Performance evaluation Image segmentation Attention mechanisms Accuracy Logic gates Real-time systems Computational efficiency Complexity theory Planning Medical diagnostic imaging

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Evaluating data-Driven Models to Enhance Employee Retention and Performance

Evaluating Data-Driven Models to Enhance Employee Retention ...

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Progressive Innovations in Intelligent Systems and data science (ICPIDS), International Conference on

作者： Rashmi Mishra Neha Tyagi Shobha Tyagi M. Tech (Data Science) Amity University Noida India Computer Science and Engineering Amity University Noida India Computer Science and Engineering Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (数字)9798331534691

ISBN: (纸本)9798331534707

Employee attrition presents considerable challenges for organizations, affecting productivity, employee morale, and financial health. This research seeks to create and evaluate data-driven models to more accurately forecast employee turnover. By analyzing various employee data, such as demographic details, job-related factors, and performance metrics, machine learning techniques are used to uncover the primary drivers of attrition. The goal is to identify the most effective model for prediction, of-fering practical insights to shape employee retention strategies. In conclusion, this study contributes to the development of proactive measures aimed at reducing attrition and enhancing workforce management. This study employs Logistic Regression and Ran-dom Forest, each bringing distinct advantages to HR analytics over conventional approaches. Logistic Regression is appreciated for its ease of interpretation, making it highly applicable for HR insights. Conversely, the ensemble nature of Random Forest enhances prediction accuracy by effectively identifying non-linear trends commonly found in employee behavior. By combining these models, the approach provides both robust predictive capabilities and valuable insights into major attrition factors, which are essential for crafting targeted retention strategies.

关键词： Logistic regression Analytical models Technological innovation Accuracy Shape Forestry Predictive models data models Robustness Random forests

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Octopus: Experiences with a Hybrid Event-Driven Architecture for Distributed Scientific Computing

arXiv

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arXiv 2024年

作者： Pan, Haochen Chard, Ryan Zhou, Sicheng Kamatar, Alok Vescovi, Rafael Hayot-Sasson, Valérie Bauer, André Gonthier, Maxime Chard, Kyle Foster, Ian Department of Computer Science University of Chicago ChicagoIL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States Department of Computer Science Southern University of Science and Technology Guangdong China Department of Computer Science Illinois Institute of Technology ChicagoIL United States

Scientific research increasingly relies on distributed computational resources, storage systems, networks, and instruments, ranging from HPC and cloud systems to edge devices. Event-driven architecture (EDA) benefits applications targeting distributed research infrastructures by enabling the organization, communication, processing, reliability, and security of events generated from many sources. To support the development of scientific EDA, we introduce Octopus, a hybrid, cloud-to-edge event fabric designed to link many local event producers and consumers with cloud-hosted brokers, and to provide a fabric for developing resilient applications. Octopus can be scaled to meet demand, permits the deployment of highly available Triggers for automatic event processing, and enforces fine-grained access control. We identify requirements in self-driving laboratories, scientific data automation, online task scheduling, epidemic modeling, and dynamic workflow management use cases, and present results demonstrating Octopus’ ability to meet those requirements. Octopus supports producing and consuming events at a rate of over 4.2 M and 9.6 M events per second, respectively, from distributed clients. © 2024, CC BY.

关键词： Access control

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