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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Lin, Zhicheng Chen, Hegang Lu, Yuyin Rao, Yanghui Xu, Hao Lai, Hanjiang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Laboratory for Big Data and Decision National University of Defense Technology Changsha China

ISBN: (纸本)9782493814104

Hierarchical topic modeling, which can mine implicit semantics in the corpus and automatically construct topic hierarchical relationships, has received considerable attention recently. However, the current hierarchical topic models are mainly based on Euclidean space, which cannot well retain the implicit hierarchical semantic information in the corpus, leading to irrational structure of the generated topics. On the other hand, the existing Generative Adversarial Network (GAN) based neural topic models perform satisfactorily, but they remain constrained by pattern collapse due to the discontinuity of latent space. To solve the above problems, with the hypothesis of hyperbolic space, we propose a novel GAN-based hierarchical topic model to mine high-quality topics by introducing contrastive learning to capture information from documents. Furthermore, the distinct tree-like property of hyperbolic space preserves the implicit hierarchical semantics of documents in topic embeddings, which are projected into the hyperbolic space. Finally, we use a multi-head self-attention mechanism to learn implicit hierarchical semantics of topics and mine topic structure information. Experiments on real-world corpora demonstrate the remarkable performance of our model on topic coherence and topic diversity, as well as the rationality of the topic hierarchy. Our code is available at https://***/Adrian-LZC/hHTM. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Embeddings

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Leveraging Deep Learning to Improve COVID-19 Forecasting Using Wastewater Viral Load

Leveraging Deep Learning to Improve COVID-19 Forecasting Usi...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Fazli, Mehrdad Shakeri, Heman George Mason University Department of Computer Science FairfaxVA United States University of Virginia School of Data Science CharlottesvilleVA United States

ISBN: (纸本)9798350324457

The outburst of COVID-19 in late 2019 was the start of a health crisis that shook the world and took millions of lives in the ensuing years. Many governments and health officials failed to arrest the rapid circulation of infection in their communities. The long incubation period and the large proportion of asymptomatic cases made COVID-19 particularly elusive to track. However, wastewater surveillance soon became a promising data source in addition to conventional indicators such as confirmed daily cases, hospitalizations, and deaths. Despite the consensus on the effectiveness of wastewater viral load, there is a lack of methodological approaches that leverage viral load to improve COVID-19 forecasting. This paper proposes a deep learning framework to automatically discover the relationship between daily cases and viral load data. We trained a Deep Temporal Convolutional Network (DeepTCN) and a Temporal Fusion Transformer (TFT) model to obtain a global forecasting model. We supplement the daily confirmed cases with viral loads and other socio-economic factors as covariates to the models. Our results suggest that TFT outperforms DeepTCN and learns a better association between viral load and daily cases. We demonstrate that equipping the models with the viral load improves forecasting accuracy and reduces uncertainty. Moreover, viral load is shown to be the second most predictive input, following the containment and health index. Our results reveal the feasibility of training a location-agnostic deep-learning model to capture the dynamics of infection diffusion when wastewater viral load data is available. © 2023 IEEE.

关键词： COVID-19 Forecasting Deep Learning Wastewater-based epidemiology

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An Automated Python Script for data Cleaning and Labeling using Machine Learning Technique

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Informatica (Slovenia) 2023年第6期47卷 219-232页

作者： Oladipupo, Matthew Abiola Obuzor, Princewill Chima Bamgbade, Babatunde Joseph Olagunju, Kazeem M. Adeniyi, Abidemi Emmanuel Ajagbe, Sunday Adeola Department of Data Science School of Science Engineering and Environment University of Salford United Kingdom Jericho GRA Ibadan Nigeria Department of Computer Engineering Ladoke Akintola University of Technology LAUTECH Ogbomoso Nigeria Department of Computer Sciences Precious Cornerstone University Ibadan Nigeria Department of Computer & Industrial Production Engineering First Technical University Ibadan Nigeria

Every employee in the company who deals with data needs to have clean, noise-free data. Since data warehouses store and update enormous amounts of data from several sources, there is a potential that some of those references may contain inaccurate data. Due to the noise, inefficacy, and poor characterization of the vast amount of accessible data, as well as the ensuing insensitivity and inefficiencies of human data cleaning and labeling, the presentation of the data has become ambiguous, and the assessment of the information has become difficult. A hole in the creation of a better data analysis method was identified. This helped to guide the creation of a Python script for automatically cleaning and labeling data. The first step in the strategy used in this study to accomplish its goals and objectives was to obtain a financial dataset from the top database, "Kaggle". Create a machine learning (ML) approach in Python that intends to automate the financial dataset cleaning. This covers ingesting data, addressing incomplete data, addressing anomalies, one-hot wrapping and label encoding, extracting date and time values, and data normalization. Implementing an unsupervised machine learning method that attempts to automate financial dataset labeling (k-means). Using the method includes the elbow principle, k-means clustering, data modeling of "age" versus "arrival," dimensionality reductions, computer vision, and dataset categorizing using the groupings. An empirical assessment of the cleaned and labeled automated trading dataset utilizing a comparison of the cleaned dataset before and after PCA adoption. The results show that the developed ML technique not only improved the performance of the audit data used in this study, but also classified the data after cleaning it and removing the unpleasant section and incomplete data, as shown by the k-means segmentation result and grouping by PCA. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Python

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Enhancing Fairness in Meta-learned User Modeling via Adaptive Sampling 24

Enhancing Fairness in Meta-learned User Modeling via Adaptiv...

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33rd ACM Web Conference, WWW 2024

作者： Zhang, Zheng Liu, Qi Hu, Zirui Zhan, Yi Huang, Zhenya Gao, Weibo Mao, Qingyang School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China School of Data Science University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China School of Computer Science and Technology University of Science and Technology of China Institute of Artificial Intelligence Hefei Comprehensive National Science Center State Key Laboratory of Cognitive Intelligence Hefei China

ISBN: (纸本)9798400701719

Meta-learning has been widely employed to tackle the cold-start problem in user modeling. Similar to a guidebook for a new traveler, meta-learning significantly affects decision-making for new users in crucial scenarios, such as career recommendations. Consequently, the issue of fairness in meta-learning has gained paramount importance. Several methods have been proposed to mitigate unfairness in meta-learning and have shown promising results. However, a fundamental question remains unexplored: What is the critical factor leading to unfairness in meta-learned user modeling? Through the theoretical analysis that integrates the meta-learning paradigm with group fairness metrics, we identify group proportion imbalance as a critical factor. Subsequently, in order to mitigate the impact of this factor, we introduce a novel Fairness-aware Adaptive Sampling framework for meTa-learning, abbreviated as FAST. Its core concept involves adaptively adjusting the sampling distribution for different user groups during the interleaved training process of meta-learning. Furthermore, we provide theoretical guarantees demonstrating the convergence of FAST. Finally, empirical experiments conducted on three datasets reveal that FAST effectively enhances fairness while maintaining high accuracy. The code for FAST is available at https://***/zhengz99/FAST. © 2024 Owner/Author.

关键词： Decision making

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An Empirical Investigation of Docker Sockets for Privilege Escalation and Defensive Strategies 5

An Empirical Investigation of Docker Sockets for Privilege E...

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5th International Conference on Innovative data Communication Technologies and Application, ICIDCA 2024

作者： Rajyashree, R. Mathi, Senthilkumar Saravanan, G. Sakthivel, M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Department of Artificial Intelligence and Data Science Erode Sengunthar Engineering College Erode India Department of Computer Science and Engineering Erode Sengunthar Engineering College Erode India

Cloud-based infrastructures often leverage virtualization, but its implementation can be expensive. Traditional coding methods can lead to issues when transitioning code from one computing environment to another. In response, the container paradigm emerged to offer cost-effective and agile delivery. Containers differ from full machine virtualization by compactly encapsulating the entire software and its dependencies. Leveraging containers, developers can create more secure and efficient applications. Docker, a prominent containerization platform, facilitates the execution of docker images. The Docker Hub serves as a popular repository for various images. Given the importance of application security, especially in the face of threats like malware, ransomware, and data breaches, ensuring robust security is imperative. The paper investigates vulnerabilities within Docker containers and proposes defensive strategies to mitigate potential breaches. In addition, it investigates attacks involving Docker sockets and suggests preventive measures for non-root users. © 2024 The Authors. Published by Elsevier B.V.

关键词： Containers

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Uncover Secrets Through the Cover: A Deep Learning-Based Side-Channel Attack Against Kyber Implementations With Anti-Tampering Covers

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IEEE Transactions on computers 2025年第6期74卷 2159-2167页

作者： Chen, Peng Li, Jinnuo Cheng, Wei Cheng, Chi China University of Geosciences School of Computer Science Wuhan430074 China City University of Macau Faculty of Data Science Macau999078 China Institut Polytechnique de Paris LTCI Télécom Paris Palaiseau91120 France Secure-IC S.A.S. Paris75014 France

The probe can directly contact the microcontrollerin a typical EM side-channel attack (SCA) targeting crypto-graphic implementations. However, in a more practical settingsuch as security level 2 of FIPS 140-3 or ISO/IEC 19790standards, the microcontroller is required to be safeguardedby an opaque anti-tampering cover. This raises an interestingproblem: Can we still launch EM attacks against microcontrollersrunning cryptographic implementations even when equippedwith the cover? This paper proposes an improved deep-learning-based profiled attack against NIST KEM standard Kyber. Ourkey observation is that the distance between the probe and the mi-crocontroller results in attenuation of signal strength. Moreover,the cover restricts the proximity of the probe, thereby limitingthe signal-to-noise ratio. We propose an Adaptive SlimmedPyramid Network (ASPN) model to instantiate a distinguisherin a plaintext-checking oracle-based SCA, which is generic andeasy to implement. The proposed ASPN approach significantlyenhances the feature extraction process by employing a pyramidnetwork structure, while simultaneously avoiding the inclusion ofexcessive parameters. Real-world experiments demonstrate thatour proposed distinguishers achieve an accuracy above 99% withan 18 mm cover and higher than 89% accuracy even with a 24mm cover. © 1968-2012 IEEE.

关键词： Signal to noise ratio

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Assessing the Feasibility and Scalability of Using Spark for Identifying Tip Burn Diseases in Strawberry Leaves 2nd

Assessing the Feasibility and Scalability of Using Spark for...

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2nd International Conference on Advances in data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Prathyuma, V. Hareesh Teja, S. Suganeshwari, G. Divya, S. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Centre for Advanced Data Science Vellore Institute of Technology Tamil Nadu Chennai6000127 India

ISBN: (纸本)9789819995233

Tip burn is a common calcium deficiency symptom found in strawberry leaves, which can deform, fold over, and discolor the tips or edges of budding leaves. To help farmers detect diseases early and manage their crops more effectively, this work aims to build a solution that employs image processing and machine learning techniques to detect tip burn in strawberry leaves on a farm. The proposed work utilizes a dataset of 1431 images of ‘diseased’ and ‘healthy’ strawberry leaves. A Random Forest classifier, which is an ensemble learning model built on decision trees, CNN, or convolutional neural networks, and VGG-16, a transfer learning-based approach were all fitted in the PySpark environment to assess the feasibility and scalability of using Spark for distributed computing. The results indicate the deep learning approaches outperformed the traditional machine learning approach in accuracy and execution speed, making them a promising tool for identifying tip burn diseases in strawberry leaves. Overall, this work provides farmers with a fast, feasible, and reliable method for detecting tip burn, aiding in crop management, and reducing crop loss. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Big data

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Power-Measurement-Based Channel Autocorrelation Estimation for IRS-Assisted Wideband Communications

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4647-4662页

作者： Sun, He Zhu, Lipeng Mei, Weidong Zhang, Rui National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications 611731 China The Chinese University of Hon g Kong School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China

Channel state information (CSI) is essential to the performance optimization of intelligent reflecting surface (IRS)-aided wireless communication systems. However, the passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed practical challenges for efficient IRS channel estimation, especially in wideband communication systems with significant multi-path channel delay spread. To tackle the above challenge, we propose a novel neural network (NN)-empowered IRS channel estimation and passive reflection design framework for the wideband orthogonal frequency division multiplexing (OFDM) communication system based only on the user’s reference signal received power (RSRP) measurements with time-varying random IRS training reflections. As RSRP is readily accessible in existing communication systems, our proposed channel estimation method does not require additional pilot transmission in IRS-aided wideband communication systems. In particular, we show that the average received signal power over all OFDM subcarriers at the user terminal can be represented as the prediction of a single-layer NN composed of multiple subnetworks with the same structure, such that the autocorrelation matrix of the wideband IRS channel can be recovered as their weights via supervised learning. To exploit the potential sparsity of the channel autocorrelation matrix, a progressive training method is proposed by gradually increasing the number of subnetworks until a desired accuracy is achieved, thus reducing the training complexity. Based on the estimates of IRS channel autocorrelation matrix, the IRS passive reflection is then optimized to maximize the average channel power gain over all subcarriers. Numerical results indicate the effectiveness of the proposed IRS channel autocorrelation matrix estimation and passive reflection design under wideband channels, which can achieve significant performance improvement compared to the existing IRS re

关键词： Orthogonal frequency division multiplexing

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Unmasking Social Robots’Camouflage:A GNN-Random Forest Framework for Enhanced Detection

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computers, Materials & Continua 2025年第1期82卷 467-483页

作者： Weijian Fan Chunhua Wang Xiao Han Chichen Lin School of Data Science and Intelligent Media Communication University of ChinaBeijing100024China School of Computer and Cyber Sciences Communication University of ChinaBeijing100024China Institute of Communication Studies Communication University of ChinaBeijing100024China State Key Laboratory of Media Convergence and Communication Communication University of ChinaBeijing100024China

The proliferation of robot accounts on social media platforms has posed a significant negative impact,necessitating robust measures to counter network anomalies and safeguard content *** robot detection has emerged as a pivotal yet intricate task,aimed at mitigating the dissemination of misleading *** graphbased approaches have attained remarkable performance in this realm,they grapple with a fundamental limitation:the homogeneity assumption in graph convolution allows social robots to stealthily evade detection by mingling with genuine human *** unravel this challenge and thwart the camouflage tactics,this work proposed an innovative social robot detection framework based on enhanced HOmogeneity and Random Forest(HORFBot).At the core of HORFBot lies a homogeneous graph enhancement strategy,intricately woven with edge-removal techniques,tometiculously dissect the graph intomultiple revealing ***,leveraging the power of contrastive learning,the proposed methodology meticulously trains multiple graph convolutional networks,each honed to discern nuances within these tailored *** culminating stage involves the fusion of these feature-rich base classifiers,harmoniously aggregating their insights to produce a comprehensive detection *** experiments on three social robot detection datasets have shown that this method effectively improves the accuracy of social robot detection and outperforms comparative methods.

关键词： Social robot detection graph neural networks random forest homophily heterophily

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Equality Generating Dependencies in Description Logics via Path Agreements 37th

Equality Generating Dependencies in Description Logics via...

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37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Feng, Eva Franconi, Enrico Patel-Schneider, Peter F. Toman, David Weddell, Grant Department of Computer Science Oxford University Oxford United Kingdom KRDB Research Centre for Knowledge and Data Free University of Bozen-Bolzano Bozen-Bolzano Italy Cheriton School of Computer Science University of Waterloo Waterloo Canada Westfield NJ United States

ISBN: (纸本)9789819603503

We consider generalizing path functional dependencies (PFDs) in Boolean complete feature logics to path description dependencies (PDDs) that now allow occurrences of inverse features. In particular, allowing such occurrences in component path descriptions of PDDs leads to a circumstance in which different notions of path agreement are now possible. We explore the cases of set intersection, set equality and non-empty set equality, studying in particular the decidability and complexity of entailment under combinations of these notions of path agreement. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computer circuits

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