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A Combined GAN and DF Based Expansion and Prediction for Small Sample data 16th

A Combined GAN and DF Based Expansion and Prediction for Sma...

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16th international conference on Modelling, Identification and Control, ICMIC 2024

作者： Wang, Li Lu, Junfeng Feng, Shanfang Wang, Xiaoyi Jin, Xuebo Yu, Jiabin Bai, Yuting Beijing Laboratory for Intelligent Environmental Protection School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing100048 China

ISBN: (纸本)9789819617760

The high cost and difficulty of data collection result in discrete intermittency and limited available data for most datasets, posing challenges to modeling and prediction needs. Thus, it is crucial to explore methodologies suitable for discrete small sample data. Concurrently, unsupervised learning methods are employed for small sample prediction to circumvent the issue of low prediction accuracy resulting from inadequate a priori knowledge. Current data modeling and prediction methods rely heavily on neural network deep learning models, which are mainly designed for continuous processes and show low accuracy for intermittent processes. This paper proposes a data expansion method based on Time Generative Adversarial Networks (TimeGAN) and a prediction method based on the combination of Generative Adversarial Networks (GAN) and Deep Forest (DF). Firstly, the TimeGAN model is proposed to learn small sample data for data expansion. Then, the GAN-DF model is constructed so that the model is used for data prediction of unsupervised discrete processes. On this basis, we introduce the DFGAN and LSTM-DFGAN models, which embed DF and LSTM-DF models as generators within the GAN framework to enhance prediction accuracy for small sample data. Simulation validation using contaminant data from a food processing process demonstrates the efficacy of the TimeGAN method in data expansion. The results demonstrate that the TimeGAN method is capable of effectively expanding the data, while the LSTM-DFGAN model exhibits the most optimal combined prediction effect. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Predicting Academic Performance of Students Using Machine Learning models

Predicting Academic Performance of Students Using Machine Le...

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2023 international conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Sharma, Neeta Sharma, Mk Garg, Umang Noida International University Department of Cs UP Greater Noida India Defence Cyber Agency New Delhi India Graphic Era Hill University Dehradun India

ISBN: (纸本)9798350322309

The NEP- 2020 of India has reemphasized the need of delivering quality education in order to decrease the drop out rate of students by the HEI. In post-cover era, retention of the students specially in Engineering and Management disciplines was more prominent. This paper is an attempt to empower HEI predict students' performance/ potential drop out using ML algorithms based on six factors namely;Family size, Study time, Time-spent on extra-curricular activities, Absenteeism, Time spent on Internet, and *** ML algorithms namely, KNN, Random Forest and Decision Tree have been implemented and compared on efficiency and accuracy criteria so as to attain at the top algorithm for subject prediction use case. The paper's findings on academic performance prediction are likely to help educational institutions and the learners by the way of early identification of potential drop out and implementation of corrective measures. A small data set of 390 students on various predictors has been utilized for ML algorithms *** paper seeks to address the problem of quality education delivery system in India and elsewhere. It is pilot research on academic performance monitoring with an aim to reduce dropout rate of students. This research work shall pave way for further scaled up research work implementing various algorithms on larger datasets to make a use case for education industry wide implementation potential. © 2023 IEEE.

关键词： Students

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SwinMixer Detector: A Two-Level Traffic Flow Detection System Based on Swin Transformer and MLP-Mixer 24th

SwinMixer Detector: A Two-Level Traffic Flow Detection Syste...

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24th international conference on algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Lu, Lu Fang, Menghao Li, Xia Li, Minghui Lei, Tianwei Kong, Zixiao School of Cyber Science and Engineering University of International Relations Beijing100091 China Institute of Disaster and Emergency Medicine Tianjin University Tianjin300072 China Marine Engineering of College Dalian Maritime University Dalian116026 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789819615414

This paper explores the problem of boundary data classification ambiguity that arises when machine learning techniques are applied in the field of intrusion detection. The features and attributes of the boundary data place them in the boundaries or boundaries between different categories in the classification decision, and these data points may be close to the decision boundaries of different categories in the feature space, resulting in relative uncertainty or some ambiguity in their classification. To address this problem, this study innovatively proposes a secondary detection system called the SwinMixer Detector, which utilises the Swin Transformer and MLP-Mixer architecture. The system consists of a primary triage detection platform utilizing Swin Transformer classifier and a secondary detection platform utilizing MLP-Mixer, which can effectively solve the problem of fuzzy classification of boundary data. By validating on the SR-BH2020 dataset and the CIC-DDOS2019 dataset, the method in this paper achieves a precision of 96.40% and 95.81%, respectively, which provides a new research idea and solution for the field of intrusion detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mixers (machinery)

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An Effective Methodology for Imbalanced data Handling in Predictive Maintenance for Offset Printing 1

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11th international conference on Mechatronics and Control Engineering, ICMCE 2023

作者： Kalafatelis, Alexandros S. Nomikos, Nikolaos Angelopoulos, Angelos Trochoutsos, Chris Trakadas, Panagiotis Department of Port Management & Shipping National and Kapodistrian University of Athens Evia Psachna34400 Greece Pressious Arvanitidis Athens15232 Greece

ISBN: (数字)9789819965236

ISBN: (纸本)9789819965229

The printing industry is one of the largest manufacturing industries in the world, being characterized by high production volumes, where continuous maintenance of machine performance is key. Predictive Maintenance (PdM) enables the use of a maintenance policy based on novel Machine Learning (ML) algorithms, in or-der to provide valuable insights for both diagnostics and prognostics. However, real-world data used for PdM model training are characterized by great class im-balances, as failure events have a significant lower rate of happening compared to the normal no failure operations. Furthermore, ML models that are subjected to imbalanced datasets, are prone to be highly biased while having misleading accuracy scores. This can prohibit systems to accurately predict machine failure, leading to excessive costs while affecting the safety of the workers. This work proposes a data sampling methodology for predictive maintenance algorithms used mainly in Offset Printing environments, aiming to improve model performance. Based on a historical dataset extracted by an Offset Printing manufacturer, a methodology consisting of multiple classification algorithms utilizing different sampling techniques (SMOTE, ADASYN, and RUS), was trained and evaluated using cross-validation. The evaluation outcomes demonstrated the ability of the proposed methodology to effectively handle data imbalances while significantly enhancing model performance, outperforming other state-of-the-art techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Industry 4.0

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Boolean and Fp-Matrix Factorization: From Theory to Practice

Boolean and Fp-Matrix Factorization: From Theory to Practice

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IEEE international conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / international Joint conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Fomin, Fedor Panolan, Fahad Patil, Anurag Tanveer, Adil Univ Bergen Dept Informat Bergen Norway IIT Hyderabad Dept CSE Sangareddy India EdgeVerve Syst Ltd Bengaluru India Amazon Chennai Tamil Nadu India

ISBN: (纸本)9781728186719

Boolean Matrix Factorization (BMF) aims to find an approximation of a given binary matrix as the Boolean product of two low-rank binary matrices. Binary data is ubiquitous in many fields, and representing data by binary matrices is common in medicine, natural language processing, bioinformatics, computer graphics, among many others. Factorizing a matrix into low-rank matrices is used to gain more information about the data, like discovering relationships between the features and samples, roles and users, topics and articles, etc. In many applications, the binary nature of the factor matrices could enormously increase the interpretability of the data. Unfortunately, BMF is computationally hard and heuristic algorithms are used to compute Boolean factorizations. Very recently, the theoretical breakthrough was obtained independently by two research groups. Ban et al. (SODA 2019) and Fomin et al. (Trans. algorithms 2020) show that BMF admits an efficient polynomial-time approximation scheme (EPTAS). However, despite the theoretical importance, the high double-exponential dependence of the running times from the rank makes these algorithms unimplementable in practice. The primary research question motivating our work is whether the theoretical advances on BMF could lead to practical algorithms. The main conceptional contribution of our work is the following. While EPTAS for BMF is a purely theoretical advance, the general approach behind these algorithms could serve as the basis in designing better heuristics. We also use this strategy to develop new algorithms for related F-p-Matrix Factorization. Here, given a matrix A over a finite field GF(p) where p is a prime, and an integer r, our objective is to find a matrix B over the same field with GF(p)-rank at most r minimizing some norm of A - B. Our empirical research on synthetic and real-world data demonstrates the advantage of the new algorithms over previous works on BMF and F-p-Matrix Factorization.

关键词： Binary matrix factorization Categorical data data mining

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Multimodal Summarization with Modality-Aware Fusion and Summarization Ranking 24th

Multimodal Summarization with Modality-Aware Fusion and Su...

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24th international conference on algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Ye, Xuming Chaomurilige Liu, Zheng Luo, Haoyu Dong, Jun Luo, Yingzhe Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of China Beijing China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan572499 China Faculty of Data Science City University of Macau Macau China

ISBN: (纸本)9789819615278

Existing multimodal summarization methods primarily focus on multimodal fusion to efficiently utilize the visual information for summarization. However, they fail to exploit the deep interaction between textual and visual modality. Moreover, optimizing the model by maximum likelihood estimation (MLE) leads to exposure bias, causing the model to generate the next word that is based on the previously generated erroneous words during inference. To address these challenges, we propose a novel modality-aware fusion module (MAF) with a summarization ranking (SumR) training objective. Specifically, the MAF module exploits the interaction in the multimodal input through multiple fusion layers, and SumR aims to align the probability order predicted by the model with actual quality metrics, therefore it is able to reduce the exposure bias problem during inference. Extensive experiments on a large-scale dataset demonstrate that our method outperforms existing models, achieving superior results in both automatic and human evaluation metrics. The generated multimodal summaries provide richer context and enhance user’s comprehension by combining the key textual information with the relevant visual content. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Maximum likelihood estimation

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Towards Robust Federated Learning: Investigating Poisoning Attacks Under Clients data Heterogeneity 19

Towards Robust Federated Learning: Investigating Poisoning A...

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19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Soubih, Abdenour Lahmer, Seyyid Ahmed Abuhamad, Mohammed Abuhmed, Tamer College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Department of Information Engineering University of Padova Padua Italy Department of Computer Science Loyola University Chicago Chicago United States

ISBN: (纸本)9798331507817

Federated Learning (FL) offers a privacy-preserving solution by enabling multiple clients to train a shared model collaboratively without centralizing data. However, the decentralized nature of FL presents challenges, particularly regarding security and performance under adversarial conditions. This paper investigates the effects of poisoning attacks under data heterogeneity. Our experiments evaluate the impact of varying malicious client fractions and poison concentration levels on the accuracy of the model. We explore the effects of poisoning attacks on FedAvg and FedNova models using medical imaging tasks. Our findings reveal that increasing data heterogeneity exacerbates the effects of poisoning, with FedNova demonstrating greater resilience compared to FedAvg. We found that the number of malicious clients plays a more significant role in degrading performance than the ratio of poisoning samples shared by each malicious client, suggesting that even modest levels of poisoning can be tolerated by most algorithms. The study highlights the importance of developing robust defense mechanisms to maintain model performance under adversarial conditions. © 2025 IEEE.

关键词： Federated learning

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KnowLog: Knowledge Enhanced Pre-trained Language Model for Log Understanding 24

KnowLog: Knowledge Enhanced Pre-trained Language Model for L...

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46th IEEE/ACM international conference on Software Engineering, ICSE 2024

作者： Ma, Lipeng Jiang, Sihang Yang, Weidong Fei, Ben Xu, Bo Liang, Jiaqing Zhou, Mingjie Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China School of Computer Science and Technology Donghua University Shanghai China School of Data Science Fudan University Shanghai China

ISBN: (纸本)9798400702174

Logs as semi-structured text are rich in semantic information, making their comprehensive understanding crucial for automated log analysis. With the recent success of pre-trained language models in natural language processing, many studies have leveraged these models to understand logs. Despite their successes, existing pre-trained language models still suffer from three weaknesses. Firstly, these models fail to understand domain-specific terminology, especially abbreviations. Secondly, these models struggle to adequately capture the complete log context information. Thirdly, these models have difficulty in obtaining universal representations of different styles of the same logs. To address these challenges, we introduce KnowLog, a knowledge-enhanced pre-trained language model for log understanding. Specifically, to solve the previous two challenges, we exploit abbreviations and natural language descriptions of logs from public documentation as local and global knowledge, respectively, and leverage this knowledge by designing novel pre-training tasks for enhancing the model. To solve the last challenge, we design a contrastive learning-based pre-training task to obtain universal representations. We evaluate KnowLog by fine-tuning it on six different log understanding tasks. Extensive experiments demonstrate that KnowLog significantly enhances log understanding and achieves state-of-the-art results compared to existing pre-trained language models without knowledge enhancement. Moreover, we conduct additional experiments in transfer learning and low-resource scenarios, showcasing the substantial advantages of KnowLog. Our source code and detailed experimental data are available at https://***/LeaperOvO/KnowLog. © 2024 IEEE Computer Society. All rights reserved.

关键词： Computational linguistics

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Multimodal Bias: Assessing Gender Bias in Computer Vision models with NLP Techniques 23

Multimodal Bias: Assessing Gender Bias in Computer Vision Mo...

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25th international conference on Multimodal Interaction (ICMI)

作者： Mandal, Abhishek Little, Suzanne Leavy, Susan Dublin City Univ Sch Comp Insight SFI Res Ctr Data Analyt Dublin Ireland Univ Coll Dublin Sch Informat & Commun Studies Insight SFI Res Ctr Data Analyt Dublin Ireland

ISBN: (纸本)9798400700552

Large multimodal deep learning models such as Contrastive Language Image Pretraining (CLIP) have become increasingly powerful with applications across several domains in recent years. CLIP works on visual and language modalities and forms a part of several popular models, such as DALL-E and Stable Diffusion. It is trained on a large dataset of millions of image-text pairs crawled from the internet. Such large datasets are often used for training purposes without filtering, leading to models inheriting social biases from internet data. Given that models such as CLIP are being applied in such a wide variety of applications ranging from social media to education, it is vital that harmful biases are detected. However, due to the unbounded nature of the possible inputs and outputs, traditional bias metrics such as accuracy cannot detect the range and complexity of biases present in the model. In this paper, we present an audit of CLIP using an established technique from natural language processing called Word Embeddings Association Test (WEAT) to detect and quantify gender bias in CLIP and demonstrate that it can provide a quantifiable measure of such stereotypical associations. We detected, measured, and visualised various types of stereotypical gender associations with respect to character descriptions and occupations and found that CLIP shows evidence of stereotypical gender bias.

关键词： bias fairness multimodal models trustworthiness

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data Platforms for Real-time Insights in Healthcare: Systematic Review 14

Data Platforms for Real-time Insights in Healthcare: Systema...

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14th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th international conference on Emerging data and Industry 4.0, EDI40 2023

作者： Miranda, Rui Alves, Carlos Abelha, Antonio Machado, Jose Centro ALGORITMI Escola de Engenharia Universidade Do Minho Campus Azurem Guimaraes4800-058 Portugal

The ever-growing usage and popularity of Internet of Things devices, coupled with Big data technologies and machine learning algorithms, have allowed for data engineers to explore new opportunities in healthcare and continuous care. Furthermore, there is a need to reduce the gap on time from when information is created to when actions and insights can be offered. However, a challenge in implementing a large-scale data processing architecture is deciding which tools are appropriate, and how to apply them in the best way possible. For example, streaming systems are now mature enough that hospitals worldwide can use their extremely large datasets, along with data producers, to predict and influence future events. Thus, the main objective of this systematic review is to identify the state-of-the-art in data platforms on healthcare that allow the creation of metrics and actions in real-time. The PRISMA guideline for reporting systematic reviews was implemented to deliver a transparent and consistent report, validating the technological advances in a critical sector. Multiple pertinent articles and papers were retrieved from the SCOPUS abstract and citation database on May 13, 2022, using several relevant keywords to identify potentially relevant documents published from January 2020 onward. These documents must have already been published in English and been already published, and accessible through the B-ON consortium that allows Portuguese students to legally download from most publishers. Over seven studies have been selected for deeper discussion based on their relevance and impact for this review, showcasing their main objectives, data sources, and tools used, as well as their approaches for interoperability and support of machine learning algorithms for decision support. In closing, the collected articles have shown that while Big data is currently in use at health institutions of all sizes, the ability of processing large amounts of data from sensors and events, a

关键词： data mining

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