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Synonym-Based Essay Generation and Augmentation for Robust Automatic Essay Scoring 23rd

Synonym-Based Essay Generation and Augmentation for Robust A...

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23rd international conference on intelligent data engineering and automated learning (IDEAL)

作者： Tashu, Tsegaye Misikir Horvath, Tomas Eotvos Lorand Univ Fac Informat Dept Data Sci & Engn Telekom Innovat Labs Pazmany Peter Setany 1-C H-1117 Budapest Hungary Wollo Univ Kombolcha Inst Technol Coll Informat Kombolcha 208 Ethiopia Pavol Jozef Safarik Univ Fac Sci Inst Comp Sci Jesenna 5 Kosice 04001 Slovakia

ISBN: (纸本)9783031217524;9783031217531

Automatic essay scoring (AES) models based on neural networks (NN) have had a lot of success. However, research has shown that NN-based AES models have robustness issues, such that the output of a model changes easily with small changes in the input. We proposed to use keyword-based lexical substitution using BERT that generates new essays (adversarial samples) which are lexically similar to the original essay to evaluate the robustness of AES models trained on the original set. In order to evaluate the proposed approach, we implemented three NN-based scoring approaches and trained the scoring models using two stages. First, we trained each model using the original data and evaluate the performance using the original test and newly generated test set to see the impact of the adversarial sample of the model. Secondly, we trained the models by augmenting the generated adversarial essay with the original data to train a robust model against synonym-based adversarial attacks. The results of our experiments showed that extracting the most important words from the essay and replacing them with lexically similar words, as well as generating adversarial samples for augmentation, can significantly improve the generalization of NN-based AES models. Our experiments also demonstrated that the proposed defense is capable of not only defending against adversarial attacks, but also of improving the performance of NN-based AES models.

关键词： Adversarial attack data augmentation Automatic essay scoring

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Development of AI Chatbot for Tourism Promotion: A Case study in Ranong and Chumphon, Thailand

Development of AI Chatbot for Tourism Promotion: A Case Stud...

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international Joint conference on Computer Science and Software engineering (JCSSE)

作者： Nasith Laosen Kanjana Laosen Piyawat Sriprasert Department of Digital Technology Faculty of Science and Technology Phuket Rajabhat University Phuket Thailand Andaman Intelligent Tourism and Service Informatics Center College of Computing Prince of Songkla University Phuket Thailand Computer Engineering Institute of Engineering Suranaree University of Technology Nakhon Ratchasima Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

Ranong and Chumphon provinces in Thailand pos-sess significant tourism potential due to their natural beauty and archaeological heritage. However, tourism development in the region remains limited due to inadequate information infrastructure and limited technology integration. This research presents a chatbot designed to promote tourism in these locations. The chatbot leverages a graph database built using data from the University to Tambon (U2T) project. Functioning as a virtual travel companion, it provides information on tourist attractions, accommodations, and dining options. Advanced natural language processing and machine learning enable the chatbot to under-stand user inquiries and effectively retrieve relevant information. Evaluation demonstrates strong performance with an F1-score of 99.56 for intent classification and 99.96 for named entity recognition. Testing with user-designed test cases confirmed the chatbot's functionality.

关键词： Adaptation models databases Training data Prototypes Machine learning Chatbots data models

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Catastrophic Forgetting in Continual Concept Bottleneck Models 21st

Catastrophic Forgetting in Continual Concept Bottleneck Mode...

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21st international conference on Image Analysis and Processing (ICIAP)

作者： Marconato, Emanuele Bontempo, Gianpaolo Teso, stefano Ficarra, Elisa Calderara, Simone Passerini, Andrea Univ Trento Trento Italy Univ Modena & Reggio Emilia Modena Italy Univ Pisa Pisa Italy

ISBN: (纸本)9783031133244;9783031133237

Almost all Deep learning models are dramatically affected by Catastrophic Forgetting when learning over continual streams of data. To mitigate this problem, several strategies for Continual learning have been proposed, even though the extent of the forgetting is still unclear. In this paper, we analyze Concept Bottleneck (CB) models in the Continual learning setting and we investigate the effect of high-level features supervision on Catastrophic Forgetting at the representation layer. Consequently, we introduce two different metrics to evaluate the loss of information on the learned concepts as new experiences are encountered. We also show that the obtained Saliency maps remain more stable with the attributes supervision. The code is available at https://***/Bontempogianpaolo1/continualExplain

关键词： Continual learning Explainable Artificial Intelligence Catastrophic Forgetting Concept Bottleneck models

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Scalable Pareto Front Approximation for Deep Multi-Objective learning 21

Scalable Pareto Front Approximation for Deep Multi-Objective...

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Ruchte, Michael Grabocka, Josif Univ Freiburg Freiburg Germany

ISBN: (纸本)9781665423984

Multi-objective optimization is important for various Deep learning applications, however, no prior multi-objective method suits very deep networks. Existing approaches either require training a new network for every solution on the Pareto front or add a considerable overhead to the number of parameters by introducing hyper-networks conditioned on modifiable preferences. In this paper, we present a novel method that contextualizes the network directly on the preferences by adding them to the input space. In addition, we ensure a well-spread Pareto front by forcing the solutions to preserve a small angle to the preference vector. Through extensive experiments, we demonstrate that our Pareto fronts achieve state-of-the-art quality despite being computed significantly faster. Furthermore, we demonstrate the scalability as our method approximates the full Pareto front on the CelebA dataset with an EfficientNet network at a marginal training time overhead of 7% compared to a single-objective optimization.

关键词： Multi-objective Optimization Deep learning Fairness

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An Image-Based Classification Module for data Fusion Anti-drone System 21st

An Image-Based Classification Module for Data Fusion Anti-dr...

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21st international conference on Image Analysis and Processing (ICIAP)

作者： Jajaga, Edmond Rushiti, Veton Ramadani, Blerant Pavleski, Daniel Cantelli-Forti, Alessandro stojkovska, Biljana Petrovska, Olivera Mother Teresa Univ Mirce Acev 4 Skopje 1000 North Macedonia Lab RaSS CNIT I-56124 Pisa Italy Ss Cyril & Methodius Univ Intelligent Syst Dept Fac Comp Sci & Engn Rugjer Boshkovikj 16 Skopje 1000 North Macedonia

ISBN: (纸本)9783031133244;9783031133237

Means of air attack are pervasive in all modern armed conflict or terrorist action. We present the results of a NATO-SPS project that aims to fuse data from a network of optical sensors and low-probability-of-intercept mini radars. The requirements of the image-based module aim to differentiate between birds and drones, then between different kind of drones: copters, fixed wings, and finally the presence or not of payload. In this paper, we outline the experimental results of the deep learning model for differentiating drones from birds. Based on the trade-off between speed and accuracy, the YOLO v4 was chosen. A dataset refine process for YOLO-based approaches is proposed. The experimental results verify that such an approach provide a reliable source for situational awareness in a data fusion platform. However, the analysis indicates the necessity of enriching the dataset with more images with complex backgrounds as well as different target sizes.

关键词： Anti-drone system Deep learning YOLO data fusion

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Weight Change Prediction for automated Depression Diagnosis 21

Weight Change Prediction for Automated Depression Diagnosis

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21st international conference on Control, Automation and Systems (ICCAS)

作者： Hong, Juyoung Shin, Jeongmin Hwang, Yujin Lee, Jeongmin Choi, Yukyung Sejong Univ Sch Intelligent Mechatron Engn Seoul 05006 South Korea

ISBN: (纸本)9788993215212

Early detection of depression through self-diagnosis is challenging. Especially survey-based self-diagnosis can lead to incorrect results because the answer contains the subjective opinion of the respondent. To overcome this problem, the multi sensor-based method using a smartphone has been proposed as an alternative for self-diagnosis of depression. These methods diagnose depression by using various sensor data that represented typical depression symptoms from the smartphone. However, sensor data have difficulty estimating weight changes, a representative symptom of depression. In this paper, we propose a facial image-based weight change estimation method for use in diagnosing depression. In particular, we show i) simple feature engineering, designed to fast and robustly compare between two facial images using 3D face landmarks, ii) face alignment processing that makes the model accurately compare facial images which were taken from various viewpoints, and iii) a novel preprocessing with monocular depth estimation to solve scale variance according to variation of distance between the camera and face.

关键词： Depression Diagnosis Mobile Systems and Applications Weight Change Prediction

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Workshop on Machine learning in Smart Mobility 21st

Workshop on Machine Learning in Smart Mobility

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21st international conference on intelligent data engineering and automated learning

作者： Ferreira, Sara Cardoso, Henrique Lopes Rossetti, Rosaldo J. F. Artificial Intelligence & Comp Sci Lab LIACC Porto Portugal Res Ctr Terr Transports & Environm CITTA Porto Portugal Univ Porto Fac Engn Porto Portugal

The workshop on Machine learning in Smart Mobility (MLSM) was co-located with the 21st international conference on intelligent data engineering and automated learning—IDEAL 2020, held online on November 4–6. © ... 详细信息

ISBN: (纸本)9783030623647;9783030623654

The workshop on Machine learning in Smart Mobility (MLSM) was co-located with the 21^st international conference on intelligent data engineering and automated learning—IDEAL 2020, held online on November 4–6. © 2020, Springer Nature Switzerland AG.

关键词： Machine learning

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Efficient Transfer learning for Visual Tasks via Continuous Optimization of Prompts 21st

Efficient Transfer Learning for Visual Tasks via Continuous ...

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21st international conference on Image Analysis and Processing (ICIAP)

作者： Conder, Jonathan Jefferson, Josephine Pages, Nathan Jawed, Khurram Nejati, Alireza Sagar, Mark Soul Machines Auckland New Zealand

ISBN: (纸本)9783031064272;9783031064265

Traditional methods for adapting pre-trained vision models to downstream tasks involve fine-tuning some or all of the model's parameters. There are a number of trade-offs with this approach. When too many parameters are fine-tuned, the model may lose the benefits associated with pre-training, such as the ability to generalize to out-of-distribution data. But, if instead too few parameters are fine-tuned, the model may be unable to adapt effectively for the tasks downstream. In this paper, we propose Visual Prompt Tuning (VPT) as an alternative to fine-tuning for Transformer-based vision models. Our method is closely related to, and inspired by, prefix-tuning of language models [22]. We find that, by adding additional parameters to a pre-trained model, VPT offers similar performance to fine-tuning the final layer. In addition, for low-data settings and for specialized tasks, such as traffic sign recognition, satellite photo recognition and handwriting classification, the performance of Transformer-based vision models is improved with the use of VPT.

关键词： Computer vision Few-shot Fine-tuning Prompt engineering Prefix-tuning CLIP Transformers Vision transformers

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Unified Fairness from data to learning Algorithm 21

Unified Fairness from Data to Learning Algorithm

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Zhang, Yanfu Luo, Lei Huang, Heng Univ Pittsburgh Elect & Comp Engn Pittsburgh PA 15260 USA

ISBN: (纸本)9781665423984

In classification problems, individual fairness prevents discrimination against individuals based on protected attributes. Fairness-aware methods usually consist of two stages, first determining a fair metric concerning the similarity between different instances and then learning the fairness-aware model. However, existing works usually consider these two stages separately and only focus on improving the individual stage. Moreover, the choice of fair metric is heavily dependent on the task or dataset of interest, which requires ad-hoc domain knowledge and introduces extra difficulty into algorithm designing. As such, this discrepancy presumably leads to sub-optimal fairness-aware pipelines for different applications. In this paper, we propose to fill in the fairness learning gap between these two stages by automatically learning an effective metric integrated into the fairness of both data and classifiers. Specifically, we formulate the fairness-aware classification as a distributional robustness optimization problem based on deep metric learning and propose an effective optimization algorithm to solve it. Meanwhile, we establish the asymptotically unbiased generalization bounds for the proposed algorithm using the techniques of U-statistics. The experimental results on popular benchmark datasets demonstrate that the proposed approach achieves consistent improvement concerning several fairness assessments.

关键词： Domain Knowledge

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A data-driven approach to predict hourly bill rates for US contingent workers 21

A data-driven approach to predict hourly bill rates for US c...

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Giuliani, Giuliano Giuliani, Fabrizio Husband, Summer M. Randstad Sourceright Data Engn & Analyt Amsterdam Netherlands Randstad Sourceright Data Engn & Analyt Houston TX USA

ISBN: (纸本)9781665424271

Contingent workers make up a large portion of the service industry workforce. A bill rate is the hourly fee a company pays to a staffing agency for the services of a temporary worker. In reality, the market for contract labor is fluid and bill rates sometimes fluctuate from week to week. Having a systematic approach that proactively aligns pay and bill rates with shifting market conditions can keep clients from underpaying or overpaying for contract talent and create a competitive advantage for recruiters and hiring managers. By using Randstad historical data, we build a machine learning model for the US labor market that predicts high confidence bill rates for a vast range of job titles at different geographical granularities (national, state and MSA) and that takes into account different experience levels.

关键词： bill rate prediction machine learning algorithm XGBoost data augmentation model evaluation

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