The International FLAIRS Conference Proceedings最新文献_第2页

A Study of Compressed Language Models in Social Media Domain 社交媒体领域的压缩语言模型研究

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133056

Linrui Zhang, Belinda Copus

{"title":"A Study of Compressed Language Models in Social Media Domain","authors":"Linrui Zhang, Belinda Copus","doi":"10.32473/flairs.36.133056","DOIUrl":"https://doi.org/10.32473/flairs.36.133056","url":null,"abstract":"Transfer learning from large-scale language models is witnessing incredible growth and popularity in natural language processing (NLP). However, operating these large models always requires a huge amount of computational power and training effort. Many applications leveraging these large models are not very feasible for industrial products since applying them into power-scarce devices, such as mobile phone, is extremely challenging. In this case, model compression, i.e. transform deep and large networks to shallow and small ones, is becoming a popular research trend in NLP community. Currently, there are many techniques available, such as weight pruning and knowledge distillation. The primary concern regarding these techniques is how much of the language understanding capabilities will be retained by the compressed models in a particular domain? In this paper, we conducted a comparative analyses between several popular large-scale language models, such as BERT, RoBERTa, XLNet-Large and their compressed variants, e.g. Distilled BERT, Distilled RoBERTa and etc, and evaluated their performances on three datasets in the social media domain. Experimental results demonstrate that the compressed language models, though consume less computational resources, are able to achieve approximately the same level of language understanding capabilities as the large-scale language models in the social media domain.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"9 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121622592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine Learning for Early Mental Health Support and Offenders Correction 早期心理健康支持和罪犯矫正的机器学习

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133338

Nelly Elsayed, Zag ElSayed, Murat Ozer

{"title":"Machine Learning for Early Mental Health Support and Offenders Correction","authors":"Nelly Elsayed, Zag ElSayed, Murat Ozer","doi":"10.32473/flairs.36.133338","DOIUrl":"https://doi.org/10.32473/flairs.36.133338","url":null,"abstract":"The effective rehabilitation and supervision of law offenders are vital to promoting community safety and enabling individuals to reintegrate into society. Community supervision presents several challenges for agencies like the Adult Parole Authority (APA), which must oversee individuals released from prisons under various forms of supervision, including courtesy supervision for different counties and interstate compact cases. With such a large number of individuals under supervision, the APA struggles to provide adequate oversight and support to guide individuals towards positive behavioral changes and reduce the risk of recidivism. To address these challenges, this paper proposes a machine learning-based system designed to monitor and support individuals under community supervision. The model would track various indicators to identify individuals at risk of self-harm or harming others and enable the APA to provide timely and appropriate support to these individuals. Improving the monitoring and support offered during the rehabilitation and supervision period would enhance the effectiveness of community supervision and contribute to safer and more stable communities.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134178444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative Local Interpretable Model-Agnostic Explanations 生成局部可解释模型不可知的解释

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133378

Mohammad Nagahisarchoghaei, Mirhossein Mousavi Karimi, Shahram Rahimi, Logan Cummins, Ghodsieh Ghanbari

{"title":"Generative Local Interpretable Model-Agnostic Explanations","authors":"Mohammad Nagahisarchoghaei, Mirhossein Mousavi Karimi, Shahram Rahimi, Logan Cummins, Ghodsieh Ghanbari","doi":"10.32473/flairs.36.133378","DOIUrl":"https://doi.org/10.32473/flairs.36.133378","url":null,"abstract":"The use of AI and machine learning models in the industry is rapidly increasing. Because of this growth and the noticeable performance of these models, more mission-critical decision-making intelligent systems have been developed. Despite their success, when used for decision-making, AI solutions have a significant drawback: transparency. The lack of transparency behind their behaviors, particularly in complex state-of-the-art machine learning algorithms, leaves users with little understanding of how these models make specific decisions. To address this issue, algorithms such as LIME and SHAP (Kernel SHAP) have been introduced. These algorithms aim to explain AI models by generating data samples around an intended test instance by perturbing the various features. This process has the drawback of potentially generating invalid data points outside of the data domain. In this paper, we aim to improve LIME and SHAP by using a pre-trained Variational AutoEncoder (VAE) on the training dataset to generate realistic data around the test instance. We also employ a sensitivity feature importance with Boltzmann distribution to aid in explaining the behavior of the black-box model surrounding the intended test instance.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134381793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Heart Murmur Classification in Phonocardiogram Representations Using Convolutional Neural Networks 用卷积神经网络对心音图表示的心脏杂音分类

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133189

Mehlam Shabbir, Xudong Liu, M. Nasseri, S. Helgeson

{"title":"Heart Murmur Classification in Phonocardiogram Representations Using Convolutional Neural Networks","authors":"Mehlam Shabbir, Xudong Liu, M. Nasseri, S. Helgeson","doi":"10.32473/flairs.36.133189","DOIUrl":"https://doi.org/10.32473/flairs.36.133189","url":null,"abstract":"Heart murmurs are sounds made by rapid blood flow in the heart. Abnormal heart murmurs can be a sign of serious heart conditions such as arrhythmia and cardiovascular diseases. Therefore, heart murmur classification is crucial for early detection of such conditions. To this end, we study the heart murmur classification problem training selected convolutional neural network (CNN) models (such as VGGNet and ResNet) using various signal representations (such as spectrogram, mel-frequency cepstral coefficient (MFCC), and shorttime Fourier transform (STFT)) of the phonocardiograms in the public PASCAL CHSC dataset. Our preliminary results show that ResNet outperforms VGGNet across all metrics and representations, consistent with the recent published works we can find in literature. Unlike some of these works, however, we see MFCC and STFT in general more effective with higher test accuracies than spectrogram across all CNN models. Looking forward, we propose to study other effective models (such as InceptionV3 and Vision Transformer) to predict heart murmur conditions in phonocardiogram representations including spectrogram, MFCC and STFT, as well as others like Wigner Ville distribution.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132831414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification of Drivers with HIV-Associated Neurocognitive Disorders using Virtual Driving Test Performance Data 利用虚拟驾驶测试数据对hiv相关神经认知障碍驾驶员进行分类

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133381

David Grethlein, Venk Kandadai, W. Dampier

{"title":"Classification of Drivers with HIV-Associated Neurocognitive Disorders using Virtual Driving Test Performance Data","authors":"David Grethlein, Venk Kandadai, W. Dampier","doi":"10.32473/flairs.36.133381","DOIUrl":"https://doi.org/10.32473/flairs.36.133381","url":null,"abstract":"In this work we focus on the problem of identifying drivers with neurocognitive impairment (NCI), specifically an NCI specific to people with HIV (PWH) called HIV-associated neurocognitive disorders (HAND) directly from driving simulator data. Since NCI-screening is typically only effective for more progressed forms of HAND, there is a critical need to identify individuals that should be referred to specialists in order to mitigate potentially dangerous driving behaviors and improve their quality of life. Data collected from (n = 81) study participants that used the virtual driving test (VDT) platform were analyzed in order to predict which drivers had NCI. Of the (n = 62) PWH participants recruited, (n = 35) had HAND; of the remaining (n = 19) HIV negative participants, (n = 7) had non-HAND NCI (e.g., Parkinson’s Disease, Alzheimer’s, etc.). In three separate experiments, subsets of VDT data were first selected via Kruskal-Wallis feature ranking and then used as ensemble inputs to classify whether or not drivers had NCI. Within the PWH population, HAND could be classified with 69.4% accuracy and a risk ratio of 2.09 (95% CI 1.52, 2.65); within the HIV negative population, non-HAND NCI could be classified with 84.2% accuracy, risk ratio of 8.25 (6.34, 10.16); and within the combined population, NCI (regardless of causation) could be classified with 63.0% accuracy, risk ratio of 1.67 (1.22, 2.11).","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133051194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

On Enhancing Security for Division Homomorphism with ElGamal 用ElGamal增强除法同态的安全性

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133266

M. Silaghi, Ameerah Alsulami

引用次数: 0

Reinforcement Learning for Guiding the E Theorem Prover 用于指导E定理证明的强化学习

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133334

Jack McKeown, G. Sutcliffe

引用次数: 0

DynamicG2B: Dynamic Node Classification with Layered Graph Neural Networks and BiLSTM DynamicG2B:基于分层图神经网络和BiLSTM的动态节点分类

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133309

Fattah Muhammad Tahabi, Xiao Luo

{"title":"DynamicG2B: Dynamic Node Classification with Layered Graph Neural Networks and BiLSTM","authors":"Fattah Muhammad Tahabi, Xiao Luo","doi":"10.32473/flairs.36.133309","DOIUrl":"https://doi.org/10.32473/flairs.36.133309","url":null,"abstract":"Most studies in graph theory assume that graphs are static, but in reality, graph structures and features change over time, leading to the concept of dynamic graphs, which is an under-researched area. Contemporary research in dynamic graph representation learning typically treats different snapshots of the graph as separate entities, disregarding the benefits of incorporating temporal information. While some techniques try to solve this problem using recurrent neural network-based solutions, these approaches still face the challenge of the vanishing or exploding gradient problem and complicated training procedures. To address these issues, we propose DynamicG2B, a BiLSTM-based graph neural architecture that computes node representations guided by attention using neighborhood aggregation. Our method applies relevant attention weights at different time steps to classify nodes in a supervised manner, utilizing dynamic edges and node feature information. Our evaluation of two benchmark datasets shows that DynamicG2B outperforms seven state-of-the-art baseline models in node classification in dynamic graphs. Additionally, our analysis of attention weights opens up opportunities for further research into exploring the importance of relationships among graph nodes.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124241472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Interactive Interpretation Viewer for Typed First-order Logic 类型化一阶逻辑的交互式解释查看器

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133073

Jack McKeown, Geoff Sutcliffe

引用次数: 0

Area Coverage Optimization using Networked Mobile Robots with State Estimation 基于状态估计的网络化移动机器人区域覆盖优化

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133074

Isaiah Chism, D. Plante, Md. Suruz Miah

引用次数: 0