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检索条件"丛书名=Lecture notes in artificial intelligence,"

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Identifying Cannabis Use Risk Through Social Media Based on Deep Learning Methods 21st

Identifying Cannabis Use Risk Through Social Media Based on ...

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21st International Conference on artificial intelligence and Soft Computing (ICAISC)

作者： Ibrahim, Doaa Inkpen, Diana Al Osman, Hussein Ottawa Univ Ottawa ON K1N 6N5 Canada

ISBN: (纸本)9783031234798;9783031234804

Cannabis is the most used drug around the world with the highest risks and associated criminal problems in many countries. This research describes the process of classifying online posts to identify cannabis use problems and their associated risks as early as possible. We annotated 11,008 online posts, which we used to build robust classification models. We tested classical and deep learning classifiers. Different CNN- and RNN-based models proved to be promising approaches to detect cannabis use posts. Our system can be used by authorities (such as parents or doctors) to monitor cannabis use-related posts. It could raise an alarm to the relevant authorities to take necessary interventions to analyze the cannabis use risks associated with the posts. To the best of our knowledge, this is the first study that uses deep learning methods successfully to detect cannabis use from any text or online posts. We tested our deep learning models on the SubUse-Cann unseen dataset which contains 17,099 tweets. It is an imbalanced dataset with only 6.9% positive cannabis use tweets. Our CNN-based model performed the best with an accuracy of 95.25% and an F1-score of 92.83% for classifying cannabis use.

关键词： CNN LSTM Attention Cannabis Marijuana Weed Substance use Classification Risk behavior

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Simplifying Aspect-Sentiment Quadruple Prediction with Cartesian Product Operation 19th

Simplifying Aspect-Sentiment Quadruple Prediction with Carte...

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19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Wang, Jigang Yang, Aimin Zhou, Dong Lin, Nankai Wang, Zepeng Huang, Weifeng Chen, Boyu Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Guangdong Peoples R China Lingnan Normal Univ Sch Comp Sci & Intelligence Educ Zhanjiang 524000 Guangdong Peoples R China Guangdong Univ Foreign Studies Sch Informat Sci & Technol Guangzhou 510006 Guangdong Peoples R China UCL Inst Hlth Informat London England

ISBN: (纸本)9789819947515;9789819947522

Aspect sentiment quad prediction (ASQP) is an emerging subtask of aspect-based sentiment analysis, which seeks to predict the sentiment quadruplets of aspect terms, aspect categories, associated sentiment polarities, and corresponding opinion items in one shot. Recent studies employ text generation models to accomplish this task. However, there are still two problems, how to effectively reduce the ASQP task's high complexity, and the possibility that the generative model may predict explicit terms that do not exist in text sentences. In order to fill the gap, this paper proposes a novel text generation model Cartesian-ASQP based on theTransformer architecture. Specifically, this paper simplifies the aspect-based sentiment quad prediction task to a sentiment triple extraction task by performing a Cartesian product operation on the aspect categories and sentiment polarity sets. For sentiment quadruplet text sentences containing pronouns as implicit terms, we present an implicit term processing strategy by semantically mapping these terms back to pronouns. On the output side, for the situation when the explicit aspect/opinion words predicted by the model are absent from input sentences, this paper introduces a two-stage term correction strategy to solve the problem. Experimental results on two publicly available datasets demonstrate that our proposed model outperforms various baseline methods and achieves outperform performance. This work also validates that our proposed model can effectively handle the task of aspect-based sentiment quad prediction with a large number of implicit aspect and opinion terms.

关键词： Aspect-based sentiment analysis Cartesian product operation Two-stage term correction

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SwinCGH-Net: Enhancing Robustness of Object Detection in Autonomous Driving with Weather Noise via Attention 19th

SwinCGH-Net: Enhancing Robustness of Object Detection in Aut...

引用

19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Cao, Shi Zhu, Qing Zhu, Wanting Beijing Univ Technol Coll Software Engn Beijing Peoples R China

ISBN: (纸本)9789819947607;9789819947614

Object detection in autonomous driving requires high accuracy and speed in different weather. At present, many CNN-based networks have achieved high accuracy on academic datasets, but their performance disastrously degrade when images contain various kinds of noises, which is fatal for autonomous driving. In this paper, we propose a detection network based on shifted windows Transformer (Swin Transformer) called SwinCGH-Net, with a kind of new detector head based on lightweight convolution attention module, which makes full use of the attention mechanism in both feature extraction and detection stages. Specifically, we use Swin Transformer as backbone to extract feature in order to obtain effective information from a small amount of pixels as well as integrate global information. Then we further improve the robustness of the network through the detector head contained lightweight attention block S-CBAM. Furthermore, we use Generalized Focal Loss to calculate loss, which effectively enhances the representation ability of the model. Experiments on Cityscapes and Cityscapes-C datasets demonstrate the superiority and effectiveness of our method in different weather condition. With the increasing level of weather noise, our method shows strong robustness compared with previous method, especially in small object detection.

关键词： Object detection Autonomous driving Robustness Attention mechanism Weather noise

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Human Activity Recognition for Online Examination Environment Using CNN 21st

Human Activity Recognition for Online Examination Environmen...

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21st International Conference on artificial intelligence and Soft Computing (ICAISC)

作者： Ramu, S. Guddeti, Ram Mohana Reddy Mohan, Biju R. Natl Inst Technol Karnataka Dept Informat Technol Mangalore India

ISBN: (纸本)9783031234798;9783031234804

Human Activity Recognition (HAR) is an intelligent system that recognizes activities based on a sequence of observations about human behavior. Human activity recognition is essential in human-to-human interactions to identify interesting patterns. It is not easy to extract patterns since it contains information about a person's identity, personality, and state of mind. Many studies have been conducted on recognizing human behavior using machine learning techniques. However, HAR in an online examination environment has not yet been explored. As a result, the primary focus of this work is on the recognition of human activity in the context of an online examination. This work aims to classify normal and abnormal behavior during an online examination employing the Convolutional Neural Network (CNN) technique. In this work, we considered two, three and four layered CNN architectures and we fine-tuned the hyper-parameters of CNN architectures for obtaining better results. The three layered CNN architecture performed better than other CNN architectures in terms of accuracy.

关键词： Convolutional neural network Human activity recognition Online exam environment

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An Augmented MetiTarski Dataset for Real Quantifier Elimination Using Machine Learning 16th

An Augmented MetiTarski Dataset for Real Quantifier Eliminat...

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16th International Conference on Intelligent Computer Mathematics (CICM)

作者： Hester, John Hitaj, Briland Passmore, Grant Owre, Sam Shankar, Natarajan Yeh, Eric Imandra Inc Austin TX 78704 USA SRI Int Menlo Pk CA 94025 USA

ISBN: (纸本)9783031427527;9783031427534

We contribute a new dataset composed of more than 41K MetiTarski challenges that can be used to investigate applications of machine learning (ML) in determining efficient variable orderings in Cylindrical Algebraic Decomposition. The proposed dataset aims to address inadvertent bias issues present in prior benchmarks, paving the way to development of robust, easy-to-generalize ML models.

关键词： MetiTarski Variable Ordering Machine Learning Datasets Generalizability Bias

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Incremental Informational Value of Floorplans for Rent Price Prediction Applications of Modern Computer Vision Techniques in Real-Estate

Incremental Informational Value of Floorplans for Rent Price...

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14th International Symposium on artificial intelligence (ISAI)

作者： Schneider, Jiyan Jonas Hoshino, Takahiro Keio Univ Grad Sch Econ Minato City Japan RIKEN AIP Ctr Chuo City Japan

ISBN: (纸本)9783031291678;9783031291685

This study strives to examine whether consideration of floorplan images of real-estate apartments could be effective for improving rental price predictions. We use a well-established computer vision technique to predict the rental price of apartments exclusively using their floorplan. Afterward, we use these predictions in a traditional hedonic pricing method to see whether its predictions improved. We found that by including floorplans, we were able to increase the accuracy of the out-of-sample predictions from an R-2 of 0.914 to an R-2 of 0.923. This suggests that floorplans contain considerable information about rent prices, not captured in the other explanatory variables used. Further investigation, including more explanatory variables about the apartment itself, could be used in future research to further examine the price structure of real estate and better understand consumer behavior.

关键词： Forecasting

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Identifying Necessary and Sufficient Conditions for the Enforcement Problem of Argumentation Frameworks 24th

Identifying Necessary and Sufficient Conditions for the Enfo...

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24th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA)

作者： Zhang, Huan Zhang, Songmao Chinese Acad Sci Acad Math & Syst Sci Key Lab MADIS Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China

ISBN: (纸本)9783031212024;9783031212031

In the dynamics research of argumentation frameworks (AFs), the enforcement problem deals with changing an AF for the purpose of ensuring that a certain set of desired arguments becomes (part of) an extension. In this paper we focus on expansions of an AF where solely the addition of new arguments and attacks is allowed and the original framework remains unchanged. Existing results about the enforcement problem under strong and normal expansion are all sufficient conditions. We argue that necessary and sufficient conditions are essential concerning the solvability of the enforcement problem. Specifically, two necessary and sufficient conditions are identified for the non-strict enforcement in respectively the odd-length cycle free and the even-length cycle free AFs under strong expansion. This result can be used to determine that when new arguments satisfying the condition are unavailable, enforcing the desired set simply becomes unsolvable.

关键词： Argumentation framework Enforcement problem Necessary and sufficient condition

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udPINNs: An Enhanced PDE Solving Algorithm Incorporating Domain of Dependence Knowledge 16th

udPINNs: An Enhanced PDE Solving Algorithm Incorporating Dom...

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16th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Chen, Nanxi Qiu, Jiyan Shi, Pengcheng Yuan, Wu Zhang, Jian Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9783031402913;9783031402920

Physics-Informed Neural Networks (PINNs) have demonstrated their effectiveness in solving partial differential equations (PDEs) by integrating PDE knowledge into the neural network training process. However, prior methods were restricted to incorporating only PDE knowledge, and could not utilize broader knowledge from computational mathematics, such as the domain of dependence of PDEs. To tackle this limitation, we introduce a distributed PINNs algorithm called udPINNs (unidirectional Physics-Informed Neural Networks), which is founded on a domain decomposition approach and capable of incorporating domain of dependence knowledge into the training process. This enhancement accelerates training speed and elevates solution accuracy. We validate udPINNs on common equations, including heat transfer and incompressible flow, and demonstrate that it surpasses existing XPINNs methods in terms of error reduction and computational efficiency.

关键词： Distributed deep learning Differential equations Knowledge-driven learning Physics-Informed Neural Networks

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Continual Model-Based Reinforcement Learning for Data Efficient Wireless Network Optimisation 1

引用

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Hasan, Cengis Agapitos, Alexandros Lynch, David Castagna, Alberto Cruciata, Giorgio Wang, Hao Milenovic, Aleksandar Huawei Ireland Res Ctr Dublin Ireland

ISBN: (数字)9783031434273

ISBN: (纸本)9783031434266;9783031434273

We present a method that addresses the pain point of long lead-time required to deploy cell-level parameter optimisation policies to new wireless network sites. Given a sequence of action spaces represented by overlapping subsets of cell-level configuration parameters provided by domain experts, we formulate throughput optimisation as Continual Reinforcement Learning of control policies. Simulation results suggest that the proposed system is able to shorten the end-to-end deployment lead-time by two-fold compared to a reinitialise-and-retrain baseline without any drop in optimisation gain.

关键词： Wireless networks

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A Novel ConvMixer Transformer Based Architecture for Violent Behavior Detection 22nd

A Novel ConvMixer Transformer Based Architecture for Violent...

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22nd International Conference on artificial intelligence and Soft Computing (ICAISC)

作者： Alfarano, Andrea De Magistris, Giorgio Mongelli, Leonardo Russo, Samuele Starczewski, Janusz Napoli, Christian Sapienza Univ Rome Dept Comp Control & Management Engn Via Ariosto 25 I-00185 Rome Italy Sapienza Univ Rome Dept Psychol Via Marsi 78 I-00185 Rome Italy Czestochowa Tech Univ Dept Comp Intelligence Al Armii Krajowej 36 PL-42200 Czestochowa Poland Italian Natl Res Council Inst Syst Anal & Comp Sci Via Taurini 19 I-00185 Rome Italy

ISBN: (纸本)9783031425073;9783031425080

Nowadays most of the streets, squares and buildings are monitored by a large number of surveillance cameras. Nevertheless, these cameras are used only to record scenes to be analyzed after crimes or thefts, and not to prevent violent actions in an automatic way. In few cases there may be a guard who checks the videosmanually in real-time, but it is a very inefficient and expensive process. In this paperwe proposes a novel approach to Violence Detection task using a recent architecture named ConvMixer, a simple CNN which uses patch-based embeddings in order to obtain superior performance with fewer parameters and computation resources. We also use an interesting technique that consists in arranging frames into super images to encode the temporal information into the spatial dimensions. Our tests on popular "Real Life Violence Situations" dataset highlight a remarkable accuracy of 0.95, placing our proposed model at the second position of the leader board on the same dataset.

关键词： ConvMixer SuperImage Violence Detection Action Recognition

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