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Attention weights accurately predict language representations in the brain

Attention weights accurately predict language representation...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Lamarre, Mathis Chen, Catherine Deniz, Fatma Institute of Software Engineering and Theoretical Computer Science Technische Universität Berlin Germany Bernstein Center for Computational Neuroscience Berlin Germany Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Helen Wills Neuroscience Institute University of California Berkeley United States

In Transformer-based language models (LMs) the attention mechanism converts token embeddings into contextual embeddings that incorporate information from neighboring words. The resulting contextual hidden state embeddings have enabled highly accurate models of brain responses, suggesting that the attention mechanism constructs contextual embeddings that carry information reflected in language-related brain representations. However, it is unclear whether the attention weights that are used to integrate information across words are themselves related to language representations in the brain. To address this question we analyzed functional magnetic resonance imaging (fMRI) recordings of participants reading English language narratives. We provided the narrative text as input to two LMs (BERT and GPT-2) and extracted their corresponding attention weights. We then used encoding models to determine how well attention weights can predict recorded brain responses. We find that attention weights accurately predict brain responses in much of the frontal and temporal cortices. Our results suggest that the attention mechanism itself carries information that is reflected in brain representations. Moreover, these results indicate cortical areas in which context integration may occur. © 2022 Association for Computational Linguistics.

关键词： Brain

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An Approach for Detecting Pneumonia from Chest X-Ray Image Using Convolution Neural Network

An Approach for Detecting Pneumonia from Chest X-Ray Image U...

引用

3rd International Conference on Intelligent Computing and Optimization, ICO 2020

作者： Kar, Susmita Akhtar, Nasim Rahman, Mostafijur Department of Computer Science and Engineering Dhaka University of Engineering and Technology Gazipur Dhaka Bangladesh Department of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9783030681531

Pneumonia is one of the most cynical problems to human beings all over the world and detecting the presence of pneumonia in an early stage is very necessary to avoid Premature Death. According to the World Health Organization above 4 million sudden deaths happen each year from domiciliary air pollution correlated diseases inclusive of pneumonia. Generally, pneumonia can be identified using chest X-ray images that are performed by an expert radiologist. But only rely on the radiologist sometimes blocks the treatment because of detecting diseases from the chest X-ray images which requires human effort, experience, and time. In this case, computer-aided diagnosis (CAD) system is required for identifying pneumonia from chest X-ray images automatically. In this research, a modified model is proposed using Convolution Neural Network (CNN) model to train sample data to relegate and diagnose the presence of pneumonia from an amassment of chest X-ray images. From the experimental result, it is found that the proposed model performs better results (89%) compared to other related existing algorithms. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Enhanced Plant Health Recognition via Humanoid Robot Advanced Sensing and AI

Enhanced Plant Health Recognition via Humanoid Robot Advance...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Manar A. Ibrahim Haneen I. Fayez Salma M. Abdo Toqa E. Ghaly Ingy A. Ghanem Ibrahim Attiya Aya M. Ai-Zoghby Khaled M. Foad Faculty of Computer Science and Engineering Artificial Intelligence Science New Mansoura University Dakhlia Egypt Computer Science Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Faculty of Engineering Computer and Communication program Faculty of Computer Science and Engineering Mansoura University New Mansoura University Dakhlia Egypt Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Computer Science Department Faculty of Computers and Artificial lntelegence Damietta University Damietta Egypt

ISBN: (数字)9798350367775

ISBN: (纸本)9798350367782

Accurate and timely detection of plant diseases is crucial for protecting crop yields and promoting sustainable agriculture. This study introduces a deep learning-based approach for plant health detection by integrating a Convolutional Neural Network (CNN) with a Humanoid robot for real-time monitoring. The approach leverages advanced tools such as TensorFlow for model development, OpenCV for image processing, and YOLOv5 for object detection. A dataset comprising 1,530 images, labeled as “Healthy,” “Powdery,” and “Rust,” was used to train, validate, and test the model. Through pre-processing techniques like rescaling, data augmentation, and feature extraction, the model achieved impressive results, with a training accuracy of 98.4%, validation accuracy of 98.2%, and testing accuracy of 99.3%. This approach marks a significant improvement in precision agriculture, offering a scalable and highly accurate solution for early plant health detection.

关键词： YOLO Training Accuracy Humanoid robots Robot sensing systems Ubiquitous computing Real-time systems Sensors Convolutional neural networks Testing

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Smart Advertising Based on Customer Preferences and Manage the Supermarket 4

Smart Advertising Based on Customer Preferences and Manage t...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Eishan Dinuka, W.H.A. Wickramasinghe, A.Y.S.W. Weerasinghe, W.S.H. Karunaratne, K.P.G. Liyanapathirana, Chethana Rupasinghe, Lakmal Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

As a developing country, Sri Lanka needs to go along with cutting-edge technologies. In the beginning phase of this digital advertising, multiple advertisements were displayed on the users' feeds, including advertisements despite their preferences. This was a terrible user experience for the users. However, smart advertising based on customer preferences can manage the flow of advertisements on the feed as per the users' preferences. This same technique can be used in handling advertisements while shopping at supermarkets. These advertisements can be directed based on demographic characteristics like face and gender and previous customer transactions. Additionally, providing the nearest supermarket they can reach based on their current location. Queue management is the next most crucial facility that needs to be provided to a supermarket. However, the manual system of queue management is not effective. But with a modernized queue management system, overcrowded supermarkets can be managed effectively. This proposed system also considers providing a chatbot service to manage customer inquiries in a reliable strategy. In this system, we mainly used the Keras model called VGGFace for face detection, the Conventional Neural Network and Keras-based model for gender detection, the TensorFlow model called Single Shot MultiBox Detection MobileNet for queue and crowd detection, the Apriori algorithm base model for predicting the buying pattern, a Keras-based model for Artificial Intelligence chatbot and finally, google map Application programming Interface for the nearest supermarket finding are models and technology. This system was developed to manage a supermarket properly. © 2022 IEEE.

关键词： Sales

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Thuryalankara: Artificial Intelligence Based Audio Plugin for Sri Lankan Percussion Instruments 6

Thuryalankara: Artificial Intelligence Based Audio Plugin fo...

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6th International Conference on Information Technology Research, ICITR 2021

作者： Fernando, P.D.C. Fernando, B.A.N. Wanaguru, I.U. Perera, M.A.P.A. Buddhika, Thilini Kodagoda, Nuwan Ganegoda, Devanshi Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665420006

Sri Lankan music is yet to prove its musical prowess by incorporating artificial intelligence tools, therefore, this research introduces a novel invention, an automated audio plugin for music producers, so the process of creating, mixing, mastering, and producing music is easier. To achieve this, the research introduces a Variational AutoEncoder (VAE) machine learning model to create and generate music, an artificial intelligence (AI) system that can automate the mastering process. This research also introduces an innovative component, a virtual instrumentation tool using MIDI technology for the Sri Lankan percussion instruments that allow users to play the instrument virtually using a MIDI keyboard, and alongside it, a preset beat generator that automatically maintain tempo consistency. Thuryalankara was able to receive a collective average of 80% accuracy rate exceeding the predicted accuracy rate of 65% from the software benchmarking test and the physical survey conducted with music producers. Finally, with the inclusion of powerful tools like this, the ultimate objective of this research is to take the Sri Lankan instruments to the international level where any producer from little to plenty experience is able to use this plugin to enhance their musical production. © 2021 IEEE.

关键词： Music

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Joint Caching and Computing Resource Reservation for Edge-Assisted Location-Aware Augmented Reality

Joint Caching and Computing Resource Reservation for Edge-As...

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IEEE International Conference on Communications (ICC)

作者： Yingying Pei Mushu Li Huaqing Wu Qiang Ye Conghao Zhou Shisheng Hu Xuemin Shen Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Canada Department of Electrical and Software Engineering University of Calgary Calgary AB Canada Department of Computer Science Memorial University of Newfoundland St. John's NL Canada

In this paper, we investigate joint caching and computing resource reservation for supporting location-aware augmented reality (AR) applications in an edge-assisted two-tier radio access network. We aim at minimizing the caching and computing resource consumption while satisfying the AR service delay requirement. Specifically, to capture the spatio-temporal AR service dynamics, the resource consumption minimization problem is formulated as a long-term stochastic optimization problem. Due to the time-varying service demands and tightly coupled multi-resource reservation decisions, we propose a novel resource reservation algorithm based on the Lyapunov optimization technique to solve the problem. We first transform the original long-term problem into multiple one-shot optimization problems, each of which is then solved by our designed iterative algorithm in an online manner. Simulation results demonstrate that the proposed algorithm can significantly reduce the overall resource consumption compared to benchmark algorithms.

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EEG Spectral Analysis for Inattention Detection in Academic Domain

EEG Spectral Analysis for Inattention Detection in Academic ...

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Frontiers in Education (FIE) Conference

作者： Sreekanth Gopi Nasrin Dehbozorgi Department of Computer Science Kennesaw State University Kennesaw GA Department of Software Engineering Kennesaw State University Kennesaw GA

The increasing rate of attention deficit among students, attributed to social media, has far-reaching consequences on their academic performance. Inattention or lack of attention is a state of absent-mindedness or not paying enough attention to the details. A rich body of literature suggests that it is highly associated with underachievement in the academic context. In particular, students who have been clinically diagnosed with Attention Deficit Hyperactivity Disorder(ADHD), a neuro-developmental disorder characterized by inattention, hyperactivity, and impulsivity symptoms are more at risk of under-performance and retention, with some leaving school without a terminal degree. In addition to the academic domain, inattention generally impacts the quality of life and future occupations. Research suggests that ADHD is also attributed to societal and unemployment excess costs as well as productivity loss and healthcare expenses which are estimated to be over $14K per adult in the United States (US). Although more than eight million adults were identified with ADHD in the US by 2018, not all inattention cases are associated with ADHD. Inattention could be caused by several other factors such as stress or anxiety and its early detection and timely intervention is critical, especially in the academic domain. There are existing studies that analyze brain signals by Electroencephalogram (EEG) scans to identify individuals who have ADHD. In this study, we developed a Machine learning (ML) pipeline model that is trained on both ADHD and inattention data to determine if a person is having an attention problem. In the first phase of developing the model, we trained a few different classifiers on a 19-channel public EEG dataset of 60 ADHD and 61 non-ADHD participants. Data analysis showed K-Nearest Neighbor (KNN) classifier outperformed other classifiers with an accuracy of 89%. While many of the existing papers focus on ADHD data, in this work we expand our model to analyze the

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IoT-enabled model for Digital Twin of Mental Stress (DTMS)

IoT-enabled model for Digital Twin of Mental Stress (DTMS)

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Ferdousi, Rahatara Hossain, M. Anwar El Saddik, Abdulmotaleb Canada King Saud University College of Computer Information Sciences Department of Software Engineering Saudi Arabia

ISBN: (纸本)9781665423908

Stress has become one of the mental health adversaries of the COVID-19 pandemic. Several stressors like fear of infection, lockdown, and social distancing are commonly accountable for the stress. The existing stress prediction systems are less compatible to handle diversly changing stressors during COVID-19. The traditional approaches often use incomplete features from limited sources (e.g., only wearable sensor or user device) and static prediction techniques. The Edge Artificial Intelligence (Edge AI) employs machine learning to make data from these sources usable for decision making. Therefore, In this study, we propose a Digital Twin of Mental Stress (DTMS) model that employs IoT-based multimodal sensing and machine learning for mental stress prediction. We obtained 98% accuracy for four widely used Machine Learning(ML) algorithms Naïve Bayes(NB), Random Forest(RF), Multilayer Perceptron(MLP), and Decision Tree (DT). The optimal Digital Twin Features (DTF) could reduce the classification time. © 2021 IEEE.

关键词： COVID-19

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Predicting the Air Pollution Level and Creating an Awareness Platform

Predicting the Air Pollution Level and Creating an Awareness...

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Advancements in Computing (ICAC), International Conference on

作者： M.N Nuha G.K.N.P Wishvajith Amitha Caldera N.T Weerasinghe G.S Weeratunga Shashika Lokuliyana Department of Computer Science and Software Engineering Sri Lanka Institute of Infromation Technology Malabe Sri Lanka Department of Computer Systems & Network Engineering Sri Lanka Institute of Infromation Technology Malabe Sri Lanka

This study proposes a comprehensive method to reduce the problems posed by global air pollution. To measure pollution levels and raise public awareness, the research uses a comprehensive strategy that incorporates image processing, time series analysis, chatbot, and facemask recognition. Time series analysis is used to build prediction models by recognizing historical patterns, and image processing techniques are used to examine cloud pictures for precise pollution predictions. Chatbots provide real time communication and may educate the public on environmental facts, health risks, and safety procedures. Additionally, it has a facemask identification system that monitors and promotes the use of masks. Importantly this not only encourages users to take action to reduce pollution but also utilizes predictive models to help mitigate pollution's consequences. These components result in improved public health, environmental conservation, and sustainability. The outcomes reflect significant advantages, with evidence of accuracy and its positive impact on both awareness and proactive measures to combat air pollution.

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Federated Non-Intrusive Load Monitoring for Smart Homes Utilizing Attention-Based Aggregation

Federated Non-Intrusive Load Monitoring for Smart Homes Util...

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IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

作者： Shamisa Kaspour Abdulsalam Yassine Department of Computer Science Lakehead University Thunder Bay Canada Department of Software Engineering Lakehead University Thunder Bay Canada

Nowadays, Non-Intrusive Load Monitoring (NILM) with Federated Learning (FL) framework has become a growing study towards providing a secure energy disaggregation system in smart homes. This study aims at deploying an attention-based aggregation (FedAtt) approach in FL to emphasize agents’ behavioral differences when consuming energy from various appliances. The goal of the proposed technique is to minimize the weighted distance between the parameters of the local model and the global model to better represent each local model’s characteristics. In this paper, we examine two different models for NILM: Short Sequence-to-Point (SS2P) and Variational Auto-Encoder (VAE). Our goal is to evaluate the effectiveness of FedAtt. The evaluation of the framework was carried out using the UK-DALE and REFIT datasets. The obtained results were then compared against centralized approaches of the models as well as FedAvg. Our findings show that FedAtt generates comparable results to the centralized model and FedAvg while improving the stability of FL at different values of added noise to local parameters.

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