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7th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2023

作者： Huseyinov, Ilham Diallo, Issa Raed, Mhd Wasim Software Engineering Department Istanbul Aydin University Istanbul Turkey Graduate Institute of Science Artificial Intelligence and Data Science Program Istanbul Aydin University Istanbul Turkey Computer Engineering Department Istanbul Aydin University Istanbul Turkey

ISBN: (纸本)9783031437885

One way to save time and resources in the human recruitment and hiring process is to post open job positions on the Internet, but the overload of applications creates challenges for hiring managers and companies to select an adequate candidate. One of the solutions is to apply intelligent tools such as Deep Learning and recommender system algorithms to speed up the hiring process and identify the right candidates. In this paper, we propose a two-fold algorithmic approach to 1) building an RNN classifier for resume classification;2) using cosine similarity for resume recommendation to find a candidate that fits job requirements best after selecting all the resumes that belong to the right category. The performance of the proposed RNN classifier is evaluated in terms of accuracy, precision, recall, F1-score, and confusion matrix criteria. The experiment results have shown that the RNN classifier performs better than the other classifiers such as GNB, Linear SVM, RF, and BERT on the same dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Recommender systems

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Bioinformatics

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IEEE Potentials 2004年第1期23卷 24-27页

作者： Doom, Travis Raymer, Michael Krane, Dan IEEE Wright State University United States Department of Computer Science and Engineering Graduate Faculty in Biomedical Sciences Dayton OH United States WSU Biomedical Sciences Ph.D. Program Forensic Bioinformatics Inc. United States Bioinformatics Research Group Computer Science Department Wright State University United States

No abstract available

关键词： Bioinformatics Biology computing Biological information theory Data analysis Computational biology Proteins Biochemistry Sequences Genomics Large-scale systems

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Lightweight Pedestrian Detection through Guided Filtering and Deep Learning 10

Lightweight Pedestrian Detection through Guided Filtering an...

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10th IEEE Global Conference on Consumer Electronics, GCCE 2021

作者： Muchtar, Kahlil Saddami, Khairun Bintang, Akhyar Cenggoro, Tjeng Wawan Pardamean, Bens Lin, Chih-Yang Ernita, Tia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Bina Nusantara University School of Computer Science Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Yuan-Ze University Department of Electrical And Engineering Taoyuan Taiwan

ISBN: (纸本)9781665436762

In this paper, we propose a novel approach to locate and detect moving pedestrians in a video. Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided filtering. This novel background subtraction algorithm allows our method to also filter unexpected noises at the same time, which could benefit the performance of our proposed method. Subsequently, the pedestrians are detected using YOLOv3 within the provided ROI. Our experiments showed that the proposed method has a competitive performance in the CDNET2014 dataset with a fast-processing time. © 2021 IEEE.

关键词： Convolutional neural networks

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Patient-specific pipeline to create virtual endoscopic third ventriculostomy scenarios

Patient-specific pipeline to create virtual endoscopic third...

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21st Medicine Meets Virtual Reality Conference, NextMed/MMVR 2014

作者： Armstrong, Ryan Eagleson, Roy De Ribaupierre, Sandrine Biomedical Engineering Graduate Program University of Western Ontario 1151 Richmond St. London ON Canada Department of Clinical Neurological Sciences University of Western Ontario Canada Department of Electrical and Computer Engineering University of Western Ontario Canada

ISBN: (纸本)9781614993742

Graphically realistic, haptics-enabled, virtual reality surgical simulations are becoming increasingly popular training tools for neurosurgical procedures and surgical skills. Compared with traditional training models, virtual simulations are safe, versatile, consistent, and are relatively cost effective. Most simulations are deficient, however, in representing the diverse anatomical variations that occur clinically. In this work, we describe the design and implementation of a pipeline to create patient-specific surgical scenarios for simulator-based training of the endoscopic third ventriculostomy: the procedure of choice for treating obstructive hydrocephalus. © 2014 The authors and IOS Press. All rights reserved.

关键词： Virtual reality

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3D Geometric Shape Assembly via Efficient Point Cloud Matching 41

3D Geometric Shape Assembly via Efficient Point Cloud Matchi...

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41st International Conference on Machine Learning, ICML 2024

作者： Lee, Nahyuk Min, Juhong Lee, Junha Kim, Seungwook Lee, Kanghee Park, Jaesik Cho, Minsu Department of Computer Science and Engineering POSTECH Pohang Korea Republic of Graduate School of Artificial Intelligence POSTECH Pohang Korea Republic of Department of Computer Science and Engineering Seoul National University Seoul Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

Learning to assemble geometric shapes into a larger target structure is a pivotal task in various practical applications. In this work, we tackle this problem by establishing local correspondences between point clouds of part shapes in both coarse- and fine-levels. To this end, we introduce Proxy Match Transform (PMT), an approximate high-order feature transform layer that enables reliable matching between mating surfaces of parts while incurring low costs in memory and compute. Building upon PMT, we introduce a new framework, dubbed Proxy Match TransformeR (PMTR), for the geometric assembly task. We evaluate the proposed PMTR on the large-scale 3D geometric shape assembly benchmark dataset of Breaking Bad and demonstrate its superior performance and efficiency compared to state-of-the-art methods. Project page: https://***/pmtr. Copyright 2024 by the author(s)

关键词： 3D modeling

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Single Layer & Multi-layer Long Short-Term Memory (LSTM) Model with Intermediate Variables for Weather Forecasting 3

Single Layer & Multi-layer Long Short-Term Memory (LSTM) Mod...

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3rd International Conference on computer Science and Computational Intelligence, ICCSCI 2018

作者： Salman, Afan Galih Heryadi, Yaya Abdurahman, Edi Suparta, Wayan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Engineering Faculty of Science and Technology Sanata Dharma University Yogyakarta55282 Indonesia

Weather forecasting has gained attention many researchers from various research communities due to its effect to the global human life. The emerging deep learning techniques in the last decade coupled and the wide availability of massive weather observation data have motivated many researches to explore hidden hierarchical pattern in the large volume of weather dataset for weather forecasting. The purposes of this research are to build a robust and adaptive statistical model for forecasting univariate weather variable in Indonesian airport area and to explore the effect of intermediate weather variable related to accuracy prediction using single layer Long Short Memory Model (LSTM) model and multi layers LSTM model. The proposed forecasting model is an extension of LSTM model by adding intermediate variable signal into LSTM memory block. The premise is that two highly related patterns in input dataset will rectify the input patterns so make it easier for the model to learn and recognize the pattern from the training dataset. In an effort to achieve a robust model for learning and recognizing weather pattern, this research will also explore various architectures such as single layer LSTM and Multiple Layer LSTM (4 layers LSTM). The dataset is weather variable data collected by Weather Underground at Hang Nadim Indonesia airport. This research used visibility as predicted data and temperature, pressure, humidity, dew point as intermediates data. The best model of LSTM in this experiment is multiple layers LSTM and the best intermediate data is pressure variable. Using the pressure variable this model has gained the validation accuracy 0.8060 and RMSE 0.0775. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Long short-term memory

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Improvement of Spatial Ambiguity in Multi-Channel Speech Separation Using Channel Attention

Improvement of Spatial Ambiguity in Multi-Channel Speech Sep...

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2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021

作者： Hong, Qian-Bei Wu, Chung-Hsien Nguyen, Thanh Binh Wang, Hsin-Min National Cheng Kung University and Academia Sinica Graduate Program of Multimedia Systems and Intelligent Computing Tainan Taiwan National Cheng Kung University Department of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9789881476890

Multi-channel speech separation has been successfully applied in a complex real-world environment such as the far-field condition. The common solution to deal with the far-field condition is using a multi-channel signal captured by a structured microphone array and leveraging the inner difference between channels to enhance the speech separation performance. The spatial feature has been widely used in recent speech separation research. This feature appears to be insufficient when the location information becomes ambiguous. This is known as the spatial ambiguity problem. In order to deal with the spatial ambiguity problem, this study proposes an attention mechanism for the Temporal-Spatial Neural Filter (TSNF), in which the channel attention on merged features and the feature map of 1D convolution block in the temporal convolution network is proposed. The proposed method is evaluated on the multi-channel reverberant dataset which is built based on the WSJ0-2mix dataset. The dataset is simulated in the real-environment room by using the Room Impulse Response generator. In the experimental results, the proposed methods produced the SI -SNR improvement of about 1.2dB in close speakers' case, while a small decrease of 0.1dB in other cases. © 2021 APSIPA.

关键词： Convolution

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Experimental analysis of coordination strategies to support wireless sensor networks composed by static ground sensors and UAV-carried sensors

Experimental analysis of coordination strategies to support ...

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IEEE International Symposium on Parallel and Distributed Processing With Applications

作者： De Freitas, Edison Pignaton Heimfarth, Tales Pereira, Carlos Eduardo Ferreira, Armando Morado Wagner, Flávio Rech Larsson, Tony School of Information Science Computer and Electrical Engineering Halmstad University Halmstad Sweden Institute of Informatics Federal University of Rio Grande do Sul Porto Alegre Brazil Computer Science Department Federal University of Lavras Lavras Brazil Defense Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil

ISBN: (纸本)9780769541907

This paper presents a comparison among different strategies to coordinate the use of heterogeneous wireless sensors aimed for area surveillance. The heterogeneity among the sensor nodes is related to their sensing and mobility capabilities. The goal of the strategies is to provide coordination among the different nodes, in order to make the wireless sensor network perform its missions with higher efficiency. Strategies combine advantages of bio-inspired and utility-based approaches to coordination. Simulations of scenarios with different characteristics were performed and the results are compared and analyzed. © 2010 IEEE.

关键词： Sensor nodes

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Act As You Wish: Fine-Grained Control of Motion Diffusion Model with Hierarchical Semantic Graphs 37

Act As You Wish: Fine-Grained Control of Motion Diffusion Mo...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Jin, Peng Wu, Yang Fan, Yanbo Sun, Zhongqian Wei, Yang Yuan, Li School of Electronic and Computer Engineering Peking University Shenzhen China Peng Cheng Laboratory Shenzhen China Tencent AI Lab China -Preferred Program Peking University Shenzhen Graduate School China

ISBN: (纸本)9781713899921

Most text-driven human motion generation methods employ sequential modeling approaches, e.g., transformer, to extract sentence-level text representations automatically and implicitly for human motion synthesis. However, these compact text representations may overemphasize the action names at the expense of other important properties and lack fine-grained details to guide the synthesis of subtly distinct motion. In this paper, we propose hierarchical semantic graphs for fine-grained control over motion generation. Specifically, we disentangle motion descriptions into hierarchical semantic graphs including three levels of motions, actions, and specifics. Such global-to-local structures facilitate a comprehensive understanding of motion description and fine-grained control of motion generation. Correspondingly, to leverage the coarse-to-fine topology of hierarchical semantic graphs, we decompose the text-to-motion diffusion process into three semantic levels, which correspond to capturing the overall motion, local actions, and action specifics. Extensive experiments on two benchmark human motion datasets, including HumanML3D and KIT, with superior performances, justify the efficacy of our method. More encouragingly, by modifying the edge weights of hierarchical semantic graphs, our method can continuously refine the generated motion, which may have a far-reaching impact on the community. Code and pre-trained weights are available at https://***/jpthu17/GraphMotion. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Topology

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A Comparative Study of Meningioma Tumors Segmentation Methods from MR Images 3

A Comparative Study of Meningioma Tumors Segmentation Method...

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3rd International Conference on Signal Processing and Information Security, ICSPIS 2020

作者： Alkhodari, Mohanad Hassanin, Omnia Dhou, Salam Biomed. Eng. Graduate Program American University of Sharjah Sharjah United Arab Emirates Dept. of Computer Science and Engineering American University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781728189987

Brain tumor segmentation from magnetic resonance (MR) images can have a great impact on improving diagnostics, growth rate prediction, and treatment planning. In this paper, we provide a comparative study of four well-known segmentation algorithms, namely k-means clustering, histogram thresholding (Otsu), fuzzy c-means thresholding, and region growing. For the region growing algorithm, the seed selection process is automated and enhanced by preprocessing the images and approximating the tumor regions using initial clustering and/or thresholding approaches. The evaluation and comparison of the algorithms is conducted using a data-set of T1-Weighted Contrast-Enhanced magnetic resonance imaging (MRI) brain images. Ground truth tumor images were provided by three experienced radiologists and are used in the evaluation process. Results showed that the enhanced region growing method had the highest mean dice similarity coefficient with a score of 0.87, and the lowest under-segmentation rate (17.46%). The fuzzy c-means thresholding method had the lowest over-segmentation rate (0.03%). This study serves as a baseline for other advanced tumor segmentation studies such as the ones using the emergent machine learning approaches. © 2020 IEEE.

关键词： Image segmentation

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