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7th ACM International Conference on Interactive Tabletops and Surfaces, ITS 2012

作者： Chua, Kien Chuan Qin, Yongqiang Block, Florian Phillips, Brenda Diamond, Judy Evans, E. Margaret Horn, Michael S. Shen, Chia School of Engineering and Applied Sciences Harvard University Cambridge 02138 United States Department of Computer Science National University of Singapore 117417 Singapore Singapore Department of Computer Science Tsinghua University Beijing 100084 China University of Nebraska State Museum Lincoln NE 68588 United States Center for Human Growth and Development University of Michigan Ann Arbor MI 48109 United States Computer Science and Learning Sciences Northwestern University Evanston 60208 United States

ISBN: (纸本)9781450312097

We present FloTree, a multi-user simulation that illustrates key dynamic processes underlying evolutionary change. Our intention is to create a informal learning environment that links micro-level evolutionary processes to macro-level outcomes of speciation and biodiversity. On a multi-touch table, the simulation represents change from generation to generation in a population of organisms. By placing hands or arms on the surface, visitors can add environmental barriers, thus interrupting the genetic flow between the separated populations. This results in sub-populations that accumulate genetic differences independently over time, sometimes leading to the formation of new species. Learners can morph the result of the simulation into a corresponding phylogenetic tree. The free-form hand and body touch gestures invite creative input from users, encourages social interaction, and provides an opportunity for deep engagement. © 2012 Authors.

关键词： Biodiversity

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Developing an Intelligent Resume Screening Tool With AI-Driven Analysis and Recommendation Features

Applied AI Letters

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Applied AI Letters 2025年第2期6卷

作者： K. L. Abhishek M. Niranjanamurthy Shonit Aric Syed Immamul Ansarullah Anurag Sinha G. Tejani Mohd Asif Shah Department of Artificial Intelligence & Machine Learning BMS Institute of Technology Affiliated to Visvesvaraya Technological University Bangalore Karnataka India Software Engineer Fidelity Investments Bangalore India Department of Management Studies University of Kashmir North Campus Srinagar Jammu and Kashmir India Tech School Computer Science Department ICFAI University Ranchi Jharkhand India Department of Computer Science RC University Palamue Bishrampur Jharkhand India Jadara University Research Center Jadara University Amman Jordan Dean of Faculty Department of Economics Kardan University Kabul Afghanistan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India Division of Research and Development Lovely Professional University Phagwara Punjab India

Current resume screening relies on manual review, causing delays and errors in evaluating large volumes of resumes. Lack of automation and data extraction leads to inefficiencies and potential biases. Recruiters face challenges in identifying qualified candidates due to oversight and time constraints. Inconsistent evaluation criteria hinder decision-making. These issues result in prolonged hiring processes, missed opportunities, and potential bias in candidate selection. The goal of this project is to develop an AI-powered Resume Analysis and Recommendation Tool, catering to the trend of recruiters spending less than 2 min on each CV. The tool will rapidly analyze all resume components while providing personalized predictions and recommendations to applicants for improving their CVs. It will present user-friendly data for recruiters, facilitating export to CSV for integration into their recruitment processes. Additionally, the tool will offer insights and analytics on popular roles and skills within the job market. Its user section will enable applicants to continually test and track their resumes, encouraging repeat usage and driving traffic. Colleges can benefit from gaining insights into students' resumes before placements. Overall, this AI-powered tool aims to enhance the resume evaluation process, benefiting both job seekers and employers. The primary aim of this project is to develop a Resume Analyzer using Python, incorporating advanced libraries such as Pyresparser, NLTK (Natural Language Toolkit), and MySQL. This automated system offers an efficient solution for parsing, analyzing, and extracting essential information from resumes. The user-friendly interface, developed using Streamlit, allows for seamless resume uploading, insightful data visualization, and analytics. The Resume Analyzer significantly streamlines the resume screening process, providing recruiters with valuable insights and enhancing their decision-making capabilities.

关键词： natural language processing parsing resume analysis tokenization

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Face recognition based on 2DPCA and fuzzy-rough technique

Face recognition based on 2DPCA and fuzzy-rough technique

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International Conference on Machine learning and Cybernetics (ICMLC)

作者： Jun-Hai Zhai Chen-Yan Bai Su-Fang Zhang Machine Learning Center Faculty of Mathematics and Computer Science Hebei University Baoding China Teaching and research of section of mathematics Hebei Information Engineering School Baoding China

To remove the redundant features extracted by using 2DPCA methods, a face recognition method is presented based on 2DPCA and fuzzy-rough technique in this paper. The proposed method selects the important features for classification by using attribute reduction in fuzzy rough sets theory. The experimental results show the proposed method outperforms the face recognition methods based on 2DPCA.

关键词： Face Face recognition Databases Feature extraction Principal component analysis Rough sets Covariance matrix

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The Curriculum development for Global AGILE Problem-Based learning in Social Entrepreneurship in Global Teams

The Curriculum Development for Global AGILE Problem-Based Le...

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Pacific Neighborhood Consortium Annual Conference and Joint Meetings (PNC)

作者： Tosh Yamamoto Chris Pang Benson Ong Juling Shih Hui-Chun Chu Meilun Shih Center for Teaching and Learning Kansai University Osaka Japan School of Business Management Nanyang Polytechnic University Singapore Department of Information and Learning Technology National University of Tainan Tainan Taiwan Department of Computer Science and Information Management Soochow University Taipei Taiwan Center for Teaching Learning & Development National Taiwan University Taipei Taiwan

This paper deals with a progress report on the execution of the sense making in the curriculum development for global AGILE Problem-Based learning incorporating computational thinking enhanced with ICT, which has been based on the collaborative endeavors between school of Business Management at Nanyang Polytechnic University in Singapore (NPU) and center for teaching and learning at Kansai University (KU), intending to foster the Vision 2020 skills as well as the future work skills defined by Institute of the Future. Although Problem-Based learning has been ubiquitous in the realm of the face-to-face onsite learning environment, the project is based on PBL in which project team members with common interests in entrepreneurship from both universities organize several teams to aim for startup business plans with simulation in the virtual learning environment. The paper will walk readers through the rationale behind such curriculum as well as the entire process of the curriculum development from the initial preparation to the final product including the assessment. The key factors of such curriculum development are elaborated in the conclusion.

关键词： Handheld computers Education computer science Information management Collaborative work Entrepreneurship

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Deep manifold-to-manifold transforming network

arXiv

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arXiv 2017年

作者： Zhang, Tong Zheng, Wenming Cui, Zhen Li, Chaolong Laboratory of Child Development and Learning Science of Ministry of Education Southeast University China Department of Information Science and Engineering Southeast University China Key Laboratory of Child Development and Learning Science of Ministry of Education Research Center for Learning Science Southeast University Nanjing Jiangsu210096 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Symmetric positive definite (SPD) matrices (e.g., covariances, graph Laplacians, etc.) are widely used to model the relationship of spatial or temporal domain. Nevertheless, SPD matrices are theoretically embedded on Riemannian manifolds. In this paper, we propose an end-to-end deep manifold-tomanifold transforming network (DMT-Net) which can make SPD matrices flow from one Riemannian manifold to another more discriminative one. To learn discriminative SPD features characterizing both spatial and temporal dependencies, we specifically develop three novel layers on manifolds: (i) the local SPD convolutional layer, (ii) the non-linear SPD activation layer, and (iii) the Riemannian-preserved recursive layer. The SPD property is preserved through all layers without any requirement of singular value decomposition (SVD), which is often used in the existing methods with expensive computation cost. Furthermore, a diagonalizing SPD layer is designed to efficiently calculate the final metric for the classification task. To evaluate our proposed method, we conduct extensive experiments on the task of action recognition, where input signals are popularly modeled as SPD matrices. The experimental results demonstrate that our DMTNet is much more competitive over state-of-the-art. Copyright © 2017, The Authors. All rights reserved.

关键词： Singular value decomposition

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Cross-database non-frontal facial expression recognition based on transductive deep transfer learning

arXiv

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arXiv 2018年

作者： Yan, Keyu Zheng, Wenming Zhang, Tong Zong, Yuan Cui, Zhen Key Laboratory of Child Development and Learning Science of Ministry of Education Department of Information Science and Engineering Southeast University China Key Laboratory of Child Development and Learning Science of Ministry of Education Research Center for Learning Science Southeast University NanjingJiangsu210096 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing Jiangsu210096 China

Cross-database non-frontal expression recognition is a very meaningful but rather difficult subject in the fields of computer vision and affect computing. In this paper, we proposed a novel transductive deep transfer learning architecture based on widely used VGGface16-Net for this problem. In this framework, the VGGface16-Net is used to jointly learn an common optimal nonlinear discriminative features from the non-frontal facial expression samples between the source and target databases and then we design a novel transductive transfer layer to deal with the cross-database non-frontal facial expression classification task. In order to validate the performance of the proposed transductive deep transfer learning networks, we present extensive cross-database experiments on two famous available facial expression databases, namely the BU-3DEF and the Multi-PIE database. The final experimental results show that our transductive deep transfer network outperforms the state-of-the-art cross-database facial expression recognition methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Database systems

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ROUGH-NEURAL IMAGE CLASSIFICATION USING WAVELET TRANSFORM

ROUGH-NEURAL IMAGE CLASSIFICATION USING WAVELET TRANSFORM

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2007 International Conference on Machine learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： XI-ZHAO WANG JUN-HAI ZHAI SU-FANG ZHANG Machine Learning Center.Faculty of Mathematics and Computer Science Hebei University Baoding 07100 Teaching and research of section of mathematics Hebei Information Engineering School Baoding 07100

Image classification plays an important role in many tasks, which is still a challenging *** paper proposes a hybrid image classification method, which integrates wavelet transform, rough set approach, and artificial neural networks (ANNs).Wavelet transform is employed to decompose the original images into different frequency sub-bands, then a set of statistical features are extracted from the wavelet coefficients, the feature set can be viewed as an information *** wavelet transform well decorrelates images, there still exist dependencies between *** the features extracted from the coefficients may be *** the features from one sub-band are dependent on the features from another sub-band, the later one can be *** set approach is utilized to remove the correlated or redundant *** reduced information system finally fed into neural network for *** performance of the method is evaluated in terms of training accuracy and testing accuracy, the experimental results confirm the effectiveness of the proposed approach.

关键词： Image classification Wavelet transform Rough sets Neural networks Statistical features

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Group-Level Emotion Recognition Using Hierarchical Dual-Branch Cross Transformer with Semi-Supervised learning

Group-Level Emotion Recognition Using Hierarchical Dual-Bran...

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Software engineering and Artificial Intelligence (SEAI), IEEE International Conference on

作者： Jinke Xu Xiaohua Huang School of Computer Engineering Nanjing Institute of Technology Nanjing China Key Laboratory of Child Development and Learning Science (Southeast University) Ministry of Education Nanjing China Research Center for Learning Science Southeast University Nanjing China

ISBN: (数字)9798350374346

ISBN: (纸本)9798350374353

Group-level emotion recognition (GER) has received attention from researchers to identify an overall emotion in a multi-person scene. To address attention issue on group dynamic in GER, a dual-branch cross-attention transformer was recently developed. However, the existing method failed to effectively distinguish confused emotion categories. On the other hand, the data scarcity of GER dataset limits the performance improvement of GER. In this paper, we propose a novel method,namely hierarchical dual-branch cross transformer with semi-supervised learning for GER. Firstly, we introduce semi-supervised learning to fully exploit the unlabelled data for dual-branch cross transformer architecture. Furthermore, hierarchical classification stage is presented in the architecture, where two scene information with various patch size was used in the first stage, while scene and the most important face information in second stage. Intensive experiments are conducted on group-level emotion database. The experimental results demonstrate that the proposed approach achieves promising performance for GER compared with the recent state-of-the-art methods.

关键词： Emotion recognition Accuracy Databases Face recognition Supervised learning computer architecture learning (artificial intelligence)

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A heterogeneous graph cross-omics attention model for single-cell representation learning

A heterogeneous graph cross-omics attention model for single...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yue Liu Junfeng Zhang Shulin Wang Wei Zhang Xiangxiang Zeng Chee Keong Kwoh College of Computer Science and Electronic Engineering Hunan University Changsha Hunan Hangzhou Machine Learning Research and Development Center Sina Weibo Hangzhou Zhejiang College of Computer Engineering & Applied Mathematics Changsha University Changsha Hunan School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Single-cell multi-omics sequencing technologies allow simultaneous measurement of transcriptome and epigenome profiles in the same cell, providing unprecedented opportunities to dissect cell heterogeneity. Despite great efforts, conjoint analysis of single-cell multi-omics data still suffers from sparsity, high dimensionality and binary. In this study, we present a heterogeneous graph cross-omics attention model (scHGA), a computational tool based on a heterogeneous graph neural network combining two attention mechanisms to jointly analyze single-cell multi-omics data based on different protocols data, including SNARE-seq, scMT-seq and sci-CAR. To avoid the cell heterogeneity of single-omics data, scHGA automatically learns a cell association graph to capture neighbor information. The latent representation of aggregated cells generated by hierarchical attention can fuse knowledge across different omics to dissect cellular heterogeneity, providing a better scheme to characterize the features of cells. scHGA is an effective exploration of graph neural networks in single-cell multi-omics analysis, providing new insights into the understanding of single-cell sequencing data.

关键词： Heating systems Analytical models Sequential analysis Correlation Protocols Data analysis Biological system modeling

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A survey on studying the social networks of students

arXiv

引用

arXiv 2019年

作者： Saxena, Akrati Saxena, Pratishtha Reddy, Harita Gera, Ralucca Department of Computer Science School of Computing National University of Singapore Singapore Singapore Department of Computer Science and Engineering Center for Advanced Studies Lucknow India Department of Computer Science and Engineering National Institute of Technology Karnataka India Department of Applied Mathematics Associate Provost for Graduate Education Teaching and Learning Commons Naval Postgraduate School MontereyCA United States

Do studies show that physical and online students’ social networks support education? Analyzing interactions between students in schools and universities can provide a wealth of information. Studies on students’ social networks can help us understand their behavioral dynamics, the correlation between their friendships and academic performance, community and group formation, information diffusion, and so on. Educational goals and holistic development of students with various academic abilities and backgrounds can be achieved by incorporating the findings attained by the studies in terms of knowledge propagation in classroom and spread of delinquent behaviors. Moreover, we use Social Network Analysis (SNA) to identify isolated students, ascertain the group study culture, analyze the spreading of various habits like smoking, drinking, and so on. In this paper, we present a review of the research showing how analysis of students’ social networks can help us identify how improved educational methods can be used to make learning more inclusive at both school and university levels and achieve holistic development of students through expansion of their social networks, as well as control the spread of delinquent behaviors. Copyright © 2019, The Authors. All rights reserved.

关键词： Students

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