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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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iGrade: an automated short answer grading system 22

iGrade: an automated short answer grading system

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proceedings of the 2022 6th international conference on Natural Language Processing and Information Retrieval

作者： Dina Hamed Alhamed Aljawharah Mohammad Alajmi Yasminah Hani Alali Tayma Abdulrahman Alqahtani Maryam Riyadh Alnassar Dina Abdullah Alabbad College of Computer science and Information Technology Imam Abdulrahman Bin Faisal University Saudi Arabia

ISBN: (纸本)9781450397629

During the COVID-19 pandemic, most countries rely on E-learning to apply social distance policy which affects the exams evaluation process. this project aimed to assist instructors in grading the short answer questions for CCSIT courses. By implanting a website application that the instructors could use to upload the students' answers and the ‘iGrade” software model will grade it. Moreover, the system will reduce the workload on the facilities members by saving time and effort as well as guarantee an objective grading for students. the model used in this project is a state-of-the-art BERT Neural Network model along with layers of BiLSTM that was trained using a dataset that has been collected from previous midterm and final exams of the CIS 211 course. the dataset consists of three categories which are (0, 0.5, 1) with around 1,128 instances. the "iGrade" test obtained an accuracy score of 85,4%, demonstrating BERT's superiority and independence from features during short answer grading as a default method in *** CONCEPTS • Computing methodologies • Artificial intelligence • Natural language processing

关键词： Automated short answer questions grading BERT BiLSTM Deep learning

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An application of Co-plot Analysis: A Multidimensional Scaling Data Visualization 23

An Application of Co-plot Analysis: A Multidimensional Scali...

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proceedings of the 2023 6th international conference on machine learning and Natural Language Processing

作者： Priyanka Poosapati Yao Shi Judith Gebauer Yang Song Department of Computer Science University of North Carolina Wilmington USA Congdon School of Supply Chain Business Analytics Information Systems University of North Carolina Wilmington USA

ISBN: (纸本)9798400709241

Exploratory graphs are a crucial element of statistical analysis and models that help us identify and interpret data patterns. As one example of exploratory graphs, multidimensional scaling (MDS) allows the visualization of large and complex datasets in a reduced dimensional space, while preserving pairwise differences and similarities between data points. Co-plot analysis extends the capabilities of traditional MDS and enables a multifaceted view of relationships between variables from different datasets. To develop a co-plot graph, the analyst starts out by standardizing the dataset and by generating a special coefficient (MADCC – median absolute deviation correlation coefficient) that subjects the dataset to MDS-embedding and helps to determine how the dataset fits the surface. In the current paper, we demonstrate how co-plot analysis can be applied to describe and compare the behavioral patterns of ecological communities and species across multiple sites and environmental contexts. We demonstrate the use of non-metric multidimensional scaling (NMDS) and robust multidimensional scaling (RMDS) techniques to visualize dissimilarities and distances of the observations in a reduced dimensional space. We also explain the use of Shepard graphs as a second technique to visualize categorical variables in a reduced-dimensional space, with points closer together indicating higher similarity. Shepard graphs also allow us to assess the goodness of fit, which is indicated by the strength of a positive linear relationship. For our application, the co-plots graphs are useful to visualize a complete set of co-relations between the species of different communities. It also allows us to plot how every factor affects the number of species against the mean and against the median and to determine the best approach: A co-plot graph against the mean may be influenced by the shape of the distribution, especially if the data is skewed, whereas a co-plot graph against the median is typica

关键词： Co-plot Data visualization Multidimensional scaling (MDS)

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4th international conference on Advances in Computing and Data Sciences, ICACDS 2020

4th International Conference on Advances in Computing and Da...

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4th international conference on Advances in Computing and Data Sciences, ICACDS 2020

ISBN: (纸本)9789811566332

the proceedings contain 46 papers. the special focus in this conference is on Advances in Computing and Data Sciences. the topics include: Intelligent Mobile Edge Computing: A Deep learning Based Approach;analysis of Clustering Algorithms in machine learning for Healthcare Data;securing Mobile Agents Migration Using Tree Parity machine with New Tiny Encryption Algorithm;an Approach to Waste Segregation and Management Using Convolutional Neural Networks;open Source Intelligence Initiating Efficient Investigation and Reliable Web Searching;a Neural Network Based Hybrid Model for Depression Detection in Twitter;Unleashing the VEP Triplet Count of Virtually Created 3D Bangla Alphabet to Integrate with Augmented Reality application;a Hybrid machine learning Framework for Prediction of Software Effort at the Initial Phase of Software Development;chronic Disease Prediction Using Deep learning;the Moderating Effect of Demographic Factors Acceptance Virtual Reality learning in Developing Countries in the Middle East;a Deep learning Based Method to Discriminate Between Photorealistic computer Generated Images and Photographic Images;Load Balancing Algorithm in Cloud Computing Using Mutation Based PSO Algorithm;statistical Model for Qualitative Grading of Milled Rice;measuring the Effectiveness of Software Code Review Comments;proposed Model for Feature Extraction for Vehicle Detection;analysis of Feature Selection Methods for P2P Botnet Detection;ELM-MVD: An Extreme learning machine Trained Model for Malware Variants Detection;real-Time Biometric System for Security and Surveillance Using Face Recognition;an Effective Block-Chain Based Authentication Technique for Cloud Based IoT;early Detection of Autism Spectrum Disorder in Children Using Supervised machine learning;digital Marketing Effectiveness Using Incrementality.

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Framing Engineering as Community Activism for Values-Driven Engineering: RFE Design and Development (Years 3-4) 129

Framing Engineering as Community Activism for Values-Driven ...

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129th ASEE Annual conference and Exposition: Excellence through Diversity, ASEE 2022

作者： Davis, Virginia A. Lakin, Joni M. Marghitu, Daniela Davis, Edward W. The University of Alabama United States

this research used a mixed-methods approach to understand how framing engineering as an altruistic, or prosocial, profession affected the engineering identity development of students from underrepresented minorities in STEM. the research was conducted through a theoretical lens encompassing the Goal Congruity Framework and Social Congruity theory. To better understand the impacts of altruistic framing, a traditional Saturday STEM program with participants from the same demographics were studied for comparison. through interviews and surveys, we found that altruistic framing led to meaningful changes in students' appreciation of engineering and, in some cases, new interests in pursuing engineering as a career. Students also increased their interest in engineering, but their definitions of the field did not broaden appreciably. Some found new interests, but they did not have the same type of transformative experience as the altruism-focused interventions. this research has shown that framing engineering as an altruistic career path can lead to meaningful changes in students' definitions of engineering and their connection of engineering to their career interests. © American Society for Engineering Education, 2022

关键词： Students

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Graph Representation learning: Foundations, Methods, applications and Systems 21

Graph Representation Learning: Foundations, Methods, Applica...

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27th ACM SIGKDD international conference on Knowledge Discovery and Data Mining (KDD)

作者： Jin, Wei Ma, Yao Wang, Yiqi Liu, Xiaorui Tang, Jiliang Cen, Yukuo Qiu, Jiezhong Tang, Jie Shi, Chuan Ye, Yanfang Zhang, Jiawei Yu, Philip S. Michigan State Univ Comp Sci & Engn E Lansing MI 48824 USA Michigan State Univ Comp Sci & Engn Dept E Lansing MI 48824 USA Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Case Western Reserve Univ Cleveland OH 44106 USA Case Western Reserve Univ Dept Comp & Data Sci Cleveland OH 44106 USA Florida State Univ Tallahassee FL 32306 USA Florida State Univ Comp Sci Tallahassee FL 32306 USA Univ Illinois Chicago IL USA Univ Illinois Comp Sci Chicago IL USA

ISBN: (纸本)9781450383325

Graphs such as social networks and molecular graphs are ubiquitous data structures in the real world. Due to their prevalence, it is of great research importance to extract meaningful patterns from graph structured data so that downstream tasks can be facilitated. Instead of designing hand-engineered features, graph representation learning has emerged to learn representations that can encode the abundant information about the graph. It has achieved tremendous success in various tasks such as node classification, link prediction, and graph classification and has attracted increasing attention in recent years. In this tutorial, we systematically review the foundations, techniques, applications and advances in graph representation learning. We first introduce the foundations on graph theory and graph Fourier analysis. We then cover the key achievements of graph representation learning in recent years. Concretely, we discuss the six topics: 1) network embedding theories and systems;2) foundations of graph neural networks (GNNs);3) CogDL toolkit for GNNs;4) scalable GNNs;5) self-supervised learning in GNNs and 6) heterogeneous graphs and heterogeneous GNNs. Finally, we will introduce the applications of graph representation learning with a focus on recommender systems.

关键词： heterogeneous graphs graph neural networks graph representation learning

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AR Interface for Teaching Students with Special Needs 4

AR Interface for Teaching Students with Special Needs

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4th international conference on Computational Linguistics and Intelligent Systems (COLINS)

作者： Supruniuk, Kateryna Andrunyk, Vasyl Chyrun, Lyubomyr Lviv Polytech Natl Univ Lvov Ukraine Ivan Franko Natl Univ Lviv Lvov Ukraine

the article is an overview of one of the practical realization of the augmented reality interface. the purpose of the study is to create an alpha version of the augmented reality application-interface to assist children with special needs in the social orientation. the objects of the study are methods and tools that can help in the education of this category of students. the interface is implemented to solve the educational problems of a certain category of students with the help of augmented reality and machine learning. the work is being completed by highlighting aspects that need further development and research.

关键词： Augmented reality machine learning Special needs

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Android Malware Classification Based on Static Features of an application 4th

Android Malware Classification Based on Static Features of a...

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4th international conference on Computing and Network Communications, CoCoNet 2020

作者： Ashwini, S.D. Pai, Manisha Sangeetha, J. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru India

ISBN: (纸本)9789813369863

Android is the most sought-after mobile platform that has changed what mobiles can do. Due to this, a continuous increase in android malware applications has been seen that poses a significant hazard to users. thus, the detection of malware applications in the Android environment has become a trending research field for cybersecurity researchers. Android malware detection depends on characterizing the Android application’s functionalities. Over the years, malware has evolved and has become more sophisticated. Hence, it cannot be detected only using a single static feature as it might result in a high number of false negatives. We propose a detection model in this paper that accurately classifies the samples as malware or benign with fewer false positives and false negatives. We have used string features that include suspicious API calls, used permissions, requested permissions, filtered intents, hardware components, and restricted API calls. We have then employed four machine learning algorithms, namely, Ridge Classifier, XGBoost Classifier, Random Forest, and Support Vector Classifier to evaluate the effectiveness of the binary feature vector formed by the combination of these string features. It was noted that Random Forest achieved the highest score for accuracy, precision, recall, area under curve, and F1 score. © 2021, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Android malware

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application of Capsule Network to Tablet Identification 21

Application of Capsule Network to Tablet Identification

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proceedings of the 2021 4th international conference on Algorithms, Computing and Artificial Intelligence

作者： Wenliang He Minling Zhu Computer School Beijing Information Science & Technology University China

ISBN: (纸本)9781450385053

To address the problem of pill defect recognition, we propose to apply capsule neural network for pill defect recognition. the effects of dynamic routing iteration rounds and different compression function constant term schemes on the performance of the capsule neural network are investigated in the context of small data sets, and the model effects are verified experimentally. the experimental results show that both the number of dynamic routing iteration rounds and different compression function constant term schemes can affect the performance of the capsule neural network, and it is essential to choose an appropriate scheme in the process of training the model. the capsule neural network is found to be suitable for the field of pill defect recognition through *** CONCEPTS • Computing methodologies∼Visual content-based indexing

关键词： capsule network image recognition Deep learning dynamic routing pill identification

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Research on air quality prediction method in Hangzhou based on machine learning 4

Research on air quality prediction method in Hangzhou based ...

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2021 4th international conference on computer Information Science and application Technology, CISAT 2021

作者： Fu, Zhongjie Lin, Haiping Huang, Bingqiang Yao, Jiana College of Information Engineering Hangzhou Vocational and Technical College Zhejiang Hangzhou310018 China Department of Robotics Engineering Zhejiang University of Science and Technology Zhejiang Hangzhou310023 China

Air pollution has become the subject of many current environmental studies, and the quality of air is directly related to the quality of life and health of human beings. In this paper, the Bayesian network model is used to predict air quality in Hangzhou. Six air pollutants SO2, NO2, O3, CO, PM2.5 and PM10 are used as the evaluation factors of the model, and AQI value is the output of the model, and then the Bayesian network model is established. Finally, the model is used to predict air quality and compare with the actual value. the results show that the accuracy of air quality prediction is over 80%, and the predicted value is close to the actual value in most cases, and this shows that Bayesian network model has a certain practical value as a means of air quality prediction. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

关键词： Air quality

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Mobile App Recommendation System Using machine learning Classification 4

Mobile App Recommendation System Using Machine learning Clas...

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4th international conference on Computing Methodologies and Communication, ICCMC 2020

作者： Jisha, R.C. Amrita, J.M. Vijay, Aswini R Indhu, G.S. Amrita Viswa Vidhyapeetham Amritapuri Department of Computer Science and Applications India

ISBN: (纸本)9781728148892

the introduction of new ideas with mobile applications can bring great change to people around the world. Nowadays thousands of apps are developed to satisfy different needs of people such as for doing jobs, transactions, entertainment etc. and distributed over the Internet. So most of the existing app stores available might face difficulties for recommending a particular app to a particular user. So there is a need for recommending apps for the users according to their personal preferences and various other limitations. We made a mobile application recommendation system with ratings, Size, and Permission as parameters and we will recommend suitable apps to the user by evaluating these parameters. Here we are using *** which is one of the famous android application markets and also makes use of Web Crawler which helps in collecting information about the website and helps in validating hyperlinks. After that by using the Clustering Algorithm, applications are grouped or clustered based on Popularity, Permission and Security aspects. this paper aims to provide a simple recommendation system without compromising rating, size and Permission aspects. © 2020 IEEE.

关键词： Recommender systems

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