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4th international computer Programming Education conference, ICPEC 2023

4th International Computer Programming Education Conference,...

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4th international computer Programming Education conference, ICPEC 2023

ISBN: (纸本)9783959772907

the proceedings contain 14 papers. the topics discussed include: I’m Sorry Dave, I’m Afraid I Can’t Fix Your Code: on ChatGPT, CyberSecurity, and secure coding;LCSMAR, an AR based tool to inspect imperative programs;can a content management system provide a good user experience to teachers?;Sifu reloaded: an open-source gamified web-based CyberSecurity awareness platform;a gamified educational escape rooms’ framework for computer programming classes;a systematic review of formative assessment to support students learning computer programming;a new approach to perform individual assessments at higher education using gamification systems;NLP/AI based techniques for programming exercises generation;and data visualization for learning analytics indicators in programming teaching.

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Analysis of computer application Software Progress Technology Based on Artificial Intelligence Background 4

Analysis of Computer Application Software Progress Technolog...

引用

4th IEEE Annual Flagship India Council international Subsections conference, INDISCON 2023

作者： Pang, Jinlong Heilongjiang Polytechnic Harbin150100 China

ISBN: (纸本)9798350333558

With the complexity and diversification of computer systems and network technology, the whole world is more closely connected with the computer, application software. Due to computer system application scope is more and more widely, more and more in-depth, computer software system is becoming more and more complex. therefore, in order to realize the automation, convenience and intelligence of computer application software progress Tech, AI technology is applied in it. therefore, on the basis of the advantages of AI technology, this article first analyzes the role of AI neural network in improving the efficiency of user work and accurately identifying the threat factors that invade computer software, and then builds an automatic progress platform for computer application software. Users can input CNN parameters and hardware resource parameters through the graphical interface of software progress environment. then the progress environment can automatically integrate the data structure of different types of parameters by analyzing the input parameters, and automatically construct processor-level network structure configuration information and instruction program. the software performance test shows that the computer application software designed by AI neural network has good performance, the accuracy rate is 97.6%, the loss function value is only 0.12, and the training time is only 6 minutes. Compared with the traditional artificial programming computer application software, the convergence speed is faster. It can be proved that the method of designing computer application software based on AI is effective and has high performance. © 2023 IEEE.

关键词： application programs

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8th EAI international conference on machine learning and Intelligent Communications, MLICOM 2023

8th EAI International Conference on Machine Learning and Int...

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8th EAI international conference on machine learning and Intelligent Communications, MLICOM 2023

ISBN: (纸本)9783031717154

the proceedings contain 18 papers. the special focus in this conference is on machine learning and Intelligent Communications. the topics include: Research on Garbage Classification Algorithm Based on machine learning;PCB Large Color Variation Image Registration with Local Optimization LoFTR;SAR Image Compression Based on Low-Frequency Suppression and Target Perception;self-guided Few-Shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models;generating a Personalised Sensor Data Generation System for the Fusion of Adversarial Networks and Behavioural Matter-of-Fact Mapping;class-Specific Noise Injection for Improved Road Segmentation;SAR Moving Target Segmentation and Removal Based on Deep learning;event Sequence-Driven Generalized and Accurate End-to-End Streaming Latency Measurement;GPT4D: Automatic Cross-Version Linux Driver Upgrade Toolkit;local-to-Global Point Supervised Object Detector via Aggregation of Discriminative Parts;leveraging Large Language Model to Generate Multi-modal Timeline Summarization;CancersQA: Federated learning with Pre-trained Models for Intelligent Medical Diagnosis;measuring Contour Similarity Based on Improved Hausdoff Distance for the Automatic Conjugation of Irregular Fragments of Ancient Manuscripts;research on Fund News Classification Method Based on Multi-level Model Fusion;a Study on the Readability of Chinese Text from a Rhetorical Perspective;application of Convolutional Neural Networks in Detecting Cropping Intensity: An Attempt Based on Global Typical Samples.

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DisContNet: Contradiction Detection in Texts using Transformers 4

DisContNet: Contradiction Detection in Texts using Transform...

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4th IEEE international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Kutty, Rida Javed Roshni, P.N. Adiga, Shreya S. Pes University Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350384628

In this study, the significance of contradiction detection in natural language text is highlighted, and the study proposes a comprehensive system that leverages advanced natural language processing (NLP) and deep learning methodologies. the approach, constructed around a dataset derived from accessible corpora, aims to classify pairs of sentences based on the type of contradiction present within them. the system not only detects contradictions but also distinguishes among various types of contradictions. Further, the performances of two kinds of Transformer Models (BERT and DistilBERT) are compared. the findings of this research pave the way for further enhancement of NLP techniques for contradiction detection, demonstrating its potential applicability across diverse NLP tasks. © 2023 IEEE.

关键词： Deep learning

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Research on Academic Risk Early Warning Model Based on the Analysis of learning Behavior Data 4

Research on Academic Risk Early Warning Model Based on the A...

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4th international conference on computer Science and Blockchain, CCSB 2024

作者： Cao, Chang Yang, Qiping Liu, Qingqing Liang, Jing Xie, Shuxiao Network & Educational Technology Institute Jinan University Guangzhou China

ISBN: (纸本)9798350356502

Accelerated by the rapid advancement of educational digital transformation, learners generate a wealth of process data during online learning. this study aims to employ educational data mining and machine learning methodologies to achieve early warnings for student academic risks. Utilizing the platform of Guangdong-Hong Kong-Macao Greater Bay Area University Online Open Course Alliance, the research collects and analyzes learning behavior data, extracting behavioral indicators closely related to student academic performance. through an optimized BP neural network algorithm, a grade prediction model is constructed that uses student academic performance ranks as output features and determines input features via Spearman correlation analysis;hyper-parameters of the hidden layer are optimized using Python and MATLAB, followed by model evaluation and prediction, ultimately resulting in a student academic risk warning model. this study defines and divides student behavior data based on Flanders Interaction Analysis theory, extracting characteristic data with representational significance. Compared with other models, this model is more pertinent in summarizing learning behavior and predicting academic performance. Experimental results demonstrate that the model exhibits excellent performance in accuracy (93.63%), recall rate (93.71%), and F1-score (0.935). this model effectively identifies students at academic risk and provides targeted early warnings, thereby reducing their academic risks. © 2024 IEEE.

关键词： Contrastive learning

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the application of Online Education and Teaching Management System Based on Network and ASP Technology 4

The Application of Online Education and Teaching Management ...

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4th international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Wang, Ningning Zibo Normal College Zibo255000 China

In order to facilitate better communication between students and teachers, submit learning questions, and make it easier for teachers to manage students during the learning process, the author has designed an online education and teaching management system based on network technology. the system utilizes ASP technology and uses SQL Server as the database to display a user-friendly interface, successfully achieving various functions of the online classroom teaching system. For the target user college students, it is hoped that the application of this system can improve teaching efficiency. In order to verify the comprehensive performance of the designed education system, tests were conducted, the test results show that the throughput rate of 20 users accessing the system is also due to the setting of a collection point every time data is submitted, and the majority of the throughput rate is concentrated after each form input and data submission. Some pages have higher throughput values due to their download task operations. this study is of great significance for developing effective educational resources, helping teachers organize and guide course teaching, making effective evaluations of learning resources and learners, and developing effective online education platforms. © 2024 the Authors.

关键词： Students

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Identifying key indicators words of suicidal ideation through ensemble voting classifier learning methods 4th

Identifying key indicators words of suicidal ideation throug...

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4th international conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Shukla, Shiv Shankar Prasad Singh, Maheshwari Prasad National Institute of Technology Patna Bihar Patna India

ISBN: (纸本)9781032911571

Suicide prevention is a pressing global concern, with a significant number of annual deaths related to suicide and numerous attempts reported each year. the rise of social media as a platform for emotional expression underscores the need for early detection of suicidal thoughts. this study introduces an innovative approach utilizing Natural Language Processing (NLP) techniques to analyze behavioral patterns and linguistic cues in social media interactions. the primary objective is to identify key indicator words in social media post i.e. Reddit posts, that may suggest the presence of suicidal ideation, through the application of advanced NLP techniques and variety of machine learning models, including Decision Tree, Random Forest, Bernoulli Naïve Bayes, Gradient Boosting, XGBoost, and an Ensemble voting classifier method that combines Random Forest, Gradient Boosting, and XGBoost. the models demonstrate impressive accuracy in the detection of posts related to suicidal ideation, providing a potential means for early intervention. On the Reddit dataset, these models achieve accuracy scores of 81%, 88%, 84%, 89%, 89%, and 92%. this research aims to identify keywords associated directly or inversely linked to suicidal ideation. the study offers a potent keywords indicator for detecting signs of suicidal thoughts in social media posts, potentially facilitating timely interventions and, ultimately, saving lives to support suicide prevention efforts. © 2025 the Author(s).

关键词： Decision trees

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(NBCI) Natural Brain (Human) and computer Interaction: Development of User Experience with computers through Adaptive Interface Method 4

(NBCI) Natural Brain (Human) and Computer Interaction: Devel...

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4th IEEE international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Packia, A. Princy Anton Arokia Babu, M. Al-Khalidi, Ayadh Abdulhasan, Maki Mahdi Erudiyanathan, Diana Naqvi, Aiman P.S.R Engineering College Department of Management Studies Sivakasi India Department of Computer Science and Engineering TN India Al-Hadi University College Baghdad10011 Iraq AL-Nisour University College Department of Medical Laboratories Technology Baghdad Iraq Department of Management Kothanur K. Narayanapura Bengaluru India Ms Ramaiah Institute of Technology Department of Chemical Engineering MSR Nagar Karnataka Bangalore India

ISBN: (纸本)9798350360165

In the current digital dispensation, Human-computer Interaction (HCI) fronts itself as key to the user experiences across all the other technological platforms. Human-computer Interaction will continue rising to the fore with rapid advancements experienced in technology and demand for seamless interactions between man and machine growing. Adaptive interfaces have been designed to meet the manifold demands and preferences of users, with the sole aim of improving the overall user experience. this paper thus goes further to try and understand the importance of adaptive interfaces within the domain of HCI toward improving user experience. How do adaptive interfaces optimally change their behavior with user inputs, context, and other determining factors to optimize a user's interaction with the system. It synthesizes prior knowledge and extracts main principles and methodologies of adaptive interface design from an extensive literature review. the basic ideas of adaptive interfaces and their roles in HCI are described. the theory could be, in return, a guide for the designers and developers for the efficient use of the adaptive techniques to develop interfaces more in accordance with users. It further explores several adaptive approaches based on advanced context-awareness, user modeling, and machine learning algorithms, and their implication on HCI. It further outlines various case studies and examples in their respective application domains, from web browsing to mobile applications and assistive technology, showing adaptive interfaces in use. © 2024 IEEE.

关键词： Adversarial machine learning

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4th EAI international conference on Technology, Innovation, Entrepreneurship and Education, TIE 2023

4th EAI International Conference on Technology, Innovation, ...

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4th EAI international conference on Technology, Innovation, Entrepreneurship and Education, TIE 2023

ISBN: (纸本)9783031593826

the proceedings contain 10 papers. the special focus in this conference is on Technology, Innovation, Entrepreneurship and Education. the topics include: Exploring Women's Role in Creative Industries through Collaborative Action Research Using Tabletop Role-Playing Games;designing a Master Course Curriculum for Innovation through Living Labs;immersive Virtual Reality, Augmented Reality and Mixed Reality for Self-regulated learning: A Review;co-creation and Personalization of an Immersive Web Environment;the Meta-Stage: Utilizing Metaverse-Enabling Technologies for Hybrid Co-presence Experiences;gamifying the Museum: Enhancing Immersive Art Experiences with Social Connections and Personal Identity Exploration;‘In the Future We Will Make Our Own Superheroes’: Reflections on the Future of Broadcast for Children Aged 7–11;flexilink: A Low Latency Solution for Packet Based Media.

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A Culture-Aware Deep Architecture for Emotional State Detection from Facial Expressions 4

A Culture-Aware Deep Architecture for Emotional State Detect...

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4th international conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Hamid, Mir Aamir Singh, Jaiteg Institute of Engineering and Technology Chitkara University Department of Computer Applications Punjab India

ISBN: (纸本)9798350369786

Emotion detection is one of the widely explored fields and finds application in almost every domain. However, culture impacts the emotional regulation of a person. thus, cultural information needs to be incorporated while designing systems for automatic detection of emotions. this study presents a culture- aware deep-learning based methodology for detection of emotions from facial expressions. Facial expressions of models belonging to Japan, North Europe and Mediterranean regions were analysed using a Multi-Layer Perceptron based deep network, in which individual layers are actually independent models trained on data of a particular region. the proposed culture-aware architecture got an accuracy of 87.9% and outperformed the individual submodels by a great margin. © 2023 IEEE.

关键词： Contrastive learning

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