The proceedings contain 21 papers. The special focus in this conference is on Software engineeringresearch and Practice. The topics include: MetaPix: A Data-Centric AI Development Platform for Efficient Manageme...
ISBN:
(纸本)9783031866432
The proceedings contain 21 papers. The special focus in this conference is on Software engineeringresearch and Practice. The topics include: MetaPix: A Data-Centric AI Development Platform for Efficient Management and Utilization of Unstructured computer Vision Data;A Novel Architecture That Examines Network Activity in a Docker-Based Multitenant to Verify Zero Trust Container Architecture (ZTCA) Compliance;VENUS: Designing a Validation Engine for User Stories;a Framework for Requirements Modeling of Safety Critical Systems: A Continuous Glucose Monitoring System Case Study;plan-Based and Agile Companies: A Comparison of Project Management Approaches;Service Availability Ratio (SAR): An Availability Metric for Microservice;Development of a Desktop Agent System Using GPT;navigating Challenges in E-Participation: A Comprehensive Meta-Analysis;The Shifting Landscape of Cybersecurity: The Impact of Remote Work and COVID-19 on Data Breach Trends;Farmchain: Empowering Smallholder Farmers Through Blockchain and DAOs: Cryptournomic Approach;privacy Strategies for Police Personnel: Co-designing a Self-assessment Tool;cybersecurity Threats: An Analysis of the Rise and Impacts of State Sponsored Cyber Attacks;moore’s Law: What Comes Next?;use of Emerging Technologies in Africa;using Survey to Investigate the Integration of Artificial Intelligence in e-Learning;students Satisfaction with the Distance Education During Covid-19 Pandemic;Improving Student Success in Math Courses Using WeBWorK;fast Food Review Online;a Systems Approach to Improving E-Learning Using Theory of Constraints.
This paper improves the ill-condition of bone-conducted (BC) speech signal by reducing the eigenvalue expansion. BC speech commonly contains a large spectral dynamic range that causes ill-condition for the classical l...
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Phishing attacks present a serious threat to enterprise systems,requiring advanced detection techniques to protect sensitive *** study introduces a phishing email detection framework that combines Bidirectional Encode...
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Phishing attacks present a serious threat to enterprise systems,requiring advanced detection techniques to protect sensitive *** study introduces a phishing email detection framework that combines Bidirectional Encoder Representations from Transformers(BERT)for feature extraction and CNN for classification,specifically designed for enterprise information ***’s linguistic capabilities are used to extract key features from email content,which are then processed by a convolutional neural network(CNN)model optimized for phishing *** an accuracy of 97.5%,our proposed model demonstrates strong proficiency in identifying phishing *** approach represents a significant advancement in applying deep learning to cybersecurity,setting a new benchmark for email security by effectively addressing the increasing complexity of phishing attacks.
Large Language Models (LLM) is a type of artificial neural network that excels at language-related tasks. The advantages and disadvantages of using LLM in software engineering are still being debated, but it is a tool...
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In order to improve the ability of engineering graduate students to process high-dimensional random information data, the advanced content of informationscience was introduced into the teaching of advanced Engineerin...
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This paper improves the performance of linear prediction (LP) in precise spectral estimation of bone-conducted (BC) speech. Inherently, BC speech contains a wide spectral dynamic range that causes ill conditioning in ...
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Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and t...
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Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.
The effects of changing learning rates, data augmentation percentage and numbers of epochs on the performance of Wasserstein Generative Adversarial Networks with Gradient Penalties (WGAN-GP) are evaluated in this stud...
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Facial identification has emerged as a key research area due to its potential to enhance biometric security. This research proposes an advanced security system for electric vehicles (EVs) based on facial identificatio...
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This research enhances the development of natural language processing (NLP) by integrating part of speech (POS) tagging and named entity recognition (NER) techniques to annotate unstructured data from advanced persist...
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