This study aims to reveal the best deep learning models that are improved and optimized by predicting undesirable behavior patterns using a dataset consisting of artificial and real exam data of students taking online...
详细信息
ISBN:
(数字)9798331509934
ISBN:
(纸本)9798331509941
This study aims to reveal the best deep learning models that are improved and optimized by predicting undesirable behavior patterns using a dataset consisting of artificial and real exam data of students taking online distance education courses in an online environment through the distance education system. Using online exam data of 129 students, the researchers conducted analysis with two different scenarios to determine the best prediction performance through regression and classification models. The model we proposed was determined as a four-layer DNN with 80.4% test performance in detecting students who “cheated” from undesirable behavior patterns, which was performed with K-10, K-5 and K-3 cross-validation. The results prove that students' online distance education exam data can be easily applied to the DNN model. The models presented in the study provide a roadmap for educational institutions to evaluate their online examination practices and develop more effective strategies for academic honesty.
The current research work addresses the problem of automating the delivery of machine learning models from MLflow to Kubernetes infrastructure. To solve the mentioned problem, a Kubernetes operator has been developed ...
详细信息
ISBN:
(数字)9798331511241
ISBN:
(纸本)9798331511258
The current research work addresses the problem of automating the delivery of machine learning models from MLflow to Kubernetes infrastructure. To solve the mentioned problem, a Kubernetes operator has been developed to automate the delivery of machine learning models to production by integrating MLflow for model tracking and Seldon Core for model serving. The developed operator allows data scientists to deploy models while maintaining the familiar MLflow environment. The operator's automatic deployment triggers upon tagging models in MLflow, greatly simplifying engineers' tasks and minimizing the need for manual infrastructure configuration. By automating configuration tasks and optimizing deployment workflows, the solution achieves a 40-50% reduction in model time to deployment (TTD) metric compared to manual processes and decreases error rates from 15% to around 3%. The practical relevance of the work is that it simplifies collaboration between data and infrastructure teams by providing a unified deployment framework, resulting in faster, more reliable, and automated integration of machine learning models into an organisation's business processes.
software Defined Network (SDN) technology is one of the modern network virtualization technologies. When implementing a virtual network on the SDN data plane, undesired effects may occur: the appearance of undesired p...
详细信息
A novel escape analysis framework that handles the Java open-world features is proposed and evaluated. The novel approach analyzes a Java program with an optimistic view that the program is in a closed world and appli...
详细信息
A novel escape analysis framework that handles the Java open-world features is proposed and evaluated. The novel approach analyzes a Java program with an optimistic view that the program is in a closed world and applies optimizations aggressively. The framework also provides a mechanism that controls the analysis complexity. The results show that the escape analysis framework, which has been implemented in Intel's Open Runtime Platform on X86, eliminated about 70% and 94% synchronization operations, and improved the runtime performance 15.77% and 31.28%, for SPECjbb2000 and 209_db respectively.
Checking for information leaks in real-world applications is a difficult task. IFlow is a model-driven approach which allows to develop information flow-secure applications using intuitive modeling guidelines. It supp...
详细信息
作者:
Mucahit SoyluResul DasInonu University
Department of Organized Industrial Zone Vocational School Computer Programming Malatya Turkiye Firat University
Faculty of Technology Department of Software Engineering 23119 Elazig Turkiye
This study proposes a hybrid approach for visualizing cyberattacks by combining the deep learning-based GAT model with JavaScript-based graph visualization tools. The model processes large, heterogeneous data from the...
This study proposes a hybrid approach for visualizing cyberattacks by combining the deep learning-based GAT model with JavaScript-based graph visualization tools. The model processes large, heterogeneous data from the UNSW-NB15 dataset to generate dynamic and meaningful graphs. In the data cleaning phase, missing and erroneous data were removed, unnecessary columns were discarded, and the data was transformed into a format suitable for modeling. Then, the data was converted into homogeneous graphs, and heterogeneous structures were created for analysis using the GAT model. GAT prioritizes relationships between nodes in the graph with an attention mechanism, effectively detecting attack patterns. The analyzed data was then converted into interactive graphs using tools like SigmaJS, with attacks between the same nodes grouped to reduce graph complexity. Users can explore these dynamic graphs in detail, examine attack types, and track events over time. This approach significantly benefits cybersecurity professionals, allowing them to better understand, track, and develop defense strategies against cyberattacks.
In this paper we tackle the problem of verifying whether a labeled partial order (LPO) is executable in a Petri net. In contrast to sequentially ordered runs an LPO includes both, information about dependencies and in...
详细信息
Control State Diagrams (CSD) are a graphical representation of Control State Abstract State Machines, a subclass of Abstract State Machines (ASM). We extend the existing semi-formal specification of this diagram type ...
详细信息
This paper introduces a powerful, efficient and generic framework for optimal routing of electric vehicles in the setting of flexible edge cost functions and arbitrary initial states. More precisely, the introduced st...
详细信息
暂无评论