Due to the lack of publicly available injection-molded product defect datasets and the diversity of defects in terms of shapes, sizes, and textures, we collects defect samples from injection molding factories to ensure the model performs well in real industrial scenarios. To ensure the quality and usability of the data, after analyzing the sample data, data cleaning is performed to remove the irregular images.
This dataset is used in my LAST Invited, keynote Speaker and 1st Special Session, AAE-2025*.
Novel Perspective of Contemplating Existing Principles of Scientific Truth : Novel B-Unified Theory, Postulates, Propositions And Models : With Applications in Impedance, Transformers, Inverters, Generators, Pumps, Solar, Machinery, Turbines, Batteries, SMPS and Short Circuit Analysis
This dataset is used in my LAST Invited, keynote Speaker and 1st Special Session, AAE-2025*.
Novel Perspective of Contemplating Existing Principles of Scientific Truth : Novel B-Unified Theory, Postulates, Propositions And Models : With Applications in Impedance, Transformers, Inverters, Generators, Pumps, Solar, Machinery, Turbines, Batteries, SMPS and Short Circuit Analysis
This dataset webpage contains datasets of existing and proposed models:
This dataset is used in my LAST Invited, keynote Speaker and 1st Special Session, AAE-2025*.
This dataset webpage contains datasets of exisiting and proposed models:
This dataset is used in my LAST Invited, keynote Speaker and 1st Special Session, AAE-2025*.
The Unified Multimodal Network Intrusion Detection System (UM-NIDS) dataset is a comprehensive, standardized dataset that integrates network flow data, packet payload information, and contextual features, making it highly suitable for machine learning-based intrusion detection models. This dataset addresses key limitations in existing NIDS datasets, such as inconsistent feature sets and the lack of payload or time-window-based contextual features.
This dataset webpage contains datasets and models
This dataset is used in my LAST Invited, keynote Speaker and 1st Special Session, AAE-2025*.
We collected programming problems and their solutions from previous studies. After applying some pre-processing steps, we queried advanced LLMs, such as GPT4, with the collected problems to produce machine-generated codes, while the original solutions were labeled as human-written codes. Finally, the entire collected dataset was divided into training, validation, and test sets, ensuring that there is no overlap among these sets, meaning no solutions in two different sets that solve the same programming problem.
Competition phase of the first cut-off stage is now closed and we have gathered submissions from student, academic and industry teams from 10 countries. Submissions are evaluated and the results of the best performing teams are given below, with individual RMSSE metrics, overall average and final score. We would like to thank everyone for their participation and valuable contributions! The competition will return soon with the new cut-off stage.
This dataset was developed in the context of the NANCY project and it is the output of the experiments involving cyberattacks against services that are running in a 5G coverage expansion scenario. The coverage expansion scenario involves a main operator and a micro-operator which extends the main operator’s coverage and can also provide additional services, such as Artificial Intelligence-based cyberattack detection.
