Please cite the following paper when using this dataset:
Vanessa Su and Nirmalya Thakur, “COVID-19 on YouTube: A Data-Driven Analysis of Sentiment, Toxicity, and Content Recommendations”, Proceedings of the IEEE 15th Annual Computing and Communication Workshop and Conference 2025, Las Vegas, USA, Jan 06-08, 2025 (Paper accepted for publication, Preprint: https://arxiv.org/abs/2412.17180).
Abstract:
This dataset contains original and augmented versions of the Korean Call Content Vishing (KorCCVi v2) dataset used in the study titled, "Enhancing Voice Phishing Detection Using Multilingual Back-Translation and SMOTE: An Empirical Study." The dataset addresses challenges of data imbalance and asymmetry in Korean voice phishing detection, leveraging data augmentation techniques such as multilingual back-translation (BT) with English, Chinese, and Japanese as intermediate languages, and Synthetic Minority Oversampling Technique (SMOTE).
TRAINING AND PLACEMENT CELL is a web based application developed in the windows platform for the training and placement department of the college in order to provide the details of its students in a database for the companies to their process of recruitment provided with a proper login. The TRAINING AND PLACEMENT CELL contains all the information about the students. The system stores all the personal information of the students, like their personal details, their aggregate marks, their skill set and their technical skills that are required in the CV to be sent to a company.
A musical instrument store is a web based application where users can view various musical
instruments along with their description. The project provides user with a flexible and attractive
GUI and shows him a list of products and carry out all the shopping activities online.
This project is developed for users to have a brief look at the instruments without actually visiting
the store. The website displays different kinds of instruments of different brands so that user can
easily get their expected instrument.
We used the broad group of 47,692 tweets from the Cyberbullying Classification dataset. This worldwide sourced dataset offers a broad range of examples of cyberbullying, guaranteeing a thorough viewpoint. Our thorough translation and modification procedure guaranteed the dataset's contextual and cultural relevance for the Tamil-speaking population, even though it is not solely from South Asia. These tweets were carefully divided into six classes, each of which represented a different facet of cyberbullying, as well as cases that weren't considered cyberbullying.
To download this dataset without purchasing an IEEE Dataport subscription, please visit: https://zenodo.org/records/13896353
Please cite the following paper when using this dataset:
Mashup and Web API dataset
This is a dataset used in Web API Recommendation which contains real data crawled from the ProgrammableWeb between 2019 and 2020. And the dataset includes 6217 Mashup services and 11930 Web APIs.
Each mashup data includes its name, tags, description text, called APIs, and categories. And each Web API data contains its name, tags and description text.
This paper addresses the problem of dynamic multi-objective optimization problems (DMOPs), by demonstrating new approaches to change prediction strategies within an evolutionary algorithm paradigm. Because the objectives of such problems change over time, the Pareto optimal set (PS) and Pareto optimal front (PF) are also dynamic.
Experiment Details:
In this paper, a multi-objective version of the recent Constraint Multi-Swarm Particle Swarm Optimization without Velocity (CMPSOWV) is developed. With the focus on wider adoption of optimization aimed at solving multi-objective constrained engineering design problems in practice, the proposed method is implemented in the popular Grasshopper framework as a new optimization plug-in “Goldfish”. As CMPSOWV, the MOCMPSOWV algorithm is supposed to obtain highly diversified optimization results thanks to incorporating the multi-swarm approach and the enlarged search scope for the best
