Machine Learning

A dataset comprising 500 data points was gathered by collecting answers to 250  computer science problems assigned in classes and quizzes from students. To generate this dataset, a response was selected from a random student for each question. The same questions were then asked to ChatGPT 3.0, and the answers were recorded. Based on the source of the response (either student or GPT), the dataset was labeled accordingly. The resulting labeled dataset includes the list of assignment and quiz questions, along with the corresponding answers from students and ChatGPT.

Accurate recognition of targets in the orchard environment is the key to vision perception for picking robots. Factors such as small, densely growing plum fruit targets and high occlusion lead to unsatisfactory recognition of plum fruit by vision algorithms. Therefore, this paper proposes an improved YOLOv5s model to detect highly occluded and dense plums in orchards. First, the backbone network of YOLOv5s is improved in this paper.

CAPTCHA (Completely Automated Public Turing Tests to Tell Computers and Humans Apart). Only humans can successfully complete this test; current computer systems cannot. It is utilized in several applications for both human and machine identification. Text-based CAPTCHAs are the most typical type used on websites. Most of the letters in this protected CAPTCHA script are in English, it is challenging for rural residents who only speak their native tongues to pass the test.

Please cite the following paper when using this dataset:

N. Thakur, K. Khanna, S. Cui, N. Azizi, and Z. Liu, “Mining and Analysis of Search Interests related to Online Learning Platforms from Different Countries since the Beginning of COVID-19” [Unpublished Paper - Paper submitted to HCI International 2023, Copenhagen, Denmark, 23-28 July 2023]

Brief Description of Dataset file - Interest_Dataset.csv:

Attribute Name: Week

Please cite the following paper when using this dataset:

N. Thakur, K. Khanna, S. Cui, N. Azizi, and Z. Liu, “Mining and Analysis of Search Interests related to Online Learning Platforms from Different Countries since the Beginning of COVID-19” [Unpublished Paper - Paper submitted to HCI International 2023, Copenhagen, Denmark, 23-28 July 2023]

Brief Description of Dataset file - Interest_Dataset.csv:

Attribute Name: Week

Please cite the following paper when using this dataset:

N. Thakur, K. Khanna, S. Cui, N. Azizi, and Z. Liu, “Mining and Analysis of Search Interests related to Online Learning Platforms from Different Countries since the Beginning of COVID-19”, Proceedings of the 25th International Conference on Human-Computer Interaction (HCII 2023), Copenhagen, Denmark, July 23-28, 2023 (Accepted for Publication)

Brief Description of Dataset file - Interest_Dataset.csv:

Attribute Name: Week

Please cite the following paper when using this dataset:

N. Thakur, K. Khanna, S. Cui, N. Azizi, and Z. Liu, “Mining and Analysis of Search Interests related to Online Learning Platforms from Different Countries since the Beginning of COVID-19” [Unpublished Paper - Paper submitted to HCI International 2023, Copenhagen, Denmark, 23-28 July 2023]

Brief Description of Dataset file - Interest_Dataset.csv:

Attribute Name: Week

Training data of Human Motion Recognition via Wearable plastic Fiber Sensing System

The uncompressed dataset consists in a folder which contains:

• A XML file per author (Twitter user) with 200 tweets. The name of the XML file correspond to the unique author id.
• A truth.txt file with the list of authors and the ground truth.