Artificial Intelligence

Ovarian cancer is among the top health issues faced by women everywhere in the world . Ovarian tumours have a wide range of possible causes. Detecting and tracking down these cancers in their early stages is difficult which adds to the difficulty of treatment. In most cases, a woman finds out she has ovarian cancer after it has already spread. In addition, as technology in the field of artificial intelligence advances, detection can be done at an earlier level. Having this data will assist the gynaecologist in treating these tumours as soon as possible.


痉挛性构音障碍说话者的视听孤立词录音。Subjects include 15 talkers with Cerebral Palsy and 13 age-matched healthy controls. Subjects were recruited based primarily on personal contact facilitated by disability support organizations. Subjects were selected based on self-report of either speech pathology or cerebral palsy. Before data were included in the UA-Speech distribution, the diagnosis of spastic dysarthria (sometimes mixed with other forms of dysarthria) was informally confirmed by a certified speech-language pathologist listening to these recordings.


In this study, 31 Chinese patients diagnosed with depression (mean age 26.60±9.21) and 33 healthy control participants (mean age 26.00±7.36) participated, during which emotional picture descriptions and interview dialogues are collected. The experiment underwent review and received approval from the hospital's Bioethics Committee and the school's Medical Ethics Committee, and all the participants signed for the informed consent.


Replication data for the fsQCA model in: "Why do companies employ prohibited unethical artificial intelligence practices?"


We release MarsScapes, the first panorama dataset for Martian terrain understanding. The dataset contains 195 panoramas of Mars surface with fine-grained annotations for semantic and instance segmentation, facilitating high-level scene understanding of Martian landforms and further enhancing the navigability of rovers over rough terrains in large areas. Note: Limited by the file size, we temporarily submit the first half of MarsScapes (i.e. from 122_1 to 527_1) as a representative subset. All samples will be provided after our paper is accepted.



Deep learning has revolutionized the field of robotics. To deal with the lack of annotated training samples for learning deep models in robotics, Sim-to-Real transfer has been invented and widely used. However, such deep models trained in simulation environment typically do not transfer very well to the real world due to the challenging problem of “reality gap”. In response, this letter presents a conceptually new Digital Twin (DT)-CycleGAN framework by integrating the advantages of both DT methodology and the CycleGAN model so that the reality gap can be effectively bridged.


Chat GPT Prompt:

Create a CSV file with 10000 rows of weather data from the fictions town named Anytown. Please include temperature, barometer readings, wind speed, wind direction, precipitation, dew point, humidity, and conditions.


Here's a sample CSV file with 10000 rows of weather data for Anytown. This data is randomly generated for demonstration purposes only, so it does not represent actual weather conditions.


Most of the existing human action datasets are common human actions in daily scenes(e.g. NTU RGB+D series, Kinetics series), not created for Human-Robot Interaction(HRI), and most of them are not collected based on the perspective of the service robot, which can not meet the needs of vision-based interactive action recognition.


The dataset includes channel frequency response (CFR) data collected through an IEEE 802.11ax device for human activity recognition. This is the first dataset for Wi-Fi sensing with the IEEE 802.11ax standard which is the most updated Wi-Fi version available in commercial devices. The dataset has been collected within a single environment considering a single person as the purpose of the study was to evaluate the impact of communication parameters on the performance of sensing algorithms.


The rocket nose-cone shapes have been generated by blending few conic sections together (two conic sections in one) and the simulated against mach number regime from subsonic through transonic to supersonic. The aerodynamic drag coefficients have been recoded for each shape for each mach number.