For GAIN, we use the source code released by the authors. Here we report the running clock time for feature imputation of different methods at test time. We adapt the same setting as in Section 4.1 and the results are shown in Appendix C. G The Douban dataset has 3000 observations and 3000 features. The Y ahooMusic dataset has 1357 observations and 1363 features. Inductive matrix completion based on graph neural networks.

A serial rapist who used dating apps to meet his victims was sentenced to 111 years to life in state prison on Thursday, according to a statement from the Ventura County district attorney's office. Dustin Ronald Alba, a 31-year-old from Oxnard, was found guilty of the rape and sexual assault of five women last month. He committed his offenses from 2012 to 2020 in the cities of Thousand Oaks, Oxnard and Los Angeles, the release said. Multiple victims of Alba said they met him online through dating apps and social media. After meeting in person, they said he would use his body weight to confine and then assault them, the statement said.

A new investigation from The Markup claims the parent company of Tinder, Hinge, OKCupid and other dating apps turns a blind eye to allegedly abusive users on its platforms. The 18-month investigation found instances in which users who were repeatedly reported for drugging or assaulting their dates remained on the apps. One such case involves a Colorado-based cardiologist named Stephen Matthews. Over several years, multiple women on Match's platforms reported him for drugging or raping them. Despite these reports, his Tinder profile was at one point given Standout status, reserved for popular profiles and often requiring in-app currency to interact with.

Robots are being designed to help people in an increasing variety of settings--but seemingly little attention has been given so far to the specific needs of women, who represent roughly half of the world's population but are highly underrepresented in robotics. Here we used a speculative prototyping approach to explore this expansive design space: First, we identified some potential challenges of interest, including crimes and illnesses that disproportionately affect women, as well as potential opportunities for designers, which were visualized in five sketches. Then, one of the sketched scenarios was further explored by developing a prototype, of a robotic helper drone equipped with computer vision to detect hidden cameras that could be used to spy on women. While object detection introduced some errors, hidden cameras were identified with a reasonable accuracy of 80\% (Intersection over Union (IoU) score: 0.40). Our aim is that the identified challenges and opportunities could help spark discussion and inspire designers, toward realizing a safer, more inclusive future through responsible use of technology.

Sexual harassment, sexual abuse, and sexual violence are prevalent problems in this day and age. Women's safety is an important issue that needs to be highlighted and addressed. Given this issue, we have studied each of these concerns and the factors that affect it based on images generated from movies. We have classified the three terms (harassment, abuse, and violence) based on the visual attributes present in images depicting these situations. We identified that factors such as facial expression of the victim and perpetrator and unwanted touching had a direct link to identifying the scenes containing sexual harassment, abuse and violence. We also studied and outlined how state-of-the-art explicit content detectors such as Google Cloud Vision API and Clarifai API fail to identify and categorise these images. Based on these definitions and characteristics, we have developed a first-of-its-kind dataset from various Indian movie scenes. These scenes are classified as sexual harassment, sexual abuse, or sexual violence and exported in the PASCAL VOC 1.1 format. Our dataset is annotated on the identified relevant features and can be used to develop and train a deep-learning computer vision model to identify these issues. The dataset is publicly available for research and development.

An artificial intelligence algorithm used by YouTube to automatically add captions to clips, has been accidentally inserting explicit language into children's videos. The system, known as ASR (Automatic Speech Transcription), was found displaying words like corn as porn, beach as bitch and brave as rape, as reported by Wired. To better track the problem, a team from Rochester Institute of Technology in New York, along with others, sampled 7,000 videos from 24 top-tier children's channels. Out of the videos they sampled, 40 per cent had'inappropriate' words in the captions, and one per cent had highly inappropriate words. They looked at children's videos on the main version of YouTube, rather than the YouTube Kids platform, which doesn't use automatically transcribed captions, as research revealed many parents still put children in front of the main version.

Slate has relationships with various online retailers. If you buy something through our links, Slate may earn an affiliate commission. We update links when possible, but note that deals can expire and all prices are subject to change. All prices were up to date at the time of publication. Nancy Jo Sales has been reporting on women's experience of the internet since well before people were aware of the unique dangers it posed.

Machine learning with missing data has been approached in two different ways, including feature imputation where missing feature values are estimated based on observed values, and label prediction where downstream labels are learned directly from incomplete data. However, existing imputation models tend to have strong prior assumptions and cannot learn from downstream tasks, while models targeting label prediction often involve heuristics and can encounter scalability issues. Here we propose GRAPE, a graph-based framework for feature imputation as well as label prediction. GRAPE tackles the missing data problem using a graph representation, where the observations and features are viewed as two types of nodes in a bipartite graph, and the observed feature values as edges. Under the GRAPE framework, the feature imputation is formulated as an edge-level prediction task and the label prediction as a node-level prediction task. These tasks are then solved with Graph Neural Networks. Experimental results on nine benchmark datasets show that GRAPE yields 20% lower mean absolute error for imputation tasks and 10% lower for label prediction tasks, compared with existing state-of-the-art methods.