The fashion industry is on the verge of an unprecedented change. The implementation of machine learning, computer vision, and artificial intelligence (AI) in fashion applications is opening lots of new opportunities for this industry. This paper provides a comprehensive survey on this matter, categorizing more than 580 related articles into 22 well-defined fashion-related tasks. Such structured task-based multi-label classification of fashion research articles provides researchers with explicit research directions and facilitates their access to the related studies, improving the visibility of studies simultaneously. For each task, a time chart is provided to analyze the progress through the years. Furthermore, we provide a list of 86 public fashion datasets accompanied by a list of suggested applications and additional information for each.

Jarvis, please pull up some quick articles to teach me about AI…Jarvis? Oh wait, my bad, I forgot that you're not real outside of Marvel. Please excuse me, I'm just going to go sob in the corner while Siri tells me she "didn't quite get that" in an endless, torturous loop. If you're the singular person on Earth who has never seen an MCU movie and you didn't quite get that, absolutely no worries (but I hope you move to a more exciting rock soon)! I'm messing with you, here's the rundown: J.A.R.V.I.S. is a fictional AI system created by billionaire genius Tony Stark, essentially a virtual assistant that can do anything from making predictions from enormous piles of data to mimicking human language (and occasionally cracking a joke), which we'll soon see is harder than it seems! Right now, some of you may be thinking WTF (Well, That's Fantastic), but I don't know what this has to do with anything? If you haven't already guessed it, today we are going to be learning about AI, i.e. Artificial Intelligence (which is what our dear J.A.R.V.I.S. is)! Let's get right into it: what exactly is AI?

José V. Fernández first got interested in using technology to ramp up financial inclusion when he first arrived in New York City from Spain around 10 years ago. His job working as a trade officer for Spain didn't impress numerous prospective landlords, none of whom would rent him an apartment because he lacked a U.S. credit score. Finally, one company agreed to sign a one-year lease, but only if Fernández paid six months of his pricey Manhattan rent ahead of time. A few years after that, Fernández co-founded a fintech firm to open up microloans to unbanked people in West Africa. Then, last year, he founded Bankuish, which aims to give gig workers and freelancers a way to access banking services they normally wouldn't be able to tap.

Acyclic model, often depicted as a directed acyclic graph (DAG), has been widely employed to represent directional causal relations among collected nodes. In this article, we propose an efficient method to learn linear non-Gaussian DAG in high dimensional cases, where the noises can be of any continuous non-Gaussian distribution. This is in sharp contrast to most existing DAG learning methods assuming Gaussian noise with additional variance assumptions to attain exact DAG recovery. The proposed method leverages a novel concept of topological layer to facilitate the DAG learning. Particularly, we show that the topological layers can be exactly reconstructed in a bottom-up fashion, and the parent-child relations among nodes in each layer can also be consistently established. More importantly, the proposed method does not require the faithfulness or parental faithfulness assumption which has been widely assumed in the literature of DAG learning. Its advantage is also supported by the numerical comparison against some popular competitors in various simulated examples as well as a real application on the global spread of COVID-19.

Large, pre-trained transformer-based language models such as BERT have drastically changed the Natural Language Processing (NLP) field. We present a survey of recent work that uses these large language models to solve NLP tasks via pre-training then fine-tuning, prompting, or text generation approaches. We also present approaches that use pre-trained language models to generate data for training augmentation or other purposes. We conclude with discussions on limitations and suggested directions for future research.

The largest dataset of Arabic speech mispronunciation detections in Egyptian dialogues is introduced. The dataset is composed of annotated audio files representing the top 100 words that are most frequently used in the Arabic language, pronounced by 100 Egyptian children (aged between 2 and 8 years old). The dataset is collected and annotated on segmental pronunciation error detections by expert listeners.

This paper reports on the evaluation of Deep Learning (DL) transformer architecture models for Named-Entity Recognition (NER) on ten low-resourced South African (SA) languages. In addition, these DL transformer models were compared to other Neural Network and Machine Learning (ML) NER models. The findings show that transformer models significantly improve performance when applying discrete fine-tuning parameters per language. Furthermore, fine-tuned transformer models outperform other neural network and machine learning models with NER on the low-resourced SA languages. For example, the transformer models generated the highest F-scores for six of the ten SA languages, including the highest average F-score surpassing the Conditional Random Fields ML model. Additional research could evaluate the more recent transformer architecture models on other Natural Language Processing tasks and applications, such as Phrase chunking, Machine Translation, and Part-of-Speech tagging.

Artificial intelligence, also known as AI, is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by humans. AI applications include advanced web search engines, recommendation systems, speech recognition, self-driving cars, and much more. Now AI is involved in the areas of writing, both fiction and non-fiction, and graphics. First, let's start out with art. The woman that you see above was created with artificial intelligence (and a little input from me).

The popularity of machine learning and artificial intelligence is driving more and more technological innovations. The tech market is also attracting several new tech professionals, both from tech and non-tech backgrounds. The emergence of machine learning hackathons has turned out to be one of the best ways for machine learning and AI practitioners to practice and show off their skills. Hackathons provide an environment for the participants to work on various kinds of projects using distinct tools to show off their skills. In this article, we talk about the top machine learning hackathons that AI professionals can choose from in 2021.

Over the past several decades, climate change has led to major disasters in Eastern Africa countries including Tanzania. From floods, chronic droughts, landslides, strong winds and earthquakes to their secondary impacts of diseases and epidemics, these are some of the recent disasters plaguing Tanzania. These disasters lead to death and displacement of people, loss of properties and livelihoods, disruption of social networks and services such as water, food, and healthcare thereby leaving communities more vulnerable and susceptible to the next extreme event. Lack of disaster preparedness and awareness makes the situation worse as communities remain helpless in the event of disasters hence face its full impact. Combining citizen science and modern technological innovation provides an opportunity to build the resilience of communities and reduce risks.