Stakeholders in the Nigerian maritime industry have identified deeper application of technology as a way to achieving an efficient port system in Nigeria. At a recent one-day Town Hall Meeting on Hitch Free Port Operations in Nigeria organised by JournalNG in Lagos, they urged the federal government to consider applying the Webb Port system being used in neighbouring Benin Republic. While making a presentation at the event, Managing Director of Webb Fontaine Nigeria Limited, Ope Babalola disclosed that his company has assisted Benin Republic in achieving ICT port system that harmonised the country's interests through a single transaction. According to him, the system has helped in saving time, producing more accurate results, protecting government revenue and facilitating trade. Tankian Coulibaly an official from Webb Fontaine in Benin Republic said his company helped in Beninois government to set up a port community integration system called Webb Port.

About the Company: Founded in 2017, RwHealth's platform combines AI machine learning and data science to give healthcare providers access to data that can aid their decision-making. RwHealth's deep analytical capability can be used to make predictions, model treatment options, improve safety and increase efficiency so that clinicians can deliver better care to more people. Its platform has been used to help UK hospitals combat bed shortages and tackle waiting list issues caused by the pandemic. The startup works with more than 40 providers in the UK and internationally and its AI technology has processed more than 10.5 million patients in the UK and a further 5.5 million across the Middle East and Australia.

Cooperative transportation by multiple aerial robots has the potential to support various payloads and to reduce the chance of them being dropped. Furthermore, autonomously controlled robots make the system scalable with respect to the payload. In this study, a cooperative transportation system was developed using rigidly attached aerial robots, and a decentralized controller was proposed to guarantee asymptotic stability of the tracking error for unknown strictly positive real systems. A feedback controller was used to transform unstable systems into strictly positive real ones using the shared attachment positions. First, the cooperative transportation of unknown payloads with different shapes larger than the carrier robots was investigated through numerical simulations. Second, cooperative transportation of an unknown payload (with a weight of about 2.7 kg and maximum length of 1.6 m) was demonstrated using eight robots, even under robot failure. Finally, it was shown that the proposed system carried an unknown payload, even if the attachment positions were not shared, that is, even if the asymptotic stability was not strictly guaranteed.

Variational Bayes (VB) applied to latent Dirichlet allocation (LDA) is the original inference mechanism for LDA. Many variants of VB for LDA, as well as for VB in general, have been developed since LDA's inception in 2013, but standard VB is still widely applied to LDA. Variational message passing (VMP) is the message passing equivalent of VB and is a useful tool for constructing a variational inference solution for a large variety of conjugate exponential graphical models (there is also a non conjugate variant available for other models). In this article we present the VMP equations for LDA and also provide a brief discussion of the equations. We hope that this will assist others when deriving variational inference solutions to other similar graphical models.

Ethics statements have been proposed as a mechanism to increase transparency and promote reflection on the societal impacts of published research. In 2020, the machine learning (ML) conference NeurIPS broke new ground by requiring that all papers include a broader impact statement. This requirement was removed in 2021, in favour of a checklist approach. The 2020 statements therefore provide a unique opportunity to learn from the broader impact experiment: to investigate the benefits and challenges of this and similar governance mechanisms, as well as providing an insight into how ML researchers think about the societal impacts of their own work. Such learning is needed as NeurIPS and other venues continue to question and adapt their policies. To enable this, we have created a dataset containing the impact statements from all NeurIPS 2020 papers, along with additional information such as affiliation type, location and subject area, and a simple visualisation tool for exploration. We also provide an initial quantitative analysis of the dataset, covering representation, engagement, common themes, and willingness to discuss potential harms alongside benefits. We investigate how these vary by geography, affiliation type and subject area. Drawing on these findings, we discuss the potential benefits and negative outcomes of ethics statement requirements, and their possible causes and associated challenges. These lead us to several lessons to be learnt from the 2020 requirement: (i) the importance of creating the right incentives, (ii) the need for clear expectations and guidance, and (iii) the importance of transparency and constructive deliberation. We encourage other researchers to use our dataset to provide additional analysis, to further our understanding of how researchers responded to this requirement, and to investigate the benefits and challenges of this and related mechanisms.

The task of assigning and validating internationally accepted commodity code (HS code) to traded goods is one of the critical functions at the customs office. This decision is crucial to importers and exporters, as it determines the tariff rate. However, similar to court decisions made by judges, the task can be non-trivial even for experienced customs officers. The current paper proposes a deep learning model to assist this seemingly challenging HS code classification. Together with Korea Customs Service, we built a decision model based on KoELECTRA that suggests the most likely heading and subheadings (i.e., the first four and six digits) of the HS code. Evaluation on 129,084 past cases shows that the top-3 suggestions made by our model have an accuracy of 95.5% in classifying 265 subheadings. This promising result implies algorithms may reduce the time and effort taken by customs officers substantially by assisting the HS code classification task.

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.