Peer assessment systems are emerging in many social and multi-agent settings, such as peer grading in large (online) classes, peer review in conferences, peer art evaluation, etc. However, peer assessments might not be as accurate as expert evaluations, thus rendering these systems unreliable. The reliability of peer assessment systems is influenced by various factors such as assessment ability of peers, their strategic assessment behaviors, and the peer assessment setup (e.g., peer evaluating group work or individual work of others). In this work, we first model peer assessment as multi-relational weighted networks that can express a variety of peer assessment setups, plus capture conflicts of interest and strategic behaviors. Leveraging our peer assessment network model, we introduce a graph convolutional network which can learn assessment patterns and user behaviors to more accurately predict expert evaluations. Our extensive experiments on real and synthetic datasets demonstrate the efficacy of our proposed approach, which outperforms existing peer assessment methods.

Super-resolution (SR) aims to increase the resolution of imagery. Applications include security, medical imaging, and object recognition. We propose a deep learning-based SR system that takes a hexagonally sampled low-resolution image as an input and generates a rectangularly sampled SR image as an output. For training and testing, we use a realistic observation model that includes optical degradation from diffraction and sensor degradation from detector integration. Our SR approach first uses non-uniform interpolation to partially upsample the observed hexagonal imagery and convert it to a rectangular grid. We then leverage a state-of-the-art convolutional neural network (CNN) architecture designed for SR known as Residual Channel Attention Network (RCAN). In particular, we use RCAN to further upsample and restore the imagery to produce the final SR image estimate. We demonstrate that this system is superior to applying RCAN directly to rectangularly sampled LR imagery with equivalent sample density. The theoretical advantages of hexagonal sampling are well known. However, to the best of our knowledge, the practical benefit of hexagonal sampling in light of modern processing techniques such as RCAN SR is heretofore untested. Our SR system demonstrates a notable advantage of hexagonally sampled imagery when employing a modified RCAN for hexagonal SR.

In this work, we fill the gap in the Semantic Web in the context of Cultural Symbolism. Building upon earlier work in, we introduce the Simulation Ontology, an ontology that models the background knowledge of symbolic meanings, developed by combining the concepts taken from the authoritative theory of Simulacra and Simulations of Jean Baudrillard with symbolic structures and content taken from "Symbolism: a Comprehensive Dictionary" by Steven Olderr. We re-engineered the symbolic knowledge already present in heterogeneous resources by converting it into our ontology schema to create HyperReal, the first knowledge graph completely dedicated to cultural symbolism. A first experiment run on the knowledge graph is presented to show the potential of quantitative research on symbolism.

Anyone waiting for the results of a medical test knows the anxious question: "Will my life change completely when I know?" And the relief if you test negative. Today, artificial intelligence (AI) is increasingly deployed to predict life-threatening diseases. But there remains a big challenge in getting the machine learning (ML) algorithms to be precise enough--specifically, in getting the algorithms to correctly diagnose if someone is sick. Machine learning (ML) is the branch of AI where algorithms learn from datasets and get smarter in the process.

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.