Areas covering algorithms that commonly require measurements of similarity within data include classification, ranking, decision-making and pattern-matching. A similarity measure can effectively substitute for a distance measure (e.g. Euclidean distance), making data types with defined similarity measures compatible with methods such as K-Nearest Neighbour [1, 2] and TOPSIS [3, 4, 5]. This study proposes a similarity measure for aggregate fuzzy numbers constructed from interval-valued data using the Interval Agreement Approach (IAA), that is when given two such fuzzy numbers the degree of similarity regarding them is computed. The experimental interval-valued data in recent literature is often an alternative representation of expert opinion.

Due to researchers'aim to study personalized recommendations for different business fields, the summary of recommendation methods in specific fields is of practical significance. News recommendation systems were the earliest research field regarding recommendation systems, and were also the earliest recommendation field to apply the collaborative filtering method. In addition, news is real-time and rich in content, which makes news recommendation methods more challenging than in other fields. Thus, this paper summarizes the research progress regarding news recommendation methods. From 2018 to 2020, developed news recommendation methods were mainly deep learning-based, attention-based, and knowledge graphs-based. As of 2020, there are many news recommendation methods that combine attention mechanisms and knowledge graphs. However, these methods were all developed based on basic methods (the collaborative filtering method, the content-based recommendation method, and a mixed recommendation method combining the two). In order to allow researchers to have a detailed understanding of the development process of news recommendation methods, the news recommendation methods surveyed in this paper, which cover nearly 10 years, are divided into three categories according to the abovementioned basic methods. Firstly, the paper introduces the basic ideas of each category of methods and then summarizes the recommendation methods that are combined with other methods based on each category of methods and according to the time sequence of research results. Finally, this paper also summarizes the challenges confronting news recommendation systems.

Interpretable machine learning addresses the black-box nature of deep neural networks. Visual prototypes have been suggested for intrinsically interpretable image recognition, instead of generating post-hoc explanations that approximate a trained model. However, a large number of prototypes can be overwhelming. To reduce explanation size and improve interpretability, we propose the Neural Prototype Tree (ProtoTree), a deep learning method that includes prototypes in an interpretable decision tree to faithfully visualize the entire model. In addition to global interpretability, a path in the tree explains a single prediction. Each node in our binary tree contains a trainable prototypical part. The presence or absence of this prototype in an image determines the routing through a node. Decision making is therefore similar to human reasoning: Does the bird have a red throat? And an elongated beak? Then it's a hummingbird! We tune the accuracy-interpretability trade-off using ensembling and pruning. We apply pruning without sacrificing accuracy, resulting in a small tree with only 8 prototypes along a path to classify a bird from 200 species. An ensemble of 5 ProtoTrees achieves competitive accuracy on the CUB-200-2011 and Stanford Cars data sets. Code is available at https://github.com/M-Nauta/ProtoTree

Machine learning-based medical anomaly detection is an important problem that has been extensively studied. Numerous approaches have been proposed across various medical application domains and we observe several similarities across these distinct applications. Despite this comparability, we observe a lack of structured organisation of these diverse research applications such that their advantages and limitations can be studied. The principal aim of this survey is to provide a thorough theoretical analysis of popular deep learning techniques in medical anomaly detection. In particular, we contribute a coherent and systematic review of state-of-the-art techniques, comparing and contrasting their architectural differences as well as training algorithms. Furthermore, we provide a comprehensive overview of deep model interpretation strategies that can be used to interpret model decisions. In addition, we outline the key limitations of existing deep medical anomaly detection techniques and propose key research directions for further investigation.

Standing just 5ft tall, Mitra navigates around the hospital wards, guided by facial recognition technology and with a chest-mounted tablet that allows patients and their loved ones to see each other. Developed in recent years by the Bengaluru startup Invento Robotics, Mitra costs around $13,600 (£10,000) and – due to the reduced risk of infection to doctors – has become hugely popular in Indian hospitals during the pandemic. Since making headlines at its debut in 2017 at an international summit, where it greeted Ivanka Trump and interacted with India's prime minister Narendra Modi, Mitra has increasingly been put to use in hospitals treating Covid-19 patients. "Mitra was originally meant for care homes, but was adapted during the pandemic to assist doctors and nurses by taking vital readings, and to help in consultations," says Balaji Viswanathan, chief executive of Invento Robotics, which now exports the robot to five countries including the US and Australia. India still only has about three robots for every 10,000 workers, but the domestic industry is growing rapidly, fuelled in no small part by the pandemic.

Esports players might be viewed as individuals who sit around, eat junk food and guzzle down sugary drinks, but a new study finds these gamers are just the opposite. A team from Queensland University of Technology uncovered uncovered players are up to 21 percent more likely to have a healthier body weight than the average person. The survey also reveals that esport gamers smoke and drink less than the general public and are significantly more active as a result of certain video games. Although a majority are in tip top shape, the study did find that 4.03 percent of esports players are more likely to be morbidly obese than the general public. A team from Queensland University of Technology uncovered uncovered players are up to 21 percent more likely to have a healthier body weight than the average person.

Deep Learning methods have been proven to be flexible to model complex phenomena. This has also been the case of Intelligent Transportation Systems (ITS), in which several areas such as vehicular perception and traffic analysis have widely embraced Deep Learning as a core modeling technology. Particularly in short-term traffic forecasting, the capability of Deep Learning to deliver good results has generated a prevalent inertia towards using Deep Learning models, without examining in depth their benefits and downsides. This paper focuses on critically analyzing the state of the art in what refers to the use of Deep Learning for this particular ITS research area. To this end, we elaborate on the findings distilled from a review of publications from recent years, based on two taxonomic criteria. A posterior critical analysis is held to formulate questions and trigger a necessary debate about the issues of Deep Learning for traffic forecasting. The study is completed with a benchmark of diverse short-term traffic forecasting methods over traffic datasets of different nature, aimed to cover a wide spectrum of possible scenarios. Our experimentation reveals that Deep Learning could not be the best modeling technique for every case, which unveils some caveats unconsidered to date that should be addressed by the community in prospective studies. These insights reveal new challenges and research opportunities in road traffic forecasting, which are enumerated and discussed thoroughly, with the intention of inspiring and guiding future research efforts in this field.

This paper primarily presents two methods of ranking aggregated fuzzy numbers from intervals using the Interval Agreement Approach (IAA). The two proposed ranking methods within this study contain the combination and application of previously proposed similarity measures, along with attributes novel to that of aggregated fuzzy numbers from interval-valued data. The shortcomings of previous measures, along with the improvements of the proposed methods, are illustrated using both a synthetic and real-world application. The real-world application regards the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm, modified to include both the previous and newly proposed methods.

Federated learning (FL), as a distributed machine learning approach, has drawn a great amount of attention in recent years. FL shows an inherent advantage in privacy preservation, since users' raw data are processed locally. However, it relies on a centralized server to perform model aggregation. Therefore, FL is vulnerable to server malfunctions and external attacks. In this paper, we propose a novel framework by integrating blockchain into FL, namely, blockchain assisted decentralized federated learning (BLADE-FL), to enhance the security of FL. The proposed BLADE-FL has a good performance in terms of privacy preservation, tamper resistance, and effective cooperation of learning. However, it gives rise to a new problem of training deficiency, caused by lazy clients who plagiarize others' trained models and add artificial noises to conceal their cheating behaviors. To be specific, we first develop a convergence bound of the loss function with the presence of lazy clients and prove that it is convex with respect to the total number of generated blocks $K$. Then, we solve the convex problem by optimizing $K$ to minimize the loss function. Furthermore, we discover the relationship between the optimal $K$, the number of lazy clients, and the power of artificial noises used by lazy clients. We conduct extensive experiments to evaluate the performance of the proposed framework using the MNIST and Fashion-MNIST datasets. Our analytical results are shown to be consistent with the experimental results. In addition, the derived optimal $K$ achieves the minimum value of loss function, and in turn the optimal accuracy performance.

Abstract--Artificial Intelligence/Machine Learning techniques have been widely used in software engineering to improve developer productivity, the quality of software systems, and decision-making. However, such AI/ML models for software engineering are still impractical, not explainable, and not actionable. These concerns often hinder the adoption of AI/ML models in software engineering practices. Then, we summarize three successful case studies on how explainable AI techniques can be used to address the aforementioned challenges by making software defect prediction models more practical, explainable, and actionable. Who should perform this task?