Reviewerly/exHarmony huggingface dataset:
Your hospital needs you: Eliciting positive cybersecurity behaviours from healthcare staff

Staff behaviour plays a key role in the cybersecurity position of an organisation. Despite this, behaviour-change interventions are not commonly applied within the field of cybersecurity. Behaviour change technique could be particularly beneficial given increasing concerns around healthcare cybersecurity risks; particularly following the 2017 WannaCry ransomware attack which had devastating results on healthcare services. Cyber-risk is particularly concerning within healthcare given the criticality of medical systems and the potential impacts of a cyberbreach or attack. In worst case scenarios, cybersecurity incidents could result in patient harm or even fatalities. Whilst there has been concerted investment in improving healthcare’s technological defences against cyberthreat, the same level of investment has not been made in healthcare staff. This has left staff behaviour as a vulnerability which can be exploited by attackers. This paper introduces a structured approach to help organisations work through four key steps that we refer to as the AIDE approach to Assess, Identify, Develop and Evaluate behaviour change techniques to facilitate more secure behaviour. We include a worked example of how we are applying this approach to the development of interventions to mitigate insecure cybersecurity behaviours in a healthcare context.
On trinomials of type X+(1 + AX(−1) + BX(−1)), n, m odd, over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mi>q</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:msub></mml:math>, q = 22+1

We provide necessary conditions for polynomials FA,B,m,n(X)=Xn+m(1+AXm(q−1)+BXn(q−1))∈Fq[X], n,m odd, over Fq2, q even, to be permutations. The main tool is the study of algebraic curves associated with the polynomial FA,B,m,n(X) and in particular the investigation of their singularities.
D2.2 FAIR Semantics: First recommendations

This document is the first iteration of recommendations for making semantic artefact FAIR. These recommendations result from initial discussions during a brainstorming workshop organised by FAIRsFAIR as co-located event with the 14th RDA Plenary meeting in Helsinki. We are proposing 17 preliminary recommendations related to one or more of the FAIR principles and 10 best practice recommendations to improve the global FAIRness of semantic artefacts. These initial recommendations should not be considered as a gold standard but rather as a basis for discussion with the various stakeholders of the semantic community.
Tree retraining in the decision tree learning algorithm

Abstract Decision trees belong to the most effective classification methods. The main advantage of decision trees is a simple and user-friendly interpretation of the results obtained. But despite its well-known advantages the method has some disadvantages as well. One of them is that decision tree learning algorithm build an “almost optimal” tree. The paper considers the way to improve the efficiency of decision trees. The paper proposes a modification of decision tree learning algorithms by retraining the part of tree at every node training. The classification problems were solved to compare standard decision tree learning algorithms with the modified ones. Algorithm efficiency refers to the percentage of correctly classified test sample objects. Statistical analysis based on Student’s t-test was carried out to compare the efficiency of the algorithms.
Belief-based Generation of Argumentative Claims

When engaging in argumentative discourse, skilled human debaters tailor claims to the audience's beliefs to construct effective arguments.Recently, the field of computational argumentation witnessed extensive effort to address the automatic generation of arguments.However, existing approaches do not perform any audience-specific adaptation.In this work, we aim to bridge this gap by studying the task of belief-based claim generation: Given a controversial topic and a set of beliefs, generate an argumentative claim tailored to the beliefs.To tackle this task, we model the people's prior beliefs through their stances on controversial topics and extend state-of-the-art text generation models to generate claims conditioned on the beliefs.Our automatic evaluation confirms the ability of our approach to adapt claims to a set of given beliefs.In a manual study, we also evaluate the generated claims in terms of informativeness and their likelihood to be uttered by someone with a respective belief.Our results reveal the limitations of modeling users' beliefs based on their stances.Still, they demonstrate the potential of encoding beliefs into argumentative texts, laying the ground for future exploration of audience reach.
Anomaly Detection for Autonomous Guided Vehicles using Bayesian Surprise

As warehouses, storage facilities and factories become more expanded and equipped with smart devices, there is a substantial need for rapid, intelligent and autonomous detection of unusual and potentially hazardous situations, also called anomalies. In particular for Autonomous Guided Vehicles (AGVs) that drive around these premises independently, unforeseen obstructions along their path-e.g. a cardboard box in the middle of a corridor or bumps in the floor-and sudden or unexpected actions executed by personnel-e.g. someone walking in a restricted area-make it hard for AGVs to navigate safely. We therefore propose a novel approach to detect such anomalies in an unsupervised manner by measuring Bayesian surprise: whenever an event is observed that does not align with the agent's prior knowledge of the world, this event is deemed surprising and could indicate an anomaly. This paper lays out the details on how to learn both the prior and posterior models of an AGV that drives around a warehouse and observes the environment through an RGBD camera. In the experiments we show that our Bayesian surprise approach outperforms a baseline that is traditionally used to detect anomalies in sequences of images.
Optical pumping of quantum dot micropillar lasers

Arrays of quantum dot micropillar lasers are an attractive technology platform for various applications in the wider field of nanophotonics. Of particular interest is the potential efficiency enhancement as consequence of cavity quantum electrodynamics effects which makes them prime candidates for next generation photonic neurons in neural network hardware. However, in particular for optical pumping their power-conversion efficiency can be very low. Here we perform an in-depth experimental analysis of quantum dot microlasers and investigate their input-output relationship over a wide range of optical pumping conditions. We find that the current energy efficiency limitation is caused by disadvantageous optical pumping concepts and by a low exciton conversion efficiency. Our results indicate that for non-resonant pumping into the GaAs matrix (wetting layer), 3.4% (0.6%) of the optical pump is converted into lasing-relevant excitons, and of those only 2% (0.75%) provide gain to the lasing transition. Based on our findings we propose to improve the pumping efficiency by orders of magnitude by increasing the aluminium content of the AlGaAs/GaAs mirror pairs in the upper Bragg reflector.
Development of dementia care training system based on augmented reality and whole body wearable tactile sensor

This study develops a training system for a multimodal comprehensive care methodology for dementia patients called Humanitude. Humanitude has attracted much attention as a gentle and effective care technique. It consists of four main techniques, namely, eye contact, verbal communication, touch, and standing up, and more than 150 care elements. Learning Humanitude thus requires much time. To provide an effective training system for Humanitude, we develop a training system that realizes sensing and interaction simultaneously by combining a real entity and augmented reality technology. To imitate the interaction between a patient and a caregiver, we superimpose a three-dimensional CG model of a patient's face onto the head of a soft doll using augmented reality technology. Touch information such as position and force is sensed using the whole body wearable tactile sensor developed to quantify touch skills. This training system enables the evaluation of eye contact and touch skills simultaneously. We build a prototype of the proposed training system and evaluate the usefulness of the system in public lectures.
Advances in Bias-aware Recommendation on the Web

The goal of this tutorial is to provide the WSDM community with recent advances on the assessment and mitigation of data and algorithmic bias in recommender systems. We first introduce conceptual foundations, by presenting the state of the art and describing real-world examples of how bias can impact on recommendation algorithms from several perspectives (e.g., ethical and system objectives). The tutorial continues with a systematic showcase of algorithmic countermeasures to uncover, assess, and reduce bias along the recommendation design process. A practical part then provides attendees with implementations of pre-, in-, and post-processing bias mitigation algorithms, leveraging open-source tools and public datasets; in this part, tutorial participants are engaged in the design of bias countermeasures and in articulating impacts on stakeholders. We conclude the tutorial by analyzing emerging open issues and future directions in this rapidly evolving research area (Website: https://biasinrecsys.github.io/wsdm2021).
Volumetric Motion Magnification: Subtle Motion Extraction from 4D Data

The extension of Phase-based Video Motion Magnification into three-dimensions is presented in this work. The technique uses a 3D complex steerable pyramid to decompose volumetric frames of 4D dataset. The resulting decomposition can be processed to filter, amplify, or attenuate subtle motions occurring locally within the 4D data, before inversion of the transform is performed to recreate the volumetric images. Recent research has used Phase Based Video Motion Magnification to extract subtle motion from 2D slices of internal deformation data. This work demonstrates how Phase Based Video Motion Magnification can be extended to work on full volumetric images to extract full 3D volumetric motions. The Volumetric Motion Magnification technique is demonstrated qualitatively, to amplify and extract the indiscernible motion of a cylinder for visual identification. Then, the ability of the technique to enhance the motion noise floor of Digital Volume Correlation by more than a factor of 10 is demonstrated.
Application of Eye Tracking in Puzzle Games for Adjunct Cognitive Markers: Pilot Observational Study in Older Adults

Recent studies suggest that computerized puzzle games are enjoyable, easy to play, and engage attentional, visuospatial, and executive functions. They may help mediate impairments seen in cognitive decline in addition to being an assessment tool. Eye tracking provides a quantitative and qualitative analysis of gaze, which is highly useful in understanding visual search behavior.The goal of the research was to test the feasibility of eye tracking during a puzzle game and develop adjunct markers for cognitive performance using eye-tracking metrics.A desktop version of the Match-3 puzzle game with 15 difficulty levels was developed using Unity 3D (Unity Technologies). The goal of the Match-3 puzzle was to find configurations (target patterns) that could be turned into a row of 3 identical game objects (tiles) by swapping 2 adjacent tiles. Difficulty levels were created by manipulating the puzzle board size (all combinations of width and height from 4 to 8) and the number of unique tiles on the puzzle board (from 4 to 8). Each level consisted of 4 boards (ie, target patterns to match) with one target pattern each. In this study, the desktop version was presented on a laptop computer setup with eye tracking. Healthy older subjects were recruited to play a full set of 15 puzzle levels. A paper-pencil-based assessment battery was administered prior to the Match-3 game. The gaze behavior of all participants was recorded during the game. Correlation analyses were performed on eye-tracking data correcting for age to examine if gaze behavior pertains to target patterns and distractor patterns and changes with puzzle board size (set size). Additionally, correlations between cognitive performance and eye movement metrics were calculated.A total of 13 healthy older subjects (mean age 70.67 [SD 4.75] years; range 63 to 80 years) participated in this study. In total, 3 training and 12 test levels were played by the participants. Eye tracking recorded 672 fixations in total, 525 fixations on distractor patterns and 99 fixations on target patterns. Significant correlations were found between executive functions (Trail Making Test B) and number of fixations on distractor patterns (P=.01) and average fixations (P=.005).Overall, this study shows that eye tracking in puzzle games can act as a supplemental source of data for cognitive performance. The relationship between a paper-pencil test for executive functions and fixations confirms that both are related to the same cognitive processes. Therefore, eye movement metrics might be used as an adjunct marker for cognitive abilities like executive functions. However, further research is needed to evaluate the potential of the various eye movement metrics in combination with puzzle games as visual search and attentional marker.
Test case generation for agent-based models: A systematic literature review

Agent-based models play an important role in simulating complex emergent phenomena and supporting critical decisions. In this context, a software fault may result in poorly informed decisions that lead to disastrous consequences. The ability to rigorously test these models is therefore essential. In this systematic literature review, we answer five research questions related to the key aspects of test case generation in agent-based models: What are the information artifacts used to generate tests? How are these tests generated? How is a verdict assigned to a generated test? How is the adequacy of a generated test suite measured? What level of abstraction of an agent-based model is targeted by a generated test? Our results show that whilst the majority of techniques are effective for testing functional requirements at the agent and integration levels of abstraction, there are comparatively few techniques capable of testing society-level behaviour. Additionally, we identify a need for more thorough evaluation using realistic case studies that feature challenging properties associated with a typical agent-based model.
Single-shot cuboids: Geodesics-based end-to-end Manhattan aligned layout estimation from spherical panoramas

It has been shown that global scene understanding tasks like layout estimation can benefit from wider field of views, and specifically spherical panoramas. While much progress has been made recently, all previous approaches rely on intermediate representations and postprocessing to produce Manhattan-aligned estimates. In this work we show how to estimate full room layouts in a single-shot, eliminating the need for postprocessing. Our work is the first to directly infer Manhattan-aligned outputs. To achieve this, our data-driven model exploits direct coordinate regression and is supervised end-to-end. As a result, we can explicitly add quasi-Manhattan constraints, which set the necessary conditions for a homography-based Manhattan alignment module. Finally, we introduce the geodesic heatmaps and loss and a boundary-aware center of mass calculation that facilitate higher quality keypoint estimation in the spherical domain. Our models and code are publicly available at https://github.com/VCL3D/SingleShotCuboids.
On-chip Phase Change Optical Matrix Multiplication Core

We demonstrated a non-volatile photonic matrix computation core which contains a 3×3 photonic phase change in-memory computing matrix to carry out matrix vector multiplication on a silicon-based substrate. We established an optical computing core model and hardware implementation based on our photonic phase change material (PCM) devices and fabricated a 3×3 matrix using the silicon photonic foundry. We demonstrated the functionality of this matrix as the linear convolution layer in a convolutional neural network (CNN) and demonstrated simple pattern recognition. Finally, we simulated scaling up matrix limits using experimental data from smaller matrices. This on-chip, non-volatile, photonic computation matrix transfers optical computing from single device to matrix, paving the way for practical wide usage of optical computing systems.
Unsupervised Emitter Clustering through Deep Manifold Learning

We perform unsupervised clustering to group Radio Frequency signals according to the device that transmitted the signal. We do so by first performing supervised training on a RiftNet classifier that contains a layer that can be used as a latent vector. We then perform the unsupervised clustering on a completely different set of devices than those used for training. During unsupervised clustering, we project each signal into its latent vector, then perform clustering in a UMAP-learned reduction of that space. This approach provides understanding and a path forward on how such semi-supervised deep-learning clustering approaches might fit in a real world RF system.
Exploring Characteristics of Inter-cluster Machines and Cloud Applications on Google Clusters

Modern cluster management systems have been evolving to cope with running and managing diverse cloud applications on heterogeneous computing clusters. Consequently, the system behaviours become complex and non-trivial to explain. In this paper we take the recently published Google trace data set version 3 (V3) as a case study to explore various aspects of intercluster differences. We analyse the distribution of underlying physical machines resource, e.g. number and types of machine, and metrics of computational job requests, e.g. job duration, utilisation and Cycles Per Instruction (CPI). We also apply an unsupervised learning algorithm on the metrics to characterise jobs. Our analysis suggests that the composition of the underlying machine resources in different cells can be substantially different, and the cells with similar machine resource structures can utilise resources differently depending on the characteristics of job requests.
An introduction to machine learning for clinicians: How can machine learning augment knowledge in geriatric oncology?

Interest in machine learning (ML) approaches to analyze patient data is in an explosive phase of growth. Underpinning this burgeoning interest are several advances in technology, including availability of computing power, evolution of ML software, and the ubiquity of electronic health records (EHRs). Personal computers now have sufficient computational power to run some ML algorithms for small to medium datasets, encompassing many of the datasets of interest in clinical medicine. Even for very large datasets (“big data”, with number of data points in the billions, trillions, or more), access to parallel computing resources is now widespread at academic medical centers and via cloud computing platforms.
Automatic image annotation based on an improved nearest neighbor technique with tag semantic extension model

Nearest Neighbor method (KNN) is a typical method to solve the problem of automatic image annotation (AIA). However, traditional AIA methods based on KNN only consider the relationships among images and labels. In this paper, we propose an improved KNN image annotation method based on a tag semantic extension model (TSEM). Our approach uses the convolutional neural network (CNN) to extract image features and predicts image tags automatically via nearest features. Different from existing work, the proposed method considers correlations among images, correlations between images and labels and those among labels. Additionally, a label quantity prediction (LQP) model is proposed to predict the number of tags, which further improves the tag prediction accuracy. Comparison experiments were performed on three typical image datasets Corel5k, ESP game and laprtc12. Experimental results show that the average F1 of our model is 0.427, which outperforms the state-of-the-art KNN image annotation methods.
A Novel Imbalanced Ensemble Learning in Software Defect Predication

With the availability of high-speed Internet and the advent of Internet of Things devices, modern software systems are growing in both size and complexity. Software defect prediction (SDP) guarantees the high quality of such complex systems. However, the characteristics of imbalanced distribution of defect data sets have led to the deviation and loss of accuracy of most software defect prediction methods. This paper presents two novel approaches for learning from imbalanced data sets to produce a higher predictive accuracy over the minority class. These two methods differ in whether the oversampling and the misclassification cost information are utilized during training stage, and they are good at different aspects of imbalanced classification, one for dealing with highly imbalanced data sets and the other for moderately imbalanced data sets. Comparing with other state-of-the-art imbalance learning algorithms on imbalanced datasets, the experimental results show that these two methods have achieved excellent results in terms of G-mean and AUC measures, and more accurately identified the defective modules to reduce the cost of detection system.
Symmetry detection of occluded point cloud using deep learning

Before our paper, some papers have approached symmetry detection in various attacks of lines. Deep learning has taken off in recent years in many fields in computer graphics, and we are thinking of using big data to solve the symmetry detection problem. Our work aims to solve a niche problem: using deep learning to detect symmetry in objects of the occluded point cloud. As far as we know, we are the first piece of work to deal with such a problem in deep learning settings. We employ points on the symmetry plane and normal vectors as double supervision to help us pinpoint the symmetry plane. Experiments conducted on the YCB-video dataset prove the effectiveness of the work and our method. To see our implementation, please visit https://github.com/Allen--Wu/dense_symmetry.