Reviewerly/exHarmony huggingface dataset:
Population exposure index – an element of seismic risk assessment

Summary This paper presents assessment of one of the main elements of the seismic risk – human exposure at the territory of Bulgaria. Seismic hazard in terms of Peak Ground Acceleration map is modelled using GIS and overlaid with a model of population distribution in order to extract a population exposure index (PEI). We use as indicator the population density to allow comparison between less populated and more populated regions. Analysis is performed on a gridded network with a single element of 1×1 kilometer. Information about the population cover the period 2011–2018 using data from the last census performed in 2011 and the updated information for the towns provided by the National Statistical Institute for 2018. As a result, a classification of region is provided based on the results obtained for PEI.
Analysis of EZproxy server logs to visualise research activity in Curtin’s online library

Purpose The purpose of this paper is to develop data visualisation proof of concept prototypes that will enable the Curtin University Library team to explore its users’ information-seeking behaviour and collection use online by analysing the library’s EZproxy logs. Design/methodology/approach Curtin Library’s EZproxy log file data from 2013 to 2017 is used to develop the data visualisation prototypes using Unity3D software. Findings Two visualisation prototypes from the EZproxy data set are developed. The first, “Global Visualisation of Curtin Research Activity”, uses a geographical map of the world as a platform to show where each research request comes from, the time each is made and the file size of the request. The second prototype, “Database Usage Visualisation”, shows the use of the library’s various subscription databases by staff and students daily, over a month in April 2017. Research limitations/implications The paper has following limitations: working to a tight timeline of ten weeks; time taken to cleanse noise data; and requirements for storing and hosting the voluminous data sets. Practical implications The prototypes provide visual evidence of the use of Curtin Library’s digital resources at any time and from anywhere by its users, demonstrating the demand for the library’s online service offerings. These prototype evidence-based data visualisations empower the library to communicate in a compelling and interesting way how its services and subscriptions support Curtin University’s missions. Originality/value The paper provides innovative approaches to create immersive 3D data visualisation prototypes to make sense of complex EZproxy data sets.
On Multi-Thread Crawler Optimization for Scalable Text Searching

Web crawlers are an important part of modern search engines. With the development of the times, data has exploded and humans have entered a “big data era”. For example, Wikipedia carries the knowledge from all over the world, records the real-time news that occurs every day, and provides users with a good database of data, but because of the large amount of data, it puts a lot of pressure on users to search. At present, single-threaded crawling data can no longer meet the requirements of text crawling. In order to improve the performance and program versatility of single-threaded crawlers, a high-speed multi-threaded web crawler is designed to crawl the network hyper-scale text database. Multi-threaded crawling uses multiple threads to process web pages in parallel, combining breadth-first and depth-first algorithms to control web crawling. The practice project is based on the Python language to achieve multi-threaded optimization network hyper-large-scale text database-Wikipedia book crawling method, the project is inspired by the article on the Wikipedia article in the Big Data Digest public number.
Addressing General Data Protection Regulations for Enterprise Applications Using Blockchain Technology

Data security has always been a concern for organisations, and with digitisation of enterprise processes over the last few decades, the need for secure enterprise application has increased tremendously. With latest incidents on misuse of social data and the emergence of new guidelines for General Data Protection Regulations (GDPR) under the European Union, the need for trusted methods to ensure data protection has grown exponentially. Blockchain technology has evolved as a solution for creating trust, especially in the banking sector and public blockchains, is slowly making their ways to ensure open data standards to avoid manipulation of available public data. Enterprise software, on the contrary, consists of data from multiple stakeholders in the form of customers, business organisations, suppliers, employees and so on, and there has always been a need to control the data flow by ensuring its authenticity and security. This article looks at available data protection techniques and how data from enterprise applications can be made more secure using a blockchain ledger that tracks every transaction by having a time-stamped record of the same. This article also explains the implications of the proposed idea on the enforced GDPR by the EUparliament.
The Impact of Padding on Image Classification by Using Pre-trained Convolutional Neural Networks

The work presented in this paper aims to investigate the effect of pre-processing on image classification by using CNN pre-trained models. By considering how different quality factors of the input images affect the performances of a CNN based classifier, we propose a pre-processing pipeline (i.e., padding) that is able to improve the classification of the model on challenging images. The presented study allows to improve the performances by only acting on the input images, instead of re-training the model or augmenting the number of CNN’s parameters. This finds very practical applications, since such model adaptation requires high amounts of labelled data and computational costs.
Neural machine translation of literary texts from English to Slovene

This publication has emanated from researchsupported in part by a research grant from ScienceFoundation Ireland (SFI) under Grant NumberSFI/12/RC/2289 (Insight), co-funded by theEuropean Regional Development Fund.
Applying Differential Evolution with Threshold Mechanism for Feature Selection on a Phishing Websites Classification

The rapid growth of data and the need for its proper analysis still presents a big problem for intelligent data analysis and machine learning algorithms. In order to gain a better insight into the problem being analyzed, researchers today are trying to find solutions for reducing the dimensionality of the data, by adopting algorithms that could reveal the most informative features out of the data. For this purpose, in this paper we propose a novel feature selection method based on differential evolution with a threshold mechanism. The proposed method was tested on a phishing website classification problem and evaluated with two experiments. The experimental results revealed that the proposed method performed the best in all of the test cases.
MetalGAN: A Cluster-Based Adaptive Training for Few-Shot Adversarial Colorization

In recent years, the majority of works on deep-learning-based image colorization have focused on how to make a good use of the enormous datasets currently available. What about when the data at disposal are scarce? The main objective of this work is to prove that a network can be trained and can provide excellent colorization results even without a large quantity of data. The adopted approach is a mixed one, which uses an adversarial method for the actual colorization, and a meta-learning technique to enhance the generator model. Also, a clusterization a-priori of the training dataset ensures a task-oriented division useful for meta-learning, and at the same time reduces the per-step number of images. This paper describes in detail the method and its main motivations, and a discussion of results and future developments is provided.
Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER

Contextual word embeddings (e.g. GPT, BERT, ELMo, etc.) have demonstrated state-of-the-art performance on various NLP tasks. Recent work with the multilingual version of BERT has shown that the model performs very well in zero-shot and zero-resource cross-lingual settings, where only labeled English data is used to finetune the model. We improve upon multilingual BERT's zero-resource cross-lingual performance via adversarial learning. We report the magnitude of the improvement on the multilingual MLDoc text classification and CoNLL 2002/2003 named entity recognition tasks. Furthermore, we show that language-adversarial training encourages BERT to align the embeddings of English documents and their translations, which may be the cause of the observed performance gains.
Vision-assisted Arm Motion Planning for Freeform 3D Printing

Rapid freeform 3D printing of heated polymeric materials to create 3D curves in aerial space instead of 2D layers is a growing research area. We develop an arm motion planning technique that can increase printing accuracy without loss of the printing speed. The method is based on an actor-critic model-free deep reinforcement learning algorithm that operates over continuous arm action spaces. The proposed technique is able to find the trajectory for the arm that can neutralize the effect of gravity and build a filament with the desired shape. Experimental results are presented to illustrate the effectiveness of this technique in printing a straight line as well as a more difficult semicircle filament in the vertical plane.
The trinity of luxury fashion recommendations

Farfetch is the leading platform for online luxury fashion shopping. We have more than 3000 brands and high-end designers with the biggest catalog of luxury products available worldwide to more than 1 million customers.
Refined Nonlinear Gaussian Quadrature Filter

Gaussian filters have been widely used in various applications due to their simplicity and effectiveness. For nonlinear estimation problems, Gaussian filters are usually developed from numerical quadrature rules to approximate the Gaussian weighted integrals in nonlinear filtering algorithms. However, the Gaussian assumption may not be viable after propagation of quadrature through nonlinear dynamics, which leads to degraded quadrature and inaccurate update of mean and covariance. In this paper, we propose a new refined nonlinear Gaussian quadrature filter in which the predicted probability density function (PDF) is not assumed Gaussian. A set of Monte Carlo samples are first propagated through nonlinear dynamics. The statistic moments can be directly obtained from the propagated Monte Carlo samples. The refined quadrature points and weights are generated from the moments' information using the arbitrary polynomial chaos method. Since the refined quadrature points contain higher order statistic information of the propagated PDF, they have the potential to better represent the uncertainty and provide a more accurate estimate. Numerical examples show the effectiveness of the proposed filter.
Design of Amazon Chess Game System Based on Reinforcement Learning

Computer Chess game is an important research field of artificial intelligence, and amazon chess is a kind of chess with complicated strategies. In this paper the author designed an amazon chess game system contains the description of the situation, the method to generate and strategy - the value evaluation. Among them, the strategy - value assessment was used in the reinforcement learning technology, relying on the neural network output and move later distribution situation valuations to assist in Monte-Carlo search trees. Monte Carlo tree search achieves self-confrontation by continuously transforming roles and performing a series of simulations. The system iterates according to the results of self-matching and continuously improves the game strategy, thus improving the chess ability of the system. In order to improve the training efficiency of neural network, in this paper, the author designed a method for the division of chess stage according to the stage characteristics of Amazon chess. At different stages, the system is encouraged to use different strategies to explore to improve training efficiency. Finally, it is verified by experiments that the performance of this kind of reinforcement learning based Amazon chess game system can be self-improved, and the training efficiency is improved compared with the general Amazon learning game system based on reinforcement learning.
Approaching SMM4H with Merged Models and Multi-task Learning

We describe our submissions to the 4th edition of the Social Media Mining for Health Applications (SMM4H) shared task. Our team (UZH) participated in two sub-tasks: Automatic classifications of adverse effects mentions in tweets (Task 1) and Generalizable identification of personal health experience mentions (Task 4). For our submissions, we exploited ensembles based on a pre-trained language representation with a neural transformer architecture (BERT) (Tasks 1 and 4) and a CNN-BiLSTM(-CRF) network within a multi-task learning scenario (Task 1). These systems are placed on top of a carefully crafted pipeline of domain-specific preprocessing steps.
Single Image Based Metric Learning via Overlapping Blocks Model for Person Re-Identification

Considering the pedestrian structure characteristics, the first step of many person re-identification algorithms is to divide the pedestrian images or feature map into several blocks, and then the blocks in the same location are used to calculate the special loss functions that metric the differences between different images, to reduce the distance between intra-samples and to increase the distance between inter-samples. However, most of those blocks based deep metric learning methods only measure the difference between different images, but ignored the metrics between different blocks in a single image. In this paper, we propose a novel blocks based method for person re-identification called Overlapping Blocks Model (OBM), in which an innovative strategy of overlapping partition on convolutional features is used to construct multiple overlapping blocks structure and a novel overlapping blocks loss function is utilized to measure the difference between different blocks in a single image, to ensure more blocks can bring more discriminate information and higher performance. We conduct thorough validation experiments on the Market-1501, CUHK03, and DukeMTMC-reID datasets, which demonstrate that our proposed Overlapping Blocks Model can effectively improve the recognition performance of networks by adding the multiple overlapping blocks structure and the overlapping blocks loss.
Multiple Digital Economy: Semiotics and Discursive Practices

The goals and objectives of the proposed paper include (1) analysis of signs’ clusters (semiotics) and the concepts as constituents of the concept-sphere “Multiple digital economy”; (2) the study of these signs clusters’ immersion as well as these concepts into the economic discourse. The research methods applied: introspection and retrospection; semiolinguistic analysis of sign formation processes in economic discourse; conceptual analysis of the concept-sphere studied; discourse analysis of the economic signs’ different types immersion into the communication environment.
Bridging the Gap Between the Digital and Print Reading Experience

This article outlines the research the author conducted to date during his PhD. His PhD, “Augmenting the Reading Experience,” looks at methods to improve the reading experience for both digital and printed methods. So far, he has developed a prototype device that uses paper as an input method to interact with digital books. Turning a physical paper page causes an e-reader device to progress through the book, allowing the reader to have the user experience of a printed book, whilst also benefiting from the digital conveniences and features. Many modern readers own the same book in a number of formats and switch between them depending on the scenario. This introduces the problem of transitioning between formats. His current project the “Digital Bookmark” looks to allow seamless transitioning from one format to another by obtaining the latest page number and broadcasting it to all formats the reader is currently using.
Almost Optimal Classical Approximation Algorithms for a Quantum Generalization of Max-Cut

Approximation algorithms for constraint satisfaction problems (CSPs) are a central direction of study in theoretical computer science. In this work, we study classical product state approximation algorithms for a physically motivated quantum generalization of Max-Cut, known as the quantum Heisenberg model. This model is notoriously difficult to solve exactly, even on bipartite graphs, in stark contrast to the classical setting of Max-Cut. Here we show, for any interaction graph, how to classically and efficiently obtain approximation ratios 0.649 (anti-feromagnetic XY model) and 0.498 (anti-ferromagnetic Heisenberg XYZ model). These are almost optimal; we show that the best possible ratios achievable by a product state for these models is 2/3 and 1/2, respectively.
A Hybrid Recommender System to Guide Assessment and Surveillance of Adverse Childhood Experiences.

Adverse Childhood Experiences (ACEs) are negative events or states that affect children, with lasting impacts throughout their adulthood. ACES are considered one of the major risk factors for several adverse health outcomes and are associated with low quality of life and many detrimental social and economic consequences. In order to enact better surveillance of ACEs and their associated conditions, it is instrumental to provide tools to detect, monitor and respond effectively. In this paper, we present a recommender system tasked with simplifying data collection, access, and reasoning related to ACEs. The recommender system uses both semantic and statistical methods to enable content and context-based filtering.
Disappearing textile interface with inherent feedforwards

Currently, interactive devices can easily disappear into a wide range of physical context due to the development of microcontrollers, sensors and actuators. However, this disappearing interaction scenario may cause confusion to the users regarding where and how to interact with it. Therefore, a research project has been conducted to investigate different inherent feedforwards for this disappearing interaction scenario in textile surfaces. A Tangible User Interface (TUI) for volume adjusting was designed, which can provide both visual and shape-changing feedforwards. This interface can be implemented in ubiquitous soft surfaces, in this demo, a textile-based Human-Machine Interaction (HMI) in the vehicle seat. The textile interface provides a both natural and enjoyable HMI concept. This report describes the theoretical background, prototype, user test and demo setup and contribution.
Distribution of mineral phases in the Eocene Green River Formation, Piceance Basin, Colorado—Implications for the evolution of Lake Uinta

The mineralogy of the Eocene Green River Formation in the Piceance Basin, Colorado, has been the subject of numerous studies since the 1920s. Most previous work has focused on the resource potential of these lacustrine mudrocks, which in addition to substantial oil shale potential (in-place resources of 353 billion barrels of synthetic crude oil for rocks yielding at least 25 gallons per ton, GPT), includes nahcolite, a currently utilized soda ash resource, and dawsonite, a potential alternative source of aluminum. Another reason to study the mineralogy in this system is that the geographic and stratigraphic distribution of various authigenic minerals may provide insights into the geochemistry and depositional environment of the long-lived Eocene Lake Uinta. In this study, legacy non-quantitative (presence/absence) X-ray diffraction (XRD) data recently published by the U.S. Geological Survey (USGS) for more than 9,000 samples collected from 30 coreholes in the Green River Formation, Piceance Basin were examined. These data were used to better define the stratigraphic and paleogeographic extent of a set of indicator minerals (illite, analcime, albite, dawsonite, and nahcolite) within the Piceance Basin lacustrine strata. This set of minerals was selected based on observations from previous work and variability in their occurrence and co-occurrence within the Piceance Basin. The USGS database has been used to (1) construct maps showing geographic variations in mineral occurrences for 14 stratigraphically defined rich and lean oil shale zones; (2) assess co-occurrences of indicator minerals; and (3) compare occurrence results with quantitative XRD datasets collected on Piceance Basin oil shales. Occurrences of many authigenic minerals (analcime, dawsonite, and nahcolite) varied in the lacustrine strata near and around the depocenter, but others, like quartz, dolomite, and feldspar (potassium + undifferentiated), were widely and consistently present (&gt;90% of samples) across the basin. Shifts in the distribution of indicator mineral occurrences generally coincide with changes identified in previous lake history descriptions and indicate that the water chemistry of Lake Uinta varied significantly going from near-shore to the depocenter and through time.
Task-Oriented Active Sensing via Action Entropy Minimization

In active sensing, sensing actions are typically chosen to minimize the uncertainty of the state according to some information-theoretic measure such as entropy, conditional entropy, mutual information, etc. This is reasonable for applications where the goal is to obtain information. However, when the information about the state is used to perform a task, minimizing state uncertainty may not lead to sensing actions that provide the information that is most useful to the task. This is because the uncertainty in some subspace of the state space could have more impact on the performance of the task than others, and this dependence can vary at different stages of the task. One way to combine task, uncertainty, and sensing, is to model the problem as a sequential decision making problem under uncertainty. Unfortunately, the solutions to these problems are computationally expensive. This paper presents a new task-oriented active sensing scheme, where the task is taken into account in sensing action selection by choosing sensing actions that minimize the uncertainty in future task-related actions instead of state uncertainty. The proposed method is validated via simulations.
Behavior fusion for deep reinforcement learning

For deep reinforcement learning (DRL) system, it is difficult to design a reward function for complex tasks, so this paper proposes a framework of behavior fusion for the actor-critic architecture, which learns the policy based on an advantage function that consists of two value functions. Firstly, the proposed method decomposes a complex task into several sub-tasks, and merges the trained policies for those sub-tasks into a unified policy for the complex task, instead of designing a new reward function and training for the policy. Each sub-task is trained individually by an actor-critic algorithm using a simple reward function. These pre-trained sub-tasks are building blocks that are used to rapidly assemble a rapid prototype of a complicated task. Secondly, the proposed method integrates modules in the calculation of the policy gradient by calculating the accumulated returns to reduce variation. Thirdly, two alternative methods to acquire integrated returns for the complicated task are also proposed. The Atari 2600 pong game and a wafer probe task are used to validate the performance of the proposed methods by comparison with the method using a gate network.