Reviewerly/exHarmony huggingface dataset:
On the Importance of Subword Information for Morphological Tasks in Truly Low-Resource Languages

Recent work has validated the importance of subword information for word representation learning. Since subwords increase parameter sharing ability in neural models, their value should be even more pronounced in low-data regimes. In this work, we therefore provide a comprehensive analysis focused on the usefulness of subwords for word representation learning in truly low-resource scenarios and for three representative morphological tasks: fine-grained entity typing, morphological tagging, and named entity recognition. We conduct a systematic study that spans several dimensions of comparison: 1) type of data scarcity which can stem from the lack of task-specific training data, or even from the lack of unannotated data required to train word embeddings, or both; 2) language type by working with a sample of 16 typologically diverse languages including some truly low-resource ones (e.g. Rusyn, Buryat, and Zulu); 3) the choice of the subword-informed word representation method. Our main results show that subword-informed models are universally useful across all language types, with large gains over subword-agnostic embeddings. They also suggest that the effective use of subwords largely depends on the language (type) and the task at hand, as well as on the amount of available data for training the embeddings and task-based models, where having sufficient in-task data is a more critical requirement.
Denoising for satellite laser altimetry full-waveform data based on EMD-Hurst analysis

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Big Data From Social Media and Scientific Literature Databases Reveals Relationships Among Risk Management, Project Management and Project Success

The literature review highlights that previous studies have been identifying risk management as an essential tool for project management and could increase the chance of successfully meeting project objectives. In addition, as found from the reviewed literature, risk management has been seen as a tool of allowing the project team to communicate risk information, so as to enhance the decision-making process towards balancing threats and opportunities. Thus, this research aims to examine participants’ views on the alignment of risk management, project management and organizational project success. Machine learning algorithms are employed to explore collective data from posts on twitter in order to obtain valuable knowledge about discussions regarding risk management, and project management. Additionally, the corresponding scientific literature obtained from Scopus database was analyzed utilizing bibliometric tools, in order to investigate diverse perceptions in academia and industry. Findings of this study will have implications for practitioners’ perception of project risk management.
Dual-projector structured light 3D shape measurement

Structured light illumination is an active three-dimensional scanning technique that uses a projector and camera pair to project and capture a series of stripe patterns; however, with a single camera and single projector, structured light scanning has issues associated with scan occlusions, multi-path, and weak signal reflections. To address these issues, this paper proposes dual-projector scanning using a range of projector/camera arrangements. Unlike previous attempts at dual-projector scanning, the proposed scanner drives both light engines simultaneously, using temporal-frequency multiplexing to computationally decouple the projected patterns. Besides presenting the details of how such a system is built, we also present experimental results demonstrating how multiple projectors can be used to (1) minimize occlusions; (2) achieve higher signal-to-noise ratios having twice a single projector’s brightness; (3) reduce the number of component video frames required for a scan; and (4) detect multi-path interference.
Modeling Alignment Error in Quantum Key Distribution Based on a Weak Coherent Source

In optical quantum cryptography, alignment error is important in secure key generation for quantum key distribution (QKD), being one of the roots of error events. However, theoretical modeling typically only reflects the behavior of a single photon, which is incompatible with a weak coherent source. This study develops a realistic model to include the propagation of multiphoton pulses in the misalignment-error analysis by defining the $l\phantom{\rule{0}{0ex}}e\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}k\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}g\phantom{\rule{0}{0ex}}e$ $l\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}g\phantom{\rule{0}{0ex}}h\phantom{\rule{0}{0ex}}t$ $r\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}o$, which can be obtained conveniently in practical QKD systems. In addition, double-click events can be depicted precisely, and thus the gap between the model and real systems is narrowed.
Monte-Carlo Tree Search in Continuous Action Spaces with Value Gradients

Monte-Carlo Tree Search (MCTS) is the state-of-the-art online planning algorithm for large problems with discrete action spaces. However, many real-world problems involve continuous action spaces, where MCTS is not as effective as in discrete action spaces. This is mainly due to common practices such as coarse discretization of the entire action space and failure to exploit local smoothness. In this paper, we introduce Value-Gradient UCT (VG-UCT), which combines traditional MCTS with gradient-based optimization of action particles. VG-UCT simultaneously performs a global search via UCT with respect to the finitely sampled set of actions and performs a local improvement via action value gradients. In the experiments, we demonstrate that our approach outperforms existing MCTS methods and other strong baseline algorithms for continuous action spaces.
Registration of Multimodal Remote Sensing Images Using Transfer Optimization

Multimodal image registration is critical yet challenging for remote sensing image processing. Due to the large nonlinear intensity differences between the multimodal images, conventional search algorithms tend to get trapped into local optima when optimizing the transformation parameters by maximizing mutual information (MI). To address this problem, inspired by transfer learning, we propose a novel search algorithm named transfer optimization (TO), which can be applied to any optimizer. In TO, an optimizer transfers its better individuals to the other optimizer in each iteration. Thus, TO can share information between two optimizers and take advantage of their search mechanisms, which is helpful to avoid the local optima. Then, the registration of the multimodal remote sensing images using TO is presented. We compare the proposed algorithm with several state-of-the-art algorithms on real and simulated image pairs. Experimental results demonstrate the superiority of our algorithm in terms of registration accuracy.
The its.analysis R Package – Modelling Short Time Series Data

Modelling data over time requires a set of unique and bespoke processes in order to deal with problems induced by temporal dependency and autocorrelation. Methods are well developed and widely applied for working with such data when sample sizes are high, but the issue of how to fit models which are methodologically robust but not under-powered or over-fitted on short time series data remains vexing. This research proposes an interrupted time series analysis model solution to this problem, and uses a Type II Sum Squares ANCOVA Lagged Dependent Variable, variance-centric approach as part of a newly introduced R package - its.analysis. Using this model switches the null hypothesis situation to a much more reliable test and allows for a much more flexible approach to adding covariates in small sample conditions. The model performs very well under test conditions, appears more conservative than existing alternative techniques, and as such is recommended to researchers for future analysis of temporal data where observations are limited (between 15 and 45 observations).
Nonlinear dynamics based machine learning: Utilizing dynamics-based flexibility of nonlinear circuits to implement different functions

The core element of machine learning is a flexible, universal function approximator that can be trained and fit into the data. One of the main challenges in modern machine learning is to understand the role of nonlinearity and complexity in these universal function approximators. In this research, we focus on nonlinear complex systems, and show their capability in representation and learning of different functions. Complex nonlinear dynamics and chaos naturally yield an almost infinite diversity of dynamical behaviors and functions. Physical, biological and engineered systems can utilize this diversity to implement adaptive, robust behaviors and operations. A nonlinear dynamical system can be considered as an embodiment of a collection of different possible behaviors or functions, from which different behaviors or functions can be chosen as a response to different conditions or problems. This process of selection can be manual in the sense that one can manually pick and choose the right function through directly setting parameters. Alternatively, we can automate the process and allow the system itself learn how to do it. This creates an approach to machine learning, wherein the nonlinear dynamics represents and embodies different possible functions, and it learns through training how to pick the right function from this function space. We report on how we utilized nonlinear dynamics and chaos to design and fabricate nonlinear dynamics based, morphable hardware in silicon as a physical embodiment for different possible functions. We demonstrate how this flexible, morphable hardware learns through learning and searching algorithms such as genetic algorithm to implement different desired functions. In this approach, we combine two powerful natural and biological phenomenon, Darwinian evolution and nonlinear dynamics and chaos, as a dynamics-oriented approach to designing intelligent, adaptive systems with applications. Nonlinear dynamics embodies different functions at the hardware level, while an evolutionary method is utilized in order to find the parameters to implement the right function.
Using Active Learning Methods Within the Andragogical Paradigm

This research paper is aimed at identifying the efficiency and at demonstrating the need for using active learning methods within the andragogical paradigm. Active learning methods intensify students’ thinking and are characterized by a high degree of interactivity, motivation, and emotional perception of learning process. Using active learning methods allows developing the cognitive and creative activities of students, enhancing the efficiency of learning process, and developing or assessing professional competences. This is exactly why the active forms of learning should be implemented in adult education to involve students in educational process as completely as possible and to activate the process of developing high-quality knowledge, including in the teaching person. Using active learning methods based on andragogical approach allows developing abilities in students, which the latter one would need later as graduates. Active learning processes also create conditions for developing critical thinking and demonstrating creativity.
Generative modeling for renal microanatomy

Generative adversarial networks (GANs) have received immense attention in the field of machine learning for their potential to learn high-dimensional and real data distribution. These methods do not rely on any assumptions about the data distribution of the input sample and can generate real-like samples from latent vector space based on unsupervised learning. In the medical field, particularly, in digital pathology expert annotation and availability of a large set of training data is costly and the study of manifestations of various diseases is based on visual examination of stained slides. In clinical practice, various staining information is required to improve the pathological diagnosis process. But when the sampled tissue to be examined is limited, the final diagnosis made by the pathologist is based on limited stain styles. These limitations can be overcome by studying the usability and reliability of generative models in the field of digital pathology. To understand the usability of the generative models, we synthesize in an unsupervised manner, high resolution renal microanatomical structures like renal glomerulus in thin tissue histology images using state-of-art architectures like Deep Convolutional Generative Adversarial Network (DCGAN) and Enhanced Super- Resolution Generative Adversarial Network (ESRGAN). Successful generation of such structures will lead to obtaining a large set of labeled data for further developing supervised algorithms for disease classification and understanding progression. Our study suggests while GAN is able to attain formalin fixed and paraffin embedded tissue image quality, GAN requires further prior knowledge as input to model intrinsic micro-anatomical details, such as capillary wall, urinary pole, nuclei placement, suggesting developing semi-supervised architectures by using these above details as prior information. Also, the generative models can be used to create an artificial effect of staining without physically tampering the histopathological slide. To demonstrate this, we use a CycleGAN network to transform Hematoxylin and eosin (H&amp;E) stain to Periodic acid-Schiff (PAS) stain and Jones methenamine silver (JMS) stain to PAS stain. In this way GAN can be employed to translate different renal pathology stain styles when the relevant staining information is not available in the clinical settings.
Forensic feature-comparison expertise: Statistical learning facilitates visual comparison performance.

Forensic feature-comparison examiners in select disciplines are more accurate than novices when comparing samples of visual evidence. This article examines a key cognitive mechanism that may contribute to this superior visual comparison performance: the ability to learn how often stimuli occur in the environment (distributional statistical learning). We examined the relationship between distributional learning and visual comparison performance and the impact of training on the diagnosticity of distributional information in visual comparison tasks. We compared performance between novices given no training (uninformed novices; n = 32), accurate training (informed novices; n = 32), or inaccurate training (misinformed novices; n = 32) in Experiment 1 and between forensic examiners (n = 26), informed novices (n = 29), and uninformed novices (n = 27) in Experiment 2. Across both experiments, forensic examiners and novices performed significantly above chance in a visual comparison task in which distributional learning was required for high performance. However, informed novices outperformed all participants, and only their visual comparison performance was significantly associated with their distributional learning. It is likely that forensic examiners' expertise is domain specific and doesn't generalize to novel visual comparison tasks. Nevertheless, diagnosticity training could be critical to the relationship between distributional learning and visual comparison performance. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Gaussian Lifting for Fast Bilateral and Nonlocal Means Filtering

Recently, many fast implementations of the bilateral and the nonlocal filters were proposed based on lattice and vector quantization, e.g. clustering, in higher dimensions. However, these approaches can still be inefficient owing to the complexities in the resampling process or in filtering the high-dimensional resampled signal. In contrast, simply scalar resampling the high-dimensional signal after decorrelation presents the opportunity to filter signals using multi-rate signal processing techniques. This work proposes the Gaussian lifting framework for efficient and accurate bilateral and nonlocal means filtering, appealing to the similarities between separable wavelet transforms and Gaussian pyramids. Accurately implementing the filter is important not only for image processing applications, but also for a number of recently proposed bilateral-regularized inverse problems, where the accuracy of the solutions depends ultimately on accurate filter implementations. We show that our Gaussian lifting approach filters images more accurately and efficiently across many filter scales. Adaptive lifting schemes for bilateral and nonlocal means filtering are also explored.
Augmented intelligent water drops optimisation model for virtual machine placement in cloud environment

Virtual machine placement in cloud computing is to allocate the virtual machines (VMs) (user request) to suitable physical machines (PMs) so that the wastage of resources is reduced. Allocation of appropriate VMs to suitable and effective PMs will lead the service provider to be a better competitor with more available resources for affording a greater number of VMs simultaneously which in turn reflects with the growth in the economy. In this research work, an augmented intelligent water drop (IWD) algorithm is used for effectively placing VMs. The preliminary goal of this proposed work is to reduce the overall resource utilisation by packing the VMs to appropriate PMs effectively. The proposed IWD model is tested under the standard simulation process as it is given in the literature. Performance of IWD is compared with the existing techniques first fit decreasing, least loaded and ant colony optimisation algorithm. Performance analysis shows the significance of the proposed method over existing techniques.
Quantum arithmetic operations based on quantum fourier transform on signed integers

The quantum Fourier transform (QFT) brings efficiency in many respects, especially usage of resource, for most operations on quantum computers. In this study, the existing QFT-based and non-QFT-based quantum arithmetic operations are examined. The capabilities of QFT-based addition and multiplication are improved with some modifications. The proposed operations are compared with the nearest quantum arithmetic operations. Furthermore, novel QFT-based subtraction, division and exponentiation operations are presented. The proposed arithmetic operations can perform nonmodular operations on all signed numbers without any limitation by using less resources. In addition, novel quantum circuits of two’s complement, absolute value and comparison operations are also presented by using the proposed QFT-based addition and subtraction operations.
Modelling Patient Behaviour Using IoT Sensor Data: a Case Study to Evaluate Techniques for Modelling Domestic Behaviour in Recovery from Total Hip Replacement Surgery

The UK health service sees around 160,000 total hip or knee replacements every year and this number is expected to rise with an ageing population. Expectations of surgical outcomes are changing alongside demographic trends, whilst aftercare may be fractured as a result of resource limitations. Conventional assessments of health outcomes must evolve to keep up with these changing trends. Health outcomes may be assessed largely by self-report using Patient Reported Outcome Measures (PROMs), such as the Oxford Hip or Oxford Knee Score, in the months up to and following surgery. Though widely used, many PROMs have methodological limitations and there is debate about how to interpret results and definitions of clinically meaningful change. With the development of a home-monitoring system, there is opportunity to characterise the relationship between PROMs and behaviour in a natural setting and to develop methods of passive monitoring of outcome and recovery after surgery. In this paper, we discuss the motivation and technology used in long-term continuous observation of movement, sleep and domestic routine for healthcare applications, such as the HEmiSPHERE project for hip and knee replacement patients. In this case study, we evaluate trends evident in data of two patients, collected over a 3-month observation period post-surgery, by comparison with scores from PROMs for sleep and movement quality, and by comparison with a third control home. We find that accelerometer and indoor localisation data correctly highlight long-term trends in sleep and movement quality and can be used to predict sleep and wake times and measure sleep and wake routine variance over time, whilst indoor localisation provides context for the domestic routine and mobility of the patient. Finally, we discuss a visual method of sharing findings with healthcare professionals.
Extending epipolar geometry for real-time structured light illumination

Structured light illumination, scanning along both horizontal and vertical directions, achieves more robust accuracy. By introducing the constraint of epipolar geometry, we previously proposed real-time 3D reconstruction using lookup tables; however, we only knew these offline derived tables were the combinations of the elements in calibration matrices of a camera and a projector, and suffered from long-time computation. In this Letter, by parameterizing the line perspectively mapping a 3D world coordinate into the camera and projector spaces, we propose to extend the epipolar analysis by defining phase and optical poles. Thus, we can geometrically address these parameters via analytic closed-form equations, with which we can (1) directly derive lookup tables in real time from the calibration matrices and (2) optimally reduce the number of tables from 11 to 5 to save much more memory space while further accelerating the processing rate. Experiments show that with the same level of accuracy, we significantly reduce the time to compute the lookup tables from more than 20 min to 20 ms, and increase the speed of computing point clouds from approximately 320 to 492 fps.