Reviewerly/exHarmony huggingface dataset:
Effects of a Monocular Laser-Based Head-Mounted Display on Human Night Vision

Head-mounted displays (HMDs) are expected to dominate the market of wearable electronics in the next 5 years. This foreseen proliferation of HMDs yields a plethora of design opportunities for revolutionizing everyday life via novel use cases, but also generates a considerable number of substantial safety implications. In this work, we systematically investigated the effect of a novel monocular laser-based HMD on the ability of our participants to see in low ambient light conditions in lab settings. We recruited a total of 19 participants in two studies and performed a series of established vision tests while using the newly available Focals by North HMD. We tested our participants' night vision after being exposed to different levels of laser luminous power and laser colors while using Focals, either with one or both eyes open. Our results showcase that the image perceived by the non-exposed eye compensates for the loss of contrast sensitivity observed in the image perceived by the laser-exposed eye. This indicates that monocular laser-based HMDs, such as Focals, permit dark adaptation to occur naturally for the non-exposed eye.
Autonomous learning and teacher guidance: Towards the improvement of EFL students’ prepared talk in speaking practice

This study was aimed to investigate the role of autonomous learning (hereafter, AL) with the teacher guidance approach in enhancing the performance and ability of students in practicing their English speaking skill. Pre-test and post-test were given to 22 first year university students to measure the improvement of their ability in the prepared talk before and after the learning strategy was implemented in the classroom. The questionnaires were also gathered to know their perception of this learning strategy. The questionnaires were in the form of closed-ended questions. Results of the pre-test and post-test were analyzed using Tableau software, meanwhile, the data from the questionnaire were calculated for frequency and percentage. From the results, the average score the students' post-test was higher than the average score they got in the pre-test, 83 and 62.2, respectively. The improvement was obvious; here, 16 students got the scores of 80-95 in their post-test, while others got a minimum of 75 and 60. The students' perception obtained from the questionnaires, in overall, received positive responses on the use of this learning strategy. It can be concluded that AL with the teacher guidance approach played a prominent role in improving the students' performance in English prepared-talk.
Exploiting Reuse in Pipeline-Aware Hyperparameter Tuning.

Hyperparameter tuning of multi-stage pipelines introduces a significant computational burden. Motivated by the observation that work can be reused across pipelines if the intermediate computations are the same, we propose a pipeline-aware approach to hyperparameter tuning. Our approach optimizes both the design and execution of pipelines to maximize reuse. We design pipelines amenable for reuse by (i) introducing a novel hybrid hyperparameter tuning method called gridded random search, and (ii) reducing the average training time in pipelines by adapting early-stopping hyperparameter tuning approaches. We then realize the potential for reuse during execution by introducing a novel caching problem for ML workloads which we pose as a mixed integer linear program (ILP), and subsequently evaluating various caching heuristics relative to the optimal solution of the ILP. We conduct experiments on simulated and real-world machine learning pipelines to show that a pipeline-aware approach to hyperparameter tuning can offer over an order-of-magnitude speedup over independently evaluating pipeline configurations.
Studying the Impact of Perturbation Methods on the Efficiency of GVNS for the ATSP

In this work we examine the impact of three shaking procedures on the performance of a GVNS metaheuristic algorithm for solving the Asymmetric Travelling Salesman Problem (ATSP). The first shaking procedure is a perturbation method that is commonly used in the literature as intensified shaking method. The second one is a quantum-inspired shaking method, while the third one is a shuffle method. The shaped GVNS schemes are tested with both first and best improvement and with a time limit of one and two minutes. Experimental analysis shows that the first two methods perform equivalently and much better than the shuffle approach, when using the best improvement strategy. The first method also outperforms the other two when using the first improvement strategy, while the second method produces results that are closer to the results of the third in this case.
SLD-Resolution Reduction of Second-Order Horn Fragments

We present the derivation reduction problem for SLD-resolution, the undecidable problem of finding a finite subset of a set of clauses from which the whole set can be derived using SLD-resolution. We study the reducibility of various fragments of second-order Horn logic with particular applications in Inductive Logic Programming. We also discuss how these results extend to standard resolution.
Half-Life Volatility Measure of the Returns of Some Cryptocurrencies

This paper explores the half-life volatility measure of three cryptocurrencies (Bitcoin, Litecoin and Ripple).Two GARCH family models were used (PGARCH (1, 1) and GARCH (1, 1)) with the student-t distribution.It was realised that, the PGARCH (1, 1) was the most appropriate model.Therefore, it was used in determining the half-life of the three returns series.The results revealed that, the half-life was 3 days, 6 days and 4 days for Bitcoin, Litecoin and Ripple respectively.This shows that, the three coins have strong mean reversion and short half-life and that it takes the respective days for volatility in each of coin to return half way back without further volatility.
The Logical Structure of the Software File Archive Formation as a Part of Industrial Management

Enhancement of electronic communication development, fast growth of information volumes stored in soft form, increase of its value make issues of corporate digital archives more and more important. The share of archiving systems in the total data storage system volume grows steadily. New requirements to secure storage appear. All this inevitably leads to review of criteria accounted for in the creation of data archiving systems. The today's file archiving systems aimed to store static unstructured data are focused on optimum information distribution and effective user access. As a rule, these solutions include specialized archived data storage system, software implementing mechanisms of interaction with client and storage control and components which ensure interaction with adjacent systems. Absence of any repository logic structure (file system, data base, etc.) allows reaching independence from the repository physical characteristics and ensures easy system scaling. Data allocated in the archive by the user or application are available to be deleted or modified only by the archive administrator or they are deleted automatically according to storage policy applied.
Artificial Neural Network for Vibration Frequency Measurement Using Kinect V2

Optical measurement can substantially reduce the required amount of labor and simplify the measurement process. Furthermore, the optical measurement method can provide full-field measurement results of the target object without affecting the physical properties of the measurement target, such as stiffness, mass, or damping. The advent of consumer grade depth cameras, such as the Microsoft Kinect, Intel RealSence, and ASUS Xtion, has attracted significant research attention owing to their availability and robustness in sampling depth information. This paper presents an effective method employing the Kinect sensor V2 and an artificial neural network for vibration frequency measurement. Experiments were conducted to verify the performance of the proposed method. The proposed method can provide good frequency prediction within acceptable accuracy compared to an industrial vibrometer, with the advantages of contactless process and easy pipeline implementation.
Continual Representation Learning for Images with Variational Continual Auto-Encoder

We propose a novel architecture for the continual representation learning for images, called variational continual auto-encoder (VCAE). Our approach builds a time-variant parametric model that generates images close to the observation by using optimized approximate inference over time. When the dataset is sequentially observed, the model efficiently learns underlying representations without forgetting previously acquired knowledge. Through experiments, we evaluate the development of test log-likelihood over time, which shows resistance to the catastrophic forgetting. The results show that VCAE has stronger immunity against catastrophic forgetting in comparison to the benchmark while VCAE requires much less time for training.
Emulating computer models with step-discontinuous outputs using Gaussian processes

In many real-world applications we are interested in approximating costly functions that are analytically unknown, e.g. complex computer codes. An emulator provides a fast approximation of such functions relying on a limited number of evaluations. Gaussian processes (GPs) are commonplace emulators due to their statistical properties such as the ability to estimate their own uncertainty. GPs are essentially developed to fit smooth, continuous functions. However, the assumptions of continuity and smoothness is unwarranted in many situations. For example, in computer models where bifurcations or tipping points occur, the outputs can be discontinuous. This work examines the capacity of GPs for emulating step-discontinuous functions. Several approaches are proposed for this purpose. Two special covariance functions/kernels are adapted with the ability to model discontinuities. They are the neural network and Gibbs kernels whose properties are demonstrated using several examples. Another approach, which is called warping, is to transform the input space into a new space where a GP with a standard kernel, such as the Matern family, is able to predict the function well. The transformation is perform by a parametric map whose parameters are estimated by maximum likelihood. The results show that the proposed approaches have superior performance to GPs with standard kernels in capturing sharp jumps in the true function.
Theorem and Algorithm Checking for Courses on Logic and Formal Methods

The RISC Algorithm Language (RISCAL) is a language for the formal modeling of theories and algorithms.A RISCAL specification describes an infinite class of models each of which has finite size; this allows to fully automatically check in such a model the validity of all theorems and the correctness of all algorithms.RISCAL thus enables us to quickly verify/falsify the specific truth of propositions in sample instances of a model class before attempting to prove their general truth in the whole class: the first can be achieved in a fully automatic way while the second typically requires our assistance.RISCAL has been mainly developed for educational purposes.To this end this paper reports on some new enhancements of the tool: the automatic generation of checkable verification conditions from algorithms, the visualization of the execution of procedures and the evaluation of formulas illustrating the computation of their results, and the generation of Web-based student exercises and assignments from RISCAL specifications.Furthermore, we report on our first experience with RISCAL in the teaching of courses on logic and formal methods and on further plans to use this tool to enhance formal education.
Automatic Analysis of Arabic Online Handwriting of Patients with Parkinson's Disease

Parkinson's disease (PD) is the second most common neurodegenerative disease affecting significant portion of elderly population. One of the most frequent hallmarks and usually also the first manifestation of PD is deterioration of handwriting. Since the diagnosis of Parkinson's disease is difficult, researchers have worked to develop a support tool based on algorithms to separate healthy controls from PD patients. Online handwriting analysis is one of the methods that can be used to diagnose PD.
A Sober Look at Neural Network Initializations

Initializing the weights and the biases is a key part of the training process of a neural network. Unlike the subsequent optimization phase, however, the initialization phase has gained only limited attention in the literature. In this paper we discuss some consequences of commonly used initialization strategies for vanilla DNNs with ReLU activations. Based on these insights we then develop an alternative initialization strategy. Finally, we present some large scale experiments assessing the quality of the new initialization strategy.
BrowStExPlus: A Tool to Aggregate Indexed<scp>DB</scp> Artifacts for Forensic Analysis

Abstract As the Internet and World Wide Web have rapidly evolved and revolutionized the applications in everyday life, it is a demanding challenge for investigators to keep up with the emerging technologies for forensic analyses. Investigating web browser usages for criminal activities, also known as web browser forensics, is a significant part of digital forensics as crucial browsing information of the suspect can be discovered. Particularly, in this study, an emerging web storage technology, called Indexed DB , is examined. Characteristics of Indexed DB technology in five major web browsers under three major operating systems are scrutinized. Also, top 15 US websites ranked by Alexa are investigated for their data storage in Indexed DB . User screen names, ids, and records of conversations, permissions, and image locations are some of the data found in Indexed DB . Furthermore, BrowStEx, a proof‐of‐concept tool previously developed, is extended and cultivated into BrowStExPlus, with which aggregating Indexed DB artifacts is demonstrated.
Developing a Prognostic Information System for Personalized Care in Real Time

Electronic medical records hold promise to transform clinical practice. However, technological and other barriers may preclude using them to guide care in real time. We used the Virtual Data Warehouse (VDW) to develop a tool that enables physicians to generate real-time, personalized prognostic information about survival after cancer.Patients with cancer often ask their oncologists, "Have you ever seen a patient like me?" To help oncologists answer this question, we developed a prototype Prognostic Information System (PRISM), a web-based tool that gathers data about the index patient from Kaiser Permanente's clinical information systems, selects a historical cohort of similar patients, and displays the survival curve of the similar patients relative to key points in their treatment course.The prototype was developed by a multidisciplinary team with expertise in oncology, research, and technology. We have completed two rounds of user testing and refinement. Successful development rested on: (1) executive support and a clinical champion; (2) collaboration among experts from multiple disciplines; (3) starting with simple cases rather than ambitious ones; (4) extensive research experience with the Virtual Data Warehouse, related databases, and an existing query tool; and (5) following agile software development principles, especially iterative user testing.Clinical data stored in health care systems' electronic medical records can be used to personalize clinical care in real time. Development of prognostic information systems can be accelerated by collaborations among researchers, technology specialists, and clinicians and by use of existing technology like the Virtual Data Warehouse.
Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records

Electronic medical records (EMRs) supports the development of machine learning algorithms for predicting disease incidence, patient response to treatment, and other healthcare events. But insofar most algorithms have been centralized, taking little account of the decentralized, non-identically independently distributed (non-IID), and privacy-sensitive characteristics of EMRs that can complicate data collection, sharing and learning. To address this challenge, we introduced a community-based federated machine learning (CBFL) algorithm and evaluated it on non-IID ICU EMRs. Our algorithm clustered the distributed data into clinically meaningful communities that captured similar diagnoses and geological locations, and learnt one model for each community. Throughout the learning process, the data was kept local on hospitals, while locally-computed results were aggregated on a server. Evaluation results show that CBFL outperformed the baseline FL algorithm in terms of Area Under the Receiver Operating Characteristic Curve (ROC AUC), Area Under the Precision-Recall Curve (PR AUC), and communication cost between hospitals and the server. Furthermore, communities' performance difference could be explained by how dissimilar one community was to others.
Document-Level $N$-ary Relation Extraction with Multiscale Representation Learning

Most information extraction methods focus on binary relations expressed within single sentences. In high-value domains, however, $n$-ary relations are of great demand (e.g., drug-gene-mutation interactions in precision oncology). Such relations often involve entity mentions that are far apart in the document, yet existing work on cross-sentence relation extraction is generally confined to small text spans (e.g., three consecutive sentences), which severely limits recall. In this paper, we propose a novel multiscale neural architecture for document-level $n$-ary relation extraction. Our system combines representations learned over various text spans throughout the document and across the subrelation hierarchy. Widening the system's purview to the entire document maximizes potential recall. Moreover, by integrating weak signals across the document, multiscale modeling increases precision, even in the presence of noisy labels from distant supervision. Experiments on biomedical machine reading show that our approach substantially outperforms previous $n$-ary relation extraction methods.
Digital Research Methods: the New Vector in Library Informatization

A large number of conferences on automation of library processes are held abroad. These events discuss the most topical and important issues facing libraries in the implementation of modern information technologies. The article provides an overview of four major international conferences, which brought together programmers, linguists and librarians, who shared their experiences and planned for future. The authors present the analysis of changes in the subject of conferences for the recent years, conclude on the significant increase in the number of studies on semantic analysis of data stored in libraries and mark the most interesting reports. In recent years, the number of reports on Linked Open Data (LOD) has been increasing. However, most of these reports focus on the publication of data in LOD, with very little research on the extent of use of already published data. The article provides examples of such studies. The authors highlight the constant interest to the topic of Digital Humanities and to the fact that the research within this area is often focused on the problems of modern society. The article notes the new direction in the activity of electronic libraries — storage of the research data, and gives the examples of implementation of these tasks.
Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling

Recurrent Variational Autoencoder has been widely used for language modeling and text generation tasks. These models often face a difficult optimization problem, also known as the Kullback-Leibler (KL) term vanishing issue, where the posterior easily collapses to the prior, and the model will ignore latent codes in generative tasks. To address this problem, we introduce an improved Wasserstein Variational Autoencoder (WAE) with Riemannian Normalizing Flow (RNF) for text modeling. The RNF transforms a latent variable into a space that respects the geometric characteristics of input space, which makes posterior impossible to collapse to the non-informative prior. The Wasserstein objective minimizes the distance between the marginal distribution and the prior directly and therefore does not force the posterior to match the prior. Empirical experiments show that our model avoids KL vanishing over a range of datasets and has better performances in tasks such as language modeling, likelihood approximation, and text generation. Through a series of experiments and analysis over latent space, we show that our model learns latent distributions that respect latent space geometry and is able to generate sentences that are more diverse.
Jointly Pre-training with Supervised, Autoencoder, and Value Losses for Deep Reinforcement Learning.

Deep Reinforcement Learning (DRL) algorithms are known to be data inefficient. One reason is that a DRL agent learns both the feature and the policy tabula rasa. Integrating prior knowledge into DRL algorithms is one way to improve learning efficiency since it helps to build helpful representations. In this work, we consider incorporating human knowledge to accelerate the asynchronous advantage actor-critic (A3C) algorithm by pre-training a small amount of non-expert human demonstrations. We leverage the supervised autoencoder framework and propose a novel pre-training strategy that jointly trains a weighted supervised classification loss, an unsupervised reconstruction loss, and an expected return loss. The resulting pre-trained model learns more useful features compared to independently training in supervised or unsupervised fashion. Our pre-training method drastically improved the learning performance of the A3C agent in Atari games of Pong and MsPacman, exceeding the performance of the state-of-the-art algorithms at a much smaller number of game interactions. Our method is light-weight and easy to implement in a single machine. For reproducibility, our code is available at this http URL