Reviewerly/exHarmony huggingface dataset:
Reproducibility and Replicability in a Fast-Paced Methodological World

Methodological developments and software implementations are progressing at an increasingly fast pace. The introduction and widespread acceptance of preprint archived reports and open-source software have made state-of-the-art statistical methods readily accessible to researchers. At the same time, researchers are increasingly concerned that their results should be reproducible (i.e., the same analysis should yield the same numeric results at a later time), which is a basic requirement for assessing the results’ replicability (i.e., whether results at a later time support the same conclusions). Although this age of fast-paced methodology greatly facilitates reproducibility and replicability, it also undermines them in ways not often realized by researchers. This article draws researchers’ attention to these threats and proposes guidelines to help minimize their impact. Reproducibility may be influenced by software development and change over time, a problem that is greatly compounded by the rising dependency between software packages. Replicability is affected by rapidly changing standards, researcher degrees of freedom, and possible bugs or errors in code, whether introduced by software developers or empirical researchers implementing an analysis. This article concludes with a list of recommendations to improve the reproducibility and replicability of results.
Movies Reviews Sentiment Analysis and Classification

As humans' opinions help enhance products efficiency, and since the success or the failure of a movie depends on its reviews, there is an increase in the demand and need to build a good sentiment analysis model that classifies movies reviews. In this research, tokenization is employed to transfer the input string into a word vector, stemming is utilized to extract the root of the words, feature selection is conducted to extract the essential words, and finally classification is performed to label reviews as being either positive or negative. A model that makes use of all of the previously mentioned methods is presented. The model is evaluated and compared on eight different classifiers. The model is evaluated on a real-world dataset. In order to compare the eight different classifiers, five different evaluation metrics are utilized. The results show that Random Forest outperforms the other classifiers. Furthermore, Ripper Rule Learning performed the worst on the dataset according to the results attained from the evaluation metrics.
Augmented Reality applications as digital experiments for education – An example in the Earth-Moon System

“You realise that the Earth is nothing but accumulated cosmic dust having formed a rock that is encompassed by a flimsy, fragile atmosphere. To grasp this, I needed the view out of the window.” German ESA Astronaut Alexander Gerst's perspective on Earth was changed sustainably by the view from the International Space Station (ISS) onto our home planet. It is possible to give pupils a very similar perspective within the means of public education due to the availability of Earth Observation data from the ISS. However, the data can be put to more educational use than providing a taste of the overview effect. Applying common remote sensing methods and modern teaching concepts to EO (Earth Observation) data from the National Aeronautics and Space Administration (NASA) High Definition Earth Viewing experiment, teaching modules for several STEM (Science, Technology, Engineering, and Mathematics) subjects could be implemented successfully. Building on this success, more ISS EO sensors are being implemented in teaching materials and new media techniques are explored. The more recent addition to the material pool are smartphone apps using Augmented Reality (AR) with which the pupils can experiment on their own. These apps are developed in a partial What You See Is What You Get (WYSIWYG) application development system called Unity with the Vuforia extension, the latter allowing the use of printed images as reference markers for AR. Complex theoretical topics can be visualised in 3-dimensional (3D) animations or turned into inexpensive, easy digital experiments. The app “The Earth-Moon System” applies this with experiments on the effects of changes in the distance between Earth and Moon and a 3D animation on the barycentre between two celestial bodies. Development of such apps is feasible for researchers to visualise their data even with no prior app development knowledge.
The development and evaluation of an educational game integrating augmented reality, ARCS model, and types of games for English experiment learning: an analysis of learning

This study developed four AR games and investigated their influence on learning in a Grade 5 English course by integrating theories of digital game-based learning, the attention relevance confidence–satisfaction (ARCS) model, and different types of digital games. Moreover, the elements between AR game design and the ARCS model were examined. 147 fifth grade students were selected as the participants in the experiment. The experimental group used AR game, while the control group used traditional learning. A pre-test and post-test, ARCS Learning Motivation questionnaire, AR game design elements questionnaire and Semi-open questionnaires were conducted with both groups. Results showed that students who received AR game learning exhibited significantly higher learning motivation than students who underwent traditional learning; however, no significant differences were observed in their learning effectiveness. A canonical correlation analysis of the AR game design elements dimensions and the learning motivation dimensions showed significant correlations, and 3D graphics dimension showed the strongest correlation with the satisfaction dimension. The finding of this study is that 3D graphics is the most important element in AR game designs, and the most popular type of AR game is role-playing. Based on the research results, high-quality 3D graphics in the design of AR games is needed to be satisfied with the other amusing storylines and interesting characters of the role-playing game is also the key of designing AR games.
Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction

Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO. In practice, the SVR method has a few parameters that should be determined through a trial-and-error procedure while developing the prediction model. This procedure usually leads to non-optimal choices for these parameters and, hence, poor prediction accuracy. Therefore, there is a need to integrate the SVR model with an optimization algorithm to achieve optimal choices for these parameters. Thus, the IPSO algorithm, as an optimizer is integrated with SVR to obtain optimal values for the SVR parameters. To examine the proposed model, two solar radiation stations, Adana, Antakya and Konya, in Turkey, are considered for this study. In addition, different models have been tested for this prediction, namely, the M5 tree model (M5T), genetic programming (GP), SVR integrated with four different optimization algorithms SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model. The sensitivity analysis is performed to achieve the highest accuracy level of the prediction by choosing different input parameters. Several performance measuring indices have been considered to examine the efficiency of all the prediction methods. The results show that SVR-IPSO outperformed M5T and MARS.
Vivewell

In this pictorial, we explore how emergent menstrual biosensing technologies compound existing concerns for the everyday ethics of extracting and analyzing intimate data. Specifically, we review the data practices of a set of existing menstrual tracking applications and use that analysis to inform the design of speculative near future technologies. We present these technologies here in the form of a product catalog for a fictional company called Vivewell. Through this work, we contribute both a set of speculative design proposals and a case study of a design project that begins with the analysis of existing data policies.
Viewport Proposal CNN for 360° Video Quality Assessment

Recent years have witnessed the growing interest in visual quality assessment (VQA) for 360° video. Unfortunately, the existing VQA approaches do not consider the facts that: 1) Observers only see viewports of 360° video, rather than patches or whole 360° frames. 2) Within the viewport, only salient regions can be perceived by observers with high resolution. Thus, this paper proposes a viewport-based convolutional neural network (V-CNN) approach for VQA on 360° video, considering both auxiliary tasks of viewport proposal and viewport saliency prediction. Our V-CNN approach is composed of two stages, i.e., viewport proposal and VQA. In the first stage, the viewport proposal network (VP-net) is developed to yield several potential viewports, seen as the first auxiliary task. In the second stage, a viewport quality network (VQ-net) is designed to rate the VQA score for each proposed viewport, in which the saliency map of the viewport is predicted and then utilized in VQA score rating. Consequently, another auxiliary task of viewport saliency prediction can be achieved. More importantly, the main task of VQA on 360° video can be accomplished via integrating the VQA scores of all viewports. The experiments validate the effectiveness of our V-CNN approach in significantly advancing the state-of-the-art performance of VQA on 360° video. In addition, our approach achieves comparable performance in two auxiliary tasks. The code of our V-CNN approach is available at https://github.com/Archer-Tatsu/V-CNN.
Sensitive-Sample Fingerprinting of Deep Neural Networks

Numerous cloud-based services are provided to help customers develop and deploy deep learning applications. When a customer deploys a deep learning model in the cloud and serves it to end-users, it is important to be able to verify that the deployed model has not been tampered with. In this paper, we propose a novel and practical methodology to verify the integrity of remote deep learning models, with only black-box access to the target models. Specifically, we define Sensitive-Sample fingerprints, which are a small set of human unnoticeable transformed inputs that make the model outputs sensitive to the model's parameters. Even small model changes can be clearly reflected in the model outputs. Experimental results on different types of model integrity attacks show that we proposed approach is both effective and efficient. It can detect model integrity breaches with high accuracy (>99.95%) and guaranteed zero false positives on all evaluated attacks. Meanwhile, it only requires up to 103× fewer model inferences, compared with non-sensitive samples.
A Hybrid Approach for Solving Optimization Problems on Small Quantum Computers

Solving larger-sized problems is an important area of research in quantum computing. Designing hybrid quantumclassical algorithms is a promising approach to solving this. We discuss decomposition-based hybrid approaches for solving optimization problems and demonstrate them for applications related to community detection.
A reversible and multipurpose ECG data hiding technique for telemedicine applications

Biomedical signals serves as a backbone of telemedicine industry and its analysis help the remote diagnosis possible. Security of biomedical data is one of the critical issues during its transmission from one storage device to another storage device. In this work, a completely reversible data hiding method is proposed for the ECG (Electrocardiogram) data. The proposed scheme is designed in such a way that it can find out false ownership claims as well as detect the tampered region of ECG data. In addition, original ECG signal is perfectly reconstructed from watermarked signal. The reversibility is tested for a large set of 460 different ECG signals generated from MIT-BIH arrhythmia database and complete reversibility (100%) is obtained each time. In proposed approach, deep neural network is used for an improved error prediction and prediction error expansion (PEE) is combined with it to ensure reversibility. Proposed method is a high capacity method and data hiding of 0.99 bps (bits per sample) is achieved with very little distortion in watermarked signal. The performance of proposed work is evaluated through percentage residual difference (PRD), signal to noise ratio (SNR) and normalized cross correlation (NCC). The most important contribution of proposed work is its multipurpose nature: ownership detection, tamper localization and 100% reversibility. Tamper detection and localization performance of the proposed method is also tested against different attacks and it is found to be quite good.
Identifying Toxicity Within YouTube Video Comment

Online Social Networks (OSNs), once regarded as safe havens for sharing information and providing mutual support among groups of people, have become breeding grounds for spreading toxic behaviors, political propaganda, and radicalizing content. Toxic individuals often hide under the auspices of anonymity to create fruitless arguments and divert the attention of other users from the core objectives of a community. In this study, we examined five recurring forms of toxicity among the comments posted on pro- and anti-NATO channels on YouTube. We leveraged the YouTube Data API to collect video and comment data from eight channels. We then utilized Google’s Perspective API to assign toxic scores to each comment. Our analysis suggests that, on average, commenters on the anti-NATO channels are more likely to be more toxic than those on the pro-NATO channels. We further discovered that commenters on pro-NATO channels tend to use a mixture of toxic and innocuous comments. We generated word clouds to get an idea of word use frequency, as well as applied the Latent Dirichlet Allocation topic model to classify the comments into their overall topics. The topics extracted from the pro-NATO channels’ comments were primarily positive, such as “Alliance” and “United”; whereas, the topics extracted from anti-NATO channels’ comments were more geared towards geographical locations, such as “Russia”, and negative components such as “Profanity” and “Fake News”. By identifying and examining the toxic behaviors of commenters on YouTube, our analysis lends aid to the pressing need for understanding this toxicity.
SoK: A Consensus Taxonomy in the Blockchain Era

Consensus is arguably one of the most fundamental problems in distributed computing, playing also an important role in the area of cryptographic protocols as the enabler of a secure broadcast functionality. While the problem has a long and rich history and has been analyzed from many different perspectives, recently, with the advent of blockchain protocols like Bitcoin, it has experienced renewed interest from a much wider community of researchers and has seen its application expand to various novel settings. One of the main issues in consensus research is the many different variants of the problem that exist as well as the various ways the problem behaves when different setup, computational assumptions and network models are considered. In this work we perform a systematization of knowledge in the landscape of consensus research in the Byzantine failure model starting with the original formulation in the early 1980s up to the present blockchain-based new class of consensus protocols. Our work is a roadmap for studying the consensus problem under its many guises, classifying the way it operates in the various settings and highlighting the exciting new applications that have emerged in the blockchain era.
Indoor Crowd Counting by Mixture of Gaussians Label Distribution Learning

In this paper, we tackle the problem of crowd counting in indoor videos, where people often stay almost static for a long time. The label distribution, which covers a certain number of crowd counting labels, representing the degree to which each label describes the video frame, is previously adopted to model the label ambiguity of the crowd number. However, since the label ambiguity is significantly affected by the crowd number of the scene, we initialize the label distribution of each frame by the discretized Gaussian distribution with adaptive variance instead of the original single static Gaussian distribution. Moreover, considering the gradual change of crowd numbers in the adjacent frames, a mixture of Gaussian models is proposed to generate the final label distribution representation for each frame. The weights of the Gaussian models rely on the frame and feature distances between the current frame and the adjacent frames. The mixed l2,1 -norm is adopted to restrict the weights of predicting the adjacent crowd numbers to be locally correlated. We collect three new indoor video datasets with frame number annotation for further research. The proposed approach achieves state-of-the-art performance on seven challenging indoor videos and cross-scene experiments.
Proof-of-Work Sidechains

During the last decade, the blockchain space has exploded with a plethora of new cryptocurrencies, covering a wide array of different features, performance and security characteristics. Nevertheless, each of these coins functions in a stand-alone manner, independently. Sidechains have been envisioned as a mechanism to allow blockchains to communicate with one another and, among other applications, allow the transfer of value from one chain to another, but so far there have been no decentralized constructions. In this paper, we put forth the first side chains construction that allows communication between proof-of-work blockchains without trusted intermediaries. Our construction is generic in that it allows the passing of any information between blockchains. Using this construction, two blockchains can be connected in a “two-way peg” in which an asset can be transferred from one chain to another and back. We pinpoint the features needed for two chains to communicate: On the source side, a proof-of-work blockchain that has been interlinked, potentially with a velvet fork; on the destination side, a blockchain with smart contract support. We put forth the smart contracts needed to implement these sidechains and explain them in detail. In the heart of our construction, we use a recently introduced cryptographic primitive, Non-Interactive Proofs of Proof-of-Work (NIPoPoWs).
Fully Automated Support System for Diagnosis of Breast Cancer in Contrast-Enhanced Spectral Mammography Images

Contrast-Enhanced Spectral Mammography (CESM) is a novelty instrumentation for diagnosing of breast cancer, but it can still be considered operator dependent. In this paper, we proposed a fully automatic system as a diagnostic support tool for the clinicians. For each Region Of Interest (ROI), a features set was extracted from low-energy and recombined images by using different techniques. A Random Forest classifier was trained on a selected subset of significant features by a sequential feature selection algorithm. The proposed Computer-Automated Diagnosis system is tested on 48 ROIs extracted from 53 patients referred to Istituto Tumori "Giovanni Paolo II" of Bari (Italy) from the breast cancer screening phase between March 2017 and June 2018. The present method resulted highly performing in the prediction of benign/malignant ROIs with median values of sensitivity and specificity of 87 . 5 % and 91 . 7 % , respectively. The performance was high compared to the state-of-the-art, even with a moderate/marked level of parenchymal background. Our classification model outperformed the human reader, by increasing the specificity over 8 % . Therefore, our system could represent a valid support tool for radiologists for interpreting CESM images, both reducing the false positive rate and limiting biopsies and surgeries.
Microcalcification detection in full-field digital mammograms: A fully automated computer-aided system

Microcalcification clusters in mammograms can be considered as early signs of breast cancer. However, their detection is a very challenging task because of different factors: large variety of breast composition, highly textured breast anatomy, impalpable size of microcalcifications in some cases, as well as inherent low contrast of mammograms. Thus, the need to support the clinicians' work with an automatic tool.In this work a three-phases approach for clustered microcalcification detection is presented. Specifically, it is made up of a pre-processing step, aimed at highlighting potentially interesting breast structures, followed by a single microcalcification detection step, based on Hough transform, that is able to grasp the innate characteristic shape of the structures of interest. Finally, a cluster identification step to group microcalcifications is carried out by means of a clustering algorithm able to codify expert domain rules.The detection performance of the proposed method has been evaluated on 364 mammograms of 182 patients obtaining a true positive ratio of 91.78% with 2.87 false positives per image.Experimental results demonstrated that the proposed method is able to detect microcalcification clusters in digital mammograms showing performance comparable to different methodologies exploited in the state-of-art approaches, with the advantage that it does not require any training phase and a large set of data. The performance of the proposed approach remains high even for more difficult clinical cases of mammograms of young women having high-density breast tissue thus resulting in a reduced contrast between microcalcifications and surrounding dense tissues.
Ontology-Aware Clinical Abstractive Summarization

Automatically generating accurate summaries from clinical reports could save a clinician's time, improve summary coverage, and reduce errors. We propose a sequence-to-sequence abstractive summarization model augmented with domain-specific ontological information to enhance content selection and summary generation. We apply our method to a dataset of radiology reports and show that it significantly outperforms the current state-of-the-art on this task in terms of rouge scores. Extensive human evaluation conducted by a radiologist further indicates that this approach yields summaries that are less likely to omit important details, without sacrificing readability or accuracy.