Reviewerly/exHarmony huggingface dataset:
Course design process in a technology-enhanced learning environment

This manuscript reports on the design strategy of a "Citizenship and Technology" course for undergraduate students under the new Tec21 Educational Model. The design was proposed in a mixed-model scheme. One model incorporated face-to-face sessions in Remote Interaction Rooms through the Zoom Rooms application, and the second model involved the use of didactic strategies using Information and Communication Technologies. After the first implementation, we detected areas of opportunity that signaled the need of making important adjustments in the activities design. These adjustments are needed for four main goals: generate evidence of competence development; make adjustments to class dynamics for the correct teaching process, simplification of the evaluation mechanisms and reconceptualization of special guests profile. The data collection was carried out through partial surveys and course grades. We analyzed the impact of these adjustments on the assessment of the evaluation by the students to the class and the teaching team.
Clinically relevant pretraining is all you need

Clinical notes present a wealth of information for applications in the clinical domain, but heterogeneity across clinical institutions and settings presents challenges for their processing. The clinical natural language processing field has made strides in overcoming domain heterogeneity, while pretrained deep learning models present opportunities to transfer knowledge from one task to another. Pretrained models have performed well when transferred to new tasks; however, it is not well understood if these models generalize across differences in institutions and settings within the clinical domain. We explore if institution or setting specific pretraining is necessary for pretrained models to perform well when transferred to new tasks. We find no significant performance difference between models pretrained across institutions and settings, indicating that clinically pretrained models transfer well across such boundaries. Given a clinically pretrained model, clinical natural language processing researchers may forgo the time-consuming pretraining step without a significant performance drop.
Basic Study for Transfer Learning for Autonomous Driving in Car Race of Model Car

Reinforcement learning, deep learning, and deep reinforcement learning can effectively acquire action rules for the autonomous motion of objects. However, some researchers have reported that the machine learning process requires a large amount of learning time. Besides, the process needs to consider the similarity of the environment between the training target and the test target. There is no such thing as driving only on a course learned in advance in actual autonomous driving. In this study, we have used a transfer learning algorithm for autonomous drivings for model cars. The training target for acquiring the learning model and the actual driving courses are changed. In this study, we report on the effectiveness of transfer learning using a model car as the basis for a learning model acquired by reinforcement learning.
3D Video Stabilization with Depth Estimation by CNN-based Optimization

Video stabilization is an essential component of visual quality enhancement. Early methods rely on feature tracking to recover either 2D or 3D frame motion, which suffer from the robustness of local feature extraction and tracking in shaky videos. Recently, learning-based methods seek to find frame transformations with high-level information via deep neural networks to overcome the robustness issue of feature tracking. Nevertheless, to our best knowledge, no learning-based methods leverage 3D cues for the transformation inference yet; hence they would lead to artifacts on complex scene-depth scenarios. In this paper, we propose Deep3D Stabilizer, a novel 3D depth-based learning method for video stabilization. We take advantage of the recent self-supervised framework on jointly learning depth and camera ego-motion estimation on raw videos. Our approach requires no data for pre-training but stabilizes the input video via 3D reconstruction directly. The rectification stage incorporates the 3D scene depth and camera motion to smooth the camera trajectory and synthesize the stabilized video. Unlike most one-size-fits-all learning-based methods, our smoothing algorithm allows users to manipulate the stability of a video efficiently. Experimental results on challenging benchmarks show that the proposed solution consistently outperforms the state-of-the-art methods on almost all motion categories.
Teach Me to Segment with Mixed Supervision: Confident Students Become Masters

Deep neural networks have achieved promising results in a breadth of medical image segmentation tasks. Nevertheless, they require large training datasets with pixel-wise segmentations, which are expensive to obtain in practice. Mixed supervision could mitigate this difficulty, with a small fraction of the data containing complete pixel-wise annotations, while the rest being less supervised, e.g., only a handful of pixels are labeled. In this work, we propose a dual-branch architecture, where the upper branch (teacher) receives strong annotations, while the bottom one (student) is driven by limited supervision and guided by the upper branch. In conjunction with a standard cross-entropy over the labeled pixels, our novel formulation integrates two important terms: (i) a Shannon entropy loss defined over the less-supervised images, which encourages confident student predictions at the bottom branch; and (ii) a Kullback-Leibler (KL) divergence, which transfers the knowledge from the predictions generated by the strongly supervised branch to the less-supervised branch, and guides the entropy (student-confidence) term to avoid trivial solutions. Very interestingly, we show that the synergy between the entropy and KL divergence yields substantial improvements in performances. Furthermore, we discuss an interesting link between Shannon-entropy minimization and standard pseudo-mask generation, and argue that the former should be preferred over the latter for leveraging information from unlabeled pixels. Through a series of quantitative and qualitative experiments, we show the effectiveness of the proposed formulation in segmenting the left-ventricle endocardium in MRI images. We demonstrate that our method significantly outperforms other strategies to tackle semantic segmentation within a mixed-supervision framework. More interestingly, and in line with recent observations in classification, we show that the branch trained with reduced supervision and guided by the top branch largely outperforms the latter.
Identification of Low‐Frequency Earthquakes on the San Andreas Fault With Deep Learning

Abstract Low‐frequency earthquakes are a seismic manifestation of slow fault slip. Their emergent onsets, low amplitudes, and unique frequency characteristics make these events difficult to detect in continuous seismic data. Here, we train a convolutional neural network to detect low‐frequency earthquakes near Parkfield, CA using the catalog of Shelly (2017), https://doi.org/10.1002/2017jb014047 as training data. We explore how varying model size and targets influence the performance of the resulting network. Our preferred network has a peak accuracy of 85% and can reliably pick low‐frequency earthquake (LFE) S‐wave arrival times on single station records. We demonstrate the abilities of the network using data from permanent and temporary stations near Parkfield, and show that it detects new LFEs that are not part of the Shelly (2017), https://doi.org/10.1002/2017jb014047 catalog. Overall, machine‐learning approaches show great promise for identifying additional low‐frequency earthquake sources. The technique is fast, generalizable, and does not require sources to repeat.
Double, triple or quadruple hits? Exploring the impact of cybercrime on victims in the Netherlands

This article explores the impact of online crime victimisation. A literature review and 41 interviews – 19 with victims and 22 with experts – were carried out to gain insight into this. The interviews show that most impacts of online offences correspond to the impacts of traditional offline offences. There are also differences with offline crime victimisation. Several forms of impact seem to be specific to victims of online crime: the substantial scale and visibility of victimhood, victimisation that does not stop in time, the interwovenness of online and offline, and victim blaming. Victims suffer from double, triple or even quadruple hits; it is the accumulation of different types of impact, enforced by the limitlessness in time and space, which makes online crime victimisation so extremely invasive. Furthermore, the characteristics of online crime victimisation greatly complicate the fight against and prevention of online crime. Finally, the high prevalence of cybercrime victimisation combined with the severe impact of these crimes seems contradictory with public opinion – and associated moral judgments – on victims. Further research into the dominant public discourse on victimisation and how this affects the functioning of the police and victim support would be valuable.
A Stochastic Subgradient Method for Nonsmooth Nonconvex Multilevel Composition Optimization

We propose a single time-scale stochastic subgradient method for constrained optimization of a composition of several nonsmooth and nonconvex functions. The functions are assumed to be locally Lipschitz and differentiable in a generalized sense. Only stochastic estimates of the values and generalized derivatives of the functions are used. The method is parameter-free. We prove convergence with probability one of the method, by associating with it a system of differential inclusions and devising a nondifferentiable Lyapunov function for this system. For problems with functions having Lipschitz continuous derivatives, the method finds a point satisfying an optimality measure with error of order $1/\sqrt{N}$, after executing $N$ iterations with constant stepsize.
Towards Domain-Specific Explainable AI: Model Interpretation of a Skin Image Classifier using a Human Approach

Machine Learning models have started to outperform medical experts in some classification tasks. Meanwhile, the question of how these classifiers produce certain results is attracting increasing research attention. Current interpretation methods provide a good starting point in investigating such questions, but they still massively lack the relation to the problem domain. In this work, we present how explanations of an AI system for skin image analysis can be made more domain-specific. We apply the synthesis of Local Interpretable Model-agnostic Explanations (LIME) with the ABCD-rule, a diagnostic approach of dermatologists, and present the results using a Deep Neural Network (DNN) based skin image classifier.
Online learning sentiment analysis during the covid-19 Indonesia pandemic using twitter data

Abstract In the last week of September 2020, Covid-19 in Indonesia has infected more than 252,000 people. The spread of the virus through physical contact forces all countries to use social distance and physical distance to reduce interactions. On the educational side, the Covid-19 pandemic has had an impact on the prevention of face-to-face teaching and learning activities. All educational institutions must stop the learning process and replace it with online learning. Nadiem Makarim, Minister of Education and Culture of the Republic of Indonesia, then affirmed this online learning by issuing a policy on online learning in case of an emergency spread of the Covid-19 virus. However, this online learning has provoked debate in the community over the preparation of the technology. The goal of this study was to analyze public opinion for online learning during the Covid-19 pandemic in October 2020. The research carried out document-based text mining and feelings on Twitter data analyzed using the Naïve Bayes method. Analysis in that period found 25 percent positive sentiment, 74 percent negative sentiment, and 1 percent neutral sentiment. Several tweets showed that the words ‘stress’ and ‘covid’ were the most commonly spoken in the month.
Visualizing large-scale high-dimensional data via hierarchical embedding of KNN graphs

Visualizing intrinsic structures of high-dimensional data is an essential task in data analysis. Over the past decades, a large number of methods have been proposed. Among all solutions, one promising way for enabling effective visual exploration is to construct a k-nearest neighbor (KNN) graph and visualize the graph in a low-dimensional space. Yet, state-of-the-art methods such as the LargeVis still suffer from two main problems when applied to large-scale data: (1) they may produce unappealing visualizations due to the non-convexity of the cost function; (2) visualizing the KNN graph is still time-consuming. In this work, we propose a novel visualization algorithm that leverages a multi-level representation to achieve a high-quality graph layout and employs a cluster-based approximation scheme to accelerate the KNN graph layout. Experiments on various large-scale datasets indicate that our approach achieves a speedup by a factor of five for KNN graph visualization compared to LargeVis and yields aesthetically pleasing visualization results.
A Trajectory Privacy Protection Method Based on Random Sampling Differential Privacy

With the popularity of location-aware devices (e.g., smart phones), a large number of trajectory data were collected. The trajectory dataset can be used in many fields including traffic monitoring, market analysis, city management, etc. The collection and release of trajectory data will raise serious privacy concerns for users. If users’ privacy is not protected enough, they will refuse to share their trajectory data. In this paper, a new trajectory privacy protection method based on random sampling differential privacy (TPRSDP), which can provide more security protection, is proposed. Compared with other methods, it takes less time to run this method. Experiments are conducted on two real world datasets to validate the proposed scheme, and the results are compared with others in terms of running time and information loss. The performance of the scheme with different parameter values is verified. The setting of the new scheme parameters is discussed in detail, and some valuable suggestions are given.
Signal propagation in a gradient-based and evolutionary learning system

Generative adversarial networks (GANs) exhibit training pathologies that can lead to convergence-related degenerative behaviors, whereas spatially-distributed, coevolutionary algorithms (CEAs) for GAN training, e.g. Lipizzaner, are empirically robust to them. The robustness arises from diversity that occurs by training populations of generators and discriminators in each cell of a toroidal grid. Communication, where signals in the form of parameters of the best GAN in a cell propagate in four directions: North, South, West and East, also plays a role, by communicating adaptations that are both new and fit. We propose Lipi-Ring, a distributed CEA like Lipizzaner, except that it uses a different spatial topology, i.e. a ring. Our central question is whether the different directionality of signal propagation (effectively migration to one or more neighbors on each side of a cell) meets or exceeds the performance quality and training efficiency of Lipizzaner. Experimental analysis on different datasets (i.e, MNIST, CelebA, and COVID-19 chest X-ray images) shows that there are no significant differences between the performances of the trained generative models by both methods. However, Lipi-Ring significantly reduces the computational time (14.2%... 41.2%). Thus, Lipi-Ring offers an alternative to Lipizzaner when the computational cost of training matters.
Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data

The scarcity of parallel data is a major obstacle for training high-quality machine translation systems for low-resource languages.Fortunately, some low-resource languages are linguistically related or similar to high-resource languages; these related languages may share many lexical or syntactic structures.In this work, we exploit this linguistic overlap to facilitate translating to and from a lowresource language with only monolingual data, in addition to any parallel data in the related high-resource language.Our method, NMT-Adapt, combines denoising autoencoding, back-translation and adversarial objectives to utilize monolingual data for lowresource adaptation.We experiment on 7 languages from three different language families and show that our technique significantly improves translation into low-resource language compared to other translation baselines.
Controversy and Conformity: from Generalized to Personalized Aggressiveness Detection

There is content such as hate speech, offensive, toxic or aggressive documents, which are perceived differently by their consumers.They are commonly identified using classifiers solely based on textual content that generalize pre-agreed meanings of difficult problems.Such models provide the same results for each user, which leads to high misclassification rate observable especially for contentious, aggressive documents.Both document controversy and user nonconformity require new solutions.Therefore, we propose novel personalized approaches that respect individual beliefs expressed by either user conformity-based measures or various embeddings of their previous text annotations.We found that only a few annotations of most controversial documents are enough for all our personalization methods to significantly outperform classic, generalized solutions.The more controversial the content, the greater the gain.The personalized solutions may be used to efficiently filter unwanted aggressive content in the way adjusted to a given person.
An Empirical Study on the Impact of Refactoring on Quality Metrics in Android Applications

Mobile applications must continuously evolve, sometimes under such time pressure that poor design or implementation choices are made, which inevitably result in structural software quality problems. Refactoring is the widely-accepted approach to ameliorating such quality problems. While the impact of refactoring on software quality has been widely studied in object-oriented software, its impact is still unclear in the context of mobile apps. This paper reports on the first empirical study that aims to address this gap. We conduct a large empirical study that analyses the evolution history of 300 open-source Android apps exhibiting a total of 42,181 refactoring operations. We analyze the impact of these refactoring operations on 10 common quality metrics using a causal inference method based on the Difference-in-Differences (DiD) model. Our results indicate that when refactoring affects the metrics it generally improves them. In many cases refactoring has no significant impact on the metrics, whereas one metric (LCOM) deteriorates overall as a result of refactoring. These findings provide practical insights into the current practice of refactoring in the context of Android app development.
Understanding Code Smell Detection via Code Review: A Study of the OpenStack Community

Code review plays an important role in software quality control. A typical review process would involve a careful check of a piece of code in an attempt to find defects and other quality issues/violations. One type of issues that may impact the quality of the software is code smells - i.e., bad programming practices that may lead to defects or maintenance issues. Yet, little is known about the extent to which code smells are identified during code reviews. To investigate the concept behind code smells identified in code reviews and what actions reviewers suggest and developers take in response to the identified smells, we conducted an empirical study of code smells in code reviews using the two most active OpenStack projects (Nova and Neutron). We manually checked 19,146 review comments obtained by keywords search and random selection, and got 1,190 smell-related reviews to study the causes of code smells and actions taken against the identified smells. Our analysis found that 1) code smells were not commonly identified in code reviews, 2) smells were usually caused by violation of coding conventions, 3) reviewers usually provided constructive feedback, including fixing (refactoring) recommendations to help developers remove smells, and 4) developers generally followed those recommendations and actioned the changes. Our results suggest that 1) developers should closely follow coding conventions in their projects to avoid introducing code smells, and 2) review-based detection of code smells is perceived to be a trustworthy approach by developers, mainly because reviews are context-sensitive (as reviewers are more aware of the context of the code given that they are part of the project's development team).
Bitcoin and stock markets: a revisit of relationship

The volatility of bitcoin (BTC) and time horizon is the center point for investment decisions. However, attention is not often drawn to the relationship between BTC and equity indices. Thus, the purpose of this paper is to investigate the volatility and time frequency domain of BTC with stock markets.
Higher quality live streaming under lower uplink bandwidth

With the growing popularity of live streaming, high video quality and low latency with limited uplink bandwidth have become a significant challenge. In this study, we propose Live Super-Resolution Based Video Coding (LiveSRVC), a novel video uploading framework that improves the quality of live streaming with low latency under limited uplink bandwidth. We design a new super-resolution-based key frame coding module to improve the coding compression efficiency. LiveSRVC dynamically selects the bitrate and the compression ratio of key frames, mitigating the influence of uplink bandwidth capacity on live streaming quality. Trace-driven emulations verify that LiveSRVC can provide the same quality while reducing up to 50% of the required bandwidth compared to the original encoding method (H.264). LiveSRVC consumes at least 10X less GPU occupation time compared to the method of reconstructing all frames with super-resolution.
Online Multi-target Tracking for Pedestrian by Fusion of Millimeter Wave Radar and Vision

Pedestrian trajectory tracking is crucial to ensure pedestrian safety in autonomous driving. Recently developed multi-sensor based multi-target tracking algorithms either overrely on the detection performance of some certain sensors or underutilize sensors' inherent information. Aiming at improving reliability and robustness of tracking under complex autonomous driving scenes, a new multi-sensor based tracking algorithm for pedestrians is proposed in this paper, which realizes sensor-fusion tracking by employing a newly proposed back-projection mechanism and a novel multi-hypothesis association approach. For the performance evaluation, massive experiments are performed to produce the dataset with 20 sequences. Qualitative experiment results demonstrate the superiority of the proposed algorithm over the single sensor based conventional algorithm. Quantitative experiment results further verify that the proposed algorithm reduces false negatives and improves tracking accuracy as compared to conventional multi-sensor based tracking algorithms.