Reviewerly/exHarmony huggingface dataset:
The Relation Between Social Media and Students’ Academic Performance in Jordan: YouTube Perspective

This study aims mainly at analyzing the relationship between social media and students’ academic performance in Jordan in the context of higher education from a YouTube perspective. It intends to explore the benefits this relationship may have in enhancing students; leaning and improving their academic performance. To successfully reach its aims, this study proposes a new model aiming at verifying the relationship of social Bookmarking, YouTube Features, Perceived Usefulness, Use of Social Media, on Jordanian students’ academic performance. To verify the validity of the proposed model, data were analyzed using Smart PLS using structural equations modeling (SEM). Data were collected from Yarmouk University in Jordan covering all the levels of study at the university. An electronic questionnaire was conducted for a target of 360 students who participated in this study. The findings of the study revealed that Social Bookmarking, YouTube Features, Perceived Usefulness, Use of Social Media are important factors to predict students’ academic performance in relation to using social networking media for e-learning purposes in Jordan.
Decoupling Representation and Classifier for Long-Tailed Recognition

The long-tail distribution of the visual world poses great challenges for deep learning based classification models on how to handle the class imbalance problem. Existing solutions usually involve class-balancing strategies, e.g., by loss re-weighting, data re-sampling, or transfer learning from head- to tail-classes, but most of them adhere to the scheme of jointly learning representations and classifiers. In this work, we decouple the learning procedure into representation learning and classification, and systematically explore how different balancing strategies affect them for long-tailed recognition. The findings are surprising: (1) data imbalance might not be an issue in learning high-quality representations; (2) with representations learned with the simplest instance-balanced (natural) sampling, it is also possible to achieve strong long-tailed recognition ability by adjusting only the classifier. We conduct extensive experiments and set new state-of-the-art performance on common long-tailed benchmarks like ImageNet-LT, Places-LT and iNaturalist, showing that it is possible to outperform carefully designed losses, sampling strategies, even complex modules with memory, by using a straightforward approach that decouples representation and classification. Our code is available at this https URL.
Approximate Trapdoors for Lattices and Smaller Hash-and-Sign Signatures

We study a relaxed notion of lattice trapdoor called approximate trapdoor, which is defined to be able to invert Ajtai's one-way function approximately instead of exactly. The primary motivation of our study is to improve the efficiency of the cryptosystems built from lattice trapdoors, including the hash-and-sign signatures. Our main contribution is to construct an approximate trapdoor by modifying the gadget trapdoor proposed by Micciancio and Peikert [Eurocrypt 2012]. In particular, we show how to use the approximate gadget trapdoor to sample short preimages from a distribution that is simulatable without knowing the trapdoor. The analysis of the distribution uses a theorem (implicitly used in past works) regarding linear transformations of discrete Gaussians on lattices. Our approximate gadget trapdoor can be used together with the existing optimization techniques to improve the concrete performance of the hash-and-sign signature in the random oracle model under (Ring-)LWE and (Ring-)SIS assumptions. Our implementation shows that the sizes of the public-key & signature can be reduced by half from those in schemes built from exact trapdoors.
Differentiable Ranking and Sorting using Optimal Transport

Sorting is used pervasively in machine learning, either to define elementary algorithms, such as $k$-nearest neighbors ($k$-NN) rules, or to define test-time metrics, such as top-$k$ classification accuracy or ranking losses. Sorting is however a poor match for the end-to-end, automatically differentiable pipelines of deep learning. Indeed, sorting procedures output two vectors, neither of which is differentiable: the vector of sorted values is piecewise linear, while the sorting permutation itself (or its inverse, the vector of ranks) has no differentiable properties to speak of, since it is integer-valued. We propose in this paper to replace the usual \texttt{sort} procedure with a differentiable proxy. Our proxy builds upon the fact that sorting can be seen as an optimal assignment problem, one in which the $n$ values to be sorted are matched to an \emph{auxiliary} probability measure supported on any \emph{increasing} family of $n$ target values. From this observation, we propose extended rank and sort operators by considering optimal transport (OT) problems (the natural relaxation for assignments) where the auxiliary measure can be any weighted measure supported on $m$ increasing values, where $m \ne n$. We recover differentiable operators by regularizing these OT problems with an entropic penalty, and solve them by applying Sinkhorn iterations. Using these smoothed rank and sort operators, we propose differentiable proxies for the classification 0/1 loss as well as for the quantile regression loss.
Disentangling Factors of Variations Using Few Labels

Learning disentangled representations is considered a cornerstone problem in representation learning. Recently, Locatello et al. (2019) demonstrated that unsupervised disentanglement learning without inductive biases is theoretically impossible and that existing inductive biases and unsupervised methods do not allow to consistently learn disentangled representations. However, in many practical settings, one might have access to a limited amount of supervision, for example through manual labeling of (some) factors of variation in a few training examples. In this paper, we investigate the impact of such supervision on state-of-the-art disentanglement methods and perform a large scale study, training over 52000 models under well-defined and reproducible experimental conditions. We observe that a small number of labeled examples (0.01--0.5% of the data set), with potentially imprecise and incomplete labels, is sufficient to perform model selection on state-of-the-art unsupervised models. Further, we investigate the benefit of incorporating supervision into the training process. Overall, we empirically validate that with little and imprecise supervision it is possible to reliably learn disentangled representations.
Path Planning With Local Motion Estimations

We introduce a novel approach to long-range path planning that relies on a learned model to predict the outcome of local motions using possibly partial knowledge. The model is trained from a dataset of trajectories acquired in a self-supervised way. Sampling-based path planners use this component to evaluate edges to be added to the planning tree. We illustrate the application of this pipeline with two robots: a complex, simulated, quadruped robot (ANYmal) moving on rough terrains; and a simple, real, differential-drive robot (Mighty Thymio), whose geometry is assumed unknown, moving among obstacles. We quantitatively evaluate the model performance in predicting the outcome of short moves and long-range paths; finally, we show that planning results in reasonable paths.
DGGAN: Depth-image Guided Generative Adversarial Networks for Disentangling RGB and Depth Images in 3D Hand Pose Estimation

Estimating 3D hand poses from RGB images is essential to a wide range of potential applications, but is challenging owing to substantial ambiguity in the inference of depth information from RGB images. State-of-the-art estimators address this problem by regularizing 3D hand pose estimation models during training to enforce the consistency between the predicted 3D poses and the ground-truth depth maps. However, these estimators rely on both RGB images and the paired depth maps during training. In this study, we propose a conditional generative adversarial network (GAN) model, called Depth-image Guided GAN (DGGAN), to generate realistic depth maps conditioned on the input RGB image, and use the synthesized depth maps to regularize the 3D hand pose estimation model, therefore eliminating the need for ground-truth depth maps. Experimental results on multiple benchmark datasets show that the synthesized depth maps produced by DGGAN are quite effective in regularizing the pose estimation model, yielding new state-of-the-art results in estimation accuracy, notably reducing the mean 3D endpoint errors (EPE) by 4.7%, 16.5%, and 6.8% on the RHD, STB and MHP datasets, respectively.
An Adaptive Social Network-Aware Collaborative Filtering Algorithm for Improved Rating Prediction Accuracy

When information from traditional recommender systems is augmented with information about user relationships that social networks store, more successful recommendations can be produced.However, this information regarding user relationships may not always be available, since some users may not consent to the use of their social network information for recommendations or may not have social network accounts at all.Moreover, the rating data (categories and characteristics of products) may be unavailable for a recommender system.In this paper, we present an algorithm that can be applied in any social network-aware recommender system that utilizes the users' ratings on items and users' social relations.The proposed algorithm addresses the issues of limited social network information or limited collaborative filtering information for some users by adapting its behavior, taking into account the density and utility of each user's social network and collaborative filtering neighborhoods.Through this adaptation, the proposed algorithm achieves considerable improvement in rating prediction accuracy.Furthermore, the proposed algorithm can be easily implemented in recommender systems.
The forgotten margins

column Share on The forgotten margins: what is community-based participatory health design telling us? Author: Christina N. Harrington DePaul University DePaul UniversityView Profile Authors Info & Claims InteractionsVolume 27Issue 3May - June 2020 pp 24–29https://doi.org/10.1145/3386381Online:17 April 2020Publication History 15citation1,551DownloadsMetricsTotal Citations15Total Downloads1,551Last 12 Months531Last 6 weeks32 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Tracking Multiple Maneuvering Targets Hidden in the DBZ Based on the MM-GLMB Filter

Tracking multiple maneuvering targets hidden in the Doppler blind zone (DBZ) is a challenging problem. To overcome the complicated problem, we proposed a tracker based on the multiple model probability hypothesis density (MM-PHD) filter. However, the PHD filter is only the first-order moment approximation of the multi-target Bayesian filter, and it cannot output track labels in principle. In order to improve the tracking performance, another novel tracker is proposed in this paper. To track multiple maneuvering targets, the proposed tracker is built on the latest multiple model generalized labeled multi-Bernoulli (MM-GLMB) filter. Moreover, it incorporates the minimum detectable velocity (MDV) to suppress the DBZ masking. Finally, a measurement-driven adaptive track initiation is introduced to address the fixed track initiation problem of the standard MM-GLMB filter. It is demonstrated through numerical examples that the proposed tracker outperforms the existing work significantly, especially in terms of both accuracy and robustness of cardinality estimation.
Powered by blockchain: forecasting blockchain use in the electricity market

Purpose The purpose of this study is to formulate the most probable future scenario for the use of blockchain technology within the next 5–10 years in the electricity sector based on today’s experts’ views. Design/methodology/approach An international, two-stage Delphi study with 20 projections is used. Findings According to the experts, blockchain applications will be primarily based on permissioned or consortium blockchains. Blockchain-based applications will integrate Internet of Things devices in the power grid, manage the e-mobility infrastructure, automate billing and direct payment and issue certificates regarding the origin of electricity. Blockchain solutions are expected to play an important big role in fostering peer-to-peer trading in microgrids, further democratizing and decentralizing the energy sector. New regulatory frameworks become necessary. Research limitations/implications The Delphi study’s scope is rather broad than narrow and detailed. Further studies should focus on partial scenarios. Practical implications Electricity market participants should build blockchain-based competences and collaborate in current pilot projects. Social implications Blockchain technology will further decentralize the energy sector and probably reduce transaction costs. Originality/value Despite the assumed importance of blockchain technology, no coherent foresight study on its use and implications exists yet. This study closes this research gap.
Digital Artifacts in Industrial Co-creation: How to Use VR Technology to Bridge the Provider-Customer Boundary

Industrial co-creation projects are often complex and ambiguous, involving high levels of interpretive uncertainty over processes and outcomes. To boost the effectiveness of such projects, firms have increasingly adopted virtual reality (VR) technology and have experienced unique benefits by utilizing digital artifacts—interactive objects in digital environments, such as factory installation layouts or design visualizations. This article provides case evidence demonstrating how VR-enabled digital artifacts support firms to effectively implement tailor-made solutions in robotics and automation projects. The adoption of new digital co-creation practices redefines the traditional customer-provider roles in industrial co-creation, increasing engagement, reducing uncertainty, and improving project outcomes.
CPC

Code comments provide abundant information that have been leveraged to help perform various software engineering tasks, such as bug detection, specification inference, and code synthesis. However, developers are less motivated to write and update comments, making it infeasible and error-prone to leverage comments to facilitate software engineering tasks. In this paper, we propose to leverage program analysis to systematically derive, refine, and propagate comments. For example, by propagation via program analysis, comments can be passed on to code entities that are not commented such that code bugs can be detected leveraging the propagated comments. Developers usually comment on different aspects of code elements like methods, and use comments to describe various contents, such as functionalities and properties. To more effectively utilize comments, a fine-grained and elaborated taxonomy of comments and a reliable classifier to automatically categorize a comment are needed. In this paper, we build a comprehensive taxonomy and propose using program analysis to propagate comments. We develop a prototype CPC, and evaluate it on 5 projects. The evaluation results demonstrate 41573 new comments can be derived by propagation from other code locations with 88% accuracy. Among them, we can derive precise functional comments for 87 native methods that have neither existing comments nor source code. Leveraging the propagated comments, we detect 37 new bugs in open source large projects, 30 of which have been confirmed and fixed by developers, and 304 defects in existing comments (by looking at inconsistencies between existing and propagated comments), including 12 incomplete comments and 292 wrong comments. This demonstrates the effectiveness of our approach. Our user study confirms propagated comments align well with existing comments in terms of quality.
Log-Linear Bayesian Additive Regression Trees for Multinomial Logistic and Count Regression Models

We introduce Bayesian additive regression trees (BART) for log-linear models including multinomial logistic regression and count regression with zero-inflation and overdispersion. BART has been applied to nonparametric mean regression and binary classification problems in a range of settings. However, existing applications of BART have been mostly limited to models for Gaussian “data,” either observed or latent. This is primarily because efficient MCMC algorithms are available for Gaussian likelihoods. But while many useful models are naturally cast in terms of latent Gaussian variables, many others are not—including models considered in this article. We develop new data augmentation strategies and carefully specified prior distributions for these new models. Like the original BART prior, the new prior distributions are carefully constructed and calibrated to be flexible while guarding against overfitting. Together the new priors and data augmentation schemes allow us to implement an efficient MCMC sampler outside the context of Gaussian models. The utility of these new methods is illustrated with examples and an application to a previously published dataset. Supplementary materials for this article are available online.
A Semi-supervised Stacked Autoencoder Approach for Network Traffic Classification

Network traffic classification is an important task in modern communications. Several approaches have been proposed to improve the performance of differentiating among applications. However, most of them are based on supervised learning where only labeled data are used. In reality, a lot of datasets are partially labeled due to many reasons and unlabeled portions of the data, which can also provide informative characteristics, are ignored. To handle this issue, we propose a semi-supervised approach based on deep learning. We deployed deep learning because of its unique nature for solving problems, and its ability to take into account both labeled and unlabeled data. Moreover, it can also integrate feature extraction and classification into a single model. To achieve these goals, we propose an approach using stacked sparse autoencoder (SSAE) accompanied by de-noising and dropout techniques to improve the robustness of extracted features and prevent the over-fitting problem during the training process. The obtained results demonstrate a better performance than traditional models while keeping the whole procedure automated.
Towards benchmark datasets for machine learning based website phishing detection: An experimental study

In this paper, we present a general scheme for building reproducible and extensible datasets for website phishing detection. The aim is to (1) enable comparison of systems using different features, (2) overtake the short-lived nature of phishing websites, and (3) keep track of the evolution of phishing tactics. For experimenting the proposed scheme, we start by adopting a refined classification of website phishing features and we systematically select a total of 87 commonly recognized ones, we classify them, and we made them subjects for relevance and runtime analysis. We use the collected set of features to build a dataset in light of the proposed scheme. Thereafter, we use a conceptual replication approach to check the genericity of former findings for the built dataset. Specifically, we evaluate the performance of classifiers on individual classes and on combinations of classes, we investigate different combinations of models, and we explore the effects of filter and wrapper methods on the selection of discriminative features. The results show that Random Forest is the most predictive classifier. Features gathered from external services are found the most discriminative where features extracted from web page contents are found less distinguishing. Besides external service based features, some web page content features are found time consuming and not suitable for runtime detection. The use of hybrid features provided the best accuracy score of 96.61%. By investigating different feature selection methods, filter-based ranking together with incremental removal of less important features improved the performance up to 96.83% better than wrapper methods.
TLMPA: Teaching-learning-based Marine Predators algorithm

&lt;abstract&gt; &lt;p&gt;Marine Predators algorithm (MPA) is a newly proposed nature-inspired metaheuristic algorithm. The main inspiration of this algorithm is based on the extensive foraging strategies of marine organisms, namely Lévy movement and Brownian movement, both of which are based on random strategies. In this paper, we combine the marine predator algorithm with Teaching-learning-based optimization algorithm, and propose a hybrid algorithm called Teaching-learning-based Marine Predator algorithm (TLMPA). Teaching-learning-based optimization (TLBO) algorithm consists of two phases: the teacher phase and the learner phase. Combining these two phases with the original MPA enables the predators to obtain prey information for foraging by learning from teachers and interactive learning, thus greatly increasing the encounter rate between predators and prey. In addition, effective mutation and crossover strategies were added to increase the diversity of predators and effectively avoid premature convergence. For performance evaluation TLMPA algorithm, it has been applied to IEEE CEC-2017 benchmark functions and four engineering design problems. The experimental results show that among the proposed TLMPA algorithm has the best comprehensive performance and has more outstanding performance than other the state-of-the-art metaheuristic algorithms in terms of the performance measures.&lt;/p&gt; &lt;/abstract&gt;
Mind the Discriminability: Asymmetric Adversarial Domain Adaptation

Adversarial domain adaptation has made tremendous success by learning domain-invariant feature representations. However, conventional adversarial training pushes two domains together and brings uncertainty to feature learning, which deteriorates the discriminability in the target domain. In this paper, we tackle this problem by designing a simple yet effective scheme, namely Asymmetric Adversarial Domain Adaptation (AADA). We notice that source features preserve great feature discriminability due to full supervision, and therefore a novel asymmetric training scheme is designed to keep the source features fixed and encourage the target features approaching to the source features, which best preserves the feature discriminability learned from source labeled data. This is achieved by an autoencoder-based domain discriminator that only embeds the source domain, while the feature extractor learns to deceive the autoencoder by embedding the target domain. Theoretical justifications corroborate that our method minimizes the domain discrepancy and spectral analysis is employed to quantize the improved feature discriminability. Extensive experiments on several benchmarks validate that our method outperforms existing adversarial domain adaptation methods significantly and demonstrates robustness with respect to hyper-parameter sensitivity.