Reviewerly/exHarmony huggingface dataset:
Scalable, High-Performance, and Generalized Subtree Data Anonymization Approach for Apache Spark

Data anonymization strategies such as subtree generalization have been hailed as techniques that provide a more efficient generalization strategy compared to full-tree generalization counterparts. Many subtree-based generalizations strategies (e.g., top-down, bottom-up, and hybrid) have been implemented on the MapReduce platform to take advantage of scalability and parallelism. However, MapReduce inherent lack support for iteration intensive algorithm implementation such as subtree generalization. This paper proposes Distributed Dataset (RDD)-based implementation for a subtree-based data anonymization technique for Apache Spark to address the issues associated with MapReduce-based counterparts. We describe our RDDs-based approach that offers effective partition management, improved memory usage that uses cache for frequently referenced intermediate values, and enhanced iteration support. Our experimental results provide high performance compared to the existing state-of-the-art privacy preserving approaches and ensure data utility and privacy levels required for any competitive data anonymization techniques.
Channel Graph Regularized Correlation Filters for Visual Object Tracking

Correlation Filters (CF) are a popular choice for visual object tracking due to their efficiency in the frequency domain. Convolutional and hand-crafted features are jointly used when learning a filter, however, these features are not uniformly important when tracking a target. Given this observation, spatial and temporal regularization and attention models have been investigated. However, these models do not consider the interaction between different feature channels. As a result, dissimilar weights are assigned to similar feature channels. To address this issue, we propose a channel attention model and study two different regularization methods for attention. We investigate the application of channel regularization to emphasize important feature channels; and graph regularization which increases the likelihood of similar feature channels obtaining similar weights. The proposed formulation can be efficiently solved via the alternating direction method of multipliers. We first show the advantages of using the proposed channel regularization by demonstrating its performance when applied to two existing CF trackers. This is followed by analyzing the effect of using the proposed channel-graph regularization for CF based tracking. The evaluation is performed on publicly available tracking datasets: OTB100, TC128, VOT-2017, VOT-2019, LaSOT, UAV123, and GOT-10k. Evaluation over multiple challenges and a comparative analysis with existing top-ranked trackers shows that our formulation improves the discriminative power of the learned CF, preventing tracker drift during challenging scenarios.
Competitive Learning of Facial Fitting and Synthesis Using UV Energy

The three-dimensional morphable model (3DMM) is the most widely used representative model for obtaining a three-dimensional (3-D) face from a target on an image. Although 3DMMs have demonstrated the powerful capability to represent various facial shapes on natural images, they are limited to capturing texture variations of in-the-wild human faces. Based on the fact that fitting a 3-D facial model to an image determines the corresponding UV map, we propose a novel method for facial fitting and synthesis by competitively training two deep learning networks for facial alignment and UV texture completion. When the completion network is trained using well-aligned UV maps, it can model facial textures precisely and, consequently, fill the missing regions more completely. Accordingly, we use a UV completion network, denoted as a UV energy-based generative adversarial network (UV EB-GAN), to discriminate whether a UV map from the alignment network is well aligned by defining the generative loss of the completion network as the energy. Competitive learning facilitates training the completion network without ground-truth facial UV maps and training the alignment network without hard constraints and regularization terms. The proposed network can be trained in an end-to-end manner. The facial texture, albedo, lighting parameters, and 3-D facial shape can be obtained through this network. The results of the experiments on 2-D alignment, 3-D reconstruction, texture synthesis, and illumination estimation verified that the proposed method achieves remarkable improvements over the state-of-the-art methods.
Remote sensing image super-resolution using cascade generative adversarial nets

Image super-resolution (SR) is a widely used and cost-effective technology in remote sensing image processing. Deep learning-based SR methods have shown promising performance, but they are prone to losing texture details. Instead, generative adversarial nets (GAN)-based methods can generate more visually acceptable results. However, GAN-based SR methods are suffering from scene variance and uncontrollable performance of discriminators as well as unstable training. Besides, both these two methods cannot yield arbitrary high-time SR images. To solve these issues, we propose a novel SR method for remote sensing images using Cascade Generative Adversarial Nets (CGAN) with introduction of content fidelity and scene constraint, which can achieve arbitrary high-time high-quality SR image. More specifically, the scene-constraint item is incorporated to constrain generated feature for avoiding the risk of scene change. Then, content fidelity is introduced to stabilize the training and avoid gradient vanishing. Besides, an edge enhancement module is designed to preserve edge detail and suppress the noise. CGAN with these three components has achieved higher quality SR results than other recent state-of-the-art methods. Compared with these methods, our proposed method outperformed average increments of 7.3% SSIM, 7.3% FSIM and 6.0% MSIM on WHU-RS19 and NWPU-RESISC45 datasets. In addition, the evaluation of GAN-train and GAN-test gained average increments of 6.3% and 4.5% on the WHU-RS19 and AID datasets, respectively.
MixMo: Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks

Recent strategies achieved ensembling "for free" by fitting concurrently diverse subnetworks inside a single base network. The main idea during training is that each sub-network learns to classify only one of the multiple inputs simultaneously provided. However, the question of how to best mix these multiple inputs has not been studied so farIn this paper, we introduce MixMo, a new generalized framework for learning multi-input multi-output deep subnetworks. Our key motivation is to replace the suboptimal summing operation hidden in previous approaches by a more appropriate mixing mechanism. For that purpose, we draw inspiration from successful mixed sample data augmentations. We show that binary mixing in features - particularly with rectangular patches from CutMix - enhances results by making subnetworks stronger and more diverse.We improve state of the art for image classification on CIFAR-100 and Tiny ImageNet datasets. Our easy to implement models notably outperform data augmented deep ensembles, without the inference and memory overheads. As we operate in features and simply better leverage the expressiveness of large networks, we open a new line of research complementary to previous works.
Adaptive Cloud-Based Extended Reality: Modeling and Optimization

Extended Reality (XR) - which includes Virtual Reality and Augmented Reality - promises to bring the virtual and telepresence experience to another level. Unfortunately, solutions leveraging these technologies require special high-performance computing platforms that degrade the cost-benefit balance. Moving processing to the cloud solves this problem but imposes strict requirements on data transmission reliability, bandwidth, and delays. The satisfaction of these requirements becomes an extremely challenging problem in the presence of other types of delay-sensitive traffic, such as remote control, industrial automation, or the control commands of the Cloud XR application itself. This article studies the joint service of the adaptive Cloud XR traffic with other high-priority delay-sensitive traffics. The paper develops an analytical model of the considered communication system. The model represents the system as a discrete state Markov chain and estimates the quality of experience for Cloud XR users in various scenarios. Using the model, the paper estimates the network capacity for the Cloud XR traffic and optimizes the bitrate adaptation function of the Cloud XR video streaming application. The goal of the optimization is to improve the visual quality of the virtual environment observed by the users, subject to the constrained probability of image impairments due to excessive delivery delays. Numerical results demonstrate the high accuracy of the developed model and the benefits provided by the optimization.
Causal learner: A toolbox for causal structure and Markov blanket learning

Causal Learner is a toolbox for learning causal structure and Markov blanket (MB) from data. It integrates functions for generating simulated Bayesian network data, a set of state-of-the-art global causal structure learning algorithms, a set of state-of-the-art local causal structure learning algorithms, a set of state-of-the-art MB learning algorithms, and abundant functions for evaluating algorithms. The data generation part of Causal Learner is written in R, and the rest of Causal Learner is written in MATLAB. Causal Learner aims to provide researchers and practitioners with an open-source platform for causal learning from data and for the development and evaluation of new causal learning algorithms. The Causal Learner project is available at http://bigdata.ahu.edu.cn/causal-learner.
Collaborative human-autonomy semantic sensing through structured POMDP planning

Autonomous unmanned systems and robots must be able to actively leverage all available information sources — including imprecise but readily available semantic observations provided by human collaborators. This work develops and validates a novel active collaborative human–machine sensing solution for robotic information gathering and optimal decision making problems, with an example implementation of a dynamic target search scenario. Our approach uses continuous partially observable Markov decision process (CPOMDP) planning to generate vehicle trajectories that optimally exploit imperfect detection data from onboard sensors, as well as semantic natural language observations that can be specifically requested from human sensors. The key innovations are a method for the inclusion of a human querying/sensing model in a CPOMDP based autonomous decision making process, as well as a scalable hierarchical Gaussian mixture model formulation for efficiently solving CPOMDPs with semantic observations in continuous dynamic state spaces. Unlike previous state-of-the-art approaches this allows planning in large, complex, highly segmented environments. Our solution is demonstrated and validated with a real human–robot team engaged in dynamic indoor target search and capture scenarios on a custom testbed.
Using modified term frequency to improve term weighting for text classification

Text classification (TC) is an essential task of natural language processing (NLP). In order to improve the performance of TC, term weighting is often used to obtain effective text representation by assigning appropriate weights to each term. A term weighting scheme is generally composed of term frequency factor, collection frequency factor and normalization factor. The normalization factor is commonly used as an optional factor to offset the influence of document length. Through the investigation of the existing term weighting schemes, we found that most of them focus on finding a more effective collection frequency factor, but rarely pay attention to finding a new term frequency factor. In this paper, we first proposed a new term frequency factor called modified term frequency (MTF). Different from the normalization factor, MTF directly modifies the raw term frequency based on the length information of all training documents. Then we proposed a new term weighting scheme by combining MTF with an existing collection frequency factor called modified distinguishing feature selector (MDFS). We denoted our scheme by MTF-MDFS (MDFS-based MTF). Extensive experimental results on 19 benchmark text datasets and 6 real-world text datasets show that our proposed MTF and MTF-MDFS are all much better than their state-of-the-art competitors in terms of the classification accuracy and the weighted average of F1 of widely used base classifiers, such as MNB, SVM and LR.
Sentiment Analysis in Dialectal Arabic: A Systematic Review

Recently, Sentiment Analysis (SA) in Arabic has gained considerable interest in the research community. Several surveys were conducted concerning Arabic sentiment analysis at one hand and the Arabic dialects on the other hand. However, analyzing the Arabic dialect sentiment analysis in social media context is still questioned and requires further examination. This study aims to systematically review and synthesizes the Arabic sentiment analysis studies related to Arabic dialects aiming to provide a full analysis of 60 research articles from 2012 to 2020. The results pointed out that SVM and NB are the most frequent research ML algorithms used to classify Arabic dialect sentiments. Besides, tweets are required by most articles to carry out their experiments besides getting results. The results also revealed that the most studied dialects are Saudi, Egyptian, Jordanian, and Algerian. To that end, this systematic review paper's outcomes offer an insight into the current trend of Arabic sentiment analysis research involving Arabic dialect studies and form an essential reference for scholars in the Arabic Natural Language Processing context.
Supporting eating behaviour of community-dwelling older adults: co-design of an embodied conversational agent

In order to support community-dwelling older adults with healthy eating behaviours, Embodied Conversational Agents (ECAs) may be an effective and engaging medium. However, ECAs have not yet been found to be capable of engendering behaviour change, which is partly attributed to the absence of a match with users' practices, needs and preferences. Hence, we describe a co-design process with older adults that informs both the content and the appearance of an ECA. Data was gathered through three consecutive iterations of co-design sessions with two groups of community-dwelling older adults in the Netherlands. Prior to the first session, participants completed a seven-day lifestyle diary. This study adds knowledge on the meaning of healthy eating, as well as on specific barriers to, and opportunities for, giving advice using an ECA in this target group. Furthermore, we translate this knowledge into general advice for designing an ECA in the context of health behaviour change, while reflecting on a co-design process with older adults.
Machine Learning for Prediction of Imbalanced Data: Credit Fraud Detection

Online transactions have increased drastically over the past decades. Credit card transactions account for a large percentage of these transactions. This leads to rise activities of credit card fraud transactions, causing losses in the finance industry. Therefore, it is vital to create reliable fraud detection systems, including two labels of fraud and no-fraud. However, there are highly unbalanced data between these two labels. In this paper, we use two resampling approaches of synthetic minority oversampling technique (SMOTE) and adaptive synthetic (ADASYN) to handle an imbalanced dataset to obtain the balanced dataset. The machine learning (ML) algorithms, named random forest, k nearest neighbors, decision tree, and logistic regression are applied to this balanced dataset. The comprehensive classification measurements, including fundamental, combined, and graphical measurements are used to evaluate the performances of these models. We observe that after resampling the dataset, the ML algorithms mentioned show the positive results of classification for fraudulent activities.
Attentive Feature Refinement Network for Single Rainy Image Restoration

Despite the fact that great progress has been made on single image deraining tasks, it is still challenging for existing models to produce satisfactory results directly, and it often requires a single or multiple refinement stages to gradually improve the quality. However, in this paper, we demonstrate that existing image-level refinement with a stage-independent learning design is problematic with the side effect of over/under-deraining. To resolve this issue, we for the first time propose the mechanism of learning to carry out refinement on the unsatisfactory features, and propose a novel attentive feature refinement (AFR) module. Specifically, AFR is designed as a two-branched network for simultaneous rain-distribution-aware attention map learning and attention guided hierarchy-preserving feature refinement. Guided by task-specific attention, coarse features are progressively refined to better model the diversified rainy effects. By using a separable convolution as the basic component, our AFR module introduces little computation overhead and can be readily integrated into most rainy-to-clean image translation networks for achieving better deraining results. By incorporating a series of AFR modules into a general encoder-decoder network, AFR-Net is constructed for deraining and it achieves new state-of-the-art results on both synthetic and real images. Furthermore, by using AFR-Net as a teacher model, we explore the use of knowledge distillation to successfully learn a student model that is also able to achieve state-of-the-art results but with a much faster inference speed (i.e., it only takes 0.08 second to process a 512×512 rainy image). Code and pre-trained models are available at (https://github.com/RobinCSIRO/AFR-Net).
MagIK: A Hand-Tracking Magnetic Positioning System Based on a Kinematic Model of the Hand

In this article, we present a hand-tracking system based on magnetic positioning. A single magnetic node is mounted on each fingertip and two magnetic nodes on the back side of the hand. A fixed array of receiving coils is used to detect the magnetic field, from which it is possible to infer the position and orientation of each magnetic node. A kinematic model of the whole hand has been developed. Starting from the positioning data of each magnetic node, the kinematic model can be used to calculate the position and flexion angle of each finger joint, plus the position and orientation of the hand in space. Relying on magnetic fields, the hand-tracking system can work also in non-line-of-sight conditions. The gesture reconstruction is validated by comparing it with a commercial hand-tracking system based on a depth camera. The system requires a small number of electronics to be mounted on the hand. This would allow building a light and comfortable data glove that could be used for several purposes: human-machine interface, sign language recognition, diagnostics, and rehabilitation.
The Role of Blockchain Technology in Aviation Industry

The current systems used to manage data in aviation industries are mostly centralized and fall short of ensuring trusted data provenance, immutability, transparency, auditablility, and traceability features. Blockchain is an emerging and disruptive technology that has a high potential to provide trusted traceability, transparency, auditability, and immutability for stored and exchanged data and transactions in a manner that is decentralized and secure without the involvement of trusted third party. In this article, we explore the potential role of blockchain technology in the aviation industry. We highlight and discuss the key opportunities brought about by blockchain technology in various aviation applications, including digitizing crew certificates, securing customer loyalty programs, and maintenance, repair, and overhaul operations. We present recently reported case studies that have leveraged blockchain technology to provide aviation-centric services. Furthermore, we introduce a framework that leverages blockchain-based smart contracts to automate the key services in the aviation industry. Finally, we highlight open research issues and challenges.
DID and VC:Untangling Decentralized Identifiers and Verifiable Credentials for the Web of Trust

Decentralized identifiers and verifiable credentials have been proposed as a self-sovereign and privacy-friendly alternative to centralized and proprietary authentication services. Currently, a W3C standard exists that attempts to unify existing proposals and to find a common layer for decentralized identification and verification. However, there are some limitations of decentralized identifiers in comparison to established, centrally controlled authentication platforms concerning trust, privacy and usability. In this paper, we first describe all workflows which are necessary to create, share and verify a verifiable credential and second, we discuss the limitations concerning trust, privacy and usability of decentralized identifiers. The paper summarizes the involved workflows for decentralized authentication as proposed by the current standard. Further, we show the existing limitations and shortcomings that need to be considered when sharing DID for practical implementations and give an overview of possible solutions and future directions.
An efficient foreign objects detection network for power substation

A power substation is susceptible to intrusions of foreign objects. The intrusions can likely result in failures of power supplies. Therefore, recognizing foreign objects becomes important to ensure constant and stable power supplies. However, existing object recognition methods fail to achieve acceptable accuracy and performance. In this paper, we propose an efficient Foreign Objects Detection Network for Power Substation (FODN4PS) to improve the recognition accuracy with less time. FODN4PS consists of a Moving Object Region Extraction Network (MORE Net) and a classification network, where the MORE Net can get the position of foreign objects, and the classification network can recognize the category of foreign objects. Experimental results show that FODN4PS is faster and more accurate in object recognition than the Fast R-CNN and Mask R-CNN.
Communicative functions of emoji sequences in the context of self-presentation: A comparative study of Weibo and Twitter users

Focusing on Weibo (a Chinese Microblogging site) and Twitter, this study adopts computer-mediated discourse analysis to examine how influencers use emoji sequences when engaging in self-presentation. It identified a variety of text-based speech acts, emoji functions, and functional relations by conducting speech act and pragmatic function analysis. ‘Claim’ is the most common text-based speech act accompanying with emoji sequences in both data groups; however, the former had a higher percentage than the later. Moreover, emoji functioning as a combination of ‘stance and action’ in sequences comprise the most prominent category in the Twitter data, whereas the ‘concept’ function accounts for the largest percentage within the Weibo data. Finally, emoji sequences serving as ‘emphasis on text’ is most employed in connection with accompanying texts in both data. This study also observed how a lack of user-desired emoji may be compensated for through collocation of emoji sequences, a device which involves significant creativity and intricacy.