Reviewerly/exHarmony huggingface dataset:
A fast and robust 3D face recognition approach based on deeply learned face representation

With the superiority of three-dimensional (3D) scanning data, e.g., illumination invariance and pose robustness, 3D face recognition theoretically has the potential to achieve better results than two-dimensional (2D) face recognition. However, traditional 3D face recognition techniques suffer from high computational costs. This paper proposes a fast and robust 3D face recognition approach with three component technologies: a fast 3D scan preprocessing, multiple data augmentation, and a deep learning technique based on facial component patches. First, unlike the majority of the existing approaches, which require accurate facial registration, the proposed approach uses only three facial landmarks. Second, the specifical deep network with an improved supervision is designed to extract complementary features from four overlapping facial component patches. Finally, a data augmentation technique and three self-collected 3D face datasets are used to enlarge the scale of the training data. The proposed approach outperforms the state-of-the-art algorithms on four public 3D face benchmarks, i.e., 100%, 99.75%, 99.88%, and 99.07% rank-1 IRs with the standard test protocol on the FRGC v2.0, Bosphorus, BU-3DFE, and 3D-TEC datasets, respectively. Further, it requires only 0.84 seconds to identify a probe from a gallery with 466 faces.
A hybrid data mining approach for identifying the temporal effects of variables associated with breast cancer survival

Predicting breast cancer survival is crucial for practitioners to determine possible outcomes and make better treatment plans for the patients. In this study, a hybrid data mining based methodology was constructed to differentiate the variables whose importance for survival change over time. Therefore, the importance of variables was determined for three different time periods (i.e. one, five, and ten years). To conduct such an analysis, the most parsimonious models were constructed by employing one regression analysis method—Least Absolute Shrinkage and Selection Operator (LASSO), and one metaheuristic optimization method, namely a Genetic Algorithm (GA). Due to the high imbalance between the number of survivals and deaths, two well-known resampling procedures—Random Under-sampling (RUS) and Synthetic Minority Over-sampling Technique (SMOTE)—were applied to increase the performance of the classification models. In the final stage, two data mining models, namely Artificial Neural Networks (ANNs) and Logistic Regression (LR), were utilized along with 10-fold cross-validation. Sensitivity analysis (SA) was conducted for each model to identify the importance of each variable for a certain model and time period. The obtained results revealed that certain variables lose their importance over time, while others gain importance. This information can assist medical practitioners in identifying specific subsets of variables to focus on in different periods, which will in turn lead to a more effective and efficient cancer care. Moreover, the study findings indicate that extremely parsimonious models can be developed by adopting a purely data-driven approach, rather than eliminating the variables manually. Such methodology can also be applied in treating other types of cancer.
Perceived exposure to and avoidance of hate speech in various communication settings

Social media platforms have been accused of spreading hate speech. The goal of this study is to test the widespread belief that social media platforms have a high level of hate speech in the eyes of survey respondents. Secondarily, the study also tests the idea that encountering perceived hate speech is related to avoiding political talk. The study analyzes data from a two-wave online survey (N = 1493) conducted before and after the 2018 U.S. Midterm Elections, and it estimates perceived exposure to hate speech across multiple venues: face-to-face, social media, mobile messaging applications, and anonymous online message boards. Results show that (a) respondents report higher levels of hate speech on social media in comparison to face-to-face communication and (b) there is a positive relationship between perceived exposure to hate speech and avoidance of political talk. Results are discussed in light of public conversations about hate speech on social media.
Cross-resolution face recognition with pose variations via multilayer locality-constrained structural orthogonal procrustes regression

In real video surveillance scenes, the extracted face regions generally have low-resolution (LR) and are sensitive to pose and illumination variations; these flaws undoubtedly degrade the subsequent recognition task. To overcome these challenges, we propose an approach named multilayer locality-constrained structural orthogonal Procrustes regression (MLCSOPR). The proposed MLCSOPR not only learns the pose-robust discriminative representation features to reduce the resolution gap between the LR image space and the high-resolution (HR) one but also strengthens the consistency between the LR and HR image space. In particular, several contributions are made in this paper: (i) Inspired by the orthogonal Procrustes problem (OPP), a matrix approximation is exploited to find an optimal correction between two data matrices. (ii) The nuclear norm constraint is applied to the reconstruction error to maintain the structural property. (iii) Based on the abovementioned learned resolution-robust representation features, a linear regression-based classification strategy is adopted to recognize the LR input face images. Experiments on commonly used face databases have shown the effectiveness of the proposed method on cross-resolution face matching with pose variations.
Assessing the quality of the steps to reproduce in bug reports

A major problem with user-written bug reports, indicated by developers and documented by researchers, is the (lack of high) quality of the reported steps to reproduce the bugs. Low-quality steps to reproduce lead to excessive manual effort spent on bug triage and resolution. This paper proposes Euler, an approach that automatically identifies and assesses the quality of the steps to reproduce in a bug report, providing feedback to the reporters, which they can use to improve the bug report. The feedback provided by Euler was assessed by external evaluators and the results indicate that Euler correctly identified 98% of the existing steps to reproduce and 58% of the missing ones, while 73% of its quality annotations are correct.
Immersive Virtual Reality and Gamification Within Procedurally Generated Environments to Increase Motivation During Gait Rehabilitation

Virtual Reality (VR) technology offers promising opportunities to improve traditional treadmill-based rehabilitation programs. We present an immersive VR rehabilitation system that includes a head-mounted display and motion sensors. The application is designed to promote the experience of relatedness, autonomy, and competence. The application uses procedural content generation to generate diverse landscapes. We evaluated the effect of the immersive rehabilitation system on motivation and affect. We conducted a repeated measures study with 36 healthy participants to compare the immersive program to a traditional rehabilitation program. Participants reported significant greater enjoyment, felt more competent and experienced higher decision freedom and meaningfulness in the immersive VR gait training compared to the traditional training. They experienced significantly lower physical demand, simulator sickness, and state anxiety, and felt less pressured while still perceiving a higher personal performance. We derive three design implications for future applications in gait rehabilitation: Immersive VR provides a promising augmentation for gait rehabilitation. Gamification features provide a design guideline for content creation in gait rehabilitation. Relatedness and autonomy provide critical content features in gait rehabilitation.
Saliency Detection via a Multiple Self-Weighted Graph-Based Manifold Ranking

As an important task in the process of image understanding and analysis, saliency detection has recently received increasing attention. In this paper, we propose an efficient multiple self-weighted graph-based manifold ranking method to construct salient maps. First, we extract several different views of features from superpixels, and generate original salient regions as foreground and background cues using boundary information via multiple graph-based manifold ranking. Furthermore, a set of hyperparameters is learned to distinguish the importance between different graphs, which can be viewed as an adaptive weighting of each graph, and then a centroid graph is generated by using these self-weighted multiple graphs. An iterative algorithm is proposed to simultaneously optimize the hyperparameters as well as the centroid graph connection. Thus, an ideal centroid graph can be obtained, offering a more clear profile of the separated structure. Finally, the saliency maps can be produced with an approximate binary image from the manifold ranking. Extensive experiments have demonstrated our method consistently achieves superior detection performance than several state-of-the-arts.
A study of the use of digital technology and its conditions with a view to understanding what ‘adequate digital competence’ may mean in a national policy initiative

(2020). A study of the use of digital technology and its conditions with a view to understanding what 'adequate digital competence' may mean in a national policy initiative. Educational Studies: Vol. 46, No. 6, pp. 727-743.
Semi-supervised facial expression recognition using reduced spatial features and Deep Belief Networks

A semi-supervised emotion recognition algorithm using reduced features as well as a novel feature selection approach is proposed. The proposed algorithm consists of a cascaded structure where first a feature extraction is applied to the facial images, followed by a feature reduction. A semi-supervised training with all the available labeled and unlabeled data is applied to a Deep Belief Network (DBN). Feature selection is performed to eliminate those features that do not provide information, using a reconstruction error-based ranking. Results show that HOG features of mouth provide the best performance. The performance evaluation has been done between the semi-supervised approach using DBN and other supervised strategies such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN). The results show that the semi-supervised approach has improved efficiency using the information contained in both labeled and unlabeled data. Different databases were used to validate the experiments and the application of Linear Discriminant Analysis (LDA) on the HOG features of mouth gave the highest recognition rate.
Ensure Hierarchal Identity Based Data Security in Cloud Environment

Cloud computing has emerged as a new promising field in the internet. It can be thought as a new architecture for the next generation of IT enterprises. It allows the user to access virtualized resources over the internet which can be dynamically scaled. Here, the owner's data is stored at a distributed data centre, which are responsible for its security constraints such as access control and data transmission to user. As the owner does not have physical access on their own data, the data centres are not trustworthy, this resulted in the cloud data security demand. Today, many cloud service providers (CSPs) are using the asymmetric and public key cryptography (PKG) for authenticating and data security purposes using the digital identity of the user. To this end, this article focuses on cloud data storage and its delivery to authorized user. For this purpose, a hierarchal identity-based cryptography method is used for data security and checking the data integrity, in order to make sure that there is no alteration or modification done by a malicious attacker or CSP for its own benefit.
Improved PPVO-based high-fidelity reversible data hiding

Most pixel-value-ordering (PVO) based reversible data hiding (RDH) methods conduct pixel prediction on image blocks, while a recently proposed pixel-based PVO (PPVO) method changes the blockwise prediction in PVO to pixelwise manner and achieves better embedding performance. However, in PPVO, the prediction is not accurate since some pixels close to the to-be-predicted one are not utilized. Moreover, pixels with different local complexity are not fully exploited and the embedding performance of PPVO is not optimized. Thus, to better determine the prediction context as well as full use the image local correlation, an improved PPVO-based RDH method is proposed in this paper. First, to improve the prediction accuracy, a new predictor is proposed in which the prediction context is properly selected. Then, for the optimized performance, a new embedding strategy is proposed based on multiple histograms generation and modification with multi-sized prediction contexts. Experimental results verify that the proposed method is superior to PPVO and some other state-of-the-art RDH methods.
A Privacy-Preserving Distributed Contextual Federated Online Learning Framework with Big Data Support in Social Recommender Systems

Nowadays, the booming demand of big data analytics and the constraints of computational ability and network bandwidth have made it difficult for a stand-alone agent/service provider to provide suitable information for every user from the large volume online data within the limited time. To handle this challenge, a recommender system (RS) can call in a group of agents to collaborate to learn users' preference and taste, which is known as a distributed recommender system (DRS). DRSs can improve the accuracy of a traditional RS by requesting agents to share information with each other. However, it is challenging for DRSs to make personalized recommendations for each user due to the large amount of candidates. In addition, information sharing among agents raises a privacy concern. Thus, we propose a privacy-preserving DRS in this paper, and then model each service provider as a distributed online learner with context-awareness. Service providers collaborate to make personalized recommendations by learning users' preferences according to the user context and users' history behaviors. We adopt the federated learning framework to help train a high quality privacy- preserving centralized model over a large number of distributed agents which is probably unreliable with relatively slow network connections. To handle big data scenario, we build an item-cluster tree to deal with online and increasing datasets from top to the bottom. We further consider the structure of social network and present an efficient algorithm to avoid more performance loss adaptively. Theoretical proofs show that our proposed algorithm can achieve sublinear regret and differential privacy protection simultaneously for service providers and users. Numerical results confirm that our novel framework can handle increasing big datasets and strike a trade-off between privacy-preserving level and the prediction accuracy.
Frame Augmented Alternating Attention Network for Video Question Answering

Vision and language understanding is one of the most fundamental and challenging problems in Multimedia Intelligence. Simultaneously understanding video actions with a related natural language question, and further produces accurate answer is even more challenging since it requires joint modeling information across modality. In the past few years, some studies begin to attack this problem by utilizing attention enhanced deep neural networks. However, simple attention mechanisms such as unidirectional attention fail to yield a better mapping between different modalities. Moreover, none of these Video QA models explore high-level semantics in augmented video-frame level. In this paper, we augmented each frame representation with its context information by a novel feature extractor that combines the advantages of Resnet and a variant of C3D. In addition, we proposed a novel alternating attention network which can alternately attend frame regions, video frames and words in the question in multi-turns. This yields better joint representations of video and question, further help the deep model to discover the deeper relationship between two modalities. Our method outperforms the state-of-the-art Video QA models on two existing video question answering datasets. Further ablation studies proved that our feature extractor and the alternating attention mechanism can improve the performance jointly.
Augmented Immersive Reality (AIR) for Improved Learning Performance: A Quantitative Evaluation

Technology-enhanced learning has attracted increasing attention of educational community focused on improvement of traditional classroom learning. Augmented immersive reality (AIR) technologies enhance users' perception of reality by augmenting it with computer-generated components such as audio, video, 2/3-D graphics, GPS data, etc. The AIR introduces new dimensions of learning experience that ensure better attention, focus, and entertainment, thereby boosting students' motivation and attainment. This work presents an award winning AIR-based educational mobile system, code-named AIR-EDUTECH, that was developed to help high school students learn chemistry. The AIR-EDUTECH introduced new AIR features to help students better understand and learn basic concepts of molecular chemistry. It offers immersive 3D visualization and visual interaction with the examined structures that provides a broader and more retentive knowledge and improves intuition around forming basic chemical reactions. The system was introduced and tested in a field study with 45 students in the 11 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> grade chemistry class, and its impact was evaluated by the formal assessment quiz along with the feedback from survey conducted after the trial. Collected data have been subjected to an in-depth multi-modal quantitative analysis that revealed that AIR-EDUTECH stimulated significant improvements in understanding and retention of the taught content as well as turned learning chemistry into a fun, interesting and interactive experience. It also uncovered a hidden structure of taught knowledge dependencies and highlighted the role that AIR technology could play in reinforcing the retention of critical knowledge that may otherwise widen student knowledge gaps.
Categorical Image Classification Based On Representational Deep Network (RESNET)

Categorical Image Classification needs thousands of images to train. Also, System needs more time to extract the features as well as classification. In the Proposed Scenario we are going to describe different deep learning approaches for image classification. In the proposed System uses more, deep convolutional neural network to categorize thousands of high-resolution images into eight different classes. We have extracted image features from a pre-trained Representational deep Neural network (RESNET), and use that features to train machine learning Support vector machine (SVM) classifier. Representational deep networks makes feature extraction easiest and fastest way use than any other conventional network methods. In this research paper we are describe Image Categorical classification using proposed representational deep networks (RESNET).
Convolutional Analysis Operator Learning: Acceleration and Convergence

Convolutional operator learning is gaining attention in many signal processing and computer vision applications. Learning kernels has mostly relied on so-called patch-domain approaches that extract and store many overlapping patches across training signals. Due to memory demands, patch-domain methods have limitations when learning kernels from large datasets - particularly with multi-layered structures, e.g., convolutional neural networks - or when applying the learned kernels to high-dimensional signal recovery problems. The so-called convolution approach does not store many overlapping patches, and thus overcomes the memory problems particularly with careful algorithmic designs; it has been studied within the "synthesis" signal model, e.g., convolutional dictionary learning. This paper proposes a new convolutional analysis operator learning (CAOL) framework that learns an analysis sparsifying regularizer with the convolution perspective, and develops a new convergent Block Proximal Extrapolated Gradient method using a Majorizer (BPEG-M) to solve the corresponding block multi-nonconvex problems. To learn diverse filters within the CAOL framework, this paper introduces an orthogonality constraint that enforces a tight-frame filter condition, and a regularizer that promotes diversity between filters. Numerical experiments show that, with sharp majorizers, BPEG-M significantly accelerates the CAOL convergence rate compared to the state-of-the-art block proximal gradient (BPG) method. Numerical experiments for sparse-view computational tomography show that a convolutional sparsifying regularizer learned via CAOL significantly improves reconstruction quality compared to a conventional edge-preserving regularizer. Using more and wider kernels in a learned regularizer better preserves edges in reconstructed images.
Face Recognition Algorithm Bias: Performance Differences on Images of Children and Adults

In this work, we examine if current state-of-the-art deep learning face recognition systems exhibit a negative bias (i.e., poorer performance) for children when compared to the performance obtained on adults. The systems selected for this work are five top performing commercial-off-the-shelf face recognition systems, two government-off-the-shelf face recognition systems and one open-source face recognition solution. The datasets used to evaluate the performance of the systems are both unconstrained in age, pose, illumination, and expression and are publicly available. These datasets are indicative of photo journalistic face datasets published and evaluated on over the last few years. Our findings show a negative bias for each algorithm on children. Genuine and imposter distributions highlight the performance bias between the datasets further supporting the need for a deeper investigation into algorithm bias as a function of age cohorts. To combat the performance decline on the child demographic, several score-level fusion strategies were evaluated. This work identifies the best score-level fusion technique for the child demographic.