Reviewerly/exHarmony huggingface dataset:
Motivational Factors in the Use of Videoconferences to Carry out Tutorials in Spanish Universities in the Post-Pandemic Period

Many of the tools used for virtual teaching during the pandemic had not been used previously, but they could continue to be used when traditional teaching returns. For this reason, this study focused on locating the key motivational factors for the possible continuation of the use of one of these tools, videoconferencing, to carry out tutorials in Spanish universities as a complement to face-to-face tutorials. For this, a literary review was conducted to obtain a list of motivational factors that may influence teachers to continuing using it, and a causal study was performed with university professors (through fuzzy cognitive maps) to identify the causal relationships among these factors and classify them by their relevance in making a decision. The most influential factors are intention, attitude and perceived compatibility with how tutorials are given, and the negative factors include quality management and trust.
Learning Privacy-preserving Optics for Human Pose Estimation

The widespread use of always-connected digital cameras in our everyday life has led to increasing concerns about the users’ privacy and security. How to develop privacy- preserving computer vision systems? In particular, we want to prevent the camera from obtaining detailed visual data that may contain private information. However, we also want the camera to capture useful information to perform computer vision tasks. Inspired by the trend of jointly designing optics and algorithms, we tackle the problem of privacy-preserving human pose estimation by optimizing an optical encoder (hardware-level protection) with a software decoder (convolutional neural network) in an end-to- end framework. We introduce a visual privacy protection layer in our optical encoder that, parametrized appropriately, enables the optimization of the camera lens’s point spread function (PSF). We validate our approach with extensive simulations and a prototype camera. We show that our privacy-preserving deep optics approach successfully degrades or inhibits private attributes while maintaining important features to perform human pose estimation.
BaGFN: Broad Attentive Graph Fusion Network for High-Order Feature Interactions

Modeling feature interactions is of crucial significance to high-quality feature engineering on multifiled sparse data. At present, a series of state-of-the-art methods extract cross features in a rather implicit bitwise fashion and lack enough comprehensive and flexible competence of learning sophisticated interactions among different feature fields. In this article, we propose a new broad attentive graph fusion network (BaGFN) to better model high-order feature interactions in a flexible and explicit manner. On the one hand, we design an attentive graph fusion module to strengthen high-order feature representation under graph structure. The graph-based module develops a new bilinear-cross aggregation function to aggregate the graph node information, employs the self-attention mechanism to learn the impact of neighborhood nodes, and updates the high-order representation of features by multihop fusion steps. On the other hand, we further construct a broad attentive cross module to refine high-order feature interactions at a bitwise level. The optimized module designs a new broad attention mechanism to dynamically learn the importance weights of cross features and efficiently conduct the sophisticated high-order feature interactions at the granularity of feature dimensions. The final experimental results demonstrate the effectiveness of our proposed model.
ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation

This paper studies Semi-Supervised Domain Adaptation (SSDA), a practical yet under-investigated research topic that aims to learn a model of good performance using unlabeled samples and a few labeled samples in the target domain, with the help of labeled samples from a source domain. Several SSDA methods have been proposed recently, which however fail to fully exploit the value of the few labeled target samples. In this paper, we propose Enhanced Categorical Alignment and Consistency Learning (ECACL), a holistic SSDA framework that incorporates multiple mutually complementary domain alignment techniques. ECACL includes two categorical domain alignment techniques that achieve class-level alignment, a strong data augmentation based technique that enhances the model’s generalizability and a consistency learning based technique that forces the model to be robust with image perturbations. These techniques are applied on one or multiple of the three inputs (labeled source, unlabeled target, and labeled target) and align the domains from different perspectives. ECACL unifies them together and achieves fairly comprehensive domain alignments that are much better than the existing methods: For example, ECACL raises the state-of-the-art accuracy from 68.4 to 81.1 on VisDA2017 and from 45.5 to 53.4 on DomainNet for the 1-shot setting. Our code is available at https://github.com/kailigo/pacl.
Collaborative Optimization and Aggregation for Decentralized Domain Generalization and Adaptation

Contemporary domain generalization (DG) and multisource unsupervised domain adaptation (UDA) methods mostly collect data from multiple domains together for joint optimization. However, this centralized training paradigm poses a threat to data privacy and is not applicable when data are non-shared across domains. In this work, we propose a new approach called Collaborative Optimization and Aggregation (COPA), which aims at optimizing a generalized target model for decentralized DG and UDA, where data from different domains are non-shared and private. Our base model consists of a domain-invariant feature extractor and an ensemble of domain-specific classifiers. In an iterative learning process, we optimize a local model for each domain, and then centrally aggregate local feature extractors and assemble domain-specific classifiers to construct a generalized global model, without sharing data from different domains. To improve generalization of feature extractors, we employ hybrid batch-instance normalization and collaboration of frozen classifiers. For better decentralized UDA, we further introduce a prediction agreement mechanism to overcome local disparities towards central model aggregation. Extensive experiments on five DG and UDA benchmark datasets show that COPA is capable of achieving comparable performance against the state-of-the-art DG and UDA methods without the need for centralized data collection in model training.
Augmented reality for preschool children: An experience with educational contents

Abstract This study was conducted to test the effect of the augmented reality‐based program on the motivation, attention and conceptual skills of preschool children. The research was conducted according to the pretest‐posttest control group quasi‐experimental design: 13 participants (6 girls, 7 boys) in the experimental group and 13 (5 girls, 8 boys) in the control group, a total of 26 children (4–5 age group) were included in the study. While the children in the experimental group participated in the applications prepared with augmented reality, the children in the control group were practised upon with the traditional teaching method. In the research, measurement tools were applied to the working groups (experiment‐control) both before and after the application. As a result, it was determined that the motivation, attention and concept skills of the children in the experimental group increased significantly compared to the children in the control group. The results show that augmented reality applications can be used in learning activities in preschool education. Practitioner notes What is already known about this topic Augmented reality applications can be adapted for preschool children. Augmented reality applications are new in pre‐school education. Augmented reality applications are interesting for preschool children. What this paper adds Augmented reality applications improve the motivational skills of preschool children. Augmented reality applications improve the attention skills of preschool children. Augmented reality applications improve the concept skills of preschool children. Implications for practice and/or policy Technology‐based classroom environments should be created in pre‐school education institutions. Augmented reality applications in pre‐school education should be expanded in line with the gains. Teachers should receive training on augmented reality applications.
Mining weighted sequential patterns in incremental uncertain databases

Due to the rapid development of science and technology, the importance of imprecise, noisy, and uncertain data is increasing at an exponential rate. Thus, mining patterns in uncertain databases have drawn the attention of researchers. Moreover, frequent sequences of items from these databases need to be discovered for meaningful knowledge with great impact. In many real cases, weights of items and patterns are introduced to find interesting sequences as a measure of importance. Hence, a constraint of weight needs to be handled while mining sequential patterns. Besides, due to the dynamic nature of databases, mining important information has become more challenging. Instead of mining patterns from scratch after each increment, incremental mining algorithms utilize previously mined information to update the result immediately. Several algorithms exist to mine frequent patterns and weighted sequences from incremental databases. However, these algorithms are confined to mine the precise ones. Therefore, in this work, we develop an algorithm to mine frequent sequences in an uncertain database in this work. Furthermore, we propose two new techniques for mining when the database is incremental. Extensive experiments have been conducted for performance evaluation. The analysis showed the efficiency of our proposed framework.
Generating Smooth Pose Sequences for Diverse Human Motion Prediction

Recent progress in stochastic motion prediction, i.e., predicting multiple possible future human motions given a single past pose sequence, has led to producing truly diverse future motions and even providing control over the motion of some body parts. However, to achieve this, the state-of-the-art method requires learning several mappings for diversity and a dedicated model for controllable motion prediction. In this paper, we introduce a unified deep generative network for both diverse and controllable motion prediction. To this end, we leverage the intuition that realistic human motions consist of smooth sequences of valid poses, and that, given limited data, learning a pose prior is much more tractable than a motion one. We therefore design a generator that predicts the motion of different body parts sequentially, and introduce a normalizing flow based pose prior, together with a joint angle loss, to achieve motion realism. Our experiments on two standard benchmark datasets, Human3.6M and HumanEva-I, demonstrate that our approach outperforms the state-of-the-art baselines in terms of both sample diversity and accuracy. The code is available at https://github.com/wei-mao-2019/gsps
Deep Transport Network for Unsupervised Video Object Segmentation

The popular unsupervised video object segmentation methods fuse the RGB frame and optical flow via a two-stream network. However, they cannot handle the distracting noises in each input modality, which may vastly deteriorate the model performance. We propose to establish the correspondence between the input modalities while suppressing the distracting signals via optimal structural matching. Given a video frame, we extract the dense local features from the RGB image and optical flow, and treat them as two complex structured representations. The Wasserstein distance is then employed to compute the global optimal flows to transport the features in one modality to the other, where the magnitude of each flow measures the extent of the alignment between two local features. To plug the structural matching into a two-stream network for end-to-end training, we factorize the input cost matrix into small spatial blocks and design a differentiable long-short Sinkhorn module consisting of a long-distant Sinkhorn layer and a short-distant Sinkhorn layer. We integrate the module into a dedicated two-stream network and dub our model TransportNet. Our experiments show that aligning motion-appearance yields the state-of-the-art results on the popular video object segmentation datasets.
Aligning Latent and Image Spaces to Connect the Unconnectable

In this work, we develop a method to generate infinite high-resolution images with diverse and complex content. It is based on a perfectly equivariant patch-wise generator with synchronous interpolations in the image and latent spaces. Latent codes, when sampled, are positioned on the coordinate grid, and each pixel is computed from an interpolation of the neighboring codes. We modify the AdaIN mechanism to work in such a setup and train a GAN model to generate images positioned between any two latent vectors. At test time, this allows for generating infinitely large images of diverse scenes that transition naturally from one into another. Apart from that, we introduce LHQ: a new dataset of 90k high-resolution nature landscapes. We test the approach on LHQ, LSUN Tower and LSUN Bridge and outperform the baselines by at least 4 times in terms of quality and diversity of the produced infinite images. The project website is located at https://universome.github.io/alis.
On the Limits of Pseudo Ground Truth in Visual Camera Re-localisation

Benchmark datasets that measure camera pose accuracy have driven progress in visual re-localisation research. To obtain poses for thousands of images, it is common to use a reference algorithm to generate pseudo ground truth. Popular choices include Structure-from-Motion (SfM) and Simultaneous-Localisation-and-Mapping (SLAM) using additional sensors like depth cameras if available. Re-localisation benchmarks thus measure how well each method replicates the results of the reference algorithm. This begs the question whether the choice of the reference algorithm favours a certain family of re-localisation methods. This paper analyzes two widely used re-localisation datasets and shows that evaluation outcomes indeed vary with the choice of the reference algorithm. We thus question common beliefs in the re-localisation literature, namely that learning-based scene coordinate regression outperforms classical feature-based methods, and that RGB-D-based methods outperform RGB-based methods. We argue that any claims on ranking re-localisation methods should take the type of the reference algorithm, and the similarity of the methods to the reference algorithm, into account.
Explaining Deep Learning using examples: Optimal feature weighting methods for twin systems using post-hoc, explanation-by-example in XAI

In this paper, the twin-systems approach is reviewed, implemented, and competitively tested as a post-hoc explanation-by-example solution to the eXplainable Artificial Intelligence (XAI) problem. In twin-systems, an opaque artificial neural network (ANN) is explained by “twinning” it with a more interpretable case-based reasoning (CBR) system, by mapping the feature weights from the former to the latter. Extensive comparative tests are performed, over four experiments, to determine the optimal feature-weighting method for such twin-systems. Twin-systems for traditional multilayer perceptron (MLP) networks (MLP–CBR twins), convolutional neural networks (CNNs; CNN–CBR twins), and transformers for NLP (BERT–CBR twins) are examined. In addition, Feature Activation Maps (FAMs) are explored to enhance explainability by providing an additional layer of explanatory insight. The wider implications of this research on XAI is discussed, and a code library is provided to ease replicability.
CasTabDetectoRS: Cascade Network for Table Detection in Document Images with Recursive Feature Pyramid and Switchable Atrous Convolution

Table detection is a preliminary step in extracting reliable information from tables in scanned document images. We present CasTabDetectoRS, a novel end-to-end trainable table detection framework that operates on Cascade Mask R-CNN, including Recursive Feature Pyramid network and Switchable Atrous Convolution in the existing backbone architecture. By utilizing a comparativelyightweight backbone of ResNet-50, this paper demonstrates that superior results are attainable without relying on pre- and post-processing methods, heavier backbone networks (ResNet-101, ResNeXt-152), and memory-intensive deformable convolutions. We evaluate the proposed approach on five different publicly available table detection datasets. Our CasTabDetectoRS outperforms the previous state-of-the-art results on four datasets (ICDAR-19, TableBank, UNLV, and Marmot) and accomplishes comparable results on ICDAR-17 POD. Upon comparing with previous state-of-the-art results, we obtain a significant relative error reduction of 56.36%, 20%, 4.5%, and 3.5% on the datasets of ICDAR-19, TableBank, UNLV, and Marmot, respectively. Furthermore, this paper sets a new benchmark by performing exhaustive cross-datasets evaluations to exhibit the generalization capabilities of the proposed method.
A Pilot Study Exploring Age Differences in Presence, Workload, and Cybersickness in the Experience of Immersive Virtual Reality Environments

Research is often focused on understanding barriers to the use and adoption of technology to support older adults’ (65+) instrumental activities of daily living (IADLs), such as communication, banking, and transportation. Less attention is paid to technology to support enhanced activities of daily living (EADLs), activities that enrich our daily lives, even though they have the potential to improve wellbeing, promote physical and emotional health, and reduce stress. Here, we explored how older adults interacted with commercial virtual reality (VR) to investigate the feasibility of using VR as an EADL support system. Older adults navigated different VR environments, including environments that were meditation, exploration, and game-oriented. Of particular interest was whether older adults ( N = 20) psychologically experienced differing degrees of presence within virtual environments compared to younger adults ( N = 20), and potential barriers to use as assessed by measures of workload and system usability. Given previously observed age-related differences in cybersickness, this was also assessed as a potential barrier. Compared to younger adults, older adults expressed a greater sense of presence in virtual environments, with nonsignificant differences in perceived workload and usability according to most measures. Contrary to expectations, older adults reported significantly less cybersickness compared to younger adults. Results suggest that VR is a promising means to support older adults’ EADLs.
Multimodal Compatibility Modeling via Exploring the Consistent and Complementary Correlations

Existing methods towards outfit compatibility modeling seldom explicitly consider multimodal correlations. In this work, we explore the consistent and complementary correlations for better compatibility modeling. This is, however, non-trivial due to the following challenges: 1) how to separate and model these two kinds of correlations; 2) how to leverage the derived complementary cues to strengthen the text and vision-oriented representations of the given item; and 3) how to reinforce the compatibility modeling with text and vision-oriented representations. To address these challenges, we present a comprehensive multimodal outfit compatibility modeling scheme. It first nonlinearly projects each modality into separable consistent and complementary spaces via multi-layer perceptron, and then models the consistent and complementary correlations between two modalities by parallel and orthogonal regularization. Thereafter, we strengthen the visual and textual representation of items with complementary information, and further induct both the text-oriented and vision- oriented outfit compatibility modeling. We ultimately employ the mutual learning strategy to reinforce the final performance of compatibility modeling. Extensive experiments demonstrate the superiority of our scheme.
Wireless Federated Learning (WFL) for 6G Networks⁴Part I: Research Challenges and Future Trends

Conventional machine learning techniques are conducted in a centralized manner. Recently, the massive volume of generated wireless data, the privacy concerns and the increasing computing capabilities of wireless end-devices have led to the emergence of a promising decentralized solution, termed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Wireless Federated Learning (WFL</i> ). In this first of the two parts letter, we present the application of WFL in the sixth generation of wireless networks (6G), which is envisioned to be an integrated communication and computing platform. After analyzing the key concepts of WFL, we discuss the core challenges of WFL imposed by the wireless ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">or mobile communication</b> ) environment. Finally, we shed light to the future directions of WFL, aiming to compose a constructive integration of FL into the future wireless networks.
Computational Model for Prediction of Malignant Mesothelioma Diagnosis

Abstract Mesothelioma is an aggressive lung cancer, harms the linings of the lungs. It is one of the deadliest cancers diagnosed in those exposed to fibrous silicate minerals (asbestos). Millions of people face severe consequences as they are diagnosed at late stages. This study presents a comparison of several machine learning approaches with distinct feature sets and addresses the issue of class imbalance. The dataset used in this study is available publicly on the University of California Irvine (UCI) machine learning repository. This study used the resampling technique, synthetic minority oversampling technique (SMOTE), and adaptive synthetic sampling (ADASYN) to handle the class imbalance. Most of the machine learning strategies performed well with the resampling technique. The best accuracy using the resampling strategy was achieved by artificial neural networks (ANN). The highest accuracy was recorded on the feature set selected by principal component analysis (PCA) is 96%. Overall, ensemble techniques performed well. The proposed stacking-based classifier achieved the highest accuracy (89%) on data balanced using SMOTE and ADASYN.
A secure and trustworthy medical record sharing scheme based on searchable encryption and blockchain

In this paper, we demonstrate the potential of using both blockchain and searchable encryption to build a medical data-sharing platform. The latter is designed to allow one to uniformly search for patients' (encrypted) data in different medical institutions. Building on the assumption that medical records are stored locally by the hospital, we use smart contracts to build a global encrypted search index and a verification index for multiple data owners on the blockchain. In our approach, fully functional access control is provided to ensure that patients and medical institutions can control access to the data. This allows the user to promptly verify the integrity of the results after the search. Our approach also supports the addition, modification, and deletion of medical records, and it complies with the requirements of forward security. The experimental results on the local Ethereum network show that our scheme efficiently facilitates secure data sharing.