Reviewerly/exHarmony huggingface dataset:
Analysis of Facial Information for Healthcare Applications: A Survey on Computer Vision-Based Approaches

This paper gives an overview of the cutting-edge approaches that perform facial cue analysis in the healthcare area. The document is not limited to global face analysis but it also concentrates on methods related to local cues (e.g., the eyes). A research taxonomy is introduced by dividing the face in its main features: eyes, mouth, muscles, skin, and shape. For each facial feature, the computer vision-based tasks aiming at analyzing it and the related healthcare goals that could be pursued are detailed.
Homogeneous network embedding for massive graphs via reweighted personalized PageRank

Given an input graph G and a node v ∈ G , homogeneous network embedding (HNE) maps the graph structure in the vicinity of v to a compact, fixed-dimensional feature vector. This paper focuses on HNE for massive graphs, e.g. , with billions of edges. On this scale, most existing approaches fail, as they incur either prohibitively high costs, or severely compromised result utility. Our proposed solution, called Node-Reweighted PageRank (NRP), is based on a classic idea of deriving embedding vectors from pairwise personalized PageRank (PPR) values. Our contributions are twofold: first, we design a simple and efficient baseline HNE method based on PPR that is capable of handling billion-edge graphs on commodity hardware; second and more importantly, we identify an inherent drawback of vanilla PPR, and address it in our main proposal NRP. Specifically, PPR was designed for a very different purpose, i.e. , ranking nodes in G based on their relative importance from a source node's perspective. In contrast, HNE aims to build node embeddings considering the whole graph. Consequently, node embeddings derived directly from PPR are of suboptimal utility. The proposed NRP approach overcomes the above deficiency through an effective and efficient node reweighting algorithm, which augments PPR values with node degree information, and iteratively adjusts embedding vectors accordingly. Overall, NRP takes O ( m log n ) time and O ( m ) space to compute all node embeddings for a graph with m edges and n nodes. Our extensive experiments that compare NRP against 18 existing solutions over 7 real graphs demonstrate that NRP achieves higher result utility than all the solutions for link prediction, graph reconstruction and node classification, while being up to orders of magnitude faster. In particular, on a billion-edge Twitter graph, NRP terminates within 4 hours, using a single CPU core.
Drawing from justice theories to support targets of online harassment

Most content moderation approaches in the United States rely on criminal justice models that sanction offenders via content removal or user bans. However, these models write the online harassment targets out of the justice-seeking process. Via an online survey with US participants ( N = 573), this research draws from justice theories to investigate approaches for supporting targets of online harassment. We uncover preferences for banning offenders, removing content, and apologies, but aversion to mediation and adjusting targets’ audiences. Preferences vary by identities (e.g. transgender participants on average find more exposure to be undesirable; American Indian or Alaska Native participants on average find payment to be unfair) and by social media behaviors (e.g. Instagram users report payment as just and fair). Our results suggest that a one-size-fits-all approach will fail some users while privileging others. We propose a broader theoretical and empirical landscape for supporting online harassment targets.
Survey of single image super‐resolution reconstruction

Image super-resolution reconstruction refers to a technique of recovering a high-resolution (HR) image (or multiple images) from a low-resolution (LR) degraded image (or multiple images). Due to the breakthrough progress in deep learning in other computer vision tasks, people try to introduce deep neural network and solve the problem of image super-resolution reconstruction by constructing a deep-level network for end-to-end training. The currently used deep learning models can divide the SISR model into four types: interpolation-based preprocessing-based model, original image processing based model, hierarchical feature-based model, and high-frequency detail-based model, or shared the network model. The current challenges for super-resolution reconstruction are mainly reflected in the actual application process, such as encountering an unknown scaling factor, losing paired LR–HR images, and so on.
Virtual/reality: how to tell the difference

With the advent of mass consumer virtual reality (VR) headsets and controllers in the second decade of the 20th century, some experts have predicted we are on a path toward losing the distinction between the real and the virtual. These predictions overstate the empirical evidence for the effects of VR; ignore its technical limitations; take for granted highly speculative claims about the nature of consciousness; and, most fundamentally, lose sight of the continuities between VR and other representational media. This article argues against thinking of VR as a magical technology for creating seamless illusions. Instead it situates VR as an emerging medium within an evolving community that is beginning to develop the media conventions to support sustained interaction and immersion. The future of VR is not an inevitable and delusional metaverse but a medium of representation that will always require our active creation of belief.
Pangolin

There is growing interest in graph pattern mining (GPM) problems such as motif counting. GPM systems have been developed to provide unified interfaces for programming algorithms for these problems and for running them on parallel systems. However, existing systems may take hours to mine even simple patterns in moderate-sized graphs, which significantly limits their real-world usability. We present Pangolin , an efficient and flexible in-memory GPM framework targeting shared-memory CPUs and GPUs. Pangolin is the first GPM system that provides high-level abstractions for GPU processing. It provides a simple programming interface based on the extend-reduce-filter model, which allows users to specify application specific knowledge for search space pruning and isomorphism test elimination. We describe novel optimizations that exploit locality, reduce memory consumption, and mitigate the overheads of dynamic memory allocation and synchronization. Evaluation on a 28-core CPU demonstrates that Pangolin outperforms existing GPM frameworks Arabesque, RStream, and Fractal by 49×, 88×, and 80× on average, respectively. Acceleration on a V100 GPU further improves performance of Pangolin by 15× on average. Compared to state-of-the-art hand-optimized GPM applications, Pangolin provides competitive performance with less programming effort.
AResU-Net: Attention Residual U-Net for Brain Tumor Segmentation

Automatic segmentation of brain tumors from magnetic resonance imaging (MRI) is a challenging task due to the uneven, irregular and unstructured size and shape of tumors. Recently, brain tumor segmentation methods based on the symmetric U-Net architecture have achieved favorable performance. Meanwhile, the effectiveness of enhancing local responses for feature extraction and restoration has also been shown in recent works, which may encourage the better performance of the brain tumor segmentation problem. Inspired by this, we try to introduce the attention mechanism into the existing U-Net architecture to explore the effects of local important responses on this task. More specifically, we propose an end-to-end 2D brain tumor segmentation network, i.e., attention residual U-Net (AResU-Net), which simultaneously embeds attention mechanism and residual units into U-Net for the further performance improvement of brain tumor segmentation. AResU-Net adds a series of attention units among corresponding down-sampling and up-sampling processes, and it adaptively rescales features to effectively enhance local responses of down-sampling residual features utilized for the feature recovery of the following up-sampling process. We extensively evaluate AResU-Net on two MRI brain tumor segmentation benchmarks of BraTS 2017 and BraTS 2018 datasets. Experiment results illustrate that the proposed AResU-Net outperforms its baselines and achieves comparable performance with typical brain tumor segmentation methods.
Memory of virtual experiences: Role of immersion, emotion and sense of presence

Virtual Reality (VR) is increasingly being used as a tool to assess or train memory. Factors specific to VR, like immersion and sense of presence, are known to modulate episodic memory of virtual environment (VE). However, the impacts of their interaction have received less attention. The present study was designed to investigate this question. Moreover, it has been shown that episodic memory is influenced by emotion. We therefore also investigated the effect of emotion on memory of VE and its interaction with immersion and sense of presence. Immersion was manipulated using two types of device (Head-mounted Display or Computer Screen) and varying the 3D model quality (High or Low). Emotion was manipulated by presenting negative, positive and neutral stimuli in VE. 108 young adults, forming four experimental groups, took part in this experiment. Sense of presence and emotional experience in different virtual places were assessed using questionnaires, and memory of the stimuli viewed in these places, determined using a free recall task, was also assessed. Results showed that, unlike emotion, immersion had no direct impact on memory. However, an emotional enhancement of memory was found only in the HMD condition. In addition, sense of presence was greater in the HMD condition, but the link between sense of presence and memory performance was weak. Crucially, emotion and device type, but not image quality, increased the sense of presence. Implications of these findings for the elaboration of virtual experiences and the limitations of this study are discussed.
Deep Coarse-to-Fine Dense Light Field Reconstruction With Flexible Sampling and Geometry-Aware Fusion

A densely-sampled light field (LF) is highly desirable in various applications, such as 3-D reconstruction, post-capture refocusing and virtual reality. However, it is costly to acquire such data. Although many computational methods have been proposed to reconstruct a densely-sampled LF from a sparsely-sampled one, they still suffer from either low reconstruction quality, low computational efficiency, or the restriction on the regularity of the sampling pattern. To this end, we propose a novel learning-based method, which accepts sparsely-sampled LFs with irregular structures, and produces densely-sampled LFs with arbitrary angular resolution accurately and efficiently. We also propose a simple yet effective method for optimizing the sampling pattern. Our proposed method, an end-to-end trainable network, reconstructs a densely-sampled LF in a coarse-to-fine manner. Specifically, the coarse sub-aperture image (SAI) synthesis module first explores the scene geometry from an unstructured sparsely-sampled LF and leverages it to independently synthesize novel SAIs, in which a confidence-based blending strategy is proposed to fuse the information from different input SAIs, giving an intermediate densely-sampled LF. Then, the efficient LF refinement module learns the angular relationship within the intermediate result to recover the LF parallax structure. Comprehensive experimental evaluations demonstrate the superiority of our method on both real-world and synthetic LF images when compared with state-of-the-art methods. In addition, we illustrate the benefits and advantages of the proposed approach when applied in various LF-based applications, including image-based rendering and depth estimation enhancement. The code is available at https://github.com/jingjin25/LFASR-FS-GAF.
A Cloud Edge Collaborative Intelligence Method of Insulator String Defect Detection for Power IIoT

Using unmanned aerial vehicles (UAVs) for equipment condition monitoring is an important application of Industrial Internet of Things (IIoT), and the limited energy is the key factor to restrict the application of UAV. In order to reduce the computational load for intelligence computing of UAV, this article proposes a cloud edge collaborative intelligent method for object detection, and applies it to insulator string recognition defect detection in the power IIoT. First, the impact of the extremely large aspect ratio of object on the detection accuracy and the computational load is analyzed, then the cloud edge collaborative intelligent method for insulator string detection and defect recognition is presented, in which on the UAV side a low cost method is proposed for estimating possible directions of insulator strings, and on the cloud side, an effective method is proposed for insulator string defect detection. The experimental results show the effectiveness of the proposed algorithm. To the best knowledge of us, this article is the first work to analyze the impact of the extremely large aspect ratio of insulator string on the detection accuracy and the computational load.
HopeEDI: A Multilingual Hope Speech Detection Dataset for Equality, Diversity, and Inclusion

Over the past few years, systems have been developed to control online content and eliminate abusive, offensive or hate speech content. However, people in power sometimes misuse this form of censorship to obstruct the democratic right of freedom of speech. Therefore, it is imperative that research should take a positive reinforcement approach towards online content that is encouraging, positive and supportive contents. Until now, most studies have focused on solving this problem of negativity in the English language, though the problem is much more than just harmful content. Furthermore, it is multilingual as well. Thus, we have constructed a Hope Speech dataset for Equality, Diversity and Inclusion (HopeEDI) containing user-generated comments from the social media platform YouTube with 28,451, 20,198 and 10,705 comments in English, Tamil and Malayalam, respectively, manually labelled as containing hope speech or not. To our knowledge, this is the first research of its kind to annotate hope speech for equality, diversity and inclusion in a multilingual setting. We determined that the inter-annotator agreement of our dataset using Krippendorff’s alpha. Further, we created several baselines to benchmark the resulting dataset and the results have been expressed using precision, recall and F1-score. The dataset is publicly available for the research community. We hope that this resource will spur further research on encouraging inclusive and responsive speech that reinforces positiveness.
Finite Time Chaos Synchronization in Time-Delay Channel and Its Application to Satellite Image Encryption in OFDM Communication Systems

This paper proposes a finite time chaos synchronization approach for the secure communication of satellite imaging. To this end, chaotic oscillators are considered in both the transmitter and receiver ends to generate the chaotic encryption/decryption keys. To mitigate the non-negligible channel time-delay between the receiver and transmitter, we propose a robust controller design. The proposed approach is designed based on the Lyapunov stability theory and the finite-time synchronization concept to attain finite time synchronization in a time-delayed channel. By using synchronized chaotic keys, a physical-layer chaotic encryption scheme for transmitting the satellite images is designed in orthogonal frequency-division multiplexing wireless network. The proposed chaotic-based satellite image encryption/decryption system is validated using a numerical simulation study. Additionally, to analyse the robustness and demonstrate the efficiency of the proposed chaotic encryption structure, a set of security analysis tools such as histogram analysis, key space analysis, correlation test, information entropy and other statistical analysis were performed.
How to design the fair experimental classifier evaluation

Many researchers working on classification problems evaluate the quality of developed algorithms based on computer experiments. The conclusions drawn from them are usually supported by the statistical analysis and chosen experimental protocol. Statistical tests are widely used to confirm whether considered methods significantly outperform reference classifiers. Usually, the tests are applied to stratified datasets, which could raise the question of whether data folds used for classification are really randomly drawn and how the statistical analysis supports robust conclusions. Unfortunately, some scientists do not realize the real meaning of the obtained results and overinterpret them. They do not see that inappropriate use of such analytical tools may lead them into a trap. This paper aims to show the commonly used experimental protocols’ weaknesses and discuss if we really can trust in such evaluation methodology, if all presented evaluations are fair and if it is possible to manipulate the experimental results using well-known statistical evaluation methods. We will present that it is possible to choose only such results, confirming the experimenter’s expectation. We will try to show what could be done to avoid such likely unethical behavior. At the end of this work, we will formulate recommendations on improving an experimental protocol to design fair experimental classifier evaluation.
Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generation

A major obstacle to the wide-spread adoption of neural retrieval models is that they require large supervised training sets to surpass traditional term-based techniques, which are constructed from raw corpora.In this paper, we propose an approach to zero-shot learning for passage retrieval that uses synthetic question generation to close this gap.The question generation system is trained on general domain data, but is applied to documents in the targeted domain.This allows us to create arbitrarily large, yet noisy, question-passage relevance pairs that are domain specific.Furthermore, when this is coupled with a simple hybrid termneural model, first-stage retrieval performance can be improved further.Empirically, we show that this is an effective strategy for building neural passage retrieval models in the absence of large training corpora.Depending on the domain, this technique can even approach the accuracy of supervised models.
BreastNet18: A High Accuracy Fine-Tuned VGG16 Model Evaluated Using Ablation Study for Diagnosing Breast Cancer from Enhanced Mammography Images

Identification and treatment of breast cancer at an early stage can reduce mortality. Currently, mammography is the most widely used effective imaging technique in breast cancer detection. However, an erroneous mammogram based interpretation may result in false diagnosis rate, as distinguishing cancerous masses from adjacent tissue is often complex and error-prone.Six pre-trained and fine-tuned deep CNN architectures: VGG16, VGG19, MobileNetV2, ResNet50, DenseNet201, and InceptionV3 are evaluated to determine which model yields the best performance. We propose a BreastNet18 model using VGG16 as foundational base, since VGG16 performs with the highest accuracy. An ablation study is performed on BreastNet18, to evaluate its robustness and achieve the highest possible accuracy. Various image processing techniques with suitable parameter values are employed to remove artefacts and increase the image quality. A total dataset of 1442 preprocessed mammograms was augmented using seven augmentation techniques, resulting in a dataset of 11,536 images. To investigate possible overfitting issues, a k-fold cross validation is carried out. The model was then tested on noisy mammograms to evaluate its robustness. Results were compared with previous studies.Proposed BreastNet18 model performed best with a training accuracy of 96.72%, a validating accuracy of 97.91%, and a test accuracy of 98.02%. In contrast to this, VGGNet19 yielded test accuracy of 96.24%, MobileNetV2 77.84%, ResNet50 79.98%, DenseNet201 86.92%, and InceptionV3 76.87%.Our proposed approach based on image processing, transfer learning, fine-tuning, and ablation study has demonstrated a high correct breast cancer classification while dealing with a limited number of complex medical images.
Multi-Granularity Anchor-Contrastive Representation Learning for Semi-Supervised Skeleton-Based Action Recognition

In the semi-supervised skeleton-based action recognition task, obtaining more discriminative information from both labeled and unlabeled data is a challenging problem. As the current mainstream approach, contrastive learning can learn more representations of augmented data, which can be considered as the pretext task of action recognition. However, such a method still confronts three main limitations: 1) It usually learns global-granularity features that cannot well reflect the local motion information. 2) The positive/negative pairs are usually pre-defined, some of which are ambiguous. 3) It generally measures the distance between positive/negative pairs only within the same granularity, which neglects the contrasting between the cross-granularity positive and negative pairs. Toward these limitations, we propose a novel Multi-granularity Anchor-Contrastive representation Learning (dubbed as MAC-Learning) to learn multi-granularity representations by conducting inter- and intra-granularity contrastive pretext tasks on the learnable and structural-link skeletons among three types of granularities covering local, context, and global views. To avoid the disturbance of ambiguous pairs from noisy and outlier samples, we design a more reliable Multi-granularity Anchor-Contrastive Loss (dubbed as MAC-Loss) that measures the agreement/disagreement between high-confidence soft-positive/negative pairs based on the anchor graph instead of the hard-positive/negative pairs in the conventional contrastive loss. Extensive experiments on both NTU RGB+D and Northwestern-UCLA datasets show that the proposed MAC-Learning outperforms existing competitive methods in semi-supervised skeleton-based action recognition tasks.
Multipartite Einstein-Podolsky-Rosen steering sharing with separable states

The distribution of quantum correlations among remote users is a key procedure underlying many quantum information technologies. Einstein-Podolsky-Rosen steering, which is one kind of such a correlation stronger than entanglement, has been identified as a resource for secure quantum networks. We show that this resource can be established between two and even more distant parties by transmission of a system being separable from all the parties. For two-user scenarios, we design a protocol allowing one to distribute one-way Gaussian steering which can be used subsequently for one-sided device-independent (1sDI) quantum key distribution. Further, we extend the protocol to three-user scenarios to distribute richer steerability properties including one-to-multimode steering and collective steering which can be used for 1sDI quantum secret sharing. All the proposed protocols can be implemented with squeezed states, beam splitters, and displacements, and thus they can be readily realized experimentally. Our findings reveal that not only entanglement but even steering can be distributed via communication of a separable system. Viewed from a different perspective, the present protocols also demonstrate that one can switch multipartite states between different steerability classes by operations on parts of the states.