Reviewerly/exHarmony huggingface dataset:
F-CAM: Full Resolution Class Activation Maps via Guided Parametric Upscaling

Class Activation Mapping (CAM) methods have recently gained much attention for weakly-supervised object localization (WSOL) tasks. They allow for CNN visualization and interpretation without training on fully annotated image datasets. CAM methods are typically integrated within off-the-shelf CNN backbones, such as ResNet50. Due to convolution and pooling operations, these backbones yield low resolution CAMs with a down-scaling factor of up to 32, contributing to inaccurate localizations. Interpolation is required to restore full size CAMs, yet it does not consider the statistical properties of objects, such as color and texture, leading to activations with inconsistent boundaries, and inaccurate localizations. As an alternative, we introduce a generic method for parametric upscaling of CAMs that allows constructing accurate full resolution CAMs (FCAMs). In particular, we propose a trainable decoding architecture that can be connected to any CNN classifier to produce highly accurate CAM localizations. Given an original low resolution CAM, foreground and background pixels are randomly sampled to fine-tune the decoder. Additional priors such as image statistics and size constraints are also considered to expand and refine object boundaries. Extensive experiments <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , over three CNN backbones and six WSOL baselines on the CUB-200-2011 and OpenImages datasets, indicate that our F-CAM method yields a significant improvement in CAM localization accuracy. F-CAM performance is competitive with state-of-art WSOL methods, yet it requires fewer computations during inference.
Efficient Wavelet Boost Learning-Based Multi-stage Progressive Refinement Network for Underwater Image Enhancement

Raw underwater images suffer from low contrast and color cast due to wavelength-selective light scattering and attenuation. The distortions in color and luminance mainly appear at the low frequency while that in edge and texture are mainly at the high frequency. However, the hybrid distortions are difficult to simultaneously recover for existing methods, which mainly focus on the spatial domain. To tackle these issues, we propose a novel deep learning network to progressively refine underwater images by wavelet boost learning strategy (PRWNet), both in spatial and frequency domains. Specifically, the Multi-stage refinement strategy is adopted to efficiently enhance the spatial-varying degradations in a coarse-to-fine way. For each refinement procedure, Wavelet Boost Learning (WBL) unit decomposes the hierarchical features into high and low frequency and enhances them respectively by normalization and attention mechanisms. The modified boosting strategy is also adopted in WBL to further enhance the feature representations. Extensive experiments show that our method achieves state-of-the-art results. Our network is efficient and has the potential for real-world applications. The code is available at: https://github.com/huofushuo/PRWNet.
E-voting adoption in many countries: A literature review

Although the number of countries that have adopted e-voting has decreased lately, the number of academic publications on e-voting adoption has increased in the last two years. To date, there is no coherent narrative in the existing literature that explains the progress of the research on e-voting adoption. This article aims to answer the following research question: “How has research on the topic of e-voting adoption progressed over the last 15 years?” The article provides a semi-systematic review of 78 studies that were conducted from 2005 to 2020. In this article, I argue that although the studies on e-voting adoption are dominated by a single case study, by research in the United States, and by the positivist paradigm, scholars have employed the term “e-voting adoption” diversely and the research on e-voting adoption has evolved to address more specific research questions. Recommendations for the future agenda of research on e-voting adoption are also discussed.
U-Net combined with multi-scale attention mechanism for liver segmentation in CT images

The liver is an important organ that undertakes the metabolic function of the human body. Liver cancer has become one of the cancers with the highest mortality. In clinic, it is an important work to extract the liver region accurately before the diagnosis and treatment of liver lesions. However, manual liver segmentation is a time-consuming and boring process. Not only that, but the segmentation results usually varies from person to person due to different work experience. In order to assist in clinical automatic liver segmentation, this paper proposes a U-shaped network with multi-scale attention mechanism for liver organ segmentation in CT images, which is called MSA-UNet. Our method makes a new design of U-Net encoder, decoder, skip connection, and context transition structure. These structures greatly enhance the feature extraction ability of encoder and the efficiency of decoder to recover spatial location information. We have designed many experiments on publicly available datasets to show the effectiveness of MSA-UNet. Compared with some other advanced segmentation methods, MSA-UNet finally achieved the best segmentation effect, reaching 98.00% dice similarity coefficient (DSC) and 96.08% intersection over union (IOU).
Semantic-Guided Relation Propagation Network for Few-shot Action Recognition

Few-shot action recognition has drawn growing attention as it can recognize novel action classes by using only a few labeled samples. In this paper, we propose a novel semantic-guided relation propagation network (SRPN), which leverages semantic information together with visual information for few-shot action recognition. Different from most previous works that neglect semantic information in the labeled data, our SRPN directly utilizes the semantic label as an additional supervisory signal to improve the generalization ability of the network. Besides, we treat the relation of each visual-semantic pair as a relational node, and we use a graph convolutional network to model and propagate such sample relations across visual-semantic pairs, including both intra-class commonality and inter-class uniqueness, to guide the relation propagation in the graph. However, since videos contain crucial sequences and ordering information, we propose a novel spatial-temporal difference module, which can facilitate the network to enhance the visual feature learning ability at both feature level and granular level for videos. Extensive experiments conducted on several challenging benchmarks demonstrate that our SRPN outperforms several state-of-the-art methods with a significant margin.
PFFN

Recently, convolutional neural network (CNN) has been the core ingredient of modern models, triggering the surge of deep learning in super-resolution (SR). Despite the great success of these CNN-based methods which are prone to be deeper and heavier, it is impracticable to directly apply these methods for some low-budget devices due to the superfluous computational overhead. To alleviate this problem, a novel lightweight SR network named progressive feature fusion network (PFFN) is developed to seek for better balance between performance and running efficiency. Specifically, to fully exploit the feature maps, a novel progressive attention block (PAB) is proposed as the main building block of PFFN. The proposed PAB adopts several parallel but connected paths with pixel attention, which could significantly increase the receptive field of each layer, distill useful information and finally learn more discriminative feature representations. In PAB, a powerful dual attention module (DAM) is further incorporated to provide the channel and spatial attention mechanism in fairly lightweight manner. Besides, we construct a pretty concise and effective upsampling module with the help of multi-scale pixel attention, named MPAU. All of the above modules ensure the network can benefit from attention mechanism while still being lightweight enough. Furthermore, a novel training strategy following the cosine annealing learning scheme is proposed to maximize the representation ability of the model. Comprehensive experiments show that our PFFN achieves the best performance against all existing lightweight state-of-the-art SR methods with less number of parameters and even performs comparably to computationally expensive networks.
Single Image Dehazing via Semi-Supervised Domain Translation and Architecture Search

Data-driven methods have demonstrated great potential in single image dehazing, most of which are trained using the hazy images synthesized by the atmospheric scattering model. However, the model cannot accurately describe the complicated degradation caused by haze. At the same time, it is impractical to obtain the pixel-to-pixel aligned clear-scene counterparts of real-world hazy images for supervised training. In this letter, we formulate dehazing as a semi-supervised domain translation problem. For better generalization, two auxiliary domain translation tasks are designed to capture the properties of real-world haze and align synthetic hazy images to real-world ones to reduce the domain gap. Dehazing and the auxiliary tasks are conducted in shared latent spaces by a unified framework, and we use differential optimization to search the architectures of the framework. We evaluate the efficacy of the proposed work using one synthetic and three real-world benchmarks that cover the challenging cases in wild scenarios, and it outperforms state-of-the-art algorithms on these benchmarks. The benefits brought by auxiliary domain translation tasks and architecture search are also verified by ablation experiments.
Dynamic Gesture Contour Feature Extraction Method Using Residual Network Transfer Learning

The current dynamic gesture contour feature extraction method has the problems that the recognition rate of dynamic gesture contour feature and the recognition accuracy of dynamic gesture type are low, the recognition time is long, and comprehensive is poor. Therefore, we propose a dynamic gesture contour feature extraction method using residual network transfer learning. Sensors are used to integrate dynamic gesture information. The distance between the dynamic gesture and the acquisition device is detected by transfer learning, the dynamic gesture image is segmented, and the characteristic contour image is initialized. The residual network method is used to accurately identify the contour and texture features of dynamic gestures. Fusion processing weights are used to trace the contour features of dynamic gestures frame by frame, and the contour area of dynamic gestures is processed by gray and binarization to realize the extraction of contour features of dynamic gestures. The results show that the dynamic gesture contour feature recognition rate of the proposed method is 91%, the recognition time is 11.6 s, and the dynamic gesture type recognition accuracy rate is 92%. Therefore, this method can effectively improve the recognition rate and type recognition accuracy of dynamic gesture contour features and shorten the time for dynamic gesture contour feature recognition, and the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>F</mi> </math> value is 0.92, with good comprehensive performance.
Robustly Pre-Trained Neural Model for Direct Temporal Relation Extraction

In this work, we studied methods to identify direct (intra-sentence) temporal relationships between clinical events and temporal expressions. In particular, we compared several variants of BERT, sampling strategies to balance training instances, and different model sizes. Our results show that the large RoBERTa model has improved overall performance by 0.0864 absolute F measure (on the 1.00 scale), thus reducing the error rate by 24% relative to the previous state-of-the-art performance achieved with SVM.
Multi-objective Conflict-based Search for Multi-agent Path Finding

Conventional multi-agent path planners typically compute an ensemble of paths while optimizing a single objective, such as path length. However, many applications may require multiple objectives, say fuel consumption and completion time, to be simultaneously optimized during planning and these criteria may not be readily compared and sometimes lie in competition with each other. Naively applying existing multi-objective search algorithms to multi-agent path finding may prove to be inefficient as the size of the space of possible solutions, i.e., the Pareto-optimal set, can grow exponentially with the number of agents (the dimension of the search space). This article presents an approach named Multi-objective Conflict-based Search (MO-CBS) that bypasses this so-called curse of dimensionality by leveraging prior Conflict-based Search (CBS), a well-known algorithm for single-objective multi-agent path finding, and principles of dominance from multi-objective optimization literature. We prove that MO-CBS is able to compute the entire Pareto-optimal set. Our results show that MO-CBS can solve problem instances with hundreds of Pareto-optimal solutions which the standard multi-objective A* algorithms could not find within a bounded time.
AITransfer

Point cloud video provides a more immersive holographic virtual experience than conventional video services such as 360 degree video and virtual reality (VR) video. However, the existing network bandwidth and transmission technology can not carry real-time point cloud video streaming due to mass data volume, high processing overheads, and extremely bandwidth-consuming. Unlike previous approaches that extend the VR video streaming, we propose AITransfer, an AI-powered bandwidth-aware and adaptive transmission technique driven by extracting and transferring key point cloud features to reduce the bandwidth consumption and alleviate the computational pressure. AITransfer has two outstanding contributions, including (1) incorporating the dynamic network bandwidth into the design of an end-to-end architecture with two fundamental contents of feature extraction and reconstruction, and (2) employing an online adapter to sense the network bandwidth and match the optimal inference model. We conduct extensive experiments on the typical dataset and develop a case study to demonstrate the efficiency and effectiveness. The results show that AITransfer can provide more than 30.72 times compression ratio under the existing network environments.
CollisionIK: A Per-Instant Pose Optimization Method for Generating Robot Motions with Environment Collision Avoidance

In this work, we present a per-instant pose optimization method that can generate configurations that achieve specified pose or motion objectives as best as possible over a sequence of solutions, while also simultaneously avoiding collisions with static or dynamic obstacles in the environment. We cast our method as a weighted sum non-linear constrained optimization-based IK problem where each term in the objective function encodes a particular pose objective. We demonstrate how to effectively incorporate environment collision avoidance as a single term in this multi-objective, optimization-based IK structure, and provide solutions for how to spatially represent and organize external environments such that data can be efficiently passed to a real-time, performance-critical optimization loop. We demonstrate the effectiveness of our method by comparing it to various state-of-the-art methods in a testbed of simulation experiments and discuss the implications of our work based on our results.
Informative Class-Conditioned Feature Alignment for Unsupervised Domain Adaptation

The goal of unsupervised domain adaptation is to learn a task classifier that performs well for the unlabeled target domain by borrowing rich knowledge from a well-labeled source domain. Although remarkable breakthroughs have been achieved in learning transferable representation across domains, two bottlenecks remain to be further explored. First, many existing approaches focus primarily on the adaptation of the entire image, ignoring the limitation that not all features are transferable and informative for the object classification task. Second, the features of the two domains are typically aligned without considering the class labels; this can lead the resulting representations to be domain-invariant but non-discriminative to the category. To overcome the two issues, we present a novel Informative Class-Conditioned Feature Alignment (IC2FA) approach for UDA, which utilizes a twofold method: informative feature disentanglement and class-conditioned feature alignment, designed to address the above two challenges, respectively. More specifically, to surmount the first drawback, we cooperatively disentangle the two domains to obtain informative transferable features; here, Variational Information Bottleneck (VIB) is employed to encourage the learning of task-related semantic representations and suppress task-unrelated information. With regard to the second bottleneck, we optimize a new metric, termed Conditional Sliced Wasserstein Distance (CSWD), which explicitly estimates the intra-class discrepancy and the inter-class margin. The intra-class and inter-class CSWDs are minimized and maximized, respectively, to yield the domain-invariant discriminative features. IC2FA equips class-conditioned feature alignment with informative feature disentanglement and causes the two procedures to work cooperatively, which facilitates informative discriminative features adaptation. Extensive experimental results on three domain adaptation datasets confirm the superiority of IC2FA.
Multi-Prototype Few-shot Learning in Histopathology

The ability to adapt quickly to a new task or data distribution based on only a few examples is a challenge in AI and highly relevant for various domains. In digital pathology, slight variations in the scanning and staining process can lead to a distribution shift that provokes significant performance degradation of classical neural networks for tasks like tissue cartography where a reliable classification is essential. To overcome this problem, we propose a few-shot learning technique, specifically a k-means extension of Prototypical Networks, to train a highly flexible model that adapts to new, unseen scanner data based on only a few examples. We evaluate our approach on a multi-scanner database comprising a total amount of 356 annotated whole slide images digitized by a base scanner for training and additional five different scanners for evaluation. We verify our method’s effectiveness by comparing it to a classically trained benchmark and Prototypical Networks, both trained on the same data. A particular focus for us is to investigate the support set, used for adapting the proto-types, to provide recommended actions for digital pathology. The best results are obtained by employing multiple prototypes per class, calculated from a distributed support set, and domain-specific data augmentation. This results in 86.9 - 88.2% accuracy for a classification task of seven tissue classes on unseen, shifted data from the automated scanners, which is almost equal to the accuracy on the in-distribution data of 89.2%.
Tailored Reality: Perception-aware Scene Restructuring for Adaptive VR Navigation

In virtual reality (VR), the virtual scenes are pre-designed by creators. Our physical surroundings, however, comprise significantly varied sizes, layouts, and components. To bridge the gap and further enable natural navigation, recent solutions have been proposed to redirect users or recreate the virtual content. However, they suffer from either interrupted experience or distorted appearances. We present a novel VR-oriented algorithm that automatically restructures a given virtual scene for a user’s physical environment. Different from the previous methods, we introduce neither interrupted walking experience nor curved appearances. Instead, a perception-aware function optimizes our retargeting technique to preserve the fidelity of the virtual scene that appears in VR head-mounted displays. Besides geometric and topological properties, it emphasizes the unique first-person view perceptual factors in VR, such as dynamic visibility and objectwise relationships. We conduct both analytical experiments and subjective studies. The results demonstrate our system’s versatile capability and practicability for natural navigation in VR: It reduces the virtual space by 40% without statistical loss of perceptual identicality.
Deep Illumination-Aware Dehazing With Low-Light and Detail Enhancement

We present a novel dehazing framework for real-world images that contain both hazy and low-light areas. Dehazing and low-light enhancements are unified by using an illumination map that is estimated using a proposed convolutional neural network. The illumination map is then used as a component for three different tasks: atmospheric light estimation, transmission map estimation, and low-light enhancement, thereby enabling the solving of interrelated low-level vision problems simultaneously. To train the neural network to perform both dehazing and low-light enhancement, we synthesize hazy and low-light images from normal images. Experimental results demonstrate that the proposed method quantitatively and qualitatively outperforms state-of-the-art algorithms in real-world image dehazing.
Phishing Websites Detection by Using Optimized Stacking Ensemble Model

Phishing attacks are security attacks that do not affect only individuals’ or organizations’ websites but may affect Internet of Things (IoT) devices and networks. IoT environment is an exposed environment for such attacks. Attackers may use thingbots software for the dispersal of hidden junk emails that are not noticed by users. Machine and deep learning and other methods were used to design detection methods for these attacks. However, there is still a need to enhance detection accuracy. Optimization of an ensemble classification method for phishing website (PW) detection is proposed in this study. A Genetic Algorithm (GA) was used for the proposed method optimization by tuning several ensemble Machine Learning (ML) methods parameters, including Random Forest (RF), AdaBoost (AB), XGBoost (XGB), Bagging (BA), GradientBoost (GB), and LightGBM (LGBM). These were accomplished by ranking the optimized classifiers to pick out the best classifiers as a base for the proposed method. A PW dataset that is made up of 4898 PWs and 6157 legitimate websites (LWs) was used for this study's experiments. As a result, detection accuracy was enhanced and reached 97.16 percent.
User Experience Methods in Research and Practice

User experience (UX) researchers in technical communication (TC) and beyond still need a clear picture of the methods used to measure and evaluate UX. This article charts current UX methods through a systematic literature review of recent publications (2016–2018) and a survey of 52 UX practitioners in academia and industry. Our results indicate that contemporary UX research favors mixed methods, and that usability testing is especially popular in both published research and our survey results. This article presents these findings as a snapshot of contemporary research methods for UX.