Reviewerly/exHarmony huggingface dataset:
RGB-D Human Matting: A Real-World Benchmark Dataset and a Baseline Method

The last decade has witnessed an increasing exploration and development of human matting. However, existing matting works primarily focus on predicting better alpha mattes from RGB images. So far few efforts have been devoted to tackling human matting in real-world activity scenarios with RGB-D information. To this end, this paper concentrates on the RGB-D human matting task, and provides the first public RGB-D human matting benchmark dataset as well as a baseline method for deep learning-based RGB-D human matting. To support the research on RGB-D human matting, a new RGB-D human-matting dataset (HDM-2K) is collected and released, which contains 2,270 high-resolution human images in various real-world scenarios and the corresponding depth maps. Additionally, a baseline method for RGB-D human matting is further proposed, which automatically generates the alpha matte by jointly exploiting the spatial structure information in the depth map and detailed texture information in the RGB image. Finally, extensive experiments conducted on the HDM-2K dataset demonstrate that the depth maps are effective for the matting task and the proposed baseline method achieves promising performance on human matting.
An Optimization Framework Based on Deep Reinforcement Learning Approaches for Prism Blockchain

Blockchains have proven to provide a high level of performance in terms of security and reliability for various applications like cryptocurrencies and Internet-of-Things (IoT). Prism is a recent blockchain algorithm that achieves the physical limit on throughput and latency without compromising security. In recent days, reinforcement learning approaches are investigated in traditional blockchains, to improve performance. In this work, we apply Deep Reinforcement Learning (DRL) to one of the promising blockchain protocols, Prism, to optimize its performance. We propose a Deep Reinforcement Learning-based Prism Blockchain (DRLPB) scheme which dynamically optimizes the parameters of the Prism blockchain and helps in achieving a better performance. In DRLPB, we apply two widely used DRL algorithms, Dueling Deep Q Networks (DDQN) and Proximal Policy Optimization (PPO). This work presents a novel approach to applying DDQN and PPO to a blockchain protocol and comparing the performance. The DRLPB scheme adapts the Prism blockchain parameters to enhance the number of votes upto 84% more than Prism, while still preserving the security and latency performance guarantees of Prism.
Immersive Learning Frameworks: A Systematic Literature Review

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> This secondary study examines the literature on immersive learning frameworks and reviews their state of the art. Frameworks have been categorized according to their purpose. In addition, the elements that compose them were also categorized. Some gaps were identified and proposed as a research roadmap. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</i> Immersive technologies for education have been used for some years. Despite this, there are few works that aim to support the development and use of virtual environments for immersive learning. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Research questions:</i> This systematic review has the following main research question: What is the state of the art of immersive learning frameworks? In order to answer this question, secondary research questions were defined: 1) What definitions of immersive learning were adopted in primary studies? 2) What are the types of frameworks and how do they support immersive learning? 3) What are the elements that compose the frameworks? 4) What are the methods used to validate the frameworks? <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methodology:</i> As per the systematic review guidelines, this study followed a rigorous and replicable process for collecting and analyzing data. From 1721 articles identified in the search engines, 15 were selected after the inclusion and exclusion criteria. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Findings:</i> Most frameworks are models that investigate the causal relationship between immersive learning factors that influence learning outcomes. Although this theoretical aspect is important for the advancement of research, the area still lacks more practical frameworks that address more technical details and support development, as well as the use of immersive virtual environments by teachers and instructors.
Automated Molecular Subtyping of Breast Carcinoma Using Deep Learning Techniques

Molecular subtyping is an important procedure for prognosis and targeted therapy of breast carcinoma, the most common type of malignancy affecting women. Immunohistochemistry (IHC) analysis is the widely accepted method for molecular subtyping. It involves the assessment of the four molecular biomarkers namely estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and antigen Ki67 using appropriate antibody reagents. Conventionally, these biomarkers are assessed manually by a pathologist, who finally combines individual results to identify the molecular subtype. Molecular subtyping necessitates the status of all the four biomarkers together, and to the best of our knowledge, no such automated method exists. This paper proposes a novel deep learning framework for automatic molecular subtyping of breast cancer from IHC images.A modified LadderNet architecture is proposed to segment the immunopositive elements from ER, PR, HER2, and Ki67 biomarker slides. This architecture uses long skip connections to pass encoder feature space from different semantic levels to the decoder layers, allowing concurrent learning with multi-scale features. The entire architecture is an ensemble of multiple fully convolutional neural networks, and learning pathways are chosen adaptively based on input data. The segmentation stage is followed by a post-processing stage to quantify the extent of immunopositive elements to predict the final status for each biomarker.The performance of segmentation models for each IHC biomarker is evaluated qualitatively and quantitatively. Furthermore, the biomarker prediction results are also evaluated. The results obtained by our method are highly in concordance with manual assessment by pathologists.Accurate automated molecular subtyping can speed up this pathology procedure, reduce pathologists' workload and associated costs, and facilitate targeted treatment to obtain better outcomes.
SCTANet: A Spatial Attention-Guided CNN-Transformer Aggregation Network for Deep Face Image Super-Resolution

Numerous CNN-based algorithms have been proposed to reconstruct high-quality face images. However, the inability of convolution operation to model long-distance relationships limits the performance of the CNN-based methods. Moreover, in the high-resolution (HR) image reconstruction stage, with the well decoded feature representations, more efficient architecture design can be explored to synthesize pixel-level image details. In this work, we propose a spatial attention-guided CNN-Transformer aggregation network (SCTANet) for face image super-resolution (FSR) tasks. The core component in the deep feature extraction stage is the Hybrid Attention Aggregation (HAA) block. The HAA block has two parallel paths, one for the Residual Spatial Attention (RSA) block, the other for the Multi-scale Patch embedding and Spatial-attention Masked Transformer (MPSMT) block. The HAA block combines the strengths of CNN and transformer to effectively exploit both local and global information. For the reconstruction stage, we propose to use the Sub-pixel MLP-based Upsampling (SMU) module instead of the conventional CNN architecture. The SMU module promotes the reconstruction of pixel-level image details and reduces computational complexity. Extensive experiments on both synthetic and real-world face datasets demonstrate the superiority of our proposed SCTANet over state-of-the-art methods.
Graph Diffusion Convolutional Network for Skeleton Based Semantic Recognition of Two-Person Actions

Graph Convolutional Networks (GCNs) have successfully boosted skeleton-based human action recognition. However, existing GCN-based methods mostly cast the problem as separated person's action recognition while ignoring the interaction between the action initiator and the action responder, especially for the fundamental two-person interactive action recognition. It is still challenging to effectively take into account the intrinsic local-global clues of the two-person activity. Additionally, message passing in GCN depends on adjacency matrix, but skeleton-based human action recognition methods tend to calculate the adjacency matrix with the fixed natural skeleton connectivity. It means that messages can only travel along a fixed path at different layers of the network or in different actions, which greatly reduces the flexibility of the network. To this end, we propose a novel graph diffusion convolutional network for skeleton based semantic recognition of two-person actions by embedding the graph diffusion into GCNs. At technical fronts, we dynamically construct the adjacency matrix based on practical action information, so that we can guide the message propagation in a more meaningful way. Simultaneously, we introduce the frame importance calculation module to conduct dynamic convolution, so that we can avoid the negative effect caused by the traditional convolution, wherein the shared weights may fail to capture key frames or be affected by noisy frames. Besides, we comprehensively leverage the multidimensional features related to joints' local visual appearances, global spatial relationship and temporal coherency, and for different features, different metrics are designed to measure the similarity underlying the corresponding real physical law of the motions. Moreover, extensive experiments and comprehensive evaluations on four public large-scale datasets (NTU-RGB+D 60, NTU-RGB+D 120, Kinetics-Skeleton 400, and SBU-Interaction) demonstrate that our method outperforms the state-of-the-art methods.
$\mathcal {R}$-Tracing: Consortium Blockchain-Based Vehicle Reputation Management for Resistance to Malicious Attacks and Selfish Behaviors

Empowered by 5G communication technology, high-speed information exchange can be realized on the Internet of Vehicles (IoV) for intelligent transportation applications. Since most applications require vehicles to report perceived information to nearby base stations, how to defend against malicious attacks and selfish behaviors of vehicles in reporting activities has become an essential security issue. Some researchers have attempted to encourage vehicles to actively report information by establishing reputations for them, but these efforts have some shortcomings. In model design, they neither verify the basis and process of reputation evaluation nor pay attention to the diversity and intelligence of vehicle behaviors. In system construction, they fail to meet the management needs of multi-party participation and provide verifiable reputation management services. In this paper, we propose <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal{R}$</tex-math></inline-formula> -tracing, a consortium blockchain-based vehicle reputation management scheme. The main contribution of our work lies in three points. First, a vehicle reputation model is designed with a reward and punishment mechanism and a regular tax mechanism. Second, a vehicle reputation management system is constructed by multiple organizations, in which all tasks of reputation update are abstracted into three types of transactions. Finally, the effectiveness of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal{R}$</tex-math></inline-formula> -tracing is verified by extensive simulations running on a large-scale traffic scenario and performance evaluation on a prototype system. Compared with the typical linear reputation model, our model not only resists the usual malicious attack and selfish behavior but also effectively deals with the on-off attack and rational selfish behavior. In the throughput test, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal{R}$</tex-math></inline-formula> -tracing achieves 600 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tps</i> , outperforming two state-of-the-art schemes.
ALR-GAN: Adaptive Layout Refinement for Text-to-Image Synthesis

We propose a novel Text-to-Image Generation Network, Adaptive Layout Refinement Generative Adversarial Network (ALR-GAN), to adaptively refine the layout of synthesized images without any auxiliary information. The ALR-GAN includes an Adaptive Layout Refinement (ALR) module and a Layout Visual Refinement (LVR) loss. The ALR module aligns the layout structure (which refers to locations of objects and background) of a synthesized image with that of its corresponding real image. In ALR module, we proposed an Adaptive Layout Refinement (ALR) loss to balance the matching of hard and easy features, for more efficient layout structure matching. Based on the refined layout structure, the LVR loss further refines the visual representation within the layout area. Experimental results on two widely-used datasets show that ALR-GAN performs competitively at the Text-to-Image generation task.
Do Not Trust the Clouds Easily: The Insecurity of Content Security Policy Based on Object Storage

The content security policy (CSP) is a World Wide Web Consortium (W3C) standard, designed to prevent and mitigate security vulnerabilities, such as cross-site scripting (XSS) attacks, data injection attacks, and clickjacking attacks on websites. In this article, we present a newly discovered front-end Web attack that uses the current object storage services vulnerability of cloud vendors to bypass CSP. We selected the object storage services from two cloud vendors with the most users, i.e., Google and Amazon, to conduct systematic and large-scale research and analysis. Three cyberspace search engines are used to retrieve data, from which we analyze the consequence and damage range of this security breach. We focus on reporting four key aspects of this security breach: 1) how to use object storage services to bypass CSP; 2) analysis on the existence of such vulnerability in real-world websites; 3) analysis on the existing security vulnerabilities in current object storage services; and 4) the new strategy on object storage services that we propose to use to eliminate the discovered security threat.
The Gender Gap in Face Recognition Accuracy Is a Hairy Problem

It is broadly accepted that there is a “gender gap” in face recognition accuracy, with females having lower accuracy. However, relatively little is known about the cause(s) of this gender gap. We first demonstrate that female and male hairstyles have important differences that impact face recognition accuracy. In particular, variation in male facial hair contributes to a greater average difference in appearance between different male faces. We then demonstrate that when the data used to evaluate recognition accuracy is gender-balanced for how hairstyles occlude the face, the initially observed gender gap in accuracy largely disappears. We show this result for two different matchers, and for a Caucasian image dataset and an African-American dataset. Our results suggest that research on demographic variation in accuracy should include a check for balanced quality of the test data as part of the problem formulation. This new understanding of the causes of the gender gap in recognition accuracy will hopefully promote rational consideration of what might be done about it. To promote reproducible research, the matchers, attribute classifiers, and datasets used in this work are available to other researchers.
Multiresolution Fusion Convolutional Network for Open Set Human Activity Recognition

In recent years, sensor-based human activity recognition (HAR) technology has been the focus of extensive research and has been successfully applied to many aspects of people’s lives, but there are still some deficiencies. Most studies only distinguish daily activities and have low accuracy for easy confusing activities. In addition, many deep learning models only consider closed set HAR, but the real world contains unknown class (UC) activities that cannot be foreseen, which makes it challenging to apply these models to practice. In view of the above problems, this article proposes a multiresolution fusion convolution network (MRFC-Net) to cover the shortcoming that confusing activities are difficult to correctly identify, thus improving the accuracy of recognition. Furthermore, a multiresolution fusion convolution variational auto-encoder network (MRFC-VAE-Net) for open set HAR is proposed. According to the reconstruction loss of the network, the corresponding threshold is set to effectively classify the known and UC activities in the open set. At the same time, a rich data set named daily-abnormal activity of special group (DAASG) is constructed, which can be applied to the daily monitoring of special groups, such as prisoners and the elderly. Experiments and analyses are carried out on the wireless sensor data mining (WISDM), physical activity monitoring for aging people (PAMAP2) and DAASG data sets, to prove the effectiveness and superiority of the proposed networks.
Data Is What Data Does: Regulating Use, Harm, and Risk Instead of Sensitive Data

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PolarPose: Single-Stage Multi-Person Pose Estimation in Polar Coordinates

Regression based multi-person pose estimation receives increasing attention because of its promising potential in achieving realtime inference. However, the challenges in long-range 2D offset regression have restricted the regression accuracy, leading to a considerable performance gap compared with heatmap based methods. This paper tackles the challenge of long-range regression through simplifying the 2D offset regression to a classification task. We present a simple yet effective method, named PolarPose, to perform 2D regression in Polar coordinate. Through transforming the 2D offset regression in Cartesian coordinate to quantized orientation classification and 1D length estimation in the Polar coordinate, PolarPose effectively simplifies the regression task, making the framework easier to optimize. Moreover, to further boost the keypoint localization accuracy in PolarPose, we propose a multi-center regression to relieve the quantization error during orientation quantization. The resulting PolarPose framework is able to regress the keypoint offsets in a more reliable way, and achieves more accurate keypoint localization. Tested with the single-model and single-scale setting, PolarPose achieves the AP of 70.2% on COCO test-dev dataset, outperforming the state-of-the-art regression based methods. PolarPose also achieves promising efficiency, e.g., 71.5% AP at 21.5FPS and 68.5%AP at 24.2FPS and 65.5%AP at 27.2FPS on COCO val2017 dataset, faster than current state-of-the-art.
Confidential High-Performance Computing in the Public Cloud

High-performance computing (HPC) in the public cloud democratizes the supercomputing power that most users cannot afford to purchase and maintain. Researchers have studied its viability, performance, and usability. However, HPC in the cloud has a unique feature—users have to export data and computation to somewhat untrusted cloud platforms. Users will either fully trust cloud providers to protect from all kinds of attacks or keep sensitive assets in-house instead. With the recent deployment of the trusted execution environment (TEE) in the cloud, confidential computing for HPC in the cloud is becoming practical for addressing users’ privacy concerns. This article discusses the threat models, unique challenges, possible solutions, and significant gaps, focusing on TEE-based confidential HPC computing. We hope this discussion will improve the understanding of this new topic for HPC in the cloud and promote new research directions.
PV-YOLO: Lightweight YOLO for Photovoltaic Panel Fault Detection

The rapid development of the photovoltaic industry in recent years has made the efficient and accurate completion of photovoltaic operation and maintenance a major focus in recent studies. The key to photovoltaic operation and maintenance is the accurate multifault identification of photovoltaic panel images collected using drones. In this paper, PV-YOLO is proposed to replace YOLOX’s backbone network, CSPDarknet53, with a transformer-based PVTv2 network to obtain local connections between images and feature maps to extract more edge-detail features of similar faults. The CBAM attention mechanism is added to enhance the effective features and improve the detection accuracy of small objects. The label assignment mechanism is optimized, and the SIoU loss functionis used to improve the uneven distribution of samples and accelerate network convergence. Experiments on the dataset prove that this method is superior to the existing technology, as the highest mAP value is 92.56%. This value is 10.46% higher than that of YOLOX, and the mAP is optimal under the same parameter magnitude,proving the model’s effectiveness.Moreover, mAP is increased by over 10%, especially for small targets. In this paper, we implemented a lightweight design for the model, and proposes four models of different sizes to be-sized models that are suitable for different detection scenarios.
Classification of Trademark Distinctiveness using OpenAI GPT 3.5 model

This paper applies artificial intelligence (AI) technology from OpenAI, which provides generative pre-trained transformer (GPT) machine learning models, to the classification of the distinctiveness of potential trademarks. The paper demonstrates the accessibility which OpenAI’s engines provide and investigates some of the challenges faced when such general purpose models are used to address legal questions. Results showed a properly defined prompt without fine tuning allowed for useful classification of the distinctiveness of marks within a specific use case which permits over classification of marks as descriptive.
Are Virtual Reality Serious Video Games More Effective Than Web Video Games?

Virtual Reality is widely used for many educational purposes. However, despite the increasing trend in the use of this technology, it is not yet clear whether it is more effective for learning than other technologies such as traditional computer video games. This paper presents a serious video game to learn Scrum, a methodology widely used in the software industry. The game is available in mobile Virtual Reality and Web (using WebGL) formats. Both versions of the game are compared in terms of knowledge acquisition and motivation enhancement through a robust empirical study involving 289 students and instruments such as pre-post tests and a questionnaire. The obtained results suggest that both formats of the game are useful for acquiring knowledge and promoting aspects such as fun, motivation, and engagement. The results also suggest, strikingly, that there is no difference in terms of learning effectiveness between the two versions of the game.