Reviewerly/exHarmony huggingface dataset:
AG3line: Active grouping and geometry-gradient combined validation for fast line segment extraction

Line segment detectors based on local image domain passively fit a line segment from a set of pixels, but no constraint on line geometry is set in the grouping process. Therefore, unstable pixels, such as the pixels in grass, clouds, or weak gradient edges, may cause false positives and fractures. This paper proposes the detector named AG3line, which employs an efficient active grouping strategy. In AG3line, the pixel for the next grouping is calculated actively with the line geometry and it can even be accurate to one pixel. To reduce the fracture caused by unstable pixels, when the adjacent pixel cannot satisfy the grouping rules, the candidate pixels for the next grouping are expanded with the line geometry constraint. To furtherly control false positives, AG3line then validates and refines the line segments by exploiting both the line geometry and the alignment of gradient magnitude. When AG3line was evaluated utilizing the image dataset with the ground truth, it outperformed both the classical and the latest detectors.The implementation of AG3line is available at https://github.com/weidong-whu/AG3line.
A Multifactorial Optimization Framework Based on Adaptive Intertask Coordinate System

The searching ability of the population-based search algorithms strongly relies on the coordinate system on which they are implemented. However, the widely used coordinate systems in the existing multifactorial optimization (MFO) algorithms are still fixed and might not be suitable for various function landscapes with differential modalities, rotations, and dimensions; thus, the intertask knowledge transfer might not be efficient. Therefore, this article proposes a novel intertask knowledge transfer strategy for MFOs implemented upon an active coordinate system that is established on a common subspace of two search spaces. The proper coordinate system might identify some common modality in a proper subspace to some extent. In this article, to seek the intermediate subspace, we innovatively introduce the geodesic flow that starts from a subspace, reaching another subspace in unit time. A low-dimension intermediate subspace is drawn from a uniform distribution defined on the geodesic flow, and the corresponding coordinate system is given. The intertask trial generation method is applied to the individuals by first projecting them on the low-dimension subspace, which reveals the important invariant features of the multiple function landscapes. Since intermediate subspace is generated from the major eigenvectors of tasks' spaces, this model turns out to be intrinsically regularized by neglecting the minor and small eigenvalues. Therefore, the transfer strategy can alleviate the influence of noise led by redundant dimensions. The proposed method exhibits promising performance in the experiments.
AI-Based Semantic Multimedia Indexing and Retrieval for Social Media on Smartphones

To cope with the growing number of multimedia assets on smartphones and social media, an integrated approach for semantic indexing and retrieval is required. Here, we introduce a generic framework to fuse existing image and video analysis tools and algorithms into a unified semantic annotation, indexing and retrieval model resulting in a multimedia feature vector graph representing various levels of media content, media structures and media features. Utilizing artificial intelligence (AI) and machine learning (ML), these feature representations can provide accurate semantic indexing and retrieval. Here, we provide an overview of the generic multimedia analysis framework (GMAF) and the definition of a multimedia feature vector graph framework (MMFVGF). We also introduce AI4MMRA to detect differences, enhance semantics and refine weights in the feature vector graph. To address particular requirements on smartphones, we introduce an algorithm for fast indexing and retrieval of graph structures. Experiments to prove efficiency, effectiveness and quality of the algorithm are included. All in all, we describe a solution for highly flexible semantic indexing and retrieval that offers unique potential for applications such as social media or local applications on smartphones.
Autonomous and Privacy-preserving Energy Trading Based on Redactable Blockchain in Smart Grid

With the development of information and communication technologies in smart grid, peer-to-peer (P2P) energy trading for distributed energy resources (DER) has achieved an efficient two-way flow of information and power. The adoption of blockchain technology makes the P2P energy trading more secure and transparent. Considering that users with extra energy may be reluctant to participate in the energy trading due to privacy concerns, many researchers have focused on potential privacy issues. However, most of the existing works are built on top of a semi-decentralized energy blockchain in which only a few certified third-party nodes are authorized to manage and verify transactions - once these authorized nodes are attacked, the system will be threatened. In this paper, we use the Ciphertext Policy Attribute-Based Encryption (CP-ABE) scheme to establish a blockchain based P2P energy trading approach with privacy preservation, which allows peer nodes, including sellers and purchasers, to manage and verify transactions autonomously without needing any additional third-party nodes. In addition, we introduce the redactable blockchain technology into our scheme to ensure users can modify their sensitive information uploaded to the blockchain. Furthermore, we improve the CP-ABE scheme to provide low latency in the system. The experimental evaluations show that our scheme is efficient and practical.
Handling Imbalance Classification Virtual Screening Big Data Using Machine Learning Algorithms

Virtual screening is the most critical process in drug discovery, and it relies on machine learning to facilitate the screening process. It enables the discovery of molecules that bind to a specific protein to form a drug. Despite its benefits, virtual screening generates enormous data and suffers from drawbacks such as high dimensions and imbalance. This paper tackles data imbalance and aims to improve virtual screening accuracy, especially for a minority dataset. For a dataset identified without considering the data’s imbalanced nature, most classification methods tend to have high predictive accuracy for the majority category. However, the accuracy was significantly poor for the minority category. The paper proposes a K-mean algorithm coupled with Synthetic Minority Oversampling Technique (SMOTE) to overcome the problem of imbalanced datasets. The proposed algorithm is named as KSMOTE. Using KSMOTE, minority data can be identified at high accuracy and can be detected at high precision. A large set of experiments were implemented on Apache Spark using numeric PaDEL and fingerprint descriptors. The proposed solution was compared to both no-sampling method and SMOTE on the same datasets. Experimental results showed that the proposed solution outperformed other methods.
An Analysis of a KNN Perturbation Operator: An Application to the Binarization of Continuous Metaheuristics

The optimization methods and, in particular, metaheuristics must be constantly improved to reduce execution times, improve the results, and thus be able to address broader instances. In particular, addressing combinatorial optimization problems is critical in the areas of operational research and engineering. In this work, a perturbation operator is proposed which uses the k-nearest neighbors technique, and this is studied with the aim of improving the diversification and intensification properties of metaheuristic algorithms in their binary version. Random operators are designed to study the contribution of the perturbation operator. To verify the proposal, large instances of the well-known set covering problem are studied. Box plots, convergence charts, and the Wilcoxon statistical test are used to determine the operator contribution. Furthermore, a comparison is made using metaheuristic techniques that use general binarization mechanisms such as transfer functions or db-scan as binarization methods. The results obtained indicate that the KNN perturbation operator improves significantly the results.
Code‐level model checking in the software development workflow at Amazon Web Services

Abstract This article describes a style of applying symbolic model checking developed over the course of four years at Amazon Web Services (AWS). Lessons learned are drawn from proving properties of numerous C‐based systems, for example, custom hypervisors, encryption code, boot loaders, and an IoT operating system. Using our methodology, we find that we can prove the correctness of industrial low‐level C‐based systems with reasonable effort and predictability. Furthermore, AWS developers are increasingly writing their own formal specifications. As part of this effort, we have developed a CI system that allows integration of the proofs into standard development workflows and extended the proof tools to provide better feedback to users. All proofs discussed in this article are publicly available on GitHub.
Learning Self-Supervised Space-Time CNN for Fast Video Style Transfer

Style transfer on images has achieved significant advances in recent years, with the deep convolutional neural network (CNN). Directly applying image style transfer algorithms to each frame of a video independently often leads to flickering and unstable results. In this work, we present a self-supervised space-time convolutional neural network (CNN) based method for online video style transfer, named as VTNet, which is end-to-end trained from nearly unlimited unlabeled video data to produce temporally coherent stylized videos in real-time. Specifically, our VTNet transfer the style of a reference image to the source video frames, which is formed by the temporal prediction branch and the stylizing branch. The temporal prediction branch is used to capture discriminative spatiotemporal features for temporal consistency, pretrained in an adversarial manner from unlabeled video data. The stylizing branch is used to transfer the style image to a video frame with the guidance from the temporal prediction branch to ensure temporal consistency. To guide the training of VTNet, we introduce the style-coherence loss net (SCNet), which assembles the content loss, the style loss, and the new designed coherence loss. These losses are computed based on high-level features extracted from a pretrained VGG-16 network. The content loss is used to preserve high-level abstract contents of the input frames, and the style loss introduces new colors and patterns from the style image. Instead of using optical flow to explicitly redress the stylized video frames, we design the coherence loss to make the stylized video inherit the dynamics and motion patterns from the source video to remove temporal flickering. Extensive subjective and objective evaluations on various styles demonstrate that the proposed method achieves favorable results against the state-of-the-arts with high efficiency.
Human interaction recognition framework based on interacting body part attention

Human activity recognition in videos has been widely studied and has recently gained significant advances with deep learning approaches; however, it remains a challenging task. In this paper, we propose a novel framework that simultaneously considers both implicit and explicit representations of human interactions by fusing information of local image where the interaction actively occurred, primitive motion with the posture of individual subject’s body parts, and the co-occurrence of overall appearance change. Human interactions change, depending on how the body parts of each human interact with the other. The proposed method captures the subtle difference between different interactions using interacting body part attention. Semantically important body parts that interact with other objects are given more weight during feature representation. The combined feature of interacting body part attention-based individual representation and the co-occurrence descriptor of the full-body appearance change is fed into long short-term memory to model the temporal dynamics over time in a single framework. The experimental results on five widely used public datasets demonstrate the effectiveness of the proposed method to recognize human interactions from videos.
Accurate and dynamic 3D shape measurement with digital image correlation-assisted phase shifting

Abstract Phase-shifting profilometry (PSP) has been widely used in structured-light (SL) systems for three-dimensional (3D) shape measurements, but the speed of the PSP technique is limited by increased phase-shifting patterns. This paper proposes an accurate and dynamic 3D shape measurement method by projecting only four patterns, including three-step phase-shifting patterns and one speckle pattern. Three-step phase-shifting images are used to obtain the initial unwrapped phase map with phase ambiguity. Based on the principle of digital image correlation and multi-view geometry, the absolute phase can be recovered reliably without requiring any embedded features or pre-defined information of the object. To improve the measurement accuracy, the projector coordinate is used as the measuring coordinate to establish a novel stereo SL system model. By solving a least square solution using the triple-view information, accurate 3D surface data can be reconstructed. The experimental results indicate that the proposed method can perform high-speed and accurate 3D shape measurements with an accuracy of 10.64 μm, which is superior to conventional methods and has certain instructive significance for 3D profilometry and measurement engineering.
Deepsum++: Non-Local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images

Deep learning methods for super-resolution of a remote sensing scene from multiple unregistered low-resolution images have recently gained attention thanks to a challenge proposed by the European Space Agency. This paper presents an evolution of the winner of the challenge, showing how incorporating non-local information in a convolutional neural network allows to exploit self-similar patterns that provide enhanced regularization of the super-resolution problem. Experiments on the dataset of the challenge show improved performance over the state-of-the-art, which does not exploit non-local information.
Towards purchase prediction: A transaction-based setting and a graph-based method leveraging price information

Targeting at boosting business revenue, purchase prediction based on user behavior is crucial to e-commerce. However, it is not a well-explored topic due to a lack of relevant datasets. Specifically, no public dataset provides both price and discount information varying on time, which play an essential role in the user’s decision making. Besides, existing learn-to-rank methods cannot explicitly predict the purchase possibility for a specific user-item pair. In this paper, we propose a two-step graph-based model, where the graph model is applied in the first step to learn representations of both users and items over click-through data, and the second step is a classifier incorporating the price information of each transaction record. To evaluate the model performance, we propose a transaction-based framework focusing on the purchased items and their context clicks, which contain items that a user is interested in but fails to choose after comparison. Our experiments show that exploiting the price and discount information can significantly enhance prediction accuracy.
Self-representation and Class-Specificity Distribution Based Multi-View Clustering

Despite the promising performance for clustering, weighted tensor nuclear norm based multi-view subspace clustering needs to artificially predefine a weighted vector when shrinking all the singular values of tensor. It is very difficult to select a suitable weighted vector due to the complex and unknown distribution of data in real applications. Another limitation is that, the learned affinity matrix does not well exploit the cluster structure. To tackle these problems, we propose a novel multi-view subspace clustering method, called SCSD-MVC. The method adaptively allocates a suitable weighted vector to all singular values. The weighted vector well characterizes the significant difference between the singular values. Drawing the inspiration of one finding that ℓ1,2-norm can well characterize class-specificity distribution, which well encodes the cluster structure, we impose ℓ1,2-norm constraint on affinity matrix such that it can well characterize the cluster structure. Experimental results on several databases indicate that SCSD-MVC outperforms state-of-the-art self-representation based multi-view subspace clustering methods.
Gender Prediction for Instagram User Profiling using Deep Learning

Instagram creates new opportunities for small businesses to expand their markets. It can be used by entrepreneurs to reach potential customers and tell them about their products. Hence, knowing the user's demographics on Instagram is essential to convert those users into potential buyers. The demographics of social media users, such as gender, are vital for personalized advertising targeting. Based on the previous study, most of them studied to predict gender based on text analysis. This study aims to implement a deep learning method called Convolutional Neural Network (CNN) to predict gender on Instagram based on an Instagram profile image. Deep learning is a widely known technique to extract hidden patterns of a specific image. Thus, it can be useful for detecting gender based on the Instagram user's profile images. According to performance analysis, the prediction of gender on Instagram based on profile pictures using CNN resulted in an accuracy value of 70.11%. Compared with research related to gender prediction based on text analysis in previous studies, this study's accuracy results are not better than the four different studies in the state-of-the-art. However, this study's accuracy results are better than the two other studies in the state-of-the-art. Furthermore, this study proves that gender prediction based on image analysis using the CNN method can be performed exceptionally well, especially image analysis based on the image profile of Instagram users.
Rapid Validation of Whole-Slide Imaging for Primary Histopathology Diagnosis

Abstract Objectives The ongoing global severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic necessitates adaptations in the practice of surgical pathology at scale. Primary diagnosis by whole-slide imaging (WSI) is a key component that would aid departments in providing uninterrupted histopathology diagnosis and maintaining revenue streams from disruption. We sought to perform rapid validation of the use of WSI in primary diagnosis meeting recommendations of the College of American Pathologists guidelines. Methods Glass slides from clinically reported cases from 5 participating pathologists with a preset washout period were digitally scanned and reviewed in settings identical to typical reporting. Cases were classified as concordant or with minor or major disagreement with the original diagnosis. Randomized subsampling was performed, and mean concordance rates were calculated. Results In total, 171 cases were included and distributed equally among participants. For the group as a whole, the mean concordance rate in sampled cases (n = 90) was 83.6% counting all discrepancies and 94.6% counting only major disagreements. The mean pathologist concordance rate in sampled cases (n = 18) ranged from 90.49% to 97%. Conclusions We describe a novel double-blinded method for rapid validation of WSI for primary diagnosis. Our findings highlight the occurrence of a range of diagnostic reproducibility when deploying digital methods.
Employing reinforcement learning to enhance particle swarm optimization methods

Particle swarm optimization (PSO) is a well-known optimization algorithm that shows good performance in solving different optimization problems. However, PSO usually suffers from slow convergence. In this article, a reinforcement learning strategy is developed to enhance PSO in convergence by replacing the uniformly distributed random number in the updating function with a random number generated from a selected normal distribution. In the proposed method, the mean and standard deviation of the normal distribution are estimated from the current state of each individual through a policy net. The historic behaviour of the swarm group is used to update the policy net and guide the selection of parameters of the normal distribution. The proposed method is integrated into the original PSO and a state-of-the-art PSO, called the self-adaptive dynamic multi-swarm PSO (sDMS-PSO), and tested with numerical functions and engineering problems. The test results show that the convergence rate of PSO methods can be improved with the proposed reinforcement learning strategy.
Leveraging Slack Capacity in IaaS Contract Cloud Services

We study capacity planning in the context of Infrastructure as a Service (IaaS) cloud services. Concretely, we propose models for optimal pricing and virtual machine allocation to maximize revenue while maintaining service reliability. Our models are based on current pricing schemes used by predominant cloud services providers in the market. These providers frequently face slack capacity and low utilization in resources, so that they require better planning in the allocation of non‐revenue‐earning resources that could significantly affect profitability. In particular, we propose two novel hybrid service models, each combining two basic types of cloud services: reserved‐instance (contract) and on‐demand, and provide conditions under which they become dominant. We compare analytical properties of different service models, derive closed‐form solutions and structural results to assess the strategic impact of the different models, and conduct numerical analysis to test how optimal solutions change with respect to critical system parameters such as service rate, market size, and service reliability.