Reviewerly/exHarmony huggingface dataset:
A supervised multi-view feature selection method based on locally sparse regularization and block computing

With the increasing scale of obtained multi-view data, how to deal with large-scale multi-view data quickly and efficiently is a significant problem. In this paper, a novel supervised multi-view feature selection method based on locally sparse regularization and block computing is proposed to solve the problem. Specifically, the multi-view dataset is firstly divided into sub-blocks according to classes and views. Then with the aid of the Alternating Direction Method of Multipliers (ADMM), a sharing sub-model is proposed to perform feature selection on each class by integrating each view’s locally sparse regularizers and shared loss that makes all views share a common penalty and regresses samples to their labels. Finally, all the sharing sub-models are fused to form the final general additive feature selection model, in which each sub-block adjusts its corresponding variables to perform block-based feature selection. In the optimization process, the proposed model can be decomposed into multiple separate subproblems, and an efficient optimization algorithm is proposed to solve them quickly. The comparison experiments with several state-of-the-art feature selection methods show that the proposed method is superior in classification accuracy and training speed.
Federated Learning: A Distributed Shared Machine Learning Method

Federated learning (FL) is a distributed machine learning (ML) framework. In FL, multiple clients collaborate to solve traditional distributed ML problems under the coordination of the central server without sharing their local private data with others. This paper mainly sorts out FLs based on machine learning and deep learning. First of all, this paper introduces the development process, definition, architecture, and classification of FL and explains the concept of FL by comparing it with traditional distributed learning. Then, it describes typical problems of FL that need to be solved. On the basis of classical FL algorithms, several federated machine learning algorithms are briefly introduced, with emphasis on deep learning and classification and comparisons of those algorithms are carried out. Finally, this paper discusses possible future developments of FL based on deep learning.
An intuitionistic fuzzy data-driven product ranking model using sentiment analysis and multi-criteria decision-making

With the advent of web 2.0 websites, the impact of customers on each other's purchasing decisions has increased, and this has changed the decision-making procedure, attitude, and consumer purchase behavior. Along with increasing the volume of customers' reviews on sites, the need for a method that can be used to rank alternative products considering a set of product features and the customer comments related to these features on websites is essential. Many attempts have been made aimed at product ranking through online customer reviews (OCRs), which have their own shortcomings. These limitations occur during different stages, including focusing only on specified features on online shopping sites or extracting features based on term frequency. Other limitations are not paying attention to low repetition, but to only important features (in identifying features), not paying attention to neutral expressions including hesitations or uncertainty in consumers’ purchase decisions (in identifying the sentiment orientations of each review), using expert-based approaches to determine the weight of features (in determining the weight of features), and ranking merely based on the star rating and ignoring the valuable information in OCRs or not considering the robustness of decision-making and most effective features (in ranking alternatives). Accordingly, this study aims to narrow these gaps by proposing an integrated framework that combines sentiment analysis (SA) and multi-criteria decision-making (MCDM) techniques based on intuitionistic fuzzy sets (IFS). In the following, the developed hybrid model has been used in a real-world case to rank five mobile phone products using the OCRs on the Amazon site to illustrate the availability and utility of the proposed methodology. Sensitivity analysis has been performed to determine the most robust method and most effective features.
Joint Clustering and Discriminative Feature Alignment for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) aims to learn a classifier for the unlabeled target domain by leveraging knowledge from a labeled source domain with a different but related distribution. Many existing approaches typically learn a domain-invariant representation space by directly matching the marginal distributions of the two domains. However, they ignore exploring the underlying discriminative features of the target data and align the cross-domain discriminative features, which may lead to suboptimal performance. To tackle these two issues simultaneously, this paper presents a Joint Clustering and Discriminative Feature Alignment (JCDFA) approach for UDA, which is capable of naturally unifying the mining of discriminative features and the alignment of class-discriminative features into one single framework. Specifically, in order to mine the intrinsic discriminative information of the unlabeled target data, JCDFA jointly learns a shared encoding representation for two tasks: supervised classification of labeled source data, and discriminative clustering of unlabeled target data, where the classification of the source domain can guide the clustering learning of the target domain to locate the object category. We then conduct the cross-domain discriminative feature alignment by separately optimizing two new metrics: 1) an extended supervised contrastive learning, i.e., semi-supervised contrastive learning 2) an extended Maximum Mean Discrepancy (MMD), i.e., conditional MMD, explicitly minimizing the intra-class dispersion and maximizing the inter-class compactness. When these two procedures, i.e., discriminative features mining and alignment are integrated into one framework, they tend to benefit from each other to enhance the final performance from a cooperative learning perspective. Experiments are conducted on four real-world benchmarks (e.g., Office-31, ImageCLEF-DA, Office-Home and VisDA-C). All the results demonstrate that our JCDFA can obtain remarkable margins over state-of-the-art domain adaptation methods. Comprehensive ablation studies also verify the importance of each key component of our proposed algorithm and the effectiveness of combining two learning strategies into a framework.
Multi-Layered Deep Learning Features Fusion for Human Action Recognition

Human Action Recognition (HAR) is an active research topic in machine learning for the last few decades. Visual surveillance, robotics, and pedestrian detection are the main applications for action recognition. Computer visio... | Find, read and cite all the research you need on Tech Science Press
Augmented reality in retail: a systematic review of research foci and future research agenda

Purpose This paper synthesises peer-reviewed published journal articles on augmented reality in retail settings to ascertain the current foci of academic research in this nascent area and develop a conceptual framework to form the basis for a future research agenda. Design/methodology/approach Thematic analysis was conducted on a sample of 76 papers published between 1997 and 2020 identified through a systematic search of high quality peer-reviewed papers. Findings Three major research avenues and theoretical bases emerged: AR adoption-based factors with technology acceptance models, AR user experience design and features that influence consumer behaviour, and AR shopping experience and value theory. The resultant S-O-R-based conceptual framework highlights the functional and experiential elements needed for an effective consumer AR experience, which could be implemented by retailers seeking to engage consumers with an augmented shopping experience and make AR applications financially viable. Originality/value This is the first systematic literature review on AR in retail settings to include multiple disciplinary perspectives (HCI and marketing/management) and research methodologies.
The Ascent of Bitcoin: Bibliometric Analysis of Bitcoin Research

Bitcoin, as the first decentralized cryptocurrency, pioneers the cryptocurrency markets, both in terms of market capitalization and scientific interest. In this paper, we performed a comprehensive bibliometric study of the Bitcoin-related literature. Using the Scopus database, we created a sample that comprises 4495 documents written in the 2011–2020 period. Furthermore, we provided insights about dimensions such as the change in the number of publications over the course of years, the main research areas, types of published documents, most important platforms and sources of Bitcoin publications, highly cited studies, productive authors, author’s countries, and finally main funders of Bitcoin-related research. Lastly, our bibliometric study manifests the current state and future path of Bitcoin literature from distinct perspectives.
The effects of cultural dimensions on algorithmic news: How do cultural value orientations affect how people perceive algorithms?

How do cultural values influence/are influenced by algorithms? A comparative study was conducted between the United States (US) and the United Arab Emirates (UAE) to investigate how users in the different cultures perceive the features of chatbot-driven news and how they view ethical issues concerning chatbot journalism. Different models of chatbot news perception reveal that the acceptance of chatbots involves a cultural dimension as the algorithms reflect the values and interests of their constituencies. How users perceive chatbot news and how they consume and interact with the chatbots depend on the cultural and social contexts in which the interaction is taking place. Our results suggest the algorithms reflect cultural values and algorithms are implicitly situated in social contexts, mediated by cultural artifacts and activities. The results resonate with ongoing debates on whether and how algorithms reinforce cultural and social values implying the co-evolving nature of algorithms and humans.
Gradient Normalization for Generative Adversarial Networks

In this paper, we propose a novel normalization method called gradient normalization (GN) to tackle the training instability of Generative Adversarial Networks (GANs) caused by the sharp gradient space. Unlike existing work such as gradient penalty and spectral normalization, the proposed GN only imposes a hard 1-Lipschitz constraint on the discriminator function, which increases the capacity of the discriminator. Moreover, the proposed gradient normalization can be applied to different GAN architectures with little modification. Extensive experiments on four datasets show that GANs trained with gradient normalization outperform existing methods in terms of both Frechet Inception Distance and Inception Score.
SOLO: A Simple Framework for Instance Segmentation

Compared to many other dense prediction tasks, e.g., semantic segmentation, it is the arbitrary number of instances that has made instance segmentation much more challenging. In order to predict a mask for each instance, mainstream approaches either follow the "detect-then-segment" strategy (e.g., Mask R-CNN), or predict embedding vectors first then cluster pixels into individual instances. In this paper, we view the task of instance segmentation from a completely new perspective by introducing the notion of "instance categories", which assigns categories to each pixel within an instance according to the instance's location. With this notion, we propose segmenting objects by locations (SOLO), a simple, direct, and fast framework for instance segmentation with strong performance. We derive a few SOLO variants (e.g., Vanilla SOLO, Decoupled SOLO, Dynamic SOLO) following the basic principle. Our method directly maps a raw input image to the desired object categories and instance masks, eliminating the need for the grouping post-processing or the bounding box detection. Our approach achieves state-of-the-art results for instance segmentation in terms of both speed and accuracy, while being considerably simpler than the existing methods. Besides instance segmentation, our method yields state-of-the-art results in object detection (from our mask byproduct) and panoptic segmentation. We further demonstrate the flexibility and high-quality segmentation of SOLO by extending it to perform one-stage instance-level image matting. Code is available at: https://git.io/AdelaiDet.
SynthTIGER: Synthetic Text Image GEneratoR Towards Better Text Recognition Models

For successful scene text recognition (STR) models, synthetic text image generators have alleviated the lack of annotated text images from the real world. Specifically, they generate multiple text images with diverse backgrounds, font styles, and text shapes and enable STR models to learn visual patterns that might not be accessible from manually annotated data. In this paper, we introduce a new synthetic text image generator, SynthTIGER, by analyzing techniques used for text image synthesis and integrating effective ones under a single algorithm. Moreover, we propose two techniques that alleviate the long-tail problem in length and character distributions of training data. In our experiments, SynthTIGER achieves better STR performance than the combination of synthetic datasets, MJSynth (MJ) and SynthText (ST). Our ablation study demonstrates the benefits of using sub-components of SynthTIGER and the guideline on generating synthetic text images for STR models. Our implementation is publicly available at https://github.com/clovaai/synthtiger.
Fast-Racing: An Open-Source Strong Baseline for $\mathrm{SE}(3)$ Planning in Autonomous Drone Racing

With the autonomy of aerial robots advances in recent years, autonomous drone racing has drawn increasing attention. In a professional pilot competition, a skilled operator always controls the drone to agilely avoid obstacles in aggressive attitudes, for reaching the destination as fast as possible. Autonomous flight like elite pilots requires planning in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathrm{SE}(3)$</tex-math></inline-formula> , whose non-triviality and complexity hindering a convincing solution in our community by now. To bridge this gap, this letter proposes an open-source baseline, which includes a high-performance <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathrm{SE}(3)$</tex-math></inline-formula> planner and a challenging simulation platform tailored for drone racing. We specify the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathrm{SE}(3)$</tex-math></inline-formula> trajectory generation as a soft-penalty optimization problem, and speed up the solving process utilizing its underlying parallel structure. Moreover, to provide a testbed for challenging the planner, we develop delicate drone racing tracks which mimic real-world set-up and necessities planning in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathrm{SE}(3)$</tex-math></inline-formula> . Besides, we provide necessary system components such as common map interfaces and a baseline controller, to make our work plug-in-and-use. With our baseline, we hope to future foster the research of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathrm{SE}(3)$</tex-math></inline-formula> planning and the competition of autonomous drone racing.
A cascade reconstruction model with generalization ability evaluation for anomaly detection in videos

Anomaly detection plays an important role in surveillance video since it maintains public safety efficiently with low cost. In current works, anomaly detection methods based on reconstruction with deep learning has been extensively studied for the powerful representation capacity. These methods use convolutional neural networks to learn model for describing normality at training and detect anomalies according to reconstruction error at testing. However, excessive representation capacity of neural networks will also bring disadvantages to anomaly detection when it is powerful enough to reconstruct abnormal information. For this reason, we proposed two solutions; firstly, a cascade model which conducts pixel reconstruction followed by optical flow prediction is designed. The conversion from frame to optical flow learns the correlation between object appearance and motion, while pixel reconstruction enlarges the optical flow prediction error to conduct effective anomaly detection. Secondly, the generalization ability evaluation based on pseudo-anomaly is proposed, which is used to evaluate the ability of model to represent anomaly, thus selecting an optimal model for anomaly detection. The selected model achieves AUC 88.9% on Avenue, 82.6% on Ped1, 97.7% on Ped2, and 70.7% on ShanghaiTech datasets. Extensive ablation experiments have verified the effectiveness of our method. Code will be released at https://github.com/Xia-Chen/Cascade_Reconstruction.
DeeSCVHunter: A Deep Learning-Based Framework for Smart Contract Vulnerability Detection

Security attacks in smart contracts have drawn extensive attention due to the financial loss and erosion of trust caused by vulnerabilities. Even worse, smart contract is a tamper proof digital agreement and fixing bugs in it is difficult, so it is necessary for developers to detect security vulnerabilities in smart contract before deployment. Researchers have proposed several methods on smart contract vulnerability detection. However, despite the numerous vulnerability-finding tools, few of them have ideal detection performance because most of them rely on fixed rules, which is inefficient. In this paper, a modularized and systematic Deep Learning-based framework is proposed to automatically detect smart contracts vulnerability, called DeeSCVHunter. Particularly, we focus on two types of smart contract vulnerabilities: reentrancy and time dependence. And we propose a novel notion of Vulnerability Candidate Slice (VCS) to help models capture the key point of vulnerability. We conduct experiments on real-world dataset and the results prove the effectiveness of VCS, which leads to 25.76% improvement in F1-score at most. And extensive experiments also show that our proposed framework significantly outperforms state-of-the-art methods.
A novel cluster detection of COVID-19 patients and medical disease conditions using improved evolutionary clustering algorithm star

With the increasing number of samples, the manual clustering of COVID-19 and medical disease data samples becomes time-consuming and requires highly skilled labour. Recently, several algorithms have been used for clustering medical datasets deterministically; however, these definitions have not been effective in grouping and analysing medical diseases. The use of evolutionary clustering algorithms may help to effectively cluster these diseases. On this presumption, we improved the current evolutionary clustering algorithm star (ECA*), called iECA*, in three manners: (i) utilising the elbow method to find the correct number of clusters; (ii) cleaning and processing data as part of iECA* to apply it to multivariate and domain-theory datasets; (iii) using iECA* for real-world applications in clustering COVID-19 and medical disease datasets. Experiments were conducted to examine the performance of iECA* against state-of-the-art algorithms using performance and validation measures (validation measures, statistical benchmarking, and performance ranking framework). The results demonstrate three primary findings. First, iECA* was more effective than other algorithms in grouping the chosen medical disease datasets according to the cluster validation criteria. Second, iECA* exhibited the lower execution time and memory consumption for clustering all the datasets, compared to the current clustering methods analysed. Third, an operational framework was proposed to rate the effectiveness of iECA* against other algorithms in the datasets analysed, and the results indicated that iECA* exhibited the best performance in clustering all medical datasets. Further research is required on real-world multi-dimensional data containing complex knowledge fields for experimental verification of iECA* compared to evolutionary algorithms.
Minimum distance calculation using skeletal tracking for safe human-robot interaction

One of the main issues about safety in human-robot interaction is the distance calculation between robot and human. The acquisition of real-time distance information between robot and human allows the robot to modify its motion to ensure safety. This paper presents a real-time distance calculation framework that enables safe human-robot interaction. The human is tracked by an RGB-D sensor, and the joint positions of the human are acquired from the skeletal tracking algorithm. The representation of the robot and the human are formed by capsules to simplify and speed up the distance calculation. The distance calculation is implemented by using the GJK (Gilbert-Johnson-Keerthi) algorithm. The framework is validated by simulations and real experiments to show the efficiency of the framework. The results demonstrate the applicability of the framework for safe human-robot interaction applications.
Bayesian deep learning for reliable oral cancer image classification

In medical imaging, deep learning-based solutions have achieved state-of-the-art performance. However, reliability restricts the integration of deep learning into practical medical workflows since conventional deep learning frameworks cannot quantitatively assess model uncertainty. In this work, we propose to address this shortcoming by utilizing a Bayesian deep network capable of estimating uncertainty to assess oral cancer image classification reliability. We evaluate the model using a large intraoral cheek mucosa image dataset captured using our customized device from high-risk population to show that meaningful uncertainty information can be produced. In addition, our experiments show improved accuracy by uncertainty-informed referral. The accuracy of retained data reaches roughly 90% when referring either 10% of all cases or referring cases whose uncertainty value is greater than 0.3. The performance can be further improved by referring more patients. The experiments show the model is capable of identifying difficult cases needing further inspection.