Reviewerly/exHarmony huggingface dataset:
A blockchain-based edge collaborative detection scheme for construction internet of things

For smart construction, it is increasingly necessary to develop innovative methods to automatically detect whether construction workers are wearing safety helmets or not. With the development of Internet of Things and object detection in deep learning, it is possible to enable object detection in end devices. In this paper, we propose a coordinated recognition scheme based on YOLOv3 to automatically detect safety helmets at construction sites. Our scheme provides coordinated recognition from multiple devices to obtain comprehensive detection results with multiple viewpoints, which improves the detection accuracy. Besides, to strengthen the security and reliability of detection devices, we propose a peer-to-peer cooperation scheme which is based on smart contract over blockchain. Our scheme ensures that only trusted devices can initiate or participate coordinated recognition tasks. All access and detection records are stored in the blockchain, which is auditable and traceable. Moreover, we propose a fair video sharing mechanism which encourages trusted devices to actively participate in the coordinated recognition tasks. Only participated nodes can access the shared detection videos from others, which forms an alliance of fair sharing with long term interest. The experimental results and analysis justify that our scheme performs well in terms of security and detection accuracy at few expense of a certain startup delay. Our framework can be employed for detecting other objects related to risks in smart construction.
On Unsupervised Methods for Medical Image Segmentation: Investigating Classic Approaches in Breast Cancer DCE-MRI

Unsupervised segmentation techniques, which do not require labeled data for training and can be more easily integrated into the clinical routine, represent a valid solution especially from a clinical feasibility perspective. Indeed, large-scale annotated datasets are not always available, undermining their immediate implementation and use in the clinic. Breast cancer is the most common cause of cancer death in women worldwide. In this study, breast lesion delineation in Dynamic Contrast Enhanced MRI (DCE-MRI) series was addressed by means of four popular unsupervised segmentation approaches: Split-and-Merge combined with Region Growing (SMRG), k-means, Fuzzy C-Means (FCM), and spatial FCM (sFCM). They represent well-established pattern recognition techniques that are still widely used in clinical research. Starting from the basic versions of these segmentation approaches, during our analysis, we identified the shortcomings of each of them, proposing improved versions, as well as developing ad hoc pre- and post-processing steps. The obtained experimental results, in terms of area-based—namely, Dice Index (DI), Jaccard Index (JI), Sensitivity, Specificity, False Positive Ratio (FPR), False Negative Ratio (FNR)—and distance-based metrics—Mean Absolute Distance (MAD), Maximum Distance (MaxD), Hausdorff Distance (HD)—encourage the use of unsupervised machine learning techniques in medical image segmentation. In particular, fuzzy clustering approaches (namely, FCM and sFCM) achieved the best performance. In fact, for area-based metrics, they obtained DI = 78.23% ± 6.50 (sFCM), JI = 65.90% ± 8.14 (sFCM), sensitivity = 77.84% ± 8.72 (FCM), specificity = 87.10% ± 8.24 (sFCM), FPR = 0.14 ± 0.12 (sFCM), and FNR = 0.22 ± 0.09 (sFCM). Concerning distance-based metrics, they obtained MAD = 1.37 ± 0.90 (sFCM), MaxD = 4.04 ± 2.87 (sFCM), and HD = 2.21 ± 0.43 (FCM). These experimental findings suggest that further research would be useful for advanced fuzzy logic techniques specifically tailored to medical image segmentation.
Heterogeneous graph neural networks with denoising for graph embeddings

With the increasing popularity of graph structures, Graph embedding, Which aims to project nodes into low dimensional space while preserving the topological structure information of graphs and the information of nodes themselves, Has attracted an increased amount of attention in recent years. most of the embedding methods based on heterogeneous graphs use a meta-path guided random walk to capture the semantic and structural correlation between different types of nodes in the graph. despite the success of the meta-path-guided heterogeneous graph embedding method, The choice of meta-path is still an open and challenging problem. the design of the meta-path scheme largely depends on domain knowledge. in this paper, We propose a heterogeneous graph neural network with denoising (HGNND) to handle the issue. considering that there are different types of nodes in heterogeneous graphs, And their features are usually distributed in different spaces, The HGNND projects features of different types of nodes into a common vector space. then, The whole heterogeneous graph is input into the graph neural network to aggregate the neighbor node information and capture the structure information of the heterogeneous graph. finally, The noise nodes that may affect the performance of the whole model are filtered out by the denoising operation. extensive experiments on three real-world datasets demonstrate that our proposed model achieves state-of-the-art performance, It further proves that the model can still effectively aggregate semantic information without using meta-paths.
A Comprehensive Review of Continuous-/Discontinuous-Time Fractional-Order Multidimensional Neural Networks

The dynamical study of continuous-/discontinuous-time fractional-order neural networks (FONNs) has been thoroughly explored, and several publications have been made available. This study is designed to give an exhaustive review of the dynamical studies of multidimensional FONNs in continuous/discontinuous time, including Hopfield NNs (HNNs), Cohen-Grossberg NNs, and bidirectional associative memory NNs, and similar models are considered in real ( [Formula: see text]), complex ( [Formula: see text]), quaternion ( [Formula: see text]), and octonion ( [Formula: see text]) fields. Since, in practice, delays are unavoidable, theoretical findings from multidimensional FONNs with various types of delays are thoroughly evaluated. Some required and adequate stability and synchronization requirements are also mentioned for fractional-order NNs without delays.
Keeping Safe Rust Safe with Galeed

Rust is a programming language that simultaneously offers high performance and strong security guarantees. Safe Rust (i.e., Rust code that does not use the unsafe keyword) is memory and type safe. However, these guarantees are violated when safe Rust interacts with unsafe code, most notably code written in other programming languages, including in legacy C/C++ applications that are incrementally deploying Rust. This is a significant problem as major applications such as Firefox, Chrome, AWS, Windows, and Linux have either deployed Rust or are exploring doing so. It is important to emphasize that unsafe code is not only unsafe itself, but also it breaks the safety guarantees of 'safe' Rust; e.g., a dangling pointer in a linked C/C++ library can access and overwrite memory allocated to Rust even when the Rust code is fully safe.
Augmented Reality in Food Promotion and Analysis: Review and Potentials

The paper presents the current state of using augmented reality (AR) in the sectors of food analysis and food promotion through products and orders. Based on an extensive literature review, 34 indicative augmented reality applications of various purposes, target audiences and implementations have been selected and presented. Applications are research-based, commercial, or oriented just for entertainment. Eight classification criteria are defined, especially for these applications, and used for presenting them, including content, context, execution scenario, markers, devices supported, implementation details and appeals based on evaluation, downloads, or sales. Additionally, 16 implementation and supportive platforms that have been used in the presented applications are described. The paper discusses advantages and limitations of current applications leading to proposals of further use of augmented reality in these food sectors towards a uniform handling of all parameters related to food processing, from production until consumption. These parameters include content use, design considerations, implementation issues, use of AR markers, etc.
Prediction on blockchain virtual currency transaction under long short-term memory model and deep belief network

The advantages of blockchain virtual currency are convenient circulation, low transaction costs, and decentralized power. At present, more and more investors have focused their investment in the blockchain virtual currency. Transaction data of blockchain virtual currency belongs to the financial time series, which is noisy and random, bringing challenges to the prediction of transaction trends. The improved deep belief network (IDBN) model and Echo state network (ESN) are constructed based on deep belief network (DBN) model to explore the long short-term memory (LSTM) model and the transaction prediction of blockchain virtual currency under the DBN and to improve the transaction prediction accuracy of the blockchain virtual currency. In addition, the parameters of IDBN model were optimized using particle swarm optimization (PSO) algorithm, which are verified with the transaction data of stocks and blockchain virtual currencies (Bitcoin, Bitcoin Cash, and Ethereum), and compared with other cash algorithms for analysis. The results show that the PSO-IDBN-based time series prediction model proposed in this study can be applied to predicting the high-latitude and high-complexity data, showing superior performance compared to the traditional time series prediction and other deep learning prediction methods.
Deep Hierarchical Representation of Point Cloud Videos via Spatio-Temporal Decomposition

In point cloud videos, point coordinates are irregular and unordered but point timestamps exhibit regularities and order. Grid-based networks for conventional video processing cannot be directly used to model raw point cloud videos. Therefore, in this work, we propose a point-based network that directly handles raw point cloud videos. First, to preserve the spatio-temporal local structure of point cloud videos, we design a point tube covering a local range along spatial and temporal dimensions. By progressively subsampling frames and points and enlarging the spatial radius as the point features are fed into higher-level layers, the point tube can capture video structure in a spatio-temporally hierarchical manner. Second, to reduce the impact of the spatial irregularity on temporal modeling, we decompose space and time when extracting point tube representations. Specifically, a spatial operation is employed to encode the local structure of each spatial region in a tube and a temporal operation is used to encode the dynamics of the spatial regions along the tube. Empirically, the proposed network shows strong performance on 3D action recognition, 4D semantic segmentation and scene flow estimation. Theoretically, we analyse the necessity to decompose space and time in point cloud video modeling and why the network outperforms existing methods.
Piracema.io: A rules-based tree model for phishing prediction

Phishing has been consolidating itself as a chronic problem due to its approach to exploiting the end-user, seen as the weakest factor. Through social engineering, the attacker seeks a carelessness of the human being to intercept sensitive data. Concomitantly, the richness in details makes it more difficult to mitigate the attack by most anti-phishing mechanisms since they are sustained in classifying a malicious page that lacks visual and textual details. This study aims to present a rule-based model approach, called piracema.io, for phishing prediction. Compared with other solutions proposed in the literature, the study believes that it has a different model that increases its efficiency in prediction as phishing presents greater richness based on page reputation-driven. In the light of the results obtained in logistic regression, the study detected static and dynamic features, considering relevance, relationship, and similarity between them. As a proof of concept, the study uses a statistical approach to evaluate the prediction modeling over the gradual depth and adherent acting strategies adopted to the proposal. As a result, the study discusses the quantitative and qualitative data obtained by the proposal, presenting contributions, threats, and limitations, as well as perspectives for future work for the continuity and improvement of the model in its current state.
Role of Blockchain Technology in Combating COVID-19 Crisis

The COVID-19 pandemic has negatively affected aspects of human life and various sectors, especially the health sector. These conditions led to the creation of new patterns of life that people have had to deal with to reduce the spread of the epidemic by committing to social distancing, among others. Therefore, governments and technological organizations had to take advantage of technological developments in the current era to overcome these challenges that were created by these conditions. In this paper, we will discuss the role of the blockchain in combating the COVID-19 crisis. Then we will review the recently recorded blockchain-based research proposals to control the COVID-19 pandemic. Finally, we will highlight the challenges of using blockchain to combat the COVID-19 pandemic and find solutions to mitigate these challenges.
Image-based visual servoing with depth estimation

For the depth estimation problem in the image-based visual servoing (IBVS) control, this paper proposes a new observer structure based on Kalman filter (KF) to recover the feature depth in real time. First, according to the number of states, two different mathematical models of the system are established. The first one is to extract the depth information from the Jacobian matrix as the state vector of the system. The other is to use the depth information and the coordinate point information of the two-dimensional image plane as the state vector of the system. The KF is used to estimate the unknown depth information of the system in real time. And an IBVS controller gain adjustment method for 6-degree-of-freedom (6-DOF) manipulator is obtained using fuzzy controller. This method can obtain the gain matrix by taking the depth and error information as the input of the fuzzy controller. Compared with the existing works, the proposed observer has less redundant motion while solving the Jacobian matrix depth estimation problem. At the same time, it will also be beneficial to reducing the time for the camera to reach the target. Conclusively, the experimental results of the 6-DOF robot with eye-in-hand configuration demonstrate the effectiveness and practicability of the proposed method.
A Novel Image Encryption Technique Based on Mobius Transformation

The nonlinear transformation concedes as S-box which is responsible for the certainty of contemporary block ciphers. Many kinds of S-boxes are planned by various authors in the literature. Construction of S-box with a powerful cryptographic analysis is the vital step in scheming block cipher. Through this paper, we give more powerful and worthy S-boxes and compare their characteristics with some previous S-boxes employed in cryptography. The algorithm program planned in this paper applies the action of projective general linear group PGL(2, GF(28)) on Galois field GF(28). The proposed S-boxes are constructed by using Mobius transformation and elements of Galois field. By using this approach, we will encrypt an image which is the preeminent application of S-boxes. These S-boxes offer a strong algebraic quality and powerful confusion capability. We have tested the strength of the proposed S-boxes by using different tests, BIC, SAC, DP, LP, and nonlinearity. Furthermore, we have applied these S-boxes in image encryption scheme. To check the strength of image encryption scheme, we have calculated contrast, entropy, correlation, energy, and homogeneity. The results assured that the proposed scheme is better. The advantage of this scheme is that we can secure our confidential image data during transmission.
Validation of XAI explanations for multivariate time series classification in the maritime domain

Due to the lack of explanation towards their internal mechanism, state-of-the-art deep learning-based classifiers are often considered as black-box models. For instance, in the maritime domain, models that classify the types of ships based on their trajectories and other features perform well, but give no further explanation for their predictions. To gain the trust of human operators responsible for critical decisions, the reason behind the classification is crucial. In this paper, we introduce explainable artificial intelligence (XAI) approaches to the task of classification of ship types. This supports decision-making by providing explanations in terms of the features contributing the most towards the prediction, along with their corresponding time intervals. In the case of the LIME explainer, we adapt the time-slice mapping technique (LimeforTime), while for Shapley additive explanations (SHAP) and path integrated gradient (PIG), we represent the relevance of each input variable to generate a heatmap as an explanation. In order to validate the XAI results, the existing perturbation and sequence analyses for classifiers of univariate time series data is employed for testing and evaluating the XAI explanations on multivariate time series. Furthermore, we introduce a novel evaluation technique to assess the quality of explanations yielded by the chosen XAI method.
Light-weight shadow detection via GCN-based annotation strategy and knowledge distillation

This paper discusses shadow detection problem, and proposes a light-weight network to achieve both accurate detection results and high computation efficiency. Firstly, we begin by presenting a compact network for real-time shadow detection. Secondly, to improve the performance of our light-weight networks, we propose two complementary and necessary strategies, i.e., the use of extra training data and knowledge distillation. Note that collecting a large amount of extra data will lead to the following challenge: shadow scenes is various, while annotating for those complex scenarios is time-consuming and expensive, sometimes even need expert help. To solve it, in the first step, we introduce a novel shadow annotation strategy based on graph convolutional networks, namely Anno-GCN, to provide extra training pairs, which obtains a complete shadow mask via only several annotation scribbles. In the second step, we can combine knowledge distillation with these sufficient GCN-labeled training data to further improve the performance of the light-weight network. Extensive experiments demonstrate that our method can achieve a state-of-the-art inference accuracy, computational efficiency, and generalizability with only about 2.97 M parameters.
Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data

The article focuses on solving an important problem of detecting suspicious reviewers in online discussions on social networks. We have concentrated on a special type of suspicious authors, on trolls. We have used methods of machine learning for generation of detection models to discriminate a troll reviewer from a common reviewer, but also methods of sentiment analysis to recognize the sentiment typical for troll's comments. The sentiment analysis can be provided also using machine learning or lexicon-based approach. We have used lexicon-based sentiment analysis for its better ability to detect a dictionary typical for troll authors. We have achieved Accuracy = 0.95 and F1 = 0.80 using sentiment analysis. The best results using machine learning methods were achieved by support vector machine, Accuracy = 0.986 and F1 = 0.988, using a dataset with the set of all selected attributes. We can conclude that detection model based on machine learning is more successful than lexicon-based sentiment analysis, but the difference in accuracy is not so large as in F1 measure.
From Conceptual Models to Knowledge Graphs: A Generic Model Transformation Platform

Semantic processing of conceptual models is a focus of research since several years, bridging the disciplines of knowledge-based systems, conceptual modeling, and model-driven software engineering. With the uptake of Knowledge Graphs, the research in this area gained further momentum. In this paper, we introduce a generic and extensible platform that enables the automated transformation of conceptual models into Knowledge Graphs. The platform can transform any model created by a state-of-the-art metamodeling platform (EMF and ADOxx) into standardized Knowledge Graph representations like GraphML, RDF, and OWL. In the paper at hand, we introduce our platform and evaluate it with a corpus of 5.000 UML models that we transform into Knowledge Graphs and subsequently exemplify the rich functionalities enabled by the graph structure by an automated detection of UML model smells.
Intention to Utilize Mobile Game-Based Learning in Nursing Education From Teachers’ Perspective: A Theory of Planned Behavior Approach

With the significant adverse impact of a pandemic like coronavirus disease 2019 (COVID-19) towards the teaching and learning experience, numerous educational institutions are looking for ways to improve their current practices and meet the challenges of this global threat. Despite the recommendations of applying Information and Communications Technologies (ICT) like video games to alleviate the negative effects of the pandemic, it is still not clear whether nursing teachers are willing to use it. Consequently, this study explored nursing teachers’ behavioral intention to employ mobile game-based learning (MGBL), and its relationship amongst core factors of the Theory of Planned Behavior (i.e., perceived behavioral control, subjective norms, and, attitude). Descriptive statistics revealed that most of the nursing teachers were female, a master’s degree holder, with an academic rank of instructor, not a licensed professional teacher, and a permanent and full-time employee at private institutions in the Visayas region of the Philippines. Moreover, they do not play mobile games and do not have an experience when it comes to MGBL. Lastly, Spearman’s correlation analysis revealed that Theory of Planned Behavior factors correlated positively with the intention of nursing teachers to use MGBL. This descriptive-exploratory study serves as a preliminary exploration of MGBL in nursing education and a future study will cover the prediction of nursing teachers’ intention to use MGBL in the classroom.
Quantum Machine Learning for Finance ICCAD Special Session Paper

Quantum computers are expected to surpass the computational capabilities of classical computers during this decade, and achieve disruptive impact on numerous industry sectors, particularly finance. In fact, finance is estimated to be the first industry sector to benefit from Quantum Computing not only in the medium and long terms, but even in the short term. This review paper presents the state of the art of quantum algorithms for financial applications, with particular focus to those use cases that can be solved via Machine Learning.