Reviewerly/exHarmony huggingface dataset:
Auction Mechanisms in Cloud/Fog Computing Resource Allocation for Public Blockchain Networks

As an emerging decentralized secure data management platform, blockchain has gained much popularity recently. To maintain a canonical state of blockchain data record, proof-of-work based consensus protocols provide the nodes, referred to as miners, in the network with incentives for confirming new block of transactions through a process of “block mining” by solving a cryptographic puzzle. Under the circumstance of limited local computing resources, e.g., mobile devices, it is natural for rational miners, i.e., consensus nodes, to offload computational tasks for proof of work to the cloud/fog computing servers. Therefore, we focus on the trading between the cloud/fog computing service provider and miners, and propose an auction-based market model for efficient computing resource allocation. In particular, we consider a proof-of-work based blockchain network, which is constrained by the computing resource and deployed as an infrastructure for decentralized data management applications. Due to the competition among miners in the blockchain network, the allocative externalities are particularly taken into account when designing the auction mechanisms. Specifically, we consider two bidding schemes: the constant-demand scheme where each miner bids for a fixed quantity of resources, and the multi-demand scheme where the miners can submit their preferable demands and bids. For the constant-demand bidding scheme, we propose an auction mechanism that achieves optimal social welfare. In the multi-demand bidding scheme, the social welfare maximization problem is NP-hard. Therefore, we design an approximate algorithm which guarantees the truthfulness, individual rationality and computational efficiency. Through extensive simulations, we show that our proposed auction mechanisms with the two bidding schemes can efficiently maximize the social welfare of the blockchain network and provide effective strategies for the cloud/fog computing service provider.
Large group activity security risk assessment and risk early warning based on random forest algorithm

With the continuous development of artificial intelligence, machine learning, the necessary way to achieve artificial intelligence, is also constantly improving, of which deep learning is one of the contents. The purpose of this paper is to evaluate and warn the security risk of large-scale group activities based on the random forest algorithm. This paper uses the methods of calculating the importance of the random forest algorithm to variables and the calculation formula of the weight of the security risk index, and combining the model parameters of the random forest algorithm The optimization experiment and the random forest model training experiment are used for risk analysis, and the classification accuracy rate reaches a maximum of 0.86, which leads to the conclusion that the random forest algorithm has good predictive ability in the risk assessment of large-scale group activities. This article takes a certain international youth environmental protection festival as an example for analysis, and better verifies the feasibility and effectiveness of this article.
Validation of the Crystallography Open Database using the Crystallographic Information Framework

Data curation practices of the Crystallography Open Database (COD) are described with additional focus being placed on the formal validation using the Crystallographic Information Framework (CIF). The cif_validate program, capable of validating CIF files against both the DDL1 and the DDLm dictionaries, is presented and used to process the entirety of the COD. Validation results collected from over 450 000 CIF files are demonstrated to be a useful resource in the data maintenance process as well as the development of the underlying ontologies. A set of programs intended to aid in the dictionary migration from DDL1 to DDLm is also presented.
Real-Time Traffic Flow Parameter Estimation From UAV Video Based on Ensemble Classifier and Optical Flow

Recently, the availability of unmanned aerial vehicle (UAV) opens up new opportunities for smart transportation applications, such as automatic traffic data collection. In such a trend, detecting vehicles and extracting traffic parameters from UAV video in a fast and accurate manner is becoming crucial in many prospective applications. However, from the methodological perspective, several limitations have to be addressed before the actual implementation of UAV. This paper proposes a new and complete analysis framework for traffic flow parameter estimation from UAV video. This framework addresses the well-concerned issues on UAV's irregular ego-motion, low estimation accuracy in dense traffic situation, and high computational complexity by designing and integrating four stages. In the first two stages an ensemble classifier (Haar cascade + convolutional neural network) is developed for vehicle detection, and in the last two stages a robust traffic flow parameter estimation method is developed based on optical flow and traffic flow theory. The proposed ensemble classifier is demonstrated to outperform the state-of-the-art vehicle detectors that designed for UAV-based vehicle detection. Traffic flow parameter estimations in both free flow and congested traffic conditions are evaluated, and the results turn out to be very encouraging. The dataset with 20,000 image samples used in this study is publicly accessible for benchmarking at http://www.uwstarlab.org/research.html.
PaDE: An enhanced Differential Evolution algorithm with novel control parameter adaptation schemes for numerical optimization

Differential Evolution (DE) variants have been proven to be excellent algorithms in tackling real-parameter single objective numerical optimization because they have secured the front ranks of these competitions for many years. Nevertheless, there are still some weaknesses, e.g. (1) improper control parameter adaptation schemes; and (2) defect in a given mutation strategy., existing in some state-of-the-art DE variants, which may result in slow convergence and worse optimization performance. Therefore, in this paper, a novel Parameter adaptive DE (PaDE) is proposed to tackle the above mentioned weaknesses and the PaDE algorithm has three advantages: (1) A grouping strategy with novel adaptation scheme for Cr is proposed to tackle the improper adaptation schemes of Cr in some state-of-the-art DE variants; (2) A novel parabolic population size reduction scheme is proposed to tackle the weakness in linear population size reduction scheme; (3) An enhanced time stamp based mutation strategy is proposed to tackle the weakness in a former mutation strategy. The novel PaDE algorithm is verified under 58 benchmarks from two Congress on Evolutionary Computation (CEC) Competition test suites on real-parameter single objective numerical optimization, and experiment results show that the proposed PaDE algorithm is competitive with the other state-of-the-art DE variants.
Lightweight and Physically Secure Anonymous Mutual Authentication Protocol for Real-Time Data Access in Industrial Wireless Sensor Networks

Industrial wireless sensor network (IWSN) is an emerging class of a generalized WSN having constraints of energy consumption, coverage, connectivity, and security. However, security and privacy is one of the major challenges in IWSN as the nodes are connected to Internet and usually located in an unattended environment with minimum human interventions. In IWSN, there is a fundamental requirement for a user to access the real-time information directly from the designated sensor nodes. This task demands to have a user authentication protocol. To satisfy this requirement, this paper proposes a lightweight and privacy-preserving mutual user authentication protocol in which only the user with a trusted device has the right to access the IWSN. Therefore, in the proposed scheme, we considered the physical layer security of the sensor nodes. We show that the proposed scheme ensures security even if a sensor node is captured by an adversary. The proposed protocol uses the lightweight cryptographic primitives, such as one way cryptographic hash function, physically unclonable function, and bitwise exclusive operations. Security and performance analysis shows that the proposed scheme is secure, and is efficient for the resource-constrained sensing devices in IWSN.
Learning Active Contour Models for Medical Image Segmentation

Image segmentation is an important step in medical image processing and has been widely studied and developed for refinement of clinical analysis and applications. New models based on deep learning have improved results but are restricted to pixel-wise fitting of the segmentation map. Our aim was to tackle this limitation by developing a new model based on deep learning which takes into account the area inside as well as outside the region of interest as well as the size of boundaries during learning. Specifically, we propose a new loss function which incorporates area and size information and integrates this into a dense deep learning model. We evaluated our approach on a dataset of more than 2,000 cardiac MRI scans. Our results show that the proposed loss function outperforms other mainstream loss function Cross-entropy on two common segmentation networks. Our loss function is robust while using different hyperparameter lambda.
Fourier-space Diffractive Deep Neural Network

In this Letter we propose the Fourier-space diffractive deep neural network (F-D^{2}NN) for all-optical image processing that performs advanced computer vision tasks at the speed of light. The F-D^{2}NN is achieved by placing the extremely compact diffractive modulation layers at the Fourier plane or both Fourier and imaging planes of an optical system, where the optical nonlinearity is introduced from ferroelectric thin films. We demonstrated that F-D^{2}NN can be trained with deep learning algorithms for all-optical saliency detection and high-accuracy object classification.
A novel selective naïve Bayes algorithm

Naïve Bayes is one of the most popular data mining algorithms. Its efficiency comes from the assumption of attribute independence, although this might be violated in many real-world data sets. Many efforts have been done to mitigate the assumption, among which attribute selection is an important approach. However, conventional efforts to perform attribute selection in naïve Bayes suffer from heavy computational overhead. This paper proposes an efficient selective naïve Bayes algorithm, which adopts only some of the attributes to construct selective naïve Bayes models. These models are built in such a way that each one is a trivial extension of another. The most predictive selective naïve Bayes model can be selected by the measures of incremental leave-one-out cross validation. As a result, attributes can be selected by efficient model selection. Empirical results demonstrate that the selective naïve Bayes shows superior classification accuracy, yet at the same time maintains the simplicity and efficiency.
Identifying Facemask-Wearing Condition Using Image Super-Resolution with Classification Network to Prevent COVID-19

The rapid worldwide spread of Coronavirus Disease 2019 (COVID-19) has resulted in a global pandemic. Correct facemask wearing is valuable for infectious disease control, but the effectiveness of facemasks has been diminished, mostly due to improper wearing. However, there have not been any published reports on the automatic identification of facemask-wearing conditions. In this study, we develop a new facemask-wearing condition identification method by combining image super-resolution and classification networks (SRCNet), which quantifies a three-category classification problem based on unconstrained 2D facial images. The proposed algorithm contains four main steps: Image pre-processing, facial detection and cropping, image super-resolution, and facemask-wearing condition identification. Our method was trained and evaluated on the public dataset Medical Masks Dataset containing 3835 images with 671 images of no facemask-wearing, 134 images of incorrect facemask-wearing, and 3030 images of correct facemask-wearing. Finally, the proposed SRCNet achieved 98.70% accuracy and outperformed traditional end-to-end image classification methods using deep learning without image super-resolution by over 1.5% in kappa. Our findings indicate that the proposed SRCNet can achieve high-accuracy identification of facemask-wearing conditions, thus having potential applications in epidemic prevention involving COVID-19.
Evaluating the factors that influence blockchain adoption in the freight logistics industry

This study proposes a technology- organization- environment (TOE) theoretical framework of critical factors that influence the successful adoption of blockchain technologies in the freight logistics industry and prioritize them using the analytic network process (ANP). The research findings indicate that ‘availability of specific blockchain tools’, ‘infrastructural facility’, and ‘government policy and support’ are the three topmost ranked significant factors that influence the adoption of blockchains in the freight logistics industry. These findings will aid government agencies, freight logistics firms and blockchain service providers in strategizing for the advancement and successful adoption of blockchain and improvement of overall organizational competitiveness.
SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth

A key limitation of deep convolutional neural networks (DCNN) based image segmentation methods is the lack of generalizability. Manually traced training images are typically required when segmenting organs in a new imaging modality or from distinct disease cohort. The manual efforts can be alleviated if the manually traced images in one imaging modality (e.g., MRI) are able to train a segmentation network for another imaging modality (e.g., CT). In this paper, we propose an end-to-end synthetic segmentation network (SynSeg-Net) to train a segmentation network for a target imaging modality without having manual labels. SynSeg-Net is trained by using (1) unpaired intensity images from source and target modalities, and (2) manual labels only from source modality. SynSeg-Net is enabled by the recent advances of cycle generative adversarial networks (CycleGAN) and DCNN. We evaluate the performance of the SynSeg-Net on two experiments: (1) MRI to CT splenomegaly synthetic segmentation for abdominal images, and (2) CT to MRI total intracranial volume synthetic segmentation (TICV) for brain images. The proposed end-to-end approach achieved superior performance to two stage methods. Moreover, the SynSeg-Net achieved comparable performance to the traditional segmentation network using target modality labels in certain scenarios. The source code of SynSeg-Net is publicly available 2.
Learning Individual Styles of Conversational Gesture

Human speech is often accompanied by hand and arm gestures. We present a method for cross-modal translation from "in-the-wild" monologue speech of a single speaker to their conversational gesture motion. We train on unlabeled videos for which we only have noisy pseudo ground truth from an automatic pose detection system. Our proposed model significantly outperforms baseline methods in a quantitative comparison. To support research toward obtaining a computational understanding of the relationship between gesture and speech, we release a large video dataset of person-specific gestures.
Dissecting Ponzi schemes on Ethereum: Identification, analysis, and impact

Ponzi schemes are financial frauds which lure users under the promise of high profits. Actually, users are repaid only with the investments of new users joining the scheme: consequently, a Ponzi scheme implodes soon after users stop joining it. Originated in the offline world 150 years ago, Ponzi schemes have since then migrated to the digital world, approaching first the Web, and more recently hanging over cryptocurrencies like Bitcoin. Smart contract platforms like Ethereum have provided a new opportunity for scammers, who have now the possibility of creating “trustworthy” frauds that still make users lose money, but at least are guaranteed to execute “correctly”. We present a comprehensive survey of Ponzi schemes on Ethereum, analysing their behaviour and their impact from various viewpoints.
The effect of Augmented Reality Technology on middle school students' achievements and attitudes towards science education

This study aims to investigate the impact of learning materials developed with augmented reality (AR) technology on middle school students' achievement and attitudes towards the course, and to determine their attitudes towards AR applications. In this study, a quasi-experimental design was used in which intact classrooms at two different schools, consisting of a total of one hundred 7th grade middle school students, were randomly assigned to either the experimental or control group. The experimental group completed the “Solar System and Beyond” module of their science course using AR technology, while the control group completed the same module using traditional methods and textbooks. Students in the experimental group were found to have higher levels of achievement and more positive attitudes towards the course than those in the control group. In addition, the results revealed that the students were pleased and wanted to continue using AR applications in the future. They also showed no signs of anxiety when using AR applications. In addition, it was found that academic achievements and attitudes of the students in the experimental group showed a positive, significant and intermediate correlation.
Batch DropBlock Network for Person Re-Identification and Beyond

Since the person re-identification task often suffers from the problem of pose changes and occlusions, some attentive local features are often suppressed when training CNNs. In this paper, we propose the Batch DropBlock (BDB) Network which is a two branch network composed of a conventional ResNet-50 as the global branch and a feature dropping branch.The global branch encodes the global salient representations.Meanwhile, the feature dropping branch consists of an attentive feature learning module called Batch DropBlock, which randomly drops the same region of all input feature maps in a batch to reinforce the attentive feature learning of local regions.The network then concatenates features from both branches and provides a more comprehensive and spatially distributed feature representation. Albeit simple, our method achieves state-of-the-art on person re-identification and it is also applicable to general metric learning tasks. For instance, we achieve 76.4% Rank-1 accuracy on the CUHK03-Detect dataset and 83.0% Recall-1 score on the Stanford Online Products dataset, outperforming the existed works by a large margin (more than 6%).
Deep Learning for Whole Slide Image Analysis: An Overview

The widespread adoption of whole slide imaging has increased the demand for effective and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision, showcasing significant improvements over previous methodologies on visual understanding. However, whole slide images have billions of pixels and suffer from high morphological heterogeneity as well as from different types of artefacts. Collectively, these impede the conventional use of deep learning. For the clinical translation of deep learning solutions to become a reality, these challenges need to be addressed. In this paper, we review work on the interdisciplinary attempt of training deep neural networks using whole slide images, and highlight the different ideas underlying these methodologies.
Ethics of instantaneous contact tracing using mobile phone apps in the control of the COVID-19 pandemic

In this paper we discuss ethical implications of the use of mobile phone apps in the control of the COVID-19 pandemic. Contact tracing is a well-established feature of public health practice during infectious disease outbreaks and epidemics. However, the high proportion of pre-symptomatic transmission in COVID-19 means that standard contact tracing methods are too slow to stop the progression of infection through the population. To address this problem, many countries around the world have deployed or are developing mobile phone apps capable of supporting instantaneous contact tracing. Informed by the on-going mapping of ‘proximity events’ these apps are intended both to inform public health policy and to provide alerts to individuals who have been in contact with a person with the infection. The proposed use of mobile phone data for ‘intelligent physical distancing’ in such contexts raises a number of important ethical questions. In our paper, we outline some ethical considerations that need to be addressed in any deployment of this kind of approach as part of a multidimensional public health response. We also, briefly, explore the implications for its use in future infectious disease outbreaks.
Healthcare Data Breaches: Insights and Implications

The Internet of Medical Things, Smart Devices, Information Systems, and Cloud Services have led to a digital transformation of the healthcare industry. Digital healthcare services have paved the way for easier and more accessible treatment, thus making our lives far more comfortable. However, the present day healthcare industry has also become the main victim of external as well as internal attacks. Data breaches are not just a concern and complication for security experts; they also affect clients, stakeholders, organizations, and businesses. Though the data breaches are of different types, their impact is almost always the same. This study provides insights into the various categories of data breaches faced by different organizations. The main objective is to do an in-depth analysis of healthcare data breaches and draw inferences from them, thereby using the findings to improve healthcare data confidentiality. The study found that hacking/IT incidents are the most prevalent forms of attack behind healthcare data breaches, followed by unauthorized internal disclosures. The frequency of healthcare data breaches, magnitude of exposed records, and financial losses due to breached records are increasing rapidly. Data from the healthcare industry is regarded as being highly valuable. This has become a major lure for the misappropriation and pilferage of healthcare data. Addressing this anomaly, the present study employs the simple moving average method and the simple exponential soothing method of time series analysis to examine the trend of healthcare data breaches and their cost. Of the two methods, the simple moving average method provided more reliable forecasting results.