Reviewerly/exHarmony huggingface dataset:
Construction of a Human Resource Sharing System Based on Blockchain Technology

Human resource data sharing is very important for the cooperation of human resource institutions. Since the human resource data-sharing service needs to take into account the needs of individuals and talent service institutions, it faces issues such as the security of information sharing and the traceability of information. Therefore, this paper constructs a human resource data-sharing service system based on blockchain technology. Its trust mechanism is based on the Fabric alliance chain, and the system makes full use of its advantages of decentralization and consensus. Our research mainly includes data sharing architecture design, consensus mechanism analysis, smart contract design, data sharing process, and blockchain construction. The contribution of this paper is mainly in two aspects. On the one hand, it explores the trust mechanism of human resource data sharing and gives the scheme of the Fabric alliance chain. On the other hand, the overall architecture and smart contract design are given on the construction of the blockchain, which provides a reference for future research on human resource data sharing.
Do information and communication technologies (ICTs) make Africans happy?

The objective of this article is to examine the effect of ICT use on the happiness of African populations. To this end, the dynamic system generalized method of moments (syst-GMM) is used to estimate the model for 31 African countries using annual data from 2006 to 2018. Our results show that the effect of ICTs depends on the type of ICT. Indeed, the results highlight the negative association of cell phones and the positive association of the internet on happiness in Africa. However, all ICTs are positively associated with happiness in countries with high life expectancy and low unemployment.
A Fast Hyperspectral Tracking Method via Channel Selection

With the rapid development of hyperspectral imaging technology, object tracking in hyperspectral video has become a research hotspot. Real-time object tracking for hyperspectral video is a great challenge. We propose a fast hyperspectral object tracking method via a channel selection strategy to improve the tracking speed significantly. First, we design a strategy of channel selection to select few candidate channels from many hyperspectral video channels, and then send the candidates to the subsequent background-aware correlation filter (BACF) tracking framework. In addition, we consider the importance of local and global spectral information in feature extraction, and further improve the BACF tracker to ensure high tracking accuracy. In the experiments carried out in this study, the proposed method was verified and the best performance was achieved on the publicly available hyperspectral dataset of the WHISPERS Hyperspectral Objecting Tracking Challenge. Our method was superior to state-of-the-art RGB-based and hyperspectral trackers, in terms of both the area under the curve (AUC) and DP@20pixels. The tracking speed of our method reached 21.9 FPS, which is much faster than that of the current most advanced hyperspectral trackers.
Online-Review-Driven Products Ranking: A Hybrid Approach

Online customer reviews (OCRs) are the real feelings of customers in the process of using products, which have great reference value for potential customers’ purchase decisions. However, it is difficult for consumers to extract helpful information from very large numbers of OCRs. To support consumers’ purchase decisions, this paper proposes a hybrid method to rank alternative products through OCRs. In this method, we use the fine-grained Bidirectional Encoder Representation from Transformers (BERT) model for aspect-level sentiment analysis (SA) and convert SA results of sub-criteria into a corresponding interval intuitionistic fuzzy number, accurately extracting customer satisfaction in OCRs and reducing the errors caused by different amounts of OCRs. Furthermore, in order to obtain the ranking results of products, the subjective and objective weights are combined to determine weight of feature. Subsequently, an improved interval intuitionistic fuzzy VIKOR method is proposed to rank mobile games. Finally, we conduct a case study and make some comparisons, which show that our method can reduce the complexity of accurately obtaining consumers’ personal preferences and help consumers make more accurate decisions.
Multilabel Text Classification with Label-Dependent Representation

Assigning predefined classes to natural language texts, based on their content, is a necessary component in many tasks in organizations. This task is carried out by classifying documents within a set of predefined categories using models and computational methods. Text representation for classification purposes has traditionally been performed using a vector space model due to its good performance and simplicity. Moreover, the classification of texts via multilabeling has typically been approached by using simple label classification methods, which require the transformation of the problem studied to apply binary techniques, or by adapting binary algorithms. Over the previous decade, text classification has been extended using deep learning models. Compared to traditional machine learning methods, deep learning avoids rule design and feature selection by humans, and automatically provides semantically meaningful representations for text analysis. However, deep learning-based text classification is data-intensive and computationally complex. Interest in deep learning models does not rule out techniques and models based on shallow learning. This situation is true when the set of training cases is smaller, and when the set of features is small. White box approaches have advantages over black box approaches, where the feasibility of working with relatively small sets of data and the interpretability of the results stand out. This research evaluates a weighting function of the words in texts to modify the representation of the texts during multilabel classification, using a combination of two approaches: problem transformation and model adaptation. This weighting function was tested in 10 referential textual data sets, and compared with alternative techniques based on three performance measures: Hamming Loss, Accuracy, and macro-F1. The best improvement occurs on the macro-F1 when the data sets have fewer labels, fewer documents, and smaller vocabulary sizes. In addition, the performance improves in data sets with higher cardinality, density, and diversity of labels. This proves the usefulness of the function on smaller data sets. The results show improvements of more than 10% in terms of macro-F1 in classifiers based on our method in almost all of the cases analyzed.
Multi-scale self-attention mixup for graph classification

Data augmentation can effectively improve the generalization performance of neural networks. However, data augmentation for the graph domain is challenging due to the fact of irregular nature of the special non-Euclidean structure. In this paper, we propose a novel graph data augmentation solution, Multi-Scale Self-Attention Mixup (MSSA-Mixup), which extends the training distribution by interpolating multi-scale graph representation with self-attention. The MSSA-Mixup improves the generalization ability of graph neural networks (GNNs) effectively. Extensive experiments illustrate that the proposed method yields consistent and robust performance boost across graph classification tasks on the frequently-used benchmark datasets.
Quantum fingerprinting for heterogeneous devices localization

In this paper, we present QHFP, a device-independent quantum fingerprint matching algorithm that addresses two of the issues for realizing worldwide ubiquitous large-scale location tracking systems: scalability of storage space and running time as well as devices heterogeneity. In particular, we present a quantum algorithm with a complexity that is exponentially better than the classical techniques, both in space and running time. QHFP also has provisions for handling the inherent localization error due to building the large-scale fingerprint using heterogeneous devices. We give the details of the entire system starting from extracting device-independent features from the raw received signal strength, mapping the classical feature vectors to their quantum counterparts, and showing a quantum cosine similarity algorithm for fingerprint matching. We implement our quantum algorithm and deploy it in a real testbed using the IBM Quantum machine simulator. We also study the effect of the different quantum error sources, such as quantum gates error and quantum measurement error, on the system accuracy. Based on this, we discuss the suitable provisions to mitigate the effect of the quantum error sources on the localization accuracy. Results confirm the ability of QHFP to obtain the correct estimated location with an exponential improvement in space and running time compared to the traditional classical counterparts. In addition, the proposed device-independent features lead to more than 20% better accuracy in median error. This highlights the promise of our algorithm for future ubiquitous large-scale worldwide device-independent fingerprinting localization systems.
Patch-Based Image Learned Codec using Overlapping

End-to-end learned image and video codecs, based on auto-encoder architecture, adapt naturally to image resolution, thanks to their convolutional aspect. However, while coding high resolution images, these codecs face hardware problems such as memory saturation. This paper proposes a patch-based image coding solution based on an end-to-end learned model, which aims to remedy to the hardware limitation while maintaining the same quality as full resolution image coding. Our method consists in coding overlapping patches of the image and reconstructing them into a decoded image using a weighting function. This approach manages to be on par with the performance of full resolution image coding using an endto-end learned model, and even slightly outperforms it, while being adaptable to different memory sizes. Moreover, this work undertakes a full study on the effect of the patch size on this solution’s performance, and consequently determines the best patch resolution in terms of coding time and coding efficiency. Finally, the method introduced in this work is also compatible with any learned codec based on a conv/deconvolutional autoencoder architecture without having to retrain the model.
An Empirical Study of Classification Models Using AUC-ROC Curve for Software Fault Predictions

Software bug prediction is the process of identifying software modules that are likely to have bugs by using some fundamental project resources before the real testing starts. Due to high cost in correcting the detected bugs, it is advisable to start predicting bugs at the early stage of development instead of at the testing phase. There are many techniques and approaches that can be used to build the prediction models, such as machine learning. We have studied nine different types of datasets and seven types of machine learning techniques have been identified. As for performance measures, both graphical and numerical measures are used to evaluate the performance of models. A few challenges exist when constructing a prediction model. In this study, we have narrowed down to nine different types of datasets and seven types of machine learning techniques have been identified. As for the performance measure, both graphical and numerical measures are used to evaluate the performance of the models. There are a few challenges in constructing the prediction model. Thus, more studies need to be carried out so that a well-formed result is obtained. We also provide a recommendation for future research based on the results we got from this study.
Semi-supervised Medical Image Segmentation with Low-Confidence Consistency and Class Separation

Deep learning has achieved a great success in various fields, such as image classification, semantic segmentation and so on. But its excellent performance tends to rely on a large amount of data annotations that are hard to collect, especially in dense prediction tasks, like medical image segmentation. Semi-supervised learning (SSL), as a popular solution, relieves the burden of labeling. However, most of current semi-supervised medical image segmentation methods treat each pixel equally and underestimate the importance of indistinguishable and low-proportion pixels which are drowned in easily distinguishable but high-proportion pixels. We believe that these regions with less attention tend to contain crucial and indispensable information to obtain better segmentation performance. Therefore, we propose a simple but effective method for semi-supervised medical image segmentation task via enforcing low-confidence consistency and applying low-confidence class separation. Concretely, we separate low- and high-confidence pixels via the maximum probability values of model’s predictions and only low-confidence pixels are kept. For these remaining pixels, in the mean teacher framework, consistency is enforced for invariant predictions between student and teacher in the output level, and class separation is applied for promoting representations close to corresponding class prototypes in the feature level. We evaluated the proposed approach on two public datasets of cardiac, achieving a higher performance than the state-of-the-art semi-supervised methods on both datasets.
Mining High Utility Itemsets Using Prefix Trees and Utility Vectors

High utility itemsets can reveal combinations of items that have a high profit, expense, or importance. Mining high utility itemsets in a database with <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> items generally results in a huge search space, composed of <inline-formula><tex-math notation="LaTeX">$2^{n}$</tex-math></inline-formula> itemsets, and heavy utility calculations for the explored itemsets. Previous algorithms using prefix tree structures perform two phases, namely candidate generation and testing. To avoid generating candidate itemsets, one-phase algorithms use list or hyper-link structures and have been proven to be superior to two-phase algorithms. However, it should be noted that a prefix tree is still an efficient structure for itemset mining problems, and especially algorithms using prefix trees such as FP-Growth have shown excellent performance for mining frequent itemsets. This paper proposes Hamm, a High-performance AlgorithM for Mining high utility itemsets. Hamm employs a novel TV (prefix Tree and utility Vector) structure and mines high utility itemsets in one phase without candidate generation. We also develop an efficient optimization which is incorporated into Hamm as a component. Using prefix trees and utility vectors, Hamm outperforms state-of-the-art algorithms on various databases in experiments. Experimental results also show that the proposed optimization remarkably reduces the search space and speeds up Hamm.
Symmetric Bayesian Personalized Ranking With Softmax Weight

Preference learning, especially pairwise preference learning, is an efficient method for modeling implicit feedback in item recommendation. However, it is insufficient and not always valid to work for a basic pairwise preference model, which assumes that users prefer interacted (i.e., bought or viewed) items to un-interacted (i.e., not bought or not viewed) items. Recently, the state-of-the-art approaches have emerged as two separate but powerful methods, namely, pairwise preferences over item-sets and asymmetric pairwise preference models, respectively, to address limitations of the basic models. In spite of the success achieved by these methods, the assumption that the horizontal pairwise preference is with respect to two items does not always hold in asymmetric pairwise preference. Hence, it is appealing to integrate them into a uniform approach. In this article, we propose a novel symmetric pairwise preference assumption. We use a weighted average through a softmax function and define the overall preferences that can better discover users’ preference patterns. With the new assumption and the weighted average method, we propose a novel recommendation algorithm to improve the recommendation quality. Extensive empirical studies show that our new algorithms can significantly outperform several state-of-the-art and baseline methods over a number of public datasets.
Feedback Network for Compact Thin Cloud Removal

The thin cloud removal (CR) technique has great practical value for the application of remote sensing images. Existing deep-learning-based methods have attained remarkable achievements. However, most of them neglect the inherent feature correlations in deeper layers due to learning in a successive manner. In this letter, we propose a compact thin cloud removal network based on the feedback (FB) mechanism, called CRFB-Net, which leverages the high-level features as feedback information to modulate shallow representations. CRFB-Net employs the recurrent architecture to achieve such a feedback scheme. Specifically, the restoration process does not terminate after obtaining an output. In this case, the output of intermediate iterations will flow into the next iteration as feedback. For better utilization of feedback, a multiscale feature fusion block (MFFB) is designed to refine the low-level representations from three scales. Furthermore, we introduce a curriculum learning strategy to train the CRFB-Net by gradually increasing the complexity of restoration, through which a sharper result is produced step by step. Extensive experiments demonstrate the superiority of our CRFB-Net, outperforming state-of-the-art.
Contextual-Assisted Scratched Photo Restoration

Printed photographs can be easily warped, wrinkled, and even deteriorated over time. Existing methods treat the restoration of scratches as a pure inpainting problem that neglects the underlying corrupted contextual knowledge. However, important underlying contents are hidden behind the scratches, which are essential hints for producing a semantically consistent result. Motivated by this insight, we explore how to harmonize the scratch-free features and noisy but essential scratch features to produce a visually consistent restoration. Specifically, in this paper, we propose an automatic retouching approach for scratched photographs with the aid of scratch/background context. We explicitly process scratch and background context in two stages. In the first stage, we mainly extract global scratch features, while the mask is introduced in the second stage to filter out and inpaint the scratches. Both contexts are carefully reciprocated for a faithful restoration. Particularly, we propose a Scratch Contextual Assisted Module (SCAM) to adaptively learn texture within the detected mask. This module utilizes the distance between the scratch mask-out feature and scratch encoder feature for modeling the pixel-wise correspondence, which determines the importance of the encoder feature within the scratch mask. Furthermore, to facilitate the evaluation of scratch restoration methods, we create two new scratched photo datasets which have 238 scratch/scratch-free photo pairs to promote the development in the scratch restoration field, namely Old Scratched Photo Dataset (OSPD) and Modern Scratched Photo Dataset (MSPD). Extensive experimental results on the proposed datasets demonstrate that our model outperforms existing methods. To extend the application, we also perform the proposed method on video samples and obtain visual-pleasing results. The code can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/cwyyt/Contextual-assisted-Scratched-Photo-Restoration</uri> .
Blockchain-Based System for Traceability of Counterfeit Medicine

Healthcare supply chain management systems (HSCM) in the world have recently undergone a tremendous change due to the unprecedented pandemic eruption in 2019. It was observed that non-automated HSCM system is making it difficult to combat rising menace of substandard and falsified products. The presence of counterfeit medication is a serious problem that needs to be totally eliminated because it prevents the intended ailment from being treated. Blockchain has emerged as a promising technology to automate global supply chain management systems. With the support of its decentralized and distributed structure, recent technological developments will lead to improved medical transactions, automated supply chain management (SCM) and more effective traceability. This article offers a thorough analysis of the automated HSCM system in use, both with and without blockchain technology. This study identifies the drawbacks of existing solutions. As a result, this research suggests a blockchain-based smart contract system that will enhance the supply chain’s visibility, traceability, and verification of medications. By altering the block sizes, number of peers, and number of clients in an organization, the presented system has been evaluated in terms of metrics, transaction throughput, and transaction latency.
Medical Image Transmission Using a Secure Cryptographic Approach for IoMT Applications

Image security is very important in the field of medical imaging. To protect medical healthcare images, many studies have been undertaken. Because it eliminates data loss, encryption is the greatest solution for image secrecy. Traditional encryption methods, on the other hand, are difficult to apply to electronic health data due to data size limitations, redundancy, and scalability, especially when the data of a patient is delivered via different networks. As a result, since images are distinct from the text in terms of data loss and confidentiality, patients may loose the confidentiality and privacy of their data. Researchers discovered these security flaws and proposed different image encryption ways to remedy the problem. In order to build a safe image encryption solution for the healthcare business, this research proposes an efficient, lightweight encryption algorithm. The suggested lightweight encryption algorithm first separates the image into multiple clusters and then employs cluster-based permutations. Other procedures like diffusion and modulation could be used after that. The suggested technique is studied, analysed, and compared to generally encrypted ones in terms of information security and time complexity. The performance of the proposed method was assessed using a range of test images. Several experiments show that the proposed image cryptosystems methodology is more efficient than existing approaches.