Reviewerly/exHarmony huggingface dataset:
Subnetworks in BlockDAG

In the article, we study the horizontal scaling of the Waterfall or similar blockDAG networks by partitioning them into subnetworks by applying hierarchical and graph-based clustering algorithms. It leads to the reducing the network load and, in addition, to the increasing of the potential performance parameters of the underlying protocol. We consider methods of topology construction, propose clustering algorithms, and perform a simulation of a network partitioning into subnetworks.
Designing a Multilingual, Multimodal and Collaborative Platform of Resources for Higher Education

While digital technologies have revolutionized how we collect, store and retrieve information, there is a lack of strategies that support academic collaboration at the institutional level. Today it is vital to know how to communicate accurately and efficiently, respecting the specificities of each communicative context. We present an interface prototype aimed to overcome the communication barriers imposed by specialized languages, which are becoming more relevant as a result of the increasing specialization of knowledge. This article presents the first iteration of the conceptualization process of the interface layout, its interaction logic and visualization structures. It is essential to consider the hierarchical structures/algorithms to represent and support the collaborative creation of academic knowledge resources. Radial tree layouts are an optimal solution for displaying large hierarchies, however the implemented radial layouts in ECharts are not being used as analysis tools, instead, they are used as a means of creating, mapping, navigating and exploring different facets and knowledge resources of academic projects, and specialized language glossaries. To sum up, this article provides a framework and fundamental guidelines for an interface layout that explores and promotes academic collaboration at the institutional level in order to overcome the lack of knowledge about the resources produced by different actors in the academic context.
Waterfall: A Scalable Distributed Ledger Technology

Waterfall is a highly-scalable smart contract platform for the development of decentralized applications (dapps) and financial services. The distributed protocol is based on Directed Acyclic Graphs (DAGs) with fast finality Proof-of-Stake (PoS) consensus. The Waterfall platform consists of the Coordinating and Shard networks achieving high transaction throughput due to parallelized block production. The DAG structure facilitates scalability, which is one of the main challenges of decentralized technologies. The Coordinating network maintains a register of Validators, and also assigns block producers, committee members, and leaders in each time slot. In addition, the linearization and finalization of the distributed ledger are performed in the Coordinating network increasing overall security and synchronization.
Public-Key Cryptography behind Blockchain Security

Blockchain (BC) technology has infiltrated many areas of our lives, from digital currencies to healthcare and beyond, thanks to its security, decentralization, and transparency properties. BC is a decentralized ledger of assets and digitally signed transactions through a peer-to-peer (P2P) computer network. Indeed, signature and consensus algorithms are behind BC’s security and performance. This paper provides a study on BC-used asymmetric cryptography. We focus on RSA, DSA, and ECDSA digital signature algorithms. We implement the algorithms using python language on a Raspberry Pi. Simulation outcomes show that the ECDSA performs better than the other algorithms and is secure despite using small keys.
Appropriate noise addition to metaheuristic algorithms can enhance their performance

Nature-inspired swarm-based algorithms are increasingly applied to tackle high-dimensional and complex optimization problems across disciplines. They are general purpose optimization algorithms, easy to implement and assumption-free. Some common drawbacks of these algorithms are their premature convergence and the solution found may not be a global optimum. We propose a general, simple and effective strategy, called heterogeneous Perturbation-Projection (HPP), to enhance an algorithm's exploration capability so that our sufficient convergence conditions are guaranteed to hold and the algorithm converges almost surely to a global optimum. In summary, HPP applies stochastic perturbation on half of the swarm agents and then project all agents onto the set of feasible solutions. We illustrate this approach using three widely used nature-inspired swarm-based optimization algorithms: particle swarm optimization (PSO), bat algorithm (BAT) and Ant Colony Optimization for continuous domains (ACO). Extensive numerical experiments show that the three algorithms with the HPP strategy outperform the original versions with 60-80% the times with significant margins.
BGC: Belief gravitational clustering approach and its application in the counter-deception of belief functions

Counter-deception information fusion is a significant issue in Dempster–Shafer evidence theory (DST). How to effectively counter the deception is the key problem in belief function. Limited work has been presented, of which the negation view, degree of falsity view, and the minimum conflict view are popular ones. However, previous work may suffer from combinatorial explosion or be limited to simple practical application. Based on our previous belief universal gravitation (BUG) model, a simple belief gravitational clustering (BGC) is proposed to model the evidential clustering process. Some goals, like no parameter adjustment, no initial condition, objective and unique cluster number, and robustness are achieved. Furthermore, BGC-based fusion strategy is ulteriorly raised to determine whether the evidence should be fused, which fully considers the abnormal feature of the body of evidence and the nature of the combination rule to perceive the deception. Both theoretical analysis and experimental results demonstrate the accuracy and flexibility of the proposed work.
The Effects of Contrast on Correlation Perception in Scatterplots

Scatterplots are common data visualizations that can be used to communicate a range of ideas, the most intensively studied being the correlation between two variables. Despite their ubiquity, people typically do not perceive correlations between variables accurately from scatterplots, tending to underestimate the strength of the relationship displayed. Here we describe a two-experiment study in which we adjust the visual contrast of scatterplot points, and demonstrate a systematic approach to altering the bias. We find evidence that lowering the total visual contrast in a plot leads to increased bias in correlation estimates and show that decreasing the salience of points as a function of their distance from the regression line, by lowering their contrast, can facilitate more accurate correlation perception. We discuss the implications of these findings for visualization design, and provide a framework for online, reproducible, and large-sample-size (N = 150 per experiment) testing of the design parameters of data visualizations.
Document-level relation extraction with multi-layer heterogeneous graph attention network

Document-level relation extraction is an essential task in natural language understanding and requires the ability to fully learn different representations of entities and multi-hop reasoning across multiple sentences. Most existing methods use graph neural networks to extract multi-hop relations. They only focus on the role of edge types and ignore node types when obtaining node representations. In addition, it is difficult for them to adequately capture the complex interactions in a document when converting a document into a graph. These problems will affect the performance of relation extraction. To solve the above problems, this paper proposes a document-level relation extraction model based on a multi-layer heterogeneous graph neural network (MHGNN). Different from previous methods that only focus on edge types between two nodes, a node type and edge type oriented heterogeneous graph attention network (NEHGAN) is proposed to further learn rich node representations. In addition, the model constructs three graphs, namely a word-level graph, a mention-level graph and an entity-level graph, for capturing the complex interactions between words, between mentions and between entities in the whole document. Experimental results demonstrate that MHGNN can effectively improve the performance of document-level relation extraction, and outperforms the state-of-the-art model by 1.04%, 1.03%, 1.69% and 0.6% in F1 on the development set and the test set of two public datasets, respectively.
Four Natural Chromite Reference Materials for the Determination of the <scp>First‐Row</scp> Transition Elements and Gallium by <scp>LA‐ICP‐MS</scp>

Four natural chromite samples (LBS13‐04, LBS13‐06 and LBS13‐13) from the Luobusa ophiolite (China) and 16SW2‐6 from the Stillwater Complex (USA) were developed as reference materials for in situ element microanalysis. Approximately 8 g of chromite fragments with grain sizes of 0.5–1.5 mm from each chromite sample were separated under a binocular microscope and analysed by EPMA, XRF, LA‐ICP‐MS and solution nebulisation ICP‐MS techniques for major and trace elements at six laboratories. The results show that the four chromite samples are homogeneous with respect to MgO, Al 2 O 3 , Cr 2 O 3 , FeO, Sc, Ti, V, Mn, Co, Ni, Zn and Ga. These samples are thus suitable to be used as reference materials for in situ microanalysis.
A Multi-Stage Security Solution for Medical Color Images in Healthcare Applications

This paper presents a robust multi-stage security solution based on fusion, encryption, and watermarking processes to transmit color healthcare images, efficiently. The presented solution depends on the features of discrete cosine transform (DCT), lifting wavelet transform (LWT), and singular value decomposition (SVD). The primary objective of this proposed solution is to ensure robustness for the color medical watermarked images against transmission attacks. During watermark embedding, the host color medical image is transformed into four sub-bands by employing three stages of LWT. The resulting low-frequency sub-band is then transformed by employing three stages of DCT followed by SVD operation. Furthermore, a fusion process is used for combining different watermarks into a single watermark image. This single fused image is then ciphered using Deoxyribose Nucleic Acid (DNA) encryption to strengthen the security. Then, the DNA-ciphered fused watermark is embedded in the host medical image by applying the suggested watermarking technique to obtain the watermarked image. The main contribution of this work is embedding multiple watermarks to prevent identity theft. In the presence of different multimedia attacks, several simulation tests on different color medical images have been performed. The results prove that the proposed security solution achieves a decent imperceptibility quality with high Peak Signal-to-Noise Ratio (PSNR) values and high correlation between the extracted and original watermark images. Moreover, the watermark image extraction process succeeds in achieving high efficiency in the presence of attacks compared with related works.
PPeFL: Privacy-Preserving Edge Federated Learning With Local Differential Privacy

Since traditional federated learning (FL) algorithms cannot provide sufficient privacy guarantees, an increasing number of approaches apply local differential privacy (LDP) techniques to FL to provide strict privacy guarantees. However, the privacy budget heavily increases proportionally with the dimension of the parameters, and the large variance generated by the perturbation mechanisms leads to poor performance of the final model. In this article, we propose a novel privacy-preserving edge FL framework based on LDP (PPeFL). Specifically, we present three LDP mechanisms to address the privacy problems in the FL process. The proposed filtering and screening with exponential mechanism (FS-EM) filters out the better parameters for global aggregation based on the contribution of weight parameters to the neural network. Thus, we can not only solve the problem of fast growth of privacy budget when applying perturbation mechanism locally but also greatly reduce the communication costs. In addition, the proposed data perturbation mechanism with stronger privacy (DPM-SP) allows a secondary scrambling of the original data of participants and can provide strong security. Further, a data perturbation mechanism with enhanced utility (DPM-EU) is proposed in order to reduce the variance introduced by the perturbation. Finally, extensive experiments are performed to illustrate that the PPeFL scheme is practical and efficient, providing stronger privacy protection while ensuring utility.
A Blockchain-Based Trust Model for Supporting Collaborative Healthcare Data Management

The development of information technology allows the collaborative business process to be run across multiple enterprises in a larger market environment. However, while collaborative business expands the realm of businesses, it also causes various hazards in collaborative Interaction, such as data falsification, inconstancy, and misuse. To solve these issues, a blockchain-based collaborative business modeling approach was proposed and analyzed. However, the existing studies lack the blockchain risk problem-solving specification, and there is no verification technique to examine the process. Consequently, it is difficult to confirm the appropriateness of the approach. Thus, here, we propose and build a blockchain-based trust model to strengthen and verify the integrity and security of the collaborative business process; Integrity and security address the validity of collaborative interactions in terms of a trust, and we construct a blockchain pattern based on trust elements to meet the required the characteristics. Specifically, a trust model can be applied to the healthcare data-sharing process, and then the achievement of the trust-based safe data-sharing process can be proven. Our model can be used as a trust-building guidance tool or for integrity and security verification with the collaborative business process in a distributed environment with blockchain.
Blockchain Assisted Optimal Machine Learning Based Cyberattack Detection and Classification Scheme

With recent advancements in information and communication technology, a huge volume of corporate and sensitive user data was shared consistently across the network, making it vulnerable to an attack that may be brought some factors under risk: data availability, confidentiality, and integrity. Intrusion Detection Systems (IDS) were mostly exploited in various networks to help promptly recognize intrusions. Nowadays, blockchain (BC) technology has received much more interest as a means to share data without needing a trusted third person. Therefore, this study designs a new Blockchain Assisted Optimal Machine Learning based Cyberattack Detection and Classification (BAOML-CADC) technique. In the BAOML-CADC technique, the major focus lies in identifying cyberattacks. To do so, the presented BAOML-CADC technique applies a thermal equilibrium algorithm-based feature selection (TEA-FS) method for the optimal choice of features. The BAOML-CADC technique uses an extreme learning machine (ELM) model for cyberattack recognition. In addition, a BC-based integrity verification technique is developed to defend against the misrouting attack, showing the innovation of the work. The experimental validation of BAOML-CADC algorithm is tested on a benchmark cyberattack dataset. The obtained values implied the improved performance of the BAOML-CADC algorithm over other techniques.
Not All Memories Created Equal: Dynamic User Representations for Collaborative Filtering

Collaborative filtering methods for recommender systems tend to represent users as a single static latent vector. However, user behavior and interests may dynamically change in the context of the recommended item being presented to the user. For example, in the case of movie recommendations, it is usually true that movies that the user watched more recently are more informative than movies that were watched a long time ago. However, it is possible that a particular movie from the past may become suddenly more relevant for prediction in the presence of a recommendation for its sequel movie. In response to this issue, we introduce the Attentive Item2Vec++ (AI2V++) model, a neural attentive collaborative filtering approach in which the user representation adapts dynamically in the presence of the recommended item. AI2V++ employs a novel context-target attention mechanism in order to learn and capture different characteristics of the user’s historical behavior with respect to a potential recommended item. Furthermore, analysis of the neural-attentive scores allows for improved interpretability and explainability of the model. We evaluate our proposed approach on five publicly available datasets and demonstrate its superior performance in comparison to state-of-the-art baselines across multiple accuracy metrics.
Using Language Models for Enhancing the Completeness of Natural-Language Requirements

[Context and motivation] Incompleteness in natural-language requirements is a challenging problem. [Question/problem] A common technique for detecting incompleteness in requirements is checking the requirements against external sources. With the emergence of language models such as BERT, an interesting question is whether language models are useful external sources for finding potential incompleteness in requirements. [Principal ideas/results] We mask words in requirements and have BERT's masked language model (MLM) generate contextualized predictions for filling the masked slots. We simulate incompleteness by withholding content from requirements and measure BERT's ability to predict terminology that is present in the withheld content but absent in the content disclosed to BERT. [Contribution] BERT can be configured to generate multiple predictions per mask. Our first contribution is to determine how many predictions per mask is an optimal trade-off between effectively discovering omissions in requirements and the level of noise in the predictions. Our second contribution is devising a machine learning-based filter that post-processes predictions made by BERT to further reduce noise. We empirically evaluate our solution over 40 requirements specifications drawn from the PURE dataset [1]. Our results indicate that: (1) predictions made by BERT are highly effective at pinpointing terminology that is missing from requirements, and (2) our filter can substantially reduce noise from the predictions, thus making BERT a more compelling aid for improving completeness in requirements.
The construction of enterprise's financial supply chain management under blockchain technology

Abstract This paper uses the advantages of blockchain technology to help construction enterprises obtain low‐interest bank loans, effectively reduce financing costs, thus improving the level of financial supply chain, innovating financial management (FM) of construction enterprises, solving the FM transformation problem of construction enterprises and promoting the sustainable development of construction enterprises, and thus doing the following work. First, this paper describes the concept and characteristics of BT and the motivation of FM of construction enterprises to use BT. Second, it analyses BT's three main application advantages in construction enterprise financing: reducing financial risks, improving the credit penetration of core enterprises, and reducing financing costs. Meanwhile, this paper summarizes the problems that should be paid attention to in implementing the current financial supply chain management strategy in enterprises. It provides a solid theoretical foundation for the research of FM transformation in construction enterprises. Finally, the overall design and functional analysis of the BT FM platform are carried out. The functions of the BT FM platform are divided into four modules: system management, credit management, contract management, and information management. Besides, their specific business processes are analysed. The research results show that BT is applicable in enterprises, and the value of each participant's financing platform can be clearly defined from the construction of BT‐based financing platform. The income quota of the owner's individual operation is 33%, and the income quota of the tripartite cooperation is also 33%, which further illustrates the effectiveness of the financial financing model of construction enterprises based on BT and the feasibility of its actual implementation in the future. In this paper, the related issues of financial supply chain management are systematically and comprehensively summarized, which has theoretical guiding significance for the research of FM transformation of construction enterprises based on BT.
Authentication of Lung CT-Scan Data in NIfTI Format Using LWT, Hessenberg Decomposition, and Affine Transform

Technological advancement in digital medical imaging changes the world health care system because various diseases are diagnosed through these technologies. In the current covid-19 phase, telemedicine plays a tremendous role in providing remote medical consultation in rural areas. But in remote consultation, various medical images send to a radiologist for diagnosis through the internet. Worldwide has seen a significant surge in digital media attacks that replicate and tamper with the digital image, resulting in a breach of authenticity and ownership. A robust and safe watermarking scheme for NIfTI images has been proposed in this paper. This novel method entails meticulously integrating a watermark in the slice of the NIfTI image. We aim to correctly incorporate the watermark with minimal distortion and retain the medical information of the selected image slice. The proposed method uses LWT transform to transform the image, allowing for surprisingly good modification during insertion. Furthermore, Hessenberg matrix decomposition is applied on the LL sab bands with the image's maximal energy to be retained. Scrambling the watermark before embedding it in the slice is accomplished using the Affine transform. A thorough study of the trade-off between security, imperceptibility, and robustness utilizing performance measures viz. NC, PSNR, SNR, and SSIM have been given. The simulation findings have been validated against image processing threats.
JointContrast: Skeleton-Based Interaction Recognition with New Representation and Contrastive Learning

Skeleton-based action recognition depends on skeleton sequences to detect categories of human actions. In skeleton-based action recognition, the recognition of action scenes with more than one subject is named as interaction recognition. Different from the single-subject action recognition methods, interaction recognition requires an explicit representation of the interaction information between subjects. Recalling the success of skeletal graph representation and graph convolution in modeling the spatial structural information of skeletal data, we consider whether we can embed the inter-subject interaction information into the skeletal graph and use graph convolution for a unified feature representation. In this paper, we propose the interaction information embedding skeleton graph representation (IE-Graph) and use the graph convolution operation to represent the intra-subject spatial structure information and inter-subject interaction information in a uniform manner. Inspired by recent pre-training methods in 2D vision, we propose unsupervised pre-training methods for skeletal data as well as contrast loss. In SBU datasets, JointContrast achieves 98.2% recognition accuracy. in NTU60 datasets, JointContrast respectively achieves 94.1% and 96.8% recognition accuracy under Cross-Subject and Cross-View evaluation metrics.