Reviewerly/exHarmony huggingface dataset:
Pervasive Container Computing–––a Fine-Grained Loosely Coupled Cloud Cluster

With the emergence of CaaS, a cloud service model that can quickly build a production environment, this model has been widely used in the industry. However, the degree of loose coupling of various containers in the cluster and the fine-grained level of function division are often difficult to achieve people’s expectations. In addition, it is difficult to achieve the desired effect in terms of cloud cluster security. Therefore, this paper takes container-based cloud computing clusters as the main research object, and proposes a fine-grained, loosely coupled cloud cluster model based on containers, is called Pervasive-container computing. The experimental results show that Pervasive-container computing not only meets the fine-grained loose coupling characteristics required by microservices, but also adopts the strategy of “created with time” and “destroy after use”, which provides guarantees in terms of endogenous security. In addition, the proposed scheduling algorithm based on Pervasive-container calculation reduces the time consumption of file transfer by 20% compared to the time consumption without the algorithm.
Anonymous Hierarchical Identity-based Encryption EHR Sharing Mechanism Based on SM9

The identity-based encryption (IBE) scheme SM9 is a commercial encryption standard in China and has been used as a basic building block for authentication. Currently, to accommodate large-scale application scenarios, a variant hierarchical identity-based encryption (HIBE) scheme has been constructed on the basis of SM9. However, there still a problem that the ciphertext may leak the identity of the recipients. An anonymous HIBE has been developed based on SM9 to realize the anonymization of data recipients. Moreover, the anonymous HIBE scheme will be proven to have adaptive secure under the standard model. The performance of the anonymous HIBE is evaluated, the experimental results are comparable to the classical AHIBE algorithm which show the practicability of the scheme.
Centralized and Distributed Consensus in Wireless Network: An Analytical Comparison

The mission-critical decisions are usually made by a central node in connected autonomous systems enabled by IoT, AI and 5G etc. The reliability of decision-making largely depends on the condition of the central node, which can have unaffordable costs and low scalability in a massive-scale mobility network. Therefore, mission-critical IoT networks have been seeking new methods to achieve the growing reliability and latency requirement of cooperative decision-making. The distributed consensus protocol, which has been widely used in distributed computing systems, can provide robustness to the liveness of mission-critical decisions that may encounter node faults. This article first analytically derives the performance of two distributed consensus protocols: Raft and Hotstuff BFT, with the extra synchronization phases. In the presence of communication failure, the results are compared with the analytically derived performance of a centralized consensus. The comparison indicates the strengths and weaknesses of these consensus mechanisms from the perspective of full consensus reliability and communication latency, which provides guidelines for deploying the appropriate distributed autonomous systems in the future.
Self-similarity verification of special images such as microscopy

At present, the self-similarity of images has received wide attention in many fields such as image compression. Most of the images have strong self-similarity through the human eye visual system observation. However, the special images with strong self-similarity for human eye observation such as biomicroscopy are not digitally reflected by a certain algorithm, this paper proposes a self-similarity coefficient algorithm that is applicable to special natural images such as biomicroscopy and aerial photography, which has high self-similarity coefficients for biomicroscopy and other images.
Free Text Keystroke Dynamics-based Authentication with Continuous Learning: A Case Study

Authenticating users based on their typing patterns has always been a target for cybersecurity professionals and researchers. Although previous studies have worked on free text-based authentication on mobile devices, none of them addressed the necessity of continuous learning under these settings. In this work, we propose a variational autoencoder (VAE) model that deals with this issue. As a case study, we consider a scenario where the model is initially trained on data collected from the user in a particular language (English). Then, the model is supposed to recognize the user when typing in another language (Korean). One way to adapt to that change is to retrain the model on a subset of the Korean data when it becomes available. By then, two scenarios can arise: 1) The English data still exists and the model is trained on the combination of English and Korean data; 2) The English data does not exist for security reasons or limited storage issues, and thus, we use the decoder part of our VAE to generate data based on what has been learned and then retrain the model on the mix. The average Equal Error Rate achieved among 50 participants was 3.23% and 3.55% for scenarios 1 and 2, respectively (~14% less than the baseline case where the model is not retrained). These results prove the need for continuous retraining of authentication models and highlight the efficiency of the proposed model and its ability to continuously learn, even without having access to the previous training data.
How to improve blockchain for blogosphere?

The article presents the impact of the problem of Byzantine generals on the blockchain-based network on the example of the decentralised blogosphere. The oral message algorithm's implementation was tested and compared to the signed message algorithm adapted to the blogosphere example. The work showed that the problem of the Byzantine generals poses a significant threat to the correct operation of the network. However, it has also been proven that, based on authentication methods, it is possible to implement an algorithm that allows for the detection of fraud attempts and an appropriate reaction without the need for many recursive calls. A further research direction was also proposed - adapting the above algorithm to situations where not all servers will receive complete information from other nodes.
An IoT Device Identification Method over Encrypted Traffic Based on t-SNE Dimensionality

Existing approaches to IoT device identification struggle to find a balance between model accuracy, computational resources and model inference speed. In this paper, we analyse the encrypted traffic of IoT devices to identify the device type and propose a t-SNE dimensionality reduction based IoT encrypted traffic device identification method. The method reduces the dimensionality of high-latitude data and retains the main features to the maximum extent. Meanwhile, various evaluation metrics are used to compare the effectiveness of the proposed algorithm with existing IoT identification algorithms. Experiments show that the proposed algorithm achieves more than 98% accuracy on IoT device data using decision tree and random forest models, while largely improving the efficiency of model inference, outperforming comparison algorithms, and meeting realtime requirements.
Knowledge Base Question Answering via Structured Query Generation using Question domain

The core problem of Knowledge Base Question Answering(KBQA) is to find queries from user questions to knowledge bases. Specifically, natural language questions need to be transformed into structured queries before associating with knowledge bases, and the answers can be found from the knowledge graph using the structured queries. We found structured queries in similar question domains tend to have repetitive reasoning steps. Also, humans often use cases identical to the question and information from these cases to assist in answering new questions. Hence, we propose a new KBQA framework based on similar question domains. We separately design the inference information retriever module to extract cases with a similar structure to the question and the relation information retriever module to narrow the scope of reasoning relation extraction. Finally, we used the retrieved inference cases and relation candidate sets as auxiliary information and generated an executable Knowledge-oriented Programming Language(KoPL) through the program generation module. Experiments have shown that the model can handle complex question answering and has a strong reasoning ability. Our methodology has resulted in new state-of-the-art performance on WebQSP and CWQ datasets.
Rep-Enhancer: Re-parameterizing Neural Network for Real-time Low-light Enhancement in Visual Maritime Surveillance

Vision-based maritime surveillance has become an essential part of the vessel traffic services system. The images collected in low-light maritime conditions often suffer from poor visibility. These images may significantly degenerate the performance of high-level visual tasks and increase the uncertainty in maritime surveillance. To address this problem, we propose a lightweight neural network (Rep-Enhancer) for low-light image enhancement. Specifically, we first design a re-parameterizable multi-branch edge extraction module, i.e., spatial domain-oriented convolution block (SDCB). Furthermore, skip connections and spatial attention operations are employed to strengthen the features. By exploiting these well-strengthened edge features, we can enhance the low-light images effectively with the encoder-decoder structure. The experimental results have shown that Rep-Enhancer can enhance the low-light image qualifiedly while maintaining great inference efficiency.
Thermal-aware Energy Efficient Task Scheduling Framework

Large energy consumption in data centers has become a challenging problem with the emergence of cloud computing and large scale data centers. In this paper, we present an architectural framework for thermal-aware resource management while considering energy efficiency. The framework consists of a layered architecture and integrates a set of easy-to-use client tools and a thermal-aware task management middleware to schedule tasks based on thermal conditions within a cluster and among different data centers. As part of this paper we focus on the development of a thermal-aware task scheduling component for a single data center. This component is fundamental to our future activities, while considering to balance the temperature distribution in a single data center, thus implicitly minimizing energy cost in data centers.
Emotional Dialogue Generation Based on Transformer and Conditional Variational Autoencoder

Human-machine dialogue is one of the most challenging tasks in the field of natural language processing, and it is also the basis for the realization of a human-machine inclusive society in the future. At present, the generative dialogue model based on deep learning is prone to generate general responses with single content and no meaningful information, and the existing research considers emotional factors relatively little. Aiming at the shortcomings of existing methods, an emotional dialogue generation model based on Transformer and conditional variational autoencoder(CVAE) is proposed, which is intended to improve the diversity of response information and embed emotional factors in the generated response. This model uses Transformer to extract the semantic features of the text sequence to improve the utilization of the semantic information of the text sequence. To increase the diversity of the response information, the latent variable of the conditional variational self-encoder is introduced into the decoder. In addition, in order to enhance the empathy ability of the model, an emotion perception encoder is used to encode user emotion information, and a pre-trained emotion classification model based on BERT is proposed to detect the emotion information implicit in the utterance. Experiments have shown that the proposed model has a stronger generation ability, more diverse information to generate responses, and a stronger empathy ability.
Relation Prediction of N-ary Knowledge Graph Based on Multi-information Propagation

Knowledge Graph (KG) is currently the most popular graph structure. For graphs, however, they are frequently incomplete due to the lack of most information in the real world. Knowledge Graph Completion (KGC) has become the most interesting topic for researchers. At the same time, the relationships in social networks have expanded from binary relationships to more complex high-order relationships. However, the current research methods on multi-relationship still lack the interaction between entities, and the corresponding entity information under each relationship is different. Based on this, we propose a high-order GCN-based multi-relation prediction model (denoted as MHGCN). Firstly, an entity adjacent matrix is constructed for each relation, and a high-order graph convolutional network (GCN) is used to propagate the neighbor information among entities within the relation. Secondly, a probabilistic calculation method that integrates entity information is designed to judge the existence of facts. Experimental analysis is carried out on three representative datasets, which illustrate the effectiveness of our proposed algorithm. In particular, for FB-AUTO dataset with unfixed number of entities, the MRR of MHGCN reaches 0.883. The MRR of the fixed entity number dataset JF17K-4 is up to 0.828. It further shows that MHGCN is not only applicable to datasets with unfixed number of entities, but also applicable to datasets with fixed number of entities.
ZRA: A Secure and Radical Auction Scheme for Blockchain with Privacy Preservation

The market for digital collections is expanding. Artists and collectors throng the market for digital collectibles. The auction of digital collections is currently a trending subject. Digital collectibles require an auction framework with continuous bidding because of their negotiable nature. The monopoly issue with digital collections is resolved by permanent or continuous pricing. In continuous pricing, malicious bidding is a possibility. Inflated prices are the result of malicious bidding. In order to combat the inflated value, the Harburg tax was implemented. So we reviewed blockchain-based digital collectibles auction solutions from three different perspectives: (i) Auction scheme with anti-monopoly characteristics for continuous pricing; (ii) Solve the problems of digital collection values that are inflated; (iii) Protect the bidder’s privacy and bid strategy. This paper combines radical market theory, the Harburg tax, and radical auction smart contracts. Our auction framework ZRA implements anti-monopoly, continuity, and privacy protection features. On simulated ethernet, ZRA can be deployed effectively. According to experiment results, ZRA can reduce communication overhead by 30%.
Using Word2Vec for news articles recommendations: Considering evaluation options for hyperparameter optimization and different input options

Evaluation of unsupervised and semi-supervised learning methods, especially in the field of information retrieval and recommender systems is a problematic and resource-intensive task. Often, there is no way to evaluate the used machine learning model until user testing is performed. We investigated hyperparameter optimization options of Gensim's Word2Vec implementation by evaluating model performance on word analogies and word pairs tests and statistics of out-of-vocabulary ratio. These automatic and task-independent offline (pre-) evaluations techniques could provide a simple way to reduce the set of final model variants used for resource-demanding user testing or hybrid recommender models, thus we investigated whether those tests were useful for the accuracy of our final task of providing similar articles to a chosen article. We also consider options of using Wikipedia articles for the model training input or the pre-trained FastText model.
An opinion mining with federated learning on the Afghan-People survey dataset

Internet and digital devices have caused a flood of information in the fourth industrial revolution era. Health-care, government, banks, military, and the banking sector, have embraced machine learning as a convenient way to recognize patterns in data. For a conventional machine learning model, the information from the data owner must be uploaded in a centralized location for training, which causes data owners to worry about the lack of their private information assurance. On the other hand, a massive amount of computing power is available to train intelligent models. In this way, federated learning becomes more and more popular. It preserves privacy of data owners and allows models to be trained over distributed clients. In this work, the federated learning approach is implemented using a neural network to model several Afghan-People opinion investigations such as the question about country's overall situation or people's security and safety in a distributed computing environment with measurable results. Further, the performance of federated learning using the same dataset is compared with centralized machine learning.
Modulation Index Estimation of CPM Signals Based on First-Order Cyclic Moments

Continuous Phase Modulation (CPM) has been widely used in satellite communication and mobile communication systems due to its good constant envelope characteristics and high bandwidth and power utilization. CPM signals can be divided into Single-h CPM signals and Multi-h CPM signals according to the amount of modulation indexes. For CPM signals, the modulation index is the most important modulation parameter, which can affect the change of signal phase information, and its size can not only affect the speed of signal phase change, but also affect the spectral efficiency of the signal. In addition, in order to realize the practical application of CPM demodulation, correct estimation of the modulation index is a very important prerequisite.Therefore, this paper proposes a modulation index estimation based on the first-order cyclic moment, which has good universality.
Metric retractions and similarity detecting algorithms

Let <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{X}=\langle X, \varrho\rangle$</tex> be a metric space, let <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A\subseteq X$</tex> and let <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon$</tex> be a positive real number. The similarity detecting problem is to find all <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$a\in A$</tex> for which <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varrho(a, x)\leq\varepsilon$</tex> where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x\in X$</tex> is a given input. In this work we study the similarity detecting problem with the additional assumption that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{X}$</tex> is an ultrametric space of finite spectrum; these assumptions seem to be natural from the point of view of practical applications. We establish model theoretical results for ultrametric spaces. More concretely, we provide sufficient conditions for the existence of metric retractions for certain ultrametric spaces. Based on these theoretical results, we propose a similarity detecting algorithm for ultrametric spaces. The time complexity of our algorithm will be discussed, as well.
A Comparative Study of the Performance of Personnel Detection based on Visible and Infrared Images

Personnel security has always been a hot topic of research especially in power systems. Effective detection of people in power scenes has become a high priority. Currently, algorithms for personnel detection have been developed considerably, and they are mainly used for visible images. Since infrared images imaged by thermal radiation can detect living blood creatures easily, they can be used in personnel detection at night. To investigate the effect of visible and infrared images on the performance of personnel detection algorithms in different environments, this thesis uses two object detection algorithms, YOLOv5 and Faster RCNN, to train personnel detection models for visible daytime, visible nighttime, infrared daytime and infrared nighttime datasets respectively. Comparative experiments are conducted to quantitatively and qualitatively analyze the performance advantages and disadvantages of the personnel detection algorithms for visible images and infrared images during the day and night. The experimental results for both models consistently show that the visible images are similar to the infrared images during daylight hours with a good target detection model, the visible image is slightly better in performance. While the infrared image is significantly better than the visible image at night, and the [email protected] can be improved by 0.2. For small human target detection, the infrared image has a significant advantage over the visible image at night, with an average detection size reduction of 48.0% and a minimum detection size reduction of 64.6%.
SCRUM Retrospective Discussions Support Based on Class-Diagram Redocumentation

Despite the fact that software developers perceive the need for good documentation, it is usually at the tail end of interest in pragmatically oriented approaches. The problem is also the fact that, as the evaluation indicators of the project, the prevailing metrics that follow the aspect of time are more important than the aspect of quality. The solution here can be the re-documentation itself, the automatic generation of documentation in the background of the developers' work from the artifacts created during the development itself. This paper analyses the approaches, documentation tools and forms in any way related to the agile Scrum approach in software development. As a result it offers its own prototype documentation tool integrated into Atlassians' Confluence platform with a link to the GitHub repository and Jira issue tracking system. He evaluates his own solution by a qualitatively controlled interview with the stakeholders.
Experimental Comparison of Array-based and Linked-based List Implementations

Lists are one of the most often used containers in practice. They provide an easy way to store and access elements of arbitrary type. The list is suitable for a wide range of use cases including problems that involve the processing of a considerably high number of elements. Such situations are common in, for example, medical databases or in reliability analysis tools. There exist two principal approaches to the implementation of the list. One is based on the use of an array and the other one on the use of linked nodes. The question of which implementation to use often raises heated debates amongst scholars and programmers. The paper aims to compare the standard implementations in scenarios testing basic list operations. Also, the paper presents how we use similar scenarios to teach computational complexities and data structures comparison within the course of Algorithms and Data Structures at the Faculty of Management Science and Informatics of University of Zilina.