Reviewerly/exHarmony huggingface dataset:
Pairwise classification using quantum support vector machine with Kronecker kernel

Abstract We investigated the potential application of quantum computing using the Kronecker kernel to pairwise classification and have devised a way to apply the Harrow-Hassidim-Lloyd (HHL)-based quantum support vector machine algorithm. Pairwise classification can be used to predict relationships among data and is used for problems such as link prediction and chemical interaction prediction. However, in pairwise classification using a Kronecker kernel, it is very costly to calculate the Kronecker product of the kernel matrices when there is a large amount of data. We found that the Kronecker product of kernel matrices can be represented more efficiently in time and space in quantum computing than that in classical computing. We also found that a pairwise classifier can be effectively trained by applying the HHL-based quantum support vector machine algorithm to the Kronecker kernel matrix. In an experiment comparing a classical algorithm with a quantum support vector machine with a Kronecker kernel run on a quantum computing simulator, the misclassification rate of the latter was almost the same as that of the former for the same pairwise classification problem in some cases. This indicates that a quantum support vector machine with a Kronecker kernel algorithm can achieve accuracy equivalent to that of the classical algorithm more efficiently and scalably. This finding paves the way for applying quantum machine learning to predicting relationships in large-scale data.
Improving CycleGAN for Image-to-Image Style Transfer by DenseNet

With the continuous progress and development of machine learning, deep learning has become a hot research field today. Among them, image-to-image style transfer is one of the research hotspots. Generally, the main goal of image style transfer is to extract the features of the original image and convert them into images with the target style through a deep neural network. Applying the cycle-consistent adversarial network known as CycleGAN to transfer the style of images is one of the existing image style transfer techniques, but CycleGAN has problems such as insufficient brightness enhancement, color distortion, and low authenticity of the generated images. To address this problem, in this paper, we construct an improved CycleGAN, use DenseNet to replace the original ResNet, and compare the image effects generated by the improved network using DenseNet and the original CycleGAN network based on ResNet. After calculating through the image evaluation indicators PSNR and SSIM, the results show that the images generated by the improved recurrent consistency network based on DenseNet have better visual effects and realism.
A Practical Website Fingerprinting Defense Approach with Universal Adversarial Perturbations

Website Fingerprinting (WF) can be used by network eavesdroppers to infer information about the content of encrypted and anonymous connections. Several defenses leverage adversarial examples to mitigate the threat of WF attacks, but they require to get entire traffic traces after network sessions have concluded to produce adversarial examples, thus offer users little protection in practical settings. In this paper, a novel practical WF defense method called WF-UAP is proposed by crafting Universal Adversarial Perturbations (UAP) to fight back against WF attacks, in which adversarial examples are produced to fool the classifiers used in WF attacks when UAPs are added to any network traffic trace in the target domain. We further present a Generative Adversarial Network (GAN) based UAP generation approach to enhance the performance of UAPs. It can efficiently generate UAPs once the generator is trained and make the adversarial traces and original traffic traces are more difficult to distinguish. Our experimental results over a public dataset demonstrate that WF-UAP reduces the accuracy of the state-of-the-art WF attacks from 98% to 15% with at most 20% increased bandwidth overhead, which outperforms the previous defenses in terms of the defense performance and overhead.
Research on the Construction of Maritime Legal Knowledge Graph

As the marine industry booms, the maritime legal documents are of great importance to the maneuver on the sea. However, the traditional way of consulting the text can not meet the demand of maritime operation nowadays. This paper aims to explore a way to extract and strengthen data from maritime legal texts to better support legal question answering. To mine knowledge from unstructured maritime laws and regulations, this paper proposes a method to build the maritime legal knowledge graph. To extract information from unstructured texts, BERT+BiLSTM+CRF is used for named entity recognition. DeepKE toolkit is used for relation extraction. And to strengthen the logics between entities, heterogeneous nodes are introduced to enhance the semantic associations in the maritime legal knowledge graph. The document-enhanced knowledge graph expanded in scale, so it can better support subsequent intelligent applications.
Detection of relevant digital evidence in the forensic timelines

Security incident handling and response are essen-tial parts of every organization's information and cyber security. Security incident handling consists of several phases, among which digital forensic analysis has an irreplaceable place. Due to particular digital evidence being recorded at a specific time, timelines play an essential role in analyzing this digital evidence. One of the vital tasks of the digital forensic investigator is finding relevant records in this timeline. This operation is performed manually in most cases. This paper focuses on the possibilities of automatically identifying digital evidence pertinent to the case and proposes a model that identifies this digital evidence. For this purpose, we focus on Windows operating system and the NTFS file system and use outlier detection (Local Outlier Factor method). Collected digital evidence is preprocessed, transformed to binary values, and aggregated by file system inodes and names. Subsequently, we identify digital records (file inodes, file names) relevant to the case. This paper analyzes the combinations of attributes, aggregation functions, local outlier factor parameters, and their impact on the resulting selection of relevant file inodes and file names.
FPGA Implementation of SIMON-128 Cryptographic Algorithm Using Artix-7

FPGA is a hardware architecture based on a matrix of programmable and configurable logic circuits thanks to which a large number of functionalities inside the device can be modified using a hardware description language. These functionalities must often be secured especially when the context is sensitive (military, banking, medical, legal, etc.). In this paper, we put forward an efficient implementation of SIMON's block cipher algorithm using Xilinx Vivado 2018.2. The proposed design is analyzed through simulation on Xilinx Artix-7. A prototype of our design is implemented using the xc7a35tcsg324-1 FPGA chip. Performance and results are discussed.
Metrics for Cyber-Physical Security: a call to action

Cyber-physical systems, by definition, have an effect on assets and people in the real world. Security factors, thus, should play a central role in every decision regarding their deployment and configuration, but this is possible only if said factors are properly defined and can be objectively measured. In this paper, we summarize the state of the art in security metrics, and advocate the need for a research effort aimed at taking the field to a new level of formal soundness and practical usability.
Cloud Computing Infrastructure Security: Challenges and Solutions

Cloud computing market has been growing with its largest over the last two years. Many businesses and education systems moved to the Cloud to support remote work and learning respectively during the COVID time. As data and applications are being migrated outside the enterprise administrative domain to the Cloud, various security challenges should be known to the consumer and the provider. This paper discusses the cloud data center building blocks and surveys the current threats, vulnerabilities, and defense mechanisms at the physical, virtual, orchestration, and application programming interfaces to enable the cloud computing infrastructure as a service.
Tenant-Grained Request Scheduling in Software-Defined Cloud Computing

Cloud providers host various services for tenants’ requests (e.g., software-as-a-service) and seek to serve as many requests as possible for revenue maximization. Considering a large number of requests, the previous works on fine-grained request scheduling may lead to poor system scalability (or high schedule overhead) and break tenant isolation. In this article, we design a tenant-grained request scheduling framework to conquer the above two disadvantages. We formulate the tenant-grained request scheduling problem as an integer linear programming and prove its NP-hardness. We consider two complementary cases: the offline case (where we know all request demands in advance), and the online case (where we have to make immediate scheduling decisions for requests arriving online). A normalization-based algorithm with an approximation factor of <inline-formula><tex-math notation="LaTeX">$ {O}(1)$</tex-math></inline-formula> is proposed to solve the offline problem and a primal-dual-based algorithm with a competitive ratio of <inline-formula><tex-math notation="LaTeX">$[(1-\epsilon), {O}(\log 3\cdot n+\log (1/\epsilon))]$</tex-math></inline-formula> is designed for the online scenario, where <inline-formula><tex-math notation="LaTeX">$\epsilon \in (0,1)$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of racks in the cloud. We also discuss how to integrate our proposed algorithms with the previous (fine-grained) request scheduling mechanism. Extensive simulation and experiment results show that our algorithms can obtain significant performance gains, e.g., the online algorithm reduces the scheduler's overhead more than <inline-formula><tex-math notation="LaTeX">$90\%$</tex-math></inline-formula> and achieves tenant isolation, while obtaining similar network performance (e.g., throughput) compared with the fine-grained request scheduling methods.
Computational Handwriting Analysis of Ancient Hebrew Inscriptions – A Survey

Ancient texts are unique evidence providing a glimpse into the thoughts, day-to-day life, and culture of people of long-gone eras. Paleography, the study of writing, aims at documenting the inscriptions, transliterating the texts, reconstructing their historical context, and studying the evolution of writing itself. The digital revolution gave rise to computational paleography, introducing new tools of data acquisition, image processing, and machine learning. Herein, we will provide an introduction to the emerging field of computational paleography through the lens of ancient Hebrew inscriptions, dating from the Iron Age through the Middle Ages. The years that passed since their composition had a great effect on their preservation level, including blurs, stains, and erosions; moreover, some documents tend to fade in the years after their discovery. Therefore, it is of paramount importance to promptly document ancient inscriptions using the most suitable imaging techniques, such as visible, infra-red, or multispectral imaging. Image analysis and processing techniques, such as binarizations, letter segmentation, and letters’ prior estimation are valuable in their own right or may serve as a stage for subsequent tasks. We will also discuss automatic handwriting analysis and writers’ identification, which could shed light on the historical background of the inscriptions.
Graph Neural Networks for Missing Value Classification in a Task-driven Metric Space

Incomplete instances with various missing values in real-world scenes have brought challenges to the classification tasks. Many existing methods impute the incomplete instances based on their neighbouring instances before classification. However, the construction of such neighbourhood relationships in the original data space may become unreliable since missing values could disturb the traditional distance measurement. Moreover, these methods decouple the imputation from the classification, which makes it difficult for the former to learn from the supervised information, resulting in sub-optimal performance. To this end, this paper proposes graph neural networks for missing values classification (GNN4MV), which directly classify the incomplete instances based on their neighbourhood relationships constructed in a novel task-driven metric space. Specifically, the supervised information is taken as additional guidance in a task-driven metric space to reduce the impact of the missing values for neighbourhood relationship construction. Furthermore, a novel neighbourhood graph convolutional network is proposed in GNN4MV, which enables direct classification of the incomplete instances without imputation by utilizing the graph topology in the constructed neighbourhood relationships. Experiments on real-world datasets demonstrate the robustness and effectiveness of the proposed algorithm.
Analysis and Design of COVID-19 Detection Supervision and Prevention System

More than 140,000 people have died from COVID-19 in Indonesia, and even, vaccines are not an absolute solution for the pandemic due to the spread of this virus; although many have been vaccinated, this only helps increase immunity to prevent infection and a more severe spread. WHO has designated Indonesia as a green country with a 99.3% reduction in cases, and the current rate of COVID spread is only 2% but learns from changes in various lines of life caused by this virus. This study took place in Bina Nusantara University environment, and as a higher education institution, this institution has to prepare all mechanisms by utilizing science and technology in order to prevent further infection and spread. With a surge in Internet use from conditions before the pandemic was 40% to 100% after the pandemic, this is an opportunity to use the internet and technology such as artificial intelligence and big data to form a COVID-19 detection and prevention system in the Bina Nusantara University environment. Therefore, in this study, a COVID-19 detection, surveillance and prevention system will be designed in the Bina Nusantara University environment by utilizing AI and big data technology.
Kernel Analysis for Handwritten Digit Recognition Using Support Vector Machine on MNIST Dataset

In this paper, we solve handwritten digit recognition problems on MNIST dataset using kernel support vector machine algorithm, the four most popular kernels will in turn be used for training to find the most suitable kernel. The hyperparameters of each kernel will also be analyzed and adjusted for the highest accuracy. From this particular problem, some new methods are also proposed for selecting the appropriate kernel with a given dataset.
Financial Reporting for Cryptocurrency

This study compares and contrasts U.S. and international accounting and financial reporting practices for cryptocurrency. We analyze the financial statements of forty global companies that have exposure to cryptocurrencies, including cryptocurrency purchases, mining, payments, trading, and investments in ICOs and early-stage blockchain ventures. We document inconsistency between GAAP and IFRS, as well as distortions that can mislead users in assessing asset value, liquidity, profitability, and cash-generating abilities across firms. In particular, firms receiving cryptocurrencies in revenue-generating activities account for cryptocurrencies as intangibles using different measurement bases and classify the associated cash inflows differently. Some firms place cryptocurrencies in the usual long-term location of intangibles, while others consider intangibles as liquid, short-term assets. Limited guidance about crypto-assets from both IFRS and GAAP lets companies choose which existing standard to apply and how to apply it. Understanding the financial and valuation implications of these new virtual assets is vital for future accounting research and professional practice.
Blockchain-based Privacy-Preserving Group Data Auditing with Secure User Revocation

Progress in cloud computing makes group data sharing in outsourced storage a reality.People join in group and share data with each other, making team work more convenient.This new application scenario also faces data security threats, even more complex.When a user quit its group, remaining data block signatures must be re-signed to ensure security.Some researchers noticed this problem and proposed a few works to relieve computing overhead on user side.However, considering the privacy and security need of group auditing, there still lacks a comprehensive solution to implement secure group user revocation, supporting identity privacy preserving and collusion attack resistance.Aiming at this target, we construct a concrete scheme based on ring signature and smart contracts.We introduce linkable ring signature to build a kind of novel meta data for integrity proof enabling anonymous verification.And the new meta data supports secure revocation.Meanwhile, smart contracts are using for resisting possible collusion attack and malicious re-signing computation.Under the combined effectiveness of both signature method and blockchain smart contracts, our proposal supports reliable user revocation and signature re-signing, without revealing any user identity in the whole process.Security and performance analysis compared with previous works prove that the proposed scheme is feasible and efficient.
Web Page Recommendation Using Distributional Recurrent Neural Network

In the data retrieval process of the Data recommendation system, the matching prediction and similarity identification take place a major role in the ontology.In that, there are several methods to improve the retrieving process with improved accuracy and to reduce the searching time.Since, in the data recommendation system, this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation process.To improve the performance of data validation, this paper proposed a novel model of data similarity estimation and clustering method to retrieve the relevant data with the best matching in the big data processing.In this paper advanced model of the Logarithmic Directionality Texture Pattern (LDTP) method with a Metaheuristic Pattern Searching (MPS) system was used to estimate the similarity between the query data in the entire database.The overall work was implemented for the application of the data recommendation process.These are all indexed and grouped as a cluster to form a paged format of database structure which can reduce the computation time while at the searching period.Also, with the help of a neural network, the relevancies of feature attributes in the database are predicted, and the matching index was sorted to provide the recommended data for given query data.This was achieved by using the Distributional Recurrent Neural Network (DRNN).This is an enhanced model of Neural Network technology to find the relevancy based on the correlation factor of the feature set.The training process of the DRNN classifier was carried out by estimating the correlation factor of the attributes of the dataset.These are formed as clusters and paged with proper indexing based on the MPS parameter of similarity metric.The overall performance of the proposed work can be evaluated by varying the size of the training database by 60%, 70%, and 80%.The parameters that are considered for performance analysis are Precision, Recall, F1-score and the accuracy of data retrieval, the query recommendation output, and comparison with other state-of-art methods.
Is Your Library Website Missing Essential Information?: A Comparison and Evaluation of Public Library Websites in Australia, Canada, and United States

This paper describes the findings of a quantitative study of 1,698 public library websites in Australia, Canada, and the United States over a period of three years using a spreadsheet protocol. The purpose of the research was to evaluate public library websites, available online sources, and whether library staff were available to respond to users’ questions and concerns regarding the website. Descriptive statistics are used to report the results. The study provides public library website information regarding which protocol criteria each country’s libraries attained.
A Collaborative Cloud Model of Auto Scaling With Load Balancing for Effective E-Commerce

Cloud computing focuses on the collaboration of distributed resource services over the network. This chapter focuses on the problem of unequal distribution of traffic load across a different number of subnets of a VPC (virtual private cloud) in a cloud computing environment. It also highlights the idea of collaborating auto scaling and load balancing to achieve an equal distribution of load across the servers. This chapter demonstrates the efficiency of our techniques through detailed process. The load balancer behaves as a single point of contact for the traffic. The benefit of this combined technique can easily be implemented in an e-commerce system (i.e., during sales/certain events, there is a huge spike in traffic at once, which may result in the crashing of the server). Here, load balancer and auto scalability can work in combination for smooth user experience. Finally, this chapter concludes that the collaboration of load balancer along with auto scaling will increase the performance of the instances.
MOTION CONTROL OF A WHEELED MOBILE ROBOT BASED ON SIMULATION

A mobile robot is a complex system capable of receiving information about the outside world and using it to move safely in a purposeful way. The work information subsystem can determine its position using sensors, as well as the location of static and dynamic objects in the workspace. The robot's movement to the target point is carried out according to various algorithms. These algorithms implement various methods of planning the trajectory of movement to the target point: to prevent the robot from colliding with barriers, to reduce the travel time and distance traveled to save battery power. Mobile robots can function in a dynamic environment with a significant degree of uncertainty. One of the main tasks of a mobile robot is planning trajectories by controlling the movement of the robot to reach target point. This article proposes a method for planning trajectory of a mobile robot by controlling its movement. The method is based on simulation of the movement of a mobile robot in an environment with fixed and movable barriers. It is assumed that the direction of dynamic obstacles can change randomly.
Application of AR technologies in the building industry

This article discusses the problem of the application of virtual augmented reality (AR) in the building industry based on the use of information modeling technologies (BIM). In the study, the author focuses on augmented reality space with a presence effect can facilitate better interaction between customers and specialists in various technical issues related to the implementation of the building project. Combined with the concept of building information modeling, AR technologies can improve the general understanding of the construction process. The article is devoted to a close analysis of using the newest augmented reality technologies allows you to work out both the entire project and its individual details as accurately as possible. Object visualization in the real world can be improved by combining AR as well as BIM technologies, since the interactive capabilities provided by AR software and access to BIM information data allow you more intuitively solve the tasks of maintenance and building management.