Reviewerly/exHarmony huggingface dataset:
A Blockchain Based Federated Ecosystem for Tracking and Validating the Authenticity of Goods

Nowadays, the presence of counterfeit goods in the markets involves billions of dollars in damage to individuals, companies, and multinationals. In recent years, Blockchain technology has become the protagonist in different sectors, thanks to its decentralized register feature and the immutability of the data and information stored within it. With this paper, we want to propose a system for tracking and validating the authenticity of tagged assets, based on a federated Blockchain environment. The federation is characterized by the presence of multiple public and private Blockchain instances. This solution allows one or more brands to store information within the private instance such as ownership of the asset, history, maintenance, and other certificates. The public Blockchain is characterized by different brands that share product information to prevent the entry of fake goods into the market. Through the federation, it is possible to cross the data stored within the different Blockchain instances to certify and validate the asset with additional levels of security.
Reconstructing quantum circuits through side-channel information on cloud-based superconducting quantum computers

Our aim in this paper is to present experimental evidence of a side-channel attack on superconducting cloud-based quantum computers, demonstrating that a quantum circuit can be analyzed indirectly using data gathered before and after its execution on a quantum computer. We hope that this can motivate further research into hardware and system stability of quantum computers as well as into more-nuanced systems-level attacks. Although much work has been done on establishing error correcting codes to address issues of decoherence, and schemes can be utilized to leverage quantum effects for the sake of intrusion detection over quantum communication channels, there is a lack of literature in the field regarding side-channel attacks on transpiled quantum circuits, particularly those which leverage quantum effects to gather information about quantum circuits running in the cloud. We explored issues of decoherence by examining quantum circuit behavior both before and after a specified circuit was executed in order to ascertain decalibration rates and potential sources of decoherence: our data clearly shows that information on an interim circuit can be acquired in this manner. Using convenient data classification techniques and various circuit diagrams consisting of one quantum gate and several measurements, we were able to distinguish between these various circuits when executed at position n in the interim phase by training our classifier on data from probing circuits executed at queue positions n-1 and n+1. This shows that there is sufficient information residing in the data before and after a circuit is executed on a cloud-based superconducting quantum computer to allow an attacker to ascertain information about an otherwise unknown third-party circuit and shows potential to form the basis of a side-channel attack in cases where queue positions can be examined to target a third party’s transpiled circuit during experimentation. This side channel attack merits further examination and extrapolation to identify more-complex circuits, and deeper statistical consideration to determine potential sources of information leakage and mitigation strategies.
Histopathological Image Analysis in Medical Decision Making

Digital pathology is one of the significant methods in the medicine field to diagnose and treat cancer. The cell morphology and architecture distribution of biopsies are analyzed to diagnose the spread and severity of the disease. Manual analyses are time-consuming and subjected to intra- and inter-observer variability. Digital pathology and computer-aided analysis aids in enormous applications including nuclei detection, segmentation, and classification. The major challenges in nuclei segmentation are high variability in images due to differences in preparation of slides, heterogeneous structure, overlapping clusters, artifacts, and noise. The structure of the proposed chapter is as follows. First, an introduction about digital pathology and significance of digital pathology techniques in cancer diagnosis based on literature survey is given. Then, the method of classification of histopathological images using deep learning for different datasets is proposed with experimental results.
RGBD-based Hardware Friendly Head Pose Estimation System via Convolutional attention module

Head pose estimation from RGB images without depth information is a challenging task, owing to the loss of spatial information and large head pose variations in the wild. However, most studies adopt deeper convolutional neural network (CNN) models, such as ResNet50, which are limited by the enormous number of parameters to be implemented on edge devices. Owing to novel technological advancements, several edge devices have also included depth cameras and obtain high-quality images. In this study, we propose a lightweight CNN for head pose estimation. By adopting attention module and feature decoupler, we resume the performance decreasing by lower parameters. Moreover, we classify the ground-truth head pose angles of the model intermittently, and adopt the multi-loss strategy to train our model. We evaluate the proposed method on three challenging benchmark datasets, and achieved optimal results for Yaw pose and average. The obtained results indicate that although the proposed model has less parameters, it still maintains a remarkable performance. The total number of parameters is 0.19 M, including RGB and depth path, which is 50% lower than FSA-Net. Consequently, the inference speed is 0.92 ms per pair RGB-D images, which is 8% faster than FSA-Net. With fewer parameters, we achieved 3.1 MAE on yaw angle, which is 22.69% lower than that of Quatnet, including 3.5 MAE on average, which is 7.40% lower than those of other advanced methods.
A Survey of Machine Learning for Big Data Processing

Today's world is a data-driven one, with data being produced in vast amounts as a result of the rapid growth of technology that permeates every aspect of our lives.New data processing techniques must be developed and refined over time to gain meaningful insights from this vast continuous volume of produced data in various forms.Machine learning technologies provide promising solutions and potential methods for processing large quantities of data and gaining value from it.This study conducts a literature review on the application of machine learning techniques in big data processing.It provides a general overview of machine learning algorithms and techniques, a brief introduction to big data, and a discussion of related works that have used machine learning techniques in a variety of sectors to process big amounts of data.The study also discusses the challenges and issues associated with the usage of machine learning for big data.
A Novel Unsupervised Anomaly Detection Approach Using Neural Transformation in Cloud Environment

As a new way of computing on the Internet, cloud computing has completely changed the abstraction and utilization of computing infrastructure and is favored by users of significant enterprises. Therefore, various large-scale cloud computing systems have emerged. Cloud computing systems capture real-time indicators in the form of multivariate time series. Therefore, it is necessary and valuable to do multivariate time series anomaly detection in the cloud computing system. However, because the data monitored in the cloud computing system is massive, redundant, contains noise and missing values, and is accompanied by the randomness and scarcity of anomalies, anomaly detection in the cloud computing system will generate high computational costs and the challenge of uncertain detection results. To solve these problems, we use neural transformation (NT) for anomaly detection, that is, Neural Transformation-Encoding-Auto Regression (NT-E-AR). NT-E-AR uses NT to generate different transformation views from the input data. Convolutional Long-Short Term Memory (ConvLSTM) encoding network and Autoregressive Long-Short Term Memory (LSTM) are combined to extract Spatio-Temporal features of time series data to achieve better anomaly detection capability. Extensive experiments show that NT-E-AR consistently outperforms all baselines on three open datasets and achieves an average F1 (0.772), increasing the average accuracy by 16.52%.
Distance learning of foreign languages in a technological university and its evolution during the pandemic COVID-19

The pandemic and quarantine in more than 80% of Russian Higher Education Institutions have intensified the transition from the traditional education system to distance education. Although its effectiveness of distance education in mass use has not been investigated, some experts and methodologists are already making analytical assessments, and indicate the lack of support and investment in the required amount. Many educational institutions, students and educators are interested in the transition to online learning. In this regard, such course management systems as Moodle are being developed. Some experience of using them has already been gained. However, there is still a need for system analytics as well as adaptation to online learning for both students and teachers. The most important task is the introduction of a distance education system in technological universities for the study of foreign languages. For example, using student volunteers acting as technologists and administrators of distance learning platforms. In this paper, a systematic analysis of the goals, objectives and platforms for distance education, as well as the most relevant problems of teaching foreign languages in technological universities, is carried out. In addition to this, the main goals and objectives of highly rated adaptive distance learning courses are highlighted.
Multi-Scale Patch Partitioning for Image Inpainting Based on Visual Transformers

Image inpainting is a challenging task that aims to reconstruct missing pixels with semantically coherent content and realistic texture using available information. Modern inpainting works rely on neural networks to generate realistic images. However, due to their limited receptive field in convolution operators, they may produce distorted content when a large region needs to be filled. Recent methods have employed transformers to deal with this problem, but their high computational cost makes it difficult to work with global image information. To address this, we propose a multi-scale patch partitioning strategy to subdivide feature maps into non-overlapping patches, and a transformer with a variable number of heads to control the computational cost growth according to the number of patches. Smaller patches enable a broader image coverage, helping to recover structural information, whereas larger patches lead to a reduced computational cost. In contrast to the fixed and small sizes employed in other literature methods, here we explore different patch sizes in the transformer blocks to achieve a good balance between the computational cost and the number of pixel references used in the reconstruction. Extensive experiments on three datasets show that our method achieves very competitive results compared to the state of the art, reaching the best scores in various scenarios, especially for metrics based on human perception. Moreover, our model presented the smallest size. Our qualitative results suggest that the proposed method is able to reconstruct structural content such as parts of human faces.
On the Angular Update and Hyperparameter Tuning of a Scale-Invariant Network

Modern deepKim, Seung Hwan neural networks are equipped with normalization layers such as batch normalization or layer normalization to enhance and stabilize training dynamics. If a network contains such normalization layers, the optimization objective is invariant to the scale of the neural network parameters. The scale-invariance induces the neural network's output to be only affected by the weights' direction and not the weights' scale. We first find a common feature of good hyperparameter combinations on such a scale-invariant network, including learning rate, weight decay, number of data samples, and batch size. Then we observe that hyperparameter setups that lead to good performance show similar degrees of angular update during one epoch. Using a stochastic differential equation, we analyze the angular update and show how each hyperparameter affects it. With this relationship, we can derive a simple hyperparameter tuning method and apply it to the efficient hyperparameter search.
Binocular stereo matching algorithm based on multi-feature aggregation

To address the issue of high mismatching rates in binocular stereo matching of objects with weak texture and repeated texture, a multi-feature aggregation-based binocular disparity estimation algorithm is proposed, taking into account the significant influence of cost aggregation on disparity prediction in stereo matching. The feature extraction network combines the left and right image features to create a cost volume, extracts effective multi-scale features using spatial pyramid pooling, and employs dilated convolution to expand the receptive field. In cost aggregation network part introduces 3d attention mechanism module, the module will be the channel distribution characteristics of different weights, after repeated convolution of the advanced features of optimization, improvement of 3d context information generated module, provide identification characteristics, and design characteristics of polymerization more modules, the fusion of high-and low-level features and context information, ultimately generated by means of regression parallax figure. The end-point-error is 1.062 on the SceneFlow dataset, and the three-pixel mismatching rate is 1.79% and 2.21% on the KITTI2012 and KITTI2015 datasets, respectively. The results show that the proposed algorithm effectively reduces the disparity error of prediction.
An Overview on Designs and Applications of Context-Aware Automation Systems

Automation systems are increasingly being used in dynamic and various operating conditions. With higher flexibility demands, they need to promptly respond to surrounding dynamic changes by adapting their operation. Context information collected during runtime can be useful to enhance the system's adaptability. Context-aware systems represent a design paradigm for modeling and applying context in various applications such as decision-making. In order to address context for automation systems, a state-of-the-art assessment of existing approaches is necessary. Thus, the objective of this work is to provide an overview on the design and applications of context and context models for automation systems. A systematic literature review has been conducted, the results of which are represented as a knowledge graph.
Using natural language processing techniques to improve manual test case descriptions

Despite the recent advancements in test automation, testing often remains a manual, and costly, activity in many industries. Manual test cases, often described only in natural language, consist of one or more test steps, which are instructions that must be performed to achieve the testing objective. Having different employees specifying test cases might result in redundant, unclear, or incomplete test cases. Manually reviewing and validating newly-specified test cases is time-consuming and becomes impractical in a scenario with a large test suite. Therefore, in this paper, we propose an automated framework to automatically analyze test cases that are specified in natural language and provide actionable recommendations on how to improve the test cases. Our framework consists of configurable components and modules for analysis, which are capable of recommending improvements to the following: (1) the terminology of a new test case through language modeling, (2) potentially missing test steps for a new test case through frequent itemset and association rule mining, and (3) recommendation of similar test cases that already exist in the test suite through text embedding and clustering. We thoroughly evaluated the three modules on data from our industry partner. Our framework can provide actionable recommendations, which is an important challenge given the widespread occurrence of test cases that are described only in natural language in the software industry (in particular, the game industry).
CompNVS: Novel View Synthesis with Scene Completion

We introduce a scalable framework for novel view synthesis from RGB-D images with largely incomplete scene coverage. While generative neural approaches have demonstrated spectacular results on 2D images, they have not yet achieved similar photorealistic results in combination with scene completion where a spatial 3D scene understanding is essential. To this end, we propose a generative pipeline performing on a sparse grid-based neural scene representation to complete unobserved scene parts via a learned distribution of scenes in a 2.5D-3D-2.5D manner. We process encoded image features in 3D space with a geometry completion network and a subsequent texture inpainting network to extrapolate the missing area. Photorealistic image sequences can be finally obtained via consistency-relevant differentiable rendering. Comprehensive experiments show that the graphical outputs of our method outperform the state of the art, especially within unobserved scene parts.
Multi-axis Gating MLP with Bidirectional Feature Extraction for Sequence Recommendation

The sequence recommendation task uses neural network models such as GRU and Self-Attention to extract potential preference characteristics from the user’s behavior data, This makes the recommendation more accurate. However, they use the sequence information of items to build embedded features, which may destroy the representation of interaction between items. Therefore, based on the traditional RNN network, we use multi-axis gating unit to capture the user behavior characteristics inside and across channels. In our experiments, we found that multi-axis gating unit can significantly improve the performance of sequence recommendation model. Compared with Self-Attention model, MLP architecture has less parameters and lower time complexity. Specifically, we propose a spatial gating unit MLP model for sequential recommendation tasks, which uses multi axis gating units to capture the behavior characteristics of multiple users in the spatial dimension. A large number of experiments on multiple real data sets show that our proposed method is superior to RNN and Self-Attention based methods.
Immersive ParaView: An Immersive Scientific Workflow for the Advancement of Measurement Science

We present ongoing work to enhance ParaView with new immersive visualization capabilities and demonstrate its use in the development of our scientific workflow in support of visual analytics in an immersive environment to advance measurement science. Two case-studies of complex meteorology and radio-frequency (RF) data as part of the knowledge discovery process reveal the benefits of interactively exploring the three-dimensional data with immersive technologies such as the CAVE and head mounted display virtual reality. By adding support of immersive technology into Para View, researchers can more naturally investigate the data of three-dimensional simulations. These efforts to provide tools for immersive visualization are guided by the need to advance measurement science, standards, and technology. To make these tools more broadly accessible and impact-ful, we promote the use of software standards related to visualization and immersive systems.
Energy Saving Using Privacy Data Secure Aggregation Algorithm

For the Internet of things (IoT) secure data aggregation issues, data privacy-preserving and limited computation ability and energy of nodes should be tradeoff. Based on analyzing the pros-and-cons of current works, a low energy- consuming secure data aggregation method (LCSDA) was proposed. This method uses shortest path principle to choose neighbor nodes and generates the data aggregation paths in the cluster based on prim minimum spanning tree algorithm. Simulation results show that this method could effectively cut down energy consumption and reduce the probability of cluster head node being captured, in the same time preserving data privacy.
A forward intersection strategy on structural parameters for robotic vision system with 3 or more DOFs

The vision-based measurement for robot with 3 or more degrees of freedom (DOFs) require on-site calibrations and auxiliary sensors to establish an object coordinate system at present. Obviously, it is time consuming and requires extensive computing resources, and these hinder the application of vision-based measurement for robot in many fields. To solve these questions, a measurement coordinate system on robot was established according to the posture of robotic parts in preset position, structure of robot and vision system. Two categories of structural parameters were proposed; posture transformation matrices of robotic parts from the preset posture to an actual posture were built; and the visual-related spatial vectors were expressed by the two categories of structural parameters, optical parameters, and posture matrices in the measurement coordinate system. According to these, a new forward intersection strategy is proposed for robotic vision system. The new structural parameters for vision system on robot were proven to be reliable. The expressions of the visual-related vectors for robot were found to be correct and effective. Experimental results demonstrated that the new forward intersection strategy was accurate and efficient, and this strategy can be applied to robotic vision platforms to facilitate control of robots.
A Model to Integrate ICH Inventories and Open Data

Using the “ICH Light” metadata model created for the QueryLab portal, we sought to collect data from different archives, harvesting and storing it from a minimal set of information, with the goal of designing a discovery section to facilitate data search and retrieval. This paper will describe the activities and problems faced while modeling the common data structure implemented aimed to design a new and extensible one, defining a format that also opens towards the Linked Open Data environment. Starting from the analysis of similar experiences applied to museum data, we studied how to apply the Europeana Data Model (EDM) classes and focused on the specific entities designed to express the intangible cultural heritage described by the CHDE model (Cultural Heritage in Digital Environments), which provides a solution using the concept of Instantiation.
Statistical Cryptanalysis of ElGamal Cryptosystem for measuring security in disruptive technology

ElGamal encryption is one of the encryption processes which employs randomization in the encryption procedure. In this paper, we applied cryptanalysis to Elgamal cryptosystem. Cryptanalysis is the study of cryptosystems to understand how they work and look for weaknesses and improve the system and algorithms that are used in that system. We have done entropy analysis, floating frequency analysis, histogram, N-gram, autocorrelation, and periodicity analysis using crypTool
Key Issues of Blockchain

Blockchain has sparked passionate debates and even controversies. The “double-edged sword” aspect of technology is especially prominent in blockchain, and its uncertainty is extremely apparent. In this chapter, a few open questions about the nature and value of blockchain are discussed, with the aim of providing some ideas for understanding and grasping this technology.