Reviewerly/exHarmony huggingface dataset:
The research of begin the Apache Giraph

Abstract Apache Giraph is an open-source image processing framework built from Apache Hadoop. Apache Giraph is based on bulk synchronous parallel and Google’s Pregel. It is suitable for running large-scale logical calculations, such as page ranking, shared links, personalized ranking, etc. Giraph focuses on social graph computing and is at the core of Facebook’s open graph tool, handling trillions of connections between users and their behaviors in a few minutes. But because the time is limited, the work just has been done with Hadoop. This paper will be mainly talked about Hadoop and how the difficulty to be solved.
Pseudo Reward and Action Importance Classification for Sparse Reward Problem

Deep Reinforcement Learning(DRL) has witnessed great success in many fields like robotics, games, self-driving cars in recent years. However, the sparse reward problem where a meager amount of states in the state space that return a feedback signal hinders the widespread application of DRL in many real-world tasks. Reward shaping with carefully designed intrinsic rewards provides an effective way to relieve it. Nevertheless, useful intrinsic rewards need rich domain knowledge and extensive fine-tuning, which makes this approach unavailable in many cases. To solve this problem, we propose a framework called PRAIC which only utilizes roughly defined intrinsic rewards. Specifically, the PRAIC consists of a pseudo reward network to extract reward-related features and an action importance network to classify actions according to their importance in different scenarios. Experiments on the multi-agent particle environment and Google Research Football game demonstrate the effectiveness and superior performance of the proposed method.
Adhesive Abrasive Detection for Diamond Images based on Improved Watershed Algorithm

Abstract Diamond images have characteristics of adhesive abrasives besides much background noise, irregular abrasive shapes and different abrasive size, which brings big challenges to accurate diamond abrasive detection. Therefore, an improved watershed algorithm is put forward to detect adhesive abrasive in this work. Firstly, the diamond abrasive image is filtered by Gaussian to suppress the background noise, and then the pre-processed diamond abrasive image is reconstructed by morphology. Through the distance transform of the reconstructed image, marking features for subsequent abrasive detection can be obtained. Finally, the watershed algorithm and extended-minima transform are used to finely detect diamond abrasives and separate adhesive abrasives, respectively, so as to realize the accurate detection of adhesive diamond abrasive image. According to the results of detection experiments, the proposed method based on improved watershed algorithm and extended-minima transform can accurately detect adhesive diamond abrasive images, the recall rate of abrasive is 94.8%, which indicates good recognition accuracy and robustness of the proposed method. The accurate detection results can be further used for subsequent image analysis and abrasive feature parameter extraction.
Outlier detection to Secure Wireless Sensor Networks Based on iForest

Wireless sensor networks (WSN) make use of low-cost and multifunctional nodes with limited at short distances utilizing wireless connectivity. It's an open media platform and it's based on application-driven technology. In addition to military applications, it can be used for environmental monitoring, the health sector, and to respond to emergencies as well. WSN security had attracted a great deal of attention due to its nature and application scenario. Construction of nodes and distributed network infrastructure is prone to a variety of attacks. To ensure its functionality of WSNs, security mechanisms are essential. In the WSN, one of the concerns is provisioning of the security with less powerful nodes and small network. The Isolation Forest algorithm is used in this study to detect the outlier nodes in WSN to secure the network. The research was carried out with a hundred nodes WSNs for temperature data. The result gave a promising result to implement in hardware structure in future.
Research on the Variable Weight Combined Prediction Based on the Optimization Weighting Method

Abstract This paper improves fixed weight combined prediction model, which is established on the predicted value of the ARIMA model and the predicted value of the BP neural network model. Based on the minimum sum of error squares, the time varying optimal weight of the combined prediction model is determined, variable weight combined prediction model is constructed. Based on the analysis of the change rule of Shaanxi province’s GDP over the years, the combined model is used to fit Shaanxi province GDP from 2018 to 2021.The result shows that the fitting errors are 0.11%, 0.01, 0.01% and 0.11%, respectively. The variable weight combined model is used to predict Shaanxi province GDP from 2022 to 2023, which is 29502 billion and 31301 billion respectively. Compared with the ARIMA and BP models, variable weight combined model improves the prediction effect.
r2ogs5: Calibration of Numerical Groundwater Flow Models with Bayesian Optimization in R

Large-scale and high-resolution groundwater models are currently becoming increasingly important in order to clarify the extent to which climate trends and extreme weather affect the groundwater balance regionally. As a result, the parameterization of groundwater models is becoming more detailed and more complex, making conventional calibration methods too time-consuming. Moderating the computational demand to find optimal solutions for the resulting potentially multi-modal objective function requires intelligent and efficient global optimization methods. Moreover, the increasing use of modern scripting languages R and Python to craft environmental analysis workflows calls to integrate groundwater flow simulators in such. Here we introduce and exemplify r2ogs5, a tool that integrates version 5 of the open-source simulation software OpenGeoSys into the programming language R. r2ogs5 allows for calibration of numerical groundwater flow models with a sequential model-based optimization approach that combines Bayesian optimization (BO) with surrogate modeling. Here, we describe the structure and function of r2ogs5 as well as the implemented calibration method. We then demonstrate the calibration method by calibrating 4 and 12 parameters of two simple groundwater flow models. The results indicate that this method needs fewer runs of the groundwater flow model than conventional gradient search and Latin hypercube sampling in case of the 12 parameter model. We believe that our method offers the potential to calibrate computationally expensive groundwater flow models. r2ogs5 supports groundwater flow modelers to access the statistical analysis and visualization capabilities of the R language and is a valuable tool for geoscientists already using R.
16<sup>th</sup> International Engineering and Computing Research Conference (16<sup>th</sup> EURECA)

The 16 th EURECA 2021 is the outcome of the hard work and dedication of researchers, research groups, academic programmes and effort of academic and non-academic staff from the School of Computer Science and Engineering, Taylor’s University, Malaysia. 16 th EURECA Conference was held on 24 th November 2021 with the double edge objectives to assist in equipping the undergraduate and post-graduate with necessary research skills as well as providing an outlet channel for the students to creatively and efficiently share their research outcome. Due to travel restrictions during pandemic, the conference was conducted virtually using Zoom as the video conferencing platform throughout the conference period. With the ever-increasing expectations of graduates to be ready to address a variety of new world challenges, the requirements to equip the graduates, especially those in the field of Engineering, Computing, Science and Technology, with research skills are more pressing. Thus, the aim of the 16 th EURECA 2021 conference is to provide the undergraduate Computing and Engineering students an opportunity to experience a research environment. This is expected to prepare them not only for careers in research but also to provide a precursor for them to use the developed research skills in their future endeavors. In addition, the 16 th EURECA 2021 also serves as a platform for postgraduate Engineering and Computing students to expand their network with other researchers as well as gaining invaluable feedback with regards to their research project from both academicians and industrial experts. List of Organizing Committee, Committee Members Secretariat, Event Management and Finance are available in this pdf.
Steganographic Information Hiding Method Based on Double Wavelet Transform

The article presents a new method of steganographic embedding of confidential information into an image based on double wavelet transform. This method is a continuation of previous studies based on a one-level discrete wavelet transform. The practical implementation of this method is demonstrated on the example of a graphic file in JPEG format. The article shows the theoretical substantiation of the presented method, the data hiding algorithm and the analysis of the research results.
Multi-level Feature Fusion Method for Long Text Classification

News classification task is essentially long text classification in the field of NLP (Natural Language Processing). Long text contains a lot of hidden or topic-independent information. Moreover, BERT (Bidirectional Encoder Representations from Transformer) can only process the text with a character sequence length of 512 at most, which may lose the key information and reduce the classification effectiveness. To solve above problems, the paper puts forward a model of mutli-level feature fusion based on BERT, which is suitable for the BERT through the hierarchical decomposition of long text. Then CNN (Convolutional Neural Networks) and stacked BiLSTM (Bidirectional Long Short-term Memory) based on attention mechanism are used to capture local and contextual features of text respectively. Finally, various features are spliced for classification task. The experimental results show that the model achieves 97.4% accuracy and 97.2% F1 score on THUCNews, 1.2% accuracy and 1.6% F1 score higher than that of BERT-CNN, 1.8% accuracy and 1.4% F1 score higher than that of BERT-BiLSTM, indicating that our model can significantly improve the effectiveness of news classification.
FT4cip: A new functional tree for classification in class imbalance problems

Decision trees (DTs) are popular classifiers partly due to their reasonably good classification performance, their ease of interpretation, and their widespread use in ensembles. To improve the classification performance of individual DTs, researchers have used linear combinations of features in inner nodes (Multivariate decision trees), leaf nodes (Model trees), or both (Functional trees). In this paper, we present a new functional tree, Functional Tree for class imbalance problems (FT4cip). FT4cip is designed to work with class imbalance problems, where one of the classes in the database has few objects compared to another class. FT4cip achieves better classification performance, in terms of AUC, than the best model tree (LMT) and functional tree (Gama) that we identified. The statistical comparison was made in 110 databases using Bayesian statistical tests. We also make a meta-analysis of classification performance per type of database, which helps us recommend a classifier given a problem. We show how each design decision taken when building FT4cip contributes to classification performance or simple models, and rank them according to their importance to classification performance. To avoid a problem of fragmentation in DT literature, we contrast each design decision taken when building FT4cip against LMT and Gama. • We introduce the Functional Tree for class imbalance problems (FT4cip). • We make a statistical comparison of FT4cip against rival methods in 110 databases. • The comparison shows FT4cip has better classification performance than rivals. • A meta-analysis lets us recommend what classifier to use given a specific problem. • The meta-analysis shows FT4cip has great performance in class imbalance problems.
JFMNet: Joint Fusion Multi-Networks for Image Dehazing and Denoising in The Port Environment

The bad weather events, such as haze, in maritime traffic dramatically reduce the visibility, which can seriously affect the ship navigation especially in areas with intensive port traffic. Meanwhile, unwanted signals are inevitably introduced by the maritime imaging device during image capturing and transmission in hazy conditions. Therefore, the captured image is not only degraded by the haze, but also may contain unwanted noise. These low-quality images interfere with the subsequent image processing and increase the potential for maritime traffic accidents. It is therefore imperative to improve the image quality in hazy conditions. To reveal the information hidden in the haze while suppress noise, this paper proposes the joint fusion multi-networks (termed JFMNet) for Image dehazing and denoising in the port environment. The multi-networks use the dehazing module (DHNet) and the denoising module (DNNet) to suppress the noise and haze. Then use the information fusion module (FNet) to integrate the results of the DNNet and DHNet with the information of the original input images to achieve the goal of dehazing and denoising while preserving the details. The modules in multi-networks are based on an encoder-decoder structure. Experiments on a number of challenging hazy images with noise are present to reveal the efficacy of this structure. Meanwhile, experiments also show our JFMNet's superiority over several state-of-the-arts in terms of dehaze quality and efficiency.
Virtual and Augmented Reality for Mechatronics based Applications

Virtual reality (VR) is a rapidly growing medium of technology in its initial stages. Innumerable applications of its use are yet to be explored or properly implemented. One such forum is in the field of education. At the state of this pandemic implementation of virtual reality could prove to be a critical solution for students facing a creative gap of understanding or overcome the difficulties faced while maintaining an engaging environment over two dimensional screens. People of all ages loves to experiment and work with new technology and are more engrossed with it. VR thereby provides the users with a new medium to interact and investigate global phenomena or scenic environments with a completely different perspective within the safe confines of a classroom. This paper highlights the various applications and implementation of this technology along with the accompanying gear. The ease with which simple applications could be created for the purpose of VR/AR display over Unity-3D was practiced. Overall, the paper highlights how Augmented and Virtual reality can reinvent the field of education with the ingenuity it provides.
Junction density based clustering algorithm for data with arbitrary shapes

Density-based clustering algorithms can deal with arbitrary shaped clusters in data. However, most of these algorithms face difficulties in handling large scale data, since they usually need to compute the distance between each pair of data points for density estimation. To alleviate this problem, we define a new type of density called junction density to measure the density of the junction region of two groups generated by K-means. Since the junction density is only computed for neighboring groups, the computation burden is small. Based on the junction density, we propose a new clustering method to merge the groups instead of directly clustering the data points. Specifically, it mines initial clusters in the groups then assigns the remaining groups to corresponding initial clusters. The experiments on several arbitrary shaped datasets demonstrate the efficiency and effectiveness of the proposed method.
Social Risks of Digitalization of Education

The article discusses the social consequences of digitalization in education, focuses on the possible risks of this process. Defining the digitalization of education as an objective consequence of the technical process, the authors draw attention to such negative consequences as the loss of basic cognitive competencies, a decrease in the overall level of training of both students in the system of school and vocational education. The consequence of digitalization is atomization both in the interpersonal interaction of students and in their communication with teachers. Digitalization is one of the mechanisms for the reproduction of social inequality in society, because is aimed at the standardization of thinking, the reproduction of “ready-made knowledge”, and not at the development of creative thinking. Formally equal diplomas do not eliminate differences in the level of training and competencies of specialists from elite and non-elite educational institutions. In the latter, students act as objects, and not as equal subjects of the educational process, and one of the reasons for this is the use of technologies without the human factor to control students’ knowledge, distance learning, minimizing contacts between teachers and students. Understanding the digitalization of education as an objective and contradictory process, the authors of the article actualize the task of preventing a number of its negative consequences.
An architecture for handling heterogenous services in a speech and language therapy platform

Modern technology platforms have a crucial role in many areas of everyday life and have applications in various fields. In this paper the architecture of a speech therapy platform is presented, which is able to support the self-management of patients with chronic speech and language disorders. The developed infrastructure comprises the main outcome in the context of Research-Create-Innovate Plan-V project (https://planv-project.gr/).
Cloud Computing for Healthcare Services: Technology, Application and Architecture

Cloud computing in healthcare becomes a growing field for improvement. The advent of cloud technology provides efficient and reliable outcomes to support healthcare services. Cloud technology reduces these costs for consumers and IT by improving clinical outcomes and quality of service for patients. Cloud technologies offer some of the best solutions for providing better medical services that easily and securely store and share medical information to improve operational efficiency and streamline costs for healthcare organizations. This article discusses the potential rule of cloud technology in healthcare, this may help to enhance the global healthcare scenario. Moreover, it presents the proposed architecture for healthcare services which is based on cloud computing.
Oscillator-based Dynamical Computing Platforms to Solve Combinatorial Optimization

Synchronized oscillator networks provide a promising pathway towards realizing application specific analog platforms capable of solving computationally intractable problems in combinatorial optimization. Specifically, electronic oscillators offer a low-power and integrated circuit (IC)-compatible hardware solution for designing such systems. In this work, we will present the opportunities as well as the challenges of realizing oscillator-based Ising machines to solve NP-hard problems like the Maximum Cut (Max-Cut) of a graph. Using experiments and simulations, we will discuss the performance and the scalability trade-offs that need to be considered in designing such platforms. Finally, we will showcase the opportunities in novel materials and beyond-CMOS devices that can be leveraged to augment the hardware implementation and performance of such systems.
Distance Learning During the Pandemic. Organizational and Managerial Problems

The pandemic has changed the world, and the education system has changed too. Previously, distance education was limited to mainly episodic experiments in its application using traditional paper textbooks and materials. Now, distance education based on digital communication technologies and a digital form of presentation of the educational content became the only possible one. If earlier such a form of learning was inherent rather in the levels of additional and professional education, then the pandemic has made it the only possible format for all levels of education. Naturally, the implementation of such education largely depended on the existence of basic digital competencies among teachers and students, on the availability of access to such education at their place of residence and on the experience of creating and implementing the content of education in the format of digital educational resources, video lectures, seminars with use of educational platforms and others. After several semesters of such training, it is already possible to talk about the first experience of mass distance learning. And when using it henceforth as an independent learning format or a component of blended learning, it is necessary to understand the attitude of all participants in the educational process towards it, their readiness for such a format of activity and its acceptance as a real alternative to traditional learning. Now we can say that in its pure form, in the format of distance learning only, professional education is practically not feasible. It requires meaningful practical content, not only knowledge, but also skills and abilities. And they are not formed outside of specialized laboratories and regular communications in the professional community in the process of teamwork.
Introduction

Abstract We meet our hosts, Alice and Bob, and an undergraduate student, Cardy. Alice and Bob tell Cardy why QC is “the next big thing.” They give a brief introduction to quantum information science (QIS) and its relation to quantum computing (QC). They also provide an overview of the rest of the book and how the book should be used to come to grips with quantum ideas that allow QCs to do things unimaginable for traditional computers. Alice and Bob emphasize that with perseverance everyone can understand the fundamentals of QC and how quantum physics enables entirely new ways of thinking about computational algorithms. They explain the book’s overarching narrative: quantum states, quantum entanglement, and quantum measurements provide the essential resources for QC and QIS.
The Dawn of Superconducting Quantum Information Processing

Quantum mechanics describes the physical world around us with exquisite precision, with no known violations of the theory. Ironically, this precision comes with some additional baggage: the theory allows for a host of complex, delicate states of the physical world, many of which are yet to be produced or observed. The debate of whether their subtle, entangled structure really captures the fundamental nature of the world, and can be used as an engineering resource, is reaching a critical moment. Experiments now straddle the threshold of quantum supremacy, where hitherto inaccessible computations are enabled with quantum computing hardware. Superconducting circuit-based quantum processors are an advanced technology platform for such quantum information processing. The basic architecture, operation, and performance of superconducting qubits and quantum processors is reviewed, along with a discussion of near-term research needs as viewed through the lens of materials science and cryogenic technology.