Reviewerly/exHarmony huggingface dataset:
Quantum optimization algorithm for solving elliptic boundary value problems on D-Wave quantum annealing device

The quantum annealing devices, which encode the solution to a computational problem in the ground state of a quantum Hamiltonian, are implemented in D-Wave systems with more than 2,000 qubits. However, quantum annealing can solve only a classical combinatorial optimization problem such as an Ising model, or equivalently, a quadratic unconstrained binary optimization (QUBO) problem. In this paper, we formulate the QUBO model to solve elliptic problems with Dirichlet and Neumann boundary conditions using the finite element method. In this formulation, we develop the objective function of quadratic binary variables represented by qubits and the system finds the binary string combination minimizing the objective function globally. Based on the QUBO formulation, we introduce an iterative algorithm to solve the elliptic problems. We discuss the validation of the modeling on the D-Wave quantum annealing system.
Digital assets in the context of the fourth industrial revolution, international integration, and Vietnamese law

The legislative practice of many countries shows that, from a cautious attitude in recognizing this type of asset in law, many countries have issued legal provisions to solve arising problems including measures to handle situational, immediate, and detailed digital assets in the law to create an official and long-term legal basis to regulate legal relations related to digital assets. It can be affirmed that the formalization of digital assets in national law will create a legal basis for exploiting the values of digital assets, thereby encouraging the development of innovative enterprises, technological innovation; and this is also a basis for collecting taxes on profits earned from investment activities that contribute to shaping a trading market of a new asset. In the condition of having deeply integrated into the world economy and actively integrating into the world as well as adapting to the fourth industrial revolution, Vietnam needs to have a roadmap for implementation from the formalization of digital assets in the Civil Code to digital assets in business activities as well as digital asset transactions, in which priority is given to the recognition of digital assets as an asset in the Civil Code to “pave the way” for digital asset regulations in the business field and laws related to transactions, transfer, control and prevent crimes that may arise from transactions related to digital assets.
Network Integrated Power Architecture for Terrestrial and Modular Data Center Contexts

The transition to renewable energy is a strategy that has been recognized for future data centers seeking to limit environmental emissions. In addition, innovation has led data center operators to install high-capacity renewable energy farms. However, the energy outputs from these farms are not totally utilized during the epochs of low server utilization. The surplus energy can be used to operate modular data center systems experiencing a power deficit. The presented research proposes an architecture to use surplus power in terrestrial data centers is used to meet the power deficit in modular data center systems. Performance evaluation shows that the proposed mechanism increases the power available to the modular data center by 78.3%. In addition, the proposed mechanism increases the number of communication epochs for modular data centers by 49.7% on average.
Blockchain-based safety production supervision system for power plants

Traditional information technology cannot solve the problems of cheating and false data in the management of safety production in power plant enterprises. Blockchain technology is widely used in the fields of distributed, Internet of Things and trusted supervision because of its characteristics of tamper-proof, traceability and openness and transparency. Taking electric power enterprises as an example, this paper discusses the use of blockchain to supervise the safety production of power plant enterprises, so as to ensure the integrity, authenticity and timeliness of supervision and prevent the occurrence of safety accidents in the electric power industry.
Comparison of current blockchain privacy protection technologies and prospects for future trends

A decentralized, unchanging, and verifiable public ledger that can track the exchange of digital assets is provided by the blockchain. After Bitcoin brought out the blockchain, the blockchain is used in new fields such as smart cities and e-health, and plays a very important role. Although the decentralization of the blockchain makes the construction of digital currency possible, its open and transparent design also risks leaking user privacy. In order to fulfill the security needs of blockchain applications for privacy related information, the relevant researchers have recently done research on blockchain privacy protection challenges and posted corresponding protection technologies, a few of which have been also successfully implemented in several prevailing blockchain applications. In view of the blockchain privacy protection technology issues, this paper investigates the latest progress of blockchain privacy protection technology, conducts a comparative analysis, and summarizes at the end, so as to make an outlook on future trends.
Information monitoring system of transmission line operation based on data analysis

As an important part of the power transmission in the power system, the safety and stability of the transmission line directly affect the availability of the power system. With the continuous development of various industries, people's demand for energy is increasing, and the requirements for power supply reliability are also getting higher and higher. Transmission lines are an important part of the power system. Once a major power outage occurs, it will cause huge losses and inconvenience to the national economy. Therefore, the equipment and operation of transmission lines need a practical and effective management system to manage. Clearly, big data analytics has great advantages in this regard.
Heterogeneous Graph Convolutional Network Based on Correlation Matrix

Heterogeneous graph embedding maps a high-dimension graph that has different sorts of nodes and edges to a low-dimensional space, making it perform well in downstream tasks. The existing models mainly use two approaches to explore and embed heterogeneous graph information. One is to use meta-path to mining heterogeneous information; the other is to use special modules designed by researchers to explore heterogeneous information. These models show excellent performance in heterogeneous graph embedding tasks. However, none of the models considers using the number of meta-path instances between nodes to improve the performance of heterogeneous graph embedding. The paper proposes a Heterogeneous Graph Convolutional Network based on Correlation Matrix (CMHGCN) to fully use of the number of meta-path instances between nodes to discover interactive information between nodes in heterogeneous graphs. CMHGCN contains two core components: the node-level correlation component and the semantic-level correlation component. The node-level correlation component is able to use the number of meta-path instances between nodes to calculate the correlation between nodes guided by different meta-paths. The semantic-level correlation component can reasonably integrate such information from different meta-paths. On heterogeneous graphs with a large number of meta-path instances, CMHGCN outperforms baselines in node classification and clustering, according to experiments carried out on three benchmark heterogeneous datasets.
Developing Conceptual Framework of Software Defect Prediction in Software Testing: The Case of Ethiopian Software Industries

Purpose: Software defect prediction is one of the most active research areas in software engineering. As our dependency on software is increasing, software quality is becoming gradually more and more important in present era. Software used almost everywhere and in every tread of life. Software consequences such as fault and failures may diminish the quality of software which leads to customer dissatisfaction. Different points that are related to the current work had been discussed.&#x0D; Methodology: This research contains a detailed explanation of the scientific methods and methodologies used for the design of proposed framework. Primary and secondary source of data has been used.&#x0D; Findings: Due to the tremendous amount of data generated daily from fields such as business that software has resulted in the generation of tremendous amount of defected data. As the organizations struggle to handle and utilize effectively all the information available in order to provide better products and services and gain competitive advantage, defect separation and the so called “big data” analytics are two fields that currently constitute matter of concern and discussion.&#x0D; Unique Contribution to Theory, Practice and Policy: There are different research areas in defect prediction using big data analytics defect segmentations one of the issues. Defect Segmentation is an important component of much organization, and the algorithm designed in this work is expected to be a significant contribution to the field, and mainly to researchers working on various aspects of defect prediction defect segmentation using big data analytics. Therefore, the researchers in the area can use the algorithm or the implemented system for processing segmentation as component in their research, mainly on machine learning applications.&#x0D;
Learning dynamic relationship between joints for 3D hand pose estimation from single depth map

3D hand pose estimation from a single depth map is an essential topic in computer vision. Most existing methods are devoted to designing a model to capture more spatial information or designing loss functions based on prior knowledge to constrain the estimated pose with prior spatial information. In this work, we focus on constraining the estimation process with spatial information adaptively by learning the mutual position relationship between joint pairs. Specifically, we propose a dynamic relationship network (DRN) with dynamic anchors. The preset fixed anchors are employed to estimate the position of each joint initially. Then, each joint is considered a dynamic anchor, which plays the role of a dynamic regressor to adjust the initially estimated position of each joint. The final estimation of each joint is the weighted sum of the results from all the dynamic anchors. Extensive experiments on benchmarks demonstrate that our method provides competitive results compared with state-of-the-arts.
Prospecting for ophiolite-type chromite deposit in Sartohay, West Junggar (NW China): Constraints from geological and geophysical data

Chromite is a critical mineral that is of great significance for the development of metallurgical, chemical, and other industries. In order to study the deep ore prospecting methods of ophiolite-type chromitite, we have carried out plane-profile geological and mineral surveys, as well as high-precision gravity, magnetic, and controlled source audio-frequency magnetotelluric (CSAMT) measurements in the Sartohay, West Junggar. Based on the analysis, interpretation, and inference of multi-source geological information, a three-dimensional (3D) geological model of the typical mining area in Sartohay District was established. The results show that Sartohay ophiolite was occurred in the form of rootless tectonic blocks and generally displayed deformation and metamorphism (e.g., oblique thrust, transverse extrusion, and lateral strike slip). Sartohay ophiolite is in tectonic contact with surrounding strata. Several northwest dipping high-angle thrust faults are developed at the contact sites and inside the ophiolite rock mass, showing a thrust imbricated structure. Meanwhile, ophiolite in the modeling area is a NW-trending monoclinic rock mass. The base surface of the ophiolite is obliquely distributed in an “N-like” shape, and the chromitite bearing lithofacies zones also have a NW-trending distribution. Geophysical data reveal that the studied mining area is characterized by a layered electrical structure in the vertical direction. The upper layer displays a high-resistivity agglomerate and a low-resistivity band distributed alternately, while the lower layer is a complete high-resistivity block. Combined with the deposit characteristics in Sartohay, it is considered that the morphological occurrence, geological structure, lithology, and lithofacies of ophiolite control the spatial distribution of related chromitites. The dunite-harzburgite lithofacies belts, highly strain zones, and strong tectonic alteration belts of the ophiolite rock mass are significant sites for the chromite ore bodies/groups occurrence. This study provides approaches for researching on the production laws of ophiolite-type chromitite and supplys a prospecting pattern for deep concealed orebodies.
Anomaly Detection using Generative Adversarial Network VAE

Fraudulent credit card transactions continue to be one of the problems facing businesses and banks. It causes us to lose billions of dollars each year. Designing efficient algorithms is one of the most important challenges in this field. This paper aims to propose an efficient approach that automatically detects fraud in credit card transactions using Generative Adversarial Network Variational Auto encoders. The effectiveness of the proposed method (Generative Adversarial Network VAE) has been proved in identifying fraud in actual data from transactions made by credit cards. However, the typical credit card data set presents an imbalanced classification landscape due to highly skewed class distributions. Researchers have proposed several strategies to address these imbalances, but draw backs still remain. The proposed method is tested on an open credit card fraud dataset, which contains 20 million transactions generated from a multi-agent virtual world simulation performed by IBM. Experimental results show that the VAE method performs better than traditional deep neural network methods. Through experiments, compared with the VAE and traditional fully connected neural networks, the results showed the proposed algorithm improves the classification accuracy of a minority class of imbalanced datasets.
A single image super resolution method based on cross residual network and wavelet transform

In this paper, we propose a method to realize single image super resolution using a network composed of residual blocks with a cross architecture and wavelet transform. For the high quality of the reconstructed high resolution image, the brightness change must be sharp at the edge and gentle at the flat region. In single image super resolution, it is important to accurately predict the high-frequency components of the image. Hence, we propose a method to realize single image super resolution by estimating the high-frequency component in the wavelet domain using neural network composed of residual blocks with two crossing skip connections.
Algorithm for detection of moving objects observed by a video camera

An algorithm to detect moving objects captured by a moving video camera is presented. The algorithm is based on detection of motion on video frames taken by a moving video camera, as well as on finding and analyzing the trajectories of moving objects. A feature of the algorithm is detection on frames of connected areas (clusters) of possible object motion. Then moving points on the detected clusters are found, and those points trajectories are built with help of the optical flow. The trajectories are used as features of moving objects. Only smooth trajectories are exploited for detection of moving objects, and the remaining ones are removed from consideration. An object is considered as moving on the current frame if it contains ends of a sufficient number of trajectories of moving points found on previous frames. The presented algorithm has a low computational complexity, which allows it to be used in real or near real time on small computers that have only a few processors of the ARM architecture without powerful parallel computing tools such as GPUs or neural network processors NPU.
Is stock market development a spur to poverty reduction? Empirical evidence from selected sub-Saharan African countries

In this study, the relationship between stock market development and poverty reduction is examined in sub-Saharan African countries during the period 1993–2019. Three proxies of stock market development were used, namely stock market capitalization, stock market traded value and stock market turnover ratio, thereby leading to three separate model specifications. A battery of panel data estimation techniques was used in the study to examine the existence of cross-sectional dependence. Furthermore, the second-generation panel unit root test and panel cointegration test were used alongside the first-generation tests to examine this linkage. Contrary to some of the previous studies, the results of the panel least squares, fully modified ordinary least squares and dynamic ordinary least squares show that stock market development has no significant impact on poverty reduction in sub-Saharan countries during the period under study. These results apply irrespective of the proxy used to measure the level of stock market development. The results were also corroborated by the heterogenous non-Granger causality test, in which a prima facie causal flow was found from poverty reduction to stock market development when stock market turnover ratio was used as a proxy. Policy implications are discussed.
Quantum key distribution over FSO channel using error reconciliation protocol

Quantum key distribution (QKD) has evolved as a robust way of secret key distribution based on renowned modern physics concepts. To transmit sensitive information in the government, private, and personal sectors, high-security levels are now necessary. All conventional cryptographic algorithms applied in the communication models, which rely on mathematical models and conceptual assumptions, are unsecure. Thus, QKD systems are the best choice for protecting this information as a countermeasure since they provide unconditional security. In this paper, a design is proposed for the FSO channel using the CASCADE protocol under different atmospheric condition such as haze, rain and snow. The suggested framework’s effectiveness and security in the context of QKD with two non-orthogonal photon states are assessed. Simulation results of this model show the percentage of original sequence recovery; the number of errors removed, optical spectrum at transmitter and receiver for a base frame length of 104 bits. Moreover, Error correction is calculated per iteration to improve signal quality at the receiver side and compared to previous work. The calculated value in this article shows a better result. Hence, both the sender and the receiver could achieve a security key using this method.
The Study of Secure Communication using Cryptography Algorithms

Network and internet applications are growing in popularity daily. Security and safety procedures are needed for sensitive information. By transforming data from its original form into an unreadable one, encryption algorithms offer the required defence against attacks by data intruders. Now a days, username/password authentication makes up the majority of web authentication. Data security can be achieved via a method known as cryptography. In the past, military data was kept secure using cryptography to safeguard national security. After the advent of communication tools, the range of uses for cryptography has significantly increased today; cryptography is primarily necessary to ensure that data are safeguarded against penetrations and to prevent the practise of spying. The state of the art for several different cryptographic methods used in networking applications is discussed in this paper with help of examples.
Exploring digital timestamping using smart contract on the Solana blockchain

Timestamps establish a means to dictate the existence of a message at a particular moment in the past. Traditional digital timestamping service utilizes Public Key Infrastructure (PKI) and thus requires the presence of a Time Stamping Authority, whose job is to ensure the message’s validity. However, with the rise of distributed computing and blockchain technology, it has become possible to obtain timestamps in a decentralized manner, eliminating the need for central authorities. Previous research involves sending transactions or utilizing a smart contract mechanism to store hashes on blockchain and validating the outcome using public ledgers. By prototyping a simple digital timestamping protocol on the Solana blockchain, this article explores the natural advantages of the Solana blockchain for timestamping, implements a smart contract of digital timestamping on the Solana blockchain, and measures its latency, costs, and performance. The initial estimation expects the accuracy of the timestamps produced by the prototype to reach the average sub-second slot time. However, due to network propagation delay, it is not always possible to hit this level. The cost incurred during the process is also analyzed and discussed. Finally, this article highlights the potential ability of the blockchain to provide modern decentralized services.
Dynamic monitoring method of mutation event network public opinion based on topic crawler

With the rapid development of Internet and the increasing amount of information, it is necessary to use big data technology to solve the bottleneck of processing speed and storage of traditional public opinion monitoring in the era of big data. In this paper, Hadoop open source platform is used to build a big data foundation, realize distributed storage of data, use MapReduce and Spark to realize distributed computing and processing of data, and process the collected data in text. The algorithm model is used to classify and cluster the text information to complete the analysis of text emotional tendency, topic discovery and tracking, and innovatively grasp the public opinion information status of network emergencies. The experimental results from the acquisition rate and average correlation test prove that the algorithm in this paper has higher calculation accuracy. It can provide real-time and effective public opinion analysis service.
Encryption methods on major telecommunication applications and thoughts

Ever since the invention and massive implementation of telecommunication invited themselves during wartime, it is crucial to keep the message as secret as possible from adversaries. Back in time, the leak of messages meant thousands of potential casualties whereas now enormous costs on fortune and confidential secrets. In this paper, we will dive into the comparison of modern communication apps such as WhatsApp, Messengers, and WeChat with their encryption methods including AES encryption and RSA encryption. Then, we build a front-to-end message app using the RSA encryption method and an easy version of a random number generator to increase the security level. We will discuss the utility of the three apps and their complexity in terms of the input, algorithm, and pros and cons of the features they provide objectively to the average consumer, regardless of occupation or special personalized need. In addition, throughout the studies of the three apps and their flow of design on how to keep the consumers safe during their sharing of messages, we will discover a trend of evolution in how the society treats the idea of information security and what improvements can be done from time to time.