Reviewerly/exHarmony huggingface dataset:
A Comprehensive, Longitudinal Study of Government DNS Deployment at Global Scale

Within the Domain Name System (DNS), government domains form a particularly valuable part of the names-pace, representing trusted sources of information, vital services, and gateways for government personnel to engage in their duties. As the COVID-19 pandemic has unfolded, governments’ digital resources have become increasingly important to provide support to populations largely in isolation. The accessibility of these resources relies largely on the trustworthiness of the domains that represent them. In this paper, we conduct an extensive measurement study focused on the availability and legitimacy of DNS records in the authoritative nameservers of government domains for over 190 countries. Our measurements reveal that thousands of domains do not use replicated authoritative name-servers, as well as a substantial increase in the trend of more domains relying on a single third-party DNS services provider. We also find more than 1,000 domains vulnerable to hijacking due to defective delegations. Our work shows that although robust overall, the deployments of authoritative nameservers in government domains still contain a non-trivial number of configurations that do not meet RFC requirements, leading to poor performance and reduced reliability that may leave domains vulnerable to hijacking.
Utilizing The Potential Of Blockchain Technology For Leading Education 4.0

Blockchain is the development of a digital data system connected to cryptography, where bitcoin and cryptocurrencies do not escape their use. From the revolutionary era of blockchain technology development, now blockchain technology is widely applied in various fields such as banking, services, entrepreneurship and so on. Therefore this research will lead to the world of education which is one that was applied in the development of previous blockchain technology and will continue to be developed, the great potential of blockchain technology in education will continue to be used to solve problems in the world of education. The result of this research is data that is blockchain in the website framework and has a novelty in the form of blockchain implementation on aid proposals and diploma certificates at universities. This research method uses 2 methods, namely: literature study method and descriptive method. With full hope that this blockchain technology can help in terms of high security in the world of education.
Speckle Reduction via Deep Content-Aware Image Prior for Precise Breast Tumor Segmentation in an Ultrasound Image

The performance of computer-aided diagnosis (CAD) systems that are based on ultrasound imaging has been enhanced owing to the advancement in deep learning. However, because of the inherent speckle noise in ultrasound images, the ambiguous boundaries of lesions deteriorate and are difficult to distinguish, resulting in the performance degradation of CAD. Although several methods have been proposed to reduce speckle noise over decades, this task remains a challenge that must be improved to enhance the performance of CAD. In this article, we propose a deep content-aware image prior (DCAIP) with a content-aware attention module (CAAM) for superior despeckling of ultrasound images without clean images. For the image prior, we developed a CAAM to deal with the content information in an input image. In this module, super-pixel pooling (SPP) is used to give attention to salient regions in an ultrasound image. Therefore, it can provide more content information regarding the input image when compared to other attention modules. The DCAIP consists of deep learning networks based on this attention module. The DCAIP is validated by applying it as a preprocessing step for breast tumor segmentation in ultrasound images, which is one of the tasks in CAD. Our method improved the segmentation performance by 15.89% in terms of the area under the precision-recall (PR) curve (AUPRC). The results demonstrate that our method enhances the quality of ultrasound images by effectively reducing speckle noise while preserving important information in the image, promising for the design of superior CAD systems.
From Decentralization to Oligopoly: A Data-Driven Analysis of Decentralization Evolution and Voting Behaviors on EOSIO

As one of the most popular blockchain systems, EOSIO has been widely used in decentralized applications (DApps). Compared with traditional proof-of-work (PoW)-based blockchain systems like Bitcoin, EOSIO achieves a high transaction throughput and an alleged decentralization with the Delegated Proof-of-Stake (DPoS) consensus protocol. However, recent reports claimed the existence of voting collusion and manipulation during the DPoS consensus procedure of EOSIO, which may greatly decline the decentralization degree, fault tolerance, and reliability of the whole system. In this article, we obtain data from up to 135 000 000 blocks of EOSIO and conduct a data-driven decentralization analysis. Specifically, we characterize the decentralization evolution of the two phases in DPoS, namely block producer election and block production. Moreover, we study the voters with similar voting behaviors and propose methods to discover abnormal mutual voting behaviors in EOSIO. The analysis results show how EOSIO gradually evolves from decentralization to oligopoly and our methods can effectively capture abnormal voting phenomena in the EOSIO, which can also provide important insights for the design and maintenance of other DPoS-based blockchains.
Binary [<i>n</i>, (<i>n</i> + 1)/2] Cyclic Codes With Good Minimum Distances

The binary quadratic-residue codes and the punctured Reed-Muller codes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal {R}}_{2}((m-1)/2, m))$ </tex-math></inline-formula> are two families of binary cyclic codes with parameters <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[n, (n+1)/2, d \geq \sqrt {n}]$ </tex-math></inline-formula> . These two families of binary cyclic codes are interesting partly due to the fact that their minimum distances have a square-root bound. The objective of this paper is to construct two families of binary cyclic codes of length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{m}-1$ </tex-math></inline-formula> and dimension near <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{m-1}$ </tex-math></inline-formula> with good minimum distances. When <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m \geq 3$ </tex-math></inline-formula> is odd, the codes become a family of duadic codes with parameters <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[2^{m}-1, 2^{m-1}, d]$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d \geq 2^{(m-1)/2}+1$ </tex-math></inline-formula> if <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m \equiv 3 \pmod {4}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d \geq 2^{(m-1)/2}+3$ </tex-math></inline-formula> if <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m \equiv 1 \pmod {4}$ </tex-math></inline-formula> . The two families of binary cyclic codes contain some optimal binary cyclic codes.
Gesture recognition from <scp>RGB</scp> images using convolutional neural network‐attention based system

Summary Hand gestures can be categorized based on their applications as conversational, manipulative, controlling, and communicative gestures. Hand gesture recognition is an aspect of human action recognition, which plays a notable role in communication among the deaf and dumb community. Accurate recognition and classification of hand gestures with similar postures is still a complex problem. The main objective of this work is to employ deep learning based convolutional neural networks for human action recognition. This article introduces a convolutional neural network based on VGG16 architecture with an attention approach to mitigate the issue. Incorporating an attention‐based module with the VGG16 architecture is the prime reason for potentially learning distinguishing image features by the network. Besides, the proposed robust system collectively recognizes and classifies all the available 36 hand gesture classes of the Massey database, where most previous work selected only a few distinguishable gestures. Experimental results show that the average recognition accuracy for characters with similar hand postures like “m” and “n” is better than the state‐of‐the‐art networks. Additionally, the proposed method attains about 3% higher recognition accuracy than the state‐of‐the‐art networks for all gesture classes. The efficacy of the proposed architecture has also been validated via precision, recall, and F‐score.
BFRA: A New Binary Hyper-Heuristics Feature Ranks Algorithm for Feature Selection in High-Dimensional Classification Data

Feature selection is one of the main issues in machine learning algorithms. In this paper, a new binary hyper-heuristics feature ranks algorithm is designed to solve the feature selection problem in high-dimensional classification data called the BFRA algorithm. The initial strong population generation is done by ranking the features based on the initial Laplacian Score (ILR) method. A new operator called AHWF removes the zero-importance or redundant features from the population-based solutions. Another new operator, AHBF, selects the key features in population-based solutions. These two operators are designed to increase the exploitation of the BFRA algorithm. To ensure exploration, we introduced a new operator called BOM, a binary counter-mutation that increases the exploration and escape from the BFRA algorithm’s local trap. Finally, the BFRA algorithm was evaluated on 26 high-dimensional data with different statistical criteria. The BFRA algorithm has been tested with various meta-heuristic algorithms. The experiments’ different dimensions show that the BFRA algorithm works like a robust meta-heuristic algorithm in low dimensions. Nevertheless, by increasing the dataset dimensions, the BFRA performs better than other algorithms in terms of the best fitness function value, accuracy of the classifiers, and the number of selected features compared to different algorithms. However, a case study of sentiment analysis of movie viewers using BFRA proves that BFRA algorithms demonstrate affordable performance.
Augmentation for Blood Doping Discovery in Sports using Random Forest Ensembles with LightGBM

Athletics bureaucrats round the globe are tackling implausible encounters owing to the partial methods of customs executed by the athletes to progress their enactment in their sports. It embraces the intake of hormonal centred remedies or transfusion of blood to upsurge their power and the effect of their coaching. On the other hand, the up-to-date direct test of discovery of these circumstances embraces the laboratory-centred technique viz restricted for the reason that of the cost factors, handiness of medical experts, etc. This ends us to pursue for indirect assessments. By the emergent curiosity of Artificial Intelligence (AI) in healthcare, it is vital to put forward a process built on blood factors to advance decision making. In this research script, a statistical and machine learning (ML) centred tactic was suggested to ascertain the concern of doping constituent rhEPO in blood units.
Hyper‐parametric improved machine learning models for solar radiation forecasting

Summary Spatiotemporal solar radiation forecasting is extremely challenging due to its dependence on metrological and environmental factors. Chaotic time‐varying and non‐linearity make the forecasting model more complex. To cater this crucial issue, the paper provides a comprehensive investigation of the deep learning framework for the prediction of the two components of solar irradiation, that is, Diffuse Horizontal Irradiance (DHI) and Direct Normal Irradiance (DNI). Through exploratory data analysis the three recent most prominent deep learning (DL) architecture have been developed and compared with the other classical machine learning (ML) models in terms of the statistical performance accuracy. In our study, DL architecture includes convolutional neural network (CNN) and recurrent neural network (RNN) whereas classical ML models include Random Forest (RF), Support Vector Regression (SVR), Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGB), and K‐Nearest Neighbor (KNN). Additionally, three optimization techniques Grid Search (GS), Random Search (RS), and Bayesian Optimization (BO) have been incorporated for tuning the hyper parameters of the classical ML models to obtain the best results. Based on the rigorous comparative analysis it was found that the CNN model has outperformed all classical machine learning and DL models having lowest mean squared error and highest R ‐Squared value with least computational time.
The Development of the Concept of an Entrepreneurial University in Russian Higher Educational Establishments: New Method of Evaluation

Launched in the summer of 2021 the «Priority 2030» Strategic Academic Leadership Program, despite being a logical extension of the Russian Academic Excellence Project, differs significantly from it in terms of its priorities. While the Russian Academic Excellence Project was primarily oriented on achieving the increase of the global competitiveness of Russian universities, the new Program primarily encourages universities to actively participate in the socio-economic development of the regions of the Russian Federation. The priorities of the new program happen to coincide a lot with the concept of an entrepreneurial university proposed by Burton R. Clark in his «Creating Entrepreneurial Universities». According to this concept, the attention is focused on such elements of university development as a strengthened material core, an expanded periphery of development, a diversified financial base, stimulated academic structures and integrated entrepreneurial culture. All of them are directly related to the function of the university as a driver of regional development. This concept has become so familiar to the general public in the recent decade that the notion of the development of universities according to it is seen as a matter of course. But are Russian universities indeed capable of following this concept in their development and are they in fact doing so? How prepared are they to actually achieve the goals set by the program «Priority 2030»? To answer these questions, the authors of the article have developed a new set of tools for accessing the development of higher educational establishments in the paradigm of the concept of entrepreneurial university.The article presents a detailed analysis of how the main elements of this concept are reflected in the practice of Russian universities. The results of the study showed that all leading universities have the characteristics of an entrepreneurial university. However, the development of the elements of an entrepreneurial university tends to be quite stretched out timewise, which creates corresponding risks affecting the effectiveness of the implementation of the «Priority 2030» project. The set of tools, suggested in the article, can be used for further analysis of the development of Russian higher educational establishments according to the concept of entrepreneurial university.
Fast non-elitist evolutionary algorithms with power-law ranking selection

Theoretical evidence suggests that non-elitist evolutionary algorithms (EAs) with non-linear selection mechanisms can efficiently overcome broad classes of local optima where elitist EAs fail. However, the analysis assumes a weak selective pressure and mutation rates carefully chosen close to the "error threshold", above which they cease to be efficient. On problems easier for hill-climbing, the populations may slow down these algorithms, leading to worse runtime compared with variants of the elitist (1+1) EA.
Automatically classifying non-functional requirements using deep neural network

Non-functional requirements are property that software products must have in order to meet the user’s business requirements, and are additional constraints on the quality and characteristics of software systems. They are generally written by software designers and documented in various parts of requirements documentation. When developing systems, developers need to classify non-functional requirements from requirements documents, and classifying these non-functional requirements requires professional skills, experience, and domain knowledge, which is challenging and time-consuming for developers. It would be beneficial to implement automatic classification of non-functional requirements from requirements documents, which could reduce the manual, time, and mental fatigue involved in identifying specific non-functional requirements from a large number of requirements. In this paper, a deep neural network model called NFRNet is designed to automatically classify non-functional requirements from software requirement documents. The network consists of two parts. One is an improved BERT word embedding model based on N-gram masking for learning context representation of the requirement descriptions, and the other is a Bi-LSTM classification network for capture context information of the requirement descriptions. We use a Softmax classifier in the end to classify the requirement descriptions. At the same time, in order to accelerate the training and improve the generalization ability of the model, the network uses multi-sample dropout regularization technology. This new regularization technology can reduce the number of iterations needed for training, accelerate the training of deep neural networks, and the networks trained achieved lower error rates. In addition, we expanded the original non-functional requirements dataset (PROMISE dataset) and designed a new dataset called SOFTWARE NFR. The new dataset far exceeds the original dataset in terms of the number of requirement description sentences and the number of non-functional requirements categories. It can be taken as a new testbed for non-functional requirements classification. Through cross-validation on the new dataset, the experimental results show that the network designed in this paper is significantly better than the other 17 classification methods in terms of Precision, Recall, and F1-score. At the same time, for the training set and the validation set, using the multi-sample dropout regularization technology can accelerate the training speed, reduce the number of iterations, and achieve lower error rates and loss.
IoT Security and Computation Management on a Multi-Robot System for Rescue Operations Based on a Cloud Framework

There is a growing body of literature that recognizes the importance of Multi-Robot coordination and Modular Robotics. This work evaluates the secure coordination of an Unmanned Aerial Vehicle (UAV) via a drone simulation in Unity and an Unmanned Ground Vehicle (UGV) as a rover. Each robot is equipped with sensors to gather information to send to a cloud server where all computations are performed. Each vehicle is registered by blockchain ledger-based network security. In addition to these, relevant information and alerts are displayed on a website for the users. The usage of UAV-UGV cooperation allows for autonomous surveillance due to the high vantage field of view. Furthermore, the usage of cloud computation lowers the cost of microcontrollers by reducing their complexity. Lastly, blockchain technology mitigates the security issues related to adversarial or malicious robotic nodes connecting to the cluster and not agreeing to privacy rules and norms.
BERT-based response selection in dialogue systems using utterance attention mechanisms

• BERT-based response selection in dialogue systems. • Utterance attention to comprehend relationships between utterances and response. • Proposed method achieved a performance improvement in DSTC8 datasets. Dialogue systems, one of the core research fields of natural language processing, attempt to understand the utterances of a user and generate an appropriate response. Response selection in a retrieval-based dialogue system involves searching for the most context-appropriate subsequent utterance. Conversations are usually composed of multiple turns; therefore, the intention of the speaker must be properly understood prior to the response selection. To accurately capture such intended meaning, we propose a retrieval-based response selection model that effectively comprehends the relationships among words and utterances in a conversation and a response candidate with word and utterance attention. Word representation is generated by using the self-attention mechanism to reflect the contextual information between intentional words in an overall conversation or individual utterance, while utterance representation is by the cross-attention mechanism to reflect the contextual information among utterances. Furthermore, since our model does not need much computation and memory size, it can be easily combined with existing other response selection models or pre-trained language models. Experiments on various utterance embedding methods were also conducted to find a proper representation of the utterance information. Our proposed model exhibits an improvement in performance of approximately 2.1%p in hit@1 in the DSTC8 ubuntu dataset compared to baseline models, as well as significant performance improvements for other datasets.