Reviewerly/exHarmony huggingface dataset:
Towards Detection of Selfish Mining Using Machine Learning

Selfish mining is an attack against a blockchain where miners hide newly discovered blocks instead of publishing them to the rest of the network. The selfish miners continue to mine on their private chain while the honest miners waste resources mining on a shorter chain. According to the blockchain protocol, a longer chain takes precedent and shorter chains are discarded which allows the selfish miners to gain an advantage by keeping their chain secret. This attack can be used by malicious miners to earn a disproportionate share of the mining rewards or in conjunction with other attacks to steal money from cryptocurrency exchanges. Several of these attacks were launched in 2018 and 2019 with the attackers stealing as much as $18 Million. Developers made several different attempts to fix this issue, but the effectiveness of the fixes is currently unknown. Although this attack is possible against both Proof-of-Work and Proof-of-Stake blockchains, this research concentrates on detection in Proof-of-Work blockchains. As is difficult to evaluate security advances in the real-time blockchain, it is imperative to focus on simulation to evaluate blockchain security properties. To this end, we extend a blockchain simulator and add the ability to simulate selfish mining attacks. Several existing simulators are examined before choosing SimBlock for this research. Our goal is to identify the factors that identify selfish mining. Using existing research, we choose several factors that could identify an attack in an unlaunched state, an active state, or historically. We plan to use simulated data to train a machine learning model to detect selfish mining. Using the modified simulator, we generate training and test data for unlaunched and active attacks. For historical attacks, we will use historical data from known selfish mining attacks. While some existing research has examined the detection of selfish mining, it only examines active attacks. In this paper, we seek to lay the groundwork for future research into detecting attacks that are unlaunched, active, or historical.
Credit Card Fraud Detection Using XGBoost Algorithm

Credit card is one of the modern payment methods widely spread all over the world. It provides excellent facilities in purchasing as well as selling operations. However, it suffers from fraud problems, causing considerable economic losses to banks, institutions, and individuals, amounting to billions of dollars annually. That has made great interest in finding systems and means with outstanding capabilities to confront fraud, whose patterns in addition to methods are increasing dramatically. One of the most prominent techniques used by researchers in this field is Machine Learning (ML) techniques. In this paper, we proposed some of the classification ML algorithms such as Logistic regression(LR), Linear Discriminant Analysis (LDA), and Naïve Bayes(NB), additionally, the boosting algorithm XGBoost to create models capable of detecting fraud. The dataset from Kaggle. We used performance metrics such as accuracy, precision, f1, recall, AUC confusion matrix to evaluate the models' performance. The XGBoost model presented the best results compared to other models.
Named-Entity Recognition for Hindi language using context pattern-based maximum entropy

This paper describes Named Entity Recognition (NER) system for Hindi language using two methodologies. An existing BaseLine Maximum Entropy-based Named Entity (BL-MENE) model and Context Pattern-based MENE (CP-MENE) framework the one proposed in this work. BL-MENE utilizes several features for the NER task but suffers from inaccurate Named Entity (NE) boundary detection, mis-classification errors, and partial recognition of NEs due to certain missing essentials. However, CP-MENE based NER task incorporates extensive features and patterns set to overcome these problems. In fact, the CP-MENE features include right-boundary, left-boundary, part-of-speech, synonyms, gazetteers and relative pronoun features. CP-MENE formulates a kind of recursive relationship to extract high ranked NE patterns that are generated through regular expressions via python@ code. Nowadays, since the Web contents in the Hindi language are rising, especially in the health-care applications, this work is conducted on the Hindi Health Data (HHD) corpus at Kaggle dataset. We conducted experiments on four NE categories- Person (PER), Disease (DIS), Consumable (CNS) and Symptom (SMP). Usually, researchers’ work upon PER NE within news articles while other NEs, especially related to the health-care domain such as DIS, CNS, and SMP NE types are left out which are incorporated in this research. CP-MENE improvised the classification performance of NEs and the F-measure achieved are 79.68% for PER, 72.50% for DIS, 68.78% for CNS, and 67.23% for SMP respectively which are comparable with respect to other NER approaches.
Music Students’ Perception Towards Music Distance Learning Education During COVID-19 Pandemic: Cross-Sectional Study in Jordan

During COVID-19 pandemic countries have faced various levels of COVID-19 infection rates, and millions of students are affected by changing the educational process. However, many music Schools have been faced with the challenge of dealing with a situation that necessitates emergency measures to continue the academic course in the midst of lock-downs and social distancing measures. Therefore, it is important to evaluate the effectiveness of online methods of learning and to decide their feasibility and appropriateness for music students. Thus, this research aimed to provide an analysis of Music Students’ Perception towered Music E-learning Education during COVID-19 Pandemic, to study the situation of musicians in COVID-19 and to study music Distance learning knowledge, attitudes and practices and to develop suggestions for solving the problems. A sample of (83) students from the music department in the University of Jordan completed a questionnaire. An online survey distributed The survey sought population and socio-economic information and information relating to electronic and online musical training; musical education during the COVID 19 pandemic; mental music assessments; and the skills, attitudes and practices of E-learning. Most respondents (76.2%) agreed that Distance learning is applicable in music department. While (54.2 %) of the respondents agreed Distance learning is a possible substitute for standard education. However, E-learning has actually been created as a modern way of improving the process of learning and improving learning performance.
Effectiveness of English for Tourism E-Learning during the Covid-19 Pandemic

The current pandemic situation has resulted in a change in learning methods from face to face to online methods, thus demanding of readiness of lecturers and students to be able to solve problems in order to achieve the learning objectives. In making this online-based tourism English learning method effective, the involvement of lecturers in preparing materials, students in receiving materials, and the use of technology is an inseparable unity. Online learning is an alternative learning method that utilizes the virtual world in the learning process. This study aims to analyze the effectiveness of the online-based English for Tourism teaching and learning process for students with a sample of 130 students taking English courses consisting of Room Division Management, Patisserie Management, Food &amp; Beverage Management, and Tourism Destinations study programs. This type of research was experimental research with pre-experimental research design in the form of the one-shot case study. The instrument by distributing questionnaires was carried out online. Hypothesis testing used one sample t-test analysis with the help of SPSS. The results showed that online-based English for Tourism learning was effective. The results of inferential parametric statistical analysis obtained t-count 83,702 &gt; t-table 1.97852, because the value of t-count was greater than α = 0.05 so H0 was rejected and H1 was accepted. So, it could be concluded that the effectiveness of online learning of English for Tourism was effectively.
Pathfinding for Mobile Robot Navigation by Exerting the Quarter-Sweep Modified Accelerated Overrelaxation (QSMAOR) Iterative Approach via the Laplacian Operator

Mobile robots are often in a situation where they need to find a bump-free path or navigation in their environment from any starting to a specific target point. Within this study, improving the navigation problem of a mobile robot iteratively by using a numerical method based on the potential field method is one of the main aims. This potential field will lean on the use of Laplace’s equation to restrain the formation of a potential function across regions within the mobile robot configuration area. The present paper proposed a Quarter-Sweep Modified Accelerated Overrelaxation (QSMAOR) approach to improve the pathfinding of mobile robots in a given environment. The experiment shows that, by using a finite difference method, it is capable of producing an optimal path and creating a smooth path between the starting and target point. The results of the simulation also show that this numerical approach works more rapidly and provides a smoother/clearer direction than the previous study.
Chinese-Uyghur Bilingual Lexicon Extraction Based on Weak Supervision

Bilingual lexicon extraction is useful, especially for low-resource languages that can leverage from high-resource languages. The Uyghur language is a derivative language, and its language resources are scarce and noisy. Moreover, it is difficult to find a bilingual resource to utilize the linguistic knowledge of other large resource languages, such as Chinese or English. There is little related research on unsupervised extraction for the Chinese-Uyghur languages, and the existing methods mainly focus on term extraction methods based on translated parallel corpora. Accordingly, unsupervised knowledge extraction methods are effective, especially for the low-resource languages. This paper proposes a method to extract a Chinese-Uyghur bilingual dictionary by combining the inter-word relationship matrix mapped by the neural network cross-language word embedding vector. A seed dictionary is used as a weak supervision signal. A small Chinese-Uyghur parallel data resource is used to map the multilingual word vectors into a unified vector space. As the word-particles of these two languages are not well-coordinated, stems are used as the main linguistic particles. The strong inter-word semantic relationship of word vectors is used to associate Chinese-Uyghur semantic information. Two retrieval indicators, such as nearest neighbor retrieval and cross-domain similarity local scaling, are used to calculate similarity to extract bilingual dictionaries. The experimental results show that the accuracy of the Chinese-Uyghur bilingual dictionary extraction method proposed in this paper is improved to 65.06%. This method helps to improve Chinese-Uyghur machine translation, automatic knowledge extraction, and multilingual translations.
Livestock Monitoring: Approaches, Challenges and Opportunities

This survey presents approaches and technologies for livestock identification, vital signs monitoring and location tracking. It first introduces the related concepts. Then, provides an analysis of existing solutions and highlights their strengths and limitations. Finally, it presents key challenges in the field, and discusses recent trends that must be factored in by researchers, implementers, and manufacturers towards future developments in the area.
Method for puncture trajectory planning in liver tumors thermal ablation based on NSGA-III

BACKGROUND: Thermal ablation of liver tumors is a conventional mode for treating liver tumors. In order to reduce the damage to normal tissue endangered by thermal ablation, the physician needs to plan the puncture path before surgery. OBJECTIVE: In this paper, a puncture trajectory planning method for thermal ablation of liver tumor based on NSGA-III is proposed. This method takes the clinical hard constraints and soft constraints into account. METHOD: The feasible puncture region is solved by the hard constraints, and after that the pareto front points are obtained under the soft constraints. When accessing the feasible puncture region, an adaptive morphological closing operation method based on K-means algorithm is adopted to process the spherical angle binary image of obstacles that might be encountered in the puncture process. RANSAC is performed to fit the tangent plane of liver surface when calculating the angle between the puncture trajectory and liver surface. In order to evaluate the puncture path obtained by this method, 6 tumors are selected as experimental subjects, and Hausdorff distance and Overlap Rate of Pareto front points with manually recommend points are calculated respectively. RESULTS: The average value of Hausdorff distance is 24.91 mm, and the mean value of the overlap rate is 86.43%. CONCLUSION: The proposed method can provide high safety and clinical practice of the puncture route.
A hybrid one‐class approach for detecting anomalies in industrial systems

Abstract The significant advance of Internet of Things in industrial environments has provided the possibility of monitoring the different variables that come into play in an industrial process. This circumstance allows the supervision of the current state of an industrial plant and the consequent decision making possibilities. Then, the use of anomaly detection techniques are presented as a powerful tool to determine unexpected situations. The present research is based on the implementation of one‐class classifiers to detect anomalies in two industrial systems. The proposal is validated using two real datasets registered during different operating points of two industrial plants. To ensure a better performance, a clustering process is developed prior the classifier implementation. Then, local classifiers are trained over each cluster, leading to successful results when they are tested with both real and artificial anomalies. Validation results present in all cases, AUC values above 90%.
Biometric Based Voting System Using Aadhar Database

Right to Vote is necessary for every citizen of a Country.Currently, electronic voting machine is introduced which managed to overcome the disadvantages like hacking and rigging. In electronic voting system. To overcome this problem, biometric based voting machine is proposed. Aadhar has become a mandatory which is used for identification. In Biometrics physical or behavioral characteristics used to identifying a person. In the proposed system, the details of the voters are stored as database. While voting, the fingerprint is registered to fetch the stored details such as name, age voter ID, place of the voter. Then, the Aadhar number is given to enable the EVM to cast the vote against the desired candidates. This method reduces chance of hacking and rigging. The proposed system minimizes the time taken for identify the voter. The design implemented in the Biometric-EVM is transportable, adaptable and with minimal power consumption.
Economic Growth, Financial Development, Income Inequality and Poverty Relationship: An Empirical Assessment for Developing Countries

The paper contributes to the current literature by investigating the effects of economic growth, income inequality, and financial development on poverty for fifteen (15) developing countries. Panel data over the period from 2002 to 2018 has been used. Pooled Mean Group (PMG) technique has been applied for estimating the results. The study employs panel unit root tests namely: LLC and IPS for checking the unit root in the data. The long results show that all variables turned significant in the model. The income inequality shows positive relationship with poverty. Financial development and economic growth reveal adverse impact on poverty. Government spending and employment (increase in labor force participation) impact on poverty are negative. Furthermore, trade openness and inflation coefficients turned significant however, its sign are not according to the theory. Moreover, in the short run income inequality and economic growth show no impact on poverty. Financial development relationship with the poverty is still positive. Inflation revealed positive relationship with poverty. Trade openness show positive relationship with poverty. And other two variables government spending and labor force participation rate coefficients remained insignificant. The error correction coefficient i.e. speed of adjustment parameter is - 0.089. On the basis of the findings it is suggest that effective policies for boosting up economic growth, advancement of financial sector and reducing income inequality can significantly contribute towards the poverty reduction in the developing countries. It is suggested that the government shall formulate appropriate policies for the development of financial sector, boosting up economic growth and reduction of income inequality which will contribute to the reduction in poverty of the selected countries.
International public opinion analysis of four olympic games: From 2008 to 2022

Since the rapid spread of the COVID-19 worldwide, the pandemic has led to a huge impact on global sporting events. As a major international event, the 2022 Beijing Winter Olympics has commonalities with the 2008 Beijing Olympics, the 2014 Sochi Winter Olympics, and the 2020 Tokyo Olympics in terms of international public opinion context and epidemiological background. In this study, over 1 million pieces of UGC(User Generated Contents) in Chinese and English languages were obtained from social media platforms such as Twitter, YouTube, as well as traditional mass media in various countries to compare the differences between the two languages in international public opinion. Using sentiment analysis, this study explores the evolution of international public opinion topics and sentiment differences among the above four Olympic Games. The analysis results show that:1) regardless of traditional mass media or online social media, there is a more obvious tendency of general politicization in the topics of the 2008 Beijing Olympics and 2022 Beijing Winter Olympics, and extreme emotional remarks of the 2022 Beijing Winter Olympics are more frequent; 2) in the topic of political opinion involving China, international Chinese public opinion presents more negative sentiment than those in English; 3) Among the topics involving COVID-19, the negative level of public opinion in Chinese and English is opposite for the 2020 Tokyo Olympics and the 2022 Beijing Winter Olympics;4) International public opinion on the topic of sports events is significantly more positive in Chinese than in English;5) YouTube’s Chinese opinion environment is better than English.
Preference-based and local post-hoc explanations for recommender systems

Post-hoc explanation aims at defining a simple local surrogate model to shed light on a prediction produced by a complex, generally black-box, model. In the general context of classification, it has been shown that local surrogates may not always be able to capture a local explanation, i.e. for a specific instance prediction, but rather depict more of a general behavior of the black-box. This problem is even more complex in a recommendation scenario where classes and decision boundaries are not explicitly defined and where data are very sparse by nature. We show in this paper that it is possible to tackle these problems with an efficient sampling around the recommendation instance to explain, to finally learn a proper local surrogate model. To this aim, this paper introduces several new approaches to capture efficiently local explanation models in the context of recommendation, all defined around a locality sample. Noticeably, and novel to this work, we show that it is possible to achieve a simple, yet better quality explanation model by not directly considering ratings, but rather implicit preferences as expressed by comparisons of pairs of ratings. We introduce to this extent a novel explainable model based on a pairwise loss RankNet architecture. Extensive experiments show that our methods can be better than state-of-the-art methods depending on the locality of the black-box model, and are much more efficient to retrieve meaningful explainable features locally.
Improved PBFT Algorithm Based on Vague Sets

The traditional PBFT consensus algorithm has several limitations in the consortium blockchain environment, such as unclear selection of primary node, excessive communication times, etc. To solve these limitations, an improved consensus algorithm VS-PBFT based on vague sets was proposed. VS-PBFT has three phases: node partition, primary node selection, and global consensus. Firstly, the nodes of the whole network are partitioned using the consistent hashing-like consensus algorithm, and then the local primary node is selected by the primary node selection algorithm in each partition. The local primary nodes run the four-phase PBFT consensus algorithm to complete the global consensus. The analysis of the VS-PBFT consistency algorithm shows that the algorithm can improve the fault-tolerant rate and reduce communication complexity, and the algorithm is dynamic; that is, node can join and quit adaptively.
Application of Deep Learning in Medical English Teaching Evaluation

Although the fact that the reform of college English teaching has yielded mixed results, medical colleges have yet to achieve a breakthrough. Deep learning (DL) is a type of education that emphasizes higher-order thinking skills. It encompasses the state of learning, the learning process, and the learning outcomes. It emphasizes critical knowledge understanding, focuses on the integration of old and new knowledge, confronts real-world problems, and pays attention to reflection. This paper establishes the EMET (evaluation of medical English teaching) model based on the improved BPNN algorithm and analyzes the performance of this model using the improved BPNN algorithm. The simulation results show that the improved BPNN algorithm proposed in this paper has fast convergence speed, strong optimization ability, and generalization ability, effectively reducing the impact of human factors on index weight determination.