Reviewerly/exHarmony huggingface dataset:
Strategic Introduction of the Blockchain Technology under Retailing Competition from New Entrants

With the rapid development of blockchain technology, the new retail has diversified intelligent empowerment modes and achieved a leap change in the service system. As the competition in the retail industry becomes increasingly fierce, whether new entrants choose to introduce blockchain technology to improve their profits becomes the key to their development. This paper considers the system consisting of the incumbent retailer and the new entrant retailer, uses the game theory method to discuss their competition, considers the introduction cost of blockchain technology, and compares the two cases of the new entrant retailer not introducing blockchain technology and introducing blockchain technology. The results show that when the introduction of blockchain technology can fully make up for consumers’ concerns about the quality of new entrant retailers’ products and enhance consumers’ trust in their products, new entrant retailers will choose to introduce blockchain technology. And only when the introduction cost of blockchain technology is less than a certain threshold, it is beneficial for new entrant retailers to introduce blockchain technology. Finally, some numerical examples are given to illustrate the correctness of the above conclusions.
The Big Data Revolution Unlocks New Opportunities for Seismology

The field of seismology is entering a new era where our understanding of earthquakes and the solid earth is increasingly driven by new Big Data experiments and algorithms.
THE MANAGEMENT OF EXAMINATION MALPRACTICE USING BLOCKCHAIN TECHOLOGY

Post examination malpractice has continued to show up in some of the marking centres at grade twelve (12) level, which is mostly the changing of grades already entered in an electronic system. This study was designed to investigate the current challenges that are poised by the current system in the entry and transmission of results from marking centres, through a mixed research study approach. To the challenges presented by the current system, a Blockchain technology approach was developed to address the challenges. The Blockchain system would use the cryptographic hash function known as SHA-256, to enable the system compute the correct input value given some output value. The function proposed because of it’s collision resistance capability. The transactions between the marking centre and the examination council is a way of recording activities occurring in a digital form on the Electronic Results Management System (ERMS).
Interactive Search Group Mining from Annotated Web Pages

Machine learning facet is a query-dependent upper layer service. Each facet is a set of items which describe and summarizes one important aspect of a query. Faceted search allows the users to search on a facet list of a query to pick out the desired one without browsing for a long time. The proposed facet mining framework is called facet mining from Annotated Documents (FMAD) integrates keyword search by category browsing and produces an interface which has several conceptual dimensions. The FMAD is designed to achieve an interactive data summarization process by liberalizing topic identification and interpretation on user side. It is an ontology-based query-dependent facet framework which follows a series of phases from initial facet weighting to facet item ranking. The FMAD produced high-quality clusters by using a context similarity approach which provides high cluster discrimination. FMAD facets automatically extracted from document description, annotated documents, and metadata records.
Improved three-point estimation method for probabilistic power flow calculation

The large-scale grid connection of new energy power generation has brought uncertainty to the power system due to its own intermittency. At present, the three-point estimation method is one of the commonly used methods of probabilistic power flow calculation for power system planning, operation and control of uncertain quantities. However, this method has the problem of insufficient tail accuracy. For this reason, this paper proposes to add a set of normal distribution tail points and combine the three-point estimation method to calculate, use Nataf transform and Cholesky decomposition to process the correlation input variables, and use Cornish- Fisher series fit to get the probability distribution function. The system of IEEE14 node, IEEE30 node and IEEE57 node was tested, and the results verified the correctness of the improved three-point estimation method of probability power flow algorithm.
Using influence functions to identify potential improvements for synthetic data generation

Computer vision, enabled by artificial intelligence and deep learning, has a nearly limitless number of possible applications, military and civilian. Object detection methods are a particularly notable type of computer vision, with broad usefulness in a variety of systems, such as autonomous vehicles, robotics, and security. Development of effective object detection methods faces many challenges; one such challenge of significance is a lack of good labeled data for the target domain, as hand labeled data is time-consuming and expensive to produce. Synthetic data generation seeks to solve this problem by programmatically generating both training data and labels simultaneously, allowing for the creation of arbitrarily large training datasets. However, synthetic data has several drawbacks { generating realistic imagery is challenging and computationally expensive, and models trained with synthetic data frequently suffer in accuracy when applied to real test data. In our research, we use model explainability techniques to connect model predictions back to the model training data, in order to identify the most important features that need to be represented accurately in synthetic training data. Influence functions score model training samples based on how influential each sample was to a particular prediction, by approximating the effect of retraining the model after leaving each individual training sample out of the training set. In this work, we seek to extend influence functions to identify the most valuable features in real and synthetic training data for use in improving our synthetic data generation tools.
Poisoning attacks on machine learning models in cyber systems and mitigation strategies

Poisoning attacks on training data are becoming one of the top concerns among users of machine learning systems. The goal of such attacks is to inject a small set of maliciously mislabeled training data into the training pipeline so as to detrimentally impact a machine learning model trained on such data. Constructing such attacks for cyber applications is especially challenging due to their realizability constraints. Furthermore, poisoning mitigation techniques for such applications are also not well understood. This paper investigates techniques for realizable data poisoning availability attacks (using several cyber applications), in which an attacker can insert a set of poisoned samples at the training time with the goal of degrading the accuracy of the deployed model. We design a white-box, realizable poisoning attack that degraded the original model’s accuracy by generating mislabeled samples in close vicinity of a selected subset of training points. We investigate this strategy and its modifications for key classifier architectures and provide specific implications for each of them. The paper also proposes a novel data cleaning method as a defense against such poisoning attacks. Our defense includes a diversified ensemble of classifiers, each trained on a different subset of the training set. We use the disagreement of the classifiers’ predictions as a decision whether to keep a given sample in the training dataset or remove it. The results demonstrate the efficiency of this strategy with very limited performance penalty.
A Framework of Non Fungible Token Marketplace using Blockchain

In this Non fungible token marketplace as the name implies,a decentralized platform where can create,buy,sell and store a non fungible token marketplace token.an non fungible token marketplace minting and trading as a global scale, while blockchain as its underlying technology ensures transferring and immutable recovering of assets tokenization and trading process.The marketplace also cares of NFT storage,NFT are units of data that represents a unique digital asset stores and verified on the blockchain in this blockchain in this paper need to import both the NFT contract and fungible token contract for sale, price changed. token purchased and sales withdrawn .in this paper a building a simple react app that contracts with flew smart contracts to validate and fetch NFTS owned by users.
Explosive hazard pre-screener based on simulated data with perfect annotation and imprecisely labeled real data

Datasets with accurate ground truth from unmanned aerial vehicles (UAV) are cost and time prohibitive. This is a problem as most modern machine learning (ML) algorithms are based on supervised learning and require large and diverse well-annotated datasets. As a result, new creative ideas are needed to drive innovation in robust and trustworthy artificial intelligence (AI) / ML. Herein, we use the Unreal Engine (UE) to generate simulated visual spectrum imagery for explosive hazard detection (EHD) with corresponding pixel-level labels, UAV metadata, and environment metadata. We also have access to a relatively small set of real world EH data with less precise ground truth – axis aligned bounding box labels – and sparse metadata. In this article, we train a lightweight, real-time, pixel-level EHD pre-screener for a low-altitude UAV. Specifically, we focus on training with respect to different combinations of simulated and real data. Encouraging preliminary results are provided relative to real world EH data. Our findings suggest that while simulated data can be used to augment limited volume and variety real world data, it could perhaps be sufficient by itself to train an EHD pre-screener.
Digital skills of Valencian university students

With the outbreak of the COVID-19 pandemic, higher education changed overnight from face-to-face to e-learning. The tech shortcomings during the lock-down period were expected to affect students from the most disadvantaged stdents in a more negative way. The aim is to detect the strengths and weaknesses of Valencian university students in relation to the e-learning modality. A sample of 18,295 students from the five public Valencian universities, answered a four-sections online form: equipment, connection, experience and attitude. Results show that the Valencian university student community, in general, has sufficient equipment to follow e-learning, nontheless 2% do not have any computer, not even in shared use. Three out of four have a broadband conection, but 0.7% of the participants cannot afford Internet access. 50% self-assess as a medium-low competence, two points in a a five-level scale. Opinions reveal a strong preference for face-to-face teaching, although e-learning advantages are positively valued
PROFESSIONAL ACTIVITY OF THE ARTIST- TEACHER OF THE NATIONAL SCHOOL (EARLY XXI CENTURY) IN THE CONTEXT OF MODERN TRENDS OF SOCIAL DEVELOPMENT

The article is devoted to finding out of main tasks of teacher of Ukrainian school in the context of scientific and social reference points, which most economically developed countries develop in obedience to. Among such principles an author considers conducting the necessity of awareness by everybody o f itself as parts of social and natural environment and analysis of the action as such, which influences on the state of all surroundings and concrete personality separately. The fragment of method of forming of similar options at students and students on the example of teaching of cycle of graphic disciplines is examined.
ANALISIS SENTIMEN KOMENTAR NETIZEN TWITTER TERHADAP KESEHATAN MENTAL MASYARAKAT INDONESIA

In today's modern era, everyone can easily exchange messages, share activities carried out in the form of images or videos using social media. Frequent use of social media can be bad for physical and mental health. Health is very important, by being healthy everyone can do activities such as studying, working, exercising, etc. Twitter as one of the social media that is widely used by the Indonesian people is used as a place to get comments that will be analyzed to find out the opinion of the Indonesian people about their mental health, this is the purpose of the analysis that has been carried out. The results obtained after analyzing the sentiments of Twitter social media users' comments on the mental health of the Indonesian people are from 2369 comment data that have been analyzed, as many as 50.8% negative, 45.1% positive and 4.1% neutral. So, it can be concluded that the sentiment analysis of social media users' comments on the mental health of Indonesian people tends to be negative. The Naïve Bayes method is used when carrying out sentiment analysis and the accuracy results are 0.7961165048543689 or rounded up to 79%.
Heavy Metals Pollutions within Lagos South Western Nigeria

Environmental pollution is a global phenomenon which could results from both natural and anthropogenic activities which has resulted to several health and physiological problems in both plants and animals. Dump sites’ soils of and Borehole waters from Ojota, Ebutte-Meta, Igando and Bariga in Lagos state were analyzed for heavy metals using atomic absorption spectrophotometer. The data obtained were further subjected to contamination factor and pollution indices analyses. The results obtained showed lead (Pb) to be most concentrated in the soils and ranged from 0.22 ppm to 2.50 ppm, this was followed by the value recorded for zinc (Zn) which was between 0.0015 ppm to 0.020 ppm. The least observed metal in all the soil sample stations was nickel (Ni) which ranged from 0.001 ppm to 0.010 ppm. On the other hand, only water samples from Ojota and Ebutte-Meta were detectable with Pb concentration values of 0.38 ppm and 0.0028 ppm, Ni concentration values of 0.0052 ppm and 0.009 ppm, Zn concentration values of 0.0039 ppm and 0.0020 ppm respectively. On subjecting the concentration of the heavy metals to contamination factor (CF) revealed very slight contamination of the different soils from different areas. There was also very slight contamination of the waters from Ojota and Ebutte-Meta by metals, except for Igando and Bariga that were moderately and severely contaminated by the metals respectively. The Pollution Index (PI) showed that the soils and waters samples are unpolluted with the heavy metals.
Novel assessment and classification of monthly average daily global solar radiation models through a figure of merit called irradiation time equivalence: Analysis of 70 regression models based on air temperature and sunshine hours predictors

Abstract Irradiation time equivalence pioneers the classification of models that predict monthly average daily global solar radiation on a horizontal surface based on their double cross-validation performances. By exploiting indigenous irradiation data, novel irradiation-based models can be created and used to classify prediction models thereby facilitating a deeper understanding of model performance beyond routine summary statistics. The concept was demonstrated by formulating novel One-hour and Two-hour irradiation-based models to predict monthly average daily global horizontal irradiation. Double cross-validation of the 2 irradiation-based models and 70 existing regression models was performed using a pair of five-year subsets. The 70 models used the measured meteorological predictors of air temperature and sunshine hours, either alone or combined. The irradiation time equivalence of a model evaluated under double cross-validation has been defined as the minimum number of hours of measured irradiation needed to predict the monthly average daily irradiation in an average year, with a root mean square error less than or equal to that of the model. Despite their intra-competitiveness, all 44 temperature-based models had an irradiation time equivalence of 1 h while the remaining 26 models which contained the sunshine-hours predictor were classified with a higher performance rank of 2 h. An irradiation time equivalence scale extending to 13 h was also developed to cater for the classification of higher-performance models. This fresh perspective on model performance directs future investigations toward determining whether prediction models exhibit global classification constancy.
Factors of effective development of leadership skills in secondary school students

The purpose of the study is to develop factors for the effective development of leadership qualities in secondary school students based on the analysis of pedagogical principles. In comparison with the types of activities existing in society, the greatest need for the development of leadership qualities is characteristic of the educational process at the general education level, which is confirmed by the results of the conducted research reflecting the practical significance of the work. This article examines the theoretical aspects of the formation of the problem of the development of leadership qualities considered in the historical concept are summarized, some theories and concepts of leadership are described, and the results of an experimental study of leadership qualities in secondary school students are also presented.
Enhancement of BIRCH Algorithm for Clusters of Different Shapes

BIRCH algorithm uses the concept of radius to manage cluster boundaries, which yields good results when clustering spherical data but unsatisfactory results when clustering non-spherical data.In which, it splits non-spherical data into several clusters in some circumstances.This paper proposed an enhancement to the BIRCH algorithm that allows it to cluster non-spherical data.The result of the experiment in this paper shows Enhanced BIRCH algorithm performs better than the BIRCH algorithm in clustering non-spherical datasets.
COMPARATIVE ANALYSIS OF CENTRALIZED AND DECENTRALIZED ALGORITHMS FOR THE MOVEMENT OF MULTICOPTER-TYPE UAVS

The development of robotics makes their group application relevant for solving varioustasks. The effectiveness of performing the tasks of detecting and determining the coordinates ofobjects by a group of robots significantly depends on the accuracy of maintaining a given formation.In this regard, the task of determining motion planning algorithms that ensure the greatestaccuracy of maintaining a given formation is of practical interest. This article is devoted to thestudy of the accuracy of maintaining the formation of a multicopter-type UAV group using a centralizedmotion planning algorithm and a decentralized algorithm. The centralized algorithm usesa master UAV, which transmits its coordinates to the slave UAVs. Based on the coordinates obtainedand the given framework of the formation, the guided UAVs plan their movement. In a decentralizedsystem, neighboring UAV groups transmit their coordinates to each other, on the basisof which the movement of a separate UAV is planned. The accuracy of the control system is investigateddepending on the errors of the navigation system and the frequency of updating data on theposition of the leading or neighboring UAVs. It is assumed that the group's UAVs determine their coordinates in discrete moments of time using an external navigation system. Centralized anddecentralized algorithms are worked out by the same motion control system. The algorithms areinvestigated in this article by numerical modeling methods. In the process of simulation, models ofkinematics, dynamics and actuators are taken into account, as well as models for the formation oferrors in the navigation system. It is shown that the de-centralized algorithm of group motionplanning provides higher accuracy compared to the centralized algorithm. However, the technicalimplementation of a decentralized algorithm is more complicated from the point of view of organizinga group communication system. In a centralized system, data transmission from the masterUAV to the slave should be implemented. In a decentralized system, it is required to implementnetwork communication.
TEACHING THE PAST PARTICIPLE THROUGH DRILLING TECHNIQUE

The study conducted at The Mushlih Institute (TMI) English Development Centre in Surabaya was to find out whether or not the drilling technique influenced TOEFL Preparation Takers' structure and written expression comprehension. The focus is on teaching past participles in the students' practice. The research included sixty students in the TOEFL Preparation class from international undergraduate and postgraduate programs. Twelve students were chosen as the experimental group's sample and twelve students as the control group. Cluster random sampling was used to collect the sample. The writer employed the match t-test to analyze the data while carrying out the research. The finding of this study has proved that the drilling strategy is influential and productive for teaching TOEFL Paper-based Test Structure: Past Participle to TOEFL Preparation Test Takers at TMI English Development Centre Surabaya.
Komparasi Performa Tree-Based Classifier Untuk Deteksi Anomali Pada Data Berdimensi Tinggi dan Tidak Seimbang

Anomaly detection is one solution to overcome the issue of data network traffic security, but is faced with the challenge of high data dimensionality and imbalanced data. High-dimensional and imbalanced data can affect the performance of the detection system. Therefore we need a feature selection technique that can reduce the dimensionality of the data by eliminating irrelevant features. In addition, the selected features need to be validated with the right classification algorithm to produce high anomaly detection performance. The purpose of this study is to produce a combination of feature selection techniques and appropriate classification algorithms to produce a system that is able to detect attacks on high-dimensional and imbalanced data. Chi-square feature selection technique was used to eliminate irrelevant features. To determine the ideal classification algorithm, in this study, a comparison of the performance of the tree-based classifer algorithm was carried out. This study also examines the performance of classification techniques in detecting traffic on high-dimensional and unbalanced data. Several Tree-based classification algorithms such as REPTree, J48, Random Tree and Random Forest were tested and compared. Testing with the best performance as a recommendation for the ideal combination of feature selection techniques and classification algorithms. This research produces an anomaly detection system that has high performance. For experimental data, the CICIDS-2017 dataset is used, which has high data dimensionality and contains unbalanced data. The test results show that Random Tree has an accuracy of 99.983% and Random Forest 99.984%.
Pooling Incomplete Data Sets

Data needed in micro studies are not always available from just a single source.In such cases it might be possible to combine two or more independent samples.The problem studied in this paper is to estimate a linear function between y and x from one sample of y-observations and another of x-observations.This is feasible if there are common variables z which can be used to predict x.A two-stage least squares estimator is propounded, which, for the model considered, is also an ML estimator.Simulation experiments show that it has a good relative efficiency and virtually no small sample bias.