Reviewerly/exHarmony huggingface dataset:
Proximity Indicator Minerals

The ore accumulations leave their trails in far more bigger area than occupied by the ore deposit itself. The expanse covered by the mineralogical footprints depends on the extent of hydrothermal alterations and other geological processes that are commonly associated with formation of ore deposits. The term proximity indicator is used in a liberal sense; and it includes in situ developed and pathfinders minerals that are associated with ore deposits; or are dislodged from prospective mineralized rocks like kimberlites. Looking at the metallogenic significance, mineralogical aspects indicating ancient evaporitic set ups form part of this chapter. The write up is well illustrated with 60 photographs, three geological maps and eight tables showing mineral chemistry of indicator minerals and their repository formations.
Improving Facial Recognition of FaceNet in a small dataset using DeepFakes

Facial recognition is affected by many factors such as low-resolution images, availability of datasets, illumination, pose invariance, ageing, expression, etc. With the increasing availability of powerful GPUs, we are using massive datasets to facilitate better accuracy. Among different datasets available to perform training of any facial recognition algorithm, very few of them can be a run on a low configuration system. Still, the same can't be used to create satisfactory FaceSwap results because of anomalies in them. A small dataset of 20 identities has been created on which the results of this paper are observed. This paper introduces the usage of DeepFakes algorithm to improve the performance of FaceNet with SqueezeNet architecture and softmax loss function. It is expected that more data leads to better performance. Then the FaceSwap variation of DeepFakes is used to swap identities and create fake images for a given identity. Then, FaceNet is used to identify faces on the newly formed dataset 250RF using fake images and the original dataset 200R. This method achieves satisfactory results on training and testing accuracy in comparison, thereby creating prospects on such a method.
Student's attendance management using deep facial recognition

Managing student attendance is a repetitive and time-consuming task for both teachers and school administrators. For this reason, we thought of automating this task by deploying the recent advances of machine learning. In this article, we propose an attendance management system based on facial detection and recognition. The classroom is continuously photographed using a camera. An in-depth analysis is applied to the captured images to detect and extract the facial features of the students. Next, a pattern recognition model predicts their identities. The results of the experiment validate the proposed architecture. The process of marking the students' attendance is maintained without any human intervention.
Situated Learning in Teaching Business English in the Age of Digital Transformations

This paper discusses the role of situated learning in teaching Business English in modern times, notable for the growing usage of the Internt, mobile technologies and social media.
A Preliminary Investigation into the Role of Virtual Sport Training Technology as Emotional Coping Mechanism During a National Pandemic Lockdown

The COVID-19 pandemic and the subsequent response by governments to introduce national lockdown regulations have confined individuals to their residential premises. As a result, no recreational or sport activities are allowed outside the (often small) boundaries of family homes, a situation often rapidly introducing social isolation. Research has proven that emotional coping mechanisms, such as sport, can lower the stressful and uncertainty burden on individuals. However, without the availability of this coping mechanism, many individuals have been forced to use virtual sport training technology to keep active. This preliminary quantitative study investigated the role of technology, in particular virtual sport training technology (if any) by cyclists as emotional coping mechanism during a period of national lockdown.The results of an online survey indicated that sport, in general, has always been an emotional coping mechanism during normal challenging situations but that slightly more respondents used sport as mechanism during the lockdown period. Respondents indicated that virtual cycling training technology enabled them to continue with using their normal coping mechanism even in a period of national lockdown.One of the benefits of a virtual training environment is the ability to socialize by riding with virtual team members. Surprisingly, the number of cyclists who preferred riding alone in the virtual cycling environment was slightly more than the cyclists who preferred to join scheduled rides with virtual team members.The research is the first step towards an in-depth investigation into the adoption of technology as an emotional coping mechanism in stressful environments.
Development of a Methodology for the Learning-Teaching Process Through Virtual and Augmented Reality

This paper presents the development of a learning-teaching methodology, based on the application of virtual and augmented reality. Its aim is to develop a teaching system based on immersive technologies containing 3D virtual environments. Lack of specialized workspace can be partially conciliated with the use of simulators for different areas of knowledge. Technical contributions of researchers in engineering areas are considered. The quality of the methodological design is verified through virtual laboratory sessions in simulated environments. Benefits of having these spaces include greater ease of access to laboratories when student populations are numerous, relieve in conducting controlled experiments without fear of experiencing equipment failures. For the execution of this work, bibliographic search on immersive methodologies applied to learning has been done. Works that contribute to virtual education, enhanced by the application of augmented reality, were selected. The second step has been the construction of a proposal of the teaching methodology for virtualized learning.
Algebraic modeling in international and local service logistical systems

International and internal service logistics are advancing at a tremendous pace in our modern life, and future forecasts for this area are optimistic. For this reason, we face a task in the control, optimization and safety and security checking of complex logistical systems with many internal agents working in a changing environment. Our paper aims to show how mathematical modeling, especially behavior algebra, can provide an opportunity for predicting the behavior and stability of logistics environments and checking their safety and security properties. The main security properties of complex logistics systems of different capacities and levels are stability of operation, resistance to external threats and detection and elimination of vulnerabilities. During the development of software systems, it is advisable to use a model approach. It involves the creation of models as tools at every stage of software development for the application of verification, testing and validation methods. Algebraic models of logistics systems can be used to analyze the behavior of all agents involved and to prove their ability to fulfill their goals and the ability of the whole system to constantly exist and remain stable. As examples of practical use the application in practice of algebra of behaviors on an example of the operating closed logistic system of a farm is considered.
Comparative analysis of Human Activity Recognition and object detection

Abstract Human Activity Recognition (HAR) is the ability to interpret human body gesture or motion and determine human activity or action. Most of the tasks can be done automatically if they can be recognized via HAR system. HAR is an essential part of various scientific research contexts and the daily defence system i.e. surveillance, healthcare and human computer interaction (HCI). In computer vision, images and videos are analysed using different techniques. While recognition of activity in the real time environment the speed of the technique matters because if the system can detect the object in the images and videos at real time then it can be forwarded for the activity recognition process. How correctly the technique detects the object and differentiates the human object and other object and how much time it is taking to give the correct object, the number of frames it processes per second all matters. This paper deals with the comparison of the performance of YOLO (You Only Look Once) and Faster R-CNN in recognizing the human activity and detecting object.
General Scientific Category of a Trace: Forensic Aspect

The purpose of the research is to analyze the general scientific category of a trace as the fundamental one to formulate the modern legal category of a forensically relevant trace. The article provides a brief review of the views of scientists who have studied the properties of the general scientific category of a trace. The general scientific category of a trace is presented as a characteristic describing and explaining a trace’s properties. The author emphasizes the necessity to distinguish real and existing traces-signs from traces-ideas. The so-called ideal traces are analyzed. The article provides the classification of such traces. The classification recognizes the traces of memory as the traces of memorization, traces of storage and traces of recollection, and mental traces. It is suggested to pay attention to electronic data traces, virtual data traces, and digital data traces.
IMPLEMENTASI DATA MINING UNTUK MEMPREDIKSI KELULUSAN SISWA DENGAN METODE NAÏVE BAYES

The problem of student grades is very important as a benchmark to see the level of student graduation. Effective value management with supporting applications that are very helpful in calculating accurate calculations. That way the use of naive Bayes technology methods is able to answer problems in the information field of any integrated data. The purpose of this study was to determine the pass rate of students in the implementation of data mining for students who passed and did not pass, based on the final school examination scores (UAS), national test scores (UN), final scores (NA) for the last 3 years, 2017 to 2019. The data study method used in this research is by observation and interviews with resource persons from SMK Putra Jaya School Batam. The auxiliary application in the study used was WEKA to calculate student graduation results. This study used 70 student data as a test of value to be processed and produced a total of 210 data with 167 students who passed and 43 students who did not pass.
Reasoning and Querying Web-Scale Open Data Based on Dl-Lite &lt;sub&gt;Α&lt;/sub&gt; in a Divide-and-Conquer Way

We propose to use DL-LiteΑ techniques to reason and query the Web-scale Open Data (knowledge bases) described by Semantic Web standards like RDF and OWL due to the low reasoning complexity and suitable expressivity of the language. When facing the real-life scalability challenge, the actual reasoning and query answering may become infeasible by the following two factors. Firstly, for both satisfiability checking and conjunctive query answering, a polynomial size of queries may need to be answered over the data layers of the corresponding knowledge bases (KBs) w.r.t. the size of the schema knowledge of these KBs. Secondly, for the KBs with massive individual assertions, evaluating a single query over the data layers may be highly time-consuming. This impels us to seek for a divide-and-conquer reasoning and query answering approach for DL-LiteA, with the basic idea of partitioning both KBs and queries into smaller chunks and decomposing the original reasoning and query answering tasks into a group of independent sub-tasks such that the overall performance can be improved by taking advantage of parallelization and distribution techniques. The challenge for designing such an approach lies in how to carry out KB and query partitioning and reasoning reduction in a sound and complete way. Motivated by hash partitioning of RDF graphs, we expect the smaller KB chunks to have the local feature for both satisfiability checking and simple-query answering. Here simple-queries are the conjunctive queries whose query atoms share a common variable or individual. For query answering, we expect to partition a query into smaller simple-queries and evaluate them over smaller KB chunks. Under these expectations, our divide-and-conquer approach is constructed from both theoretical and practical perspectives. Theoretically, definitions of KB partitions and query partitions are presented, and the sufficient and necessary conditions are identified to determine whether a KB partition holds the desired features. Practically, based on the theoretical results, the concrete ways of partitioning KBs and queries as well as evaluating query partitions over KB partitions are described. Moreover, a strategy of optimizing the procedure of evaluating query partitions over KB partitions is provided to improve the overall query answering performance. To verify our approach, two Web-scale open datasets, DBpedia and BTC 2012 dataset, have been chosen. The experimental results indicate that the proposed approach opens new possibilities for realizing performance-critical applications on the Web with both high expressivity and scalability.
A vision to mitigate bioinformatics software development challenges

Developers construct bioinformatics software to automate crucial analysis and research related to biological science. However, challenges while developing bioinformatics software can prohibit advancement in biological science research. Through a human-centric systematic analysis, we can identify challenges related to bioinformatics software development and envision future research directions. From our qualitative analysis with 221 Stack Overflow questions, we identify six categories of challenges: file operations, searching genetic entities, defect resolution, configuration management, sequence alignment, and translation of genetic information. To mitigate the identified challenges we envision three research directions that require synergies between bioinformatics and automated software engineering: (i) automated configuration recommendation using optimization algorithms, (ii) automated and comprehensive defect categorization, and (iii) intelligent task assistance with active and reinforcement learning.
Research on Film Data Preprocessing and Visualization

Data is the core of information, and good data quality is a prerequisite for many data analysis. Data cleaning is to increase the fault tolerance rate by correcting the error value of detected data. This paper aims to solve the problem of data set processing and visualization in the recommendation algorithm, so as to better apply in the field of recommendation algorithm. The recommendation algorithm and data sets Movielens and IMDB are analyzed theoretically. First, data set A was processed from data reading and movie score calculation; Again, the IMDB is processed in four steps to make it more suitable for the recommendation algorithm field; Finally, the plot function is used to visualize the key information. experiment shows: The data set sorted out by the above methods can effectively improve the quality and availability of data and provide relevant basis for better application in the algorithm.
The Machine as Author

The use of Artificial Intelligence (AI) machines using deep learning neural networks to create material that facially looks like it should be protected by copyright is growing exponentially. From articles in national news media to music, film, poetry and painting, AI machines create material that has economic value and that competes with productions of human authors. The Article reviews both normative and doctrinal arguments for and against the protection by copyright of literary and artistic productions made by AI machines. The Article finds that the arguments in favor of protection are flawed and unconvincing and that a proper analysis of the history, purpose, and major doctrines of copyright law all lead to the conclusion that productions that do not result from human creative choices belong to the public domain. The Article proposes a test to determine which productions should be protected, including in case of collaboration between human and machine. Finally, the Article applies the proposed test to three specific fact patterns to illustrate its application.
On the problem of intensification of introduction of informational communicative technologies into educational process at regional universities

Revealed is the essence of the problem of introducing information and communication technologies in educational process at regional universities against the background of a new wave of transformation of Russian education. The article highlights shortcomings of educational process at higher education institutions, incl. formalization of approaches to introduction of informational and communicative technologies at regional universities in the absence of clear technical and material base. Based on an empirical study of two regional universities, specifics of using informational and communicative technologies are revealed. The purpose of the research is to develop the strategy of the triad “education — practice — business”, that involves intensification of efforts to find approaches to attracting potential employers to development of informational and communicative technology base of regional universities. Conclusion is made that currently it is important to involve business representatives into creation of modern informational and communicative environment of universities that promotes integration of curricula in real business situations, in construct of modern markets, as well as in forming students’ flexibility in learning process.
Paths Planning of Unmanned Aerial Vehicles based on Grey Wolf Optimizer

Trajectories planning for Unmanned Aerial Vehicles (UAV) is a essential task in autonomous flight control design. In this paper, a method based on a Grey Wolf Optimizer (GWO) is present and favorably implemented to solve the path planning problem, reformulated as a hard optimization problem with constraints. The objective function is reformulated based on the path length and environmental constraints specific to the UAV while avoiding all the obstacles. The Water Cycle Algorithm (WCA), Crew Search Algorithm (CSA), Salp Swarm Algorithm (SSA), Differential Evolution (DE) and Particle Swarm Optimization (PSO) metaheuristics are retained as comparison tools for statistical analysis. In order to observe the performance of the GWO compared to the other algorithm, many indicators are used as the performance criteria. The obtained results, conducted by numerical simulation, are satisfactory, perfectible and very encouraging in the aim of a future practical implementation on a hardware target of the proposed planning approach.
Noise Resilient Thresholding Based on Fuzzy Logic and Non-linear Filtering

This chapter proposes a novel image thresholding technique resilient to noise based on the fuzzy logic and the non-linear filtering. The rationale of the proposed technique is to use adaptive Kuwahara filtering, which essentially suppresses the noise while retaining the image texture information. Finally, an optimized threshold selection process is formalized using the fuzzy logic properties. The performance of the proposed technique is then evaluated by extensive experiments on different types of synthetic, real-world images, and floor plan images. Finally, the superiority of the proposed technique is carried out by detailed comparison with the existing methods.
Economic Policy Uncertainty and Cross-Border Lending

During times of high economic policy uncertainty, domestic banks increase cross- border syndicated lending. We control for credit demand by including time-varying borrower country fixed effects in our regressions. The credit migration effects are strongest for banks with diverse income, and when banks face fiercer competition, either in the domestic banking sector or from the bond market. Additionally, using elections as a source of plausibly exogenous variation, which positively affects political uncertainty, we provide causal evidence on the effect of political uncertainty on cross-border lending. In countries with exogenous election timings, banks increase cross-border lending during the election period, especially when elections are closely fought. Compared to the extant literature, which extensively documents the negative effect of uncertainty on real investment, our findings show that uncertainty affects investments in financial assets differently.
A Comparative Analysis on Offline Signature Verification System Using Deep Convolutional Neural Network

The objective of offline signature verification is to identify that the signature is produced by the legitimate user (genuine) or produced by imitator (Forged). This is a challenging task because of static (offline) scenario that uses scanned images of signatures. Offline signature verification and identification is concerned with behavioural biometrics and has fetched significant research attention over the last thirty years. The problem still persist and open for researcher because of need of improvement in accuracy and high variations of signature of same user. Identifying features which can best discriminate and classify signature as genuine or forged is needed. Many studies have taken place in literature which exploited hand-crafted features for task. No feature extractor is found satisfactory in terms of performance. Under this study, we have used pertained convolutional neural network to extract features and classical machine learning algorithm as classifier. This paper compares performance of models for particular dataset.
Proposed metamodels transformation from Predictive methodologies to Agile methodologies

Project management models are constantly evolving, among the most widely answered models we find Agile models or else called “Adaptive” and “Predictive” models. A study by the Standish group confirms that Agile projects achieve the desired result three times more than projects that are carried out by conventional (predictive) Methodologies. Thus, many organizations tend to apply the new project management model or migrate from the standard model to the agile one. In this article, building on previous research, we will build on the principles of MDA to define a metamodel of Agile and Predictive Methodologies, and then we will affect a transformation that will help organizations transform project management.
Research on Named Entity Structure Information for Machine Translation

Due to the existing single machine translation model for named entity markup is too rough and not detailed to meet the requirements. This paper proposes an integrated analysis of lexical and syntactic integration of named entity information to improve the performance of Tibetan-Chinese machine translation. Experiments show that the performance of the model is better than that of the model based on independent integration and the model based on independent grammar. In the stage of syntactic analysis, the combination of named entity information and comprehensive information can improve the performance of syntactic analysis.