Reviewerly/exHarmony huggingface dataset:
Optimal Common Job Block Table (CJBT) to improve the Performance in Hadoop framework

&lt;p&gt;By rapid transformation of technology, huge amount of data (structured data and Un Structured data) is generated every day. With the aid of 5G technology and IoT the data generated and processed every day is very large. If we dig deeper the data generated approximately 2.5 quintillion bytes.&lt;br&gt; This data (Big Data) is stored and processed with the help of Hadoop framework. Hadoop framework has two phases for storing and retrieve the data in the network.&lt;/p&gt; &lt;ul&gt; &lt;li&gt;Hadoop Distributed file System (HDFS)&lt;/li&gt; &lt;li&gt;Map Reduce algorithm&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;In the native Hadoop framework, there are some limitations for Map Reduce algorithm. If the same job is repeated again then we have to wait for the results to carry out all the steps in the native Hadoop. This led to wastage of time, resources. If we improve the capabilities of Name node i.e., maintain Common Job Block Table (CJBT) at Name node will improve the performance. By employing Common Job Block Table will improve the performance by compromising the cost to maintain Common Job Block Table.&lt;br&gt; Common Job Block Table contains the meta data of files which are repeated again. This will avoid re computations, a smaller number of computations, resource saving and faster processing. The size of Common Job Block Table will keep on increasing, there should be some limit on the size of the table by employing algorithm to keep track of the jobs. The optimal Common Job Block table is derived by employing optimal algorithm at Name node.&lt;/p&gt;
About Pedagogical Skills, Psychological Obstacles in Learning and Science and Imaginative Thinking

The article deals with the psychological and mathematical problems of teaching mathematics in higher education in the context of the humanitarization of mathematical education. An attempt is made to identify objective reasons that hinder the successful assimilation of the subject.
A Case in Which All Students Gave Their Presentations on a Video-sharing Site in a Course of Statistical Modeling

University courses pertained to data-science subjects typically involve students′ presentations. However, due to the temporal limitation of the courses, all student attendees may not be allowed to have their opportunities of presentations. This paper introduces a method in which all students give their presentations by utilizing a video-sharing-site. Furthermore, a double-blind mutual evaluation was attempted to ensure the perceived fairness of evaluation and students′ active engagement in the question and answer section. These detailed methods are described and their potential effects are discussed.
Understanding the Future Prospects in The Indian Financial Sector Through Blockchain Technology Related Innovations

Outcomes of the blockchain technology and of the resultant innovations will bring in a new renaissance in the Indian financial sector, if adequate measures can be taken for its efficient implementation. The concerned technology supports the entire structure for the delivery of services by the Indian financial sector in a more cost effective, faster, secure and accurate manner for a gigantic customer base. Moreover, the resultant virtual currency innovation through this technology will create a new space in the existing field of means of exchange of the world.
Post-Hoc Interpretability and Explanations

Post-hoc techniques represent a vast collection of methods created to specifically address the black-box problem, where we do not have access to the internal feature representations or model structure. There are considerable advantages to using post-hoc methods. They can work for a wide variety of model algorithms. They allow for different representations to be used for internal modeling and explanation. They can also provide different types of explanations for the same model. However, there is a trade-off between the fidelity and comprehensibility of explanations.
The Applicability of Greimassian Semiotics to Meaningful Procedural Quest Generation

We present the semiotics of Algirdas J. Greimas as an analytical method that may be useful to the procedural generation of quest narratives in interactive games. We focus on summarizing the tools introduced by Greimas to describe a narrative's meaning, and we explain them using a sample analysis of Monterroso's microstory "The Dinosaur". Finally, we sketch our vision of how these tools can be used in the context of a generative system that could create stories where a deeper meaning is identifiable by players.
Panoramic Image Stitching Techniques Based on SURF and Singular Value Decomposition

The fundamental goal of stitching images is to discover a group of images of a single scene, combine them to form one image for a wide scene. The handheld camera has limited resolution and a narrow field of view. Image stitching faces many challenges such as alignment of image, illumination variation, bad lighting, different scale, and low resolution resulting from stitching. The objective of this paper is to produce a high-resolution panorama in different uncontrolled environments. The suggested method starts by detecting the overlapping areas based on detecting the features extracted by the Speeded-Up Robust Features (SURF) algorithm and Singular Value Decomposition (SVD). Images are automatically aligned based on discovering the geometric relation sips among the images, then images concatenated to create a panoramic image. The suggested algorithm was experimentally tested with different uncontrolled environments such as different resolutions, different image sizes, different numbers of input images, different illumination, and bad lighting. The results showed that the proposed algorithm could stitch correctly with a different overlapping area up to 20% and sometimes more for the images obtained by a wide lens with a different wide-angle. The results were promised and dependable, and the tests prove the ability of the proposed algorithm to solve many image stitching challenges.
Classification-Aware Path Planning of Network of Robots

We propose a classification-aware path planning architecture for a team of robots in order to traverse along the most informative paths with the objective of completing map classification tasks using localized (partial) observations from the environment. In this method, the neural network layers with parallel structure utilize each agent’s memorized history and solve the path planning problem to achieve classification. The objective is to avoid visiting less informative regions and significantly reduce the total energy cost (e.g., battery life) when solving the classification problem. Moreover, the parallel design of the path planning structure reduces the training complexity drastically. The efficacy of our approach has been validated by a map classification problem in the simulation environment of satellite campus maps using quadcopters with onboard cameras.
Generating Targeted Attack Scenarios against Availability for Critical Infrastructures

Attack and defense are both essential to information security. Focusing on critical infrastructures, we aim to generate targeted attack scenarios against availability for attack, and propose corresponding monitoring and detection deployment for defense. We start with producing an asset inventory automatically with an open-source network exploration and security auditing tool, and assess vulnerabilities for each asset with open-source intelligence. With vulnerability metrics, we can construct more exploitable attack scenarios with the most impact on availability. With the adversary techniques derived from real-world incidents and attacks, we can construct more realistic attack scenarios integrated with industrial control systems. With current security advisories, we can construct more targeted attack scenarios against specific critical infrastructure sectors. As a result, we can propose proactive monitoring and timely detection deployment across all levels of the PURDUE model in order of the sequences of kill chain phases for defense.
Intelligent Machine Translation of Chinese English News in the Context of Communication Observations and a Priori Modeling

People of various countries communicate more frequently, and as a new medium for recording and disseminating information, online news, with its real-time, authenticity, and wide coverage, has enabled more and more people to learn more through the window of news. Information from abroad. However, language barriers have become the biggest obstacle to communication and exchanges between countries and people in the region. During the critical period of actively promoting the “Belt and Road” strategy, the demand for translation of news reports, government documents and other texts in various languages has increased greatly. However, manual translation cannot meet the existing large-scale text translation needs. This paper studies and analyzes the intelligent machine translation of Chinese English news based on the background of communication perspectives and a priori model.
TYPES OF A HIGHER EDUCATION TEACHER’S PERSONALITY ORIENTATIONS AND SCIENTIFIC AND PEDAGOGICAL ACTIVITY

The proposed article analyzes the types of personality and scientific-professional orientation of higher education institutions teachers, characterized by a high level of competence and creative productivity. The relevance of this study stems from the fact that teachers’ scientific and pedagogical activities are connected with their creative developmentandprofessionalself-improvement. Therefore, theobjectives of thearticle are tostudythepersonality and psychological characteristics of the relevant cohort of high school teachers, the leading types of personality orientationandscientificandpedagogicalactivitiesoftheseteachers. Inaddition, animportanttaskofourresearch is to analyze the relevant types in the context of systems research, which allows to deepen the understanding of this scientific problem. With the help of Yu.A. Urmantsev’s general systems theory the systematic correlation of the main categories of pedagogy in the context of the scientific orientation of the teacher is clarified. It is shown that the personality-oriented, subjective orientation of the teacher is realized in the context of the interaction of epistemological and axiological ways of mastering the world by man and humanity. Acmeological orientation is realized in the system-functional plane of interaction of epistemology and praxeology. Creative innovation orientation is realized in the focus of combination of axiological and praxeological ways of mastering the world by a man.
5G Service Architecture Automatic Code Generation

Code generation can effectively reduce the defect rate in software development. The 5G system adopts a service-based architecture. For various application scenarios, developers need to write a large amount of code according to specifications to realize the construction of different logical networks. This process has a long cycle, low efficiency, and many code vulnerabilities. In response to the above problems, this paper introduces the neural network to map the natural language intentions related to the 5G core network into code. The experimental results show that our system is effective and can learn the grammatical knowledge of the target language.
College Physical Education Teaching Platform Based on Information Technology

With the arrival of 5G era, mobile Internet is gradually becoming the development direction of Internet industry, and the information construction of college physical education is gradually valued. This paper further integrates the needs of the college sports information teaching platform, providing more convenient sports information services for College students, teachers, and sports management departments, with the help of the GPS function of mobile phones to explore the assessment methods of students’ extracurricular physical exercises. By using the methods of questionnaire and field interview, this paper theoretically analyzes the problems that should be paid attention to in the development of university sports informatization and the applied research. Through the demand analysis of university sports informatization service, on the basis of the business demand, it establishes the main business functions of sports teaching information inquiry, accurate information push and students’ extracurricular exercise: through the detailed through the investigation and analysis of users’ needs, the detailed needs of students in each functional module are established. Through the analysis of functional needs, the basic goal of function realization is established for further development. This paper compares the mainstream mobile phone operating system in the current market, and summarizes the characteristics and current situation of the development of sports informatization in Colleges and universities.
Depth Privileged Object Detection with Depth-Enhanced DCN

Deep learning has achieved excellent performance in object detection but remains challenging due to the poor illumination, object occlusion, etc. Some works have attempted to leverage the additional depth images to assist for the RGB object detection since the depth images can provide complementary information for RGB images. However, owing to the less prevalence of depth sensors, depth images are not always available in general scenarios. In this paper, we focus on the depth privileged object detection (DPOD), in which the depth images are privileged and only available in the training stage. Specifically, we propose a novel depth-enhanced deformable convolutional network (DEDCN) to adapt to the depth input and transfer the knowledge in depth information to RGB information using learned offsets and derived modulations. Furthermore, we design a three-branch network equipped with DCNv2/DEDCN to fulfill the offset and modulation hallucination. Extensive experiments on NYUDv2 and SUN RGB-D demonstrate that our method outperforms the state-of-the-art approaches for DPOD.
Quantitative assessment of areal geological complexity

Background. The article presents a new approach to assessing the geological complexity — quantitative assessment of areal complexity, as well as an alternative methodology for assessing complexity in 1D. &#x0D; &#x0D; Aim. Developing a numerical metric for assessing the geological complexity and creating an algorithm for complexity maps construction. &#x0D; &#x0D; Materials and methods. Generally, complexity describe the reservoir in one number, that often underestimates the real complexity of the deposit. Geological complexity, presented in the article consists of 5 groups: structuraltectonic, facies-lithological, permeability and porosity, secondary alteration and fluid properties, 13 characteristics describe the complexity space of these groups. Each of these characteristics could be presented not only in 1D but also in 2D. The proposed methodology was tested on the company’s assets. &#x0D; &#x0D; Results. The presented examples of complexity maps for several fields show the advantage of 2D complexity estimation in comparison with 1D. The analysis of decomposed complexity estimation (for individual groups) on the company’s assets showed that the key groups of complexity are structural-tectonic, facies-lithological characteristics. Therefore, characteristics that describe these groups should be taken into account during the decision-making process and assets ranking. &#x0D; &#x0D; Conclusion. A methodology of quantitative assessment of areal geological complexity has been developed. This areal assessment allows identify the most “problematic” areas, analyzing existing sources of uncertainty, and also ranking and screening company assets when making strategic decisions.
Personalized Travel Recommendation System Using an Ontology

The rapid growth of the web and its applications has created immense importance for recommender systems. Recommender systems were designed to generate suggestions for items or services based on user interests with the applications to different domains. However, the integration of multiple data sources while resolving semantic ambiguity of entities involved in the integration has been overlooked in many recommender systems developed for travel recommendation. This research proposes an ontology-based travel recommender system to overcome such deficiencies in the current travel recommender systems. The developed ontology facilitates the integration of multi-model data for personalized travel recommendations. The similarity analysis of entities to be interconnected is performed by using a semantic data classification technique that integrates a hybrid filtering approach to classify similar entities, including tours and visitors. The proposed ontology-based approach for travel recommendation outperforms other methods and with higher accuracies.
NSPIS: Mining Negative Sequential Patterns with Individual Support

Negative sequential pattern (NSP) mining is crucial and sometimes carries more enlightening information than positive sequential pattern (PSP) mining in data mining. Owing to its computational complexity and exponential search space, the task of discovering NSPs is often much more difficult and challenging than that for PSPs. To date, a few NSP mining algorithms have been proposed. However, most algorithms only consider a single support, thus can not present good results in many special real-world applications. To solve this problem and achieve better efficiency on a long sequence database or a large-scale database, we propose a novel algorithm called Negative Sequential Patterns with Individual Support (NSPIS) in this paper. The projection mechanism is adopted to NSPIS, which allows greatly reduce the search space and simultaneously improve the efficiency. Finally, detailed results of the experiments show that NSPIS can achieve better performance and it uses less memory on large datasets compared to the state-of-the-art algorithm.
Robot Path Planning Based on Fusion Improved Ant Colony Algorithm

Robot path planning is a hot issue in the robotics field. Based on many algorithms, we consider the efficiency and accuracy of path planning. This paper proposes a path planning method that combines improved ant colony algorithm and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{A}^{\ast}$</tex> algorithm. First, by improving the pheromone update method, state transition probability and heuristic function in the ant colony algorithm, an ant colony algorithm with better performance is obtained. Afterwards, aiming at the defect that the same initial pheromone concentration in the ant colony algorithm leads to the purposeless search of the first-generation ant colony, the fusion improved ant colony algorithm and the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{A}^{\ast}$</tex> algorithm are proposed, and simulation experiments are carried out. The experimental results show that the path is planned through the fusion algorithm the search time is shortened, the number of iterations when reaching convergence is reduced, and a better path can be obtained, which shows that the algorithm can achieve good results when dealing with path planning problems.
Autoencoder-based MIMO Communications with Learnable ADCs

With the growing demand for high-speed data transmission, multiple-input multiple-output (MIMO) technology has been widely used in 5G wireless communication systems. Practically, analog-to-digital converters (ADCs) are employed to reduce the high power consumption problem caused by using a large number of antennas. However, one significant bottleneck restricting the performance of such MIMO systems is the infeasibility to jointly design the transceiver and the ADCs. To address this problem, this paper proposes an autoencoder solution driven by deep learning methods, for MIMO Rayleigh fading channels. Specifically, by viewing the entire MIMO system as an auto encoder, the encoding-decoding pair and the ADCs can be trained simultaneously and optimized in an end-to-end manner, which can also be easily adapted for different information transmission rates. Moreover, in order to enable the gradient passing for ADCs, a smooth function (i.e., arctan(.)) is used to finely approximate the quantization layers of original ADCs. Finally, simulation results reveal the improved performance on both accuracy and stability of the proposed autoencoder based framework in MIMO systems.
Exploring Network Communication in Classroom Using a Homemade Optical Fiber System

We designed a device that converts sound into light through an optoelectronic circuit including a blue LED connected to a single optical fiber in order to discuss fast and long distance communication in classroom.
Data Mining Driven Modelling on the Individual Heterogeneity of Economic Preference: A Metaphor Corpus of Neuroeconomics

This paper proposes an economic preference theory based on neuroeconomics and economic metaphor, and initially constructs a metaphorical mapping context-corresponding economic preference convolutional neural network metaphor corpus model; machine deep learning algorithms and Markov jump system chain thought are both applied, with the aid of convolutional neural network model structure visualization experimental tools as empirical research methods and experimental means, the economic preference theory of economic metaphor context and experience mapping has been carried out empirically.
Analysis of Effort Estimation for Test Suite using Control Graph

The software test estimation is a vital process for business prospects. The testing effort estimates with test case generation and execution time of test data. This paper evaluates the effort estimated for test cases by branch coverage on Control Flow Graph (CFG). Develop CFG for the programs, and extract all independent paths. The graph covers the information flow among all the classes, their methods, functions, and statements. Examine the number of test cases by assessing the cyclomatic complexity metrics of the graph. We also formed software test metrics with Halstead measurement on two different versions of a program. The empirical evaluation is portrayed on a segment of python code. Test efforts are analyzed on the additional test cases, and a comparative analysis is performed on testing effort estimated for the changed version of source code. This work aims to analyze testing efforts on old and modified versions of a program and measure the difference between the two. The experiment results show that the modified code regression test takes 0.791 sec less time than the complete test.
Isogeny Computation on Twisted Jacobi Intersections

Isogenies between elliptic curves act as a key role in isogeny-based cryptography. Formulas for isogenies on different elliptic curve models such as Weierstrass, Edwards, Huff and Hessian have been proposed. In this paper, we construct isogenies on twisted Jacobi intersections for the first time including a 2-isogeny and a generalized \(\ell \)-isogeny for any odd \(\ell \). We also introduces \(\omega \)-coordinate systems for twisted Jacobi intersections which provides biquadratic relations like the Montgomery model. As a result, such \(\omega \)-coordinate systems would significantly simplify the computation of isogenies on twisted Jacobi intersections.