Reviewerly/exHarmony huggingface dataset:
Synchronous Cable Sensing Network

This paper describes the creation of a wired sensor network used for seismic exploration. The network is built on the basis of the UDP protocol. A distinctive feature is the ability to install network nodes in the break of a twisted pair cat5 TIA / EIA-S68, as well as synchronous measurements (with an error less than 1 μs) and the absence of additional power cables. Each node of the network is a complex of two main blocks: a microswitching unit and a data collection unit. The microswitching unit organizes the sequential transfer of information from the data collection units to the client. A target load, for example, a geophone or a magnetometer is connected to the data collection unit. The block regulates the signal level from the load and collects data from it. This paper presents the architecture of the system and the structure of the blocks, shows the results of measurements and notes some of the features of the system operation.
An Empirical Study of English Writing Based on Big Data

English writing can effectively promote the development of learners' basic language knowledge, skills and comprehensive application ability. It is a major and difficult point in college English teaching. Based on the big data behind the correction website, combined with the statistical retrieval of WordSmith Tools and other data, and referring to the online corpus big data resources such as BNC and COCA, this article analyzes the significance of corpus and big data in English writing, correction and teaching, and improves the corpus-based Big data English writing, correction, teaching effect, efficiency and effectiveness.
Innovative Forms of Social Non-formal Youth Education

The article reveals the features of non-formal education, its connection with formal education; various definitions of the concept of "non-formal education" are presented; This article analyzes scientific sources of researchers who have dealt with the issue of non-formal education, the main forms of non-formal education, which are known in Ukraine. The purpose of the article is to carry out a theoretical analysis of the scientific problem and highlight the features of the introduction of non-formal education into the process of preparing social workers to work with young people. In this research used methods of analysis, comparison and generalization of sources from the researched problem.
Notice of Removal: Estimate The Efficiency Of Multiprocessor’s Cash Memory Work Algorithms

Removed.
On the effectiveness and role of university elective modules of artistic orientation: the experience of implementing the elective module “Theory and Practice of Contemporary Art" in Moscow City Pedagogical University

This article describes the practical experience of a group of pedagogues, organizers of the elective module &amp;ldquo;Theory and Practice of Contemporary Art&amp;rdquo; in Moscow City Pedagogical University (D. S. Matyunina, M. V. Nozdracheva, M. A. Semenova). The authors share their opinion on the role and effectiveness of the system of elective modules in modern university education, outline the primary tasks, and discuss the peculiarities of teaching general university courses of artistic orientation. The focus attention is the questions of motivation of students, who chose the module, possibility of flexible arrangement of the content of disciplines thereof in accordance with attainment level and requests of the student audience. The researchers assume that elective courses allow the students to adjust the content of their professional education. Based on the three-year experience of conducting a joint elective course, the co-authors conclude that elective courses can expand the professional competencies of students in adjacent fields, as well as give confidence to the students majoring in other disciplines to change their own educational trajectory. The article is addressed to the pedagogues and students of higher education institutions.
Sources of IR Radiation in the Earth’s Atmosphere in Connection with the PeTa Effect

The PeTa (Perelman-Tatartchenko) effect is the radiation of the energy of a first-order phase transition during the transition from a less condensed phase to a more condensed one. The effect was independently discovered by M. Perelman and the author of this paper. Six papers on the PeTa effect have been published in this journal over the past nine years. They are devoted to the development of PeTa models to explain the following phenomena: IR radiation from cold surfaces, cavitation luminescence/sonoluminescence (CL/SL), laser-induced bubble luminescence (LIBL), and vapor bubble luminescence (VBL) in underwater geysers. This paper describes the sources of PeTa radiation in the Earth’s atmosphere. These sources of infrared radiation have been investigated by numerous research groups, but their interpretation either does not exist at all, or it is erroneous. The following phenomena are specifically considered: PeTa radiation during the formation of clouds and fog; a pulse laser based on the PeTa radiation; condensation explosions as sources of PaTa radiation; measurement of the concentration of water vapor in the atmosphere using PeTa radiation; atmospheric scintillation of infrared radiation in the atmosphere due to the PeTa effect; PeTa radiation as a source of comfort for the igloo; the influence of PeTa radiation on living organisms; PeTa radiation due to characteristics of tropical storms; PeTa radiation as a possible precursor to earthquakes. The problem of global warming, which worries everyone, as it turns out, is also associated with the PeTa effect.
Research on hybrid bounding box interference check algorithm for robot offline simulation system

Abstract Aiming at the problem that the traditional industrial robot may interfere with the workpiece when running in the offline simulation environment, a hybrid bounding box algorithm based on octree is proposed. First, AABB is used for rough interference checks, which quickly eliminates the pairs of impossible objects to intersect, then OBB hierarchical bounding box is used for precise interference checks. A new OBB construction method is proposed, which recalculates the semi-axis length and centre of the constructed traditional OBB to make it surround the object as much as possible and reduce the space gap. A traversal method based on distance priority is proposed, which traversed the closest node every time by maintaining a priority queue. And in the update strategy, “update on demand” is adopted, which discards updating the entire tree before each intersection and only updates the current node during the intersection traversal to achieve the purpose of partial update. Experiments show that the proposed hybrid bounding box algorithm can detect interference faster and perform better real-time performance and accuracy.
Encoding Test Cases using Execution Traces

Test case minimization can be critical to meeting the release date of a software product. Identifying redundant test cases can help improve the quality of the test suite and speed up the testing process. Thus, there is a need to uniquely characterize test cases. This identification can support the test engineer to remove redundancy in the test suite and prioritize test cases that are highly affected by the most recent modification in source code. This paper proposes a test case encoding approach that allows engineers to facilitate execution traces to classify and identify their test cases. It will empower test engineers and allow them to minimize the time and cost of testing by reducing the number of test cases, especially in regression testing. Furthermore, it enhances the documentation of the testing process by providing a one-to-one mapping between test cases and their corresponding execution traces, each of which is a sequence of execution events triggered during the execution of the test case. The one-to-one mapping allows the approach to uniquely represent the control-flow and data-flow within the source code. This trace can be used as a signature for the test case. Whenever a modification occurred in the source code, the newly captured signatures are compared against the previous ones; any mismatch indicates that the test case has been affected by the modification. Repeating this process will help classify the test suite into four groups of test cases. This provides the ability to put the testing efforts where it is needed. Additionally, keeping a hashed value for each of the captured sequences simplifies the comparison and unifies the mapping between test cases and captured traces. It also allows detection of minor modifications in the traced events, and reduces the lengthy traces to a set of fixed size hashed values.
Literature-Based Augmented Reality: Integrating Urban Novels with Context-Aware Augmented Environments

This chapter investigates the ways in which the dialogism and unfinalizability of cultural heritage experiences redefine place attachment, so that inhabitants and visitors of cities can recognize and process the urban multivariate collective cultural and aesthetic distinctiveness. The suggested approach is founded on the amalgamation of novels with mobile/context-aware augmented reality. Urban novels play a threefold role. First, they reveal a multiplicity of embodied situations as spatiotemporal representations of the surrounding built environment. Second, they create socially mediated memories related to groups and places. Third, the reading of novels offers strong bodily enactment, opening up the minds of readers to the vivid collective, social and cultural potential, surprise and aesthetic awareness of the urban environment. Augmented Reality integrated with novels can render possible the exploration of such characteristics; as a result, a mutated, transformed urbanscape emerges as a multilayered experience capable of defining durable relationships with the city’s hidden collective, cultural, and aesthetic memories.
Reproducible Software Vulnerability Testing with IaC

Cybersecurity is more important now than ever. Attackers maliciously use vulnerabilities to exploit IT systems. Software developers are responsible for fixing the reported vulnerabilities in their own products, while system administrators should assess the impacts of attacks against their working systems. However, they are wasting time to make the problem appear because provisioning is a complex and troublesome process. This paper presents a model of vulnerability testing and our original system which enables efficient and automatic reproduction of the vulnerable environments and replicable execution of attacks. We utilize the Infrastructure as Code (IaC) principle. Vulnerability testing is represented as code and the system automates the process of provisioning vulnerable environments and executing attacks through machine-readable definitions, rather than manual configuration and execution. A simple human-readable YAML language is used for writing testing definitions. If the definitions are maintained in publicly accessible repositories, security experts have a chance to contribute their knowledge and expertise to the community and an open-source style ecosystem may be created.
Phishing Attack Detection using Python and Machine Learning

In this digital era, information security has become a very important domain as all sorts of information are publicly available in the web. Even though the security measures and the research performed in this field are evolving, still different types of security attacks are prevailing. Also information has become a great business importance in recent times. Even the data of large companies are prone to attacks and are in the danger of losing their data. In particular, human weaknesses are targeted by various social engineering techniques to manipulate people and steal their sensitive information. Inspite of the advances, information security domain is very young and still it has a wider research scope. More efficient research works are required to analyze the emerging security attacks like Man-in-the-middle, phishing attack, drive-by attack, password attack, SQL injection attack, etc. This paper mostly concentrates on phishing attacks by studying and analyzing the PCAP file generated by wireshark at the time of attack and the results are presented in a visualized and understandable format. After which the attack will be categorized. The other method will utilize the machine learning algorithm. In addition to this, different methods are presented to prevent the phishing attacks.
Video captioning based on multi-feature fusion with object awareness

This paper proposes a novel method to utilize three source features for video captioning. It fuses global video features with local object and regional features to model the relationships among objects and their motions and applies object tags instead of visual features to guide the generation of descriptions. Specifically, Multi-feature is firstly extracted by pretrained models and treated as separate inputs alongside video frames. Secondly, an object awareness attention block is designed to fuse the different features information and to learn a joint video representation which has both visual and linguistic semantics. Experiments on MSVD and MSR-VTT datasets have shown the effectiveness of the proposed method, and the ablation studies have verified the contribution of each component.
The Evolution of Context-Aware RDF Knowledge Graphs

The many benefits of knowledge graphs using or based on the Resource Description Framework (RDF) well justify the utilization and wide deployment of a simple yet powerful, formally grounded data model, its serialization formats, vocabulary, and well-defined interpretation to be used for efficient querying, data integration, and automated reasoning. However, the simplicity of RDF comes at a price: there is no built-in mechanism for RDF statements to store metadata and context. This chapter is a critical review of different approaches proposed over the years in the Semantic Web research community to address this limitation, which are used for capturing different types of information, such as data provenance, spatiotemporal data, and certainty, which are crucial in data science applications to make statements context-aware, authoritative, verifiable, and reproducible.
A Novel Approach to Data Augmentation for Document Image Classification Using Deep Convolutional Generative Adversarial Networks

Data augmentation is a procedure where new samples are generated from the training dataset by applying various techniques and algorithms to improve machine and deep learning models’ accuracy and generalization ability. The recent advances in deep learning and computer vision techniques have made scanned document classification a painless and straightforward process. However, such approaches require a lot of labeled data before training and validating the classifiers, which can be done by augmenting the existing dataset by different means. In this contribution, we explored using a system based on deep convolutional adversarial networks (DCGAN) to generate fake document images using an existing scanned documents dataset. Moreover, we compared conventional data augmentation techniques (rotation, zoom, random cropping, etc.) to DCGAN-based augmentation in a document classification context. The newly generated data could then be used along with the labeled data to train a Convolutional Neural Network (CNN) to classify scanned document images. This experiment compared the performances of a system trained on different datasets (original labeled data, GAN-augmented dataset, hybrid augmented dataset). Its results revealed the effectiveness of the proposed approach.
Genetic algorithm niching by (Quasi-)infinite memory

Genetic Algorithms (GA) explore the search space of an objective function via mutation and recombination. Crucial for the search dynamics is the maintenance of diversity in the population.
A comparison of Pattern Recognition Approaches for Recognizing Handwriting in Arabic Letters

For Arabic letters recognition, we achieve three of pattern recognition approaches namely gray level co-occurrence matrix (GLCM), local binary pattern recognition (LBP) and artificial neural network (ANN) and compare between them to result best performance. Two of these methods level co-occurrence matrix and local binary pattern recognition are used for feature extraction whereas in artificial neural network (ANN) we use the intensity values of pixels for input of the neural network. Two classifiers are used, the K-Nearest Neighbor classifier (KNN) for the LBP, GLCM and neural network classifier for (ANN) artificial neural network. Also, we evaluate the results by using leave one person out approach, fold classification and leave one out.
Heterogeneous-Aware Online Cloud Task Scheduler Based on Clustering and Deep Reinforcement Learning Ensemble

Taking advantage of the powerful computing and flexibility charge abilities of cloud computing, an increasing number of applications moved to the cloud. As a result, how to assign the large-scale and dynamical workloads to the cloud servers with the trade-off the performance of applications and resource utilization of virtual machines (VMs) is an important challenge in the cloud environment. Most of the existing methods focus on using heuristic evolutionary algorithms to solve the optimized assign problem, which is not suitable to the time-sensitive workloads due to the high computation costs. To address this issue, we propose an intelligent cloud task scheduler to automatically assign the incoming workloads to cloud servers with variant hardware configurations, according to the performance request and resource utilization. The proposed scheduling framework integers a clustering algorithm and a deep reinforcement learning algorithm. In details, we adopt the k-means clustering method to identify the variant workload types, since different workload type has the varying requirement on performance and cloud resources. After that, the deep Deterministic Policy Gradient (DDPG) method is presented to find the optimized dispatch solution. Finally, we conduct experiments on Alibaba’s real workloads. They show that the proposed scheduler outperforms the other existing methods in terms of response time and resource utilization.
IMPLEMENTASI SISTEM INFORMASI PAYROLL PADA SEKOLAH GEMANURANI

The development of the Gemanurani Integrated Islamic School information system encountered obstacles because it was carried out based on the personal needs of the developer at that time. So that the development carried out is not necessarily appropriate or has optimal benefit value in the future. Along the way, there have been many changes to forms, reports to system procedures. This shows that the development of the information system is not based on existing needs, so management is difficult to make decisions. Gemanurani Integrated Islamic School started the development of Information Systems for New Student Admissions (PSB), Finance, Administration (TU), Study Programs (PRODI), Students and Teachers without Human Resources Development (HRD). One of HRD's routine tasks is payroll where the implementation is still manually using MS. Excel, problems often arise when calculating teacher intervals, teachers across school programs and employee deductions. By prioritizing the development of a payroll information system that suits the needs, it is hoped that it will facilitate the HRD department in processing employee and teacher salaries. The goal is to make it easier for schools in time management and salary calculation security. Keywords: payroll, information system, HRD . DOI :  http://doi.org/10.5281/zenodo.4958348
On Assisted Quantum Key Authentication Protocol

This paper has developed a quantum authentication protocol along with the encryption encapsulation by entangled photon states that are forwarded from a trusted server to two users. It is shown to provide second tier security against impersonation and eavesdropping but with the ease of decryption limited only for two legitimate users. In this process, qutrit photon states are used to identity encryption and a one-way-function containing the phase information of the trits is used for basis selection. The protocol is analyzed to test different eavesdropping strategies and is found to provide unconditional security.
A Novel Scheme for Crawling and Mining of Housing Transaction Data

Housing transaction data from online housing platform contains many valuable information about development trends of real estate, but is also the first-hand reference for citizens to carry out housing transaction. However, how to find the key information hidden in housing transaction data from Internet is still a serious challenge. To solve it, based on web crawling technology and visual analysis technique, this paper proposes a novel scheme for gaining and analyzing housing transaction data. It includes data crawling, data prepossessing, data analysis and other steps. An empirical research is conducted with housing transaction data of Hangzhou, China. The results show the spatial distribution characters of housing price, and the most cost-effective type of real estate in Hangzhou.
Not all users are the same: Providing personalized explanations for sequential decision making problems

There is a growing interest in designing robots that can work alongside humans. Such robots will undoubtedly be expected to explain their behavior and decisions. While generating explanations is an actively researched topic, most works tend to focus on methods that generate explanations that are one size fits all. As in the specifics of the user-model are completely ignored. The handful of works that look at tailoring their explanation to the user’s background rely on having specific models of the users (either analytic models or learned labeling models). The goal of this work is thus to propose an end-to-end adaptive explanation generation system that begins by learning the different types of users that the robot could interact with. Then during the interaction with the target user, it is tasked with identifying the type on the fly and adjust its explanations accordingly. The former is achieved by a data-driven clustering approach while for the latter, we compile our explanation generation problem into a POMDP. We demonstrate the usefulness of our system on two domains using state-of-the-art POMDP solvers. We also report the results of a user study that investigates the benefits of providing personalized explanations in a human-robot interaction setting.
Tingkat Keberhasilan Program Pendidikan Pengguna (User Education) di Perpustakaan Institut Teknologi Sepuluh Nopember Bagi Mahasiswa Tingkat Akhir

The user education program is one of the university library's efforts in improving the information literacy skills of users, especially students. The user education program aims to guide students so that they can take advantage of the library and carry out information searches effectively and efficiently. In general, user education programs in university libraries are intended for all users, but some programs are intended for students, including final-year students. This research uses a descriptive quantitative research method, with purposive random sampling method as the sampling method, and final-year students of the Institut Teknologi Sepuluh Nopember as the research population. The research was conducted using Kirkpatrick's program effectiveness model, in which the effectiveness of a program can be determined through four levels, namely reaction, learning, behavior, and results. The results showed that three of the four levels were in the high category, while there was one level that was in the medium category, to be specific the behavior level. On the whole, the user education program at the Library of Institut Teknologi Sepuluh Nopember has been effective, but there is attention needed to service accessibility so that what users have learned in the program can become a habit and have a high level of behavior as an