Reviewerly/exHarmony huggingface dataset:
Research and application of cable information intelligent platform

This project aims to solve the problems of accurately mastering the cable distribution, trend and buried depth within the management scope of distribution underground cables, and the theoretical accuracy can reach the centimeter level. Through the establishment of line information location archiving, combined with GIS technology, it can realize the visual operation of cable distribution, detailed survey of cable attribute information, and improve from paper-based to electronic information. Therefore, based on GIS geographic information and RTK difference technology, all-round, all-weather, all-time and high-precision characteristics, through the effective combination of unique vector map display and geographic analysis functions (cable inflection point marking, overlay analysis, path planning, etc.) and distributed database system (attribute query and mathematical statistical analysis, etc.), It can realize the joint operation of spatial and non spatial data, and it can make the economic benefit develop rapidly and sustainably.
Survey on Social Distance Analyser Using Deep Learning Techniques

Covid – 19 Virus spreading over day by day, throughout the world, billions of people are infected. "Social distance" is the only key to decreasing the COVID 19 rate. The best way to deal with such a dangerous situation is to follow the rules made by the government. Maintaining social distancing is only the option to control the spread of the diseases. The ongoing COVID-19 virus outbreak has caused a global disaster with its deadly spreading. Due to the absence of effective remedial agents and the shortage of immunizations against the virus, population vulnerability increases. Social distancing is thought to be an adequate precaution (norm) against the spread of the pandemic virus. The risks of virus spread can be minimized by avoiding physical contact among people. To effectively control the Covid-19 virus situation, the survey relied on twelve research papers focusing on detecting social gap violators using in-depth learning algorithms. The goal is to detect violations of the social distancing rule. Many studies have been done and various innovative methods have been suggested. The purpose of this paper is to compare the proposed methods and their results. This survey paper will discuss the technologies used for this in detail.
Relative Pose Estimation for RGB-D Human Input Scans via Implicit Function Reconstruction

To achieve a promising performance on relative pose estimation for RGB-D scans, a considerable overlap between two RGB-D inputs is often required for most existing methods. However, in many practical applications for human scans, we often have to estimate the relative poses under arbitrary overlaps, which is challenging for existing methods. To deal with this problem, this paper presents a novel end-to-end and coarse-to-fine optimization method. Our method is self-supervision which firstly combines implicit function reconstruction with differentiable render for RGB-D human input scans at arbitrary overlaps in relative pose estimation. The insight is to take advantage of the underlying human geometry prior as much as possible. First of all, for stable coarse poses, we utilize the implicit function reconstruction to dig out abundant hidden cues from unseen regions in the initialization module. To further refine the poses, the differentiable render is leveraged to establish a self-supervision mechanism in the optimization module, which is independent of standard pipelines for feature extracting and accurate correspondence matching. More importantly, our proposed method is flexible to be extended to multiview input scans. The results and evaluations demonstrate that our optimization module is robust for real-world noisy inputs, and our approach outperforms considerably than standard pipelines in non-overlapping setups.
Mobile-Assisted Language Learning (MALL) in Indonesia: Case on English Language Learning for Higher Students during COVID 19

The present paper, therefore, aims to develop deeper students’ understanding and perception regarding the use of mobile assisted language learning in the learning process. All schools and universities should eliminate offline learning and switch to online education. When this is enforced, not a few students find it difficult to access online learning. Some students may have privileges in the form of computers and internet access. This one can easily follow the learning delivered through online media used by the school/Learning management system.This study adopted qualitative research design where case study is used. In this study, the research participants are five students taken from university students who are currently taking English education. To hinder the ethical issues during the data collection and also the research process, the anonymity of the participants is implemented. The research participants also filled out the informed research consent form in order to share the agreement to be part of this study. There are some benefits gaining from implementing MALL such as learning flexibility and easiness. Students are not limited by the place and time to start studying and access some English sources. However, the comprehension supports for students from surrounding in mastering the technology use will enhance the learning process.
Hate Speech, Fake News and Content Regulation on Social Networks in Europe

This chapter analyses the current controversies surrounding the regulation of online content—regarding migrants, but also beyond—showing how the reconfiguration of relations between actors in the information market influences the conditions for the exercise of freedom of expression on the Internet. It shows that the regulation of online public speech goes far beyond relations between States and platforms, and implies a great diversity of actors: the media rely on fact-checking and certification of information; activists engage in collective actions of counter-speech or practice the “name and shame” policy to fight extremist websites or manipulators; companies put on market solutions of “brand safety”, the Internet users engage in initiatives of “participatory vigilance”.
The Personality of the Character Jagat in the Novel Tamu Kota Seoul by Yusri Fajar

Aims: Describe the personality of the character in the Tamu Kota Seoul novel by Yusri Fajar based on psychological studies.&#x0D; Study Design: Multidisciplinary.&#x0D; Place and Duration of Study: Graduate Program of Indonesian Bahasa and Literature Education University of Lampung, August 2019 until now.&#x0D; Methodology: Qualitative descriptive method by using a literary psychology approach. The literary psychology approach is used to see the relationship between the data in the literary work with the personality of the character and psychosocial facts. The source of the data in this research is the Tamu Kota Seoul novel by Yusri Fajar. Data collection is using library and note-taking techniques. The data analysis technique uses an interactive model analysis technique which consists of three components of analysis, including data reduction, data presentation, and drawing conclusions or verification.&#x0D; Results: The results show that the personality of the character found in the Tamu Kota Seoul novel by Yusri Fajar can be described as the character Jagat experiences various personality dynamics as well as the emergence of a strong id and ego problems of meeting needs, but superego strength, mature personality, and psychological qualities possessed are able to control personality strongly. Although, the highest needs aspect (self-actualization) of the characters have been fulfilled, the physiological needs, security, love, and self-esteem remain as needs that continue to revolve conditionally and want to be fulfilled as well.
The Dynamic Construal and Generation of Abstract Coherence with the Framework of Cognitive Reference Point, Target Concept Point and the Processing Mental Path

Langacker’s model of Cognitive Reference Point (CRP) and Wang Yin’s model of Remedied Cognitive Reference Point (RCRP) could be integrated to form a cognitive trinity to construe and generate the text coherence. This trinity framework is composed of cognitive reference point, target concept point and the processing mental path (CTM) within certain semantic domain. This research tested the efficacy of CTM and found it promotes the dynamic construal and generation of local and global coherence in Abstracts of scientific papers. Activating the related information schema and integrating the mental representation with the semantic information embedded in the Abstract sentences, the three-dimensional CTM establishes a cognitive referential framework for Abstract viewers. This CTM framework assists Abstract viewers in efficiently construing and constructing the local and global coherence of Abstracts.
A balancing act: finding harmony between institutional needs and user feedback during a website redesign

In this column, an academic library’s web team shares their experience of managing a website redesign while also navigating institutional changes and branding requirements. They detail the obstacles and challenges that needed to be managed in order to build a new website with limited staff and technological resources to better meet the needs of library users.
Visual Analytics for the International Trade

International trade brings more and more attention to the public. Understanding the trade behaviour is essential for the policy makers as well as business leaders for decision making. Since the trade events are numerous and complex, it is challenging for people to catch up with the trade transactions let alone decision making. This paper applies the visualization techniques to show the trade trends and behaviours. With the visualization interface, it is much clear to understand the international trade and catch the significant trends. In addition, we also analyse the behaviour via mathematical modelling and data mining techniques. Our work assists a lot for government and business leaders to discover the trade patterns and make decisions.
Applying Sharing Session in Encouraging and Motivating Students in Language Learning Class

The purpose of this paper is to analyze the importance of sharing sessions in encouraging and motivating students in the language learning class. Not all of the students are active in the class, some students are passive in the class because of some factors. When the teacher asks the question, students preferably keep silent in the class. They are becoming passive participants in the class because they do not know the topic and feel unconfident to speak in front of others. They are becoming afraid to speak because they do not feel comfortable in the class situation and condition. They do not have good socialization in the class like they do not respond well and give feedback in the class. For this problem, this paper attempts to analyze how to apply the sharing session steps in language learning class and what are the benefits of applying them. The teacher can give motivation like intrinsic or extrinsic motivation in a sharing session class which also can encourage students in language learning class. This paper applies qualitative research analysis and also a descriptive method to describe the finding. Two steps can be used to apply sharing sessions in encouraging and motivating students in the language learning class. The first step is to choose the learning materials that are appropriate for the learners, and the second step is to create good classroom management. This paper finally underlines that the use of applied learning materials that are appropriate with the learners and create good classroom management can improve students’ confidence, and it makes them be the independent learners in the classroom.
Doppler Indıces Evaluatıon In Malıgnant Breast Lesıons: A Revıew

The incidence of breast malignancy has displayed a rapid rise in recent times. It is one of the most common malignancies found in women. Women should be educated and made aware about self breast examination in order to detect malignancies at an early stage so that the prognosis can be improved. Breast pathologies can be classified into malignant and benign ones on the basis of signs and symptoms and by ultrasonography but can be confirmed on histopathology which remains the gold standard. Breast ultrasonography is the most common non ionizing radiological investigation used to assess breast lesions especially in women with dense or painful breasts. Breast carcinomas appear irregularly shaped hypoechoic lesions with non-circumscribed margins, but these features can rarely be manifested by benign lesions as well. Recent introduction of doppler ultrasound has led to an increase in the sensitivity of detecting breast carcinoma since most of the breast malignancies are hyper vascular and they show high resistivity index on spectral analysis. Angiogenesis in malignant lesions lead to formation of structurally abnormal and tortuous vessels with increased resistive index. Several studies on doppler analysis of breast malignancies have shown Doppler ultrasound to be a safe and effective modality showing acceptable diagnostic accuracy for non-invasive characterization of malignant breast lesions. Therefore, it can be employed as an alternative to histopathology in patients who present with breast lesions.
Decoupling of Direction and Length for Cumulative Step Size Adaptation

Covariance matrix adaptation evolution strategy (CMA-ES) is a successful evolutionary algorithm for continuous optimization. The cumulative step size adaptation (CSA) is the default method for adapting the step size in CMA-ES and achieves remarkable performance in practice. It follows the idea of exploiting the correlations between search directions in consecutive generations. In this paper, we investigate the effects of the update directions and the length in CSA. We find that sampling from uniform sphere distribution can yield comparative performance to the default setting, and normalizing the update direction in CSA does not hurt the performance. Further, the CSA with actual search direction cannot perform well, and the correlation between the default direction of CSA and the actual search direction is rather small. It indicates that CSA does not accumulate the actual search directions. We argue that the step size and the covariance matrix should be updated by decoupled search information, and accumulating the actual search direction results in inferior performance due to the same update direction in two evolution paths for the step size and covariance matrix updates.
Research on Webshell Detection Based on Semantic Analysis and Text-CNN

Webshell, a kind of web page-based backdoor, is widely used in network attacks. There are many methods used for Webshell detection. A method based on bytecode of JSP scripts and Abstract Syntax Tree of PHP fails to see the similarities between different script languages. What's more, an approach based on raw script's content and CNN model only focuses on PHP Webshell and does poorly in the extraction of Webshell's features. Therefore, this paper proposes a generic Webshell detection model based on Text-CNN by extracting the node sequence of the Abstract Syntax Tree of PHP and JSP Webshell. The experimental results show that, compared to the traditional static detection model, the proposed detection model significantly improves detection rate, where the detection accuracy achieves as high as 99.5%.
Web Page Classification Based on an Accurate Technique for Key Data Extraction

The continuously growing number of web pages challenge web users, search engines, information retrieval, and recommendation systems. Hence, the importance of automatic Web page classification becomes obvious. This paper proposes a method to classify web pages after extracting the key data. Our extraction system aims to reduce the noisy contents and improve the classification model performance. We test our method on a large dataset of categorized websites and we conclude that SVM outperforms Gradient Boosting, K Nearest Neighbors, Multinomial Logistic Regression, Multinomial Naïve Bayes, and Random Forest.
Application of Failure Prediction via Ensemble Techniques

Fault is considered one of the most common issues in the software life cycle. Some of these errors are not identified during the testing phase, causing problems throughout the production process and execution time. The technology and complexity in the field of software development are increasing day by day, increasing the difficulties in fault prediction prior to the production process. Earlier, the prediction of failure in software is pushed into production by enhancing the system’s reliability and quality. The wastage of time and cost due to failure in runtime can be avoided by implementing a better prediction system. Some traditional methods are available for predicting failures in different aspects and cannot provide proper output. This paper proposes an advanced failure prediction system by using a machine learning (ML) algorithm. It is capable to detect software faults in a very fast and accurate manner. This system is developed by combining different ML techniques for comparing the performance of each technique and it also determines how they influence results. Ensemble learners are used for combining ML algorithms by soft voting and plurality voting for performing early prediction in software module failure. A graphical user interface (GUI) and the dashboard have been developed for obtaining better output representation.
Research and application of sensitive data intelligent identification technology in power grid data protection

The purpose of this paper is to study the security control measures when data is distributed and shared, and necessary privacy data protection measures should be taken. On the one hand, when data leakage occurs after data is distributed, it can accurately locate which responsibility link occurs according to the added data watermark information, and then trace the responsibility and strengthen the weak link control. On the other hand, through the joint query technology in the data encryption sharing scenario, this paper studies how to build a trusted security system to ensure the complete trust between the data participants and provide effective support for big data analysis.
Deception for Characterizing Adversarial Strategies in Complex Networked Systems

There is a need for systematic characterization of complex networked systems involving friendly forces and opponent forces to understand the adversary opportunities and capabilities to cause harm and develop counter-strategies that would minimize the adversarial impact. Specifically, we need the ability to quantify the incurred cost (cost of protecting friendly forces) and induced cost (opponent cost to cause damage to friendly systems). In this paper, we address the problem of characterizing adversarial strategies and develop a suite of metrics that quantify the opportunity and capability of the adversary. We also propose a deception strategy that would force the opponent to redirect away from mission-critical systems by optimally placing decoy nodes and thereby expending opponent resources with little gain. Our adversary strategy characterization involves an integrated graph theoretic and Bayesian network that tracks lateral propagation of adversaries in a complex networked sys-tem. Our deception strategy is devised by a Partially Observable Monte Carlo Planning (POMCP) Framework that provides a sequence of actions that force an attacker to be redirected to a decoy network with high probability.
Automated Classification using SVM and Back Propogation Learning Technique

In this paper the comparative study of two supervised machine learning techniques for classification problems has been done. Due to the real-time processing ability of neural network, it is having numerous applications in many fields. SVM is also very popular supervised learning algorithm because of its good generalization power. This paper presents the thorough study of the presented classification algorithm and their comparative study of accuracy and speed which would help other researchers to develop novel algorithms for applications. The comparative study showed that the performance of SVM is better when dealing with multidimensions and continuous features. The selection and settings of the kernel function are essential for SVM optimality.
Retraction Notice to: Recent Approaches for Text Summarization Using Machine Learning &amp; LSTM0

Cite This Article N. Kumar Sirohi, M. Bansal and S. N. Rajan, "Retraction notice to: recent approaches for text summarization using machine learning & lstm0," Journal on Big Data, vol. 3, no.2, pp. 97–97, 2021.
The Implementation of Character Education in Teaching English at SMP Muhammadiyah 4 Pekanbaru

This research was aimed to describ about the implementation of character education in teaching English in online learning. Since there is transition between tradisional learning to online learning, this topic will be interesting to be researched. This was conducted at SMP Muhammadiyah 4 Pekanbaru.This research can be categorized as phenomenological research which employs the qualitative approach. The instruments used were observation sheets and interview guides. The data were collected through observation, interview, and documents. The subjects of the research were the English teacher of SMP Muhammadiyah 4 Pekanbaru.The result of this study shows that the implementation of character education in teaching English in online learning of SMP Muhammadiyah 4 Pekanbaru. It was integrated into the planning stage, the teaching and learning stage, and the evaluation stage by developing essential virtues, including being religious, discipline, honesty, independence, responsibility, hardwork/persistence, curiosity, creativity, honesty, friendliness/ communicative, appreciation of acheivement. There are some supporting and inhibiting factors faced in implementing character education including (1) teacher (2) students (3) parents who support to implement the values in teaching and learning process and also some challenges such as (1). Lack of training (2) no direct interaction (3) lack of students’ motivation and manner (4) lack of parents’ support and ( technical error). In addition there are some ways that teacher do to solve the problems in this research such (1) Learn again and again. (2) approach the students (3) collaborate with parents.Keywords: implementation, character education, online learning.
Modelo de madurez tecnológica de estudiantes de instituciones de educación superior en Colombia

The purpose of this paper is to explorer the level of technological maturity of undergraduate students from institutions of higher education. Which is why it was developed a model of technological maturity, with variables directly related to the teaching-learning process. The focus of the study was quantitative. The study was designed as non-experimental with a causal cut. The study was carried out in the city of Bogotá, Colombia, with a non-probabilistic, a questionnaire was designed as a data collection instrument, which was distributed by internet to 481 undergraduate students from different academic units participated and during different semesters. The results confirmed the direct relationship among the familiarity and trust towards digital transformation, while, the trust has a negative impact on the attitude. The results indicate that the model adjustment indicators were acceptable, the positive influence of the variable trust in the model is evidenced, and how the attitude towards digital transformation has a negative impact on the expectations of the future use of digital tools.
Data scrutiny in IUCr journals

Peer review of supporting data for submitted research articles is currently assuming great significance