Reviewerly/exHarmony huggingface dataset:
Verbality and creolization in the hypertext space professional IT-Blogs

The aim of the research is to investigate the textual property of connectedness in the electronic hypertext system of a professional Internet IT blog, and to identify its expressive means in the English and Russian languages. This article presents the results of the analysis of connectedness in hypertexts of the English Internet blog infoworld.com and the Russian blogs it-guild.com, itglobal.com and tproger.ru, dedicated to IT: development of cloud computing, big data technologies, machine learning, software development, and etc. for 2018-2021. The Internet blog is considered as hypertext formed by both homogeneous (verbal) and heterogeneous (creolized) posts, connected thematically and by means of hyperlinks. The study of professional IT blogs, undertaken on the material of the English and Russian languages, not only contributes to the development of the issue of hypertext linguistic status, but also reveals the mechanisms of creating cohesion and coherence of the Internet discourse texts by means of different languages. The research material was the current English publications of the IT blog infoworld.com and the Russian Internet IT blog posts it-guild.com, itglobal.com and tproger.ru for 2018-2021.
Secure serverless internet (TheOoL.net)

A system of private Internet is presented, which, in addition to an overlay data transfer protocol, includes serverless hosting and cloud computing, a serverless search engine, and an integrated payment system. At the software implementation level, it is a universal distributed decentralized operating system designed to protect distributed computing, isolate private networks without limiting the possibilities of useful interaction, cryptographic protection, unauthorized access protection using biometrics, and an innovative blockchain-based information interaction protocol with automatic topology control. This system offers the user total protection of personal data, high speed of data access and computation, a system of cataloging and access to information that is unique for such networks, secure network audio-video communications, and secure payments.
Deep Learning - Prediksi dan Resiko Investasi Enam Saham Bank di Indonesia

Stock prediction and risk level are important for investors, but this ability can only be done by experts and takes a long time. The rapid development of technology today demands that decisions be made quickly and precisely. Deep Learning (DL) is one of the Artificial Intelligence (AI) methods that are able to analyze and predict stock values accurately, in real-time, and without much human intervention. Analysis of risk and correlation between stocks by calculating daily returns using the moving average (MA) method. Dataset of 6 bank shares obtained from yahoo-finance, namely Bank Central Asia (BBCA), Bank Rakyat Indonesia (BBRI), Bank Mandiri (BMRI), Bank Nasional Indonesia (BBNI), Bank Rakyat Indonesia Syariah (BRIS), and Bank Tabungan Negara (BBTN). The volume of share sales increased significantly only in BBRI and BBNI shares, although 5 bank shares (except BRIS) experienced price increases. The highest correlation occurred between BBNI and BMRI shares with a value of 97%, 92% between BMRI and BBCA shares, and 91% between BBNI and BBCA. Analysis of risk and expected return shows that BRIS has the highest risk and expected return of 0.042245 and 0.002986, respectively. BBCA shares have the lowest risk and expected return at 0.015392 and 0.000695, respectively. The results show that future predictions have decreased, namely BBRI, BBNI, and BBTN, and rose for BBCA, BMRI, and BRIS stocks.
AN EVALUATION ON TWO SAHABATKU INDONESIA COURSEBOOKS IN LITTLEJOHN’S TEXTBOOK ANALYSIS FRAMEWORK

The use of proper learning materials in foreign language learning have a pivotal role. Feasibility analysis on language learning material surely different from other subjects’. Sahabatku Indonesia coursebooks has been main reference for BIPA learning programs. This qualitative descriptive research evaluates Sahabatku Indonesia BIPA 3 and Sahabatku Indonesia untuk Pelajar BIPA 3 using Andrew Littlejohn's Textbook Analysis Framework in three steps of analysis. The result of the evaluation shows both coursebooks could accommodate in process of learning BIPA relatively effective. It appears from the linguistic aspect and cultural context in the coursebooks provided comprehensively. Nonetheless, the learning methods still can be improved to make more room for the learners to be more engaged in the language through more active roles in bringing or producing their related texts can still be improved.&#x0D; Keywords: foreign language, learning materials, Indonesian language learning materials, evaluation.
An Automatic Search Image Augmentation Algorithm for UAV Identification

Currently, it is difficult to obtain a large amount of training data for UAV identification, so data augmentation is needed, which is an effective method to improve the accuracy of object detection. However, current data augmentation implementations are manually designed commonly. In this paper, we describe a data augmentation strategy for an automated search. Specifically, we design a search space where each policy consists of $$N$$ operations, each of which is an image processing function such as translation, rotation, and the probability and magnitude of applying these operations. In the actual automatic search process, an evolutionary algorithm is used to find the optimal operation combination. In addition, to further improve the recognition rate of the algorithm under the condition of a few samples, we also use an improved genetic algorithm to tune the hyperparameters. Experiments show that after using the hybrid strategy, the $$mAP$$ of the network increases from $$71.45\%$$ to $$78.61\%$$ , and the recognition accuracy is also significantly improved.
PID with Deep Reinforcement Learning and Heuristic Rules for Autonomous UAV Landing

Unmanned Aerial Vehicle (UAV) landing is a challenging task in dynamic environments. The PID controller can be used, but it suffers from the problem of manual parameter tuning. In this paper, we propose that PID can be combined with deep reinforcement learning to learn the PID parameters autonomous learning. Besides, heuristic rules of how to adjust the PID parameters can be used to further speed up the learning. We demonstrate the effectiveness of the proposed method in a simulated quadrotor UAV landing task.
A study on the selection of important factors by progress stage in the reconstruction project through the AHP analysis

This study carried out the analysis on the selection of important factors in the reconstruction project that takes place in the progress stage while conducting the reconstruction project. For this study, a preliminary study is carried out to analyze individual elements for the reconstruction project and for the importance survey of reconstruction project. The variables extracted from it are applied to analyze the importance of key decision making works that take places in the progress stage among the construction project. The weighted value was calculated by applying the importance of decision making matters per progress stage in the reconstruction project and the results from the order was as follows: Rank 1: moving expense payment condition, rank 2: communication between promoting subjects, rank 3: calculation of premium value, rank 4: union members' contribution and rank 5: union members' selling price. As the reconstruction project is a maintenance project for new housing supply and for enhancing the quality of residence, it is expected that, from the results of this study, the rapid reconstruction project is to be carried out by deciding the importance of decision making issues per progress stage of the reconstruction business.
Posterior Densities, Ill-Conditioning,and Classical Regularization

Now that the basic principles guiding the design of likelihoods and priors have been introduced, we are ready to welcome on the stage the main character in the Bayesian play of inverse problems, the posterior distributionPosterior distribution, and in particular, the posterior density. Bayes’ formula is the way in which prior and likelihood combine into the posterior density. In this chapter, we show through some examples how to explore and analyze posterior distributions. In later chapters, particular attention will be given to the design of numerical schemes of reduced complexity to deal with posteriors for high-dimensional inverse problems. In this chapter, we will build connections between posterior densities and classical regularization methods.
Relative State Estimate of Non-cooperative Target by Multiple Spacecraft Collaborative Observation

For solving the weak observability problem of angle-only relative state estimate, a multi-spacecraft collaborative observation method is proposed. The multi-spacecraft collaboration angle-only relative state estimate model is developed, including state equation and measurement equation. Firstly, based on the second-order relative motion equation, the system state equation is established; secondly, considering the line-of-sight angle measured by different spacecrafts, the system measurement equation is established; then the unscented Kalman filter algorithm is used to estimate the target’s relative state. Finally, the numerical simulation validates that the aforementioned method can effectively improve the weak observability of angle-only relative state estimate, and with the increasing of the number of spacecrafts, the convergence speed and accuracy of the relative position, velocity are also higher. In addition, the performance of the aforementioned method under uncertainty is also validated by two scenarios where the measurement data is sometimes lost and the target is maneuvered.
Bayesian Filtering for Gaussian Densities

In the previous chapter, we considered dynamic inverse problems where the posterior density is updated sequentially as new observations become available. The particle filter approach is fully general and does not assume anything particular about the probability densities, as they were approximated by particle-based point mass distributions. However, if parametric forms of the distributions are known, or if the distributions can be approximated by parametric ones, computationally efficient methods for Bayesian filtering are available. In this section, we review methods that make use of Gaussian densities which, being completely characterized by the mean and the covariance, allow a natural parametric representation. In particular, we derive the classical Kalman filtering algorithm, and discuss some generalizations of it.
Asynchronous and Decentralized Multiagent Trajectory Planning in Dense Environments

This paper proposes an online decentralized and asynchronous multiagent trajectory planning algorithm in dense environments. In our algorithm, the optimization problem is transformed into a quadratic programming (QP) problem to reduce the computational complexity by constructing the optimal linear flight corridors (OLFC). A cooperation-based deconfliction framework is designed to ensure the safety and feasibility under the decentralized and asynchronous architecture. We conduct a large number of simulations to verify the reliability and efficiency of our algorithm in dense environments with higher success rate, less computational time and total navigation time, which is more aggressive and cooperative.
VITON-Based CT Image Registration for Medical Surgery Application

Augmented Reality (AR) technology enables augmentation and information display through camera mounted on a wearable device. In medical field, it tackles asynchronization between hands and eyes and concentration decrease of the surgeon during surgery by providing medical image information by the wearable device. Inspired by virtual try-on algorithms, we propose a method based on VITON to register and display a Computed Tomography (CT) image with tumor information on the patient’s chest without markers using a camera mounted on an AR device. The proposed method consists of two stages: (1)Image Pre-process stage to prepare CT image and the corresponding CT image mask and human presentations given reconstructed CT image and reference model image; (2)Image Registration stage to register warped CT image on reference model image. Extensive experiments demonstrate that the proposed method achieves high-fidelity registration results both qualitatively and quantitatively. In particular, the generation of CT registered image of size 1024×768 enhances the ability to seek the patient’s tumor in organ area with higher accuracy during surgery, strengthening the contribution to medical field.
COUNTERFEIT PRODUCT DETECTION SYSTEM USING GRAPHICAL QRCODE IN BLOCKCHAIN

Nowadays people buy products through online and even from shops, but could not distinguish which is a counterfeit product and which is a genuine product. There are many famous companies whose product is duplicated and made some changes and are sold in the market, where even the manufacturer get confused with the duplicate product. The customer buys the product and comes to a bad opinion about the company which is completely a negative part on the side of the manufacturer and the seller who sells the product. So in order to overcome this, manufacturers need to generate a graphical Quick response code that will be linked with the blockchain. When there is a usage of a normal Quick response code (QR code) which has a combination of black and white colors it will be easy for the hackers to duplicate the data. So manufacturers need to include a graphical-based QR code where hackers could not duplicate the product which may lead to ink smearing. When they link the Graphical QR code to the blockchain all the data will be stored in the form of blocks and could not change any of the data which is stored in blocks, on the customer side where they could scan the graphical QR code and find whether the Product is genuine or not. For each transaction in the blockchain, aspecific hash value will be generated and this hash when the customer scan needs to match the hash value what the manufacturer who added the block for the product. In this way, the counterfeit product could be eradicated from the market.
Anomaly detection in the course evaluation process: a learning analytics–based approach

Purpose Course evaluations are formative and are used to evaluate learnings of the students for a course. Anomalies in the evaluation process can lead to a faulty educational outcome. Learning analytics and educational data mining provide a set of techniques that can be conveniently applied to extensive data collected as part of the evaluation process to ensure remedial actions. This study aims to conduct an experimental research to detect anomalies in the evaluation methods. Design/methodology/approach Experimental research is conducted with scientific approach and design. The researchers categorized anomaly into three categories, namely, an anomaly in criteria assessment, subject anomaly and anomaly in subject marks allocation. The different anomaly detection algorithms are used to educate data through the software R, and the results are summarized in the tables. Findings The data points occurring in all algorithms are finally detected as an anomaly. The anomaly identifies the data points that deviate from the data set’s normal behavior. The subject which is consistently identified as anomalous by the different techniques is marked as an anomaly in evaluation. After identification, one can drill down to more details into the title of anomalies in the evaluation criteria. Originality/value This paper proposes an analytical model for the course evaluation process and demonstrates the use of actionable analytics to detect anomalies in the evaluation process.
THE EFFECTIVENESS OF USING LMS SIPDA COMPARED WITH THE GOOGLE CLASSROOM LMS IN THE DEPARTMENT OF COMMUNITY EDUCATION UNIVERSITY OF THE STATE OF MEDAN

In online learning that uses e-learning, whether it is effective or not is largely determined by the effectiveness of the LMS model used. This study aims to describe the effectiveness of using LMS SIPDA compared to LMS Google Classroom at the Department of Public Education, State University of Medan. The assessment indicators used in this study include indicators of teaching quality, level of teaching, intensity, and time. The research method used is quantitative research with a descriptive approach. The research instrument used in this study was a questionnaire distributed via Google Form. The data analysis technique of this research used descriptive quantitative analysis in the form of percentages. The results obtained an average of 77.66% with the category agreeing that the level of effectiveness of using SIPDA LMS is higher than Google Classroom. So it can be concluded that users feel more effective in using SIPDA LMS than Google Classroom, with a negative value on internet connection (WiFi or Internet Quota) in accessing SIPDA LMS which is much stronger and more stable than accessing Google Classroom.
IMPLEMENTATION OF PACKAGE C GROUP DISCUSSION LEARNING METHODS IN PKBM DEVELOPMENT OF CITIZENS, PANGKALAN DISTRICT, KARAWANG REGENCY

The aims of this study are to (1) describe the implementation process of the package C pursuit discussion learning method in PKBM Bina Warga, Pangkalan District, Karawang Regency; (2) Describe the supporting and inhibiting factors of the implementation of the package C pursuit discussion learning method at PKBM Bina Warga, Pangkalan District, Karawang Regency. In this study, the author uses a qualitative approach with a case study method. This research was conducted at PKBM Bina Warga, Pangkalan District, Karawang Regency. The subjects of this study consisted of: (1) the source of information consisted of three respondents, namely one manager of PKBM Bina Warga, and two tutors, (2) the source of the informant consisted of two learning residents. Determination of the subject using purposive sampling technique. Research data obtained through data collection techniques by observation, interviews and documentation studies. The research stages use the orientation stage, the exploration stage, and the member check stage. Data analysis was carried out using data reduction techniques, data presentation, and conclusion drawing. The results of the interviews obtained show that in the process of implementing group discussions there are steps that must be carried out, namely (1) determining the teaching materials to be discussed, (2) determining discussion groups and the number of participants in group discussions, (3) determining the time of discussion activities. groups, (4) reflection of each group from the results of the discussion, (5) conclude and explain the results of the discussion. Factors that can affect the learning process of group discussions are divided into 2, namely internal and external factors. Internal factors include interests, talents, awareness, interest and attention. Then external factors include tutors, the environment as well as the facilities and infrastructure available during the learning process.
A Novel Technique Based on Crow Search Algorithm to Solve Optimal Control Problem of a Single-Link Rigid Robotic Manipulator

This study shows how Meta-heuristic optimization programming may be implemented to solve constrained optimal control problems. With a single-link rigid manipulator (SLM) model as an example, the proposed Crow search algorithm (CSA) is contrasted with the differential evolution algorithm (DE), whale optimization algorithm (WOA), and particle swarm optimization (PSO) in terms of performance index, which has demonstrated the notable outcomes on another algorithm.
Looking from a Higher-Level Perspective: Attention and Recognition Enhanced Multi-scale Scene Text Segmentation

Scene text segmentation, which aims to generate pixel-level text masks, is an integral part of many fine-grained text tasks, such as text editing and text removal. Multi-scale irregular scene texts are often trapped in complex background noise around the image, and their textures are diverse and sometimes even similar to those of the background. These specific problems bring challenges that make general segmentation methods ineffective in the context of scene text. To tackle the aforementioned issues, we propose a new scene text segmentation pipeline called Attention and Recognition enhanced Multi-scale segmentation Network (ARM-Net), which consists of three main components: Text Segmentation Module (TSM) generates rectangular receptive fields of various sizes to fit scene text and integrate global information adequately; Dual Perceptual Decoder (DPD) strengthens the connection between pixels that belong to the same category from the spatial and channel perspective simultaneously during upsampling, and Recognition Enhanced Module (REM) provides text attention maps as a prior for the segmentation network, which can inherently distinguish text from background noise. Via extensive experiments, we demonstrate the effectiveness of each module of ARM-Net, and its performance surpasses that of existing state-of-the-art scene text segmentation methods. We also show that the pixel-level mask produced by our method can further improve the performance of text removal and scene text recognition.
Causal-SETR: A SEgmentation TRansformer Variant Based on Causal Intervention

We present a novel SEgmentaion TRansformer variant based on causal intervention. It serves as an improved vision encoder for semantic segmentation. Many studies have proved that vision transformers (ViT) can achieve a competitive benchmark on these downstream tasks, which shows that they can learn feature representations well. In other words, it is good at observing the instance from the image. However, in the human visual system, to recognize the objects in the scene, it is necessary to observe the objects themselves and introduce some prior knowledge for producing higher confidence results. Inspired by this, we introduced a structural causal model (SCM) to model images, category labels, and context. Beyond observing, we propose a causal intervention method by removing the confounding bias of global context and plugging it in the ViT encoder. Unlike other sequence-to-sequence prediction tasks, we use causal intervention instead of likelihood. Besides, the proxy training objective of the framework is to predict the contextual objects of a region. Finally, we combine this encoder with the segmentation decoder. Experiments show that our proposed method is flexible and effective.
MUSH: Multi-scale Hierarchical Feature Extraction for Semantic Image Synthesis

Semantic image synthesis aims to translate semantic label masks to photo-realistic images. Previous methods have limitations that extract semantic features with limited convolutional kernels and ignores some crucial information, such as relative positions of pixels. To address these issues, we propose MUSH, a novel semantic image synthesis model that utilizes multi-scale information. In the generative network stage, a multi-scale hierarchical architecture is proposed for feature extraction and merged successfully with guided sampling operation to enhance semantic image synthesis. Meanwhile, in the discriminative network stage, the model contains two different modules for feature extraction of semantic masks and real images, respectively, which helps use semantic masks information more effectively. Furthermore, our proposed model achieves the state-of-the-art qualitative evaluation and quantitative metrics on some challenging datasets. Experimental results show that our method can be generalized to various models for semantic image synthesis. Our code is available at https://github.com/WangZC525/MUSH.