Reviewerly/exHarmony huggingface dataset:
Application of Three-Dimensional Direct Least Square Method for Ellipsoid Anisotropy Fitting Model of Highly Irregular Drill Hole Patterns

Ellipsoid or geometric anisotropy is a widely used method in geostatistical analysis to obtain variograms with different ranges in different directions (azimuth) and relatively similar sill variance. Ellipsoid anisotropy is indispensable in mining when a resource geologist intends to understand the spatial continuity of variables related to any geological controls of the mineralization. For example, when dealing with mineralization related to tabular deposits, a porphyritic deposit with an irregular drill hole pattern (fan drilling), three-dimensional ellipsoid anisotropy is quite challenging to model. We assume that the variables’ spatial continuity is isotropic, and we model it using a three-dimensional omnidirectional variogram. However, if the actual spatial continuity of the variables has a three-dimensional anisotropy, then assuming a three-dimensional omnidirectional variogram will generate imprecise resource estimations. This study presents a new practical three-dimensional ellipsoid model-fitting method using a three-dimensional direct least square method. We investigated a zinc (Zn) dataset from thousands of irregular drill hole patterns from a porphyritic system associated with skarn orebodies for the case study.
Efficient Task Scheduling in Cloud Systems with Adaptive Discrete Chimp Algorithm

Successful task scheduling is one of the priority actions to increase energy efficiency, commercial earnings, and customer satisfaction in cloud computing. On the other hand, since task scheduling processes are NP-hard problems, it is difficult to talk about an absolute solution, especially in scenarios with large task numbers. For this reason, metaheuristic algorithms are frequently used in solving these problems. This study focuses on the metaheuristic-based solution of optimization of makespan, which is one of the important scheduling problems of cloud computing. The adapted Chimp Optimization Algorithm, with enhanced exploration and exploitation phases, is proposed for the first time to solve these problems. The solutions obtained from this adapted algorithm, which can use different mathematical functions, are discussed comparatively. The proposed solutions are also tested in the CloudSim simulator for different scenarios and they prove their performance in the cloud environment.
Forensics Analysis of Resampling via ConvNeXt Block

Images play an important role in transmitting visual information in our life. It could lead to severe consequences if images are manipulated or tampered maliciously. Digital forensics is an important research area to secure multimedia information. Many forensics technologies are applied to protect our community from the abuse of digital information. In many cases, after tampering, attackers could apply operations such as resampling, JPEG compression, blurring, etc. to cover the traces of tampering. Therefore, it is necessary to detect these manipulations in image forensics before exposing forgeries. In this paper, we propose to employ the prediction error filters, ConvNeXt blocks and convolution modules to classify images with different compression quality factors and resampling rates. By tracing the inconsistencies of resampling rates and compression quality factors, it could provide supplementary information for forensics researchers to expose possible forgeries. The proposed method could achieve great classification performance regardless of the interpolation algorithms. Also, it is highly robust against JPEG compression. In addition, the proposed method can be applied for estimating quality factors of JPEG compression.
THE GREEDY RANDOMIZED EXTENDED KACZMARZ ALGORITHM FOR NOISY LINEAR SYSTEMS

For solving the noisy linear systems, we propose a new greedy randomized extended Kaczmarz algorithm by introducing an effective greedy criterion for selecting the working rows and a randomized orthogonal projection for reducing the influence of the noisy term. We prove that the solution of the proposed greedy randomized extended Kaczmarz algorithm converges in expectation to the least squares solution of the given linear system. Theoretical analysis indicate that the convergence rate of the greedy randomized extended Kaczmarz algorithm is much faster than the randomized extended Kaczmarz method, and numerical results also show that the proposed greedy randomized extended Kaczmarz algorithm is superior to the randomized extended Kaczmarz method. Moreover, for noisy linear systems, the proposed algorithm is more effcient than the greedy randomized Kaczmarz algorithm.
On the Cost-Effectiveness of Stacking of Neural and Non-Neural Methods for Text Classification: Scenarios and Performance Prediction

Nowadays, neural networks algorithms, such as those based on Attention and Transformers, have excelled on Automatic Text Classification (ATC). However, such enhanced performance comes at high computational costs. Stacking of simpler classifiers that exploit algorithmic and representational complementarity has also been shown to produce superior performance in ATC, enjoying high effectiveness and potentially lower computational costs than complex neural networks. In this master's thesis, we present the first and largest comparative study to exploit the cost-effectiveness of Stacking in ATC, consisting of Transformers and non-neural algorithms. In particular, we are interested in answering the following research question: Is it possible to obtain an effective ensemble with significantly less computational cost than the best learning model for a given dataset? Besides answering that question, another main contribution of this thesis is the proposal of a low-cost oracle-based method that can predict the best ensemble in each scenario using only a fraction of the training data.
Optically generated graphene plasmons for optoelectronic logic operations

Leveraging the counter-propagating and temporally-synchronized laser comb pulses, we demonstrate all-optical generation and electrical control of multiple plasmon polarizations in 0 ~ 50 THz, enabling AND/OR/NOR optoelectronic logic operations in one single device.
Deep Learning-based Virtual Immunohistochemical HER2 staining of Label-Free Breast Tissue

We present deep learning-based virtual immunohistochemical (IHC) HER2 staining of label-free breast tissue sections, matching the standard IHC HER2 staining performed by histotechnologists. © 2022 The Author(s)
Utilizing Marker to Integrate Augmented Reality in Furniture Using Unity 3D

The development of technology has had an effect on online shopping, whilst the use of Augmented Reality technology has provided customers a new method to see things in their actual context, particularly large and heavy products like furniture. Customer satisfaction, scheduling constraints, and the inability to picture the actual product are cited as problems. Augmented Reality is a three-dimensional technology that enables people to see and comprehend the actual environment while being surrounded by virtual objects. In other words, augmented reality comprises of computer-generated three-dimensional images that link the virtual world to the actual world. Finding from research papers has shown two types of AR application for furniture, which is marker based and markerless. Virtual Furniture, a project developed using Unity 3D, Google Cloud database and C# programming language is an AR marker based application. This application will further help furniture industry businesses to assist customers in visualizing an object in a real environment before purchasing it.
Cybercrime in the Russian Federation: Criminological and Criminal Law Analysis

We live in a time when cyberspace as part of the global information space has become an important object of legal, political, economic and social relations. Almost every modern branch of science and technology uses digital technologies and techniques. Therefore, since the beginning of 2000, the theory that disruption of these networks can disable the whole state has been expanding. And a number of incidents that have come to the attention of the public, such as the massive attacks by hackers on large institutions and organizations around the world in 2017, make it possible to explain the significance of studying cybercrime around the world.
Multi-objective Metaheuristics with Intelligent Deep Learning Model for Pancreatic Tumor Diagnosis

Pancreatic tumor is the deadliest disease which needs earlier identification to reduce the mortality rate. With this motivation, this study introduces a Multi-Objective Metaheuristics with Intelligent Deep Learning Model for Pancreatic Tumor Diagnosis (MOM-IDL) model. The proposed MOM-IDL technique encompasses an adaptive Weiner filter based pre-processing technique to enhance the image quality and get rid of the noise. In addition, multi-level thresholding based segmentation using Kapur’s entropy is employed where the threshold values are optimally chosen by the barnacles mating optimizer (BMO). Besides, densely connected network (DenseNet-169) is employed as a feature extractor and fuzzy support vector machine (FSVM) is utilized as a classifier. For improving the classification performance, the BMO technique was implemented for fine-tuning the parameters of the FSVM model. The design of MOBMO algorithm for threshold selection and parameter optimization processes shows the novelty of the work. A wide range of simulations take place on the benchmark dataset and the experimental results highlighted the enhanced performance of the MOM-IDL technique over the recent state of art techniques.
Educación a distancia coyuntura y paradigma

In this paper, the authors will talk about the development of technological platforms and tools that will support the creation and implementation of techno-pedagogical paradigms to tech the XXI Century generations. By proving how the innovative model of the open and distance learning universities (UNAD, for its Spanish acronym) is not run out and can be renewed, taking as an example the COVID-19 pandemic and analyzing the carriers that provide change to a new education, where the need of innovation in higher education in open and distance modalities is an urgent matter. Undoubtedly, learning is one of the human capacities that has most influenced their development and evolution. Thus, the e-education concept is presented as an effort to develop talents and human capacities using the Internet and new information and communication technologies (ICT). This will allow introducing a generalized remote mode, which will create a different culture adjusting the school curriculum to an online strategy, and not a rigid one, that involves technological improvements, so general and specialized knowledge are combined to the needs of scientific, technological, and social development. Teachers work with students differently and provide help through more knowledge and experience. Recent studies are showing that there are more open and online options for teacher professional development and teachers access the Internet to expand their development opportunities through social media platforms. In this sense, the non-attendance process of education has improved its relevance globally. Although, the COVID-19 pandemic caused an accelerator effect on the digital transformation of the educational institutions.
PHY-NET: A Channel Autoencoder Approach for Wireless PHY Layer Design

In this work, we present a deep learning model for creating wireless communications waveforms with tailored physical layer attributes. A multi-task learning framework based on the notion of a channel autoencoder is presented and two secondary learning goals are introduced. Data bits are the input to the deep learning model and the output is a latent representation that can be interpreted as complex baseband samples. This output representation is perturbed by a wireless channel model and distributed to the decoder, modeling a classical communications demodulator. In addition, the output representation is distributed to one or many expert-designed loss functions that serve the purpose of providing feedback to the encoder such that specific physical layer design constraints can be imposed. Synthesis of a waveform is demonstrated with example energy and cyclostationary design constraints. The bit error rate of the synthesized waveform is compared to that of a conventional BPSK modulation. When trained to optimize transmit energy, the model learned to reduce transmit power until a balance between bit error rate and transmit power is found. When trained to tune cycle features, the model learned to impose an atypical pulse shape exhibiting suppressed cycle features and a 1 dB bit error rate performance loss. These models are learned without applying expert knowledge to the waveform design process; rather, the framework uses a set of constraints to guide the development.
Prediction of Online News Popularity using ANN Deep Learning

Online news is incredibly popular these days because of the growth of the Internet and web expansion. At the same time, it is dynamic and chaotic on many levels, thus it gives an interesting research opportunity for the prediction of online news popularity. ANN (Artificial Neural Network) was used in this paper on a online news popularity based dataset. The goal was to increase prediction accuracy using deep learning. Dataset was preprocessed to use for a multiclass classification. The model was created with appropriate features needed and it produced more than 96 percent accuracy. Moreover, the false negative value of each multiclass was very low and precision, recall, and f1 score was high in our proposed model. All the results were discussed for the prediction model. This can help the online news authors to increase their news popularity.
Testbed for the Experimental Evaluation of Road Anomaly Detection Algorithms

The goal of this article is to present a hardware and software architecture developed for the evaluation of road anomaly detection algorithms, its different components and the solutions we developed to optimize the use of the bandwidth during communications. It also presents some significant results obtained with the new lightweight road-anomaly detection methods we developed in the scope of this architecture.
AN ANALYSIS OF USING WEBTOON IN IMPROVING STUDENTS’ READING MOTIVATION AT FIRST SEMESTER

The purpose of this study was to analyze the use of Webtoon to improve students' reading motivation in the first semester of the English department, Bandar Lampung University. The population used in this study was 15 Students. This research used purposive sampling technique. Data was collected using observations, questionnaires and interviews. Interviews were used to find out more about using the Webtoon that is used. The technique used in this study was purposive sampling. The results of the study show that most students were motivated to read when using a Webtoon, it was caused reading using the media, would influence students' desire to do something. This research could have implications for English language teachers, students, and further researchers.
THE IMPLEMENTATION OF EXTENSIVE READING AND EXTENSIVE LISTENING IN XREADING AS ENGLISH LEARNING APPROACH

This research is based on the implementation of ER and EL in Xreading as the 1st semester students’ English learning approach. The population of this research is students of English Education, Universitas Bandar Lampung. This research aimed to know how the principles of extensive reading and extensive listening implemented in ER and EL process through Xreading website and what students' perceptions of the implementation of ER and EL in Xreading as the 1st semester students’ English learning approach. The instruments of this research were observation, interview and TOEL test. Then, the data were analyzed by coding compression. The result showed that most of the ER principles had been implemented well in the implementation of ER in Xreading. On the other side, the implementation of EL in Xreading, only several principles of EL have been implemented. Thus, the implementation of the ER process in Xreading was more dominated than the EL process. Even though EL also helped students to learn the pronunciation of words. But, the students did not implement principles of EL optimally. Consequently, ER in Xreading was suitable for the 1st semester students’ English learning approach, but EL in Xreading was not suitable for the 1st semester students’ English learning approach. During the ER and EL process in Xreading, students preferred reading to listening to the material.
INVESTIGATION OF THE IMPACT OF PRIMARY DATA PROCESSING ON THE RESULTS OF NEURAL NETWORK TRAINING FOR SATELLITE IMAGERY RECOGNITION

Proper preparation of materials for training a neural network is one of the key stages that directly affects all subsequent and results. The research provides two trials, the first with raw data and the second with specially prepared for training. The training of the neural network uses data for three different classes, water, trees and field, test results and information about potential flaws provided after each test as well as recommendations for improving results. The results were analyzed and concluded that the improvement of primary materials for teaching the neural network to recognize satellite images directly affects the result for the better, namely by 27.6% in the case of using prepared data in this study, they may differ depending on the conditions.
PENGARUH SISTEM INFORMASI AKUNTANSI DALAM PELAPORAN KEUANGAN PEMERINTAH DAERAH

An accounting information system is an information system that has an information processing process that produces financial reports. This research is causative. The data used in this study were from several previous studies. This study discusses an accounting information system that is able to assist the financial reporting process and an effective system in the financial reporting process.&#x0D;
Implementation and Optimization of Zero-Knowledge Proof Circuit Based on Hash Function SM3

With the increasing demand for privacy protection in the blockchain, the universal zero-knowledge proof protocol has been developed and widely used. Because hash function is an important cryptographic primitive in a blockchain, the zero-knowledge proof of hash preimage has a wide range of application scenarios. However, it is hard to implement it due to the transformation of efficiency and execution complexity. Currently, there are only zero-knowledge proof circuits of some widely used hash functions that have been implemented, such as SHA256. SM3 is a Chinese hash function standard published by the Chinese Commercial Cryptography Administration Office for the use of electronic authentication service systems, and hence might be used in several cryptographic applications in China. As the national cryptographic hash function standard, the zero-knowledge proof circuit of SM3 (Chinese Commercial Cryptography) has not been implemented. Therefore, this paper analyzed the SM3 algorithm process, designed a new layered circuit structure, and implemented the SM3 hash preimage zero-knowledge proof circuit with a circuit size reduced by half compared to the automatic generator. Moreover, we proposed several extended practical protocols based on the SM3 zero-knowledge proof circuit, which is widely used in blockchain.
Analogy-Based Post-treatment of CNN Image Segmentations

Convolutional neural networks (CNNs) have proven to be efficient tools for image segmentation when a large number of segmented images are available. However, when the number of segmented images is not so large, the CNN segmentations are less accurate. It is the case for nephroblastoma (kidney cancer) in particular. When a new patient arrives, the expert can only manually segment a sample of scanned images since manual segmentation is a time-consuming process. As a consequence, the question of how to compute accurate segmentations using both the trained CNN and such a sample is raised. A CBR approach based on proportional analogy is proposed in this paper. For a source image segmented by the expert, let a be the CNN segmentation of this image, b be its expert segmentation and c be the CNN segmentation of a target image close to the source image. The proposed approach aims at solving the analogical equation “a is to b as c is to d” with unknown d: the solution d of this equation is proposed as a segmentation of the target image. This approach and some of its improvements are evaluated and show an accuracy increase of the segmentation with respect to the CNN segmentation.
User Authentication Techniques of the Internet of Things

Internet of Things (IoT) is a growing technology that has got the attention of researchers from different fields. As more of IoT resources increasing, the number of users' sensitive data also increasing. Authentication process is one of the security factors which used to protect the users' sensitive data and identify whether the user is legitimate or not. IoT facing many authentication challenges due to its complicated environment it contains a massive number of connected devices, different layers, big data, cloud computing, and the open channels. The truly important challenge of the user authentication process for IoT resources is how to do it more securely and easily utilize for computer-illiterate users. In general, the password is the first defense line of user confidentiality, but the use of alphanumeric-based passwords is still widely, even with the existence of alternatives designed to overcome its weakness. Graphical-based Password is one of the practical alternatives which is developed to improve the security of the user authentication. This paper aims to explore the various authentication aspects that can be used in the IoT technology. Also, an overview of the graphical passwords which can be used as an alternative of the weak alphanumeric-based passwords in IoT technology.
ANALISIS PENGGUNAAN APLIKASI SISTEM AKUNTANSI PERSEDIAAN (ASAP) TERHADAP EFEKTIVITAS PELAPORAN PERSEDIAAN PADA BADAN PENGELOLAAN KEUANGAN DAN ASET DAERAH KABUPATEN MUSI BANYUASIN

This research is used to answer two main problems, namely how to use the Inventory Accounting System Application (ASAP) on the effectiveness of inventory reporting at the Regional Financial and Asset Management Agency of Musi Banyuasin Regency and what efforts should be made to increase the effectiveness of using the Inventory Accounting System Application (ASAP)The results of this study indicate that the financial report reporting of the Inventory Information System Application (ASAP) has been used effectively considering the final results of the inventory obtained are itemized and, however, if viewed from the point of view of using the application in inputting, it can be said to be less effective because inputting errors often occur data where the available menu does not match the data inputted so it is necessary to make adjustments in the input and efforts made by the local government through the Regional Financial and Asset Management Agency of Musi Banyuasin Regency, namely intrinsically and exterinsically in the form of operator assistance, routine checks / checks, application updates. as well as ASAP operator outreach / training.
Investigating Communication Tools in SE Capstone Courses

Software engineering (SE) curriculum typically presents students with a final, team-based capstone project towards the end of their collegiate career. This capstone project provides students with a simulated real world development project, requiring students to learn skills such as communication and collaboration to complete the project. However, these capstone projects are prone to team dysfunction such as team miscommunication or an overall team communication breakdown. We propose to identify the challenges students and developers have communicating through online communication tools. Additionally, we aim to determine if we can maintain open lines of communication for each team member through bots in communication tools.