Reviewerly/exHarmony huggingface dataset:
On the Transferability of Single Image Height Estimation for SAR Intensity Imagery

Height estimation from single images has become a highly requested topic in the remote sensing community. While most methods use optical data, a first attempt with SAR intensity imagery was recently performed and achieved promising results. The actual practical value of any Deep Learning-based method such as this, however, then depends on how well it can be applied to sceneries unknown to the model, maybe even recorded under different acquisition conditions. This paper focuses on that very aspect of this methodology. For this purpose, the differences of distinct data types and test scenes are highlighted and the obtained results by the pre-trained models are evaluated and interpreted.
Classification of Movie Review Sentiment Analysis Using Chi-Square and Multinomial Naïve Bayes with Adaptive Boosting

Sentiment analysis problems have attracted the attention of researchers. Sentiment analysis is a process that aims to determine the sentiment polarity of text. Nowadays, sentiment from product reviews has become a piece of important information for producers and potential customers. This paper conducted a sentiment analysis classification on a movie review from the IMDb site. In the classification analysis, the sentiment of movie reviews used the multinomial naïve Bayes algorithm. Adaboost was applied to boosting the accuracy of multinomial naïve Bayes. Feature selection is used to reduce the number of features and irrelevant features. The chi-square feature selection used was employed in the current study. The accuracy obtained in movie review sentiment analysis classification using the multinomial naïve Bayes algorithm is 81.39%. Meanwhile, the accuracy of the multinomial naïve Bayes algorithm by applying chi-square is 85.37%. The final result of multinomial naïve Bayes algorithm accuracy by applying AdaBoost and chi-square feature selection is 87.74%.
Comparative Analysis of Certainty Factor Methods and Dempster Shafer Methods in Pest Management in Hydroponic Vegetables (Case Study: Celebes Hydroponics Bengkulu)

Celebes Hydroponics is one of the hydroponic vegetable cultivators in Bengkulu City. At Celebes Hydroponics, there are various kinds of hydroponic vegetables grown, namely Pakcoy, Sawi, Kailan, Kale, Pagoda, Lettuce, Spinach, Kangkung, and others. So far, the handling of hydroponic vegetable pests has been carried out by employees at Celebes Hydroponics with the direction of expert cultivators (the owner of Celebes Hydroponics). However, problems occur when expert cultivators are not available when pests attack hydroponic vegetables. This makes it difficult for employees to handle these pests because so far they have always received guidance from expert cultivators. The expert system for controlling pests on hydroponic vegetables at Celebes Hydroponic Bengkulu is used to make it easier for employees to overcome problems faced by hydroponic vegetables, especially vegetable pests. This expert system was created using the Visual Basic .Net programming language which can be accessed by Celebes Hydroponic Bengkulu employees. This expert system application was created to help make it easier for employees at Celebes Hydroponics to identify pests that attack hydroponic vegetables by entering the symptoms experienced. Of course this will speed up handling when you know the pests that attack the hydroponic vegetables. Based on the tests that have been carried out, the functionality of the pest management expert system on hydroponic vegetables at Celebes Hydroponics Bengkulu runs as expected, and the application is able to display the results of pest diagnosis on hydroponic vegetables from the selected symptoms
Dynamic structural testing of a composite structure by 3D projected digital image correlation

Complex composite structures have unexpected mechanical behaviour, specially under high dynamic loads or in bucking conditions. The complexity and the number of sensors of these types of structural testing require non-standard instrumentation in order to detect local phenomena before critical failure appears. The main problem introduced by accelerometers or strain sensors is the cabling, due to the weight and complexity introduced. Digital Image Correlation (DIC) is a strain and deformation measurement technique well know and extendedly applied in composite structural testing. In this paper it is proposed a new DIC methodology based on project the aleatory patter over the structure instead of paint it. Furthermore, an UAV wing full made of composite will be tested at high frequency and vibrations modes will be observed with a stroboscope and, simultaneously, the aleatory patter will be projected in order to measure the shape of the vibration mode applying the DIC technique. Additionally, strain and shape sensing at high frequencies will be validated with Distributed Fiber Optic at high frequency rates. In this paper, the different technologies will be tested and compared in order to validate their capabilities and limitations.
The Importance of Reducing the Risks of Electronic Auditing in Improving the Business Performance of Commercial Banks : A Prospective Study on Commercial Banks Registered in the Iraqi Stock Exchange

This topic is considered one of the important topics in order to improve the financial situation of commercial banks through of the expansion of banking services.Therefore, the research aims to identify the relationship between the independent variable, the risks of electronic audit, and the dependent variable in improving banking performance and to identify the extent of awareness of workers in commercial banks (in question) of the concept of electronic auditing risks.The deductive method based on statistical measurement tools has been used in scientific research by using a questionnaire to find out the opinions of a sample of workers in commercial banks, which are (5) banks, (110) questionnaire forms have been distributed on the aforementioned sample, (100) questionnaires have been retrieved from them for statistical analysis and identification of the results.The research has reached some important conclusions: there is a tangible weakness in the use of electronic auditing programs by auditors, and this contributes to the poor performance of banks 'work.The most important research recommendations is the need for commercial banks to choose competent auditors in developing and managing electronic audit programs to prevent errors and Travesties.
Book Review: “Octavian Goga” County Library Cluj, The National Association of Public Librarians and Libraries in Romania, Cluj Branch Library, Library Science for Beginners, 2018, Casa Cărții de Știință, Cluj-Napoca.

The handbook Library Science for Beginners is definitely a much-needed appearance in our editorial landscape. Coordinated by the Centre for Professional Development from „Octavian Goga“ County Library Cluj, the handbook contains chapters and subchapters written by librarians with experience, such as Diana Baciu, Aura Câmpan, Delia Chira, Ghizela Cosma, Tatiana Costiuc, Ana Maria Dudescu, Simona Floruțău, Octavia Hulpoi, Floarea Elena Moșoiu, Anca-Maria Pop, Monica Sărăcuț, Luminița Sima, Georgeta Topan, Liana Vescan, Adriana Zotea and last, but not least, by the manager Sorina Stanca. The volume was published with the help of the Cluj County Council. [...]
Algorithmically Flexible Style Composition Through Multi-Objective Fitness Functions

Creating a musical fitness function is largely subjective and can be critically affected by the designer's biases. Previous attempts to create such functions for use in genetic algorithms lack scope or are prejudiced to a certain genre of music. They also are limited to producing music strictly in the style determined by the programmer. We show in this paper that musical feature extractors, which avoid the challenges of qualitative judgment, enable creation of a multi-objective function for direct music production. The main result is that the multi-objective fitness function enables creation of music with varying identifiable styles. To demonstrate this, we use three different multi-objective fitness functions to create three distinct sets of musical melodies. We then evaluate the distinctness of these sets using three different approaches: a set of traditional computational clustering metrics; a survey of non-musicians; and analysis by three trained musicians.
Toward a Computational Model of Character Personality for Planning-Based Narrative Generation

Authoring narrative content for interactive digital media can be both difficult and time consuming.The research proposed here aims at enhancing the capabilities of content creators through the development of a computational model that improves the quality of automatically generated stories, potentially decreasing the burden placed on the author. The quality and believability of a story can be significantly enhanced by the presence of compelling characters. To achieve this objective, I aim to develop a choice-based computational model that facilitates the automatic generation of narrative that includes characters that are made more compelling by the presence of distinct personality characteristics.
MOBILE TECHNOLOGY FOR ENHANCING TEACHING AND LEARNING AT THE COLLEGE OF BUSINESS EDUCATION, TANZANIA: AN EXPLORATORY STUDY

Mobile phone technology has increasingly been used as an important gadget for enhancing teaching and learning in higher education institutions, Tanzania in particular. However, despite the fact that many students in Tanzanian higher education institutions have access to mobile phones in the learning context, little is known about how those mobile phones are effectively used to enhance teaching and learning process. This study aimed to explore the extent to which mobile phone technology particularly smartphones are used for enhancing effective teaching and learning process at the College of Business Education in the campus of Dar es Salaam. The study collected data through structured and open-ended questionnaires. Purposive and stratified sampling techniques were used to obtain one hundred and five (105) student respondents from the department Business Administration, Accountancy and Marketing. The data were analys6ed and interpreted using descriptive analysis techniques. The findings indicate that, students use mobile phones to facilitate learning including communicating with their fellow students and lecturers on academic issues, downloading materials and accessing student information related to examinations results. Also, the study revealed that poor cellular networks, cost of purchasing airtime and handset are the main challenges hindering the effectiveness of mobile phones usage for enhancing learning and teaching in higher education institutions. The significance of this study is highlighted in the development of technology in the field of education for enhancing teaching and learning
Implementation of an RNS Reverse Converter for General Moduli Sets Based on LUTs with Reference Points

Residue Number System (RNS) is a nonpositional number system that can be used to implement parallel arithmetic operations.This feature allows achieving high performance in hardware.However, algorithms for some operations are complex and hard to realize effectively.One of the most common operations in RNS is reverse conversion (from RNS to binary).In this paper we propose a new approach to its implementation, based on a combination of LUTs and simple arithmetic calculations.The main advantage of this method is flexibilitythe design can be easily changed to achieve the best solution for given hardware constraints and performance criteria.This approach also does not require any special set of moduliall integers just are to be coprime.Experimental results show that proposed implementation outperforms the method based on the modification of the Chinese remainder theorem in terms of computational speed while maintaining moderate hardware costs.More compact solutions can also be created at the expense of loss in timing.
Quantum-Classical Variational Approaches with Single-Qubit Operation on Near-Term Quantum Processors

Currently developed quantum devices have the potential to show a computational advantage if quantum applications exploit the devices efficiently. Variational quantum algorithms (VQAs) combine a quantum processor with a classical computer to minimize the expectation value of a problem Hamiltonian. An important element of VQAs is the state-preparation circuit, referred to as variational quantum circuit. To improve the ability for solving combinatorial optimizations with VQAs, we show that the variational quantum circuit with single-qubit gates only can find the ground state more quickly. We demonstrate our VQA by using real quantum processors through IBM Quantum. As a result, the variational circuit with single-qubit gates takes four times smaller numbers of function evaluations than that with entangling gates, such as controlled-X gates.
Comparing Training Strategies Using Multi-Assessor Segmentation Labels for Barrett’s Neoplasia Detection

In medical imaging, segmentation ground truths generally suffer from large inter-observer variability. When multiple observers are used, simple fusion techniques are typically employed to combine multiple delineations into one consensus ground truth. However, in this process, potentially valuable information is discarded and it is yet unknown what strategy leads to optimal segmentation results. In this work, we compare several ground-truth types to train a U-net and apply it to the clinical use case of Barrett's neoplasia detection. To this end, we have invited 14 international Barrett's experts to delineate 2,851 neoplastic images derived from 812 patients into a higher- and lower-likelihood neoplasia areas. Five different ground-truths techniques along with four different training losses are compared with each other using the Area-under-the-curve (AUC) value for Barrett's neoplasia detection. The value used to generate this curve is the maximum pixel value in the raw segmentation map, and the histologically proven ground truth of the image. The experiments show that random sampling of the four neoplastic areas together with a compound loss Binary Cross-entropy and DICE yields the highest value of 94.12%, while fusion-based ground truth clearly performs lower. The results show that researchers should incorporate measures for uncertainty in their design of networks.
Data Uncertainty Learning for Single Image Camera Calibration

Although deep learning-based calibration methods can predict extrinsic camera parameters from a single image, the accuracy of these methods is severely degraded due to "data uncertainty" e.g., noisy and outlying input images. To address this problem, we propose a novel Data Uncertainty-Driven Loss (DUD Loss), which could derive the uncertainty from the input image, during the camera calibration process. Instead of estimating the camera extrinsic parameter as scalars, the proposed method models it as a Gaussian distribution with its variance represents the uncertainty of the input image. Hence, each camera parameter is no longer a deterministic scalar, but a probabilistic value with diverse distribution possibilities. With the help of DUD loss, the network can be trained to alleviate the perturbations caused by noisy input images. Furthermore, in the real-world single image camera calibration scenario, noisy input images, which yield larger variance/uncertainty could be effectively omitted without increasing the computation cost. To evaluate our method, we propose a large-scale Vehicle-Infrastructure Collaborative Autonomous Driving dataset, which contains millions of objects (e.g., cars, trucks, pedestrians) along with the camera calibration parameters (i.e., roll, pitch, yaw angles, and height of the camera setting position), dubbed as VICAD. Extensive experiments conducted on the proposed database demonstrate the effectiveness of the proposed method. Experimental results show that our proposed DUD loss has achieved a more accurate camera calibration prediction result than the state-of-the-art without further increasing computation cost.
Recognizing Hate with NLP: The Teaching Experience of the #DeactivHate Lab in Italian High Schools

The possibility of raising awareness about misbehaviour online, such as hate speech, especially in young generations could help society to reduce their impact, and thus, their consequences. The Computer Science Department of the University of Turin has designed various technologies that support educational projects and activities in this perspective. We implemented an annotation platform for Italian tweets employed in a laboratory called #DeactivHate, specifically designed for secondary school students. The laboratory aims at countering hateful phenomena online and making students aware of technologies that they use on a daily basis. We describe our teaching experience in high schools and the usefulness of the technologies and activities tested.
Tree Cache

This program generates a single search tree, compacts it to reduce memory/disk space, then utilizes it to very quickly generate valid paths in an 8-connected gridworld. This technique is similar in principle to RRTs used in robotics.
Development of a machine tool simulator based on VR

In the manufacturing industry, various types of machine tools are widely used by many engineers. Some inexperienced users of machine tools, such as students and trainees, operate machine tools when they are training to acquire skills. However, machine tools have mechanisms that rotate at high speed and exert large forces, which can be dangerous for inexperienced users, and therefore safe training of personnel is required. Virtual reality (VR) is inexpensive compared to machine tools and can provide an immersive training environment for a large number of personnel. In this study, we developed a training system for machining training in a virtual space for a desktop milling machine tool. Based on the input NC program, the movement of each axis and the rotation of the main axis are represented, associated with the control panel by C# script. As a training function, interference between the tool and workpiece, hand contact with the tool, and overrun of each axis are determined by interference checks for each part, and an interface is created that displays warnings on the user's line of sight. In addition, the machined shape is reproduced by using a function called “stencil” in Unity. Furthermore, in order to realize the digital twin, the behavior of the actual machine and that of the virtual machine can be synchronized using TCP/IP, and the behavior of the table of the actual machine tool can be monitored.
The Correlation Between Students’ Creativity And Their Writing Skill At Ma Nw Peseng

This research was aimed to find out the correlation between students’ creativity and their writing skill. The kind of this research was simple correlational study (quantitative approach). The population of this research was 77 of the second grade students of MA NW Peseng. The sample of this research was 21 consist of one class. The sample technique that used in this research was cluster random sampling technique by using lottery. The intruments that was used in this research were tests, that were verbal creativity test and writing test. Then the technique that used in analyzing the data was Pearson Product Moment formula. From the research, the r of xy score was higher than r-table (0.829&gt;0.456) in 5% and 1% level of significance. Based on the result of this research, it could be concluded that there was a significant correlation between students’ creativity and their writing skill meant Alternative Hypothesis (Ha) was accepted and Null hypothesis (H0) was rejected.
Multi-scale Multi-target Domain Adaptation for Angle Closure Classification

Deep learning (DL) has made significant progress in angle closure classification with anterior segment optical coherence tomography (AS-OCT) images. These AS-OCT images are often acquired by different imaging devices/conditions, which results in a vast change of underlying data distributions (called “data domains”). Moreover, due to practical labeling difficulties, some domains (e.g., devices) may not have any data labels. As a result, deep models trained on one specific domain (e.g., a specific device) are difficult to adapt to and thus may perform poorly on other domains (e.g., other devices). To address this issue, we present a multi-target domain adaptation paradigm to transfer a model trained on one labeled source domain to multiple unlabeled target domains. Specifically, we propose a novel Multi-scale Multi-target Domain Adversarial Network (M2DAN) for angle closure classification. M2DAN conducts multi-domain adversarial learning for extracting domain-invariant features and develops a multi-scale module for capturing local and global information of AS-OCT images. Based on these domain-invariant features at different scales, the deep model trained on the source domain is able to classify angle closure on multiple target domains even without any annotations in these domains. Extensive experiments on a real-world AS-OCT dataset demonstrate the effectiveness of the proposed method.
Hybrid System of Dual Axis Photovoltaic Tracking System Using ANFIS-ACO

In this paper, the efficiency of the photovoltaic panels is improved by adding two solar tracking systems. The solar tracking system is used to track the sun so that the photovoltaic always faces the sun. This system uses a dual axis consisting of a horizontal rotation axis and a vertical rotation axis. The motion of the horizontal axis of rotation follows the sun's azimuth angle from north to south. Then to follow the sun's azimuth angle from east to west is the vertical axis motion. Both types of movements are controlled using PID and Fuzzy controllers, which are optimized with an artificial intelligence approach, namely Ant Colony Optimization (ACO). Experiments with PID control, Fuzzy control, and ANFIS control approaches optimized by the ACO method (ANFIS-ACO Hybrid method). The experimental results show that ANFIS-ACO has the best performance in terms of settling time and overshoot.
Evaluation of Lightning Electromagnetic Induction on Buried Signaling Cable of High-speed Railway Considering the Frequency Dependence of Soil Electrical Parameters

Evaluation of the lightning coupling on the buried signaling cable nearby when the throughground wire is used as the discharge channel of lightning current requires accurate models for the calculation of the underground lightning electromagnetic field and the induced current of this field on the signaling cable conductor.To accomplish this, a full-wave approach based on the finite-element method (FEM) is used, which incorporates the frequency dependence of soil conductivity and relative permittivity into in the model.The numerical results show that for soils characterized by relatively low resistivity values (less than 4000 Ω•m), the frequency dependence of the electrical properties of the soil has a negligible influence on the horizontal component of the electric field and the vertical component of the magnetic field.However, the distribution of the lightning electromagnetic field is markedly affected by the distance between the air-soil interface and the buried signaling cable.We also find that the coupling strength of the lightning electromagnetic field to the buried signaling cable is strongly dependent on the wave shape of the lightning current, soil resistivity, the distance between the cable and the air-soil interface, and the distance between the cable and the lightning strike point.Finally, the common grounding methods of the cable shielding layer in cable protection are compared.Results show that single-layer double-terminal grounding is the most effective anti-interference measure for the electromagnetic field coupling between the through-ground wire and the buried signal cable near the lightning point of the high-speed railway.The desired shielding effect properties with the frequency from dc to 1 MHz can be achieved using this method.