Reviewerly/exHarmony huggingface dataset:
INTERACTION BETWEEN TYPES OF ARTIFICIAL INTELLIGENCE

Artificial intelligence is called any entity that has the ability to calculate, perceive their own environment and respond to various actions to achieve their goals. AI is a series of algorithms for developing machines with capabilities close to those of a human being.
DEVELOPMENT AND IMPLEMENTATION OF AN AUTOMATED PILOT SYSTEM FOR A FIXED-WING TWIN-ENGINE AIRPLANE UAV

When controlling a UAV, we have to rely on the line of site of the operator and aft wards on the communication with the ground control station. This implies expensive equipment used in communication, a skilled operator and a UAV with low autonomy. In order to address these issues an automated piloting system needs to be implemented thus offering the needed autonomy to the UAV. An automated pilot can also stabilize the aircraft and allow for the control of the UAV to be done by people without intensive training or special skills. In the present paper we present the development and implementation of such a system, built on Cube Orange flight controller, and implemented on a fixed-wing UAV with twin-engines, fabricated using additive manufacture technologies.
MISSION MANAGEMENT FOR AN AUTOMATED PILOT SYSTEM MOUNTED ON A FIXED-WING TWIN-ENGINE AIRPLANE UAV

UAV systems used automated pilots which can be configured based on the mission. The entry level market for this type of systems is not focus mainly on fixed-wing configurations. On this paper we asses, develop and simulate missions for an automated piloting system, built on a Cube Orange architecture. The flight controller is mounted on a fixed-wing twin-engine airplane built using additive manufacturing technologies. For the management and simulation of UAV mission we use Mission Planner, and open-source software from ArduPilot. The missions is simulated to obtain an improvement of the automated piloting system, evaluate the terrain from the flight path, avoid dangers, keep out of restricted areas and find the optimal routes. .
DRONE DETECTION USING DEEP LEARNING

Drones have widespread application in real life and the industry is expanding rapidly. As they are growing increasingly it is more accessible to the public at cheaper prices. They are used for espionage and can be converted into gruesome weapons. Hence it is very important to monitor and detect unauthorized drones entering into the restricted regions in order to maintain peace and prevent chaos. The Technology stack implied here is You Only Look Once (YOLO v5) which is a real time object detection system. In recent times Yolo is a profound algorithm used for real time object or image detection . The Yolo trained model is trained with pictures of drones and birds so that the trained model can differentiate between and prevent false prediction of drones . Everytime a drone is detected in the camera an alert message is sent to the higher officials so that the drone could be eliminated. This algorithm comprises 3 techniques namely: Residual blocks, Bounding box regression and Intersection over Union. The camera used here is 360 degree so that maximum area of visibility is obtained. KEYWORDS: Drones, real time object detection , YOLO v4.
The Effects of Cohesive Devices Used in the Background of the Study of English Department Students’ Undergraduate Theses

This study entitled The Effects of Cohesive Devices Used in the Background of the Study of English Department Students' Undergraduate Theses aims to analyse the effects of the overuse, underuse, and misuse cohesive devices employed by the English Department students in the background of the study in their undergraduate theses. The data of this study is taken from ten backgrounds of the studies written by the English Department students, Faculty of Humanities, Udayana University submitted in 2021 in various topic. This study is conducted using descriptive qualitative method. In collecting the data, documentation method utilized by a checklist instrument and note-taking technique are used to collect the data. The method for presenting the data analysis in this study could be formal and informal. In analysing the data, this study employs the theory from Halliday and Hassan (1976) about the cohesive devices and supporting theory by Ong (2011). The results of this study show that there are several types of cohesive devices are overused, underused, and misused. The overuse of cohesive devices is dominated by the overuse of repetition, conjunction, reference, and synonym. Then, the underuse of cohesive devices found in conjunction and reference. Lastly, the misuse of cohesive devices is found in reference and conjunction. The effects from these overuse, underuse, and misuse of cohesive devices affect the text, especially the connections of the ideas become not in cohesion. Those cases also affect the effectiveness, clarity, and the way the reader comprehend the meaning of the text.
JPR volume 59 issue 3 Cover and Back matter

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Q-Learning in enormous action spaces via amortized approximate maximization

Applying Q-learning to high-dimensional or continuous action spaces can be difficult due to the required maximization over the set of possible actions. Motivated by techniques from amortized inference, we replace the expensive maximization over all actions with a maximization over a small subset of possible actions sampled from a learned proposal distribution. The resulting approach, which we dub Amortized Q-learning (AQL), is able to handle discrete, continuous, or hybrid action spaces while maintaining the benefits of Q-learning. Our experiments on continuous control tasks with up to 21 dimensional actions show that AQL outperforms D3PG (Barth-Maron et al, 2018) and QT-Opt (Kalashnikov et al, 2018). Experiments on structured discrete action spaces demonstrate that AQL can efficiently learn good policies in spaces with thousands of discrete actions.
Robust Template Matching via Hierarchical Convolutional Features from a Shape Biased CNN

Finding a template in a search image is an important task underlying many computer vision applications. Recent approaches perform template matching in a deep feature-space, produced by a convolutional neural network (CNN), which is found to provide more tolerance to changes in appearance. In this article we investigate if enhancing the CNN's encoding of shape information can produce more distinguishable features that improve the performance of template matching. This investigation results in a new template matching method that produces state-of-the-art results on a standard benchmark. To confirm these results we also create a new benchmark and show that the proposed method also outperforms existing techniques on this new dataset. Our code and dataset is available at: https://github.com/iminfine/Deep-DIM.
Leveraging Special-Purpose Hardware for Local Search Heuristics

As we approach the physical limits predicted by Moore's law, a variety of specialized hardware is emerging to tackle specialized tasks in different domains. Within combinatorial optimization, adiabatic quantum computers, CMOS annealers, and optical parametric oscillators are few of the emerging specialized hardware technology aimed at solving optimization problems. In terms of mathematical framework, the Ising optimization model unifies all of these emerging special-purpose hardware. In other words, they are all designed to solve optimization problems expressed in the Ising model or equivalently as a quadratic unconstrained binary optimization model. Due to variety of constraints specific to each type of hardware, they usually suffer from a major challenge: the number of variables that the hardware can manage to solve is very limited. Given that large-scale practical problems, including problems in operations research, combinatorial scientific computing, data science and network science require significantly more variables to model than these devices provide, we are likely to witness that cloud-based deployments of these devices will be available for parallel and shared access. Thus hybrid techniques in combination with both hardware and software must be developed to utilize these technologies. Local search meta-heuristics is one of the approaches to tackle large scale problems. However, a general optimization step within local search is not traditionally formulated in the Ising form. In this work, we propose a new meta-heuristic to model local search in the Ising form for the special-purpose hardware devices. As such, we demonstrate that our method takes the limitations of the Ising model and current hardware into account, utilizes a given hardware more efficiently compared to previous approaches, while also producing high quality solutions compared to other well-known meta-heuristics.
Harnessing the Power of Serverless Runtimes for Large-Scale Optimization

The event-driven and elastic nature of serverless runtimes makes them a very efficient and cost-effective alternative for scaling up computations. So far, they have mostly been used for stateless, data parallel and ephemeral computations. In this work, we propose using serverless runtimes to solve generic, large-scale optimization problems. Specifically, we build a master-worker setup using AWS Lambda as the source of our workers, implement a parallel optimization algorithm to solve a regularized logistic regression problem, and show that relative speedups up to 256 workers and efficiencies above 70% up to 64 workers can be expected. We also identify possible algorithmic and system-level bottlenecks, propose improvements, and discuss the limitations and challenges in realizing these improvements.
Critical Discourse Analysis of Literary Texts of NCTB English for Today (Class II - Class VIII) Textbooks: A Qualitative Study

This research has attempted an analysis of the Literary Texts included in the NCTB English for Today (Class II - Class VIII) Textbooks from the perspective of Critical Discourse Analysis (CDA) which is fundamentally a sociolinguistic paradigm to find and locate the internal ideological, social, ethical, moral and political implications encoded in the texts. CDA has been adopted both as a theoretical and methodological framework to carry out the research. Literary texts like poetry, short story etc. have been used as a secondary source materials and data collected from students and teachers from 20 Government primary schools and 10 secondary schools covering 1500 students and 150 teachers as primary data for this research. The primary data has been collected through survey questionnaire and focused group discussion. The collected data has been analyzed using qualitative methods like Content analysis, Narrative analysis, Thematic analysis, Grounded theory (GT) and Interpretive phenomenological analysis (IPA). Various Computer-assisted qualitative data analysis software (CAQDAS) like ATLAS.ti, Dedoose (mixed methods), MAXQDA (mixed methods), NVivo and QDA MINER have been used where necessary to analyze in-depth interviews. The collected data have been analyzed using NVivo software depending on the similarities; the data have been then grouped into categories and themes. These patterned themes have emerged objectively out of the data as emergent themes. The findings from the data shows that 33% of the total student respondents do not at all read the literary texts, 33% of the teacher respondents do not teach or discuss literary texts in the classrooms and only 16% of the students read the literary texts on their own. The literary texts are totally skipped and ignored by around 8% of the student respondents and only 8% of students and teachers read and teach the literary texts effectively as intended. Other results from the data analysis show that majority ..............
Mapping of Factors Affecting Land Fires in the Media Frame for Disaster Mitigation in the Future

Land fires are national and international disasters that cause crucial environmental problems and are of local and global concern. This research was conducted to determine the mapping of the factors that affect land fires in a media frame that can be used for disaster mitigation so that the impact of fires can be minimized in the future. In addition, with mitigation, it is hoped that fires can be reduced, namely through restoration programs, including wetting, revegetation and community economic revitalization. This study uses time series data from 2012-2021 and uses the framing analysis method and content analysis. The results of the study show that the mapping of factors that affect land fires in the research area from several media from September 2019 to June 2021 is a factor of human activity and natural factors. The dominant human activities causing fires in the research area are land clearing for agriculture and settlements, land preparation, illegal logging, and lack of public awareness of the dangers of fire. The natural factors that dominate causing fires in the research area are the dry season, not residential areas.
Classification of 8-divisible binary linear codes with minimum distance 24

We classify 8-divisible binary linear codes with minimum distance 24 and small length. As an application we consider the codes associated to nodal sextics with 65 ordinary double points.
Fréchet Sufficient Dimension Reduction for Random Objects

We in this paper consider Fr\'echet sufficient dimension reduction with responses being complex random objects in a metric space and high dimension Euclidean predictors. We propose a novel approach called weighted inverse regression ensemble method for linear Fr\'echet sufficient dimension reduction. The method is further generalized as a new operator defined on reproducing kernel Hilbert spaces for nonlinear Fr\'echet sufficient dimension reduction. We provide theoretical guarantees for the new method via asymptotic analysis. Intensive simulation studies verify the performance of our proposals. And we apply our methods to analyze the handwritten digits data to demonstrate its use in real applications.
SIMPLIFIKASI MORFOLOGI ARAB (SHARF) DENGAN PENDEKATAN KONSTRUKTIVISME DAN ANALOGI

This study aims to develop an "Arabic morphological simplification model with a constructivism approach and the analogy of learning sharf in Fast Time." The research approach used is qualitative and quantitative types of development research (RD). Researchers will develop an Arabic morphological model that can make neural learning successful in this fast time through surveys, interviews, documentation studies, and FGDs. It found that model was developed has succeeded in learning. Arabic education experts provide positive support to continue developing Arabic morphology models based on constructivism and analogy. Many inputs are used as references for researchers in developing the model designs that have been made. In the next stage, students as objects and public test subjects feel that neural learning becomes meaningful. Students also get a new, productive, and accessible learning model, even for those who are very 'basic,' who have always been seen as having difficulty mastering nerves.
Implementation of Aptitude Treatment Interaction (ATI) in Nahwu Learning: Identification of Student Ability Characteristics

Aptitude Treatment Interaction (ATI) is used as a learning method that treats differences in student abilities; this is considered appropriate for Nahwu learning. The purpose of this study is to elaborate on Nahwu's learning activities using Aptitude Treatment Interaction (ATI) through the involvement of lecturers and students. This study uses a qualitative approach; the participants are non-Arabic speaking Nahwu students in Indonesia, totaling 40 students. Data collection techniques are using observation, structured interviews, and using questionnaires. The results showed that there were implications for the use of Aptitude Treatment Interaction (ATI) in learning motivation, as indicated by an increase in scores on the evaluation of students' Nahwu understanding abilities and perceptions that showed a good assessment of this method. The recommendation of this research is the preparation of teaching materials based on Aptitude Treatment Interaction (ATI) for Nahwu learning that can be done at universities in Indonesia.
Pengaruh Kompetensi Auditor dan Implementasi e-Audit terhadap Kualitas Audit

Abstract. This study aims to examine the effect of auditor competence and e-audit implementation on audit quality. This research is a quantitative research with descriptive verification method with a survey approach. The sample in this study consisted of 31 respondents who were auditors at the West Java Province BPKP Representative Office. The source of data used in this study is the primary data source. The data collection technique used is a questionnaire distributed to auditors who work at the BPKP Representative Office of West Java Province. Testing the hypothesis used in this study using multiple regression analysis. The test results show that the competence of the auditor and the implementation of e-audit have a significant effect on audit quality. For further researchers, it is hoped that they can add research samples so that the results can generalize the theory more, besides that it is hoped that they can add other variables to examine the dependent variable of audit quality.&#x0D; Abstrak. Penelitian ini bertujuan untuk menguji pengaruh kompetensi auditor dan implementasi e-audit terhadap kualitas audit. Penelitian ini merupakan penelitian kuantitatif dengan metode deskriptif verifikatif dengan pendekatan survey. Adapun sampel dalam penelitian ini berjulam 31 responden yang merupakan auditor pada Kantor Perwakilan BPKP Provinsi Jawa Barat. Sumber data yang digunakan dalam penelitian ini yaitu sumber data primer. Adapun teknik pengumpulan data yang digunakan adalah kuesioner yang disebarkan pada auditor yang bekerja pada Kantor Perwakilan BPKP Provinsi Jawa Barat. Pengujian hipotesis yang digunakan dalam penelitian ini menggunakan analisis regresi berganda. Hasil pengujian hipotesis menunjukkan bahwa kompetensi auditor dan implementasi e-audit berpengaruh signifikan terhadap kualitas audit. Bagi peneliti selanjutnya, diharapkan dapat menambah sampel penelitian agar hasilnya lebih menggeneralisasikan teori, selain itu diharapkan mampu menambahkan variable lainnya untuk meneliti variable dependen kualitas audit.
MRI to CT Translation with GANs

We present a detailed description and reference implementation of preprocessing steps necessary to prepare the public Retrospective Image Registration Evaluation (RIRE) dataset for the task of magnetic resonance imaging (MRI) to X-ray computed tomography (CT) translation. Furthermore we describe and implement three state of the art convolutional neural network (CNN) and generative adversarial network (GAN) models where we report statistics and visual results of two of them.
Estimation of Global Solar Radiation (GSR) From the Sunshine Hour of Guranshe, Surkhet, Nepal

The variation in Global Solar Radiation (GSR) was studied by using experimental data via an automatic weather station (CMP3 pyranometer) located at Guranshe (Surkhet), a hilly area with elevation 2194m from the sea level. Also the estimated sunshine hour data of the area were used to calculate the GSR for the purpose of comparison with the measurements. Our study finds a good agreement between the measured (15.65 MJ/m2/day) and estimated/calculated (19.18 MJ/m2/day) values of annual average GSR. The estimated GSR uses Angstrom-Prescott type model for calculating sunshine hours, and the coefficients (a=0.28) and (b=0.57) were obtained using the Sangeetas and Tiwari model. The annual average estimated sunshine hours was found to be 6.86hr which is suitable for GSR based applications. The measured data were used to study the seasonal, monthly and diurnal variations of GSR. The maximum and minimum values of GSR were found during the spring and summer seasons respectively. The minimum GSR in summer is because of heavy rainfall, high relative humidity, and cloud formation in the sky during the time. The coefficient of regression(R) and the coefficient of determination (R2) were obtained as R=0.881 and R2=0.776 respectively. Here the high value of R (88.1%) indicates that of dependent variable (GSR) is nicely predicted by the independent variables. Also the coefficient of determination (R2) tells us the strength of the relationship between two variables. BIBECHANA 19 (2022) 150-159
Metaverse - The Future of Virtual World

Since the popularization of the Internet in the 1990s, cyberspace has kept evolving. In addition to social networks, video conferencing, virtual 3D worlds (like VR Chat), augmented reality software (like Pokemon Go), and non-fungible token games, we have also developed other computer-mediated virtual environments (e.g., Upland). Such virtual environments, albeit non-perpetual and unconnected, have brought us various degrees of digital transformation. The term `Metaverse' has been coined to further facilitate digital transformation in every aspect of our physical lives. At the core of the metaverse stands the vision of an immersive Internet as a gigantic, unified, persistent and shared realm. While the metaverse may seem futuristic, catalyzed by emerging technologies such as Extended Reality, 5G and Artificial Intelligence, the digital `big bang' of our cyberspace is not far away. Technologies are the enablers that drive the transition from the current Internet to the metaverse. Enabling technologies that drive this transition rigorously are Extended Reality, User Interactivity (Human-Computer Interaction), Artificial Intelligence, Blockchain, Computer Vision, IoT and Robotics, Edge and Cloud computing and Future Mobile Networks. In terms of applications, the metaverse ecosystem allows human users to live and play within a self-sustaining, persistent, and shared realm. User-centric factors are Avatar, Content Creation, Virtual Economy, Social Acceptability, Security and Privacy and Trust and Accountability.
CREDIT GROWTH DETERMINANTS IN ROMANIA

Through an empirical analysis, using a multiple linear regression model, the aim of this paper is to study the effects on credit growth of some of the macroeconomic factors as well as indicators of assets and liabilities of the Romanian banking system. Credit growth is determined by the dependent variable of bank credit to the private sector as percent of GDP. The results showed a direct statistically significant relationship between the dependent variable and factors such as ROE, bank lending-deposit interest rate spread, capital investment, and the indicators of bank assets as percent of GDP and the unemployment rate.
FINANCIAL INCLUSION IN THE REPUBLIC OF MOLDOVA: RECENT IMPACTS AND EVIDENCE

This paper analyses the relationships between remittances and financial inclusion in the Republic of Moldova. Using a dataset are created the linear regression models, that show the interdependency of the level of the remittances and the level of financial inclusion. It is also mentioned that the level of financial education places an important role in using financial services. From the policy perspective, is suggested that the Government has to implement the National Financial Inclusion Strategies in order to elaborate an action plan that takes into account all the ways for increasing the level of financial inclusion.