Reviewerly/exHarmony huggingface dataset:
Effect of Meteorological Parameters on the Performance of Photovoltaics Installed under the Guinea Savannah Atmosphere in Ogoja, Cross Rivers State, Nigeria

A thorough experimental investigation was carried out to study the effect of some meteorological parameters on the performance of photovoltaics installed under the guinea savannah atmosphere in Ogoja, Cross River State, Nigeria by in-situ measurement approach using a precision digital solar power meter, an accurate digital hygrometer, precision digital infrared gun thermometer and a digital high precision photovoltaic smart panel maximum power point tracker (MPPT) tester to track and determine the maximum power, voltage and current produced by the photovoltaic module. The result reveals that stability in voltage should be expected between relative humidity levels of 67% to 47% while erratic drop in voltage below 47% of relative humidity should be expected which hinders the PV module from attaining 100% efficiency. In addition, the temperature of the study location seems favorable as on the average the panel temperature will rarely exceed 400C for months besides January which makes it possible for the PV module to attain 90% efficiency. Also, with an altitude of 85m above sea level coupled with the fact that as early as 9:00am the solar power level is close to 800W/m2 makes the location a good site for the installation of solar farm for harvesting solar energy.
Editorial policy in promoting scientific periodicals (on the example of the &lt;i&gt;Gyroscopy and Navigation&lt;/i&gt; journal)

The paper describes the editors’ experience of development and promotion of a scientific and technical journal Gyroscopy and Navigation intended for scientists and specialists in the art, and published in the Russian and English versions. The effectiveness of various tools for increasing the citation of the journal is analyzed, and the examples of their practical use are given. The features of interaction with the authors and readership of both the Russian and English-language versions of the journal, i.e., specialists in navigation and motion control of various moving objects are shown. Work with the Russian-speaking audience of the journal is based on direct contacts with researchers through online seminars, which facilitates the formation of a community of scientists loyal to the publication and interested in the journal development. At present, one of the main goals of the journal is to increase the level of its global reputation and influence. This is solved by sending regular information letters about the publication of new issues of the journal with a brief description of published materials to particular addressees. As a result of the measures taken, the number of downloads of full texts of articles in the English version of the journal, as well as their citation, has increased significantly (2.5 times), which, in turn, has considerably increased the rating of the journal in the Scopus database. For example, the CiteScore index increased from 1.7 to 2.6 between 2019 and 2021. In this paper, it is shown that the difficulties in promoting the Russian-language version of the journal are due to the small audience of Russian-speaking specialists in this subject area, and due to the specific procedure of journal ratings calculation in the Russian Science Citation Index.
DITES: A Lightweight and Flexible Dual-Core Isolated Trusted Execution SoC Based on RISC-V

A Trusted Execution Environment (TEE) is an efficient way to secure information. To obtain higher efficiency, the building of a dual-core system-on-chip (SoC) with TEE security capabilities is the hottest topic. However, TEE SoCs currently commonly use complex processor cores such as Rocket, resulting in high resource usage. More importantly, the cryptographic unit lacks flexibility and ignores secure communication in dual cores. To address the above problems, we propose DITES, a dual-core TEE SoC based on a Reduced Instruction Set Computer-V (RISC-V). At first, we designed a fully isolated multi-level bus architecture based on a lightweight RISC-V processor with an integrated crypto core supporting Secure Hashing Algorithm-1 (SHA1), Advanced Encryption Standard (AES), and Rivest–Shamir–Adleman (RSA), among which RSA can be configured to five key lengths. Then, we designed a secure boot based on Chain-of-Trust (CoT). Furthermore, we propose a hierarchical access policy to improve the security of inter-core communication. Finally, DITES is deployed on a Kintex 7 Field-Programmable-Gate-Array (FPGA) with a power consumption of 0.297 W, synthesized using TSMC 90 nm. From the results, the acceleration ratios of SHA1 and RSA1024 decryption/encryption can reach 75 and 1331/1493, respectively. Compared to exiting TEE SoCs, DITES has lower resource consumption, higher flexibility, and better security.
Comparison of A* Algorithm and Greedy Best Search in Searching Fifteen Puzzle Solution

Artificial Intelligence is an exciting field to research. Artificial Intelligence itself is a broad subject. The application of artificial intelligence in daily routine is various. One of the usages of artificial intelligence is finding the shortest route on a map. In general, the algorithm which can be used for finding the shortest route is A*. A* is often used in finding the shortest route in a graph or map. Generally speaking, A* is used to make a game, especially for finding the shortest route of an intelligent agent inside it. In this paper, the finding solution of puzzle game using A* and Greedy Best First Search is to be discussed. The puzzle game which is discussed is the Fifteen Puzzle. This research compares the two algorithms used, A* and Greedy Best First Search. This research shows that Greedy Best First Search gives a slightly faster solution than A*.
Comparative Analysis of the Performance of Single Sign-On Authentication Systems with OpenID and OAuth Protocols

A vast number of people use the internet on a regular basis. The growing number of users will inadvertently bring new issues for both users and administrators as user managers. Users forget their user accounts and passwords when they have too many accounts to surf the internet. Web-based application services at University of Technology Yogyakarta include the Academic Information System (SIA) and E-Learning without exception. Both have the same issue: figuring out how to establish an authentication mechanism that will prevent users from forgetting their accounts on the system. The goal of this research is to create a prototype using Single Sign On (SSO) and compare the performance of the two SSO protocols utilized, OpenID and OAuth. The Explicate Problem, Define Requirements, Design and Develop Artifact, Demonstrate Artifact, Evaluate Artifact, and Communication processes are all part of this study. The results of prototype testing are obtained by attempting to log in using an academic service system account, and users are not required to login/authenticate again while accessing the e-learning page. Performance studies on both protocols revealed that the highest number of users who could login to the system at the same time was 1230 (OpenID) and 1219 (OAuth). In comparison to the OpenID protocol, the OAuth protocol is more consistent in terms of average response time for handling user requests. A greater specification is also required to suit the demands of additional users.
Drone Tracking with Drone using Deep Learning

With the development of technology, studies in fields such as artificial intelligence, computer vision and deep learning are increasing day by day. In line with these developments, object tracking and object detection studies have spread over wide areas. In this article, a study is presented by simulating two different drones, a leader and a follower drone, accompanied by deep learning algorithms. Within the scope of this study, it is aimed to perform a drone tracking with drone in an autonomous way. Two different approaches are developed and tested in the simulator environment within the scope of drone tracking. The first of these approaches is to enable the leader drone to detect the target drone by using object-tracking algorithms. YOLOv5 deep learning algorithm is preferred for object detection. A data set of approximately 2500 images was created for training the YOLOv5 algorithm. The Yolov5 object detection algorithm, which was trained with the created data set, reached a success rate of approximately 93% as a result of the training. As the second approach, the object-tracking algorithm we developed is used. Trainings were carried out in the simulator created in the Matlab environment. The results are presented in detail in the following sections. In this article, some artificial neural networks and some object tracking methods used in the literature are explained.
Some aspects of the use of cloud computing in the training of physics teachers

The specifics of the training of physics teachers lies in the extensive use of experimental research. E-learning, the role of which has increased due to quarantine restrictions, has the means of experimental research in its arsenal. The international educational community has developed and uses a variety of electronic educational resources, which are integrated into collections and libraries. The professional training of physics teachers is not an exception. Electronic educational resources make it possible not only to deliver educational material in a variety of ways, sometimes impossible without ICT, but also to simulate events and processes for their wider study in educational purposes. The use of cloud calculations in the form of virtual laboratory workshops is a way to solve existing problems in the training of future physics teachers. The use of cloud calculations in the educational activities of future physics teachers contributes not only to the improvement of educational material but also to the improvement of digital competence of all participants of the educational process. The research analyzed the available virtual laboratory practices, the scheme of using physics simulations was suggested, the examples of their use during physics teaching and the role of cloud calculations in the training of physics teachers were given. An analysis of the use of cloud computing and the best practices of this activity is carried out.
Determining the role of digital literacy in EFL teaching concerning the views of Turkish EFL Teachers

The use of digital technologies in English as a foreign language (EFL) teaching has not been addressed enough. Furthermore, the COVID-19 pandemic has emphasized the significance of digital technology and digital literacy in EFL field. This study aims at finding out EFL in-service secondary school (SS) and high school (HS) teachers’ views on the role of digital literacy in their EFL teaching; revealing the level of EFL teachers’ digital literacy and its effects on their teaching, and understanding the facilities of the digital tools and challenges in EFL teachers’ instructions. Further, this study aims to examine the difference between the thoughts of SS teachers and HS teachers about the role of digital literacy in EFL. To achieve these aims, this study utilized a qualitative paradigm to determine the role of digital literacy in EFL classes concerning the views of Turkish EFL teachers, and their perceptions about digital teaching and learning tools in their instruction. First it was found that HS teachers knew the term digital literacy more than the SS teachers. Both SS and HS teachers were confident using digital tools and being digitally literate although some of them could not define the term properly. Secondly digital literacy was claimed to have positive effects on language teaching. Thirdly, all the participants agreed with the fact that the innovations of the digital technologies facilitated their language instruction. Lastly, digital tools had many challenges due to some problems besides their advanteages. All in all, when taken together, the results of this study confirmed the vital role of digital literacy in EFL teaching in the digital age that teachers should acquire and use it actively. Therefore, EFL teachers should incorporate digital literacy in using different digital technologies such as Web 2 tools having several facilities into their instructions. The EFL teachers also admitted the need for trainings and seminars to help them using those tools and become digital natives.
Conditional Generative Adversarial Networks for Domain Transfer: A Survey

Generative Adversarial Network (GAN), deemed as a powerful deep-learning-based silver bullet for intelligent data generation, has been widely used in multi-disciplines. Furthermore, conditional GAN (CGAN) introduces artificial control information on the basis of GAN, which is more practical for many specific fields, though it is mostly used in domain transfer. Researchers have proposed numerous methods to tackle diverse tasks by employing CGAN. It is now a timely and also critical point to review these achievements. We first give a brief introduction to the principle of CGAN, then focus on how to improve it to achieve better performance and how to evaluate such performance across the variants. Afterward, the main applications of CGAN in domain transfer are presented. Finally, as another major contribution, we also list the current problems and challenges of CGAN.
EMO-NET ARTIFICIAL NEURAL NETWORK: A ROBUST AFFECTIVE COMPUTING PREDICTION SYSTEM FOR EMOTIONAL PSYCHOLOGY USING AMIGOS

Affective computing has become an increasingly popular area of study that focuses on developing a robust system that automatically recognizes human emotions.Any changes in human emotion directly impact brain stimulation and physiological parameters.Emotions are critical parameters that directly impact the behaviour of an individual.Collecting the neurophysiological data from humans is utilized here.The ultimate goal of the presented work is to develop a robust methodology to predict human real affect.The present system considers the Amigos data set in which physiological signals such as ECG, EEG, and Galvanic Skin Response (GSR) are considered.The proposed framework is modelled using Gaussian mixture models to produce an expectation-maximization technique (GEM).Along with the measurement of statistical parameters such as mean, standard deviation, and Sigma, these are helpful for the system to identify the real class.The comprehensive analysis of various participant data collected through the AMIGOS dataset was split into training data and testing data.The raw data preprocessing using the Synthetic Minority Oversampling Technique (SMOTE) model in which the data has cleaned up random values or removed junk values or removed.Since data is provided to the GEM algorithm in which the expectation-maximization parameters such as mean median standard deviation and sigmoid are calculated.Based on the evaluated model, part of the test signals are given from the amigo dataset.The system can compare an interpreter to validate the parameters-based emotion labelling.A novel EmoNet_ANN (ENA) system is further improved by including deep learning models to find the detailed covariate values helpful to identify the personality traits of the participants.The proposed optimized Novel EmoNet_ANN (ENA) perceives the accuracy benefit of 92.5% with less computational time.A novel EmoNet_ANN(ENA) Proposed system is comparatively studied and further improved by evaluating deep learning models to find the detailed covariate values present in the data set.The statistical measures on Sigma, Variance from the GEM model concerning the correlation metric from ANN are further used to validate the emotion detection.Accuracy, Precision, Recall and F1score form the analysis module which is used to determine the overall performance statistics.Emotion analysis and affect impacted parameters are modelled using Novel EmoNet_ANN (ENA).The AMIGOS dataset analyses physiological signals to determine the actual emotions.With reduced computation time and iteration run till Zero error on complete analysis.
Bounded-Memory Runtime Enforcement

Runtime Enforcement (RE) is a monitoring technique to ensure that a system obeys a set of formal requirements (properties). RE employs an enforcer (a safety wrapper for the system) which modifies the (untrustworthy) output by performing actions such as delaying (by storing/buffering) and suppressing events, when needed. In this paper, to handle practical applications with memory constraints, we propose a new RE paradigm where the memory of the enforcer is bounded/finite. Besides the property to be enforced, the user specifies a bound on the enforcer memory. Bounding the memory poses various challenges such as how to handle the situation when the memory is full, how to optimally discard events from the buffer to accommodate new events and let the enforcer continue operating. We define the bounded-memory RE problem and develop a framework for any regular property. The proposed framework is implemented and its performance evaluated via some examples from application scenarios indicates that the enforcer has reasonable execution time overhead.
Inspecting Decorative Ceramic Defects by Fusing Convolutional Neural Network and Image Recognition

The intelligent inspection of ceramic decorative defects is one of the hot research at present. This work aims to improve the defect inspection automation of finished decorative ceramic workpieces. First, it introduces the multi-target detection algorithm and compares the performance of different network models on the public data set. Second, the initial images are collected on the spot. The initial pictures are easy to produce noise in actual deployment, affecting the image quality. Therefore, image preprocessing is performed for the initial images, and a median filtering method is used to calculate the denoising. Finally, the original You Only Look Once version 3 network model is realized. Based on this, the decorative ceramic-oriented Automated Surface Defect Inspection model is proposed. Then, decorative ceramic defect images are inputted for model training. The experimental conclusions are deeply studied and analyzed. The results show that the proposed decorative ceramic-oriented Automated Surface Defect Inspection model based on Deep Learning technology has good feature extraction and inspection ability. The detection accuracy is 94.90% on the test set, and the detection speed reaches 25 frames per second. Compared with the traditional manual inspection method, the proposed model greatly improves the inspection effect and can meet the on-site inspection requirements of surface defects of decorative ceramics under complex backgrounds. It is of great significance to improve the quality inspection efficiency and economic benefits of China's decorative ceramics industry.
The effect of entrepreneurship on the sustainability of small enterprises and productivity financed by the ministry of social development of Jordan

This study aimed to identify the impact of entrepreneurship on the sustainability of small enterprises and productivity financed by the ministry of social development of Jordan. To achieve the objectives of the study, a descriptive analytical method was adopted, and a questionnaire was designed and distributed to members of the study sample consisting of owners of productive projects funded by the Ministry of Social Development in Jordan. The results show that Entrepreneurship has a significant impact on the sustainability of small enterprises and productivity financed by the ministry of social development of Jordan.
Control and Path Planning of Mobile Swarm Robots Using Blockchain Technology with Particle Swarm Optimization

Technological advancement has made robots become rampant in industrial automation and globalization, as it’s fast and efficient in delivering tasks in industries with no supervision. This work shows the use of blockchain technology (smart contract) in controlling a swarm of robots combined with particle swarm optimization for solving the navigation path. The technique in this research modeled a new fitness function, that uses the optimal path generation and barrier avoidance for the mobile robot’s movement within the swarm, while the blockchain smart contract was integrated to control the robot’s speed. Simulated results validate path optimization and speed control with PSO and BT.
Multimodal Failure Matching Point Based Motion Object Saliency Detection for Unconstrained Videos

Inspired by classical feature descriptors in motion matching, this paper proposes a multimodal failure matching point collection method, which is defined as FMP. FMP is, in fact, a collection of unstable features with a low matching degree in the conventional matching task. Based on FMP, a novel model for the saliency detection of motion object is developed. Models are evaluated on the DAVIS and SegTrackv2 datasets and compared with recently advanced object detection algorithms. The comparison results demonstrate the availability and effectiveness of FMP in the detection of motion object saliency.
CED-Net: A more effective DenseNet model with channel enhancement

In recent years, deep convolutional neural network (CNN) has been applied more and more increasingly used in computer vision, natural language processing and other fields. At the same time, low-power platforms have more and more significant requirements for the size of the network. This paper proposed CED-Net (Channel enhancement DenseNet), a more efficient densely connected network. It combined the bottleneck layer with learned group convolution and channel enhancement module. The bottleneck layer with learned group convolution could effectively increase the network's accuracy without too many extra parameters and computation (FLOPs, Floating Point Operations). The channel enhancement module improved the representation of the network by increasing the interdependency between convolutional feature channels. CED-Net is designed regarding CondenseNet's structure, and our experiments show that the CED-Net is more effective than CondenseNet and other advanced lightweight CNNs. Accuracy on the CIFAR-10 dataset and CIFAR-100 dataset is 0.4 and 1% higher than that on CondenseNet, respectively, but they have almost the same number of parameters and FLOPs. Finally, the ablation experiment proves the effectiveness of the bottleneck layer used in CED-Net.
Implementation of School Learning Continuity Plan (SLCP) in Selected Public Secondary Schools of Aborlan North District in the Division of Palawan

This study was conducted from February to April 2022 to determine the Implementation of the School Learning Continuity Plan (SLCP) in the selected public secondary schools of Aborlan North District in the Division of Palawan. This study which is quantitative in nature, employed a descriptive research design, specifically the survey method. A total of forty (40) permanent teachers and three (3) school heads were enumerated from the selected junior high schools as the respondents of this study. The data were gathered through survey questionnaires using Google Forms. Descriptive statistics were utilized for data analysis, particularly frequency distribution, percentages, and mean. The results showed that most of the respondents were married females in their middle age. They had been in the service for a mean of 12 years, and 44 percent of them were Secondary School Teacher I. The perceived level of implementation of the different programs and activities in the School Learning Continuity Plan was noted to be “great extent”. The insufficient fund was considered the number one problem in the implementation of programs and projects, and activities in the School Learning Continuity Plan.