Reviewerly/exHarmony huggingface dataset:
Spatial assessment of solar PV-based irrigation potential in Kenya

In this paper, we present a spatial modeling framework for assessing solar PV-based groundwater-fed irrigation potential in Kenya at subcounty resolution for maize, onion, and tomato crops. The model results show that 56 out of the 290 subcounties are technically and economically viable for solar PV irrigation farming when the depth-to-groundwater table is limited to 25m. These 56 subcounties are concentrated in Western and Mt. Kenya regions where current agricultural activities take place. Using the net present value of cashflows, the model results show that maize crop is not economically viable for solar PV-based irrigation farming. On the other hand, tomato is a more economically profitable crop than onion. Using the tomato crop as a reference, the model results show that all the 56 viable subcounties can be well served by systems of less than 4kWp/ha and the maximum capital cost ${\$}$27,000/ha. The payback period for the tomato and onion crops for the 56 subcounties is less than 2 years and 4 years respectively.
Island violations in stripping constructions

Clausal ellipsis (e.g. sluicing and fragment answers) involves a movement that can sometimes appear to violate island constraints. This squib focuses on a relatively understudied type of clausal ellipsis, stripping, and argues based on its unique properties that its apparent ability to violate islands is an illusion created by the evasion strategy – a parse that does not involve any islands. I also argue that stripping has the same underlying operations as sluicing and fragment answers, and thus the evasion strategy available to stripping should also be available to those other clausal ellipses, supporting Barros et al.'s (2014) evasion-based analysis for sluicing.
An Analysis of Longitudinal Relationships between Discussion/Debate Activities and Creativity

This study analyzed the longitudinal relationship between discussion/debate activities and creativity using data from the 2nd and 5th years of the Busan Education Longitudinal Study (BELS) 2016. Descriptive statistics and correlation analysis for each variable and autoregressive cross delay models were analyzed using longitudinal data for 4 years from 5th grade to 2nd grade of middle school of 2,359 students in Busan. As a result, first, there was a positive correlation between discussion/debate activities and creativity. Second, discussion/debate activities and creativity were stable over time. Third, the discussion/debate activity for the previous time point had a significant positive effect on the creativity for the next time, and it was found that the creativity for the previous time point also had a significant positive effect on the discussion/debate activity for the next time point. Thus, when the longitudinal relationship between discussion/debate activity and creativity was analyzed, it was found that there was a positive influence relationship in both directions.&#x0D; Based on this, the following were suggested. First, it is necessary to continuously present various types of discussion/debate activities to students in the educational field. Also, it is necessary to develop and disseminate various teaching and learning strategies for developing students' abilities. Second, it is necessary to maximize the effect by strengthening the virtuous cycle structure based on the continuous positive influence between discussion/debate activities and creativity. In addition, in the development and dissemination of various teaching and learning strategies, it should be possible to continuously strengthen their virtuous cycle structure and increase its effectiveness.
Exploring the Characteristics of ‘Future Education District’ as a Deepening Model for Innovative Education Districts: Focusing on the Case of “C” Future Education District

The purpose of this study is to confirm the characteristics of how future education districts are making a model for community education cooperation with a focus on public-private education governance as a deepening model for innovative education districts. To this end, the C Future Education District, which is the 6th year of the Innovation Education District and the 3rd Year of the Future Education District, was selected as an examples. The main research results are as follows. First, by reorganizing the existing cooperative system, it was possible to establish a cooperative system so that public-private-academic governance can work well. Second, activities were carried out to jointly develop and operate the village education curriculum to vitalize the link between schools and regions. Third, in order to reflect regional characteristics and needs, we actively utilized regional multi-ministerial project linkages to promote special projects. In conclusion, it is the focus and characteristic of the Future Education District project to develop a mindset for educational cooperation in the region by discovering the actors (people, groups, and institutions) to connect and cooperate for local education, and to connect and communicate with them. The purpose of the Future Education District is to lead the qualitative leap forward and sustainability of the Innovative Education District.
A Comparative Analysis of the Terminology of Guard and Security Using Big Data

Gaurd and Security have Since there are many problems that do not match the rapidly changing trend of the times, the overall perception of society was compared and analyzed. In order to provide basic data and provide meaningful implications, data were collected through TEXTOM, and word cloud analysis, LDA topic modeling analysis, emotional analysis, and semantic network analysis were conducted.&#x0D; First, in 'Gaurd', 'Security' has many associations, and 'Gaurd' was suitable to include the meaning of 'Security’. Second, ‘Gaurd’ is a term that allows people to think of related tasks more without being confused than "Security", and it was a term suitable to include the meaning of related tasks comprehensively. Third, it was confirmed that 'Security' clearly had more negative perceptions than 'Gaurd', and 'Gaurd' was found to be a relatively positive term. Fourth, among the terms with strong connection centrality in 'Gaurd', there was 'security' as a term that could replace 'Gaurd' and 'Security', but in 'Security', no term that could imply the meaning of the two terms did not appear.&#x0D; Security, a term that is already widely used in academia and industry and can be replaced without confusion, is expected to be the most suitable replacement for "Gaurd" and "Security" in a large framework and include both meanings.
Phenotypic complexity and evolvability in evolving robots

The propensity of evolutionary algorithms to generate compact solutions have advantages and disadvantages. On one side, compact solutions can be cheaper, lighter, and faster than less compact ones. On the other hand, compact solutions might lack evolvability, i.e. might have a lower probability to improve as a result of genetic variations. In this work we study the relation between phenotypic complexity and evolvability in the case of soft-robots with varying morphology. We demonstrate a correlation between phenotypic complexity and evolvability. We demonstrate that the tendency to select compact solutions originates from the fact that the fittest robots often correspond to phenotypically simple robots which are robust to genetic variations but lack evolvability. Finally, we demonstrate that the efficacy of the evolutionary process can be improved by increasing the probability of genetic variations which produce a complexification of the agents' phenotype or by using absolute mutation rates.
The Effects of Weighting on the Estimator of 3-Level Growth Model in the analysis of missing longitudinal data

This study seeks to clarify the effect of weighting on the fixed effect and random effect parameter estimators of the 3-level growth model. This study considered five weighting methods; ① non-weighting, ② sampling weighting, ③ longitudinal weighting, ④ multi-level weighting, ⑤ scaled multi-level weighting. The simulation study was conducted to statistically reveal the effect of weighting methods on the parameter estimation of the multi-level growth model. For the study, population, sampling data, and missing longitudinal data were each generated. The parameters of the 3-level growth model were estimated for each of the five weighting conditions using the missing longitudinal data. All conditions were repeated 100 times. The properties of the estimator were evaluated with bias, relative bias, and RMSE(root mean square error) from the viewpoint of bias and efficiency. The result is as follows. First, all fixed and random effects parameters were estimated inconsistently when non-weights or scaled multi-level weights were used in the 3-level growth model. Second, the 3-level growth model had the highest efficiency of fixed-effect and random-effect parameter estimators when non-weights or scaled multi-level weights were used. Based on the above results, suggestions for researchers and follow-up studies were presented.
Design of immersive scenarios and pieces of light art

The implementation of new methodological possibilities for the creation of images is a transcending aspect of the art sector. In this paper, we present a method for designing and projecting caustic images through optical systems, based on the application of the physical principles of light and inter-disciplinary cooperation. Prior to the experimental work, we conduct literature research to gain a better understanding of the production of caustic images and we conduct a comprehensive literature study to get an understanding of the scientific principles affecting light and how this may be applied to the art world. In addition, we examine comparable approaches created by scientists, engineers, and visual artists, confirming how significant the relationship between artists and scientists has become, especially in recent times. Under this perspective, we set out to explore how caustic images may be generated in an optics laboratory and subsequently included in the design of an immersive installation. The purpose of this study is to exhibit the technique we created for projecting these images on various surfaces. We test multiple variations that can be created in an exhibition area by utilizing light. We can appreciate the opportunity for artistic expression and the creation of fresh knowledge that this method offers.
Accelerating Pooling Layers in Photonic Convolutional Neural Networks

We propose a photonic neural network where multiply-accumulate and, for the first time, average pooling are realized exploiting a Mach-Zehnder interferometer mesh. Average pooling outperforms the max pooling counterpart, enabling an optical neural network layer.
IPSG: Invariant Proof Score Generator

Many previous case studies on formal verification of invariant properties by writing proof scores have demonstrated that the approach is powerful and flexible. Writing proof scores by hand, however, is prone to human errors and time-consuming, especially with complicated systems or specifications. Therefore, we propose an approach and implement a tool (called Invariant Proof Sore Generator or IPSG) that can automatically generate proof scores for formal invariant property verification. We demonstrate the practicability of the tool with three mutual exclusion protocols and one authentication protocol. The experiments show that IPSG can completely generate proof scores to prove that those protocols enjoy some desired properties.
Research on SAR Image Processing Performance Based on OpenMP and CUDA Parallel Model

In the field of remote sensing image processing, Synthetic Aperture Radar(SAR) image has a large amount of data and takes a long time to process. Especially in the military field and disaster detection, conventional serial processing methods cannot meet the requirements of real-time performance. To solve this problem, the high-performance computing advantages of parallel processing are utilized. The optimization effect of SAR image processing performance was studied based on OpenMP shared memory parallel processing method and CUDA parallel model. Combined with the use of OpenCV computer vision library, basic image processing tasks and parallel processing framework were designed. According to the comparison of the execution time between parallel processing and serial processing, the SAR image preprocessing speed is increased by 2 to 16 times. It is concluded that the parallel computing model is an effective fast SAR image processing optimization scheme.
An adaptive learning model for predicting and analyzing student performance on flight training tasks

As complexity and diversity of military assets increase, ensuring that military forces are well-trained becomes complex and costly when relying on traditional classroom instruction. Human instructors bear the burden of manually creating training datasets with tools that are often not geared to their missions, tasks, and objectives. Further, analyzing how well students learned their tasks often requires collecting and managing student performance over time, which may not be feasible in time-critical situations, and may consume instructor time and attention that could be spent facilitating learning. In addition, while one-to-one human tutoring has proven to be effective, it is costly and impractical to provide in every task domain. We present Multi-task Adaptive Learning Tutor (MALT), a concept for an intelligent tutoring system (ITS) for the psychomotor domain that flexibly responds to a user’s current tasking, information needs, and cognitive ability to interpret information. As the user performs a series of complex psychomotor sub-tasks drawn from flight procedures implemented in a simulator, MALT will learn to predict which features contributed most to their performance. In a proof-of-concept study, we trained MALT using data collected from pilots, ranging from new student pilots to Certified Flight Instructors, while performing different flight procedures. This paper presents the MALT concept, and methods and results associated with the proof-of-concept study focused on MALT’s diagnostic capability. We believe MALT to be among the first to expand the ITS beyond traditional cognitive tasks such as problem solving to include complex psychomotor tasks.
Construction of College Aesthetic Education Quality Improvement Model under Big Data Network

In view of the importance and urgency of college aesthetic education, this paper constructs a quality improvement model of college aesthetic education under the big data network. Firstly, it puts forward the characteristics of planning, step-by-step, scientific, and centralized aesthetic education in colleges and universities and then expounds the importance of aesthetic education in colleges and universities, including the importance of aesthetic education in spiritual education and in the construction of socialist spiritual civilization. Integrating the functions of data mining under the big data network, using the analytic hierarchy process to construct the improvement index, and carrying out the consistency test at the same time, we can get the improvement index system of aesthetic education quality in colleges and universities. The fuzzy judgment matrix is constructed to obtain the final improvement result. The experimental results show that the constructed model verifies the importance and urgency of aesthetic education in colleges and universities, has a high degree of freedom, and reduces the complexity of quality improvement.
Deep Learning Algorithm Based Support Vector Machines

This paper proposes a new deep learning model which replaces the softmax activation function with support vector machines. To evaluate the performance of the model, we have completed a total of four sets of codes, including the traditional svm classification model, the traditional cnn model, the model of svm behind the fully connected layer, and the model of svm instead of softmax. In order to compare the accuracy of these four groups of models, we trained and tested three data sets, namely the mnist data set, the CIFAR-10 data set and the compass x-ray data set .
Performance Optimization of Sparse Deep Neural Networks Based on GPU

Deep neural networks are widely used in various fields. However, due to the large scale of the latest deep neural networks, the research on the sparsity of deep neural networks is constantly carried out. The implementation of the sparse deep neural network on GPU can further accelerate the computing speed of a sparse deep neural network. The performance of the GPU code of the CUDA version is far superior to the CPU codes of the Matlab version, which confirms the great superiority of the sparse deep neural network implementation on GPU. And the GPU code of the CUDA version is up x1.61 faster than the CUSPARSE version when the deep neural network has 1024 neurons and the 1920 layers.
ATM Model for a Trusted Third Party System (TTPs)

The history of banking transaction has been with the use of cheque books, which direct the bank to pay or transfer specific amount to the bearer – third party on behalf of account owner. Identity card (ID) is the means of authentication and the third party is asked to append his or her signature and address on the back of the cheque for the bank to grant the request. Therefore, human beings cannot do without the help of third party, for their daily activities as nature can call for such service at any time. Even today, may be as a result of sickness, people do give their ATM card and PIN to third party to withdraw money on their behalf. The negative effect of involving third party to withdraw money with an ATM card on behalf of an account owner is at alarming rate. This action to say the least is prone to many security risks which must be avoided at all costs. Therefore, a system is designed in this study to recognize the use of third party in an ATM bank transaction in order to alleviate the negative effect of using the third party. An algorithm was designed for third party ATM transaction using Bracket IDE and Xampp as programming tools. The developed system, allows a trusted third party to have access to ATM card and to withdraw money on behalf of an account owner. The account owner initiates the process through the platform created in this study by registering the third party details and the transaction to be carried out on behalf of an account owner. The system generates an OTP that allows the third party to perform a restricted transaction on behalf of the account owner. The amount to be withdrawn is embedded in the OTP generated for the third party, such that there is no checking of account balance, no withdrawal of an amount less or above amount specified in the OTP, and no money transfer. Therefore, since the system allows restricted transactions to be performed by the third party, it becomes very difficult for third party to use the privilege of performing ATM transaction against the account owner. Keywords: ATM, One Time Password (OTP), Trusted Third Party (TTP), Third Party
Knowing When to Stop

Consider not just the value of features in isolation, but what it means for the value of the product as a whole if they are included.
Trade-off between Privacy, Quality and Risk: Anonymization Strategy Evaluation for Data Warehouses

The transformation of big data to the cloud requires us to reconsider trust. Trust in all parties involved in the data management, the infrastructure as well as all groups with an access interest. A common way to mitigate the risk of the identification of individuals in case of privacy breaches is anonymization, which consequently also leads to information loss. Depending on the assumed level of confidence, a data processor can control the risk for privacy breaches in changing the point where anonymization gets applied. We examined anonymization points in data warehouse scenarios to evaluate their effects on utility and re-identification risk. Our evaluation showed that data quality can differ up to 4,80% while the re-identification risk is reduced by up to 16,82 %. With still improved quality, the re-identification risk differs up to 53,49 % in another configuration.
Laughing Online: A Morpho-Semantic Study on Typed Laughter-Derived Expressions Used in Hijazi Twitter and WhatsApp Instant Messages

This study aims to explore the morpho-semantic features of Typed Laughter-Derived Expressions (TLDEs) used by sixty young Hijazi females aged (18-25) through instant messaging (IM) in online chatting on two social networking sites (SNSs) confined in this study to Twitter and WhatsApp. It focuses on the ways those Hijazi females express their online laughter in the absence of face-to-face interactions. Forty-five Twitter messages and one hundred and nine WhatsApp messages were screenshot by those Hijazi females and were submitted for morpho-semantic analysis. The results show that the following morpho-semantic processes are used: onomatopoeia, borrowing some English acronyms, hyperbole, parts of the body that are physically associated with laughter, haphazard letters, and repetition of a letter. Even though this study does not focus on emojis, emojis are found to be important in packing up those TLDEs to make the virtual situation similar to the real face-to-face one and to mark social interaction among online users as well.
Repercussion of Expressions in Facial Age Estimation

This digital Aeon has a remarkable growth in soft computing and technical innovation. In digitization, the role of the images and interpretation through image data is inevitable. There are several innovations carried on in the field of image processing like recognition, verification, interpretation and analysis which are imposed in the form of images and visual presentations which are obligatory in many situations. The era attunes attention to recognition and extraction of features from a face. The digital world seeks heed in the area of bio-recognition, age estimation and gender identification. The proposed system concentrates on processing the facial image, extracting the features from the input image to classify the age group of the input image. Extraction of features and establishment of classifiers are the major considerations in the process of facial image processing and estimation includes features availed from texture, geometric measures, fiducial value and matching the results with the database reading. With the features extracted from the face image, the classification of age is done after processing the image and subjecting to deep neural classification. The result of accuracy is tested in images which encompass expressions. The accuracy of estimation is achieved better with the analysis of muscle movements and the impact of expressions in the variation in texture of the face.