Reviewerly/exHarmony huggingface dataset:
Ketersediaan dan Kebutuhan Air Daerah Irigasi Ameroro Kabupaten Konawe

In general, sources of agricultural irrigation in Indonesia still rely on rainwater, so the productivity of wetland agriculture, especially rice, is highly dependent on rainwater as an agricultural input. In this position, irrigation (weirs) is important in maintaining the availability of water for agricultural irrigation needs. This study aims to analyze water availability and water demand from Ameroro Irrigation. Both of these results provide information (pattern) of farmer planting. Research data comes from primary and secondary. Comparative analysis of the supply and demand for irrigation water using the Penman method and the F.J. Mock method to determine the amount of water flow from rainfall data, the hydrological characteristics of the catchment area, and evapotranspiration. The results of this study position that the construction of the Ameroro Dam has a vital role in maintaining the balance of irrigation (inflow and outflow). Maintaining this balance is at the same time the basis for irrigation engineering management efforts for agricultural activities. For farmers, those who live on or cultivate functional agricultural land A, B, and C start the planting season in May or in another month taking into account that the maximum plant growth period does not occur in the October period.
Integrating Hybrid Learning and Team-Based Project in EFL Writing Class

This study aimed to describe the integration of hybrid learning and team-based projects in learning to write and to explain the effectiveness of implementing hybrid and team-based projects in improving students' writing performances. This research method was mixed with quantitative analysis using quasi-experiments and questionnaires, while qualitative analysis used observations in writing classes. The instruments applied were writing tests, questionnaires, and observations. The results of this study were feasible to be used and tested in a small-scale class in the third semester of English Education at the Universitas Muhammadiyah Semarang. The average result of the validation is more than four scores, so it is in the excellent category. Meanwhile, the results of the analysis of student perspectives on learning through Team-Based Projects are divided into four, namely; student perspectives and motivation regarding the implementation of hybrid learning, student perspectives on the effectiveness of Team-Based Projects, student perceptions of independent learning through team-based projects and hybrid learning, and student perceptions about working in groups. All of the indicators were categorized as excellent. In addition, the integration of hybrid learning and team-based project in the English Language Education Study Program with the participation of seventy-two students with the final grades of all students being in the complete category above seventy so that the integration of hybrid learning and team-based projects is effectively implemented in genre text writing classes. Therefore, the results of this study can make a good contribution because they can motivate students to write texts and improve independent student learning.
Lessons learned from the 2022 CONVERSE Monogenetic Volcanism Response Scenario Exercise

When volcanic unrest occurs, the scientific community can advance fundamental understanding of volcanic systems, but only with coordination before, during, and after the event across academic and governmental agencies. To develop a coordinated response plan, the Community Network for Volcanic Eruption Response (CONVERSE) orchestrated a scenario exercise centered around a hypothetical volcanic crisis in Arizona’s San Francisco Volcanic Field (SFVF). The exercise ran virtually from February 4 to March 4, 2022. Over 60 scientists from both academic and governmental spheres participated. The scenario exercise was assessed for its effectiveness in supporting collaborative production of knowledge, catalyzing transdisciplinary collaboration, supporting researcher confidence, and fostering a culture of inclusion within the volcanology community. This identified a need to support early career researchers through community and allyship. Overall, the 2022 CONVERSE exercise demonstrated how a fully remote, extended scenario can be authentically implemented and help broaden participation within the volcano science community.
Impact of Scaffolding Learning Strategy on Pre-Service Teachers’ Attitude to Cybercrime Prevention

The study investigated the impact of scaffolding learning strategy on pre-service teachers’ attitude to cybercrime prevention. The study adopted the quasi-experimental research design of the survey type. The following instrument was developed by the researcher and used for the study, namely: Scaffolding Learning Strategy Guide and Lecture Note (SLSG), Conventional Method Guide and Lecture Note (CMG), Attitude to Cybercrime Prevention Scale (ACPS) and Computer Literacy Test (CLT). The participants for this study were selected using multistage sampling procedure. The first stage involved the use of simple random sampling technique to select three universities (one university for each learning strategy) in the region i.e. Southwest, Nigeria. This was followed by the use of purposive sampling technique to select the department of Social Studies education in each of the universities was selected. Consequently, intact class in each of the selected universities was used alongside three research assistants and three lecturers of the department. The data obtained through the administration of the instruments was analysed using descriptive and inferential statistics. All the hypotheses were tested and decisions were taken at 0.05 level of significance. The findings of the study revealed that Scaffolding Learning Strategy (SLS) has no effect on the attitude of Social Studies Pre-service Teachers’ attitude to cybercrime prevention, teachers’ gender does not affect their attitude towards cybercrime prevention and computer literacy does not influence the attitude of social studies pre-service teachers to cybercrime prevention. Therefore, it was recommended that the use of instructional strategies should be well articulated so as to bring about change in the attitude of the learners, gender of the learners should not be seen as factor affecting the attitude of learners towards learning. Hence, during teaching, teachers should give room for gender disparity among the learners and emphasis should be place on the computer literacy of learners. That is, learners should be encouraged to have knowledge of the use of computers in teaching and learning process.
Improving Right Ventricle Contouring in Cardiac MR Images Using Integrated Approach for Small Datasets

Deep learning methods are showing progressive development in the medical imaging field. The accuracy of segmentation is improving challengingly with various deep techniques. Cardiac imaging is a very useful modality to evaluate functional analysis of the right ventricle. We propose an integrated model with the learning approach and Active Contour Method in this paper. We have customized UNet architecture on the basis of performance parameters such as dice metric and Haussdorff distance, training accuracy and testing accuracy etc. with MICCAI 2012 RVSC data. Six layer (6L)-UNet architecture was selected for learning the model. The predicted results of UNet are given to active contour model. These are considered as seed points to extract RV contours. Thus, our model replaces the semi-automatic active contour method into fully automatic segmentation method. It shows promising average results of dice metric and Haussdorff distance such as 0.9 and 2 mm. The active contour method is significant to remove undesired surroundings from extracted ROI.
A Comparative Study of GANs (Text to Image GANs)

Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications like creating cartoon characters from the description, creating the room interiors, and so on. Deep Convolutional Generative Adversarial Networks (GANs) are very powerful and have simple architecture. It generates images from the text description and random noise. Combining the random noise and text embedding creates a multi-modal problem. The generator generates the image from the random noise and the discriminator identifies that the generator image is fake and rejects it. This model of generator and discriminator is like the Min–Max problem. The major problem with this DCGAN is that it takes more time and computation power. In our architecture, we proposed that, instead of initializing the random noise vector, we take the basic image features from the pre-trained VGG-19 layers. It makes the whole computation simple and reduces the time complexity. In the final result, we are going to compare the computation and time complexity between Modified DCGAN (i.e. our architecture) and Stackgan.
The problem of loss of integrity of modern philosophical and scientific knowledge

The article deals with the actual problem of social development, the development of sciences and, in general, the development of human civilization – a gradual departure from reliance on a system of established generally accepted ideas and, as a result, the loss of the integrity of philosophical and scientific knowledge. The ability of various models to reliably describe areas of cognition that are outside the area on the basis of knowledge about which these models are formed is considered. The general theory of systems is considered, the central idea of which is the existence of isomorphism of forms and laws in various subject areas and at various levels of the universe, through which the integrity of the world is manifested. The necessity of relying on a system of generally accepted ideas about nature, society, ethics and aesthetics is justified, even if these ideas are not indisputable and final. The necessity of returning philosophy to the leading role in cognition is stated, since only philosophy is able to ensure the integrity of the knowledge system. It is stated that such a property is possessed by models that are able to fit into a holistic picture of the world. The idea is put forward that the General theory of systems can become the basis for building a holistic picture of the world. To do this, it should be expanded by defining the methodology of formation and describing particular manifestations of isomorphism, as well as supplemented with an ontological part containing an explanation of the genesis of isomorphism.
Derivation-Graph-Based Characterizations of Decidable Existential Rule Sets

This paper establishes alternative characterizations of very expressive classes of existential rule sets with decidable query entailment. We consider the notable class of greedy bounded-treewidth sets ( $$\textbf{gbts}$$ ) and a new, generalized variant, called weakly gbts ( $$\textbf{wgbts}$$ ). Revisiting and building on the notion of derivation graphs, we define (weakly) cycle-free derivation graph sets ( $$\mathbf {(w)cdgs}$$ ) and employ elaborate proof-theoretic arguments to obtain that $$\textbf{gbts}$$ and $$\textbf{cdgs}$$ coincide, as do $$\textbf{wgbts}$$ and $$\textbf{wcdgs}$$ . These novel characterizations advance our analytic proof-theoretic understanding of existential rules and will likely be instrumental in practice.
The effectiveness of question student method in social studies learning on activity and learning outcomes

The purpose of this study is to analyze the effectiveness of the question students have a method in social studies learning to increase student activity and learning outcomes. This research uses quantitative methods. The population included seventh-grade students at junior high school in Yogyakarta, with a sample of 122. The sampling techniques cluster random sampling, while data collection used observations, interviews, and instruments. Data analysis used an independent sample t-test and an N-gain score test to determine student activity and learning outcomes. The results showed that the question student has a method in social studies learning was able to increase student activity and learning outcomes compared to conventional classes, based on the independent sample t-test test, and the N-gain score test was included in the medium category. The questions students have a method, have a very significant impact, thus making social studies learning more quality.&#x0D; Keywords: Activity; learning methods; learning outcomes; questions students have; social science.
Multi-solution tracking in shifting sphere function using gravitational particle swarm algorithm

In this study, we propose a novel optimization method, which can track multiple optimal solutions for dynamic problems. In our previous study, we proposed a gravitational particle swarm algorithm (GPSA), which is able to search for multiple optimal solutions. In this paper, first, we apply the original GPSA to a multi-solution tracking problem and reveal its drawback. Second, we propose a modified GPSA, called 𝜏GPSA, by replacing original GPSA’s update rule for personal bests with a tolerance update rule. Finally, we demonstrate that 𝜏GPSA can track multiple optimal solutions in an one-dimensional shifting sphere function.
The impact of job motivation on organizational commitment: A study on travel agencies

This study examines job motivation's influence on organizational commitment among employees of Group A travel agencies operating in Istanbul. Adopting a qualitative research design, data for this study were collected through face-to-face surveys using a convenience sampling method from travel agency employees in Istanbul from January to April 2023. The analysis of the obtained data reveals a positive impact of job motivation on organizational commitment. This study provides a valuable contribution to understanding the relationship between job motivation and organizational commitment, as well as enhancing the performance of agency employees. The findings can assist businesses in developing strategies to increase employee motivation.
Automation of Steel Drums Visual Inspection Using Deep Learning and Image Recognition

In this paper, we propose a technology for diagnosing the state of objects for automated inspection of facility infrastructure. As part of efforts to link and integrate non-destructive inspection data, we automatically estimate the deterioration state of steel drums that store waste based on image recognition. Specifically, the area and type of rust on the steel drums surface are automatically estimated by using semantic segmentation.
ROLE OF STATISTICS IN ARTIFICIAL INTELLIGENCE

Artificial intelligence (AI) is a rapidly developing field that involves the use of algorithms and machine learning to enable machines to perform humanlike tasks. Statistics is a branch of mathematics that deals with collection, analysis, interpretation, presentation, and organization of data. Statistical methods are widely used in AI to improve performance and accuracy. The purpose of this paper is to examine the role of Statistics in AI. The paper aims to explore the theoretical foundations of AI and Statistics, the statistical methods used in AI, the applications of Statistics in AI, the benefits of integrating Statistics in AI, the challenges and limitations, and the future directions.
Novel hybrid generative adversarial network for synthesizing image from sketch

In the area of sketch-based image retrieval process, there is a potential difference between retrieving the match images from defined dataset and constructing the synthesized image. The former process is quite easier while the latter process requires more faster, accurate, and intellectual decision making by the processor. After reviewing open-end research problems from existing approaches, the proposed scheme introduces a computational framework of hybrid generative adversarial network (GAN) as a solution to address the identified research problem. The model takes the input of query image which is processed by generator module running 3 different deep learning modes of ResNet, MobileNet, and U-Net. The discriminator module processes the input of real images as well as output from generator. With a novel interactive communication between generator and discriminator, the proposed model offers optimal retrieval performance along with an inclusion of optimizer. The study outcome shows significant performance improvement.
A novel visual tracking scheme for unstructured indoor environments

In the ever-expanding sphere of assistive robotics, the pressing need for advanced methods capable of accurately tracking individuals within unstructured indoor settings has been magnified. This research endeavours to devise a realtime visual tracking mechanism that encapsulates high performance attributes while maintaining minimal computational requirements. Inspired by the neural processes of the human brain’s visual information handling, our innovative algorithm employs a pattern image, serving as an ephemeral memory, which facilitates the identification of motion within images. This tracking paradigm was subjected to rigorous testing on a Nao humanoid robot, demonstrating noteworthy outcomes in controlled laboratory conditions. The algorithm exhibited a remarkably low false detection rate, less than 4%, and target losses were recorded in merely 12% of instances, thus attesting to its successful operation. Moreover, the algorithm’s capacity to accurately estimate the direct distance to the target further substantiated its high efficacy. These compelling findings serve as a substantial contribution to assistive robotics. The proficient visual tracking methodology proposed herein holds the potential to markedly amplify the competencies of robots operating in dynamic, unstructured indoor settings, and set the foundation for a higher degree of complex interactive tasks.
Choosing wave energy devices for community-led marine energy development

As we pursue more justice and community driven renewable energy projects, more decision making is put in the hands of community leaders. Appropriate tools and resources are necessary for communities to determine what wave energy developer might fit their community needs and wave resource. While there are methods and tools for communities, researchers and wave energy developers to determine the wave resource or potential locations for wave energy development, we are lacking a tool to help communities take the next steps in pursuing wave energy.&#x0D; A community driven design process was undertaken in Sitka, AK, in conjunction with evaluating the local area for wave energy resource. The community had interest in wave energy development, a decent wave resource area accessible, and existing maritime expertise that could aid in operations and maintenance. Further, as a remote island, the community viewed alternative energy development positively as a way to achieve more energy independence. Still, in this community driven process, we identified a gap that while the community may be interested in a wave energy project, there was little data on which wave energy devices would be appropriate for the community and few tools to help them choose.&#x0D; This presentation will highlight questions that the participants from Sitka, AK still have after engaging in a community centered design process. We compare the requirements that the community prioritizes with the tools that researchers and developers have to evaluate those requirements, allowing assessment of whether these tools are useable and adequate for community use. We also show an analysis of a selection of current wave energy developers with some of the community focused factors and proposals for tools that may be useful in future community energy development. This research ultimately highlights what factors must be presented to a community for them to choose to pursue installation of a device.
How to backdoor split learning

Split learning, a distributed learning framework, has garnered significant attention from academic and industrial communities. In contrast to federated learning, split learning offers a more flexible architecture for participants with limited computing resources. However, the security of split learning has been questioned due to the separation of data and model control rights from usage rights. Currently, most research work focuses on inference attacks in split learning. In this paper, we first reveal the vulnerability of split learning to backdoor attacks and present two backdoor attack frameworks from both the server and client perspectives. Regarding the client-side attacker, we insert backdoor samples into the training data by utilizing the client’s direct control over local data, and propose two methods for labeling backdoor samples that can be adapted to various application scenarios. Due to the server’s lack of control over the client in split learning, it is infeasible for server-side attackers to inject backdoor samples into training data. Our strategy involves leveraging the server’s control over the training process to shape the optimization direction of the client model, thereby enabling it to encode backdoor samples. Moreover, we introduce an auxiliary model into the attack framework to enhance the effectiveness of the backdoor attack. The auxiliary model can increase the distinction between backdoor samples and clean samples in the feature space to improve the sensitivity of the client model to backdoor samples. Extensive evaluations demonstrate the high attack accuracy of both proposed attack frameworks without causing any compromise to the performance of the main task. Our research uncovers the potential security risks and rings the alarm for the application of split learning.
Adopting DevOps practices: an enhanced unified theory of acceptance and use of technology framework

&lt;div align="center"&gt;&lt;span lang="EN-US"&gt;DevOps software development approach is widely used in the software engineering discipline. DevOps eliminates the development and operations department barriers. The paper aims to develop a conceptual model for adopting DevOps practices in software development organizations by extending the unified theory of acceptance and use of technology (UTAUT). The research also aims to determine the influencing factors of DevOps practices’ acceptance and adoption in software organizations, determine gaps in the software development literature, and introduce a clear picture of current technology acceptance and adoption research in the software industry. A comprehensive literature review clarifies how users accept and adopt new technologies and what leads to adopting DevOps practices in the software industry as the starting point for developing a conceptual framework for adopting DevOps in software organizations. The literature results have formulated the conceptual framework for adopting DevOps practices. The resulting model is expected to improve understanding of software organizations’ acceptance and adoption of DevOps practices. The research hypotheses must be tested to validate the model. Future work will include surveys and expert interviews for model enhancement and validation. This research fulfills the necessity to study how software organizations accept and adopt DevOps practices by enhancing UTAUT.&lt;/span&gt;&lt;/div&gt;