Reviewerly/exHarmony huggingface dataset:
Eğitim Yönetiminde Enformatik Çerçevede Bilişim Sistemlerinin İncelenmesi

The word knowledge corresponds to two different concepts in our language. Firstly, information is characterized as a generally held opinion about things or concepts, and secondly as processed data that describes various qualities of things or concepts. Informatics is the science of regular and intelligent processing of information that a person uses in his communication in technical, economic and social fields and which is the basis of science, especially through electronic machines. A branch of applied science that studies the phenomenon of information, information storage, access strings, methods of processing, transferring and using information, taking into account the benefits of society and humanity. Informatics, which is an interdisciplinary teaching and service segment, also covers topics related to the design, development and production of various tools used in informatics and information access systems, including computers inside. Dec. In addition, many issues that fall within the field of automation, which covers techniques related to the automatic regulation of all kinds of industrial production, are also included in the scope of informatics in a broad sense. In this sense, information, like information, has existed throughout historical periods, even though it has been differentiated within the temporal process. Information has a dynamic and variable structure. Since the second half of the twentieth century, the information society and the information society are mentioned with the information technologies that have settled on the world agenda and daily life with increasing speed step by step, especially with the development and change created in the last decade of the century. More information is more easily accessible in a shorter time. The desired information can be easily disseminated to the desired number of people and institutions in a short time and more information can be stored. Thus, with the innovations caused by information technologies, many applications are gaining international quality. Key Words: Educational Management, Information, Informatics, Information Systems
Text To Image Generation By Using Stable Diffusion Model With Variational Autoencoder Decoder

Abstract: Imagen is a text-to-image diffusion model with a profound comprehension of language and an unmatched level of photorealism. Imagen relies on the potency of diffusion models for creating high-fidelity images and draws on the strength of massive transformer language models for comprehending text. Our most important finding is that general large language models, like T5, pretrained on text-only corpora, are surprisingly effective at encoding text for image synthesis: expanding the language model in Imagen improves sample fidelity and image to text alignment much more than expanding the image diffusion model.
Upaya Peningkatan Pengetahuan Bencana melalui Prototipe Aplikasi LANDIE (Landslide Disaster Education)

The high incidence of landslides during the rainy season still occurs frequently. Efforts have been made to increase knowledge about landslide disasters among the public. The use of technology through applications in providing knowledge about landslide disasters generally uses text and images. To address this gap, we developed a new application prototype with disaster knowledge and evacuation route navigation features called LANDIE (Landslide Disaster Education). The method used is quantitative descriptive in the form of mean and standard deviation analysis; descriptive percentage; kolmogorov-smirnov and paired sample t-test. The research results reveal that the LANDIE application prototype is effective in increasing knowledge of landslide disasters. This is supported by the findings that initial knowledge about landslide disasters is at medium criteria, with the application design being suitable for use with revisions, the application is suitable for use as a medium in disaster training, and the LANDIE application prototype is declared effective in increasing knowledge about landslide disasters.
RANCANGAN SISTEM PAKAR DIAGNOSA PENYAKIT PADA UDANG MENGGUNAKAN METODE FORWARD CHAINING BERBASIS WEB

The cultivation of vannamei shrimp has become the primary choice for shrimp farmers and breeders today. The superiority of this shrimp species lies in its resilience to stress and extreme weather conditions. However, the occurrence of diseases in cultivated shrimps remains a significant concern as it can lead to substantial losses if not properly addressed. This research aims to develop a web-based expert system to assist shrimp farmers and breeders in identifying the types of diseases affecting vannamei shrimp. The expert system employs the Forward Chaining method, allowing the system to infer and reach accurate disease diagnoses based on observed symptoms in the shrimp. The web-based nature of the system enables easy access for farmers and breeders through internet-connected devices. Utilizing PHP as the programming language and appserver as the database, the system gathers data provided by users about observed symptoms in the shrimp, subsequently performing inference to deliver precise diagnoses of potential diseases in vannamei shrimp. Leveraging the knowledge and expertise of fisheries experts, the system aims to provide more effective guidance in disease prevention and identification in vannamei shrimp. The expected outcome of this research is to contribute significantly to improving the management of vannamei shrimp cultivation, particularly in preventing and addressing diseases that may affect the shrimp. The expert system aims to empower farmers and breeders to swiftly recognize various diseases and take appropriate measures to maintain the health and productivity of vannamei shrimp cultivation.
Positive Definite Kernels for Partitions

This paper presents a comprehensive exploration of various families of positive definite kernels for comparing partitions. It not only reviews existing examples from the literature but also introduces novel classes of positive definite kernels. These new classes include kernels based on agreement and ones designed using the concept of hidden variables. The study also focuses on assessing the compatibility of these kernels with structural properties that capture the intrinsic notion of proximity between partitions. Notably, agreement-based kernels are demonstrated to align well with this notion. Moreover, the paper provides two generic procedures for designing hidden-feature-based kernels that also adhere to the specified structural properties.
PENERAPAN DATA MINING DENGAN METODE NAIVE BAYES CLASSIFIER PADA PENJUALAN BARANG UNTUK OPTIMASI STRATEGI PEMASARAN

Along with technological developments, market developments are also increasing rapidly and competition is starting to occur between entrepreneurs and business people, which requires entrepreneurs to take advantage of existing technology. At this time the Universal Homeware Store does not realize that this data can be processed effectively to find out the development of a business or business that is being run, besides that the procurement of goods that customers need is often unavailable. One of the data processing technologies is by applying data mining. Data mining is a form of extracting information from data that is large enough to obtain data patterns so as to produce information data that is useful for making decisions and increasing profits in developing marketing strategies. One of the uses of data mining is using the Naive Bayes Classifier algorithm. Naive Bayes Classifier is one of the existing algorithms in data mining which is used to deal with uncertainty problems by emphasizing the concept of probability of use and measuring the level of accuracy so that it can predict purchasing strategies from past sales analysis. with research results Using 36 data and with rapid minner tools produces an accuracy value of 80%
An Analysis of Multi Agent Systems Agent Based Programming

The effectiveness of agent-based modeling as a simulation modeling methodology has resulted in its application in diverse settings, including the resolution of pragmatic business challenges, in recent times. The domain of symbolic artificial intelligence, which investigates intelligent and self-governing entities, is preoccupied with the mechanisms by which these entities arrive at determinations regarding their conduct in reaction to, or in expectation of, stimuli from the external environment. The scope of the methods employed encompasses a diverse array of techniques, spanning from negotiations to agent simulations, as well as multi-agent argumentation and planning. The present article scrutinizes the utilization of agent-based computing in multi-agent systems and provides an all-encompassing analysis of the relevant literature. This study delves into the examination of both traditional and contemporary agent programming languages, including their respective extensions, comparative analyses, and instances of their application in published literature.
PEIPNet: Parametric Efficient Image-Inpainting Network with Depthwise and Pointwise Convolution

Research on image-inpainting tasks has mainly focused on enhancing performance by augmenting various stages and modules. However, this trend does not consider the increase in the number of model parameters and operational memory, which increases the burden on computational resources. To solve this problem, we propose a Parametric Efficient Image InPainting Network (PEIPNet) for efficient and effective image-inpainting. Unlike other state-of-the-art methods, the proposed model has a one-stage inpainting framework in which depthwise and pointwise convolutions are adopted to reduce the number of parameters and computational cost. To generate semantically appealing results, we selected three unique components: spatially-adaptive denormalization (SPADE), dense dilated convolution module (DDCM), and efficient self-attention (ESA). SPADE was adopted to conditionally normalize activations according to the mask in order to distinguish between damaged and undamaged regions. The DDCM was employed at every scale to overcome the gradient-vanishing obstacle and gradually fill in the pixels by capturing global information along the feature maps. The ESA was utilized to obtain clues from unmasked areas by extracting long-range information. In terms of efficiency, our model has the lowest operational memory compared with other state-of-the-art methods. Both qualitative and quantitative experiments demonstrate the generalized inpainting of our method on three public datasets: Paris StreetView, CelebA, and Places2.
Some Aspects of Using Authentic Video Materials in ESP Courses

Despite the fact that the use of authentic video materials in the process of teaching English is no longer a novelty, for the full-scale implementation of the aforementioned teaching aids, a number of issues still need to be researched and clarified especially, in the process of teaching ESP courses. Authentic video materials are not still used intensively which indicates the existace of certain problems in this regard. Therefore, we consider it relevant to research the current situation in Georgian higher educational institutions regarding the use of authentic video materials in the process of teaching ESP courses and to search for the solutions to the problems that will contribute to the integration of these audio-visual aids in the English language teaching process. The purpose of our research is to study and analyze the problems of the existing practice and experience of using authentic video materials in the process of teaching ESP courses in higher educational institutions. The article also explores the different &nbsp;types of video materials used in the process of teaching a foreign language and examines the advantages and disadvantages of each of them. The following research methods were used in our study: literature review on the research topic; survey research of English language teachers. The given paper is interesting for both researchers interested in ESP-related issues and teachers who want to make the process of teaching ESP courses even more effective.
An Analysis of Government Support Programs for Small Business Development and Growth

&lt;p class="normal"&gt;Small and medium-sized businesses (SMEs) are very important for many countries' economic and social growth. They constitute a significant proportion of employment, innovation, and value creation across diverse sectors and regions. But small businesses also have many problems that make it hard for them to grow and be good at what they do. For example, they might not have enough money or the right tools or information to compete. To help small businesses succeed, governments around the world have created programs and policies that give them money, help with their business needs, and help with their organization.&lt;/p&gt;&lt;p class="normal"&gt;The impact of government support programs on SME development and growth holds significant interest and significance for policymakers, researchers, practitioners, and entrepreneurs. Nonetheless, the evidence regarding the efficacy and efficacy of these programs is ambiguous and uncertain. Some studies have shown that government support helps SME performance indicators, such as sales, profits, employment, productivity, innovation, and exports. Other studies have found that government support for SME outcomes is negative or doesn't affect them at all, or that there is a lot of variation among different types of programs, beneficiaries, contexts, and methods.&lt;/p&gt;This paper aims to provide a comprehensive and critical analysis of the literature regarding the impact of government support programs on the development and growth of small and medium enterprises. It includes both theoretical and empirical studies that examine the rationale, design, implementation, evaluation, and outcomes of various forms of government support for SMEs. A &lt;em&gt;questionnaire based survey&lt;/em&gt; consisting of geographically unique SMEs was conducted. 68 SMEs had opted to provide responses to the questionnaire based on research the hypothesis of this study. In addition, it discusses the main challenges and limitations of the existing literature, and provides recommendations for further research and practice.
A Study on Prognostic Factors in Management of Breast Carcinoma in A Tertiary Care Hospital

Breast cancer is the most common malignant tumor and leading cause of death in women worldwide. It accounts for 15% of all cancer deaths According to World Health Organisation (WHO), approximately 70% of breast malignancies occur in women with unknown risk factorsThe prognostic factor can be defined as a measurable variable which correlates with natural history of the disease. The most significant factor which influences prognosis in breast cancer is axillary lymph node involvement, which is usually assessed at time of surgery using sentinel lymph node biopsy or axillary lymph node dissection. The number of lymph nodal involvement is also significant. Involvement of 1-3 lymph nodes carry better prognosis than 4-9 and more than 9 lymph nodes involvement. Size of the tumour has long been recognized as a prognostic factor and as predictor of axillary node status, with larger tumours being associated with a bad prognosis and an increased incidence of nodal metastasis. This study is conducted in Department of General Surgery, SVRRGGH, Sri Venkateswara Medical College, Tirupati for a period of more than one and half year after getting approval from Institutional Ethical Committee on March 2021. A prospective study of 50 patients who fulfilled Inclusion Criteria is taken into consideration to know how prognostic factors like tumor size &amp; grade, axillary lymphnode involvement are influencing the prognosis and management of breast carcinoma. Patients who took part in the study were from 28-68 years of age. Majority of people were in the age group of 46-55 years (38%). Increasing age, involvement of axillary lymph nodes, tumors with larger size and higher grade, and lymphovascular invasions are all associated with worse prognosis Keywords: Estrogen Receptor, Progesterone Receptor, Locally Advanced Breast Carcinoma
Enhancement in Stemmer Design: Natural Language Semantics Perspective

Objective: To enhance the performance and accuracy of the stemming process. Method: The Porters stemmer is used conventionally for removing common morphological and inflectional endings (suffixes) from the words in the English language. It uses a set of pre-defined rules that are less complex when compared to other existing stemmers. We have identified several imprecisions encountered during the stemming process and proposed solutions to remove and invalidate the same. Findings: The experiment was performed on a set of 762 words starting with characters “a”, “b”, and “c”. It was found that out of 762 words used for system validation and testing, the results of 355 words were different when stemmed with MPS [Modified Porter Stemmer], and the remaining 407 words resulted in the same stemmed word after using both stemmers. The Modified Porter Stemmer presented in the current paper with Python implementation has given better results for 46% of words. Novelty: This paper highlights the encountered errors while using the algorithm and provides solutions to enhance the performance and accuracy of the stemming process. The designed stemmer is named “Modified Porter Stemmer” [MPS]. Keywords: Natural Language Processing; Stemmer; Porter’s Stemmer; Enhancement; Stemming Process
Twitter's Mirroring of the 2022 Energy Crisis: What It Teaches Decision-Makers - A Preliminary Study

The paper pertains to the field of opinion mining on social networks in view of decision-making. It was designed to determine the effect of the energy crises on the socio-ethical and ethical worries and concerns related to the role of Information and Communication Technology (ICT) in the crisis, as reflected on Twitter. Knowing public opinions during crises is a key component of decision-making. Other purposes include the determination of the perception of the correlations between these categories of concerns and the determination of specificities of distributions of such perceptions, as represented by words. A mixture of tools from lexical, topical, and semantical analysis were applied for generating the bag of words and for analyzing a set of tweets collected during the initial stage of an energy crisis, with special emphasis to coherence indices. The results confirm that energy crises may be associated with such concerns, as expected, but the association probability is low for the 2022 energy crisis, indicating at least a partial success of the mitigation decisions of the respective authorities. Some surprising relationships also occurred from the study. The paper is dedicated to Acad. Florin Gheorghe Filip, at the 75th anniversary.
Project-based learning, its role and importance

The education system plays a major role in the era of modern globalization, in the process of formation and development of a civilized society. Modern education, high-quality learning and acquisition of appropriate knowledge is a social requirement. This requirement is met at each step using various progressive methods. Project-based teaching is one of the important, progressive, and relevant methods of modern teaching. This method has undergone a certain evolution over the years, but its main essence remains unchanged: in the process of teaching with this method, students' interests are aroused, concentrated and stimulated in relation to specific problems and their solutions. It is based on the development of students' cognitive ability and the level of critical and creative thinking through orientation in the modern information space. In order to better study and implement the modern teaching method, project teaching method, research was conducted on the mentioned topic during the academic year. Relevant literature was studied. Discussion of the legal framework and the experience of existing studies. An observation was made. Focus group selection, survey. Data were collected during observation. Available data, survey and observation results were sorted and processed. The results were analyzed and based on that intervention was made. Priorities were outlined. During the research process, there was active cooperation between the teachers. The discussion on subject chairs was in progress. Feedback was clear, specific and easy to understand. An interesting discussion was also held on the shortcomings of the project teaching method. The process and result of the research showed once again that the main goal of teaching with such a method is to develop the creative and intellectual ability of students, so that they have more self-realization, independent thinking ability, to make important decisions. As the Chinese proverb says: "Tell me and I'll forget, show me and I'll remember, turn on and I'll understand."
An Optional Memory Balancing Algorithm for Big Data Based on Distributed File System

Background/Objectives: Load balancing algorithms are a type of algorithm that assists with efficient workloads spread. To reduce server overload, increase resource usage, lower latency, and maximize throughput, efficient distribution is required. There are several load balancing algorithms, all of these algorithms have flaws that render them ineffectual in many real-world settings. The current paper aims to improvise the algorithm by proposing a heuristic to decide the next job to be allotted to reduce the net response time. This is achieved by storing the incoming jobs in a priority queue (heap) and using ageing to prevent starvation. Methods: In the current research, we assign the incoming client request to the virtual machine such that estimated finish time of the service is minimum. We calculate finishTime for all the virtual machines, then the VM which gives the minimum finishTime is assigned the incoming client request. Once a client request is assigned to a VM, it gets added to the virtual Machine Queue. The virtual Machine Queue behaves like a priority queue; it gives priority to the task which has the least instruction Count. This ensures that the Net Response Time of the load balancer is minimized. Findings: Significant improvements have been achieved with respect to response time and throughput. We calculated the Response Time and the Throughput Time for the Load Balancing Using Estimated Finish Time Algorithm compared to IEFTA. The numbers of client requests were varied by a factor of 10, starting from 10, and we got an efficient distribution. Novelty: By using our proposed Algorithm Pseudocode of Load Balancing Algorithm, different coefficient of VMs were calculated. We compared the Estimated Finish Time Algorithm to IEFTA, we suggest an approach that addresses this limitation while also improving responsiveness and processing time. Keywords: Cloud Computing; Distributed Systems; Load Balancing; Virtual Machines; Data Migration