Reviewerly/exHarmony huggingface dataset:
RANCANG BANGUN SISTEM INFORMASI MANAJEMEN KESEHATAN KLINIK PRATAMA UNIKAL HEALTH CENTER (UHC)

&lt;p&gt;The success of health management is largely determined by the availability of health data and information. This data and information serve as the basis for decision-making in the health sector. To obtain quality health data and information, and accurate health information system is needed. The development of a strong Health Information System in producing quality information in decision-making in the health sector requires knowledge of the basic concepts of the health information system itself and the basics in its development. Primary Clinic Unikal Health Center (UHC) is a public clinic under the auspices of a private university called Pekalongan University. The problem that is still encountered is that the recording of medical record data is still not optimal because it is done manually so it is still less effective and efficient in processing the data. Based on this, it is necessary to develop a health management information system to be implemented at the Unikal Health Center (UHC) Primary Clinic. The purpose of this study is to replace the manual recording and data processing system with computerization, to overcome the obstacles that often occur related to the inability to provide information quickly, accurately, and on time with the management information system of the Unikal Health Center Clinic. (UHC) the required reports can be available instantly, such as patient reports, doctor reports, registration reports, medical record reports, prescription reports, and other data. The final result of this research is a prototype of the health information management system of the UHC Pratama clinic.&lt;/p&gt;
ANALISIS KEBUTUHAN (NEED ANALYSIS) UNTUK PENGEMBANGAN LEMBAR KERJA SISWA (LKS) DENGAN BANTUAN DIGITAL MIND MAPS

Teaching materials are an important part of the learning process, one of which is the Student Worksheet (LKS). An educator needs to increase creativity in preparing teaching materials to be delivered. Teaching materials used by students need to follow the development of science and technology. A digital mind map is a mind map that is packaged in digital form that displays brief lesson notes in the form of a mind map with the help of the mindomo application. This needs analysis was carried out to find out the needs needed in the Student Worksheet (LKS), and responses regarding the development of LKS with the help of digital mind maps. This type of research method is descriptive qualitative research. Research data was obtained by observation, interviews, and distributing questionnaires. The results obtained from this analysis are the responses of teachers and students to the development of worksheets with the help of digital mind maps are quite good. The worksheets used by educators are monotonous but understandable, there is no digital mind maps task to increase student creativity. Students also agree that there is an interesting, easy-to-understand LKS development by using their Android smartphones. Therefore, the data from this needs analysis becomes a preliminary study on the development of LKS with the help of digital mind maps, then it is developed into an LKS with the help of digital mind maps that will be used.Kata Kunci: Analisis Kebutuhan, Lembar Kerja Siswa (LKS), Digital Mind Maps
Survey on Content Modeling Using Natural Language Processing

Abstract: Content modelling is the procedure of formation is a compressed version of a text document which makes sure the definition or the meaning of the information isn’t changed from the meaning of original text. Automatic text summarization turns into a beneficial method to findsignificant information accurately in lengthy text in a little amount of time with negligible trouble. Automatic text summarization has taken part in an essential part in assisting users to acquire important key details by adding a large amount of data while also has an advantage of developed technology. Previously, some of the other papers are interconnected in solving the issue to stir up brief outline of the content by using machine learning (ML). Content modelling using natural language processing, it is a prominent requisition exert to bring out suitable details with narrowing huge amount of text. Prevailing observation show that it usescodeword rooted methods to classify text, who doesn’t give the file a real idea. We granted effective entity modelling method joins basic information about the data along with the outline inherited text. Besides, merging the features of the text abstract, shows competent outcome contrast to the synopsis given by controlled replicas of abstractive and extractive summarization.
Realistic Deep Learning May Not Fit Benignly

Studies on benign overfitting provide insights for the success of overparameterized deep learning models. In this work, we examine the benign overfitting phenomena in real-world settings. We found that for tasks such as training a ResNet model on ImageNet dataset, the model does not fit benignly. To understand why benign overfitting fails in the ImageNet experiment, we analyze previous benign overfitting models under a more restrictive setup where the number of parameters is not significantly larger than the number of data points. Under this mild overparameterization setup, our analysis identifies a phase change: unlike in the heavy overparameterization setting, benign overfitting can now fail in the presence of label noise. Our study explains our empirical observations, and naturally leads to a simple technique known as self-training that can boost the model's generalization performances. Furthermore, our work highlights the importance of understanding implicit bias in underfitting regimes as a future direction.
Implementasi Aplikasi Keuangan 69 Volley Ball Club Berbasis Web

69 Volley Ball Club is one of the volleyball associations located in Tangerang Regency, Banten, whose performance is getting better in terms of achievements and the number of athletes. To improve the services of athletes, coaches, training administration, matches, and financial administration, it is necessary to create a computer application that can help record data automatically.&#x0D; The cash flow information system for 69 Volley Ball Club is designed to be web-based so that it can be easily accessed anywhere. The development methodology used to build this cash flow information system is by using the object oriented method: Unified Modeling Language (UML). The system design is described using Activity Diagrams, Use Case Diagrams, Use Case Descriptions, Sequence Diagrams and Mockup views. The results of the design, a cash flow application program was made by the Laravel framework.&#x0D; The implementation stage begins with a field study using the observation method, by observing directly the administrative activities at the 69 Volley Ball Club in order to obtain explanations and obtain data that will be needed in designing of the information systems. Then the design was made which was finally implemented in the form of a web-based 69 Volley Ball Club financial application program.
PROFESSIONAL TRAINING OF THE TEACHER- PSYCHOLOGIST IN THE PROCESS OF LEARNING A FOREIGN LANGUAGE CULTURE

The article considers the professional training of the teacher-psychologist in the process of learning a foreign language.The main features of the professional training of a teacherpsychologist in modern education
Optimizing Time Complexity of Searching Operation: A Noble Algorithm

By the passage of time , there are many searching algorithm and data structure evolved to make basic operations more efficient with respect to time and space complexity like storing, searching, deleting the data and they play very important role in real-world applications. Based on mechanism of searching, Searching algorithm can be classified into three types of algorithm: linear, binary, hashing, but excluding these, there are many searching techniques proposed by many researchers. The aim of all these proposed searching technique is to optimize the time complexity of searching operation generally compared to Binary Search, which have average and worst case time complexity of log(n), where n is number of elements. This paper proposes a noble searching algorithm that works for sorted array data structure and performs better in most of the cases than that of binary search. The proposed searching Technique will either find index of target element or find a small space within array space by using statistical operation, and then binary search will be applied to that small space, thus it will reduce the overall time complexity. This paper briefly discusses, proposed algorithm and it’s time complexity and comparing the efficiency of the algorithm with some of the well-known other algorithms, using graphs and tables and its practical applications. We will analyze the behavior of algorithm in different parameters like:-size of input data, Nature of elements in list, Different Case Scenario (worst, best, and avg. case) etc.
Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization

We tackle real-world problems with complex structures beyond the pixel-based game or simulator. We formulate it as a few-shot reinforcement learning problem where a task is characterized by a subtask graph that defines a set of subtasks and their dependencies that are unknown to the agent. Different from the previous meta-rl methods trying to directly infer the unstructured task embedding, our multi-task subtask graph inferencer (MTSGI) first infers the common high-level task structure in terms of the subtask graph from the training tasks, and use it as a prior to improve the task inference in testing. Our experiment results on 2D grid-world and complex web navigation domains show that the proposed method can learn and leverage the common underlying structure of the tasks for faster adaptation to the unseen tasks than various existing algorithms such as meta reinforcement learning, hierarchical reinforcement learning, and other heuristic agents.
Gambaran Pengelolaan Penyimpanan Obat Di Apotek X Lamongan

Apotek X Lamongan is a health service facility that manages supplies in the pharmaceutical sector. Based on preliminary studies that have been carried out, the results show that at Apotek X Lamongan there are often problems in the storage and dispensing of drugs. One of the incidents of wrong drug storage that occurs is not recording the intake and expenditure of drugs. This causes the service to be less than optimal, allegedly because the storage system at the X Lamongan Pharmacy is not fully in accordance with the pharmaceutical service standards at the pharmacy, and there has never been any research on the drug storage system at the X Lamongan Pharmacy. Based on the importance of the impact of drug storage in a pharmacy, it is necessary to evaluate the drug storage system at the X Lamongan Pharmacy. This study aims to evaluate and determine the drug storage system at Apotek X Lamongan. This research is a descriptive observational study by making direct observations of the pharmacy storage system in the form of a checklist and conducting direct interviews with the person in charge of the pharmacy. It shows that in general the drug storage system has a score of 60.41% which is categorized as quite good. The drug storage system includes spatial arrangement with the percentage obtained is 66.67% in the good category, the method of preparation of drugs with a percentage value of 100% in the very good category, recording of stock cards with a percentage value of 0% in the very poor category, and observation of drug quality with a value of 0%. percentage 75% good category. This shows that X Lamongan Pharmacy has not met the storage standards based on Permenkes No. 73 of 2016 and based on BPOM No. 4 of 2018.
Indian Sign Language Generation using Natural Language Processing and Audio Speech

Abstract Language plays a vital role in the communication of ideas, thoughts, and information to others. Deaf and dumb people also communicate their thoughts using a language known as Sign Language. Every country has a different sign language which is based on their native language. In our research paper, our major focus is on Indian Sign Language which is mostly used sign language by hearing and speaking impaired communities in India. Sharing feelings and thoughts with family and friends is the easiest way for normal people to express their emotions but what if other people are unable to understand your language. This situation is faced by nearly every deaf and dumb person in our society. This led to the idea of introducing Audio to the Indian Sign Language translation system which can erase this gap in communication between deaf people and society. The system accepts audio and text as input and shows corresponding sign movements based on grammar rules of Indian Sign Language as output. The heart of the system is natural language processing which gives life to the system using tokenization, parsing, lemmatization, and parts of speech tagging.
Thinking as a function and its decomposition into a Taylor series

The paper hypothesizes the possibility of applying the mathematical construction of the Taylor series in the semantic space. Then symbolic forms, like some spiritual functions that display the immanent in semantic space and are explicated in the form of verbal constructions, can be tried to decompose into a Taylor series. The first terms of the Taylor series of thinking functions carry basic meanings that are conjectured by secondary forms, tertiary, etc., as in the case of the usual Taylor series, where the first term of the series is a constant, the second term is linear, determined by the first derivative of the function, the third is quadratic, otherwise acceleration. The paper shows that all of the above can be found in the paradigms of thinking called Foucault epistems. The world in ancient philosophy seemed unchangeable or cyclical, which refers us to the concept of a constant or the first term of the Taylor series of the function of thinking. With the advent of Christianity, the circle of time turned into a straight line, the concept of evolution appeared. This stage was completed by Galileo, who introduced the idea of speed into the paradigm of thinking, which completed the formation of the second, linear term of the Taylor series of the thinking function. The third term of the series appeared in Newton's theory and entered the next episteme with the idea of acceleration. Deductively, one can continue the decomposition of the thinking function into a Taylor series and imagine the appearance of a member of the series responsible for the third derivative.
UNIVERSITY EDUCATIONAL ENVIRONMENT AND FUNDAMENTAL VALUES

Multi-vector geopolitical processes taking place in the world provoke the transmutation of fundamental values, actualizing the information and educational environment of the university, which is dictated by the need for the scientific and pedagogical community to comprehend issues related to the features of the axiological guidelines of the individual.
Forecasting homogenized cloud cover time series over Greece using generalized regression neural networks

Abstract Cloud cover is an important meteorological parameter that affects the earth’s ra-diative energy balance and precipitation, playing a major role in the hydrologicalcycle. Long records of cloud cover data are the result of human estimations madeduring the synoptic observation hours at each meteorological station. Here, adoptinga homogenization methodology of the cloud cover time series of the Hellenic NationalWeather Service (HNMS) network and using monthly average cloud cover time series,we analyze 6 meteorological stations in the region of Crete, Greece for a period be-tween 1975 and 2004. To analyze these time series data, we propose the generalizedregression neural network (GRNN), a special type of neural network. By taking ad-vantage of several GRNN properties (i.e., a single design parameter and fast learning)and by incorporating several design strategies, the algorithm investigates effectivelythe possible seasonal and trend patterns found in cloud cover time series data. Fore-casting experiment results implemented under various strategies and time horizonsreveal that the GRNN methods outperforms previously adopted forecasting methodbased on exponential smoothing and ARIMA methodology. Our results reveal thatthe selected GRNN specifications may vary across monitoring stations and forecasting horizons.
Prevention of VM Side-Channel Timing Attack in Cloud Computing using Randomized Timing Approach in AES - 128

Abstract Cloud computing is one of the most widely used technologies in the world. More and more services are offered to clients by cloud service providers (CSP). Today Cloud computing services are facing constant threats and vulnerabilities unique to such systems. One of the most important cloud-specific attacks is the VM side-channel timing attack. In this paper, we propose a new method to prevent VM side-channel timing attacks in AES-128.
CREDIT CARD FRAUD DETECTION USING ISOLATION FOREST AND LOCAL OUTLIER FACTOR

Nowadays, card fraud is increasing due to the prevalence of modern technology. Thus, Automatic systems to detect and prevent against card fraud are a significant tool in the financial industries battle against card crime. Machine learning and novelty detection techniques approaches are taken in Credit Card Fraud Detection field, since they are effective technologies and methodologies and easy to apply at the same time. It is used in order to reduce fraud activities. The main aim of this project was to create a program that detects and Identifies potentially fraudulent credit card transactions from a given data set, and evaluate its performance to be compared with other classifiers with evaluation metric. The program was trained with the given data set, based on the some of the most popular Machine Learning and Deep Learning Classification algorithms which are; Random Forest, Isolation Forest and Neural Networks Algorithm Load Balancing and Feature Selection were maintained throughout the project, and it was implemented using Python programming language. However, there were no autonomous system that will be able to categorically define a transaction as fraud. The objective was to highlight those transactions that have a high probability of being fraudulent based on some criteria, known or otherwise learnt. Key Words: isolation forest and local outlier factor, Pandas, Json, Matplotlib and seaborn.
Machine Learning with Applications in Breast Cancer Detection

Abstract: This briefing will give an overview of Machine Learning Techniques (MLT) and their applications. It's used to identify patterns in data, analyze trends and make predictions using algorithms. It evolved from a study in computational theory and pattern recognition to touch industries that range from education to government. It is widely considered a key tool in decisionmaking. We present a review on the most recent ML methods used in modeling cancer progression. We present the latest works using ML techniques to model patient outcomes or cancer risk, in light of the increasing use of ML methods for cancerresearch.
Comparative Analysis of PSO Algorithm in Cloud Computing

Abstract: Cloud computing makes it possible to access applications and data from anywhere so this has become new technology. The goals of the paper are to provide additional insights to suggest ways in which performance might be improved by incorporating features from one paradigm into the other. The Reasearch Paper focus on particle swarm optimization (PSO) heuristic-based algorithm based on Scheduling. By scheduling, applications are scheduled to the cloud resources that are used for computation of cost and transmission cost of data in the cloud. by using PSO total cost of execution is minimized. Since the inception of Inertia Weight in PSO, a large number of variations of the Inertia Weight strategy have been proposed. In order to propose one or more than one Inertia Weight strategies that are efficient than others, this paper studies popular Inertia Weight strategies and compares their performance on optimization test problems. Keywords: Particle Swarm Optimization, Inertia weight, Population, Cognitive component, Optimization Function.
ISPRS-SHY – OPEN DATA COLLECTOR FOR SUPPORTING GROUND TRUTH REMOTE SENSING ANALYSIS

Abstract. The 2021 Scientific Initiatives in ISPRS funded this project called ISRS-SHY from “SHare mY ground truth”. It was intended as a collector of geographic data to support image analysis by sharing the necessary ground truth data needed for rigorous analysis. Regression and classification tasks that use remote sensing imagery necessarily require some control on the ground. The rationale behind this project is that often data on the ground is collected during projects, but is not valued by sharing across projects and teams globally. Internet has improved the way that data are shared, but there are still limitations related to discoverability of the data and its integrity. In other words, data are usually kept in local storage or, if in an accessible server, they are not documented and therefore they will not be picked up during search. In this initiative we created a portal using the Geonode environment to provide a hub for sharing data between research groups and openly to the community. The portal was then tested within the framework of three projects, with several participants each. The data that was uploaded and shared covered all types of geographic data formats and sizes. Further sharing was done in the context of teaching activities in higher education.The results show the importance of creating easy means to find data and share it across stakeholders. Qualitative results are discussed, and future steps will focus on quantitative assessment of the portal’s usage, e.g. number of registered users in time, number of visits, and other key performance indicators. The results of this project are to be considered also in light of the effort in the scientific community to make research data available, i.e. FAIR - Findability, Accessibility, Interoperability, and Reuse of digital assets.
Meta-SysId: A Meta-Learning Approach for Simultaneous Identification and Prediction

In this paper, we propose Meta-SysId, a meta-learning approach to model sets of systems that have behavior governed by common but unknown laws and that differentiate themselves by their context. Inspired by classical modeling-and-identification approaches, Meta-SysId learns to represent the common law through shared parameters and relies on online optimization to compute system-specific context. Compared to optimization-based meta-learning methods, the separation between class parameters and context variables reduces the computational burden while allowing batch computations and a simple training scheme. We test Meta-SysId on polynomial regression, time-series prediction, model-based control, and real-world traffic prediction domains, empirically finding it outperforms or is competitive with meta-learning baselines.