Reviewerly/exHarmony huggingface dataset:
An efficient implementation of Hybrid Signcryption Processor using Flexible Encryption and Signature Techniques

In today’s digital era, with the enormous growth in the Internet of Things, everyday humans utilize IoT in various applications such as banking, online transactions, purchasing data forwarding, etc. Cryptography is all about the design and implementation of protocols that keep the data safe and secure from third parties. The design of the cryptographic processor present in the Field Programmable Gate Array (FPGA) incurs more power, area, and throughput. In this article, an Efficient Hybrid Signcryption Processor (EHSP) which operates based on the principle of encryption and signature is proposed. Generally, a hybrid signcryption operation comprises of Data Encapsulation Method (DEM) and Key Encapsulation Method (KEM). and in EHSP, we utilize the improved form of the Modified Kurosawa and Desmedt (MKD) hash scheme to encapsulate the key; the Elliptic Curve Cryptography (ECC) processor is used to encapsulate the message. The ECC processor is designed in a flexible manner and the flexibility is achieved by the flexible bit-serial multiplier, which reduces the power, and area consumption and maximizes the throughput. The proposed EHSP architecture is designed and simulated in Xilinx tool with various field programmable gate array families such as Virtex4 (XC4VLX60), Virtex5 (XC5VLX50), and Virtex7 (XC7V330T). The simulation result depicts the efficiency of the proposed EHSP design with respect to total power consumption, utilization of hardware, and throughput.
Implementation of Blended Learning in Tertiary Education of Health and Social Care Professionals During Covid-19 Pandemic

Blended learning is a relatively new e-learning method that combines traditional classroom learning with computer-mediated instruction. Its roots can be traced back to the 1840s when Sir Isaac Pitman sent tasks to his students on postcards for evaluation. It represents a new way of teaching, employing modern technologies and the online environment, whilst simultaneously emphasizing the importance of traditional teaching methods and personal contact between students and teachers. Blended learning has proven to be an effective way to combine scheduled and self-paced classwork, ensuring its better accessibility and flexibility. This article discusses the implementation and effectiveness of a blended-learning course designed to train communication skills in students studying healthcare-related programmes at the Faculty of Health and Social Sciences of the University of South Bohemia, Czech Republic. The course uses a combination of face-to-face instruction, individual student work, and multimedia online materials. It is divided into modules specific to the student's field of study, such as nursing, nutritional therapy, and civil protection. Each module includes theoretical and practical sections, and the course covers communication skills with seniors, adults, children, and disabled clients. A questionnaire survey was conducted to assess the effectiveness of the course and student satisfaction in order to obtain feedback for further development and improvement of the course. The data obtained from the survey were analysed using descriptive statistics and the Gini index. The results show that the blended-learning course was effective in training communication skills and reducing the number of failures.
Health promotion in elderly patients with hyponatremia related to polypharmacy: The role of nurses

Hyponatremia is a condition of low sodium concentration in the body, which occurs occasionally in the long-lived, with greater aggravation in a scenario of poly-medications. Objective: To analyze articles on the topic: The importance of nurses' role in promoting health in elderly people with hyponatremia. Method: This work is a systematic bibliographical review of integrative evaluation. The results show the necessary correlation between professional development, the characteristics of elderly people with hyponatremia and their implications in terms of medication. Nurses are the main agents in health promotion, knowing all the particularities of their patients, planning, implementing and participating in the whole process. This work was developed with the aim of raising awareness and providing learning for professionals, so that they are more engaged and participate in a more assertive way in promoting the health of the population in which they work
Build Android-Based Go Service Electronic Applications Using React Native Framework and Firebase Realtime Database

Many entrepreneurs provide electronic services, especially on cellphones and PCs, but their marketing is very limited due to the location of the store and banners that are not strategic so it is difficult to know where the service is located, on the other hand customers are also difficult to know the estimated cost of repairing electronic devices and often potential customers I was disappointed when I arrived at the location but the service was closed. With these problems, the author built an Android-based Go Service Electronic application by utilizing React Native Framework technology to build Android-based applications and using the Firebase Realtime Database as data storage. The application is also equipped with consulting features, a booking system, calculating the estimated cost of equipment repair services, as well as providing information on the best service services through an assessment system that has been provided by customers who have booked previous services. With the Go Service Electronic application, it is easier for the public to find out where the location of service providers is, find out the estimated repair costs, and can consult directly through the application, as well as being able to increase business opportunities for electronic service providers.
A methodology for model clone detection using statistics of design metrics

Model clone detection has predominantly gained momentum in the field of software development process and model driven engineering. Reuse and mutations of the existing models will increase the complexity of managing the software’s organisation’s internal repositories. Detecting semantic and structural similarities among the models finds various uses like fault prediction, estimation of maintenance and refactoring. Literature in the domain witnesses very few works focussing on model clone detection. This work proposes a model clone detection technique by leveraging the statistical and lexical properties of the UML diagrams. The primary contribution of the work is the construction of Similarity Measure (SM) by analysing the design metrics from different perspectives namelyi)measuring the statistical variability between the models and ii) estimating the lexical similarity among design metrics. The results of the model indicates that the proposed method was able to detect the semantically similar model clones of the banking use case. Also, the method is very robust and computationally inexpensive, so that it could find its applicability in all fields where model driven engineering is used.
A heuristic based linear time O(N) novel solution to N-Queen problem

N-Queen problem is a classic puzzle in which ‘N’ number of queens and a chessboard of size N X N is given. The puzzle is to lay all the given queens on the chessboard in a way that all queens are safe (i.e., no queen on the chessboard is intersecting the other queen). In this article we have proposed an algorithm to solve the N-Queen puzzle in O(N) time-complexity, without affecting the space-complexity.
Implementation of a Butterworth digital filter using Python libraries

Formulation of the problem. In digital signal processing, noise refers to unwanted or random elements of a signal that can distort or corrupt its information content. Such noise can arise from various sources, including electromagnetic interference, thermal noise, quantization errors, and degradation of the transmission channel. It is necessary to deal with noise in a digital signal, as it can degrade the quality and reliability of the signal, lead to errors, distortions and loss of information. Moreover, noise can limit the performance of many digital signal processing systems, such as communications and audio processing systems. Therefore, various methods and algorithms have been developed to reduce noise and improve the quality of digital signals, the main of which is filtering. Target. Increasing the efficiency of digital signal processing systems using filters that allow you to programmatically change the signal filtering parameters. Development of a signal processing system in the Python programming language that implements the Butterworth digital filter algorithm. Results. The architectures of filters with infinite impulse response (IIR filters), features of the Butterworth filter are considered. A software model of a Butterworth digital filter in the Python programming language has been developed, and its verification has been carried out in relation to an audio file. The work of the model is based on the use of Python libraries such as Matplotlib, SciPy and Librosa, which allow you to implement the required algorithm and get the visualization of the signal. Practical significance. The results obtained can later be used as a module of a programmable universal digital signal processing system.
Comparison Of Feat Examination Results Using Direct Slide And Sedimentation Methods For Detecting Soil Transmidet Helmint (STH) Worm Infection In Primary School Students

The worms are parasitic infections that occur frequently and are estimated to infect 1-45 billion individuals worldwide. There are several types of worms that infect humans, namely roundworms (Ascaris lumbricoides), whipworms (Trichuris trichura), and hookworms or hookworms (Ancylostoma duodenale and necator americanus). The method used in this research is descriptive analytical method, which is the method used to view the description of the examination results from a sample. The population of this study was elementary school students with a sample of 50 students. Based on the results of the study using the direct slide method, negative results were obtained with a percentage of 100% with a total frequency of 50 students. If using the sedimentation method, 3 students were infected with Ascaris lumbrichoides with a percentage of 6.0% and 47 students were not infected with a percentage of 94.0% with a total sample of 50 students. Based on the results of the research using the direct slide and sedimentation methods, the results showed that the results of the two methods used were influenced by different sensitivity.
Construction and evaluation of an anomaly detection system using System Invariant Analysis Technology(SIAT) for sound data

Here is a summary of a paper that presents case studies on the application of SIAT, a machine learning technique, for anomaly detection in plants and industrial machinery, with a focus on sound-based anomaly detection as a new application of SIAT: This paper explains case studies on anomaly detection using SIAT as a machine learning technique. SIAT is specialized in analyzing time-series data and is widely used for anomaly prediction in plants and industrial machinery. In recent years, the application of SIAT has been extended to sound-based anomaly detection, and this paper presents some case studies on this topic. Specifically, the paper provides several examples of sound-based anomaly detection, such as detecting abnormal sounds or predicting machinery failures. In these cases, SIAT was used to analyze sound data collected from multiple sensors, and anomalies were detected successfully. The results of these anomaly detection methods were then used to take preventive measures such as maintenance or repairs, leading to improvements in productivity and safety. This paper demonstrates the usefulness of SIAT for sound-based anomaly detection and suggests the potential for expanding the scope of SIAT’s applications.
Cyber Security Essentials

Cybersecurity acumen is crucial in today's interconnected society. With the information in this book, readers will be better prepared to face the many cyber security issues they will face in their professional and personal lives. Information management practises, tactics, techniques, and technologies that may prevent, detect, and respond to cybercrime and cyberattacks are thoroughly explored. Both cybercrime and cybersecurity are discussed, with an emphasis on the role that evolving and improving digital technologies have played.
The PetShop Dataset -- Finding Causes of Performance Issues across Microservices

Identifying root causes for unexpected or undesirable behavior in complex systems is a prevalent challenge. This issue becomes especially crucial in modern cloud applications that employ numerous microservices. Although the machine learning and systems research communities have proposed various techniques to tackle this problem, there is currently a lack of standardized datasets for quantitative benchmarking. Consequently, research groups are compelled to create their own datasets for experimentation. This paper introduces a dataset specifically designed for evaluating root cause analyses in microservice-based applications. The dataset encompasses latency, requests, and availability metrics emitted in 5-minute intervals from a distributed application. In addition to normal operation metrics, the dataset includes 68 injected performance issues, which increase latency and reduce availability throughout the system. We showcase how this dataset can be used to evaluate the accuracy of a variety of methods spanning different causal and non-causal characterisations of the root cause analysis problem. We hope the new dataset, available at https://github.com/amazon-science/petshop-root-cause-analysis/ enables further development of techniques in this important area.
Description Of The Results Of The Cytological Examination Of Pleural Fluid Using Giemsa Staining

Many objects after being fixed and then glued with entellan or balsam become so transparent that their structure is not obvious when viewed with a microscope. To overcome this difficulty, in general, preparations are colored with dyes that can clarify their structure. Staining aims to sharpen or clarify various tissue elements, especially the cells, so that they can be distinguished and studied under a microscope. This research was conducted at the Anatomical Pathology Laboratory of Grandmed Lubuk Pakam Hospital in February 2020 - July 2020. The research design was descriptive. The population of this study were all pleural effusion fluid cytology preparations at the Anatomical Pathology Laboratory of Grandmed Lubuk Pakam Hospital. The sampling technique used is accidental sampling, namely the technique of determining the sample by chance. In this case, the samples found were 16 samples. The results showed that microscopic images of pleural fluid cytology preparations using Giemsa staining showed poor (43.75%) and good (56.25%) results. The results of this study are expected to be input for the Anatomical Pathology Laboratory about the description of the results of the pleural fluid cytology examination using Giemsa staining to improve service quality.
Development of an Android Educational Game Magic Square -Megaforce Assisted to Improve Students' Creative Thinking Skills

Abstract:The development of educational games is very relevant to learning activities in schools today. This research developed the Android educational game Magic Square – Megaforce to be valid, practical, and effective in improving students' creative thinking skills. This research is a development research with a 4-D model. The validity of educational games reached a high category with a correlation coefficient value of 0.95. This educational game is also declared practical with an average learning implementation of 100%. The effectiveness of this educational game was assessed based on classical learning completeness of 85.7%, student learning activities of 80%, and student response questionnaires of 85.5%, all of which were in the Good category. Educational game design can still be refined for further research so that it is more varied.Abstrak:Pengembangan game edukasi sangat relevan dengan kegiatan pembelajaran di sekolah saat ini. Penelitian ini mengembangkan game edukasi Android Magic Square – Megaforce hingga valid, praktis, dan efektif meningkatkan kemampuan berpikir kreatif siswa. Penelitian ini merupakan penelitian pengembangan dengan model 4-D. Validitas game edukasi mencapai kategori tinggi dengan nilai koefisien korelasi 0,95. Game edukasi ini juga dinyatakan praktis dengan rata-rata keterlaksanaan pembelajaran sebesar 100%. Efektifitas game edukasi ini dinilai berdasarkan ketuntasan belajar klasikal sebesar 85,7%, aktivitas belajar siswa sebesar 80% dan angket respon siswa sebesar 85,5% yang semuanya berada pada kategori Baik. Desain game edukasi masih dapat disempurnakan untuk penelitian selanjutnya sehingga lebih variatif.
Differential Diagnosis with Active Testing

The diagnosis of complex systems benefits greatly from a differential, multistep approach that narrows down the list of possible conditions or failures that share the same observable effects to a single root cause. We provide a suitable and practically applicable methodology for this. In extension to existing work, it covers all types of diagnostic actions, i.e., the observation of system properties, active testing and system interventions like providing a dedicated diagnostic input or forcing the system into discriminating states, but also the replacement of components. Combining all these possible steps into one probabilistic and causal reasoning framework, we I) stepwise generate the diagnostic model systematically to correctly cover the interplay of observations and diagnostic interventions, and II) provide decision support based on counterfactuals for the selection of the next diagnostic step, countering the vast number of possible actions that arise in machine diagnostic processes. We developed and successfully tried our methodology for diagnosing cyber-physical systems in the high-tech industry, but we found that it supports more processes, such as computing intervention actions for autonomous robots.
Abnormal Detection Using Two-Stage Method in Combined Power Plant

Complex systems, such as power plants, demand precise and reliable anomaly detection mechanisms. Traditional supervised learning approaches often fall short due to the challenges of imbalanced data and the scarcity of labeled abnormal instances. This paper introduces a two-stage methodology to address these challenges. The first stage emphasizes feature engineering, mitigating redundant sensor effects, and reducing dimensionality through Kmeans clustering and PCA. The second stage employs an LSTM-Autoencoder for abnormal event detection. Validated using data from a combined power plant, our approach demonstrates superior performance over existing techniques in terms of accuracy and computational efficiency. This research not only advances the field of anomaly detection in power plants but also offers insights for other complex systems.
Influence of the range Doppler coupling error on the characteristics of second-order filters

This paper mainly considers a growing-memory filter for tracking an object moving with constant acceleration and detected by chirp radar. Chirp radars employ linear frequency modulated (LFM) waveforms for object tracking which yields a displacement of measured position from a true range of moving objects. This effect may enable better track accuracy in range. The second order degree polynomial describes coordinates law for a constantly accelerating maneuvering object. The issues of lag errors, covariance matrix of the filtered state vectors, impulse responses and Kalman gains are discussed. For example, the growing-memory filter is most often used for initiating track. The growing-memory filter is derived in recursive and non-recursive form based on least-squares. It is shown that the growing-memory filter estimating a true object range (with the range-Doppler coupling coefficient) can be represented by the growing-memory filter estimating a biased object range (without the range-Doppler coupling coefficient) and compensating the estimated biased range by the estimated the range-Doppler coupling error. Expressions giving an interrelation between lag errors, covariance matrix of the filtered state vectors, impulse responses and Kalman gains of growing-memory filters estimating true and biased range of moving objects are derived.
Proposal of a Time Series Anomaly Detection Method Using Image Encoding Techniques

Time series anomaly detection is considered to play a major role in many areas of society. Models using RNN, which are well suited for time series data, have been studied. However, models using RNN have the problem of high cost; image en- coding approaches combining Gramian Angular Fields(GAF) and Autoencoder are less expensive than RNN. However, the accuracy in existing studies is not as good as RNN. In this paper, we propose a time-series anomaly detection frame- work that first focuses on the structural issues of GAF and the reconstruction accuracy of Autoencoder. Experiments were conducted to verify the effectiveness of the framework. The results showed that the approach focusing on the structural is- sues of GAF achieved a significant improvement in accuracy, while the approach focusing on improving the reconstruction accuracy of the Autoencoder network decreased the anomaly detection accuracy. The reason for the lower accuracy was found to be that the networks with higher reconstruction accuracy accurately reconstructed even the anomaly images, making anomaly detection based on L1 errors impossible. These results indicate that an approach that focuses on the structural problems of GAF is effective, while an approach that improves the reconstruction accuracy of Autoencoder is not necessarily effective.
A Review of Methodologies in Machine Learning For Face Shape Analysis

Abstract: Face finding and face analysis have been employed as the basis of many researchers in recent years. The outline of an object gives the structure and look of an object. In applications like face recognition, a form of the face acts as a significant role. This paper reviews different methods adopted in different works to frame the shape of the human face that would be useful for many other future works. A list of existing machine learning and deep learning algorithms for face shape analysis is discussed and reviewed. It also discusses the result and accuracy of different methods that act as intermediate steps in achieving the shape of a human face and also a number of suggestions for achieving optimization in the routine of the work. A review of various performance measures such as time taken, error rate, classification rate, and memory are discussed.
PENTINGNYA KETERAMPILAN BERBAHASA INDONESIA PADA KEGIATAN PEMEBELAJARAN SEKOLAH DASAR

Human life is not without language activities, language is used as a means of interpersonal communication, and even language is considered a perfect language for interacting with others. To improve the ability to communicate and speak the language, it is necessary to learn the language from the beginning, since I was sitting at the basics. Then the supporting aspect of improving communication ability and good language is to master a lot of vocabulary, to master. To speak well four skills must be mastered, namely the skills of sympathy, fluency of speech, reading skills, and writing skills. The method that will be used to build this journal is library study.