Reviewerly/exHarmony huggingface dataset:
NOVEL PRIVACY PRESERVING ASSOCIATION RULE MINING FOR HORIZONTALLY PARTITIONED DATABASES

Advancement in scientific research depends on the availability and sharing of information and ideas. Many authors proposed many methodologies for isolation preserving data mining for centralized as well as for distributed database. The core intend of this paper work is, to develop efficient methodologies to find privacy preserving association rule mining in centralized as well as in distributed database environment without violating any privacy constraints. The issue of privacy constraints for centralized database environment is entirely different from distributed database environment. So the approaches for privacy preserving association rule mining in centralized are also different from approaches in distributed environment. Achieving privacy preserving goals in both these environments in the process of mining is a challenging task.
Autonomous Tool for Monitoring Multi-Morbidity Health Conditions in UAE and India

Multi-morbidity is the presence of two or more long-term health conditions, including defined physical or mental health conditions, such as diabetes or schizophrenia. One of the regular and critical health cases is an elderly person with a multi-morbid health condition and special complications who lives alone. These patients are typically not familiar with advanced Information and Communications Technology (ICT), but they are comfortable using smart devices such as wearable watches and mobile phones. The use of ICT improves medical quality, promotes patient security and data security, lowers operational and administrative costs, and gives the people in charge to make informed decisions. Additionally, the use of ICT in healthcare practices greatly reduces human errors, enhances clinical outcomes, ramps up care coordination, boosts practice efficiencies, and helps in collecting data over time. The proposed research concept provides a natural technique to implement preventive health care innovative solutions since several health sensors are embedded in devices that autonomously monitor the patients' health conditions in real-time. This enhances the elder's limited ability to predict and respond to critical health situations. Autonomous monitoring can alert doctors and patients themselves of unexpected health conditions. Real-time monitoring, modeling, and predicting health conditions can trigger swift responses by doctors and health officials in case of emergencies. This study will use data science to stimulate discoveries and breakthroughs in the United Arab Emirates (UAE) and India, which will then be reproduced in other world areas to create major gains in health for people, communities, and populations.
Location-Guided Coarse-to-Fine Network for Whole Heart Segmentation

The human heart is a hollow muscular organ with very large variations in shape and size, automatic and accurate segmentation of the whole heart is challenging for medical image analysis. To tackle this challenge, a simple and effective fully automatic coarse-to-fine network is proposed to enable whole heart segmentation. It comprehensively captures the anatomical dependencies of the heart and its substructures as explicit guidance to assist the segmentation of these challenging substructures. Based on the anatomical location of the heart in CT slices, we design a Slice Channel Attention (SCA) module to aggregate specific categories of response channels to highlight feature representation of different slices, thereby enhancing the features of the same substructure and avoiding confusion between different substructures. Furthermore, we propose a Location-aware Object Generation (LOG) module, which can generate the heart mask for guiding heart substructures segmentation by processing the CNN feature map. Experimental results show that our method achieves state-of-the-art on MICCAI 2017 MM-WHS dataset. Ablation studies and visualizations are provided to understand our method.
MPTGAN: A Multimodal Prior-Based Triple-Branch Network for Fast Prostate Mri Reconstruction

As the most commonly used radiological examination for prostate disorder diagnosis, Magnetic Resonance Imaging (MRI) acquisition is very time-consuming. To accelerate prostate MRI reconstruction while maintaining high quality, this paper provides MPTGAN, a multimodal prior-based triple-branch network. MPTGAN guides the reconstruction of time-consuming MRI modality by utilizing time-efficient MRI modality as prior knowledge, thus the massive loss of low and high-frequency information in under-sampled data could be replenished. In particular, a hybrid feature interaction module (HFIM) is employed to integrate and fuse the latent relation clues between multimodal MRIs. Besides, we also design a triple-attention upsampling module (TAUM) to capture the salient features from the fused multimodal information. We collected 7300 pairs of T1 and T2 images from 387 prostate disorder patients and applied MPTGAN on them. Experiments show that MPTGAN significantly improved the reconstruction quality of prostate MRI and outperformed state-of-the-art methods.
On $(q^2+q+1)$-Sets of Plane-Type $(m, n, r)_2$ in $\mathrm{PG}(3, q)$

In this paper (q2+q+1)(q2+q+1)-sets of points in PG(3,q)PG(3,q) of type (m,n,r)(m,n,r) with respect to planes are studied, and as a by-product for qq odd a characterization of quadratic cones is obtained.
Timbre and Expressive Means of Modern Saxophone Performance

A new substrate of timbre-sound model of modern saxophone sound in chamber-instrumental music is considered, which is approved due to the emergence of new and modification of established methods and techniques of saxophone playing. It is found that the timbre-sound instrumental model of the saxophone is realized through the timbre color of the sound, pitch characteristics of the sound range of the instrument and related ideas about: registers, surround effects (vibrato and frullato), spectral sound effects (flageolets), dynamic possibilities of sounds in different registers, realization of possibilities of longness of sound, etc. The technology of using additional alternative fingering and flageolet sounds on the saxophone is described, which contributes to greater diversity in timbre between notes within the same octave. It is emphasized that the coloring of the timbre or bisbigliando is carried out on the instrument by alternating ordinary and flagellate sounds, as well as with the help of additional (auxiliary) fingering. The timbre of the sound from the position of saturation of the melody is highlighted. It is stated that recently in modern performing practice on the saxophone are widely used both performing techniques that affect the timbre of the sound during its extraction, and techniques that generate individual timbre and sound structures. The method of changing the sound of the instrument with the help of specific work of the earbud (earbud effects) is substantiated. It is established that timbre ornamentation involves the use of klangfarbenmelodie, intricately ornamented notes, glissando, trills and tremolo. Emphasis is placed on the possibility of composers introducing a polyphonic element into the saxophone (solo) part as a combination of several sound voices — doubletone. It is proved that one of the biggest innovations of modern saxophone performance is the ability to perform chordal polyphony. The author's concept of using alternative (auxiliary) fingering to reproduce consonance on a fundamentally unanimous saxophone due to the performance of double flageolets, splitting tones, etc. is proposed.
Adaptable Text Matching via Meta-Weight Regulator

Neural text matching models have been used in a range of applications such as question answering and natural language inference, and have yielded a good performance. However, these neural models are of a limited adaptability, resulting in a decline in performance when encountering test examples from a different dataset or even a different task. The adaptability is particularly important in the few-shot setting: in many cases, there is only a limited amount of labeled data available for a target dataset or task, while we may have access to a richly labeled source dataset or task. However, adapting a model trained on the abundant source data to a few-shot target dataset or task is challenging. To tackle this challenge, we propose a Meta-Weight Regulator (MWR), which is a meta-learning approach that learns to assign weights to the source examples based on their relevance to the target loss. Specifically, MWR first trains the model on the uniformly weighted source examples, and measures the efficacy of the model on the target examples via a loss function. By iteratively performing a (meta) gradient descent, high-order gradients are propagated to the source examples. These gradients are then used to update the weights of source examples, in a way that is relevant to the target performance. As MWR is model-agnostic, it can be applied to any backbone neural model. Extensive experiments are conducted with various backbone text matching models, on four widely used datasets and two tasks. The results demonstrate that our proposed approach significantly outperforms a number of existing adaptation methods and effectively improves the cross-dataset and cross-task adaptability of the neural text matching models in the few-shot setting.
Utilizing Educational Technology as a Learning Solution in the New Normal Era of Community Life

AbstractThis virus disaster has proven to be able to overwhelm almost all the people of the world, especially the people of Indonesia. In addition to having claimed many lives it has also damaged all systems of community life, one of which is the education system. To prevent this pathway from spreading the pandemic, the central government established policies specifically in the field of education which required students to carry out teaching and learning activities carried out at their respective homes online, both at the elementary, junior high, high school, and college level. The High School X educational institution is one of the educational institutions, which was the object of this study, which also experienced the impact of this pandemic and was forced to change its teaching and learning system, namely from offline learning activities and converted into online teaching and learning activities. Also related to the impact of the pandemic on the education system also affects the absence of the National Examination and changes the registration system for new students from High School X who will continue to the next level of education.Keyword: Modernization, Technology, Covid-19 Pandemic, Online AbstrakBencana virus ini terbukti mampu melanda hampir seluruh masyarakat dunia, khususnya masyarakat Indonesia. Selain telah memakan banyak korban jiwa juga telah merusak seluruh sistem kehidupan masyarakat, salah satunya adalah sistem pendidikan. Untuk mencegah jalur penyebaran pandemi ini, pemerintah pusat menetapkan kebijakan khusus di bidang pendidikan yang mewajibkan peserta didik untuk melakukan kegiatan belajar mengajar yang dilakukan di rumah masing-masing secara online, baik di tingkat SD, SMP, SMA, maupun SMK. tingkat perguruan tinggi. Institusi pendidikan SMA X merupakan salah satu institusi pendidikan yang menjadi objek penelitian ini, yang juga mengalami dampak dari pandemi ini dan terpaksa merubah sistem belajar mengajarnya yaitu dari kegiatan pembelajaran offline menjadi pembelajaran online. dan kegiatan belajar. Selain itu terkait dampak pandemi terhadap sistem pendidikan juga berdampak pada tidak adanya Ujian Nasional dan perubahan sistem pendaftaran siswa baru dari SMA X yang akan melanjutkan ke jenjang pendidikan berikutnya.Kata kunci: Modernisasi, Teknologi, Pandemi Covid-19, Daring
DESENVOLVIMENTO DE DIABETES MELLITUS TIPO 2 FRENTE CONTAMINAÇÃO PELO VÍRUS SARS-COV-2

Objective: To describe through the literature the relationship between pain and cardiac frequency in neonates in the ICU.Methods: This is a literature review developed in 2021, based on the articles from 2010 to 2021, the research was carried out through the search in Scielo, Bireme and VHL through the key terms using the boolean expression AND between the terms.Results: At the end of the reading it can be observed that the conducts and procedures with greater manual touch on the skin of neonates cause pain yes and it can be verified through the high cardiac frequency and that this can alter the development of the nervous system and sequentially the behavior.And, that pain should still be a preventive condition even if it is in a preventive environment.Conclusion: It can be concluded that the professional team that works in the newborn is an important factor for pain minimization, as well as it was seen that the ICU has important equipment and resources for the evaluation and intervention.
Evaluation of a Multi-agent “Human-in-the-loop” Game Design System

Designing games is a complicated and time-consuming process, where developing new levels for existing games can take weeks. Procedural content generation offers the potential to shorten this timeframe, however, automated design tools are not adopted widely in the game industry. This article presents an expert evaluation of a human-in-the-loop generative design approach for commercial game maps that incorporates multiple computational agents. The evaluation aims to gauge the extent to which such an approach could support and be accepted by human game designers and to determine whether the computational agents improve the overall design. To evaluate the approach, 11 game designers utilized the approach to design game levels with the computational agents both active and inactive. Eye-tracking, observational, and think-aloud data was collected to determine whether designers favored levels suggested by the computational agents. This data was triangulated with qualitative data from semi-structured interviews that were used to gather overall opinions of the approach. The eye-tracking data indicates that the participating game level designers showed a clear preference for levels suggested by the computational agents, however, expert designers in particular appeared to reject the idea that the computational agents are helpful. The perception of computational tools not being useful needs to be addressed if procedural content generation approaches are to fulfill their potential for the game industry.
Undergraduate Students’ Perceived Self-Efficacy in Academic Reading

Reading academic materials is necessary for acquiring knowledge and new insights to perform well in all academic demands. Nevertheless, many students were still struggling in understanding academic texts due to their high level of complexity and so avoided reading. Thus, self-efficacy as a motivating force and continuous effort in the reading process must be activated. This study aimed to investigate the undergraduate students’ perception of their self-efficacy in handling the academic reading materials. One research question was proposed namely, to what extent do undergraduate students perceive their self-efficacy in reading academic texts? To answer this question, a mixed method was undertaken. The respondents consist of 35 undergraduate students from two private universities in Yogyakarta, Indonesia. The findings indicated that most undergraduate students prefer to read academic texts as a process of constructing meaning. Moreover, to succeed in reading academic text, both external (effective strategies) and internal factors (self-efficacy) were required. One of the implications for teachers and lecturers, namely applying learning procedures that can equip undergraduate students with powerful reading strategies and more importantly stimulate their self-efficacy belief to a stable level which enables them to overcome various difficulties in reading academic texts, such as articles.
A More Accurate Approach for Prediction using Gradient Descent

Accuracy is one of the most important concerned when we are dealing the problem related to machine learning.Artificial intelligence of also one of the most popular and emerging field which used optimization to improve accuracy.Gradient descent is a new and most power techniques which integrate machine learning and AI to achieve optimization.There are so many techniques are available for optimization and Gradient descent is one of then it is a kind of an iterative algorithm and most used for discovering the minimum cost function.This technique help to take efficiently and successfully decisions, by the use of derivatives.Derivative is useful when we want to calculate slope of the graph from a particular point.The value of the slope is defined by representation a tangent line to the graph at the point.In this technique we have to calculate this tangent line, with help of this we are able to calculate and decide the direction to reach the minima.It is the best techniques for optimization in machine learning.It is based on first-order optimization.This technique used a objective function, in which we have to update parameter in the reverse direction for every iteration.In the paper apply this optimization techniques to find better linear regression line and try to fit as best regression line for given data set.Our objective is to reduce SSE error and improve cost function value.
An Innovative Method for Detection of Malicious Behaviours in Automated Vehicle System Using Hybrid Fuzzy C-Means Algorithm with Neural Network Algorithm Based Accuracy and Cost

Aim: To improve the predictive accuracy and cost analysis for malicious behaviors in automated vehicle systems using the Hybrid Fuzzy C-Means algorithm (HFCM) and Neural Network algorithm (NN). Materials and Methods: Accuracy is performed with two groups Fuzzy C-Means Algorithm and the Neural Network algorithm of sample size per group (N = 125). G power 80% threshold 0.05%, CI 95%. Mean and Standard deviation. Result: Independent sample T-Test was carried out using Fuzzy C-Means and Neural Network. C-means (92.1%) perform better than NN (89.6%). There is a statistically significant difference between Fuzzy C-means and with (p&lt;0.01). Conclusion: The results conclude that the proposed Fuzzy C-Means algorithm helps to identify and detect with better accuracy and cost percentage at event systems.
Design and Development of Supporting Applications “E-MPKK” For Leraning Media of Midwife and Enterpreneurship Managament Clinic Practice

Learning Center for Midwifery Service Management and Entrepreneurship to manage management reports, proposals, and role plays. The first purpose and benefit of this research is to design and build a prototype software application "E-MPKK" as a Learning Media for Clinical Practice Management of Midwifery and Entrepreneurship Services for Midwifery Department Students, FKUB so that the learning process is more effective and efficient, including easy access to reporting, monitoring student assignments, and the effectiveness of the duration of clinical guidance between lecturers and students. With this software application, students are expected to be able to analyze the correct information with the right method and time. The right time is meant to shorten the time to work on data collection, problem analysis, and making strategic plans. the process of storing the resulting soft file. assessment, analysis results, and planning to be better, structured, and measurable. With this software application students can analyze the correct information with the right method and time. storage of soft files of assessment results, analysis results, and planning to be better, structured, and measurable. by assessing the effectiveness and efficiency of working on reports with software applications. With this software application, it is expected that Midwifery Service Management reporting will be more effective; and reporting time is shorter, storage is also better and safer and reduces paper usage With this software application students can analyze the correct information with the right method and time. storage of soft files of assessment results, analysis results, and planning to be better, structured, and measurable. by assessing the effectiveness and efficiency of working on reports with software applications. With this software application, it is expected that Midwifery Service Management reporting will be more effective; and reporting time is shorter, storage is also better and safer and reduces paper usage With this software application students can analyze the correct information with the right method and time. storage of soft files of assessment results, analysis results, and planning to be better, structured, and measurable. by assessing the effectiveness and efficiency of working on reports with software applications. With this software application, it is expected that Midwifery Service Management reporting will be more effective; and reporting time is shorter, storage is also better and safer and reduces paper usage With this software application, it is expected that Midwifery Service Management reporting will be more effective; and reporting time is shorter, storage is also better and safer and reduces paper usage With this software application, it is expected that Midwifery Service Management reporting will be more effective; and reporting time is shorter, storage is also better and safer and reduces paper usage.
Interactive Instructional Reading Materials: A Responsive and Comprehensive Intervention for the Struggling Readers

The study was a quasi-experimental research conducted to investigate the effect of Interactive Instructional Reading Materials: A Responsive and Comprehensive Intervention to Struggling Readers. The participants of the study were the identified 74 struggling readers from Grades 2 to 10 enrolled during the 2nd quarter SY 2021-2022 at Tawantawan Integrated School. One group was assigned as control group who was exposed to plainly printed reading materials while the other one was experimental group who was exposed to interactive reading materials. This study utilized interactive instructional reading materials to sustain the interest of the learners as teachers promote a holistic pedagogical strategy to make learning gratifying, engaging, and effective. The performance of the learners were measured using their test scores. To determine if the interactive reading materials influence the reading ability of the learners, the Analysis of Covariance model (ANCOVA) was utilized at 0.05 level of significance. Results revealed that the Interactive Instructional Reading Materials helped learners read on their own and with comprehension.