Reviewerly/exHarmony huggingface dataset:
A new hybrid method combining search and direct based construction ideas to generate all 4 × 4 involutory maximum distance separable (MDS) matrices over binary field extensions

This article presents a new hybrid method (combining search based methods and direct construction methods) to generate all $4 \times 4$ involutory maximum distance separable (MDS) matrices over $\mathbf{F}_{2^m}$. The proposed method reduces the search space complexity at the level of $$\sqrt n $$, where n represents the number of all $4 \times 4$ invertible matrices over $\mathbf{F}_{2^m}$ to be searched for. Hence, this enables us to generate all $4 \times 4$ involutory MDS matrices over $\mathbf{F}_{2^3}$ and $\mathbf{F}_{2^4}$. After applying global optimization technique that supports higher Exclusive-OR (XOR) gates ( e.g ., XOR3, XOR4) to the generated matrices, to the best of our knowledge, we generate the lightest involutory/non-involutory MDS matrices known over $\mathbf{F}_{2^3}$, $\mathbf{F}_{2^4}$ and $\mathbf{F}_{2^8}$ in terms of XOR count. In this context, we present new $4 \times 4$ involutory MDS matrices over $\mathbf{F}_{2^3}$, $\mathbf{F}_{2^4}$ and $\mathbf{F}_{2^8}$, which can be implemented by 13 XOR operations with depth 5, 25 XOR operations with depth 5 and 42 XOR operations with depth 4, respectively. Finally, we denote a new property of Hadamard matrix, i.e ., (involutory and MDS) Hadamard matrix form is, in fact, a representative matrix form that can be used to generate a small subset of all $2^k\times 2^k$ involutory MDS matrices, where k &gt; 1. For k = 1, Hadamard matrix form can be used to generate all involutory MDS matrices.
Sosialisasi Media Informasi Kebutuhan Gizi Harian Pada Unit Pelaksana Teknis (UPT) Puskesmas Mandomai

In serving the local community, the Mandomai Health Center Technical Implementation Unit (UPT) does not only handle the treatment of sick people, but also usually handles complaints from people who are malnourished. All types of food contain different composition of nutrients. The human body will experience malnutrition if it consumes less nutritious food. It is very important to choose a variety of foods according to the body's nutritional needs so that you do not experience a deficiency or excess of nutrients which can cause various diseases, such as malnutrition, obesity, hypertension, diabetes, and other diseases. In order to help the UPT staff at the Mandomai Health Center and the surrounding community in understanding the calculation of nutritional needs and recommendations for a balanced diet, an information application will be disseminated, especially one based on Android mobile in the form of a system that will present information on daily nutritional needs, namely Media Information on Daily Nutritional Needs which will be socialized to UPT Puskesmas Mandomai staff and the surrounding community.
The Universal Decimal Classification in the Organization of Knowledge: Representing the Concept of Ethics

Training in knowl­edge organization (KO) involves an understanding of theories for the construction, maintenance, use, and evaluation of logical documentary languages. Teaching these KO concepts in LIS programs are related basically to accessing documents and retrieving their intellectual content. This study focuses on access to documents and exploring the ethical theme in all its dimensions as applied to the teaching of an undergraduate discipline as part of a Bachelor of Library Science degree offered at the Federal University of Minas Gerais (UFMG). As a methodology, a Project-based Pedagogy strategy is used in the teaching of a discipline called “Classification Systems: UDC” for students to classify a documentary resource from a collection on ethics. The teaching of bibliographic classification requires students to learn how to use the mechanisms available to form a notation as well as to use a syntax schema (tables) appropriately. Students also learn to determine a place for the document in the collection, considering the knowl­edge represented in the collection as a whole. Altogether, such a practice can help students to understand the theory underlying a classification system. The results show that the students were able to understand the basic concepts of knowl­edge organization. The students were also able to observe that the elements of the different tables of a classification tool are essential mechanisms for the organization of knowl­edge in other contexts, especially for specific purposes.
Analysis of effective techniques and algorithms in terms of “text mining” to predict the authorship in Albanian language

Natural Language Processing has gained a special importance and development in recent years, where the analysis of written texts through various techniques of “text mining” and the extraction of all their features is a prerequisite to be used and be further implemented for various purposes. In this paper it will be compared some of the most effective techniques and algorithms in termsof “text mining”, to predict the authorship of a written text in the Albanian language, using for training the model, a fund of articles written by some of the most well- known bloggers of Albanian journalism. When talking about finding the authorship of a text it must kept in mind many important elements such as: number of sentences, sentence structure, number of words in a sentence, repetition of the same word, length of words used, frequency of the use of punctuation, literary figures used; elements which best display the unique narrative style for each author. This paper can serve as a good starting point to go further to its specific objective, predicting the authorship of an anonymous text, but also for other applications related to “text mining”, referring to the Albanian language.
Text sentiment analysis using deep convolutional networks

The ability to process language is a difficult task. A computer system that can extract meaningful information from raw text data is considered to be equipped with intelligent behaviour. Sentiment analysis is a form of information extraction of growing research and commercial interest, especially because of the exponential increase of data in recent years. This paper presents a machine learning approach built upon Neural Networks that equips a computer system with the ability to learn how to detect sentiment in a given text and to correctly classify previously unencountered text with respect to predefined sentiment categories. The developed approach does not rely on any hard-coded parameters. Rather, a model is learned through a training procedure performed on raw data alone with the objective of searching to identify patterns and discovering feature predictors that allow the algorithm to classify text into positive, negative, or neutral sentiments, while additionally focusing on nuances such as humorous or sarcastic text. Experimental validation shows promising results. The learning algorithm is fed with high-volume textual data extracted from the Twitter social media platform. The trained model is then tested on a separate dataset, previously unknown by the model. The classification algorithm is able to predict the sentiment associated with a given text with high accuracy.
Cost-Efficient Request Bundling for O2O Home Services

With the advent of mobile internet, Online-to-Offline (O2O) home services have emerged, such as home healthcare and repair services, greatly facilitating our lives. Customers book services through online platforms, and workers provide the requested services at the customers’ homes offline. However, each time workers travel to customers’ homes, they incur opportunity cost, leading to increased cost for home services. In this paper, we propose to bundle several O2O home service requests close to each other and match them with a worker. Therefore, requests that are in a bundle can split the worker’s opportunity cost. Specifically, we formalize the request bundling problem for O2O home services, which aims to minimize the overall cost of completing all requests while satisfying the time and Quality of Service(QoS) constraints. We present three Bi-layer Greedy request bundling(BiG) algorithms to solve it, including BiG-LEV, BiG-DIST, and BiG-COST. Besides, a cost accounting method based on Shapley value is designed to calculate the actual cost of each service for in-depth analysis. Finally, we illustrate a case of bundling requests for home healthcare services and compare the performance of the three algorithms.
Reliable prediction intervals with directly optimized inductive conformal regression for deep learning

By generating prediction intervals (PIs) to quantify the uncertainty of each prediction in deep learning regression, the risk of wrong predictions can be effectively controlled. High-quality PIs need to be as narrow as possible, whilst covering a preset proportion of real labels. At present, many approaches to improve the quality of PIs can effectively reduce the width of PIs, but they do not ensure that enough real labels are captured. Inductive Conformal Predictor (ICP) is an algorithm that can generate effective PIs which is theoretically guaranteed to cover a preset proportion of data. However, typically ICP is not directly optimized to yield minimal PI width. In this study, we propose Directly Optimized Inductive Conformal Regression (DOICR) for neural networks that takes only the average width of PIs as the loss function and increases the quality of PIs through an optimized scheme, under the validity condition that sufficient real labels are captured in the PIs. Benchmark experiments show that DOICR outperforms current state-of-the-art algorithms for regression problems using underlying Deep Neural Network structures for both tabular and image data.
Generalized heterophily graph data augmentation for node classification

Graph data augmentations have demonstrated remarkable performance on homophilic graph neural networks (GNNs). Nevertheless, when transferred to a heterophilic graph, these augmentations are less effective for GNN models and lead to reduced performance. To address this issue, we propose a unified augmentation approach called GePHo, a regularization technique for heterophilic graph neural networks based on self-supervised learning, leveraging graph data augmentation to acquire extra information to guide model learning. Specifically, we propose to generate a pseudo-homophily graph that is type-agnostic, enabling us to apply GePHo to both homophilic and heterophilic graphs. Then, we regularize the neighbors with a sharpening technique for data augmentation and generate the auxiliary pseudo-labels to classify the original GNN’s output, whose operations are to constrain the local and global node representation, respectively. Extensive experiments on three homophilic graph and six heterophilic graph datasets demonstrate the competitive effectiveness of GePHo in node classification task, and the ablation experiments verify the efficacy of our GePHo in graph data augmentation.
Differential Complete Blood Count for Diagnosis of COVID-19?

Background: The World Health Organization (WHO) has declared COVID-19 a public health emergency of international concern. In this context, effective and affordable diagnostic procedures are essential for identifying and managing cases. Complete blood counts (CBC) are among the most common and readily available diagnostic tests. The current study aimed to evaluate the efficacy of CBC in diagnosing COVID-19 and identifying cases. Patients and Methods: A case-control study was conducted on 173 patients at Ain Shams University Hospitals over a period of three months. Patients were allocated into two groups according to COVID-19 PCR results: Group 1 included patients with COVID-19 positive PCR, and Group 2 included patients with COVID-19 negative PCR. Results: The study found that differential CBC had significant value in diagnosing COVID-19 disease. Many COVID-19 patients had lymphopenia and leucopenia compared to non-COVID-19 suspected patients. The low values of leukocytes, neutrophils, lymphocytes, and eosinophils with a CBC test were found to be valuable in the initial diagnosis of COVID-19. Conclusion: The definitive diagnosis of COVID-19 requires RT-PCR analysis, which is time-consuming and less accessible. Thus, the initial diagnosis and treatment of patients may be delayed. This study suggests that CBC, which is easily available and affordable, can be valuable in the early identification of COVID-19 cases, allowing for prompt treatment and management.
English Learning Styles of the Eleventh Grade Students in SMA Negeri 07 Kendari

This study aimed to find out the English learning styles of the eleventh-grade students in SMA Negeri 07 Kendari and the contribution of students learning styles to English achievement. Those learning styles were visual, auditory, read/write, and kinesthetics. This study applied a quantitative approach under the survey research design. The sample for this study was 84 students of grade 11 in SMA Negeri 07 Kendari, who were divided into 4 classes. A closed-ended questionnaire was used to collect the data. This study used descriptive statistics and simple linear regression to analyze the data. The results of this study showed that the majority of grade 11 students in SMA Negeri 07 Kendari used auditory learning as their major English learning style preference. Meanwhile, visual learning was their minor English learning style preference. Followed by read-write and kinesthetics learning styles. There was a significant relationship between the students’ learning styles and their English achievement, with the significance value (Sig.) being 0.000 smaller than the probability of 0.05, and the contribution of the students’ learning styles to the English achievement of the eleventh-grade students in SMA Negeri 07 Kendari was 30%. The R square value is 0.300. It means that learning style is not the main factor contributing to the students’ academic achievement. _________________________________________________________________________________________________________________________________________ Penelitian ini bertujuan untuk mengetahui gaya belajar bahasa Inggris siswa kelas sebelas di SMA Negeri 07 Kendari dan kontribusi gaya belajar siswa terhadap prestasi bahasa Inggris. Gaya belajar tersebut adalah visual, auditori, baca/tulis, dan kinestetik. Penelitian ini menerapkan pendekatan kuantitatif dengan desain penelitian survei. Sampel penelitian ini adalah 84 siswa kelas 11 SMA Negeri 07 Kendari yang terbagi dalam 4 kelas. Kuesioner tertutup digunakan untuk mengumpulkan data. Penelitian ini menggunakan statistik deskriptif dan regresi linier sederhana dalam menganalisis data. Hasil penelitian ini menunjukkan bahwa mayoritas siswa kelas 11 SMA Negeri 07 Kendari menggunakan pembelajaran auditory sebagai preferensi gaya belajar bahasa Inggris utama mereka. Sementara itu, pembelajaran visual merupakan preferensi gaya belajar bahasa Inggris minor mereka. Dilanjutkan dengan gaya belajar baca/tulis dan kinestetik. Terdapat hubungan yang signifikan antara gaya belajar siswa dan prestasi bahasa Inggris mereka, dengan nilai signifikansi (Sig.) sebesar 0,000 lebih kecil dari probabilitas 0,05, dan kontribusi gaya belajar siswa terhadap prestasi bahasa Inggris siswa kelas sebelas di SMA Negeri 07 Kendari sebesar 30%. Nilai R Square sebesar 0,300. Artinya, gaya belajar bukanlah faktor utama yang berkontribusi terhadap prestasi akademik siswa.
IMPLEMENTATION OF CERTAINTY FACTOR METHOD IN WEB-BASED EAR NOSE THROAT (ENT) DISEASE DIAGNOSIS EXPERT SYSTEM

Ear Nose Throat Disease (ENT) is a disease that affects many Indonesian people. Beji Mulyo Village is a village in the Tungkal Jaya sub-district, Banyuasin district, Palembang City. This village only has a health center and a midwife as the head of health in the village. The problem experienced by Beji Mulyo Village is the lack of experts to treat ENT diseases. Besides that, to find an ENT specialist, the community has to travel quite a distance to find a hospital in the city. Therefore it is necessary to implement an expert system with the certainty factor method with the aim of being able to help and facilitate the community in providing diagnostic information related to diseases and initial treatment to detect diseases that can be given to people affected by ear disease. The results of testing the system from 20 patient data with the application of the certainty factor method obtained an accuracy rate above 98%.
Designers’ Roles and Challenges Conducting Sustainable Design Process Under ESG : Qualitative research through comparison between Korea and Europe

The purpose of this research is to explore women’s wear designers’ roles and challenges when practicing ESG. Qualitative research was conducted through 1:1 in-depth interviews with six participants (three professional designers in Korea and three professional designers in Europe) to explore practical designers’ viewpoints on how ESG implementation differs within Korea and Europe. Semi-structured questionnaires were set up for the interviews, and the interviews were recorded with participants’ permission upon IRB approval for data collection. The analyzed data results garners through the three-level coding process are as follows. First, we identified disparities between Korea and Europe regarding fashion designers’ roles and working system. Designers in Korea participated in every levels of production, striving to create luxury brand quality within the high-street brand’s working system. Conversely, designers in Europe focus on leading an intensive sampling process for products within the pre-order working system. This approach enables designers in Europe to develop insightful products through collaborative discussions with experts in each department. Secondly, we identified differences in the external conditions, such as management and social conditions, affecting ESG implementation within Korea and Europe. These differences stem from each region’s unique historical context regarding sustainability issues. The European fashion industry can draw upon past experiences to provide more practical and comprehensive conditions for practicing ESG. Third, all participants emphasized the importance of deliberate and efficient planning, sustainable materials usage, sustainable product processing, and a shift in designers’ perceptions when practicing ESG in design. However, there were variations in how designers in Korea and Europe perceived practical roles. Fourth, we identified the different limitations in implementing ESG among designers due to varying working systems, external factors, and practical experiences between Korea and Europe. Therefore, we advocate for employing distinct regulations and systems to promote ESG in the design process, including integrated planning that considers practical designers’ viewpoints, establishes a clear vision within companies, and promotes sustainability education with national-level support in Korea. This study has significant value in exploring through qualitative research designers’ accumulated experiences and viewpoints.
IoT-Based Medical Ecosystem

The Internet of Things (IoT) is an evolving technology in the emerging digital transformation domain. The healthcare system is also growing, using IoT to improve human life and save more lives. With the assistance of IoT technology, physicians can easily monitor patients’ health conditions in real-time. A cardiac pacemaker is a medical device connected to the IoT environment to improve the efficiency of healthcare. However, low-quality IoT design will bring disadvantages, such as cyber-attacks. Every process of building IoT medical devices should evaluate the product before launching it to the market. Manufacturers or hospitals should organise their critical infrastructure orderly to protect confidential data. The data should achieve the confidentiality, integrity, and availability of the CIA triad, which is the foundation of information security. This paper aims to study the vulnerabilities of IoT medical devices, the methods of possible attacks from hackers, and organisational and operational security to address cyber security in the healthcare industry. Moreover, it proposes a framework for the IoT medical ecosystem between the patient and the hospital to improve the existing IoT medical ecosystem.
Does splitting make sentence easier?

In this study, we focus on sentence splitting, a subfield of text simplification, motivated largely by an unproven idea that if you divide a sentence in pieces, it should become easier to understand. Our primary goal in this study is to find out whether this is true. In particular, we ask, does it matter whether we break a sentence into two, three, or more? We report on our findings based on Amazon Mechanical Turk. More specifically, we introduce a Bayesian modeling framework to further investigate to what degree a particular way of splitting the complex sentence affects readability, along with a number of other parameters adopted from diverse perspectives, including clinical linguistics, and cognitive linguistics. The Bayesian modeling experiment provides clear evidence that bisecting the sentence leads to enhanced readability to a degree greater than when we create simplification with more splits.
WEBSITE UNTUK PENDATAAN BALITA POSYANDU CEMPAKA RW 11 KELURAHAN ANTAPANI KIDUL KOTA BANDUNG

Posyandu Cempaka in RW 11, Antapani Kidul Village, Antapani District, Bandung City has a low to moderate maternal and infant participation rate of 40% to 45%. In addition, the growth and development of toddlers is recorded manually or manually on the health card page (KMS). The absence of KMS often makes it difficult for parents and posyandu staff to find history about their child's development. This activity aims to increase the number of toddlers who come to Posyandu. In addition, this activity aims to make it easier for parents of toddlers to obtain information about Posyandu action plans, information on child growth and development, as well as Posyandu health information and programs. As for posyandu cadres, this activity can help collect data and report child growth and development, and can facilitate reporting to the sub-district or puskesmas. This activity utilizes the method of applying technology by creating a Posyandu Website. This website is a collection of components consisting of text, images, sound, and animation so it is interesting to visit (Sholechul Azis, 2013). The electronic-based data collection method serves as the basis for activity planning and implementation. Based on the measurement results, this community service activity is able to fulfill all indicators of success, including: the number of mothers of children under five who use the application reaches 45%, the number of children under five who come to Posyandu increases by 75%, all children under five at Posyandu Cempaka are registered digitally, the level of community satisfaction is high , and the activity suitability level reached 100%.