Reviewerly/exHarmony huggingface dataset:
Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised Learning

With the rapid development of self-supervised learning (e.g., contrastive learning), the importance of having large-scale images (even without annotations) for training a more generalizable AI model has been widely recognized in medical image analysis. However, collecting large-scale task-specific unannotated data at scale can be challenging for individual labs. Existing online resources, such as digital books, publications, and search engines, provide a new resource for obtaining large-scale images. However, published images in healthcare (e.g., radiology and pathology) consist of a considerable amount of compound figures with subplots. In order to extract and separate compound figures into usable individual images for downstream learning, we propose a simple compound figure separation (SimCFS) framework without using the traditionally required detection bounding box annotations, with a new loss function and a hard case simulation. Our technical contribution is four-fold: (1) we introduce a simulation-based training framework that minimizes the need for resource extensive bounding box annotations; (2) we propose a new side loss that is optimized for compound figure separation; (3) we propose an intra-class image augmentation method to simulate hard cases; and (4) to the best of our knowledge, this is the first study that evaluates the efficacy of leveraging self-supervised learning with compound image separation. From the results, the proposed SimCFS achieved state-of-the-art performance on the ImageCLEF 2016 Compound Figure Separation Database. The pretrained self-supervised learning model using large-scale mined figures improved the accuracy of downstream image classification tasks with a contrastive learning algorithm. The source code of SimCFS is made publicly available at https://github.com/hrlblab/ImageSeperation.
Reconstructing Gaussian bipartite states with a single polarization-sensitive homodyne detector

We present a novel method to fully estimate Gaussian bipartite polarization states using only a single homodyne detector. Our approach is based on [Phys. Rev. Lett.102, 020502 (2009)10.1103/PhysRevLett.102.020502], but circumvents additional optics, and thereby losses, in the signal path. We provide an intuitive explanation of our scheme without needing to define auxiliary modes. With six independent measurements, we fully reconstruct the state's covariance matrix. We validate our method by comparing it to a conventional dual-homodyne measurement scheme.
Conversion of companies and source code comment ontology into RelOntoUML

In this article, the conversion of a Companies and a source code commenting (SEM) ontology system into the RelOntoUML modelling system is presented. The two ontology systems are in OWL format available on github. After the simplification of the OWL files, I converted these two systems to RelOntoUML. RelOntoUML is a proprietary modelling that combines ontology, UML, and relational database modeling.
RelOntoUML model of the archaeological findings

In this article, an ontology describing archaeological finds (DhiQar) is converted into the RelOntoUML model, which combines relational database modelling, ontology, and UML modelling. The ontology describing the archaeological finds can be found in OWL on github, it is open source. In this article, the original OWL ontology is also presented using the graphical representation system of the Protége editor. And the RelOntoUML model of the system is also detailed.
The role of information and digital technologies in ensuring the sustainable development of economic entities - Participants of the ecosystem

The COVID-19 pandemic has caused serious damage to the global economy, left threatening socio-economic consequences, forced employers to overcome enormous difficulties associated with the need to survive and maintain decent working conditions.At the same time, the COVID-19 pandemic gave an impetus to the development of digitalization in all spheres of activity around the world, forming a digital fault line and highlighting the urgent need to improve information and digital technologies of ecosystems in which economic entities operate.New technologies have radically changed the paradigm of life, caused economic polarization, job instability and alienation of people. In this regard, when volatility is high, uncertainty is high, and complexity and ambiguity are maximum, it is important to analyze the role of information and digital technologies in ensuring the sustainable development of economic entities, to study the relationship between digitalization and the sustainable development of ecosystems.
Application of Machine Learning Algorithm in Identification of Anaemia Diseases

Anaemia is the reason for lot of serious health-related issues, and if it is detected at an early stage accurately, it can help to avoid problems like fatigue, pregnancy complications, heart problems, and life-threatening complications. In this study, five supervised machine learning (ML) algorithms for detecting anaemia based on red blood cell (RBC) parameters such as haemoglobin, haematocrit or packed cell volume (PCV), RBC count, mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH) and mean corpuscular haemoglobin concentration (MCHC) were developed. The ML algorithms were applied on actual patient anaemia data, collected from a clinical laboratory, of a sample size of 2000 data samples. Multiclass classification of these data into five different conditions of anaemia, namely beta thalassemia trait (BTT), dimorphic anaemia (DA), and macrocytic blood picture (MBP), microcytic hypochromic anaemia (MHA), including normocytic normochromic blood picture (NNBP) indicating no anaemia, was performed by the five ML algorithms, and the best algorithm for accurately detecting anaemia was identified. The result indicated that the decision tree and random forest algorithms were superior to other algorithms in terms of accuracy, sensitivity, and specificity of the identification process.
An AIoT-Based Environment Control System for Outdoor Barbecue Restaurant in Penghu, Taiwan

This paper proposes an outdoor barbecue restaurant environment control system based on AIoT technology in Penghu, Taiwan, which provides a more convenient restaurant management method for barbecue restaurant service providers and improves the comfort of diners in barbecue restaurants. The proposed system can automatically adjust the temperature and humidity of the restaurant, which uses image processing technology to count the number of diners in the restaurant and set up cameras in the food storage refrigerator to identify whether there are empty plates in time. In response to the COVID-19 epidemic when entering a restaurant, mask identification is carried out using object recognition so that diners who wear masks abnormally can be detected in time, and the virus infection problem can be reduced. All information can be monitored through the proposed IoT monitoring platform.
Undefined Object Detection Based on Convolutional Neural Network Using Small Dataset

In this paper, we propose a CNN-based method for undefined object detection. This method utilizes a small dataset acquired by multiple cameras at different views. The first procedure is to divide an image into <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$64\times 64$</tex> patches, and then the patches with the annotations input to CNN architecture for training a classification model. The annotation is a binary value determined by the area ratio of a marked area to the patch. In the detection process, our method applies the trained model with two estimated thresholds to detect undefined objects in the monitored area. The experiment results demonstrate that Resnet18 outperforms the compared CNN models.
Text as data

This chapter evaluates a variety of strategies for computational text analysis, otherwise known as "text as data" (TAD). We begin by providing general guidance on constructing a corpus - or a collection of documents - relevant to a research question, followed by an explanation of the necessary steps to prepare raw text for a variety of computational models. We then describe three TAD modeling strategies. First, we discuss dictionary methods, which leverage the frequency and tone of words to measure documents on a quantity of interest and/or classify them into a particular category. Second, we review supervised classification, where a machine learning algorithm assigns documents to predetermined categories. Finally, we address unsupervised techniques, which seek to discover new ways of organizing texts that are theoretically useful, but perhaps understudied or previously unknown.
An Improved Force-Directed Automatic Layout Method for Undirected Compound Graphs

In software engineering, the undirected compound graph serves as a medium for human-machine communication, translating human intent into machine language to efficiently deliver and demonstrate people’s demands by utilizing its outstanding analysis capability. Still, the industrial adoption of goal models is hindered by the scalable data and the massive manual workload it incurs. Overt problems, including the uncertainty of iteration direction and the difficulty of controlling the number of edge crossings, remain unsolved. We introduce the method of graph folding, which continuously collapse indecisive nodes to reduce the data scale and remain the basic structure of the original graph simultaneously to provide a possible solution to this dire state. The folding method confirms the direction of the iterations and considerably reduces its number, which greatly improves the efficiency of the man-machine communication. We repeatedly operate adjustment and expansion of the graph to ensure the optimal solution, and the results are promising.
Development Employee Payment Information System on CV Borneo Andeska Maja Samarinda

This research is motivated by the unavailability of an employee payroll information system that facilitates data processing on CV Borneo Andeska Maja Samarinda in finance/treasury. Current the company from the finance/treasury section is still using Microsoft Excel in making employee salary recaps. Therefore the purpose of making a payroll information System in this study is to provide convenience and help reduce administrative costs or administrative costs and of course it can be a better information system. As for the development using the System Development Life Cycle (SDLC) with the PHP Programing language Framework Laravel. The data structure used is Entity Relationship Diagram (ERD) and Physical Diagram Model (PDM) and for the database using phpMySQL. The result of this study are expected to provide convenience for the company CV Borneo Andeska Maja Samarinda, especially in the finance department in terms of employee payroll which is carried out every month.&#x0D;
A study on the actual conditions of the operation of the Military exam in Jeju during the Joseon Dynasty

This study confirmed specific cases of the implementation of the military exam in Jeju. As a result of the study, it was confirmed that the first Jeju military exam was continuously conducted by assigning three personnel belonging to Jeolla Province, and through the records, 73 tests were conducted and 124 people passed.&#x0D; Next, an official who was assigned a special mission was sent to Jeju to take a military exam. A total of 19 tests were conducted and 156 soldiers passed.&#x0D; During the reign of King Cheoljong, special military exams were conducted only in Jeju, and in two tests, Lee Eon-Gil and Yang Je-ho took first place. In 1854, eight soldiers passed the special military exam conducted in Jeju. Finally, the operation of Jeju Mabyeongdosi(馬兵都試) and Byeolpo-Gwa(別砲科) was considered.&#x0D; Through this study, 285 people who passed the Jeju military exam were confirmed, and this number is considered to be the minimum number of those who passed the Jeju military exam.
Business Strategy of AI Voucher Support Project: Focused on a Level of Perceived Performance for the Participating Companies

To overcome the lacks of new competitive capabilities in domestic AI industry such as technology expert and employee, Korea government has planned and carried out AI voucher support project. However, the executive organization confronted various problems and risks, and the project participating companies had difficulties in preparing the project. Therefore, the study aims to provide both the executive organization and the project participating companies with the guidelines for business strategy establishment. To complete the research purpose, the study traced various clues for effects of AI voucher support project through investigating the degrees of satisfaction of the project process and performance experience for AI solution supply and demand companies. The study recommended that the executive organization should focus on manpower recruitment and that the participating companies should clarify the vision and goals for the project.
Analysis on the Transitional Process of Schools’ Number and Its Influential Factors in Jeju Special Self-Governing Province

The purpose of this study was to identify the transitional process of schools’ number and to analyze the essential factors affecting the school transition based upon the change in the number of schools in Jeju. To accomplish it, the study used literature research and interview surveys. In-depth interviews were conducted from Oct. 17th through Nov. 28th, 2020, with the former four principals who had experienced managers, directors, or supervisors of the educational office. Major results of the study are as follows: First, the transitional process of schools in Jeju was revealed in four stages of fomentation, foundation, quantitative expansion, and qualitative transition. Second, the essential factors that influenced the transition of schools in Jeju were non-policy factors rather than policy factors on all stages except for the foundation one. The results would be used as the basic resources for future policy making in the educational office.
PENINGKATAN KOMPETENSI GURU MELALUI PEMANFAATAN TEKNOLOGI INFORMASI [TEACHER COMPETENCE ENHANCEMENT THROUGH USE OF INFORMATION TECHNOLOGY]

&lt;p&gt;&lt;em&gt;During the COVID-19 pandemic, teaching and learning activities in Indonesia, from basic education to university levels, must be carried out online. The main problem experienced by teachers in online teaching and learning activities is the lack of mastery of technology by teachers. Based on this, it is necessary to conduct mentoring and training programs to improve the ability of teachers to use technology to support online teaching and learning activities. For this reason, in this community service activity, training has been carried out which aims to provide knowledge to teachers to improve teacher competence using information technology. The method of implementing activities is through counseling and training. Activities have been evaluated using a questionnaire, the results achieved are 90.77% of teachers can use information technology to support online teaching and learning activities.&lt;/em&gt;&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt;BAHASA INDONESIA ABSTRACT: &lt;/strong&gt;Pada masa pandemi COVID-19 kegiatan belajar mengajar di Indonesia, mulai dari tingkat pendidikan dasar hingga perguruan tinggi harus dilaksanakan secara daring. Permasalahan utama yang dialami oleh guru dalam kegiatan belajar mengajar daring adalah kurangnya penguasaan teknologi oleh guru. Berdasarkan hal tersebut, perlu dilakukan program pendampingan dan pelatihan untuk meningkatkan kemampuan guru-guru dalam menggunakan teknologi guna mendukung kegiatan belajar mengajar daring. Untuk itu, pada kegiatan pengabdian masyarakat ini telah dilakukan pelatihan yang bertujuan memberikan pengetahuan kepada guru-guru untuk meningkatkan kompetensi guru melalui pemanfaatan teknologi informasi. Metode pelaksanaan kegiatan melalui penyuluhan dan pelatihan. Kegiatan telah dievaluasi dengan menggunakan kuesioner, hasil yang dicapai adalah 90.77% guru dapat menggunakan teknologi informasi dalam mendukung kegiatan belajar mengajar daring.&lt;/p&gt;
Arduino Uno Programming Training for Senior High School in the City of Tasikmalaya

This study aims to program basic Arduino UNO. First, we create an Arduino simulation circuit to turn on the Light Emitting Diode (LED) in the Proteus software. Next, make Arduino programming for the running LED. Finally, enter the Arduino program into the Arduino circuit on Proteus. This Arduino UNO programming training will be introduced to teachers in online training activities. With this training, it is hoped that teachers will be able to increase innovation and creativity in the subjects taught,
HAIM: Holistic AI for Medicine

Artificial intelligence (AI) systems hold great promise to improve healthcare over the next decades. Specifically, AI systems leveraging multiple data sources and input modalities are poised to become a viable method to deliver more accurate results and deployable pipelines across a wide range of applications. In this work, we propose and evaluate a unified Holistic AI in Medicine (HAIM) framework to facilitate the generation and testing of AI systems that leverage multimodal inputs. Our approach uses generalizable data pre-processing and machine learning modeling stages that can be readily adapted for research and deployment in healthcare environments. We evaluate our HAIM framework by training and characterizing 14,324 independent models based on MIMIC-IV-MM, a multimodal clinical database (N=34,537 samples) containing 7,279 unique hospitalizations and 6,485 patients, spanning all possible input combinations of 4 data modalities (i.e., tabular, time-series, text, and images), 11 unique data sources and 12 predictive tasks. We show that this framework can consistently and robustly produce models that outperform similar single-source approaches across various healthcare demonstrations (by 6-33%), including 10 distinct chest pathology diagnoses, along with length-of-stay and 48-hour mortality predictions. We also quantify the contribution of each modality and data source using Shapley values, which demonstrates the heterogeneity in data modality importance and the necessity of multimodal inputs across different healthcare-relevant tasks. The generalizable properties and flexibility of our Holistic AI in Medicine (HAIM) framework could offer a promising pathway for future multimodal predictive systems in clinical and operational healthcare settings.
Dark-Matter Search Optimiser

A version of the Gravitational Search Algorithm that takes advantage of the repulsive forces of dark matter to explore the space is presented in this article. The classification of matter into dark and gravitational particles is used to balance between exploration and exploitation of the feasible set of solutions, respectively. The idea is to overcome some problems of the Gravitational Search Algorithm as, for instance, trapping into local optima and the choice of parameters. Three different problems are used to demonstrate the potential of the algorithm when compared with the Particle Swarm Optimiser. In the future, we plan to do a thorough assessment of the algorithm on benchmark problems as well as on some applications.
Anomaly Detection

Anomaly detection identifies unexpected events, observations, or items that differ significantly from the norm. To detect anomalies, first, a model is trained to learn what normal behavior is. Then, a statistical technique is applied to determine, if a specific data point is an anomaly.
Using Oracle Cloud Functions

In the previous chapter, we created a photo album that used the OCI Object Storage to store the original images. These are large files that we do not necessarily want to render in full resolution until the user explicitly requests to view them. We can further enhance our application with image and/or data processing to create smaller preview images for rendering, thereby improving the page load performance.
Cybercrime Detection Using Live Sentiment Analysis

AbstractCyberbullying is a continual act that harasses, humiliates, threatens, or hassles people through electronic devices and online social networking Websites. Cyberbullying through the Internet is considered additionally more dangerous than any form of bullying done in the past, because it can probably amplify the humiliation to a vast online audience. Current models have a variety of issues which our proposed system try to address through our proposed work. Many of the other models use small, heterogeneous datasets, without a thorough evaluation of applicability. At the same time, many models yield small datasets that fail to capture the required complex social dynamics and impede direct comparison of progress. Our model uses real-time data from Twitter API which is preprocessed using regex. It is then fed to the LSTM neural network which will filter out negative tweets and also passed through a sentiment analyzer. After passing through these components, if the sentence is found to be negative in nature and contains an abusive word, then we classify it as an act of cyberbullying. In the coming years, we may see people trying to find different ways to harass each other on social media, so by an over proposed method, we have the model to detect sentiment analysis of sentences as well as we have the reference as to why the sentence is passing negative impressions to people using sentiment analyzer.KeywordsCyberbullyingLSTM neural networkTwitter APISentiment analyzerStemmerMachine learningWeb app
Organizational learning: personalization, blended learning, and tailor-made learning solutions

Chapter Three explores how changes in the world of work contribute to the need for employees and workers to increase knowledge and upgrade skills and how organizations adapt accordingly, abandoning traditional training methods for a plethora of learning solutions focusing on the learner. Increasingly, we argue, organizations will utilize Hybrid and Blended learning paradigms using up-to-date technologies (such as virtual reality) tailored to both the employee and the organization. Finally, building on the recent work of Kraiger and Ford (2021), we posit that future research will move towards a comprehensive and parsimonious model that integrates all the positive elements and requirements of instruction, learning paradigms, methodologies, and outcomes under the rubric of a "science of workplace instruction."
Towards a shared understanding of genuine co-design with people with lived experience

Co-design is becoming a well-known term outside of the design domain, reflecting a growing awareness of the potential and value of applying a design-led process to address complex social and systemic issues. However, an increase in the adoption of ‘co-design’ methods has also led to multiple and sometimes conflicting understanding of what it means and how it is enacted in practice. ‘What does genuine co-design mean? What does it look like? And how does it make a difference?’ – we have been exploring these questions through our participatory design practice and research spanning over seven years. In this chapter, we share our learnings and reflections on genuine co-design, using examples to articulate characteristics of co-design and conditions for enabling genuine participation in the co-design process. We invite further dialogue with the wider practice and research communities to develop a shared understanding of what is (and what is not) genuine co-design.