Reviewerly/exHarmony huggingface dataset:
Analysis of trends and challenges of public open data in health care industry using Artificial Intelligence

Understanding the public open data being gathered and analyzed is necessary before we can discuss health data analytics and its function in the healthcare industry. A significant quantity of health data is also being obtained, kept, and analyzed, in addition to data on the operations and procedures of the commercial side of the healthcare industry. Any information about a patient’s or a population’s health is referred to as “health data.” Medical professionals and administrators may find areas that need improvement or are in danger by using data from the health industry. With this knowledge, they may take steps to improve any areas where patient care is deficient and elevate the standard of care for all patients. Lab findings, vital sign recordings, prescription diaries, and computerized medical records all include enormous amounts of data. A change in the patient’s health or the possibility of experiencing a major consequence may be detected by physicians and nurses using artificial intelligence (AI) techniques to spot data trends. Due to the complexity and expansion of data in the healthcare sector, AI will be employed there with greater frequency. Numerous types of AI are already being utilized by health insurance companies, medical organizations, and biological sciences enterprises. Solutions can be put into three main categories: operational tasks, patient engagement and participation, and medication and diagnosis recommendations. The health sector uses AI and data engineering to improve the processing and analysis of health data, compensation settlements, and other clinical records. The objective of this chapter is to learn about the capabilities of AI in using public open data as well as the trends and challenges in patient data.
A Blockchain Framework in Compliance with Data Protection Law to Manage and Integrate Human Knowledge by Fuzzy Cognitive Maps: Small Business Loans

New firms have minimum accessibility to data, unlike large data repositories of businesses operating for longer periods. They rely on the knowledge exchange and perceptions of domain experts outside the organisation to enhance the capability of the decision support systems. This paper presents a framework on blockchain technology to develop a secure knowledge management service which addresses the challenges imposed by European and US data protection laws and high on-chain computational costs. It presents a hybrid off-chain and on-chain smart contract computation methodology for the scalable application of blockchain technology. It requires low on-chain storage cost and low cost of frequent data imports for off-chain AI computations. The framework demonstrates a real-world application of knowledge management by blockchain to integrate the judgment of multiple financial experts to design loan eligibility policies.
Improved Schemes for Managing Reputation in a Blockchain-based Decentralized Marketplace

Very recently, in an attempt to reduce the uncertainty associated with notoriously unreliable buyer feedback, researchers proposed a blockchain-based trust and reputation management system, where a Smart Contract manages all aspects of a transaction: setting up a contract, interacting with the two parties, and finally evaluating and providing feedback on the buyer/seller performance at the end of each transaction. They have also proposed a data structure that manages seller reputation scores inside the blockchain. In this paper, we provide two novel reputation management schemes that significantly improve on these methods. Our first such scheme is adaptive; the second one is randomized, wherein decisions about managing the underlying data structure are made based on coin flips. We provide analytical performance predictions and verify, empirically, by extensive simulation, the accuracy of our predictions. Our schemes are shown to result in more efficient query execution with enhanced block structure schemes.
Design and application of integrated energy control system for National Speed Skating Stadium

With the increasing degree of information integration between new generation information technology and management applications, overall energy services are ushering in a period of opportunity for very fast development. Combining the actual needs of energy system operation and control, in the Beijing Speed Skating Stadium scenario, the interaction and visualization design of the energy management control system is carried out. We will further analyze and design the core functions of the energy control system for the Beijing Speed Skating Stadium. The system platform is built to make the system more intelligent, more powerful, more efficient and flexible in operation, and more convenient for users to interact and use. By building an energy control system, we can realize energy detailed management, improve comprehensive energy utilization level and reduce energy consumption effectively.
Classification for Covid-19 Affected Family Cash Aid Recipients Using Naïve Bayes Algorithm

The COVID-19 pandemic that occurred in Indonesia had a huge impact on the country's economy. One of the solutions set by the government in dealing with COVID-19 is to use APBD funds for social assistance in the form of cash, namely "Village Direct Cash Assistance" (BLT DD). With the hope that the people affected by COVID-19 can be helped by this assistance. There are several problems in the distribution of social assistance, one of which is recipients who are not on target. Therefore, it is necessary to use methods to correctly classify recipients. This study uses the Naïve Bayes method to classify people who receive and do not receive aid. From the results obtained on the confussion matrix, the people who received BLT DD assistance and were predicted to receive were as many as 33 people/KK, the people who did not receive BLT DD and were predicted not to receive as many as 34 people/KK, the people who received BLT DD and were predicted not to receive as many as 2 people/KK , and people who do not receive BLT DD and are predicted to receive as many as 6 people/families. As for the classification accuracy value obtained using the Naïve Bayes method is 89%, while the error rate obtained is 11%.
Automated Extraction of Fire Protection System Design Information from Architectural Drawings Using Artificial Intelligence

This study aimed to develop an artificial intelligence-based fire protection system design engineering solution. We developed an automatic fire protection system design element extraction algorithm from architectural overview tables using various artificial intelligence libraries. The deep learning-based libraries used in this study were TableNet, OpenCV, and EasyOCR. Levenshtein distance was also used to check the similarity of the characters. Approximately 1,000 tables and the Marmot dataset, which is open-source training data, were trained for the development of the table detection model. The performance metrics used for performance evaluation were recall, precision, and F1 score. The final model selected had the highest F1 score of 0.63. The results confirmed that fire protection system design elements could be efficiently extracted through the developed algorithm. We expect various positive effects of this newly developed algorithm, such as improvements in engineers’ working productivity, functional suitability of designs, and reliability.
Perception and simulation during concept learning.

A key component of humans' striking creativity in solving problems is our ability to construct novel descriptions to help us characterize novel concepts. Bongard problems (BPs), which challenge the problem solver to come up with a rule for distinguishing visual scenes that fall into two categories, provide an elegant test of this ability. BPs are challenging for both human and machine category learners because only a handful of example scenes are presented for each category, and they often require the open-ended creation of new descriptions. A new type of BP called physical Bongard problems (PBPs) is introduced, which requires solvers to perceive and predict the physical spatial dynamics implicit in the depicted scenes. The perceiving and testing hypotheses on structures (PATHS) computational model, which can solve many PBPs, is presented and compared to human performance on the same problems. PATHS and humans are similarly affected by the ordering of scenes within a PBP. Spatially or temporally juxtaposing similar (relative to dissimilar) scenes promotes category learning when the scenes belong to different categories but hinders learning when the similar scenes belong to the same category. The core theoretical commitments of PATHS, which we believe to also exemplify open-ended human category learning, are (a) the continual perception of new scene descriptions over the course of category learning; (b) the context-dependent nature of that perceptual process, in which the perceived scenes establish the context for the perception of subsequent scenes; (c) hypothesis construction by combining descriptions into explicit rules; and (d) bidirectional interactions between perceiving new aspects of scenes and constructing hypotheses for the rule that distinguishes categories. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
CDBC: A novel data enhancement method based on improved between-class learning for darknet detection

&lt;abstract&gt; &lt;p&gt;With the development of the Internet, people have paid more attention to privacy protection, and privacy protection technology is widely used. However, it also breeds the darknet, which has become a tool that criminals can exploit, especially in the fields of economic crime and military intelligence. The darknet detection is becoming increasingly important; however, the darknet traffic is seriously unbalanced. The detection is difficult and the accuracy of the detection methods needs to be improved. To overcome these problems, we first propose a novel learning method. The method is the Chebyshev distance based Between-class learning (CDBC), which can learn the spatial distribution of the darknet dataset, and generate "gap data". The gap data can be adopted to optimize the distribution boundaries of the dataset. Second, a novel darknet traffic detection method is proposed. We test the proposed method on the ISCXTor 2016 dataset and the CIC-Darknet 2020 dataset, and the results show that CDBC can help more than 10 existing methods improve accuracy, even up to 99.99%. Compared with other sampling methods, CDBC can also help the classifiers achieve higher recall.&lt;/p&gt; &lt;/abstract&gt;
My Tally -A Personal Book Keeping Mobile Application

Bookkeeping has a long history and is charming and colorful.Throughout the ages, people have used the experience of abacus bookkeeping; Later, we used pens and books to keep accounts, supplemented by calculation results; In the information age of the 21st century, the popularization of computer software makes us turn to excel or other desktop software with powerful analysis function for computer bookkeeping.However, all the above methods are limited by time and space.For example, they can't record anytime and anywhere, so they will delay the best recording time, or they need to spend a lot of time on account statistical analysis in the later stage.In this way, people's bookkeeping habits cannot continue, nor can they better carry out their own financial analysis.More importantly, they cannot achieve the purpose of financial planning.In modern economic society, people's economic activities are frequent and there are many kinds of consumption.In this case, there is an urgent need to study an application dedicated to daily consumption bookkeeping.The personal bookkeeping application designed in this paper not only reflects the convenience of recording income and expenditure information, but also analyses the financial situation, so that people can record information faster and in real time, and provide users with the ability to record and manage daily financial events.
Ensuring Trust and Security in IoT Systems through Blockchain Integration

The authors of this paper suggest a new security architecture based on blockchain technology to keep the Internet of Things (IoT) ecosystem safe. The use of smart contracts and microservices in the design improves the security and interoperability of the system. It addresses difficulties with data privacy, security, and accessibility caused by IoT devices and networks. The framework consists of three tiers: application layer, microservices layer, and blockchain layer. The microservices layer can manage intricate activities and interactions among diverse IoT devices and networks. The smart contracts layer automates the execution of security rules and processes, while the blockchain layer ensures the validity and reliability of recorded data. The study proves the architecture's efficiency in addressing security concerns like denial-of-service attacks, malicious code injection, and data manipulation. It also improves the ability of different IoT networks and devices to connect with one another in a secure manner. The proposed technique can be customized to address specific use cases and data security concerns and integrated into existing IoT architectures. Overall, the blockchain-based security architecture offers a feasible strategy for the interoperable and decentralized protection of IoT networks and devices, making it easier to implement blockchain technology within the IoT framework.
Propositional proof complexity

Propositional proof complexity studies efficient provability of those statements that can be expressed in propositional logic, in various proof systems, and under various notions of “efficiency.” Proof systems and statements of interest come from a variety of sources that, besides logic and combinatorics, include many other areas like combinatorial optimization and practical SAT solving. This article is an expanded version of the ECM talk in which we will attempt to convey some basic ideas underlying this vibrant area.
The 'Birth of Doubt' and 'The Existence of Other Possibilities': Exploring How the ACAD Toolkit Supports Design for Learning

The circumstances in which humans live and learn are subject to constant change. Given these cycles of change, educational designers (teachers, instructional designers, and others) often search for new models and frameworks to support their work, to ensure their designs are in alignment with valued forms of learning activity. Our research foregrounds the entanglement of people (the relational), tasks (the conceptual) and tools (the digital and material) in formal and informal learning settings. In this paper, we explore the use of the ACAD toolkit with the aim of understanding how this analytical tool supports design for learning. A thematic analysis of five workshops attended by 40 educators from diverse professional and academic backgrounds in Spain and Argentina, reveals how ACAD supports educational designers in four distinctive ways: encouraging dynamic engagement with key elements and concepts; supporting the visualization of (dis)connections and (in)coherence in designs; prompting critical reflection on past practices and contexts; and stimulating discussion about future teaching practices. A key contribution of this article is the discussion about how the ACAD toolkit helps educators see the ways in which all learning is situated, subject to constraints and affordances at multiple scale levels, and oriented towards certain pedagogical purposes or values.
Can We Explain Privacy?

Web users want to protect their privacy while sharing content online. This can be done through automated privacy assistants that are capable of taking actions by detecting privacy violations and recommending privacy settings for content that the user intends to share. While these approaches are promising in terms of the accuracy of their privacy decisions, they lack the ability to explain to the end user why certain decisions are being made. In this work, we study how privacy assistants can be enhanced through explanations generated in the context of privacy decisions for the user content. We outline a methodology to create explanations of privacy decisions, discuss core challenges, and show example explanations that are generated by our approach.
Ising Hamiltonians for Constrained Combinatorial Optimization Problems and the Metropolis‐Hastings Warm‐Starting Algorithm

Abstract Quantum approximate optimization algorithm (QAOA) is a promising variational quantum algorithm for combinatorial optimization problems. However, the implementation of QAOA is limited due to the requirement that the problems be mapped to Ising Hamiltonians and the nonconvex optimization landscapes. Although the Ising Hamiltonians for many NP hard problems have been obtained, a general method to obtain the Ising Hamiltonians for constrained combinatorial optimization problems (CCOPs) has not yet been investigated. In this paper, a general method is introduced to obtain the Ising Hamiltonians for CCOPs and the Metropolis‐Hastings warm‐starting algorithm for QAOA is presented which can provably converge to the global optimal solutions. The effectiveness of this method is demonstrated by tackling the minimum weight vertex cover (MWVC) problem, the minimum vertex cover (MVC) problem, and the maximal independent set problem as examples. The Ising Hamiltonian for the MWVC problem is obtained first time by using this method. The advantages of the Metropolis‐Hastings warm‐starting algorithm presented here is numerically analyzed through solving 30 randomly generated MVC cases with 1‐depth QAOA.
Recognition of text areas with personal data on diagnostic images

The aim of the study is to develop a method for detecting areas of text with private data on medical diagnostic images using the Tesseract module and the modified Levenshtein distance. Materials and methods. For threshold filtering, the brightness of the points belonging to the text characters in the images is determined at the initial stage. The dynamic threshold is calculated from the histogram of the brightness of the pixels of the image. Next, the Tesseract module is used for primary text recognition. Based on the tag values from DICOM files, a set of strings was formed to search for them in the recognized text. A modified Levenshtein distance was used to search for these strings. A set of DICOM files of the “Dose Report” type was used to test the algorithm. The accuracy was assessed by experts marking up blocks of private information on images. Results. A tool has been developed with a set of metrics and optimal thresholds for choosing decisive rules in finding matches that allow detecting areas of text with private data on medical images. For this tool, the accuracy of localization of areas with personal data on a set of 1131 medical images was determined in comparison with expert markup, which is 99.86%. Conclusion. The tool developed within the framework of this study allows identifying personal data on digital medical images with high accuracy, which indicates the possibility of its practical application in the preparation of data sets.
Switching: Cultural fluency sustains and cultural disfluency disrupts thinking fast

Abstract Culture-as-situated cognition theory provides insight into the system 1 monitoring algorithm. Culture provides people with an organizing framework, facilitating predictions, focusing attention, and providing experiential signals of certainty and uncertainty as system 1 inputs. When culture-based signals convey that something is amiss, system 2 reasoning is triggered and engaged when resources allow; otherwise, system 1 reasoning dominates.
PENGARUH MODEL PROJECT BASED LEARNING TERHADAP HASIL BELAJAR MATEMATIKA MATERI PENYAJIAN DATA

This study aims to determine the influence of the Project Based Learning model on the learning outcomes of mathematics data presentation material. This research conducted at SDIT Al Hikmah in second semester of the 2022/2023 school year. The research method used was a two group quasi-experiment design. The subject of this study fifth grade students SDIT Al Hikmah with a total 36 students. Data collection was carried out to obtain learning outcomes or N-gain is obtained using pretest and posttest. Researchers conducted a normality test with the lilliefors test of two samples normally distributed because the values of two samples were smaller than Ltabel. In the Project Based Learning class group, Lcount ≤ Ltable values are 0.024 ≤ 0.200, in the control class group the Lcalculate ≤ Ltable values are 0.072 ≤ 0.200. the homogeneity test the data is homogeneous because it is smaller than Ftable, the values are 1.006 ≤ 2.27. Furthermore, in the hypothesis test, tcount of 25.89 is greater than the ttable 2.03224, indicating that H0 is rejected and Ha is accepted. Thus, there is an influence of the Project Based Learning model on mathematics learning outcomes of data presentation material in class V SDIT Al Hikmah.
Plagiarism Detection in Software Projects Using Abstract Syntax Trees

Plagiarism is a hot topic in modern education and science.It requires special attention since committing plagiarism is very easy with the use of the internet.This problem can be fought against utilizing prevention or detection methods, which have been both used in this work.This paper introduces an implementation of a submission scheme of students' projects in classes taught at the Brno University of Technology.Scripts for an automatic hand-in space for each student were created.Students have restricted privileges within these spaces on the GitLab cloud service.For plagiarism detection, a tool written in Python was developed.This tool utilizes Abstract Syntax Trees compiled from the source code, which is a part of the Students' solutions.The output of the comparison is represented with a tabular file of the format .xlsx,which allows a detailed view.Ongoing implementation is focused on widening the tool's usability by adding a Python similarity comparison engine.
Training Image Synthesis for Shelf Item Detection reflecting Alignments of Items in Real Image Dataset

We propose a novel cut-and-paste approach to synthesize a training dataset for shelf item detection, reflecting the alignments of items in the real image dataset. The conventional cut-and-paste approach synthesizes large numbers of training images by pasting foregrounds on background images and is effective for training object detection. However, the previous method pastes foregrounds on random positions of the background, so the alignment of items on shelves is not reflected, and unrealistic images are generated. Generating realistic images that reflect actual positional relationships between items is necessary for efficient learning of item detection. The proposed method determines the pasting positions for the foreground images by referring to the alignment of the items in the real image dataset, so it can generate more realistic images that reflect the alignment of the real-world items. Since our method can synthesize more realistic images, the trained models can perform better.
User-irrelevant Cross-domain Association Analysis for Cross-domain Recommendation with Transfer Learning

Cross-domain recommendation (CDR) is an effective approach to boost user experience and expand business. Traditional CDR methods generally rely on sharing user-relevant data between domains (e.g., user-item interaction data or user-overlap information). However, this approach is unrealistic in many practical applications, due to user data policies. Some works attempt to circumvent this limitation by leveraging other forms of overlapped data (e.g., item-, content-, or tag-overlap). However, such a solution is not always possible, especially if these forms of overlap are either non-existent or unknown. Until now, there have been limited studies focusing on the intractable CDR task without relying on sharing user-relevant data or using other types of overlap information.