Reviewerly/exHarmony huggingface dataset:
Indigenous Sand Drawings as Predictors of the Cell Response to Nanoparticle Therapy

A technique for predicting the response of cells and tissues to a physicochemical stimulus without the use of expensive molecular markers and at time points before any morphological changes can be visibly spotted would be a meaningful addendum to the current set of bioimaging tools. One such method was developed here based on correlating transformed distance matrices of populations of cultured cells and digital checkerboard patterns derived from traditional central African drawings in the sand. Similarity measurements were made at an early time point in the therapy administered to bone cancer cells in the form of composite magnetic nanoparticles. At this early of a time point, the cell viability was mildly reduced, but no gross alterations to the cell morphology or density were visible yet. Similarity score evaluation demonstrated a significantly higher degree of similarity between the patterns derived from the sand drawings and the cells subjected to the treatment than between the former patterns and the untreated cell controls. The treated cells produced more ordered and symmetric patterns than the control ones after the processing of their pairwise distant matrices, explaining their better geometric correlation with the ancestral sand arabesques, which were monolinear and commonly comprised multiple mirror planes and rotational symmetry axes. This has suggested that the course of the therapy could be predicted by a relatively simple comparison between raw optical images of cells and indigenous ideographs using the metrics postulated here. The interdisciplinary method developed in this study may prove applicable for in situ monitoring of the response of cells and tissues to various therapies, allowing for the early indications of adverse effects to be noticed based on the simple optical observations of cells and acted upon before the progression toward nonviable states becomes irreversible. The method elaborated here may also provide an impetus for a broader search for solutions to problems plaguing the modern medicine outside of the scope of its mainstream analytical frameworks and in the ancestral heritage of relatively obscure ethnic traditions.
Public auditing for real-time medical sensor data in cloud-assisted HealthIIoT system

With the advancement of industrial internet of things (IIoT), wireless medical sensor networks (WMSNs) have been widely introduced in modern healthcare systems to collect real-time medical data from patients, which is known as HealthIIoT. Considering the limited computing and storage capabilities of lightweight HealthIIoT devices, it is necessary to upload these data to remote cloud servers for storage and maintenance. However, there are still some serious security issues within outsourcing medical sensor data to the cloud. One of the most significant challenges is how to ensure the integrity of these data, which is a prerequisite for providing precise medical diagnosis and treatment. To meet this challenge, we propose a novel and efficient public auditing scheme, which is suitable for cloud-assisted HealthIIoT system. Specifically, to address the contradiction between the high real-time requirement of medical sensor data and the limited computing power of HealthIIoT devices, a new online/offline tag generation algorithm is designed to improve preprocessing efficiency; to protect medical data privacy, a secure hash function is employed to blind the data proof. We formally prove the security of the presented scheme, and evaluate the performance through detailed experimental comparisons with the state-of-the-art ones. The results show that the presented scheme can greatly improve the efficiency of tag generation, while achieving better auditing performance than previous schemes.
An Improved Infrastructure for Privacy-Preserving Analysis of Patient Data

Incorporating healthcare data from different sources is crucial for a better understanding of patient (sub)populations. However, data centralization raises concerns about data privacy and governance. In this work, we present an improved infrastructure that allows privacy-preserving analysis of patient data: vantage6 v3. For this new version, we describe its architecture and upgraded functionality, which allows algorithms running at each party to communicate with one another through a virtual private network (while still being isolated from the public internet to reduce the risk of data leakage). This allows the execution of different types of algorithms (e.g., multi-party computation) that were practically infeasible before, as showcased by the included examples. The (continuous) development of this type of infrastructure is fundamental to meet the current and future demands of healthcare research with a strong emphasis on preserving the privacy of sensitive patient data.
Explaining Any ML Model? -- On Goals and Capabilities of XAI

An increasing ubiquity of machine learning (ML) motivates research on algorithms to explain ML models and their predictions -- so-called eXplainable Artificial Intelligence (XAI). Despite many survey papers and discussions, the goals and capabilities of XAI algorithms are far from being well understood. We argue that this is because of a problematic reasoning scheme in XAI literature: XAI algorithms are said to complement ML models with desired properties, such as "interpretability", or "explainability". These properties are in turn assumed to contribute to a goal, like "trust" in an ML system. But most properties lack precise definitions and their relationship to such goals is far from obvious. The result is a reasoning scheme that obfuscates research results and leaves an important question unanswered: What can one expect from XAI algorithms? In this article, we clarify the goals and capabilities of XAI algorithms from a concrete perspective: that of their users. Explaining ML models is only necessary if users have questions about them. We show that users can ask diverse questions, but that only one of them can be answered by current XAI algorithms. Answering this core question can be trivial, difficult or even impossible, depending on the ML application. Based on these insights, we outline which capabilities policymakers, researchers and society can reasonably expect from XAI algorithms.
DayDreamer: World Models for Physical Robot Learning

To solve tasks in complex environments, robots need to learn from experience. Deep reinforcement learning is a common approach to robot learning but requires a large amount of trial and error to learn, limiting its deployment in the physical world. As a consequence, many advances in robot learning rely on simulators. On the other hand, learning inside of simulators fails to capture the complexity of the real world, is prone to simulator inaccuracies, and the resulting behaviors do not adapt to changes in the world. The Dreamer algorithm has recently shown great promise for learning from small amounts of interaction by planning within a learned world model, outperforming pure reinforcement learning in video games. Learning a world model to predict the outcomes of potential actions enables planning in imagination, reducing the amount of trial and error needed in the real environment. However, it is unknown whether Dreamer can facilitate faster learning on physical robots. In this paper, we apply Dreamer to 4 robots to learn online and directly in the real world, without simulators. Dreamer trains a quadruped robot to roll off its back, stand up, and walk from scratch and without resets in only 1 hour. We then push the robot and find that Dreamer adapts within 10 minutes to withstand perturbations or quickly roll over and stand back up. On two different robotic arms, Dreamer learns to pick and place multiple objects directly from camera images and sparse rewards, approaching human performance. On a wheeled robot, Dreamer learns to navigate to a goal position purely from camera images, automatically resolving ambiguity about the robot orientation. Using the same hyperparameters across all experiments, we find that Dreamer is capable of online learning in the real world, establishing a strong baseline. We release our infrastructure for future applications of world models to robot learning.
Ugaling Pinoy: A Mobile Learning Game Application to Enhance Learning in Values Subject of Elementary Pupils

The fact that elementary students are still in the pick of their childhood who loves playing, learning values could be much easier if a game-type of learning will be used. The Ugaling Pinoy: A Mobile Learning Game Application About Edukasyon sa Pagpapakatao (Values Subject), is a mobile learning game developed to teach young children good manners and right conduct in different places and situations. This study was conducted to develop a learning game in values for elementary pupils and to evaluate its effect in their learning specifically in values subject. The system used various software to make the system more interesting and attractive to the end-users such as Basic for Android for coding, Adobe Photoshop for graphics, and Adobe Flash for animation. It was validated by ten (10) IT experts and five (5) values teachers. A total of 30 pupils were selected as the respondents of the study using purposive sampling. Mean, percentage and t-test were the statistical tools employed in the study. The result revealed that the developed learning game has a positive effect on the pupils’ learning in values subject. It was concluded that the system was user friendly and is very good in its performance. Furthermore, the study identifies positive effects of learning games - an aspect of intervention plan and demonstrates the potential of such learning games in improving the good manners and right conduct of the pupils. &#x0D; Keywords: learning values, mobile learning games, Filipino values, t-test
Sensors fingerprints using Machine Learning: a case study on dam monitoring systems

This paper presents a comprehensive approach to getting sensors’ fingerprints (i.e., identifiers based on physical properties) using machine learning classifiers. The object of study is a dam’s structure health monitoring system, which constitutes a suitable tool to protect these critical infrastructures against hazard events. Since cybersecurity is a vital requirement in these systems, the correct identification of each component (including sensors) constitutes a basic premise to provide security mechanisms such as authentication and access control. We develop our study in both theoretical and practical aspects. After presenting the conjectures that basis our strategy, we implement an experiment that simulates conditions observed in a real dam’s structure health monitoring system. We evaluate our experiment by testing four different machine learning classifiers. Our contribution portrays the hypothesis that the physical characteristics of piezoelectric sensors and the environment in which they are inserted are propagated through the captured signal. The results indicate an accuracy of over 90% in the correct sensors identification and attest our approach as a promising cybersecurity solution alternative.
The New Approach Optimization Markov Weighted Fuzzy Time Series using Particle Swarm Algorithm

Markov Weighted Fuzzy Time Series is a forecasting method that applies fuzzy logic to form linguistic variables from existing data.The formation of linguistic variables makes it possible for the forecasting process to be more accurate by considering the uncertainty aspect in decision-making.Its formation is started by grouping the data into a certain number of clusters.The next steps are fuzzification, transition matrix formation, and defuzzification for forecasting.In the process of grouping, the existing data will be grouped into several clusters so that it results in the interval length of each cluster.One of the problems of this grouping is the absence of a base standard in the clustering process so it is prone to have a different value in forecasting accuracy.The difference in the number of the class or interval length will result in different accuracy even though the clustering method that is used is the same.In this study, the author proposes the idea of using Particle Swarm Optimization to improve the interval length.The initial interval that is already obtained through the K-means clustering algorithm will be evaluated using the Particle Swarm Optimization method so that it will have a new interval that later will be used in the fuzzification process and forecasting.The accuracy of forecasting can be calculated by using Mean Absolute Percentage Error from Markov Weighted Fuzzy Time Series conventional method and Markov Weighted Fuzzy Time Series method with Particle Swarm Optimization.The result of this study gives an improvement in error value from 8.03% to 5.88%.
Stage Performance Characteristics of Minority Dance Based on Human Motion Recognition

Due to the different development history of different nationalities, there is a big gap between traditional cultures. Especially, as an important embodiment and component of traditional culture, ethnic minority dance has unique characteristics. With the development of machine vision technology, human motion recognition has gradually become a hot research direction, but its application research in the field of dance motion recognition is still in its infancy. In this paper, the characteristics of stage performances of ethnic minority dances in the field of intelligent auxiliary training based on human motion recognition technology are introduced. The human body regions in the frame are obtained by using human posture, and 3D_SIFT and optical flow features are extracted from each region. Then, we use the extracted key frames and DTW (dynamic time warping) algorithm to realize the motion recognition of the motion capture data and carry out simulation experiments. The test results show that the algorithm can identify the minority dance video database, effectively identify the dance movements, and realize the movement correction of dancers.
study on English translation of Chinese four-character idioms

Chinese Four-character idioms are fixed phrases with four Chinese characters that have been used for a long time in Chinese. They are language units with richer meanings and equivalent grammatical functions than words. They are concise, easy to remember, and easy to use. Many idioms have two or more meanings. It is precisely for this reason that the English translation of Chinese idioms has brought considerable difficulties. In the process of translating idioms into English, it is difficult to be accurate, profound, and complete. Therefore, a variety of English translation techniques should be combined for translating Chinese four-character idioms. This paper analyzed the definition, characteristics, classification, and translatability of Chinese four-character idioms, and concluded three commonly applied strategies for translating Chinese four-character idioms into English: the literal translation, the free translation, and the combination of literal and free translation. Finally, some problems existing in the translation of Chinese four-character idioms are analyzed and summarized: poor acceptance by foreigners, loss of cultural elements, and unbalanced cultural status.
Motivation Behind the Preference of Distance Education of Higher Education Students

Aim. The aim of the study is to investigate into the motives of learning that are inherent in students with different attitudes to distance learning. The main objectives of the study are as follows: to analyze the attitude to different forms of distance learning of students of higher education institutions; to investigate the main forms of scientific motivation of students of higher education institutions; to compare how the scientific motivation of students with different attitudes to distance learning differs.&#x0D; Methods. The study involved 211 students majoring in science (physics, chemistry). The study uses the following methods: 1) attitude questionnaire; 2) Science Motivation Questionnaire II; 3) statistical methods. The following is used for statistical data processing: descriptive statistics methods (to measure the percentage of people with different attitude towards organization of distance learning); ANOVA (to compare levels of science motivational types of students with different preferences in educational models).&#x0D; Results. It was determined that 38% of students are positive about distance education and see great opportunities for its implementation. But only 22.75% of students consider distance learning the best option of educational process organisation, and 21.8% are in favor of an exclusive face-to-face format. The most popular form among students is mixed form of education (55.45%). Students with a positive attitude to distance education are characterized by the highest scores on intrinsic motivation, self-efficacy and career motivation. Therefore, proponents of distance education are mostly students who have a good understanding of their own goals and learning needs, plan their career advancement, and are well-organized in terms of educational and professional development.&#x0D; Conclusions. Distance forms of education cannot completely replace traditional ones. But it is already becoming obvious that their use in a mixed format can increase the effectiveness of the educational process. Students' sympathy for the mixed format should be taken into account by the developers of educational programs and university administrations in order to improve the quality of education and meet the requirements of the times.
Classification of Challenges and Threats in Healthcare Cybersecurity: A Systematic Review

The rapid development of electronic health has highlighted the essential position information security holds today in the healthcare industry. Indeed, healthcare organisations have increasingly become targets for cyberattacks. The authors investigated cybersecurity challenges through identifying cases within the academic literature related to cybersecurity threats and vulnerabilities present within healthcare settings. The fast adoption of healthcare information systems has exposed the healthcare industry to numerous kinds of cyberattacks, thereby prompting the international academic community to investigate these threats. There are various cybersecurity challenges and vulnerabilities within healthcare that could later be exploited and lead to cybercrimes. As security systems continue to develop, the interest in cybersecurity as a form of defense and protection is expected to grow.
Comparative analysis of software for smart homes

The article presents the results of a comparative analysis of smart home software. For this purpose, the domestic market of producers of such solutions was analyzed and selected 17 systems that are among the most popular. A comparative methodology was developed to determine which of these solutions turned out to be the best. The results are presented in tables and graphs.
Academic Information System Management To Improve Service Quality To Students During The Covid-19 Pandemicat Universities In Jambi Province

Universities that have good academic service management quality standards are the goal in every university's efforts. In Jambi Province in particular, good management applied to a university is one of the important factors to improve the quality of service both for students, the community or for the university itself. Not a few universities whose academic service quality still does not meet the satisfaction threshold in the assessment, one of which is offline-based services that will usually hinder the delivery of existing information. UNAMA colleges (Universitas Stikom Dinamika Bangsa) and IAI Tebo in Jambi Province are universities where each delivery of academic information focuses on the use of computers and technology, both of these universities have implemented academic information system management, but each of these universities has advantages and disadvantages in the information system that is run, so a study is needed to be able to combine each of the advantages and disadvantages found in the two universities. By applying qualitative methods, it is hoped that it will provide useful research results,the focus of this research is directed at (1). Planning, (2). Implementation, (3). Evaluation (4). Solutions or follow-ups in improving the quality of academic services at universitiesusing Deming's cycle management theory. OnThis study uses a descriptive method with a qualitative approach and data collection by conducting observations, interviews, and documentation studies.so from the results of this study can conclude a more complete management information system that can be applied to other universities, especially those that are still offline.
An Efficient Deep Learning Classification Model for Predicting Credit Card Fraud on Skewed Data

Due to fast-evolving technology, the world is moving to the use of credit cards rather than money in their daily lives, giving rise to many new opportunities for fraudsters to use credit cards maliciously. Based on the Nilson report, losses related to global cards were estimated to be over $35 billion by 2020. In order to maintain the security of users of these cards, the credit card company must develop a service to ensure that users are protected from any risks they may be exposed to. For this reason, we introduce a fraud detection model, denoted ST-BPNN, which is based on machine and deep learning approaches to identify fraudulent transactions. ST-BPNN was applied on real fraud detection data provided by the European bank. Comparing the obtained results from ST-BPNN with a recent state-of-the-art approach shows that our proposed model demonstrates high predictive performance for detecting fraudulent transactions.
Effectiveness of a Mental Health Program using Instagram (Friendship Town) for Newly Entered Nursing Students during the COVID-19 Pandemic

Purpose: The purpose of this study was to evaluate the effectiveness of a mental health program utilizing Instagram for nursing students.Methods: A non-equivalent control group pretest-posttest design was used. Fifty-seven nursing students who enrolled in a university during the COVID-19 pandemic in South Korea participated in the study: 30 nursing students (26 females and 4 males) in the experimental group and 27 nursing students (21 females and 6 males) in the control group. The Instagram Program for mental health provided 8 sessions for each topic. The data collected via questionnaires were statistically analyzed.Results: Significant differences were found between the two groups in depression (t=-2.32, &lt;i&gt;p&lt;/i&gt;=.024) and anxiety (t=-2.43, &lt;i&gt;p&lt;/i&gt;=.018), but there was no significant difference in loneliness (t=0.64, &lt;i&gt;p&lt;/i&gt;=.263).Conclusion: The Instagram program is accessible for incoming nursing students and effective in decreasing their depression and anxiety during the COVID-19 era. Therefore, this intervention could enhance the mental health of nursing students by allowing them to express their thoughts and feelings through mutual interaction on Instagram.