Reviewerly/exHarmony huggingface dataset:
Improving Variational Autoencoders Reconstruction Using Prototypes

Abstract Variational autoencoders implement latent space regularization with a known distribution, which enables stochastic synthesis from straightforward sampling procedures. Most of the various existing regularization approaches struggle with balancing proper regularization and good reconstruction quality. Here, we propose to circumvent such limitations by implementing a distributed latent space regularization using prototypical deep clustering with known distributions. Our approach enables high-quality data synthesis by simply decoding samples from such distributions around prototypes and disregards training any further auxiliary network to explicitly learn the decoder synthesis from codes. Such schema enables obtaining an appropriate number of clusters for solid regularization with better reconstruction quality and improved synthesis control. We experiment with our method using widespread exploratory benchmarks and report that regulariza-tion centered on optimal prototypes’ coordinates or cluster centroids neutralizes the adverse effects regularization terms often have on autoencoder reconstruction quality, matching non-regularized autoencoders’ performance. We also report appealing results for interpreting data representatives with simple prototype synthesis and controlling the synthesis of samples with prototype-like characteristics from decoding white noise around prototype coordinates.
Design and Build a Web-Based Village Information System for Acceleration of Village Services in Gampong Jantho Makmur

Today, information systems are very important in village governance. Jantho Makmur is one of the villages which currently still uses the conventional system in various ways, this method is not very effective considering that the times are developing and technology is getting more advanced. To provide efficient services to the public, we need an information system that can replace the conventional system. Based on this, a web-based village information system was developed. In its creation, this system uses the PHP programming language with Laravel as the Framework and MySQL as database management. This village information system provides an online feature for making certificates and public complaints. Submissions of letters and complaints that have been validated will be sent to the WhatsApp community in PDF format using the WhatsApp API Gateway. This application also implements Ajax and Datatables technology so it is very light and efficient to use. Besides that, this application also has features for filing incoming and outgoing letters, managing population data, writing articles and photo documentation of community activities.
Segmentation Techniques based on Image Quality and Edge Detection Algorithms

Segmentation is one of the fundamental tasks in the area of digital image processing and analysis. Segmentation highlights parts of the image that have common features. Such areas of the image are called Region of Interest (ROI). The choice of segmentation algorithm depends on the nature of the origin images and there is no single, universal method that can always be applied. When choosing a segmentation algorithm for a particular image, it is very important to test multiple methods and choose the one that gives the best results. This paper presents a comparison of several segmentation algorithms on different origin images. The comparison was performed based on standard parameters like Mean-Square Error (MSE), Signal to Noise Ratio (SNR), Peak Signal-To-Noise Ratio (PSNR), Structure Similarity Index (SSIM) etc. for image quality assessment.The results of this work can help in the selection of the edge detection algorithm and as a preparation for image segmentation.
Cobalt distribution in the Boleo stratiform sediment-hosted ore deposit: a transtension rift basin in the central Baja California peninsula, Mexico

Abstract. The Miocene Santa Rosalia Basin, located in Baja California, Mexico, is a unique basin that contains the Boleo copper deposit. This basin provides a remarkable opportunity to study the early stages of tectonic evolution, volcanic activity, ore genesis, and sedimentary deposition in the incipient Gulf of California. Previous research suggested the presence of Co in areas between Cu and Zn, but the factors responsible for cobalt distribution in the basin remained unclear. This study utilized multiple methods, including historical geologic maps, core drilling, field measurements, XRF readings, petrographic analysis, and mineral composition analysis, to comprehend the Co mineralization of the Boleo Formation. Our research found Co anomalies in five localities, with the highest values situated at the base of a slump block in manto 3 near the Neptuno mine. The presence of elevated cobalt readings in areas with uplifted and eroded gypsum suggests the possibility of uplift and dissolution of earlier gypsum deposits, and an association with brine waters. Moreover, the result of the study indicates that gypsum interfingers with both the clastic facies and ore mineralization in mantos 1, 2, and 3, signifying a north-to-south expansion of the basin. The study also identified manganese oxides and Cu-Fe sulfides, including villamaninite, along laminated gypsum beds. The findings of this study suggest that cobalt enrichment is influenced by structural factors and occurs near subsurface evaporitic deposits. This supports the notion that cobalt mineralization forms near the redox front and through sulfate reduction processes. The study's findings offer valuable insights for further exploration and understanding of cobalt mineralization in similar geological settings.
Habits and goals in synergy: a variational Bayesian framework for behavior

Behaving efficiently and flexibly is crucial for biological and artificial embodied agents. Behavior is generally classified into two types: habitual (fast but inflexible), and goal-directed (flexible but slow). While these two types of behaviors are typically considered being managed by two distinct systems in the brain, recent studies have revealed a more sophisticated interplay between them. We introduce a theoretical framework using variational Bayesian theory, incorporating a Bayesian "intention" variable. Habitual behavior depends on the prior distribution of intention, computed from sensory context without goal-specification. In contrast, goal-directed behavior relies on the goal-conditioned, posterior distribution of intention, inferred through variational free energy minimization. Assuming that an agent behaves using a synergized intention, our simulations in vision-based sensorimotor tasks explain the key properties of their interaction as observed in experiments. Our work suggests a fresh perspective on the neural mechanisms of habits and goals, shedding light on future research in decision-making.
Gesture Recognition for Enhancing Human Computer Interaction

Gesture recognition is critical in human-computer communication. As observed, a plethora of current technological developments are in the works, including biometric authentication, which we see all the time in our smartphones. Hand gesture focus, a frequent human-computer interface in which we manage our devices by presenting our hands in front of a webcam, can benefit people of different backgrounds. Some of the efforts in human-computer interface include voice assistance and virtual mouse implementation with voice commands, fingertip recognition and hand motion tracking based on an image in a live video. Human Computer Interaction (HCI), particularly vision-based gesture and object recognition, is becoming increasingly important. Hence, we focused to design and develop a system for monitoring fingers using extreme learning-based hand gesture recognition techniques. Extreme learning helps in quickly interpreting the hand gestures with improved accuracy which would be a highly useful in the domains like healthcare, financial transactions and global business
Using BiLSTM Structure with Cascaded Attention Fusion Model for Sentiment Analysis

In the last decade, sentiment analysis has been a popular research area in the domains of natural language processing and data mining. Sentiment analysis has several commercial and social applications. The technique is essential to analyse the customer experience to develop customer loyalty and maintenance through better assistance. Deep Neural Network (DNN) models have recently been used to do sentiment analysis tasks with promising results. The disadvantage of such models is that they value all characteristics equally. We propose a Cascaded Attention Fusion Model-based BiLSTM to address these issues (CAFM-BiLSTM). Multiple heads with embedding and BiLSTM layers are concatenated in the proposed CAFM-BiLSTM. The information from both deep multi-layers is merged and provided as input to the BiLSTM layer later in this paper. The results of our fusion model are superior to those of the existing models. Our model outperforms the competition for lengthier sentence sequences and pays special attention to referral words. The accuracy of the proposed CAFM-BiLSTM is 5.1%, 5.25%, 6.1%, 12.2%, and 13.7% better than RNN-LSTM, SVM, NB, RF and DT respectively.
The Cataloguing Code of Ethics 2021: conception, community and continuation

Cataloguing has existed for millennia but until January 2021 there was no formal code of ethics for this branch of the information profession. But what is the Code? Who created it and why? How was it produced? Is the Code immutable and what relevance does it have in day-to-day cataloguing work now? Can institutional experiences of encountering and resolving ethical dilemmas in creating, sharing, enriching and maintaining metadata be shared for mutual benefit? This article, based on a presentation delivered by the authors as part of the ARLIS UK Cataloguing and Classification Ethics series delivered online between April and June 2022, 1 provides examples of how the ten ethical statements of principles can be used to inform cataloguing in art libraries.
A Scoring Matrix For Pre-Left Ventricular Device Patient Selection: Predicting Hospital Readmissions

Introduction Groundbreaking progress in mechanical support devices for end stage heart failure has created such advanced therapies that the complexity of left ventricular assist device (LVAD) implantation rivals that of a heart transplant. Yet while transplantation is associated with well respected patient selection criteria, such criteria for LVAD candidates is lacking. LVAD implantation is a serious undertaking on behalf of the patient and health care community. Recent data suggests the cost of readmissions for end stage heart failure was lower before LVAD implantation. Simulated lifetime models reveal LVAD therapy, including cost of the therapy, initial admission and readmissions, almost doubles estimated total treatment costs when compared with medical management. In order to improve this healthcare and patient burden, we need to identify at risk patients and reduce hospital readmissions. The purpose of the LVAD matrix is to appropriately asses candidates’ risk in an effort to minimize a major cost of readmission and maximize quality of life outcomes. Hypothesis The LVAD risk matrix will serve as a successful scoring tool to identify patients who are at risk for an increase in proportion of days in hospital after 30 days post LVAD implantation. Methods 196 LVAD patients who were implanted from October 2012-May 2021were retrospectively assigned a risk matrix score that was based on clinical risk factors categorized into four sections: psychosocial, frailty, medical and surgical. The main sections were divided into further subgroups giving the matrix score a maximum of 12. Patients with higher scores indicated higher risk, with patients totaling 6-9 points needing further evaluation and patients with 10+ points were declined as potential LVAD recipients. We then examined the relationship between the risk matrix score and proportion of days in-hospital after 30 days post-LVAD implantation. Results Of the 196 patient's who were assigned a risk matrix score, patients with higher risk matrix scores tended to have commonalities such as older age, more debilitated condition, Caucasian race and a self identified mental illness. Thirty days post LVAD implantation, patients with higher scores of 4-5 and 6-9 had increased proportion of days in the hospital than outside of the hospital. Conclusions The risk matrix scoring tool is a strong predictor of the proportion of days spent in-hospital after the initial 30 days post-LVAD implantation. This scoring tool allows higher risk patients to be identified prior to implantation. Subsequently, such patients can then undergo more evaluation to hopefully minimize these high risk areas and reduce rates of readmission.
Spiritual and Moral Education of Students within the Framework of Teaching the Discipline “Fundamentals of Applied Arts”

At present, during the period of complex socio-economic transformations in the Republic of Kazakhstan, scientific and pedagogical searches are increasingly being conducted to form the spiritual and moral qualities of students by strengthening the ethno-cultural heritage of the people. This allows you to fully understand and realize the values and heritage of the culture of the Kazakh people. It originates from the basics of arts and crafts. The situation with coronavirus has strengthened the role of information and remote technologies. However, it is worth highlighting the positive value of the possibilities of the Internet: access to in-depth study of the subject, comfortable conditions for obtaining information. Information technologies now allow not only conducting classes online, but also to take part in creative competitions, virtually visiting famous world museums - Louvre, Tretyakov Gallery, Vatican, watch cultural and historical films. The pedagogical experiment authors conducted made it possible to highlight the contradictions that arose in the study of the discipline “Fundamentals of arts and crafts” and its educational aspect in the formation of the spiritual and moral qualities of the student. Purpose of the study – conduct a pedagogical experiment on the formation of the spiritual and moral qualities of a student based on the content of the discipline “Fundamentals of arts and crafts”. Research methods: research of scientific-theoretical and methodological literature on the problem of this research; diagnostic techniques, blitz survey, direct observation. The final result was the development of a special training combination of methods, techniques and techniques in the discipline “Fundamentals of arts and crafts”, aimed at the formation of the spiritual and moral qualities of the student.
The Method of Project as a Way of Organizing and Activating the Students’ self-work

The project method is a way of organizing and activating students' self-work or a creative approach to solving a specific problem situation. The project method allows solving many educational tasks and creates conditions for communicative interaction of students studying a foreign language (English) in the process of active cognitive work. In this article the author considers the method of projects that provide the following opportunities: 1) development of students' ability to think independently; 2) formation of skills to use theoretical knowledge in solving creative problems; 3) help to increase student's motivation; 4) improvement of students' language potential; 5) development of students' communicative competence on the basis of cooperation in doing self-work; 6) development of the student's creative potential in performing individual tasks. 7) increasing the student's self-esteem in the learning process. In addition, the author of this article defines the functions of independent work based on the research of a number of methodologists: developing, informative, stimulating, research and educational. In this article the author dwells on the special role and functional position of the teacher in the organization of students’ self-work as the assistant and adviser. Based on the practical materials of the article’s author, the stages of inserting and implementation of the project method as a way of organizing and activating students' self-work were determined.
A Study on the Effects of a Tea-Meditation Program on the Daily Stress of Children in Community Child Centers

The purpose of this study was to examine the effect of a tea-meditation program on the daily stress levels of children in community child centers in G-city, G-do. The study comprised an experimental group of 14 children and a control group of 14 students and was based on the loving-kindness and compassion tea-meditation program of Wonheo Ji-woon’s Jabi-dasun. The study covered 5 sessions of 10 classes from June 12 to July 17, 2022. A daily stress level test was conducted on all children before and after the program and the Jamovi version 2.3.18 software was used for data analysis to examine the effects of the program. The main research results were as follows: There was a significant effect (p&lt;0.001) of the tea-meditation program on the daily stress of children in community child centers in the experimental group compared to the control group. Therefore, it may be concluded that the tea-meditation program has a positive effect on the daily stress of children in community child centers. Thus, in the future, it would be helpful to develop a tea-meditation program targeting elementary school children in various environments.
FROM FRINGE TO MAINSTREAM: QANON NARRATIVES IN CENTRAL EUROPEAN DIGITAL SPACES

The paper analyses the spread of QAnon adjacent conspiracy narratives in Czech and Slovak speaking digital spaces, with focus on influencers communities on social media (TikTok, Instagram). Based on case study of a two specific narratives about COVID-19 and the anti-covid vaccines, it proposes a 5-step structure of how conspiracy content moves from a) English to non-English speaking contexts online and b) accross platforms, from fringe to mainstream spaces. It focuses on the role of influencers and digital opinion leaders in mainstreaming fringe political content and analyses how both political and lifestyle influencers adopt the role of cultural translators in the process. The paper is based on mixed method approach, combining digital ethnography and ethnographic content analysis.
SHAPING APP DATA PUBLICS AS A NATIONAL SECURITY ISSUE: A COMPARATIVE ANALYSIS OF THE CFIUS-GRINDR AND CFIUS-TIKTOK RULINGS

In March 2019, the Committee on Foreign Investment in the United States (CFIUS) labelled Grindr as a risk for national security and forced the Chinese conglomerate Kunlun Tech to divest from the queer dating app by June 2020. Later that year, the Committee launched an investigation into the Chinese company ByteDance, owner of the popular app TikTok, after it acquired the app Musical.ly in 2017. The objective of this paper is to compare how the CFIUS-TikTok and CFIUS-Grindr rulings were shaped as national security controversies, as well as to highlight how app data publics are increasingly the subject of discursive struggles to determine whether or how they constitute risk, especially amid the ongoing US-China trade conflict. Drawing from a political economy approach to communication, we collected and analyzed newspaper articles pertaining to the CFIUS-TikTok (n=294) and the CFIUS-Grindr (n=107) rulings. Our analysis highlights how both rulings were framed by construing the Chinese ownership of popular social media apps as a potential threat to US national security by conjuring anti-Chinese sentiment. However, a closer examination revealed that TikTok’s and Grindr’s data publics were made to matter differently. If the CFIUS-TikTok ruling was predominantly framed as a human rights issue, the CFIUS-Grindr investigation was made to matter by depicting LGBTQ+ citizens as higher security risks for the US because of their sexual and gender identities. This paper concludes by raising the implications that datafication poses for the LGBTQ+ communities who have little control over the regulatory frames that oversee their activities online.
Comparison performance analysis of autoregressive integrated moving average and deep learning long-short term memory forecasting weather data

Information about the weather is crucial in assisting human activities and labor because the weather is a factor that cannot be separated and is closely related to all human activities. The purpose this study to compare performance the Autoregressive Integrated Moving Average (AIMA) and Long-Short Term Memory (LSTM) algorithm models with case studies of weather forecasting. This study uses comparison of two methods, forecasting using AIMA and LSTM methods. LSTM method provides the best forecasting performance for attribute minimum temperature, maximum temperature, and average temperature with the Root mean squared error value below 1.45 and the Mean Absolute Error value below 1.14. For attributes of average humidity and solar radiation with a Root mean squared error value of 2.62 to 3.82 and a Mean Absolute Error value of 2.21 to 3.2. Precipitation forecasting has the highest error value with a root mean squared error value of 9.99 and a mean absolute error of 6.5. The AIMA method provides the best forecasting performance on the attribute minimum temperature, maximum temperature, and average temperature with the Root mean squared error value below 1.47 and the Mean Absolute Error value below 1.16. For the sun exposure attribute with a Root mean squared error value of 2.91 to 3.05. Whereas the average humidity attribute has the highest error with the Root mean squared error value reaching 4.97 and the Mean Absolute Error reaching 3.99. LSTM method is better in terms of forecasting results and in terms of computation time. From every forecast made, the LSTM method produces a smaller error value.
PATHWAYS TO RADICALISM: EXAMINING ONLINE COMMUNITY AND POLITICAL PATHWAYS TO MORE RADICAL BELIEFS ON REDDIT

Recent research shows that “algorithmic radicalization” and “echo chambers”—the idea that recommendation algorithms on social media have a strong independent effect on radicalization and silo people into ideologically homogeneous communities—are not as prevalent nor influential as once feared. Yet, online political discourse is as toxic as ever while political misinformation continues to plague social media platforms. This begs the question: If algorithms aren’t encouraging radicalization, then what is producing it? Drawing from church-sect theory, this study interviews politically active Reddit users to better understand _how_ they arrived at their current media use, online engagement, and political beliefs. Results show that participants have a deep mistrust of mainstream media, leading them to seek alternative sources of political content. Reddit participation is also driven by a desire for “earnest” political discussions with like-minded individuals and cross-partisans, in part to “reject” partisan polarization. Despite engaging on more extreme subreddits, participants said their beliefs were unchanged, but that other Redditors had moved to more extreme beliefs over time. And, participants perceived their Reddit participation as necessary to _prevent_ radicalization and partisan polarization. Collectively, these results provide preliminary insight into the media and social/psychological pathways that could lead to online radicalization, providing an alternative explanation to algorithmic radicalization. This study also underscores the importance of interrogating the ecological pathways to radicalization for researchers and policy-makers; future interventions should account for attribution bias and the individual-level factors related to radicalization.