Reviewerly/exHarmony huggingface dataset:
Multi-modal Asymmetric Autoencoders for Massive Photo Collection Applications

There has been an abundant use of applications where many photos obtained from camera-equipped devices can be leveraged and exploited to enable emerging services, e.g., mobile crowdsourcing. These systems usually collect a large data stream of images coming from different heterogeneous sources (e.g, IoT devices and humans) in an inadvertent way. Due to the limitations and challenges related to communication bandwidth, storage, and processing capabilities, it is unwise to transfer unselectively all the photos since most of them often either contain duplicate information, are inaccurate, or are just falsely submitted. In this paper, we propose to design a smart image selection procedure using an asymmetric multi-modal neural network autoencoder to select a subset of photos that has high utility coverage for multiple incoming streams. The proposed system enables selecting high context data from an evolving picture stream and ensures relevance. The approach uses the photo's metadata such as geo-location and timestamps along with the pictures' semantics to decide which photos can be submitted and which ones must be discarded. Simulation results for two different multi-modal autoencoder architectures indicate that a mixed asymmetric stacked autoencoder approach can yield better results outperforming the concatenated input autoencoder while leveraging user-side rendering to reduce bandwidth consumption and computational overhead.
Automatic multi-modal processing of language and vision to assist people with visual impairments

In recent years, the study of the intersection between vision and language modalities, specifically in visual question answering (VQA) models, has gained significant appeal due to its great potential in assistive applications for people with visual disabilities. Despite this, to date, many of the existing VQA models are nor applicable to this goal for at least three reasons. To begin with, they are designed to respond to a single question. That is, they are not able to give feedback to incomplete or incremental questions. Secondly, they only consider a single image which is neither blurred, nor poorly focused, nor poorly framed. All these problems are directly related to the loss of the visual capacity. People with visual disabilities may have trouble interacting with a visual user interface for asking questions and for taking adequate photographs. They also frequently need to read text captured by the images, and most current VQA systems fall short in this task. This work presents a PhD proposal with four lines of research that will be carried out until December 2025. It investigates techniques that increase the robustness of the VQA models. In particular we propose the integration of dialogue history, the analysis of more than one input image, and the incorporation of text recognition capabilities to the models. All of these contributions are motivated to assist people with vision problems with their day-to-day tasks.
An Empirical Study for Comparison of Estimation Methods for Value at Risk, Tail Value at Risk, and Adjusted Tail Value at Risk Using Extreme Value Theory Approach to Stock Market Index

Risk management helps the financial industry to manage and estimate the risks that may occur by using risk measures. Financial series data mostly have a heavy tail distribution which causes the probability of extreme values to occur. To overcome these extreme values, it is necessary to apply a mathematical model in calculating risk estimates in financial data combined with the Extreme Value Theory approach. The Adjusted-TVaR model is a measure of the risk of modification of the TVaR model to eliminate outliers in the tail of the distribution. The purpose of this study is to measure the accuracy of the Value at Risk, Tail Value at Risk, and Adjusted Tail Value at Risk using the Peak Over Thresholdapproach in Extreme Value Theory Models.The results of the risk estimation research using the POT approach method, show that the higher the level of confidence and the chosen constant, the higher the value of Adj-TVaR presented. This value represents that the potential loss will be higher. The estimation results obtained that the VaR value is smaller than Adj-TVaR and Adj-TVaR is smaller than TVaR. This shows that Adj-TVaR is more efficient to use in terms of predicting risk value when compared to TVaR with the Peak Over Threshold approach
Improving Language Model Fine-tuning with Information Gain Filtration

Language model fine-tuning is essential for modern natural language processing. The effectiveness of fine-tuning is limited by the inclusion of training examples that negatively affect performance. Here we present Information Gain Filtration, a general fine-tuning method, for improving the overall final performance of a fine-tuned model. We define Information Gain of an example as the improvement on a validation metric after training on that example. A secondary learner is then trained to approximate this quantity. During fine-tuning, this learner filters informative examples from uninformative ones. We show that our method is robust and has consistent improvement across datasets, fine-tuning tasks, and language model architectures.
Modern forms and methods of ecological literacy advancement: The current experience of mass cultural, methodological, educational work

The history of humankind is inextricably linked with the history of nature. Today, the traditional interdependence have escalated to the global ecological problem. If the humans fail to care about the nature, they will extinguish themselves for we depend on one another completely and totally. This is why we have to cultivate ecological literacy, culture and responsibility since our childhood. Ecological education, awareness and ecological skills must be promoted which is the mission of libraries cooperating with educational institutions. Thus, Bataysk Lermontov Library cooperates with the Ecological and Biological Center. The experience of the Library’s literature and science club «The young ecologist» operating since 1994, is also discussed.
Ecological innovations in open access

The author discusses patent information as an ecology-related segment of the vast array of information resources. Over other types of sci-tech information, the patent information has an advantage in its uniqueness, effectiveness, reliability; its coverage of all scientific disciplines and technologies; it is published in over 140 countries [1]. The examples of ecology-related patent documents are provided. This information is accessible through the ROSPATENT information resources.
Ecological education in Bashkortostan Republican M. Kh. Tukhvatshin Special Library for the Blind

The experience of Bashkortostan Republican Special Library for the Blind in ecological education is discussed. Diverse related activities, e.g. contests, publishing, exhibitions, lectures, etc., are described.
Library’s publishing books in ecology: The experience of the publisher’s of Yuzhno-Sakhalinsk O. P. Kuznetsov Central City Library

The experience of the municipal library in publishing books in ecology is discussed. The route from projecting to publishing is described in detail. The author also reviews the use of ecological publications in library services.
The role of library-type cultural institutions in ensuring sustainable socioeconomic development of the Russian Federation

The article analyzes the role of library institutions in achieving sustainable state socio-economic development at the present stage. The conditions for the transitionto sustainable development in the activities of the relevant cultural institutions are studied, directions for modernization are proposed, based on the specifics of the activity, available opportunities, current tasks and development prospects. The research methods were: analysis and synthesis, deduction and induction, systematic approach, structural-functional method, classification, abduction method, content analysis method, special legal methods of cognition: formal legal, historical legal, comparative legal.
Design and Implementation of Text to Speech Synthesizer

Current research introduces a novel, efficient, and less expensive way for users to hear than to read the content of text images in real time. Includes Optical Character Recognition (OCR) ideas and a Text to Speech Synthesizer on Raspberry Pi (TTS). This type of technology uses a visual connection to allow visually impaired people to communicate with computers successfully. Extraction of text from colored images is a serious problem in computer vision. Converting text into speech is a process that scans and translates English letters and numbers into pictures using recognizable letter recognition (OCR) and converts them into words.&#x0D;
Maharshi Dyanand Dvawara vrnit Shikshan Paddatiyan

Teaching methods should be such that the set goals can be achieved. We should not be slaves of any one method. Teaching methods should be such that the internal powers of the students can be fully developed. For this, experiments, activities and projects and games, lectures and reasoning methods can be used. There are many methods from which we can choose those, which can meet our needs in time and in the best way.
'Alexa, What Do You Mean to Me?': A Scoping Review of Users' Relationships with Smart Speakers

Background: Smart speakers encourage users to view them as social entities, tapping into a human tendency to anthropomorphise objects around them. In this paper, we conduct a scoping review on the social aspects of smart speaker use, focusing on the predictive factors to, and outcomes of, forming a relationship with a smart speaker.Methods: A database search conducted in January 2023 identified 26 papers reporting on the relationships users form with their smart speakers that were included in this scoping review. The procedure of identifying these papers followed guidance set out by Arksey and O’Malley and the PRISMA guidelines for scoping reviews.Results: Living alone, isolation and age were associated with forming a ‘relationship’ with a smart speaker. Users tended to classify their smart speaker as a companion, friend, quasi-other, assistant or as loving. Some users drew social value from their smart speaker (e.g., comfort and avoiding feelings of loneliness), but were sometimes frustrated by its unnatural speech and lack of personalisation options. The methods used in the literature are discussed.Implications: This review identifies isolated and older individuals as being the most likely to form a relationship with their smart speaker. Additional findings that smart speakers can reduce users’ loneliness indicates the potential for future applications as an accessible digital intervention for loneliness. However, there is a lack of understanding as to why these users are most likely to form relationships, although underlying loneliness and subsequent social motivation is hypothesised.
A Survey on Deep Bayesian Representation Learning

Representation learning has emerged as a crucial area of machine learning, especially with the rise of self-supervised learning. Bayesian techniques have the potential to provide powerful learning representations both in a self-supervised and supervised fashion. Unlike optimization-based approaches, Bayesian methods use marginalization and provide access to the entire posterior, which can be effectively utilized. Additionally, Bayesian representations have unique properties, such as quantization of uncertainty, that make them more valuable than MAP-based representations. Paper presents a comprehensive survey of the domain of Bayesian representation learning. The authors introduce the most critical Bayesian inference techniques necessary to understand the Bayesian representation learning framework. The focus is on two significant classes of models: variational autoencoders, which generate representation as a probability distribution, and Bayesian neural networks, which allow access to every possible model with the parameters settings weighted by their posterior probabilities. The survey outlines recent advances in organizing knowledge and characterizing the representations obtained with generative and discriminative methods. The authors argue that Bayesian representation learning is a promising area of research that has the potential to yield significant advancements in machine learning. This paper provides an essential resource for researchers and practitioners interested in exploring Bayesian techniques for representation learning.
Assessment of Effectiveness in Stabilization/Solidification of Arsenic-Contaminated Soil: Long-Term Leaching Test and Geophysical Measurement

This study focused on evaluating the effectiveness of stabilizers in immobilizing As in contaminated soil using both geochemical and geophysical monitoring methods. The effluent from the stabilizer application and control columns was analysed, and the status of the columns was monitored using electrical resistivity (ER) and induced polarisation (IP) methods. The stabilizer delayed As and Ca leaching time and reduced As leaching amount significantly. The coefficients of As and Fe leaching were high (control column: R2=0.955; AMDS(Acid Mine Drainage Sludge) column: R2=0.908; and SS(SteelSlag) column: R2=0.833). As the concentration of the leached Fe decreased, the IP also decreased. The coefficients of IP and Fe leaching were high (control column R2=0.768; AMDS column R2=0.807, and SS column R2=0.818). IP measurements can be used to monitor and evaluate the retention effectiveness of applied stabilizer in As-contaminated soil, providing important information for long-term monitoring of stabilization sites. This study suggests a rapid and continuous monitoring method for solidification/stabilization sites in the field.
Adaptive Directional Learning in Games

In this paper, we develop a novel learning model that combines directional learning and automata reinforcement. We show that directional learning can be defined on a cyclic order in addition to its original domain (interval) and that pattern learning by reinforcing parts of automata [Chernov, 2020] can be used with directions instead of actions as a basis of automata. We test our model on two laboratory experimental datasets (Oligopoly [Duersch et al., 2010] and Rock-Paper-Scissors [Chernov, 2020]). Fitting participants’ trajectories and aggregating areas where players’ average payoffs converge, we establish that our model outperforms classical models (Fictitious Play and Reinforcement Learning) and produces a behavior aligned with human decisions while making fewer mistakes.
Identifying Multiple Soil Pollutions of Potentially Contaminated Sites Based on Multi-Gate Mixture-of-Experts Network

With the rapid increase in the amount and sources of big data, using big data and machine learning methods to identify site soil pollution has become a research hotspot. However, previous studies that used basic information of sites as pollution identification indexes mainly have problems of low accuracy and efficiency when conducting complex model predictions for different soil pollution types. In this study, we collected the environmental data of 199 sites in 6 typical industries involving heavy metal and organic pollution. After feature fusion and selection, 10 indexes based on pollution sources and pathways were used to establish the soil pollution identification index system. The Multi-gate Mixture-of-Experts network (MMoE) were constructed to carry out the multi-tasks of soil heavy metals, VOCs and SVOCs pollution identification simultaneously. The SHAP method was used to reveal the significance of pollution identification indexes on the multiple outputs of MMoE and obtain their driving factors. The results showed that the MMoE has better accuracy and efficiency than BP neural networks of single tasks. The indexes of raw material containing organic compounds, enterprise scale, soil pollution traces and industry types have the different significant importance on site soil pollutions. This study proposed a more efficient and accurate method to identify site soil pollutions and their driving factors, which offers a step towards realizing intelligent identification and risk control of site soil pollution globally.
Accurate White Light Phase-Shifting Interferometry Under Pzt Scanning Error

White light phase-shifting interferometry (WLPSI) has obvious advantages such as large field of view, high precision and non-contact, so it is widely used in three-dimensional (3-D) topography measurement of micro-nano photoelectric devices. In this paper, the problems for large phase unwrapping error and low phase calculation accuracy under the scanning error of piezoelectric transducer (PZT) in current phase shifting measurement are studied. The novel and effective phase-shifting measurement method under scanning error are proposed. This method can be used to accurately recover the surface phase and complete the surface reconstruction under random scanning error with maximum for 10nm. Finally, the proposed method is verified by simulation and measurement experiments.
"ABYSS" BY YU. V. MAMLEEV: GENESIS, DYNAMICS OF DOCTRINE, ONTOLOGICAL PROBLEM

The article is devoted to the genesis, dynamics, and ontological problem of Mamleev`s doctrine of the Abyss. The author identifies five reasons for the emergence of such an extraordinary doctrine: the crisis of the 20th century and underground philosophizing, rootedness in Russian culture and Mamleev`s national self-identity, the influence of oriental metaphysics and integral traditionalism, artistic origins of Mamleev`s way of thinking and the connection between philosophy and literature, Mamleev`s mystical experience and his spiritual insights. Considering the dynamics of the doctrine of Abyss, the author of the article points out three stages of its development: formation of I-utrism as an opportunity to preserve the personality in the form of a higher I upon reaching the Absolute; enlightenment the depths of the invisible layers of space and creating the "Last Doctrine"; revealing the idea of Eternal Russia as a mediator between the Absolute and the Abyss. In addition, the author of the article examines the possibility of reconciling radicalism Mamleev's I-utrism with his own doctrine of Eternal Russia. Reconciliation becomes possible precisely within the context of the dynamics of Mamleev's doctrine of the Abyss. Studying the ontological problem of the ratio of the Absolute and the Abyss, the author notes, that at the metaphysical level, which is esoteric in nature, the Absolute and the Abyss are identical and cannot be conceived separately. However, at the religious level, which is exoteric in nature, they can be opposed to each other. Wherein, the transcendence of the Absolute is regarded as given to man and which can be attained, and the transcendence of the Abyss regarded as not given to man and as fundamentally unattainable. The author of the article concludes that consideration of the genesis and dynamics of Mamleev's doctrines can be helpful in understanding and solving the paradoxes, which arise when the Abyss is introduced into traditional metaphysics.
Simple Correlation Dimension Estimator and its Use to Detect Causality

Revisiting the Grassberger - Procaccia algorithm inspires us to introduce an extremely simple estimator of the correlation dimension. For the new estimator, we also provide guidance on determining the extent to which the correlation dimension may be underestimated, given the size of the data and the dimensionality of the state space.As a topical application, the new estimates, along with some established intrinsic dimension calculations, are used to detect causal relationships. For the demonstration, we use a challenging example of coupled chaotic oscillators.
Synchronization of Non-Smooth Chaotic Systems Via an Improved Reservoir Computing

The reservoir computing (RC) is increasingly used to learn the synchronization behavior of chaotic systems as well as the dynamical behavior of complex systems, but it is scarcely applied in studying synchronization of non-smooth chaotic systems likely due to its complexity leading to the unimpressive effect. Here proposes a simulated annealingbased differential evolution (SADE) algorithm for the optimal parameter selection in the reservoir, and constructs an improved RC model for synchronization, which can work well not only for non-smooth chaotic systems but for smooth ones. Extensive simulations show that the trained RC model with optimal parameters has far longer prediction time than those with empirical and random parameters. More importantly, via the model the well-trained RC system can be well synchronized to its original chaotic system as well as its replicate RC system via one shared signal, whereas the traditional RC system with empirical or random parameters fails for some chaotic systems, particularly for some non-smooth chaotic systems.
The Effect of Think Talk Write Strategy toward Students’ Information Literacy in Immune System Concept

Students' lack of information literacy skills are influenced by some factors, including educators' use of a learning strategy and their inability to link it to their students. The purpose of this study is to ascertain whether or not the Think Talk Write strategy enhances students' literacy abilities regarding immune system content. A non-equivalent pretest-posttest design and a quasi-experimental design were used in this research. Fifty-two of 11 th grade students dari MAS Raudhatul Akmal Batang Kuis served as the sample who was chosen by cluster random sampling technique. An information literacy questionnaire was used as research instrument which was developed by following the information literacy principles. The ANCOVA test was used to analyze the data. The implementation of Think Talk Write strategy had a significant impact on students' information literacy skills. Keywords: Think Talk Write strategy, information literacy, biology learning. Abstrak: Kurangnya keterampilan literasi informasi siswa dipengaruhi oleh beberapa faktor, salah satunya adalah penggunaan model pembelajaran oleh pendidik dan ketidakmampuan mereka menghubungkannya dengan siswa yang menjadi fokus pengembangan literasi informasi di sekolah. Tujuan penelitian ini adalah untuk mengetahui apakah strategi pembelajaran Think Talk Write dapat meningkatkan kemampuan literasi siswa terkait materi sistem imun. Metode penelitian menggunakan desain pretest-posttest non-equivalent dan quasi-experimental design. Penelitian ini menggunakan sampel sebanyak 5 2 siswa kelas XI di MAS Raudhatul Akmal Batang Kuis yang dipilih dengan teknik cluster random sampling . I nstrumen penelitian yang digunakan adalah angket informasi berdasarkan prinsip literasi informasi. Tes ANCOVA digunakan untuk menganalisis data. P enerapan strategi pembelajaran Think Talk Write memberikan pengaruh yang signifikan terhadap kemampuan literasi informasi siswa . Kata kunci: pembelajaran Think Talk Write , literasi informasi, pembelajaran biologi. DOI: http://dx.doi.org/10.23960/jpmipa/v24i1.pp01-12
Exploiting Deep Learning to Compute Maximum Common Subgraphs

Finding the maximum common induced subgraph is a fundamental problem in computer science. Proven to be NP-hard in the 1970s, nowadays, it has countless applications that still motivate the search for efficient algorithms and practical heuristics. In this work, we extend a state-of-the-art branch-and-bound exact algorithm with new techniques developed in the deep-learning domain, namely graph neural networks and node embeddings, effectively transforming an efficient, yet uninformed depth-first search into an effective best-first. The change enables the algorithm to find suitable solutions within a limited time budget, pushing forward the time-efficiency and the applicability of the method on larger graphs. The experimental analysis on standard graphs compares our heuristic against the original algorithm and a recently tweaked versions that exploits reinforcement learning. Results clearly demonstrate the effectiveness and scalability of the proposed approach.