Reviewerly/exHarmony huggingface dataset:
Asymmetric Color Transfer with Consistent Modality Learning

The mono-color dual-lens system widely exists in the smartphone that captures asymmetric stereo image pairs, including high-resolution (HR) monochrome images and low-resolution (LR) color images. Asymmetric color transfer aims to reconstruct an HR color image by transferring the color information of the LR color image to the HR monochrome image. However, the inconsistency of spectral resolution and spatial resolution between stereo image pairs poses a challenge for establishing reliable stereo correspondence for precise color transfer. Previous works have not adequately addressed this issue. In this paper, we propose a dual-modality consistency learning framework to assist the establishment of reliable stereo correspondence. According to the complementarity of color and frequency information between stereo images, a dual-branch Stereo Information Complementary Module (SICM) is devised to perform the consistent modality learning in feature domain. Specifically, we meticulously design the stereo frequency and color modulation mechanism equipped in the SICM for capturing the information complementarity between dual-modal features. Furthermore, a parallax attention distillation is proposed to drive consistent modality learning for better stereo matching. Extensive experiments demonstrate that our model outperforms the state-of-the-art methods in the Flickr1024 dataset and has superior generalization ability over the KITTI dataset and real-world scenarios. The code is available at https://github.com/keviner1/SICNet.
Convolutional Neural Network to Predict the Penetration Coefficient of Metamaterials Based on Their Structure and Composition

The work is devoted to the development of a technique for predicting the coefficient passage of metamaterials based on topological structure and chemical composition with the use of machine learning techniques, namely artificial neural networks using convolution. In modern scientific and technical research, the methods of machine learning, namely: convolutional neural networks, occupy the most rapidly researched method in the design tasks of metamaterials and their properties. The advantage of this technique is the ease of implementation, the availability of data for this approach, the speed of calculations compared to the exact methods of predicting properties and topological structure based on physical laws. Of course, artificial neural networks are a rather complex process that has its own drawbacks – the need for a large amount of data, the relative complexity of optimization, and the complexity of problem formulation. With the development of machine learning technologies, these disadvantages are more and more eliminated, and therefore their use becomes more accessible. A large amount of information about metamaterials from relevant sources was used, namely the topology, chemical composition and measurement conditions of metamaterials. Software environments were used for writing digital code and building 3D objects of metamaterials with defined properties. An algorithm for predicting the transmission coefficient based on the structure, chemical composition of metamaterials based on a convolutional neural network using experimental data of laboratory metamaterials has been developed. An algorithm for saving information about the chemical composition of metamaterials has been developed. It is shown that using information about the electromagnetic properties of chemical elements, it is possible to predict the transmission coefficient of metamaterials. The process of presenting the coefficient of passage of metamaterials in a form convenient for training a convolutional neural network is described. Two methods were used to compare the effectiveness of both methods. It is shown that the method of representing experimental characteristics in the form of polynomial coefficients is faster, but not suitable for solving problems of predicting the characteristics of metamaterials. Data augmentation is shown to be the most effective method for improving forecasting results. Nevertheless, performance improvement methods based on architecture changes and hyperparameter changes should be continually evaluated and used whenever possible.
The Russian Book Chamber in the press (1992–2021)

The bibliographical lists of printed publications from the post-Soviet Russian Book Chamber and about it are submitted. The peculiarities of its production, scientific, instruction and methodical activities in the spheres of bibliography, statistics and bibliology are reflected for the period of 1992–2021.
Rapid identification of hazardous heavy metal-containing waste by combining EDXRF with machine learning: Taking zinc smelting waste as an example

To address the time-consuming identification and poor timeliness of environmental pollution tracing problems of traditional chemical detection methods for hazardous solid waste, a rapid identification and characterization methodology for hazardous waste containing heavy metals is established by using the EDXRF spectroscopy principle combined with an optical intelligent optimization algorithm. Two years of long-time serial optical spectrum information are collected from nine typical types of hazardous waste containing heavy metals produced in the zinc smelting process using EDXRFs, online analytical instruments installed on different process lines. Then, different machine learning models are applied to identify the key spectral factors that are first screened after noise reduction and standardization to pretreat all spectrum information data to distinguish different characteristics of each type of hazardous waste. Finally, the random forest model is selected and optimized to improve its identification accuracy. The results show that the spectral line signals corresponding to heavy metal elements such as Fe, Zn, Cu, Cd, and Ag in zinc smelting materials can be used as important factors to distinguish different types of zinc smelting solid waste. Because different smelting processes lead to different heavy metal contents in different types of zinc smelting hazardous waste, the optimized random forest model can be implemented to accurately identify and characterize nine typical types of hazardous waste containing heavy metals, and an accuracy rate of 100% is achieved. This method provides an efficient tool for the environmental management of hazardous waste and the rapid tracing of environmental risks in the future. Especially in tracking soil or surface water pollution caused by the illegal dumping of solid waste, the rapid identification and characterization of solid waste are of great significance for the quality supervision of regional ecological environments.
PENGEMBANGAN MEDIA PEMBELAJARAN MATEMATIKA BERBASIS ANDROID

AbstractMathematics education in elementary schools is very important, because it is the basis of the use of mathematics at the next level. The notion of mathematics as a frightening lesson must be eliminated and replaced with a pleasant lesson. In elementary schools most of the subject matter is delivered conventionally. So the material looks less attractive. For this reason, a learning innovation is needed so that the material looks more interesting and motivates students to learn. Especially the basic geometrical building calculation operations most frequently studied by elementary school students are calculating the area, perimeter, and volume of basic geometric shapes.This study aims to optimize the use of smartphones, especially Android in the world of education. T Pengembangan Media Pembelajaran Matematika Berbasis Androidhe method used by the author in the design of this application is the Waterfall method because this method is a structured approach, this research certainly there are still many shortcomings and things that still need to be studied and developed again.
Comparative study of classical test theory and item response theory using item analysis results of quantitative chemistry achievement test

Classical Test Theory (CTT) and Item Response Theory (IRT) are the two major test theories used to analyse test responses. This study compared the results from the CTT and IRT 3 parameter Logistic model analyses to ascertain which is better fit for the analysis of Quantitative Chemistry Achievement Test (QCAT) items. Fifty objective test items on Senior Secondary 2 quantitative chemistry (calculations in chemistry) were used and Survey research-type of non-experimental design was adopted. Randomly selected sample of 1105 students participated. Item analyses were done based on CTT and IRT using Bilog MG, Dimtest and R software. The QCAT items certified the IRT assumptions on dimensionality (unidimensional), local independence and model-data fit (3 parameter logistic model). Items from CTT analysis with rpbs≥0.20 and 0.30≤p≤0.80 were considered good items and selected. Items from IRT analysis with -3≤b ≤+3 were considered good items and selected. The result revealed that only 11 items survived the CTT analysis while 20 items survived the IRT analysis. The reliability of the items from CTT (0.27) was lower than that of IRT (0.60). IRT is a more effective method of item analysis for objective test item responses and should be used by scale developers. &nbsp
A Tutorial on Visual Representations of Relational Queries

Query formulation is increasingly performed by systems that need to guess a user's intent (e.g. via spoken word interfaces). But how can a user know that the computational agent is returning answers to the "right" query? More generally, given that relational queries can become pretty complicated, how can we help users understand existing relational queries, whether human-generated or automatically generated? Now seems the right moment to revisit a topic that predates the birth of the relational model: developing visual metaphors that help users understand relational queries. This lecture-style tutorial surveys the key visual metaphors developed for visual representations of relational expressions. We will survey the history and state-of-the art of relationally-complete diagrammatic representations of relational queries, discuss the key visual metaphors developed in over a century of investigating diagrammatic languages, and organize the landscape by mapping their used visual alphabets to the syntax and semantics of Relational Algebra (RA) and Relational Calculus (RC).
Rethinking Cross-Domain Pedestrian Detection: A Background-Focused Distribution Alignment Framework for Instance-Free One-Stage Detectors

Cross-domain pedestrian detection aims to generalize pedestrian detectors from one label-rich domain to another label-scarce domain, which is crucial for various real-world applications. Most recent works focus on domain alignment to train domain-adaptive detectors either at the instance level or image level. From a practical point of view, one-stage detectors are faster. Therefore, we concentrate on designing a cross-domain algorithm for rapid * one-stage detectors that lacks instance-level proposals and can only perform image-level feature alignment. However, pure image-level feature alignment causes the foreground-background misalignment issue to arise, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., the foreground features in the source domain image are falsely aligned with background features in the target domain image. To address this issue, we systematically analyze the importance of foreground and background in image-level cross-domain alignment, and learn that background plays a more critical role in image-level cross-domain alignment. Therefore, we focus on cross-domain background feature alignment while minimizing the influence of foreground features on the cross-domain alignment stage. This paper proposes a novel framework, namely, background-focused distribution alignment (BFDA), to train domain adaptive one-stage pedestrian detectors. Specifically, BFDA first decouples the background features from the whole image feature maps and then aligns them via a novel long-short-range discriminator. Extensive experiments demonstrate that compared to mainstream domain adaptation technologies, BFDA significantly enhances cross-domain pedestrian detection performance for either one-stage or two-stage detectors. Moreover, by employing the efficient one-stage detector (YOLOv5), BFDA can reach 217.4 FPS (640×480 pixels) on NVIDIA Tesla V100 (7~12 times the FPS of the existing frameworks), which is highly significant for practical applications. The code from this study will be made publicly available.
Deep Inductive Logic Programming meets Reinforcement Learning

One approach to explaining the hierarchical levels of understanding within a machine learning model is the symbolic method of inductive logic programming (ILP), which is data efficient and capable of learning first-order logic rules that can entail data behaviour. A differentiable extension to ILP, so-called differentiable Neural Logic (dNL) networks, are able to learn Boolean functions as their neural architecture includes symbolic reasoning. We propose an application of dNL in the field of Relational Reinforcement Learning (RRL) to address dynamic continuous environments. This represents an extension of previous work in applying dNL-based ILP in RRL settings, as our proposed model updates the architecture to enable it to solve problems in continuous RL environments. The goal of this research is to improve upon current ILP methods for use in RRL by incorporating non-linear continuous predicates, allowing RRL agents to reason and make decisions in dynamic and continuous environments.
MOON: A Subspace-Based Multi-Branch Network for Object Detection in Remotely Sensed Images

The effectiveness of training-based object detection heavily depends on the amount of sample data. But in the field of remote sensing, the amount of sample data is difficult to meet the needs of network training due to the non-cooperative imaging modes and complex imaging conditions. Moreover, the imbalance of the sample data between different categories may lead to the long-tail problem during the training. Given that similar sensors, data acquisition approaches, and data structures could make the targets in different categories possess certain similarities, those categories can be modeled together within a subspace rather than the entire space to leverage the amounts of sample data in different subspaces. To this end, a subspace-dividing strategy and a subspace-based multi-branch network is proposed for object detection in remotely sensed images. Specifically, a combination index is defined to depict this kind of similarity, a generalized category consisting of similar categories is proposed to represent the subspace, and a new subspace-based loss function is devised to address the relationship between targets in one subspace and across different subspaces to integrate the sample data from similar categories within a subspace and to balance the amounts of sample data between different subspaces. Furthermore, a subspace-based multi-branch network is constructed to ensure the subspace-aware regression. Experiments on the DOTA and HRSC2016 datasets demonstrated the superiority of our proposed method.
Understanding Collective Intelligence: Investigating the Role of Collective Memory, Attention, and Reasoning Processes

As society has come to rely on groups and technology to address many of its most challenging problems, there is a growing need to understand how technology-enabled, distributed, and dynamic collectives can be designed to solve a wide range of problems over time in the face of complex and changing environmental conditions-an ability we define as "collective intelligence." We describe recent research on the Transaction Systems Model of Collective Intelligence (TSM-CI) that integrates literature from diverse areas of psychology to conceptualize the underpinnings of collective intelligence. The TSM-CI articulates the development and mutual adaptation of transactive memory, transactive attention, and transactive reasoning systems that together support the emergence and maintenance of collective intelligence. We also review related research on computational indicators of transactive-system functioning based on collaborative process behaviors that enable agent-based teammates to diagnose and potentially intervene to address developing issues. We conclude by discussing future directions in developing the TSM-CI to support research on developing collective human-machine intelligence and to identify ways to design technology to enhance it.
The value of Augmented Reality in surgery — A usability study on laparoscopic liver surgery

Augmented Reality (AR) is considered to be a promising technology for the guidance of laparoscopic liver surgery. By overlaying pre-operative 3D information of the liver and internal blood vessels on the laparoscopic view, surgeons can better understand the location of critical structures. In an effort to enable AR, several authors have focused on the development of methods to obtain an accurate alignment between the laparoscopic video image and the pre-operative 3D data of the liver, without assessing the benefit that the resulting overlay can provide during surgery. In this paper, we present a study that aims to assess quantitatively and qualitatively the value of an AR overlay in laparoscopic surgery during a simulated surgical task on a phantom setup. We design a study where participants are asked to physically localise pre-operative tumours in a liver phantom using three image guidance conditions - a baseline condition without any image guidance, a condition where the 3D surfaces of the liver are aligned to the video and displayed on a black background, and a condition where video see-through AR is displayed on the laparoscopic video. Using data collected from a cohort of 24 participants which include 12 surgeons, we observe that compared to the baseline, AR decreases the median localisation error of surgeons on non-peripheral targets from 25.8 mm to 9.2 mm. Using subjective feedback, we also identify that AR introduces usability improvements in the surgical task and increases the perceived confidence of the users. Between the two tested displays, the majority of participants preferred to use the AR overlay instead of navigated view of the 3D surfaces on a separate screen. We conclude that AR has the potential to improve performance and decision making in laparoscopic surgery, and that improvements in overlay alignment accuracy and depth perception should be pursued in the future.
Utilization of DSS to the Promotion and Conclusion of Charters in the Shipping and Supply Chain Context

In recent times, the importance of shipping services has been rapidly increasing. As a consequence, shipping companies seek to capitalize on every available strategy and opportunity to promote their services and manage to promote and conclude charters. This highlights the role of digital marketing and the utilization of customer data originating from corporate websites. Moreover, to this aim, shipping companies can utilize various Decision Support Systems (DSSs) available, based on their corporate goals. This research focused on gathering big data from the web pages of the 5 largest shipping companies in the worldwide economic context, which were analyzed by utilizing various DSSs to evaluate their effect on the performance of the shipping firms. The collected data were elaborated through statistical analyses (correlation and linear regression), and their coefficients were used as inputs for the deployed Fuzzy Cognitive Mapping (FCM) DSS model. As a result, from the utilization of the DSS with the website analytical data from the shipping companies, the authors found that it is essential for these firms to reduce the bounce rate of their website customers and visitors in order to enhance their ranking in search engines. Thus, by ranking higher in their customers’ search engine results, shipping companies would be able to promote and conclude more charters. The role of DSSs in this goal of shipping companies is highlighted by this study as a determinant.
About the metallogeny of Kazakhstan and its significance for the forecast of mineral deposits

The metallogeny of Kazakhstan is considered from the standpoint of the concept of the Earth's crust stepwise development and geotectonogens. Using examples, new regularities are established that significantly complement the well-known scientific and theoretical ideas about metallogeny and the forecast of minerals, which have not previously been given due attention. Currently, for many priority types of minerals, especially those that are city-forming, there is already an acute shortage. Therefore, the replenishment of mineral reserves is an extremely urgent and important task for the country. In solving the problem, the predictive-metallogenic direction of the metallogenic school of Kazakhstan, which was foundet by K.I. Satpaev. We have described new regularities in the formation and distribution of mineral deposits, especially unconventional ones. In selected promising areas, nodes and areas, large-scale integrated forecasting and prospecting and exploration work is proposed. Considering the established patterns, special attention is paid to the ore content of the Precambrian, Volcanogenic belts, Weathering crusts and stratiform mineralization. The role of predictive metallogenic studies of regional, special and local is shown, with an analysis of existing and new materials based on the latest scientific ideas. The use of advanced technologies will make it possible to establish new patterns of the deposit’s development in time and space, to identify new, including non-traditional ore-bearing areas for Kazakhstan.
A framework for understanding label leakage in machine learning for health care

Abstract Introduction The pitfalls of label leakage, contamination of model input features with outcome information, are well established. Unfortunately, avoiding label leakage in clinical prediction models requires more nuance than the common advice of applying “no time machine rule.” Framework We provide a framework for contemplating whether and when model features pose leakage concerns by considering the cadence, perspective, and applicability of predictions. To ground these concepts, we use real-world clinical models to highlight examples of appropriate and inappropriate label leakage in practice. Recommendations Finally, we provide recommendations to support clinical and technical stakeholders as they evaluate the leakage tradeoffs associated with model design, development, and implementation decisions. By providing common language and dimensions to consider when designing models, we hope the clinical prediction community will be better prepared to develop statistically valid and clinically useful machine learning models.