Reviewerly/exHarmony huggingface dataset:
High-Speed Tracking with Mutual Assistance of Feature Filters and Detectors

Object detection and tracking in camera images is a fundamental technology for computer vision and is used in various applications. In particular, object tracking using high-speed cameras is expected to be applied to real-time control in robotics. Therefore, it is required to increase tracking speed and detection accuracy. Currently, however, it is difficult to achieve both of those things simultaneously. In this paper, we propose a tracking method that combines multiple methods: correlation filter-based object tracking, deep learning-based object detection, and motion detection with background subtraction. The algorithms work in parallel and assist each other's processing to improve the overall performance of the system. We named it the "Mutual Assist tracker of feature Filters and Detectors (MAFiD method)". This method aims to achieve both high-speed tracking of moving objects and high detection accuracy. Experiments were conducted to verify the detection performance and processing speed by tracking a transparent capsule moving at high speed. The results show that the tracking speed was 618 frames per second (FPS) and the accuracy was 86% for Intersection over Union (IoU). The detection latency was 3.48 ms. These experimental scores are higher than those of conventional methods, indicating that the MAFiD method achieved fast object tracking while maintaining high detection performance. This proposal will contribute to the improvement of object-tracking technology.
Creating Universum for class imbalance via locality and its application in multiview subspace learning

Since the existing methods for generating Universum fail in class imbalance cases, we propose a new method for creating Universum via locality and betweenness, named L-CIBU. Compared to existing methods for generating Universum, L-CIBU not only generates appropriate Universum in the case of class imbalance but also has higher efficiency. Given the existence of class imbalance in multiview learning, we introduce Universum to multiview subspace learning (MvSL) to further enhance its performance and L-CIBU happens to be able to overcome this problem. As far as we know, this is the first time to introduce Universum into MvSL with more than two views. Specifically, we introduce Universum created by L-CIBU into multiview discriminant analysis (MvDA) and propose Universum MvDA (UMvDA). By analyzing several randomly generated imbalanced datasets, it is intuitively demonstrated that L-CIBU is more effective than FIBU and CIBU in the case of class imbalance. The experimental results on two real datasets show that the time required for creating the Universum using L-CIBU is 25%∼95% less than that of FIBU and CIBU. Moreover, UMvDA achieves an accuracy improvement of approximately 1%∼6% compared to MvDA, Universum canonical correlation analysis (UCCA), and Universum discriminant canonical correlation analysis (UDCCA).
Multi-kernel learning for multi-label classification with local Rademacher complexity

Multi-label classification aims to construct prediction models from input space to output space for multi-label datesets. However, the feature space of multi-label dataset and the hypothesis space of classifier are always complex. In order to obtain high performance multi-kernel learning algorithms, the core problem is to design algorithms that compress both hypothesis space and feature space. In this paper, by combining the local Rademacher complexity and Hilbert-Schmidt independence criterion (HSIC), an effective multi-kernel learning algorithm for multi-label classification is proposed to compress the feature space and hypothesis space simultaneously. Based on the tail sum of eigenvalues of the integral operator corresponding to kernels, the upper bound of local Rademacher complexity of linear functions class in the feature space is determined. The Hilbert-Schmidt independence criterion is applied to maximize the correlation between the convex combination of the input kernel matrices and the ideal kernel matrix in the label space. Finally, the Laplace regularization model is built by controlling the tail sum of eigenvalues of the integral operator corresponding to kernels of this criterion. Therefore, the above process obtains a combined kernel, simultaneously by sorting the coefficients of the combined kernel, a feature selection algorithm can be constructed. On the basis, the combined kernel can also be used to design a new binary classifier for multi-label classification. The experimental results verify that our proposed multi-kernel learning algorithms can effectively compress the hypothesis space, while the feature selection algorithms can effectively compress the feature space.
A Privacy-Preserving Consensus Mechanism for ADMM-Based Peer-to-Peer Energy Trading

In the electricity market, prosumers are becoming more and more prevalent due to the fast development of distributed energy resources and demand response management, which also promote the appearance of peer-to-peer (P2P) trading mechanisms for energy. Optimization-based methods are efficient tools to design the P2P energy trading negotiation mechanism. However, the main drawback for market mechanisms based on optimization methods is that the incentive compatibility cannot be satisfied, which means participants can obtain more profit by providing untruthful biddings. To overcome this challenge, a novel consensus mechanism based on Proof of Solution (PoSo) is proposed for P2P energy trading. The optimization results will be verified by neighboring agents according to the KKT conditions in a fully decentralized and symmetric manner, which means agents will check each other’s solutions. However, the verification process may leak the private information of agents, and a privacy-preserving consensus mechanism is designed using Shamir’s secret sharing method. After that, we explore a method to realize that trusted agents can recover the right information even under the misbehavior of malicious agents by inheriting the philosophy of Practical Byzantine Fault Tolerance (PBFT). The numerical results demonstrate the effectiveness and efficiency of our proposed consensus mechanisms. In more detail, (1) when the message delivery success rate is not lower than 0.7, the consensus mechanisms almost guarantee success; (2) if the proportion of untrusted agents satisfies 4f+1≤Nωn, the proposed method guarantees the correctness of the consensus verification results; (3) the communication times among agents can be highly reduced by more than 60% by only verifying the optimality of the received results for the first three and last few iterations.
Improved Parallel Implementation of 1D Discrete Wavelet Transform Using CPU-GPU

This work describes a data-level parallelization strategy to accelerate the discrete wavelet transform (DWT) which was implemented and compared in two multi-threaded architectures, both with shared memory. The first considered architecture was a multi-core server and the second one was a graphics processing unit (GPU). The main goal of the research is to improve the computation times for popular DWT algorithms for representative modern GPU architectures. Comparisons were based on performance metrics (i.e., execution time, speedup, efficiency, and cost) for five decomposition levels of the DWT Daubechies db6 over random arrays of lengths 103, 104, 105, 106, 107, 108, and 109. The execution times in our proposed GPU strategy were around 1.2×10−5 s, compared to 3501×10−5 s for the sequential implementation. On the other hand, the maximum achievable speedup and efficiency were reached by our proposed multi-core strategy for a number of assigned threads equal to 32.
Algebraic Attacks against Grendel: An Arithmetization-Oriented Primitive with the Legendre Symbol

The rise of modern cryptographic protocols such as Zero-Knowledge proofs and secure Multi-party Computation has led to an increased demand for a new class of symmetric primitives. Unlike traditional platforms such as servers, microcontrollers, and desktop computers, these primitives are designed to be implemented in arithmetical circuits. In terms of security evaluation, arithmetization-oriented primitives are more complex compared to traditional symmetric cryptographic primitives. The arithmetization-oriented permutation Grendel employs the Legendre Symbol to increase the growth of algebraic degrees in its nonlinear layer. To analyze the security of Grendel thoroughly, it is crucial to investigate its resilience against algebraic attacks. This paper presents a preimage attack on the sponge hash function instantiated with the complete rounds of the Grendel permutation, employing algebraic methods. A technique is introduced that enables the elimination of two complete rounds of substitution permutation networks (SPN) in the sponge hash function without significant additional cost. This method can be combined with univariate root-finding techniques and Gröbner basis attacks to break the number of rounds claimed by the designers. By employing this strategy, our attack achieves a gain of two additional rounds compared to the previous state-of-the-art attack. With no compromise to its security margin, this approach deepens our understanding of the design and analysis of such cryptographic primitives.
Image Authentication and Restoration Using Block-Wise Variational Automatic Encoding and Generative Adversarial Networks

The Internet is a conduit for vast quantities of digital data, with the transmission of images being especially prevalent due to the widespread use of social media. However, this popularity has led to an increase in security concerns such as image tampering and forgery. As a result, image authentication has become a critical technology that cannot be overlooked. Recently, numerous researchers have focused on developing image authentication techniques using deep learning to combat various image tampering attacks. Nevertheless, image authentication techniques based on deep learning typically classify only specific types of tampering attacks and are unable to accurately detect tampered images or indicate the precise location of tampered areas. The paper introduces a novel image authentication framework that utilizes block-wise encoding through Variational Autoencoder and Generative Adversarial Network models. Additionally, the framework includes a classification mechanism to develop separate authentication models for different images. In the training phase, the image is first divided into blocks of the same size as training data. The goal is to enable the model to judge the authenticity of the image by blocks and to generate blocks similar to the original image blocks. In the verification phase, the input image can detect the authenticity of the image through the trained model, locate the exact position of the image tampering, and reconstruct the image to ensure the ownership.
Approximate blocked Gibbs sampling for Bayesian neural networks

Abstract In this work, minibatch MCMC sampling for feedforward neural networks is made more feasible. To this end, it is proposed to sample subgroups of parameters via a blocked Gibbs sampling scheme. By partitioning the parameter space, sampling is possible irrespective of layer width. It is also possible to alleviate vanishing acceptance rates for increasing depth by reducing the proposal variance in deeper layers. Increasing the length of a non-convergent chain increases the predictive accuracy in classification tasks, so avoiding vanishing acceptance rates and consequently enabling longer chain runs have practical benefits. Moreover, non-convergent chain realizations aid in the quantification of predictive uncertainty. An open problem is how to perform minibatch MCMC sampling for feedforward neural networks in the presence of augmented data.
Spatial oblivion channel attention targeting intra-class diversity feature learning

Convolutional neural networks (CNNs) have successfully driven many visual recognition tasks including image classification. However, when dealing with classification tasks with intra-class sample style diversity, the network tends to be disturbed by more diverse features, resulting in limited feature learning. In this article, a spatial oblivion channel attention (SOCA) for intra-class diversity feature learning is proposed. Specifically, SOCA performs spatial structure oblivion in a progressive regularization for each channel after convolution, so that the network is not restricted to a limited feature learning, and pays attention to more regionally detailed features. Further, SOCA reassigns channel weights in the progressively oblivious feature space from top to bottom along the channel direction, to ensure the network learns more image details in an orderly manner while not falling into feature redundancy. Experiments are conducted on the standard classification dataset CIFAR-10/100 and two garbage datasets with intra-class diverse styles. SOCA improves SqueezeNet, MobileNet, BN-VGG-19, Inception and ResNet-50 in classification accuracy by 1.31%, 1.18%, 1.57%, 2.09% and 2.27% on average, respectively. The feasibility and effectiveness of intra-class diversity feature learning in SOCA-enhanced networks are verified. Besides, the class activation map shows that more local detail feature regions are activated by adding the SOCA module, which also demonstrates the interpretability of the method for intra-class diversity feature learning.
Training cost analysis during covid-19 pandemic

The Covid-19 pandemic that has changed learning method in training including in the Financial Education and Training Agency (BPPK). The online learning method replaces the classical face-to-face learning method. This study aims to provide empirical evidence of changes in training costs at BPPK from the aspect of size, component structure, and the level of cost savings.The analysis was carried out using a descriptive quantitative method on training costs at BPPK in 2019 (before the Covid-19 pandemic) and 2020 (during the Covid-19 pandemic). The results of the analysis show that there was a significant change in the amount of training costs before the Covid-19 pandemic, from Rp180.58 billion to Rp43.99 billion during the Covid-19 pandemic, or dropped drastically to only 24.36% of the previous cost. On the other hand, there was an increase in the number of training participants from 76,176 people to 403,897 people or experienced a change of more than five times compared to before the Covid-19 pandemic. Thus, if the rough average cost of training per participant is calculated, there is a cost savings of Rp2,370,575 per person to Rp108,912 per person. Furthermore, the analysis of changes in the training cost structure showed a significant increase in the proportion of training costs at the curriculum development stage (Develop) from 0.23% to 3.46% and the learning program design stage (Design) from 0.32% to 0.62 %. On the other hand, the proportion of training costs in the learning needs analysis (Analyze) stage decreased from 0.17% to 0.02%, the training implementation stage (Implement) from 97.99% to 95.36%, and the learning evaluation stage (Evaluate) from 1.29% to 0.54%. The benefit of this research can be used by government training agency in calculating the costs required for online-based learning.
Participation of Miriskusnik Artists in the Book Projects of the Publisher Z. Grzhebin (1917-1922)

The paper is devoted to the study of the printed heritage of famous artists belonging to the “Mir iskusstva” (World of Art) society. The research aims to shed light on the history of participation of Miriskusnik artists (members of the “Mir iskusstva” society) in the book projects of the publisher Z. Grzhebin (1917-1922). A new chapter is opened, i.e. the printed works of Miriskusniks in children’s books, a completely new side of activities for graphic artists of the late XIX – early XX century. The paper presents the search in the design and creation of children’s books – richly illustrated books, providing insight into the dramatic plot through drawings, having an educational impact. The research is novel in that it determines various creative search options chosen by Miriskusnik artists when working on illustrations: the predominance of the drawing over the text, change of capital letters and inscriptions, introduction of a jobbing font set, which promoted a new interpretation of book images. As a result, it has been substantiated that the publishing house of Zinovii Isaevich Grzhebin remained a stronghold of high aesthetic taste, compliance with the demands of an intelligent public and a laboratory of creativity for artists and the publisher.
Secure Version Management of BIM Using Blockchain and Smart Contract Cluster

Versioning in Building Information Modeling (BIM) is essential for design collaboration. However, current version control systems risk data manipulation because they rely on centralized versioning architecture, which might result in rewriting, losing design traceability, and causing arguments. Blockchain technology offers a decentralized, immutable, and traceable database model, making it a possible solution for secure version management. Therefore, this study presents a blockchain-based framework with two key contributions. First, a distributed versioning environment is established, leveraging blockchain and common data environment (CDE). Second, a smart contract cluster (SCC) is developed to automate versioning operations in the blockchain. The proposed framework is evaluated and validated in design scenarios based on an actual project. Results show that the blockchain is a promising solution for efficient and secure BIM versioning.
Identification of Medical Outliers with the use of Machine Learning

Machine learning has the potential to not only improve outcomes in healthcare data, but also to lessen the burden on healthcare workers. This algorithm has the potential to aid in the resolution of problems and the identification of new information crucial to the development of medical data. As part of our study, we provide a novel method for identifying outliers across many data sets. if one considers the possibility that information gleaned from medical records might shed light on the dynamics at play in an illness or person’s way of life. The presented approach uses a combination of supervised and unsupervised learning to achieve its goals. It’s probable that this programme might detect any discrepancies in the patient’s medical file. How well regional and international data sources cooperate to detect anomalies in medical records in real time. The model is used here regardless of whether or not it was trained and verified using actual patient data. The cleaning method takes full use of all the advantages of analogous processes. The utilisation of medical data sets in research is common. The arithmetical results demonstration that the machine learning-based approach to outlier identification is more accurate.
Assessment of the ecological state of a water body for biodiversity monitoring according to biological and hydrochemical indicators

The purpose of the work is to assess the ecological state of the reservoir for biodiversity monitoring. Biodiversity monitoring is the systematic and continuous observation and measurement of the diversity of life in a given area or ecosystem. It is carried out to assess changes in biodiversity, identify trends, and determine the effectiveness of management measures and conservation programs. Biodiversity monitoring is a key tool for understanding the state and changes in natural systems, and its results can be used to make decisions in the field of nature conservation and sustainable use of resources. Bioindication can be used for long-term environmental monitoring. Biological organisms can be collected and analyzed over a long period of time, which makes it possible to assess trends in ecosystem changes and their relationship to anthropogenic factors. This takes into account the complex nature of pollution, that is, not only the concentration of certain substances, but also their interaction, synergistic effects and other aspects.
Technology of low-altitude aeromagnetic survey for prospecting for iron ores as a direction of sustainable development of modern geology

Exploration is essential to ensure the achievement of the UN Sustainable Development Goals through the provision of ferrous and non-ferrous metal resources. At the same time, geology as an industry should be based on the principles of sustainable development. The purpose of the study is to develop and test the technology of aeromagnetic survey for prospecting for iron ores that meets the requirements of the sustainable development of modern geology. Unmanned aerial vehicles with GNSS receivers, a camera, a magnetometer, as well as Trimble Business Center, Agisoft Metashape Professional, and QM Center software were used. A search for magnetic radiation anomalies over an area of 6.20 square kilometres was carried out. Areas were established where the radiation level is 59000–65860 nanotesl (background – 57803 nanotesl), that is, there are reserves of magnetite ores. The binding of these sites to the exact geographical coordinates has been completed. The results of the low-altitude aeromagnetic survey are compared with the control data of the ground-based magnetic survey performed earlier, and the correctness of the low-altitude aeromagnetic survey data is shown. Since ground surveys provide more detailed information, it is advisable to use low-altitude aeromagnetic surveys as a method of fast, low-cost screening of large areas. This will make it possible to make decisions on the expediency of carrying out expensive ground works. The study found that labour productivity in low-altitude aeromagnetic surveys exceeds that in ground-based surveys by about 75 times. In addition, low-altitude aeromagnetic surveys have virtually no impact on ecosystems and do not lead to greenhouse gas emissions. This corresponds to the main directions of sustainable development of geology.
Monitoring of biodiversity of water communities

Biodiversity monitoring is a process of systematic collection, analysis and evaluation of data on the diversity of living organisms, their populations, communities and ecosystems in a certain territory or area. The purpose of biodiversity monitoring is to study changes in the composition and structure of biological systems over time. Biodiversity monitoring is carried out at different levels - from global to local, and may include both long-term programs and short-term studies. It is important to take into account the scale, methods and resources required for monitoring, as well as to ensure proper processing and analysis of the data obtained. Thus, biodiversity monitoring plays a key role in assessing the state of natural systems, adapting to climate change, developing strategies for nature conservation and supporting sustainable development.