Reviewerly/exHarmony huggingface dataset:
Research on Image Steganography Based on Multiple Expansion Generation Adversarial Network

Image steganography is a method of hiding secret information in images. This method can be applied to many important fields, such as military, business, medicine, copyright protection and so on. In this paper, a multi dilated generation countermeasure network (Multi Dilated GAN) model is proposed to improve the information steganography quality of images. Based on the discriminator of steganogan model, multiple convolution and expansion convolution are adopted. By selecting different expansion rates in the expansion convolution and matching with different receptive fields, different feature layers of the network can be effectively extracted; Multiple convolution processing is carried out on different feature layers to connect the features extracted from different directions, and distinguish the steganographic image from the carrier image, so that the discriminator can more accurately distinguish the difference between the steganographic image and the carrier image, so as to improve the steganographic ability of the encoder of the model. Experiments show that this model can effectively improve the steganogan model&#x0027;s steganographic accuracy and steganographic quality while maintaining a high steganographic capacity of 4.4bit/pixel.
Formation of Educational Independence of Primary Schoolchildren by Means of Project Tasks

The article reveals the essence of the concept of “educational independence”, which became a subject of study back in the 19th century. and&#x0D; remains one of the most pressing problems of modern education. The main criteria for its formation have been determined. Approaches to the formation of educational independence in younger students are considered. The idea is substantiated that the project task is an effective means of developing the educational independence of students. Specific examples of assignments for solving design problems with the aim of developing educational independence of primary schoolchildren are given.
The Application of Factor Analysis and Loading Matrix in Computer Intelligent Comprehensive Evaluation Model of Grades

There is a need to have a comprehensive evaluation on the students’ grade scientifically and reasonably in the field of teaching management. Firstly, this paper use the method of factor analysis and cluster analysis to establish comprehensive evaluated model. A empirical analysis on the marks of 32 students in the department of mathematics and information science was given. Secondly, the comprehensive ranking of the students based on three main factors extracted from 11 subjects were given. Finally, the advantages and disadvantages of the students in various disciplines were also analyzed objectively. The results provided a good reference for the comprehensive quality training of the students and improving the teaching quality.
Fast Vision Transformer via Query Vector Decoupling

Although transformer structure has become the defacto standard of the natural language processing task, it still has limited application in computer vision. In vision, attention is either combined with or replaces certain modules in a convolutional network, while keeping its overall framework intact. It proves that this reliance on CNNs is unnecessary for the pure transformer directly applied to image patch sequence can successfully classify the images. When large amounts of data are pre-trained and transmitted to multiple small and medium-sized image recognition benchmark (ImageNet, CIFAR-100, VTAB, etc.), the Vision Transformer (ViT) achieves remarkable results with less computational resources required than existing convolutional networks. Unlike the previous calculation that weighs attention, the model in this paper has been optimized, reducing the time complexity from O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) to O(n • logn), largely improving the model’s speed. Therefore, a new model is developed—Fast Vision Transformer (Fast VIT).
Improving Monocular Depth Estimation by Semantic Pre-training

Knowing the distance to nearby objects is crucial for autonomous cars to navigate safely in everyday traffic. In this paper, we investigate monocular depth estimation, which advanced substantially within the last years and is providing increasingly more accurate results while only requiring a single camera image as input. In line with recent work, we use an encoder-decoder structure with so-called packing layers to estimate depth values in a self-supervised fashion. We propose integrating a joint pre-training of semantic segmentation plus depth estimation on a dataset providing semantic labels. By using a separate semantic decoder that is only needed for pre-training, we can keep the network comparatively small. Our extensive experimental evaluation shows that the addition of such pre-training improves the depth estimation performance substantially. Finally, we show that we achieve competitive performance on the KITTI dataset despite using a much smaller and more efficient network.
Towards a robust object classifier for autonomous vehicles by feature synthesis

As the AVs’ perception system, there are many different types of sensors in the car to provide data support for the automatic driving system's judgment. The judgment system, which is made up of convolutional neural networks, is then lacking in robustness. When confronted with input data such as adversarial samples and malicious tampering, it will provide some options that significantly deviate from the correct answer. In this article, we first use FGSM as an adversarial sample generation method, and then used the generated adversarial samples to successfully disrupt the system's results. After that, the adversarial sample data was then added to the original data set and trained in the neural network. We successfully trained a classifier with high robustness after incorporating hyperparameters and feature fusion.
A virtual platform skin tears learning tool

With the advance of technology video games became more realistic, with better graphics and accessible for all. With them gamification was inserted as a feature to push the boundaries of learning tools. In this work, a learning tool was developed, with a main focus on delivering a skin tears evaluation experience. The player will enter in an attention center where the user will be able to move freely and interact with an examination of several images in which will have to answer what's the correct type of image shown and categorize it correctly, whenever the user gets a correct answer he/she will receive points, if the answer is incorrect, no points will be granted, at the end of the activity the user will have an qualification depending on its choices. Results were promising. According to the validation performed with users, all of them thought that the tool was easy to use and agreed that the tool generates an engaging and learning experience.
A Tractable Multi-target Detection Model for Line-of-Sight Measurements

This paper propose a tractable yet realistic detection model for Line-of-Sight (LoS) measurements/sensors that permits efficient multi-object tracking via the generalized labeled multi-Bernoulli (GLMB) filter. Most detection models in the multi-object tracking literature do not account for occlusions. Our method exploits the property of LoS, whereby objects that are not in the LoS are considered occluded and thus most likely to generate no measurement. While the resulting Bayes multi-object filter is not a GLMB filter, we propose tractable GLMB filter approximation and demonstrated improved tracking performance via a bearings-only sensors example.
Filter design for two-dimensional discrete-time T-S fuzzy systems described by FM LSS model

This our work is concerned with the problem of filter design in two-dimensional (2D) discrete-time non-linear systems in Takagi-Sugeno (T-S) fuzzy model described by Fornasini-Marchesini local state-space (FM LSS) Model. The problem to be solved in the paper is to find a $H_{\infty}$ filter model such that the filtering error system is asymptotically stable and has a reduced $H_{\infty}$ performance index. Via the use of the Lyapunov functions approach and parameterize slack matrices, new design conditions guaranteeing the $H_{\infty}$ T-S fuzzy filter method of FM LSS model are developed by solving linear matrix inequalities (LMIs). At last, the simulation results are provided to show the effectiveness and the validity of the proposed T-S fuzzy of FM LSS model strategy by a practical application has been made.
Lexical Means of (Re)Creating of the Sound Effects in the Original and Translation: A Case Study Based on the Novel by J.-M. G. Le Clézio Ritournelle De La Faim

Discussion of the translation process is complex and fraught with pitfalls, especially when it concerns the notions of translation equivalence and adequacy. This issue is of crucial importance concerning the translation of literary works. This article is dedicated to the study of the fundamental problem which translators of all times and all countries encounter, that is, to convey the creative idea of the author of the original work without perverting it, to preserve, throughout the translation process, all the semantic and stylistic nuances which form its content. This is the question around which our research problem revolves. We study here the approaches chosen by the Ukrainian translator Yarema Kravets, in order to recreate in his translation, the sound image that J. M. G. Le Clézio succeeded in creating in his novel Ritournelle de la faim. In order to render this research more usable, we have adopted the following methodological approach, founded on these two axes: the collection of lexical units from the source text which serve to create the sound image; and the comparative analysis of the original text with its translation, of the novel by J. M. G. Le Clézio, Ritournelle de la faim, with the goal of recreating the sound of the source text by identifying its means of transmission. The anticipated result of the given study, which is dedicated to the recreation of the sound effects in the translation of the J. M. G. Le Clézio’s novel Ritournelle de la faim, will be either to confirm or refute our primary hypothesis: that the translation proposed by Y. Kravets respects the spirit and the letter of the source text.
Introduction

Abstract When physical travel to a specific place is prohibited or otherwise difficult or impossible, digital travel provides a promising alternative. The technology to do this is now widely available and many people have the possibility to meet with others digitally, and thus alleviate the social effects of physical isolation. Digital travel can also be source of pleasure and entertainment, and many people spend time exploring and interacting in digital places, realistically rendering in 3D games. But despite the recent upsurge in virtual social interaction, it does not meet many of the psychosocial aspects of the travel experience. In this book, we take a fresh look at the nature of the telepresence experience in digital environments. We also address a number of relevant questions, such as whether these experiences can seem real to the digital traveller and, if so, under what conditions and on what grounds.
Introduction to Bayesian Statistical Inference

Abstract We present basic concepts of Bayesian statistical inference. We briefly introduce the Bayesian paradigm. We present the conjugate priors; a computational convenient way to quantify prior information for tractable Bayesian statistical analysis. We present tools for parametric and predictive inference, and particularly the design of point estimators, credible sets, and hypothesis tests. These concepts are presented in running examples. Supplementary material is available from GitHub.
Effects of Motion-Relevant Knowledge From Unlabeled Video to Human–Object Interaction Detection

The existing works on human-object interaction (HOI) detection usually rely on expensive large-scale labeled image datasets. However, in real scenes, labeled data may be insufficient, and some rare HOI categories have few samples. This poses great challenges for deep-learning-based HOI detection models. Existing works tackle it by introducing compositional learning or word embedding but still need large-scale labeled data or extremely rely on the well-learned knowledge. In contrast, the freely available unlabeled videos contain rich motion-relevant information that can help infer rare HOIs. In this article, we creatively propose a multitask learning (MTL) perspective to assist in HOI detection with the aid of motion-relevant knowledge learning on unlabeled videos. Specifically, we design the appearance reconstruction loss (ARL) and sequential motion mining module in a self-supervised manner to learn more generalizable motion representations for promoting the detection of rare HOIs. Moreover, to better transfer motion-related knowledge from unlabeled videos to HOI images, a domain discriminator is introduced to decrease the domain gap between two domains. Extensive experiments on the HICO-DET dataset with rare categories and the V-COCO dataset with minimum supervision demonstrate the effectiveness of motion-aware knowledge implied in unlabeled videos for HOI detection.
Methods of Working With Local Digital Resources on History

This study aimed to identify the best techniques and approaches to working with digital resources on history suitable for both scientific research and teaching. The methodological basis of this study was the author’s methodology for measuring the level of students’ involvement in working with digital resources on history. The conducted empirical research revealed this level to be average since as much as 98% of respondents turned to the resources of e-libraries for educational purposes, and 46% of interviewed used online-based historical archives for academic activity. The study showed that the main way of conducting historical research in the context of digitalization is to work with images and scanned copies of scientific papers. The obtained materials and conclusions can be included in the curricula of academic disciplines for students of historical specialties at the undergraduate and graduate levels of Russian and foreign universities.
Standard Greedy Non Maximum Suppression Optimization for Efficient and High speed Inference

Recent studies to improve the performance of non maximum suppression (NMS) have accuracy fluctuations when applied only to inference of models trained with standard greedy NMS. In this paper, we propose an optimization method of standard greedy NMS without degradation of accuracy. The proposed method combines NMS input and output characteristics to reduce NMS loop operations, supports high-speed inference of CNN-based object detection, and is optimized for data pipelining in hardware and CPU implementations. Experiments show that the speed of NMS in inference is improved by about ×6 compared to standard greedy NMS.
Few-shot modulation recognition using Recurrence Plot Algorithm

Deep learning techniques have shown high performance for Automatic Modulation Classification (AMC) tasks. However, the tasks need a burden of collecting large-scale annotated data, where the trained network model should be re-trained if new classes are given. In this paper, Few-shot learning (FSL) based AMC is introduced to handle this problem. Also imaging algorithm using recurrence plot (RP) is considered to make the input data more suitable for few shot learning. The results demonstrate that the proposed approach is able to classify images of new classes with high accuracy without further updating the network.
Heterogeneous Network Embedding With Enhanced Event Awareness Via Triplet Network

Network analysis is an unavoidable topic in data mining today, and network embedding is an important means to help solve network analysis. With the increasing of network data volume, the content is increasingly complicated, the embedding scenario of homogeneous graph has been gradually replaced by heterogeneous graph. More and more embedding algorithms for heterogeneous graphs are proposed. Heterogeneous network can naturally integrate different aspects of information, so heterogeneous network embedding is a relatively effective method to solve the diversity of big data. It is helpful in the areas of anomaly detection, user clustering and intent recommendation. Here we propose a Siamese Neural Network optimization method based on event relations and meta graphs. This method ensures the semantic integrity and event integrity of heterogeneous graphs by using events and meta graphs respectively. Then put the graph information in Triplet Network for training, and the embedding results are produced. A classification task on a dataset for the true network are designed to prove the method. A real network data set classification task is designed to prove that this method is helpful for heterogeneous graph analysis.
Model Quantization and Synthetic Aperture Data Analyses Increasing Throughput and Energy Efficiency

New model quantization techniques have been researched to inform future information/data processing approaches. This hardware evaluation and the associated data exploitation methods developed support systems that require high performance computing (HPC) and machine learning (ML) models where significant throughput is desired. Specifically, these applications include, but are not limited to, low cost compute that supports the detection and classification of objects in a scene, where it is not feasible to spend valuable resources on HPC capabilities. Additional applications include data exploitation upstream, near sensors, where size, weight and power (SWAP) are constrained. This research included the analyses of representative data, synthetic aperture radar (SAR) imagery called the Moving and Stationary Target Acquisition and Recognition (MSTAR) data. The NVIDIA Tesla, Xavier, and Titan compute architectures were used and analyzed as part of this research. These graphics processing units (GPU) represent architectures that span various operating powers (~10 to over a few 100 Watts). Additionally, the energy utilization per frame was determined and analyzed, e.g., the energy use of the Tesla went from 104.4 to 50.68 micro-Joules/frame when quantization was reduced to an 8-bit integer. The Tesla architecture also improved processing throughput from 448 frames per second (FPS) to 1085 FPS when quantized to 8-bit integers. An important part of this new research showed the compute systems retained SAR detection/classification performance of over 97% mean average precision (mAP) on the MSTAR imagery data after quantization – thereby retaining its capability to detect and classify objects.
Use of technoloy of development of critical thinking in informatics lessons at school

The article summarizes the pedagogical experience of applying the techniques of technology for the development of critical thinking in informatics lessons at school. The purpose, structure, the tasks of the technology for the development of critical thinking are determined, the algorithm of its application in teaching is described. The features of the implementation of each phase of the technology for the development of critical thinking are described: challenge, comprehension, reflection. An example of the classification of methods for the development of critical thinking in students in accordance with the structural components of thinking is given. On the example of specific themes of the school course of informatics ("Information", "Social Informatics", "Algorithmization and Programming", etc.), the possibilities of using the technology of developing critical thinking to activate and develop the thinking abilities of students are shown. The use of the technology of critical thinking in informatics lessons at school allows students to master the methods of information analysis, the basics of semantic reading, that is important for the successful assimilation of subject knowledge
Look Before You Act: Boosting Pseudo-LiDAR with Online Semantic Embedding

Vision-based 3D object detection is a research focus in the field of autonomous driving system. While recently proposed pseudo-LiDAR is a promising solution, its performance is severely restricted by the image-based depth estimator, leading to a considerable performance gap against the LiDAR-based counterparts. In this paper, substantial advances are developed along an orthogonal direction to the previous efforts in the pseudo-LiDAR pipeline. Concretely, we propose a plug- and-play module, called Online Semantic Embedding (OSE), aligning image semantics with the pseudo-LiDAR detection in an end-to-end manner. On the KITTI object detection benchmark, existing stereo-based baselines integrated with our approach show impressive improvements without bells and whistles. Furthermore, we emphasize that OSE works in retrieving the performance under geometric imperfection conditions.
A Technical Approach for Promotion of Yakshagana - A Literature Survey

Background/Purpose: Yakshagana is a theatre style that combines varieties of make-up styles and dressing styles depending on the type of character or role that is to be played on the stage. This article briefly discusses certain areas of Yakshagana where technology can be used for object detection and make-up recognition to classify and identify the characters. Objective: The main objective of this literature survey is to summarize the techniques used to identify different objects in a Yakahagana image &amp; to identify the different makeup styles to recognize the type of characters. This paper also recommends the technical methods that can be used to promote Yakshagana. Methodology: Information is collected from various scholarly articles, books, and web pages. Findings/Results: Based on the analysis of the papers reviewed, it is observed that, no much work is carried out in the field of object detection &amp; make-up detection in Yakshagana. This survey paper recommends techniques and the methodology that can be applied to the Yakshanaga field to identify and classify the objects and detect the patterns in makeup in Yakshagana roles. Limitations: The study is limited to crown &amp; jewelry detection and pattern recognition in facial makeup in Yakshagana Originality: This paper focuses on the summary of techniques used to identify the objects, facial recognition, and pattern in make-up detection. Paper Type: A Literature survey paper on techniques and methods used to meet the required objectives.
Legion: Tailoring Grouped Neural Execution Considering Heterogeneity on Multiple Edge Devices

Distributing workloads that cannot be handled by a single edge device across multiple edge devices is a promising solution that minimizes the inference latency of deep learning applications by exploiting model parallelism. Several prior solutions have been proposed to partition target models efficiently, but most studies have focused on finding the optimal fused layer configurations, which minimize the data-transfer overhead between layers. However, as recent deep learning network models have become more complex and the ability to deploy them quickly has become a key challenge, the search for the best fused layer configurations of target models has become a major requirement. To solve this problem, we propose a lightweight model partitioning framework called Legion to find the optimal fused layer configurations with minimal profiling execution trials. By finding the optimal configurations using cost matrix construction and wild card selection, the experimental results showed that Legion achieved a similar performance to the full configuration search at a fraction of the search time. Moreover, Legion performed effectively even on a group of heterogeneous target devices by introducing a per-device cost-related matrix construction. With three popular networks, Legion shows only 3.4% performance loss as compared to a full searching scheme (FSS), on various different device configurations consisting of up to six heterogeneous devices, and minimizes the profiling overhead by 48.7× on average.