Reviewerly/exHarmony huggingface dataset:
Dehazing of an Images by Discrete Wavelet Transform with Weight Map and De-Noised Wavelet Method

Haze diminishes contrast in an image and results in colour loss, both of which have an adverse impact on future object recognition. The haze will be different in day and night time, where the day and night time haze removal is a difficult job. Here, a unique method multi-scale wavelets is proposed. Both have a propensity to cause colour discrepancies. Initially an images which are hazy is divided into one lower frequency and three higher frequency sub-images using discrete wavelet transform (DWT). A lower frequency sub image uses the Weight Balance Weight Map (WBWM) algorithm for dehazing the particular image. Three higher frequency sub images uses the wavelet de-noising method for dehazing. Then, a dehazed image is attained by reconstruction of sub-images by using an Inverse Discrete Wavelet Transform (IDWT). The focus of the suggested approach is on dehazing and de-noising on low frequency images (LFI) and high frequency images (HFI), so that the image's features may be well retained. Finally the output dehazed image is analysed by consider its parameters. The obtained outputs are discussed and compared with previous works.
Visual Sentiment Analysis with Semantic Correlation Enhancement

Visual sentiment analysis is in great demand as it provides a computational method to recognize sentiment information in abundant visual contents from social media sites. Most of existing methods use CNNs to extract varying visual attributes for image sentiment prediction, but they fail to comprehensively consider the correlation among visual components, and are limited by the receptive field of convolutional layers as a result. In this paper, we propose a visual semantic correlation network VSCNet, a Transformer-based visual sentiment prediction model. Precisely, visual features are captured through a kernel attention network that benefits from both CNNs and Transformers. An off-the-shelf object query tool is used to determine the candidates of potential affective regions, by which redundant and noisy visual proposals are filtered out. All candidates considered affective are embedded into a computable semantic space. Finally, a fusion strategy integrates semantic representations and visual features for sentiment analysis. Extensive experiments reveal that our method outperforms previous studies on 5 annotated public image sentiment datasets without any training tricks. More specifically, it achieves 1.2% higher accuracy on Emotion6 benchmark compared with other state-of-the-art methods.
Multimodal Feature Fusion for Illumination-Invariant Recognition of Abnormal Human Behaviors

This paper presents a fusion of feature maps extracted from visible and thermal infrared surveillance videos to recognize abnormal human behaviors in low illumination conditions. Abnormal human behaviors often occur at night or in darkness, where a visible camera alone may not capture sufficient visual information for analysis. The proposed scheme uses additional data captured by a thermal infrared camera to enhance information on human appearance and motion. Thermal and visible data are assumed to be informative for analyzing human behaviors, yet they contribute differently to certain areas of video frames. This paper proposes a multimodal feature fusion (MFF) to produce a weight matrix that determines how much thermal or visible input should contribute to obtain optimal performance. Experiment results demonstrate that MFF effectively recognizes abnormal human behaviors in darkness. MFF achieves an overall accuracy of 91.01%, which is at least 4.53% higher than individual thermal or visible data and 2.17% higher than the state-of-the-art fusion-based abnormal human behavior recognition techniques.
3d Product Desaign as a Virtual Museum at the Museum of History, Universitas Airlangga, Surabaya Indonesia

A museum is a place to preserve and collect historical relics, has a form an intangible form can be learned and remembered by the public. The aims to develop a 3D product for a collection of artifacts as a virtual museum in the Android-based. This study uses the R&D development model to develop a virtual tour application. The development consists of 12 stages, namely potential and problem, data collection, software analysis, hardware analysis, product design, software testing, system repair, design validation test, design revision, usage test, product revision, and final product. The result that virtual reality applications in the 3D form are feasible to be used as a substitute for physical museums. The results were obtained using three tests, black box, validation, and SUS testing. The application of virtual museums as an alternative media for introducing the History Museum, seen from the test results, is in the very feasible category.
New Generalized Mathematical Model for Evaluating Global Solar Radiation in the Sense of Joseph Fourrier's Heat Equation

This article proposes a new approach for evaluating or characterizing the dispersion of solar radiation through the atmosphere in the sense of Joseph Fourrier's heat equation. From which will result a new formula for predicting the solar deposit on a horizontal plane for a given duration of sunshine and in any region of the terrestrial globe. To validate the new approach and its formulas, we rely on the use of data on effective sunshine and global solar irradiation collected by measurements between the years 1991 and 1993 in the regions of Garoua in Cameroon and El Jadida in Morocco. According to the regression analysis and the calculations of the statistical performance indicators, it is deduced that the average values and their global solar irradiation curves on a horizontal plane obtained from the proposed approach are very well correlated and close to the values measured. In addition, the models proposed for the prediction of global solar radiation on a horizontal plane in the cities of Garoua and El Jadida, presented better performance compared to models derived from the Angstrom-Prescott formula and others new model formats cited in the literature, particularly in terms of precision and flexibility or simplicity.
Heterogeneous Relation and Progressive Aggregation Network for Person Re-Identification

Person Re-identification methods generally use ResNet-50 as a backbone because it learns good features. However, these ResNet-50 - based methods rely on deep-level features and ignore some of the detailed features, missing from the shallow-level features lost by the down-sampling operation in ResNet-50. In this paper, we propose a new network, the Heterogeneous Relation and Progressive Aggregation Network. It constructs a heterogeneous graph with shallow-level features of different stages as nodes, fuses features with different levels in an orderly manner, and explores details features ignored. For shallow-level features, we designed a Multi-level Attention module to optimize shallow-level features’ representation ability further. And we construct a heterogeneous graph with the features from different stages as nodes. Then we map all features to the same feature space to calculate similarity for fusion. Finally, we map back to the original feature space and transfer the information based on progressive aggregation strategy. For the deep-level features, we use the deep-level features extracted directly from ResNet-50.Furthermore, experiments on the three datasets Market1501, DukeMTMC-Re-ID, and MSMTT17 show that our HRPA can further mine the neglected detailed features and achieve the state-of-the-art results on the three benchmark datasets.
V-Shaped Neural Network Structure Based on Multi-Scale Features for Image Denoising

Due to the good performance of deep learning, more and more image denoising methods incorporating deep learning are implemented, including fixed-scale-based methods and multi-scale-based methods. It is easy for fixed-scale-based neural networks to achieve a better denoising performance when their depth is increased, whereas feature extraction of multiple scales from images can be obtained using multi-scale-based neural networks. In this work, a multi-scale image denoising method has been proposed which is mainly based on fixed-scale but combines the method of obtaining feature information from shallow image sampling. We propose a diamond-shaped module and a V-shaped subnetwork for extracting the features of images obtained from shallow sampling and improve the up-sampling and down-sampling units to obtain a better sampling effect. Densely connecting images of same size in MSDN can solve the problem of gradient vanishing since increasing the depth of the network and shallow image sampling prevent the loss of image details due to excessive sampling. Experiments show that our method can efficiently retain the image details during image denoising, and this method is especially helpful for preserving minute image details as compared to the other state-of-the-art denoising algorithms.
Modelling and Predicting Environmental Issues, Environmental Quality and Environmental Destruction for Environmental Quality Management

This paper presents general equations of environmental issues, environmental quality and environmental destruction. The paper employed the concepts of the Ideal Gas equation, Malthus model, IPAT model and population dynamics to relate human population and resource depletion in order to estimate environmental issues over time. This resulted in the generation of three equations; equations for estimating environmental issues, environmental quality and environmental destruction. The dataset on global population growth and resource records was obtained from World Bank and Food and Agricultural Organisation Indicators to test and validate the equations. The findings of the study demonstrated that the models are ecologically efficient and sound. However, the only limitation of the models is that they are resource, population and time-dependent.
A New Induced Seismicity Indicator Using Accumulated Earthquake's Frictional Energy

The effects of induced seismicity caused by mining activities are one of the main problems that the mining industry faces. Although they have been documented as risks by countries with a vast mining tradition (e.g., Canada, Australia and Chile) for over a century, the problems have intensified throughout the years due to the greater depths that mineral extraction reaches, where the stress levels and environmental conditions increase the possibility of a rockburst occurrence. Seismic events inside the mine cause important losses of human lives and infrastructure damage. To deal with this problem it is necessary to implement several kinds of safety protocols. For example, seismic indicators allow to identify, through the analysis of a set of seismic parameters -magnitude, energy, time, decay rate, among others-, periods of time when a seismic event of considerable magnitude is most likely to occur. The objective of this work is to use the accumulated frictional energy generated by microearthquakes inside the mine as a seismic indicator, linking it with the occurrence of large magnitude seismic events which are outer the background activity. Thus, we seek to determine the set of seismic parameters which allow the best performance of the frictional energy/heat indicator. In the present study, we considered magnitude, frictional energy, and time as seismic parameters to study the efficiency of the proposed indicator. The optimal set of parameters was determined using two different methodologies: Accumulated Frictional Heat (AFH) and Alert Bands (AB). The Piece Skill Score and the Accuracy indexes were used to evaluate each set of parameters. The AFH methodology shows a high rate of true positive and true negative hits, forecasting 3 out of 4 high magnitude events (Mw > 3.0).
A Security Enhancement Scheme For Raft Consensus Algorithm Against Term Forgery Attacks

The consensus algorithm is the core component of blockchain technology and a key link to maintaining the security of the blockchain network. In the Raft consensus algorithm, since the term growth range is not specified in the algorithm, malicious nodes can obtain the leader status by increasing their term value, and the fairness of the Raft cluster leader election is damaged. To solve the above problems, in this paper, we restore the attack experiment, which is a malicious node gaining leader status by increasing its term value. Identify malicious nodes by dynamically adjusting the growth range of the term, thus achieving a defensive effect. The optimized algorithm resists malicious node attacks with a 79.4% probability and reduces the election time by 11.4% compared to the Raft consensus algorithm in the presence of malicious nodes. On the premise of ensuring high efficiency, the fault tolerance and security of the algorithm have also been significantly improved.
Handwritten Chinese Character Recognition Based on Convolutional Neural Networks and TrueType Font Template Matching

Convolutional neural networks(CNN) have made significant advances in offline handwritten Chinese character recognition, CNN recognize characters based on local feature but not making use of the overall topology of the character to improve recognition accuracy. In this paper, we propose a method for handwritten Chinese character recognition by using CNN combined with TrueType font template matching. Firstly, a trained CNN is used to recognize the handwritten Chinese character, the output Top-N are selected as the candidate characters, and then the handwritten Chinese character is linearly transformed and matched with the TrueType font templates of the candidate characters respectively, the best matching candidate character is selected as the result. The experiments show that the method can combine the speed of CNN and the accuracy of template matching effectively, has higher accuracy than conventional CNN, especially for specific types of handwritten Chinese characters recognition.
A Stacking Ensemble Approach for Supervised Video Summarization

Existing video summarization methods are classified into either shot-level or frame-level methods, which are individually used in a general way. This paper investigates the underlying complementarity between the frame-level and shot-level methods, and a stacking ensemble approach is proposed for supervised video summarization. Firstly, we build up a stacking model to predict both the key frame probabilities and the temporal interest segments simultaneously. The two components are then combined via soft decision fusion to obtain the final scores of each frame in the video. A joint loss function is proposed for the model training. The ablation experimental results show that the proposed method outperforms both the two corresponding individual method. Furthermore, extensive experimental results on two benchmark datasets shows its superior performance in comparison with the state-of-the-art methods.
Polyamide 12 Pickling Equipment for 3D Printed Parts

In this review article, we would like to describe the development and production of equipment called MorPa, designed for pickling of components extruded from Polyamide 12 material using multi jet fusion technology. The goal of this project is to create an affordable and reliable device that will change color and physical properties of the components. In the following article, we describe the hardware part of the device, the software part and compare the printed parts before and after pickling. The article also includes a description of the control of the device based on the Arduino microcomputer with the used peripherals.
Innovative Concept of STEAM Education at Primary Schools in the Czech Republic - Support for Implementation in School Practice

This paper presents for expert discussion partial results of the scientific research project. The aim of the project is to contribute to the innovation of STEAM education at lower secondary schools by means of applied research and through eliminating the barriers resisting the implementation of the new curriculum aimed at the development of technical thinking and creativity. In direct cooperation with the Ministry of Education, Youth, and Sports and other partners the project focuses on the following: 1) designing, creating, and verifying the on-line system (software) for setting the adequate form of technical education that would enable schools to successfully reach the expected learning results; 2) designing and verifying learning situations; 3) developing innovative teaching aids (mobile toolkits enabling sharing). The above results and products will make a positive contribution to the development of pupils' competencies - in particular, basic scientific and technical competences consisting of cognitive, motoric, social, social-moral and information-communication competencies that ensure that individuals “participate in the use of technology as an element of culture”. Specifically, the following can be mentioned: the ability to solve problems, the ability to apply creative ideas in pupils' work, the ability to take responsibility, the ability to be self-sufficient, the ability to evaluate and express one's opinions, the ability to self-identify and self-assess in the direction of one's own professional orientation, the ability to respond flexibly to changes in the labour market.
Text Detection and Recognition in Natural Scenes Based on TWO-DIMENSIONAL Attention

In this paper, we improve the natural scene text detection and recognition technology based on 2d attention and encoder-decoder framework. Firstly, the related work of text detection and recognition in different natural view is discussed. Secondly, we work on the basis of encoder-decoder framework and two-dimention module, and improve it through aggregation and hybridisation. Finally, we discussed and analyzed the results,and figured out the possible shortcomings of the model.
Modeling an author's specific expertise based on educational-related artifacts analysis

The paper deals with the problem of an author's specific expertise modeling. Research is fundamentally based on the analysis of an author's output - any artifacts that have educational potential. From the many available, it is limited to those of a textual nature. The work aims to create an author model and its confrontation with a reference model. Due to the real absence of the reference model, the result of the work is also its design and implementation. The paper evaluates several alternative proposals in the form of candidates for the reference model. Based on the results obtained from experiments, we conclude that estimating the author's expertise by comparing the author's model with a reference model of knowledge in any domain has potential.
Psychological Advisor Chatbot

Chatbots improve a customer relationship by re-sponding to requests faster and meeting their expectations at the same time. In our paper, we investigate the increasing psychological problems, the pressure, and the lack of time for some people due to the rapid development of the IT era. We propose a knowledge-based chatbot that interacts with end users using natural language input. It is trained in advance to detect the user's psychological situation (fear, anger,…) and suggests solutions to overcome this.
Survey On Using Audio Video for True Random Number Generation

True random number generation is essential for modern and future security and cryptography. This paper surveys five existing methods of using audio and video to generate numbers, and compares their hardware, software, and randomness to find the best method for TRNG. The entropy of each method is compared, and the randomness of each method can be found. Each paper generates random numbers by utilizing the inherent noise created by transitioning analogue data to digital data, unlike other TRNG methods which use the conditions of internal hardware, erroneous data from wireless networks, or other non-noise-based methods.
Few-shot handwritten Chinese character generation based on generative adversarial network

Chinese characters are different from the English alphabet that English consists of 26 simple words, whereas Chinese characters contain multiple strokes, complex structures and a variety of styles, which makes the study of Chinese character style transfer a challenging problem. In recent years, generative adversarial networks have been used to automatically generate handwritten Chinese characters, but these methods often produce Chinese characters with a variety of stroke errors, such as multiple strokes and missing strokes. Especially, few works focus on the problem of few-shot handwritten Chinese character generation. A further problem that is worth exploring is that in practice, people often want to use only a small number of handwritten Chinese characters, then let the model automatically generate a large number of handwritten Chinese characters with the same style. Therefore, in this paper, few-shot handwritten Chinese character generation is discussed in detail. The experimental results show that it is possible to use only a few tens of handwritten characters to automatically generate a library of handwritten characters with special style.
Unsupervised Time Series Anomaly Detection under Data Contamination

Unsupervised learning utilizes unlabeled data to alleviate the reliance on large amounts of labeled data, and it has made great progress in time series anomaly detection. However, there are still some thorny issues unsolved, especially the incapability to maintain the robustness of the model when the time series dataset is contaminated. In this work, we propose an unsupervised time series anomaly detection method on data contamination, named distribution alignment autoencoder (DAAE), in which a temporal sliding reward function combines the adversarial distribution alignment to mine the contaminated data. Specifically, the dimensionality reduction and reconstruction of the time series are implemented by an autoencoder. Next, using two discriminators, the latent features obtained by dimensionality reduction are mapped to the Gaussian distribution, and the high-confidence normal time series are generated by sampling noise from the Gaussian distribution. Then, we compute the mean square error between high-confidence normal samples and the training data. Finally, we feed the error values to the temporal sliding reward function to mine the contaminated training samples. Experimental results compared with baselines on public datasets demonstrate the effectiveness of DAAE.
A Person Re-identification Method Fusing Bottleneck Transformer andRelation-aware Global Attention

Person re-identification can quickly locate and find all the specified targets in complex scenes with multiple cameras, which has been widely applied in intelligent video surveillance and security system. As a state-of-art method proposed recently, ResNet exhibits promising performances on person re-identification. However, without intermediate fully connected layer, ResNet fails to fully grasp the global information in the detection process. To overcome the above problem, this paper proposes a person re-identification method named RG-BoTNet by fusing the Relation-aware Global Attention mechanism into BoTNet. Since relation-aware global attention is good at grasping the global information of the image, RG-BoTNet is powerful in extracting personal features. The good performances conducted on cuhk03 dataset in terms of Mean Average Precision (MAP) and Rank-1demonstrate the effectiveness of RG-BoTNet for person re-identification task.