Reviewerly/exHarmony huggingface dataset:
Design of Work System for Reducing Pollution and Forest Fire Smoke

Abstract Air quality has an impact on human life. The incidence of forest and land fires has caused many casualties. On the other hand, poor air quality as a result of forest and land fires also threatens human life directly. Therefore a Pollution and Smoke Reduction Tool was designed as a solution to the problems of pollution and smoke due to forest fires. The purpose of writing this article is to describe the design and manufacture of Pollution and Smoke Reducers as well as its working principles. The writing method used is descriptive qualitative, with data collection techniques in the form of literature studies to strengthen ideas. Pollution and Smoke Reducers are tools that can convert particulate CO (PM) PM10 and PM2.5 into CO_2 and burn particulates PM10 and PM2.5 until they disappear. Pollution and Smoke Reducers are the development of research on catalytic converters and diesel particulate filters by utilizing a fan/blower as a smoke suction agent and a heater to heat the smoke until it burns completely. Pollution and Smoke Reducers are also equipped with wire mesh and fiber to trap particulates and hold them until they burn entirely. Pollution and Smoke Reducers are designed with several materials and tools specifically designed to reduce the direct impact of forest and land fires by burning PM 10 and PM2.5 and lowering CO emissions. The smoke reduction capability based on the designed design has smoke and particulate reduction capacity of ±43,4769880184/ft 3 . The capacity of the smoke and particulate suction rate is 21500ft 3 /minutes with the ability of the significant smoke suction rate so that smoke and particulates due to forest fires can enter the equipment and do not fly freely under the wind. Hence, the air that is inhaled by the community has better potential and safe for health.
Manifolk: A 3D t-SNE Visualizer

Manifolk is a tool to visualize the output of dimensionality reduction algorithms like t-SNE, PCA etc. One of this tool’s main uses is that it de-clutters graphs by plotting data points pertaining to a subset of labels. The subset of labels to be plotted can be selected using the provided checkboxes. A case study on data from a publicly available action recognition dataset like UCF101 shows how this tool can help find outliers. With the rise in self-supervised methods for training deep neural networks, this tool helps researchers better visualize the embeddings learned by the model.
Partial-ACO as a GA mutation operator applied to TSP instances

A recent novel modification to Ant Colony Optimisation (ACO) known as Partial-ACO can be successfully used to solve Travelling Salesman Problems (TSP) by making partial modifications. The approach also dispenses with a pheromone matrix using the population to build pheromone levels on edges enabling scaling to large problems. Consequently, being population based the approach can be also used within a Genetic Algorithm as a mutation operator. Results demonstrate significant improvements when using Partial-ACO as a mutation operator with a range of crossover operators.
Phase singularity reduction in speckle interferometry by tailoring the spatial coherence of light

Reducing the number of phase singularities in speckle interferometry by physical means increases the quality of the phase deconvolution and enhances the separability of fringe systems, especially for highly dynamic fringe systems. The reduction is achieved by tailoring the spatial coherence of the light source at the entrance of the interferometer. The reduction mechanism is non-intuitive and rests on a change of size and of position of the speckle patterns as a result of the spatially shaped coherence. After demonstrating the amount of reduction that may be achieved, the reduction mechanism is explained theoretically and via simulations, and it is vindicated in an experiment using a Michelson–Mach–Zehnder interferometer hybrid.
Exploiting image translations via ensemble self-supervised learning for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) aims to improve the generalization capacity of models when they are tested on a real-world target domain by learning a model on a source labeled domain. Recently, a UDA method was proposed that addresses the adaptation problem by combining ensemble learning with self-supervised learning. However, this method uses only the source domain to pretrain the model and employs a limited amount of classifiers to create target pseudo labels. To mitigate these deficiencies, in this work, we explore the usage of image translations in combination with ensemble learning and self-supervised learning. To increase the model’s exposure to more variable pretraining data, our method creates multiple diverse image translations, which encourages the learning of domain-invariant features, desired to increase generalization. With these image translations, we are able to learn translation-specific classifiers, which also allows to maximize the amount of ensemble’s classifiers resulting in more robust target pseudo labels. In addition, we propose to use the target domain in pretraining stage to mitigate source domain bias in the network. We evaluate our method on the standard UDA benchmarks, i.e., adapting GTA V and Synthia to Cityscapes, and achieve state-of-the-art results on the mIoU metric. Extensive ablation experiments are reported to highlight the advantageous properties of our UDA strategy.
An Improved Theta*-based Trajectory Planner for Autonomous Vehicle With Obstacle Avoidance

Generating a comfortable and safe trajectory in the dynamic scenario is an essential issue for autonomous driving. This paper presents an improved theta*-based trajectory planning framework to deal with the obstacle avoidance problem. The road is simplified in the Frenet frame, and an ameliorated Theta* is implemented along with the B-spline curve to generate an optimal path. Then, the velocity of the self-driving car is determined in an ST-graph. Two challenging scenarios with static and moving obstacles are designed, and the simulation results verify the effectiveness of the proposed method.
TAMIZHİ: Historical Tamil-Brahmi Script Recognition Using CNN and MobileNet

Computational epigraphy is the study of an ancient script where the computer science and mathematical model is relatively built for epigraphy. The Tamil-Brahmi inscriptions are the most ancient of the extant written of the Tamil. The inscriptions furnish valuable information on many aspects of life in the ancient Tamil country from a period anterior to the literary age of Sangam. The recognition of the script and systematic analysis of the script is required. The recognition of this script is complex, containing various curves for a single character and the style of writing overlap with curves and lines. Generating corpus of the script is necessary, since it is the initial step for computational epigraphy. The archaeological department has supported the raw data that helped to develop a corpus of Tamizhi. In this article, we have implemented a convolution neural network in various ways, i.e., (i) Training the CNN model from scratch a Softmax classifier in a sequential model (ii) using MobileNet: Transfer learning paradigm from a pre-trained model on a Tamizhi dataset (iii) Building Model with CNN and SVM (iv) SVM for evaluation of best accuracy to recognize handwritten Brahmi characters. To train the CNN Model an extensive TAMIZHİ handwritten Brahmi Dataset of 1lakh and 90,000 isolated samples for the character has been created and deployed. The designed dataset consists of 9 vowels and 18 consonants and 209 class so researchers can use machine learning. MobileNet outperformed among all the models implemented with the accuracy of 68.3%, whereas other algorithm ranges from 58% to 67% with respect to the Tamizhi dataset. MobileNet model is trained and tested for the dataset of vowels (8 class), consonants (18 class), and consonants vowels (26 class) with the accuracy of 98.1%, 97.7%, 97.5%, respectively.
Technology Probes to Explore How Children Learn About Gender Stereotypes

There are widespread gender stereotypes in society that would influence children's physical and mental cognitive development and future career choices. Gender equality education for children is critical. However, few studies have explored how technology can be used to promote gender equality education and developed educational tools to help children learning gender equality. To explore the HCI form of learning gender stereotypes for children in early adolescence, this paper introduced three technology probes to test with children and drew several conclusions.
Analysis of the Level of Physical Fitness of Students under the Impact of Distance Learning

The article deals with the problem of the influence of distance learning on the level of physical fitness of students. The use of modern information and communication technologies has led to the creation of a new educational environment, which, as part of the training, has provided students with the opportunity to independently acquire knowledge in the context of constantly updated information. The discipline «Physical Education» is one of the main practical classes in the university program, and in order to assess how the new proposed training conditions will affect the quality of physical fitness of students, we will conduct a study within the framework of physical education classes. The aim of the study is to analyze the impact of distance learning on the level of physical fitness of students. Objectives of the study: to consider the content of distance learning, to analyze the passing of the standard running 100 m., to conduct an experiment and check the impact of distance learning on the level of physical fitness of students, to draw conclusions and make recommendations. The research methods are the generalization of scientific and methodological literature, the method of mathematical statistics, and the pedagogical experiment. Organization and results of the study. The study was conducted with students of the 1st year of full-time study at the Perm State Technical University. As a result of the experiment, it was revealed that during distance learning, the theoretical part of the students mastered better, and in the practical part (running 100 m.), the results deteriorated, since most of the physical culture program was presented in the form of theoretical and methodological-practical sections, and there was no possibility of active and systematic running, mastering technical and tactical training techniques and improving them.
A Regression Analysis Based Model for Defect Learning and Prediction in Software Development

The development of software undergoes multiple regression phases to deliver quality software. Therefore, to minimize the development effort, time and cost it is very important to understand the probable defects associated with the designed modules. It is possible that occurrence of a range of defects may impact the designed modules which need to be predicted in advance to have a close inter-association with the depended modules. Most of the existing defect prediction classifier mechanisms are derived from the past project data learning, but it is not sufficient for new project defect predicting as the new design may have a different kind of parameters and constraints. This paper recommends Regression Analysis (RA) based defect learning and prediction Defect Prediction (RA-DP) mechanism to support the defective or non-defective prediction for quality software development. The RA-DP approach provides two methods to perform this prediction analysis. It initially presents an association learning through RA to construct the regression rules from the learned knowledge required for the defect prediction. The constructed regression rules are used for defect prediction and analysis. To measure the performance of the RA-DP a regression experimental evaluation is performed over the defect-prone PROMISE dataset from NASA project. The outcome of the results is analyzed through measuring the prediction Accuracy, Sensitivity and Specificity to demonstrate the improvisation and effectiveness of the proposal in comparison to a few existing classifiers.
A Low Distortion Audio Self-Recovery Algorithm Robust to Discordant Size Content Replacement Attack

Although the development of watermarking techniques has enabled designers to tackle normal processing attacks (e.g., amplitude scaling, noise addition, re-compression), robustness against malicious attacks remains a challenge. The discordant size content replacement attack is an attack against watermarking schemes which performs content replacement that increases or reduces the number of samples in the signal. This attack modifies the content and length of the signal, as well as desynchronizes the position of the watermark and its removal. In this paper, a source-channel coding approach for protecting an audio signal against this attack was applied. Before applying the source-channel encoding, a decimation technique was performed to reduce by one-half the number of samples in the original signal. This technique allowed compressing at a bit rate of 64 kbps and obtaining a watermarked audio signal with an excellent quality scale. In the watermark restoration, an interpolation was applied after the source-channel decoding to recover the content and the length. The procedure of decimation–interpolation was taken because it is a linear and time-invariant operation and is useful in digital audio. A synchronization strategy was designed to detect the positions where the number of samples in the signal was increased or reduced. The restoration ability of the proposed scheme was tested with a mathematical model of the discordant size content replacement attack. The attack model confirmed that it is necessary to design a synchronizing strategy to correctly extract the watermark and to recover the tampered signal. Experimental results show that the scheme has better restoration ability than state-of-the-art schemes. The scheme was able to restore a tampered area of around 20% with very good quality, and up to 58.3% with acceptable quality. The robustness against the discordant size content replacement attack was achieved with a transparency threshold above −2.
Automatic knowledge acquisition in interactive oral translation

In order to improve the accuracy of automatic knowledge acquisition in interactive oral translation, an automatic knowledge acquisition method is designed. First, the target mining includes four steps: automatic acquisition of bilingual abstract resources, extraction of candidate translation units, translation selection and statistical model. Then, the phrase table is filtered and the context is reordered. Finally, a matching template is established to realize the knowledge acquisition in interactive spoken translation. Experimental results show that the proposed method can effectively improve the accuracy and inclusion rate.
Artificial intelligence model for the prediction of malignant tumors using a set of medical images from mammography studies

Abstract Currently, the diagnosis of tumors and malignant cells through imaging studies is a great challenge for expert medical personnel, due to the complexity of achieving an early prediction of cancer cells, which would allow to accelerate early medical treatments. Today, technologies have become a fundamental ally for the health sector, specifically the area of artificial intelligence, which has permeated many disciplines, generating important advances. Advances in parallel computing, GPU technology, and deep learning have made real-time image processing easier. The main objective of this research was to generate a deep learning model for the prediction of malignant cells in medical images of diagnosed mammograms. Using the previously trained model based on Faster R-CNN, with the ResNet function extractor. This model works in the Python programming language, using the Tensorflow framework and the OpenCv library. The algorithms were previously trained through the DDSM and MIAS open medical image databases, published on the web. This model not only focuses on recognizing and classifying malignant cells in the image, but also on the location of objects within it, appropriately drawing a bounding box. One of the latent challenges of these models since their inception has been the consumption of computing, but today they have been optimized so much that they allow freezing the pre-trained models by loading them in the memory of the devices, managing to use them in computers without GPUs. As a result, it was found that the Faster R-CNN method with the Resnet 101 extractor offers great advantages of precision and speed when it comes to detecting malignant tumors, studies that can serve as a great contribution to the bets of this algorithm in the health sector.
Information Retrieval in Business Industry Using Blockchain Technology and Artificial Intelligence

Industry 5.0 promotes automation in an optimized way. Collaboration with blockchain technology and artificial intelligence helps to enrich Industry 5.0 with its quantifiers and qualifiers. In the business industry, information plays an iconic role. When we consider the issues of storage and retrieval, we need to think about blockchain technology where the data will be stored and shared in a secure way. Here, the data will be distributed across the network in an encrypted format; hence, the original data can be viewed only by the owner of the data. Blockchain stores the information in the form of blocks. Every block has three sections. The first section holds the hash value of the previous block, the second one holds the information to be stored in a block, and the third one holds the hash value of an upcoming block. It does not allow an intruder to hack or modify the data without user's knowledge as these blocks are interconnected on both the sides with their hashes. This synergy of technologies brings supremacy in the field of business industries which will be discussed in this chapter.
Subgraph Isomorphism Building on A Hierarchical Query Graph

Subgraph isomorphism is an essential problem of graph theory. It has broad application on information retrieval in many research field, such as biology, chemistry, knowledge graph and social network. The settlement to graph isomorphism is to find the embedding details from a given query graph Gq to a given data graph G. Till now, most subgraph isomorphism research build on searching from one fixed query graph. However, in practice, fixed query graph will lead to heavy query graph building work. In this paper, we proposed a subgraph matching framework built on a hierarchical query graph to alleviate the burden of query graph building work. The vertices of query graph are classified into three type: compulsory vertex, optional vertex and forbidden vertex. We proposed a modified VF2 algorithm to tackle this task. It creatively handle the order problem by converting the vertices matching into label matching and using a sieving method to map the label to the vertex. We conducted experiments on a financial semantic relationship datasets. Results show our modified VF2 algorithm can accurately map the query graph Gq to the subgraph of data graph G with acceptable time efficiency.
Magnesite and bauxite ores as petrophysical objects of spectral-grade estimation based on electrometry (from the archives of oxide-metric exploration in the Ural deposits)

Research subject. Distribution of magnesite and bauxite ores in mining blocks of deposits according to electrical conductivity in connection with signals of scattered phases of Ca, Mg and Al oxides. Petrophysical objects and differentiation of the properties of oxides detected in the boundary area and at the boundary with dielectrics, the semiconductor range in the geochemical environment of metamorphic formations from carbonate sediments. Materials and methods. An approach of searching for stationary features was applied. First, a study at the elemental level (review) was undertaken. Further, the authors’ electrometric measurements of the deposits were analyzed. Results. In the review part, the analysis of the metal constants – the relative electronegativity of the OEO (Luo and Benson scale), the electronic potentials E0, and the relationship of electrical resistance and pressure in pure elements was performed. The inversion of the properties of CaO ↔ MgO was revealed (for the first time in 2002–2004); Ca was identified as a reducing agent (CaO is a solid dielectric), which contradicts the current ideas about the comparative additivity of light Mg. It was established that MgO is a separate source of semiconductor carriers. Favourable conditions for studying the conductivity during the experimental part of the work were the absolute exchange increments of ore oxides at geological contacts. The properties of ores, rocks and oxides were measured and calculated. The relationships of the varietal growth of conductivity with compaction, increased basicity and magnetic susceptibility (magnetization) were predicted. This, in turn, provides a basis for studying cores, samples from debris, shredding and recycled geo-material, including concentrates, tailings and waste. Conclusions. The spectral properties of scattered oxides with atomic numbers ≤ 20 were identified. The activation effect of MgO and Al2O3 in the ionic medium (CaCO3 = CaO+CO2) was noted. In the phenomena of the semiconductor nature (n- and psources), a version of the donor-acceptor mechanism was proposed. The established properties are a factor in the implementation of measuring identification of oxides and ore grades in the conditions of deposits, both in the estimates of carbonate chemical types of terrigenous fossils, and plagioclase-pyroxene – in igneous ones. Varietal exploration and prototype electrometry were discussed from the standpoint of high-precision interpretation in the potential and replenishment combination of various petrophysical meters.
FPGA Design of Enhanced Scale-Invariant Feature Transform with Finite-Area Parallel Feature Matching for Stereo Vision

In this paper, we propose an FPGA-based enhanced-SIFT with feature matching for stereo vision. Gaussian blur and difference of Gaussian pyramids are realized in parallel to accelerate the processing time required for multiple convolutions. As for the feature descriptor, a simple triangular identification approach with a look-up table is proposed to efficiently determine the direction and gradient of the feature points. Thus, the dimension of the feature descriptor in this paper is reduced by half compared to conventional approaches. As far as feature detection is concerned, the condition for high-contrast detection is simplified by moderately changing a threshold value, which also benefits the reduction of the resulting hardware in realization. The proposed enhanced-SIFT not only accelerates the operational speed but also reduces the hardware cost. The experiment results show that the proposed enhanced-SIFT reaches a frame rate of 205 fps for 640 × 480 images. Integrated with two enhanced-SIFT, a finite-area parallel checking is also proposed without the aid of external memory to improve the efficiency of feature matching. The resulting frame rate by the proposed stereo vision matching can be as high as 181 fps with good matching accuracy as demonstrated in the experimental results.