Reviewerly/exHarmony huggingface dataset:
Multiple-datasets and multiple-label based color normalization in histopathology with cGAN

Computational histopathology algorithms can interpret very large volumes of data, which can navigate pathologists to assess slides promptly, and also aid in the localization and quantification of abnormal cells or tissues. In recent years, taking place of conventional imaging processing methods, deep learning has become the mainstream methodology to interpret cancer pathology images. However, similar as conventional computer vision methods, stain normalization in tissue identification with convolutional neural networks (CNNs) is still essential for the diagnostic accuracy. Traditional prior knowledge-oriented color matching, as well as a particular style based pure learning in generative adversarial networks, may be encompassed with accuracy decrease when data centers are many. In this paper, we propose a novel color normalization method with a conditional generative adversarial network (cGAN). It is a learning-based interpolation approach with probability distribution space on multiple datasets training. A target template is designed to be label-dependent to overcome the improper color mapping problem caused by data heterogeneity. The tests are performed on histopathology datasets from The Cancer Genome Atlas (TCGA) and the proposed method outperforms other previous works in classification accuracy. This approach has potential in clinical practice for better recognition of cancer in digital pathology and can be implemented in a decentralized setting.
Tracking Methods in Augmented Reality – Explore the Usage of Marker-Based Tracking

The advancement in technology has created new challenges using different techniques. Augmented Reality (AR) is the amalgamation of a true object viewed by a user which is augmented by the virtual scene generated by a computer with additional information. In AR, the system augments with the help of a mechanism to combine virtual and real-world but the user maintains a sense of presence in real-world. It is one of the emerging technologies that has attracted researchers in the field of enhancing information for education and training, product visualization, and virtual try in the field of marketing, finding a way for tourism and many more applications to a very great extent. A lot of factors influence an effective AR experience. A discussion is carried on about the important factors like Graphical rendering, Tracking, Calibration, display that contributes to an effective AR experience depending on the AR application. This article highlights different tracking techniques that can be classified as Sensor-based and Vision-based tracking. Sensor-based tracking can additionally be divided into other tracking such as Optical, Magnetic, Acoustic, and Inertial. Vision-based tracking is further categorized into Marker-based and Markerless Tracking. The usage of the tracking technique depends on the AR application. This paper projects the advantages and disadvantages of each of these tracking techniques which are listed out. Finally, the article deals with the requirements of marker based application and its usage in detail.
Optimizing Automated Programming Contracts with Modified Ant Colony Optimization

Development of sufficiently optimized software coding Contracts with insignificant association from developers directs at lowering the setbacks in abiding by the input specifications of the software, delivering systematic testing of several real life software.The associated framework put forward primarily speculates dependency aspects signifying both behavior and semantic attributes well-defined as conditions in a Decision tree as Automated Contracts for conformance with input specs.The contracts are perfected based on their feasibility with a modified form of Ant Colony Optimization algorithm motivated by pursuing behavior of ant colonies of real world, with considerable comprehensiveness in the Fault identification potential.This research focuses on the decision tree classifiers are J48, RandomForest classifiers have same precision value which is 0.96 of precision value.RandomTree and REPTree classifiers have same precision value which is 0.95 of precision value, HoeffdingTree classifier is 0.82 of precision, DecisionStump is zero precision value.The J48, RandomForest classifiers have same recall value which is 0.96 of recall value.RandomTree and REPTree classifiers have same recall value which is producing 0.95 of recall value, HoeffdingTree classifier is having 0.76 of recall value, DecisionStump is having zero precision value.The RandomForest is having highest ROC value whichi is 1.The J48 classifier , REPTree classifier have 0.99 ROC value.HoeffdingTree classifier is 0.98 of recall value, RandomTree classifier has 0.97 ROC value and finally the lowest ROC value is produced by DecisionStump Decision classifier.The RandomForest, RandomTree, REPTree classifiers are having highest PRC Area value which is 0.99.The J48 classifier is having 0.92 PRC Area, HoeffdingTree classifier is 0.85 of PRC Area, and DecisioStump classifier is having 0.23 PRC Area value.Among the set of decision classifiers, the J48 decision classifier is producing the best results based on the accuracy, precision, recall, ROC and PRC area values which are compare with other models.
Automatic Feature Selection for Improved Interpretability on Whole Slide Imaging

Deep learning methods are widely used for medical applications to assist medical doctors in their daily routine. While performances reach expert’s level, interpretability (highlighting how and what a trained model learned and why it makes a specific decision) is the next important challenge that deep learning methods need to answer to be fully integrated in the medical field. In this paper, we address the question of interpretability in the context of whole slide images (WSI) classification with the formalization of the design of WSI classification architectures and propose a piece-wise interpretability approach, relying on gradient-based methods, feature visualization and multiple instance learning context. After training two WSI classification architectures on Camelyon-16 WSI dataset, highlighting discriminative features learned, and validating our approach with pathologists, we propose a novel manner of computing interpretability slide-level heat-maps, based on the extracted features, that improves tile-level classification performances. We measure the improvement using the tile-level AUC that we called Localization AUC, and show an improvement of more than 0.2. We also validate our results with a RemOve And Retrain (ROAR) measure. Then, after studying the impact of the number of features used for heat-map computation, we propose a corrective approach, relying on activation colocalization of selected features, that improves the performances and the stability of our proposed method.
Research on PIV Image Matching Algorithm of Turbulent Velocity Field Based on Circular Projection

Based on the complexity and particularity of the images processed by the particle image velocimetry technology and the non-rigid motion mode of the fluid, a particle image matching algorithm with low complexity, high accuracy, anti-deformation, anti-rotation, high robustness to illumination and wide measuring range of velocity is proposed by combining the nature of the rotation invariance of circle and the theory of image warping. The algorithm uses transformation of circular projection instead of FFT to ensure that the information of interpretation window and the search area has sufficient similarity; the complexity of the algorithm proposed is greatly reduced by setting the quarter circle and establishing relative coordinate system; the size of the interpretation window can be changed with the characteristics of the flow field by setting the threshold of the number of particles and the threshold of the window size, which improves the accuracy and applicability of the algorithm; it makes the algorithm suitable for turbulent flow fields with high Reynolds number and large deformation by warping the image iteratively. Experiments have proved that the algorithm is suitable for matching images of turbulent flow which with rotational transformation, the matching rate is over 99%, and the complexity of the algorithm is the same order of magnitude as the cross-correlation algorithm based on fast Fourier transform.
Experimental Archaeology: Making, Understanding, Story-telling

Experimental Archaeology: Making, Understanding, Story-telling is an edited volume that presents the results of a two-day workshop on experimental archaeology hosted by the Irish Institute of Helle...
Reduced Complexity Optimal Convolution Based on the Discrete Hirschman Transform

The Discrete Hirschman Transform (DHT) is more computationally attractive than the Discrete Fourier Transform (DFT). Based on its derived linear convolution, we have confirmed that the DHT-based convolution filter shows its superiority in reducing computations conditionally, while compared with the conventional DFT-based convolution filter in our previous work. Since the DHT-based convolution has many configurations depending on parameter choices, we conjecture that there should be an optimal case for the largest reduction in computations. In this paper, for the DHT-based convolution, we express the requirement in real computations and propose an approach of how to determine the optimal parameters to reduce computations. We further compare the computational load of the optimal DHT-based convolution with that of other popular convolutions. Moreover, its reduction in clock cycles has also been estimated using a Digital Signal Processor (DSP) TMS320C5545. Results indicate that the optimal DHT-based convolution can reduce real computations (multiplications by 9.09%-50% and additions by 1.12%-51.09%) and clock cycles, according to the input length and filter size, except for some cases with identical performance to the radix-2 FFT-based competitor.
Pose-Guided Multi-Scale Structural Relationship Learning for Video-Based Pedestrian Re-Identification

How to extract discriminative features from redundant video information is a key issue for video pedestrian re-identification.Factors such as occlusion, perspective, and posture changes in complex environments pose severe challenges to pedestrian re-identification based on local methods.In this paper, a posture-guided multi-scale structural relationship learning pedestrian re-identification method is proposed.The purpose is to analyze the video sequence of pedestrians based on the reference pose and the pose alignment model, and extract the sample frame with the highest image quality and the most complete spatial information in the reference pose.The method based on posture guidance can more accurately eliminate the interference of background, occlusion and perspective factors.To further explore the potential relationship between local regions, this paper calculates the relationship matrix between the local regions based on the relationship model to further calculate the relationship weight, and the graph convolutional network based on the relationship weight learns the structural relationship feature of multi-scale regions.The input of the graph convolutional network is a local region divided by a multi-scale method, and the output is a pose-guided multi-scale structural relationship feature.The experimental results on three public datasets show that the proposed method performs favorably against state-of-the-art methods.
Evolutionary Attention Network for Medical Image Segmentation

Medical image segmentation is an active research topic to analyse medical images to find an organ or possible abnormalities in an image. Using a Convolutional Neural Network (CNN) is a successful technique for medical image segmentation. However, developing a CNN is a difficult task, especially when it includes complex structures, such as an attention mechanism. A CNN equipped with an attention mechanism is able to focus on a specific part of an image to extract a Region Of Interest (ROI), that can play a significant role to increase the accuracy of an image segmentation. Due to the difficulty of developing an attention network, in this paper, we introduce a new evolutionary technique to generate an attention network automatically for medical image segmentation. To the best of our knowledge, this is the first attempt to create an attention network using an evolutionary technique. To do this, a new encoding model is introduced to create a network topology, along with its training parameters, to ease the complexity of developing a CNN. Also, a Genetic Algorithm (GA) is applied to evolve the networks. To show the capability of the proposed technique, we used three publicly available medical segmentation datasets. The obtained results show that the proposed model can generate networks corresponding to each dataset, such that the developed networks have high performance for medical image segmentation.
Cloud Variance Impact on the Voltage and Power Flow of Distribution Networks with Photovoltaic Generators

The present paper aims to investigate the effect of different types of clouds on the photovoltaic power generators and electricity distribution network in which they are connected. The developed methodology classifies solar irradiance data according to the GOES-13 satellite image processing, which contain information regarding the types of clouds. The image processing results are then compared with real solar irradiance data in order to analyze the influence of shading and thus classify the measured data as: clear sky, partly cloudy and cloudy. The OpenDSS software is employed for simulating the varying generation and load profiles using the IEEE 34 bus system as a testbed. The result analysis shows the power flow and electrical losses, as well as the generation profiles impact on voltage levels and the operation of voltage regulating equipment. Voltage fluctuations and tap changes are also analyzed considering different tap delays.
A New Framework for Video-based Frequent Iris Movement Analysis towards Anomaly Observer Detection

This paper suggested a new framework for detecting abnormal behavior, specifically based on frequent iris movements.It contributed to a decision whereas an individual is dubious or unsuspected from a video.One of the key components of questionable observer detection is to detect some specific suspicious activity.According to the writer, various areas of the body movement and human behaviors may be an indicator of suspicious behavior.In this research, we considered the movement of human eyes to identify suspicious activity.This working field is also a significant aspect of machine vision and artificial intelligence, and a big part of the understanding of human behavior.The system framework comprises three parts to monitor suspicious video activities.First, we used the Multi-task Cascaded Convolutional Networks (MTCNN) classifier to detect eyes.Second, we observe irises from eye representations with the use of Circular Hough Transformation (CHT).Finally, we calculated the average distance of iris movement from eye images using a new morphological method called TRM using some properties of the iris movement.We have observed a particular phenomenon of frequent iris movement.Hence, we are making a case of someone being an abnormal person and referring it to a suspicious observer.To vouch for our work, we created our data set with 100 videos where 30 individuals volunteered to validate this research.Each video comprises 200 frames with a duration of 6-10 seconds.We've reached an accuracy of 94% on detecting a frequent iris movement.Rather the goal is to minimize people's burdens so they can focus on a small range of cases for investigation in more depth.This research's sole purpose is to indicate a person's anomalous behavior on the basis of frequent iris movement.Our research outstrips much of the current literature on abnormal iris movement and dubious investigator identification.
Multiple-Criteria Fuzzy Optimization of the Heat Treatment Processes for Two Steel Rolled Products

This paper presents a developed method for fuzzy multiple-criteria optimization of the rolled-steel heat treatment processes in the modern metallurgical plant. At the first stage of the study, by means of passive industrial experiments or a mathematical simulation of heat transfer processes, and using statistical methods, the regression dependencies of the output parameters of process quality on the input variables that are technological parameters are established. Then, based on the quality parameters, membership functions are formed that represent local criteria of the process quality, and their ranks are calculated using the matrix of pairwise comparisons. The practically useful methodology of the fuzzy multiple-criteria optimization of technological processes is proposed. To illustrate this methodology’s practical efficiency, the solutions of two optimization problems are found by maximizing the global criterion that aggregates local criteria using their ranks. It is shown that the efficiency of the obtained optimal heat treatment modes significantly exceeds the efficiency of the technology used earlier in the plant.
DEEP LEARNING APPROACH FOR INDIAN CURRENCY CLASSIFICATION

Currency is an unavoidable part of our day-today life.Despite the rapidly expanding utilization of master cards and additional electronic payment categories, money is considerably utilized for everyday exchanges because of its comfort.The current day monetary self-service gives birth to currency recognition, which plays a vital role in the automated banking procedure.Therefore we propose a novel method for currency recognition that identifies Indian currency in different views on the scale.It is straightforward for a typical human being to comprehend and recognize any banknote easily, but it is undoubtedly troublesome for anyone with a visually impaired or blind individual to accomplish a similar task.Banknotes commonly have unique designs according to the denomination and can be sorted with surplus human errors in the bank.These errors lead to difficulties in evaluating and recognition.If computers or mobile apps recognize currency, it will immensely boost the precision of recognition and ameliorate people's workload efficiently.As money has a significant role in daily life for any business transactions, real-time detection and recognition of banknotes become necessary for a person, especially blind or visually impaired, or for a system that sorts the data.The model which we worked on essentially classifies the currency note into distinct denominations like Rs10, Rs50, Rs100, Rs500, Rs2000.The currency will be recognized and classified by using image processing techniques, deep learning techniques.We have implemented transfer learning theory, a deep learning domain where we will reuse the weights in one or more layers from an already trained model into a new model by either maintaining the weights fixed, fine-tuning them, or adapting the weights completely when training the model.We aim to enhance techniques that have been missing in most contemporary works that have been done so far.Therefore our proposed currency recognition system can be efficiently run on the web application/mobile app, where a user would upload an image of the currency note, and it will deliver an audio output and a text output.
A language to analyze, describe, and explore collections of visual art

Abstract A vast quantity of art in existence today is inaccessible to individuals. If people want to know the different types of art that exist, how individual works are connected, and how works of art are interpreted and discussed in the context of other works, they must utilize means other than simply viewing the art. Therefore, this paper proposes a language to analyze, describe, and explore collections of visual art (LadeCA). LadeCA combines human interpretation and automatic analyses of images, allowing users to assess collections of visual art without viewing every image in them. This paper focuses on the lexical base of LadeCA. It also outlines how collections of visual art can be analyzed, described, and explored using a LadeCA vocabulary. Additionally, the relationship between LadeCA and indexing systems, such as ICONCLASS or AAT, is demonstrated, and ways in which LadeCA and indexing systems can complement each other are highlighted.
Single Image Super-resolution Model Based on Improved Sub-pixel Convolutional Neural Network

In order to improve the clarity of a single image after super-resolution and reduce the complexity of calculation, this paper uses a neural network model based on sub-pixel convolution to speed up the image processing speed and make the image details after super-resolution more clear. In the feature extraction, the image features are first extracted by using a smaller convolution kernel, then the image features are enlarged through the up-sampling process, and finally the feature extraction is performed through the convolution operation again. At the same time, in order to better preserve the image features, this paper also adds a feature compensation module. When the magnification is 3, the PSNR value is higher than the ESPCN (+1.03db). The sub-pixel convolutional network model in this paper effectively reduces the computational complexity and improves the image quality, and provides an idea for the optimization of single image super-resolution.
Digital Competences in the Elderly and University Students: Didactic Interaction from the Use of Social Networks

Digital Competences mediate the population of vulnerable Latin American contexts. In these there is the need to expand social communication as a way of learning and self-improvement. This research proposes to determine the effects of transfer of digital competences in two types of interaction: university students-adults, adults-schoolchildren. Therefore, two groups of individuals participate in two experimental phases of didactic transfer through the use of social networks, based on the execution of a Research Seed Project and another Project of Professional Practices of Social Responsibility in vulnerable contexts. We found effectiveness in the transfer of Digital Competences in both cases during more than six months of project execution. We report greater predisposition for the acquisition of information, communication, self-evaluation and access to information in adults. The schoolchildren managed to reinforce their skills to apply Digital Safety and Problem Solving, improving notably in this group. This program is seen as part of the improvement of teaching that every school needs as a means of curricular strengthening, whether they are teachers of advanced age or students of the primary stage. We believe that these skills will open up to the professional world with better visionary prospects for developing countries.