Reviewerly/exHarmony huggingface dataset:
Trustless Construction Project Information Exchanging Using Hyperledger Blockchain

The distributed ledger is an immutable record for data stored inside it. This technology can be helpful for construction project events and communication recording. This article investigates the Hyperledger project, a modular enterprise Blockchain with pluggable products to create a construction communication and data exchange ledger. Hyperledger is an open-source modular Blockchain architecture designed to help enterprises run distributed ledgers to benefit from the technological advancement provided by Blockchain technology. These ledgers are immutable, distributed, and trustless. These features allow for transparent communication between stakeholders, which could positively impact construction project workflow and the participation of projects actors. The article uses Hyperledger products Fabric and Composer to showcase a hypothetical Blockchain to exchange project metadata and communication between construction project participants. The article presents an example of using Blockchain for the construction project process by designing a Blockchain to handle the Request for Information (RFI) process.
Remote Sensing Digital Image Pixel Processing Theory IV: Time Domain Sampling

The continuous spatial information of the natural world must be transformed into the discrete information needed by the computer, that is, time series information. Therefore, the sampling process is a prerequisite for computer expression analyses.
Pattern Recognition (Image Segmentation)

Chapter guidance: The recognition of information in images is the most important purpose of remote sensing digital image processing. To extract useful information in the image, it is necessary to subject the image to pattern recognition processing. Pattern recognition involves three steps: segmentation, extraction and classification, that is, image segmentation, feature extraction and classification, respectively. This section describes image segmentation, which is a key part of remote sensing applications. Feature extraction and classification are described in the subsequent chapter.
Applications of Remote Sensing Digital Image Processing IV—Color Transform and 3-D Reconstruction

After appropriately processing remote sensing images and extracting useful information, it is necessary to properly express the information to ensure that the image is suitable for human eyes and mimics the real world. To this end, proper color transformation and 3D reconstruction of the image processing results must be performed.
Bengali Document Retrieval Using Model Combination

In this paper, we present an approach for Bengali information retrieval that combines two Information Retrieval (IR) models. Our proposed hybrid model combines a TFIDF-based IR model with a Latent Semantic Indexing (LSI)-based IR model. Since the TFIDF-based model exhibits poor recall and the LSI-based model achieves higher recall, the combination of these two models improves the retrieval performance. The experimental results demonstrate that the proposed Bengali IR model performs significantly better than some baseline models.
Geometric Deep Learning: A Temperature Based Analysis of Graph Neural Networks

We examine a Geometric Deep Learning model as a thermodynamic system treating the weights as non-quantum and non-relativistic particles. We employ the notion of temperature previously defined in [5] and study it in the various layers for GCN and GAT models. Potential future applications of our findings are discussed.
Deep Visualisation-Based Interpretable Analysis of Digital Pathology Images for Colorectal Cancer

Colorectal cancer is one of the most widespread cancers in western countries. As with most forms of cancers, its diagnosis can only be made by performing a histopathological examination from tissue collected in the suspicious area. This process is time-consuming for the pathologist and consequently for the patient, whereas recent advances in computer vision and machine learning have shown that models can match the performance of human experts in classification and segmentation challenges. However the medical and ethical responsibilities that come with the use of artificial intelligence in healthcare demand that predictions are interpretable, and not just be the output of a black-box model. This paper demonstrates how visualisation of learned features play an important role in making the decision-making process transparent and helps to justify alignment with clinical features of colorectal cancer.
Cryptanalysis of Markle Hellman Knapsack Cipher Using Cuckoo Search Algorithm

This research work demonstrates the cryptanalysis of Markle Hellman Knapsack cipher using Cuckoo Search Algorithm. Cuckoo search is a population-based metaheuristic nature encouraged optimization technique. Firstly, we have initialized different cuckoo search parameters and generated a random population of nests with egg. Each egg is a possible candidate plaintext. Then cuckoo search algorithm has been accomplished to find out the best possible solution. The proposed cuckoo search attack algorithm has been characterized by population generation, fitness function evaluation, Levy flight, mutation, and perturbation method. The algorithm gradually discovers the optimal solution by comparing the fitness values of the intermediate solution. Experimental outcomes are compared with the results that are obtained using genetic algorithm and binary firefly algorithm. Experimental results exhibit that the proposed cryptanalysis technique outperforms the previous results of cryptanalysis. Moreover, the proposed algorithm successfully recovers the plaintext from the ciphertext.
Optimization of Character Classes in Devanagari Ancient Manuscripts and Dataset Generation

In any OCR system, the character classes define the set of characters or shapes to be recognized during the recognition process. So, it is necessary to identify character classes for an OCR system. The Devanagari script consists of several basic characters (vowels and consonants), half-forms of characters, modifiers, diacritics, and conjuncts. An analysis of the characters in Devanagari ancient documents has been presented in this chapter. Based on the analysis of such documents, two sets of character classes have been categorized in this work. One set contains only basic characters (vowels and consonants) of the Devanagari script. It does not contain any modifier, conjunct or diacritical symbols and is labeled as DATASET-A. DATASET-A contains 33-character classes. The other set contains all identifiable shapes of Devanagari ancient manuscripts and is labeled as DATASET-B. A total of 236-character classes have been identified in DATASET-B. In this work, the character class count for DATASET-B has been optimized to 116 characters. In this work, a character dataset of 22,522 characters has been used. Discrete Cosine Transform (DCT) zigzag features have been extracted and a Support Vector Machine (SVM) classifier has been used for the recognition of ancient Devanagari characters. A maximum accuracy of 88.69% has been obtained with the above-mentioned setup.
Object-Background Partitioning on Images: A Ratio-Based Division

Image segmentation is a process of partitioning an image’s distinct objects from the background and has wide applications in diverse fields that include medical images, video surveillance, scene understanding, partial image encryption, image compression, etc. Segmentation is accomplished by thresholding some image attributes, thereby dividing image data into two parts. In this work, image data is considered as an array of gray values in the form of a line segment comprising objects and background pixels. Then an iterative thresholding on intensity values is done in the framework of dividing a line segment internally. The image is partitioned into non-overlapping blocks and segmented in an iterative manner based on cumulative frequency of the gray values in the local histogram. On the segmented blocks, the probabilities of the correct detection ( $$P_d$$ ) of object pixels are computed, which are then used to find the same for the whole image, called global probability of object detection, denoted as $$P_D$$ . The iteration stops when the global $$P_D$$ meets some predefined threshold value. A large set of simulation results show the efficiency of the proposed segmentation scheme over the existing methods in terms of object detection probability (0.9) and false detection probability of background (0.1) at faster convergence (four to six iterations).
Document Region Classification

After region segmentation, it is necessary to identify the functional labels of the segmented regions. This is because OCR engines can only consider text regions. Presence of non-text may degrade their performance. On the other hand, different non-texts follow different ways to present information. Therefore, these require separate processing. To satisfy this need, classification of the segmented regions in various types like text, image, table, chart, separator, etc. is essential. In this chapter, different methods developed for document region classification are discussed along with their pros and cons. This chapter also discusses the standard datasets and metrices developed for performance evaluation of these methods.
Document Image Binarization

Documents often get affected by noise due to various issues like casual handling occurred during storing, or image acquisition processes. These noises not only affect the quality of the documents but also degrade the visual appearance of the content. Therefore, these have high potential to downgrade the final outcome. To suppress the noise, occur in an input document image at the initial stage, it is essential to use an efficient pre-processing before further analysis. Pre-processing converts the input image to a specific form, suitable for further analysis without disturbing the knowledge content. Among all, binarization is very popular and commonly performed pre-processing before many document image processing tasks. Therefore, in the present chapter, a detailed discussion on document image binarization and the recent progresses in this domain are presented. Additionally, in this chapter, various degradations related to document image, and various noise models are also described to make the discussion complete.
Method for Analyzing the Structure of Noisy Images of Administrative Documents

The problem of recognition of images of administrative documents is considered. Administrative documents are used in an organization or group of organizations to accompany business processes. The structure of administrative documents contains static elements and fill areas (fields). The main stages of recognition are the linking stage (finding areas of filling) and the stage of recognition (extracting text or graphic information from these areas). The chapter considers the stage of linking field boundaries. The linking is carried out using a model based on static reference elements. The reference elements in the model are the line segments (separating lines) and the words of the text. The model consists of a set of descriptors. A simple text descriptor contains words and attributes to distinguish it from similar descriptors. A text descriptor has an order relationship with other text descriptors. It is proposed to use combined descriptors consisting of words and segments. Linking is carried out by superimposing descriptors and a set of recognized words and detected segments in the document page image. The SDK Smart Document Engine was used as OCR in the experiment. For our private dataset, consisting of universal transfer documents, it is shown that the extraction of object sets improves the accuracy of recognition of document fields during recognition by 16%, and the accuracy of extracting information from a document image by 15%.
Introduction

Documents often carry crucial information, covering almost every aspect of human society. Even in this era of cyber-physical systems, many of us prefer to read paper documents. These facts raise the concern of careful storage and management of these documents. However, preserving these documents in their physical form has many risks and also limit their access. To address these problems, it is required to convert these paper-based documents into their electronic form. However, mere electronic conversion would not be of pronounced help for properly preserving the documents as well as automatic information retrieval. Therefore, a system for efficiently analyzing the layout of these documents becomes a pressing need. In this chapter, a quick introduction to Document layout analysis and its constituent stages are presented. This chapter also discusses various challenges associated with the task of layout analysis.
A Geometric View on the Role of Nonlinear Feature Maps in Few-Shot Learning

We investigate the problem of successfully learning from just a few examples of data points in a binary classification problem, and present a brief overview of some recent results on the role of nonlinear feature maps in this challenging task. Our main conclusion is that successful learning and generalisation may be expected to occur with high probability, despite the small training sample, when the nonlinear feature map induces certain fundamental geometric properties in the mapped data.
Case Study

Historical documents carry many crucial information regarding a place or person for a specific time period, whereas contemporary documents cover recent status. However, contemporary documents possess mostly non-Manhattan layout, even overlapping layout in some cases. In contrast to that layouts of historical documents are comparably constrained, but these documents suffer from huge degradations. These facts say analysis of layout present in documents from these categories are very challenging. In this chapter, a method named as ABCD (Analysis of Basic Contents in Documents) is presented, which automatically segments and classifies the regions of early Indian printed document images. This method was submitted to REID2019 competition for Task 1, and it outperforms all other participating methods from different industrial and academic sectors in terms of layout analysis and becomes the winner. In this chapter, also a method name BINYAS is presented which was developed to deal with contemporary documents. This method secures the 3rd position in RDCL2019 competition.
Feature Selection for Nepali Part-of-Speech Tagging in a Conditional Random Fields-Based System

The features used for training any statistical model have an enormous impact on the model’s performance. The selection of features that contribute most to the prediction variable or output of concern is a crucial task. We used conditional random fields (CRF) to conduct experiments on the best feature set selection for part-of-speech (POS) tagging of Nepali text. Nepali is a resource-poor language, and its processing is still in its infancy. In this work, we have studied the effect of using various features (non-linguistic affixes, word length coupled with contextual information, along with digit and symbol checking) for improving the tagger’s performance without using any external sources like the gazetteer list, morphological rules, etc. A thorough error analysis has been carried out, followed by the manual correction of the wrongly annotated words in the training data. The proposed CRF-based POS tagger’s efficacy has been validated with 95 $$\%$$ accuracy.
Shape-from-Template with Camera Focal Length Estimation

One of the major and open research objectives in computer vision is to automatically reconstruct the 3D shape of a deformable object from a monocular image. Shape-from-template (SfT) methods use prior knowledge embodied in a template that provides the object’s 3D shape in a known reference position, and a physical model that constrains deformation. SfT methods have shown great success in recent years; however, accurate methods require an intrinsically calibrated camera. This is an important practical limitation because the intrinsics of many real cameras are not available, so they must be estimated with a dedicated calibration process. In this chapter, we present a novel SfT method that handles unknown focal length (a critical intrinsic of the perspective camera). The other intrinsics such as the principal point and aspect ratio are assumed to take canonical values, which is valid for many real cameras. We call this problem fSfT, and we solve it by gradient-based optimization of a large-scale non-convex cost function. This is not trivial for two main reasons. First, it requires suitable initialization, and we present a multi-start approach using a small set of candidate focal lengths (typically fewer than three are required). We combine this with a mechanism to avoid repeated exploration of the search space from different starts. Furthermore, we present cost normalization strategies, allowing the same cost function weights to be used in a diverse range of cases. This is crucial to make the method practical for real-world use. The method has been evaluated on twelve public datasets, and it significantly outperforms a previous state-of-the-art fSfT method in both focal length and deformation accuracy.
Software Package for Simulation Modeling of Mobile Robots

In this chapter, the possibility of using Unity as a simulation environment for mobile robots is considered, and the possibilities of Unity Editor for use in simulation modeling are determined. The possibility of using a standard set of development tools to simulate the behavior of mechanical connections, kinematic circuits, and rigid body physics is evaluated. The architecture diagram of the developed simulation modeling system is compiled. The methods of implementation and functionality of simulating modules are described, such as ModuleBaseScript for the simulation of rigid mechanical connections, HingeBaseScript for axial connections, ServoMotorScript and MotorBaseScript for electric motors and servos, respectively, MicrocontrollerBaseScript, GPIOBaseScript, DataTransferMasterScript for microcontrollers, MotorDriverScript for electric motor drivers, RangeFinderBaseScript for rangefinders and DataTransferSlaveScript for transmission protocols data. The received package of tools was tested, and a virtual model of a mobile robot with a rangefinder was created. The shortcomings of the project have been identified and ways to solve problems have been proposed.
Document Region Segmentation

Documents possess a hierarchy of physical modules which is referred as the structure of the document. Document structure includes both geometrical and logical aspects. Geometrical aspect of a document structure is the geometrical representation of various constituent regions’ boundaries and their spatial arrangements. The logical one refers to the labelling of these regions. For analysing the layout of an input document image, it is essential to extract the geometrical structure of the document first. Therefore, in this chapter, various document region segmentation methods are discussed along with their pros and cons. Additionally, this chapter discusses various standard datasets and different evaluation metrices developed for the evaluation of the said methods.
EcoWallet: A Design Intervention for Promoting Sustainability and Raising Awareness of Environmental Issues

In the modern world that we live in today, innovative and creative solutions are being developed to make daily activities increasingly convenient for us. Design is an omnipresent tool that is being used to bring those solutions to life and revolutionize the world around us. The present project is done with a similar goal of defining the issue of lack of sustainability and proposing an idea of an application, ‘EcoWallet’ which can be used to promote sustainability in individuals of all age groups. The platform is aimed at extrinsically motivating people and promoting sustainability. The idea of EcoWallet is primarily centered around people’s tendency to shop online. EcoWallet could be a platform that would connect people to businesses selling sustainable goods and some non-profit organizations near them. The platform could make it easier for people to shop for sustainable products with great deals, be socially responsible, and be aware of the organizations in need.
Extraction of Information Fields in Administrative Documents Using Constellations of Special Text Points

This chapter considers administrative documents with a simple flexible structure. To analyze the content of documents we propose a model based on constellations of special text points. A special text point is understood as a word unique for some neighborhood of the text. The descriptors of points extracted from the document images by OCR are considered. Local text singularities are defined. A modified normalized Levenshtein distance for comparison of words considering recognition errors is described. To resolve conflicts when compared words are distant in meaning but differ by a small Levenshtein distance, we propose penalty functions for character mismatches in a subset of symbols in words. Constellations are defined as sets of special text points grouped into terms and chains. The representation of keywords and terms as a set of alternatives is specified. An algorithm for matching the set of words of a recognized document and the set of constellation models is described. The experimental verification of the implemented algorithm on private test datasets of administrative documents is provided. The developed method can be applied in modern CAD systems to analyze the content of recognized textual files.
Photometric Stereo with Non-Lambertian Preprocessing and Hayakawa Lighting Estimation for Highly Detailed Shape Reconstruction

In many realistic scenarios, the use of highly detailed photometric 3D reconstruction techniques is hindered by several challenges in given imagery. Especially, the light sources are often unknown and need to be estimated, and the light reflectance is often non-Lambertian. In addition, when approaching the problem to apply photometric techniques at real-world imagery, several parameters appear that need to be fixed in order to obtain high-quality reconstructions. In this chapter, we attempt to tackle these issues by combining photometric stereo with non-Lambertian preprocessing and Hayakawa lighting estimation. At hand of a dedicated study, we discuss the applicability of these techniques for their use in automated 3D geometry recovery for 3D printing.
Self-adaptation Can Improve the Noise-tolerance of Evolutionary Algorithms

Real-world optimisation often involves uncertainty. Previous studies proved that evolutionary algorithms (EAs) can be robust to noise when using proper parameter settings, including the mutation rate. However, finding the appropriate mutation rate is challenging if the occurrence of noise (or noise level) is unknown. Self-adaptation is a parameter control mechanism which adjusts mutation rates by encoding mutation rates in the genomes of individuals and evolving them. It has been proven to be effective in optimising unknown-structure and multi-modal problems. Despite this, a rigorous study of self-adaptation in noisy optimisation is missing. This paper mathematically analyses the runtimes of 2-tournament EAs with self-adapting two mutation rates, fixed mutation rates and uniformly chosen mutation rate from two given rates on LeadingOnes with and without symmetric noise. Results show that using self-adaptation achieves the lowest runtime regardless of the presence of symmetric noise. In supplemental experiments, we extend analyses to other types of noise, i.e., one-bit and bit-wise noise. We also consider another self-adaptation mechanism, which adapts the mutation rate from a given interval. Self-adaptive EAs adapt their mutation rate to the noise level and outperform static EAs in these experiments. Overall, self-adaptation can improve the noise-tolerance of EAs in the noise-models studied here.