Reviewerly/exHarmony huggingface dataset:
Automated Text and Tabular Data Extraction from Scanned Document Images

Currently, many businesses are using a manual approach for digitization of paper-based documents. This is a long and error-prone process and can be significantly sped up by automating the data extraction from the scanned documents. The automation will also significantly reduce the time and effort needed for the data extraction and can be a valuable opportunity for an organization to reduce costs. A solution is proposed which makes use of open-source components that can automate this process with minimal user input. The solution is also capable of handling common problems with images such as image skew and rotation caused by scanning the page at a slight angle or incorrect orientation respectively, along with watermark removal for improved extraction accuracy. It also handles full text pages along with forms or tabular data. The extraction of these can be performed with the help of a configuration file which contains keywords that help in identifying the fields in a form and map them into a structured format, which can be saved as XML and can be used for loading into a database system.
A Sequential Framework Towards an Exact SDP Verification of Neural Networks

Although neural networks have been applied to several systems in recent years, they still cannot be used in safety-critical systems due to the lack of efficient techniques to certify their robustness. A number of techniques based on convex optimization have been proposed in the literature to study the robustness of neural networks, and the semidefinite programming (SDP) approach has emerged as a leading contender for the robust certification of neural networks. The major challenge to the SDP approach is that it is prone to a large relaxation gap. In this work, we address this issue by developing a sequential framework to shrink this gap to zero by adding non-convex cuts to the optimization problem via disjunctive programming. We analyze the performance of this sequential SDP method both theoretically and empirically, and show that it bridges the gap as the number of cuts increases.
Improving Image Tracing with Artificial Intelligence

Image tracing describes the task to convert a raster image into a vector format. This paper investigates whether dimensionality reduction techniques can improve the results of image tracing by producing simpler vector graphic files while keeping image quality reasonably high. In particular, an Autoencoding Neural Network and Principal Component Analysis are investigated as pre-processing steps before performing the actual tracing. Results indicate that using an Autoencoder as pre-processing step before image tracing can reduce SVG file complexity by over 70% with acceptable impact on image quality.
A Mining Software Repository Extended Cookbook: Lessons learned from a literature review

The main purpose of Mining Software Repositories (MSR) is to discover the latest enhancements and provide an insight into how to make improvements in a software project. In light of it, this paper updates the MSR findings of the original MSR Cookbook, by first conducting a systematic mapping study to elicit and analyze the state-of-the-art, and then proposing an extended version of the Cookbook. This extended Cookbook was built on four high-level themes, which were derived from the analysis of a list of 112 selected studies. Hence, it was used to consolidate the extended Cookbook as a contribution to practice and research in the following areas by: 1) including studies published in all available and relevant publication venues; 2) including and updating recommendations in all four high-level themes, with an increase of 84% in comments in this study when compared with the original MSR Cookbook; 3) summarizing the tools employed for each high-level theme; and 4) providing lessons learned for future studies. Thus, the extended Cookbook examined in this work can support new research projects, as upgraded recommendations and the lessons learned are available with the aid of samples and tools.
Auto Complementary Exposure Control for High Dynamic Range Video Capturing

Recent trends in consumer electronics have led to a proliferation of high dynamic range (HDR) video acquisition and transmission studies. High-end video capturing systems increase the bit depth of each pixel to adapt to the high-contrast ambient light. However, the extended bit depth is pathetically meager compared with the far wider dynamic range of the incident light. What’s more, incompatibility issues for the subsequent compression and transmission system may be arisen along with the increased bit depth. In this paper, we propose an auto complementary exposure (ACE) algorithm for optimal exposure parameter estimation under Siamese Trigger (ST) mode to maximize the entropy of the output HDR image. With the proposed algorithm, neither the sensor nor other related hardware needs to be modified. Only the auto-exposure algorithm in the firmware needs to be updated. In the proposed ACE algorithm, every two consecutive images are treated as Siamese twins, and the exposure parameters for the twin images are calculated simultaneously. The target of the ACE algorithm is not to maximize the entropy of each image but to maximize that of the fused image. Compared to existing auto exposure or bracketed fusing algorithms, the proposed algorithm has three significant advantages. Firstly, no extra bit-depth is needed to embrace the details of the scene. Thus the image transmission system is not affected. Secondly, only two raw images are necessary for one output fused HDR image. The computation load is relatively low. Thirdly, the proposed method can fit video recording, which has a broader application prospect than HDR image capturing. Experiment results on a self-built database and publicly accessible database demonstrate that the algorithm works well under various types of scenarios. Furthermore, the experiment results on the real-time system show that the captured video image reveals many details under high dynamic scenes.
A New Deep Learning Method for Multi-label Facial Expression Recognition based on Local Constraint Features

Human emotions always have been reflected by the facial expression. In recent year, the facial expression recognition has been found that it can be treated as a multi-label task and some databases (such as JAFFE, FER+, RAF-ML.) which include information of multi-label facial expression also have been utilize to address relate issue. Simultaneously, some deep learning methods also be used to solve multi-label facial expression task, such as VGG13 and Deep Bi-Manifold CNN (DBM-CNN). But there are also have many weakness such as the inaccurate recognition of multi-label expressions. To overcome this drawback, a novel Deep learning with local constraint framework, called DL- LC framework, is proposed. The proposed framework will use the MTCNN as an implement to crop the local constraints features which include the infromation of facial expression. And the ResNet18 has been applied as a backbone network to extract the feature from the global and local constraint images, which can get more details of original image after incorporating local constraints in this new framework. The effectiveness of this model has been testified through rigorous experiments in this study. Comprehensive analyses reveal that, this model is outperform the recent state-of-the-art approaches for multi-label facial expression recognition.
Learning Associations between Features and Clusters: An Interpretable Deep Clustering Method

Clustering is a challenging problem when many features are irrelevant to separate clusters. Also, different clusters may relate to various feature subsets. This work proposes a deep clustering algorithm that localizes the search for instance clusters and their relevant features. The relevant features of each cluster are defined as those with high associations (dependency) within that cluster. Given the number of clusters <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> , we formulate the problem as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> . -parallel auto-reconstructive learning, where low-rank graph learning, rooted in graph Laplacian theory, is used to explore the unknown feature associations of each cluster. The model performs automatic feature weighting on residuals to minimize loss from the corresponding cluster. Through such design, different feature subsets can be learned to calculate the loss from different clusters. Subsequently, the associations between features and clusters can be acquired, and better clustering result can be achieved. Moreover, the associated features of each cluster can be used to interpret the clustering patterns.
Securing Arabic Contents Algorithm for Smart Detecting of Illegal Tampering Attacks

The most common digital media exchanged via the Internet is in text form. The Arabic language is considered one of the most sensitive languages of content modification due to the presence of diacritics that can cause a change in the meaning. In this paper, an intelligent scheme is proposed for improving the reliability and security of the text exchanged via the Internet. The core mechanism of the proposed scheme depends on integrating the hidden Markov model and zero text watermarking techniques. The watermark key will be generated by utilizing the extracted features of the text analysis process using the third order and word level of the Markov model. The Embedding and detection processes of the proposed scheme will be performed logically without the effect of the original text. The proposed scheme is implemented using PHP with VS code IDE. The simulation results, using varying sizes of standard datasets, show that the proposed scheme can obtain high reliability and provide better accuracy of the common illegal tampering attacks. Comparison results with other baseline techniques show the added value of the proposed scheme.
Exploration and Practice on Blended Teaching of Mathematical Analysis with Information Technology

Educational informatization has entered a new stage of development. With introducing information technology such as computers and internet into higher education, online teaching combining modern information technology and teaching activities has not only promoted the innovation of teaching models, but also created a new learning environment. Mathematics analysis is a very important basic course for mathematics majors. Based on the construction of the course online platform, this study examines instructor strategies for designing and participating in the blended teaching model, which integrates learning online and traditional classes. The experimental results show that the blended teaching of mathematics analysis influences students’ course participation and performance, plays an important role in promoting the development of students’ personality, cultivating the innovative spirit and mathematics core literacy.
Secure Data Model for the Healthcare Industry in Ecuador Using Blockchain Technology

Patients’ and doctors’ mistrust of data security in third-party information storage is a latent and relevant problem in the healthcare sector in Ecuador. This paper presents a data model to improve information security in Ecuador’s healthcare sector by using Blockchain technology. A descriptive research is conducted using an exploratory-deductive, qualitative methodology to analyze relevant studies referencing Blockchain and Smart Contract technologies in the healthcare industry. The results of the study present the definition of participants and roles for a Blockchain-based healthcare model in Ecuador, a conceptual architecture to improve information security in the healthcare sector and impact factors. It is concluded that Blockchain technology allows sharing information securely; the proposed model is developed in the Hyperledger platform with 56% of information analyzed remaining as a private network.
On Constructions of One-Lee Weight Codes Over Z₄

Let Z4 be the integer ring of residue classes modulo 4. In this paper, we construct four infinite families of Z4-codes with one nonzero Lee weight by their generator matrices. Furthermore, we study the linearity of their Gray images and obtain a family of optimal binary codes.
Implementation of Service-Oriented Architecture for Static and Dynamic Objects Interval Modeling Software

Construction of interval models of static and dynamic systems based on methods of analysis of interval data leads to the formation of common structural elements, based on the specifics of their use for specific subject areas. The use of common structural elements in their software implementation inevitably leads to duplication and redundancy of program code.This problem can be solved by creating a single software package for computer simulation of static and dynamic systems implemented on the algorithms of the bee colony with guaranteed accuracy.This article focuses on the implementation of software architects, which includes requirements analysis, implementation of use cases, as well as information software. The article also describes the specifics of the implementation of service-oriented software architecture, as one of the options for using the developed database of model experiments for use in various subject areas.
Story Visualization: Generation of Scenes sequentially for a given fable using NLP and Image Processing

Humans have a usual tendency to paint pictures in their minds when a story is being told. Undoubtedly, a picture conveys information more effectively than words. So it is necessary to instill visualization capability at an early age. In this project Story Visualization, we have aimed to improve the visualization of fables along with narration of the story. This is achieved by performing pronoun replacement to enhance story character extraction and improving fragmentation of the story to accurately depict the story, which is received as input in the form of text. The extracted attributes for scene generation are then sent to the image processing model which chooses appropriate images from the labeled dataset, transforms them relatively, and positions them based on grids, thereby generating a meaningful scene that nearly depicts the origin segment. Furthermore, the story is narrated, and the interactions between the story characters are uniquely voiced while they are exhibited through speech balloons. The comprehensive website presented abstracts NLP model, image processing model, and TextToSpeech model while giving a fairly simple user interface for users to interact with. To cover a broader audience, we have experimented in Indian languages and as proof of concept, Kannada language fables are visualized.
Intelligent Recognition of Digital Archives of Application and Installation in Power Business Expanding Based On Image Recognition Technology

The existing intelligent recognition methods have the problem of fuzzy characteristics of digital archives, resulting in slow recognition speed. A digital archives intelligent recognition method based on image recognition technology is designed. Optimize the application and installation in power business expanding, quantify the information flow, select the attribute with the maximum information gain rate as the new node of the decision tree, extract the digital file features, construct the signaling recognition layer of power supply enterprises with image recognition technology, find the port mapping table, and formulate the intelligent recognition mode according to the linear summation function attribute.
Throughput-based Service Composition

Web Service Composition has been studied intensively in the past two decades, considering the many aspects of the problem, like: semantic modeling, general QoS metrics, services state, and much more. In this paper, our focus is on integrating throughput metric with the automation formalism, without any limitation on automation, i.e. allowing full automation with meaningful throughput propagation. The user request and each service in the respiratory are described by their throughput values that are: an extra requirement for the request; and a maximum allowed load for services. In the proposed version, it is possible that to satisfy a request’s throughput, multiple traditional compositions are used concurrently, adding up for the required throughput. This highly increases the problem complexity both in how to model the combining throughput values, and also computationally, as the algorithms are required to search for multiple compositions. However, we also propose an algorithm that reduces the original problem to multiple constraint satisfaction problem instances and can find compositions for requests and considerable-sized repositories, which we prove through a synthetic evaluation.
Research on Cost-sensitive Classification Methods for Imbalanced Data

This paper is concerned with the class imbalance problem which has been known to hinder the learning performance of classification algorithms. The problem occurs when there are significantly more training samples available for some classes than for others. Various real-world datasets suffer from this phenomenon. The cost-sensitive classification algorithm has shown good classification performance on imbalanced datasets. Besides, active learning has shown potential for solving the class imbalance problem by providing the classifier more balanced datasets. In this paper, we propose a novel hybrid algorithm combining active learning and cost sensitive classification within the Metacost framework. Based on the selection strategies in active learning, our method can construct more informative balanced datasets for each base classifier in the Metacost framework, which can improve the classification performance. Our experimental results show that the proposed algorithm can achieve more competitive prediction performance in terms of Recall, G_mean and Area Under roc Curve.
A Real-time Inference Method of Graph Attention Network Based on Knowledge Graph for Lung Cancer

The continuous increase of medical data promotes the development of knowledge graphs in specific fields. Currently, lung cancer ranks first in terms of morbidity and mortality. In the context of medical intelligence, the top-down and bottom-up methods are combined to semi-automatically construct a knowledge graph of lung cancer. The constructed domain knowledge graph is usually incomplete and lacking the relationship between entities. Therefore, the paper presents a real-time reasoning method of graph attention network to automatically learn reasoning from knowledge graph. This method not only considers neighbor information, but also introduces an attention mechanism, and it can make full use of all nodes and edges in the knowledge graph. The convergence time is reduced by changing the activation function and loss function in the network. This paper adopts three traditional data sets and a new data set for experiment comparison. The experiment results show the proposed method has good performance.
An Approach of Utilizing Exponential Rank and In-Inverse Closeness Centrality on Recommender Systems

Following the recent trends and involvement of social networks in practically every field of life, this work represents an approach to create a recommender system that utilizes social network analysis (SNA) trustworthiness metrics in our two-mode network, which is composed by taking into account social and touristic profiles of the users. By using Exponential Ranking and In-Inverse centrality metrics on ranking the recommendations, we achieved feasible results on satisfaction levels for the proposed touristic locations.
Structure Adaptive Filtering for Edge-Preserving Image Smoothing

In this paper, we propose a new edge-preserving image smoothing technique. A simple and effective scheme that classifies a pixel as situating on a corner, an edge or a plane has been developed. For the central pixel to be processed, nine adjacent support regions are constructed, leading to nine dimensional variation. Then the selected support region is adaptively determined by the coefficient of variation and variance, and finally the center pixel is updated iteratively according to the selected support region. More specifically, we show that a pixel at a location with very small variation is very likely situating on a plane (a smooth region). Otherwise, When the coefficient of variation is larger than the mean, then it is likely an edge pixel, otherwise it is a corner pixel. We adaptively select the appropriate filtering windows based on the local image structures to achieve excellent edge-preserving image smoothing. We present experimental results to show the effectiveness of our new technique.
Automatic Warning System for Drivers using Deep Learning Algorithm

The problem related to traffic is a complex one requiring proper design and planning for developing an efficient solution. Drivers are expected to pay attention to identify traffic signs, interpret and follow them while driving. Many of the accidents occur because drivers lose their attention and ignore the street signs. An automatic system in a car which detects, recognizes, interprets and gives warning to the driver would be a great help in reducing the number of road accidents. This paper proposes an embedded system based on Raspberry Pi 3 and uses CNN (Convolution Neural Network) algorithm to detect and identify the captured traffic signs and activate the motor accordingly. Among the different activation functions ReLu is better but its derivative is zero fo r negative values. Hence in this paper a new activation function is proposed which gives a better accuracy, less loss and execution time when compared with ReLu. The detection of traffic sign boards will send the output signal to PIC (Peripheral Interface Controllers) microcontroller which will compare the input value from Raspberry Pi with the rotating speed of DC motor. According to the control signal from PIC microcontroller, servomotor turns 45 deg for controlling ABS system and the speed of the vehicle is thus reduced when a sign board is detected. LCD displays the speed in which the vehicle is running. The CNN model with proposed activation function gives an accuracy of 93% with sensitivity and specificity above 70% and 55% respectively.
Analysis of Risk Factors in the Scope of Distributed Software Team Structure

The structure of a distributed software team represents a type of development that enables team members to be available at different remote locations throughout the software lifecycle.Therefore, it forms a network of remote sub-teams.Nowadays, the development of software projects with geographically remote teams obligates flexible software development environments.In this case, it is of great significance for software managers to organize and administer the distributed team management well.With the inclination of distributed teams across countries and regions, more stakeholders are being brought onto the scene of software projects.While risk management is the most important assurance of obtaining the expected result from the project, these processes should be considered the common responsibilities and commitment of the stakeholders involved in the project in all fields.The 'in-house' development of the software poses a series of risks.Meanwhile, its partial development in another geography causes the risks in question to increase exponentially.The management of risks should start at the initial stages of the project and continue as a monitoring and follow-up activity throughout the life-cycle of projects.Within the scope of this study, the process steps developed to manage the main risk factors in the distributed team structure in accordance with the Capability Maturity Model Integration (CMMI) instead of the conventional project management approach are explained in detail.Moreover, this study aimed to reveal the components involved in the risk management process regarding the distributed software development.