Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised OCR Model Evaluation Using GAN

Optical Character Recognition (OCR) has achieved its state-of-the-art performance with the use of Deep Learning for character recognition. Deep Learning techniques need large amount of data along with ground truth. Out of the available data, small portion of it has to be used for validation purpose as well. Preparing ground truth for historical documents is expensive and hence availability of data is of utmost concern. Jenckel et al. jenckel came up with an idea of using all the available data for training the OCR model and for the purpose of validation, they generated the input image from Softmax layer of the OCR model; using the decoder setup which can be used to compare with the original input image to validate the OCR model. In this paper, we have explored the possibilities of using Generative Adversial Networks (GANs) gan for generating the image directly from the text obtained from OCR model instead of using the Softmax layer which is not always accessible for all the Deep Learning based OCR models. Using text directly to generate the input image back gives us the advantage to use this pipeline for any OCR models even whose Softmax layer is not accessible. In the results section, we have shown that the current state of using GANs for unsupervised OCR model evaluation.
Can Domain Theory Combined with the Resource-Based View Demonstrate the Missing Link in IT Value Creation?

The purpose of this paper is to apply the rationale of Domain Theory (DT) with the taxonomy of the Resource-Based View (RBV) to explain how information technology (IT) creates value. Based on literature, a conceptual model was developed, which is tested in a case study. Findings demonstrate that both theories combined are a novel way to describe the IT value creation. Particularly, breaking down the IT value construct into three domains, each described by functions and properties, demonstrates the IT value creation. Describing the IT value creation by functions and properties enables practitioners to both highlight the impact of IT and derive meaningful measurements to prove the value of IT. This research contributes to literature by applying the rationale of DT to describe the IT value creation.
A Meaningful Information Extraction System for Interactive Analysis of Documents

This paper is related to a project aiming at discovering weak signals from different streams of information, possibly sent by whistleblowers. The study presented in this paper tackles the particular problem of clustering topics at multi-levels from multiple documents, and then extracting meaningful descriptors, such as weighted lists of words for document representations in a multi-dimensions space. In this context, we present a novel idea which combines Latent Dirichlet Allocation and Word2vec (providing a consistency metric regarding the partitioned topics) as potential method for limiting the "a priori" number of cluster K usually needed in classical partitioning approaches. We proposed 2 implementations of this idea, respectively able to: (1) finding the best K for LDA in terms of topic consistency; (2) gathering the optimal clusters from different levels of clustering. We also proposed a non-traditional visualization approach based on a multi-agents system which combines both dimension reduction and interactivity.
The Effectiveness of Rolling Stock Maintenance on Quality Assurance at the Largest South African Rail Company

This paper presents the results of the evaluation of the effectiveness that rolling stock maintenance (RSM) has on quality assurance (QA) at the largest rail, port and pipeline company in South Africa, as a case study conducted at its Koedoespoort depots and factories. Using a quantitative approach descriptive in nature, the researchers aimed to gain an insight into the research problem and to investigate the effectiveness RSM has on QA at freight rail company. Data was collected from a sample of 30 employees randomly selected at the engineering division. The evidence collected shows that the engineering division does not have a criterion to monitor the effectiveness of the current maintenance plan, the division does not have a maintenance system that is well understood by the artisans as well as technicians. The recommendations emphasized on a continuous training program on quality planning and implementation for the whole engineering division to ensure that the proposed maintenance strategy delivers as expected right the first time.
ReS2TIM: Reconstruct Syntactic Structures from Table Images

Tables often represent densely packed but structured data. Understanding table semantics is vital for effective information retrieval and data mining. Unlike web tables, whose semantics are readable directly from markup language and contents, the full analysis of tables published as images requires the conversion of discrete data into structured information. This paper presents a novel framework to convert a table image into its syntactic representation through the relationships between its cells. In order to reconstruct the syntactic structures of a table, we build a cell relationship network to predict the neighbors of each cell in four directions. During the training stage, a distance-based sample weight is proposed to handle the class imbalance problem. According to the detected relationships, the table is represented by a weighted graph that is then employed to infer the basic syntactic table structure. Experimental evaluation of the proposed framework using two datasets demonstrates the effectiveness of our model for cell relationship detection and table structure inference.
Digitalization: Size Doesn't Matter, Put Focus on Product-and-Service, Not on Process

Digitalization is opening new era in business as it provides clear advantages for companies to make the whole value chain customer and data centric, innovative, better quality, more convenient working conditions, less labor demanding, and more efficient. Despite of long list of potential benefits it is not known how good companies are to implement them and how it affects the performance. Quantitative data from 132 Estonian companies is used to reveal the impact. Estonia is European Union country known for high innovativeness and companies' emphasis on process improvements. Results indicate that process digitalization is not as important as product/service digitalization. Managerial decisions have more effect than staff activities. Impact on performance is surprisingly marginal. There is no size related variance.
Post-OCR Error Detection by Generating Plausible Candidates

The accuracy of Optical Character Recognition (OCR) technologies considerably impacts the way digital documents are indexed, accessed and exploited. Post-processing approaches detect and correct remaining errors to improve the quality of OCR texts. However, state-of-the-art approaches still need to be improved. Most of the existing post-OCR techniques use predefined error position lists or apply simple techniques to detect errors. In this paper, we describe a novel error detector using different features from character-level (including character noisy channel, index of peculiarity) to word-level (such as frequencies of n-grams, skip-grams, part-of-speech) Experimental results show that our approach outperforms the best performing techniques in the ICDAR 2017 Competition on Post-OCR text correction.
Full Factorial Design of Experiment Approach to Quantify the Effect of Forming Parameters on Wrinkling Effect of Deep Drawn Cylindrical Cups

With the advancement of the manufacturing industry and the increase in competition, the need of quality has become an ever-growing challenge in metal forming industry. Deep drawing is a typical sheet metal forming process which is widely used to manufacture thin walled automotive panels, aircraft body panels and utensils. Blank holding force, drawing ratio, lubrication and die profile are few key process parameters that effect on the quality of the final product. Since the complicated interaction of these forming parameters, deep drawn parts are susceptible to various defects such as ironing, earing and thinning. Wrinkles are a common defect in sheet formed products. This study focuses on improving the quality of deep drawing products by minimizing the formation of wrinkles. The effect of Blank Holding Force (A), Blank Sheet Thickness (B) and Punch Force Applying Rate (C) were considered. Full Factorial Design of Experiments (DOE) conducted by simultaneously changing each parameter. Wrinkles were measured using surface roughness machine. Results shows that there is a significant interaction between Blank Sheet Thickness and Blank Holding Force at 5 % significance level. Results were validated by conducting several confirmation runs with optimum parameter settings.
A Case Study of Intellectual Property Rights Management with Capability Maturity Model

Capability Maturity Model, CMM, was proposed to assess the software development organizations. In a company, the nature of an Intellectual Property Rights Management (IPRM) Department is like the organization of software development. It is possible to use CMM for assessing the performance of the IPRM Department. The IPRM Department in Company A is responsible for handling US patent applications filing. The department is a professional organization and the performance is relatively difficult to assess. In our case study, the Capability Maturity Model was introduced into the department to assess the performance. The schedule and cost of the US patent applications is controlled and measurable.
A Semi-supervised Approach for Steam Turbine Health Prognostics Based on GAN and PF

Steam turbines are indispensable part of power plants, they will cause serious impact once fail. Health prognostics is an effective technology to keep steam turbine in healthy condition and avoid unnecessary losses. This paper proposed a semi-supervised framework for health prognostics on steam turbines. The framework is based on Generative Adversarial Networks (GAN) and Particle Filter (PF). In this framework, anomaly detection and remaining useful life (RUL) prediction are conducted. During anomaly detection, a health index (HI) is constructed to indicate steam turbine operation conditions and an anomaly threshold is used to detect anomalies. During predicting the RUL, an approach based on PF is employed to predict the RUL. Real case are demonstrated to validate the effectiveness of the proposed framework. Results indicate that the proposed framework has good performance on anomaly detection and RUL prediction.
Semantic and Interaction: When Document Image Analysis Meets Computer Vision and Machine Learning

Researchers in the DIA field have proposed different interesting techniques for the extraction of elements of contents inside images at different levels: lexical, syntactical, semantical. Besides, different knowledge representation techniques have been involved for the analysis of relations between them. From these observations, it seems very interesting to propose a comparative study showing the increasing similarity between CV and DIA problems and discussing the recent trends that have been or can be proposed in a short future to try to deal more efficiently with semantic and user interaction in these fields (computer vision and robust reading). By selecting and explaining interesting recent works from the CV and DIA communities, this paper tries to bring answers to the following questions: Can we do more than automatic features selection with CNN based architectures? Can we reach a more semantical point of view on the data? What are the solutions to define adaptable semantic models that can be easily learned or user-defined? How can we get more generic systems by combining machine learning & user interaction?
Semi-Synthetic Data Augmentation of Scanned Historical Documents

This paper proposes a fully automatic new method for generating semi-synthetic images of historical documents to increase the number of training samples in small datasets. This method extracts and mixes background only images (BOI) with text only images (TOI) issued from two different sources to create semi-synthetic images. The TOIs are extracted with the help of a binary mask obtained by binarizing the image. The BOIs are reconstructed from the original image by replacing TOI pixels using an inpainting method. Finally, a TOI can be efficiently integrated in a BOI using the gradient domain, thus creating a new semi-synthetic image. The idea behind this technique is to automatically obtain documents close to real ones with different backgrounds to highlight the content. Experiments are conducted on the public HisDB dataset which contains few labeled images. We show that the proposed method improves the performance results of a semantic segmentation and baseline extraction task.
Bernardo Autonomous Emotional Agents Increase Perception of VR Stimuli

Video games are high emotional vectors. They play with the emotions of players by eliciting and increasing them. The importance of the induction of basic emotions has been a long forestay and is favoured by video game publishers, as they are quite easily mobilized. Video game publishers look to produce more complex social emotions like empathy, and compassion. In games framework with narrative context, designers frequently use cinema movies methods, like cinematic non-interactive Cutscenes. These methods temporarily exclude the player from interactivity to leave his first viewpoint view and move the camera focusing on the narrative stimuli. Cutscenes were used abundantly and are now rejected, the new development wave is often trying to develop in a “zero cinematic” way. For the same reason, cinematics are also not usable in new Virtual Reality. If VR games and simulations provides a high level of presence, VR environments needs certain rules related in particular to the continuation of free will and the avoidance of possible Break in Presence. We propose in this paper a concept of Emotionally Intelligent Virtual Avatars, which when they perceive an important narrative stimulus, share their emotions through, gestures, facial nonverbal expressions, and declarative sentences to stimulate the player's attention. This will lead players to focus on the narrative stimuli. Our research studies the impact of the use of Bernardo Agents Emotional Avatars involving n = 51 users. The statistical analysis of the results shows a significant difference in the narrative perception of the stimuli and in Presence, correlated to the use of Agents Bernardo. Overall, our emotional Agent Bernardo is a unique concept for increasing the perception of narrative stimuli in virtual environments using HMD, and may be useful in all virtual environments using an emotional narrative process.
Factors that Influence Sharing Behaviors in Sharing Economy Based on the Theory of Social Capital and Social Exchange: Example of Taiwan-Based USPACE

Sharing economy has been a phenomenon in many countries for years owing to a number of factors such as the economic recession which contributes to a pervasive thinking of renting over buying. This paper examines the sharing behavior in sharing economy based on social capital theory and social exchange theory, and looks at the example of USPACE, a start-up company providing shared parking services in metropolis, Taiwan. The research covers the urban areas in Taipei and Kaohsiung and collects data of USPACE users. A total of 160 copies of questionnaire have been distributed and 151 valid copies returned, a valid response rate of 94.4%. The Partial Least Squares (PLS) has been used for empirical analysis. The findings show that trust and identification will significantly affect the intention to share, so will extrinsic rewards and the enjoyment in helping others, while norms and reciprocity do not have significant impacts on the sharing intention. The results not only extend the application of social capital and social exchange theory, but also provide directions for future studies from sharing economy's perspective of various industries.
Recognition of Barriers in Brownfield Redevelopment PPP Project

The brownfield redevelopment projects (BRPs) have attracted much attention due to the increasing shortage of land resources. Public-Private-Partnership (PPP) model is an effective way for local governments to carry out infrastructure projects. However, when PPP is applied to the brownfield redevelopment, the project is still delayed or interrupted due to many factors. This paper uses literature research, AHP and Fuzzy-DEMATEL method to construct a set of barriers related to brownfield redevelopment PPP project, and identifies four main barriers: inefficiency of government approval, variability of government decision-making, insufficient management ability of enterprise PPP project, lack of professionals and institutions. This paper lays a theoretical foundation and provides practical guidance for brownfield redevelopment PPP application in order to promote China brownfield redevelopment PPP project process.
Case Study: A Semi-Supervised Methodology for Anomaly Detection and Diagnosis

In this paper, a semi-supervised methodology for anomaly detection and diagnosis is proposed. The approach combines techniques of non-parametric statistics, quality control, and deep learning to provide a tool that allows an adequate and online detection of faults in a production system and a diagnosis of the factors associated with the failure. We propose a semi-supervised neural network for detection and a particular control chart called Open Up for the diagnosis. This neural network is composed of the adjustment of an autoencoder followed by a Long Short-Term Memory model (LSTM). Open Up is used in the last stage to identify the variables associated with the anomaly. This proposal achieves a high correct classification rate using real data of a monitoring system in paper manufacturing and simulated data from the Tennessee Eastman Process.
A homozygous mutation in CMAS causes autosomal recessive intellectual disability in a Kazakh family

Intellectual disability (ID) describes a wide range of serious human diseases caused by defects in central nervous system development and function. Some mutant genes have been found to be associated with these diseases, but not all cases can be explained, thus suggesting that other disease‐causing genes have not yet been discovered. Sialic acid is involved in a number of key biological processes, including embryo formation, nerve cell growth, and cancer cell metastasis, and very recently it has been suggested that N‐acetylneuraminic acid synthase‐mediated synthesis of sialic acid is required for brain and skeletal development. CMP‐sialic acid synthetase (CMAS) is one of four enzymes involved in NeuNAc metabolism, as it catalyzes the formation of CMP‐NeuNAc. Before the present study, no links between mutations in CMAS and incidences of human ID had been reported. In the current study, we recruited a recessive nonsyndromic ID pedigree with consanguineous marriage in which all patients have typical clinical manifestations of ID. We identified the NM_018686. 3:c.563G > A (p.Arg188His) substitution in CMAS as being responsible for the disease in this family. Conservation analysis, structural prediction, and enzyme activity experiments demonstrated that (p.Arg188His) influences protein dimerization and alters CMAS enzyme activity. Our results offer a new orientation for future research and clinical diagnosis.
Neuroimaging of Language: The Contribution of fMRI

Since the start of contemporary neuroimaging techniques during the late 20th century, analysis of the brain’s organization of language has represented one of the major topics of research [1]. Different types of neuroimaging procedures have been developed to analyze both morphology and brain activity. Although functional Magnetic Resonance Imaging (fMRI) represents the main neuroimaging research and clinical technique, other procedures including Positron Emission Tomography (PET), Magneto-Encephalography (MEG), Event Related Potentials (ERP), Diffusion Tensor Imaging/Tractography DTI-Tractography, and Transcranial Magnetic Stimulation, are also frequently used [2]. However, because of its versatility and lack of invasiveness fMRI has become particularly popular during the last decades and dozens of research studies are continuously published worldwide [3]. Furthermore, fMRI has opened new research approaches, in particular the study of resting-state-fMRI (rs-fMRI) [4]. Because of these reasons, most of the content of this paper will be devoted to fMRI. Alfredo Ardila1* and Byron Bernal2 1Department of Communication Sciences and Disorders, Florida International University, Miami, USA 2Radiology Department / Brain Institute, Nicklaus Children’s Hospital, Miami, USA *Corresponding author: Alfredo Ardila, Department of Communication Sciences and Disorders, Florida International University, Miami, USA, Email: ardilaa@fiu.edu
LambdaNet: Probabilistic Type Inference using Graph Neural Networks

As gradual typing becomes increasingly popular in languages like Python and Typescript, there is a growing need to infer type annotations. While type annotations help with tasks like code completion and static error catching, these annotations cannot be fully inferred by compilers and are tedious to annotate by hand. This paper proposes a probabilistic type inference scheme for Typescript based on a graph neural network. Our approach first uses lightweight source code analysis to generate a program abstraction called a type dependency graph, which links type variables with logical constraints as well as name and usage information. Given this program abstraction, we then use a graph neural network to propagate information between related type variables and eventually make type predictions. Our neural architecture can predict both standard types, like number or string, as well as user-defined types that have not been encountered during training. Our experimental results show that our approach outperforms prior work in this space by 14% (absolute) on library types, while having the ability to make type predictions that are out of scope for existing techniques.