Reviewerly/exHarmony huggingface dataset:
Automation of Business Cards

Business card is shared as hardcopy, the data present in business card will be highly useful if it is available in digital format. The task of manually entering the details of all business cards is laborious and time-consuming. Document image analysis is used in this paper for automating this process. This will be accomplished by performing OCR and then using the text to extract the Meta data. One more important component of business card is the logo of the organization. The text extraction OCR will be done using the Tesseract API. After conversion of the image to text, the data will be saved in the database. The raw data will be saved in the database, which will later be segregated and stored in the appropriate fields. It is generally ignored in the process of saving text information, in this paper it is extracted and stored in database. For logo detection various techniques like Gabor Filter, Harris edge detection technique, MSER, etc., are compared to determine the best technique for acquiring the most accurate logo extraction. Gabor filter gives the best result is used for the extracting logo and storing in database. Java language on NetBeans IDE platform which use the Spring MVC framework is used to implement this work.
Pilot experiment of a 2D trajectory representation of quaternion-based 3D gesture tracking

This paper presents ongoing research work on developing a protocol framework for human motion recognition using complex and continuous 3D motion data into more intuitive 2D trajectory representation based-on the quaternion visualization. Quaternions are very compact and free from gimbal lock for representing orientations and rotations of objects in 3D space. In this study, the focus is only on the arm orientation and not the position. In our pilot experimental evaluation, we examine our approach to visually recognize several biceps curl using quaternions data collected using wireless inertial sensors attached to the human arm. The results of the analysis indicate that the proposed framework makes it possible to represent 3D motion data in the form of a 2D trajectory for continuous motion patterns.
Video-based Person Re-identification Based on Feature Learning of Valid Regions and Distance Fusion

Abstract Video-based person re-identification (re-id) is a very challenging problem because of the occlusion and the changes of viewpoints, pedestrian postures and illumination. Most existing methods of video-based person re-id usually concatenates the extracted appearance and space-time features directly, which do not consider the discrepancy of different features fully. In order to deal with this problem well, we propose a simple but effective method based on feature learning of valid regions and distance fusion, which combines three distances. The first distance is local distance of valid regions calculated using the Gaussian of Gaussian(GOG) feature. Pedestrian images are divided in horizontal directions, and then regions with smaller distances are selected as valid ones to be reserved and regions with larger distances are as invalid ones to be removed due to occlusions and posture changes. The other two distances are obtained by independent metric learning using the histogram of oriented gradient 3D (HOG3D) feature and the Local Maximal Occurrence (LOMO) feature. The three distances are added as the final distance between pedestrian pair, so the matching rank of gallery is obtained. Extensive experiments are performed on the iLIDS-VID and PRID-2011 datasets, and the results prove the effectiveness of our methods.
Identification of fuzzy blocks using sum distance metric

— In this paper, new concepts are introduced to enhance the process of block identification in fuzzy graphs using sum distance metric and special type of cycles, which is called a strongest strong cycle, locamin cycle. An Algorithm is developed for retrieving information from Big-Data using block i dentification.
Novel Texture Descriptor Family for Face Recognition

This paper presents a novel image descriptor family. The shaped local binary patterns are an extension of the popular local binary patterns (LBPs). It takes into consideration larger neighbourhoods of the central pixel. The main novelty is that this descriptor allows using varying shapes of the neighbourhood instead of just one point. This property ensures better robustness and gives the opportunity to fine-tune the descriptor for a given task. We evaluate the descriptor on the face recognition task in the frame of an application for recognition of real-world face images. The results on two standard face corpora show improved performance over the basic LBP method.
Adaptive Recognition for Tracking of Moving Objects

An adaptive multilocator for online recognition and tracking of moving objects is considered. The vision system is based on the fast real time sub image localization module aimed to recognize patterns in order to track the motion of the selected object. In order to reduce mistakes concerned with the identity, the system is able to recognize several similar objects simultaneously. Nevertheless, the operator can point out an object on the screen to correct the tracking process. An adaptation of the actual patterns according to the lastly localized objects in the area of interest is performed in each step of the real time process. The aim of the paper is to present an effective adaptation and correction procedure based on constant primary patterns defined by the operator and varying secondary patterns defined by the automatic adaptation.
A comprehensive intelligent compression method on DICOM images

Virtual medical imaging has overgrown in recent years and later implemented in such situations - all of the radiological modalities which include Computed tomography (CT scanners), Magnetic resonance imaging (MRI), ultrasound (US), positron emission tomography (PET), X-Ray (radiographs) made employing more than one providers and hosted by one or many websites which communicated over the DICOM network. The DICOM images required huge hard disk space and excellent transfer speed, which need to compress the DICOM images for effective capacity and transmission over the internet. The compression process is applied through utilized a recurrent neural network algorithm establishing Trainscg as the activation function. Extraordinary high-quality metrics like mean squared error (MSE), Peak signal-to-noise ratio (PSNR), Compression ratio (CR), and Compression time are computed on several medical test images. The proposed compression method shows better experimental results than the existing techniques based on performance parameters except for the compression time for the large image only.
Multi-sensor cross cueing technology based on grey wolf algorithm

Abstract In order to solve the problem of poor convergence and low precision of the current Multi-sensor Detection Network algorithm, a multi-sensor cross cueing technology based on Grey Wolf algorithm is proposed. By endowing the grey wolf with autonomous decision-making ability, the algorithm can jump out of the local optimal solution as soon as possible, and the convergence degree of the algorithm is improved. The simulation results show that the algorithm can find the optimal solution in a short time and maintain good stability, and the optimization ability is further strengthened. By comparing with other algorithms, it is more effective to prove that the algorithm has better solution quality.
22‐3: 2400 Nits Edge‐Lit LCD and Adaptive EOTF for HDR and Brilliant Images

For displaying HDR images, an Edge‐Lit LCD was amended and achieves 2400 nits brightness. A new adaptive EOTF combining the strength of Gamma function and Barten CSF is developed. Based on brilliance analysis, dull images are displayed at lower power without glaring viewers, while brilliant images appear vivid and naturalistic.
Research on Mutual Information Registration Method Based on Gradient Edge Information

Aiming at the large difference of information of multi-source images, this paper proposes a registration method based on edge gradient information and normalized mutual information as the similarity criterion, and uses pyramid decomposition method to optimize the speed of the algorithm. The infrared image and the visible light image of the same scene are used as multi-source images, and the registration experiments are carried out. It is proved that the algorithm can achieve multi-source image registration.
Differential Evolution Trained Fuzzy Cognitive Map: An Application to Modeling Efficiency in Banking

In this work, we developed a Differential Evolution (DE) trained Fuzzy Cognitive Map (FCM) for predicting the bank efficiency. We developed two modes of training namely (i) sequential and (ii) batch modes. We compared the DE trained FCM models with the conventional Hebbian training in both modes. We employed Mean Absolute Percentage Error (MAPE) as an error measure while predicting the efficiency from Return on Assets (ROA), Return on Equity (ROE), Profit Margin (PM), Utilization of Assets (UA), and Expenses Ratio (ER). We employed 5x2-fold cross-validation framework. In the first case i.e. sequential mode of training, the DE trained FCM statistically outperformed the Hebbian trained FCM and in the second case i.e. batch mode of training, DE trained FCM is statistically the same as the Hebbian trained FCM. To break the tie in the batch mode, the training time is compared where DE trained FCM turned to be 19% faster than the Hebbian trained FCM. The proposed model can be applied to solving similar banking and insurance problems.
Implementing Hartmann type null screens to test cemented doublet lenses

A new design of Hartmann type null screens to test qualitatively cemented doublet lenses is presented. The designs of these null screens are based on the knowledge of caustic surface and an exact ray tracing.
SMC: Single-Stage Multi-location Convolutional Network for Temporal Action Detection

Temporal action detection in untrimmed videos is an important and challenging visual task. State-of-the-art works always adopt a multi-stage pipeline, i.e., a class-agnostic segment proposal followed by a multi-label action classification. This pipeline is computationally slow and hard to optimize as each stage need be trained separately. Moreover, a desirable method should go beyond segment-level localization and make dense predictions with precise boundaries. We introduce a novel detection model in this paper, Single-stage Multi-location Convolutional Network (SMC), which completely eliminates the proposal generation and spatio-temporal feature resampling, and predicts frame-level action locations with class probabilities in a unified end-to-end network. Specifically, we associate a set of multi-scale default locations with each feature map cell in multiple layers, then predict the location offsets to the default locations, as well as action categories. SMC in practice is faster than the existing methods (753 FPS on a Titan X Maxwell GPU) and achieves state-of-the-art performance on THUMOS’14 and MEXaction2.
3D Reconstruction Under Weak Illumination Using Visibility-Enhanced LDR Imagery

Images of objects captured under poor illumination conditions inevitably contain noise and under-exposed regions where important geometric features may be hidden. Using these images for 3D reconstruction may impair the quality of the generated models. To improve 3D reconstruction under poor illumination, this paper proposes a simple solution for reviving buried features in dark images before feeding them into 3D reconstruction pipelines. Nowadays, many approaches for improving the visibility of details in dark images exist. However, according to our knowledge, none of them fulfills the requirements for a successful 3D reconstruction. Proposed approach in this paper aims not only to enhance the visibility but also contrast of features in dark images. Experiments conducted using challenging datasets of dark images demonstrate a significant improvement of generated 3D models in terms of visibility, completeness, and accuracy. It also shows that the proposed methodology outperforms state-of-the-art approaches that tackle the same problem.
Appropriate Data Density Models in Probabilistic Machine Learning Approaches for Data Analysis

This paper investigates the mathematically appropriate treatment of data density estimators in machine learning approaches, if these estimators rely on data dissimilarity density models. We show exemplarily for two well-known machine learning approaches for classification and data visualization that this dependence is apparently analyzing the respective mathematical models. We show by numerical experiments that data sets generate different data dissimilarity densities depending on the dissimilarity measure in use. Thus an appropriate choice in machine learning models is mandatory to process the data consistently.
Inter-Team Coordination in Agile Development: Learning from Non-Software Contexts

As agile development is increasingly pursuing its relevance for large-scale development, the focus on coordination shifts from an intra-team perspective to an inter-team perspective. However, inter-team coordination in large-scale agile software development is still an understudied subject and has become one of the main challenges for researchers and practitioners in the field. Based on an ongoing ethnographic study we propose that relevant learnings about inter-team coordination can be made from non-software development. To illustrate this we provide the example of mechanical integration engineers and their work in large-scale and complex hardware development.
Alex McLean and Roger T. Dean (eds.), The Oxford Handbook of Algorithmic Music. New York: Oxford University Press, 2018. ISBN: 978-0-190-22699-2 (hardback).

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Augmented Reality Hologram

This paper presents the development of a mobile application that enables the creation of holographic objects and displays holographic objects on mobile devices. This project aims to demonstrate how the functionalities are implemented, and testing would be conducted to ensure the accuracy of creation and exhibitions of the holographic objects. During the app testing, there would be two approaches, which were tested based on two different lightings conditions. Users would be able to create their holographic objects with minimum lighting changes based on the results of the two approaches.
Mathematical model’s parameters identification in a prototype of solar panel’s motor

Solar panel can optimally convert energy when its position is upright to the sun, thus the innovation of solar tracker is needed. Solar tracker needs a motor so that the solar tracker is able to track the sun. This research studies a motor of solar panel prototype called motor DC. This research identifies parameter value of motor DC mathematical model. Identification is performed on resistance (Ra), inductance (La), back emf constant (Kb), torque constant (Km), rotor inertia (J), coefficient of viscous friction (B). Furthermore, simulation is applied to motor DC mathematical model with the identified parameter. Simulation is repeated 4 times with combinations of back emf constant and torque constant. Result of the simulation system is angular velocity. Then, angular velocity from simulation is compared to the result of angular velocity from the experiment. Comparison indicates that the third simulation shows the best value with the confidence level of absolute average relative deviation of 97.317587123%.
Structurization of the Common Criteria Vulnerability Assessment Process

The paper deals with the Common Criteria Evaluation Methodology (CEM), especially with its part related to the vulnerability assessment. The aim of the paper is better structurization of the vulnerability assessment process, allowing its future automatization. The ontological approach will be applied to develop the models of processes and data. The elementary evaluation processes are defined on the basis of the analysis of the CEM vulnerability assessment. The process activities, input and output information, are identified and specified in a pseudocode. The process verification against CEM is performed. The conclusions summarize the verification and propose future works to build the ontology, knowledge base and the vulnerability assessment tool.
MOSCEPA(Adaptive Pedagogocal Scripting Model): Towards a New Adaptive Model of Educational Scriptwriting for LMS

In recent years, Senegal has adopted the Bachelor Master PhD system (BMP) in higher education institutions. It is a model that has several advantages for learners, such as mobility at national and international level and the flexibility of transit conditions. Our research focuses on this second point, the conditions of passage. More specifically, our research problem concerns the question of the management of students admitted to higher education but who take up teaching units of the previous class. These students “in conditional passage” have difficulties to follow at the same time the courses taken and the current school year. Based on this observation, we proposed an adaptive learning scenario model based on the student's academic results. Thus the pedagogical model becomes hybrid, with classroom courses and online courses. These are implemented in an online training platform whose data are derived from the educational computer system. In our previous research, we conducted a comparative study of the scripting models used in online training platforms. Studies show that some models are general, others specific for specific purposes. Hence, a generalist model is still not the most appropriate to meet the needs of pedagogical models of higher education. Most models emphasize the scripting of activities and resources of the e-learning platform without taking into account external parameters such as data from the school's educational information system. In this article, we present MOSCEPA (Adaptive Pedagogical Scripting Model), a new model of adaptive pedagogical scripting based on the IMS LD formalism and data from the pedagogical information system.
Using mutual distance plot and warped time distance chart to compare scan-paths of multiple observers

The aim of the research is the introduction of new techniques that enable a visual comparison of scan-paths. Every eye tracking experiment produces many scan-paths, and one of the main challenges of eye tracking analysis is how two compare these scan-paths. A classic solution is to extract easily measurable features such as fixation durations or saccade lengths. There are also many more sophisticated techniques that compare two scan-paths using only spatial or both spatial and temporal information. These techniques typically return a value (or several values) that may be used as scan-path similarity/distance measure. However, there is still a lack of widely adopted methods that offer not only the measure but enable a visual comparison of scan-paths. The paper introduces two possible options: the Mutual Distance Plot for two scan-paths and the Warped Time Distance chart for the comparison of the theoretically unlimited number of scan-paths. It is shown that these visualizations may reveal information about relationships between two or more scan-paths on straightforward charts. The informativeness of the solution is analyzed using both artificial and real data.
IMPROVEMENT OF TECHNICAL SUPPLY OF PROJECTS OF ROBOTIZED MONITORING OF UNDERWATER CONDITIONS IN SHALLOW WATER AREAS

The intensification of industrial activity in shallow water areas and the growing requirements for the safety of their use determine the urgency of developing new technologies for monitoring their underwater environment. The monitoring tasks include mapping of the bottom surface, inspection of hydraulic structures, search for sunken objects, control of unauthorized access to protected areas, etc. A promising direction for improving the efficiency of monitoring projects for such water areas is the use of uninhabited autonomous and remote-controlled underwater robots. However, the use of traditional types of such equipment has low efficiency because of the impossibility of the operational management of the missions of autonomous vehicles in real time and the complexity and high cost of using remote-controlled vehicles. As an alternative to the robotic support of underwater monitoring projects in shallow water areas, it is proposed to use autonomous underwater vehicles with a radio beacon, since they make it possible to survey large areas, perform high-quality and comprehensive search and mapping work, while providing operators with real-world underwater conditions of time. A generalized structure and composition of the equipment for an autonomous underwater vehicle with a radio beacon is described, its main underwater missions and types of underwater operations are described. In order to assess the resources for the execution time of projects for the robotized monitoring of shallow water areas, dependencies are proposed for calculating the time costs for different trajectories of the movement of the underwater vehicle-robot. Using this methodology, time expenses were calculated and the number of underwater vehicle operation cycles for the main ports of Ukraine was estimated during the examination of the mooring walls with video equipment of the underwater vehicle and examination of the shallow water area by its sonar. The obtained results are a preliminary assessment of the time spent on the study of shallow-water offshore and port water areas, and also form the theoretical basis for the formation of a technical assignment for the creation of modern robotic support for monitoring projects in such water areas.
Hardware Accelerators for Data Search

This chapter is dedicated to searching networks, which permit to find extreme values in a set of data and to check if there are items satisfying some predefined conditions or limitations, indicated by given thresholds. The simplest task is retrieving the maximum and/or the minimum values. More complicated procedures permit the most frequent value/item to be found and a set of the most frequent values/items above a given threshold or satisfying some other constraint to be retrieved. The described above tasks may be solved for entire data sets, for intervals of data sets, or for specially organized structures, such as Boolean/ternary matrices. Different architectures are proposed that rely on: combinational and iterative searching networks, address-based technique, and some others. They are modelled in software (using Java language) and implemented in FPGA on the basis of the design technique described in the previous chapter. All necessary details for software and the basic VHDL modules for FPGA are presented and discussed. Searching maximum/minimum items in very large data sets and pipelining are also overviewed. The networks of this chapter will be used as components of more complicated systems considered in subsequent chapters.