Reviewerly/exHarmony huggingface dataset:
Adding Virtual Objects to Realtime Images; A Case Study in Augmented Reality

Augmented reality applications related with faces such as make-up, hair design, wearing glasses are mostly prepared for entertainment purposes. Facilitating the preparation of augmented reality applications and more accurate analysis of real-world data in applications will enable these applications to be used more widely in different sectors such as R&amp;amp;D, education and marketing. In generally, the steps in image-based augmented reality applications can be listed as follows; detection of the targeted object, finding two reference points for each targeted object in 2D images, determining the boundaries of virtual object in its image and inserting the virtual object in real time. In this study, the problems that may be encountered in preparations of these augmented reality applications expected to be used more in the future are examined through a case study. Firstly, haar cascade classifiers, used to find different face areas, are compared and as a result of the comparison, it is decided to use eye haar cascade. Afterwards, rule-based approaches have been used to eliminate the wrong ones among the found eyes and to match the eyes of the same face. Then the position, size and angle of the virtual object to be added are calculated and it is added to the face using affine transformations. The problems encountered in augmented reality and algorithms used for problem solving are explained through the virtual hat application, but these simply prepared algorithms, can be used for different objects such as hair and glasses by changing the target points.
Dynamic Texture Analysis Using Auto-correlation Function of Histogram Similarity Measure From Galois-Field Texture Representation of Water Flow Video

This paper propose a method for determining the periodicity of dynamic textures from water video using spatial feature similarity measure of Galois-Field (GF) texture representation. Auto correlation function are use to analyze the extracted spatial feature from the representation to determine the periodicity of dynamic textures. There are two type of spatial feature to be compared, the first is histogram and second is normalize cumulative histogram (NCH). Two type of experiment are conducted, the first is virtual rotation where video is rotated virtually from 0 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> until 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> , the second is actual rotation where camera are rotated physically. Experiments show that although GF improves the performance of the Histogram similarity measure, overall NCH shows better performance. In virtual rotation experiment, GF representation prove to minimize variability due to rotation of camera, the maximum variability produce by NCH is 27%, while when GF are not use the maximum variability is 106%. Contrary, in actual rotation experiment, GF is not proven to minimize variability where NCH produce maximum variability is 57%, while where GF are not use the maximum variability is 9%. The difference in variability pattern between virtual and actual rotation, shows that Galois Field is good at handling dynamic texture rotation, but not against other factors that affect variability.
A Hybrid Approach for Automatic Aorta Segmentation in Abdominal 3D CT Scan Images

Due to rapid advancement in medical imaging, human anatomy is now observable in finer details bringing new dimensions to diagnosis and treatment. One such area which benefitted from advancement in medical imaging is aorta segmentation. Aorta segmentation is achieved by using anatomical features (shape and position of aorta) using specialized segmentation algorithms. These segmentation algorithms are broadly classified into two categories. The first type comprises of fast algorithms which exploits spatial and intensity properties of images. The second type are iterative algorithms which use optimization of a cost function to track aorta boundaries. Fast algorithms offer lower computation cost, whereas iterative algorithms offer better segmentation accuracy. Therefore, there is a tradeoff between segmentation accuracy and computational cost. In this work, a hybrid approach for aorta segmentation in 3D Computed Tomography (CT) scan images is proposed. The proposed approach produces high segmentation accuracy of intensity based (fast) approaches at reduced computational cost. The proposed technique is evaluated using real world 3D abdominal CT scan images. The proposed approach can either be used as a fast-standalone segmentation procedure, or as a pre-segmentation procedure for iterative and more accurate approaches.
An Empirical Investigation of Spikes in Agile Software Development

The use of spikes in agile software development (ASD) can enable organizations to produce quality software by employing the required technical expertise, planning the entire development cycle and ensuring that the client's requirements are adhered to. This study aims to examine the use of spikes in ASD. It explores the role, efficiency and efficacy of spikes in various software development domains through the different agile methods. An exploratory research design is adopted to achieve this purpose, whereby mixed methods are used to collect concurrently both qualitative and quantitative data from the experts recruited to the study. Based on the study's findings, this paper shows spikes' impact on risk reduction and their role in the estimation process, as well as how their use is related to the team's understanding and the consistency and reliability of the story estimate. Through interviews, it establishes that the primary role of spikes is risk management through investigations to understand user stories and reveal any uncertainty. Both the efficiency and efficacy of spikes were found to be high. The findings further showed that spikes improve the quality of the end product.
Min-max Path Planning of Multiple UAVs for Autonomous Inspection

In this paper, we study the path planning for autonomous inspection with multiple UAVs in a certain area to minimize the maximum flight distance of the UAV. Particularly, the UAVs are dispatched from the same depot to visit all inspection points, and finally return to the depot. Each inspection point is visited by only one UAV. Since the min-max path planning is NP-hard, we transform this problem by replacing the minimum Hamiltonian cycle length of each UAV with the average cycle length. Then, we decouple it into two subproblems, i.e., task allocation and trajectory design, and solve them respectively. Simulation results show that the proposed algorithm achieves better performance than conventional GA-mTSP method.
Application of the case based reasoning &amp; sorensen-dice coefficient method for fitness exercise program

Abstract Fitness Training Program Recommendation is a web-based system using C # language and SQL Server database that can help find the results of fitness training program recommendations for system users. The making of this application aims to recommend a fitness training program in accordance with input data that are considered attributes in the selection of fitness training programs, gender, age, weight, height, sports activities, and muscle specialization. The making of this application uses the system development method of system development life cycle and the process of selecting an exercise program using the case based reasoning method with the stages of retrieve, reuse, revise, retain and rule base methods to determine the results of a more optimal fitness training program recommendation. The results of testing the fitness exercise recommendation system programs that are based on the user as a whole shows 51.14% of respondents stated strongly agree, 40.19% of respondents agreed that the system was able to recommend a fitness training program and provide information needed by the user.
A type-safe structure editor calculus

Structure editors make syntax errors impossible, but they still allow construction of programs with incomplete semantics, leading to program states that cannot be evaluated. We introduce a structure editor calculus for a simple functional programming language that allows for incomplete programs. Our editor expressions may interleave construction and evaluation of programs and can thus describe the history of the development of a program. We extend our editor calculus with types and define a resource-aware type system that prohibits editor expressions introducing type errors in the abstract syntax tree and prove that the type system is sound.
VOIS: The First Speech Therapy App Specifically Designed for Myanmar Hearing-Impaired Children

The hearing-impaired children's education is challenging because they are unlikely to develop normal speech and language ability. We propose a mobile application VOIS, which is the first speech therapy application for hearing-impaired children in Myanmar. This mobile application uses a Convolutional Neural Network (CNN) based offline Burmese speech recognition system. It can help hearing-impaired children to train with the language pre-requisites at their own pace. To effectively help the hearing-impaired children to understand the basics of the language, this system provides one-syllable and two-syllable structured Myanmar words collected in real-life educational and communication materials. The experimental result shows that the prediction rate of this system is nearly 60%. Experiments also show the hearing-impaired children can learn and operate the language freely through a simple practice using this application. The expectation is that this application can bring both opportunities and life-quality improvements for children with hearing loss in Myanmar.
Iterative Visual Relationship Detection via Commonsense Knowledge Graph

Scene Graph Generation, which discovers the interaction between pairs of entities in an image, plays a significant role in image understanding. Most recent studies only consider visual features, ignoring the implicit effect of commonsense. We propose a novel model to take the advantage of commonsense knowledge in Scene Graph Generation, named Iterative Visual Relationship Detection with Commonsense Knowledge Graph (IVRDC). IVRDC consists of two modules: a feature module that predicts predicates by visual features and semantic features with a bi-directional recurrent neural network; and a commonsense knowledge module that constructs a specific commonsense knowledge graph for predicate prediction. These two modules roll out iteratively and cross-feed predictions from and to each other. The final predictions are made by taking the result of every iteration into account with an attention mechanism. Experimental results on the Visual Relationship Detection (VRD) dataset and the Visual Genome (VG) dataset demonstrate that our proposed model is competitive.
Reasoning with Failures

Safety Instrumented Systems (SIS) protect major hazard facilities, e.g. power plants, against catastrophic accidents. An SIS consists of hardware components and a controller software – the “program”. Current safety analyses of SIS’ include the construction of a fault tree, summarising potential faults of the components and how they can arise within an SIS. The exercise of identifying faults typically relies on the experience of the safety engineer. Unfortunately the program part is often too complicated to be analysed in such a “by hand” manner and so the impact it has on the resulting safety analysis is not accurately captured. In this paper we demonstrate how a formal model for faults and failure modes can be used to analyse the impact of an SIS program. We outline the underlying concepts of Failure Mode Reasoning and its application in safety analysis, and we illustrate the ideas on a practical example.
Quantum Minimum Searching Algorithm and Circuit Implementation

This paper addresses the quantum minimum searching algorithm and provides simulation results using qiskit library by IBM Q. We propose a whole procedure to implement the quantum minimum searching algorithm with high accuracy and design a quantum circuit for 5-qubit searching. The circuit consists of several oracles(phase inverter) and Grover operators(amplifier) and a comparator. To implement 5-qubit searching, we suggest three things. Modifying the expected number of iterations described in Section II.C, applying the concept of QRAM as an interface between quantum data and classical data in Section II.D, and designing quantum comparator using constant adder by Thomas in 2017 in Section III.A.
Part-Of-Speech Tagger in Malayalam Using Bi-directional LSTM

The majority of activities performed by humans are done through language, whether communicated directly or reported using natural language. As technology is increasingly making the methods and platforms on which we communicate ever more accessible, there is a great need to understand the languages we use to communicate. By combining the power of artificial intelligence, computational linguistics and computer science, natural language processing (NLP) helps machines “read” text by simulating the human ability to understand language. Part-of-speech tagging (POS Tagging) is done as a pre-requisite to simplify a lot of different NLP applications like question answering, speech recognition, machine translation, and so on. Here, we attempt a comparison between part-of-speech taggers in Malayalam using decision tree algorithm and bi-directional long short term memory (BLSTM). The experiments presented in this paper use two corpora, one of 29076 sentences and the other of 500 sentences for performance evaluation. The experiments demonstrate the potential of architectural choice of BLSTM-based tagger over conventional decision tree-based tagging in Malayalam.
Generalized Operational Classifiers for Material Identification

Material is one of the intrinsic features of objects, and consequently material recognition plays an important role in image understanding. The same material may have various shapes and appearance, while keeping the same physical characteristic. This brings great challenges for material recognition. Besides suitable features, a powerful classifier also can improve the overall recognition performance. Due to the limitations of classical linear neurons, used in all shallow and deep neural networks, such as CNN, we propose to apply the generalized operational neurons to construct a classifier adaptively. These generalized operational perceptrons (GOP) contain a set of linear and nonlinear neurons, and possess a structure that can be built progressively. This makes GOP classifier more compact and can easily discriminate complex classes. The experiments demonstrate that GOP networks trained on a small portion of the data (4%) can achieve comparable performances to state-of-the-arts models trained on much larger portions of the dataset.
From Decoupling to Reconstruction: A Robust Graph Neural Network Against Topology Attacks

Graph neural networks (GNNs) are criticized for poor robustness; their performance will decline seriously even with unnoticeable edge perturbations in node classification tasks. To against topology attacks, recent works punish edges based on data and attack characteristics (such as node similarity or SVD), or utilize the graph data distribution to absorb attacks (such as BayesianGCN or RGCN). We propose a robust GNN model based on topology reconstruction, which does not explicitly assume data or attack distributions. Our proposed method decouples graph data into attribute subgraph and topology subgraph and makes classifier cooperate with topology generator to label nodes. The classifier starts training from the attribute subgraph and labels some high-confidence nodes to join the trainset. Meanwhile, the generator reconstructs topology based on classification loss, topology subgraph, and link prediction, providing the classifier with new information. Extensive node classification experiments show that under the same attack conditions, our model has higher classification accuracy and stronger robustness compared with other latest robust GNN models.
Image Registration using Bio-inspired Algorithms

Image registration is one of the most essential applications of image processing. In image registration, two images are compared to find a similarity metric and necessary adjustments are made to one of the images to minimize the similarity metric and align it to the other one (reference image). This minimization is performed using an optimization algorithm. Here, some of the newly developed meta-heuristic algorithms, namely Bat Algorithm and Grey Wolf Optimization are used to implement the image registration process with Mutual Information as the similarity metric. Along with these a Particle Swarm Optimization based image registration is also performed to the same sample sets. The performance results of these three implementations are compared on basis of both speed and quality of registration to find the overall best solution. The three algorithms are found to be very competitive when compared as optimizer in image registration process.
Improved Patch-based View Rendering for Focused Plenoptic Cameras with Extended Depth-of-Field

This paper proposes a patch-based framework to improve view rendering from light fields captured by focused plenoptic cameras with extended depth-of-field. A three-step approach is performed to minimise discontinuities across neighbouring patches and to maximise the information captured from different types of micro-lens. Firstly, the useful patch size of each micro-image is determined to limit the contribution of low light intensity regions into the rendered images. Secondly, anisotropic inpainting is applied to smooth the discontinuities between neighbouring patches. Thirdly, a multi-focus image fusion algorithm is used to efficiently combine the information captured by the different types of micro-lens comprising plenoptic cameras with extended depth-of-field. Significant improvements are obtained in the rendered views, by reducing visible discontinuities between adjacent micro-images, while at the same time increasing the average sharpness of image content by 20%, measured by the Energy of Laplacian (i.e., a focus metric), in comparison to the conventional rendering approach, which always selects the best focused lens type.
Product recommendation system on building materials shopping using FP-Growth algorithm

The application program created is an e-commerce website based application program that is used to provide product choices for building materials using the FP- Growth algorithm. This algorithm is used to find frequent itemset (data that most often appears) on store transaction data from January 2019 to the most recent transaction. The use of FP-Tree which is used simultaneously with the FP-Growth algorithm only scans the database 2 times. The first scanning is done to read the previous store sales transaction data. The second scanning on the tree trace to form a pattern. After obtaining a number of linkages that are higher than the minimum support from the transaction data, product choices can be generated on the website. This application was created using the Hypertext Processor (PHP) programming language base which is supported by the MySQL database and SQL Server. It is expected that the presence of web-based applications can improve store sales and the marketing process of building products for the better.
Sequential Learning and Regularization in Variational Recurrent Autoencoder

Latent variable model based on variational autoen-coder (VAE) is influential in machine learning for signal processing. VAE basically suffers from the issue of posterior collapse in sequential learning procedure where the variational posterior easily collapses to a prior as standard Gaussian. Latent semantics are then neglected in optimization process. The recurrent decoder therefore generates noninformative or repeated sequence data. To capture sufficient latent semantics from sequence data, this study simultaneously fulfills an amortized regularization for encoder, extends a Gaussian mixture prior for latent variable, and runs a skip connection for decoder. The noise robust prior, learned from the amortized encoder, is likely aware of temporal features. A variational prior based on the amortized mixture density is formulated in implementation of variational recurrent autoencoder for sequence reconstruction and representation. Owing to skip connection, the sequence samples are continuously predicted in decoder with contextual precision at each time step. Experiments on language model and sentiment classification show that the proposed method mitigates the issue of posterior collapse and learns the meaningful latent features to improve the inference and generation for semantic representation.
Practical Trade-Offs in Integrity Protection for Binaries via Ethereum

Ensuring the integrity of executable binaries is of vital importance to systems that run and depend on them. Additionally, supply-chain attacks and security related bugs demonstrate that binaries, once deployed, may need to be revoked and replaced with updated versions.
From Silo to the Web: Library Cataloging Data in an Open Environment

The silos of library cataloging data have long been recognized as barriers to the seamless discovery of library resources via the Web and to the interaction of library data with Web data and other uses. In order to better serve users, library cataloging data must be available in a more open environment, and libraries are looking to linked data to present library resources in the data stream of the Web in a way that is seamless to users. This entails changing cataloging formats and tools to Web standards. While there is a lot of discussion and activity around linked data, this chapter approaches the highly technological topic in a way that is geared more toward a general and practical cataloging perspective.
Music teachers’ understanding of blended learning in Korean elementary music classes

As information and communication technologies (ICT) have been rapidly developed, the computers and internet are widely used in many fields. The purpose of this study was to examine music teachers’ experiences and perceptions of blended learning in elementary music classes. Using a multiple case study method, I observed four elementary music teachers in different schools located in Seoul, South Korea. The following research questions were addressed: (1) How do the elementary music teachers utilise blended learning in their music classes? (2) What are the challenges the music teachers face in implementing blended learning into their classes? and (3) What are the music teachers’ perceptions of blended learning that could be beneficial to their students and to their teaching? The results showed that the music teachers employed the station-rotation model of blended learning. This study also revealed that the teachers acknowledged educational effects of blended learning. Nevertheless, they shared concerns about motivational factors and teacher education for blended learning. It is suggested that continuing professional development is needed to increase the use of blended learning in Korean elementary music classes.
Mathematical modelling of a search and rescue robot with TriSTAR locomotion units

Abstract In this paper the authors present the mathematical model of a search and rescue robot with hybrid locomotion system. The robot uses a locomotion system with two TriSTAR units and two conventional wheels. Details are also presented regarding the design of the robot and the TriSTAR locomotion unit, the robot locomotion and the robot actuation and control.