Reviewerly/exHarmony huggingface dataset:
A framework for projected light intensity prediction in high dynamic range three-dimensional shape measurement

Fringe projection technique has been widely used in numerous areas including manufacturing, medical science, computer science, entertainment, and documentation of cultural artifacts. However, there are problems when measuring a complex scene with a large range of reflectivity variations, where minimum human intervention is usually desirable. This paper presents a framework that can determine the optimal number of projected light intensities and corresponding intensity values for high-quality three-dimensional (3D) shape measurement with digital fringe projection technique. By using the k-means clustering algorithm, we can calculate the distribution of surface reflectivity and thus predict the optimal number of projected light intensities and corresponding intensity values. Experimental results demonstrate that the proposed framework can be successfully applied to the measurement of complex scenes whose histogram does not show apparent peaks and troughs.
SemSegDepth: A Combined Model for Semantic Segmentation and Depth Completion

Holistic scene understanding is pivotal for the performance of autonomous machines. In this paper we propose a new end-to-end model for performing semantic segmentation and depth completion jointly. The vast majority of recent approaches have developed semantic segmentation and depth completion as independent tasks. Our approach relies on RGB and sparse depth as inputs to our model and produces a dense depth map and the corresponding semantic segmentation image. It consists of a feature extractor, a depth completion branch, a semantic segmentation branch and a joint branch which further processes semantic and depth information altogether. The experiments done on Virtual KITTI 2 dataset, demonstrate and provide further evidence, that combining both tasks, semantic segmentation and depth completion, in a multi-task network can effectively improve the performance of each task. Code is available at https://github.com/juanb09111/semantic depth.
Visual Literacy as a New Frontier of Digital Social Innovation

The research aims to analyze the characteristics of visual literacy as an incentive for digital social innovation in vocational education. Data analysis was performed using the Inquiry graphics (IG) framework to analyze visual (20 online photography book projects) communication messages and Qualitative Deductive Content analysis was used to analyze textual (interviews with 20 vocational students) messages. QSR NviVo 12 program was used for data processing. The research results reveal features of digital social innovations in students' projects and their significance in professional media education. The limitations of the research are that IG is merely a methodological and analytical tool and that applying semiotic approaches does not necessarily contribute to educational or vocational education theory or theoretical development but only to methods and teaching practice. The study results indicate the study's future directions regarding the binding of digital social innovation with global and research competence.
Face Expression Emoji as Avatar Creation and Detection

One of the latest emerging trends, the flexible way of communication in an entertainment manner is through emoji (smile emoji, sad emoji, etc), many top companies like Facebook, Snapchat, Instagram, and many more are using this type of trend but now also they are in progress of this trend for the creation of avatar for the virtual world. Therefore we will discuss the backend of this type of work creation, different technologies used for the implementation, all the set of algorithms, and past, present, and future of the Face emoji creation (FEC) and Face emoji detection (FED) with the review in avatar formation. We get to know different advantages and the disadvantages related to the above technology. One of the interesting things for everyone is the avatar of their face. So, we get to know the concepts of AC (avatar creation) with expression oriented and how it is going to be related with the virtual world, which is going to be experienced by the future generation.
Depth-of-Field Segmentation for Near-lossless Image Compression and 3D Reconstruction

Over the years, photometric three-dimensional (3D) reconstruction gained increasing importance in several disciplines, especially in cultural heritage preservation. While increasing sizes of images and datasets enhanced the overall reconstruction results, requirements in storage got immense. Additionally, unsharp areas in the background have a negative influence on 3D reconstructions algorithms. Handling the sharp foreground differently from the background simultaneously helps to reduce storage size requirements and improves 3D reconstruction results. In this article, we examine regions outside the Depth of Field (DoF) and eliminate their inaccurate information to 3D reconstructions. We extract DoF maps from the images and use them to handle the foreground and background with different compression backends, making sure that the actual object is compressed losslessly. Our algorithm achieves compression rates between 1:8 and 1:30 depending on the artifact and DoF size and improves the 3D reconstruction.
Quantitative Comparison of Two Chain-Selection Protocols Under Selfish Mining Attack

The longest-chain and Greedy Heaviest Observed Subtree (GHOST) protocols are the two most famous chain-selection protocols to address forking in Proof-of-Work (PoW) blockchain systems. Inclusive protocol was proposed to lower the loss of miners who produce stale blocks and increase the blockchain throughput. This paper aims to make an analytical-model-based quantitative comparison of their capabilities against selfish mining attack. Analytical models have been developed for the longest-chain protocol but less to the GHOST protocol. However, the blockchain dynamics and evolution are different when adopting different chain-selection protocols. Therefore, the corresponding analytical models and/or the formulas of calculating metrics (such as miner profitability and system throughput) may be different. To address these challenges, this paper first develops a novel Markov model and the formulas of evaluation metrics, in order to analyze a GHOST-based blockchain system under selfish mining attack. Then extensive experiments are conducted for comparison and we observe that: (i) The GHOST protocol is more resistant to selfish mining attack than the longest-chain protocol from the aspect of relative revenue of selfish miners. (ii) Inclusive protocol can promote the security (evaluated in terms of miner profitability) improvement of the system which has little total computational power or a high forking probability. Additionally, the longest-chain protocol is more sensitive to inclusive protocol than GHOST protocol. (iii) It is hard for each of the two common-used difficulty adjustment algorithms to achieve higher system throughput and security.
Slurm PaaS in a cloud environment for development and debug purposes

Nowadays specialized distributed services and sig-nificant amounts of computing resources are used for solving tasks in Big Data processing and analysis. Cloud environments allow people to create virtual clusters with necessary characteris-tics and tune specialized software for planning jobs and resource allocation. Slurm belongs to such systems class. Unfortunately, lots of problems appear during manual setup and Slurm usage in cloud infrastructure. The article offers the solution to the problem of automating deployment of the Slurm cluster in cloud infrastructure for development and testing purposes. User acquires access using REST API request to get ready for use virtual cluster with preinstalled and preconfigured Slurm cluster. This work also includes analysis for Slurm/HPC clusters effi-ciency running in Openstack environment using virtual machines interconnected with an overlay network compared to classical bare-metal HPC clusters.
Particle Swarm Optimization with Weighted Extreme Learning Machine for Software Change Prediction

Software Change Prediction (SCP) is a branch of research that reduces maintenance efforts by predicting change-prone classes prior to the software release. The past SCP studies have highly motivated the use of techniques to handle the two major issues in SCP datasets, i.e., feature representation and imbalance handling. This study proposes a novel combination of particle swarm optimization for feature selection and weighted extreme learning machine for imbalance handling and classification to tackle these issues. The experiment is conducted on six Java datasets that have been collected using tools and open-source repository. This study uses AUC and F-measure as the performance measure with Friedman and Wilcoxon statistical tests to evaluate and compare the results of the proposed model PSOWE with ten state-of-the-art techniques. PSOWE achieved an AUC-ROC median value of 0.9337 and an F-Measure median value of 0.6883 which prove the superiority of the proposed model with the majority of the techniques.
Machine learning algorithms for integrating clinical features to predict intracranial hemorrhage in patients with acute leukemia

Intracranial hemorrhage (ICH) in acute leukemia (AL) patients leads to high morbidity and mortality, treatment approaches for ICH are generally ineffective. Thus, early identification of which subjects are at high risk of ICH is of key importance. Currently, machine learning can achieve well predictive capability through constructing algorithms that simultaneously exploit the information coming from clinical features.After rigid data preprocessing, 42 different clinical features from 948 AL patients were used to train different machine learning algorithms. We used the feature selection algorithms to select the top 10 features from 42 clinical features. To test the performance of the machine learning algorithms, we calculated area under the curve (AUC) values from receiver operating characteristic (ROC) curves along with 95% confidence intervals (CIs) by cross-validation.With the 42 features, RF exhibited the best predictive power. After feature selection, the top 10 features were international normalized ratio (INR), prothrombin time (PT), creatinine (Cr), indirect bilirubin (IBIL), albumin (ALB), monocyte (MONO), platelet (PLT), lactic dehydrogenase (LDH), fibrinogen (FIB) and prealbumin (PA). Among the top 10 features, INR, PT, Cr, IBIL and ALB had high predictive performance with an AUC higher than 0.8 respectively.The RF algorithm exhibited a higher cross-validated performance compared with the classical algorithms, and the selected important risk features should help in individualizing aggressive treatment in AL patients to prevent ICH. Efforts that will be made to test and optimize in independent samples will warrant the application of such algorithm and predictors in the future.
Recent Techniques in Image Retrieval: A Comprehensive Survey

In recent days of image processing, retrieval of images (IR) is very popular, important, and rapidly developing area of research in multimedia technology. There is a rapid increase in image transactions in the digital computer world. For various activities, most of the digital equipment generates images. This creates a massive picture archive. In recent years, a large amount of visual content from various fields, such as social media sites, medical images, and robotics, has been created and shared. Searching databases for similar information, i.e., content-based image retrieval (CBIR), is a long-established area of study, and real-time retrieval involves more effective and accurate methods. There are enormous methods of image retrieval. One of the approaches for obtaining low-level image characteristics is CBIR. Color, shape, texture and spatial position are some of the features. We have done extensive survey to understand CBIR, various retrieval techniques, image attributes, standard image datasets aimed at promoting a global view of the CBIR sector.
Research on the Application of Mathematical Methods in Image Denoising Technology

In the process of acquisition, compression, transmission and storage, digital images are often interfered by imaging equipment and the external environment, resulting in image distortion. Image denoising aims to truly reconstruct the image from the observation data contaminated by noise to provide more accurate and reliable information for the subsequent processing of the image. Therefore, image noise reduction is the most basic and most important link in the field of image processing research. In the past decade or so, the application of theories and methods of partial differential equations (PDEs) in various fields of image processing has attracted more and more attention. This article researches and analyzes the image processing methods based on partial differential equations. First, it introduces the research background of this article, the development history, principles of partial differential equations and the status quo of image processing; then, it summarizes and analyzes the application of partial differential equations to images Classical model of denoising. Through the analysis of the classic model, it can be known that the image noise reduction algorithm based on partial differential equations can be based on the structural characteristics of the image. This method adopts a corresponding smoothing strategy, and it can achieve satisfactory image denoising effects.
Static Analytical Reasoning of Directed Cyclic Graph in the Discrete Case Weight Combination Method

As being defined, a Bayesian Network (BN) is based on a directed acyclic graph (DAG). However, when statistical data to learn a BN are collected in a set of groups independently by different experts, in other words, when we do not have the entire data covering all variables, we may have to construct a set of small pieces of BNs independently based on these different groups of data, and then combine these BNs together as a whole BN by fusing the same variables in different BNs. But directed cyclic graphs (DCGs) may appear. If we allow such cyclic BN, what is its definition? What is its physical meaning? What is its algorithm to make the inference? The answers are provided in this paper. Note that a cyclic BN cannot be understood as a dynamic BN (DBN), because variables are not in a doubtless time sequence.
Freedom of Speech and Information

Having discussed the risk of increased authoritarianism from the use of emergency powers in Chapters 2 , this chapter will consider freedom of information and speech. Brown argues that the norms of allowing citizens free access to information, and the freedom to express their views on government responses, is vital, to not only keep check on government, but also to create a trustful environment in which pandemic responses can be effective. The chapter first seeks to engage with the thought of Hobbes, Schmitt, and Sorell on freedom of information and expression before turning to consider this issue in relation to China and the UK.
Generating Text to Realistic Image using Generative Adversarial Network

Generative Adversarial Network (GANs) has become one of the most interesting ideas in the last years in Machine Learning. Generative Adversarial Network is a very exciting area and that’s why researchers are so excited about building generative models as they are set to vary what machines can do for humans. This paper proposes the generation of realistic images according to their semantics based on text description using a Knowledge Graph alongside Knowledge Guided Generative Adversarial Network (KG-GAN) that comes with the embeddings generated from the Knowledge Graph (KG) into GAN. The Knowledge Graph is made from the text description by making the machine understand from the Natural Language Processing (NLP) techniques. The Knowledge Graph produced from the text description is converted to its embeddings by utilizing a Graph Convolutional Networks (GCN) and is fed into the GAN for generating realistic images by training the generators and discriminators and also the performance is evaluated. The experimental study is completed on a Caltech-UCSD Birds 200-2011 (CUB-200-2011) dataset and results that the approach using the knowledge graph for image generation using GAN has performed well and with high accuracy in comparison to the other established techniques generated in the past years for text to image generation in GAN.
Perceptually improved T1-T2 MRI translations using conditional generative adversarial networks

Magnetic Resonance Imaging (MRI) encompasses a set of powerful imaging techniques for understanding brain structure and diagnosing pathology. Various MRI sequences including T1- and T2-weighted provide rich complementary information. However, significant equipment costs and acquisition times have inhibited uptake of this critical technology, adversely impacting health equity globally. To ameliorate these costs associated with brain MRIs, we present pTransGAN, a generative adversarial network (GAN) capable of translating both healthy and unhealthy T1 scans into T2 scans, potentially obviating T2 acquisition. Extending prior GAN-based image translation, we show that the addition of non-adversarial losses, like style and content loss, improves the translations provided, especially making the generated images sharper, and making the model more robust. Additionally in previous studies, separate models have been created for healthy and unhealthy brain MRI. Thus here, we also present a novel simultaneous training protocol that allows pTransGAN to concurrently train on healthy and unhealthy data sampled from two open brain MRI datasets. As measured by novel metrics that closely match perceptual similarity of human observers, our simultaneously trained pTransGAN model outperforms the models individually trained on just healthy or unhealthy data. These encouraging results should be further validated with independent paired and unpaired clinical datasets.
Comparative Study of Optimization Methods Proposed for the Extreme Learning Machine (ELM)

The Extreme Learning Machine (ELM) is a learning algorithm used for training a single hidden layer feed-forward neural network (SLFN). It leads to a better generalization performance and fast learning speed and many more advantageous properties. The first step of an ELM is to assign random values to the input weights and biases. Then it will determine the output weights in a single step using the generalized Moore-Penrose method. The random initialization of weights and biases and a large number of hidden nodes can affect the performance of the ELM. Several optimizers were proposed aiming to increase generalization performance and produce more compact networks. This paper presents a comparative study of ELM optimization in the state of the art using existing algorithms namely Optimally Pruned ELM (OP-ELM), Grey Wolf Optimizer (GWO), Salp Swarm Algorithm (SSA), Bat Algorithm (BA), and Particle Swarm Optimization (PSO) which are swarm intelligence-based ELMs. Also Genetic Pruning Algorithm (GPA) and Enhanced Genetic Algorithm (EGA) which are pruning methods using evolutionary algorithms. Results prove that SSA-ELM improves the accuracy in most benchmark datasets and shows better results than the other comparing methods while OP-ELM gives the most compact model with a high level of accuracy.
German case ambiguities at the interface: Production and comprehension

This article discusses a new approach to the interface between phonology/ prosody and syntax with regard to two perspectives: production and comprehension. Themodel assumes two transfer processes responsible for the exchange of information at the interface: the transfer of vocabulary, which operates at the word-level and below, and the transfer of structure, which is concerned with the association of syntactic and prosodic phrasing above the word-level. These transfer processes at the interface are illustrated by means of syntactically ambiguous German genitive/dative case constructions which can be resolved via prosodic phrasing. The relevant prosodic cues for the disambiguation were determined via a production experiment which also showed that the use of acoustic cues is not uniform across speakers. The proposedmodel allows for a straightforward and elegant resolution of the syntactic ambiguities at the interface to the prosodic module and can furthermore be extended to include speaker variability as well.
Spatiotemporal Initialization for 3D CNNs with Generated Motion Patterns

The paper proposes a framework of Formula-Driven Supervised Learning (FDSL) for spatiotemporal initialization. Our FDSL approach enables to automatically and simultaneously generate motion patterns and their video labels with a simple formula which is based on Perlin noise. We designed a dataset of generated motion patterns adequate for the 3D CNNs to learn a better basis set of natural videos. The constructed Video Perlin Noise (VPN) dataset can be applied to initialize a model before pre-training with large-scale video datasets such as Kinetics-400/700, to enhance target task performance. Our spatiotemporal initialization with VPN dataset (VPN initialization) outperforms the previous initialization method with the inflated 3D ConvNet (I3D) using 2D ImageNet dataset. Our proposed method increased the top-1 video-level accuracy of Kinetics-400 pre-trained model on {Kinetics-400, UCF-101, HMDB-51, ActivityNet} datasets. Especially, the proposed method increased the performance rate of Kinetics-400 pre-trained model by 10.3 pt on ActivityNet. We also report that the relative performance improvements from the baseline are greater in 3D CNNs rather than other models. Our VPN initialization mainly helps to enhance the performance in spatiotemporal 3D kernels. The datasets, codes and pre-trained models used in this study will be publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Gender Recognition by Voice Using Machine Learning

Recently, gender recognition has become an important area of research. Gender recognition can be used in various fields like for security purposes, speaker identification and speaker recognition. Various techniques has been used to identify the gender of person like using facial analysis, voice identification, machine learning, deep learning, using features like LPC and MFCC. This paper deals with identifying the gender using Acoustic properties of voice using Machine learning and how the accuracy vary when dataset goes through different transformation. It shows that we achieve maximum accuracy when uniform transformation is applied on dataset in case of KNN and SVM both.
Variational Multi-Prototype Encoder for Object Recognition Using Multiple Prototype Images

In the recent research of Variational Prototyping-Encoder (VPE), the problem of classifying 2D flat objects of the unseen class has been addressed. VPE solves this problem by pre-learning the image translation task from real-world object images to their corresponding prototype images as a meta-task. VPE uses a single prototype for each object class. However, in general, a single prototype is not sufficient to represent a generic object class because the appearance can change significantly according to viewpoints and other factors. In this case, using VPE and a single prototype for each class in training can result in overfitting or performance degradation. One solution may be the use of multiple prototypes. However, this also requires costly sub-labeling for dividing the input class into smaller classes and assigning a prototype to each. Therefore, we propose a new learning method, the variational multi-prototype encoder (VaMPE), which can overcome the limitations of VPE and use multiple prototypes for each object class. The proposed method does not require additional sub-labeling other than simply adding multiple prototypes to each class. Through various experiments, we demonstrate that the proposed method outperforms VPE.