Reviewerly/exHarmony huggingface dataset:
Using Smart Contracts via Blockchain Technology for Effective Cost Management in Health Services

Societies evolved towards a knowledge-based construct with technology by providing a change in the methods of traditional production and service presentation. In today’s information societies, digital technologies enable peers to transfer data and knowledge without needing any central authority or intermediary. Morever, blockchain technology, which is becoming more and more widespread, has begun to be renowned as Bitcoin and crypto currencies enter the markets, and practiced as a business strategy in different sectors with the technology it relies on. There are findings that Blockchain Technology will eliminate inefficiency in many sectors and will contribute to the reduction of costs by conducting transactions among parties without a central authority through smart contracts. Recently, the use of this technology in the health sector has been keeping the agenda occupied and sector specific solutions have been becoming prevalent. In this study, the health services where E-government applications, Tele-Medicine and Artificial Intelligence are reviewed and the effects of the sharing of the data about patients and diseases among health sector parties with the Blockchain Technology through smart contracts have been investigated. The theoretical framework of blockchain technology has been investigated within the existing framework and the applications of countries such as Estonia, Sweden, and the U.S.A, who use Blockchain Technology in the health sector have been analyzed and their effects on the costs of health services were evaluated.
From 2D to 3D: Component Description for Partial Matching of Point Clouds

We present a method to compute the descriptor of components of point clouds, therefore, a novel component-oriented partial matching of point clouds is achieved based on the component descriptor. We observe that 3D components can be constructed by stacking 2D shapes using certain criteria so that the centers of the 2D shapes form a curve called a skeletal curve that is the trajectory of the 2D shapes. In addition, the scaling factors of the 2D shapes also impact the shape of the 3D components. Motivated by these observations, the computation of the component descriptor that is termed 2to3SSC (from 2D to 3D: 2D Shape and Skeletal Curve) is formulated as a 2D shape and skeletal curve extraction problem, and the component-oriented partial matching of the point clouds is based on the dissimilarity measure of 2to3SSCs of the components. Furthermore, for the 2D shape matching, which is crucial to the matching of the components, we present a novel 2D shape descriptor called VDTL (Vertical Distances to the Tangent Line). The proposed method outperforms previously proposed methods because it simultaneously encodes the local and global features of the components as opposed to only encoding the local or partial features as in previous studies. Finally, the effectiveness and performance of 2to3SSCs are compared with those of state-of-the-art feature description and matching methods for different point cloud datasets. Further, the benefits and the applicability of the proposed method are demonstrated; favorable results are obtained for real-world point clouds of Terracotta fragments.
Community Preserving Node Embedding Based on Seed-Expansion Sampling

Social network analysis focuses on extraction of various network properties and facilitates many applications like link prediction and community detection. As one of those popular and promising analytical approaches, network embedding aims to represent the network in a low-dimensional space with preservation of the network's structure and inherent properties. Existing network embedding methods are mainly targeted at network's microscopic structure, particularly at first-order and second-order proximities of nodes. However, the demand of scalable community structural information preservation is ignored. In this paper, we propose a novel Seed-Expansion sampling based Network Embedding model (SENE) to capture mesoscale community information into our learned vectors. We also present an improved network sampling algorithm based on XS strategy to capture node sequences that contain more information. We then feed the sampled node context to Skip-Gram model. Extensive experimental results on several sorts of social networks demonstrate the superior performance of the proposed method over the state-of-the-arts.
Foreground Objects Segmentation in Videos with Improved Codebook Model

Extraction of foreground objects in real-time is a significant topic for applications in computer vision. Most of the proposed techniques use background subtraction technique to detect moving or static foreground objects in the scene. Despite ongoing lots of research, the domain has not reached mature status and needs more advanced and improved solutions. In this proposed system, background subtraction is done by improved codebook model-based method to get segmented foreground objects. In background modeling, the L*a*b* color space is used instead of RGB color space. This method has been tested with standard datasets and the accuracy of segmentation results are also evaluated. The experimental results demonstrate that the proposed method perform well under difference background subtraction challenges such as dynamic background, shadow, illumination changes and bad weather.
On the Performance Analysis of SPHINCS&lt;sup&gt;+&lt;/sup&gt; Verification

SPHINCS+, an updated version of SPHINCS, is a post-quantum hash-based signature scheme submitted to the NIST post-quantum cryptography standardization project. To evaluate its performance, SPHINCS+ gives the theoretical number of function calls and the actual runtime of a reference implementation. We show that the theoretical number of function calls for SPHINCS+ verification is inconsistent with the runtime and then present the correct number of function calls.
Using Convolutional Neural Network in Cross-Domain Argumentation Mining Framework

Argument Mining has become a remarkable research area in computational argumentation and Natural Language Processing fields. Despite its importance, most of the current proposals are restricted to a text type (e.g., Essays, web comments) on a specific domain and fall behind expectations when applied to cross-domain data. This paper presents a new framework for Argumentation Mining to detect argumentative segments and their components automatically using Convolutional Neural Network (CNN). We focus on both (1) argumentative sentence detection and (2) argument components detection tasks. Based on different corpora, we investigate the performance of CNN on both in-domain level and cross-domain level. The investigation shows challenging results in comparison with classic machine learning models.
Semi-Random Forest Based on Representative Patterns for Noisy and Non-Stationary Data Stream

Noise often exists in the data stream, and the data distribution may change as time evolves, that is, concept drift, which makes the previous decision boundary of classifier is no longer suitable to new data, resulting in poor performance. To deal with these issues, this paper proposes a pattern-based classifier named Closed Frequent Pattern based Semi-Random Forest (CFPSRF), which adopts closed frequent patterns for the representation of the raw data to remove redundant information and noise. Meanwhile, a change measure for pattern sets is proposed, which measures the magnitude of distribution change by the mined patterns to determine whether the classifier needs to be updated. To evaluate the performance of CFPSRF, we perform experiments using real-world datasets and synthetic datasets respectively under MOA. The experimental results show that our method outperforms the related algorithms used for comparison in average classification accuracy, and can deal with the issues of concept drift and noise effectively.
Deep Domain Adaptation on Vehicle Re-Identification

For vehicle re-identification, it is a task of searching all pictures in gallery and finding all vehicle images with the same ID as the given images. Despite using deep learning, we have achieved excellent results in vehicle re-identification. However, there is a huge challenge in vehicle re-identification. The vehicle model we have trained can only work well on particular set of datasets. When transferring to other datasets, its performance is not satisfactory. Domain adaptation is mainly used to solve the problem. As far as we know, the paper first use domain adaptation for vehicle re-identification, used to improve vehicle re-identification in cross dataset performance. The paper uses resnet as the basic skeleton network, adding Maximum Mean Discrepancy (MMD) to the optimization goal of the network, and extend it to multiple-kernel. It is found that the performance of vehicle re-identification has been improved on the basis of transferring directly through experiments.
Anomaly Detection Based on the Global-Local Anomaly Score for Trajectory Data

Anomaly detection of trajectory data is important and challenging in many real applications. Many anomalous trajectory detection algorithms have been developed, however most of them cannot well handle the complex trajectories with varying local densities. Additionally, trajectory similarity measure is usually difficult for complex trajectory data. In this paper, to address these two issues, we develop a novel anomalous trajectory detection technique with two important points. First we propose a new abnormal score, Global-Local Anomaly Score (GLAS), to sensitively quantify the abnormal degree of trajectories with varying local densities. Second an effective and fast measure, Extended Power enhanced Euclidean Distance (EPED), is designed to calculate the trajectory similarity. The proposed technique is evaluated on both synthetic and real-world trajectory data, showing that the new anomaly detector outperforms both classic and state-of-the-art methods.
Advanced Decisions in Technical and Medical Applications: An Introduction

This chapter presents a brief description of chapters pertaining to advanced decisions for technical and medical systems. Recent research results in the image and videos processing, transmission, and image analysis are included in Part I, while a wide spectrum of algorithms for medical image processing are included in Part II of this book. Each chapter involves detail practical implementations and explanations.
Speaker Verification Systems: A Comprehensive Review

In this paper, we presentBaruah, Ujwala a detailed review of the approaches for speaker verification.Laskar, Rabul Hussain Speaker verification is a binary class problem of eitherPurkayashtha, Biswajit accepting or rejecting a claimant speaker on the basis of the matching accuracy of the claimant’s test utterance with his/her train utterance. It has been observed that text-independent speaker verification gives much more flexibility as compared to text-dependent speaker verification. However, it is easier to attain better verification accuracy in the text-dependent mode as compared to its counterpart. In addition, our focus has been on giving an overview of the most popular databases available for this purpose as well as on the various feature extraction methods and modeling tools used in the speaker verification systems.
Environmental Geochemistry of Heavy Metals and Plagioclase Background Enrichment Factor in Coastal Sediments at Lumpue - Parepare, South Sulawesi, Indonesia

Abstract Lumpue Beach is an urban area in Parepare Bay with a harbour, tourism, and a landfill site. The purpose of this research was to determine the provenance, level and distribution of heavy metals in coastal sediments. Surface sediment samples were analysed for grain size, mineragraphy, petrography and total heavy metal content (Cu, Zn, Pb, Co, Ni, Fe). The minimum-maximum background enrichment factor (EF) range was calculated from the plagioclase mineral content of trachyte porphyry and andesite. Weathering alteration was quite high in trachyte porphyry rocks; andesite mineragraphy produced predominantly sedimentary plagioclase (25-45%), hornblende (10-40%), and rock fragments (40-45%). The grab sampling method produced samples dominated by plagioclase (30-50%), hornblende (15-40%) and alkaline chemical conditions were important in Fe, Ni, Pb, Cd, Cr, and Cu enrichment. Enrichment of heavy metals was supported by the mesokurtic-very leptokurtic dynamic sorting characteristics of Lumpue beach, becoming very leptokurtic - platykurtic to the north (Parepare Bay). The enrichment factor (EF) of Pb &gt; 1 was calculated from the concentration of various non-earth sources. The order of heavy metal concentration based on EF 0.5 ≤ EF ≤ 1.5 was Pb&gt; Zn&gt; Cr&gt; Fe&gt; Cu&gt; Ni, with Cu ≤ 0.5, indicating heterogeneous heavy metal sources correlated with urban area, river and ultrabasic regional lithology, with Trachyte-andesite alteration, especially enrichment in heavy metals Ni, Cr, Cu. Index of geoaccumulation (Igeo) was in the ranges: uncontaminated to heavily contaminated for Fe; uncontaminated to moderately contaminated for Cu, Zn, Ni, Cr and Cd; and uncontaminated for Pb. The intensity of heavy metal contamination followed the direction of sedimentation from various sources. Euclidian Cluster distance showed heavy metal distribution patterns correlated with rivers passing through urban areas and with regional geological lithology.
Virtio Front-End Network Driver for RTEMS Operating System

Virtualization technology can provide a transparent and efficient software development environment at the development phase of embedded systems. In addition, virtualization can be exploited to provide strong resource isolation between real-time applications. However, since virtualization incurs noticeable overheads, we need to minimize these to utilize virtualization technology for either a software development environment or a runtime environment of real-time embedded systems. In this letter, we particularly focus on virtualization overheads of network I/O and present an implementation of standardized (i.e., virtio) front-end network driver for a real-time operating system (RTOS). Although there have been several studies for virtio on general-purpose OS, our implementation is the first open-source virtio for an RTOS. The measurement results show that our implementation can improve the bandwidth and latency up to 168% and 52%, respectively. In addition, the memory usage can be saved up to 36%.
Authoring Tool for Generating Multiple Experiences of 360° Virtual Reality

360° video is a special form of digital content which provides visual details of 360° world around the viewer. While watching such video using head mounted display (HMD), it immerses the viewer with experience of a different place. This paper introduces a novel authoring tool which enables the viewers to create multiple interesting experiences of a 360° video. Viewers can read descriptions of experiences provided by their creators and choose anyone to watch. This tool assists users to efficiently follow the selected experiences while watching 360° video. Major challenge in existing authoring tools is that they are difficult to learn. We have made authoring easy for user to create experiences of 360° video by simply watching in virtual reality by wearing an HMD. Another problem in previous authoring tools is that it is difficult to author multiple video experiences of a 360° video. This authoring tool has made it easy.
Enhanced adaptive patch based exemplar image inpainting

Image inpainting is a process in which missing or deteriorated portions of an image is reconstructed. In the proposed framework, an adaptive patch updating strategy for the basic Criminisi's exemplar-based image inpainting is implemented. Patch matching is the supreme stage of exemplar-based inpainting, but Criminisi uses fixed size patches for the matching process. The proposed method improves the existing approach by adaptively setting the size of patches. A four-level discrete wavelet transform is used to identify whether the host image is a smooth or detailed one. The proposed method could exhibit faster inpainting results in smooth images. For smooth images, it will make large patches, and with these large patches, the algorithm process patch matching and inpainting. Series of experiments conducted for inpainting different images. Compared to existing systems, the outcomes of the proposed framework were favorable in smooth images.
Leveraging asynchronous parallel computing to produce simple genetic programming computational models

Traditionally, reducing complexity in Machine Learning promises benefits such as less overfitting. However, complexity control in Genetic Programming (GP) often means reducing the sizes of the evolving expressions, and past literature shows that size reduction does not necessarily reduce overfitting. In fact, whether size consistently represents complexity is itself debatable. Therefore, this paper proposes evaluation time of an evolving model - the computational time required to evaluate a model on data - as the estimate of its complexity. Evaluation time depends upon the size, but crucially also on the composition of an evolving model, and can thus distil its underlying complexity. To discourage complexity, this paper takes an innovative approach that asynchronously evaluates multiple models concurrently. These models race to their completion; thus, those models that finish earlier, join the population earlier to breed further in a steady-state fashion. Thus, the computationally simpler models, even if less accurate, get further chances to evolve before the more accurate yet expensive models join the population. Crucially, since evaluation times vary from one execution to another, this paper also shows how to significantly minimise this variation.
Augmented Reality-Based Instructional Media for Electrical Power Protection Learning

Abstract Augmented reality is a technology that combines two-dimensional and/or three-dimensional virtual objects into a real three-dimensional environment and projects those virtual objects in real time. This study was aimed at discussing (1) the development of an instructional media for power system protection learning, especially the fuse cut out, using augmented reality, (2) the appropriateness of the developed media as evaluated by both material and media experts, and (3) the users’ responses toward the developed media. This media was developed using the waterfall model proposed by Pressman, consisting of four main stages: analyzing, designing, coding, and testing. The performance test proved that this media functioned properly (100%). This media was categorized as “highly appropriate” in terms of the materials presented as evaluated by the material experts with a mean score of 96.5 out of 112. Meanwhile, the media was “appropriate” as evaluated by the media experts with a mean score of 68 out of 84. For the end users of this media, 75% of them perceived this media as “highly appropriate”, while the rest considered it as “appropriate”.
Model evaluation in computational historical linguistics

Abstract This is a reply to the comments by Hammarström et al. (This volume) and List (This volume) on the target article Computational Historical Linguistics (This volume). There I proposed several methodological principles for research in Computational Historical Linguistics pertaining to suitable techniques for model fitting and model evaluation. Hammarström et al. debate the usefulness of these principles, and List proposes a novel evaluation measure specifically aimed at the task of proto-form reconstruction. This reply will focus on the role of model evaluation in our field.
Intra-Modality Feature Interaction Using Self-attention for Visual Question Answering

Better capturing the interactions of different modality is a hot research topic in visual question answering (VQA) recently. Inspired by human vision information processing, a method of VQA based on intra-modality features interactive with self-attention mechanism (IMFI-SA) is proposed. We adopted object-level features with bottom-up attention instead of feature mapping to extract the fine-grained information in images. Moreover, the interactions of intra-modality in the question and the image modality is also extracted by proposed IMFI-SA model respectively. Finally, we combined the enhanced object-level features interaction using top-down cross-attention and the question features interaction to predict the answer given a question and image. Experimental results on the VQA2.0 dataset show that the proposed method is superior to the existing method in the reasoning answer generating, especially in counting problems.
ComNE: Reinforcing Network Embedding with Community Learning

Learning network embedding for large-scale networks have been attracting increasing attention due to their importance in supporting numerous network analytic and data mining tasks such as node classification, clustering and visualization. In this paper, we present a novel framework for learning large-scale network embedding incorporating network topology and community structural information. Most existing network embedding methods tend to embed network topology and ignore the partially labeled community structure information that exist in real-world networks and thus are unable to efficiently learn and capture the community structure of real-world networks. Unlike existing works, our framework integrates the network topology and community structure into the learning process. We propose a deep autoencoder model to generate low-dimensional feature representations efficiently through learning network reconstruction and community classification tasks. The experimental results on several real-world networks show that our framework outperforms the state-of-the-art methods.
Deep Learning Framework for Single and Dyadic Human Activity Recognition

Recently, human activity recognition in videos attracts much attention in the computer vision community because of its broad real-life applications. In this context, we introduced a robust two-stream deep learning model with less complexity which utilized only the raw RGB color sequences and their dynamic motion images (DMIs) to recognize complex human activities. The RGB frames are trained using a pre-trained Inception-v3 network and CNN-LSTM with end-to-end training and for dynamic image stream, we fine-tuned the last few layers of the pre-trained model. Through our two-stream network, the features extracted from both, are max fused to increase the classification accuracy. The proposed approach has been evaluated over single-person activity dataset MIVIA Action as well as dyadic SBU Interaction dataset. Our model obtained significant performance improvement over existing similar approaches.
Distributed Online Ensemble Learning Based on Orthogonal Transformation

A new distributed online learning scheme for classifying data captured from distributed data sources is proposed in this paper. The scheme consists of multiple distributed learners that independently classify different streams of data. Each local learner uses an ensemble classifier trained by shared data to make a prediction. We propose a novel form of shared data, that is, the covariance matrix and mean vector, that has small and stable network traffic when transmitted between nodes. Then, we provide a systematic online ensemble learning approach based on these shared data. In contrast to boosting and bagging, our proposed learning approach is based on orthogonal transformation, which can increase the differences between individual learners without a significant loss in accuracy. Moreover, we discuss the ensemble maintenance method based on weight to adapt the underlying data dynamics. Empirical studies demonstrate the effectiveness of our approach in comparison to existing state-of-the-art methods on several datasets.