Reviewerly/exHarmony huggingface dataset:
A Blockchain Applications Overview

This paper presents a general review of blockchainBlockchain applicationsApplications. It addresses financial applicationsApplications that are the origin of this recent technology, Internet of things (IoT) applicationsApplications and also logistics and supply chain areas. Additionally, other application areas are reviewed as reference models for further research. The most widely employed modelling approaches and software tools are also identified. Here HyperLedger, which is a private blockchainBlockchain, seems to be the most convenient one for supply chains. Finally, a critical discussion about the reviewed articles is carried out.
A Variational Bayesian Approach for Partly Resolvable Group Tracking

The partly resolvable group tracking is significant for the anti multi-agent system, in which some individual targets of the group may generate only one measurement, and the association relationships between the individual targets and measurements are unknown. To estimate the group state, the individual target states, and the association variables accurately, this paper proposes a novel tracking algorithm considering the resolvability of group. Firstly, a unified Bayesian framework for partly resolvable group tracking is formulated, including data association and state estimation. Secondly, a variational Bayesian approach is derived using the Kalman filter and random matrix model, which recursively estimates the states of the group and its individual targets. Thirdly, to reduce the computational burden, the complex data association problem is modeled by a factor graph that is solved by message passing. Finally, simulation results show that the proposed algorithm has a significant improvement over the previous variational group tracking algorithm in terms of state estimation.
IFPDSO-PS: A Hybrid Approach for Global and Local Optimization

The nature inspired algorithms have motivated the practitioners to solve complex real-world problems. These algorithms are more capable to approach the optimal solution faster than conventional methods. The proposed algorithm uses the exploration capability of the Improved Flower Pollination algorithm with dynamic switch probability and swap operator (IFPDSO) and exploitation capability of Pattern Search (PS) to approach the optimal solution efficiently. The hybridization of IFPDSO and Pattern Search (IFPDSO-PS) has been validated on various benchmark functions and compared with other hybrid algorithms to evaluate its better performance.
Integrating Nôm Language Model Into Nôm Optical Character Recognition

To help preserve Nôm script—an ancient ideographic writing system of Viet Nam, we work on the task of building Nôm Optical Character Recognition (OCR) model for Nôm woodblock-print images. Among top-N results of a character prediction, we pick out a single and more accurate result using Nôm language model (LM). An OCR-LM weighting mechanism is introduced to integrate the language model into the OCR baseline as a post-processing stage. Our OCR baseline’s top-1 achieves 98.91% mAP on training dataset Truyện Kiều (1871), 71.80% mAP on testing dataset Truyện Kiều (1902) and 72.96% mAP on testing dataset Truyện Lục Vân Tiên. The integration of the Nôm language model (whose training corpus is limited) helps boost top-1 performance by a small margin: +0.04% on an already high result of the training dataset, +2.72 and +0.14% on the two testing datasets, respectively.
A Novel Approach Based on RCNN for Object Detection Through Webcam

Today’s generation object detection has become a forefront in applications of deep learning techniques in which extracting of features are better compared to conventional techniques. In this regard detecting desired object has become an important aspect in many applications ranging from autonomous vehicles to video compression techniques. In this paper the author proposes a Region Convolution Neural Networks for detecting desired objects through a webcam.
Evolutionary Ensemble Adversarial Learning for Identifying Terrorists Among High-Speed Rail Passengers

As one of the most salient features of China’s economic development, high-speed rail (HSR) is considered to be an attractive target and travel mode for terrorists. Distinguishing potential terrorists from normal passengers is of critical importance to public security, but very challenging because terrorists constitute only a very small fraction of HSR passengers, especially when they can disguise their attributes and behaviors to deceive the classifiers. For this extremely imbalanced classification problem, we propose a novel evolutionary generative adversarial network (GAN) ensemble method, where each GAN in the ensemble simultaneously trains a discriminator to identify abnormal samples from a large number of passenger profiles and trains a generator to produce abnormal samples that are disguised as normal ones in a subspace of the sample space, and the final classifier combines these GANs using an evolutionary fusion method. Experiments on benchmark problems demonstrate that the proposed ensemble adversarial learning method has very competitive performance compared to popular imbalanced classifiers. The successful applications in terrorist identification for China Railway also demonstrate the feasibility and effectiveness of our approach.
A Binary PSO-Based Model Selection for Novel Smooth Twin Support Vector Regression

The recently proposed smooth twin support vector regression, denoted by STSVR, gains better training speed compared with twin support vector regression (TSVR). In the STSVR, sigmoid function is used for the smooth function, however, its approximation precision is relatively low, leading to the generalization performance of STSVR is not good enough. Moreover, STSVR has at least three parameters that need regulating, which affects its practical applications. In this paper, we increase the regression performance of STSVR from two aspects. First, by introducing Chen-Harker-Kanzow-Smale (CHKS) function, a new smooth version for TSVR, termed as smooth CHKS twin support vector regression (SCTSVR) is proposed. Second, a binary particle swarm optimization (PSO)-based model selection for SCTSVR is suggested. Computational results on one synthetic as well as several benchmark datasets confirm the great improvements on the training process of proposed algorithm.
BugCat: A Novel Approach to Bug Number Categorization with Multi-modal Time Series Learning

It is of great importance to categorize the number of bugs over time for both software project managers and its end users. This paper proposes a novel approach called BugCat (i.e., Bug number Categorization) to categorize the bug numbers of software with multi-modal time series learning. The time series derived from the five modalities are used as the inputs of the proposed BugCat approach as the bug number, the day of the week, the day off, bug severity and bug priority. Then, the LSTM (Long Short-Term Memory) embedding is conducted on the five modalities of times series separately and, the concatenated vectors derived from data fusion on the five LSTM embeddings are used as the input of the full-connected neural network with ReLU (Rectified Linear Unit) activation to categorize the bug numbers of software. The extensive experiments with the Mozilla Firefox bug data demonstrate the superiority of the proposed BugCat approach over state-of-the-art techniques including multi-layer perceptron (MLP), fully convolutional network (FCN) and LSTM.
Explainable, Stable, and Scalable Network Embedding Algorithms for Unsupervised Learning of Graph Representations

Network embedding that maps nodes in a graph to vectors in a Euclidean space is a very powerful method to address various tasks on a graph. However, most network embedding algorithms, in particular, graph neural networks (GNNs), are difficult to interpret and do not scale well to handle millions of nodes. In this article, we tackle the problem from a new perspective based on the equivalence of three constrained optimization problems: the network embedding problem, the trace maximization problem of the modularity matrix in a sampled graph, and the matrix factorization problem of the modularity matrix in a sampled graph. The optimal solutions to these three problems are the dominant eigenvectors of the modularity matrix. We propose two unsupervised learning algorithms that belong to a special class of graph convolutional networks (GCNs) for solving these problems: 1) Clustering As Feature Embedding (CAFE) and 2) Sphere. Both algorithms are stable trace maximization algorithms and yield good approximations of dominant eigenvectors. Moreover, there are linear-time implementations for sparse graphs. Various experiments are conducted to evaluate our algorithms and show that our proposed algorithms outperform several baseline methods.
A Classification of Information-Oriented Pos Technology from Customer Perception

PoS technologies are not only very widespread today, but are capable of mastering different requirements. When it comes to investigating how customers use PoS technologies or which elements might promote their adoption, it is important to know, which characteristics are particularly relevant and important from the perspective of customers. Based on three more recent approaches of PoS technology systematisation, this research empirically categorizes PoS technologies from the customer's point of view. We investigated PoS technologies that are information-oriented and contain a certain form of interaction capability. In a first step, we selected seven information-oriented PoS technologies for the research. These PoS technologies were used to generate a similarity matrix using multidimensional scaling (MDS). Hereby, an online study (N = 830) was conducted. In a next step, three independent clusters of PoS technologies were identified: (A) comprehendable PoS technologies, (B) information-integration PoS technologies, (C) entertainment-related PoS technologies.
Saliency Map Using Features Derived from Spiking Neural Networks of Primate Visual Cortex

We propose a framework inspired by biological vision systems to produce saliency maps of digital images. Well-known computational models for receptive fields of areas in the visual cortex that are specialized for color and orientation perception are used. To model the connectivity between these areas we use the CARLsim library which is a spiking neural network(SNN) simulator. The spikes generated by CARLsim, then serve as extracted features and input to our saliency detection algorithm. This new method of saliency detection is described and applied to benchmark images.
Development of AR-Manner 1.0 as a Learning Media for Communication Ethics to Contact Lecturers in Higher Educations using Augmented Reality Technology

Innovative learning techniques need to be carried out in the 21st century today, where technological developments are growing very rapidly and require innovative learning to improve ethics education in a higher education environment.One of the innovative technologies that have a major impact on scholars is Augmented Reality (AR), [1).The purpose of this research is to develop AR-Manner 1.0 as a medium to introduce communication ethics using Augmented Reality in the PTI UMS environment.The method used in this research is Research and Development.The first stage is a needs analysis and literature study.The second stage is to design media using use cases.The third stage is the implementation or manufacture of learning media AR-Manner 1.0 and testing the functionality and compatibility tests by media experts.The fourth stage is the software testing stage which includes 2 stages of testing, namely verification and validation tests and knowing the quality and feasibility.The results of the analysis show that the AR-Manner 1.0 learning media is feasible to use.However, this research still needs further research for the development of better media.
Image Enhancement for Surveillance Videos using Quick Browsing system

Surveillance cameras have been widely installed in large cities to monitor and record human activities for different applications. Since surveillance cameras often record all events 24 hours/day, it necessarily takes huge workforce watching surveillance videos to search for specific targets, thus a system that helps the user quickly look for targets of interest is highly demanded. This paper proposes a quick surveillance video browsing system with colour image enhancement. The basic idea is to collect all of moving objects which carry the most significant information in surveillance videos to construct a corresponding compact video by tuning positions of these moving objects. The compact video rearranges the spatiotemporal coordinates of moving objects to enhance the compression, but the temporal relationships among moving objects are still kept. The compact video can preserve the essential activities involved in the original surveillance video. This paper presents the details of browsing system and the approach to producing the compact video from a source surveillance video. At the end we will get the compact video with high resolution.
In-App Cryptographically-Enforced Selective Access Control for Microsoft Office and Similar Platforms

The interplay between cryptography and access control has been widely investigated in the literature. For example, attribute-based encryption (ABE) is a leading candidate of a cryptographic tool going beyond the all-or-nothing approach of public-key encryption by supporting fine-grained access control for encrypted data. Unfortunately, the deployment and adoption of ABE have been slow, and (to the best of our knowledge) few commercial widely-used products use it to date. In particular, selective and fine-grained control over what is shared, and with whom, is absent from common data products and formats, such as those generated by commercial authoring products, e.g., Microsoft Word documents, Excel spreadsheets, PowerPoint slides. This lack of selective and fine-grained control results in users simply not sharing. This major usability shortcoming impacts defense and military coalition operations, as well as commercial settings, such as life sciences, healthcare, and the financial sectors. This paper addresses the above usability problem head-on by proposing a crypto- graphically enforced selective access control in Microsoft Office products and similar platforms. We focus on Excel as an illustrative use-case, but note that our work is applicable to (and is already implemented for) other Microsoft products such as Word, PowerPoint, and Outlook. Using the JavaScript API for Microsoft Office, we designed and developed simple add-ins that enable cell encryption according to a policy, and requires a key that embeds attributes satisfying the policy in order to decrypt. Our performance evaluation not only shows that cryptographic-based selective sharing of information in widely-deployed and widely-used commercial authoring and collaboration platforms is possible, but also practical.
Power Grid Fault Diagnosis Model Based on the Time Series Density Distribution of Warning Information

Fast and intelligent power grid fault diagnosis is a key means to improve the intelligence level of power grid dispatching. Currently, data-driven power grid fault diagnosis lacks a method to quantify the time series characteristics of alarm information, and it is difficult to build a deep learning model. This paper proposes a power grid fault diagnosis method based on the time series density distribution of alarm information. First, the alarm information is classified, quantized time sequence feature extraction is performed for the device, and the obtained quantized time sequence features are superimposed. After graphical coding, a time series density distribution map of the alarm information of each device and a mosaic map reflecting the overall fault event are generated, providing a unified data representation for the deep learning model. Then, a faulty equipment type classification model and equipment state judgment model are constructed based on a residual network. The faulty equipment and fault type are determined using a sequential evaluation strategy of first determining the type of faulty equipment and then determining the state of the equipment. The proposed method can meet the requirements of on-line fault diagnosis of a power grid and provide emergency decision support for dispatchers.
Comparison of Different Supervised and Self-supervised Learning Techniques in Skin Disease Classification

For years now, The International Skin Imaging Collaboration has been providing datasets of dermoscopic images. Several studies show that dermoscopy provides improved diagnostic accuracy, in comparison to standard photography. Excellent results have been obtained that even exceed human performance. In this paper we broke the state of the art for the dataset provided for the ISIC 2019 challenge. In this work were compared the performance of various convolutional networks, various data augmentations, and various cost functions and optimizers. Results obtained using transfer learning from IMAGENET were compared with the performance obtained using BYOL (bootstrap your own latent), a self-supervised technique. Moreover, it has been demonstrated that self-supervised learning techniques can be used in this field improving the performance of the network compared to training from scratch. We were obtained a balanced multiclass accuracy (BCA) of 87% in the test and validation dataset and with a top-2 accuracy of 97%.
A Hybrid Machine Learning Method for Movies Recommendation

Recently, the application of machine learning algorithms is very useful in marketing by companies nowadays. Overall, it has become a big factor on the companies success and growth in term of the number of users or revenues, since it helps to suggest the right content to the right people in an easy way without going through a long complicated process to choose an element in a list of millions elements. This research has a goal of evaluating several recommending mining algorithms in machine learning by adopting a model that combines the content-based (constrained system to people) and collaborative approach and compares it with a paralleled algorithm, and we assume that can help to get the right recommendations to users. The model’s results show that it can positively solve this issue and help users to find the right content that they want to watch, and also predict if they like the new trending content.
Ontology Engineering Methodologies: State of the Art

Recently, the use of ontologies has become more popular in both academia and industry fields. However, many ontology projects have failed due to, at least in part, a lack of discipline in the development process; that is, the poorly specified, underspecified, or lack of requirements and evaluation criteria. Therefore, it is reasonable to ask: what is the most prominent methodology to develop an ontology? To answer this question in this paper, we reviewed the most prominent ontology engineering methodologies, and analyzed the most mature and suitable approaches for the development of ontology based on a set of criteria. We also provide a better understanding of ontology engineering methodology, most used ontology engineering methodologies, current prominent methods, and future research scope for standard ontology engineering methodology. This study shows that no methodology enjoys consensus among the community, and none of these methodologies are mature enough and without limitations. Finally, we concluded that Krisnadhi & Hitzler methodology remains very practical and much more detailed in a step-by-step manner. The fact that it is based on ontology design patterns ensures that there is a trade-off between interoperability, on the one hand, and over-commitment and conflicting requirements on the other hand.
Digital Inclusion, Psychological Well-Being, and Digital Competence

Although a large proportion of adolescent students regularly use digital devices and the internet, the effects of digital technology on psychological health and digital competence are still a matter of policy and scholarly debates. Using 2018 PISA data, this chapter examines the influences of ICT use at home and in school on adolescents’ sense of belonging at school, positive psychological feelings, sense of purpose and meaning in life, and perceived ICT competence. Results of multilevel analyses across 22 developed countries suggest that ICT use for schoolwork both at home and in school increases adolescents’ perceived ICT competence. Although school ICT use has minimal positive effects on students’ psychological well-being in a few countries, the benefits of home ICT use are substantial. ICT use has equal or greater positive effects on perceived ICT competence and psychological well-being for low-SES students in half of the developed countries (e.g., the United Kingdom and Iceland), and greater positive effects for high-SES students in the other half. This suggests that some countries are more capable than others in implementing ICT use in education, which may mitigate some of the disparities in adolescent students’ psychological well-being.
When Virtual Reality Helps Fathom Mathemusical Hyperdimensional Models

Mathemusicians have always produced models for understanding, analyzing or computing music. We are used to visualize some of them on paper, in a theater or on a computer screen. Even if they refer to multidimensional spaces (3D-4D), while displaying these models on a computer screen the viewer ends up with a 2D picture, or a movie. Planar projection limits the perception, nowadays, in the era of virtual reality, we propose tools and solutions to better apprehend these models and give the viewer an improved immersive experience. Taking advantage of methods used in air traffic simulations, we are developing techniques that we will apply to existing mathemusical visualizations, beginning with Tonnetze and Hyperspheres. We herewith introduce two recently revealed mathemusical models that we have created: 2D: The Shadow Tonnetz, our latest extension of the Tonnetz that keeps trace of a harmonic path. 4D: The Entangled Hyperspheres, a combination of two Planet-4D models that enables us to visualize microtonal music. The images in this paper are extracted from immersive virtual reality world; during MCM we intend to presented the movies with adapted 3D equipment. All videos including virtual ones will be available on www.mathemusic.net .
A Novel Optimization Approach Towards Improving Compactness and Separability of Clusters

The objective functions in optimization models of the sum-of-squares clustering problem reflect intra-cluster similarity and inter-cluster dissimilarities and in general, optimal values of these functions can be considered appropriate measures for the compactness of clusters. However, the use of the objective function alone may not lead to the finding of separable clusters. To address this shortcoming in existing models for clustering, we develop a new optimization model where the objective function is represented as a sum of two terms reflecting the compactness and separability of clusters. Based on this model we develop a two-phase incremental clustering algorithm. In the first phase, the clustering function is minimized to find compact clusters and in the second phase, a new model is applied to improve the separability of clusters. The Davies-Bouldin cluster validity index is applied as an additional measure to compare the compactness of clusters and silhouette coefficients are used to estimate the separability of clusters. The performance of the developed algorithm is demonstrated and compared with that of two other algorithms on some synthetic and real-world data sets. Numerical results clearly show that in comparison with other algorithms the new algorithm is able to find clusters with better separability by preserving the compactness of clusters.