Reviewerly/exHarmony huggingface dataset:
An Emerging Digital Ecosystem: Blockchain Competence Certification Networks

In this paper, we investigate how blockchain technologies improve the certification system and generate added value for different involved actors - learners, educational institutions, and businesses. An exploratory study is proposed to systematize the overall impacts of blockchain in the field of digital certification while focusing on university education as the main research area. We carried out a desk analysis, direct observation/focus groups and semi-structured interviews with key players of the two Italian universities which had first adopted certification and blockchain-based-certification systems. The aim is to understand the drivers and value generation conditions in the new scenario introduced by the development of DACS (Digital Academic Certification System) through insight into the relationships between actors in the ecosystem that characterize the different types of complementarities between actors and services before and after the introduction of blockchain-based platforms and the personal wallet containing titles and certifications. Three main findings arose from the study related to actors, ecosystem, and complementarities. The main theoretical contributions of this study is that it produces the first empirical evidence of the new Ecosystem Theory proposed by Jacobides et al. []. From the managerial perspective, this study contributes to better identify and debate the conditions and success factors unlocking value generation and benefits embedded in Digital Certification Ecosystems.
Iterated Lower Bound Formulas: A Diagonalization-Based Approach to Proof Complexity.

We propose a diagonalization-based approach to several important questions in proof complexity. We illustrate this approach in the context of the algebraic proof system IPS and in the context of propositional proof systems more generally. We use the approach to give an explicit sequence of CNF formulas {φn} such that VNP ≠ VP iff there are no polynomial-size IPS proofs for the formulas φn. This provides a natural equivalence between proof complexity lower bounds and standard algebraic complexity lower bounds. Our proof of this fact uses the implication from IPS lower bounds to algebraic complexity lower bounds due to Grochow and Pitassi together with a diagonalization argument: the formulas φn themselves assert the non-existence of short IPS proofs for formulas encoding VNP ≠ VP at a different input length. Our result also has meta-mathematical implications: it gives evidence for the difficulty of proving strong lower bounds for IPS within IPS. For any strong enough propositional proof system R, we define the *iterated R-lower bound formulas*, which inductively assert the non-existence of short R proofs for formulas encoding the same statement at a different input length, and propose them as explicit hard candidates for the proof system R. We observe that this hypothesis holds for Resolution following recent results of Atserias and Muller and of Garlik, and give evidence in favour of it for other proof systems.
Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of \(\sim \)7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).
The Applications of the PHM in INS Area

PHM is an effective technique of which can advance the capability of the colligating Fault Diagnosis and reduce the use and the RMS (Reliability maintainability Security) fee of the government equipment. By absorbing the advanced overseas experience, PHM is embedded researched. It is combined the idiographic points of INS, and confirmed frame of PHM. The INS fault prognosis model which is based on PHM is proposed in the paper. It is detailedly expatiated on the INS PHM method, and take an example about the key circuits in INS to explain the scenario of the PHM. At last, it is expatiated the manage points about PHM in INS area. By the applications of the system, it is a large right-about change about the INS (Inertia navigation system) maintenance methods, viz. from the traditional Diagnosis method which based on the sensor to the prognosis method which based on the intellectualized system and from the afterwards or time-lapse maintain to the maintain method which based on the state maintain.
Multi-objective Automatic Clustering with Gene Rearrangement and Cluster Merging

As an unsupervised machine learning method, clustering is an important approach to understanding structural information in data. However, current adaptive clustering approach using multi-objective optimization framework have two apparent limitations. The first is that prior knowledge is needed to identify the correct cluster number. The second is difficulty in evaluating the best clustering solutions from the Pareto Optimal Front (POF) generated by a multi-objective optimization. These problems become severer in non-category datasets. Therefore, the primary goal of this research is to establish a genetic optimization based multi-objective clustering framework, in which multiple clustering validity indexes (CVIs) can be tested simultaneously to automatically obtain the optimal cluster number without knowing any sample label information in advance. In this effort, we will not only be able to consider clustering measurements such as cluster cohesion and separation, but also take other aspects, such as compactness, connectivity, variation among data elements, into consideration as well. Then, we aim to design a procedure to recommend three best solutions from the POF by using appropriate combination of CVIs without increasing computational cost. This procedure is expected to control the cluster number in a reasonable range and consequently decrease the difficulty in best solution recommendation. Finally, since we have the knowledge that using gene rearrangement in the genetic optimization does not affect partition, we take this advantage to merge clusters effectively and significantly speed the convergence of the algorithm. Our approach can outperform the state-of-the-art counterparts across diverse benchmark datasets in terms of partitioning accuracy and performance, as demonstrated in three experiments conducted on both artificial and typical real-world datasets.
Development of a 3D User Interface for Programming a Robotic Arm Using Virtual Reality

At present, there has been an increase in the use of virtual tools in different fields such as: medicine, education, agriculture and industrial security. The present paper is based on developing a graphical environment that enables to carry out the simulation of the Mitsubishi RV-2AJ robot movements by means of VR, implementing a virtual environment which combines the Visual Studio and Unity development platforms. The virtual scene was made using the software Unity and the robotic arm was designed using Autodesk Inventor Professional; the environment has a menu that enables interacting by means of the stereoscopic visualization helmet (HTC VIVE PRO) through the action of elements such as the slider and the buttons where the movements are reproduced in the virtual environment; this application involves an extensive study of the degrees of freedom and joints of the robotic arm, so that working ranges can be established together with the virtual menu, in turn, when analyzing through the homogeneous matrix, it is possible to obtain the necessary equations to establish said ranges. The project intends to be an educational innovation projection and a training guide for the student, increasing the interactivity with the functionalities of the robot in the virtual environment.
Exploration and Thinking on Comprehensive Practice Teaching of the 3D Printing Engineering Training

Engineering training, as an important part of the practice teaching of modern universities, plays a vital role in cultivating high-quality, high-level and innovative talents. Traditional engineering training solves the practice teaching problems in talents training. However, under the background of the manufacturing power strategy and emerging engineering education construction in the new era, it can no longer satisfy the needs of high-level, high-quality, and innovative talents in the new era. In order to play the role of the comprehensive practice of engineering training in cultivating innovative talents in the new era, it is urgent to explore and think a new model of comprehensive practice teaching of engineering training. This paper attempts to reform the comprehensive practice teaching model of engineering training, and cultivate high-level, high-quality and innovative talents by constructing an engineering training innovation system, integrating upstream and downstream related knowledge structures, building an open practice teaching platform, and forming a resource-complementary comprehensive training model. Finally, take the 3D printing practice training as an example to demonstrate the comprehensive practice teaching of engineering training.
Knowledge-based Sense Disambiguation of Multiword Expressions in Requirements Documents

Understanding the meaning and the senses of expressions is essential to analyze natural language requirements. Disambiguation of expressions in their context is needed to prevent misinterpretations. Current knowledge-based disambiguation approaches only focus on senses of single words and miss out on linking the shared meaning of expressions consisting of multiple words. As these expressions are common in requirements, we propose a sense disambiguation approach that is able to detect and disambiguate multiword expressions. We use a two-tiered approach to be able to use different techniques for detection and disambiguation. Initially, a conditional random field detects multiword expressions. Afterwards, the approach disambiguates these expressions and retrieves the corresponding senses using a knowledge-based approach. The knowledge-based approach has the benefit that only the knowledge base has to be exchanged to adapt the approach to new domains and knowledge. Our approach is able to detect multiword expressions with an F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -score of 88.4% in an evaluation on 997 requirement sentences. The sense disambiguation achieves up to 57% F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -score.
High-Dimensional Dataset Simplification by Laplace-Beltrami Operator

With the development of the Internet and other digital technologies, the speed of data generation has become considerably faster than the speed of data processing. Because big data typically contain massive redundant information, it is possible to significantly simplify a big dataset while maintaining the key information. In this paper, we develop a high-dimensional (HD) dataset simplification method based on the eigenvalues and eigenfunctions of the Laplace-Beltrami operator (LBO). Specifically, given a dataset that can be considered as an unorganized data point set in an HD space, a discrete LBO defined on the HD dataset is constructed, and its eigenvalues and eigenvectors are calculated. Then, the local extremum and saddle points of the eigenvectors are proposed to be the feature points of the HD dataset, constituting a simplified dataset. Moreover, we develop feature point detection methods for the functions defined on an unorganized data point set in HD space, and devise metrics for measuring the fidelity of the simplified dataset to the original set. Finally, examples and applications are demonstrated to validate the efficiency and effectiveness of the proposed methods, demonstrating that the developed HD dataset simplification method is feasible for processing a maximum-sized dataset using a limited data processing capability.
Math Word Problem Solving with Ensemble Learning

In the field of natural language processing, math word problem solving can understand the meaning of math problems through model learns data features and outputs corresponding answers. However, there are still problems such as low accuracy of the answers, weak algorithm robustness. This paper proposes an ensemble learning on Unified pre-trained Language Model (UniLM) and Graph2Tree, which is combined with graph-based encoder and tree-based decoder, to generate solution expressions. A combination model which combines multiple complementary models using weighted voting method to complete the automatically math word problems solving task of elementary school. This method won the third prize in the 8th CCF big data and computational intelligence competition in 2020 - primary school mathematics application question automatic solution track, and further confirmed the effectiveness of ensemble learning in transforming math text into expression.
Deep Collaborative Filtering based Recommendation System

A recommendation system seeks to make relevant suggestions to users based on their individual preferences. It primarily analyses existing data and identifies latent patterns within the same. Based upon these patterns user behaviour is predicted and thereby, suggestions are offered. In this paper, a collaborative filtering approach has been applied to a deep learning model to develop a recommendation system. An autoencoder was exercised as the fundamental building block in the construction of the model architecture. To achieve working functionality first, the model was trained on two datasets from movielens. Next, this model was compared against a Restricted Boltzmann Machine run model. The results were then compared and analysed.
Vector-Quantized Autoencoder With Copula for Collaborative Filtering

In theory, the variational auto-encoder (VAE) is not suitable for recommendation tasks, although it has been successfully utilized for collaborative filtering (CF) models. In this paper, we propose a Gaussian Copula-Vector Quantized Autoencoder (GC-VQAE) model that differs prior arts in two key ways: (1) Gaussian Copula helps to model the dependencies among latent variables which are used to construct a more complex distribution compared with the mean-field theory; and (2) by incorporating a vector quantisation method into encoders our model can learn discrete representations which are consistent with the observed data rather than directly sampling from the simple Gaussian distributions. Our approach is able to circumvent the "posterior collapse'' issue and break the prior constraint to improve the flexibility of latent vector encoding and learning ability. Empirically, GC-VQAE can significantly improve the recommendation performance compared to existing state-of-the-art methods.
Constructivism-Based Magnetic KIT Design as a Science Learning Media

Abstract Some students still feel foreign to hear about electrical material because the material that is passed by some considers it to be still abstract. So that the concept of electric magnetism is still a lot of misconceptions. Based on the research, there are also some teachers who make assignments to students for experiment at their respective homes which makes students not do experiment but imitate the existing theory. The research aims to design a magnetic KIT that can be used for practicum at school. This study uses a 4D development model which is limited to the media development stage. The development of KIT magnets designed with four experiments with precision values in each of experiment have a very high range from 0.93 to 0.99 which it can be categorized high and the highest precision. So that from very high precision the error percentage of the magnetic KIT experiments are the lowest ranging a the lowest precentage 1.8%-9.7% which is categorized as a very low percentage of the magnetic KIT device. So the accuracy of the magnetic KIT device can be said to be very high. For the first and second experiments the results obtained are not much different from the existing theory. The percentage of errors for the first and second experiments ranged from 1.8%-8.6% while the third and fourth experiments were carried out repeatedly but the results were not much different or almost the same.
Industrialization of quantum communication technologies

Abstract The growing number of cyber‐attacks together with the complexities of cyber risk management are driving the development of quantum‐based data security solutions. In this article, we look at the applications, challenges, and future business opportunities in the field of quantum key distribution, which is emerging as a promising solution to ensure highly secure data transfer, particularly in the telecom and finance sectors.
Photo-Realistic Virtual Try-On with Enhanced Warping Module

An image-based virtual try-on system focuses on virtually transferring a clothing item to a given person. In most of the approaches, garment transfer on a target image involves human parsing with pose estimation generating warped cloth, followed by an inpainting module. The generated output depends on the attributes of the final and intermediate stages. In our paper, we organized a relative study of methods adopted on different existing stages to bring up a better solution. We conduct our studies with reference to a state-of-the-art try-on model named adaptive content generating and preserving network (ACGPN). ACGPN transfers reference cloth to the target person and gives photo-realistic try-on results but fails when there is large dissimilarity in reference person image and cloth image which arises due to errors in the warping module. We propose an improved ACGPN model, with a key-points based warping module to improve the result.
Vision-based Semantics for Scene Comprehension

A stark increase in the vision-based applications in recent years has made the interpretation of imagery data a challenging problem at scale. For accurate visual scene comprehension, the need for accurate segmentation modules is witnessed to rise exponentially. This raises demands analysis of granular pixel information captured in different scenarios (direct/low lightning, reflections, fog/rain). This paper presents two non-trivial issues of existing semantic segmentation techniques which proves to be impeding factors for accurate scene understanding. Firstly, a review of existing semantic segmentation techniques for analysis of image data leading to the identification of a problem is presented. Second, the possible factors triggering the problem are identified which eventually marks the failure of optimal semantic segmentation results. Then, empirical evidence of the presence of the problem through erroneous results produced by testing existing solutions is presented. Finally, a hybrid framework is proposed by visually presenting different integrated modules to tackle the identified problem. The proposed framework has relevance to wide range of applications including facial recognition in online learning setups where image analysis of dynamic images is expected.
Characteristics of the Levels of Mechanisation in Arc Welding

Abstract Improvement of quality, reduction of the subjective possibilities of faults may be facilitated with the help of the technically rational and economically justifiable mechanisation of productive serial production as well as the use of the wide range of hardware and software IT possibilities. Mechanisation is usually understood as the mechanisation of technological operations that may otherwise be performed by manual operations (human movements and effort). Mechanisation is therefore the substitution, in whole or in part, of manual labour with the help of specialised equipment and the mechanical operation of the various movements. The present article examines the particular features of mechanisation in the field of outstandingly high significance in industrial applications, i.e. arc welding, assisting the forms of training applied in the specialty area.
Convolutional Autoencoder for Image Denoising: A Compositional Subspace Representation Perspective

This study explores a convolutional autoencoder for image denoising with a proposed compositional subspace method. This modeling approach presents a structural and rigorous mathematical abstraction to understand a convolutional autoencoder's functional computation layers. The theoretical basis is that the best way to model a complex learning function is by using a composition of simple functions to form a multilayer successive cascaded function for complex representation. The proposed method has experimented with the Fashion-MNIST dataset. Experimental results are discussed and were consistent with the theoretical expectation.
Image Recognition Using Unsupervised Learning Based Automatic Fuzzy Clustering Algorithm

This article proposes a novel techniques for unsupervised learning in image recognition using automatic fuzzy clustering algorithm (AFCA) for discrete data. There are two main stages in order to recognize images in this study. First of all, new technique is shown to extract sixty four textural features from n images represented by a matrix n × 64. Afterwards, we use the proposed method based on Hausdorff distance to simultaneously determine the appropriate number of clusters. At the end of the unsupervised clustering process, discrete data belonging to the same cluster converge to the same position, which represents the cluster’s center. After determining number of cluster, we have probability of assigning objects to the established clusters. The simulation result built by Matlab program shows the effectiveness of the proposed method using the corrected rand, the partition entropy, and the partition coefficients index. The experimental outcomes illustrate that the proposed method is better than the existing ones as Fuzzy C-mean. As a result, we believe that the proposed method is filled with a potential possibility which can be applied in practical realization.
FMAGAN:Fusing multiple attention and generative adversarial network to enhance underwater image

Underwater image enhancement has always been a hot spot in underwater vision research. However, due to complicated underwater environment, a lot of problems such as the color distortion and low brightness of underwater raw images are very likely to occur. In response to the above situation, we proposed a generative adversarial network that integrated multiple attention to enhance underwater images. In the generator, we introduced multi-layer dense connections and CSAM modules, of which the former could capture more detailed features and make use of previous features, while the latter could improve the utilization of the feature map. Meanwhile, we improved the enhancement effect of the generated image by combining VGG19 content loss function and SmoothL1 loss function. Finally, we verified the effectiveness of the proposed model through qualitative and quantitative experiments, and compared the results with the performance of several latest models. The results show that the methods proposed in this paper are superior to the existing methods.