Reviewerly/exHarmony huggingface dataset:
A decision support system for DM algorithm selection based on module extraction

Data mining techniques are needed in various fields. However, most data researchers do not have sufficient knowledge and experience. Due to the significant number of algorithms and parameters for data analysis, intuition-based decisions can’t lead to optimal solutions. Ontology is widely adopted to build knowledge-driven decision support systems since it is suited to encapsulate the concepts and relationships of terms associated with various domains. It is suitable for capturing knowledge by computers, allowing sharing and reusing it whenever necessary. All concepts and relationships about data analysis can be described using Web Ontology Language (OWL). Its reasoning mechanism is vital in any knowledge-based system. Ontology can be reasoned to recommend suitable solutions for a specific analysis task by considering the data characteristics and task requirements. In the previous work, we have developed a system that describes comprehensive knowledge and logical internal relationships about data mining. We found that the reasoning and query complexity is high in the large size ontology, so this paper focuses on the modeling and implementation of an ontology-based decision support system for data mining algorithm selection that contains a new sub-ontology extraction method. Extracting effective content can significantly reduce the size of the ontology. This extraction method can improve query efficiency on the premise of ensuring the quality of information.
Beyond Camera Motion Blur Removing: How to Handle Outliers in Deblurring

Camera motion deblurring is an important low-level vision task for achieving better imaging quality. When a scene has outliers such as saturated pixels, the captured blurred image becomes more difficult to restore. In this paper, we propose a novel method to handle camera motion blur with outliers. We first propose an edge-aware scale-recurrent network (EASRN) to conduct deblurring. EASRN has a separate deblurring module that removes blur at multiple scales and an upsampling module that fuses different input scales. Then a salient edge detection network is proposed to supervise the training process and constraint the edges restoration. By simulating camera motion and adding various light sources, we can generate blurred images with saturation cutoff. Using the proposed data generation method, our network can learn to deal with outliers effectively. We evaluate our method on public test datasets including the GoPro dataset, Kohler's dataset and Lai's dataset. Both objective evaluation indexes and subjective visualization show that our method results in better deblurring quality than other state-of-the-art approaches.
WITHDRAWN: EGIA: A new node splitting method for decision tree generation: Special application in software fault prediction

Software Fault Prediction (SFP) is a method of estimation of faulty modules of the software. It helps developers, testers as well as maintainers to develop good quality software modules with proper refactoring and less complexity. Many classification models have been developed by researchers to classify faulty modules in software like Decision Tree, SVM, ANN, and Random Forest, etc. Out of these techniques, the Decision Tree is one of the widely used approaches, which works on the greedy strategy for node splitting. Many alternates of node splitting have been attempted in the recent past to generate the decision tree for SFP such as Gini-Index, CHID, Gain Ratio, and Information Gain, etc. each of these has its pros and cons, but none of these individually perform well in terms of accuracy and/or efficiency. This paper proposes a new node splitting method based on combination of Gini-Index and Entropy for generating the decision tree. A new node splitting method called EGIA has been developed by integrating both node-splitting alternates. The proposed approach has been tested on 18 open source datasets of software implemented in different languages (C, C++, and Java). Results show that the proposed approach generates more accurate decision trees and can predict the software faulty modules better than the existing approaches. At the same time, the proposed method of node splitting is more dynamic than previous alternates because it can be modified according to the user's need and can be integrated with different optimization methods.
CompCertO: compiling certified open C components

Since the introduction of CompCert, researchers have been refining its language semantics and correctness theorem, and used them as components in software verification efforts. Meanwhile, artifacts ranging from CPU designs to network protocols have been successfully verified, and there is interest in making them interoperable to tackle end-to-end verification at an even larger scale.
Additive noise model structure learning based on rank correlation

To examine the structural learning of the additive noise model in causal discovery, a new algorithm, i.e., RCB (Rank-Correlation-Based), is proposed in combination with the method of Rank Correlation. This algorithm can effectively process multivariate linear Gaussian, non-Gaussian and multivariate nonlinear non-Gaussian data. In this article we have made three contributions. First, it is proven that rank correlation can be used as the criterion of the independence test. Second, through a series of experiments, the optimal threshold of rank correlation is found to select the potential neighbors of the target node. Thus, the RCB algorithm greatly reduces the search space and achieves good time performance. The third contribution is the improvement of the RCB algorithm in combination with the hypothesis testing method, and the RCS (Rank-Correlation-Statistics) algorithm is proposed to solve the theoretical basis for the threshold selection. Compared with the existing technology on 7 networks, the RCS algorithm is superior to existing algorithms in terms of both accuracy and time performance. In addition, simulations show that the RCS algorithm can achieve a good time performance and accuracy on low-dimensional large samples, high-dimensional large samples and real data.
Soft subspace clustering of interval-valued data with regularizations

Data analysis plays an indispensable role in understanding different phenomena. One of the vital means of handling these data is to group them into a set of clusters given a measure of similarity. Usually, clustering methods deal with objects described by single-valued variables. Nevertheless, this representation is too restrictive for representing complex data, such as lists, histograms, or even intervals. Furthermore, in some problems, many dimensions are irrelevant and can mask existing clusters. In this regard, new interval-valued data clustering methods with regularizations and adaptive distances are proposed. These approaches consider that the boundaries of the interval-valued variables have the same and different importance for the clustering process. The algorithms optimize an objective function alternating three steps for obtaining the representatives of each group, a fuzzy partition, and the relevance weights of the variables. Experiments on synthetic and real data sets corroborate the robustness and usefulness of the proposed methods.
Managerial Approach for Foreign Language Learning and Fostering in a Smart University Environment

This article reveals both the basic definitions and concepts of managerial theories of motivation implemented into the educational process. Innovative methods for science studies students at smart university are represented. The importance of methodical developments, content and innovative methods used during the English lessons to form a stable positive learning motivation is emphasized. The stages for classroom and extra curriculum educational activities are identified and the main methods used at each of them are described. The well-structured system of feedback enabling the designing of English learning and fostering on the basis of knowledge transfer at Togliatti State University is described.
ALBFL: A novel neural ranking model for software fault localization via combining static and dynamic features

Automatic software fault localization serves as a significant purpose in helping developers solve bugs efficiently. Existing approaches for software fault localization can be categorized into static methods and dynamic ones, which have improved the fault locating ability greatly by analyzing static features from the source code or tracking dynamic behaviors during the runtime respectively. However, the accuracy of fault localization is still unsatisfactory. To enhance the capability of detecting software faults with the statement granularity, this paper puts forward ALBFL, a novel neural ranking model that combines the static and dynamic features, which obtains excellent fault localization accuracy. Firstly, ALBFL learns the semantic features of the source code by a transformer encoder. Then, it exploits a self-attention layer to integrate those static features and dynamic features. Finally, those integrated features are fed into a LambdaRank model, which can list the suspicious statements in descending order by their ranked scores. The experiments are conducted on an authoritative dataset (i.e., Defect4J), which includes 5 open-source projects, 357 faulty programs in total. We evaluate the effectiveness of ALBFL, effectiveness of combining features, effectiveness of model components and aggregation on method level. The results reflect that ALBFL identifies triple more faulty statements than 11 traditional SBFL methods and outperforms 2 state-of-the-art approaches by on average 14% on ranking faults in the first position. To improve the precision of automatic software fault localization, ALBFL combines neural network ranking model equipped with the self-attention layer and the transformer encoder, which can take full use of various techniques to judge whether a code statement is fault-inducing or not. Moreover, the joint architecture of ALBFL is capable of training the integration of these features under various strategies so as to improve accuracy further. In the future, we plan to exploit more features so as to improve our method's efficiency and accuracy.
Three‐dimensional transfer function of optical microscopes in reflection mode

Abstract Three‐dimensional (3D) transfer functions build the basis for a comprehensive characterization of optical imaging systems in the spatial frequency domain. Utilizing the projection‐slice theorem, the 2D modulation transfer function of an incoherent imaging system can be derived from a 3D transfer function by integration with respect to the axial spatial frequency. For a diffraction limited microscope with homogeneous incoherent pupil illumination, the modulation transfer function equals the 2D autocorrelation function of a circular disc. However, until now to the best of our knowledge no 3D transfer function has been published, which exactly leads to the 2D modulation transfer function of a diffraction limited microscope in reflection mode. In this article, we derive a formula, which after integration with respect to the axial spatial frequency coordinate perfectly fits to the diffraction limited 2D modulation transfer function. The inverse three‐dimensional Fourier transform of the 3D transfer function results in a complex‐valued 3D point spread function, from which the depth of field, the lateral resolution and, in addition, the corresponding 3D point spread function of both, a conventional and an interference microscope, can be obtained.
Influence of virtual reality on student learning in undergraduate construction education

Virtual Reality (VR) allows for an active learning environment that can enhance a student's comprehension of material. This research explores the actual and perceived benefit of introducing a VR simulation into a materials and methods of construction course in undergraduate education. The study included the development and implementation of a virtual simulation environment to support learning in the materials and methods of construction course. Actual student performance was then compared between a control group, who did not use the simulation, and the intervention group, those who used the simulation. Other factors such as student self-efficacy, learning motivation, and technology acceptance for the intervention group were also examined. Findings suggest that students who utilized the simulation have a better understanding of the course material represented in the simulation compared to those that were only exposed to traditional course materials.
Kernel Risk-Sensitive Loss based Hyper-graph Regularized Robust Extreme Learning Machine and Its Semi-supervised Extension for Classification

Kernel Risk-Sensitive Loss (KRSL) is a nonlinear similarity measure defined in kernel space, which enable the gradient based method to achieve higher accuracy while effectively weakening the negative effects caused by noise and outliers. Defined as a kernel function expectation between two random variables, KRSL has been successfully applied in robust machine learning and signal processing. Extreme Learning Machine, as one of the most popular methods of machine learning, has attracted great attention in supervised learning and semi-supervised learning. However, when the data contains noise and outliers, the manifold structure of the data is not considered or the neural network structure is too complex, the performance of traditional ELM methods will decline. Therefore, based on KRSL, hyper-graph regularization and L2,1-norm, we first propose a more robust ELM method named Kernel Risk-Sensitive Loss Based Hyper-graph Regularized Robust Extreme Learning Machine (KRSL-HRELM). In KRSL-HRELM, KRSL is introduced into ELM to enhance its ability to handle noise and outliers. Moreover, the hyper-graph regularization is integrated into the method to learn the higher-order geometric structure information between the data. In addition, the L2,1-norm is introduced to constrain the output weight matrix to obtain a sparse network model. Inspired by other semi-supervised ELM methods, we extend KRSL-HRELM to semi-supervised learning and propose its semi-supervised version semi-supervised KRSL-HRELM (SS- KRSL-HRELM). Empirical studies on a large number of real-world datasets show that the proposed methods are competitive with other advanced supervised or semi-supervised learning methods in terms of robustness and efficiency.
Deep Hashing with Hash Center Update for Efficient Image Retrieval

In this paper, we propose an approach for learning binary hash codes for image retrieval. Canonical Correlation Analysis (CCA) is used to design two loss functions for training a neural network such that the correlation between the two views to CCA is maximum. The main motivation for using CCA for feature space learning is that dimensionality reduction is possible and short binary codes could be generated. The first loss maximizes the correlation between the hash centers and the learned hash codes. The second loss maximizes the correlation between the class labels and the classification scores. In this paper, a novel weighted mean and thresholding-based hash center update scheme for adapting the hash centers is proposed. The training loss reaches the theoretical lower bound of the proposed loss functions, showing that the correlation coefficients are maximized during training and substantiating the formation of efficient feature space for retrieval. The measured mean average precision shows that the proposed approach outperforms other state-of-the-art methods.
A Survey on Crawlers used in developing Search Engine

A vast collection of information, which is developed over a period, using HTML (Hyper Text Markup Language) formatted documents that are interlinked to each other is called as World Wide Web (WWW). With the increasing size of World Wide Web (WWW), obtaining meaningful information from the web is becoming a tedious task. Search Engine is developed for extracting information from the web. It works as an interface between the web and the user. The three important components of a search engine are: Crawler, Indexer and Page Ranking. The ambiguity of data along with its vast availability on the web is increasing at a greater pace due to the tremendous increase in data on the World Wide Web (WWW). For a naïve user this implies a challenge while surfing on the web for retrieving relevant and required information based on the search. Crawler is a component of search engine responsible for traversing webpages and fetching relevant links from the web. This represents huge dependency of any search engine on the crawlers. So, a detailed study about all the available crawlers for understanding the drawbacks and the insights about the working methodology undertaken is necessary before proceeding to develop a smart crawler. For developing a smart crawler which is future scope, a comparative analysis of widely used crawlers like Focused Crawler, Inference-based Crawler, Incremental Crawler, Parallel Crawler and Distributed Crawler is done.
New Lattice Two-Stage Sampling Technique and Its Applications to Functional Encryption – Stronger Security and Smaller Ciphertexts

This work proposes a new lattice two-stage sampling technique, generalizing the prior two-stage sampling method of Gentry, Peikert, and Vaikuntanathan (STOC '08). By using our new technique as a key building block, we can significantly improve security and efficiency of the current state of the arts of simulation-based functional encryption. Particularly, our functional encryption achieves $$(Q,\mathsf {poly})$$ simulation-based semi-adaptive security that allows arbitrary pre- and post-challenge key queries, and has succinct ciphertexts with only an additive O(Q) overhead. Additionally, our two-stage sampling technique can derive new feasibilities of indistinguishability-based adaptively-secure $$\mathsf {IB} $$ - $$\mathsf {FE} $$ for inner products and semi-adaptively-secure $$\mathsf {AB} $$ - $$\mathsf {FE} $$ for inner products, breaking several technical limitations of the recent work by Abdalla, Catalano, Gay, and Ursu (Asiacrypt '20).
A space hybridization theory for dealing with data insufficiency in intelligent power equipment diagnosis

Absence of fault samples of power equipment is a big bottleneck that limits the development of diagnostic method. This paper proposes a Space Hybridization Theory for dealing with diagnostic data insufficiency. First, the experimental datasets of transformer winding failure are obtained, constituting the real space. Then, the transformer digital space models are constructed similarly to obtain the simulation datasets, constituting the virtual space. After that, the features of the samples in two spaces are extracted through windowing feature calculation, and the real-virtual space is obtained through feature hybridization. The final samples in the space are taken as the auxiliary dataset during the intelligent diagnosis process. The diagnostic accuracy with the proposed method increased by several percent than without. While the MAPE between the real space and the virtual space is less than 9.4%, it can comprehensively improve the diagnostic effect.
Federated Learning Research: Trends and Bibliometric Analysis

Federated learning (FL) allows machine learning algorithms to gain insights into a broad range of datasets located at different locations, enabling a privacy-preserving model development. Since its announcement in 2016, FL has gained interest from a variety of entities—both, in academia and industry. To understand what are the research trends in this area, a bibliometric analysis is conducted to objectively describe the research profile of the FL area. In this regard, 476 documents written in English were collected through a thorough systematic search in the Scopus database and examined from several perspectives (e.g., growth trends, top-cited papers, subject area), productivity measures of authors, institutions, and countries. Further, a co-word analysis through VOSviewer was carried out to identify the evolving research themes in FL. There has seen exponential growth in FL literature since 2018. There are five research themes, namely internet of things, wireless communication, privacy and security, data analytics, and learning and optimization, which were surfaced in the analysis. We also found that most of the documents related to FL were published in computer science, followed by engineering disciplines. It was also observed that China is at the forefront in terms of the frequency of documents in this area followed by the United States of America and Australia.
A Comprehensive Discussion on Deep Reinforcement Learning

Deep reinforcement learning (RL) is an important method to provide solutions for the perception and decision-making problems of complex systems. It has provided great contributions in natural language processing, drug design, gaming, and some other fields. This paper gives an overview of recent achievements in deep reinforcement learning. First, some basic concepts regarding deep learning, reinforcement learning and deep reinforcement learning are first introduced in this paper. Then, the milestone related work of deep RL and two methods of deep RL: deep Q-learning-based and policy gradient-based are reviewed. At last, an overview of deep RL applications is presented, and the future of deep RL is also proposed.
Efficient Multi-Signature Scheme Using Lattice

Abstract With the help of a multi-signature scheme, we can reduce the cost of storage and bandwidth in case of many signers signing the same message. Therefore, multi-signature schemes can be used in bitcoin to reduce the size of a blockchain. In this paper, we propose a lattice-based multi-signature scheme with the following highlighted features. Our lattice-based multi-signature scheme supports signature compression and public key aggregation. The only existing lattice-based multi-signature scheme by Kansal and Dutta (Africacrypt, 2020) that supports both signature compression and public key aggregation has communication and storage cost $\widetilde{\mathcal{O}}(n^2)$, whereas our communication and storage cost is $\mathcal{O}(n)$. Our multi-signature scheme is in the plain public key model where the special registration of the public key is not necessary and it is secure under the rogue key attack. Our multi-signature scheme is secure under the hardness of ring short integer solution problem in the random oracle model.