Reviewerly/exHarmony huggingface dataset:
A Fuzzy String Matching-Based Reduplication with Morphological Attributes

String matching is a common problem in the field of computer science, and it is a common operation in various language processing tasks. Several efficient algorithms have been developed for string matching problems like Knuth-Morris-Pratt (KMP) algorithm [1], Rabin-Karp’s algorithm [2], matching using a finite-state machine, etc. But in natural language processing, the problem related to string matching is much complex and the conventional string matching algorithm does not fulfil their requirement. This paper presented one such issue related to the string matching on partial reduplication. In that context, a fuzzy-based string matching technique has been proposed. In this approach, the fuzzy membership is not only considered based on the character/symbol or sub-string matching rather some other grammatical information like morphological information, prosodic pattern, etc., are considered. The experiment is done on the Bengali dataset, and finally, the system is tested on real-life text to measure the accuracy.
PREP: Prerequisite Relationship Extraction Using Position-Biased Burst Analysis

An intelligent tutoring system (ITS) provides personalized instructions and feedback to learners without requiring expert intervention. One of the components of an ITS is the domain modeling module where the system stores the dependencies between the concepts in the domain using a concept map. These concept dependencies are used by an ITS to determine how to sequence the teaching of concepts to a student and as a representation of student knowledge. This requires an expert to manually find concepts in the domain and enter the dependencies between concepts. We aim to automate the process of domain modeling by finding relationships among concepts in a domain. One such type of relationship is a prerequisite relationship which represents the learning order of a pair of concepts. Determining prerequisite relationships helps in developing an effective concept map for an ITS. These relationships can be extracted by finding patterns between occurrences of the concepts in the text. We propose a method “Prerequisite Relationship Extraction using Position-biased burst analysis” (PREP) based on burst analysis to determine prerequisite relationships between the concepts present in unstructured text using the order of occurrence of concepts. We have evaluated the proposed method using textbooks spanning multiple domains like data mining, geometry, precalculus, and physics to test its robustness. The proposed method results in as much as a 17% improvement in precision over existing methods.
MZI-Based Electro-optic Reversible XNOR/XOR Derived from Modified Fredkin Gate

Arithmetic logic unit (ALU) is the heart of any computational logic module, and most of the operations in it are supported by XNOR/XOR gates, which also play a vital role even in encryption/decryption circuits. Thus, efficient and reliable operation of XNOR/XOR using emerging technologies has become a point of interest. The recent advancement of IC technology finds itself in a spot of bother due to the excessive heat dissipation, which is needed to be addressed, and reversible logic (RL) has emerged as a potential candidate. This paper unveils the exploration of reversible XNOR/XOR logic using a single cell of 4 × 4 MFG. Electro-optic Mach–Zehnder interferometer (MZI) is considered to realize the proposed logics under beam propagation method using OptiBPM tool. The power model of output ports is calculated and verified through MATLAB simulation.
Kernelized Random Vector Functional-Link Network

Random vector functional-link (RVFL) networks are closed-form solution-based methods that evaluate a linear function in the output layer. The input vectors are fed to the output layer using the direct links. In this study, similar to the kernel-based methods, the input space is mapped to a higher dimensional feature space before feeding them to the output layer of RVFL. Thus, the proposed method utilizes the benefits of the nonlinear kernel functions of the kernel-based methods and the nonlinear activation functions of the artificial neural networks. The proposed method achieves complete nonlinearity at the output layer. Experiments performed on real-world datasets establish efficacy of the proposed approach.
FPGA Implementation of Optimized Code Converters with Reversible Logic Gates

In today’s technology development, there is vast advancement present the low power VLSI; it is possible to decrease the power dissipation. The reversible concept is one of the significant technologies for attaining the more interest because of low power dissipation and high speed operation. In electronics, the code converters are widely used because it enhances the security of the information, reducing the arithmetic operations complexity and thus decreasing the required hardware, power saving, the high speed of operation, etc. In this paper, an optimized design of gray to binary, binary to gray, Ex-3 to BCD, and BCD to Ex-3 code converters with reversible logic gates is proposed. The simulation and synthesis process can be done with Xilinx ISE software, and it is dumped on FPGA Spartan-6E.
Understanding Quantum Computing Through Drunken Walks

Quantum random walks have caught the attention of quantum information theorists in recent years. Classical walks have been used to solve or design several highly efficient randomized algorithms, and they are also used in many quantum algorithms, but quantum walks provide an exponential speedup over classical walks since they can solve some oracle problems. For several practical problems, such as the element distinctness problem, the triangle finding problem, and evaluating NAND trees, quantum walks provide polynomial speedups over classical algorithms. In this paper, we propose a new quantum random walk representation based on the drunken walk and the classical random walk. This current portrayal would make it easier for people to comprehend the potential of quantum computing. It will also be shown how to solve a large deviation analysis through quantum walks. This paper includes an explanation of how an inebriated person might locate his friend in a bar after leaving the restroom, as well as a comparison of quantum walks and classical walks.
Theoretical aspects of school education reform in the Republic of Finland

The article attempts to theoretically investigate the theoretical aspects of school education reform in the Republic of Finland. According to the results of research, the stages of reforming school education in Finland are analyzed. The relationship between the needs of society to solve economic and social problems and the improvement of the education sector and the features of legal regulation of educational changes in the political and administrative environment of Finland are revealed. The relevance of the study is due to the fact that the education sector of the Republic of Finland has a high rating among the indicators of international education quality assessment systems such as PISA, PIRLS and TIMSS, which is closely related to the modernization of school education for six decades. Finnish education is characterized by quality, accessibility and innovation. Particular attention is paid to the correlation of education reform with the economic development of Finland. The stages of economic development of the country in the postwar period are presented, in particular: increasing the level of opportunities in education simultaneously with the transition from agrarian to industrial society; reforming public secondary education along with the growth of the services sector and raising the level of technology in Scandinavian society; improving the quality of basic education and improving higher education at the same time as globalizing the country. The transformation of the concept of “equality” of school education from the quantitative basis to the quality of education is revealed. Attention is paid to the main drivers in the modernization of education in the context of flexible economic change. Emphasis was placed on the fact that the leading role in economic recovery was: strong social cohesion, strong security system and educational system. Organizational and content changes, features of structural innovations in the field of education are characterized The stages of reforming Finnish school education, which are reflected in many works, in particular in the works of Pasi Sahlberg, are analyzed. Emphasis is placed on the need for consistent and flexible actions to modernize the theoretical and methodological foundations of the education sector, improve the effectiveness of structures and management in education.
SiamOT: An Improved Siamese Network with Online Training for Visual Tracking

As a prevailing solution for visual tracking, Siamese networks manifest high performance via convolution neural networks and weight-sharing schemes. Most existing Siamese networks have adopted various offline training strategies to realize precise tracking by comparing the extracted target features with template features. However, their performances may degrade when dealing with unknown targets. The tracker is unable to learn background information through offline training, and it is susceptible to background interference, which finally leads to tracking failure. In this paper, we propose a twin-branch architecture (dubbed SiamOT) to mitigate the above problem in existing Siamese networks, wherein one branch is a classical Siamese network, and the other branch is an online training branch. Especially, the proposed online branch utilizes feature fusion and attention mechanism, which is able to capture and update both the target and the background information so as to refine the description of the target. Extensive experiments have been carried out on three mainstream benchmarks, along with an ablation study, to validate the effectiveness of SiamOT. It turns out that SiamOT achieves superior performance with stronger target discrimination abilities.
Digital Competence of university students with disabilities and factors that determine it. A descriptive, inferential and multivariate study

Taking into account that 15% of the world population suffers from some type of disability, few investigations have been concerned with knowing the level of Digital Competence that students with functional diversity have. This research is based on an initial sample of 17,301 newly enrolled university students from the Andrés Bello University (Chile), of which 650 are selected for having some type of disability. Its main objective is to know the level of self-perception in the Digital Competence of university students with disabilities and the variables that explain it. To do this, an evaluation instrument consisting of 6 dimensions (technological literacy, information search and processing, critical thinking, problem solving and decision making, communication and collaboration, digital citizenship, and communication and collaboration) is designed and applied. To calculate the validity of the instrument, the partial least squares (PLS) method is applied through structural equation modeling. Next, data is provided regarding the level of Digital Competence of the students and if there are differences between the types of disability. Finally, the data is subjected to a logistic regression to estimate the relationship between the dependent variable (Digital Competence) and a set of independent variables (sociodemographic variables). The results reveal that the instrument used shows high levels of reliability, which guarantees its use in students with disabilities. Likewise, the average level of Digital Competence is medium low. The factors that affect this variable are discussed and the necessary structuring of digital literacy plans is highlighted. Finally, this paper discusses new techniques for diagnosing students' digital skills.
White Blood Cells Detection Using Saturation Level

Abstract Blood is made up of three main components; erythrocytes, leukocytes and thrombocytes. Each of these blood components all have their own roles in the human body. Leukocytes, which can be divided into five types; Basophil, Neutrophil, Eosinophil, Lymphocytes and Monocytes are all part of the body’s defence mechanism to fight against pathogens that could harm the body. Identifying the presence of these blood cells is one of the fundamental ways to diagnose a disease. Hence, blood tests are always being run by physicians in clinical practice. Manually identifying leukocytes is a tedious and time-consuming process, and does not guarantee standardised results as it depends fully on the operator’s skills. Therefore, many works have been done to develop an automated method of leukocyte identification, which aims to reduce the processing time, cost-effective and is efficient in producing standardised results. The proposed method uses the technique of segmenting the nucleus and cytoplasm of leukocytes by extracting it from the Saturation level of the image.
The Can-Do Power

If today there is agreement on one fundamental thing, it refers to the fact that the world is not evolving well. And this, regardless of whether this characterization refers to realities within or between states, to political or social issues, to short-term or long-term ones. An atmosphere of mistrust and uncertainty has set in recent years, which we can only understand as a result of a major confluence of several trends; some in the process of exhaustion, others just beginning, but all suggesting that they are out of control. The pandemic has only developed the state of the world today and strengthened the atmosphere of uncertainty we were talking about.
Participatory Design

Unlike traditional design methods in which professionals require prospective users to give their opinion on the finished product, participatory design (PD) sees users as collaborators and co-designers whose daily life experiences truly contribute to the product conceptualization and development. This design approach is especially valuable for social groups whose life experiences significantly differ from those of designers and researchers. Although there is an agreement that the involvement of people with autism spectrum disorders (ASD) in the design and evaluation process positively affects the adaptation of technology to their needs, most technologies for people with ASD have been developed with either their minimal involvement or without it. In addition to a short overview of PD principles, methods, and techniques, this chapter also presents research studies in which children and adults with ASD were directly involved in designing technology for this population. People with ASD mainly have positive experiences in PD and manage to develop a partnership with researchers during the design. Research results show that, despite their initial anxiety, people with ASD enjoy PD activities and believe that their contribution is appreciated. However, it is noticeable that most studies involve school-age participants with preserved speech and higher abilities.
Microclustering-Based Multi-Class Classification on Imbalanced Multi-Relational Datasets

In a relational database, classification algorithms are used to look for patterns across several interconnected relations. Most of the methods for multi-relational classification algorithms implicitly assume that the classes in the target relation are equally represented. Thus, they tend to produce poor predictive performance over the imbalanced dataset. In this paper, the authors propose an algorithm-level method MCMRC_IB for the classification of imbalanced multi-relational dataset. The proposed method extends MCMRC which is for balanced datasets. MCMRC_IB exploits the property of the imbalanced datasets that the minority class is represented by a smaller number of records usually 20-30% of the total records and is to be dealt accordingly by giving them weightages. The proposed method is able to handle multiple classes. Experimental results conðrm the efficiency of the proposed method in terms of predictive accuracy, F-measure, and G-mean.
Application of artificial intelligence technology in recognition of sports athletes’ running foul

In order to solve the problems of low recognition efficiency, low recognition rate and large recognition error of traditional methods, an application method of artificial intelligence technology in athletes’ running foul recognition was proposed. Build the image acquisition model of sports athletes’ running foul, divide each frame of the image samples into static area and motion area, and get the motion direction estimation results; K-means in the field of artificial intelligence is used to cluster the characteristics of sports athletes’ rush foul action, and LLE algorithm is used to reduce the dimension of features; The background subtraction method is used to detect the foul target of rush, and the Bayesian algorithm is used to construct the recognition model of sports athletes’ foul of rush, which is used to identify the foul target. The experimental results show that the recognition rate of this method has reached more than 72%, and continues to increase, and the recognition error is only 2%, which effectively improves the recognition rate and reduces the recognition error, which is feasible and effective.
DPre: Effective preprocessing techniques for social media depressive text

Depression has become one of the most common public health issues. Several people with depression rely on social media to express their grief. The text data generated by these users can be exploited to promote study in this area in order to detect early-stage depression and provide support. However, to develop a reliable automatic depression detection system, the social media text cannot be used directly as there is a lot of irrelevant, inaccurate, and noisy information available. Moreover, the basic preprocessing steps which are used with most of the machine learning models have limited functionality and thus lead to lots of information loss. This loss of information is not affordable especially in the domain of affective computing (mental health) for text. In this paper, we present various preprocessing techniques for depressive text, DPre, to obtain readable text from raw and noisy tweets. This method can help in minimizing the loss of information and expressions hidden in the raw tweet. Moreover, the processed and clean text will be ready to input into any machine learning algorithm. The readability of the processed text is evaluated and compared with raw tweets using four readability scores: Flesch Reading Score, Flesch_kincaid Score, the Coleman-Liau Index, and Dale_Chall Score. Compared to basic state-of-art preprocessing methods, the proposed method significantly improved the readability score.
A panoramic photo mosaic method based on asymmetric two-way flow field

The main problems of panoramic photo stitching are discontinuity of stitching area and ghost, which has a great impact on the realization of panoramic video stitching. A panoramic photo mosaic method for asymmetric two-way flow field is proposed. In the analysis of asymmetric two-way flow field technology, through optical flow motion analysis and virtual close computer vision. Based on this technology, according to the similarity and smoothness between images, the discontinuous method is used to estimate the fusion area and realize image fusion processing. Considering that the image gray space contains a large amount of smoothing information, one-step method is selected as the flow field estimation method, and the panoramic photo mosaic is realized by combining the occlusion calculation and flow field estimation results. The empirical results show that the average stitching time of 10 panoramic images per frame is 21.588 s, and the minimum and maximum resolution of panoramic photos are 4040 respectively × 1080 dpi and 4580 × 1080 dpi. Subjective evaluation, objective evaluation and fuzzy evaluation methods show that 10 groups of video mosaic images have good mosaic effect. The research results provide the possibility for the high-quality implementation of panoramic photos and video stitching.
Abnormal motion signal detection of mobile robot based on deep learning

In order to solve the problem of high false positive rate and false negative rate of mobile robot motion signal anomaly detection, a new method based on deep learning is designed. The abnormal state of mobile robot is analyzed, and the feature of mobile robot running data is extracted by using correlation dimension. The PNN training is completed by adopting the multi-neural network structure of deep learning to deal with the abnormal state sample data of the robot. Based on the motion control method and double evolutionary probability neural network, the abnormal motion signal is detected by fuzzy weighting method and fuzzy matching. Experimental results show that the method can effectively solve the problem of high false alarm rate and false positive rate, and promote the development of robot motion signal anomaly detection technology.
Enhanced regression model using cluster based sampling techniques

This paper aims to develop the methodology for enhancing the regression models using Cluster based sampling techniques (CST) to achieve high predictive accuracy and can also be used to handle large datasets. Hard clustering (KMeans Clustering) or Soft clustering (Fuzzy C-Means) to generate samples called clusters, which in turn is used to generate the Local Regression Models (LRM) for the given dataset. These LRMs are used to create a Global Regression Model. This methodology is known as Enhanced Regression Model (ERM). The performance of the proposed approach is tested with 5 different datasets. The experimental results revealed that the proposed methodology yielded better predictive accuracy than the non-hybrid MLR model; also, fuzzy C-Means performs better than the KMeans clustering algorithm for sample selection. Thus, ERM has potential to handle data with uncertainty and complex pattern and produced a high prediction accuracy rate.
An approach for data integrity authentication and protection in fog computing

The data integrity verification process in cloud has become more promising research area in several Internet of Things (IoT) applications. The traditional data verification approaches use encryption in order to preserve data. Moreover, fog computing is considered as extensively employed virtualized platform and it affords various services including storage as well as services interconnected to computing and networking between user and data center based on standard cloud computing. Moreover, fog computing is an extensive description of cloud computing. Thus, fog servers effectively decrease the latency by integrating fog servers. In this paper, novel model for data integrity authentication and protection is designed in IoT cloud-fog model. This method mainly comprises fog nodes, cloud server, IoT nodes, and key distribution center. Here, dynamic and secure key is produced based on the request to key distribution center based on hashing, Exclusive OR (XOR), homomorphic encryption and polynomial. The fog nodes are employed to encrypt the data gathered from IoT nodes as well as allocate the nearby nodes based on Artificial Bee Colony-based Fuzzy-C-Means (ABC FCM) – based partitioning approach. The proposed data integrity authentication approach in IoT fog cloud system outperformed than other existing methods with respect to detection rate, computational time and memory usage of 0.8541, 34.25 s, and 54.8 MB, respectively.