Reviewerly/exHarmony huggingface dataset:
Kernel Regression on Graphs in Random Fourier Features Space

This work proposes an efficient batch-based implementation for kernel regression on graphs (KRG) using random Fourier features (RFF) and a low-complexity online implementation. Kernel regression has proven to be an efficient learning tool in the graph signal processing framework. However, it suffers from poor scalability inherent to kernel methods. We employ RFF to overcome this issue and derive a batch-based KRG whose model size is independent of the training sample size. We then combine it with a stochastic gradient-descent approach to propose an online algorithm for KRG, namely the stochastic-gradient KRG (SGKRG). We also derive sufficient conditions for convergence in the mean sense of the online algorithms. We validate the performance of the proposed algorithms through numerical experiments using both synthesized and real data. Results show that the proposed batch-based implementation can match the performance of conventional KRG while having reduced complexity. Moreover, the online implementations effectively learn the target model and achieve competitive performance compared to the batch implementations.
Most-Cited Research Publications on Educational Leadership and Management: A Bibliometric Analysis

There is a growing interest to understand the knowledge base in the area of educational leadership and management (EDLM) due to exceedingly large number of articles published. This study aims to explore the knowledge base within the highly cited papers of EDLM and their characteristics using science mapping. The 100 most-cited papers out of 5680 publications generated from Scopus database were utilised in bibliometric analysis. VOSviewer software was used to conduct bibliometric analysis such as co-citation and co-occurrence. The analysis identified that 100 most-cited papers were published between 1981 to 2018. The results showed that 12 countries contributed to the 100 most-cited publications with the most productive country being the United States, followed by United Kingdom, Canada, Netherlands, Hong Kong, and Australia. Findings also revealed United States representing maximum collaboration with other countries. The analysis identified that Kenneth Leithwood and Philip Hallinger were the greatest contributors to the highly cited articles. The most repeated key words were; leadership, school leadership, social justice, transformational leadership, educational leadership, and principals. Main schools of thought such as leadership for social justice were related with these key terms. The majority of publications came from Educational Administration Quarterly followed by Journal of Educational Administration, and School Leadership and Management. The results of this study helped to identify important areas guiding impactful research opportunities. Scholars looking forward to undertake research on EDLM can consider the major schools of thought that were identified in this analysis.
Categorizing Text Documents Using Naïve Bayes, SVM and Logistic Regression

Categorizing Text documents is the method of arranging different types of documents into labelled data. The field of this paper is to combine the Data mining Technology, Data extraction and Artificial Intelligence for text categorization. This paper will showcase the features of the technologies involved. There are three machine learning algorithms (SVM, Multinomial Naïve Bayes and Logistic Regression) used in this paper for text categorization, i.e. arrange documents into different categories of dataset 20 news groups. In the evaluation of the above classification techniques, SVM classifier outperforms other classifiers for text categorization.
Sentiment Analysis for Sudanese Arabic Dialect Using comparative Supervised Learning approach

Sentiment analysis is several methods, techniques, and tools that are used to determine the polarity of the text (positive, negative, or neutral). The most popular approaches to address this problem, is the machine learning approach, lexicon-based approach, and hybrid approach. This project focuses on extracting and analyzing Sudanese social media feeds about ridesharing services. This project aims to tackle the issue of Sudanese Arabic dialect analysis by conducting a comparative analysis to measure the performance of the machine learning algorithms using Sudanese dialect corpus comparing different preprocessing approaches. For this study, a stop word list that combines a modern standard Arabic list and a Sudanese stop word list was built to be conducted through the analysis as one of the preprocessing steps. with four classifiers applied on a dataset consist of 2116 tweets. In particular, Naïve Bayes (NB), Support vector machine (SVM), Logistic Regression, and K-Nearest Neighbor (KNN) had been trained and measured the performance. The results of the selected classifiers against the dataset which had been applied to various preprocessing steps revealed that SVM with stemming only gives the highest F1-score (0.71), and the best accuracy (0.95).
Genetics and Human Language: A Critical Discourse Analysis of a Father and a Daughter’s language.

Human language is a byproduct of diverse sociological, psychological and other associated factors. How does parents’ genes affect the progeny’s language and how these effects appear in the siblings’ language are the less explored area in the fields of genetics and language research. The present study has evaluated the effects of parental gene on progeny’s written discourse through a qualitative cum quantitative research by using textual data that has been excerpted from books of two prominent political figures who have been former prime ministers of Pakistan. The selected books are the autobiographical accounts of the authors who have experienced different ups and downs in their political careers and consequent deaths. Zulfiqar Ali Bhutto is former prime minister of Pakistan and has been hanged on convictions of certain charges. If I am assassinated is a book written by Zulfiqar Ali Bhutto during his prison life destined to be hanged. Daughter of Destiny is the book authored by Benazir Bhutto written in backdrop of his father’s death, her own troublesome life account and associated factors. For collection and analysis of valid and reliable results, the researchers have employed Python programming language platform which provides numerous useful tools like libraries and packages to analyze textual files. Among the prominent features of the Natural Language Processing (NLP) tasks used in literature studies, the one feature is the python libraries which assists in finding frequency of the words and their counts, total number of sentences in a file, total number of the words, finding specific words in a file, parts of speech tagging, analyzing contents and comparing texts from two different sources of textual data intended for classification purposes. One of the most important python libraries for NLP is Natural Language Processing Toolkit (NLTK). Finding the results of diverse variables on the books, we employed different softwarese.g. we used Twinwood emotion analysis API in our work; while cosine similarity metric was used to measure the similarity between the two books as it determines how similar the documents are regardless of their size. We also exercised state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) model developed by Google to measure how similar the two books are as BERT mainly focuses on context and semantics of the textual data. The researchers have found close similarity in language pattern of the selected characters. Human gene play an important role in development of human language and therefore, the premise tested was to evaluate whether father has influenced the language of his daughter or not? Through computer programming and machine analysis, the researcher have found the effects of genes on progeny‘s language by endorsing the proposition that fathers affect the daughters’ written language.
Cloud based Sign Language Interpreter using Deep Learning

There are people around us who are verbalized and auricularly discerning-impaired. They communicate utilizing sign languages with each other but the situation becomes worse when the other person doesn’t know that dactylology. This has become a major issue. We conducted a survey and got acquainted with the fact that American dactylology is the most commonly used dactylology around the globe. Nowadays, laptop vision-primarily predicated solutions stay absolutely preferred to get their signal language understandable to general people. Many analysts have conducted experiments on the Apperception of Hand Gestures utilizing a computer vision-predicated system. It has been a popular area of study in recent times. These studies show the involvement of some deep learning models for Hand Gesture apperception utilizing computer vision. Through this research work, our aim is to eradicate the communication barrier between verbalization/aurally perceiving impaired people and general people. This is done by engendering a model which uses CNN (Convolutional Neural Network)
Searchable encryption using secret sharing scheme that realizes direct search of encrypted documents and disjunctive search of multiple keywords

The main search functions realized by searchable encryption can be classified into the search function using one query and that using multiple queries. Searchable encryption using one query has been widely studied and researched; however, few searchable encryption methods can accommodate the search function using multiple queries. Additionally, most of the methods proposed thus far utilize the concept of index search. Therefore, an additional process of updating or deleting an index when new documents are added or removed is required, increasing the overall computational cost. Furthermore, a document that is not registered in the index cannot be searched. Therefore, herein, using a secret sharing scheme that is known to offer a low computational cost, we propose a method that can realize the direct search function over sentences using the conjunctive search function without the construction of any index. We also propose a method that realizes the search function with multiple search queries using the disjunctive search function. Additionally, we implement our proposed methods, and demonstrate that they can perform a direct search over sentences using multiple keywords.
Redirection Using Alignment

Unobstructed locomotion in virtual reality (VR) using natural walking is often not possible due to the differing placement of obstacles in the physical and virtual environments. Redirected walking is a technique that helps to enable natural walking in VR by imperceptibly rotating the virtual environment such that users adjust their physical trajectory and avoid physical obstacles. Traditionally, the algorithms responsible for applying redirected walking focused mainly on steering users away from physical obstacles, with little regard for the locations of obstacles in the virtual environment. However, recent work showed that considering both the physical and virtual environments when applying redirected walking can lead to significantly fewer collisions with physical obstacles. We formalize the notion of alignment: the concept of comparing the physical and virtual environments according to some feature present in both environments. We provide a generalized definition of alignment that allows it to be used in any research problem, and we present an example of how alignment can be used to yield significant improvements in VR locomotion with redirected walking.
Virtual blox

There are a plethora of brick systems on the market these days. Children and young adults play very creatively with the interlocking bricks. Even adults like the colorful bricks and build models with them quickly and easily. In this paper we present our brick system solution for the virtual world, playable with the HTC Vive and two controllers. For our solution, we transferred the brick properties of the real world into the virtual one. Play patterns like grabbing a brick, connecting it to other bricks or releasing it needed to be converted for the virtual world. Virtual reality is capable of enhancing the play experience in a way that would not be possible in the real world. To support the building process, we implemented several forms of assistance. These were positively evaluated by players.
Security of Automated Teller Machines (ATM’s)

ATM-s are the tools that perform cash transactions and different banking operations, as they contain and perform sensitive data of customers. Differently from the past, where attackers were focused on physical attacks, nowadays they use logical attacks to capture those sensitive data or to gain access to cash inside ATMs. In this paper we have assessed risk on an ATM platform to be aware of risks that come from logical attacks. Through this risk assessment we have pointed out some important issues that are responsible for vulnerabilities of ATMs platform. In this paper, we have also discussed the possible countermeasures to mitigate the risk and provide a secure environment on banks and ATM’s.
A pedagogical profession in the context of the latest educational research and innovative pedagogical knowledge (consider the pedagogical profession in the context of preschool education)

This article aims to establish how effective it is to introduce information about innovative pedagogical knowledge into training courses, how educators perceive the latest educational research and innovations, practices of preschool educational space, how to motivate integration into professional activities; identified several characteristics of forecasting (attitude to innovation, alarming use of innovation, external barriers to integration of innovation into pedagogical practice, courses related to innovative pedagogical practices and previous experience, readiness to use innovative pedagogical knowledge in the space of preschool education). Empirical, statistical, and theoretical methods were used in this research. The result of this study is to prove that training programs for educators in preschool education, new pedagogical knowledge, and courses, related to innovations, have the effect of enabling preschool teachers to use innovations in their professional practice. A further perspective is the study of effective ways to introduce innovative pedagogical knowledge to teachers in the field of preschool education.
Unsupervised Domain Adaptation for 3D Medical Image with High Efficiency

Domain adaptation is a fundamental problem in the 3D medical image process. The current methods mainly cut the 3D image into 2D slices and then use 2D CNN for processing, which may ignore the inter-slice information of the 3D medical image. Methods based on 3D CNN can capture the inter-slice information but lead to extensive memory consumption and lots of training time. In this paper, we aim to model the inter-slice information in 2D CNN to realize the unsupervised 3D medical image's domain adaptation without additional cost from 3D convolutions. To better capture the inter-slice information, we train the model from the adjacent (local) slices and the global slice sequence perspective. We first propose the Slice Subtract Module method (SSM), which can easily embed into 2D CNN and model the adjacent slices by introducing very limited extra computation cost. We then train the adaptation model with a distance consistency loss supervised by the LSTM component to model the global slice sequence. Extensive experiments on BraTS2019 and Chaos datasets show that our method can effectively improve the quality of domain adaptation and achieve state-of-the-art accuracy on segmentation tasks with little computation increased while remaining parameters unchanged.
Verification of Functional Correctness of Code Diversification Techniques

Code diversification techniques are popular code-reuse attacks defense. The majority of code diversification research focuses on analyzing non-functional properties, such as whether the technique improves security. This paper provides a methodology to verify functional equivalence between the original and a diversified binary. We present a formal notion of binary equivalence resilient to diversification. Moreover, an algorithm is presented that checks whether two binaries – one original and one diversified – satisfy that notion of equivalence. The purpose of our work is to allow untrusted diversification techniques in a safety-critical context. We apply the methodology to three state-of-the-art diversification techniques used on the GNU Coreutils package. Overall, results show that our method can prove functional equivalence for 85,315 functions in the analyzed binaries.
Depth from Mono Accuracy Analysis by Changing Camera Parameters in the CARLA Simulator

Depth estimation is an important task in robotics and autonomous driving. By estimating depth and relying only on a single camera, it is no longer necessary to add and calibrate additional sensors - usually a second camera. However, such an approach requires training on extensive datasets and obtaining real-world datasets is time consuming and costly. Given that, using photorealistic simulators can be beneficial, since a multitude of varoius scenes can be created. In this paper we present an approach to training a deep neural network based on the ResNet architecture for estimating depth from a single camera. We target road vehicle scenes and use the CARLA simulator. We evaluate the trained network on the real-world KITTI dataset images and in the CARLA simulator. In the simulated experiments, we compare the performance with respect to the changes in camera intrinsic and extrinsic calibration parameters with respect to the ego vehicle frame.
Sample Efficient Subspace-Based Representations for Nonlinear Meta-Learning

Constructing good representations is critical for learning complex tasks in a sample efficient manner. In the context of meta-learning, representations can be constructed from common patterns of previously seen tasks so that a future task can be learned quickly. While recent works show the benefit of subspace-based representations, such results are limited to linear-regression tasks. This work explores a more general class of nonlinear tasks with applications ranging from binary classification, generalized linear models and neural nets. We prove that subspace-based representations can be learned in a sample-efficient manner and provably benefit future tasks in terms of sample complexity. Numerical results verify the theoretical predictions in classification and neural-network regression tasks.
Blended Teaching Model in Foreign Language Course - An Action Research based on the Analysis of SPSS

In order to promote teaching reform and improve teaching quality, the study uses the Internet technology to assist foreign language teaching. Three rounds of action research have been carried out to explore the blended teaching mode combining students' online self-study and offline project-based learning in the English course Intercultural Communication. With the interaction provided by the intelligent teaching platform named Super Star, the study tried to promote the cooperation between students (SS) as well as teachers and students (TS). SPSS is used to analyze the data formulated from the teaching platform such as learning behavior, learning achievement and of survey. The study found that the blended teaching model assisted students to evaluate their own academic performance and promote their autonomous learning ability.
Personalized Ontology-Based Adaptive E-Learning System

This chapter illustrates novel methods to provide personalized and adaptive content to the learners. This chapter illustrates a new methodology of automatically constructing concept maps using ontology to measure the learners' understanding for a particular topic, thereby teachers can adopt adaptive teaching based on the learners knowledge structures as reflected in the concept maps. The teachers can dynamically revise and deliver instructional materials according to the learners' current progress. In the approach, the authors provide dynamic content to the learners based on neuro fuzzy domain ontology extraction algorithm. This method also provides a personalized ontology model of a learner to learn the ontological user profiles from both world knowledge base and user local instance repositories. The main quality of the innovative work is to mine the personalized ontology of the learners to extract their knowledge through ontology mining using Inc Span+ algorithm.
Classification of Documents Using Machine Learning and Genetic Algorithms

In the past few years, there has been rampant growth in the amount of complex documents that stand in need of a deeper understanding of machine learning methods to classify them in many applications. The success of these methods depends on their ability to understand complex patterns and nonlinear relationships in data. Yet, finding the right structures, architectures, and techniques for text classification is often a challenge for researchers. In this article, we present an automated document classification system based on two axes; the first regard the processing of natural language (NLP), along with the second that focuses on Machine Learning (ML) algorithms. In addition, a hybrid system that combines the best of classification models in a single strong system with a very high percentage of accuracy that we came to give rise to with the genetic algorithms (GA).
Research on a Real-time Computer Network Trend Analysis Algorithm Based on Dynamic Data Flow under the Background of Big Data

With the continuous development of information technology, the Internet technology and computing technology are constantly improving and developing, and the demand of users is constantly increasing in this process, so the network specification is particularly important in this context. Traditional behavior identity is mostly based on a static model, which has a strong dependence on sample eigenvalues, but lacks flexibility and adaptability to individual users' behavior habits. In this paper, the data stream is segmented and fitted based on the total least square method, and the traditional curve analysis algorithm and online data segmentation are combined and improved. The variable sliding window algorithm is used to realize the reasonable segmentation of the data stream and improve the accuracy of trend analysis. Experimental results show that the algorithm has high recognition rate and good adaptability to users.
Image-based Table Cell Detection: a Novel Table Structure Decomposition Method with New Dataset

Recently deep learning has been applied to decompose table structure with the main ideas of detecting table lines and then forming table cells. However, the existing methods face problems in dealing with tables with rotation or no internal table lines. To tackle these problems, we propose a novel table structure decomposition method, which directly detects table cells as objects and creates table structure. Extensions to the existing object detection models including effective table projection module are proposed to adapt to the table cell detection. To support the training of the enhanced models, we create a large image-based table dataset TableCell with cell level annotations. A novel and efficient semi-supervised method is proposed to annotate this new dataset. Experiments demonstrate that our proposed table structure decomposition method is simple, effective and robust to the tables without table lines or with rotation. Our dataset and code will be made available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> https://github.com/weidafeng/TableCell.