Reviewerly/exHarmony huggingface dataset:
An Efficient Handwritten Digit Recognition Based on Convolutional Neural Networks with Orthogonal Learning Strategies

The predictions of characters/text/digits from the handwritten images have made the research community spotlight towards recognition. There are enormous applications and ambiguity that made prediction possible with Deep Learning (DL) approaches. Primarily, there are four necessary steps to be carried out with handwriting prediction. First, consideration of a dataset that is more appropriate for DL validation an inefficient manner. Here, Special Database 1 and Special Database 2 are used, which are combined and modified by the National Institute of Standards and Technology (NIST). Next is pre-processing of input handwritten digit recognition data by data normalization, extraction of efficient features which provides better prediction accuracy. The proposed idea uses pixel values as features with the analysis of hyper-parameters to enhance near-human performance. With SVM, non-linear and linear models are built to extract the appropriate features for further processing. The features are separate and placed over the Bag of Features (BoF), which is used by the next processing stage. Finally, a novel Convolutional Neural Network (CNN) is by built modifying the network structure with Orthogonal Learning Particle Swarm Optimization (CNN-OLPSO). This modification is adopted for evolutionarily optimizing the number of hyper-parameters. This proposed optimizer predicts the optimal values from the fitness computation and shows better efficiency when compared to various other conventional approaches. The novelty which relies on CNN adoption is to endeavor a suitable path towards digitalization and preserve the handwritten structure and help automatic feature extraction using CNN by offering better computation accuracy. The optimization approach helps to avoid over-fitting and under-fitting issues. Here, metrics like accuracy, elapsed time, recall, precision, and [Formula: see text]-measure are evaluated. The results of CNN-OLPSO give better accuracy, reduced error rate and better execution time (s) compared to other existing methods. Thus, the proposed model shows better tradeoff in the recognition rate of handwritten digits.
On the Upper Bound of Filter Length in the Design of Polynomial Graph Filter

In this paper, the upper bound of filter length in the design of polynomial graph Alter is studied. First, the characteristic polynomial of graph Laplacian matrix is used to show that the upper bound of filter length is the number of vertex of graph. Then, two methods are presented to reduce the transfer matrix of graph filter if its length is greater than upper bound. One is degree reduction method, the other is long division method. Next, the numerical examples of cycle graph and path graph are illustrated to show the effectiveness of the proposed reduction methods. Finally, the lowpass graph filter design in the temperature data sensor network is applied to demonstrate the usefulness of proposed filter length reduction methods.
Session-persistent Load Balancing for Clustered Web Servers without Acting as a Reverse-proxy

A Load balancer is an essential component of a clustered web-server system which distributes requests among servers. Layer-4 load balancers process requests faster than Layer-7 ones, but they cannot provide session-persistent load balancing. On the other side, layer-7 load balancers, which are known as reverse-proxies, can direct all requests of an application-level session to the same server, but they impose a high processing cost due to processing requests at the application level. We propose a session-persistent load balancing approach which uses TLS session data instead of processing at the application level. It outperforms existing approaches in terms of transaction rate and response time.
Multi-scale Object Detection Algorithm Based on Faster R-CNN

Most object detection methods have low detection accuracy for small and dense objects and susceptibility to noise interference. In response to these problems, this paper designs an improved multi-scale object detection algorithm based on Faster R-CNN, aiming to improve the detection accuracy of small and dense objects. This paper introduces depthwise separable Convolution to reduce the number of network parameters. The amount of calculation uses dilated Convolution to improve the feature extraction of small objects and dense objects by increasing the receptive field, uses Convolution instead of 3 × 3 conventional Convolution to solve the problem that the feature extraction of small objects caused by Convolution is easy to be lost, distorted or the feature extraction redundancy of large objects is too high. The spatial attention mechanism is introduced to efficiently screen features that are more beneficial to object detection and improve the model's ability to deal with large object scale differences in multi-scale object detection. The network model in this paper was trained and verified on the datasets VOC2007 and VOC2012, and the mean average precision reached 86.3%.
Research on Chinese Sentiment Analysis Based on Bi-LSTM Networks

Chinese sentiment analysis is a very important branch of natural language processing. It has been receiving much attention in recent years. The bidirectional long and short-term memory network (Bi-LSTM) model has been well applied in the field of sentiment analysis because of its own characteristics. This experiment hopes to further explore the performance and application of the Bi-LSTM model in sentiment analysis. There are three main steps in the experiment. First, the collected Chinese reviews are segmented and vectorized. Then, the Bi-LSTM is trained and tested. Finally, the sentiment analysis result is obtained. With the help of the hyper-parameter adjustment and the dropout mechanism, the evaluation indicators of the experimental model have reached about 89%. What's more, based on the same experimental environment and experimental data, this experiment tested the accuracy of CNN, LSTM, CNN_LSTM, and Bi-LSTM. In addition, the trained Bi-LSTM was used to analyze reviews from Taobao and JD.COM. The specific operation is to collect reviews on a certain product from Taobao and JD.COM to perform a specific analysis with the model. Then find the advantages and disadvantages of the model in practical applications, so that the model can continue to be improved.
Risk Assessment of Web Application Penetration Testing on Cross-Site Request Forgery (CSRF) Attacks and Server-Side Includes (SSI) Injections

Cross-Site Request Forgery (CSRF) and Server-Side Includes (SSI) are the typical web applications vulnerabilities, posing severe threats to the web applications that lack security implementations. Thus, demonstrating various types of attack techniques is necessary to create awareness for web developers regarding the concepts of CSRF and SSI exploitation. However, the current approaches provided limited information since they did not include the risk assessment of severity level for each attack, the limitations, and advantages of the attack techniques. This state of the currently limited information regarding CSRF, and SSI attacks is the primary motivation for this study. Therefore, this research aims to perform risk assessment for CSRF attacks and SSI injections based on each attack technique's characteristics, severity level, limitations, and the advantages by using penetration testing. This research is separated into three main stages. Stage one focuses on finding suitable target web applications and reviewing related studies on existing attack techniques for both vulnerabilities. The second stage involves intelligence gathering, vulnerability scanning using Burp Suite and OW ASP Zed Attack Proxy (ZAP), and exploitation using seven planned testing techniques. The third stage focuses on analyzing the results of testing techniques, including the pros and cons, perform a risk assessment of the results/penetration test findings to identify the severity level of each attack technique used in this study. From the risk assessment results, it is found that SSI vulnerabilities pose an overall higher level of criticality than CSRF vulnerabilities.
Time-Series Cross-Validation Parallel Programming using MPI

Time-series data has a natural chronological arrangement with modeling and cross-validation techniques, highly dependent on sequential processing of data, which challenges its parallelization. Since the running time of the modeling algorithm largely consists of a main operation of solving the modeling algorithm several times with various training and testing dataset sizes, it is the prime target for optimizing the running time. This research presents a parallel implementation of time-series cross-validation on a rolling basis that involves using a subset of the dataset for training purposes, expanding each dataset with each run to obtain test accuracy. The evaluation is conducted using parallel speedup, parallel efficiency, and computational time. The proposed algorithm distributes the task of modeling among several processors to run independently and obtain results. The computational time recorded ranged from 15.47 seconds at best with five processors to 28.33 seconds at worst when done with a single processor. The parallel system speedup was found to be sub-linear with an efficiency decreasing with the increase in the number of processors.
Towards Semantic Description of Explainable Machine Learning Workflows

Machine learning (ML) has outperformed humans in many areas, and that is why it has been adopted by a large variety of applications, such as computer vision, speech recognition, and robotics. However, their functioning and the reason why they generated specific results usually are not clear to the user, being often considered as black-boxes. Explainable Artificial Intelligence (XAI) aims to make AI systems results better understandable to humans, enabling the optimization of the learning models and trust by users. Semantic Web Technologies (SWT) has been applied to ML models because they provide semantically interpretable tools and allow reasoning on knowledge bases that can help explain ML systems. Nevertheless, current solutions usually limit their explanations to the logic of the results, lacking the description or explanations of the other steps of the ML process, which can restrict the understanding experience of the user, making it difficult to identify in which step corrections and adjustments should take place. In this paper, we give an overview of XAI and SWT, and discuss the importance of providing a holistic solution, by means of an ontology. This challenge has to be addressed to improve understandability of the whole ML process and explanation process.
Adopting Agility in Academia through Pilot Projects

This innovate practice work in progress paper presents our initial findings about the application of agile process in the day to day operation of an academic department. The benefits of agile methods, which are processes that are lightweight and people-oriented, are well-known. Agile methods provide the ability to respond faster to the changing needs of stakeholders, improved product quality, team over individual goals, and better transparency. Therefore, one would expect adaption of the agile process within academic environment would be welcomed, however, unique issues exist within faculty that impact adoption; faculty have different goals that change through their tenure, and faculty have their own priorities that drive their time investment. For the last 18 months, the Electrical Engineering and Computer Science department at the Embry-Riddle Aeronautical University has been involved in adopting the use of agile process in its day-to-day operations, namely Scrum, which is one type of Agile method. To assess the feasibility of departmental adaptation of agile process, we established two pilot projects to identify advantages and disadvantages such adaptation. In this paper we report on the outcome of the projects in addition to the observations, challenges, and opportunities regarding the adaptation of the agile processes in the day-to-day operation of an academic department.
A fuzzy clustering algorithm based on hybrid surrogate model

Data clustering based on regression relationship is able to improve the validity and reliability of the engineering data mining results. Surrogate models are widely used to evaluate the regression relationship in the process of data clustering, but there is no single surrogate model that always performs the best for all the regression relationships. To solve this issue, a fuzzy clustering algorithm based on hybrid surrogate model is proposed in this work. The proposed algorithm is based on the framework of fuzzy c-means algorithm, in which the differences between the clusters are evaluated by the regression relationship instead of Euclidean distance. Several surrogate models are simultaneously utilized to evaluate the regression relationship through a weighting scheme. The clustering objective function is designed based on the prediction errors of multiple surrogate models, and an alternating optimization method is proposed to minimize it to obtain the memberships of data and the weights of surrogate models. The synthetic datasets are used to test single surrogate model-based fuzzy clustering algorithms to choose the surrogate models used in the proposed algorithm. It is found that support vector regression-based and response surface-based fuzzy clustering algorithms show competitive clustering performance, so support vector regression and response surface are used to construct the hybrid surrogate model in the proposed algorithm. The experimental results of synthetic datasets and engineering datasets show that the proposed algorithm can provide more competitive clustering performance compared with single surrogate model-based fuzzy clustering algorithms for the datasets with regression relationships.
Question Generation using Knowledge Graphs with the T5 Language Model and Masked Self-Attention

Question generation is helpful for understanding reading comprehension, spontaneous questioning in chatting systems, and expanding datasets for answering questions. In previous studies, many models have been used to generate questions from contexts, but none was suitable in large-length contexts. To overcome this challenge, we generated questions from an intermediate representation of a context, such as knowledge graphs. In this study, we focused on developing questions using knowledge graphs with the T5 language model. We used the language model to create questions using the knowledge graph and mask the self-attention of the encoder to train the model by explicitly preserving the graph’s structure. As a result of the automatic evaluation, the T5 language model with and without mask was comparable with the bidirectional Graph2Seq model (G2S), known as the QG model, using knowledge graphs. More-over, the masked language model was slightly better than the non-masked model in t5-small on four benchmarks. The code and data are publicly available at https://github.com/Macho000/T5-for-KGQG.
PRE-SERVICE TEACHERS’ TRAINING FOR THE INNOVATIVE EXTRACURRICULAR WORK

The research proves that the level of pre-service teachers’ readiness for innovative extracurricular work at school will be increased together with the general index of their satisfaction with the extracurricular work at university after the implementing the designed holistic program of their training. The participants of the pedagogical experiment were 148 students, divided into experimental and control groups; the experts were 18 teachers of Philological Faculty of the Hryhorii Skovoroda University in Pereiaslav and 10 General Secondary Schools teachers (Pereiaslav, Ukraine). After implementation of the program, 27% of participants were ready for innovative extracurricular work at the high level, 62% of students increased their level of readiness for innovative extracurricular work to the middle one. The suggested program also increased the students' satisfaction with the extracurricular work at university. On the basis of the research results, the authors suggest methodical recommendations for educators.
Data Confidentiality for IoT Networks: Cryptographic Gaps and Physical-Layer Opportunities

The conventional solution for providing data confidentiality is by means of encryption (a branch of cryptography). However, encryption schemes are generally designed to provide a certain level of security without necessarily taking resource consumption into account. This poses an issue for Internet of Things (IoT) devices which are limited in terms of storage capacity and computational capabilities. In this paper, we discuss the capabilities of cryptographic solutions for providing data confidentiality and we evaluate whether these solutions are appropriate for IoT networks in terms of resource consumption. Based on the identified drawbacks of cryptographic solutions, we discuss opportunities within the area of physical-layer security (PLS). Finally, we provide an overview of PLS schemes which aim to enhance data confidentiality in IoT networks.
Intelligent Classification Model for Biomedical Pap Smear Images on IoT Environment

Biomedical images are used for capturing the images for diagnosis process and to examine the present condition of organs or tissues. Biomedical image processing concepts are identical to biomedical signal processing, which includes the investigation, improvement, and exhibition of images gathered using x-ray, ultrasound, MRI, etc. At the same time, cervical cancer becomes a major reason for increased women's mortality rate. But cervical cancer is an identified at an earlier stage using regular pap smear images. In this aspect, this paper devises a new biomedical pap smear image classification using cascaded deep forest (BPSIC-CDF) model on Internet of Things (IoT) environment. The BPSIC-CDF technique enables the IoT devices for pap smear image acquisition. In addition, the pre-processing of pap smear images takes place using adaptive weighted mean filtering (AWMF) technique. Moreover, sailfish optimizer with Tsallis entropy (SFO-TE) approach has been implemented for the segmentation of pap smear images. Furthermore, a deep learning based Residual Network (ResNet50) method was executed as a feature extractor and CDF as a classifier to determine the class labels of the input pap smear images. In order to showcase the improved diagnostic outcome of the BPSIC-CDF technique, a comprehensive set of simulations take place on Herlev database. The experimental results highlighted the betterment of the BPSIC-CDF technique over the recent state of art techniques interms of different performance measures.
A Novel Hybrid Tunicate Swarm Naked Mole-Rat Algorithm for Image Segmentation and Numerical Optimization

This paper provides a new optimization algorithm named as tunicate swarm naked mole-rat algorithm (TSNMRA) which uses hybridization concept of tunicate swarm algorithm (TSA) and naked mole-rat algorithm (NMRA). This newly developed algorithm uses the characteristics of both algorithms (TSA and NMRA) and enhance the exploration abilities of NMRA. Apart from the hybridization concept, important parameter of NMRA such as mating factor is made to be self-adaptive with the help of simulated annealing mutation operator and there is no need to define its value manually. For evaluating the working capabilities of proposed TSNMRA, it is tested for 100-digit challenge (CEC 2019) test problems and real multi-level image segmentation problem. From the results obtained for CEC 2019 test problems, it can be seen that proposed TSNMRA performs well as compared to original TSA and NMRA. In case of image segmentation problem, comparison of TSNMRA is performed with multi-threshold electro magnetism-like optimization (MTEMO), particle swarm optimization (PSO), genetic algorithm (GA), bacterial foraging (BF) and found superior results for TSNMRA.
Metallic debossed characters industrial online non‐segmentation identification based on improved multi‐scale image fusion enhancement and deep neural network

This paper investigates accurate industry online scene text recognition techniques for metallic debossed characters (MDCs). As MDCs have low contrast with its background and easy to interfere with corrosion rust, oxidate skin or specular reflection and so forth, a multi-scale image fusion algorithm is first proposed to restore the MDCs’ shape and appearance information to enhance the sample's contrast by using the MDCs' depth characteristic with the help of four-directional illumination. With this method, the three most important reflection-related picture quality evaluation standards, namely contrast, saturation, and exposure, can be enhanced simultaneously. Next, based on the fusion result, a U-shaped network based on VGG-16 network architecture is used for text localization. In order to improve network receptive fields, an improved post-processing output module that combines a text sequence score map for coarsening localization with a single-character score map for fine localization is proposed, so that the network's adaptability and accuracy for long texts are improved. Finally, a convolutional recurrent neural network is adopted to realize the recognition of sequence characters. The advantages and effectiveness of the proposed method are statistically analyzed with the data from a liquefied petroleum gas cylinder annual periodic inspection production line.
Identification of argumentative sentences in Russian scientific and popular science texts

Abstract In this study we analyze the applicability of specific machine learning algorithms to the task of detecting sentences containing argumentation in Russian text. We employ a collection of scientific and popular science texts with manually annotated argumentation to evaluate the quality of identifying argumentative sentences in terms of precision, recall, and F -measure. The experiment involves three algorithms: MNB, SVM, and MLP. The bag of words model is used for representing texts. Lemmas of words in analyzed sentences serve as features for the classification. We perform the automatic selection of informative features in accordance with Variance and χ 2 criteria combined with the weight-based filtration of lemmas (via TF*IDF and EMI). The training set includes around 800 sentences, while the test set contains 180. The MNB algorithm demonstrates the highest F -measure and recall scores on almost all feature sets (maximal values reached equal 68.7% and 89% respectively), while the MLP algorithm shows the best precision for about half of feature selection variations (the maximal value is 72.5%).
A model to characterize exercises using probabilistic methods

Many studies have been conducted on modeling learners in education using probabilistic methods to infer different indicators. However, little research has been done on modeling content in education by estimating different content characteristics using probabilistic methods. Based on the existing gap in this area, this paper presents a model for exercises using probabilistic methods with interactions carried out by students to obtain content characteristics and their relationship such as the number of attempts, grade, and time spent related with student efficiency. Simulations were performed to train the model and find the different curves that best characterize the exercises based on programmatically generated interactions. This model allows the teacher to redesign the exercises to improve the student’s learning process.
Stroke Classification Model using Logistic Regression

Abstract This study aims to determine the factors that significantly affect the classification of stroke. The response variable used is the type of stroke, namely non-hemorrhagic stroke and hemorrhagic stroke. The predictors used were cholesterol level, blood sugar level, temperature, length of stay, pulse rate, and gender. By using logistic regression, the results obtained modeling accuracy of 74.8% where the predictors that have a significant effect (alpha &lt;0.05) are total cholesterol and length of stay.