Reviewerly/exHarmony huggingface dataset:
On the Use of the Convolutional Autoencoder for Arabic Writer Identification Using Handwritten Text Fragments

Convolutional autoencoders (CAE) are designed to reconstruct the input image to the output in a near-perfect way via a compact data namely encoded data containing relevant features. The encoded data can be used in various applications as for compressing or classifying the image. The present paper tries to investigate the use of the CAE for writer identification using handwritten text fragments. Hence, the CAE is used for generating features, which is fed to the distance-based classifier. Experimental evaluation is performed on the well-known IFN/ENIT dataset containing 411 writers. During training, a subset is selected from the 411 writers containing only 11 writers allowing to produce a lite CAE. Experimental results show an identification rate of 92.70% using the whole dataset when the feature vector is appropriately normalized.
Joint Entity and Relation Extraction with Triple Discrimination

Joint entity and relation extraction, extracting relational triples (subject, relation, object) from unstructured natural texts, is a significant task in information extraction and automatic knowledge graph constructions. Existing methods usually utilize identified possible entities to predict relations. However, the extraction results of almost existing methods contain two types of errors: invalid triples and unrecognized positive triples. Intuitively, generating the confidence of triples and learning the gold triples information by utilizing knowledge graph embedding techniques are effective to solve issues above. In this research, we propose a novel joint method with triple discrimination for extracting entities and relations. Specifically, two modules are contained in the proposed method: a triple extractor, a triple discriminator. The former is used to extract triple candidates, and the latter is used to generate the confidence of triples. Experiment results indicate that the proposed approach achieves the state-of-the-art performance on public NYT and WebNLG datasets. Moreover, the results prove the method is effective to reduce errors usually contained in the results obtained by previous methods.
A Comparison of Input Devices for Gaming: Are Gamepads Still Useful in PC Environment?

As the PC has emerged as a new video game platform since its rapid performance progress, the cross-platform compatibility has grown to prominence for developers and designers. Besides, the number of users who want to use their gamepads, which is the conventional input devices of video game consoles, has been gradually increasing. This study was designed to compare the performance and usability of two input devices, the gamepad and the keyboard-mouse setup, the traditional setup used by most PC users, by task type and difficulty level. The goal of this study is to explore the interface design guidelines for each device. In this study, we measured the reaction time for the performance and the perceived workload for usability. The results indicated that the keyboard-mouse setup showed higher performance than the gamepad in general, and that the difference by the difficulty level was intensified in the physical task group. However, this tendency was not found in the cognitive task group. The keyboard-mouse setup also showed lower mental demand and physical demand than the gamepad. This result suggests that designers should not require too many simultaneous inputs when designing interfaces for gamepads, and that users should understand the characteristics of each device, in order to choose the one appropriate for their intended goals.
Volume Exploration Using Multidimensional Bhattacharyya Flow

We present a novel approach for volume exploration that is versatile yet effective in isolating semantic structures in both noisy and clean data. Specifically, we describe a hierarchical active contours approach based on Bhattacharyya gradient flow which is easier to control, robust to noise, and can incorporate various types of statistical information to drive an edge-agnostic exploration process. To facilitate a time-bound user-driven volume exploration process that is applicable to a wide variety of data sources, we present an efficient multi-GPU implementation that (1) is approximately 400 times faster than a single thread CPU implementation, (2) allows hierarchical exploration of 2D and 3D images, (3) supports customization through multidimensional attribute spaces, and (4) is applicable to a variety of data sources and semantic structures. The exploration system follows a 2-step process. It first applies active contours to isolate semantically meaningful subsets of the volume. It then applies transfer functions to the isolated regions locally to produce clear and clutter-free visualizations. We show the effectiveness of our approach in isolating and visualizing structures-of-interest without needing any specialized segmentation methods on a variety of data sources, including 3D optical microscopy, multi-channel optical volumes, abdominal and chest CT, micro-CT, MRI, simulation, and synthetic data. We also gathered feedback from a medical trainee regarding the usefulness of our approach and discussion on potential applications in clinical workflows.
AVX C++ Programming: Part 1

The previous chapter explained SIMD fundamentals including packed types, arithmetic calculations, and data manipulation operations. It also highlighted a few details regarding the history of x86-AVX and its computational capabilities. The focus of this chapter is AVX integer arithmetic using 128-bit wide operands. The first section contains several concise source code examples that illustrate how to use C++ SIMD intrinsic functions to perform packed integer arithmetic. This followed by a section that highlights common programming operations with packed integers including bitwise logical operations and shifts. The third and final section includes source code examples that demonstrate elementary image processing tasks using C++ SIMD intrinsic functions. As you will soon see, SIMD techniques are ideal for many types of image processing algorithms.
Study on Segmentation Technology of Thyroid Nodules Based on Improved U-Net

Ultrasound segmentation of thyroid nodules is one of the key technologies to discover and identify malignant thyroid tumors [1]. The research content of this paper mainly includes three parts: data set selection, image preprocessing, and segmentation of thyroid nodules. The image preprocessing includes ROI selection, adaptive histogram equalization that limits the contrast, and data set expansion. In the segmentation of thyroid nodules, this article chooses to improve the U-Net model. The BN and GN modules are added to the U-Net model, and the activation function is changed to Leaky ReLU. In the end, the model's Miou reached 80.14%, the PA reached 94.33%, the Precision rate reached 90.37%, and the Recall rate reached 88.91 %. In addition, this article also uses PyQt5 to systematically visualize the preprocessing and nodule segmentation. The system can complete the segmentation of thyroid nodules in 4–5 seconds.
Conclusions

The Data, Information, and Time (DIT) model makes a clear distinction between the terms data and information and illustrates how the meaning of a data item and the sense of a proposition can change over time.
Cybersecurity Risk Assessment Platform for State Government

A working knowledge of cybersecurity by high-level stakeholders is necessary for the effective management of complex digital infrastructures. High-level stakeholders within state government must strengthen cybersecurity platforms and prepare for the consequences of inevitable failures. This paper presents a communication platform for bridging the gap between stakeholders and cybersecurity professionals. The tool incorporates the evaluative framework described in the National Institute of Standards and Technology (NIST) 800-53 and NIST 800-171 as a basis for comparison to a series of best- practice cybersecurity toolsets. The implementation stage and level of importance of each NIST subcategory is also recorded. A description of how the resulting information is incorporated into the strategic planning process is provided, as well as a discussion of how the risk information is used in support of a common risk treatment alternative (insurance).
Collaboration and advance planning across campus create more cybersecure universities

“Cybersecurity is a threat that changes all the time, so addressing it involves everybody living and breathing security as second nature.” This was a key theme on Sept. 22, 2021, when the National Student Clearinghouse hosted a webinar titled “CyberSecurED: Building a Cyber‐Ready Campus.” The webinar panelists — experts from EDUCAUSE, the Cyber Readiness Institute, REN‐ISAC, and NSC — discussed the role of university registrars and enrollment managers in preventing and responding to cyberattacks.
An improved fuzzy adaptive teaching learning-based optimisation algorithm for generating pairwise test suites

Pairwise testing has proved its applicability to adequately test software with a huge number of inputs. It can avoid the otherwise impractical exhaustive testing by employing an efficient sampling strategy. Strategies based on meta-heuristic algorithms offer optimal pairwise test suite sizes for software applications. A fuzzy adaptive teaching learning-based optimisation (ATLBO) algorithm has shown competitiveness against other meta-heuristic-based strategies in terms of pairwise test suite generation. Although useful, the present design of ATLBO is lacking in dealing with stagnation or abnormal convergence after some iterations. A remedial operator is introduced in ATLBO in order to address this issue and hence further enhance its convergence speed. With this modification, ATLBO is used for the pairwise test suite generation problem. From the conducted experiments, it can be concluded that the performance of ATLBO with remedial operator is comparable with other pairwise strategies based on hyper-heuristic, meta-heuristic and greedy algorithms.
Research and Practice of Video Recognition Based on Deep Learning

This paper used tensorflow and keras library to build a deep learning environment. Designed and established a deep 3D convolutional network model and Long Short-Term Memory network model, using UCF-101 dataset known category videos as training samples to train the network. Some videos in the dataset were used as test samples to verify the recognition performance of the network model and realize classification. Finally, Tensorboard was used to visually analyze the network training process. The experimental results show that the model has better video recognition performance.
Multi-robot Visual Navigation Structure Based on Lukas-Kanade Algorithm

This paper presents an efficient control structure of two mobile robots based-visual navigation methods in an indoor environment. The proposed navigators are based on decision systems employed the necessary values estimated by a Lukas-Kanade (LK) algorithm of optical flow (OF) approach. The robots control systems use the generated motion values in order to detect and estimate the positions of the nearest obstacles and objects around each mobile robot. The multi-robot system task is to navigate autonomously in their environment safely without collisions. Obstacles are identified and detected by the employed cameras of each robot based on video acquisition and image processing steps. The efficiency of the proposed approach is verified in simulation using Visual Reality Toolbox. Simulation results demonstrate that the visual based control system allows autonomous navigation without any collision with obstacles.
Parallel Improved Quantum Evolutionary Algorithm for Complex Optimization Problems

As for the problems of premature convergence, slow convergence and long computing time in solving complex continuous function optimization by traditional quantum evolutionary algorithm, a dynamic parallel quantum evolutionary algorithm for solving complex continuous function optimization problem is proposed in this paper. Multi population co-evolution is adopted, and each sub-population evolves in different search areas according to their own evolution objectives to form a parallel search mode, which can speed up the algorithm convergence and avoid premature convergence; Quantum computation is introduced into the differential evolution algorithm. In this method, the probability amplitude representation of qubits is applied to the real number coding of chromosomes, the chromosome position is updated by quantum mutation, quantum crossover and quantum selecting operations, the two probability amplitudes of qubits are exchanged by quantum non-gate, and an adaptive operator is introduced to improve the population diversity, It can not only prevent the premature convergence of the algorithm, but also make the algorithm converge faster and improve the problem-solving ability of the optimization algorithm. Taking the function extreme value problem as an example, the effectiveness of the algorithm is verified by this algorithm.
OTT (Netflix) Film Recommender System Using Data Mining

With the current COVID-19 situation on hand, the load of entertainment has completely fallen onto the OTT platforms for over a year now, and as a significant rise in viewership has been seen in the recent years and especially the past year, it is particularly important that these systems are used in best way. Along this increase a big issue becoming among the viewers is the quality and content of the films, documentary films, etc., which depends highly on some factors like choice of actors, genre, availability in different languages and most importantly reviews. So, to solve this issue, a decision was made to try new approach and put some new life into an old and trusted system, incidentally which has been one of the earliest implementations in the field of machine learning and is fondly known or called by the name of the recommender system or the recommendation system. This paper emphasizes on providing with an over-the-top media-service provider (OTT) film recommendation system which aims to deliver a personalized choice of the films as per the new trends among the viewers, the ratings, the reviews and sometimes as per the previous experiences of the user. The so-called recommender systems have been in use for more than a decade now, but the interesting thing with these machine learning-based systems is that there is always scope for some new features or improvements to be added or channelled in and further on when you think that it is perfect and requires no changes or additions to it, but in fact everything in machine learning can be designed and implemented or given a new direction as per your own perspective. The thought to simply redefine this system mainly grew because of a big issue that is the wastage of time in selecting appropriate films as per the choice, non-engaging films and in a bid to make people watch more quality and knowledgeable content. This paper reviews a host of previous work done in this field along with chalking out issues which can be improved along the way. This paper further talks about the methodology for conceiving the system and as well as sheds light on the implementation of the system using Python language and open-source tools. The paper further moves onto dictate about the applications of the system for OTT services and also take a bit of sneak peek into the applications beyond the OTT services.
Quantum error mitigation for parametric circuits

Reducing errors is critical to the application of modern quantum computers. We investigate the quantum error mitigation considering parametric circuits accessible by classical computations in some range of their parameters. We present a method of global randomized error cancellations mitigating quantum errors by summing a linear combination of additionally randomized quantum circuits with weights determined by comparison with the referent classically computable data. We illustrate the performance of this method with real quantum simulations of the $n=4$ spins antiferromagnetic Ising model in the transverse field.
Call Python Functions from MATLAB

The MathWorks introduced the py module for MATLAB in 2014. This module provides a direct binary interface between MATLAB and Python, opening vast new possibilities for both languages. In addition to making it possible to call any Python function, MATLAB’s py module allows one to create native Python objects within MATLAB. Such variables retain their full complement of Python methods—for example, a native Python string created in MATLAB can call all its 40 methods, from .capitalize() to .zfill().
Development of Application Usability Evaluation Scale for Seniors

Purpose: Applications for seniors are emerging because of the increasing number of seniors due to an aging society and popularization of smartphones by the 4th industrial revolution. The purpose of this paper is to establish a usability evaluation scale to develop more convenient and useful applications for seniors. Research design, data and methodology: For this study, first, the necessity of developing an application usability evaluation scale for seniors is discussed by examining the definition and context of seniors. Second, the primary usability evaluation factors were derived from collecting the factors of usability evaluation such as applications, seniors and conducting in-depth interviews with experts. Third, the second usability evaluation factors were derived through survey and statistical analysis based on the usability evaluation factors. Results: As a result, the usability evaluation scale was established with five factors – Cognition, Navigation, Feedback, Error, Aesthetic - and 20 items. Conclusions: This study can be the basis for guidelines of development and design in applications for seniors. Therefore, not only the young generation but also seniors can feel convenience in their daily lives by using customized applications for seniors.
Using Wearables to Optimize Learning at Home

During the COVID-19 pandemic, there has been a shift from learning in classrooms to learning at home. However, currently applied learning methods are not optimized for learning at home. Therefore, we explore how the unique benefits of a home environment could be used to inform the design of learning experiences. In particular, we explore the applicability of wearables and IoT devices as flexible and affordable learning tools. Based on our findings, we present our prototype of an NFC-based learning application for mobile devices.
A Comprehensive Review of Fraudulent Email Detection Models

Emails have become an integral part of our lives; be it personal or professional, emails are used everywhere. Most websites require the customers to enter their email ID, increasing the risk of exposure to spammers who attack them by sending spam messages. To overcome this problem, researchers have come up with different techniques to classify emails as legitimate or spam mails. The conducted literature survey includes 32 relevant papers out of the 47 selected papers. The paper is based on a comprehensive six research question-based approach which has been implemented to find the Machine Learning (ML) and Deep Learning (DL) techniques used, datasets used, pre-processing methods implemented, spam types, different evaluation metrics used and the strengths and weaknesses of the models in the literature. The domains analyzed in ML and DL are Naïve Bayes (NB), Support Vector Machines (SVM), K-Nearest Neighbor (K-NN), Random Forest, Neural Networks (NN), etc. This review will help the researchers in identifying the present and the future context of research in the different approaches for the classification of spam emails.
Styling Your GUIs

The GUIs you have created up until now have mainly focused on functionality and less on appearance and customization. Creating an interactive, coherent, and professional-looking GUI can be achieved not only with widgets and layout managers but also by modifying the look and behavior of each object in the interface. Choosing the right style, colors, fonts, and subtle forms of feedback can help create a consistent, easy-to-navigate, user-friendly experience.
Quantitative evaluation of denoising techniques of lung computed tomography images: an experimental investigation

Appropriate selection of denoising method is critical component of lung computed tomography (CT)-based computer aided diagnosis (CAD) systems since noises and artefacts may deteriorate the image quality significantly thereby leading to incorrect diagnosis. This study presents a comparative investigation of various techniques used for denoising lung CT images. Current practices, evaluation measures, research gaps and future challenges in this area are also discussed. Experiments on 20 real-time lung CT images indicate that Gaussian filter with 3 × 3 window size outperformed others achieving high picture signal-to-noise ratio (PSNR), Pratt's figure of merit (PFOM), signal-to-noise ratio (SNR) and root mean square error (RMSE) of 45.476, 97.964, 32.811, 0.948 and 0.008, respectively. Further, this approach also demonstrates good edge retrieval efficiency. Future work is needed to evaluate various filters in clinical practice along with segmentation, feature extraction, and classification of lung nodules in CT images.
An Attempt at Underwater Robot Competition which Aimed at Improvement of Learning Motivation

Underwater robot competitions provide students with the opportunity to voluntarily learn engineering knowledge and skills. It is expected that students will be interested in the marine engineering and manufacturing by participating in such underwater robot competitions, and that interest will lead to motivation for university learning. In this study, we will conduct extracurricular activities for 8 students for about half a year with the aim of participating in underwater robot competitions, and evaluate their educational effectiveness. In the evaluation, changes in interest in the marine engineering and manufacturing were evaluated by questionnaire. In addition, changes in learning motivation were evaluated from the university GPA from 2018 to 2020. As a result, it was confirmed that the participating students were interested in the marine engineering and manufacturing through extracurricular activities and maintained high learning motivation.
Development of Teaching Materials KaitCV for Learning Autonomous Driving Technology and Practice of Class

Educating engineers who can be involved in automobile development requires expertise in mechanical, electrical, and information systems. Until now, there has been specialized education in each field. However, there were few practical educational cases in which specialized fields were integrated into vehicles. In addition, there was no teaching material that could effectively and inexpensively teach each of “driving environment sensing,” “driving behavior generation,” and “vehicle motion control,” which are essential basic technologies for realizing autonomous driving. The purpose of this study is to develop “KaitCV”, an automatic driving learning support teaching material that uses simple and inexpensive teaching materials such as hobby cars, so that all students can practice the acquisition of automatic driving technology. In addition, I will explain that the educational practice was carried out using the developed teaching materials and that a high educational effect was obtained.
Interacting with the File System

A program that reads or writes files must sometimes interact with the file system in other ways, for example, checking whether or not a file or directory exists, seeing if a file can be read or written to, getting file size or modification time, reading a directory’s contents, and so on.