Reviewerly/exHarmony huggingface dataset:
A Novel Inverse Procedure Via Creating TubeNet with Constraint Autoencoder for Feature-Space Dimension-Reduction

TubeNet has the simplest possible tubular configuration with the uniform number of neurons in all layers and enables explicit inversion. To create a TubeNet, dimension-reduction is a prerequisite for the inverse problems so that the numbers of neurons in the input and output layerscan be made the same. This study introduces a novel procedure to construct inverse TubeNets. The proposed procedure has three major sequential steps. (1) An autoencoder will be used to extract the necessary number of features from a large number of features in a high-dimensional space; (2) Constraints will be imposed to the autoencoder guided by the concepts of the principal component analysis (PCA), so that the extracted features possess the important orthogonality; (3) An L2 regularizer is proposed to adequately impose these constraints on the off-diagonal entries in the weight matrix of the autoencoder, ensuring quality orthogonality. The benchmark problems of inverse identification of material constants of composite laminated plates are used to evaluate the effect of the present TubeNet procedure with the constraint autoencoder, standard autoencoder and PCA, implemented in TubeNet. The study shows that the present constrained autoencoder can effectively overcome the shortcomings of PCA and standard autoencoder, and offers an effective way for dimension-reduction for inverse TubeNet.
Unsupervised domain adaptation for the segmentation of breast tissue in mammography images

Breast density refers to the proportion of glandular and fatty tissue in the breast and is recognized as a useful factor assessing breast cancer risk. Moreover, the segmentation of the high-density glandular tissue from mammograms can assist medical professionals visualizing and localizing areas that may require additional attention. Developing robust methods to segment breast tissues is challenging due to the variations in mammographic acquisition systems and protocols. Deep learning methods are effective in medical image segmentation but they often require large quantities of labelled data. Unsupervised domain adaptation is an area of research that employs unlabelled data to improve model performance on variations of samples derived from different sources.First, a U-Net architecture was used to perform segmentation of the fatty and glandular tissues with labelled data from a single acquisition device. Then, adversarial-based unsupervised domain adaptation methods were used to incorporate single unlabelled target domains, consisting of images from a different machine, into the training. Finally, the domain adaptation model was extended to include multiple unlabelled target domains by combining a reconstruction task with adversarial training.The adversarial training was found to improve the generalization of the initial model on new domain data, demonstrating clearly improved segmentation of the breast tissues. For training with multiple unlabelled domains, combining a reconstruction task with adversarial training improved the stability of the training and yielded adequate segmentation results across all domains with a single model.Results demonstrated the potential for adversarial-based domain adaptation with U-Net architectures for segmentation of breast tissue in mammograms coming from several devices and demonstrated that domain-adapted models could achieve a similar agreement with manual segmentations. It has also been found that combining adversarial and reconstruction-based methods can provide a simple and effective solution for training with multiple unlabelled target domains.
Health-care information technologies for dispersed knowledge management

Purpose The health-care industry has multiple stakeholders, with knowledge dispersed among clinicians, experts and patients and their families. As the adoption of health-care information technologies (HITs) depends on multiple factors, this study aims to uncover the motivators for adopting them. Design/methodology/approach The study considers 391 respondents, representing the health-care sector, to evaluate the motivators for adopting HITs for better-dispersed knowledge management. The authors analyze the responses using exploratory factor analysis (EFA) to identify the actual structure of the factors, followed by confirmatory factor analysis (CFA). Findings EFA categorized the factors into four classes: quality management; information sharing; strategic governance; and available technological infrastructure. CFA revealed that the strategic governance factor is most predictive of successfully adopting HITs that model the normative pressure of Institutional theory in health-care organizations. These results indicate that, along with considerations of finances, care quality and infrastructure, effective government involvement and policy-making are important for successful HIT adoption. Practical implications Results reveal that stakeholders’ motivating factors for HIT adoption in a developed economy like the United Arab Emirates are based on considering HITs as a knowledge management mechanism. These factors may help other nations in HIT implementation and drive valuable innovations in the health-care sector. This research presents the implications for health-care professionals and stakeholders in relation to adopting HITs and their role in knowledge flow for efficient care. Originality/value HITs offer an affordable and convenient platform for collaboration among diverse teams in the health-care sector. Apart from this, it helps in facilitating an interactive platform for knowledge creation and transfer for the benefit of users and providers.
Improving Vehicle Classification and Detection with Deep Neural Networks

Vehicle detection and classification are major functions of advanced driver assistance systems (ADAS). In this paper, a deep-learning approach for vehicle detection and classification is discussed and improved. More specifically, we utilized the state of the art object detection model “YOLOV4” with a clear focus on vehicles which made the detection process more robust. We conducted real life tests on vehicle images from Egypt at El-Sahil bridge, Imbaba and El-Hussary where our improved model detects the vehicles reliably. Our approach mainly relied on using ResNet50 and VGG16 as a classification backbones for YOLOv4, we used the GTI dataset to train and fine tune both networks to get the one with the better classification accuracy. The mean average precision (MAP) increased by 6.7% for VGG (84.49% to 91.02%) and 7.35% for ResNet (85.3% to 92.653%) then we trained YOLOv4 using OpenImages and Highway data sets for vehicle detection reaching an improvement of nearly 6.3% MAP (86.5% to 92.82%).
Nested Dense Attention Network for Single Image Super-Resolution

Recently, deep convolutional neural networks (CNNs) are widely used in single image super-resolution (SISR) and have recorded impressive performance. However, most of the existing CNNs architectures can not fully utilize the correlation of feature maps in the middle layers, and abundant features of different levels are lost. Furthermore, convolution operation is limited by processing one local neighborhood at a time, which lacks global information. To address these issues, we propose the nested dense attention network (NDAN) for generating more refined and structured high-resolution images. Specifically, we propose nested dense structure (NDS) to better integrate features of different levels extracted from different layers. Besides that, in order to capture inter-channel dependencies more efficiently, we propose the adaptive channel attention module (ACAM) to adaptively rescale channel-wise features by automatically adjusting the weights of different receptive fields. Furthermore, to better explore the global-level context information, we design hybrid non-local module (HNLM) and hybrid non-local up-sampler (HNLU) to upscale the images by capturing spatial-wise long-distance dependencies and channel-wise long-distance correlation. Numerous experiments demonstrate the effectiveness of our model by achieving higher PSNR and SSIM scores and generating images with better structures against the state-of-the-art methods.
Automated derivation of variants in manufacturing systems design

The Logistics Specification and Analysis Tool (LSAT) is a modelbased engineering tool used for design-space exploration of flexible manufacturing systems. LSAT provides domain specific languages to model a manufacturing system and means to analyze the productivity characteristics of such a system. In LSAT, developers can specify a system and model its deterministic operations as a set of activities. Given a set of activities, it is possible to construct an individual activity sequence that represents one valid system execution, and with minor variations in the specification individual systems can be obtained. To avoid modeling each variant separately, which means cloning and maintaining the common parts, new functionality is needed to deal with the variability of system specifications. In this study, we aim to establish integration between LSAT and product line engineering techniques. Specifically, we provide a realization of a toolchain including variability representation of LSAT realization artifacts and automated variant derivation for the LSAT model variants. Delta modeling, a transformational variability realization mechanism, is employed to model the variability within LSAT realization artifacts. Using the toolchain, we develop an industry-related case for a product line, the so called Extended Twilight System, a Cyber Physical System (CPS) inspired by the CPSs of our industrial partner.
Implementing QR Code–Based Contact Tracing Framework

In 1994, Denso Wave from Japan was introduced the Quick Response (QR) code at first. At present our world is in the hands of digital developments, like tablet, smart phones, iPad and others. A small object with millions benefits with starting from user needs is almost complete. All users are widely using Smartphone, tablet and iPad. Even electronic transactions in Smartphone are also following the trend. To improve the time efficiency at the public premises, electronic transaction aims check in and checkout using android mobile App and QR code. So that the check in and checkout does not need to be served manually. The main objective of this paper is to reduce the queues that occur at the public premises in this Covid19 pandemic situation. This paper explains the design and development of a mobile application to speed up the check-in and check-out process. In conclusion, adaption of this QR code will improve a positive change in the field of check in and check out across the public premises.
Medical-Based Text Classification Using FastText Features and CNN-LSTM Model

Text classification is a fundamental task that is often carried out upstream of natural language processing (NLP) techniques. Therefore, this task plays an essential role in information retrieval and extraction, and has a wide range of applications in many areas. Many text classification techniques have been proposed, with promising results. However, to propose an efficient model, the particularity of the application domain also needs to be addressed to better grasp the syntactic and semantic complexity of the texts. In this paper, we proposed a classification model for medical text classification that is based on a convolutional neural network (CNN) combined with a long short term memory (LSTM) neural networks. The proposed CNN-LSTM is using word vectors computed with FastText to achieve the highest accuracy. We compared our proposed model results with other state-of-the-art models such as CNN, support vector machines, decision trees, naive Bayes, and K-nearest neighbor. The performance of all used models were evaluated in terms of accuracy, precision, recall, and F1 score. The CNN-LSTM outperforms all other models in terms of all evaluation parameters and achieved 86.34%, 90.68%, 91.72%, 90.67% accuracy, precision, recall, and F1 score, respectively.
Sequential Rule Mining on the Student Behavior Data of an E-Learning Platform in the Field of Financial Sciences: Case Study

Today, we observe that innovative technologies are needed to increase students' success on e-learning platforms. There is a need to develop a system that monitors students' activities, identifies common repetitive behaviors, and makes suggestions. Within the scope of this research, we perform sequential rule mining on sequential action sequences. We introduce a software architecture, which mines the most repetitive sequential action sequences and enables rules based on these sequences. To demonstrate the usability of the proposed method, we develop a prototype application for an e-learning platform that provides training in financial sciences. We carry out tests on the developed prototype application in terms of performance and scalability. The results reveal that the proposed software architecture successfully extracts rules from sequential action sequences obtained from student behavior data.
Multi-task CNN for Abusive Language Detection

Abusive language detection serves to ensure a compelling user experience via high-quality content. Different sub-categories of abusive language are closely related, with most aggressive comments containing personal attacks and toxic content and vice versa. We set a multi-task learning framework to detect different types of abusive content in a mental health forum to address this feature. Each classification task is treated as a subclass in a multi-class classification problem, with shared knowledge used for three related tasks: attack, aggression, and toxicity. Experimental results on three sub-types of Wikipedia abusive language datasets show that our framework can improve the net F1-score by 7.1%, 5.6%, and 2.7% in the attack, aggressive, and toxicity detection. Our experiments identified multi tasking framework act as an effective method in abusive language detection.
Chatbot Driven Web-based Platform for Online Safety and Sexual Exploitation Awareness and Reporting in Namibia

Technological devices have become a commodity, access to information can be achieved within seconds, connecting people across the globe is easy and possible. Where there is growth, exploitation lurks too. Online sex offenders have taken this opportunity to seduce, groom and make contact with children online. The objective of the research was to design an interactive website with a reporting mechanism that serves as a safe haven where children can find awareness materials and report online incidences of abuse. The use of chatbots on such a website can be beneficial, as queries reported can be attended to. The research used the Software Development Life Cycle (SDLC) focusing on Agile Method to develop the interactive website prototype and chatbot. Qualitative primary data was collected from 42 pre-teens and teens selected from 2 private and 1 public schools in Windhoek, Namibia. The schools were purposefully selected to reflect the extent of the challenge as they offer Computer Studies or Computer Science subject, the children mostly have access to digital technologies daily. Results from the study yielded opinions that teens and pre-teens would make use of the chatbot and reporting portal, and also supported it by sharing innovative ideas for a platform that they would want to utilise. The prototype was evaluated for Usability by five participants using the unmonitored remote usability testing and the feedback was used to further refine the design.
Variants of Jacobi polynomials in coding theory

In this paper, we introduce the notion of the complete joint Jacobi polynomial of two linear codes of length n over $${\mathbb {F}}_{q}$$ and $${\mathbb {Z}}_{k}$$ . We give the MacWilliams type identity for the complete joint Jacobi polynomials of codes. We also introduce the concepts of the average Jacobi polynomial and the average complete joint Jacobi polynomial over $${\mathbb {F}}_{q}$$ and $${\mathbb {Z}}_{k}$$ . We give a representation of the average of the complete joint Jacobi polynomials of two linear codes of length n over $${\mathbb {F}}_{q}$$ and $${\mathbb {Z}}_{k}$$ in terms of the compositions of n and its distribution in the codes. Further we present a generalization of the representation for the average of the $$(g+1)$$ -fold complete joint Jacobi polynomials of codes over $${\mathbb {F}}_{q}$$ and $${\mathbb {Z}}_{k}$$ . Finally, we give the notion of the average Jacobi intersection number of two codes.
A Path Planning Method for the Spherical Amphibious Robot Based on Improved A-star Algorithm

In order to improve the autonomous navigation ability and mobility of spherical amphibious robot. A multiple target points path planning algorithm based on global static planning was proposed. Firstly, the A* algorithm is optimized for bidirectional smoothness, and the distance of each path is calculated. Secondly, according to this distance, the ant colony algorithm is used to sort multiple targets, and the global optimal path is planned. Finally, a series of simulations and experiments verify the proposed path planning method. The experimental results show that the robot successfully avoids obstacles from the starting point to the end point. On the one hand, the path distance of the robot is greatly shortened, on the other hand, the robot completes the path planning of multiple target points.
Homography Ranking Based on Multiple Groups of Point Correspondences

Homography mapping is often exploited to remove perspective distortion in images and can be estimated using point correspondences of a known object (marker). We focus on scenarios with multiple markers placed on the same plane if their relative positions in the world are unknown, causing an indeterminate point correspondence. Existing approaches may only estimate an isolated homography for each marker and cannot determine which homography achieves the best reprojection over the entire image. We thus propose a method to rank isolated homographies obtained from multiple distinct markers to select the best homography. This method extends existing approaches in the post-processing stage, provided that the point correspondences are available and that the markers differ only by similarity transformation after rectification. We demonstrate the robustness of our method using a synthetic dataset and show an approximately 60% relative improvement over the random selection strategy based on the homography estimation from the OpenCV library.
Enhanced Spatial–Temporal Map-Based Video Analytic Platform and Its Local- Versus Cloud-Based Deployment with Regional 511 Camera Network

This paper explores the cloud- versus server-based deployment scenarios of an enhanced computer vision platform for potential deployment on low-resolution 511 traffic video streams. An existing computer vision algorithm based on a spatial–temporal map and designed for high-angle traffic video like that of NGSIM (Next Generation SIMulation) is enhanced for roadside CCTV traffic camera angles. Because of the lower visual angle, determining the directions, splitting vehicles from occlusions, and identifying lane changes become difficult. A motion-flow-based direction determination method, a bisection occlusion detection and splitting algorithm, and a lane-change tracking method are proposed. The model evaluation is conducted by using videos from multiple cameras from the New Jersey Department of Transportation’s 511 traffic video surveillance system. The results show promising performance in both accuracy and computational efficiency for potential large-scale cloud deployment. The cost analysis reveals that at the current pricing model of cloud computing, the cloud-based deployment is more convenient and cost-effective for an on-demand network assessment. In contrast, the dedicated-server-based deployment is more economical for long-term traffic detection deployment.
Improving Accuracy of Deep Learning-Based Compression Techniques by Introducing Perceptual Loss in Industrial IoT

Rani, Poonam Jain, Vibha Saif, Mohammad Mugloo, Saahil Hussain Hirna, Mitul Jain, SomilOver the years there has been a great increase in the industrial Internet of things, and consequently, over 20 billion devices are now connected through the Internet. Internet of things is the connection of a wide variety of devices or entities over the Internet for storage and gathering of data. This will raise a need for compression techniques for remote sensing images, which is an enabling technology for the Internet of things. While various conventional technologies exist for the compression of remote sensing images, we require a low complexity technique that preserves the quality of the images without compromising over the various spatial parameters that these images possess. Deep learning methods through the use of convolution neural networks have proved to be a reasonable solution for the compression problem. The proposed method uses a deep learning-based method that uses autoencoders for encoding and decoding that incorporates perceptual loss to improve the accuracy of the reconstructed images. The loss functions we utilize are mean squared error loss, content loss, and style loss. We have also made use of lossless compression techniques using LZMA and deflate compression algorithms which do not result in any information loss. Experimental results show that our proposed compression method outperforms standard benchmark techniques.
Fair Rewarding Mechanism for Sharding-based Blockchain Networks with Low-powered Devices in the Internet of Things

Contributing to mining algorithms using low-powered devices is one of the principal concerns in the growth of the Internet of Things (IoT) paradigm. To address this limitation, we introduce a new concept of allocating mining devices based on their computation power in sharding-based blockchain systems for the IoT. This study proposes two scenarios based on different ways of partitioning the transactions into the disjoint shards: Scenario 1 (Number of processors are equally distributed among shards); Scenario 2 (Number of processors are distributed among shards using computation power-based criterion). We also adopt an adversary model to obtain the fraction of participating processors that must be compromised by the adversaries to compromise our proposed method of sharding based blockchain networks. Our results demonstrate that the total computation cost of the entire system increases when the computing cost for verifying signatures and the number of shards increases. We observe that the total reward of each processor increases when the number of less powerful processors increases. The total number of mined processors of each shard decreases with a high number of less powerful processors. Thus, the total reward of the entire network is distributed among a few processors. We demonstrate that our new method of allocating mining devices based on their computation power (Scenario 2) provides fair reward distribution. Additionally, when the total number of processors in a network is higher than 500, each processor's total reward increment shows an insignificant change. However, when the total number of processors is lower than 500, each processor's total reward shows an upward linear trend and contradicts the assumption of Scenario 1. Thus, our results show that the proposed model (Scenario 2) is more suitable for allocating mining devices based on their computation power in sharding-based blockchain systems for IoT, especially devices with different computational capabilities or processors with selfish behavior. This concept motivates powerful processors to verify and validate new transactions to be added to the blockchain.