Reviewerly/exHarmony huggingface dataset:
Creating a ubiquitous reading environment for children in undeveloped rural areas: An action research project

Reading literacy in children is very low in undeveloped, rural areas compared to reading literacy levels in urban children. Action research is effective in exploring collaboration among libraries, schools, and families to improve rural children's reading levels. To create a ubiquitous reading environment for rural children, a three-stage and two-year action framework was initially designed and evaluated in this study. Ten action research circles were carried out within this framework involving 10 rural libraries, 11 primary schools and 127 families in China. Findings show positive effects of the improved reading environment. Five themes were identified to interpret why the research project exerted positive impacts on rural areas and to guide future action for libraries and other sectors to develop children's literacy as strategies.
A smart building security system with intelligent face detection and recognition

Abstract The security system is the most important application that protects several threats from inside and outside of an organization. The purposes of several security building applications such as the system will know criminals, disgruntled former employees enter the workplace, and enabling building security to work proactively for ensuring that associates and customers are safe from harm. By implementing face detection and recognition of the security system, video surveillance in the building can be improved by upgrading the system of video surveillance with artificial intelligence. The aim of the research is to develop the face detection and recognition in the security system in the building with recognizing human faces and image capture to the cloud storage. The result shows that the video will stream and recognize human faces whether the person is authorized or unauthorized in the system image using Raspberry Pi 3. For analysis purpose, the image captured and stored from the two type of cloud storage namely Cloudinary and Dropbox had been analyzed. Based on the analysis, the image captured and stored into Dropbox cloud storage give the best performance features which gained more 75% of quality images.
Semantic Image Segmentation Guided By Scene Geometry

Semantic image segmentation is an important functionality in various applications, such as robotic vision for autonomous cars, drones, etc. Modern Convolutional Neural Networks (CNNs) process input RGB images and predict per-pixel semantic classes. Depth maps have been successfully utilized to increase accuracy over RGB-only input. They can be used as an additional input channel complementing the RGB image, or they may be estimated by an extra neural branch under a multitask training setting. Contrary to these approaches, in this paper we explore a novel regularizer that penalizes differences between semantic and self-supervised depth predictions on presumed object boundaries during CNN training. The proposed method does not resort to multitask training (which may require a more complex CNN backbone to avoid underfitting), does not rely on RGB-D or stereoscopic 3D training data and does not require known or estimated depth maps during inference. Quantitative evaluation on a public scene parsing video dataset for autonomous driving indicates enhanced semantic segmentation accuracy with zero inference runtime overhead.
SmithNet: Strictness on Motion-Texture Coherence for Anomaly Detection

Anomaly detection is a key functionality in various vision systems, such as surveillance and security. In this work, we present a convolutional neural network (CNN) that supports the detection of anomaly, which has not been defined when building the model, at frame level. Our CNN, named SmithNet, is structured to simultaneously learn commonly occurring textures and their corresponding motion. Its architecture is a combination of: 1) an encoder extracting motion-texture coherence from each video frame and 2) two decoders that separately reconstruct the input as well as predict its typical motion from the estimated coherence. We also introduce an encoding block, which is specifically designed for the task of anomaly detection. The optimization is performed on only data of normal events, and the network is expected to determine the ones that are unusual, i.e., have not been seen before. According to the experiments on eight benchmark datasets of different environments with various anomalous events, the performance of our network is competitive or outperforms current state-of-the-art approaches.
Sentiment Classification of News Text Data Using Intelligent Model

Text sentiment classification is a fundamental sub-area in natural language processing. The sentiment classification algorithm is highly domain-dependent. For example, the phrase "traffic jam" expresses negative sentiment in the sentence "I was stuck in a traffic jam on the elevated for 2 h." But in the domain of transportation, the phrase "traffic jam" in the sentence "Bread and water are essential terms in traffic jams" is without any sentiment. The most common method is to use the domain-specific data samples to classify the text in this domain. However, text sentiment analysis based on machine learning relies on sufficient labeled training data. Aiming at the problem of sentiment classification of news text data with insufficient label news data and the domain adaptation of text sentiment classifiers, an intelligent model, i.e., transfer learning discriminative dictionary learning algorithm (TLDDL) is proposed for cross-domain text sentiment classification. Based on the framework of dictionary learning, the samples from the different domains are projected into a subspace, and a domain-invariant dictionary is built to connect two different domains. To improve the discriminative performance of the proposed algorithm, the discrimination information preserved term and principal component analysis (PCA) term are combined into the objective function. The experiments are performed on three public text datasets. The experimental results show that the proposed algorithm improves the sentiment classification performance of texts in the target domain.
Cybersecurity Ontology for Dynamic Analysis of IT Systems

Today’s IT systems are characterized by a high complexity and the increasing number of sub-systems, as well as the physical equipment needed. That raises the problem of keeping the whole architecture secure, as it has been revealed in recent years by numerous scandals related to data leaks from inadequately secured systems. Several ontology-based approaches tied to the cybersecurity domain have been developed aiming at solving the problem of identifying vulnerabilities in real world systems. Unfortunately, these approaches do not address the essential problem: since real IT system architectures are dynamic and highly variable, it is necessary to enable real-time inventory and observation of all system components, as well as to retrieve up-to-date data about those systems and potential risks. The current paper has a threefold purpose. First, we examine the existing cybersecurity ontologies and identify the deficiencies that prevent us from using them in real, dynamic IT systems. Next, we introduce and propose a framework based on the Dynamic CyberSecurity Ontology, which fills the existing gaps in current solutions. Finally, we outline a monitoring system based on the developed ontology, which implements automatic data mining mechanisms that aggregate results from dynamic knowledge sources, such as Shodan or Censys. As a result, the ontological examination of systems over time becomes possible.
An Explainable Online Password Strength Estimator

Human-chosen passwords are the dominant form of authentication systems. Passwords strength estimators are used to help users avoid picking weak passwords by predicting how many attempts a password cracker would need until it finds a given password.In this paper we propose a novel password strength estimator, called PESrank, which accurately models the behavior of a powerful password cracker. PESrank calculates the rank of a given password in an optimal descending order of likelihood. PESrank estimates a given password’s rank in fractions of a second—without actually enumerating the passwords—so it is practical for online use. It also has a training time that is drastically shorter than previous methods. Moreover, PESrank is efficiently tweakable to allow model personalization in fractions of a second, without the need to retrain the model; and it is explainable: it is able to provide information on why the password has its calculated rank, and gives the user insight on how to pick a better password.We implemented PESrank in Python and conducted an extensive evaluation study of it. We also integrated it into the registration page of a course at our university. Even with a model based on 905 million passwords, the response time was well under 1 s, with up to a 1-bit accuracy margin between the upper bound and the lower bound on the rank.
Extracting material parameters of silicone elastomers under biaxial tensile tests using virtual fields method and investigating the effect of missing deformation data close to specimen edges on parameter identification

In this work, virtual fields method (VFM) is applied to extract the constitutive parameter of silicone elastomers under equi-biaxial and general biaxial tests; the influence of missing deformation data near specimen edges and noise introduced by digital image correlation (DIC) on parameter identification using VFM is investigated. The results indicate that the negative impact of the missing data and noise can be mitigated by scaling the load applied to the specimen boundary properly and extracting parameters based on the deformation fields of all loading steps through the least square method.
Identical Twins as a Facial Similarity Benchmark for Human Facial Recognition

The problem of distinguishing identical twins and non-twin look-alikes in automated facial recognition (FR) applications has become increasingly important with the widespread adoption of facial biometrics. This work presents an application of one of the largest twin datasets compiled to date to address two FR challenges: 1) determining a baseline measure of facial similarity between identical twins and 2) applying this similarity measure to determine the impact of doppelgangers, or look-alikes, on FR performance for large face datasets. The facial similarity measure is determined via a deep convolutional neural network. The proposed network provides a quantitative similarity score for any two given faces and has been applied to large-scale face datasets to identify similar face pairs.
Exploring the Impact of Data Poisoning Attacks on Machine Learning Model Reliability

Recent years have seen the widespread adoption of Artificial Intelligence techniques in several domains, including healthcare, justice, assisted driving and Natural Language Processing (NLP) based applications (e.g., the Fake News detection). Those mentioned are just a few examples of some domains that are particularly critical and sensitive to the reliability of the adopted machine learning systems. Therefore, several Artificial Intelligence approaches were adopted as support to realize easy and reliable solutions aimed at improving the early diagnosis, personalized treatment, remote patient monitoring and better decision-making with a consequent reduction of healthcare costs. Recent studies have shown that these techniques are venerable to attacks by adversaries at phases of artificial intelligence. Poisoned data set are the most common attack to the reliability of Artificial Intelligence approaches. Noise, for example, can have a significant impact on the overall performance of a machine learning model. This study discusses the strength of impact of noise on classification algorithms. In detail, the reliability of several machine learning techniques to distinguish correctly pathological and healthy voices by analysing poisoning data was evaluated. Voice samples selected by available database, widely used in research sector, the Saarbruecken Voice Database, were processed and analysed to evaluate the resilience and classification accuracy of these techniques. All analyses are evaluated in terms of accuracy, specificity, sensitivity, F1-score and ROC area.
LiterallyWikidata - A Benchmark for Knowledge Graph Completion Using Literals

In order to transform a Knowledge Graph (KG) into a low dimensional vector space, it is beneficial to preserve as much semantics as possible from the different components of the KG. Hence, some link prediction approaches have been proposed so far which leverage literals in addition to the commonly used links between entities. However, the procedures followed to create the existing datasets do not pay attention to literals. Therefore, this study presents a set of KG completion benchmark datasets extracted from Wikidata and Wikipedia, named LiterallyWikidata. It has been prepared with the main focus on providing benchmark datasets for multimodal KG Embedding (KGE) models, specifically for models using numeric and/or text literals. Hence, the benchmark is novel as compared to the existing datasets in terms of properly handling literals for those multimodal KGE models. LiterallyWikidata contains three datasets which vary both in size and structure. Benchmarking experiments on the task of link prediction have been conducted on LiterallyWikidata with extensively tuned unimodal/multimodal KGE models. The datasets are available at https://doi.org/10.5281/zenodo.4701190.
Development of learning-based noise reduction and image reconstruction algorithm in two dimensional Rayleigh thermometry

Consistent research efforts have been invested to deal with particles' Mie scattering interference in Rayleigh measurements. This work reports an alternative learning-based image denoising and reconstruction algorithm to remove high value Mie scattering noise in Rayleigh thermometry. The algorithm, named denoising generative adversarial network (DNGAN), consists of a generator and a discriminator to learn the texture features and infer denoised image with a given noisy counterpart. Training data of the neural network come from artificially generated noise-free Rayleigh image superimposed with experimentally acquired Mie scattering image. Extensive evaluations are then conducted to prove the superiority of the proposed DNGAN network, compared with several other neural networks reported in literature. Quantitative results show that a convincing denoising with an overall reconstruction error of ~ 0.6% a peak signal-to-noise ratio of ~ 43 dB can be reached. Moreover, the pre-trained network is able to denoise image of the jet flame with a different Reynolds number without retraining the network parameters.
Introduction to User-Centred Design Sprint

This course will introduce attendees to the challenges and benefits of integrating User-Centred Design (UCD) methods into the Google Design Sprint (GDS) process. The course will introduce this new User-Centred Design Sprint process, and participants will practice selected methods from the process during the course. By the end of the course, participants will know why, when and how to use User-Centred Design Sprint. Delivered by experienced members of IFIP TC13, this course will appeal to researchers and developers working in the early stages of designing software products.
Offline Natural Scene Character Recognition Using VGG16 Neural Networks

Recognizing printed characters is a little easier than when compared to handwritten characters by offline OCR machines. When the images of the characters are taken from the natural scene, they become hazy and in various angles. Then they are harder to recognize, even though they are printed. In this study, an attempt has been made to identify characters that are colored and in different orientations, using a deep convolutional neural network known as VGG16. The effect on classification accuracy with respect to epochs on the datasets is projected by conducting some experiments. Two Latin character datasets have been considered, such as Chars74kImg and Binary alphanumeric. The Chars74kImg dataset consists of RGB isolated character images taken from the real world, while the Binary alphanumeric dataset comprises binary versions of distinct characters in different orientations. With the Chars74kImg dataset, the suggested model obtains the highest accuracy of 78.04%, far better than the previous techniques found in the literature. Finally, along with quantitative analysis, a comparison of the suggested method to a few preexisting systems is presented.
Texture-based latent space disentanglement for enhancement of a training dataset for ANN-based classification of fruit and vegetables

The capability of Convolutional Neural Networks (CNNs) for sparse representation has significant application to complex tasks like Representation Learning (RL). However, labelled datasets of sufficient size for learning this representation are not easily obtainable. The unsupervised learning capability of Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) provide a promising solution to this issue through their capacity to learn representations for novel data samples and classification tasks. In this research, a texture-based latent space disentanglement technique is proposed to enhance learning of representations for novel data samples. A comparison is performed among different VAEs and GANs with the proposed approach for synthesis of new data samples. Two different VAE architectures are considered, a single layer dense VAE and a convolution based VAE, to compare the effectiveness of different architectures for learning of the representations. The GANs are selected based on the distance metric for disjoint distribution divergence estimation of complex representation learning tasks. The proposed texture-based disentanglement has been shown to provide a significant improvement for disentangling the process of representation learning by conditioning the random noise and synthesising texture rich images of fruit and vegetables.
Can AI Teach Humans? Humans AI Collaboration for Lifelong Machine Learning

Humans always play the role of a teacher to AI in the last decades. One day, AI will become wiser than humans in various fields. At that time, humans need to learn from AI to improve themselves. However, most of the current high-performance machine learning models are black boxes and challenging to understand. So a system with better explainability is needed to help humans to understand AI. Therefore, the authors proposed a double-track approach to use expert systems to supplement the current machine learning paradigm to solve this problem. Under lifelong machine learning, the double-track approach can be wiser and wiser and achieve high performance but keeps outstanding explainability.
Dispersive and dissipative coupling of photon Bose-Einstein condensates

Abstract The synchronization of coherent states of light has long been an important subject of basic research and technology. Recently, a new concept for analog computers has emerged where this synchronization process can be exploited to solve computationally hard problems - potentially faster and more energy-efficient than what can be achieved with conventional computer technology today. The unit cell of such systems consists of two coherent centers that are coupled to one another in a controlled manner. Here, we experimentally characterize and analyze the synchronization process of two photon Bose-Einstein condensates, which are coupled to one another, either dispersively or dissipatively. We show that both types of coupling are robust against a detuning of the condensate frequencies and show similar time constants in establishing mutual coherence. Significant differences between these couplings arise in the behaviour of the condensate populations under imbalanced optical pumping. The combination of these two types of coupling extends the class of physical models that can be investigated using analog simulations.
Reversible training of waveguide-based AND/OR gates for optically driven artificial neural networks using photochromic molecules

Artificial neural networks (ANNs) are inspired by the biological nervous system. The high performance of such ANNs is achieved through the dynamic change of the synaptic weights by applying self-optimizing learning algorithms. Despite the simple operations for each single element in an ANN, a network with a huge number of simulated elements consumes lots of computing capacity using von Neumann computer architectures. To overcome this issue, neuromorphic devices facilitate the design of hardware ANNs that emulate the synaptic functions. Here we demonstrate the viability of such an approach using photonic waveguides in combination with a photochromic diarylethene (DAE) molecule. By positioning and irradiating DAE molecules on single waveguides, we modulate the intensity and thereby emulate the plasticity of the synaptic weights. To run the photonic device as an ANN we firstly characterize the modulation range and encode a learning procedure accordingly. As the proof of concept, we operate a y-shaped waveguide performing basic AND/OR logic gate functions, with the capability to switch between these two gate functions by using specific training sets.
Enhancement of Breast Mammography to Rapid Screen Abnormalities Using <scp>2D</scp> Spatial <scp>Fractional‐Order</scp> Feature Extraction and Multilayer Machine Vision Classifier

While breast tumor symptoms [benign (B) or malignant (M) tumors] or lymph node metastasis can be detected at an early stage, the timely discovery of abnormalities could simultaneously improve survival rates and enhance therapeutic efficacy. During first‐line mammography examination, upon finding any solid‐white region, clinicians or radiologists can manually select the region of interest (ROI) from lateral‐view images with a specific bounding box (BB). This study used two‐dimensional (2D) fractional‐order convolution (FOC) operations with fractional‐order parameters ( v = 0.3 − 0.4) to sharpen and retain the structure of possible focus, inhibit the features of background tissue, and remove unwanted noise. The gray‐scale values of breast mammography can be readjusted to enhance the image contrast using a nonlinear intensity transformation function. Abnormal contour levels can automatically be searched on the basis of the edge detection of normalized gradients to interpret the main ROI region using a contour algorithm. Hence, the main ROI can be rapidly identified on each breast mammography. The central region of the main ROI can then be divided into five sub ROIs, which are fed to a multilayer machine vision classifier. We used a general regression neural network (GRNN)‐based classifier to separate normal (no tumor) images from abnormal (B or M tumor) ones and obtain an automatic screening support tool that can decisively confirm suspicious lesions and achieve accurate detection. Using images from the Mammographic Image Analysis Society (MIAS) digital breast mammogram database, we applied the proposed GRNN‐based classifier with K‐fold cross‐validation to emphasize the possible focus and provide additional confidence during imaging examination. Results showed an average recall (%), an average precision (%), an average accuracy (%), and an average F1 score of 87.31%, 90.50%, 88.67%, and 0.8883%, respectively, for the rapid screening of B and M tumors on digital breast X‐ray images. The GRNN‐based classifier is superior to traditional techniques in terms of adaptive learning schemes, computational time consumption, and design cycle. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.