Reviewerly/exHarmony huggingface dataset:
Use of Augmented and Virtual Reality for Enhancement of Aerospace Cabin and Cockpit Experience

Ryerson University’s Mixed-Reality Immersive Motion Simulation (MIMS) lab conducts a variety of research on Mixed Reality (MR) application for aerospace cockpits and cabins. The research team at the MIMS aims to integrate Commercially Available Off-the-Shelf (COTS) equipment with flight simulator data for faster and more reliable development of aerospace systems. The MIMS team has successfully used MR in crew training, rapid development of aircraft cockpits, and visualization of cabin interiors. This chapter provides an overview of three research projects completed at the MIMS lab using MR technology.
Learning to Match Workers and Tasks via a Multi-View Graph Attention Network

The worker-task matching problem brings up unique characteristics that are not present in traditional matching scenarios, i.e., the huge flow of tasks with short lifespans, the importance of workers’ capabilities, and the quality of the completed tasks. These characteristics further pose significant challenges of data sparsity and comprehensive modeling.To address the two challenges, this paper proposes MvkGAN, a multi-view attention network on a bi-collaborative knowledge graph (BicKG). The core ideas of our work are 1) building BicKG from the data of workers, tasks, their interactions, and domain knowledge, and then leveraging it to reveal the latent interactions between workers and tasks to mitigate the data sparsity challenge; and 2) designing a multi-view knowledge graph attention network (MvkGAN) which learns to match workers and tasks, to meet the comprehensive modeling challenge. In this network, different features are organized as multiple views and these views are further connected by the attention mechanism.We have implemented MvkGAN and evaluated it against five state-of-the-art approaches (Wide&Deep, DeepFM, KGAT, Crow-dRex and PJFNN) on two real-world datasets. The evaluation results show that MvkGAN improves the accuracy by 6.90% and F1-score by 5.52% on average, and also has the ability of generating reasonable explanations.
Who is Selling to Whom – Feature Evaluation for Multi-block Classification in Invoice Information Extraction

The invoice information extraction task aims at unifying the automatized processing of invoices in structured forms and in the form of a scanned image. Recognizing the pieces of information where a specific value is identified with a keyword (such as the invoice date) is a relatively well-managed task. On the other hand, identification of multi-block information on the invoice, such as distinguishing the seller, buyer, and the delivery address, is much more challenging due to versatile invoice layouts.In this work, we present a new technique of feature extraction and classification to recognize the seller, buyer, and delivery address text blocks in scanned invoices based on a combination of complex layout and annotated text features. The method does not only consider the block positional features but also the relation between blocks and block contents at a higher level. The technique is implemented as a module of the OCRMiner system. We offer its detailed evaluation and error analysis with a dataset of more than five hundred Czech invoices reaching the overall macro average F1-score of 94%.
Research on Invulnerability Evaluation of Optical Network Geography Disaster

In view of the lack of an effective evaluation model for optical network survivability evaluation caused by geographical disasters, this paper has studied and proposed an evaluation model for optical network survivability considering geographical disasters, designed and proposed an evaluation index for survivability based on communication efficiency. On this basis, a survivability assessment method considering protection and unprotected conditions is proposed. Simulation results show that this method can effectively evaluate the invulnerability of optical networks under geographical disasters.
PSYCHOLOGICAL FEATURES OF INFORMATION PERCEPTION IN THE ONLINE MODE

This article examines the psychological features of the perception of information online and offline, the formation of a visual image and its connection with verbal information. The same image placed in different contexts can acquire dissimilar, often opposite meanings. This duality leads to divergent thinking. In most cases, visual images are combined with a verbal message and creolized texts are formed.
Approximation Algorithms for Confidence Bands for Time Series

Confidence intervals are a standard technique for analyzing data. When applied to time series, confidence intervals are computed for each time point separately. Alternatively, we can compute confidence bands, where we are required to find the smallest area enveloping k time series, where k is a user parameter. Confidence bands can be then used to detect abnormal time series, not just individual observations within the time series. We will show that despite being an NP-hard problem it is possible to find optimal confidence band for some k. We do this by considering a different problem: discovering regularized bands, where we minimize the envelope area minus the number of included time series weighted by a parameter \(\alpha \). Unlike normal confidence bands we can solve the problem exactly by using a minimum cut. By varying \(\alpha \) we can obtain solutions for various k. If we have a constraint k for which we cannot find appropriate \(\alpha \), we demonstrate a simple algorithm that yields \( \mathcal {O} \mathopen {}\left( \sqrt{n}\right) \) approximation guarantee by connecting the problem to a minimum k-union problem. This connection also implies that we cannot approximate the problem better than \( \mathcal {O} \mathopen {}\left( n^{1/4}\right) \) under some (mild) assumptions. Finally, we consider a variant where instead of minimizing the area we minimize the maximum width. Here, we demonstrate a simple 2-approximation algorithm and show that we cannot achieve better approximation guarantee.
Analysis of the frame rate limit for the estimation of the natural frequency of vibration in a mechanical system using optical techniques

In this work, we analyze the advantages and limitations of the systems of low speeds of frames per second (fps) for the estimation of the vibrations measurements in systems where optical techniques are used. The acquisition systems with low fps are interesting because they are not expensive. In this way, the aim of this work is to compute the limit speed to obtain good resolutions in data collection. Laser triangulation technique is implemented to determine the natural frequency of vibration of a system using a cantilever beam, as a standard example. The results are compared, with a commercial accelerometer.
A Study of Word Presentation in Vietnamese Sentiment Analysis

Sentiment analysis is a field of research that analyzes people’s attitudes, psychology, emotions about a particular issue. In recent years, deep learning techniques have yielded remarkable success in sentiment analysis. One of the paramount factors that affect the effectiveness of the deep learning model in sentiment analysis is the representation of the words in the input documents. In this paper, first, we have evaluated the effectiveness of some recent word representation in sentiment analysis for Vietnamese reviews. Second, we have proposed a new feature representing a document named BERT-POS feature. This feature has both contextual semantic and syntactic information of the sentences in a comment. Thus, it helps to improve the accuracy of the sentiment analysis problem. Third, we have conducted several extensive experiments to prove the effectiveness of our proposed feature.
Automated Marking of Underwater Animals Using a Cascade of Neural Networks

In this work, a multifactorial problem of analyzing the seabed state of plants and animals using photo and video materials is considered. Marine research to monitor benthic communities and automatic mapping of underwater landscapes make it possible to qualitatively assess the state of biomes. The task includes several components: preparation of a methodology for data analysis, their aggregation, analysis, presentation of results. In this work, we focused on methods for automating detection and data presentation.For deep-sea research, which involves the detection, counting and segmentation of plants and animals, it is difficult to use traditional computer vision techniques. Thanks to modern automated monitoring technologies, the speed and quality of research can be increased several times while reducing the required human resources using machine learning and interactive visualization methods.The proposed approach significantly improves the quality of the segmentation of objects underwater. The algorithm includes three main stages: correction of image distortions underwater, image segmentation, selection of individual objects. Combining neural networks that successfully solve each of the tasks separately into a cascade of neural networks is the optimal method for solving the problem of segmentation of aquaculture and animals.Using the results obtained, it is possible to facilitate the control of the ecological state in the world, to automate the task of monitoring underwater populations.
QUALITATIVE STUDY OF A COMPUTER MODEL IN THE CYBERNETIC FIELD

The starting point of this study described in this paper is the desire to create a model that will be useful in various fields related to mathematical modeling and that will contain a new perspective of what we call and know about feedback. This feedback has appeared in cyber studies. The modeling of this computer system involves the transfer of information from the data of a problem written in a practical language to the language specific to the feedback contained in cellular automata and algebraic fractals. Both cellular automata and algebraic fractals are fundamental in the development of technical solutions used in the fields related to quantum mechanics. The bases of these researches are from the articles conceived by Prof. Dr. Colceag, in which he mentions information fields, structural fractals, but also about modeling and the models that emerge from this modeling. All this complex modeling structure will describe more complex objects with characteristics such as: feedback cycles, projective relationships in the projective space and specific transformations that describe how this model was obtained, these being common features for everything that means description and modeling the phenomena of physics, chemistry, biology, economy etc. At the beginning, such a model focuses on feedback cycles, and will also develop commutative diagrams based on automorphisms. After this first phase, the model is organized on progressive levels where its structure is divided into new and stable structures that self-determine, this leading to a fractalized modeling in which a feedback structure is inserted in the form of a loop.
Intelligent Failure Prediction in Industrial Vehicles

We propose a data-driven approach for predicting potential malfunction of concrete pump vehicles. Our approach is based on a novel machine learning model called Aggregate Cluster-Based Classifier (ACBC). It is comprised of several weak models, each represents a work states cluster, to learn the work status of several vehicle types and cluster them separately. The ACBC model also introduces a score voting process that decides on using a linear model with customised loss function or gradient boost decision tree to aggregate the outputs of the weak models. We evaluate our ACBC model using real data collected from IoT sensors attached to concrete piston vehicles. Our experimental analysis demonstrates that the ACBC model can achieve an overall accuracy of 68 % from all vehicle types and an accuracy above 80 % for certain vehicle types. Our experiments also shows that the proposed ACBC model consistently outperforms the LSTM model in terms of prediction accuracy and training time.
The Role of the “Reflective Practice” Competence in the Professional Training of Prison Service Officers

This paper presents the model of "reflective practice" according to D. Schön's conception, with special regard to the phenomenon of simultaneous thinking in action, which was applied in the implementation of a pilot postgraduate study program for the future managing staff of penitentiary units.The author describes theoretical sources of inspiration for the training of officers to the profile of professional competence defined as the "Reflective Practitioner".The presented approaches to education and training are based on the trend of reflective practice methodology that could be quickly implemented in daily professional practice of experienced and newly trained prison service officers.
Estimation of Sharing Dependencies in Personal Storage Clouds Using Ensemble Learning Approaches

Multi-cloud computing is emerging and acquired many computing spaces in various offices. It provides choices in choosing vendors, disaster avoidance, and compliance. Multi-cloud brought greater mobility and easy access to facilitate compute anywhere anytime. At the same time, it raises the demand for intelligent resource provisioning based on the dynamic need of the customers. It is essential for vendors and users for estimating future trends in demand and requirements. Many machine learning techniques are proposed to predict the dynamic requirements of the customers. Ensemble learning is a kind of machine learning technique where multiple classifier outputs are combined to produce effective prediction/classification. Ensemble learning is a model that makes predictions based on several different models. By combining individual models, the ensemble model tends to be more flexible and less data-sensitive. It addresses biasing issues in learning. There are two most popular ensemble methods: bagging and boosting. Bagging is training a bunch of individual models in a parallel way. Each model is trained by a random subset of the data. Boosting is training a bunch of individual models sequentially. Each model learns from mistakes made by the previous model. This chapter presents a prediction of the sharing dependency of files stored on storage cloud using ensemble learning methods. The sharing dependency is vital and widely utilized in office networks. Such cloud resources should be available to all the intended users without break. As well, the users should also know the future requirements. This will ensure smooth computation on sharing resources. The NEC personal cloud dataset is experimented with to find the best model for predicting sharing dependency. The experiments showed that the bagged tree model of the ensemble classifier is found to be the best model for the NEC Personal Cloud Dataset that provides 99.8% accuracy and an AUC of 1.
Small Lesion Segmentation in Brain MRIs with Subpixel Embedding

We present a method to segment MRI scans of the human brain into ischemic stroke lesion and normal tissues. We propose a neural network architecture in the form of a standard encoder-decoder where predictions are guided by a spatial expansion embedding network. Our embedding network learns features that can resolve detailed structures in the brain without the need for high-resolution training images, which are often unavailable and expensive to acquire. Alternatively, the encoder-decoder learns global structures by means of striding and max pooling. Our embedding network complements the encoder-decoder architecture by guiding the decoder with fine-grained details lost to spatial downsampling during the encoder stage. Unlike previous works, our decoder outputs at $$2\times $$ the input resolution, where a single pixel in the input resolution is predicted by four neighboring subpixels in our output. To obtain the output at the original scale, we propose a learnable downsampler (as opposed to hand-crafted ones e.g. bilinear) that combines subpixel predictions. Our approach improves the baseline architecture by $$\approx $$ 11.7% and achieves the state of the art on the ATLAS public benchmark dataset with a smaller memory footprint and faster runtime than the best competing method. Our source code has been made available at: https://github.com/alexklwong/subpixel-embedding-segmentation .
Quantum Differential Collision Distinguishing Attacks on Feistel Schemes

Feistel schemes are important components of symmetric ciphers, which have been extensively studied in the classical setting. We examine the extension methods of differential distinguishers of Feistel key-function and Feistel function-key schemes. The schemes are subjected to quantum differential collision distinguishing attacks based on the methods. The results show that the complexity is lower than that of differential attacks using only Grover algorithm, and the complexity of differential collision attack based on the Brassard-Høyer-Tapp and Grover algorithms is lower than that of quantization when using only the Grover algorithm. The results also show that different algorithms and methods can be combined to produce a more effective cryptanalysis approach. This provides a research direction for post-quantum cryptographic analysis and design.
Training in the prevention of emotional stress as a condition of psychological readiness for the profession of a teacher

One of the factors that negatively affect psychological readiness for the profession of a teacher is emotional stress. The article examines the definition and approaches to the description of stress, proves the relevance of the problem of teaching students training to become teachers the methods and ways of preventing and correcting stress, improving stress resistance, and preventing emotional burnout in professional activity, the methods of emotional stress prevention are analyzed. The purpose of the article is to study the method of sound therapy (neuroacoustics) and to analyze the effect of a neuroacoustic program in increasing stress resistance in students – future teachers. To test the hypothesis on the influence of neuroacoustic program on the increase of stress tolerance in students training to become teachers, the authors conduct a study using the Perceived Stress Scale by S. Cohen and G. Williamson at the ascertaining and control stages and deploying the developed neuroacoustic program at the formative stage. The conducted study allows to determine and prove that the mastery of neuroacoustic methods increases stress resistance in students training to become teachers. Constant overload, disruptive student behavior, and increased demands are the causes of emotional stress in teachers. The presented method teaches future specialists to cope with stress factors in professional pedagogical activity, allows them to resist emotional and professional burnout, and thereby creates optimal conditions for psychological readiness for the profession of a teacher. The technique can be used in organizations of secondary and higher education.
Web Security: An Overview and Current Trend

Websites are the mirrors of the modern IT Age. It is very difficult to find out the areas and sectors where Websites are not using. Initially, only big organizations and multinational companies being uses websites but gradually small organizations and institutions and individuals are also having websites of different kinds and purposes. It is (Website) the collection of resources of different network web resources viz. multimedia, contents, etc and published in one or more servers. In websites domain names, IP addresses are a very important concern. There may be different kinds of websites viz. static, dynamic and in all these abilities to keep a large amount of data. In the recent past, websites increased radically. Due to the wide uses and number of websites, today a huge amount of data stored into the systems and thus their security also becomes an important concerned. Thus, the concept of Web Security or Web Application Security has emerged. Today it is an important concern of Information Technology Security. Web Security is the affairs of security related to the websites, web services and web based applications as a whole. This paper is a kind of review and trend analysis of website and web security. Paper described the basics of web security, the reasons/ threats including various defending mechanism as well. Paper highlighted the challenges and issues as well in a simple context.
Bounding Box Repairing Algorithm for Underwater Object Detection Based on IoU Optimization

In recent years, deep learning based methods have achieved excellent performance in standard object detection. However, these methods lack sufficient capabilities to handle underwater object detection due to these challenges: (1) underwater images have color shift and low contrast, and (2) various noises (sediments, bubbles, etc.) would cause blurring in underwater images. To address these problems, our work investigates optimization policies to detect low-contrast and blurred mine. We propose a bounding box repairing algorithm based on Intersection over Union (IoU) optimization. The algorithm mainly optimizes Mask Scoring R-CNN network. Firstly, we use the Mask Scoring R-CNN model to generate the coarse-grained detection results of mines. Secondly, we perform IoU between the coarse-grained boxes and the annotated boxes in the dataset to obtain the best matching area. Finally, we use the best matching area to repair the coarse-grained boxes. Experimental results show that our method achieves promising object detection and localization performance in underwater environment.
Speaker-Invariant Speech-to-Intent Classification for Low-Resource Languages

Deep Neural Networks based speech embedding techniques have delivered significant results in speech processing applications such as automatic speech recognition and spoken intent detection systems. Still, the presence of para-linguistic information such as speaker characteristics, accent, pronunciation, and emotional expression cause performance degradation in speech tasks where only the linguistic content is required. Over time many techniques have been proposed to address this problem by disentangling the underlying para-linguistic content from the speech signal. The most common approach is to incorporate speaker representations in speech recognition systems. However, it has been less studied for speech intent identification and these speech recognition models require large amounts of labeled training data. In the case of low resource languages, when only a limited amount of data is available, transfer learning approach is adopted. In this paper, we present a speech-to-intent classification model with i-vector based speaker normalization evaluated on Sinhala, and Tamil speech intent datasets. We explore the use of pre-trained acoustic models to address the problem of data scarcity. Experimental results show that the proposed approach is effective in improving the performance of the speech intent classification system.
Semantics for Global and Local Interpretation of Deep Convolutional Neural Networks

A large number of saliency methods have been proposed to explain individual decisions of deep convolutional neural networks (DCNNs). They work by identifying the relevance of each input feature to the predicted output class. However, the feature representations of hidden layers are difficult to interpret semantically. In this work, human-interpretable semantic concepts are associated with vectors in feature space. The association process is mathematically formulated as an optimization problem. The semantic vectors obtained from the optimal solution are applied to interpret deep neural networks globally and locally. The global interpretations are useful to understand the knowledge learned by DCNNs. The interpretation of local behaviors can help to gain a better understanding of the individual decisions made by DCNNs. The empirical experiments demonstrate how to use identified semantics to interpret the existing DCNNs.