Reviewerly/exHarmony huggingface dataset:
Design and Implementation of an Attention-Based Interpretable Model for Document Classification

Abstract With the rapid development of artificial intelligence and machine learning, deep learning technology has been successfully applied to various tasks in the field of natural language processing. However, the erroneous decisions produced by the deep learning model and the characteristics of its black box cause users to doubt its decisions. We urgently need the model to have the ability to make rationalized explanations of its decisions, that is, interpretability. In this paper, we propose an attention-based interpretable model for document classification, which aims to have a better model performance and at the same time have better interpretability. Experiments show that our classification model is close to the performance of other mainstream models. Also, the attribution score explanation provided by the interpretation method are effective.
ScreenSeg: On-Device Screenshot Layout Analysis

We propose a novel end-to-end solution that performs a Hierarchical Layout Analysis of screenshots and document images on resource constrained devices like mobilephones. Our approach segments entities like Grid, Image, Text and Icon blocks occurring in a screenshot. We provide an option for smart editing by auto highlighting these entities for saving or sharing. Further, this multi-level layout analysis of screenshots has many use cases including content extraction, keyword-based image search, style transfer, etc. We have addressed the limitations of known baseline approaches, supported a wide variety of semantically complex screenshots, and developed an approach that is highly optimized for on-device deployment. In addition, we present a novel weighted NMS technique for filtering object proposals. We achieve an average precision of about 0.95 with a latency of around 200ms on the Samsung Galaxy S10 Device for a screenshot of 1080p resolution. The solution pipeline is already commercialized in Samsung Device applications i.e. Samsung Capture, Smart Crop, My Filter in Camera Application, Bixby Touch.
A Study of Local Optima for Learning Feature Interactions using Neural Networks

In many fields such as bioinformatics, high energy physics, power distribution, etc., it is desirable to learn non-linear models where a small number of variables are selected and the interaction between them is explicitly modeled to predict the response. In principle, neural networks (NNs) could accomplish this task since they can model non-linear feature interactions very well. However, NNs require large amounts of training data to have a good generalization. In this paper we study feature selection in the data-starved regime where a NN is trained on a relatively small amount of training data. Feature selection is known to improve generalization for linear models. As an extreme case of data with feature selection and feature interactions we study the XOR-like data with irrelevant variables. We experimentally observed that the cross-entropy loss function on XOR-like data has many non-equivalent local minima, and the number of local minima grows exponentially with the number of irrelevant variables. To deal with the local minima and for feature selection we propose a node pruning and feature selection algorithm that improves the capability of NNs to find better local minima even when there are irrelevant variables. Finally, we show that the performance of a NN on real datasets can be improved using pruning, obtaining compact networks on a small number of features, with good prediction and interpretability.
Image inpainting based on priority values

Image inpainting is a means of inferring the content of the missing information based on the remaining information of the image, and inpainting the missing information, requiring the inpainted image to meet the human eye perception to the greatest extent. The image inpainting method based on the weight function takes arbitrary points for inpainting during inpainting, which results in poor inpainting effect at the junction of objects. Therefore, this paper adds the priority values calculation of inpainting points to improve the inpainting effect. Finally, comparing the inpainted image based on the priority calculation with the previous one, the inpainting effect at the boundary of the object is significantly better than the image inpainting method based solely on the weight functions.
Single sequence phase shifting spectrally resolved interferometry for an in-line thin film thickness measurement using spectral reflectance and phase

A novel, to the best of our knowledge, thickness measurement scheme based on a phase shifting spectrally resolved interferometry is proposed for a thin film measurement from spectral reflectance and phase in a single sequence. This purpose is realized by introducing a spectral reflectance phase surface (SRPS). The spectral phase, originally existing in the form of an imaginary term, is revealed as a real term with the spectral reflectance due to the proposition of SRPS. Therefore, the reflectance and phase are contained in a single parameter, SRPS, and are measured in a single sequence. The phase shift error compensation is presented to form the accurate SRPS and ensure the performance of the measurement. The thickness of the sample is determined as the measured SRPS, and the theoretical SRPSs are compared by the cross correlation method. The performance of the proposed system is validated by measuring several samples of the SiO2 on a silicon substrate and comparing the results with the commercial ellipsometer.
Position paper: GPT conjecture: understanding the trade-offs between granularity, performance and timeliness in control-flow integrity

Abstract Performance/security trade-off is widely noticed in CFI research, however, we observe that not every CFI scheme is subject to the trade-off. Motivated by the key observation, we ask three questions: ➊ does trade-off really exist in different CFI schemes? ➋ if trade-off do exist, how do previous works comply with it? ➌ how can it inspire future research? Although the three questions probably cannot be directly answered, they are inspiring. We find that a deeper understanding of the nature of the trade-off will help answer the three questions. Accordingly, we proposed the GPT conjecture to pinpoint the trade-off in designing CFI schemes, which says that at most two out of three properties (fine granularity, acceptable performance, and preventive protection) could be achieved.
Development of the Digital Literacy Barriers Scale: A Validity and Reliability Study

This research aims to develop a digital literacy barriers (DILBAR) scale. The survey method was used. A total of 278 participants, including 177 students and 101 faculty members from Akdeniz University, Bartın University and Bayburt University in Turkey, were included in the study. According to the factor analysis results KMO value of DILBAR scale is 0.905 and the value of Barlett’s test is 1023,211 (Df= 55, p=0,000). The DILBAR scale can be used in single and multidimensional forms. The scale explains 60,113% of the total variance. In the analysis results for DILBAR scale, it is seen that the factor loads vary between 0.453-0.819 and the item-total correlations vary between 0.523-0.752. The correlation coefficient between the two halves of the scale was 0.769. The Cronbach’s Alpha Coefficient of DILBAR scale is 0.859. The scale consists of 11 items. In line with these results, the scale can be said to be valid and reliable. After its validity and reliability were ensured, confirmatory factor analysis of the DILBAR scale was performed with the AMOS program. (Chi-square = 59,882, Df = 40, GFI = 0,965, CFI = 0,980, RMSEA = 0.042). As the results suggest, the scale is valid and reliable and can be applied to university students and faculty members.
Complementary Feature Pyramid Network for Human Pose Estimation

Human pose estimation plays an important role in human action recognition, human-computer interaction, animation. Most existing methods commonly utilize cascaded pyramid or stacked hourglass network to fuse multi-scale feature from different levels, which greatly enhances the performance but brings a tremendous amount of computing, so it is difficult to achieve real-time human pose estimation. In this paper, we propose a newly-designed network named Complementary Feature Pyramid Network (CFPNet) for human pose estimation with a focus on efficient multi-scale feature generate and fusion method. CFPNet extends the range of receptive fields for each network layer with the help of Feature Mix Bottleneck (FMB) block which constructs hierarchical connections and mixes multiple receptive fields features in a single bottleneck block. In order to reduce the redundant gradient information during the network optimization and construct a lightweight network, Cross Stage Partial (CSP) connection is introduced into the CFPNet. Complementary Feature Fusion (CFF) block is proposed, which can adaptively select complementary information from different levels for fusion to maximize the effective feature in the output of CFPNet. Through the above improvements, CFPNet comprises more affluent multi-scale feature and lower model complexity. Especially, CFPNet-101 achieves the 72.3% AP at 31.7 FPS on the MS COCO dataset only with 1.96 GFLOPs and 10.5M Params. Compared with the existing methods, CFPNet has competitive accuracy and can run in real-time.
StoryTracker: A Semantic-Oriented Tool for Automatic Tracking Events by Web Documents

Media vehicles play an essential role in investigating events and keeping the public informed. Indirectly, logs of daily events made by newspapers and magazines have been built rich collections of data that can be used by lots of professionals such as economists, historians, and political scientists. However, exploring these logs with traditional search engines has become impractical for more demanding users. In this paper, we propose StoryTracker, a temporal exploration tool that helps users query news collections. We focus our efforts (i) to allow users to make queries by adding information from documents represented by word embbedings and (ii) to develop a strategy for retrieving temporal information to generate timelines and present them using a suitable interface for temporal exploration. We evaluated our solution using a real database of articles from a huge Brazilian newspaper and showed that our tool can trace different timelines, covering different subtopics of the same theme.
BiLSTM Embedding Pretraining for Relation Extraction

Many recent relation extraction methods are proposed based on distantly supervised learning to address the issue of expensive human labelling. They automatically align relation instances from a knowledge base with unstructured text. Although these methods solve the problem of insufficient labelled data and are scalable, due to automatic labelling, they suffer from a large amount of noisy data. In this paper, we propose a relation extraction method based on a BiLSTM approach to alleviate the noise by automatic labelling. It firstly performs unsupervised pretraining for word embeddings, dependency path tokens and entity embeddings. The attention mechanism we employed in the pretraining module helps select relevant information. As pretraining denoise and encode the semantic information, the relation types can be more efficiently classified by supervised learning using less labelled training data. Experiments show that our approach significantly outperforms previous state-of-the-art baselines when labelled training data is insufficient.
Monocular 3D Target Detection Based on Cross-Modal and Mass Perceived Loss

In recent years, the monocular 3D target detection algorithm based on pseudo-LiDAR has achieved great accuracy improvement on the KITTI data set. However, due to the large amount of noise contained in the point cloud obtained by depth estimation, the detection accuracy is affected. In this paper, we propose a monocular 3D target detection network that adaptively fuses image and pseudo-LiDAR information, and realizes quality perception of the prediction boxes. First, we propose an adaptive feature fusion mechanism, which uses the attention mechanism to achieve effective fusion of different modal information, to improve the precision of target detection. Then, to gain awareness about the quality of the forecast box, we propose to construct an independent 3D detection box confidence prediction network, and put forward a quality perception loss to train the confidence prediction network. Finally, the experimental verification on the KITTI data set shows that the proposed algorithm is higher than that of other algorithms.
The Use of Augmented Reality and 3D Visualization in Petroleum Engineering Education

Abstract The objective of this paper is to showcase the applications of augmented reality and 3D visualization to enhance the learning process of petroleum engineering concepts. In this study, an educational magazine covering various components of an offshore drilling rig with embedded 3D models is used. In addition, the magazine includes simplified text describing the definition and functionality of various drilling components. The prototype of this magazine was sent to both current and former students of Texas A&amp;M University at Qatar and was accompanied by a survey designed to evaluate the efficacy of the magazine. The participants showed a significant favor towards the use of augmented reality and 3D visualization in petroleum engineering education through the survey. Overall, the augmented reality experience provided has given the participants a new dimension in the interactive-learning field. Also, the magazine provides a unique experience to visualize petroleum engineering operations and concepts in the midst of COVID-19, where many trips and experiences are limited.
An Adaptive Resonance Theory-based Neural Network for Autonomous Learning via Iterative Knowledge Redescription

This paper introduces a neural network architecture called ARTIR, which autonomously learns classifications of arbitrary input sequences via iterative redescription of knowledge that it builds up during learning. Knowledge redescription is achieved via changes to both the network architecture and the connection weights between neurons. Based on ARTMAP, the supervised version of the Adaptive Resonance Theory (ART) neural network, and motivated by the Representational Redescription hypothesis in cognitive science, the ARTIR network creates new, successively deeper internal features iteratively. The process is driven by the complexity of the classification task as measured by the number of input categories the ARTMAP network develops during training. The network is forced to discover a new internal feature that better discriminates the output classes every time the currently perceived difficulty of the learning task exceeds a certain limit as indicated by the number of input categories the ARTMAP network develops in response to an input-target sequence. Once a new candidate feature - found to correlate well enough with an output class - is accepted, ARTIR re-learns the mapping using the additional feature and uncommits its categories that are no longer needed as a result of re-learning. The iterative learning process then continues where the newly created feature, along with all original inputs and internal features created during previous stages of learning, becomes subject of internal knowledge redescription for future iterations. Experiments carried out on two binary decision problems demonstrate the viability of the approach, and the results point to further questions that will be the subject of future research.
Multi-distorted Image Restoration with Tensor 1 × 1 Convolutional Layer

Image restoration with corruptions from combined multiple types of distortion is a challenging and practical problem. Recent studies show that a promising technique is to perform parallel “operations” to handle different types of distortion, which can be modeled by a deep neural network framework. However, the reconstruction may be dominated by a small number of operations due to the heterogeneous features generated by different operations. To handle this issue, we introduce a tensor 1×1 convolutional layer by leveraging high-order tensor fusion, which can not only harmonize the heterogeneous features but also take high order statistical information into account. To efficiently learn the large-scale kernel tensor resulted from the tensor product, we employ tensor network to represent kernels, which is able to convert the exponential growth of the dimension to linear growth. Armed with this new layer, we propose high-order operation-wise attention network for the task of multi-distorted image restoration. The experimental results demonstrated that the proposed method outperforms the method with vanilla 1 × 1 convolutional layer in several typical tasks and is promising for more difficult tasks. Code is available at https://github.com/ZihaoH/High-order-OWAN.
Survey on Image Processing and Its Techniques

Increase in the number of applications being dominated by computer vision and image processing has led to further research on this area. Computer vision is a process which allows computers to associate with the images same as human vision does, it is used in applications like artificial intelligence, signal processing, pattern recognition, semantic division, subject detection, and instance division. One of the methods used for computer vision is digital image processing (DIP), which processes images. In this paper, we aim at analyzing all the techniques by comparing them based on their algorithms.
Modern artificial intelligence technologies application in learning foreign languages by bachelors of energy

E-learning provides new and unique opportunities for professional education, including language education. One of the most innovative technologies is artificial intelligence. As a result of comparison of the use of artificial intelligence technology, the prospects of its use in e-learning a foreign language are revealed. Artificial intelligence is an integral part of the technologies of e-learning a foreign language, but it is not able to replace them completely.
Robust Spectral Clustering: A Locality Preserving Feature Mapping Based on M-estimation

Dimension reduction is a fundamental task in spectral clustering. In practical applications, the data may be corrupted by outliers and noise, which can obscure the underlying data structure. The effect is that the embeddings no longer represent the true cluster structure. We therefore propose a new robust spectral clustering algorithm that maps each high-dimensional feature vector onto a low-dimensional vector space. Robustness is achieved by posing the locality preserving feature mapping problem in form of a ridge regression task that is solved with a penalized M-estimation approach. An unsupervised penalty parameter selection strategy is proposed using the Fiedler vector, which is the eigenvector associated with the second smallest eigenvalue of a connected graph. More precisely, the penalty parameter is selected, such that, the corresponding Fiedler vector is Δ-separated with a minimum information loss on the embeddings. The method is benchmarked against popular embedding and spectral clustering approaches using real-world datasets that are corrupted by outliers.
Internet memes as a new sociocultural phenomenon

The subject of this research is the Internet meme as a phenomenon in modern culture, while the object is the study of global Internet environment for the existence of this phenomenon. The author explores the role of the Internet meme in culture in the context of globalization and Internet development; communication function of the Internet meme in modern society; as well as the use of Internet memes as informational and semantic signals that affect the existing media markets. Special attention is given to interrelation between the Internet meme as a cultural object and the Internet as a global environment for people&amp;rsquo;s communication worldwide. The main conclusions lies in determination of the role of the Internet meme in the modern social life, as well as its place in the cultural sphere. The author's special contribution lies in description of the mechanisms of impact of the Internet memes on media markets, as well as the market and information field of news, advertising, marketing, and propaganda. The relevance of this work is defined by the analysis of emergence of the Internet meme not only as a result of creativity, but also as a commercial product and means of information manipulation. The novelty of consists in examination and analysis of such aspects of the Internet memes as a phenomenon that have become significant in the last few years, which is proven by the active attention paid of businesses and the government.
Disabled children and digital technologies: learning in the context of inclusive education

"Disabled children and digital technologies: learning in the context of inclusive education." Learning, Media and Technology, 46(4), pp. 585–586
Task and Situation Structures for Case-Based Planning

This paper introduces two new representation structures for tasks and situations, and a comprehensive approach for case-based planning (CBP). We focus on everyday tasks in open or semi-open domains, where exist a variety of situations that a planning (and execution) agent must deal with. This paper first introduces a new, generic structure for representing tasks and task plans. The paper, then, introduces a generic situation structure and a methodology of situation handling. The proposed structures support encoding all domain knowledge in cases while avoiding hard-coding domain rules.
Challenges and Competences in Master Degrees from a Comprehensive Security Perspective

Nowadays, the security threats faced by society in general, and organizations in particular, are reaching unprecedented highs in terms of complexity and sophistication. Organizations must face up to new challenges in order to protect their assets (e.g., personnel, goods, information, or reputation, among others), so they need new solutions for the immediate future to achieve their business objectives efficiently and effectively. Right now, corporate security and cybersecurity within organizations are dependent on themselves and complementary at the same time, so the coordination of their activities is crucial. This joint action for a comprehensive security requires a training and educational process that provides necessary knowledge and skills. This article highlights the limitations of the current master's degrees in cybersecurity from the perspective of comprehensive security and proposes a new master's degree focused on it as a solution, which takes advantage of the skills of current degrees, and is aligned with the needs of the European Higher Education Area. This master’s degree promotes sustainability through efficiency and rationalization on the general asset protection process, improving the security culture inside the organization, reducing the number of security incidents, and increasing the reputation and image of the organization.
A study on the SPOC Blended Teaching Practice Based on the Online Platform : A Case Study of "Korean as a Second Foreign Language"

SPOC is a blended teaching method that combines online resources and physical classrooms, integrating internet and digital media technology with regular face-to-face teaching, which is conducive to realizing a real "student-centered classroom" and an important way to carry out deep teaching. This study attempts to build a blended teaching model and conduct teaching practice based on this model to prove the feasibility and effectiveness of SPOC blended teaching in second foreign language courses. The results show that students have a high degree of adaptation to online learning and evaluate the model positively, which is considered to help improve learning effectiveness and learning ability. At the same time, the model places higher demands on both students and teachers.