Reviewerly/exHarmony huggingface dataset:
Robust Image Hashing With Saliency Map And Sparse Model

Abstract Image hashing is an effective technology for extensive image applications, such as retrieval, authentication and copy detection. This paper designs a new image hashing scheme based on saliency map and sparse model. The major contributions are twofold. The first contribution is the construction of a weighted image representation by combining a visual attention model called Itti model and the matrix of color vector angle (CVA). Since the Itti model can efficiently detect saliency map and CVA fully captures color information of image, they contribute to a visually robust and discriminative image representation. The second contribution is the hash extraction from the weighted image representation via sparse model. A classical sparse model called robust principal component analysis is exploited to decompose the weighted image representation into a low-rank component and a sparse component. As the low-rank component can describe intrinsic structure of image, hash calculation with low-rank component can achieve good discrimination. The efficiencies of the proposed scheme are validated by extensive experiments with open databases. The results demonstrate that the proposed scheme is superior to some state-of-the-art schemes in terms of classification performance between robustness and discrimination.
Cyber-Attack Behavior Knowledge Graph Based on CAPEC and CWE Towards 6G

6G-oriented network intelligence needs the support of knowledge from inside and outside the network. CAPEC and CWE are network security databases targeting attack patterns and weaknesses respectively, which are relatively complete knowledge from outside the network. Constructing the important entities and relationships in CAPEC and CWE as knowledge graphs is conducive to comprehensively grasping the strategies and behaviors of certain attacks, thus providing a supplement for network internal knowledge and guidance for attack prediction and network situational awareness. Therefore, this paper analyzes the content and organizational structure of CAPEC and CWE, and proposes a method to construct cyber-attack knowledge graph based on CAPEC and CWE, which is implemented in the graph database Neo4j. This paper also introduces the application of the knowledge graph in DDoS flood attack and multi-stage attack.
Problems of education in construction. Introduction of digital pedagogics in the system of the higher education in construction

There is a new concept "digital pedagogics" due to the distribution of digital technologies, which application has to bring process of professional training to the qualitatively new level. Until recently digitalization of teaching represented only partial replacement of traditional training methods with digital analogs, without affecting the content of educational process. Since September, 2021 digital competences were included into all the educational standards. That raised quality requirements of training of teachers of higher educational institutions and made significant changes to the content of disciplines of the curriculum. This article opens a cycle of publications covering the results of updating of subject matters of training of students of a construction profile taking into account requirements of digital pedagogics and the national project "Digital Economy»".
Corpus linguistics and the study of social media: a case study using multi-dimensional analysis

Social media refers to the communication generated on platforms that enable users to send text messages and multimedia content to a group of other users. Despite its growing importance, social media has received scant attention in our field due in part to the kinds of text used in social media posing challenges for corpus methods and techniques. In the study reported in this chapter, a multi-dimensional (MD) analysis was carried out to detect the major dimensions underlying the variation across platforms, user groups and individual users in social media. MD analysis is a framework for corpus analysis that uses multivariate statistical analysis to identify correlations among linguistic features across the texts in a corpus. Two dimensions were identified: dim. 1: Formal, informational, argumentative discourse; dim. 2: Informal, interactive, speaker-oriented discourse. Each dimension corresponds to a set of linguistic features commonly occurring across the texts. After scoring the texts for each dimension, the variation accounted for by platform, user groups and individual groups was calculated. The findings suggest that the variation seems to be driven primarily by the individual users rather than by the platform or the types of user.
Explorative Experiments: A Paradigm Shift to Deal with Severe Uncertainty in Autonomous Robotics

Abstract This paper presents a case of severe uncertainty in the development of autonomous and intelligent systems in Artificial Intelligence and autonomous robotics. After discussing how uncertainty emerges from the complexity of the systems and their interaction with unknown environments, the paper describes the novel framework of explorative experiments. This framework presents a suitable context in which many of the issues relative to uncertainty, both at the epistemological level and at the ethical one, in this field should be reframed. The case of autonomous robot systems for search and rescue is used to make the discussion more concrete.
Adversarial Regularized Reconstruction for Anomaly Detection and Generation

We propose ARN, a semisupervised anomaly detection and generation method based on adversarial reconstruction. ARN exploits a regularized autoencoder to optimize the reconstruction of variants of normal examples with minimal differences, that are recognized as outliers. The combination of regularization and adversarial reconstruction helps to stabilize the learning process, which results in both realistic outlier generation and substantial detection capability. Experiments on several benchmark datasets show that our model improves the current state-of-the-art by valuable margins because of its ability to model the true boundaries of the data manifold.
Identifying physiological correlates of cybersickness using heartbeat-evoked potential analysis

Abstract Many studies have consistently proven that repeatedly watching virtual reality (VR) content can reduce cybersickness. Moreover, the discomfort level decreases when the VR content includes an unusual orientation, such as an inverted scene. However, few studies have investigated the physiological changes during these experiences. The present study aimed to identify psychophysiological correlates, especially the neural processing, of cybersickness. Twenty participants experienced two types of VR orientation (upright and inverted), which were repeated three times. During the experience, we recorded the participants’ subjective levels of discomfort, brain waves, cardiac signals, and eye trajectories. We performed a heartbeat-evoked potential (HEP) analysis to elucidate the cortical activity of heartbeats while experiencing cybersickness. The results showed that the severity of cybersickness decreased as the participants repeatedly watched the VR content. The participants also reported less nausea when watching the inverted orientation. We only found a significant suppression at the fronto-central HEP amplitudes in the upright orientation for the physiological changes. This study provides a comprehensive understanding of bodily responses to varying degrees of cybersickness. In addition, the HEP results suggest that this approach might reflect the neural correlates of transient changes in heartbeats caused by cybersickness.
PerSEEption: Mobile and Web Application Framework for Visually Impaired Individuals

Visually impaired people have tools, assistant devices, or organizations that guide them throughout their lives but at a certain point, other visually impaired are lack of material especially in learning. In terms of other visually impaired organizations, some of them also facing difficulties in handling their members together with their information records. That is why the developers decided to propose a system entitled PerSEEption: A mobile and web application for visually impaired individuals which will address the challenges that they have encountered. PerSEEption mobile will offer an emergency feature for visually impaired users and an introductory for basic Braille such as alphabet, numbers, and punctuations that work together with audio and vibration. This application is not designed to replace the traditional material for braille but rather as an option that can be accessed through mobile devices. However, the web application is targeted to be developed for the organization that exclusively handles the guardian of the visually impaired individual. This will also serve as aplatform for the communication between the organization and the sighted members which is also the parent of a visually disabled individual. Researchers believed that having an environment that supports visually disabled people will serve as their great foundation throughout their lives. PerSEEption is designed to help not just the visually impaired but also the people who are established to guide them.
Global-Guided Asymmetric Attention Network for Image-Text Matching

Image-text matching is a vital yet challenging task in the field of vision and language. Unlike previous methods that usually adopt a symmetrical network to independently embed images and sentences into a joint latent space, we propose a novel Global-guided Asymmetric Attention Network (GAAN) to represent the two modalities more comprehensively. Specifically, we first design a Global Information-guided Transformer Encoder (GITE) to effectively mitigate the drawback of the lack of contextual information of the region features. Taking full advantage of the image global information, GITE is able to model the regional relations and region-global relations simultaneously, so as to obtain a more accurate visual representation. Then, we adopt a Textual Self-Attention (TSA) module to explore the word-word relations and produce the context-aware word representations. Finally, we deploy an Image-guided Textual Attention (ITA) module to explore the fine-grained correspondence between image regions and sentence words. By using context-aware visual information to guide textual representation learning, we can build asymmetric connections between vision and language to better exploit textual information. Experimental results on two benchmark datasets including MSCOCO and Flickr30k show that GAAN significantly surpasses state-of-the-art methods.
Data Security in Healthcare Using Blockchain Technology

Data are the most important assets in all kinds of industries, as every organization is moving to cloud storage but still data security is a challenging one in many fields. One of the most important fields is Healthcare because it handles with most sensitive information. Since the data in healthcare includes patient's medical information, prescriptions, their medical insurance, etc. Due to the sensitive nature of the information stored within the EHRs(Electronic Health Records) data security is very essential. Blockchain is a technology that is developed based on two major mathematical concepts such as hashes and cryptography. Blockchain stores data as an immutable ledger that is duplicated and shared throughout all networks in a delegation from a central authority. In this paper, we provide how data can securely be stored and accessed in healthcare. Since one node blockchain is often used for worldwide electronic health records (EHR) it will store and manage patient data in a very verifiable manner & blockchain can be in public gettable in real time by anyone within the aid service supplier chain. This paper aims to discuss the issues in the existing systems and solutions to the challenges of using blockchain technology.
Handwritten Digit Recognition With Machine Learning Algorithms

Nowadays, the scope of machine learning and deep learning studies is increasing day by day. Handwriting recognition is one of the examples in daily life for this field of work. Data storage in digital media is a method that almost everyone is using nowadays. At the same time, it has become a necessity for people to store their notes in digital media and even take notes directly in the digital environment. As a solution to this need, applications have been developed that can recognize numbers, characters, and even text from handwriting using machine learning and deep learning algorithms. Moreover, these applications can recognize numbers, characters, and text from handwriting and convert them into visual characters. This project, investigated the performance comparison of machine learning algorithms commonly used in handwriting recognition applications and which of them are more efficient. As a result of the study, the accuracy was 98.66% with artificial neural network, 99.45% with convolutional neural network, 97.05% with K-NN, 83.57% with Naive Bayes, 97.71% with support vector machine and 88.34% with decision tree. This study also developed a handwriting recognition system for numbers similar to these mentioned applications. A desktop application interface was developed for end users to show the instant performance of some of these algorithms and allow them to experience the handwriting recognition system.
A Component-detection-based Approach for Interpreting Off-line Handwritten Chemical Cyclic Compound Structures

Recently, off-line handwritten chemical structure formulas recognition draws considerable attention from re-searchers for its research challenges and supports to numerous in-teresting applications in education. The existing solutions belong to the rule-based category or the end-to-end trainable category, which suffer from the problems of low generalization capability or lacking the accurate alignment information between the input and output. To tackle these problems to enable auto correction of handwritten chemical assignments at a fine-grained level, in this paper we propose a component-detection-based approach for interpreting the spatial structure of off-line handwritten chem-ical cyclic compound structure formulas. Specifically, we define different components of cyclic compound structure formulas as objects (including graphical objects and text objects) and adopt the deep learning detector to detect them. Then, considering the property of chemical notations, we propose the Non-Maximum Area Suppression (NMAS) algorithm to improve the detection results. Finally, with these detection results and the geometric relationships of detected objects, this paper design a holistic algorithm for interpreting the spatial structure of handwritten cyclic compound structure formulas. The proposed method is evaluated on a self-collected dataset consisting 2100 samples and achieved an accuracy of 89.5%
A preliminary evaluation of Echo State Networks for Brugada syndrome classification

In the present study recurrent neural networks, in particular Echo State Networks (ESNs), have been applied for the prediction of Brugada Syndrome (BrS) from electrocardiogram (ECG) signals. The research lays its foundations in BrAID (Brugada syndrome and Artificial Intelligence applications to Diagnosis), a project aimed at developing an innovative system for early detection and classification of BrS Type 1. The ultimate objective of the BrAID platform is to help clinicians to improve the BrS diagnosis process, to detect a pattern in ECG, and to combine them with multi-omics information through Artificial Intelligence (AI) - Machine Learning (ML) models, such as ESNs. We report novel preliminary results of this approach, presenting the first baseline results, in terms of accuracy, for BrS recognition using ECG analysis, with the application of ESNs. Such results are particularly encouraging and may shed light on the possibility of using this model as a computational intelligence clinical support system tool for healthcare applications.
An Analysis on Semantic Interpretation of Tamil Literary Texts

The interaction between a computer and a human or natural language is known as Natural Language Processing (NLP). The ultimate goal is to make the natural language text understandable, which in turn, requires its meaning to be captured. Text can be analyzed on several levels, such as lexical, syntax, semantics, discourse, and pragmatics. These NLP tasks deal with text at different levels, such as word, phrase, sentence, paragraph, and document. Discourse analysis is a type of text analysis that goes beyond the sentence level. The discourse analysis is currently performed on expository (essay) type of texts. There are currently no state-of-the-art NLP applications that handle Tamil literary texts at a discourse level. Tamil classical literature is rich with ethical, moral, and philosophical values that should be explored for the benefit of society. This paper proposes an automatic semantic interpretation framework for Tamil literary texts using discourse parsing by giving works on discourse parsing, text classification, discourse-based clustering and information retrieval, and Tamil language and Tamil literatures. This semantic interpretation can be developed as a smart mobile application using multimedia components. This paper also discusses how the Tamil literary text processing differs from the essay type of text.
Patterns on Deriving APIs and their Endpoints from Domain Models

Domain-Driven Design (DDD) places the domain model at the center of all software development practices. Remote API design is crucial for developing distributed systems including, for example, microservice-based systems. While software practitioners realize APIs based on DDD models, clear guidance on how to derive APIs and API endpoints from domain model elements is still missing. Based on prior in-depth studies of practitioner sources on this and related topics, we have mined patterns to address these design problems. In particular, we present the domain model facade as api pattern which describes how to derive an API from a Domain Model. To explain further how derive API endpoints constituting the API from Domain Model elements, we present the aggregate roots as api endpoints, domain services as api endpoints, and domain processes as api endpoints patterns. In addition, we relate these patterns to the previously published patterns api description and api contract, both explaining how to describe APIs formally.
DFAN: Dual Feature Aggregation Network for Lightweight Image Super-Resolution

With the power of deep learning, super-resolution (SR) methods enjoy a dramatic boost in performance. However, they usually have a large model size and high computational complexity, which hinders the application in devices with limited memory and computing power. Some lightweight SR methods solve this issue by directly designing shallower architectures, but it will adversely affect the representation capability of convolutional neural networks. To address this issue, we propose the dual feature aggregation strategy for image SR. It enhances feature utilization via feature reuse, which largely improves the representation ability while only introducing marginal computational cost. Thus, a smaller model could achieve better cost-effectiveness with the dual feature aggregation strategy. Specifically, it consists of Local Aggregation Module (LAM) and Global Aggregation Module (GAM). LAM and GAM work together to further fuse hierarchical features adaptively along the channel and spatial dimensions. In addition, we propose a compact basic building block to compress the model size and extract hierarchical features in a more efficient way. Extensive experiments suggest that the proposed network performs favorably against state-of-the-art SR methods in terms of visual quality, memory footprint, and computational complexity.
Natural language processing: A window to understanding skincare trends

Reddit is a popular social media discussion forum. Reddit data can be analyzed with natural language processing techniques to gain insights into public health questions by tracking frequency of discussion on relevant topics over time and analysis of discussion content.To apply natural language processing techniques to categorize, track, and gain insights from comments regarding skincare-related topics on Reddit using sentiment analysis and word search techniques.Historical Reddit comments available on Google BigQuery from the r/SkincareAddiction subreddit were selected and preprocessed. Latent Dirichlet Allocation was applied to create topics. Selected topics were further investigated for interest over time, by determining comment frequencies of words of interest. Sentiment analysis was also applied to each topic.>3,000,000 comments were analyzed and classified into 25 topics. Topics related to sunscreen, diet, and exfoliants were examined for response frequencies over time, demonstrating seasonal variation. Taking comment frequencies demonstrated peaks containing "coral" and "oxybenzone" that corresponded to media coverage of sunscreen-associated coral bleaching. Queries containing "physical" and "mineral" demonstrated an evolution in word choice describing physical/mineral sunscreens over time. Sentiment analysis demonstrated a range from mildly positive to moderately positive sentiment across the five examined skincare topics.Our analysis was limited to one subreddit category. Additionally, Latent Dirichlet Allocation is an unsupervised model; its accuracy cannot be readily assessed. Taking comment frequencies for words, while powerful, cannot be used to find word trends that are not intentionally queried by the user.Natural language processing is a powerful tool to examine large dermatology discussion forums and gain insights into patient perceptions of the field.
Block-VC: A Blockchain-Based Global Vaccination Certification

This paper proposes a global blockchain-based vaccination certification. To facilitate issuing and verifying certificates globally, the framework is lightweight on the user's side. The essential certification operations, issuing certificate, verifying certificate, and verifying vaccine, are implemented preserving user's privacy. Our framework's smart contracts design is implemented on the Ethereum blockchain test network to support indexing and querying the certification data. The experimental results show the effectiveness of the proposed approach in improving the performance of the primary functionalities. The results show the retrieving time of certificate information is efficient no matter how long the chain is, compared with scanning the blocks to find the target data.
Bi-Level Attention Graph Neural Networks

Recent graph neural networks (GNNs) with the attention mechanism have historically been limited to small-scale homogeneous graphs (HoGs). However, GNNs handling heterogeneous graphs (HeGs), which contain several entity and relation types, all have shortcomings in handling attention. Most GNNs that learn graph attention for HeGs learn either node-level or relation-level attention, but not both, limiting their ability to predict both important entities and relations in the HeG. Even the best existing method that learns both levels of attention has the limitation of assuming graph relations are independent and that its learned attention disregards this dependency association. To effectively model both multi-relational and multi-entity large-scale HeGs, we present Bi-Level Attention Graph Neural Networks (BA-GNN), scalable neural networks (NNs) that use a novel bi-level graph attention mechanism. BAGNN models both node-node and relation-relation interactions in a personalized way, by hierarchically attending to both types of information from local neighborhood contexts instead of the global graph context. Rigorous experiments on seven real-world HeGs show BA-GNN consistently outperforms all baselines, and demonstrate quality and transferability of its learned relation-level attention to improve performance of other GNNs.