Reviewerly/exHarmony huggingface dataset:
Role of Resource Description Framework in Knowledge Discovery in World Wide Web

Semantic web is not just a matter of translation from HTML to RDF/OWL languages. It adds logic to the web and represents the content of the web through knowledge graphs. The main challenge posed is to transform unstructured data from plain HTML files to structured data consisting of concepts, named entities and relationships between different entities. The authors have designed and implemented a semantic framework for conceptualization of unstructured web data and deriving knowledge from it. Currently, the model is tested for research data in computer and management areas, but the model is generic and extensible and can be applied to any domain. The semantic framework comprises of five different modules which are implemented employing different semantic technologies such as RDF, RDF Schema, FOAF, ontology and XML. The current paper focuses on RDF generator and validator module employed for modeling relationship between different parts of unstructured web data. The RDF document generated is provided as an input to HTML table generator AJAX module which renders HTML table containing queried research data to an end user.
Chatbot for Hospital Management Using AI

As the world becomes more digitalized, technologies have been enhancing day by day and are interconnected in all aspects. It is tedious to book an appointment with doctors for all the health-related issues in person. The aim is to create a medical bot application using AI from where we can consult a doctor from our comfort at home. This will assist by reducing travel expenses and enhancing availability to medical understanding through a medical chatbot under our roof. It is among the most efficient and time-saving innovations, but in need to do many chores, chatbots must be made better in the medical community. The proposed design is using AI to develop a medical chatbot that can diagnose disorders and provide basic information before approaching a doctor. To overcome this limitation, this project creates a platform for humans to interact with a talkbot that has been intensively developed on data sets using machine learning methods. Instead of taking a logical approach to computation, machine learning algorithms take a more natural approach. The information is kept in the directory in order to identify the code word, resolve the query, and respond to the question.
OVERVIEW OF PEDESTRIAN DETECTION METHODS BASED ON DEEP LEARNING

With a large number of cameras being deployed in public places and the rapid development of artificial intelligence, pedestrian detection systems are widely used in life. Both object detection and re-identification have been hot topics in computer vision research in recent years. It drives the development of smart security, smart video surveillance, and smart retrieval.
Reducible Theories and Amalgamations of Models

Within knowledge representation in artificial intelligence, a first-order ontology is a theory in first-order logic that axiomatizes the concepts in some domain. Ontology verification is concerned with the relationship between the intended models of an ontology and the models of the axiomatization of the ontology. In particular, we want to characterize the models of an ontology up to isomorphism and determine whether or not these models are equivalent to the intended models of the ontology. Unfortunately, it can be quite difficult to characterize the models of an ontology up to isomorphism. In the first half of this article, we review the different metalogical relationships between first-order theories and identify which relationship is needed for ontology verification. In particular, we will demonstrate that the notion of logical synonymy is needed to specify a representation theorem for the class of models of one first-order ontology with respect to another. In the second half of the article, we discuss the notion of reducible theories and show we can specify representation theorems by which models are constructed by amalgamating models of the constituent ontologies.
Topic-Based Unsupervised and Supervised Dictionary Induction

Word translation is a natural language processing task that provides translation between the words of a source and a target language. As a task, it reduces to the induction of a bilingual dictionary, which is typically performed by aligning word embeddings of the source language to word embeddings of the target language. To date, all the existing approaches have focused on performing a single, global alignment in word embedding space. However, semantic differences between the various languages, in addition to differences in the content of the corpora used for training the word embeddings, can hinder the effectiveness of such a global alignment. For this reason, in this article we propose conducting the alignment between the source and target embedding spaces by multiple mappings at topic level. The experimental results show that our approach has been able to achieve an average accuracy improvement of +3.30 percentage points over a state-of-the-art approach in unsupervised dictionary induction from languages as diverse as German, French, Italian, Spanish, Finnish, Turkish, and Chinese to English, and +3.95 points average improvement in supervised dictionary induction.
Synthetic Behavior Sequence Generation Using Generative Adversarial Networks

Due to the increase in life expectancy in advanced societies leading to an increase in population age, data-driven systems are receiving more attention to support the older people by monitoring their health. Intelligent sensor networks provide the ability to monitor their activities without interfering with routine life. Data collected from smart homes can be used in a variety of data-driven analyses, including behavior prediction. Due to privacy concerns and the cost and time required to collect data, synthetic data generation methods have been considered seriously by the research community. In this article, we introduce a new Generative Adversarial Network (GAN) algorithm, namely, BehavGAN , that applies GAN to the problem of behavior sequence generation. This is achieved by learning the features of a target dataset and utilizing a new application for GANs in the simulation of older people’s behaviors. We also propose an effective reward function for GAN back-propagation by incorporating n-gram-based similarity measures in the reinforcement mechanism. We evaluate our proposed algorithm by generating a dataset of human behavior sequences. Our results show that BehavGAN is more effective in generating behavior sequences compared to MLE, LeakGAN, and the original SeqGAN algorithms in terms of both similarity and diversity of generated data. Our proposed algorithm outperforms current state-of-the-art methods when it comes to generating behavior sequences consisting of limited-space sequence tokens.
WhyMyFace: A Novel Approach to Recognize Facial Expressions Using CNN and Data Augmentations

Refat, Md Abu RummanSarker, SoumenKaushal, ChetnaKaur, AmandeepIslam, Md KhairulAptitude, in terms of human facial recognition, cases prior one of digital image's fundamental parts. This conveys facial parameters in many social contexts. Medical imaging, robotics, intrusion detection system with sentiment analysis, and automation and some industries use computer vision to understand human facial expressions. Studying human facial expressions using deep learning has become popular in recent years, and several efforts have been made. However, facial expression recognition remains challenging because of the wide range of persons with similar facial expressions. This paper proposed a 16-layer efficient CNN technique to categorize human facial expressions with data augmentation. Then, we evaluated our proposed approach on a well-known facial expression recognition, the FER2013 benchmark dataset. And, the proposed technique achieves state-of-the-art testing accuracy of 89.89 $$\%$$ exceeding some prior research.
Analysis of Misogynistic and Aggressive Text in Social Media with Multilayer Perceptron

Cyber-aggression and misogynistic aggression is a form of abusive text that has been risen on various social media platforms. Any type of cyber-aggression is harmful to society and social media users, and it also provokes hate crimes. Misogynistic aggression shows hatred toward women. Automatic detection models for hateful tweets can combat this problem. Contextual analysis of tweets can improve existing detection models. We propose a novel syntactic and semantic feature-based classification model where paragraph embedding enriches the contextual analysis of aggressive tweets and TF-IDF identifies the importance of each term in tweet and corpus. This study considers Trac-2 shared task English language dataset for multitasking classification. This novel research shows achievable performance against a baseline model and various classifiers. Among the various machine learning and deep learning classifiers, multilayer perceptron (MLP) portrays the preeminent performance.
Residual dense collaborative network for salient object detection

Owing to the renaissance of deep convolutional neural networks (CNN), salient object detection based on fully convolutional neural networks (FCNs) has attracted widespread attention. However, the scale variation of prominent objects, complex background features and fuzzy edges have historically been a great challenge to us. All these are closely associated with the utilization of multi-level and multi-scale features. At the same time, deep learning methods meet the challenges of computation and memory consumption in practice. To address these problems, the authors propose a different salient object detection method based on residuals learning and dense fusion learning framework. The proposed network is named Residual Dense Collaborative Network (RDCNet). First of all, the authors design a multi-layer residual learning (MRL) module to extract salient object features in more detail, getting the utmost out of the object's multi-scale and multi-level information. Then, on the basis of the vigoroso stage-wise convolution feature, the authors put forward the dilated convolution module (DCM) to acquire a rough global saliency map. Finally, the final accurate saliency detection map is obtained through dense cooperation learning (DCL), and the remaining learning is also used to improve gradually, so as to achieve high compactness and high-efficiency results. Experimental results show that this method is the most advanced method for five widely used datasets (DUTS-TE, HKU-IS, PASCAL-S, ECSSD, DUT-OMRON) without any pre-processing and post-processing. Especially on the ECSSD dataset, the F-measure of RDCNet achieves 95.2%.
Speculations on the Future Design School Structure

In this chapter, Benjamin Stopher and Tobias Revell present four Speculations that examine the potential future design school structure. Particular emphasis is placed on the roles of technology in the Academy and on students’ holistic learning experiences. The authors’ Speculations address a learning environment that is continuously shaped by shifting national, regional, political, economic, and socio-technological contexts. “Speculation One: Status Quo, Like Now, But More” predicts the Academy's financial conditions, the growth of students’ agency and consumer rights, and the greater influence of corporations in directing curricula. “Speculation Two: AI Studios and the Metaverse” anticipates the expansion of technologies like artificial intelligence (AI), augmented reality (AR), virtual reality (VR), and cryptocurrencies. Technological advancements such as these will affect the Academy's student enrollment, course delivery, and sense of “place” in the physical and digital realms. “Speculation Three: The Networked Studio and Other Dreams” envisions a decentralized Academy, one that leverages digital networks to cultivate new learning communities around the world. “Speculation Four: The Post-Crisis ‘No-School’” predicts a world where the structure that was the design school has collapsed under political, financial, and technological pressures. Subsequently, design education is provided to small, mobile, and eclectic learning groups that travel to specific areas to develop curricula that respond to a community's needs.
Economic Efficiency Substantiation of the Use of a Sectional Coil Exchanger in an Individual Heating Point System

The environmental problem associated with the concentration of greenhouse gases and the consequent global warming of the climate should be addressed in an integrated manner. The authors propose the calculation of economic efficiency of a fundamentally new design of a heat exchanger based on intensification of processes of hydrodynamics and heat exchange. Switching to a new type of heat exchange equipment will save operating costs in residential apartment buildings, reducing CO2 concentration. When performing economic calculations, the methods of calculating the net present value of the project (NPV), discounted payback period (PBP), as well as the method of calculating the expected monetary result (EMV) for the risk insurance budget were used. Calculations were performed for two scenarios: basic and strategic. The discounted payback period from the replacement of one heat exchanger unit for the base scenario was 5.66 years, and for the strategic scenario – 2.73 years. The heat exchangers in question have a wide range of industrial applications, including: chemistry and petrochemicals, power engineering, household appliances, food and medical industries, shipbuilding and a number of related industries. Market analysis has shown that 4,000 units of new heat exchange equipment can be installed in the Republic of Tatarstan by 2033. At the same time, the reduction of greenhouse gas emissions in 2033 will amount to 81,160 tons of carbon dioxide equivalent.
Qualitative Analysis of Use of ICTs and Necessary Personal Competencies (Self-Efficacy, Creativity and Emotional Intelligence) of Future Teachers: Implications for Education

(1) Background: The acquisition of Information and Communication Technologies (ICTs) skills and competencies has become enormously important in regard to the effective management of studies or schooling. The questions that guided this study were: What are future teachers’ perceptions regarding the use of new technologies in teaching/learning? What are their opinions of the personal competencies necessary to appropriately make use of ICTs? The purpose of this study was to discover the opinions of future teachers—current master’s degree students—regarding the use of ICTs in teaching/learning, as well as the personal competencies involved in their best use, such as self-efficacy, creative intelligence, and emotional intelligence. (2) Methods: Three focus groups were conducted, comprising 27 graduate students. The participants were asked about their knowledge of ICTs, how they had acquired this knowledge, their attitudes when faced with a new tool, whether they considered themselves to be creative, and whether they considered themselves to be emotionally competent. (3) Results: The data analyzed revealed different opinions on the use of new technologies for innovation, as well as other factors involved in academic or professional performance. Three themes were extracted: Training experiences and ICT skills; Feelings experienced when confronted with an ICT tool; and Task-oriented personal competencies. The identification of these three themes enabled us to extract eight sub-themes. (4) Conclusions: Professionals’ evident lack of knowledge and training regarding ICTs makes it necessary to promote ICTs’ importance and to propose training programs directed at future teachers so they can improve their competencies, and thus, become better able to prepare their students, which, in turn, would allow the students to master these new technologies and make adequate use of them.
Graph Embedding-Based Domain-Specific Knowledge Graph Expansion Using Research Literature Summary

Knowledge bases built in the knowledge processing field have a problem in that experts have to add rules or update them through modifications. To solve this problem, research has been conducted on knowledge graph expansion methods using deep learning technology, and in recent years, many studies have been conducted on methods of generating knowledge bases by embedding the knowledge graph’s triple information in a continuous vector space. In this paper, using a research literature summary, we propose a domain-specific knowledge graph expansion method based on graph embedding. To this end, we perform pre-processing and process and text summarization with the collected research literature data. Furthermore, we propose a method of generating a knowledge graph by extracting the entity and relation information and a method of expanding the knowledge graph using web data. To this end, we summarize research literature using the Bidirectional Encoder Representations from Transformers for Summarization (BERTSUM) model based on domain-specific research literature data and design a Research-BERT (RE-BERT) model that extracts entities and relation information, which are components of the knowledge graph, from the summarized research literature. Moreover, we proposed a method of expanding related entities based on Google news after extracting related entities through the web for the entities in the generated knowledge graph. In the experiment, we measured the performance of summarizing research literature using the BERTSUM model and the accuracy of the knowledge graph relation extraction model. In the experiment of removing unnecessary sentences from the research literature text and summarizing them in key sentences, the result shows that the BERTSUM Classifier model’s ROUGE-1 precision is 57.86%. The knowledge graph extraction performance was measured using the mean reciprocal rank (MRR), mean rank (MR), and HIT@N rank-based evaluation metric. The knowledge graph extraction method using summarized text showed superior performance in terms of speed and knowledge graph quality.
BSML: Bidirectional Sampling Aggregation-based Metric Learning for Low-resource Uyghur Few-shot Speaker Verification

In recent years, text-independent speaker verification has remained a hot research topic, especially for the limited enrollment and/or test data. At the same time, due to the lack of sufficient training data, the study of low-resource few-shot speaker verification makes the models prone to overfitting and low accuracy of recognition. Therefore, a bidirectional sampling aggregation-based meta-metric learning method is proposed to solve the low-accuracy problem of speaker recognition in a low-resource environment with limited data, termed bidirectional sampling multi-scale Fisher feature fusion (BSML). First, the BSML method was used for effective feature enhancement in the feature extraction stage; second, a large number of similar and disjoint tasks were used to train the models to learn how to compare sample similarity; finally, new tasks were used to identify unknown samples by calculating the similarity of the samples. Extensive experiments are conducted on a short-duration text-independent speaker verification dataset generated from the THUYG-20 low-resource Uyghur with limited data, which comprised speech samples of diverse lengths. The experimental result has shown that the metric learning approach is effective in avoiding model overfitting and improving model generalization, with significant results in the identification of short-duration speaker verification in low-resource Uyghur with few-shot. It also demonstrates that BSML outperforms the state-of-the-art deep-embedding speaker recognition architectures and recent metric learning approach by at least 18%–67% in the few-shot test set. The ablation experiments further illustrate that our proposed approaches can achieve substantial improvement over prior methods and achieves better performance and generalization ability.
Metrological Evaluation of the Demosaicking Effect on Colour Digital Image Correlation with Application in Monitoring of Paintings

A modified 3D colour digital image correlation method (3D cDIC) is proposed for efficient displacement measurements of colour objects with natural texture. The method is using a separate analysis of correlation coefficient (sigma) value in the RGB channels of CCD cameras by utilising local information from the channel with the minimum sigma. In this way, merged U, V and W displacement maps are generated based on the local correlation quality. As the proposed method applies to colour filter array cameras, the images in RGB channels have to undergo a demosaicking procedure which directly influences the accuracy of displacement measurements. In the paper, the best performing demosaicking methods are selected. The metrological analysis of their influence on the results of canvas paintings investigations obtained by unmodified and modified 3D cDIC processing is presented.
Coherent Potts machine based on an optical loop with a multilevel phase-sensitive amplifier

The Potts model is an extended version of the Ising model that has been intensively studied for applications in physical computers solving combinatorial optimization problems. This study proposes a coherent Potts machine (CPM), named after the coherent Ising machine (CIM), which is a type of Ising-model-based physical computer. A phase-sensitive amplifier (PSA) used in a CIM based on an optical loop with a measurement feedback system is replaced with a multilevel PSA, owing to which the optical pulses in the loop behave as Potts spins of multivalued discrete states. The operation of the proposed CPM was analyzed and numerically simulated using a traveling-wave model, demonstrating its capability to solve a graph coloring problem.
Dark-Forest: Analysis on the Behavior of Dark Web Traffic via DeepForest and PSO Algorithm

The dark web is a shadow area hidden in the depths of the Internet, which is difficult to access through common search engines.Because of its anonymity, the dark web has gradually become a hotbed for a variety of cyber-crimes.Although some research based on machine learning or deep learning has been shown to be effective in the task of analyzing dark web traffic in recent years, there are still pain points such as low accuracy, insufficient real-time performance, and limited application scenarios.Aiming at the difficulties faced by the existing automated dark web traffic analysis methods, a novel method named Dark-Forest to analyze the behavior of dark web traffic is proposed.In this method, firstly, particle swarm optimization algorithm is used to filter the redundant features of dark web traffic data, which can effectively shorten the training and inference time of the model to meet the realtime requirements of dark web detection task.Then, the selected features of traffic are analyzed and classified using the DeepForest model as a backbone classifier.The comparison experiment with the current mainstream methods shows that Dark-Forest takes into account the advantages of statistical machine learning and deep learning, and achieves an accuracy rate of 87.84%.This method not only outperforms baseline methods such as Random Forest, MLP, CNN, and the original DeepForest in both large-scale and small-scale dataset based learning tasks, but also can detect normal network traffic, tunnel network traffic and anonymous network traffic, which may close the gap between different network traffic analysis tasks.Thus, it has a wider application scenario and higher practical value.