Reviewerly/exHarmony huggingface dataset:
An Indicate System for Danger Detection and Its Soldering Motion Analysis

Soldering is prone to human error due to lack of concentration and corner-cutting caused by simple tasks. Therefore, it is expected that accidents can be reduced by having instructors give indication during dangerous actions or inappropriate postures during soldering iron work. In this paper, in order to solve these problems, we propose an indicate system for danger detection and carry out soldering motion analysis. Also, we show the experimental results for dangerous detection during soldering iron. The experimental results show that the proposed system has a good accuracy for detecting dangerous situations.
Improving the Science of Annotation for Natural Language Processing: The Use of the Single-Case Study for Piloting Annotation Projects

Researchers need guidance on how to obtain maximum efficiency and accuracy when annotating training data for text classification applications. Further, given wide variability in the kinds of annotations researchers need to obtain, they would benefit from the ability to conduct low-cost experiments during the design phase of annotation projects. To this end, our study proposes the single-case study design as a feasible and causally-valid experimental design for determining the best procedures for a given annotation task. The key strength of the design is its ability to generate causal evidence at the individual level, identifying the impact of competing annotation techniques and interfaces for the specific annotator(s) included in an annotation project. In this paper, we demonstrate the application of the single-case study in an applied experiment and argue that future researchers should incorporate the design into the pilot stage of annotation projects so that, over time, a causally-valid body of knowledge regarding the best annotation techniques is built.
An IoT Authentication Framework for Urban Infrastructure Security Using Blockchain and Deep Learning

Internet of Things (IoT) enables increased connectivity between devices. However, this benefit also intrinsically increases cybersecurity risks as cyber attackers are provided with expanded network access and additional digital targets. To address this issue, this paper presents a holistic digital and physical cybersecurity user authentication framework, based on Blockchain and Deep Learning (BLDE) algorithms. The introduced user authentication framework, provides an additional layer of resilience against Cybersecurity attacks that may arise through the IoT. Moreover, it controls digital access through the seven OSI layers and via the physical user’s identity, such as finger prints or self-images, before the user is accepted in the IoT network. Finally, it offers many layers of security through its decentralized and distributed nature, in order to reduce the system’s vulnerability from cyber threats.
Supporting Natural Language Interaction with the Web

Conversational AI is disrupting the way information is accessed. However, there is still a lack of conversational technologies leveraging the Web. This paper introduces an approach to support the notion of Conversational Web Browsing. It illustrates design patterns for navigating websites through conversation and shows how such patterns are sustained by a Web architecture that integrates NLP technologies.
RES-Q: Toward Semantic Interoperability for Risk and Disaster Management in Smart Cities

The COVID-19 pandemic has imposed new challenges in preserving the goal of developing smart and sustainable cities worldwide while improving urban resilience. In the smart city domain, disaster or crisis management operations require contributions and collaboration from different types of entities with various functions, rules, and protocols, forming complex contexts in decision-making or event coordination. The management of the corresponding information usually coming from multiple heterogeneous sources and sometimes with attributes revealing semantic inconsistencies constitutes an emerging challenge. Furthermore, the demand for interoperability between the various services and IoT devices at local and national level is imperative. Yet, existing literature highlights that the conceptualization of a holistic reference schema that covers all the dimensions of the smart city disaster/crisis management domain and allows the exchange of information through different agents has not been fully addressed so far. We present the RES-Q (RESCUE) semantic model, which includes the needed domain knowledge streams for the smart city crisis management domain. This model aims for data consolidation and linkage in order to be further utilized for the implementation of a common knowledge repository and advanced analysis. In this context, semantic web technologies are proposed as a promising solution for providing semantic interoperability in crisis and/or disaster management in the smart city discourse. Finally, data consolidation and harmonization methodology is presented, which is used for the integration of different data sources, according to the RES-Q model.
Mask Compliance Detection on Facial Images

The COVID-19 pandemic has significantly changed our ways of living. Government authorities around the world have come up with safety regulations to help reduce the spread of this deadly virus. Covering the mouth and nose using facial masks is identified as an effective step to suppress the transmission of the infected droplets from one human to the other. While the usage of facial masks has been a common practice in several Asian societies, this practice is fairly new to the rest of the world including modern western societies. Hence, it can be noticed that the facial masks are either worn incorrectly (or sometimes not worn) by a significant number of people. Given the fact that the majority of the world population is only getting accustomed to this practice, it would be essential for surveillance systems to monitor if the general population is abiding by the regulatory standards of correctly wearing a facial mask. This paper uses deep learning algorithms to track and classify face masks. The research proposes a mask detection model based on Convolutional Neural Networks to discern between a correct usage of facial masks and its incorrect usages or even lack of it. Different architectures have been tested (even on real-time video streams) to obtain the best accuracy of 98.9% over four classes. These four classes include correctly worn, incorrectly worn on the chin, incorrectly worn on mouth and chin, and not wearing a mask at all. The novelty of this work is in the detection of the type of inaccuracy in wearing the face mask rather than just detecting the presence or absence of the same.
Distance Education and the COVID-19 Pandemic

The sudden change in the educational process paradigm with the transition to predominantly distance learning was due to quarantine restrictions as a result of the COVID-19 pandemic. Therefore, the purpose of the research was to study the psychological motivational aspects of the transition to predominantly distance learning. The study involved 226 graduate students and 223 undergraduate students. The research revealed that without direct contact and the implementation of real practical tasks during distance learning, students lose interest in the subject under study. The conclusion is made about the uncertainty of life values among undergraduate students, which reflects their psychological immaturity, and the presence of intrapersonal contradictions may be associated with a dependent position and a struggle of motives.
Picture Fuzzy Simple Additive Weighting Method for Food Presentations Scoring of Gastronomy Students

Eating is one of the basic activities that people carry out throughout their lives to survive and to get pleasure. Today, this basic activity is handled within the framework of gastronomy, which is also defined as the “art of living”, which includes many industrial sub-fields and various research disciplines. Service providers of the food and beverage industry compete fiercely for the privilege of being favored by the customers. For gastronomy education institutes, introducing unique products for restaurant menus and creating innovative recipes is vital. This is due to the fact that one of the qualities required of prospective chefs is ingenuity. In this study, scoring the food presentations created by the students within the scope of the Korean Cuisine course taught in a gastronomy education institution is considered as a multi-criteria decision-making problem. Food presentations by the students in the final examination are evaluated by considering the criteria such as its visuality, creativity of the name, taste perception created by the expert, and the fusion balance of the product (coming together of different cultures in the product). The Picture fuzzy Simple Additive Weighting (SAW) method was used for this multi-criteria evaluation, which was considered for the scoring of food presentations of the students. There is no doubt that the developed methodology can be applied to many different evaluations related to the cuisine.
Crowd Detection Algorithm for Complex Scene Based on Improved Yolov3

Accurate detection of the crowd in the complex scene is much conducive to reducing the risk of crowding and trampling and ensuring the safety of the crowd. Implicit anchors-based detection frameworks, such as YOLO-based, can effectively solve the problem of small targets and light occlusions. However, YOLOv3 also possesses limitations, such as a large model size and slow response time. The number of the network parameters will also increase significantly as the number of channel attributes extracted increases. In this work, a novel improved yolov3-based framework (TDS-YOLOv3) was proposed for crowd detection in the complex scene. Firstly, a deep separable convolutional kernel to reconfigure the output module of YOLOv3 to reduce the number of parameters. Secondly, the training process of the proposed model was accelerated by transfer learning, which included mainly two stages, freezing the main backbone Darknet53 and unfreezing the entire network for global fine-tuning. Then, the VOC07trainval+test dataset with Hieve train was used for fully supervised training for complex scenarios. Finally, generalization performance evaluation was carried out two datasets, CUHK Occlusion and Hieve test. The experimental results show that our model achieves a significant improvement in detection speed and effective improvement in model size at the expense of certain detection accuracy.
The Role of Information Modelling and Computational Ontologies to Support the Design, Planning and Management of Urban Environments: Current Status and Future Challenges

Over the past decades the understanding of urban environment has undergone profound changes. This has been accelerated by the advent of information science and big data, and the ever-increasing quantity of data produced by smart devices located in urban areas and remote-sensing. This development has been accompanied by advanced information and communication technologies designed to take advantage of this data deluge. Computational ontologies have been proposed to turn this data into knowledge that can be exploited for a variety of tasks ranging from information retrieval to decision support. In this chapter, we review how recent approaches for information modelling in urban environments implement computational ontologies. This survey highlights the problems these approaches intend to solve, as well as their limitations. Based on this analysis, a roadmap of future research is drawn that needs to meet the environmental and ecological challenges raised by urbanization.
Using Support Vector Machine (SVM) in Modeling Water Resources Systems

Another supervised learning method is the support vector machine (SVM) method. This method, like the artificial neural network method, is one of the basic data methods that can classify or predict data after the training process. This theory was later used as a powerful tool for classifying data in various sciences especially in water and environmental science. In this chapter, before examining and studying the details of the SVM model, first the basics and basic concepts of classification and then the SVM model are stated. After that, the details and relations of the ruler and the types of functions used in it are discussed. At the end of this chapter, various software applications for this tool will be introduced, and how some of the work will be described.
Blockchain-Based COVID-19 Detection Framework Using Federated Deep Learning

With the increase of COVID-19 instances worldwide, a reliable method for diagnosing COVID-19 cases is needed. The major issue in detecting COVID-19 clients is a lack of diagnostic techniques that are both reliable and affordable. Due to the virus's rapid dissemination, medical professionals are having difficulties finding positive cases. The second real-life issue is sharing data across clinics worldwide but keeping in mind the organizations' privacy concerns. Developing a collaborative approach and protecting personal information are two important issues while creating a global classifier. This article offers a system that uses Ethereum - based federated learning to gather a modest quantity of data from many sources and train a global deep learning model. The data is authenticated using blockchain technology, and federated learning trained the system worldwide while maintaining the institution's anonymity. The suggested structure may make use of current data to enhance diseases recognition. Our findings show that our method is more effective in detecting COVID-19 participants.
Text Normalization Through Neural Models in Generating Text Summary for Various Speech Synthesis Applications

Machine learningP N Varalakshmi K Jagadish S Kallimani, like neural network methods, has been implemented in the natural language processing of virtually every domain. With speech utilizations like vocabulary to speech organization, coalescence challenge that has been adequately immune directly toward successful machine attainments learning approaches is fundamentals normalization. Considering example, in this application it must be determined that 123 is verbalized in signatures as one hundred and twenty-three but in sovereign potentate Ave as one twenty-three. Modern industrial systems for this role are heavily dependent on hand-recorded penned double speak-specific stratification. We introduce neural interconnection miniatures well-known regard text notarization for as a streak to progression problem, where input is admission taking in token in history, and turn out gain would be that token’s verbalization.
Legal Vacuum to Regulate AI

In contrast to rapidly developing AI, the international community has been struggling to come to an agreement on how to regulate it. Consequentially, there is a legal vacuum with respect to AI.
A Deep Learning Approach: Frontal Facial Emotion Prediction Using Residual Network and Machine Learning

A computer based Facial emotion recognition has been an essential to understand the needs and coding skills of human. Facial Emotion Recognition (FER) deals with facial expressions captured in both static photos and video to deduce a person's current emotional state. The facial expressions are a reflection of human brain activity, and they reveal a lot about human behavior. With the advancement of neural network, researchers have developed diverse models to identify facial expression using different deep learning techniques for monitoring human activities. To be motivated by this, we propose a facial recognition model by applying Residual Network where HAARCascade classifier extracts features from the facial areas of the images, while the main model concentrates on detection. We have trained the model using different datasets and evaluated the performance of the model. The model demonstrates around 85%-87% training accuracy.
Feature Selection and Diagnosis Performance Evaluation of Breast Cancer

The most vicious malady among women is breast cancer. The expeditious detection will aid to escalate the survival rate and also dwindle the mortality rate due to breast cancer. A machine learning-based system is proposed for the explicit diagnosis of breast cancer. Support vector machine (SVM), K-nearest neighbour (K-NN), Naive Bayes (NB), decision tree (DT) and random forest (RF) are being used for the development of the model. The significant features for the model are selected by using least absolute shrinkage and selection operator (Lasso). Accuracy, misclassification, sensitivity, specificity, F1 score, Mathew’s correlation coefficient (MCC) and Youden’s index (J) were calculated for model diagnosis assessment.
Optimal Information and Security Design

Abstract An asset owner designs an asset-backed security and a signal about its value. After privately observing the signal, he sells the security to the monopolistic liquidity supplier. Any optimal signal structure guarantees the security sale and commits the issuer not to learn too positive private information about the security value. The optimal security design is pure equity. It is risky debt under additional liquidity requirements akin to Basel III regulation. Thus, the standard intuition behind debt optimality as the least informationally sensitive security holds only under additional restrictions (e.g., regulatory) on security or signal design.
Csse - an Agnostic Method of Counterfactual, Selected, and Social Explanations for Classification Models

Achieving high predictive capability is not always enough for a machine learning model. In many scenarios, it is necessary to understand the model's decisions to increase confidence in the predictions and direct the actions to be taken from them. Therefore, it is essential to provide interpretable models. However, some authors have pointed out the need for improvements in current interpretability methods to provide adequate explanations, especially for non-specialists in machine learning. The solution is to expand studies beyond computational issues to understand the best way for people to receive explanations. Based on the literature, we identified three aspects to be considered in the explanations: being contrastive, selected, and social. The counterfactual approach, contrastive in nature, generates examples that show people how to obtain a different prediction. Given this, the objective of this work is to present the Agnostic Method of Counterfactual, Selected, and Social Explanations (CSSE) capable of generating local explanations that consider the highlighted aspects for an end-user-oriented approach. The method uses a strategy that combines genetic algorithms and the SHAP approach as central elements. The experiments showed that CSSE presents relevant results compared to some existing approaches. The work also includes a case study of predicting the academic performance of children and adolescents with ADHD, in which we applied the CSSE. Thus, the proposed method presents advances in interpretability, since it offers explanations oriented to the end-user, generating greater acceptance, confidence, and understanding regarding the models' decisions. The method implementation is available at https://github.com/marcelobalbino/CSSE.
Local measurements of nonlinear embeddings with information geometry

A basic problem in machine learning is to find a mapping f from a low-dimensional latent space Y to a high-dimensional observation space X. Modern tools such as deep neural networks are capable to represent general nonlinear mappings. A learner can easily find a mapping which perfectly fits all the observations. However, such a mapping is often not considered as good, because it is not simple enough and can overfit. How to define simplicity? We try to make a formal definition on the amount of information imposed by a nonlinear mapping f. Intuitively, we measure the local discrepancy between the pullback geometry and the intrinsic geometry of the latent space. Our definition is based on information geometry and is independent of the empirical observations, nor specific parameterizations. We prove its basic properties and discuss relationships with related machine learning methods.
Intelligent Slice Admission Control in Cloud-Ran by Approximate Reinforcement Learning

Abstract — Network slicing is considered to be a crucial feature of 5G cellular system. This technology gives the infrastructure provider (InP), the ability to support a variety of services on a shared platform by creating a customized slice for each service. According to the business model of InPs, their revenue depends on accepted slice requests received form the service providers. Hence, the revenue maximization of any InP, can be achieved by selecting those slice requests that bring more benefit for InP. Moreover, the fundamental decision is which request must be accepted to be served. In this paper, we employ the approximate reinforcement learning to design an intelligent slice admission control (SAC) mechanism, that tries to maximize the InP revenue. Our ostensive simulation shows that the proposed scheme, called ARL, outperforms deep reinforcement learning and Q-learning in term of maximizing revenue, utilization, and accepting more numbers of requests.
Residual Vector Product Quantization for Approximate Nearest Neighbor Search

Product Quantization is popular for approximate nearest neighbor search, which decomposes the vector space into Cartesian product of several subspaces and constructs separately one codebook for each subspace. The construction of codebooks dominates the quantization error that directly impacts the retrieval accuracy. In this paper, we propose a novel quantization method, residual vector product quantization (RVPQ), which constructs a residual hierarchy structure consisted of several ordered residual codebooks for each subspace. The proposed method minimizes the quantization error by jointly optimizing all the codebooks in each subspace using the efficient mini-batch stochastic gradient descent algorithm. Furthermore, an efficient encoding method, based on H-variable Beam Search, is also proposed to reduce the computation complexity of encoding with negligible loss of accuracy. Extensive experiments show that our proposed method outperforms the-state-of-the-art on retrieval accuracy while retaining a comparable computation complexity.
Position and Time Augment System Security Management Technology for SGIN Based on Consortium Blockchain

Global Satellite Navigation System (GNSS) can provide accurate time and space information for Space-Ground Integrated Network (SGIN) to support the application in various fields. However, GNSS has inherent vulnerability and cannot provide continuous and reliable positioning and timing services for SGIN users. The quasi-GNSS augment system (QGAS) is used to provide positioning and timing enhancement services for SGIN. QGAS distributes over a wide area, and the heterogeneity of the network poses a significant risk to the security management of the system. The traditional centralized network security technology is not suitable for QGAS. In view of the network spoofing threat of QGAS, a novel security management technology based on consortium blockchain for QGAS is proposed. The architecture and the workflow of the system are given. Security analysis show that the proposed framework can be used as an efficient way to ensure the reliability of PT information, supporting QGAS as a supplement and backup to GNSS. Together, they can provide SGIN with accurate time and space information