Reviewerly/exHarmony huggingface dataset:
Blockchain for Industry 4.0: A Far reaching Survey

Because of the multiplication of ICT during the most recent couple of many years, there is a dramatic expansion in the use of different brilliant applications, for example, shrewd cultivating, savvy medical care, production network and coordinated factors, business, the travel industry and friendliness, energy the executives and so forth Nonetheless, for all the previously mentioned applications, security and protection are main pressing issues keeping taking into account the use of the open channel, i.e., Web for information move. Albeit numerous security arrangements and guidelines have been proposed throughout the years to improve the security levels of previously mentioned brilliant applications, yet the current arrangements are either founded on the incorporated engineering (having weak link) or having high calculation and correspondence costs. Also, a large portion of the current security arrangements has focussed just on a couple of viewpoints and neglect to address adaptability, strength, information stockpiling, network dormancy, suitability, changelessness, and detectability. To deal with the previously mentioned issues, blockchain innovation can be one of the arrangements. Spurred from these realities, in this paper, we present an orderly audit of different blockchain-based arrangements and their pertinence in different Industry 4.0-based applications. Our commitments in this paper are in four creases. First and foremost, we investigated the present status of-the-craftsmanship arrangements in the blockchain innovation for shrewd applications. Then, at that point, we outlined the reference design utilized for the blockchain relevance in different Industry 4.0 applications. Then, at that point, benefits and bad marks of the customary security arrangements are additionally talked about in contrast with their countermeasures. At long last, we gave a correlation of existing blockchain-based security arrangements utilizing different boundaries to give profound bits of knowledge to the perusers about its appropriateness in different applications.
Introduction to remote sensing using GNSS signals of opportunity

Chapter Contents: 18.1 A quick historical review of GNSS-R 18.2 Basic concepts on GNSS 18.2.1 Measurement principle 18.2.2 Structure of the GNSS signals 18.2.3 Received power of the GNSS signals 18.2.4 Atmospheric and ionospheric effects 18.2.5 Satellite navigation systems 18.3 GNSS-R 18.3.1 Spatial resolution 18.3.2 Received power: coherent and incoherent components 18.3.3 The Woodward ambiguity function 18.3.4 GNSS-R observables and techniques 18.3.5 SNR computation 18.4 GNSS-R ocean applications 18.4.1 Ocean Scatterometry 18.4.2 Ocean altimetry 18.4.3 Ocean imaging 18.5 Conclusions Acknowledgment References
Quantifying the impact of illicit financial flows on sustainable development

This chapter explores the potential relationship between IFFs, structural transformation and sustainable development. It examines how IFFs may be negatively associated with productivity increases across sectors and highlights the role of institutions in channelling such effects. Over the past decade, in most countries in Africa, productivity increases have been low, despite relatively high economic growth rates. The findings indicate how curbing IFFs could contribute to achieving higher levels of economic productivity (target 8.2), supporting productive capacities (target 8.3) and improving resource efficiency (target 8.4) in Africa.
Telaah Problematika Anak Slow Learner dalam Pembelajaran

This study reviews the problems of slow learners in learning. Literary references slow learner to teachers providing learning and parents in providing assistance to children at home during online learning. Based on the results of the study that has been carried out, it is concluded that slow learner children are children who experience learning delays. This can be shown in situations where a child has not been able to master the learning information conveyed by the teacher within a certain time limit. The characteristics of these children are one level above mentally retarded children so they need teacher responses and regular assistance to help them develop themselves. Teachers must observe and identify children who are slow learners to be given counseling assistance that can stimulate cognitive, affective, physical, and intuitive aspects. Counseling should also educate children to use devices in a positive way during online learning.
Handling Imbalanced Datasets in the Case of Credit Card Fraud

Credit card fraud is a major problem in the banking sector. This is shown through the financial loss that many organizations, companies and government agencies face due to the rapid increase in the rate of fraudulent activities in recent years. To manage this increase, researchers focused on the early detection of fraudulent behaviors using advanced machine learning techniques. However, the dataset on credit card fraud is highly imbalanced which means that the model will be biased towards the majority samples and may consider a fraudulent transaction a normal one. Therefore, the classification algorithm will face a major difficulty during the detection of fraud since the percentage of fraud is very low. To resolve this issue, we used different resampling methods such as undersampling, oversampling and Synthetic Minority Oversampling Technique. All these methods have been applied in the credit card dataset to overcome rare events in the dataset. Furthermore, the three resampled datasets were classified using classification techniques. The performances were measured by their accuracy, precision, recall and F1 score. The results revealed that by resampling the dataset, the models performed and were statistically better, more particularly, the random forest outperformed the other models.
Lightweight Cyber Security for Decision Support in Information Security Risk Assessment

Cyber-Security in the Internet of Things (IoT) is a major concern for information exploitation which hinder the growth of information system. To address security levels and issues, security risk assessment is considered an effective tool for system security, products, process, and readiness. Effective system vulnerabilities guidance is involved in the prioritization of security risk assessment. At present, the differential equation provides a significant tool for risk assessment. However, for second-order derivatives, the error rate is higher which impacts on overall risk assessment model. To overcome those limitations, this paper presented Decision Support Light Weight Risk Assessment Model (DSLiRAM). The proposed DSLiRAM is the domain-specific framework for security assessment. The proposed DSLiRAM is adopted in four stages for the specification of practices applied for cybersecurity and organizational characteristics. The proposed DSLiRAM includes a fuzzy differential equation with a second-order derivative. To minimize error rate Taylor series expansion is integrated with Fredholm for risk assessment. The proposed DSLiRAM is examined in three scenarios, RT server, BPCS, and HMI. Analysis of results stated that the proposed DSLiRAM significantly predicts risk and prevents the attack.
Anomaly Detection: Case of Mobile Money Transactions with Isolation Forrest

Generally, the risk of fraud involves any intentional deception made for financial gain. Managing the risks associated with mobile money is a difficult task, particularly with regard to the risk of fraud. Fraud not only results in financial loss for the customer, it also damages the reputation of the service and endangers the reputation of the industry as a whole. As such, mitigating the risk of fraud is a primary objective of a robust risk management strategy. The detection of fraudulent money transactions is an important application in the detection of anomalies. Different approaches exist to detect anomalies such as k-mean, SVM, regression, decision trees, etc. However, each algorithm has its own limits depending on the model and the case. The objective of this document is to have a system for predicting fraudulent monetary transactions capable of overcoming the various problems associated with the detection of anomalies and having high accuracy. We compare three different techniques in order to distinguish the best approach. We choose the isolation forest with the best test evaluation and best time response.
Dual Attention Modeling between Users and Items Using Review for Recommendation

Recommendation systems were developed to predict the users&#x2019; preferences by tracking the interactions between users and items. While the interaction history and the basic information have been the prevailing data of the predictive models, other available types of information, like user feedback, could also provide abundant and subjective information. However, there is no efficient way to extract emotional information, and hence user comments are rarely fully utilized. With the recent development of Natural Language Processing (NLP) in sentiment extraction, user and item reviews could be processed to sequential word embeddings via Transformer encoders and then further extracted as features, which enables itself as a piece of auxiliary information added to the recommendation systems. Thus, this paper aims to propose a dual attention model using review for recommendation called DAM4R, which employs deep bidirectional self-attention to model user preferences and item characters respectively from review text and predict the score via an MLP layer. Through evaluation on Yelp and Amazon datasets, we show that our model can match the predictive performance of prior work.
Research on the construction of a blockchain-based intelligent logistics data management system

Blockchain is a cutting-edge technology with multiple functions such as data storage and security encryption, and is widely used in many fields. This paper studies the establishment of intelligent logistics data management system based on blockchain technology, optimizing the logistics service data management system from four aspects: logistics traceability, data analysis, intelligent decision-making and logistics finance. A new decentralized multilateral logistics transaction mechanism is proposed in combination with improved ant colony algorithms to achieve intelligent decision making for multi-user transaction matching, while using smart contracts to ensure the transaction process is safeguarded and constrained. The experimental results show that the stability and decision-making process of the intelligent decision-making model based on the improved ant colony algorithm are significantly better than those of the traditional ant colony algorithm and the basic particle swarm optimization algorithm. The blockchain-based intelligent logistics data management system effectively improves the intelligence and automation of logistics service information management and analysis, and has important practical value for the modern development of logistics service industry.
Security and Privacy in Distributed Health Care Environments

© 2022. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).
Confidence intervals for ratios of means applied to corpus-based word frequency classes

The words we choose when we communicate with one another convey meaning and information. In written or spoken language, we tend to employ a relatively small number of words repeatedly whereas a large number of words in the lexicon are seldom used. By considering a ratio of means of the most prevalent word in a body of texts (or corpus) compared to that of the word in question, one can quantify the prevalence of the word in question. Furthermore, the concept of word classes or grouping words having similar measures of prevalence enables the investigator to compare the words. Using a sample of texts having varying lengths from a corpus, the sample mean relative frequency of a word and the maximum likelihood estimator using the zero-inflated beta distribution serve as two measures of the prevalence of a word. We construct and then compare asymptotic confidence intervals involving ratios of means for a number of words in the British National Corpus, a 100 million-word collection of written and spoken language of a wide range of British English. We also examine the sample sizes required to meet specific objectives regarding word classes and ratios of means.
Data-inspired co-design for museum and gallery visitor experiences

Abstract The capture and analysis of diverse data is widely recognized as being vital to the design of new products and services across the digital economy. We focus on its use to inspire the co-design of visitor experiences in museums as a distinctive case that reveals opportunities and challenges for the use of personal data. We present a portfolio of data-inspired visiting experiences that emerged from a 3-year Research Through Design process. These include the overlay of virtual models on physical exhibits, a smartphone app for creating personalized tours as gifts, visualizations of emotional responses to exhibits, and the data-driven use of ideation cards. We reflect across our portfolio to articulate the diverse ways in which data can inspire design through the use of ambiguity, visualization, and inter-personalization; how data inspire co-design through the process of co-ideation, co-creation, and co-interpretation; and how its use must negotiate the challenges of privacy, ownership, and transparency. By adopting a human perspective on data, we are able to chart out the complex and rich information that can inform design activities and contribute to datasets that can drive creativity support systems.
HybridEI: Smartly Face Detection System in Resource Constrained Edge Environment

With the digital revolution of the Internet of Things (IoT), data is rapidly emerging at the network&#x2019;s edge, where it is collected using artificial intelligence (AI) and processed in a variety of applications, including cybersecurity, image recognition, and human tracking. Despite this, facial expression recognition (FER) remains a challenging problem in computer vision. Inconsistencies in the sophistication of FER and variations among expression classes are often ignored in most facial expression detection systems due to the various expressions of emotion and unpredictable environmental influences. An HybridEI (Hybrid Edge Intelligence), a smartly face detection system trained in a more powerful cloud environment and inferenced at the resource-constrained edge environment, is proposed for FER in this study. Face classification complexity, memory minimization, and computational time were all well addressed by our proposed HybridEI approach. With an accuracy of 82.78% and 1.27 frames per second (FPS), the experimental results on FER-2013 dataset demonstrated the effectiveness and superiority over other state-of-the-art (SOTA) methods. Furthermore, both the memory capacity and computational time were significantly decreased.
Electric Power Monitor tracking algorithm based on Improved SiamFC

With the deep application of intelligent safety monitoring technology in the field of power engineering construction, computer vision technology represented by object tracking has become one of the important research contents in the field of smart grid. In the industrial visual recognition and early warning system, it is difficult to achieve accurate and real-time tracking. In this study, we propose a real-time target tracking algorithm based on improved SiamFC. The second convolution layer in the original siamese network structure is replaced by deeply separable convolution, improves the tracking speed by reducing parameter calculation, meet the need of real-time tracking in practical application. In the third convolution layer, mixed deep convolution is used to extract features through convolution kernels of different dimensions to achieve multi-feature fusion, extract features with stronger robustness, and improve the network's ability to distinguish objects and backgrounds. The performance of the algorithm is tested on OTB2015 data set and power construction site surveillance video. Experimental results show that compared with the SiamFC algorithm, the algorithm has a certain improvement in the tracking success rate, tracking accuracy and tracking speed, and can meet the tracking requirements in power construction scenarios.
Research on Fault Early Warning Technology of Key Operation Nodes in Power Communication Network

In the process of power grid operation and maintenance, the current fault early warning technology has the problem of single access channel, which leads to more misjudgments. This paper designs a research on fault early warning technology of key nodes in power communication network operation. Using stratified sampling method, combined with data mining technology to detect abnormal network operation data, switching communication network access channels, developing link control procedures, using three-scale analytic hierarchy process to identify key nodes, sending information symbols and check symbols at the same time, eliminating the problem of information dispersion, setting fault early warning mode. Experimental results: the design of fault early warning technology, and the other two kinds of early warning technology, the average number of misjudgment is 8.06, 9.8, 10.06, which proves that the design of fault early warning technology has higher use value.
Feature Selection Technique for Microarray Data Using Multi-objective Jaya Algorithm Based on Chaos Theory

Microarray datasets are high-dimensional data that contain the gene expression profiling information used for cancer classification. This causes a curse of dimensionality and poses multiple challenges in gene classification. Moreover, the microarray datasets are imbalanced as they have a very small sample size, which deteriorates the performance of machine learning algorithms. In order to address these problems, dimensionality reduction technique is required. Meta-heuristic optimization algorithm-based feature selection is the efficient dimensionality reduction technique. This paper introduces a feature selection technique for microarray data classification using multi-objective Jaya algorithm based on chaos theory. The two objectives: minimization of selected gene subset and maximization of classification accuracy are considered. Chaos theory is used to improve the convergence of Jaya algorithm. Five benchmark microarray datasets were used to evaluate the performance of the proposed feature selection technique. Furthermore, a comparative study is carried out where the performance of the proposed technique is compared with four other competitors.
Multimodal Summarization: A Concise Review

Rapid increase in multimodal data from online sources necessitates the need for the development of methods and techniques that use diverse modes along with text to generate summaries. Meeting recordings, CCTV footages, and sports coverages are vast information resources that incorporate modes such as speech, text, and images. Multimodal data contains rich information which could help in the improving the quality of summaries. Recent developments in the field of neural networks have furthered the research in this area. This paper is an attempt to contribute to the growing body of research in multimodal summarization by drawing attention to state-of-the-art techniques, datasets, and evaluation strategies. Besides covering the recent work in the field, the paper also highlights the major issues and challenges.
Pretraining Multi-modal Representations for Chinese NER Task with Cross-Modality Attention

Named Entity Recognition (NER) aims to identify the pre-defined entities from the unstructured text. Compared with English NER, Chinese NER faces more challenges: the ambiguity problem in entity boundary recognition due to unavailable explicit delimiters between Chinese characters, and the out-of-vocabulary (OOV) problem caused by rare Chinese characters. However, two important features specific to the Chinese language are ignored by previous studies: glyphs and phonetics, which contain rich semantic information of Chinese. To overcome these issues by exploiting the linguistic potential of Chinese as a logographic language, we present MPM-CNER (short for Multi-modal Pretraining Model for Chinese NER), a model for learning multi-modal representations of Chinese semantics, glyphs, and phonetics, via four pretraining tasks: Radical Consistency Identification (RCI), Glyph Image Classification (GIC), Phonetic Consistency Identification (PCI), and Phonetic Classification Modeling (PCM). Meanwhile, a novel cross-modality attention mechanism is proposed to fuse these multimodal features for further improvement. The experimental results show that our method outperforms the state-of-the-art baseline methods on four benchmark datasets, and the ablation study also verifies the effectiveness of the pre-trained multi-modal representations.
AI-Powered “Voice Recognition Avatar”

Consumers like to try innovative technologies when they perceive them to be approachable, convenient, and entertaining. The purpose of this study was to explore the adoption of AI voice recognition avatar in the gameplay. This study's interesting insights show us that gamers are keen and interested in voice recognition technology. The study augments value to TAM theories, perceived value theory, and the flow theory with localization's moderating role. The 218 respondents from China provided useful insights. In China, gaming is a vast industry, and such gaming options can attract current and new gamers. Thus, AI can benefit gamers in this regard and give them the freedom to interact with their avatar through the power of voice recognition.
Facial Gender Classification Using Deep Learning

Automatic facial gender prediction is an essential sub-task of any facial analysis system. Given the current rapid advancements of artificial intelligence and the ease of connectivity, gender prediction models are being utilised in many applications, such as soft biometrics, surveillance, targeted marketing, social media, human-computer interaction applications and many more. Nonetheless, when gender prediction models attempt to classify images taken in unrestrained conditions with different poses and illuminations, the classification accuracy drops significantly. One solution to solve this issue is employing a very deep architecture to extract as many features as possible from an image. However, such models are usually complex to train and might not be deployable to devices with limited computation power. Therefore, the main objective of this study is to close the gap between the classification of unrestrained facial images and the need for an efficient architecture. We achieve this objective by presenting a novel lightweight deep convolutional neural network to classify male and female facial images taken in uncontrolled environments. We train our model on the UTKFace dataset and produce competitive accuracies compared to several state-of-the-art methods. We then carefully analyse and discuss our results to better understand the variables that control the performance of our model. In addition, we present potential suggestions and ideas to enhance our proposed method for future research.