Reviewerly/exHarmony huggingface dataset:
Hand gesture recognition and voice conversion for deaf and Dumb

In this paper, we purpose a Hand gesture recognition model which can be used in real time application. This model is based on the mediapipe frame work of the google, Tensor flow in openCv and python and classification using feed forward neural network with keras model. The structure of the proposed work consists of 3 modules: Grabbing the frames, detecting hand landmarks and classification. The proposed model has the accuracy 95.7% at recognizing 10 kinds of hand gestures(Thumbs up, Thumbs down, Peace, Smile, Rock, Ok, Fist, livelong, call me, stop). A hand gesture recognition model that reacts rapidly and with generally acceptable accuracy is one of this work’s primary achievements and pre trained model for feature extraction. The unique approach of the suggested approach is that it detects hand landmarks using Google’s MediaPipe, which is faster and more accurate than traditional methods that rely on geometry, form, and edge data. For modelling sequence data and for recognising gestures, the LSTM model has proven to be quite successful.
TruFLaaS: Trustworthy Federated Learning as a Service

The increasing availability of data generated by Internet of Things (IoT) and Industrial Internet of Things (IIoT) devices, as well as privacy and law regulations, have significantly boosted the interest in collaborative machine learning (ML) approaches.In this direction, we claim federated learning (FL) as a promising ML paradigm where participants collaboratively train a global model without outsourcing on-premises data.However, setting up and using FL can be extremely costly and time-consuming.To effectively promote the adoption of FL in real-world scenarios, while limiting the overhead and knowledge of the underlying technology, service providers should offer federated learning as a service (FLaaS).One of the major concerns while designing an architecture that provides FLaaS is achieving trustworthiness among involved typically unknown participants.This article presents a blockchain-based architecture that achieves Trustworthy federated learning as a service (Tru-FLaaS).Our solution provides trustworthiness among 3rd-party organizations by leveraging blockchain, smart contracts, and a decentralized oracle network.Specifically, during each FL round, the service provider supplies a sample, without overlapping, of its validation set to validate all partial models submitted by clients.By doing so, poor models, which tend to degrade performance or introduce malicious backdoors, are identified and discarded.Due to the transparency of the blockchain, not changing the validation set would enable participants to forge a malicious partial model that passes the validation phase.We evaluate our approach over two well-known IIoT datasets: the reported experimental results show that TruFLaaS outperforms the state-of-the-art literature solutions in the field.
Monte Carlo-based improved ant colony optimization for path planning of welding robot

For spot-welding process, rational path planning of weld points can improve productivity for welding robot. In basic ant colony optimization, at the beginning of the iteration, the pheromone concentration is only related to the path length, but at this time, the path often contains a lot of redundant parts. Therefore, the pheromone in the initial iteration is almost difficult to reflect the advantages and disadvantages of the node, and it requires multiple iterations to slowly show the advantages and disadvantages of the path. For this shortcoming, a Monte Carlo-based improve ant colony optimization (MC-IACO) is proposed to solve the path planning of welding robots. The MC-IACO algorithm samples the path results generated in each generation and converts them into Monte Carlo indexes of the nodes. By substituting the Monte Carlo index of the feasible node into the sigmoid function, the probability that the corresponding node may constitute the optimal path, namely the Monte Carlo factor, is calculated to help ants make decisions. The new node transfer formula constructed with this factor can effectively improve the path finding efficiency of ants and the efficiency of the algorithm to complete the path planning task. The feedback adjustment factor is introduced to make the judgment of the advantages and disadvantages of feasible nodes also refer to the advantages and disadvantages of subsequent nodes. The simulation results show that compared with the basic ant colony optimization and other improved ant colony optimization, the MC-IACO algorithm in this paper improves significantly in solving the path planning problem of the welding robot, and its overall performance is better.
Detection of Adversarial Attacks against the Hybrid Convolutional Long Short-Term Memory Deep Learning Technique for Healthcare Monitoring Applications

Deep learning (DL) models are frequently employed to extract valuable features from heterogeneous and high-dimensional healthcare data, which are used to keep track of patient well-being via healthcare monitoring systems. Essentially, the training and testing data for such models are collected by huge IoT devices that may contain noise (e.g., incorrect labels, abnormal data, and incomplete information) and may be subject to various types of adversarial attacks. Therefore, to ensure the reliability of the various Internet of Healthcare Things (IoHT) applications, the training and testing data that are required for such DL techniques should be guaranteed to be clean. This paper proposes a hybrid convolutional long short-term memory (ConvLSTM) technique to assure the reliability of IoHT monitoring applications by detecting anomalies and adversarial content in the training data used for developing DL models. Furthermore, countermeasure techniques are suggested to protect the DL models against such adversarial attacks during the training phase. An experimental evaluation using the public PhysioNet dataset demonstrates the ability of the proposed model to detect anomalous readings in the presence of adversarial attacks that were introduced in the training and testing stages. The evaluation results revealed that the model achieved an average F1 score of 97% and an accuracy of 98%, despite the introduction of adversarial attacks.
Blank Strip Filling for Logging Electrical Imaging Based on Multiscale Generative Adversarial Network

The Fullbore Formation Micro Imager (FMI) represents a proficient method for examining subterranean oil and gas deposits. Despite its effectiveness, due to the inherent configuration of the borehole and the logging apparatus, the micro-resistivity imaging tool cannot achieve complete coverage. This limitation manifests as blank regions on the resulting micro-resistivity logging images, thus posing a challenge to obtaining a comprehensive analysis. In order to ensure the accuracy of subsequent interpretation, it is necessary to fill these blank strips. Traditional inpainting methods can only capture surface features of an image, and can only repair simple structures effectively. However, they often fail to produce satisfactory results when it comes to filling in complex images, such as carbonate formations. In order to address the aforementioned issues, we propose a multiscale generative adversarial network-based image inpainting method using U-Net. Firstly, in order to better fill the local texture details of complex well logging images, two discriminators (global and local) are introduced to ensure the global and local consistency of the image; the local discriminator can better focus on the texture features of the image to provide better texture details. Secondly, in response to the problem of feature loss caused by max pooling in U-Net during down-sampling, the convolution, with a stride of two, is used to reduce dimensionality while also enhancing the descriptive ability of the network. Dilated convolution is also used to replace ordinary convolution, and multiscale contextual information is captured by setting different dilation rates. Finally, we introduce residual blocks on the U-Net network in order to address the degradation problem caused by the increase in network depth, thus improving the quality of the filled logging images. The experiment demonstrates that, in contrast to the majority of existing filling algorithms, the proposed method attains superior outcomes when dealing with the images of intricate lithology.
Natural Language to SQL: Automated Query Formation Using NLP Techniques

In this era of information world, given any topic, we are able to get relevant data or documents at a mouse click. The flexibility that internet provides is the user friendly language or Natural Language to search for required topic. Natural Language Querying and Retrieval has made internet popular. It is implicit for business user to understand what the business data is indicating to find better business opportunities. Querying for required data the business users are using SQL. To effectively Query such systems, the Business users has to master the Language. But many business users may not be aware of the SQL language or may not be aware of the databases and some users feel difficulty to write the long SQL Queries. Therefore, it is equally important to query the database very easily. The work here presents a case study to help the business users to type a query in Natural Language, which then converts into SQL statement and process this SQL query against the Databases and get the expected result. This work proposes QCNER approach to extract SQL properties from Natural Language Query. The proposed approach after the application of SMOTE technique depicts 92.31 accuracy over the existing models. 1
The Future of Education Utilizing an Artificial Intelligence Robot in the Centre for Independent Language Learning: Teacher Perceptions of the Robot as a Service

This book chapter provides an overview of Temi, an autonomous, video-oriented personal assistant robot which was deployed within the Centre for Independent Language Learning (CILL) at The Hong Kong Polytechnic University. The artificial intelligence robot was chosen principally because of its role as a Robot as a Service (RaaS). Such a service can deliver greater self-improvement and better learning strategies (e.g. Cohen, A. D. (2014). Strategies in learning and using a second language (2nd ed.). Routledge., Dörnyei et. al., 2015, Wenden, Learner strategies for learner autonomy, Prentice Hall, 1991, Yang, Frontiers in Psychology 12:600, 218–600, 218, 2021) as well as foster beneficial attitudes and skills towards the users’ long-term language learning success. Through its cloud-based system, Temi offers users access to dynamic interactions and enhanced CILL services, during the COVID-19 pandemic. As a whole, it appears that the introduction of Temi has proven to be an effective strategy to augment learners’ autonomy. It further allows administrators to rethink how CILL services are conducted during human resource shortages.
StruNet: Perceptual and low‐rank regularized transformer for medical image denoising

Various types of noise artifacts inevitably exist in some medical imaging modalities due to limitations of imaging techniques, which impair either clinical diagnosis or subsequent analysis. Recently, deep learning approaches have been rapidly developed and applied on medical images for noise removal or image quality enhancement. Nevertheless, due to complexity and diversity of noise distribution representations in different medical imaging modalities, most of the existing deep learning frameworks are incapable to flexibly remove noise artifacts while retaining detailed information. As a result, it remains challenging to design an effective and unified medical image denoising method that will work across a variety of noise artifacts for different imaging modalities without requiring specialized knowledge in performing the task.In this paper, we propose a novel encoder-decoder architecture called Swin transformer-based residual u-shape Network (StruNet), for medical image denoising.Our StruNet adopts a well-designed block as the backbone of the encoder-decoder architecture, which integrates Swin Transformer modules with residual block in parallel connection. Swin Transformer modules could effectively learn hierarchical representations of noise artifacts via self-attention mechanism in non-overlapping shifted windows and cross-window connection, while residual block is advantageous to compensate loss of detailed information via shortcut connection. Furthermore, perceptual loss and low-rank regularization are incorporated into loss function respectively in order to constrain the denoising results on feature-level consistency and low-rank characteristics.To evaluate the performance of the proposed method, we have conducted experiments on three medical imaging modalities including computed tomography (CT), optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA).The results demonstrate that the proposed architecture yields a promising performance of suppressing multiform noise artifacts existing in different imaging modalities.
Does institutional quality modulate the effect of capital flight on economic growth in sub-Saharan Africa?

Purpose Sub-Saharan African (SSA) region has been battling illegal outflow of capital over the years, with little success recorded so far. Without adequate attention, unemployment, infrastructure deficiencies and inefficient capital might be worse in the future. The purpose of this study is to investigate if institutional quality mitigates the effect of capital flight (CF) on economic growth. Design/methodology/approach The panel data from 26 SSA countries spanning 1998 to 2018 are used. The analysis of this study was carried out through a two-step generalized method of moments technique. The principal component index is used to group the institutional quality/governance indicators into three categories: political governance, economic governance and institutional governance. Findings The study found that CF is harmful to the economic growth of the SSA region. The study also found that, among the indicators of institutional quality, only the rule of law and control of corruption stimulate economic growth. Contrary to expectation, the finding indicates that institutional quality does mitigate the effect of CF on economic growth in the SSA region. Originality/value This study provides an insight into the relevance of institutional quality in mitigating CF in sub-Saharan African region.
Inducing AI-powered chatbot use for customer purchase: the role of information value and innovative technology

Purpose This study aims to understand a customer-purchase mechanism in the artificial intelligence (AI)-powered chatbot context based on the elaboration likelihood model (ELM) and technology acceptance model (TAM). The first objective is to examine how to boost chatbot adoption. The second objective is to investigate the role of information characteristics, technology-related characteristics and attitude toward AI in purchase intention. Design/methodology/approach Data was collected from a sample of 492 users in Vietnam, who are potential customers of chatbots for purchase. Structural equation modeling was applied for data analysis. Findings Results illustrate that chatbot adoption is significantly influenced by information credibility, technology-related factors (i.e. interactivity, relative advantage and perceived intelligence), attitude toward AI and perceived usefulness. Moreover, information quality and persuasiveness motivate information credibility. Information credibility and attitude toward AI are the essential motivations for perceived usefulness. Finally, chatbot adoption and information credibility determine purchase intention. Practical implications The results are insightful for practitioners to envisage the importance of chatbot use for customer purchase in the AI scenario. Additionally, this research offers a framework to practitioners for shaping customer engagement in chatbots. Originality/value The value of this work lies in the incorporation of technology-related characteristics into the two well-established theories, the ELM and TAM, to identify the importance of AI and its applications (i.e. chatbots) for purchase and to understand the formation of perceived usefulness and chatbot use through information credibility and attitude toward AI.
Investigation of carbon-aramid hybrid braided composites using digital volume correlation

Hybrid braided composites combine two or more reinforcing fibres in a matrix. Limited research on hybrid braided composites has been conducted. The Digital Image Correlation (DIC) method has been used to understand hybrid braided composites' external deformation and strain. However, DIC measurements do not provide sufficient information on the internal behaviour of hybrid braids. Therefore, this study focuses on understanding the behaviour of hybrid braids Digital Volume Correlation (DVC). DVC is a micro-computed tomography (Micro-CT) based volumetric strain measurement technique that allows for the internal strain fields of an object. Tensile loading of the Carbon-Aramid hybrid braided samples is achieved using an integrated material testing stage. DVC is used to generate three-dimensional displacement and strain fields and understand the internal strain behaviour of hybrid braided composites. The peak and strain observed for the sample was around −0.065 (µm/µm) and 0.017 (µm/µm), respectively. The effect of contrast-enhancing tracking particles on the DVC resulting DVC strain fields were also examined. Samples with and without contrast particles were subjected to in-situ loading and then analyzed using the DVC process. From this comparison, it was found that the addition of contrast-enhancing particles improves the volumetric strain results from the DVC measurements.
Mapping geological configuration using geophysics data: an investigative approach in targeting iron ore, gold mineralization and other commodities, a case study of Toko-Nlokeng area (Nyong Greenstone Belt, SW Cameroon)

ABSTRACTABSTRACTRegional geophysical data are important for detecting the presence of mineral deposits under cover. This study used geophysical data coupled with field geology of the Toko-Nlokeng area, Cameroon, to locate and map structures and lithologies as part of an exploration campaign for mineral deposits. The airborne geophysical survey in the Toko-Nlokeng area located in the Nyong complex revealed a predominantly EW striking highly folded series result of polyphase deformation of iron formations and host rocks divided into domains based on magnetic and radiometric character. The geophysical data indicate that major faults and shear zones strike NE-SW, with N–S striking units on the western margin and mainly E–W striking units in the rest of the area. These data suggest a major N–S thrust fault similar to the Borborema Province (NE Brazil) terrane marked by accretionary processes during the assembly of Western Gondwana. Several significant NE–SW striking shear zones are present. The data revealed many moderate to highly magnetic bodies that are interpreted as magnetite-bearing iron formation, mafic–ultramafic intrusions and flows, skarns, carbonatites and kimberlites. The magnetic and radiometric trends reveal the internal regional rock fabric and strike, and iron ore targets with magnetic anomaly up to 27,000 nT related to iron formations and other significant targets for gold, potassium (alteration) and uranium. The Toko-Nlokeng iron deposit is comprised of magnetite-bearing quartzite beds and is concordant with the surrounding host rocks and stands as one of a broad group of magnetite-dominated iron formations that occur throughout West and Central Africa. A new geological map was produced from mapped lithologies and microstructure characteristics of the iron formations, interpretations of the aeromagnetic data and structural features. The study results have important implications for regional exploration programs.KEYWORDS: Geophysical surveylithology configurationiron–gold depositstructural analysisToko-Nlokeng areaNyong complex AcknowledgementsYannick Saturnin Evina Aboula and Paul-Désiré Ndjigui dedicate this paper to Professor Joseph Mvondo Ondoa who has just left us in the revision status on this paper. The authors would like to thank the mining exploration company IMIC. PLC, in particular Mr. Simo Emmanuel, for permission to access unpublished data and to collect core samples from the Ntem iron project (Toko-Nlokeng iron deposit). We thank Pr. Sylvestre Ganno for his support and availability for the processing of the drilling data. We thank the anonymous reviewers for their critical and constructive comments that greatly reorganize this manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).
Enhancing Supply Chain Efficiency Through Blockchain Integration

Blockchain technology offers several beneficial features that make it well-suited for supply chain management (SCM). Particularly, it can improve transparency, traceability, security, efficiency, and accountability in supply chains (SC). This chapter focuses on how blockchain technology can completely transform SC processes due to its inherent features. By implementing blockchain, businesses can cut costs, improve efficiency, and ensure accountability. The chapter explains the basics of blockchain technology and its potential benefits for SCM. The various beneficial features of blockchain technology such as trust, immutability, reliability, and security are discussed and highlighted in the chapter. The chapter showcases real-world uses of blockchain technology in SCM to depict the possible benefits. Benefits and challenges of incorporating blockchain into SC processes are also discussed in the chapter. Finally, this chapter emphasizes how crucial it is for companies to identify the factors that weaken their SC and to ensure that blockchain is applied to address such issues.
Overview of Day-ahead Solar Power Forecasts Based on Weather Classifications

Solar power forecasting technology includes the procedure of data processing and the establishment of forecasting models. There is a high correlation exists between power generation and solar irradiance. Thus, if solar irradiance can be predicted accurately, the forecasting accuracy of solar power generation can be greatly improved. However, there is still room for improving the solar irradiance forecasting due to the limits of numerical weather prediction (NWP). Therefore, it is significant to improve other forecasting related technologies, such as data processing, to improve the power forecasting accuracy. According to literature reports, the classification of weather patterns is one of the important methods for data preprocessing. If different weather patterns can be classified, and the corresponding prediction model can be established in each weather classification, the forecasting accuracy can be improved effectively. This paper attempts to establish a variety of different methods to classify weather patterns; these methods include typical clustering classification, seasonbased classification, time-based classification (morning or afternoon), and the classification according to data’s amplitude and variance. The research results demonstrate that the forecasting methods that consider weather classification can improve the accuracy of day-ahead forecasts.