Reviewerly/exHarmony huggingface dataset:
MPCNet: Smart Contract-Based Multiparty Computing Network for Federated Learning

Stepping into the era of big data, with more resources shared, the machine learning algorithms are more likely to derive a better solution, and those complicated computations can be finished in a shorter time. The existing works about multiparty computing mainly focus on how to perform the computation when the involved partners are given, but hardly consider the process during which the partners find each other. In this work, we proposed a framework of the multiparty computing network (MPCNet) for the agents propose and collaborate, where R3 Corda is harnessed to establish a blockchain platform where the convener is able to look for some other partners, and a crowdsourcing process is performed to verify the validity of the conveners proposal and the partners applications. Furthermore, a reward mechanism is proposed in order to motivate the verifiers to participate. Once all the agents joining the computing task are confirmed, they communicate with each other to perform the computing task, following the plan that is mentioned in the proposed smart contract. Experimental results demonstrated the feasibility, usability, and scalability of our proposed approach.
Biomechanical modeling and simulation of a human organ using an augmented reality technique during open surgery

Basically, augmented reality (AR) technology grants innovative ways so as to manipulate and visualize a three-dimensional (3D) model of an object through superimposing computer-generated images onto another object interactively. Being able to interact in real-time with digital and spatial information offers further chances in order to manipulate medical data efficiently and easily. In fact, surgeons encounter multiple challenges handling digital patient data during surgical interventions. Multiple techniques are invested in order to visualize the operative areas, as for example fluoroscopy and ultrasound methods. The latter display certain deficiencies. Therefore, the AR technique can stand for a good candidate in order to project a 3D model of a target organ into the surgeon’s perspective and view field so as to enhance the efficiency and accuracy of the medical intervention intraoperatively. The basic target of this paper is to simulate a generated biomechanical model of the liver organ and visualize its deformations through the use of an AR headset during the open surgery. The proposed approach is validated by the use of acquired CT scans of a human liver organ.
Human motion tracking and system design for dance choreography teaching based on deep learning

This paper studies the dynamic multi-path environment, mainly solves the problems of poor data transmission and high image recognition error in the existing system, and designs and develops a moving image monitoring and tracking tool, which can effectively complete the tasks of illumination balance, target recognition and target tracking. In addition, according to the teaching mode and actual situation of dance choreography in colleges and universities, the actual demand of teaching dance choreography in colleges and universities is analyzed, the teaching system of dance choreography is designed, and the system is tested in strict accordance with the design and development ideas of software engineering. The results show that the choreography teaching system proposed in this paper has certain advantages, which can effectively deal with different problems in the dynamic multipath environment, and also provide effective teaching solutions for choreography teaching in universities. The development and innovation of choreography teaching system can provide reference for other similar fields.
Use of statistical methods in translation and interpreting research

Abstract The study reported on in the article examines the patterns and trends of statistical analysis in translation and interpreting (T&amp;I) research, based on a longitudinal quantitative analysis of more than 3300 research articles sampled from eleven leading T&amp;I journals (2000–2020). This evidence-based review is the first study to provide a systematic mapping of statistical methods used by T&amp;I researchers. Our analyses suggest that (a) about 40% of the articles use statistics, and the use of statistics has been increasing over time; (b) the most frequently used inferential statistical techniques are the t -test, Pearson’s correlation, and chi-squared test; and (c) although the use of statistical methods has become increasingly diversified, about 90% of the methods used are basic-level statistics. We discuss these findings in relation to statistical teaching and learning for relevant stakeholders, especially T&amp;I researchers.
Application of speech software based on mobile network technology in oral English teaching and classroom feedback

Mobile network education is a new education model, and the spoken language teaching software based on mobile app device instant feedback is an indispensable part of the model. The use of the software can effectively improve students’ speaking skills, as it can provide real-time feedback in class to help students correct pronunciation and grammar mistakes in a timely manner, as well as provide speech analysis and assessment, so that students can better understand their oral expression ability. In addition, the software can also increase students’ learning interest and enthusiasm, so that they are more willing to participate in oral English teaching, improve the teaching results and students’ participation. The test results of the software verify the effectiveness of the system, and also prove that the software can be used in oral English teaching and classroom feedback. English teaching based on instant voice feedback of mobile app devices is an innovative way of education, which can better meet the needs of students and adapt to the development needs of today’s society. It will play an increasingly important role in the future education field.
Development of VGG-16 transfer learning framework for geographical landmark recognition

Computer vision mandates the development of landmark recognition paradigms for efficient Image Recognition. In this article, the concept of Visual Geometry Group Network (VGG-16) transfer learning is used to develop an AI model over a geographical landmarks’ dataset. The dataset is a small part of Google Landmarks dataset V2 which originally consists of over 4M images. A VGG-16 model trained on ImageNet dataset is used to transfer knowledge. A positive transfer of knowledge is seen and it was observed that the trained model was a highly generalized model for our dataset. Not only a training accuracy of more than 0.85 is observed but equivalent validation accuracy suggests that the developed model is highly robust with minimal overfitting. A comparison of our proposed approach was made with classical machine learning techniques like KNN (K Nearest Neighbor), Decision Trees, Random Forest, SVM (Support Vector Machines) and a 3 Layered CNN. The results clearly depict that the proposed approach outperforms all other machine learning classifiers in consideration.
An OER on digital historical research on European historical newspapers with the NewsEye platform

In this article, we introduce an Open Education Resource (OER) on digital historical research with historical newspapers,11The URL will be given with the camera-ready version of this paper. intended to give students the means to understand the induced risks in working with large collections of digitised documents, as well as the keys to benefit from the advances of natural language processing over large multilingual collections of European historical newspapers. This resource exploits results of the NewsEye Horizon 2020 research and innovation project. It is part of a set of 7 OERs developed and shared within the Erasmus+ project Digital Methods Platform for Arts and Humanities (DiMPAH).
A new real-time image semantic segmentation framework based on a lightweight deep convolutional encoder-decoder architecture for robotic environment sensing

Background: Image semantic segmentation can be understood as the allocation of a predefined category label to each pixel in the image to achieve the region segmentation of the image. Different categories in the image are identified with different colors. While achieving pixel classification, the position information of pixel points of different categories in the image is retained. Purpose: Due to the influence of background and complex environment, the traditional semantic segmentation methods have low accuracy. To alleviate the above problems, this paper proposes a new real-time image semantic segmentation framework based on a lightweight deep convolutional encoder-decoder architecture for robotic environment sensing. Methodology: This new framework is divided into three stages: encoding stage, decoding stage and dimension reduction stage. In the coding stage, a cross-layer feature map fusion (CLFMF) method is proposed to improve the effect of feature extraction. In the decoding stage, a new lightweight decoder (LD) structure is designed to reduce the number of convolutional layers to speed up model training and prediction. In the dimension reduction stage, the convolution dimension reduction method (CDR) is presented to connect the encoder and decoder layer by layer to enhance the decoder effect. Results: Compared with other state-of-the-art image semantic segmentation methods, we conduct comparison experiments on datasets Cityscapes, SUN RGB-D, CamVid, KITTI. The Category iIoU combined with the proposed method is more than 70%, and the Category IoU is as high as 89.7%. Conclusion: The results reflect that the new method can achieve the better semantic segmentation effect.
A new on-line self-adapting fuzzy controller design using unidimensional input-output with dynamical membership functions

Here, we develop a fuzzy controller using a new online self-adapting design. The objective of this work is to control a nonlinear process by using a one-dimensional input rule variable, instead of error and error variation. The initial limits of the fuzzy logic membership functions are mostly depend on experiments and previous knowledge of the dynamic process behaviors. Generally, the membership function parameters have a significant impact on control signal amplitude and, consequently on the convergence and stability of the controller-plant system. The proposed technique determines the limits of the antecedent membership functions online using the kth and k - 1th outputs of the controlled plant and reference model, respectively. Meanwhile, the limits of the consequent membership functions are calculated using error and error variation. This approach ensures: (i) that the input/output variables have the required fuzzy space, (ii) the controlled plant follows the desired reference model, and (iii) the control signal amplitude is within acceptable limits. Additionally, (iiii) it takes into account the dynamic variability of the process and the existence of an overshoot. The membership function parameters are updated continuously through a self-adapting procedure, ensuring improved control performance. Ultimately, the proposed approach is improved using two nonlinear systems.
Prediction of Breast cancer using integrated machine learning-fuzzy and dimension reduction techniques

In the last two decades, regardless of epidemiological, and clinical studies, the incidence of breast cancer (BC) is still increasing. However, so far, a lot of research has been done in this field to diagnose BC, and some of them have been discussed in the literature section. But still, happening major issues while dealing with fault feature matrix, generated from traditional feature extraction methods. As a result, the complexity of fault classification has raised, which will negatively impact fault identification’s accuracy and effectiveness. Thus, in this research, a novel hybridized machine learning-fuzzy and dimension reduction (MLF-DR) model has been proposed to improve the decision capabilities and efficiency of an ML model. A feature-based class-togetherness fuzzification method has been used for every feature. The novelty of our research work is to find all possibilities between cancerous and non-cancerous cells by implementing a fuzzy inference system (FIS) in the data analysis phase, and DR techniques at preprocessing phase to select the best optimizing features. This research tries to reduce the incidence of BC and prevent needless deaths, thus will probably follow necessary action to perform i.e. (i) FIS to interpret input values; (ii) principal component analysis (PCA), and recursive feature elimination (RFE) to select best features, and (ii) logistic regression (LR) and random forest (RF) models to predict BC with these features. Furthermore, all the experiments have been done on Wisconsin Breast Cancer Dataset (WBCD), freely available on the Kaggle repository using Python programming on Jupyter Notebook version 6.4.3. The key findings of this research are that the LR-PCA (8 components) model can reliably and successfully obtain the defect diagnosis results with 99.1% accuracy, as compared to individual LR and RF models.
Intelligence system for sentiment classification with deep topic embedding using N-gram based topic modeling

Multi-modal information outbreak is consistently increasing in social media. Classification of tweet sentiments using various information modalities will help the recommender systems to achieve success in digital marketing. Moreover, aspect-level sentiment analysis categorizes a target’s sentiment polarity in a specific environment. Using topic modelling in aspect-level sentiment analysis enables the identification of more accurate aspect-based tweet sentiments. The existing sentiment classification techniques used for the development of recommendation systems do not focus on the aspect-based approach modelled using deep learning classifier with temporal analysis on the social media data. Hence, this paper proposes an efficient sentiment classification model that highlights the impact of topic modelling-based word feature embedding for improvising the classification of Twitter sentiments and product reviews based on temporal reasoning and analysis for performing predictive analysis. For tweets context analysis, Latent Dirichlet Allocation based topic modelling is used in this work which generates the topics. For each topic, the sentiment is calculated separately and the topic guided feature expansion is done using Senti-wordnet. Moreover, an extended deep learning classification algorithm called Long Short-Term Memory (LSTM) with word embedding and temporal reasoning(LSTMWTR) is proposed in this paper for improving the classification accuracy. Finally, the labelled data are classified using the existing machine learning algorithms namely Naïve Bayes, Support Vector Machines and also using the deep learning models such as Convolution Neural Network(CNN),LSTM, Recurrent Neural Networks (RNN) and the transformer model namelyBi-directional Encoder Representation from Transformers (BERT),Convolution Bi-directional Recurrent Neural Network (CBRNN) and the proposed deep learning algorithm namelyLSTMWTR. These sentiment classification algorithms have been evaluated with word embedding for tweet sentiment classification and product review classification. The results obtained from this work show that the proposed LSTMWTR algorithm emerges as the highly accurate model for tweet sentiment and product review classification.
What's in a Decade? Transforming Faces Through Time

Abstract How can one visually characterize photographs of people over time? In this work, we describe the Faces Through Time dataset, which contains over a thousand portrait images per decade from the 1880s to the present day. Using our new dataset, we devise a framework for resynthesizing portrait images across time, imagining how a portrait taken during a particular decade might have looked like had it been taken in other decades. Our framework optimizes a family of per‐decade generators that reveal subtle changes that differentiate decades—such as different hairstyles or makeup—while maintaining the identity of the input portrait. Experiments show that our method can more effectively resynthesizing portraits across time compared to state‐of‐the‐art image‐to‐image translation methods, as well as attribute‐based and language‐guided portrait editing models. Our code and data will be available at facesthroughtime.github.io.
Assessment of implementation barriers of blockchain technology in public healthcare: evidences from developing countries

The widespread use of Blockchain technology (BT) in nations that are developing remains in its early stages, necessitating a more comprehensive evaluation using efficient and adaptable approaches. The need for digitalization to boost operational effectiveness is growing in the healthcare sector. Despite BT's potential as a competitive option for the healthcare sector, insufficient research has prevented it being fully utilised. This study intends to identify the main sociological, economical, and infrastructure obstacles to BT adoption in developing nations' public health systems. To accomplish this goal, the study employs a multi-level analysis of blockchain hurdles using hybrid approach. The study's findings provide decision- makers with guidance on how to proceed, as well as insight into implementation challenges.
Covid-19 in Pseudo BH-algebra

In this paper, we introduce a new of a BH-algebra and a new of pseudo BH-algebra. We call this covid-19 of pseudo BH-algebra. Also, we give the concepts of ( BH-algebra, pseudo BH-algebra and covid-19 ). And we study some relationships between them.
Generating and Detecting Face Morphing Using Texture Techniques

Biometric forms major and very effective role nowadays in many fields such as health, reliability, devices, phones, banking, airport security, and others because of its unique characteristics for each person that cannot be replicated in another person. Therefore, most security systems rely and verify biometric properties. Airport security systems rely directly on facial recognition, but these systems may be exposed to attacks by the use of morphing faces in the passport image that allows multiple users to use the same passport. This paper presents a complete system consist of three stage, the first stage generating morphing faces based on edge detection to determine landmark and combine between landmarks to produce morphing. The second stage passing images on to the face recognition system that using Local Binary Pattern to features extraction, the final stage how to detect image bona fide or morph using texture techniques represented by each Local binary pattern and Gray-Level Co-Occurrence Matrix. With the use of the Wasserstein Distance measure, which has not previously been used in this field. The method gave effective results showing the mechanism of reducing morphing attack.
Study of Efficient Cloud Storage Architectures for the Security Environment

Lately, cloud computing has acquired notoriety. Data storage is an essential and beneficial area of concentrate in cloud computing. The term "cloud data storage" describes the process of transferring data to an external, up-to-date storage system. You store data to a far-off database instead of your PC's hard plate or another nearby storage gadget. Your PC and the database are associated over the web. The user's requirements for hardware and software are reduced via cloud storage. The security issues associated with accessing and storing data on cloud storage render the cloud information inconsistent and untrustworthy. Through the internet, the cloud computing provides service with dynamically scalable resources. Users gain from cloud computing services in terms of cost and usability. Security issues must be addressed by cloud computing services when sending sensitive data and important applications to shared and public cloud environments. For the needs of data processing and storage, cloud environments are expanding up significantly. The use of cloud computing has a number of benefits as well as drawbacks for service users' data security. The primary security concerns that exist in cloud computing systems are highlighted in this study. The essential security worries with cloud computing are examined in this paper, alongside a straightforward, secure, and protection saving architecture for between cloud data sharing that depends on an encryption/unscrambling calculation that expects to shield data put away in the cloud from unapproved access.
Genetic Algorithm Based Clustering Optimization A Survey

Genetic algorithm has an important role in improving clustering methods. When it comes to getting into a dataset, one of the most common methods used is clustering. Recent years have seen a rise in the number of articles interested in clustering, which may be attributed to the development of various new fields of application. Most clustering method's performance depends on initial values of parameters or the preprocessing of a dataset, so clustering needs to optimize. The improvement of clustering in two directions, either we improve the parameters or improve the input data. Ga is an evolutionary technique well-known in optimization. This survey deals with the methods that use a genetic algorithm to choose parameter values and input data. This study shows many important performance metrics used to get the optimal result in each research.
Masked Face Recognition Using Convolutional Neural Networks

Since the COVID-19 epidemic's rise in 2020, Cover face recognize achieve advanced significantly in the range of computer vision. Face cover is important to stop or limit the COVID-19 disease's spread due to the global outbreak. Face recognize is among of the most commonly used biometric recognition approach, because it can beutilized for monitoring systems, identity management, security verifying, and a lot of applications. The majority features of faces were hidden by mask, leaving just a quite some, including eyes plus head-region, that’s utilized for recognize. This challenge may reduce the recognition percentage because of the limited area to extract features. Due to the popularity of deep learning to extract and recognize deep features in many research areas especially computer vision,In this work, a covered face recognize system is introduced. utilizing Convolutional neural network (CNN), one of the most widely common deep learning algorithms. The final layer in the CNN architecture, the softmax activation function, was utilized to identify the facial characteristics after they had been extracted using CNN from the masked face's eyes, forehead, and brow regions. In the Study employ the "Extended Yale B database," which has issues with changes in placement and lighting. additionally, they covered faces in Dataset with medical masks. In comparison to other approaches to solving this problem, our strategy showed to be successful and promising with a recognition accuracy for "Extended Yale B" of 95%.