Reviewerly/exHarmony huggingface dataset:
Deep learning model for deep fake face recognition and detection

Deep Learning is an effective technique and used in various fields of natural language processing, computer vision, image processing and machine vision. Deep fakes uses deep learning technique to synthesis and manipulate image of a person in which human beings cannot distinguish the fake one. By using generative adversarial neural networks (GAN) deep fakes are generated which may threaten the public. Detecting deep fake image content plays a vital role. Many research works have been done in detection of deep fakes in image manipulation. The main issues in the existing techniques are inaccurate, consumption time is high. In this work we implement detecting of deep fake face image analysis using deep learning technique of fisherface using Local Binary Pattern Histogram (FF-LBPH). Fisherface algorithm is used to recognize the face by reduction of the dimension in the face space using LBPH. Then apply DBN with RBM for deep fake detection classifier. The public data sets used in this work are FFHQ, 100K-Faces DFFD, CASIA-WebFace.
Computer Vision-Based Path Planning for Robot Arms in Three-Dimensional Workspaces Using Q-Learning and Neural Networks

Computer vision-based path planning can play a crucial role in numerous technologically driven smart applications. Although various path planning methods have been proposed, limitations, such as unreliable three-dimensional (3D) localization of objects in a workspace, time-consuming computational processes, and limited two-dimensional workspaces, remain. Studies to address these problems have achieved some success, but many of these problems persist. Therefore, in this study, which is an extension of our previous paper, a novel path planning approach that combined computer vision, Q-learning, and neural networks was developed to overcome these limitations. The proposed computer vision-neural network algorithm was fed by two images from two views to obtain accurate spatial coordinates of objects in real time. Next, Q-learning was used to determine a sequence of simple actions: up, down, left, right, backward, and forward, from the start point to the target point in a 3D workspace. Finally, a trained neural network was used to determine a sequence of joint angles according to the identified actions. Simulation and experimental test results revealed that the proposed combination of 3D object detection, an agent-environment interaction in the Q-learning phase, and simple joint angle computation by trained neural networks considerably alleviated the limitations of previous studies.
Breast cancer histopathological images classification based on deep semantic features and gray level co-occurrence matrix

Breast cancer is regarded as the leading killer of women today. The early diagnosis and treatment of breast cancer is the key to improving the survival rate of patients. A method of breast cancer histopathological images recognition based on deep semantic features and gray level co-occurrence matrix (GLCM) features is proposed in this paper. Taking the pre-trained DenseNet201 as the basic model, part of the convolutional layer features of the last dense block are extracted as the deep semantic features, which are then fused with the three-channel GLCM features, and the support vector machine (SVM) is used for classification. For the BreaKHis dataset, we explore the classification problems of magnification specific binary (MSB) classification and magnification independent binary (MIB) classification, and compared the performance with the seven baseline models of AlexNet, VGG16, ResNet50, GoogLeNet, DenseNet201, SqueezeNet and Inception-ResNet-V2. The experimental results show that the method proposed in this paper performs better than the pre-trained baseline models in MSB and MIB classification problems. The highest image-level recognition accuracy of 40×, 100×, 200×, 400× is 96.75%, 95.21%, 96.57%, and 93.15%, respectively. And the highest patient-level recognition accuracy of the four magnifications is 96.33%, 95.26%, 96.09%, and 92.99%, respectively. The image-level and patient-level recognition accuracy for MIB classification is 95.56% and 95.54%, respectively. In addition, the recognition accuracy of the method in this paper is comparable to some state-of-the-art methods.
An Efficient and Robust Fracture Detection in Femur Bones

There are several bones in the body but the femur is especially the important bone in the body which is from the hip to knee. The Red blood cells(RBC) are created because of bone called femur. In this paper we have given a method to know where the bone has broken by the methods of image processing..We will preprocess the image in order to show the interested domain . In this paper, foreground is taken as our interested domain in order to hide the background details. There are many mathematical and morphological operations which are used for this process, by using these methods and operations we highlight the foreground and the objects in the foreground will be highlighted by using edge detection. The support vector machine in the preprocessed image to know where the bone has fractured and where the bone was not fractured
A blockchain-based multisignature approach for supply chain governance: A use case from the Australian beef industry

This paper designed and implemented a blockchain-based multisignature approach to digitally transform supply chain governance in multi-tier food supply chains, particularly in a geographically dispersed beef supply chain. An exploratory case study was utilised to demonstrate the design, implementation, and evaluation of a blockchain-based multisignature approach that was deployed on the Smart Trade Networks (STN) Proof of Authority (PoA) blockchain system for data collection and validation in a beef supply chain context. The multisignature approach was implemented with a use case to track a shipment of 92 cattle and meat products through key events from farm to food service. The use-case deployment records approximately 6000 data points registered on the STN PoA blockchain system. The real-case deployment illustrates the capability of the blockchain-based multisignature approach to digitally improve beef supply chain governance by enabling whole-of-chain transparency and trustworthy information sharing and supports supply chain professionals to have a better understanding of how to unlock blockchain potential for supply chain transformation.
XFUND: A Benchmark Dataset for Multilingual Visually Rich Form Understanding

Multimodal pre-training with text, layout, and image has achieved SOTA performance for visually rich document understanding tasks recently, which demonstrates the great potential for joint learning across different modalities.However, the existed research work has focused only on the English domain while neglecting the importance of multilingual generalization.In this paper, we introduce a human-annotated multilingual form understanding benchmark dataset named XFUND, which includes form understanding samples in 7 languages (Chinese, Japanese, Spanish, French, Italian, German, Portuguese).Meanwhile, we present Lay-outXLM, a multimodal pre-trained model for multilingual document understanding, which aims to bridge the language barriers for visually rich document understanding.Experimental results show that the LayoutXLM model has significantly outperformed the existing SOTA cross-lingual pre-trained models on the XFUND dataset.The XFUND dataset and pre-trained LayoutXLM models have been publicly available at https:// aka.ms/layoutxlm.
Financial supply chain management: A bibliometric analysis for 2006-2022

Supply Chain Finance (SCF) is playing an increasingly important role in operational and financial practices and attracted growing attention from academia and industry alike. However, researchers have not yet been totally able to reach a consensus on the definition of this phenomenon. The study presented in the paper provides an overview of the advancements present in research on Financial Supply Chain Management. Using a bibliometric analysis approach, the paper summarizes the trends of development and the status quo of the Financial Supply Chain Management. The aim is to provide the reader with guidance and a solid conceptual framework for future research.
Specific class center guided deep hashing for cross-modal retrieval

Hashing approaches show excellent retrieval efficiency and low storage usage in search tasks. In general, most existing deep hashing approaches mainly focus on constructing the pairwise similarity matrix by exploiting the supervised information. However, they rarely construct a label network using the labels to guide hash code learning and thus cannot generate accurate hash codes in some cases. To alleviate this issue, a novel supervised hashing model, named Specific Class Center Guided Deep Hashing (SCCGDH), is proposed in this paper. The purpose of SCCGDH is to learn the specific class centers from the neural network and guide the hashing learning of multi-media data. We design three different neural networks: label network, image network and text network. Specifically, the label network outputs the hash codes of the center of each category. The hash codes from the image network and the text network are encouraged to approximate the corresponding specific centers, reducing the intraclass variation of multi-media data. Furthermore, we seek hash codes of different modalities to be consistent by minimizing the inter-modal invariance loss. We integrate three neural networks into a unified end-to-end hashing learning framework. Experimental results on three cross-modal datasets show that our proposed SCCGDH approach can obtain the better performance than other state-of-the-art hashing approaches.
Super-resolution reconstruction of digital rock CT images based on residual attention mechanism

Computer tomography technology is widely used in geological exploration because it is a nondestructive and three-dimensional imaging method that can be integrated with computer simulation. However, the large-scale application of the computer tomography technique is limited by economic costs and time consumption. Therefore, it is challenging and intractable to indicate the pore structure characteristics of rock. To address this issue, a super-resolution reconstruction algorithm based on convolutional neural networks, residual learning, and attention mechanism was proposed to generate super-resolution images in this study. This algorithm was applied to the reconstruction of carbonate rock and sandstone. The performance of two-dimensional image reconstruction was evaluated by quantitative extraction and qualitative visualization. The results from experiments indicate that the built model performs well on different upscaling factors and is superior to the existing super-resolution approaches based on convolutional neural network. Cited as: Shan, L., Bai, X., Liu, C., Feng, Y., Liu, Y., Qi, Y. Super-resolution reconstruction of digital rock CT images based on residual attention mechanism. Advances in Geo-Energy Research, 2022, 6(2): 157-168. https://doi.org/10.46690/ager.2022.02.07
Sentiment analysis of COVID-19 social media data through machine learning

Pandemics are a severe threat to lives in the universe and our universe encounters several pandemics till now. COVID-19 is one of them, which is a viral infectious disease that increased morbidity and mortality worldwide. This has a negative impact on countries’ economies, as well as social and political concerns throughout the world. The growths of social media have witnessed much pandemic-related news and are shared by many groups of people. This social media news was also helpful to analyze the effects of the pandemic clearly. Twitter is one of the social media networks where people shared COVID-19 related news in a wider range. Meanwhile, several approaches have been proposed to analyze the COVID-19 related sentimental analysis. To enhance the accuracy of sentimental analysis, we have proposed a novel approach known as Sentimental Analysis of Twitter social media Data (SATD). Our proposed method is based on five different machine learning models such as Logistic Regression, Random Forest Classifier, Multinomial NB Classifier, Support Vector Machine, and Decision Tree Classifier. These five classifiers possess various advantages and hence the proposed approach effectively classifies the tweets from the Twint. Experimental analyses are made and these classifier models are used to calculate different values such as precision, recall, f1-score, and support. Moreover, the results are also represented as a confusion matrix, accuracy, precision, and receiver operating characteristic (ROC) graphs. From the experimental and discussion section, it is obtained that the accuracy of our proposed classifier model is high.
Deep learning based on ultrasound images assists breast lesion diagnosis in China: a multicenter diagnostic study

Abstract Background Studies on deep learning (DL)-based models in breast ultrasound (US) remain at the early stage due to a lack of large datasets for training and independent test sets for verification. We aimed to develop a DL model for differentiating benign from malignant breast lesions on US using a large multicenter dataset and explore the model’s ability to assist the radiologists. Methods A total of 14,043 US images from 5012 women were prospectively collected from 32 hospitals. To develop the DL model, the patients from 30 hospitals were randomly divided into a training cohort ( n = 4149) and an internal test cohort ( n = 466). The remaining 2 hospitals ( n = 397) were used as the external test cohorts (ETC). We compared the model with the prospective Breast Imaging Reporting and Data System assessment and five radiologists. We also explored the model’s ability to assist the radiologists using two different methods. Results The model demonstrated excellent diagnostic performance with the ETC, with a high area under the receiver operating characteristic curve (AUC, 0.913), sensitivity (88.84%), specificity (83.77%), and accuracy (86.40%). In the comparison set, the AUC was similar to that of the expert ( p = 0.5629) and one experienced radiologist ( p = 0.2112) and significantly higher than that of three inexperienced radiologists ( p &lt; 0.01). After model assistance, the accuracies and specificities of the radiologists were substantially improved without loss in sensitivities. Conclusions The DL model yielded satisfactory predictions in distinguishing benign from malignant breast lesions. The model showed the potential value in improving the diagnosis of breast lesions by radiologists.
From Shielding to Avoidance: Passenger Augmented Reality and the Layout of Virtual Displays for Productivity in Shared Transit

Passengers spend considerable periods of time in shared transit spaces, relying on smartphones and laptops for work. However, these displays are limited in size and ergonomics compared to typical multi-monitor setups used in the office, impairing productivity. Augmented Reality (AR) headsets could provide large, flexible virtual workspaces during travel, enabling passengers to work more efficiently. This paper investigates the factors affecting how passengers choose to layout virtual displays in car, train, subway and plane environments, studying the affordances of each mode of transport and the presence of others. Results from our experiment showed: significant usage of the physical environment to align displays; strong social effects meant avoiding placing displays over other passengers or their belongings; and use of displays for shielding oneself from others. Our findings show the unique challenges posed by the mode of transport and presence of others on the use of AR for mobile productivity in the future.
Practical use of cloud services for organization of future specialists professional training

The article is devoted to the peculiarities of the practical use of cloud services for the organization of qualitative professional training of future specialists. It is established that in order to implement state policy, there is an essential need for using various ICT, in particular cloud services, which are not only economically acceptable in the new educational environment, but also a powerful tools of obtaining new knowledge, skills and abilities.The advantages and disadvantages of using cloud services in the educational process of higher education are substantiated; the examples discuss the methods of using cloud services in the process of studying fundamental disciplines. The object of the study is the professional training of students in higher education institutions. The subject of research is the process of organizing professional training of future specialists with the use of cloud services. To achieve the set goals, a set of general scientific (analysis, synthesis, comparison) and specific scientific (bibliographic, problem-based) was used. Observation and conversation manipulation allowed to highlight the advantages and disadvantages of using cloud services and draw conclusions from the problem under investigation.The foreign experience of using cloud services has been researched and the features of the application of traditional and distance technology training abroad have been determined.It describes the use of the blog as a media-educational technology during the advent of pedagogical practice. The methods of using cloud-based services on the example of creation of a distance course “Linear algebra and analytic geometry” are considered.The prospects of research, which consist in getting acquainted with cloud technologies of the humanitarian profile future specialists at the second higher education, are determined. It has been established that the practical application of cloud technologies in the educational process will promote more qualitative and progressive learning; the formation of a close interaction between the teacher and student; development of professional skills and abilities of independent work.
General multivariate arctangent function activated neural network approximations

Here we expose multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or \(\mathbb{R}^{N}\), \(N\in \mathbb{N}\), by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We treat also the case of approximation by iterated operators of the last four types. These approximations are derived by establishing multidimensional Jackson type inequalities involving the multivariate modulus of continuity of the engaged function or its high order Frechet derivatives.&#x0D; Our multivariate operators are defined by using a multidimensional density function induced by the arctangent function. The approximations are pointwise and uniform. The related feed-forward neural network is with one hidden layer.
A unified low-order information-theoretic feature selection framework for multi-label learning

The approximation of low-order information-theoretic terms for feature selection approaches has achieved success in addressing high-dimensional multi-label data. However, three critical issues exist in such type of approaches: (1) existing approaches are devised based on single heuristic variable correlation assumption, which biases towards some specific scene; (2) high-order variable correlations are ignored by cumulative summation low-order information-theoretic terms; (3) abundant approaches confuse researchers to devise and utilize appropriate approaches. To address these issues, two types of probability distribution assumption in terms of candidate features and labels are derived based on low-order variable correlations. Afterwords, clearing up all information-theoretic terms, we propose a unified feature selection framework including three low-order information-theoretic terms for multi-label learning named Selected Terms of Feature Selection (STFS). STFS contains high-order variable correlations in the form of low-order information-theoretic terms. Furthermore, many previous multi-label feature selection approaches can be reduced to special forms of STFS. Finally, extensive experiments conducted on twelve benchmark data sets in comparison to seven state-of-the-art approaches demonstrate the classification superiority of STFS.
GCNDepth: Self-supervised monocular depth estimation based on graph convolutional network

Depth estimation is a challenging task of 3D reconstruction to enhance the accuracy sensing of environment awareness. This work brings a new solution with improvements, which increases the quantitative and qualitative understanding of depth maps compared to existing methods. Recently, convolutional neural networks (CNN) have demonstrated their extraordinary ability to estimate depth maps from monocular videos. However, traditional CNN does not support a topological structure, and they can work only on regular image regions with determined sizes and weights. On the other hand, graph convolutional networks (GCN) can handle the convolution of non-Euclidean data, and they can be applied to irregular image regions within a topological structure. Therefore, to preserve object geometric appearances and objects locations in the scene, in this work, we aim to exploit GCN for a self-supervised monocular depth estimation model. Our model consists of two parallel auto-encoder networks: the first is an auto-encoder that will depend on ResNet-50 and extract the feature from the input image and on multi-scale GCN to estimate the depth map. In turn, the second network will be used to estimate the ego-motion vector (i.e., 3D pose) between two consecutive frames based on ResNet-18. The estimated 3D pose and depth map will be used to construct the target image. A combination of loss functions related to photometric, reprojection, and smoothness is used to cope with bad depth prediction and preserve the discontinuities of the objects. Our method and performance are improved quantitatively and qualitatively. In particular, our method provided comparable and promising results with a high prediction accuracy of 89% on the publicly available KITTI dataset. Our method also offers 40% reduction in the number of trainable parameters compared to the state of the art solutions.In addition, we tested our trained model with Make3D dataset to evaluate the trained model on a new dataset with low resolution images. The source code is publicly available at (https://github.com/ArminMasoumian/GCNDepth.git)