Reviewerly/exHarmony huggingface dataset:
ABAFOR: A Blockchain-based Privacy-Preserving Architecture for Efficient Contact Tracing and GIS Analysis

The Coronavirus disease 2019 has manifested into a global pandemic spreading into almost all the countries and territories in the world. Contact tracing, followed by testing and isolation, have been identified as important tools in containing the proliferation of the disease. Because of manual contact tracing limitations, smartphone apps for digital contact tracing have been deployed by authorities in multiple countries. However, many of these apps have faced criticism because of interoperability, privacy and security issues. This paper proposes an open architecture based on blockchain technology that addresses some of these criticisms. It enables interoperability through a publicly-readable consortium blockchain database, preserving privacy by distributing users’ partial identities among multiple decentralised authorities and providing security through digital signature and asymmetric key encryption. In addition, the architecture provides a fast proof-of-authority based consensus algorithm using reputation as a stake to add and validate blocks on the blockchain efficiently and a swift contact tracing algorithm using Spatio-temporal and key-valued databases.
Efficient approximations of the fisher matrix in neural networks using kronecker product singular value decomposition

We design four novel approximations of the Fisher Information Matrix (FIM) that plays a central role in natural gradient descent methods for neural networks. The newly proposed approximations are aimed at improving Martens and Grosse’s Kronecker-factored block diagonal (KFAC) one. They rely on a direct minimization problem, the solution of which can be computed via the Kronecker product singular value decomposition technique. Experimental results on the three standard deep auto-encoder benchmarks showed that they provide more accurate approximations to the FIM. Furthermore, they outperform KFAC and state-of-the-art first-order methods in terms of optimization speed.
Follow-Up of Robotic Mitral Valve Repair: A Single Tertiary Institution Experience in China

Surgical treatment of mitral valve diseases has become minimally invasive. This study analyzed the follow-up results of patients after mitral valve repairs (MVRep) using the da Vinci robot. The clinical data of patients who underwent minimally invasive MVRep using the da Vinci robot between January 2016 and June 2021 and completed follow-ups were prospectively collected. All operations were performed by the same surgeon and assistants. The data of a total of 120 patients were available for analysis, including 78 males (65%) and 42 females aged 49.9 ± 12.1 years (range, 19–73 years). Among them, there were 30 cases (25%) of mitral valve prolapse, 87 cases (72.5%) of mitral regurgitation, and 40 cases of combined tricuspid regurgitation. Edwards Physio II annuloplasty rings were implanted intraoperatively, followed by continuous sutures. The intraoperative cardiopulmonary bypass time was 152.32 ± 45.77 min, and the aortic occlusion time was 95.13 ± 5.64 min. After surgery, patients were followed up regularly with echocardiography with a follow-up period of 3–57 months postoperatively. One patient died in the early stage, and five patients required sternotomy due to postoperative bleeding. Follow-up transesophageal echocardiography showed that the end-systolic diameter, end-diastolic diameter, and ejection fraction of the left ventricular all improved after surgery. Among Chinese patients, MVRep using the da Vinci robot is a safe and effective surgical approach.
Shadow Detection for Remote Sensing Images

Shadow detection of remote sensing images is an essential work, as the presence of shadow always reduce the robustness of computer vision algorithms such as image segmentation, object recognition, target tracking and feature extraction. In this paper, a shadow detection method based on orthogonal decomposition is proposed for remote sensing images. We first decompose input image into an illumination invariant component and an illumination component with pixel-wise orthogonal decomposition. Then, we get non-shadow illumination component by taking advantage of intrinsic characteristic that the pixels in same object share the same illumination invariant component whether in shadow area or not. Finally, we generate shadow mask by analyzing the attenuation of illumination component in shadow area. The proposed method is compared to several representative methods on the public dataset, and results show the effectiveness and robustness of proposed method.
Automatic Identification of High-Impact Bug Report by Product and Test Code Quality

Bug reports are submitted by the software stakeholders to foster the location and elimination of bugs. However, in large-scale software systems, it may be impossible to track and solve every bug, and thus developers should pay more attention to High-Impact Bugs (HIBs). Previous studies analyzed textual descriptions to automatically identify HIBs, but they ignored the quality of code, which may also indicate the cause of HIBs. To address this issue, we integrate the features reflecting the quality of production (i.e. CK metrics) and test code (i.e. test smells) into our textual similarity based model to identify HIBs. Our model outperforms the compared baseline by up to 39% in terms of AUC-ROC and 64% in terms of F-Measure. Then, we explain the behavior of our model by using SHAP to calculate the importance of each feature, and we apply case studies to empirically demonstrate the relationship between the most important features and HIB. The results show that several test smells (e.g. Assertion Roulette, Conditional Test Logic, Duplicate Assert, Sleepy Test) and product metrics (e.g. NOC, LCC, PF, and ProF) have important contributions to HIB identification.
Crypto Currency Market Price Prediction using Data Science Technique

Cryptocurrency is commonly called as digital currency where the coin ownership records are stored in an electronic ledger existing in a form of a computerized database using strong cryptography approach which is used to maintain the creation and updation of an addition coin in a market, it is also used to check and maintain the current ownerships of the coins. Nowadays cryptocurrency is used in largescale and there is a sudden rise or decrease in their share and it is difficult to predict the price of the crypto currency. In this project a machine learning model is built to predict the price of crypto currency. The application of data science process is applied for getting the better model for predicting the outcome. Variable identification and data understanding is the main process in building the successful model. Different machine learning algorithms are applied on the pre-processed data and the accuracy are compared to see which algorithm performed better other performance metrics like precision, recall, scores are also taken in consideration for evaluating the model. The machine learning model is used to predict the crypto currency outcome.
Attention Awareness Multiple Instance Neural Network

Multiple instance learning is qualified for many pattern recognition tasks with weakly annotated data. The combination of artificial neural network and multiple instance learning offers an end-to-end solution and has been widely utilized. However, challenges remain in two-folds. Firstly, current MIL pooling operators are usually pre-defined and lack flexibility to mine key instances. Secondly, in current solutions, the bag-level representation can be inaccurate or inaccessible. To this end, we propose an attention awareness multiple instance neural network framework in this paper. It consists of an instance-level classifier, a trainable MIL pooling operator based on spatial attention and a bag-level classification layer. Exhaustive experiments on a series of pattern recognition tasks demonstrate that our framework outperforms many state-of-the-art MIL methods and validates the effectiveness of our proposed attention MIL pooling operators.
Configuring CNN architectures for performance

Machine learning has exhibited significant advances through the development and application of new deep learning methods. Convolutional neural networks (CNNs) in particular are a powerful tool for object recognition in imagery and are widely used in for computer vision applications. Implementing a CNN solution involves decisions about the parameters defining the architecture, including the convolutional filter window size and number of filters, the size of the latent space, and the number of hidden layers. Generally, developers have chosen these parameters by relying on heuristics or empirical investigations. In this study, we build on previous research to understand the trade-offs associated with these design choices for a CNN. The approach explicitly models the performance, as measured by the correct classification rate, and the cost, as measured by computer times for training and testing. We develop a performance model that captures these measures as a function of the design parameters and can guide developers in assessing the tradeoffs.
Deep Face-Swap Model Combining Attention Mechanism and CycleGAN

Abstract Deep face swapping is a hot field in computer vision, which uses deep learning technology to replace the face in the original image while keeping other contents unchanged. To solve the problem of discontinuities across the blending boundaries in existing mainstream deepfake methods, we propose a face swapping model combining attention mechanism and CycleGAN. Firstly, a generator incorporating the attention module is used to obtain the generated graph. Then, the discriminator based on PatchGAN is used to discriminate if the graph is synthesized. Finally, the least square loss and SmoothL1 loss are used to improve the loss function. Experimental results manifest that our method reduces the difference by 0.43 in facial region and increases the similarity by 0.56 in background region. The model we proposed can improve the quality of face swapping images effectively, and enrich the existing face swapping methods, which has great significance for both generation and detection of deep face swapping research.
ANALYSIS OF EMPLOYMENT SENTIMENT IN THE INDONESIAN TELEMATICS FIELD USE MULTINOMIAL NAIVE BAYES AND VECTOR SPACE MODEL

Indonesia in 2030 experienced a demographic bonus in the sense that Indonesia would have far more labor supply than in previous years. Then there is a discourse that this 4.0 industrial revolution will replace a lot of work, especially low-skilled work or does not require special skills and rough jobs replaced by machinery and artificial intelligent (AI). To obtain the value of the percentage of positive, negative and neutral sentiments from the public regarding the impact of the industrial revolution 4 against labor and employment on online news media sites and social media Twitter, the authors conducted a study "analysis of employment sentiment in Indonesian telematics using multinomial naïve bayes. " The author uses the preprocessing stages including the case folding, tokenizing, stopword, and stemming. Then weighting with Term Frequency - Invers Document Frequency (TF-IDF). After that the classification stage was done using the multinomial Naive Bayes Classifier method and compare it with the Vector Space model classification. The evaluation used is the Confusion Matrix evaluation method. This study produced an evaluation value in the multinomial method of Naïve Bayes for news data to produce an accuracy of 81.75%, average precision 82.77%, and the average recall of 78.15%. Whereas with the Vector Space model method for news data produces an accuracy of 67.88%, average precision 65.59%, and the average recall of 70.56%. On Twitter data with the Multinomial Naïve Bayes method resulted in an accuracy of 88.80%, average precision 93.75%, and the average recall of 74.44%. On Twitter data with the Vector Space Model method resulted in 85.60% accuracy, average precision 76.44% and average recall of 86.07%.
CCC-Quality2: Cross-Cloud Service Composition Based on Quality of Clouds and Web Services for Business Applications

Composition of web services is a promising approach whenever it is not possible to satisfy the requirements with a single service. Combining the services deployed in a single cloud prevents to utilize the benefits offered by the other clouds. Hence, it is beneficial to compose the required services available in multiple clouds. The web services providing the similar functionality differ in their quality of service (QoS). The characteristics of the cloud platform where these services are deployed also have an impact on the quality of the services. Therefore, web services must be chosen based on their QoS as well as QoS of the deployed cloud to improve the QoS of the composite service. Towards this objective, it is proposed to compose services deployed in different clouds platforms based on QoS parameters of web services as well as cloud platforms. The proposed approach maximizes the satisfaction level offered by the composition in terms of QoS. The proposed method has been experimented with 2507 web services of QWS dataset deployed in 10 different cloud platforms. The impact on increasing the number of services deployed in each cloud as well as the number of cloud platforms available on the satisfaction level of web service selection has been studied.
Value Refinement Network (VRN)

In robotic tasks, we encounter the unique strengths of (1) reinforcement learning (RL) that can handle high-dimensional observations as well as unknown, complex dynamics and (2) planning that can handle sparse and delayed rewards given a dynamics model. Combining these strengths of RL and planning, we propose the Value Refinement Network (VRN), in this work. Our VRN is an RL-trained neural network architecture that learns to locally refine an initial (value-based) plan in a simplified (2D) problem abstraction based on detailed local sensory observations. We evaluate the VRN on simulated robotic (navigation) tasks and demonstrate that it can successfully refine sub-optimal plans to match the performance of more costly planning in the non-simplified problem. Furthermore, in a dynamic environment, the VRN still enables high task completion without global re-planning.
Research Status and Development Trend of UAV Path Planning Algorithms

Abstract This paper introduces the basic concepts of UAV track planning, the relationship between track planning and algorithms, and focuses on the analysis and induction of several commonly used algorithms at home and abroad in recent years. The advantages and disadvantages of current path planning algorithms in different application scenarios are summarized. Finally, the future development trend of UAV path planning algorithm is prospected, and it is pointed out that real-time online path planning algorithm research, multi-UAV swarm planning algorithm, perfect path planning problem model and algorithm hybrid optimization are the next research trends.
Pronunciation Dictionary-Free Multilingual Speech Synthesis by Combining Unsupervised and Supervised Phonetic Representations

This paper proposes a multilingual speech synthesis method which combines unsupervised phonetic representations (UPR) and supervised phonetic representations (SPR) to avoid reliance on the pronunciation dictionaries of target languages. In this method, a pretrained wav2vec 2.0 model is adopted to extract UPRs and a language-independent automatic speech recognition (LI-ASR) model is built with a connectionist temporal classification (CTC) loss to extract segment-level SPRs from the audio data of target languages. Then, an acoustic model is designed, which first predicts UPRs and SPRs from texts separately and then combines the predicted UPRs and SPRs to generate mel-spectrograms. The results of our experiments on six languages show that the proposed method outperformed the methods that directly predicted mel-spectrograms from character or phoneme sequences and the ablated models that utilized only UPRs or SPRs.
Exploring the Relationship between Opportunity and Self-Control in Youth Exposure to and Sharing of Online Hate Content

The rise of the Internet has dramatically increased the degree to which youth may be exposed to online hate content, and simplified the process of sharing this content with others. Viewing messages that contain hate speech or language vilifying others can increase an individual’s risk of radicalization to extremist views and the acceptance of violent ideologies. Researchers have begun to explore the risk factors for exposure to such content, with prior studies demonstrating a relationship between low self-control and online activities being important correlates. Few studies have utilized youth samples to assess these relationships, or explored the voluntary consumption and sharing of content. This study attempts to address this gap in the literature using self-report responses provided by a sample of 1,193 youths in South Australia. A series of quantitative models are estimated assessing the relationships between self-control, opportunities to view content using both on and off-line measures, and four dependent variables related to exposure to or sharing of hate content. The implications of this analysis for our understanding of the utility of criminological theory to radicalization and countering violent extremism are discussed in detail.
Report On Image Denoising for Gaussian Noise Affected Images

In this paper, owing to the random variance of pixel values, salt and pepper noise and gaussian noise appeared in the images are of varying standard. For denoising these images, it’s critical to employ a variety of filtering techniques. Advanced images can be a slanted image with a range of noise. Poisson noise, Gaussian noise, etc. Filters such as Median filters and Weiner filters have been proposed to eliminate noise from pepper images in order to achieve substantial performance. Using show parameters such as Mean Square Error, Root Mean Square Error, Peak Signal to Noise Ratio and this paper compares various noise-removal filters. The wiener filter is shown to be the best filter for eliminating noise from pepper images in this paper. We utilized the MATLAB software for simulating the outcome.
Exponential kernelized feature map Theil-Sen regression-based deep belief neural learning classifier for drift detection with data stream

Rapid expansion in technological advancement leads to exponential growth in the volume of data generated.Information shared by the people across the world through the Internet and also the data from hardware technologies such as sensors, mobile etc. leads to the growth of digital data.The data generated are dynamic in nature and is referred to as streaming data or data streams [1].Data streams change over time, making it difficult to analyze large amounts of data in a distributed environment.The data streams are potentially large in size and thus it is not possible to process various machine learning techniques and approaches.
Noise reduction and mammography image segmentation optimization with novel QIMFT-SSA method

Breast cancer is one of the most dreaded diseases that affects women worldwide and has led to many deaths. Early detection of breast masses prolongs life expectancy in women and hence the development of an automated system for breast masses supports radiologists for accurate diagnosis. In fact, providing an optimal approach with the highest speed and more accuracy is an approach provided by computer-aided design techniques to determine the exact area of breast tumors to use a decision support management system as an assistant to physicians. This study proposes an optimal approach to noise reduction in mammographic images and to identify salt and pepper, Gaussian, Poisson and impact noises to determine the exact mass detection operation after these noise reduction. It therefore offers a method for noise reduction operations called Quantum Inverse MFT Filtering and a method for precision mass segmentation called the Optimal Social Spider Algorithm (SSA) in mammographic images. The hybrid approach called QIMFT-SSA is evaluated in terms of criteria compared to previous methods such as peak Signal-to-Noise Ratio (PSNR) and Mean-Squared Error (MSE) in noise reduction and accuracy of detection for mass area recognition. The proposed method presents more performance of noise reduction and segmentation in comparison to state-of-arts methods. supported the work.
Index of Physical activity and Fall Efficacy scale classification through biomechanical signals and Machine Learning.

The rapid increase of the elderly population and chronic diseases have augmented disability in today's world. This situation has led researchers and engineers to create tools and technologies that allow health caregivers, physical trainers, and health policymakers to understand, measure, and treat people with disabilities. Nowadays, artificial intelligence techniques have been applied to improve the performance of these technologies. This article shows the development of a novel classifier that utilizes Machine Learning (ML) algorithms and biomechanical signals to predict a subject's International Physical Activity Questionnaire (IPAQ) and Falls Efficacy Scale (FES). Three ML algorithms were applied K-Nearest Neighbors (KNN), Decision tree, and Support Vector Machine (SVM). Results show the accuracy of classification over 95%, 99%, and 89%, respectively, and validate the correlation between qualitative scales and biomechanical responses in balance training. This classifier poses as an innovative tool to help professionals adjust and improve physical training programs.
Robust fused hypergraph neural networks for multi-label classification

Deep neural networks have been adopted in multi-label classification for their excellent performance, however, existing methods fail to comprehensively utilize the high-order correlations between instances and the high-order correlations between labels, and these methods are difficult to deal with label noise effectively. We propose a novel end-to-end deep framework named Robust Fused Hypergraph Neural Networks for Multi-Label Classification (RFHNN), which can effectively utilize the two kinds of high-order correlations and adopt them to mitigate the impact of label noise. In RFHNN, Hypergraph Neural Networks (HNNs) are adopted to mine and utilize the high-order correlations of the instances in the feature space and the label space respectively. The high-order correlations of the instances can not only improve the accuracy of the classification and the discrimination of the proposed model, but also lay the foundation for the subsequent noise correction module. Meanwhile, a hypergraph construction method based on the Apriori algorithm is proposed to realize Hypergraph Neural Networks (HNNs), which can mine robust second-order and high-order label correlations effectively. Effective classifiers are learned based on the correlations between the labels, which will not only improve the accuracy of the model, but can also enhance the subsequent noise correction module. In addition, we have designed a noise correction module in the networks. With the help of the high-order correlations among the instances and the effective classifier, the framework can effectively correct the noise and improve the robustness of the model. Extensive experimental results on datasets demonstrate that our proposed approach is better than the state-of-the-art multi-label classification algorithms. When dealing with the multi-label training datasets with noise in the label space, our proposed method also has great performance.