Reviewerly/exHarmony huggingface dataset:
Improving blockchain performance in clinical trials using intelligent optimal transaction traffic control mechanism in smart healthcare applications

Blockchain technology has revolutionized the ways of processing and storing data in terms of reliability and security. Blockchain plays a pivotal role in transferring the processing hurdle from the client–server to a decentralized and secured platform. Blockchain is deemed to be an efficient technology in a forthcoming era that would beneficiate multifarious industries. An issue that becomes a bottleneck for blockchain-based applications is their restricted ability to process in comparison to distributed database systems. In this paper, we present intelligent traffic control using a hybrid model based on the PSO-based optimization algorithm and fuzzy logic in order to improve blockchain performance. The real-time network feedback model is designed and used as an input to control the transaction traffic across the entire network in a robust way without human intervention. In order to evaluate the effectiveness of the designed model, a clinical trial service framework as a test network is implemented on top of Hyperledger Fabric. The case study is further compared with baseline network, network with fuzzy approach, and network with optimized parameter. The experiments show that the proposed model not only enhanced the network by maximizing the network throughput and minimizing the network latency. A smart contract is implemented to automate the transaction flow as per real-time data of network conditions. An open-source blockchain framework, Hyperledger Fabric, is harnessed for implementation of the experiment environment in order to signify the potential of the proposed model. The outcome of this study indicated a remarkable increase in transaction throughput (i.e., 38.5%) and a decrease in transaction latency of 40.5%. Moreover, the proposed model can easily be integrated with other existing blockchain-based performance-enhancing tools.
DEAF-BSL: Deep lEArning Framework for British Sign Language recognition

The recent development of disability studies in academic bodies has expedited the promotion of investigation on disability. With computer-aided tools, communication between the impaired person and someone who does not understand sign language could be accessible. A large number of people across the world are using sign language (e.g., British Sign Language (BSL) , Asian Sign Language (ASL) , Indian Sign Language (ISL) , etc.) with hand gestures for communication. In BSL recognition, the involvement of both hands overlapping each other becomes the main challenge. Moreover, BSL comprises ambiguous signs concerning viewpoint. However, existing traditional techniques seem in-stable, less accurate, and inefficient. In this work, the BSL fingerspelling alphabet recognition problem explores using a Deep learning framework to address the above-mentioned concerns. Convolutional Neural Network (CNN) is employed to detect and recognize for classification of 26 alphabets after being trained on the BSL corpus dataset. The proposed work outperforms the existing works with better precision (6%), recall (4%), and F-measure (5̃%). It reported better results on the BSL corpus dataset and webcam videos. The model achieved better accuracy (98.0%) for a large lexicon of words than previous models (Goh &amp; Holden [ 6 ]: 69.5%, Rambhau [ 9 ]: 79.2%, and Liwicki et al. [ 8 ]: 92.5%). The 3D CNN-based proposal performs robust hand detection, much more accurate sign recognition, more scalability, and less ambiguity in BSL finger-spelling recognition.
A two stream convolutional neural network with bi-directional GRU model to classify dynamic hand gesture

Dynamic hand gesture recognition is still an interesting topic for the computer vision community. A set of feature vectors can represent any hand gesture. A Recurrent Neural Network (RNN) can recognize these feature vectors as a hand gesture that analyzes the temporal and contextual information of the gesture sequence. Thus, we proposed a hybrid deep learning framework to recognize dynamic hand gestures. In the Hybrid model GoogleNet is pipelined with a Bidirectional GRU unit to recognize the dynamic hand gesture. Dynamic hand gestures consist of many frames, and features of each frame need to be extracted to get the temporal and dynamic information of the performed gesture. As RNN takes input as a sequence of feature vectors, we extract features from videos using pretrained GoogleNet. As Gated Recurrent Unit is one of the variants of RNN to classify the sequential data, we created a feature vector that corresponds to each video and passed it to the bidirectional GRU (BGRU) network to classify the gestures. We evaluate our model on four publicly available hand gesture datasets. The proposed method performs well and is comparable with the existing methods. For instance, we achieved 98.6% accuracy on Northwestern University Hand Gesture(NWUHG), 99.6% on SKIG, 99.4% on Cambridge Hand Gesture (CHG) datasets respectively. We performed our experiments on DHG14/28 dataset and achieved an accuracy of 97.8% with 14-gesture classes and 92.1% on 28-gesture classes. DHG14/28 dataset contains skeleton and depth data, and our proposed model used depth data and achieved comparable accuracy.
AI for Detection of Missing Person

Identifying missing people and bringing them back to their families has become a universal issue. Various research publications are examined in this paper. Each of the existing mechanisms has merits and demerits. But the issues related to bringing back missing people have not been perfected 100% Computing technology has evolved in recent years to include a wide range of flavors that can be used in practically every sector. Information plays a critical function in the computing system, notwithstanding the rapid-fire rise of technology. Every day, a considerable number of people, including children, teenagers, mentally challenged people, elderly people with Alzheimer's disease, and others, go missing around the world. In India, more than 500 missing person concerns are estimated to go unaddressed every day. Face recognition technologies have become increasingly important in recent decades. A facial recognition system is a computer application that can recognize or verify a person by analyzing a digital image or a video frame from a video source. Facial feature detection and recognition are extensively used in current world scenarios and technologies. Artificial intelligence, on the other hand, has given solutions to the issues of the ultramodern world. Artificial intelligence (AI) has been developed to help humans and machines communicate more effectively. The proposed mechanism has been successfully implemented to accurately identify a face with a precision of 90% when compared to 59% using KNN and 43% using SVM with PCA.
Site adaptation of global horizontal irradiance from the Copernicus Atmospheric Monitoring Service for radiation using supervised machine learning techniques

Satellite and reanalysis-derived solar products have gained great attention due to the inadequate number of radiometric stations worldwide, however, they are associated with considerable uncertainties. This study deals with the ground-based validation of Global Horizontal Irradiance from CAMS radiation service (GHICAMS) and the application of supervised machine learning algorithms (MLAs) to site-adapt GHICAMS. The validation of GHICAMS against measurements shows significant systematic and dispersion errors for all-sky (nMBE = 4.9% and nRMSE = 15.7%) and cloudy conditions (nMBE = 17.6% and nRMSE = 38.8%). Under clear skies, CAMS performs adequately (nMBE <1% and nRMSE <5%). All MLAs lead to reduced errors for the site-adapted irradiances. MBE is improved by more than 50%, accompanied by significant reductions in RMSE for various solar zenith angles and cloud fractions. The best results are revealed for the tree-based MLAs and especially for Random Forests.
Engineers, Aware! Commercial Tools Disagree on Social Media Sentiment: Analyzing the Sentiment Bias of Four Major Tools

Large commercial sentiment analysis tools are often deployed in software engineering due to their ease of use. However, it is not known how accurate these tools are, and whether the sentiment ratings given by one tool agree with those given by another tool. We use two datasets - (1) NEWS consisting of 5,880 news stories and 60K comments from four social media platforms: Twitter, Instagram, YouTube, and Facebook; and (2) IMDB consisting of 7,500 positive and 7,500 negative movie reviews - to investigate the agreement and bias of four widely used sentiment analysis (SA) tools: Microsoft Azure (MS), IBM Watson, Google Cloud, and Amazon Web Services (AWS). We find that the four tools assign the same sentiment on less than half (48.1%) of the analyzed content. We also find that AWS exhibits neutrality bias in both datasets, Google exhibits bi-polarity bias in the NEWS dataset but neutrality bias in the IMDB dataset, and IBM and MS exhibit no clear bias in the NEWS dataset but have bi-polarity bias in the IMDB dataset. Overall, IBM has the highest accuracy relative to the known ground truth in the IMDB dataset. Findings indicate that psycholinguistic features - especially affect, tone, and use of adjectives - explain why the tools disagree. Engineers are urged caution when implementing SA tools for applications, as the tool selection affects the obtained sentiment labels.
A dual evolutionary bagging for class imbalance learning

A C TBagging, as a commonly-used class imbalance learning method, combines resampling techniques with ensemble learning to provide a strong classifier with high generalization for a skewed dataset.However, integrating different numbers of base classifiers may obtain the same classification performance, called multi-modality.To seek the most compact ensemble structure with the highest accuracy, a dual evolutionary bagging framework composed of inner and outer ensemble models is proposed.In inner ensemble model, three sub-classifiers are built by SVM, MLP and DT, respectively, with the purpose of enhancing the diversity among them.For each sub-dataset, a classifier with the best performance is selected as a base classifier of outer ensemble model.Following that, all optimal combinations of base classifiers is found by a multi-modal genetic algorithm with a niche strategy in terms of their average G-mean.A combination that aggregates the smallest number of base classifiers by the weighted sum forms the final ensemble structure.Experimental results on 40 KEEL benchmark datasets and a practical one of coal burst show that dual ensemble framework proposed in the paper provides the simplest ensemble structure with the best classification accuracy for imbalance datasets and outperforms the state-of-the-art ensemble learning methods.
Measuring Representational Harms in Image Captioning

Previous work has largely considered the fairness of image captioning systems through the underspecified lens of “bias.” In contrast, we present a set of techniques for measuring five types of representational harms, as well as the resulting measurements obtained for two of the most popular image captioning datasets using a state-of-the-art image captioning system. Our goal was not to audit this image captioning system, but rather to develop normatively grounded measurement techniques, in turn providing an opportunity to reflect on the many challenges involved. We propose multiple measurement techniques for each type of harm. We argue that by doing so, we are better able to capture the multi-faceted nature of each type of harm, in turn improving the (collective) validity of the resulting measurements. Throughout, we discuss the assumptions underlying our measurement approach and point out when they do not hold.
HFM: A Hybrid Feature Model Based on Conditional Auto Encoders for Zero-Shot Learning

Zero-Shot Learning (ZSL) is related to training machine learning models capable of classifying or predicting classes (labels) that are not involved in the training set (unseen classes). A well-known problem in Deep Learning (DL) is the requirement for large amount of training data. Zero-Shot learning is a straightforward approach that can be applied to overcome this problem. We propose a Hybrid Feature Model (HFM) based on conditional autoencoders for training a classical machine learning model on pseudo training data generated by two conditional autoencoders (given the semantic space as a condition): (a) the first autoencoder is trained with the visual space concatenated with the semantic space and (b) the second autoencoder is trained with the visual space as an input. Then, the decoders of both autoencoders are fed by the test data of the unseen classes to generate pseudo training data. To classify the unseen classes, the pseudo training data are combined to train a support vector machine. Tests on four different benchmark datasets show that the proposed method shows promising results compared to the current state-of-the-art when it comes to settings for both standard Zero-Shot Learning (ZSL) and Generalized Zero-Shot Learning (GZSL).
Heterogeneous Pseudo-Supervised Learning for Few-shot Person Re-Identification

How to obtain good retrieval performance in the case of few-shot labeled samples is the current research focus of Person Re-Identification. To facilitate formal analysis, we formally put forward the concept of Pseudo-Supervised Learning (PSL) to represent a series of research works based on label generation under few-shot condition. Through extensive investigations, we find that the main problem that needs to be solved of PSL is how we can improve the quality of pseudo-label. To solve this problem, in this work, we proposed a simple yet effective Heterogeneous Pseudo-Supervised Learning (H-PSL) framework based on classical PSL to implement asynchronous match, which boosts the feature expression and then a better label prediction in the following. Specifically, a novel isomer is constructed as the feature extractor and is trained with a much larger amount of pseudo-supervised data, i.e., samples with pseudo-labels. In this way, the isomer obtains advanced feature expression. We then deliberately implement a cross-level asynchronous match mechanism between model and pseudo-supervised data. As a result, the quality of pseudo-label is greatly improved and the feature expression performance also be optimized accordingly. In addition, to make better use of pseudo-supervised data, we also designed a knowledge fusion strategy to integrate the pseudo labels and their confidence which are easily obtained by the base model and isomer. Encouragingly, knowledge fusion strategy further removes the noise-labeled samples from candidate data. We conduct experiments on four popular datasets to fully verify the universality of the proposed method. The experimental results show that the proposed method improves the performance of all compared baseline works.
Research on AUV Energy Saving 3D Path Planning with Mobility Constraints

This paper aims to focus on the path planning problem of AUV in the marine environment. As well as considering the path length and safe obstacle avoidance, ocean currents should not be ignored as the main factor affecting the navigation energy consumption of AUV. At the same time, the path should satisfy the mobility constraint of AUV; otherwise, the path is inaccessible to AUV. For the above problems, this paper presents a path planning algorithm based on an improved particle swarm (EPA-PSO); the fitness function is designed based on path length, energy consumption, and mobility constraints. The updated law of particle velocity and the initialization law of particles are improved, and the possible optimal solutions are stored in the feasible solution set; finally, the optimal solutions are obtained by comparison. The local jumping ability is given to the particle swarm so that the particles can jump out of the local optimal solution. The path planning simulation experiment is compared with the traditional PSO algorithm. The results show that the EPA-PSO algorithm proposed in this paper can be used in the AUV three-dimensional path planning process. It can effectively save energy and make the navigation path of AUV satisfy the requirements of maneuverability. The field experiment was completed in Shanghai, China, and the experiment proved that it was feasible to obtain a path satisfying the maneuverability constraints with optimal energy consumption for the problems studied in this paper.
G-YOLOX: A Lightweight Network for Detecting Vehicle Types

In recent years, vehicle type detection has had an important role in traffic management. A lightweight detection network based on multiscale ghost convolution called G-YOLOX is designed in this paper. It is suitable for practical applications for an embedded device. Specifically, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>3</mn> <mo>×</mo> <mn>3</mn> </math> convolutions and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mn>5</mn> <mo>×</mo> <mn>5</mn> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mn>7</mn> <mo>×</mo> <mn>7</mn> </math> ghost convolutions are combined to fully utilize different feature information. A series of linear transformations was designed to generate ghost feature maps to ensure that the network is lightweight. Moreover, a dataset of images showing different vehicles in a city environment was established. Altogether, 20,000 road scene images were collected, and seven categories of vehicles were identified. Extensive experiments with the benchmark datasets VOC2007 and VOC2012 and with our dataset demonstrate the superiority of the proposed G-YOLOX over the original YOLOX. The proposed G-YOLOX can achieve a nearly invariable mean average precision of 0.5, while the size of the weight file decreased by 40% and the number of parameters decreased by 67% compared to the original YOLOX network.
Fully Bayesian analysis of the relevance vector machine classification for imbalanced data problem

Abstract Relevance Vector Machine (RVM) is a supervised learning algorithm extended from Support Vector Machine based on the Bayesian sparsity model. Relevance Vector Machine classification suffers from theoretical limitations and computational inefficiency mainly because there is no closed‐form solution for the posterior of the weight parameters. We propose two advanced Bayesian approaches for RVM classification, namely the Enhanced RVM and the Reinforced RVM, to perfect the theoretic framework of RVM and extend the algorithm to the imbalanced data problem, which has an arresting skew in data size between classes. First, the Enhanced RVM conducts a strict Bayesian sampling process instead of the approximation method in the original one to remedy its theoretic limitations, especially the non‐convergence of the iterations. Secondly, we conjecture that the hierarchical prior makes the Reinforced RVM achieve consistent estimations of the quantities of interest compared with the non‐consistent estimations of the original RVM. Consistency is necessary for RVM classification since it makes the model more stable and localises the relevant vectors more accurately in the imbalanced data problem. The two‐level prior also renders the Reinforced one competitive in the imbalanced data problem by building the inner connection of parameter dimensions and alloting a more vital relevance to the small class data weight parameter. The theoretic proofs and several numeric studies demonstrate the merits of our two proposed algorithms.
Document-Level Sentiment Analysis Using Attention-Based Bi-Directional Long Short-Term Memory Network and Two-Dimensional Convolutional Neural Network

Due to outstanding feature extraction ability, neural networks have recently achieved great success in sentiment analysis. However, one of the remaining challenges of sentiment analysis is to model long texts to consider the intrinsic relations between two sentences in the semantic meaning of a document. Moreover, most existing methods are not powerful enough to differentiate the importance of different document features. To address these problems, this paper proposes a new neural network model: AttBiLSTM-2DCNN, which entails two perspectives. First, a two-layer, bidirectional long short-term memory (BiLSTM) network is utilized to obtain the sentiment semantics of a document. The first BiLSTM layer learns the sentiment semantic representation from both directions of a sentence, and the second BiLSTM layer is used to encode the intrinsic relations of sentences into the document matrix representation with a feature dimension and a time-step dimension. Second, a two-dimensional convolutional neural network (2DCNN) is employed to obtain more sentiment dependencies between two sentences. Third, we utilize a two-layer attention mechanism to distinguish the importance of words and sentences in the document. Last, to validate the model, we perform an experiment on two public review datasets that are derived from Yelp2015 and IMDB. Accuracy, F1-Measure, and MSE are used as evaluation metrics. The experimental results show that our model can not only capture sentimental relations but also outperform certain state-of-the-art models.
Deep learning with weak annotation from diagnosis reports for detection of multiple head disorders: a prospective, multicentre study

A large training dataset with high-quality annotations is necessary for building an accurate and generalisable deep learning system, which can be difficult and expensive to prepare in medical applications. We present a novel deep-learning-based system, requiring no annotator but weak annotation from a diagnosis report, for accurate and generalisable performance in detecting multiple head disorders from CT scans, including ischaemia, haemorrhage, tumours, and skull fractures.Our system was developed on 104 597 head CT scans from the Chinese PLA General Hospital, with associated textual diagnosis reports. Without expert annotation, we used keyword matching on the reports to automatically generate disorder labels for each scan. The labels were inaccurate because of the unreliable annotator-free strategy and inexact because of scan-level annotation. We proposed RoLo, a novel weakly supervised learning algorithm, with a noise-tolerant mechanism and a multi-instance learning strategy to address these issues. RoLo was tested on retrospective (2357 scans from the Chinese PLA General Hospital), prospective (650 scans from the Chinese PLA General Hospital), cross-centre (1525 scans from the Brain Hospital of Hunan Province), cross-equipment (1484 scans from the Chinese PLA General Hospital), and cross-nation (CQ500 public dataset from India) test datasets. Four radiologists were tested on the prospective test dataset before and after viewing system recommendations to assess whether the system could improve diagnostic performance.The area under the receiver operating characteristic curve for detecting the four disorder types was 0·976 (95% CI 0·976-0·976) for retrospective, 0·975 (0·974-0·976) for prospective, 0·965 (0·964-0·966) for cross-centre, and 0·971 (0·971-0·972) for cross-equipment test datasets, and 0·964 (0·964-0·966) for CQ500 (with only haemorrhage and fracture). The system achieved similar performance to four radiologists and helped to improve sensitivity and specificity by 0·109 (95% CI 0·086-0·131) and 0·022 (0·017-0·026), respectively.Without expert annotated data, our system achieved accurate and generalisable performance for head disorder detection. The system improved the diagnostic performance of radiologists. Because of its accuracy and generalisability, our computer-aided diganostic system could be used in clinical practice to improve the accuracy and efficiency of radiologists in different hospitals.National Key R&D Program of China, National Natural Science Foundation of China, and Beijing Natural Science Foundation.