Reviewerly/exHarmony huggingface dataset:
Clothing Image Classification with a Dragonfly Algorithm Optimised Online Sequential Extreme Learning Machine

This study proposes a solution for the issue of the low classification accuracy of clothing images. Using Fashion-MNIST as the clothing image dataset, we propose a clothing image classification technology based on an online sequential extreme learning machine (OSELM) optimised by the dragonfly algorithm (DA). First, we transform the Fashion-MNIST dataset into a data set that we extract from the corresponding grey image. Then, considering that the input weight and hidden layer bias of an OSELM are generated randomly, a DA is proposed to optimise the input weight and hidden layer bias of the OSELM to reduce the influence of random generation on the classification results. Finally, the optimised OSELM is applied to the clothing image classification. Compared to the other seven types of classification algorithms, the proposed clothing image classification model with the DA-optimised OSELM reached 93.98% accuracy when it contained 350 hidden nodes. Its performance was superior to other algorithms that were configured with the same number of hidden nodes. From a stability analysis of the box-plot, it was found that there were no outliers exhibited by the DA-OSELM model, whereas some other models had outliers or had lower stability compared to the model proposed, thereby validating the efficacy of the solution proposed.
Making marine image data FAIR

Underwater images are used to explore and monitor ocean habitats, generating huge datasets with unusual data characteristics that preclude traditional data management strategies. Due to the lack of universally adopted data standards, image data collected from the marine environment are increasing in heterogeneity, preventing objective comparison. The extraction of actionable information thus remains challenging, particularly for researchers not directly involved with the image data collection. Standardized formats and procedures are needed to enable sustainable image analysis and processing tools, as are solutions for image publication in long-term repositories to ascertain reuse of data. The FAIR principles (Findable, Accessible, Interoperable, Reusable) provide a framework for such data management goals. We propose the use of image FAIR Digital Objects (iFDOs) and present an infrastructure environment to create and exploit such FAIR digital objects. We show how these iFDOs can be created, validated, managed and stored, and which data associated with imagery should be curated. The goal is to reduce image management overheads while simultaneously creating visibility for image acquisition and publication efforts.
Low-Light Enhancement Using a Plug-and-Play Retinex Model With Shrinkage Mapping for Illumination Estimation

Low-light photography conditions degrade image quality. This study proposes a novel Retinex-based low-light enhancement method to correctly decompose an input image into reflectance and illumination. Subsequently, we can improve the viewing experience by adjusting the illumination using intensity and contrast enhancement. Because image decomposition is a highly ill-posed problem, constraints must be properly imposed on the optimization framework. To meet the criteria of ideal Retinex decomposition, we design a nonconvex Lp norm and apply shrinkage mapping to the illumination layer. In addition, edge-preserving filters are introduced using the plug-and-play technique to improve illumination. Pixel-wise weights based on variance and image gradients are adopted to suppress noise and preserve details in the reflectance layer. We choose the alternating direction method of multipliers (ADMM) to solve the problem efficiently. Experimental results on several challenging low-light datasets show that our proposed method can more effectively enhance image brightness as compared with state-of-the-art methods. In addition to subjective observations, the proposed method also achieved competitive performance in objective image quality assessments.
SVM-SMO-SGD: A hybrid-parallel support vector machine algorithm using sequential minimal optimization with stochastic gradient descent

The Support Vector Machine (SVM) method is one of the popular machine learning algorithms as it gives high accuracy. However, like most machine learning algorithms, the resource consumption of the SVM algorithm in terms of time and memory increases linearly as the dataset grows. In this study, a parallel-hybrid algorithm that combines SVM, Sequential Minimal Optimization (SMO) with Stochastic Gradient Descent (SGD) methods have been proposed to optimize the calculation of the weight costs. The performance of the proposed SVM-SMO-SGD algorithm was compared with classical SMO and Compute Unified Device Architecture (CUDA) based approaches on the well-known datasets (i.e., Diabetes, Healthcare Stroke Prediction, Adults) with 520, 5110, and 32,560 samples, respectively. According to the results, Sequential SVM-SMO-SGD is 3.81 times faster in terms of time, and 1.04 times more efficient RAM consumption than the classical SMO algorithm. The parallel SVM-SMO-SGD algorithm, on the other hand, is 75.47 times faster than the classical SMO algorithm in terms of time. It is also 1.9 times more efficient in RAM consumption. The overall classification accuracy of all algorithms is 87% in the Diabetes dataset, 95% in the Healthcare Stroke Prediction dataset, and 82% in the Adults dataset.
(In)visible moderation: A digital ethnography of marginalized users and content moderation on Twitch and Reddit

Research suggests that marginalized social media users face disproportionate content moderation and removal. However, when content is removed or accounts suspended, the processes governing content moderation are largely invisible, making assessing content moderation bias difficult. To study this bias, we conducted a digital ethnography of marginalized users on Reddit’s /r/FTM subreddit and Twitch’s “Just Chatting” and “Pools, Hot Tubs, and Beaches” categories, observing content moderation visibility in real time. We found that on Reddit, a text-based platform, platform tools make content moderation practices invisible to users, but moderators make their practices visible through communication with users. Yet on Twitch, a live chat and streaming platform, content moderation practices are visible in channel live chats, “unban appeal” streams, and “back from my ban” streams. Our ethnography shows how content moderation visibility differs in important ways between social media platforms, harming those who must see offensive content, and at other times, allowing for increased platform accountability.
Ensemble learning using three-way density-sensitive spectral clustering

As one popular clustering algorithm in the last few years, spectral clustering is advantageous over most existing clustering algorithms. Although spectral clustering can perform well in many instances, the algorithm still has some problems. The clusters obtained by spectral clustering have crisp boundaries, which cannot reflect the fact that one cluster may not have a well-defined boundary in the real situations. Furthermore, the frequently-used distance measures in spectral clustering cannot satisfy both global and local consistency, especially for the data with multi-scale. In order to address the above limitations, we firstly present a three-way density-sensitive spectral clustering algorithm, which uses the core region and the fringe region to represent a cluster. In the proposed algorithm, we use density-sensitive distance to produce a similarity matrix, which can well capture the real data structures. An overlap clustering is introduced to obtain the upper bound (unions of the core regions and the fringe regions) of each cluster and perturbation analysis is applied to separate the core regions from the upper bounds. The fringe region of the specific cluster is the differences between the upper bound and the core region. Because a single clustering algorithm cannot always achieve a good clustering result, we develop an improved ensemble three-way spectral clustering algorithm based on ensemble strategy. The proposed ensemble algorithm randomly extracts feature subset of sample and uses the three-way density-sensitive clustering algorithm to obtain the diverse base clustering results. Based on the base clustering results, voting method is used to generate a three-way clustering result. The experimental results show that the three-way density-sensitive clustering algorithm can well explain the data structure and maintain a good clustering performance at the same time, and the ensemble three-way density-sensitive spectral clustering can improve the robustness and stability of clustering results.
A Conceptual Investigation on the Image Processing using Artificial Intelligence and Tensor Flow Models through Correlation Analysis

Artificial intelligence plays a vital role in image processing and tensor flow models. Due to increasing demand of generating data from image as well as to make distinction between images the necessity and relevance of these two topics are also growing. Image classification and processing is a recent trend where traffic lights and other objects are identified by Machine Learning (ML) and Deep Learning (DL) technologies. These technologies are further implemented inside self-driven vehicles for developing autonomous processing&#x0027;s and information transfer. However, an accuracy and speed of detection process is a concern in today&#x0027;s world. Thus, this study paper provides an insight into image processing accuracy by &#x201C;single-stage object detection model&#x201D; or SSD MobileNetV2 and &#x201C;Regional-based Convolutional Neural Network&#x201D; or RCNN. Pearson correlation analysis and mean was obtained using the input data. The output data were further analysed and discussed. Findings suggested that RCNN is 80.90&#x0025; accurate when it is allowed to detect traffic lights present at 11.9 metres apart; whereas SSD MobileNet V2 has shown 71.8&#x0025; of accuracy when the traffic light was detected present at the same distance. On average, the RCNN shows higher accuracy than SSD MobileNet V2. The discussion found that RCNN is more accurate and slower than SSD MobileNetV2.
Blockchain-envisioned access control for internet of things applications: a comprehensive survey and future directions

With rapid advancements in the technology, almost all the devices around are becoming smart and contribute to the Internet of Things (IoT) network. When a new IoT device is added to the network, it is important to verify the authenticity of the device before allowing it to communicate with the network. Hence, access control is a crucial security mechanism that allows only the authenticated node to become the part of the network. An access control mechanism also supports confidentiality, by establishing a session key that accomplishes secure communications in open public channels. Recently, blockchain has been implemented in access control protocols to provide a better security mechanism. The foundation of this survey article is laid on IoT, where a detailed description on IoT, its architecture and applications is provided. Further, various security challenges and issues, security attacks possible in IoT and their countermeasures are also provided. We emphasize on the blockchain technology and its evolution in IoT. A detailed description on existing consensus mechanisms and how blockchain can be used to overpower IoT vulnerabilities is highlighted. Moreover, we provide a comprehensive description on access control protocols. The protocols are classified into certificate-based, certificate-less and blockchain-based access control mechanisms for better understanding. We then elaborate on each use case like smart home, smart grid, health care and smart agriculture while describing access control mechanisms. The detailed description not only explains the implementation of the access mechanism, but also gives a wider vision on IoT applications. Next, a rigorous comparative analysis is performed to showcase the efficiency of all protocols in terms of computation and communication costs. Finally, we discuss open research issues and challenges in a blockchain-envisioned IoT network.
Recent Application of Artificial Intelligence in Non-Gynecological Cancer Cytopathology: A Systematic Review

State-of-the-art artificial intelligence (AI) has recently gained considerable interest in the healthcare sector and has provided solutions to problems through automated diagnosis. Cytological examination is a crucial step in the initial diagnosis of cancer, although it shows limited diagnostic efficacy. Recently, AI applications in the processing of cytopathological images have shown promising results despite the elementary level of the technology. Here, we performed a systematic review with a quantitative analysis of recent AI applications in non-gynecological (non-GYN) cancer cytology to understand the current technical status. We searched the major online databases, including MEDLINE, Cochrane Library, and EMBASE, for relevant English articles published from January 2010 to January 2021. The searched query terms were: "artificial intelligence", "image processing", "deep learning", "cytopathology", and "fine-needle aspiration cytology." Out of 17,000 studies, only 26 studies (26 models) were included in the full-text review, whereas 13 studies were included for quantitative analysis. There were eight classes of AI models treated of according to target organs: thyroid (n = 11, 39%), urinary bladder (n = 6, 21%), lung (n = 4, 14%), breast (n = 2, 7%), pleural effusion (n = 2, 7%), ovary (n = 1, 4%), pancreas (n = 1, 4%), and prostate (n = 1, 4). Most of the studies focused on classification and segmentation tasks. Although most of the studies showed impressive results, the sizes of the training and validation datasets were limited. Overall, AI is also promising for non-GYN cancer cytopathology analysis, such as pathology or gynecological cytology. However, the lack of well-annotated, large-scale datasets with Z-stacking and external cross-validation was the major limitation found across all studies. Future studies with larger datasets with high-quality annotations and external validation are required.
An Improved Algorithm for Low-Light Image Enhancement Based on RetinexNet

Due to the influence of the environment and the limit of optical equipment, low-light images produce problems such as low brightness, high noise, low contrast, and color distortion, which have a great impact on their visual perception and the following image understanding tasks. In this paper, we take advantage of the independent nature of YCbCr color channels and incorporate RetinexNet into the brightness channel (Y) to reduce color distortion in the enhanced images. Meanwhile, to suppress the image noise generated during the enhancement, the enhanced image is also denoised. Finally, the original color and the enhanced brightness are recombined in the channel direction, converted back to the RGB color space, and adjusted to generate an enhanced result. The proposed algorithm is compared with other recently published counterparts on the LOL dataset. The experimental results demonstrate that the proposed algorithm achieved better performance in terms of both quantitative metrics and visual quality.
Accurate Clinical and Biomedical Named Entity Recognition at Scale

We introduce an agile, production-grade clinical and biomedical Named entity recognition (NER) algorithm based on a modified BiLSTM-CNN-Char DL architecture built on top of Apache Spark. Our NER implementation establishes new state-of-the-art accuracy on 7 of 8 well-known biomedical NER benchmarks and 3 clinical concept extraction challenges: 2010 i2b2/VA clinical concept extraction, 2014 n2c2 de-identification, and 2018 n2c2 medication extraction. Moreover, clinical NER models trained using this implementation outperform the accuracy of commercial entity extraction solutions, AWS Medical Comprehend and Google Cloud Healthcare API by a large margin (8.9% and 6.7% respectively), without using memory-intensive language models.
A fuzzy clustering technique for enhancing the convergence performance by using improved Fuzzy c-means and Particle Swarm Optimization algorithms

Fuzzy clustering is a well-established technique among the well-known clustering techniques in several real-world applications due to easy implementation and produces satisfactory clustering result. However, it has some deficiency such as sensitive to outliers, result dependency on choosing initial centroid, etc. To eradicate the shortcoming of FCM algorithm, this article introduces a robust clustering technique, particle swarm optimization improved fuzzy c-means is developed by the hybridization of particle swarm optimization and improved fuzzy c-means techniques, to deal with noisy data and initialization problem. In this article, a fuzzy clustering technique is developed to increase the convergence performance of clustering techniques. Fuzzy c-means is improved by developing a new metric to tolerate the noisy environment. Particle swarm optimization has an inbuilt guidance strategy which leads the solution in particle swarm optimization to obtain useful information from the better solution and thereby helping them improve their own solution. To handle the initialization problem of fuzzy c-means, particle swarm optimization technique is used. PSO effectively enhance the performance of improved FCM to increase the effectiveness of clustering. The effectiveness of the proposed clustering technique over existing techniques in literature has been illustrated by adopting eight real worlds and three artificial data sets. The results show that the proposed algorithm generates encouraging results as compared to the established clustering technique in literature.
The Weighted Cross-Modal Attention Mechanism With Sentiment Prediction Auxiliary Task for Multimodal Sentiment Analysis

Human brain extracts the spatial and temporal semantic information by processing the multi-modalities, which has contextually meaningful for perceiving and understanding the emotional state of an individual. However, there are two main challenges in modeling multimodal sequences: 1) the different sampling rates from multimodal data make the cross-modal interactions very difficult; 2) how to efficiently fuse unimodal representations and effectively capture relationships among multimodal data. In this paper, we design the weighted cross-modal attention mechanism, which not only captures the temporal correlation information and the spatial dependence information of each modality, but also dynamically adjusts the weight of each modality across different time steps. And the unimodal subtasks are led for assisting the representation learning of specific modality to jointly train the multimodal tasks and unimodal subtasks to explore the complementary relationships of each modality. Our model gets a new state-of-the-art record on the CMU-MOSI dataset and brings noticeable performance improvements on all the metrics. For the CMU-MOSEI dataset, the F1 score of the binary classification, the 7-class task, and the regression task of our model are still the highest among all models and the proposed model is only lower than the multimodal split attention fusion (MSAF) model with aligned data on the accuracy of the binary classification, showing the great performance of the suggested method.
Redactable Blockchain From Decentralized Chameleon Hash Functions

Blockchain is a technology with decentralization and immutability features and has been employed for auditing by many applications. However, immutability sometimes limits the application of blockchain technology. For example, vulnerable smart contracts on blockchain cannot be redacted due to immutability. The existing redactable blockchain solutions either have a low efficiency or violate the decentralization feature. Moreover, those solutions lack mechanisms for tracing redaction history and checking block consistency. In this paper, we present an efficient redactable blockchain with traceability in the decentralized setting. Specifically, we propose a decentralized chameleon hash function for redactable blockchain that every redaction must be approved by multiple blockchain nodes. We also design a redactable blockchain structure that maintains all redactions of a block and encodes the redacted blocks into an RSA accumulator. Then, we propose an efficient block consistency check protocol based on the RSA accumulator. Finally, we conduct experiments and compare our scheme with another decentralized redactable blockchain to demonstrate that our solution is efficient in practice.
An approach to sport activities recognition based on an inertial sensor and deep learning

In recent years, due to changing the human lifestyle, the number of sport trainers has been increased. The conventional classifiers as Naive Bayes (NB), Decision Trees (DT) and Convolutional Neural Networks (CNNs) can be used in this domain to recognize and count sports activities of subjects and provide them qualified feedback. This paper uses literature studies and selected sport activities, namely squats, pull-ups and dips as the dataset based on three UWB sensors with additional inertial data, which contains the reduced data set consisting of 17 training sets and next for CNN training the 1444 samples describing exercises and 2024 samples with breaks, which were grouped in the ratio 70:15:15. The recognition accuracy of the NB and DT were 89.4 and 92.9 accordingly. Next, the extensive performance analysis of the CNN based on experiments for different kernel sizes, different number of filters for single and dual layer networks was carried out. Moreover, the innovative model for sport activities recognition in the form the combination of several networks forming Ensemble Neural Network (ENN) was created. The accuracy was at the level 94.81 of CNN and exceeded 95% of ENN. The proposed prototype of the measurement system and data acquisition platform for sport activities recognition was highlighted as the great potential in the privacy-training sport system.
A new hybrid feature selection based on Improved Equilibrium Optimization

Feature selection is one of the most important preprocessing steps for analyzing high-dimensional data in the data mining and machine learning fields. In machine learning problems, Classification accuracy is highly dependent on the selected features of a dataset. The main purpose of feature selection is to remove additional and irrelevant features from the data feature set to increase the performance of algorithms. A novel feature selection model is proposed in this paper. In the first step, the features are scored based on the mRMR feature selection approach, and those with a higher score are selected. In the second step, the Wrapper feature selection approach is used to extract the best features based on the Improved Equilibrium Optimization (IMEO) algorithm. IMEO uses a new operator called Entropy-based to improve the performance of the Equilibrium Optimizer (EO) algorithm and prevents the algorithm from getting trap in local optima by controlling the exploration and exploitation abilities. Also, Levy flight is used to find new solutions in the search space and to improve the exploration phase of the EO algorithm. This mechanism will avoid local optimum by creating many short jumps and sometimes large jumps in the search space. The proposed IMEO algorithm is first tested on 23 benchmark test functions; after ensuring the efficiency of the proposed method, the Binary Improved Equilibrium Optimization (BIMEO) algorithm is defined based on the sigmoid transfer function to solve the feature selection problem. The simulation results of the BIMEO algorithm and state-of-the-art algorithms on 18 standard benchmark datasets show the performance of the proposed model compared to the other algorithms. In addition, the results of experiments on two chemical datasets prove the efficiency of the proposed algorithm, BIMEO, in terms of selective features and classification accuracy.