Reviewerly/exHarmony huggingface dataset:
Cross-modal fusion for multi-label image classification with attention mechanism

For multi-label image classification, existing studies either utilize a poor multi-step training workflow to explore the (local) relationships between the image target regions and their corresponding labels with attention mechanism or model the (global) label dependencies via graph convolution network (GCN) but fail to efficiently fuse these image features and label word vectors. To address these problems, we develop Cross-modal Fusion for Multi-label Image Classification with attention mechanism (termed as CFMIC), which combines attention mechanism and GCN to capture the local and global label dependencies simultaneously in an end-to-end manner. CFMIC mainly contains three key modules: (1) a feature extraction module with attention mechanism which helps generate the accurate feature of each input image by focusing on the relationships between image labels and image target regions, (2) a label co-occurrence embedding learning module with GCN which utilizes GCN to learn the relationships between different objects to generate the label co-occurrence embeddings and (3) a cross-modal fusion module with Multi-modal Factorized Bilinear pooling (termed as MFB) which efficiently fuses the above image features and label co-occurrence embeddings. Extensive experiments on MS-COCO and VOC2007 verify CFMIC greatly promotes the convergence efficiency and produces better classification results than the state-of-the-art approaches.
Target-level sentiment analysis for news articles

The rapid growth of social media, news sites, and blogs increases the opportunity to express and share an opinion on the Internet. Researchers from different fields take advantage of nearly limitless data. Thus, in the past decade, opinion mining or sentiment analysis has become an important research discipline. In this paper, we focus on the target-level sentiment analysis, wherein the task is to predict the sentiment concerning specific (multiple) entities that appear as coreference mentions throughout the document. We created a new annotated dataset of Slovene news articles, additionally annotated with named entities and coreferences that are the basis for the proposed task. Using entity-document representation, we compared the task with the traditional sentiment analysis, evaluating traditional machine learning and deep neural network approaches. According to existing approaches, the proposed task represents a challenging problem. The results show that we can achieve the best results using a customised BERT adapter (a minor improvement over a standard text-classification adapter). We outperformed existing aspect-based state-of-the-art approaches by 13%, reaching up to 77% accuracy and a 73% F1 score.
Sentiment Analysis on COVID-19 Twitter Data Streams Using Deep Belief Neural Networks

Social media is Internet-based by design, allowing people to share content quickly via electronic means. People can openly express their thoughts on social media sites such as Twitter, which can then be shared with other people. During the recent COVID-19 outbreak, public opinion analytics provided useful information for determining the best public health response. At the same time, the dissemination of misinformation, aided by social media and other digital platforms, has proven to be a greater threat to global public health than the virus itself, as the COVID-19 pandemic has shown. The public's feelings on social distancing can be discovered by analysing articulated messages from Twitter. The automated method of recognizing and classifying subjective information in text data is known as sentiment analysis. In this research work, we have proposed to use a combination of preprocessing approaches such as tokenization, filtering, stemming, and building N-gram models. Deep belief neural network (DBN) with pseudo labelling is used to classify the tweets. Top layers of the base classifiers are boosted in the pseudo labelling strategy, whereas lower levels of the base classifiers share weights for feature extraction. By introducing the pseudo boost mechanism, our suggested technique preserves the same time complexity as a DBN while achieving fast convergence to optimality. The pseudo labelling improves the performance of the classification. It extracts the keywords from the tweets with high precision. The results reveal that using the DBN classifier in conjunction with the bigram in the N-gram model outperformed other models by 90.3 percent. The proposed approach can also aid medical professionals and decision-makers in determining the best course of action for each location based on their views regarding the pandemic.
Investigation of E-voting system using face recognition using convolutional neural network (CNN)

An Election is a method of selection of individuals to hold the public office in democracy. Ballot is basically a piece of paper that is used to cast vote during election. In ballot paper voting system each voter uses a ballot paper which is not shared and basically it is a paper printed with the name and symbols of the candidates. The Electronic Voting Machine is basically a memory recorder which records the vote casted by the voters. In this paper, main advantages of E-voting systems for country is highlighted. For constructing E-voting systems, every countries need to do great attention to Verification and Validation requirements. In this research, E-voting scheme with face recognition using deep learning technique is proposed. The process of casting vote is accomplished by blockchain technology and blind signature mechanism. The main objective of the proposed scheme is to explore the positive effects of security and safety in online voting system.
Knowledge tools to organise software engineering Data: Development and validation of an ontology based on ECSS standard

This paper describes an OWL-based ontology to manage the data and artifacts created as part of software development projects based on the ECSS-E-ST-40C standard for SW development. ECSS standards are the main reference in aerospace projects. ECSS-E-ST-40C establishes the process, activities, requirements, and work products that must be generated. As part of its requirements, ECSS-E-ST-40C implicitly refers to specific data that must be generated, collected, and managed. The ontology identifies these data and the relationships that are inferred from the requirements in the standard, intending to set up the basis for building advanced information systems where data coming from different projects can be integrated and connected into a coherent set. The definition of this ontology provides the conceptual framework to develop data exchange processes and interfaces between the different tools and information systems used by the different players in the aerospace industry.
Measuring the Deployment of 5G Security Enhancement

The fifth-generation(5G) cellular network is entering an era of rapid development. Not only is 5G supposed to be fast, it also offers enhanced security based on 5G security specifications developed by the 3rd Generation Partnership Project (3GPP). However, little is known about 5G security in real world deployment. This paper analyzes 5G security features and measures their implementation in commercial 5G networks. By collecting and analyzing signaling messages between a cell phone and several commercial 5G networks, we measured multiple aspects of 5G security in real world deployment including, crypto algorithms used in the control plane, user plane (UP) security activation, subscriber identifier protection, and initial None-Access Stratum(NAS) message protection. We evaluated the compliance of commercial 5G networks with 5G security specifications. The results show that major discrepancy exists between 5G security standards and real world deployment, especially in the areas of UP protection and subscriber identifier protection. Therefore, well-known security risks, such as user data leakage, location exposure and Denial-of-Service(DoS) attacks, still apply to 5G commercial networks.
Multi-human Fall Detection and Localization in Videos

There has been a delay in the exploration of the benefits of deep learning for human action and activity recognition applications. Within these fields, the detection of falls attracts attention due to its excellent public utility. Fall detection can be implemented in facilities such as nursing homes, areas with public cameras, and the homes of older people who live alone, as the vast majority of fatalities related to falls occur in these locations. The YOLO object detection algorithm is combined with temporal classification models and the Kalman filter tracking algorithm, which are used to detect falls individually on video streams. The following steps are taken when the proposed approach is used: (i) the region of the image in which the fall occurred is located; (ii) the features that comprise the fall in a temporal sequence of images are tracked and extracted, and a series of actions associated with a given person are formed; and (iii) a model is built to classify the consecutive sequence of images and aggregate the temporal information. Based on these steps, two versions of the proposed approach, YOLOK+3DCNN and YOLOK+2DCNN+LSTM, are created. The proposed model is compared with other methods in the literature using well-known metrics. Experimental simulations using a custom dataset and state-of-the-art models show that the best results in most evaluated metrics are achieved using the proposed approach.
Deep Reinforcement Learning With Modulated Hebbian Plus Q-Network Architecture

In this article, we consider a subclass of partially observable Markov decision process (POMDP) problems which we termed confounding POMDPs. In these types of POMDPs, temporal difference (TD)-based reinforcement learning (RL) algorithms struggle, as TD error cannot be easily derived from observations. We solve these types of problems using a new bio-inspired neural architecture that combines a modulated Hebbian network (MOHN) with deep Q-network (DQN), which we call modulated Hebbian plus Q-network architecture (MOHQA). The key idea is to use a Hebbian network with rarely correlated bio-inspired neural traces to bridge temporal delays between actions and rewards when confounding observations and sparse rewards result in inaccurate TD errors. In MOHQA, DQN learns low-level features and control, while the MOHN contributes to high-level decisions by associating rewards with past states and actions. Thus, the proposed architecture combines two modules with significantly different learning algorithms, a Hebbian associative network and a classical DQN pipeline, exploiting the advantages of both. Simulations on a set of POMDPs and on the Malmo environment show that the proposed algorithm improved DQN's results and even outperformed control tests with advantage-actor critic (A2C), quantile regression DQN with long short-term memory (QRDQN + LSTM), Monte Carlo policy gradient (REINFORCE), and aggregated memory for reinforcement learning (AMRL) algorithms on most difficult POMDPs with confounding stimuli and sparse rewards.
Computational intelligence based sustainable computing with classification model for big data visualization on map reduce environment

Abstract In recent years, the researchers have perceived the modifications or transformations motivated by the presence of big data on the definition, complexity, and future direction of the real world optimization problems. Big Data visualization is mainly based on the efficient computer system for ingesting actual data and producing graphical representation for understanding large quantity of data in a fraction of seconds. At the same time, clustering is an effective data mining tool used to analyze big data and computational intelligence (CI) techniques can be employed to solve big data classification process. In this aspect, this study develops a novel Computational Intelligence based Clustering with Classification Model for Big Data Visualization on Map Reduce Environment, named CICC-BDVMR technique. The proposed CICC-BDVMR technique intends to perform effective BDV using the clustering and data classification processes on the Map Reduce environment. For clustering process, a grasshopper optimization algorithm (GOA) with kernelized fuzzy c-means (KFCM) technique is used to cluster the big data and the GOA is mainly utilized to determine the initial cluster centers of the KFCM technique. GOA is a recently proposed metaheuristic algorithm inspired by the swarming behaviour of grasshoppers. This algorithm has been shown to be efficient in tackling global unconstrained and constrained optimization problems. Based on the modified GOA, an effective kernel extreme learning machine model for financial stress prediction was created. Besides, big data classification process takes place using the Ridge Regression (RR) and the parameter optimization of the RR model is carried out via the Red Colobuses Monkey (RCM) algorithm. The design of GOA and RCM algorithms for parameter optimization processes for big data classification shows the novelty of the study. A wide ranging simulation analysis is carried out using benchmark big datasets and the comparative results reported the enhanced outcomes of the CICC-BDVMR technique over the recent state of art approaches. The broad comparison research illustrates the CICC-BDVMR approach’s promising performance against contemporary state-of-the-art techniques. As a result, the CICC-BDVMR technique has been demonstrated to be an effective technique for visualising and classifying large amounts of data.
VenusAI: An artificial intelligence platform for scientific discovery on supercomputers

Since the machine learning platform can provide one-stop artificial intelligence (AI) application solutions, it has been widely used in the industrial and commercial internet fields in recent years. Based on the heterogeneous accelerator cards, scientific discovery using large-scale computation and massive data is a significant tendency in the future. However, building a platform for scientific discovery remains challenging, including large-scale heterogeneous resource scheduling and support for massive multi-source data. To free researchers from tedious resource management and environmental configuration, we propose a VenusAI platform for large-scale computing scenarios in scientific research, based on heterogeneous resources scheduling framework. This paper firstly illustrates the VenusAI platform architecture design scheme based on the supercomputers and elaborates on the virtualization and containerization of the underlying hardware resources. Next, a technical framework for heterogeneous resource aggregation and scheduling is proposed. A unified resource interface in the application service layer is introduced. Considering the core three parts of the AI scenario: data, model, and computing power, modularized service decoupling is carried out. Furthermore, three types of experiments are evaluated on the supercomputers and show that the performance of the scheduling framework on virtual clusters is better than that on common clusters. Finally, three scientific discovery applications deployed on VenusAI, i.e., new energy forecasting, materials design, and unmanned aerial vehicle planning, demonstrate the advantages of the platform in solving practical scientific problems.
Computer-Aided Depth Video Stream Masking Framework for Human Body Segmentation in Depth Sensor Images

The identification of human activities from videos is important for many applications. For such a task, three-dimensional (3D) depth images or image sequences (videos) can be used, which represent the positioning information of the objects in a 3D scene obtained from depth sensors. This paper presents a framework to create foreground-background masks from depth images for human body segmentation. The framework can be used to speed up the manual depth image annotation process with no semantics known beforehand and can apply segmentation using a performant algorithm while the user only adjusts the parameters, or corrects the automatic segmentation results, or gives it hints by drawing a boundary of the desired object. The approach has been tested using two different datasets with a human in a real-world closed environment. The solution has provided promising results in terms of reducing the manual segmentation time from the perspective of the processing time as well as the human input time.
Amplitude and Temporal Dynamics of Motion Sickness

The relationship between the amplitude of motion and the accumulation of motion sickness in time is unclear. Here, we investigated this relationship at the individual and group level. Seventeen participants were exposed to four oscillatory motion stimuli, in four separate sessions, separated by at least 1 week to prevent habituation. Motion amplitude was varied between sessions at either 1, 1.5, 2, or 2.5 ms-2. Time evolution was evaluated within sessions applying: an initial motion phase for up to 60 min, a 10-min rest, a second motion phase up to 30 min to quantify hypersensitivity and lastly, a 5-min rest. At both the individual and the group level, motion sickness severity (MISC) increased linearly with respect to acceleration amplitude. To analyze the evolution of sickness over time, we evaluated three variations of the Oman model of nausea. We found that the slow (502 s) and fast (66.2 s) time constants of motion sickness were independent of motion amplitude, but varied considerably between individuals (slow STD = 838 s; fast STD = 79.4 s). We also found that the Oman model with output scaling following a power law with an exponent of 0.4 described our data much better as compared to the exponent of 2 proposed by Oman. Lastly, we showed that the sickness forecasting accuracy of the Oman model depended significantly on whether the participants had divergent or convergent sickness dynamics. These findings have methodological implications for pre-experiment participant screening, as well as online tuning of automated vehicle algorithms based on sickness susceptibility.
Dilated residual grooming kernel model for breast cancer detection

Breast cancer is the most significant cause of mortality among women. When detected and treated early, it saves lives. Breast cancer detection is becoming more accessible and accurate thanks to machine learning and deep learning models. This research aims to enhance medical science and technology by employing a deep learning model to detect small cancer cells with pinpoint accuracy. The proposed model uses datasets from the Breast Cancer Histopathological Image Classification (BreakHis) and Breast Cancer Histopathological Annotation and Diagnosis (BreCaHAD). Next, the image processing procedure employs strain normalization to rectify color divergence caused by using different slide scanners, staining processes, and biopsy materials. The data augmentation with nineteen parameters such as scaling, rotation, flip, resize, and gamma value tackles the overfitting problems. The augmented images are then processed using the dilated residual (DR) model; the DR model combines the proposed dilated spatial convolution unit, fully connected dilation unit, and dilated channel convolution. The first unit is the dilated spatial convolution, which handles all channels equally and amplifies the valuable aspects. The second unit is a fully connected dilation unit; it displays low-level properties such as edges, contours, and color. The third unit is dilated channel convolution, which detects tiny objects and thin boundaries without adding complexity. The proposed dilated residual grooming kernel (DRGK) model is a 14-layer deep learning model that stretches the receptive field while retaining feature information, using the proposed DR unit and ghost model, as well as convolution, pooling, downsampling, and dilated convolution. Dilated convolutions are extensively used in the proposed model to extract features. Accuracy, the area under the curve, average precision score, precision, sensitivity, and f1 score all improve with a learning rate of 0.001. With 96.33% and 93.35% marks, the proposed approach surpasses several state-of-the-art methods.
A Comprehensive Comparison of the Performance of Metaheuristic Algorithms in Neural Network Training for Nonlinear System Identification

Many problems in daily life exhibit nonlinear behavior. Therefore, it is important to solve nonlinear problems. These problems are complex and difficult due to their nonlinear nature. It is seen in the literature that different artificial intelligence techniques are used to solve these problems. One of the most important of these techniques is artificial neural networks. Obtaining successful results with an artificial neural network depends on its training process. In other words, it should be trained with a good training algorithm. Especially, metaheuristic algorithms are frequently used in artificial neural network training due to their advantages. In this study, for the first time, the performance of sixteen metaheuristic algorithms in artificial neural network training for the identification of nonlinear systems is analyzed. It is aimed to determine the most effective metaheuristic neural network training algorithms. The metaheuristic algorithms are examined in terms of solution quality and convergence speed. In the applications, six nonlinear systems are used. The mean-squared error (MSE) is utilized as the error metric. The best mean training error values obtained for six nonlinear systems were 3.5×10−4, 4.7×10−4, 5.6×10−5, 4.8×10−4, 5.2×10−4, and 2.4×10−3, respectively. In addition, the best mean test error values found for all systems were successful. When the results were examined, it was observed that biogeography-based optimization, moth–flame optimization, the artificial bee colony algorithm, teaching–learning-based optimization, and the multi-verse optimizer were generally more effective than other metaheuristic algorithms in the identification of nonlinear systems.
Solving medicine’s data bottleneck: Nightingale Open Science

Open datasets, curated around unsolved medical problems, are vital to the development of computational research in medicine, but remain in short supply. Nightingale Open Science, a non-profit computing platform, was founded to catalyse research in this nascent field.
Object Detection Algorithm for Wheeled Mobile Robot Based on an Improved YOLOv4

In practical applications, the intelligence of wheeled mobile robots is the trend of future development. Object detection for wheeled mobile robots requires not only the recognition of complex surroundings, but also the deployment of algorithms on resource-limited devices. However, the current state of basic vision technology is insufficient to meet demand. Based on this practical problem, in order to balance detection accuracy and detection efficiency, we propose an object detection algorithm based on a combination of improved YOLOv4 and improved GhostNet in this paper. Firstly, the backbone feature extraction network of original YOLOv4 is replaced with the trimmed GhostNet network. Secondly, enhanced feature extraction network in the YOLOv4, ordinary convolution is supplanted with a combination of depth-separable and ordinary convolution. Finally, the hyperparameter optimization was carried out. The experimental results show that the improved YOLOv4 network proposed in this paper has better object detection performance. Specifically, the precision, recall, F1, mAP (0.5) values, and mAP (0.75) values are 88.89%, 87.12%, 88.00%, 86.84%, and 50.91%, respectively. Although the mAP (0.5) value is only 2.23% less than the original YOLOv4, it is higher than YOLOv4_tiny, Eifficientdet-d0, YOLOv5n, and YOLOv5 compared to 29.34%, 28.99%, 20.36%, and 18.64%, respectively. In addition, it outperformed YOLOv4 in terms of mAP (0.75) value and precision, and its model size is only 42.5 MB, a reduction of 82.58% when compared to YOLOv4’s model size.