Reviewerly/exHarmony huggingface dataset:
The aspect of vast data management problem in healthcare sector and implementation of cloud computing technique

In general the task of data management in the healthcare sector is a gargantuan duty. There are millions of patients and the data need to be stored carefully for the utilization in the future for general and public concern. In order to cope up with the situation the healthcare industry tries to merge their system with cloud computing for better implementation The main purpose of the present study is to evaluate the implementation of cloud computing in the management of vast data in the healthcare sector. Different units use the cloud computing technology in recent times for upbringing the work culture and in order to overcome the issues related to the system. Secondary data has been collected from different authentic sources for the present research and systematic and thematic analysis have been employed. The data has been collected from journals and scholarly articles from 2016 to 2021. The healthcare unit is basically a multidimensional system that aims to prevent, diagnose and treat diseases in order to develop a society with less health and mental complications. The research has shown a relationship between the data management in the healthcare industry and the cloud computing system that can be helpful for the sector. In many of the cases the big industries or business organizations need a proper implication of data management platforms in order to improve the customer engagement system that can help in harnessing the business. Cloud computing can become a definite part of the healthcare industry in future. In the present scenario the healthcare industry is facing enormous challenges due to the pandemic and the need for advanced technology is increasing in this sector.
Optimally configured Gated Recurrent Unit using Hyperband for the long-term forecasting of photovoltaic plant

The photovoltaic generation inherits the instability due to the variability and non-availability of solar irradiation at times. Such unstable generation will cause grid management, planning, and operation issues. Researchers have proposed several classical and advanced algorithms to forecast the power generation of photovoltaic plants to avoid such unsuitability issues. Artificial Neural Networks advancement has pushed them in power forecasting, ranging from yearly to hourly prediction of power generation. These networks' performance depends on the choice of hyperparameters, which includes the number of layers, neurons in each layer, activation function, and learning rate. In power forecasting, these parameters are selected through trial and error, which is inefficient and does not ensure the optimal selection. In this paper, we have presented the Hyperband Gated Recurrent Unit model for power, voltage, and current forecasting of the photovoltaic power plant. The model has a monthly prediction horizon with a temporal resolution of a day. We used the Hyperband technique for the optimal selection of the hyper-parameters. It is an iterative process where several configurations are tried on each trial, and only n best configurations qualify for the subsequent trial. The inputs to the model are irradiation, temperature, and wind speed, and the model's output includes current, voltage, and power. We trained our model on 11 months of data and predicated the outputs for the 12th month. The network is tested with other variants of Recurrent Neural Networks based on several evaluation metrics. The proposed model achieved promising results with minimum error.
Cybercrime on the menu? Examining cafeteria-style offending among financially motivated cybercriminals

Criminologists have frequently debated whether offenders are specialists, in that they consistently perform either one offense or similar offenses, or versatile by performing any crime based on opportunities and situational provocations. Such foundational research has yet to be developed regarding cybercrimes, or offenses enabled by computer technology and the Internet. This study address this issue using a sample of 37 offender networks. The results show variations in the offending behaviors of those involved in cybercrime. Almost half of the offender networks in this sample appeared to be cybercrime specialists, in that they only performed certain forms of cybercrime. The other half performed various types of crimes on and offline. The relative equity in specialization relative to versatility, particularly in both on and offline activities, suggests that there may be limited value in treating cybercriminals as a distinct offender group. Furthermore, this study calls to question what factors influence an offender's pathway into cybercrime, whether as a specialized or versatile offender. The actors involved in cybercrime networks, whether as specialists or generalists, were enmeshed into broader online offender networks who may have helped recognize and act on opportunities to engage in phishing, malware, and other economic offenses.
Transfer Learning Approach for Classification of Histopathology Whole Slide Images

The classification of whole slide images (WSIs) provides physicians with an accurate analysis of diseases and also helps them to treat patients effectively. The classification can be linked to further detailed analysis and diagnosis. Deep learning (DL) has made significant advances in the medical industry, including the use of magnetic resonance imaging (MRI) scans, computerized tomography (CT) scans, and electrocardiograms (ECGs) to detect life-threatening diseases, including heart disease, cancer, and brain tumors. However, more advancement in the field of pathology is needed, but the main hurdle causing the slow progress is the shortage of large-labeled datasets of histopathology images to train the models. The Kimia Path24 dataset was particularly created for the classification and retrieval of histopathology images. It contains 23,916 histopathology patches with 24 tissue texture classes. A transfer learning-based framework is proposed and evaluated on two famous DL models, Inception-V3 and VGG-16. To improve the productivity of Inception-V3 and VGG-16, we used their pre-trained weights and concatenated these with an image vector, which is used as input for the training of the same architecture. Experiments show that the proposed innovation improves the accuracy of both famous models. The patch-to-scan accuracy of VGG-16 is improved from 0.65 to 0.77, and for the Inception-V3, it is improved from 0.74 to 0.79.
MGSeg: Multiple Granularity-Based Real-Time Semantic Segmentation Network

Recent works on semantic segmentation witness significant performance improvement by utilizing global contextual information. In this paper, an efficient multi-granularity based semantic segmentation network (MGSeg) is proposed for real-time semantic segmentation, by modeling the latent relevance between multi-scale geometric details and high-level semantics for fine granularity segmentation. In particular, a light-weight backbone ResNet-18 is first adopted to produce the hierarchical features. Hybrid Attention Feature Aggregation (HAFA) is designed to filter the noisy spatial details of features, acquire the scale-invariance representation, and alleviate the gradient vanishing problem of the early-stage feature learning. After aggregating the learned features, Fine Granularity Refinement (FGR) module is employed to explicitly model the relationship between the multi-level features and categories, generating proper weights for fusion. More importantly, to meet the real-time processing, a series of light-weight strategies and simplified structures are applied to accelerate the efficiency, including light-weight backbone, channel compression, narrow neck structure, and so on. Extensive experiments conducted on benchmark datasets Cityscapes and CamVid demonstrate that the proposed method achieves the state-of-the-art performance, 77.8%@50fps and 72.7%@127fps on Cityscapes and CamVid datasets, respectively, having the capability for real-time applications.
Fast Convergence Algorithm for Analog Federated Learning

In this paper, we consider federated learning (FL) over a noisy fading multiple access channel (MAC), where an edge server aggregates the local models transmitted by multiple end devices through over-the-air computation (AirComp). To realize efficient analog federated learning over wireless channels, we propose an AirComp-based FedSplit algorithm, where a threshold-based device selection scheme is adopted to achieve reliable local model uploading. In particular, we analyze the performance of the proposed algorithm and prove that the proposed algorithm linearly converges to the optimal solutions under the assumption that the objective function is strongly convex and smooth. We also characterize the robustness of proposed algorithm to the ill-conditioned problems, thereby achieving fast convergence rates and reducing communication rounds. A finite error bound is further provided to reveal the relationship between the convergence behavior and the channel fading and noise. Our algorithm is theoretically and experimentally verified to be much more robust to the ill-conditioned problems with faster convergence compared with other benchmark FL algorithms.
Time-varying Black–Litterman portfolio optimization using a bio-inspired approach and neuronets

The Black–Litterman model is a very important analytical tool for active portfolio management because it allows investment analysts to incorporate investor’s views into market equilibrium returns. In this paper, we define and study the time-varying Black–Litterman portfolio optimization under nonlinear constraints (TV-BLPONC) problem as a nonlinear programming (NLP) problem. More precisely, the nonlinear constraints refer to transaction costs and cardinality constraints. Furthermore, a speedy weights-and-structure-determination (WASD) algorithm for the power-activation feed-forward neuronet (PFN) is presented to solve time-series modeling and forecasting problems. Inhere, the investor’s views in the TV-BLPONC problem are considered as a forecasting problem and, thus, they are produced by the WASD-based PFN. In addition, using the beetle antennae search (BAS) algorithm a computational method is introduced to solve the TV-BLPONC problem. For all we know, this is an innovative approach that integrates modern neural network and meta-heuristic optimization methods to provide a solution to the TV-BLPONC problem in large portfolios. Our approach is tested on portfolios of up to 90 stocks with real-world data, and the results show that it is more than 30 times faster than other methods. Our technique’s speed and precision are verified in this way, showing that it is an outstanding alternative to ordinary methods. In order to support and promote the findings of this work, we have constructed two complete MATLAB packages for the interested user, which are freely available through GitHub.
Consortium Blockchain-Based Public Integrity Verification in Cloud Storage for IoT

The applications of Internet of Things have emerged in every aspect of people’s life. The volume of data gathered can be enormous. Enterprises and personal consumers are increasingly reliant on cloud storage services instead of local storage. While they enjoy the convenience of cloud storage services, they also worry about the integrity of the cloud-stored data since they do not physically own the data. To enable public integrity auditing, third-party auditors as trusted ones verify data integrity on behalf of the data owner. However, the vulnerability of auditors should also be considered. We propose a consortium blockchain-based public integrity verification system (CBPIV). In CBPIV, the auditor behaviors are recorded in the consortium blockchain so that authorized parties can audit the auditor to see if the verification results are correct. A smart contract is deployed to check the behavior of the auditor automatically, which can trigger alerts for unusual behaviors. The evaluation on both security and performance shows that our proposed scheme is secure and alleviates the burden on data owners of limited computation capability.
Exploiting Simultaneous Communications to Accelerate Data Parallel Distributed Deep Learning

Synchronous stochastic gradient descent (S-SGD) with data parallelism is widely used for training deep learning (DL) models in distributed systems. A pipelined schedule of the computing and communication tasks of a DL training job is an effective scheme to hide some communication costs. In such pipelined S-SGD, tensor fusion (i.e., merging some consecutive layers' gradients for a single communication) is a key ingredient to improve communication efficiency. However, existing tensor fusion techniques schedule the communication tasks sequentially, which overlooks their independence nature. In this paper, we expand the design space of scheduling by exploiting simultaneous All-Reduce communications. Through theoretical analysis and experiments, we show that simultaneous All-Reduce communications can effectively improve the communication efficiency of small tensors. We formulate an optimization problem of minimizing the training iteration time, in which both tensor fusion and simultaneous communications are allowed. We develop an efficient optimal scheduling solution and implement the distributed training algorithm ASC-WFBP with Horovod and PyTorch. We conduct real-world experiments on an 8-node GPU cluster of 32 GPUs with 10Gbps Ethernet. Experimental results on four modern DNNs show that ASC-WFBP can achieve about 1.09 × -2.48× speedup over the baseline without tensor fusion, and 1.15× -1.35× speedup over the state-of-the-art tensor fusion solution.
Converting Optical Videos to Infrared Videos Using Attention GAN and Its Impact on Target Detection and Classification Performance

To apply powerful deep-learning-based algorithms for object detection and classification in infrared videos, it is necessary to have more training data in order to build high-performance models. However, in many surveillance applications, one can have a lot more optical videos than infrared videos. This lack of IR video datasets can be mitigated if optical-to-infrared video conversion is possible. In this paper, we present a new approach for converting optical videos to infrared videos using deep learning. The basic idea is to focus on target areas using attention generative adversarial network (attention GAN), which will preserve the fidelity of target areas. The approach does not require paired images. The performance of the proposed attention GAN has been demonstrated using objective and subjective evaluations. Most importantly, the impact of attention GAN has been demonstrated in improved target detection and classification performance using real-infrared videos.
Heuristic Path Planning Method for Multistatic UAV-Borne SAR Imaging System

Multistatic unmanned aerial vehicle-borne synthetic aperture radar (MuUAV-SAR) plays an important role in the applications of environmental monitoring and disaster warning because its distributed platforms can provide high-resolution imagery by fusing the multiple measurements.However, the flight paths of the multiple platforms are limited for such an unmanned system since the flight safety and the path length are basic conditions for guaranteeing the effective observation.This paper first studies the observation signal model of MuUAV-SAR imaging system, and then analyzes the factors that determine the imaging resolution, while these factors are all determined by the flight path of UAVs.Secondly, MuUAV-SAR imaging path planning problem is established as a constrained multiobjective optimization problem (CMOP), which considers the navigation and imaging performance of UAV in the process of completing path planning task in detail.For this CMOP, a heuristic search method is proposed to solve it, which can ensure that each step achieves local optimum, and it can also list all feasible solutions to meet the application requirements for selection.Finally, experimental results verify the effectiveness and practicability of the proposed heuristic path planning method.
Sentimental analysis of Indian regional languages on social media

The idea of sentimental analysis is getting attention for the last few years. The key challenges in a sentimental analysis are the collection of huge data from the sources, applying appropriate algorithms or techniques, and classifying them into different sentiments. In this fast-spreading internet world, social media provides a platform for individuals to express their sentiments. With the changing ways of things in different areas in our day-to-day life, the way of expressing one's view or opinion has also changed. People tend to express themselves in their regional language or in a way convenient to them. These individual reviews play an important role in decision-making. With the huge amount of data that is obtained on social media, it is of no use if the opinions are not classified based on their sentiments. This paper provides information about the tweets posted by the customer are positive, negative, or neutral. For this the proposed model first scrape the tweets from Twitter by using Twitter APIs, then later by using text blob, the customer reviews are given different sentiment scores and classify them as positive, negative, or neutral by using text classification model. This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 8th International Conference on Through-Life Engineering Service – TESConf 2019.
Bloom’s Learning Outcomes’ Automatic Classification Using LSTM and Pretrained Word Embeddings

Bloom's taxonomy is a popular model to classify educational learning objectives into different learning levels for three domains including cognitive, affective and psycho motor.Each domain is further detailed into different levels.The cognitive domain includes knowledge, comprehension, application, analysis, synthesis and evaluation levels.In educational institutions, designing course learning outcomes (CLOs) as per different levels of Bloom and mapping of assessment items on designed CLOs is an important task -every semester, faculty and administrators read thousands of statements to complete the tedious task of such mapping of CLOs and assessment items into Bloom's levels for an improved student learning.This paper proposes LSTM based deep learning model to perform classification of CLOs and assessment items in different levels of Bloom in cognitive domain.Although, there has been some attempts in the literature to automatically assign Bloom's taxonomy category using keywords-based approach but it suffers from the problem of low accuracy and overlapping of keywords.Initially, when we performed keywords-based approach on our datasets we achieved the overall accuracy of 55% for classification of CLOs and assessment items into Bloom's taxonomy.The proposed model predicts Bloom's level for CLO and assessment question item, respectively.The proposed model is simple in terms of the architecture as compared to other deep learning models reported in literature and achieves classification accuracy of 87% and 74% on CLOs and assessment question items, respectively.The proposed model obtained 3% increase in overall accuracy comparing to an existing study for the same task.To the best of our knowledge, this is first attempt towards applying deep learning on classifying educational objectives in Bloom's levels.
GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network

Panoptic segmentation as an integrated task of both static environmental understanding and dynamic object identification, has recently begun to receive broad research interest. In this paper, we propose a new computationally efficient LiDAR based panoptic segmentation framework, called GP-S3Net. GP-S3Net is a proposal-free approach in which no object proposals are needed to identify the objects in contrast to conventional two-stage panoptic systems, where a detection network is incorporated for capturing instance information. Our new design consists of a novel instance-level network to process the semantic results by constructing a graph convolutional network to identify objects (foreground), which later on are fused with the back-ground classes. Through the fine-grained clusters of the foreground objects from the semantic segmentation back-bone, over-segmentation priors are generated and subsequently processed by 3D sparse convolution to embed each cluster. Each cluster is treated as a node in the graph and its corresponding embedding is used as its node feature. Then a GCNN predicts whether edges exist between each cluster pair. We utilize the instance label to generate ground truth edge labels for each constructed graph in order to supervise the learning. Extensive experiments demonstrate that GP-S3Net outperforms the current state-of-the-art approaches, by a significant margin across available datasets such as, nuScenes and SemanticPOSS, ranking 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> on the competitive public SemanticKITTI leaderboard upon publication.
A novel multi-objective CR-PSO task scheduling algorithm with deadline constraint in cloud computing

• A multi-objective hybrid method for task scheduling problem is proposed. • Represented the NP-Complete task scheduling problem in the form of mathematical model. • Proposed a task scheduling framework for processing the applications/tasks in efficient way. • The proposed method is a hybrid optimization technique using the Chemical Reaction Optimization (CRO) and Particle Swarm Optimization (PSO). • Proposed CR-PSO framework is simulated at CloudSim simulator to evaluated the performance. In cloud computing, efficient task scheduling espouses many challenges. To schedule the multiple cloudlets with deadline constraints on hybrid cloud resources while meeting the various quality requirements is a challenging issue. The purpose of this research work is to address the task scheduling problem of cloud computing. A novel hybrid task scheduling algorithm named Chemical Reaction Partial Swarm Optimization has been proposed for the allotment of multiple independent tasks on the available virtual machines. It enhances the classical chemical reaction optimization and partial swarm optimization and does hybridization by combining the features for the optimal schedule sequence where tasks can be processed based upon the demand and deadline simultaneously to improve the quality in terms of factors like cost, energy, and makespan. We present the comprehensive simulation experiment using the CloudSim toolkit, which shows the effectiveness of the proposed algorithms. To analyse average execution time, comparative experiments have been carried out using various combinations of virtual machines and the number of tasks. The results bring out a significant reduction in execution time of the order of 1–6 percent, which further improves even more than 10 percent in some cases. The results of the makespan reflect the effectiveness of the algorithm in order of 5–12 percent, and the outcome of total cost 2–10 percent and energy consumption rate shows the 1–9 percent improvement.