Reviewerly/exHarmony huggingface dataset:
A Tweet Sentiment Classification Approach Using a Hybrid Stacked Ensemble Technique

With the extensive availability of social media platforms, Twitter has become a significant tool for the acquisition of peoples’ views, opinions, attitudes, and emotions towards certain entities. Within this frame of reference, sentiment analysis of tweets has become one of the most fascinating research areas in the field of natural language processing. A variety of techniques have been devised for sentiment analysis, but there is still room for improvement where the accuracy and efficacy of the system are concerned. This study proposes a novel approach that exploits the advantages of the lexical dictionary, machine learning, and deep learning classifiers. We classified the tweets based on the sentiments extracted by TextBlob using a stacked ensemble of three long short-term memory (LSTM) as base classifiers and logistic regression (LR) as a meta classifier. The proposed model proved to be effective and time-saving since it does not require feature extraction, as LSTM extracts features without any human intervention. We also compared our proposed approach with conventional machine learning models such as logistic regression, AdaBoost, and random forest. We also included state-of-the-art deep learning models in comparison with the proposed model. Experiments were conducted on the sentiment140 dataset and were evaluated in terms of accuracy, precision, recall, and F1 Score. Empirical results showed that our proposed approach manifested state-of-the-art results by achieving an accuracy score of 99%.
Evaluation of energy generation in Iraqi territory by solar photovoltaic power plants with a capacity of 20 MW

Abstract The study evaluates the visibility of solar photovoltaic power plant construction for electricity generation based on a 20 MW capacity. The assessment was performed for four main cities in Iraq by using hourly experimental weather data (solar irradiance, wind speed, and ambient temperature). The experimental data was measured for the period from 1st January to 31st December of the year 2019, where the simulation process was performed at a 1 h time step resolution at the same resolution as the experimental data. There are two positionings considered for solar photovoltaic modules: (i) annual optimum tilt angle and (ii) two-axis tracking system. The effect of the ambient temperature and wind on the overall system energy generated was taken into consideration. The study is targeted at evaluating the potential solar energy in Iraq and the viability of electricity generation using a 20 MW solar photovoltaic power plant. The results showed that the overall performance of the suggested power plant capacity is highly dependent on the solar irradiance intensity and the ambient temperature with wind speed. The current 20 MW solar photovoltaic power plant capacity shows the highest energy that can be generated in the mid-western region and the lowest in the northeast regions. The greatest influence of the ambient temperature on the energy genrated by power plants is observed in the southern regions.
Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019-2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA.
BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model

Pretrained language models have served as important backbones for natural language processing.Recently, in-domain pretraining has been shown to benefit various domain-specific downstream tasks.In the biomedical domain, natural language generation (NLG) tasks are of critical importance, while understudied.Approaching natural language understanding (NLU) tasks as NLG achieves satisfying performance in the general domain through constrained language generation or language prompting.We emphasize the lack of in-domain generative language models and the unsystematic generative downstream benchmarks in the biomedical domain, hindering the development of the research community.In this work, we introduce the generative language model BioBART that adapts BART to the biomedical domain.We collate various biomedical language generation tasks including dialogue, summarization, entity linking, and named entity recognition.BioBART pretrained on PubMed abstracts has enhanced performance compared to BART and set strong baselines on several tasks.Furthermore, we conduct ablation studies on the pretraining tasks for BioBART and find that sentence permutation has negative effects on downstream tasks.
Hierarchical Amortized GAN for 3D High Resolution Medical Image Synthesis

Generative Adversarial Networks (GAN) have many potential medical imaging applications, including data augmentation, domain adaptation, and model explanation. Due to the limited memory of Graphical Processing Units (GPUs), most current 3D GAN models are trained on low-resolution medical images, these models either cannot scale to high-resolution or are prone to patchy artifacts. In this work, we propose a novel end-to-end GAN architecture that can generate high-resolution 3D images. We achieve this goal by using different configurations between training and inference. During training, we adopt a hierarchical structure that simultaneously generates a low-resolution version of the image and a randomly selected sub-volume of the high-resolution image. The hierarchical design has two advantages: First, the memory demand for training on high-resolution images is amortized among sub-volumes. Furthermore, anchoring the high-resolution sub-volumes to a single low-resolution image ensures anatomical consistency between sub-volumes. During inference, our model can directly generate full high-resolution images. We also incorporate an encoder with a similar hierarchical structure into the model to extract features from the images. Experiments on 3D thorax CT and brain MRI demonstrate that our approach outperforms state of the art in image generation. We also demonstrate clinical applications of the proposed model in data augmentation and clinical-relevant feature extraction.
Online public opinion during the first epidemic wave of COVID-19 in China based on Weibo data

Abstract As COVID-19 spread around the world, epidemic prevention and control policies have been adopted by many countries. This process has prompted online social platforms to become important channels to enable people to socialize and exchange information. The massive use of social media data mining techniques, to analyze the development online of public opinion during the epidemic, is of great significance in relation to the management of public opinion. This paper presents a study that aims to analyze the developmental course of online public opinion in terms of fine-grained emotions presented during the COVID-19 epidemic in China. It is based on more than 45 million Weibo posts during the period from December 1, 2019 to April 30, 2020. A text emotion extraction method based on a dictionary of emotional ontology has been developed. The results show, for example, that a high emotional effect is observed during holidays, such as New Year. As revealed by Internet users, the outbreak of the COVID-19 epidemic and its rapid spread, over a comparatively short period of time, triggered a sharp rise in the emotion “fear”. This phenomenon was noted especially in Wuhan and the immediate surrounding areas. Over the initial 2 months, although this “fear” gradually declined, it remained significantly higher than the more common level of uncertainty that existed during the epidemic’s initial developmental era. Simultaneously, in the main city clusters, the response to the COVID-19 epidemic in central cities, was stronger than that in neighboring cities, in terms of the above emotion. The topics of Weibo posts, the corresponding emotions, and the analysis conclusions can provide auxiliary reference materials for the monitoring of network public opinion under similar major public events.
Image-Based Model Parameter Optimization Using Model-Assisted Generative Adversarial Networks

We propose and demonstrate the use of a model-assisted generative adversarial network (GAN) to produce fake images that accurately match true images through the variation of the parameters of the model that describes the features of the images. The generator learns the model parameter values that produce fake images that best match the true images. Two case studies show excellent agreement between the generated best match parameters and the true parameters. The best match model parameter values can be used to retune the default simulation to minimize any bias when applying image recognition techniques to fake and true images. In the case of a real-world experiment, the true images are experimental data with unknown true model parameter values, and the fake images are produced by a simulation that takes the model parameters as input. The model-assisted GAN uses a convolutional neural network to emulate the simulation for all parameter values that, when trained, can be used as a conditional generator for fast fake-image production.
Semantic enrichment for enhancing LAM data and supporting digital humanities. Review article

With the rapid development of the digital humanities (DH) field, demands for historical and cultural heritage data have generated deep interest the data provided by libraries, archives, and museums (LAMs). In order to enhance LAM data’s quality and discoverability while enabling a self-sustaining ecosystem, “semantic enrichment” becomes a strategy increasingly used by LAMs during recent years. This article introduces a number of semantic enrichment methods and efforts that can be applied to LAM data at various levels, aiming to support deeper and wider exploration and use of LAM data in DH research. The real cases, research projects, experiments, and pilot studies shared in this article demonstrate endless potential for LAM data, whether they are structured, semi-structured, or unstructured, regardless of what types of original artifacts carry the data. Following their roadmaps would encourage more effective initiatives and strengthen this effort to maximize LAM data’s discoverability, use- and reuse-ability, and their value in the mainstream of DH and Semantic Web.
Mobile educational follow-up application for patients with peripheral arterial disease

ABSTRACT Objective: to describe the development of a prototype mobile educational application for nursing follow-up aimed at patients diagnosed with peripheral arterial disease. Method: a prototype-based technological production study. The construction followed the contextualized instructional design model using two steps: analysis and design and development. Results: the pedagogical content of the application was based on a survey of needs of patients with Peripheral Arterial Disease and treatments recommended in the literature. The prototype developed contained concepts, risk factors, signs and symptoms, treatment, importance of medications and their side effects, frequent doubts, necessary health care, and follow-up of patients by monitoring the evolution of the cicatricial process of lesions and possible complications, clarification of doubts and stimulus for continuation of treatment. Conclusion: the use of health applications is a technological tool with the potential to improve the follow-up of patients regarding the progress of the disease and self-care, monitoring of risk factors, co-participation of the patient in the treatment, family participation, as well as planning of individualized care, and cost reduction for the health system.
A Novel Image-Based Inertial Impact Test (IBII) for the Transverse Properties of Composites at High Strain Rates

Current methods for testing the high strain rate properties of composites require multiple assumptions that limit achievable strain rates. Therefore, this study presents a new method for testing the transverse properties of composites at high strain rates using ultra-high speed imaging. The image-based inertial impact test developed here uses the reflection of a compressive stress wave to generate tensile stress in the specimen. Throughout the test, full-field displacement measurements are taken. The acceleration and strain fields are then derived from the displacement fields. The acceleration is then used to calculate the average stress in the specimen. This paper describes the optimisation of the experimental configuration using simulations and the experimental validation of the technique. The elastic modulus and tensile strength were identified at strain rates of ∼ 2000 s−1. The results showed an increase of 8% in elastic modulus and an increase of 57% in strength compared to quasi-static values.
Sentiment Analysis of Amazon Products Using Ensemble Machine Learning Algorithm

In recent years, Sentimental Analysis is used in all online product firms. The number of users using the particular product has increased which makes the industry to improvise the characteristics of the product. These days, many users who are using websites, blogs, online shopping tends to review the products they used. These reviews were taken into consideration by other users during their search for products. Hence the industry has found the root of delivering the correct product searched by the user based on the reviews of the users using the concept of sentimental analysis. Sentimental Analysis is the concept of data analysis where the collections of reviews are taken into consideration, and those reviews are analyzed, processed and recommended to the user. The reviews given are longer and which consist of a few paragraphs of content. In this paper, the dataset is collected from the official product sites. Initially, these reviews must be pre-processed in order to remove the unwanted data’s such as stop words, be verbs, punctuations, and conjunctions. Once, the pre-processing is over the trained dataset is classified using Naive Bayes and SVM algorithm. These existing algorithms provided the accuracy which is not worth enough. Hence, an ensemble approach has been applied to enhance the accuracy of the given reviews. An ensemble is a classification approach by combining two or more algorithms and calculate the mode value based on the vote reference for every algorithm which is used. In this paper, Naive Bayes, SVM, and Ensemble algorithm are combined. We proposed an Ensemble method that helps in providing better accuracy than the current existing algorithm. Once the accuracy is calculated, based on the reviews, the particular product is recommended for the user.
Highly parallel simulation and optimization of photonic circuits in time and frequency domain based on the deep-learning framework PyTorch

Abstract We propose a new method for performing photonic circuit simulations based on the scatter matrix formalism. We leverage the popular deep-learning framework PyTorch to reimagine photonic circuits as sparsely connected complex-valued neural networks. This allows for highly parallel simulation of large photonic circuits on graphical processing units in time and frequency domain while all parameters of each individual component can easily be optimized with well-established machine learning algorithms such as backpropagation.
On the Latency of USB-Connected Input Devices

We propose a method for accurately and precisely measuring the intrinsic latency of input devices and document measurements for 36 keyboards, mice and gamepads connected via USB. Our research shows that devices differ not only in average latency, but also in the distribution of their latencies, and that forced polling at 1000 Hz decreases latency for some but not all devices. Existing practices - measuring end-to-end latency as a proxy of input latency and reporting only mean values and standard deviations - hide these characteristic latency distributions caused by device intrinsics and polling rates. A probabilistic model of input device latency demonstrates these issues and matches our measurements. Thus, our work offers guidance for researchers, engineers, and hobbyists who want to measure the latency of input devices or select devices with low latency.
Game-based adaptive anomaly detection in wireless body area networks

Wireless Body Area Network (WBAN) is a quite suitable communication tool for medical IoT devices that are deployed to collect physiological parameters and forecast real-time events in order to facilitate the diagnostic decision-making for the medical staff. However, sensor readings may be inaccurate due to resource-constrained devices, sensor misplacement, hardware faults, and other environmental factors. Therefore, anomaly detection is envisioned as a promising approach to deal with unreliable and malicious data injection to improve remote patient monitoring systems and reduce false medical diagnosis. In this context, several data analysis and machine learning tools have been proposed to detect abnormal deviations in WBAN. Nevertheless, no one considers the dynamic context changes of WBAN to provide adaptive and dynamic outlier detection. In addition, most of them ignore the co-existence of strong spatial and temporal correlations between monitored physiological attributes. To this end, we propose a two-level lightweight and adaptive anomaly detection approach to discard false alarms caused by faulty measurements and raise alarms only when a patient seems to be in emergency situations. In the first level, a game-theoretic technique is introduced wherein body-worn sensor nodes exploit the spatiotemporal correlation among readings to locally and adaptively detect anomalous events according to the dynamic context changes of WBAN. In the second level, we apply the Mahalanobis distance in the Local Processing Unit (LPU) which has a global view for multivariate analysis. Our main objective is to ensure a tradeoff between detection accuracy, false positive rates, and network performance while considering the WBAN environment constraints. The proposed approach is evaluated through numerical simulations on a real physiological data set. Simulation results prove the effectiveness of the proposed approach in terms of achieving high detection accuracy with low false alarm rate and energy consumption.
Fast Graph Fourier Transforms Based on Graph Symmetry and Bipartition

The graph Fourier transform (GFT) is an important tool for graph signal processing, with applications ranging from graph-based image processing to spectral clustering. However, unlike the discrete Fourier transform, the GFT typically does not have a fast algorithm. In this work, we develop new approaches to accelerate the GFT computation. In particular, we show that Haar units (Givens rotations with angle $\pi/4$) can be used to reduce GFT computation cost when the graph is bipartite or satisfies certain symmetry properties based on node pairing. We also propose a graph decomposition method based on graph topological symmetry, which allows us to identify and exploit butterfly structures in stages. This method is particularly useful for graphs that are nearly regular or have some specific structures, e.g., line graphs, cycle graphs, grid graphs, and human skeletal graphs. Though butterfly stages based on graph topological symmetry cannot be used for general graphs, they are useful in applications, including video compression and human action analysis, where symmetric graphs, such as symmetric line graphs and human skeletal graphs, are used. Our proposed fast GFT implementations are shown to reduce computation costs significantly, in terms of both number of operations and empirical runtimes.
Using distant supervision to augment manually annotated data for relation extraction

Significant progress has been made in applying deep learning on natural language processing tasks recently. However, deep learning models typically require a large amount of annotated training data while often only small labeled datasets are available for many natural language processing tasks in biomedical literature. Building large-size datasets for deep learning is expensive since it involves considerable human effort and usually requires domain expertise in specialized fields. In this work, we consider augmenting manually annotated data with large amounts of data using distant supervision. However, data obtained by distant supervision is often noisy, we first apply some heuristics to remove some of the incorrect annotations. Then using methods inspired from transfer learning, we show that the resulting models outperform models trained on the original manually annotated sets.
The Construction Contract Execution Through the Integration of Blockchain Technology

This paper aims to analyze the Blockchain level of implementation, focusing on the AEC sector that has always suffered from lack of trust, incomplete sharing, and transparency of information flow throughout the process execution. In this context, the progressive introduction of BIM based on the Blockchain technology can provide a trustworthy infrastructure for information management during the design, tender, and construction phases.
Securing DICOM images by a new encryption algorithm using Arnold transform and Vigenère cipher

This study presents a novel encryption method for the security of DICOM (Digital Imaging and Communications in Medicine) images used in medical applications. The proposed algorithm splits the original image into blocks of size 16 × 16 pixels, then encrypts it through three steps. Firstly, the keys k1, k2, k3 and k4 are transformed from four vectors of 16 pixels to a matrix of 16 × 16 pixels. Then, the proposed algorithm encrypts the image block-by-block using the Vigenère cipher algorithm. For each block, the proposed algorithm modifies the key using Arnold transform. The proposed encryption algorithm is scalable with colour images and JPEG (Joint Photographic Experts Group) compression. The cryptanalysis of the proposed algorithm demonstrates that it passed the cryptography attacks tests with success. Its running time shows that it is faster than a typical and recent image encryption algorithm.