Reviewerly/exHarmony huggingface dataset:
Adaptable physics-based super-resolution for electron backscatter diffraction maps

Abstract In computer vision, single-image super-resolution (SISR) has been extensively explored using convolutional neural networks (CNNs) on optical images, but images outside this domain, such as those from scientific experiments, are not well investigated. Experimental data is often gathered using non-optical methods, which alters the metrics for image quality. One such example is electron backscatter diffraction (EBSD), a materials characterization technique that maps crystal arrangement in solid materials, which provides insight into processing, structure, and property relationships. We present a broadly adaptable approach for applying state-of-art SISR networks to generate super-resolved EBSD orientation maps. This approach includes quaternion-based orientation recognition, loss functions that consider rotational effects and crystallographic symmetry, and an inference pipeline to convert network output into established visualization formats for EBSD maps. The ability to generate physically accurate, high-resolution EBSD maps with super-resolution enables high-throughput characterization and broadens the capture capabilities for three-dimensional experimental EBSD datasets.
A Novel Hybrid Network for Arabic Sentiment Analysis using fine-tuned AraBERT model

The pre-trained word embedding models become widely used in Natural Language Processing (NLP), but they disregard the context and sense of the text.We study in this paper, the capacity of pre-trained BERT model (Bidirectional Encoder Representations from Transformers) for the Arabic language to classify Arabic tweets using a hybrid network of two famous models; Bidirectional Long Short Term Memory (BiLSTM) and Gated Recurrent Unit (GRU) inspired by the great achievement of deep learning algorithms.In this context, we finetuned the Arabic BERT (AraBERT) parameters and we used it on three merged datasets to impart its knowledge for the Arabic sentiment analysis.For that, we lead the experiments by comparing the AraBERT model in one hand in the word embedding phase, with a statics pretrained word embeddings method namely AraVec and FastText, and on another hand in the classification phase, we compared the hybrid model with convolutional neural network (CNN), long short-term memory (LSTM), BiLSTM, and GRU, which are prevalently preferred in sentiment analysis.The results demonstrate that the fine-tuned AraBERT model, combined with the hybrid network, achieved peak performance with up to 94% accuracy.
SGBANet: Semantic GAN and Balanced Attention Network for Arbitrarily Oriented Scene Text Recognition

Scene text recognition is a challenging task due to the complex backgrounds and diverse variations of text instances. In this paper, we propose a novel Semantic GAN and Balanced Attention Network (SGBANet) to recognize the texts in scene images. The proposed method first generates the simple semantic feature using Semantic GAN and then recognizes the scene text with the Balanced Attention Module. The Semantic GAN aims to align the semantic feature distribution between the support domain and target domain. Different from the conventional image-to-image translation methods that perform at the image level, the Semantic GAN performs the generation and discrimination on the semantic level with the Semantic Generator Module (SGM) and Semantic Discriminator Module (SDM). For target images (scene text images), the Semantic Generator Module generates simple semantic features that share the same feature distribution with support images (clear text images). The Semantic Discriminator Module is used to distinguish the semantic features between the support domain and target domain. In addition, a Balanced Attention Module is designed to alleviate the problem of attention drift. The Balanced Attention Module first learns a balancing parameter based on the visual glimpse vector and semantic glimpse vector, and then performs the balancing operation for obtaining a balanced glimpse vector. Experiments on six benchmarks, including regular datasets, i.e., IIIT5K, SVT, ICDAR2013, and irregular datasets, i.e., ICDAR2015, SVTP, CUTE80, validate the effectiveness of our proposed method.
Culture Matters: A Survey Study of Social Q&amp;A Behavior

Online social networking tools are used around the world by people to ask questions of their friends, because friends provide direct, reliable, contextualized, and interactive responses. However, although the tools used in different cultures for question asking are often very similar, the way they are used can be very different, reflecting unique inherent cultural characteristics. We present the results of a survey designed to elicit cultural differences in people’s social question asking behaviors across the United States, the United Kingdom, China, and India. The survey received responses from 933 people distributed across the four countries who held similar job roles and were employed by a single organization. Responses included information about the questions they ask via social networking tools, and their motivations for asking and answering questions online. The results reveal culture as a consistently significant factor in predicting people’s social question and answer behavior. The prominent cultural differences we observe might be traced to people’s inherent cultural characteristics (e.g., their cognitive patterns and social orientation), and should be comprehensively considered in designing social search systems.
Exploring Students' Perception of Using Grammarly to Check Grammar in Their Writing

There are many online platforms that can help students to check their grammar. To correct their writing, students usually use online grammar checkers such as Grammarly. This research was conducted to know the English department students’ perspective of Grammarly software as the media to check the grammar in their writing. This research was conducted by using a descriptive qualitative method. The data of this research were taken through a questionnaire. By conducting this research, researchers hope that students can have an idea about Grammarly as one of the online grammar checkers. The result of this research showed that Grammarly gives beneficial for students. Grammarly really helps the students to correct their writing. However, Grammarly still has some drawbacks, such as miscorrection and limited features for the free version, yet students agree that Grammarly helps them a lot.
PoseGPT: Quantization-Based 3D Human Motion Generation and Forecasting

We address the problem of action-conditioned generation of human motion sequences. Existing work falls into two categories: forecast models conditioned on observed past motions, or generative models conditioned on action labels and duration only. In contrast, we generate motion conditioned on observations of arbitrary length, including none. To solve this generalized problem, we propose PoseGPT, an auto-regressive transformer-based approach which internally compresses human motion into quantized latent sequences. An auto-encoder first maps human motion to latent index sequences in a discrete space, and vice-versa. Inspired by the Generative Pretrained Transformer (GPT), we propose to train a GPT-like model for next-index prediction in that space; this allows PoseGPT to output distributions on possible futures, with or without conditioning on past motion. The discrete and compressed nature of the latent space allows the GPT-like model to focus on long-range signal, as it removes low-level redundancy in the input signal. Predicting discrete indices also alleviates the common pitfall of predicting averaged poses, a typical failure case when regressing continuous values, as the average of discrete targets is not a target itself. Our experimental results show that our proposed approach achieves state-of-the-art results on HumanAct12, a standard but small scale dataset, as well as on BABEL, a recent large scale MoCap dataset, and on GRAB, a human-object interactions dataset.
Blockchain and NFTs for Trusted Ownership, Trading, and Access of AI Models

The demand for high-quality Artificial Intelligence (AI) models is ever-increasing in this digital era.However, most of the existing methods leveraged for managing the ownership, trading, and access of AI models fall short of providing traceability, transparency, audit, security, and trustful features.In this paper, we propose a solution based on blockchain and Non-fungible Tokens (NFTs) to manage ownership rights and exchange of AI models in a transparent, traceable, auditable, secure, and trustworthy manner.Smart contracts are employed to enforce ownership, ease of access, and exchange policies for the unique NFT linked to an AI model.We use decentralized storage of the InterPlanetary File System (IPFS) and proxy re-encryption oracles to securely fetch, store, and share data related to AI models.We present algorithms along with their implementation, testing, and validation details.The proposed solution is evaluated using cost and security analyses to show its affordability and resiliency against security threats and attacks.All smart contract codes are made publicly available on GitHub.
A Smart System for Personal Protective Equipment Detection in Industrial Environments Based on Deep Learning at the Edge

Real-time object detection is currently used to automate various tasks in industrial environments. One of the most important tasks is to improve the safety of workers by monitoring the correct use of Personal Protective Equipment (PPE) in dangerous areas. In this context, usually, a monitoring system analyzes the stream of videos from surveillance cameras to assess PPE usage in real time. When a worker not wearing the appropriate PPE is detected, an acoustic or visual alarm is triggered automatically to raise attention and awareness. The solutions proposed so far are mostly cloud-based systems: images from the site are continuously offloaded to the cloud for analysis. This centralized architecture requires significant network bandwidth to transmit the video feeds through an internet connection that must be reliable, as a network outage would disrupt the service. In this work, we propose a system for real-time PPE detection based on video streaming analysis and Deep Neural Network (DNN). We adopt the edge computing model in which the application for image analysis and classification is deployed on an embedded system installed in proximity of the camera and directly connected to it. The system does not require continuous image transmission towards a cloud system, thus ensuring bandwidth efficiency, reliability, and workers’ privacy. A prototype of the proposed system is developed exploiting a low-cost commercial embedded system, i.e. a Raspberry PI, equipped with an Intel Neural Compute Stick 2. We tested the system with five different pre-trained convolutional neural networks (CNNs), fine-tuned to detect different PPEs, namely helmets, vests, and gloves. In our experimental evaluation, we first compared the five CNNs in terms of classification performance and inference latency. Then, we deployed each CNN on the real system and evaluated the system’s throughput regarding the number of video frames analyzed each second.
Characterizing Retweet Bots: The Case of Black Market Accounts

Malicious Twitter bots are detrimental to public discourse on social media. Past studies have looked at spammers, fake followers, and astroturfing bots, but retweet bots, which artificially inflate content, are not well understood. We present the first study focusing exclusively on retweet bots. We characterize retweet bots that have been uncovered by purchasing retweets from the black market. We detect whether they are fake or genuine accounts involved in inauthentic activities and what they do in order to appear legitimate. We also analyze their differences from human-controlled accounts. From our findings on the nature and life-cycle of retweet bots, we also point out several inconsistencies between the retweet bots used in this work and bots studied in prior works. Our findings challenge some of the fundamental assumptions related to bots and in particular how to detect them.
BCTrustFrame: Enhancing Trust Management via Blockchain and IPFS in 6G Era

Beginning in 2030, sixth generation (6G) mobile communication is expected to play a key role by collecting billions of things, humans, and robots, resulting in zettabytes of digital information. The key features of 6G include inherent connected intelligence in the telecommunication networks with devices from different vendors. Hence, trust should be enforced among devices, sub-networks, and applications, especially for mission-critical services. Currently, trust-based intrusion detection can measure an entity's trustworthiness by collecting statistics or sending consultation messages, while it may become inefficient in the era of 6G due to the overloaded traffic and potential latency. In this article, we discuss the challenges of deploying trust-based intrusion detection in 6G, and design BCTrustFrame, a framework of blockchain-based trust management with IPFS, which aims to enhance the effectiveness of trust evaluation via the collaboration of blockchain and IPFS technology. We also perform a case study to demonstrate the viability and performance.
Context-Aware Deep Learning Model for Detection of Roman Urdu Hate Speech on Social Media Platform

Over the last two decades, social media platforms have grown dramatically.Twitter and Facebook are the two most popular social media platforms, with millions of active users posting billions of messages daily.These platforms allow users to have freedom of expression.However, some users exploit this facility by disseminating hate speeches.Manual detection and censorship of such hate speeches are impractical; thus, an automatic detection mechanism is required to detect and counter hate speeches in a real-time environment.Most research in hate speech detection has been carried out in the English language.Still, minimal work has been explored in other languages, mainly Urdu written in Roman Urdu script.A few research have attempted machine learning, and deep learning models for Roman Urdu hate speech detection; however, due to a scarcity of Roman Urdu resources, and a large corpus with defined annotation rules, a robust hate speech detection model is still required.With this motivation, this study contributes in the following manner: we developed annotation guidelines for Roman Urdu Hate Speech.Second, we constructed a new Roman Urdu Hate Speech Dataset (RU-HSD-30K) that was annotated by a team of experts using the annotation rules.To the best of our knowledge, the Bi-LSTM model with an attention layer for Roman-Urdu Hate Speech Detection has not been explored.Therefore, we developed a contextaware Roman Urdu Hate Speech detection model based on Bi-LSTM with an attention layer and used custom word2vec for word embeddings.Finally, we examined the effect of lexical normalization of Roman Urdu words on the performance of the proposed model.Different traditional as well as deep learning models, including LSTM and CNN models, were used as baseline models.The performance of the models was assessed in terms of evaluation matrices like accuracy, precision, recall, and F1-score.The generalization of each model is also evaluated on a cross-domain dataset.Experimental results revealed that Bi-LSTM with attention outperformed the traditional machine learning models and other deep learning models with an accuracy score of 0.875 and an F-Score of 0.885.In addition, the results demonstrated that our suggested model (Bi-LSTM with Attention Layer) is more general than previous models when applied to unseen data.The results confirmed that lexical normalization of Roman Urdu words enhanced the performance of the suggested model.
Estimation of solar radiation by model dependent on extreme temperatures for the region of Puno, Peru

The objective of this research was to identify a model to estimate solar radiation for the Puno region based on extreme temperatures. For which seven models have been considered to estimate global solar radiation, to make the adjustments of the models, d a ta from the 2016-2018 period of the DAVIS meteorological station for the city of Puno were considered, where the coefficients of the models were adjusted considering 1097 data. To choose the appropriate model, the correlation coefficient between measured and estim a ted so la r radiation data was used. Then, the data measured by SENAMHI for the thirteen provinces of the Puno region have been considered. Concluding, it was possible to identify two models to estimate solar radiation in the Puno region, considering as input variable the difference in extreme temperatures and the latitude of the place, a correlation coefficient of 0.855 was obtained for the measured and estimated solar radiation data. . . . For the estimation of solar radiation in the Puno region, the data on extreme temperatures from SENAMHI for the thirteen provinces have been considered, reaching estimates of solar radiation through the regression model of order four.
Blockchain-Based Processing of Health Insurance Claims for Prescription Drugs

The current legacy system used in processing health insurance claims causes a huge amount of financial loss every year due to fraud claims. It is also highly prone to privacy and security threats due to the use of traditional methods. Health insurance claims for prescription drugs are one such claim that is highly prone to being tampered with. Also, there is a lack of linkage in the prescription data between the medical care provider and the pharmacy, which also leads to miscommunication and the use of false prescriptions. In this paper, we propose processing health insurance claims for drug prescriptions using blockchain technology that manages the processing of prescription drugs in a private, secure, trustworthy, and decentralized manner. The proposed system utilizes a private Ethereum blockchain. The system includes two smart contracts: registration and approval smart contracts, which provide traceability and trustworthiness to the system. The system was integrated with off-chain storage (IPFS) and a fronted decentralized applications (DApps), which improves the accessibility of centralized patient information by authorized parties. To maintain the privacy of the data, a gateway is used to filter the data that can be viewed by the participants. We present the system architecture, sequence diagrams, entity-relationship diagram, and algorithms to demonstrate the working principles of the proposed process for processing health insurance claims for prescription drugs using blockchain. The performance of the proposed solution is evaluated by conducting security analysis and comparing it to the existing solutions. Our smart contract code is publicly available on GitHub.
Artificial Intelligence and Cardiovascular Risk Prediction: All That Glitters is not Gold

Artificial intelligence (AI) is a broad term referring to any automated systems that need 'intelligence' to carry out specific tasks. During the last decade, AI-based techniques have been gaining popularity in a vast range of biomedical fields, including the cardiovascular setting. Indeed, the dissemination of cardiovascular risk factors and the better prognosis of patients experiencing cardiovascular events resulted in an increase in the prevalence of cardiovascular disease (CVD), eliciting the need for precise identification of patients at increased risk for development and progression of CVD. AI-based predictive models may overcome some of the limitations that hinder the performance of classic regression models. Nonetheless, the successful application of AI in this field requires knowledge of the potential pitfalls of the AI techniques, to guarantee their safe and effective use in daily clinical practice. The aim of the present review is to summarise the pros and cons of different AI methods and their potential application in the cardiovascular field, with a focus on the development of predictive models and risk assessment tools.
Prerequisites for learning in networked immersive virtual reality.

There has been growing interest in using virtual reality (VR) as a solution for many of the challenges facing distance education, such as fostering a sense of connectedness with classmates.However, implementing VR in distance education has its share of challenges, such as hardware accessibility and a scarcity of content which match curricula.In this exploratory, mixed-methods study, we examined 19 students' use of head-mounted displays to meet with classmates inside social VR.For 4 weeks, students worked together in small groups on various tasks inside a virtual environment.We present quantitative results on attitudes foundational to fostering ideal learning environments.Entitativity ("group-ness"), enjoyment, realism, and presence did not change over time, likely due to a small sample size resulting from technical difficulties in collecting data.We present qualitative observations on instructors' and students' experiences across time and with VR use, and how these may inform curricula development.First, it is critical to provide ample training time to allow students to grow accustomed to the medium before investigating how response to VR changes over time.Without learning how to use VR first, students cannot learn inside VR.Second, we discuss task type and content considerations within and outside of VR and provide recommendations on how to reduce cognitive load and encourage social interaction.Third, we address technological and social issues that are likely to arise.Overall, we focus on ways to create a sense of connectedness and reduce psychological distance and challenges that may disrupt meaningful interactions from taking place.
Retracted: Scientific programming using optimized machine learning techniques for software fault prediction to improve software quality

Abstract Retraction: [Muhammad Shafiq, Fatemah H. Alghamedy, Nasir Jamal, Tahir Kamal, Yousef Ibrahim Daradkeh, Mohammad Shabaz, Scientific programming using optimized machine learning techniques for software fault prediction to improve software quality, IET Software 2023 ( https://doi.org/10.1049/sfw2.12091 )]. The above article from IET Software , published online on 6 January 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.