Reviewerly/exHarmony huggingface dataset:
pyRDF2Vec: A Python Implementation and Extension of RDF2Vec

This paper introduces pyRDF2Vec, a Python software package that reimplements the well-known RDF2Vec algorithm along with several of its extensions. By making the algorithm available in the most popular data science language, and by bundling all extensions into a single place, the use of RDF2Vec is simplified for data scientists. The package is released under an MIT license and structured in such a way to foster further research into sampling, walking, and embedding strategies, which are vital components of the RDF2Vec algorithm. Several optimisations have been implemented in pyRDF2Vec that allow for more efficient walk extraction than the original algorithm. Furthermore, best practices in terms of code styling, testing, and documentation were applied such that the package is future-proof as well as to facilitate external contributions.
Clinical named entity recognition and relation extraction using natural language processing of medical free text: A systematic review

Natural Language Processing (NLP) applications have developed over the past years in various fields including its application to clinical free text for named entity recognition and relation extraction. However, there has been rapid developments the last few years that there's currently no overview of it. Moreover, it is unclear how these models and tools have been translated into clinical practice. We aim to synthesize and review these developments.We reviewed literature from 2010 to date, searching PubMed, Scopus, the Association of Computational Linguistics (ACL), and Association of Computer Machinery (ACM) libraries for studies of NLP systems performing general-purpose (i.e., not disease- or treatment-specific) information extraction and relation extraction tasks in unstructured clinical text (e.g., discharge summaries).We included in the review 94 studies with 30 studies published in the last three years. Machine learning methods were used in 68 studies, rule-based in 5 studies, and both in 22 studies. 63 studies focused on Named Entity Recognition, 13 on Relation Extraction and 18 performed both. The most frequently extracted entities were "problem", "test" and "treatment". 72 studies used public datasets and 22 studies used proprietary datasets alone. Only 14 studies defined clearly a clinical or information task to be addressed by the system and just three studies reported its use outside the experimental setting. Only 7 studies shared a pre-trained model and only 8 an available software tool.Machine learning-based methods have dominated the NLP field on information extraction tasks. More recently, Transformer-based language models are taking the lead and showing the strongest performance. However, these developments are mostly based on a few datasets and generic annotations, with very few real-world use cases. This may raise questions about the generalizability of findings, translation into practice and highlights the need for robust clinical evaluation.
PPADT: Privacy-Preserving Identity-Based Public Auditing With Efficient Data Transfer for Cloud-Based IoT Data

Public auditing is a significant technique in cloud-based Internet of Things (IoT) systems, which enables the verifier to check the integrity of IoT data stored in the cloud. Nowadays, data become a core property for owners. Once the data of one owner are sold to another one, the ownership of these data has to be transferred. However, the existing public auditing schemes with data transfer require all the authenticators corresponding to the transferred data to be transformed to the new ones for integrity auditing. It incurs significant computation cost because of recomputing the new authenticators for all transferred data, especially when a vast quantity of data is being transferred. In addition, the data privacy and the identity privacy of the data owner cannot be protected for the verifier in such schemes. Thus, how to achieve efficient data transfer and privacy protection are key challenges in public auditing with data transfer for cloud-based IoT data. In this article, we propose a privacy-preserving identity-based public auditing scheme with efficient data transfer for cloud-based IoT data (PPADT). In PPADT, all the authenticators corresponding to the transferred data blocks do not need to be transformed. We only need to transform an aggregated authenticator in the integrity auditing phase. It means that the computation cost of data transfer is independent of the number of transferred data blocks. Furthermore, the data owner’s identity privacy can be ensured with the assistance of the private key generator. The data privacy can also be guaranteed by employing the random masking technique.
Sentiment analysis of tweets and government translations: Assessing China’s post-COVID-19 landscape for signs of withering or booming

This article aims to gain insights into the prevailing public sentiment during the policy relaxation period by examining whether the post-COVID-19 landscape reflects signs of withering or booming conditions. Employing methods from natural language processing (NLP) and machine learning (ML), the analysis reveals a predominance of positive sentiment from December 7, 2022 to May 17, 2023, indicative of an optimistic perspective and a potentially flourishing environment. A predictive model based on logistic regression emerges as a notably effective tool for sentiment prediction, suggesting potential utility in predicting future public health crises. A comparison of sentiments in translations by the government aligns with previous research, revealing a less favorable depiction of translated texts compared to the source texts. Furthermore, the commonality index, a measure of group consensus value, surpasses the typical range, while the certainty index, a measure of confidence, slightly falls below the norm. These findings offer valuable insights for policy considerations while highlighting areas for international communication and understanding improvement.
Semi-Supervised Person Detection in Aerial Images with Instance Segmentation and Maximum Mean Discrepancy Distance

Detecting sparse, small, lost persons with only a few pixels in high-resolution aerial images was, is, and remains an important and difficult mission, in which a vital role is played by accurate monitoring and intelligent co-rescuing for the search and rescue (SaR) system. However, many problems have not been effectively solved in existing remote-vision-based SaR systems, such as the shortage of person samples in SaR scenarios and the low tolerance of small objects for bounding boxes. To address these issues, a copy-paste mechanism (ISCP) with semi-supervised object detection (SSOD) via instance segmentation and maximum mean discrepancy distance is proposed (MMD), which can provide highly robust, multi-task, and efficient aerial-based person detection for the prototype SaR system. Specifically, numerous pseudo-labels are obtained by accurately segmenting the instances of synthetic ISCP samples to obtain their boundaries. The SSOD trainer then uses soft weights to balance the prediction entropy of the loss function between the ground truth and unreliable labels. Moreover, a novel evaluation metric MMD for anchor-based detectors is proposed to elegantly compute the IoU of the bounding boxes. Extensive experiments and ablation studies on Heridal and optimized public datasets demonstrate that our approach is effective and achieves state-of-the-art person detection performance in aerial images.
Effects of a self-regulated-based gamified virtual reality system on students’ English learning performance and affection

ABSTRACTABSTRACTScholars have suggested English classrooms with sufficient context and fun activities to increase English performance and promote learning motivation. With the rapid growth of technology-enhanced learning, virtual reality (VR) has become increasingly popular in English learning. Using VR without a well-designed learning environment, however, may not guide students to focus on the learning content to achieve the learning goals. Hence, this study proposed a self-regulated-based gamified VR learning system to trigger students to monitor their learning process to achieve their learning goals. To examine the effectiveness of the proposed system, a quasi-experiment was conducted in a middle school in China involving three groups: students who adopted the self-regulated-based gamified VR system, students who learned with the gamified VR system, and those who learned with a conventional VR system. The results showed that the self-regulated-based gamified VR system significantly improved the students' English learning performance regarding their English vocabulary and speaking, self-regulation, learning motivation, learning attitudes, and meta-cognition tendency. Moreover, their reflections on the self-regulated-based gamified VR system were revealed and discussed.KEYWORDS: Virtual realityEnglish language learningself-regulationgamified learningsecondary education Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsJia-Hua ZhaoMs. Jia-Hua Zhao is a PhD student at the Institute for Innovative Learning, Mahidol University. Her research interests include e-learning and game-based learning.Patcharin PanjabureeDr. Patcharin Panjaburee is an associate professor at Faculty of Education, Khon Kaen University. Her research interests include technology-enhanced learning, mobile learning environment, digital game-based learning, adaptive web-based learning, computer testing and diagnostic systems, and ubiquitous learning environment.Gwo-Jen HwangDr. Gwo-Jen Hwang is a chair professor at the Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology and the honorary chair professor at Graduate Institute of Educational information and Measurement, National Taichung University of Education, Taiwan. His research interests include mobile learning, digital game-based learning, flipped classroom and AI in education.Wararat WongkiaDr. Wararat Wongkia is an assistant professor at the Institute for Innovative Learning, Mahidol University. His research interests include educational science, mathematics education, and technology-enhanced learning.
Accelerating automatic hate speech detection using parallelized ensemble learning models

With increasing number of social media users and online engagement, it is essential to study hate speech propagation on social media platforms (SMPs). Automatic hate speech detection on social media is of utmost importance as hate speech can create discomfort among users and potentially generate a strong reaction in society. Ensemble learning algorithms are helpful in addressing sentiment-based classification due to their fault tolerance and efficiency. However, a simple, scalable, and robust framework is required to deal with large-scale data efficiently and accurately. Therefore, we propose parallelization to the standard ensemble learning algorithms to speed up the automatic hate speech detection on SMPs. In this study, we parallelize bagging, A-stacking, and random sub-space algorithms and test their serial and parallel versions on the standard high-dimensional datasets for hate speech detection. The experiments are performed using six datasets that address hate speech propagation during events like the COVID-19 pandemic, the US presidential election (2020), and the farmers' protest in India (2021). Our parallel models observe a significant speedup with high efficiency, claiming that the proposed models are suitable for the considered application. Also, one of the main motivations of this study is to highlight the importance of generalization by testing the models under the cross-dataset environment. We observed that the accuracy is not affected while parallelizing the algorithms compared with serial algorithms executing on a single machine.
Coherent dynamics of a photon-dressed qubit

We consider the dynamics and stationary regime of a capacitively-shunted transmon-type qubit in front of a mirror, affected by two signals: probe and dressing signals. By varying the parameters of these signals and then analyzing the probe signal (reflected by the atom-mirror system), it is possible to explore the system dynamics, which can be described by the Bloch equation. The obtained time-dependent occupation probabilities are related to the experimentally measured reflection coefficient. The study of this type of dynamics opens up new horizons for better understanding of the system properties and underlying physical processes, such as Landau-Zener-Stuckelberg-Majorana transitions.
Contextual Embeddings based on Fine-tuned Urdu-BERT for Urdu threatening content and target identification

Identification of threatening text on social media platforms is a challenging task. Contrary to the high-resource languages, the Urdu language has very limited such approaches and the benchmark approach has an issue of inappropriate data annotation. Therefore, we present robust threatening content and target identification as a hierarchical classification model for Urdu tweets. This study investigates the potential of the Urdu-BERT (Bidirectional Encoder Representations from Transformer) language model to learn universal contextualized representations aiming to showcase its usefulness for binary classification tasks of threatening content and target identification. We propose to exploit a pre-trained Urdu-BERT as a transfer learning model after fine-tuning its parameters on a newly designed Urdu corpus from the Twitter platform. The proposed dataset contains 2,400 tweets manually annotated as threatening or non-threatening at the first level and threatening tweets are further categorized into individual or group at the second level. The performance of fine-tuned Urdu-BERT is compared with the benchmark study and other feature models. Experimental results show that the fine-tuned Urdu-BERT model achieves state-of-the-art performance by obtaining 87.5% accuracy and 87.8% F1-score for threatening content identification and 82.5% accuracy and 83.2% F1-score for target identification task. Furthermore, the proposed model outperforms the benchmark study.
A magnification-independent method for breast cancer classification using transfer learning

Breast cancer is the most common and diagnosed cancer among women worldwide. Doctors use breast imaging reporting and data systems to classify breast density. This study proposes a transfer learning-based artificially intelligent (AI) system to classify breast cancer from the histopathological images of the breast. Three different Deep Convolutional Neural Networks (DCNN) architectures, namely, the Visual Geometry Group-16 (VGG-16), Depthwise Separable Convolutions (Xception), and Dense Convolutional Network-201 (Densenet-201), have been used as a base model in the transfer learning method used in this work. Each test image has been classified into benign or malignant class after extracting the features from each test image using the three pre-trained base models. The proposed classification system is independent of the magnification factors of the images. The performance of the proposed breast cancer classification system has been evaluated on two widely used public datasets known as the Invasive Ductal Carcinoma (IDC) dataset and BreaKHis dataset. The classification accuracies of 99.42% (IDC dataset) and 99.12% (BreaKHis dataset) have been obtained from the proposed system. The results demonstrate that the proposed breast cancer classification system outperforms the state-of-the-art methods in this research domain.
Disrupting hate: The effect of deplatforming hate organizations on their online audience

How does removing the leadership of online hate organizations from online platforms change behavior in their target audience? We study the effects of six network disruptions of designated and banned hate-based organizations on Facebook, in which known members of the organizations were removed from the platform, by examining the online engagements of the audience of the organization. Using a differences-in-differences approach, we show that on average the network disruptions reduced the consumption and production of hateful content, along with engagement within the network among periphery members. Members of the audience closest to the core members exhibit signs of backlash in the short term, but reduce their engagement within the network and with hateful content over time. The results suggest that strategies of targeted removals, such as leadership removal and network degradation efforts, can reduce the ability of hate organizations to successfully operate online.
Quality Grading of River Crabs Based on Machine Vision and GA-BPNN

The prices of different quality river crabs on the market can vary several times. Therefore, the internal quality identification and accurate sorting of crabs are particularly important for improving the economic benefits of the industry. Using existing sorting methods by labor and weight to meet the urgent needs of mechanization and intelligence in the crab breeding industry is difficult. Therefore, this paper proposes an improved BP neural network model based on a genetic algorithm, which can grade the crab quality. We comprehensively considered the four characteristics of crabs as the input variables of the model, namely gender, fatness, weight, and shell color of crabs, among which gender, fatness, and shell color were obtained by image processing technology, whereas weight is obtained using a load cell. First, mature machine vision technology is used to preprocess the images of the crab's abdomen and back, and then feature information is extracted from the images. Next, genetic and backpropagation algorithms are combined to establish a quality grading model for crab, and data training is conducted on the model to obtain the optimal threshold and weight values. Analysis of experimental results reveals that the average classification accuracy reaches 92.7%, which proves that this method can achieve efficient and accurate classification and sorting of crabs, successfully addressing market demand.
EC-Crypto: Highly Efficient Area-Delay Optimized Elliptic Curve Cryptography Processor

Elliptic Curve Cryptography (ECC) based security protocols require much shorter key space which makes ECC the most suitable option for resource-limited devices as compared to the other public key cryptography (PKC) schemes. This paper presents a highly efficient area-delay optimized ECC crypto processor over the general prime field ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}_{p}$ </tex-math></inline-formula> ). It is structured on a new novel finite field multiplier (FFM) where several optimization techniques have been incorporated to shorten the latency and hardware resource consumption. The proposed FFM architecture is embedded with a finite field adder/subtractor (FFAS) unit which is utilized to perform FFAS operations instead of deploying a dedicated unit. The Common Z (Co-Z) coordinates with the Montgomery ladder method are used to compute point multiplication, a core operation in all ECC-based crypto protocols. The work also proposes an efficient scheduling strategy to execute low-level finite field arithmetic primitives with minimum latency on the employed finite field arithmetic units. Due to these techniques, the proposed ECC processor is optimized for hardware resources, latency, and throughput. It is captured in Verilog-HDL, synthesized, and implemented on Virtex-7, Kintex-7, and Virtex-6 FPGA platforms using Xilinx Vivado and ISE Design Suite tools. On the Virtex-7 FPGA platform, it computes a single 256-bit scalar multiplication primitive in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.7~m\text{s}$ </tex-math></inline-formula> , consumes just 6.2K slices, and delivers a throughput of 1428 operations per second. The implementation results show that it is a highly efficient design outperforming the state-of-the-art by providing a better area-delay product and higher efficiency. Therefore, it has the potential to be deployed in many applications where both latency and resource requirements are critical.
Geochemical and remote sensing integrated with satellite gravity data of Darhib and Atshan talc deposits, South Eastern Desert, Egypt

The current contribution conducted new geochemical, remote sensing integrated with gravity detailed studies of talc deposits to identify the talc protolith as well as its extension, depth, and structures. There are two examined areas, distributed from north to south, Atshan and Darhib and both belong to the southern sector of the Egyptian Eastern Desert. They occur as individual lenses or pocket bodies in ultramafic-metavolcanics following NNW-SSE and E-W shear zones. Geochemically, among the investigated talc, Atshan samples have high contents of SiO2 (av. 60.73 wt.%), and higher concentrations of transition elements such as Co (av. 53.92 ppm), Cr (781 ppm), Ni (av. 1303.6 ppm), V (av. 16.67 ppm), and Zn (av. 55.7 ppm). Notably, the examined talc deposits contain low contents of CaO (av. 0.32 wt.%), TiO2 (av. 0.04 wt.%), SiO2/MgO (av. 2.15), and Al2O3 (av. 0.72 wt.%), which is comparable with ophiolitic peridotite and forearc setting. False color composite (FCC), principal component analysis (PCA), minimum noise fraction (MNF), and band ratio (BR) have been used to distinguish talc deposits in the investigated areas. Two new proposed band ratios were created to separate talc deposits. FCC band ratios (2/4, 4/7, 6/5) and (4 + 3/5, 5/7, 2 + 1/3) have been derived to focus on talc deposits in two case studies, Atshan and Darhib areas. The application of regional, residual, horizontal gradient (HG), and analytical signal (AS) techniques to gravity data are used in interpreting the structural directions of the study area. The analysis of this technique displays several notable faults trending in NW-SE, NE-SW, NNW-SSE, and E-W directions. Two techniques of gravity depth calculation were applied in the study areas, namely source parameter image (SPI), and Euler deconvolution (EU). The analysis of these techniques reflects that the depth of subsurface sources ranges between 383 and 3560 m. Talc deposits may be attributed to greenschist facies metamorphism or to a magmatic solution that is (associated with granitic intrusions) interacted with the surrounding volcanic rocks forming metasomatic minerals.
An Improved YOLOv7 Model Based on Visual Attention Fusion: Application to the Recognition of Bouncing Locks in Substation Power Cabinets

With the continuous progress of intelligent power system technology, in order to meet the needs of substation operation and maintenance, a target detection algorithm is applied to identify the status of equipment switches. YOLOv7, as the latest achievement of YOLO (You Only Look Once) series algorithms, has good speed and accuracy in target detection tasks. However, when the generalized network is applied in a specific scenario, its advantages are not obvious due to its high weight and poor portability. In this paper, an improved GF-YOLOv7 network model is proposed to apply in the recognition of the status of bounce locks in a substation. The MobileViT module is used to improve the feature extraction ability of the backbone network. Referring to the CBAM feature attention mechanism, the channel attention module and the spatial attention module are used to design a more lightweight feature fusion network. The experimental results in the test set show that the proposed network can significantly reduce the network weight and improve the detection accuracy on the basis of a small reduction in the detection speed, and the accuracy reaches 97.8%, which can meet the needs of the detection task of substation bounce locks.