Reviewerly/exHarmony huggingface dataset:
Live line strain clamp's DR image anomaly detection based on unsupervised learning

Abstract Due to the high‐risk working environment of high‐voltage transmission lines, defect samples of strain clamps cannot be fully and completely collected. As a result, the deep learning method based on defect sample tags cannot effectively identify all abnormalities. To solve this problem, an unsupervised anomaly detection method based on knowledge distillation is proposed, which only requires a small number of normal samples to drive the model for anomaly detection. ResNet is the framework of the teacherstudent model, and the feature activation layer after ResBlock is used for knowledge transfer. Residual‐assisted attention and pyramid‐splitting attention were used to enhance the spatial perception and multi‐scale information utilization ability of the model. This model only transmits the information of normal samples and is sensitive to abnormal samples. The proposed model outperformed the baseline by 23% and individual categories by 78% on the MVTec AD (Anomaly Detection Dataset) and outperformed the baseline by 45% and individual categories by 10% on the CIFAR10 and is also reliable for Mnist and Fashion Mnist. This method performs best (82.71%) over the existing method on the self‐built data set.
Fusion weighted features and BiLSTM-attention model for argument mining of EFL writing

Argument mining (AM), an emerging field in natural language processing (NLP), aims to automatically extract arguments and the relationships between them in texts. In this study, we propose a new method for argument mining of argumentative essays. The method generates dynamic word vectors with BERT (Bidirectional Encoder Representations from Transformers), encodes argumentative essays, and obtains word-level and essay-level features with BiLSTM (Bi-directional Long Short-Term Memory) and attention training, respectively. By integrating these two levels of features we obtain the full-text features so that the content in the essay is annotated according to Toulmin's argument model. The proposed method was tested on a corpus of 180 argumentative essays, and the precision of automatic annotation reached 69%. The experimental results show that our model outperforms existing models in argument mining. The model can provide technical support for the automatic scoring system, particularly on the evaluation of the content of argumentative essays.
Leveraging the Power of Crowdsourcing and Linked Open Data: Transformation of the Schoenberg Database of Manuscripts and the SDBM Name and Place Authorities

Linked data technologies are natural fits for building and managing controlled vocabularies and authorities, especially in a crowd-sourced environment where managing data integrity can be a challenge. This paper considers the role of researchers in contributing to the health and maintenance the Schoenberg Database of Manuscripts, particularly as it relates to its local Name Authority. It will also demonstrate how applying Linked Open Data technologies and processes in the SDBM can facilitate the symbiotic relationship between the SDBM user community–the “crowd”– and its data as well as prepare the data for reuse in other LOD contexts like Wikidata.
Multi-head second-order pooling for graph transformer networks

Graph transformer networks (GTNs) have great potential in graph-related tasks, particularly graph classification. GTNs use self-attention mechanism to extract both semantic and structural information, after which a class token is used as the global representation for graph classification.However, the class token completely abandons all information held by nodes, which leads to a huge loss of information and neglects the dependencies among different feature dimensions. Therefore, we propose a Multi-head Global Second-Order Pooling (MGSOP) to utilize the rich second-order statistics of all nodes. To this end, a second-order pooling module incorporated with multi-head structure generates multiple covariance representations for GTNs. Then, the first-order representation and concatenation of all covariance matrices are combined by an adaptive fusion module for better performance on different types of data. Furthermore, we introduce an efficient dropout-based normalization to utilize the geometry of covariance representation. Experiments conducted on six real-world graph benchmarks show our MGSOP outperforms its counterparts and achieves competitive results against state-of-the-arts.
Automated Generation of Human-readable Natural Arabic Text from RDF Data

With the advances in Natural Language Processing (NLP), the industry has been moving towards human-directed artificial intelligence (AI) solutions. Recently, chatbots and automated news generation have captured a lot of attention. The goal is to automatically generate readable text from tabular data or web data commonly represented in Resource Description Framework (RDF) format. The problem can then be formulated as Data-to-text (D2T) generation from structured non-linguistic data into human-readable natural language. Despite the significant work done for the English language, no efforts are being directed towards low-resource languages like the Arabic language. This work promotes the development of the first RDF data-to-text (D2T) generation system for the Arabic language while trying to address the low-resource limitation. We develop several models for the Arabic D2T task using transfer learning from large language models (LLM) such as AraBERT, AraGPT2, and mT5. These models include a baseline Bi-LSTM Sequence-to-Sequence (Seq2Seq) model, as well as encoder-decoder transformers like BERT2BERT, BERT2GPT, and T5. We then provide a detailed comparative study highlighting the strengths and limitations of these methods setting the stage for further advancement in the field. We also introduce a new Arabic dataset (AraWebNLG) that can be used for new model development in the field. To ensure a comprehensive evaluation, general-purpose automated metrics (BLEU and Perplexity scores) are used as well as task-specific human evaluation metrics related to the accuracy of the content selection and fluency of the generated text. The results highlight the importance of pre-training on a large corpus of Arabic data and show that transfer learning from AraBERT gives the best performance. Text-to-text pre-training using mT5 achieves second best performance results even with multilingual weights.
Using Semantic Web to Create and Explore an Index of Toponyms Cited in Medieval Geographical Works

Western thought in European history was mainly affected by the image of the world created during the Middle Ages and Renaissance. The most popular reason to travel during the Middle Ages was taking a pilgrimage. Jerusalem, Rome, and Santiago de Compostela were the most popular destinations. It is not surprising that a lot of works written by travellers as guides for pilgrims exist. By the beginning of the Renaissance, a more precise image of the world was defined, thanks to the discovery of ancient geographical models, especially the work of Ptolemy. The Italian National Research Project (PRIN) IMAGO --- Index Medii Aevi Geographiae Operum --- (2020-2023) aims to provide a systematic overview of the medieval and renaissance Latin geographical literature using the Semantic Web technologies and the LOD paradigm. Indeed, until now, this literature has not been studied using digital methods. In particular, this article presents how we formally represented the knowledge about the toponyms, or place names, in the IMAGO ontology. To maximise the interoperability, we developed the IMAGO ontology as an extension of two reference vocabularies: the CIDOC CRM and its extension FRBRoo, including its in-progress reformulation, LRMoo. Furthermore, we used Wikidata as reference knowledge base. As case study, we chose to represent the knowledge related to the toponyms cited by the Italian poet Dante Alighieri in his Latin works. We carried out a first experiment for visualising the knowledge about these toponyms on a map and in the form of tables and CSV files.
Incorrectly Generated RSA Keys: How I Learned To Stop Worrying And Recover Lost Plaintexts

Abstract When generating primes $p$ and $q$ for an RSA key, the algorithm specifies that if $p-1$ and $q-1$ must be relatively prime to the public exponent $e$. If this is not done, then the decryption exponent is not well defined. However, what if a software bug allows the generation of public parameters $N$ and $e$ of an RSA key with this property and then it is subsequently used for encryption? Though this may seem like a purely academic question, a software bug in a preview release of the Windows 10 operating system makes this question of more than purely theoretical. Without a well defined decryption exponent, plaintexts encrypted to such keys will be undecryptable thus potentially losing user data, a serious software defect. Though the decryption exponent is no longer well defined, it is in fact possible to recover the a small number of potential plaintexts, if the prime factors $p$ and $q$ of the public modulus $N$ are known. This paper presents an analysis of what steps fail in the RSA algorithm and derives a plaintext recovery algorithm. The runtime of this algorithm is $O(e)$ making it practical to use, and it has been implemented in python.
Modern Physics and Quantum Mechanics

Physics stood at the cusp of dramatic change near the end of the nineteenth century and the beginning of the twentieth century. Prior to that, there was a sense that physics was mature, complete and well-established, and broad subjects like mechanics, electromagnetism, and thermodynamics, which we call classical physics now, captured all the big ideas necessary to understand nature. It was as if all that was left to do was to work out some details of physics.
ABEE: automated bio entity extraction from biomedical text documents

Purpose The purpose of this study was to design a multitask learning model so that biomedical entities can be extracted without having any ambiguity from biomedical texts. Design/methodology/approach In the proposed automated bio entity extraction (ABEE) model, a multitask learning model has been introduced with the combination of single-task learning models. Our model used Bidirectional Encoder Representations from Transformers to train the single-task learning model. Then combined model's outputs so that we can find the verity of entities from biomedical text. Findings The proposed ABEE model targeted unique gene/protein, chemical and disease entities from the biomedical text. The finding is more important in terms of biomedical research like drug finding and clinical trials. This research aids not only to reduce the effort of the researcher but also to reduce the cost of new drug discoveries and new treatments. Research limitations/implications As such, there are no limitations with the model, but the research team plans to test the model with gigabyte of data and establish a knowledge graph so that researchers can easily estimate the entities of similar groups. Practical implications As far as the practical implication concerned, the ABEE model will be helpful in various natural language processing task as in information extraction (IE), it plays an important role in the biomedical named entity recognition and biomedical relation extraction and also in the information retrieval task like literature-based knowledge discovery. Social implications During the COVID-19 pandemic, the demands for this type of our work increased because of the increase in the clinical trials at that time. If this type of research has been introduced previously, then it would have reduced the time and effort for new drug discoveries in this area. Originality/value In this work we proposed a novel multitask learning model that is capable to extract biomedical entities from the biomedical text without any ambiguity. The proposed model achieved state-of-the-art performance in terms of precision, recall and F1 score.
SaFiDe: Detection of saccade and fixation periods based on eye-movement attributes from video-oculography, scleral coil or electrooculography data

In this work we present SaFiDe, a deterministic method to detect eye movements (saccades and fixations) from eye-trace data. We developed this method for human and nonhuman primate data from video- and coil-recorded eye traces and further applied the algorithm to eye traces computed from electrooculograms. All the data analyzed were from free-exploration paradigms, where the main challenge was to detect periods of saccades and fixations that were uncued by the task. The method uses velocity and acceleration thresholds, calculated from the eye trace, to detect saccade and fixation periods. We show that our fully deterministic method detects saccades and fixations from eye traces during free visual exploration. The algorithm was implemented in MATLAB, and the code is publicly available on a GitHub repository.•The algorithm presented is entirely deterministic, simplifying the comparison between subjects and tasks.•Thus far, the algorithm presented can operate over video-based eye tracker data, human electrooculogram records, or monkey scleral eye coil data.
Family Parents’ Perspective of Different Social-Economical Statuses about the Online and Offline Classes during the COVID-19 Pandemic

This research work aimed to characterize the perception of parents of educational institutions of various socioeconomic strata of Barranquilla-Colombia about the teaching of classes in the synchronous and asynchronous mode in times of Covid-19. With a qualitative approach and an interpretative-hermeneutical design, 6 parents were chosen and an unstructured interview was applied in which aspects such as connectivity, strategies used by teachers and interaction with them during the pandemic were addressed. The main finding was that parents who are enrolled in low-income institutions agree that they do not have the guarantees for their children to receive a quality education under the modality at home; For their part, the parents who have their children enrolled in a high socioeconomic level assure that despite the fact that educational institutions have resources to teach the contents of the areas of knowledge, their attendants were demotivated by the new modality of teaching.
Optimization Design of Server for Mobile Offline Data of Mechanical Equipment

Mechanical equipment data acquisition is indispensable to enterprises moving forward with intelligent transformation and digital transformation. However, in remote areas or in the occasion of underground operation where network is inaccessible, or when in industrial plants under exposure to electromagnetic interference with poor wireless telecommunication signals, the problem of mechanical equipment data acquisition is still to be addressed urgently. Therefore, mobile terminal becomes an inevitable option to collect and transmit off-line data of the mechanical equipment to the server. Based on the conventional service of mobile terminal PDA, we analyzed the characteristics of mobile service data online processing, established a general model of business data request processing with platform thinking, and designed a low-cost, efficient and decoupled solution for mobile offline data acquisition. The offline server is matched with the offline mobile terminal to form a complete mobile offline data acquisition scheme.
A Named Entity Recognition Method for Cigarette Production Process Standard Based on BERT-BiLSTM-CRF Model

Extracting useful information from unstructured cigarette production process standard text data is the key to building process standard knowledge base. To address this issue, a process text entity recognition method based on BERT-BiLSTM-CRF deep learning was presented in this paper. In comparison with the traditional methods, its advantage lies in the improvement of efficiency and accuracy by solving the key problems of dependence on contextual features, interference of multiple modifiers in knowledge entities, and the semantic polysemy. Finally, experiment was conducted with this method in this paper, and the results show this model can achieve 82.67% precision, 83.22% recall and 82.94% F1 value.
A Data Preprocessing Method for Strip Steel

Effective data preprocessing is crucial to establish a correct model and ensure the accuracy of the model. To solve the problems of poor integrity, high noise and irregular distribution for strip steel data, a preprocessing approach for data cleaning and data transformation of strip steel production data is proposed. According to the different characteristics of the strip steel data, several operations such as filling missing values, smoothing noise, normalizing and discretizing are carried out to assure the quality of the data used in subsequent modeling, thus improving the accuracy of the strip steel model.
Measurement Fusion Kalman Filter for the Multisensor Unmanned Aerial Vehicle Systems

In the flight control system of unmanned aerial vehicle (UAV), the measurement accuracy of single sensor is not high enough to get the true flight altitude. So it is necessary to utilize multiple sensors to improve the accuracy. First, state space model is built by combing the motion model and all sensor measurement models. Second, the noise variance recursive estimators are obtained for unknown sensor noise statistics. Finally, the self-tuning measurement fusion Kalman filter is proposed for the taking off and hovering state of UAV. The simulation results show that the positioning accuracy in the vertical direction is improved effectively, which can ensure the effectiveness and reliability of the UAV flight system.
Improving the Lightweight Object Detection Method for YOLOv5

As an excellent algorithm, YOLOv5 is an object detection model with the advantages of high flexibility and fast speed, but it has problems with many network parameters, complex model structure, and low boundary regression accuracy for the target. For the above problems, this study improves on the YOLOv5s algorithm and proposes a new model YOLO-GC with lower hardware requirements, fewer network parameters and higher boundary regression accuracy. First, the upsampling module of YOLOv5s is replaced by the CARAFE upsampling module to increase the receptive field and semantic features, the Ghost module to reduce the number of parameters and calculation, and the CBAM attention mechanism to combine the spatial and channel attention map, the model pays more attention to the key areas to improve the model accuracy. The test results of this research method on the PASCAL VOC object detection benchmark dataset show that compared with YOLOv5s, the number of parameters is reduced by 44.2%, the model size is reduced by 42.8%, the is increased by 1.2%, and the :0.95 An increase of 5.5%.
Development and Research of Vision Inspection System for Metro Machine Room

At present, the inspection of the subway machine room is still done by the staff, which not only occupies a lot of manpower and material resources, but also has the problem of insufficient real-time and accuracy. Therefore, the inspection efficiency and accuracy of subway machine room are improved by using inspection robot and embedded system inspection software written in Python language. Firstly, the image is pre-processed, and then the contour extraction algorithm is used to extract the contour in the binary image, and the state detection is carried out after the contour screening. Among them, the traditional template matching localization algorithm is not accurate, and the improved template matching and YOLO based localization algorithm are used to find the area to be detected. The experiment proves that the accuracy of the detection system reaches 90%, which can be applied in the production of enterprises.
An Unmanned Ground System Based on ROS

With the development of technology, autonomous driving and remote control technology are becoming more and more common in our lives. Unmanned ground systems can help or replace humans in various complex and dangerous environments, and have important practical significance for improving work efficiency and reducing casualties. In this study, mainly designs the hardware scheme independently on the basis of the three types of chassis of the car, and builds the overall hardware part of the system; studies the overall framework of ROS, completely designs the overall software process framework of the system, and conducts relevant test experiment on the designed parts. The experimental results show that the ground unmanned system designed in this study can meet the operator's remote operation requirements for the platform, and can perform real-time map construction, autonomous positioning and obstacle detection for the unknown environment, and achieve a certain degree of human-machine separation.
Analyses of seven writing studies journals, 2000–2019, Part II: Data-driven identification of keywords

Keywords are often used to shed light on shared words and their meanings, including their contestation. Often these keywords are determined using small samples or author inferences. However, identification of large sample, data-driven keywords is important for writing studies to avoid a range of biases including socioeconomic, confirmation, and sampling. We use the methodologies of “term frequency-inverse document frequency” (TF-IDF) and collocation on a corpus of journal articles from seven major writing studies journals: College Composition and Communication, College English, Computers and Composition, Research in the Teaching of English, Rhetoric Review, Rhetoric Society Quarterly, and Written Communication. By examining approximately 99% of the research articles published in these journals between 2000 and 2019 (N = 2738), we determine the evolution of keywords over time. Changes in keywords suggest attention to the impact of technology.
Combining Beaufort cipher and RSA-CRT algorithm in a hybrid scheme to secure images

Abstract RSA algorithm is one type of algorithms in public-key cryptographic systems where the private keys are kept secret and the public keys can be disseminated. Beaufort Cipher algorithm is a type of classical symmetric algorithm. Meanwhile, the RSA-CRT is one variant of RSA cryptosystem. The RSA-CRT is not efficient for encrypting large files such as image files, since it uses modular exponentiation, which is slow, and the resulting ciphertext would be very much larger than the message. To avoid this problem, a hybrid cryptosystem scheme using RSA-CRT and Beaufort algorithm is designed to encrypt images. Beaufort Cipher algorithm is used for encryption and decryption of digital images, while RSA-CRT algorithm for decryption and decryption of beaufort cipher keys. The results showed that the size of the image (*.BMP) files and the encrypted image files are the same.
A tutorial on using ElGamal cryptosystem and RC4-P1 cipher in a hybrid scheme

Abstract Digital document is a form of information that can be transmitted over a digital channel. Public key cryptosystem such as ElGamal may provide secure transmission of digital document. However, securing a large document in .docx extension, it is usually not efficient to use public key cryptosystem directly, since the ciphertext generated is usually much larger than the original document itself, making it difficult to transmit. In this research, this problem is avoided by using a scheme called hybrid cryptosystem. A combination of ElGamal algorithm and RC4-P1 cipher will be used to perform the hybrid cryptosystem scheme for data security. The RC4-P1 cipher is the latest variant of RC4 cipher and relatively shorter time to do the encryption and decryption process, but only by using single-key.