Reviewerly/exHarmony huggingface dataset:
A novel blind medical image watermarking scheme based on Schur triangulation and chaotic sequence

Abstract Image watermarking is a potentially effective and powerful solution for multimedia security. It is a technique that plays a fundamental role in protecting copyright, proofing ownership, and helps to authenticate sensitive data. Watermarking can cause a decrease in image quality, especially when it comes to medical images. This could provoke false diagnoses, which can lead to serious consequences on the patient's health. In fact, it becomes necessary to think for new more imperceptible, secure watermarking techniques. To provide such techniques, we proposed a novel blind watermarking scheme for medical images, based on Schur decomposition and chaotic sequence (CS). An efficient chaotic method is applied on the watermark and the cover image to obtain encrypted images divided into subblocks. A decomposition based on Schur is utilized to embed the encrypted watermark bits in the cyphered cover image blocks. The same CS is applied to extract the original watermark. We compared the proposed technique with other watermarking schemes. The experimentation results demonstrate that the suggested method achieving good image fidelity and acceptable robustness.
Abnormal event detection, identification and isolation in nuclear power plants using LSTM networks

Increases in concerns regarding system safety and reliability challenge nuclear energy's attractiveness among the public. To alleviate such concerns, being able to prevent a developing event from escalating into a severe accident is indispensable, which requires an abnormal event to be identified in its early stage. In this study, several long short-term memory (LSTM)-based networks for abnormal event detection, identification, and isolation are proposed to help maintain the safe operations of nuclear power plants (NPPs). With the proposed model for predicting normal operation sensing readings, an abnormal event is detected if the discrepancies between the acquired and predicted readings exceed a preset threshold. Through LSTM's superior capability in time series analysis, the process information for sensing reading generation and the interrelations among the sensors in the event recordings can be extracted to enable valid event identification. Via the proposed autoencoders for sensing reading reconstruction, the plausible type for the ongoing event can be further verified to prevent an unseen event from being wrongly linked to any class in the event set. Results from experiments utilizing data of 13 event classes generated by a Maanshan NPP simulator illustrate the efficacy of the proposed models.
Physical Adversarial Attacks on an Aerial Imagery Object Detector

Deep neural networks (DNNs) have become essential for processing the vast amounts of aerial imagery collected using earth-observing satellite platforms. However, DNNs are vulnerable towards adversarial examples, and it is expected that this weakness also plagues DNNs for aerial imagery. In this work, we demonstrate one of the first efforts on physical adversarial attacks on aerial imagery, whereby adversarial patches were optimised, fabricated and installed on or near target objects (cars) to significantly reduce the efficacy of an object detector applied on overhead images. Physical adversarial attacks on aerial images, particularly those captured from satellite platforms, are challenged by atmospheric factors (lighting, weather, seasons) and the distance between the observer and target. To investigate the effects of these challenges, we devised novel experiments and metrics to evaluate the efficacy of physical adversarial attacks against object detectors in aerial scenes. Our results indicate the palpable threat posed by physical adversarial attacks towards DNNs for processing satellite imagery <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Combinatorial Learning of Graph Edit Distance via Dynamic Embedding

Graph Edit Distance (GED) is a popular similarity measurement for pairwise graphs and it also refers to the recovery of the edit path from the source graph to the target graph. Traditional A* algorithm suffers scalability issues due to its exhaustive nature, whose search heuristics heavily rely on human prior knowledge. This paper presents a hybrid approach by combing the interpretability of traditional search-based techniques for producing the edit path, as well as the efficiency and adaptivity of deep embedding models to achieve a cost-effective GED solver. Inspired by dynamic programming, node-level embedding is designated in a dynamic reuse fashion and suboptimal branches are encouraged to be pruned. To this end, our method can be readily integrated into A* procedure in a dynamic fashion, as well as significantly reduce the computational burden with a learned heuristic. Experimental results on different graph datasets show that our approach can remarkably ease the search process of A* without sacrificing much accuracy. To our best knowledge, this work is also the first deep learning-based GED method for recovering the edit path.
A Novel Machine Learning-Based Analysis Model for Smart Contract Vulnerability

In recent years, a lot of vulnerabilities of smart contracts have been found. Hackers used these vulnerabilities to attack the corresponding contracts developed in the blockchain system such as Ethereum, and it has caused lots of economic losses. Therefore, it is very important to find out the potential problems of the smart contracts and develop more secure smart contracts. As blockchain security events have raised more important issues, more and more smart contract security analysis methods have been developed. Most of these methods are based on traditional static analysis or dynamic analysis methods. There are only a few methods that use emerging technologies, such as machine learning. Some models that use machine learning to detect smart contract vulnerabilities cost much time in extracting features manually. In this paper, we introduce a novel machine learning-based analysis model by introducing the shared child nodes for smart contract vulnerabilities. We build the Abstract-Syntax-Tree (AST) for smart contracts with some vulnerabilities from two data sets including SmartBugs and SolidiFI-benchmark. Then, we build the Abstract-Syntax-Tree (AST) of the labeled smart contract for data sets named Smartbugs-wilds. Next, we get the shared child nodes from both of the ASTs to obtain the structural similarity, and then, we construct a feature vector composed of the values that measure structural similarity automatically to build our machine learning model. Finally, we get a KNN model that can predict eight types of vulnerabilities including Re-entrancy, Arithmetic, Access Control, Denial of Service, Unchecked Low Level Calls, Bad Randomness, Front Running, and Denial of Service. The accuracy, recall, and precision of our KNN model are all higher than 90%. In addition, compared with some other analysis tools including Oyente and SmartCheck, our model has higher accuracy. In addition, we spent less time for training .
A multi-dimensional relation model for dimensional sentiment analysis

Dimensional sentiment analysis has received considerable attention because it can represent affective states as continuous numerical values on multiple dimensions such as valence (positive–negative) and arousal (excited–calm). Compared to the categorical approach, which represents affective states as several discrete classes (e.g., positive and negative), the dimensional approach can provide more fine-grained (real-valued) sentiment analysis. Traditional approaches to predicting dimensional sentiment scores typically treat each dimension independently without consideration of relations between dimensions. In fact, different dimensions may correlate with each other. For example, expressions with a higher valence score usually have a higher arousal score, And higher irony expressions usually have a lower valence score. Such relations between dimensions are useful for dimension score prediction. To this end, this study proposes a multi-dimensional relation model to incorporate relations between dimensions into deep neural networks for dimension score prediction. The proposed method has two modes: internal and external. The internal mode incorporates the relations between dimensions into sentence representations before prediction, whereas the external mode builds a linear regression model that can capture the relations between dimensions to refine the predicted scores after prediction. To evaluate the proposed method, we created a Chinese three-dimensional corpus with valence-arousal-irony (VAI) ratings. Experiments using various neural network architectures demonstrate that the proposed multi-dimensional relation model outperformed those that treat each dimension independently. In addition, the internal mode outperformed the external mode, and a combination of the two modes achieved the best performance.
Using garnet geochemistry discriminating different skarn mineralization systems: Perspective from Huangshaping W-Mo-Sn-Cu polymetallic deposit, South China

Important Mid-Late Jurassic W-Mo-Sn and Cu polymetallic skarn deposits are widely distributed in South China, yet differences in ore-forming processes and physiochemical conditions between these two skarn deposit types are poorly constrained. The large Huangshaping deposit in South China developed both W-Mo-Sn skarn (named 301 system) and Cu skarn (named 304 system) mineralization systems, which are genetically linked to granite porphyry and quartz porphyry, respectively. Both skarn systems are characterized by abundant garnet. Three types of garnet were distinguished in the 301 system, i.e., the texturally homogeneous Grt301a and veined Grt301b from deep-level (−176 m), and oscillatory-zoned Grt301c from shallow-level (−96 m). Moreover, Grt301a is commonly cut by Grt301b vein. Meanwhile, two types of garnet were identified in the 304 system, i.e., the texturally anisotropic Grt304a and oscillatory zoned Grt304b, with the former being commonly replaced by the latter. Trace element geochemistry shows that REE3+ entered garnet mainly by YAG-type substitution ([Ca2+]VIII −1[REE3+]VIII + 1[Si4+]IV −1[Z3+]IV + 1) in the 301 system and menzerite substitution ([Ca2+]VIII −1[REE3+]VIII + 1[Al3+]VI −1[Y2+]VI + 1) in the 304 system. Grt301a, Grt301b and Grt304a are HREE-enriched, suggesting that they were formed under nearly-neutral pH and low water/rock (W/R) ratios in closed and stable systems via diffusive metasomatism. Meanwhile, Grt301c and Grt304b are LREE-enriched, suggesting that they were formed under mildly-acidic pH and high W/R ratios in open and fluctuating systems via infiltration metasomatism. The elevated Sn content in Grt301c (median 5039 ppm) indicates its higher oxygen fugacity (fO2) than Grt301a (median 1861 ppm) and Grt301b (median 1776 ppm). Similarly, the high Sn content in Grt304b (median 1444 ppm) also suggests higher fO2 than that of Grt304a (median 338 ppm). The different formation conditions between deep-level garnet (Grt301a and Grt301b) and shallow-level garnet (Grt301c) in the 301 system are not consistent with garnet in the 304 system that shows similar texture, trace element geochemistry and formation conditions at different depths. This reveals differences of formation environments between two skarn systems at Huangshaping. Partial least squares-discriminant analysis (PLS-DA) suggests that W in garnet can discriminate the 301 and 304 skarn systems, which may be related to the lower fO2 and higher fractionation of the ore-causative granite porphyry in the former, which favors W enrichment in corresponding magmatic-hydrothermal fluids in 301 system. Moreover, Mo can discriminate garnet from the deep- and shallow-levels of the 301 system, which may be related to the higher fO2 in the latter. We highlight the importance of garnet trace element geochemistry on tracing hydrothermal fluid evolution and discriminating between different skarn systems.
Introducing Knowledge‐based Vocabulary Lists (KVL)

TESOL JournalVolume 12, Issue 4 e622 CURRENT ISSUES IN TESOL Introducing Knowledge-based Vocabulary Lists (KVL) Norbert Schmitt, Corresponding Author Norbert Schmitt norbert.schmitt@nottingham.ac.uk orcid.org/0000-0001-8590-5471 University of Nottingham Correspondence norbert.schmitt@nottingham.ac.ukSearch for more papers by this authorKaren Dunn, Karen Dunn orcid.org/0000-0002-7499-9895 British CouncilSearch for more papers by this authorBarry O’Sullivan, Barry O’Sullivan orcid.org/0000-0002-2256-8209 British CouncilSearch for more papers by this authorLaurence Anthony, Laurence Anthony orcid.org/0000-0002-8249-2824 Waseda UniversitySearch for more papers by this authorBenjamin Kremmel, Benjamin Kremmel orcid.org/0000-0002-2536-0091 University of InnsbruckSearch for more papers by this author Norbert Schmitt, Corresponding Author Norbert Schmitt norbert.schmitt@nottingham.ac.uk orcid.org/0000-0001-8590-5471 University of Nottingham Correspondence norbert.schmitt@nottingham.ac.ukSearch for more papers by this authorKaren Dunn, Karen Dunn orcid.org/0000-0002-7499-9895 British CouncilSearch for more papers by this authorBarry O’Sullivan, Barry O’Sullivan orcid.org/0000-0002-2256-8209 British CouncilSearch for more papers by this authorLaurence Anthony, Laurence Anthony orcid.org/0000-0002-8249-2824 Waseda UniversitySearch for more papers by this authorBenjamin Kremmel, Benjamin Kremmel orcid.org/0000-0002-2536-0091 University of InnsbruckSearch for more papers by this author First published: 24 August 2021 https://doi.org/10.1002/tesj.622Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume12, Issue4December 2021e622 RelatedInformation
Medical image inpainting with edge and structure priors

Image inpainting techniques are widely applied to repair distorted or missing regions in the image. However, unreasonable results are usually generated due to lack of structural information. In order to repair the structure rationally, we propose a model based on edge and structure information for medical image inpainting (ESMII). In addition, for overcoming the degradation problem of the deep network, multi-scale residual blocks (MRB) are used to extract features from the image at three scales. Meanwhile, the multi-level loss is proposed to motivate the network synthesizing more significant structural textures. Experimental results on COVID CT, Abdominal CT and Abdominal MRI datasets indicate that ESMII can generate more reasonable structures and fine textures compared to other models. Furthermore, ablation studies for justification verify the effectiveness of different modules in the network. We show that our approach can effectively solve the distortion of medical images.
AI-Enabled Object Detection in UAVs: Challenges, Design Choices, and Research Directions

Unmanned aerial vehicles (UAVs) are emerging as a powerful tool for various industrial and smart city applications. UAVs coupled with various sensors can perform many cognitive tasks such as object detection, surveillance, traffic management, and urban planning. Deep learning has emerged as a popular technique to speed up the processing of high-dimensional data like images and videos, which has led to several applications in surveillance and autonomous driving. However, the area of aerial object detection has been understudied. This work proposes a deep learning approach for detection of objects in aerial scenes captured by UAVs. Our work first categorizes the current methods for aerial object detection using deep learning techniques and discusses how the task is different from general object detection scenarios. We delineate the specific challenges involved and experimentally demonstrate the key design decisions that significantly affect the accuracy and robustness of models. We further propose an optimized architecture that utilizes these optimal design choices along with the recent Res-NeSt backbone to achieve superior performance in aerial object detection. Lastly, we propose several research directions to inspire further advancement in aerial object detection.
A Multimodal Author Profiling System for Tweets

The rising usage of social media has motivated to invent different methodologies of anonymous writing, which leads to an increase in malicious and suspicious activities. This anonymity has created difficulty in finding the suspect. Author profiling deals with the characterization of an author through some key attributes such as gender, age, language, dialect region variety, personality, and so on. Identifying the gender of the author of a suspect document is a salient task of author-profiling. The linguistic profile of a user can help in determining his/her demographics. Different social media platforms, such as Twitter, Facebook, and Instagram, are used regularly by users for sharing their daily life activities. Moreover, users often post images along with text on different social media platforms; thus, the usage of multimodal information is very common nowadays. In this article, the task of automatic gender prediction from multimodal Twitter data is posed as a classification problem and an efficient multimodal neural framework is proposed for solving this. The popularly used BERT_base is utilized for learning the encoded representation for the text part of the tweet, and recently introduced EfficientNet is used for extracting the features from images. Finally, a direct product-based fusion strategy is applied for fusing the text and image representations, followed by a fully connected layer for predicting the gender of a Twitter user. Plagiarism detection authorship analysis near end duplicate detection (PAN)-2018 author profiling data are used for evaluating the performance of our proposed approach. Our proposed model achieved accuracies of 82.05%, 86.22%, and 89.53% for pure-image, pure-text, and multimodal setting, respectively; outperforming the previous state-of-the-art works in all the cases. Moreover, a deep analysis is carried out to interpret the produced results; different words that serve as clues for gender classification are identified characterizing different gender classes. The supplementary file and the source codes for the proposed approach are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/chanchalIITP/GenderTCSS</uri> .
Approximate analog computing with metatronic circuits

Abstract Analog photonic solutions offer unique opportunities to address complex computational tasks with unprecedented performance in terms of energy dissipation and speeds, overcoming current limitations of modern computing architectures based on electron flows and digital approaches. The lack of modularization and lumped element reconfigurability in photonics has prevented the transition to an all-optical analog computing platform. Here, we explore, using numerical simulation, a nanophotonic platform based on epsilon-near-zero materials capable of solving in the analog domain partial differential equations (PDE). Wavelength stretching in zero-index media enables highly nonlocal interactions within the board based on the conduction of electric displacement, which can be monitored to extract the solution of a broad class of PDE problems. By exploiting the experimentally achieved control of deposition technique through process parameters, used in our simulations, we demonstrate the possibility of implementing the proposed nano-optic processor using CMOS-compatible indium-tin-oxide, whose optical properties can be tuned by carrier injection to obtain programmability at high speeds and low energy requirements. Our nano-optical analog processor can be integrated at chip-scale, processing arbitrary inputs at the speed of light.
Developing students’ digital competences through collaborative game design

This exploratory study engaged teams of elementary and middle school students in the collaborative design of digital games. Game design is theoretically examined in this study as a form of knowledge-creating learning that is characterized by collaborative efforts to advance a shared object of activity, i.e., the game being designed. Using mixed methods, we examined how students experienced the game design project and how the project fostered connected learning, that is, integration of students' personal interests and supportive peer relations with their schoolwork, and how their self-assessed digital competences developed. The digital competences of 98 comprehensive school students across Finland were traced using pre- and post-questionnaires. The post-questionnaire also included validated measures on connected learning. Quantitative methods were used to analyze structured measures, and qualitative methods were used to analyze open-ended measures. Students experienced game design as an inspiring, challenging activity. Game design engaged student teams in sustained, collaborative efforts to create shared digital artifacts. Their efforts involved a great deal of mutual support and knowledge sharing. Participation also improved students' self-reported technical and artistic digital competences. The game design project fostered informal, interest-driven, sociodigital participation; inspired learning engagement; and improved schoolwork practices. The game design project appeared to be a pedagogically meaningful way of engaging students in knowledge-creating learning and of connecting students' formal and informal learning. The project sparked students' motivation to learn, fostered digital competences, and enriched the learning environment.
A Knowledge-Based Deep Learning Architecture for Aspect-Based Sentiment Analysis

The task of sentiment analysis tries to predict the affective state of a document by examining its content and metadata through the application of machine learning techniques. Recent advances in the field consider sentiment to be a multi-dimensional quantity that pertains to different interpretations (or aspects), rather than a single one. Based on earlier research, the current work examines the said task in the framework of a larger architecture that crawls documents from various online sources. Subsequently, the collected data are pre-processed, in order to extract useful features that assist the machine learning algorithms in the sentiment analysis task. More specifically, the words that comprise each text are mapped to a neural embedding space and are provided to a hybrid, bi-directional long short-term memory network, coupled with convolutional layers and an attention mechanism that outputs the final textual features. Additionally, a number of document metadata are extracted, including the number of a document’s repetitions in the collected corpus (i.e. number of reposts/retweets), the frequency and type of emoji ideograms and the presence of keywords, either extracted automatically or assigned manually, in the form of hashtags. The novelty of the proposed approach lies in the semantic annotation of the retrieved keywords, since an ontology-based knowledge management system is queried, with the purpose of retrieving the classes the aforementioned keywords belong to. Finally, all features are provided to a fully connected, multi-layered, feed-forward artificial neural network that performs the analysis task. The overall architecture is compared, on a manually collected corpus of documents, with two other state-of-the-art approaches, achieving optimal results in identifying negative sentiment, which is of particular interest to certain parties (like for example, companies) that are interested in measuring their online reputation.