Reviewerly/exHarmony huggingface dataset:
AvgNet: Adaptive Visibility Graph Neural Network and Its Application in Modulation Classification

Our digital world is full of time series and graphs which capture the various aspects of many complex systems. Traditionally, there are respective methods in processing these two different types of data, e.g., Recurrent Neural Network (RNN) and Graph Neural Network (GNN), while in recent years, time series could be mapped to graphs by using the techniques such as Visibility Graph (VG), which simultaneously captures relevant aspects of both local and global dynamics in an easy way, so that researchers can use graph algorithms to mine the knowledge in time series and gain special latent graph representation features. Such mapping methods establish a bridge between time series and graphs, and have high potential to facilitate the analysis of various real-world time series. However, the VG method and its variants are just based on fixed rules and thus lack of flexibility, largely limiting their application in reality. In this paper, we propose an Adaptive Visibility Graph (AVG) algorithm that can adaptively map time series into graphs, based on which we further establish an end-to-end classification framework AvgNet, by utilizing GNN model DiffPool as the classifier. We then adopt AvgNet for radio signal modulation classification which is an important task in the field of wireless communication. The simulations validate that AvgNet outperforms a series of advanced deep learning methods, achieving the state-of-the-art performance in this task.
Face mask detection in COVID-19: a strategic review

With the outbreak of the Coronavirus Disease in 2019, life seemed to be had come to a standstill. To combat the transmission of the virus, World Health Organization (WHO) announced wearing of face mask as an imperative way to limit the spread of the virus. However, manually ensuring whether people are wearing face masks or not in a public area is a cumbersome task. The exigency of monitoring people wearing face masks necessitated building an automatic system. Currently, distinct methods using machine learning and deep learning can be used effectively. In this paper, all the essential requirements for such a model have been reviewed. The need and the structural outline of the proposed model have been discussed extensively, followed by a comprehensive study of various available techniques and their respective comparative performance analysis. Further, the pros and cons of each method have been analyzed in depth. Subsequently, sources to multiple datasets are mentioned. The several software needed for the implementation are also discussed. And discussions have been organized on the various use cases, limitations, and observations for the system, and the conclusion of this paper with several directions for future research.
Recent advances in earthquake monitoring II: Emergence of next-generation intelligent systems

Seismic data processing techniques, together with seismic instrumentation, determine our earthquake monitoring capability and the quality of resulting earthquake catalogs. This paper is intended to review the improvement of earthquake monitoring capability from the perspective of data processing. Over the past two decades, seismologists have made considerable advancements in seismic data processing, partly thanks to the significant development of computational power, signal processing, and machine learning techniques. In particular, the wide application of template matching and increasing use of deep learning significantly enhance our capability to extract signals of small earthquakes from noisy data. Relative location techniques provide a critical tool to elucidate fault geometries and seismicity migration patterns at unprecedented resolution. These techniques are becoming standard, leading to emerging intelligent software systems for next-generation earthquake monitoring. Prospective improvements in future research must consider the urgent needs in highly generalizable detection algorithms (for both permanent and temporary deployments) and in emergency real-time monitoring of ongoing sequences (e.g., aftershock and induced seismicity sequences). We believe that the maturing of intelligent and high-resolution processing systems could transform traditional earthquake monitoring workflows and eventually liberate seismologists from laborious catalog construction tasks.
ET: Edge-Enhanced Transformer for Image Splicing Detection

A key challenge of image splicing detection is how to localize integral tampered regions without false alarm. Although current forgery detection approaches have achieved promising performance, the integrality and false alarm are overlooked. In this paper, we argue that the insufficient use of splicing boundary is a main reason for poor accuracy. To tackle this problem, we propose an Edge-enhanced Transformer (ET) for tampered region localization. Specifically, to capture rich tampering traces, a two-branch edge-aware transformer is built to integrate the splicing edge clues into the forgery localization network, generating forgery features and edge features. Furthermore, we design a feature enhancement module to highlight the artifacts of the edge area in forgery features and assign weight values to the resulting tensor in spatial domain for vital signal strengthening and noise suppression. Extensive experimental results on CASIA v1.0, CASIA v2.0 and NC2016 demonstrate that the proposed method can accurately localize tampered regions in both pixel and edge levels and outperforms state-of-the-art methods.
Artificial neural networks for power output forecasting from bifacial solar PV system with enhanced building roof surface Albedo

The main objective of this research study is to evaluate the performance of bifacial solar PV systems (bPV) installed on flat roof buildings with controlled surface albedo and to develop forecasting models to anticipate the power output from bifacial solar PV systems due to the enhancement of surface albedo. The originality of this study and the novelty include integrating innovative bifacial solar PV and cool roof technologies to increase solar PV panel power output and anticipate electricity generation in advance to balance supply and demand. For the methodology, modeling and simulation analysis was used to test the performance of bPV system installed on a building with a surface albedo of 0.2–0.8. An Artificial Neural Network was used as a machine learning approach for bifacial solar PV power and energy forecasting. The significant findings show that an increase in surface albedo from 0.2 to 0.5 and 0.2 to 0.8 will help to boost the annual bifacial solar PV power production by 7.75% and 14.96%, respectively. The predictive models are exceptionally accurate in anticipating the bifacial solar PV power. The monthly power from bPV rose from 10.87 to 21.54%, with a 0.3 (from 0.2 to 0.5) to 0.6 (from 0.2 to 0.8) albedo increase. The coefficient of correlation R values for all of the power data ranges from 0.99145 to 0.99382 for the roof surface albedo, ranging from 0.2 to 0.8. The ANN models illustrate how effectively the model mimics the modeling and simulation data. For the research implications, the developed forecast models will assist the industry in predicting ahead of the power production, building operations and maintenance, demand-side management, and advanced energy purchases. • Performance of bifacial solar PV (bPV) system with low and high surface albedo. • Bifacial solar PV system integrated with cool roof technology. • Accurate ANN-based bPV power and energy output forecasting models. • Albedo enhancement can increase annual bifacial solar PV by 7.75–14.96%. • bPV monthly power rose from 10.87 to 21.54%, with a 0.3-to-0.6 albedo increase.
Gated Broad Learning System Based on Deep Cascaded for Soft Sensor Modeling of Industrial Process

With the advancement of computer and sensor technology, soft sensors have been more and more extensively used in industrial processes. Soft sensors based on deep learning often need to redesign the structure and retrain the model when the prediction results are poor, which consumes a lot of time. Therefore, a deep cascade-gated broad learning system with fast update capability is proposed for industrial process soft sensor modeling. Being inspired by deep learning, the hidden layer features extracted by the autoencoder (AE) are used in the feature nodes of the broad learning system (BLS) to obtain the deep-BLS (D-BLS), which can circumvent the problem of insufficient feature extraction caused by stochastically generated weights in the feature nodes of BLS. On this basis, each feature node is integrated and sent to the enhancement nodes through the gated neurons. The enhancement nodes are cascaded to construct the deep cascaded-gated BLS (DC-GBLS), which can improve the prediction effect of the model while enhancing the utilization rate of the hidden layer features. Finally, a fast update method is developed for the model when the accuracy is insufficient. The validity and superiority of proposed model are demonstrated by two industrial processes.
MTRRE-Net: A deep learning model for detection of breast cancer from histopathological images

Histopathological image classification has become one of the most challenging tasks among researchers due to the fine-grained variability of the disease. However, the rapid development of deep learning-based models such as the Convolutional Neural Network (CNN) has propelled much attentiveness to the classification of complex biomedical images. In this work, we propose a novel end-to-end deep learning model, named Multi-scale Dual Residual Recurrent Network (MTRRE-Net), for breast cancer classification from histopathological images. This model introduces a contrasting approach of dual residual block combined with the recurrent network to overcome the vanishing gradient problem even if the network is significantly deep. The proposed model has been evaluated on a publicly available standard dataset, namely BreaKHis, and achieved impressive accuracy in overcoming state-of-the-art models on all the images considered at various magnification levels.
ScalableViT: Rethinking the Context-Oriented Generalization of Vision Transformer

The vanilla self-attention mechanism inherently relies on pre-defined and steadfast computational dimensions. Such inflexibility restricts it from possessing context-oriented generalization that can bring more contextual cues and global representations. To mitigate this issue, we propose a Scalable Self-Attention (SSA) mechanism that leverages two scaling factors to release dimensions of query, key, and value matrices while unbinding them with the input. This scalability fetches context-oriented generalization and enhances object sensitivity, which pushes the whole network into a more effective trade-off state between accuracy and cost. Furthermore, we propose an Interactive Window-based Self-Attention (IWSA), which establishes interaction between non-overlapping regions by re-merging independent value tokens and aggregating spatial information from adjacent windows. By stacking the SSA and IWSA alternately, the Scalable Vision Transformer (ScalableViT) achieves state-of-the-art performance on general-purpose vision tasks. For example, ScalableViT-S outperforms Twins-SVT-S by 1.4% and Swin-T by 1.8% on ImageNet-1K classification.
L2 learners' perceptions of a chatbot as a potential independent language learning tool

Independent language learning is paramount for those wishing to develop proficiency in a second or foreign language. Language learners often have few opportunities to communicate and interact actively in their target language. In this two-phase study, a chatbot was developed to assist second-language learners at a tertiary education institution in Hong Kong with independent language learning. I employed a questionnaire (N = 128) followed by semi-structured interviews (N = 12) to gain holistic insight into learners' experiences with the chatbot. The results suggested that the participants enjoyed interacting with the chatbot both in and out of class and perceived that it improved their English skills. These findings have implications for language teachers and the future development of chatbots.
An Empirical Investigation of Company Response to Data Breaches

Companies may face serious adverse consequences as a result of a data breach event. To repair the potential damage to relationships with stakeholders after data breaches, companies adopt a variety of response strategies. However, the effects of these response strategies on the behavior of stakeholders after a data breach are unclear; differences in response times may also affect these outcomes, depending on the notification laws that apply to each company. As part of a multimethod study, we first identified the adopted response strategies in Study 1 based on content analysis of the response letters issued by publicly traded U.S. companies (n = 204) following data breaches; these strategies include any combination of the following: corrective action, apology, and compensation. We also found that breached companies may remain silent and adopt a “no action” strategy. In Studies 2 and 3, we examined the effects of various response strategies and response times on the predominant stakeholders affected by data breaches: customers and investors. In Study 2, we focused on customers and present a moderated-moderated-mediation model based on the expectancy violation theory. To test this model, we designed a factorial survey with 15 different conditions (n = 811). In Study 3, we focused on investors and conducted an event study (n = 166) to examine their reactions to company responses to data breaches. The results indicate the presence of moderating effects of certain response strategies; surprisingly, we did not find compensation to be more effective than apology. The magnitude of the moderating effects of response strategies is contingent upon response time. We also found that the negative effects of data breaches disappear after six months. We interpret the results and provide implications for research and practice.
Planning in Factored Action Spaces with Symbolic Dynamic Programming

We consider symbolic dynamic programming (SDP) for solving Markov Decision Processes (MDP) with factored state and action spaces, where both states and actions are described by sets of discrete variables. Prior work on SDP has considered only the case of factored states and ignored structure in the action space, causing them to scale poorly in terms of the number of action variables. Our main contribution is to present the first SDP-based planning algorithm for leveraging both state and action space structure in order to compute compactly represented value functions and policies. Since our new algorithm can potentially require more space than when action structure is ignored, our second contribution is to describe an approach for smoothly trading-off space versus time via recursive conditioning. Finally, our third contribution is to introduce a novel SDP approximation that often significantly reduces planning time with little loss in quality by exploiting action structure in weakly coupled MDPs. We present empirical results in three domains with factored action spaces that show that our algorithms scale much better with the number of action variables as compared to state-of-the-art SDP algorithms.
The influence of social distance on speech behavior: Formality variation in casual speech

Abstract An important dimension of linguistic variation is formality. This study investigates the role of social distance between interlocutors. Twenty-five native Dutch speakers retold eight short films to confederates, who acted either formally or informally. Speakers were familiarized with the informal confederates, whereas the formal confederates remained strangers. Results show that the two types of interlocutors elicited different versions of the same stories. Formal interlocutors (i. e. large social distance) elicited lower articulation rates, and more nouns and prepositions, both indicators of explicit information. Speakers addressing the informal interlocutors, to whom social distance was small, however, provided more explicit information with an involved character (i. e. adjectives with subjective meanings). They also used the word and more often as a gap filler or as a way to keep the floor. Furthermore, a small social distance elicited more laughter, interjections, first-person pronouns and direct speech, which are all indicators of involvement, empathy and subjectivity.
Private Compound Wildcard Queries Using Fully Homomorphic Encryption

Fully homomorphic encryption (FHE) brings a paradigm shift in cryptographic engineering by enabling us to resolve various unsolved problems. Among them, this work solves the problem to design a private database query (PDQ) protocol that supports compound queries with wildcard conditions on encrypted databases using FHE. More precisely, we consider a setting where clients outsource an encrypted database using FHE to a remote server, and later request results of compound queries including a wildcard search condition-given a set of attribute values {A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ; A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ; ...; A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> } and a search pattern W, retrieve a set of all attribute values A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> 's in which the pattern W occurs. To this end, we first develop an algorithm for testing whether an encrypted string contains an encrypted pattern without revealing any information of the pattern, taking auxiliary encryptions as additional inputs. Then, using this algorithm, we design PDQ protocols on encrypted databases, which support compound queries using wildcard search conditions. Finally, we demonstrate proof-of-concept implementation results of our protocols by exploiting single-instruction-multiple-data operations and multi-threading techniques.
It Wasn’t Me!

Ring signatures, introduced by [RST01], are a variant of digital signatures which certify that one among a particular set of parties has endorsed a message while hiding which party in the set was the signer. Ring signatures are designed to allow anyone to attach anyone else’s name to a signature, as long as the signer’s own name is also attached. But what guarantee do ring signatures provide if a purported signatory wishes to denounce a signed message—or alternatively, if a signatory wishes to later come forward and claim ownership of a signature? Prior security definitions for ring signatures do not give a conclusive answer to this question: under most existing definitions, the guarantees could go either way. That is, it is consistent with some standard definitions that a non-signer might be able to repudiate a signature that he did not produce, or that this might be impossible. Similarly, a signer might be able to later convincingly claim that a signature he produced is indeed his own, or not. Any of these guarantees might be desirable. For instance, a whistleblower might have reason to want to later claim an anonymously released signature, or a person falsely implicated in a crime associated with a ring signature might wish to denounce the signature that is framing them and damaging their reputation. In other circumstances, it might be desirable that even under duress, a member of a ring cannot produce proof that he did or did not sign a particular signature. In any case, a guarantee one way or the other seems highly desirable. In this work, we formalize definitions and give constructions of the new notions of repudiable, unrepudiable, claimable, and unclaimable ring signatures. Our repudiable construction is based on VRFs, which are implied by several number-theoretic assumptions (including strong RSA or bilinear maps); our claimable construction is a black-box transformation from any standard ring signature scheme to a claimable one; and our unclaimable construction is derived from the lattice-based ring signatures of [BK10], which rely on hardness of SIS. Our repudiable construction also provides a new construction of standard ring signatures.
Semi-automatic extraction and mapping of dyke swarms based on multi-resolution remote sensing images: Applied to the dykes in the Kuluketage region in the northeastern Tarim Block

The geometry and spatial distribution of mafic dyke swarms provide information about geodynamics. However, mapping large-scale dyke swarms manually is a time-consuming and expensive process. Here we present an efficient method to map dyke swarms semi-automatically by using multi-resolution remote sensing images. This method is applied in the Kuluketage region in the northeastern Tarim Block, northwest China where mafic dykes of multiple trends are widely exposed and with the dominant NW trend being of probable Neoproterozoic age. The tracing of dykes, including their orientation, length, thickness, density, and distribution is done by integrating 30-m multispectral Landsat 8 and 2-m panchromatic CORONA KH-4B imagery. The Gramm-Schmidt Spectral Sharpening approach is used to generate color composite and fusion images with 2-m resolution. The Canny edge detector and Hough transform are used to detect dyke edges. Combining the detection of dyke edges with ArcGIS™, the dyke orientation, length, density, thickness, and exposed area are measured. The accuracy of the semi-automatic method exceeds 75% in comparison with manual mapping of the dykes in the Kuluketage region.
Segmentation of breast MR images using a generalised 2D mathematical model with inflation and deflation forces of active contours

In medical computer aided diagnosis systems, image segmentation is one of the major pre-processing steps used to ensure only the region of interest, such as the breast region, will be processed in subsequent steps. Nevertheless, breast segmentation is a difficult task due to low contrast and inhomogeneity, especially when estimating the chest wall in magnetic resonance (MR) images. In fact, the chest wall comprises fat, skin, muscles, and the thoracic skeleton, which can misguide automatic methods when attempting to estimate its location. The objective of the study is to develop a fully automated method for breast and pectoral muscle boundary estimation in MR images. Firstly, we develop a 2D breast mathematical model based on 30 MRI slices (from a patient) and identify important landmarks to obtain a model for the general shape of the breast in an axial plane. Subsequently, we use Otsu's thresholding approach and Canny edge detection to estimate the breast boundary. The active contour method is then employed using both inflation and deflation forces to estimate the pectoral muscle boundary by taking account of information obtained from the proposed 2D model. Finally, the estimated boundary is smoothed using a median filter to remove outliers. Our two datasets contain 60 patients in total and the proposed method is evaluated based on 59 patients (one patient is used to develop the 2D breast model). On the first dataset (9 patients) the proposed method achieved Jaccard = 81.1% ±6.1 % and dice coefficient= 89.4% ±4.1 % and on the second dataset (50 patients) Jaccard = 84.9% ±5.8 % and dice coefficient = 92.3% ±3.6 %. These results are qualitatively comparable with the existing methods in the literature.