Reviewerly/exHarmony huggingface dataset:
Indexing of electron back-scatter diffraction patterns using a convolutional neural network

Accurate indexing of EBSD patterns presents a challenging problem. We propose a new convolutional neural network (EBSD-CNN) to realize real-time indexing of EBSD patterns; we implement a disorientation loss function to adapt a standard CNN model for crystallographic orientation indexing. The indexing accuracy, rate, and robustness against noise are evaluated using both simulated and experimental data, and compared with other indexing methods (Hough-based indexing, dictionary indexing, and spherical indexing). The results suggest that a CNN can provide an alternative to commercial Hough-transform-based indexing with comparative accuracy and rate. We obtain insight into the network functionality by visualization of selected filters.
New activation functions for single layer feedforward neural network

Artificial Neural Network (ANN) is a subfield of machine learning and it has been widely used by the researchers. The attractiveness of ANNs comes from their remarkable information processing capability. An important key parameter of ANN is activation function (AF). Different AFs can significantly affect the performance of an ANN, and therefore choosing a good AF is important. This study aims to introduce some new AFs that combine the advantages of predefined AFs and perform better than them. For this purpose, we propose some new AFs, which we called generalized swish, mean-swish, ReLU-swish, triple-state swish, sigmoid-algebraic, triple-state sigmoid, exponential swish, sinc-sigmoid and derivative of sigmoid AFs. Then, we compare the proposed AFs with some well-known and recently proposed AFs. To investigate the performance of these AFs, we use four different data sets, which are simulated data, optical interconnection network data, specifications of cars, and average house costs data.
From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset

Sarcasm is one of the main challenges for sentiment analysis systems. Its complexity comes from the expression of opinion using implicit indirect phrasing. In this paper, we present ArSarcasm, an Arabic sarcasm detection dataset, which was created through the reannotation of available Arabic sentiment analysis datasets. The dataset contains 10,547 tweets, 16% of which are sarcastic. In addition to sarcasm the data was annotated for sentiment and dialects. Our analysis shows the highly subjective nature of these tasks, which is demonstrated by the shift in sentiment labels based on annotators’ biases. Experiments show the degradation of state-of-the-art sentiment analysers when faced with sarcastic content. Finally, we train a deep learning model for sarcasm detection using BiLSTM. The model achieves an F1 score of 0.46, which shows the challenging nature of the task, and should act as a basic baseline for future research on our dataset.
Modeling Intra and Inter-modality Incongruity for Multi-Modal Sarcasm Detection

Sarcasm is a pervasive phenomenon in today's social media platforms such as Twitter and Reddit.These platforms allow users to create multi-modal messages, including texts, images, and videos.Existing multi-modal sarcasm detection methods either simply concatenate the features from multi modalities or fuse the multi modalities information in a designed manner.However, they ignore the incongruity character in sarcastic utterance, which is often manifested between modalities or within modalities.Inspired by this, we propose a BERT architecture-based model, which concentrates on both intra and inter-modality incongruity for multi-modal sarcasm detection.To be specific, we are inspired by the idea of self-attention mechanism and design intermodality attention to capturing inter-modality incongruity.In addition, the co-attention mechanism is applied to model the contradiction within the text.The incongruity information is then used for prediction.The experimental results demonstrate that our model achieves state-of-the-art performance on a public multi-modal sarcasm detection dataset.
Fast Multi-View Clustering via Nonnegative and Orthogonal Factorization

The rapid growth of the number of data brings great challenges to clustering, especially the introduction of multi-view data, which collected from multiple sources or represented by multiple features, makes these challenges more arduous. How to clustering large-scale data efficiently has become the hottest topic of current large-scale clustering tasks. Although several accelerated multi-view methods have been proposed to improve the efficiency of clustering large-scale data, they still cannot be applied to some scenarios that require high efficiency because of the high computational complexity. To cope with the issue of high computational complexity of existing multi-view methods when dealing with large-scale data, a fast multi-view clustering model via nonnegative and orthogonal factorization (FMCNOF) is proposed in this paper. Instead of constraining the factor matrices to be nonnegative as traditional nonnegative and orthogonal factorization (NOF), we constrain a factor matrix of this model to be cluster indicator matrix which can assign cluster labels to data directly without extra post-processing step to extract cluster structures from the factor matrix. Meanwhile, the F-norm instead of the L2-norm is utilized on the FMCNOF model, which makes the model very easy to optimize. Furthermore, an efficient optimization algorithm is proposed to solve the FMCNOF model. Different from the traditional NOF optimization algorithm requiring dense matrix multiplications, our algorithm can divide the optimization problem into three decoupled small size subproblems that can be solved by much less matrix multiplications. Combined with the FMCNOF model and the corresponding fast optimization method, the efficiency of the clustering process can be significantly improved, and the computational complexity is nearly <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n)$ </tex-math></inline-formula> . Extensive experiments on various benchmark data sets validate our approach can greatly improve the efficiency when achieve acceptable performance.
Relevant information undersampling to support imbalanced data classification

Traditional classification algorithms suppose that the sample distribution among classes is balanced. Yet, such an assumption leads to biased performance over the majority class. This paper proposes a Relevant Information-based UnderSampling (RIUS) approach to select the most relevant examples from the majority class to improve the classification performance for imbalanced data scenarios. RIUS builds on the information-preservation principle that extracts the majority class’s underlying structure with fewer samples. Additionally, we couple our RIUS approach to the well-known Clustering-based Undersampling algorithm (CBUS) to enhance the data representation, and named this RIUS enhancement as CRIUS. Experimental results show that RIUS and CRIUS reveal the data’s relevant structure and reduce the loss of information by selecting the most informative instances.
Deep Learning Anomaly Detection for Cellular IoT With Applications in Smart Logistics

The number of connected Internet of Things (IoT) devices within cyber-physical infrastructure systems grows at an increasing rate. This poses significant device management and security challenges to current IoT networks. Among several approaches to cope with these challenges, data-based methods rooted in deep learning (DL) are receiving an increased interest. In this paper, motivated by the upcoming surge of 5G IoT connectivity in industrial environments, we propose to integrate a DL-based anomaly detection (AD) as a service into the 3GPP mobile cellular IoT architecture. The proposed architecture embeds autoencoder based anomaly detection modules both at the IoT devices (ADM-EDGE) and in the mobile core network (ADM-FOG), thereby balancing between the system responsiveness and accuracy. We design, integrate, demonstrate and evaluate a testbed that implements the above service in a real-world deployment integrated within the 3GPP Narrow-Band IoT (NB-IoT) mobile operator network.
The case of HyperLedger Fabric as a blockchain solution for healthcare applications

The healthcare industry deals with highly sensitive data which must be managed in a secure way. Electronic Health Records (EHRs) hold various kinds of personal and sensitive data which contain names, addresses, social security numbers, insurance numbers, and medical history. Such personal data is valuable to the patients, healthcare service providers, medical insurance companies, and research institutions. However, the public release of this highly sensitive personal data poses serious privacy and security threats to patients and healthcare service providers. Hence, we foresee the requirement of new technologies to address the privacy and security challenges for personal data in healthcare applications. Blockchain is one of the promising solutions, aimed to provide transparency, security, and privacy using consensus-driven decentralised data management on top of peer-to-peer distributed computing systems. Therefore, to solve the mentioned problems in healthcare applications, in this paper, we investigate the use of private blockchain technologies to assess their feasibility for healthcare applications. We create testing scenarios using HyperLedger Fabric to explore different criteria and use-cases for healthcare applications. Additionally, we thoroughly evaluate the representative test case scenarios to assess the blockchain-enabled security criteria in terms of data confidentiality, privacy and access control. The experimental evaluation reveals the promising benefits of private blockchain technologies in terms of security, regulation compliance, compatibility, flexibility, and scalability.
Security of Blockchain-Based Supply Chain Management Systems: Challenges and Opportunities

Blockchain is a revolutionary technology that is being used in many applications, including supply chain management. Although, the primary motive of using a blockchain for supply chain management is to reduce the overall production cost while providing the comprehensive security to the system. However, current blockchain-based supply-chain management (BC-SCM) systems still hold the possibility of cyber attacks. Therefore, the goal of this study is to investigate practical threats and vulnerabilities in the design of BC-SCM systems. As a starting point, we first establish key requirements for the reliability and security of supply chain management systems, i.e., transparency, privacy and traceability, and then discern a threat model that includes two distinctive but practical threats including computational (i.e., the ones that threaten the functionality of the application) and communication (i.e., the ones that threaten information exchange among interconnected services of the application). For investigation, we follow a unique approach based on the hypothesis that reliability is pre-requisite of security and identify the threats considering (i) design of smart contracts and associated supply chain management applications, (ii) underlying blockchain execution environment and (iii) trust between all interconnected supply management services. Moreover, we consider both academic and industry solutions to identify the threats. We identify several challenges that hinder to establish reliability and security of the BC-SCM systems. Importantly, we also highlight research gaps that can help to establish desired security of the BC-SCM. To the best of our knowledge, this paper is the first effort that identifies practical threats to blockchain-based supply chain management systems and provides their counter measures. Finally, this work establishes foundation for future investigation towards practical security of BC-SCM system.
Edge computing-based joint client selection and networking scheme for federated learning in vehicular IoT

In order to support advanced vehicular Internet-of-Things (IoT) applications, information exchanges among different vehicles are required to find efficient solutions for catering to different application requirements in complex and dynamic vehicular environments. Federated learning (FL), which is a type of distributed learning technology, has been attracting great interest in recent years as it performs knowledge exchange among different network entities without a violation of user privacy. However, client selection and networking scheme for enabling FL in dynamic vehicular environments, which determines the communication delay between FL clients and the central server that aggregates the models received from the clients, is still under-explored. In this paper, we propose an edge computing-based joint client selection and networking scheme for vehicular IoT. The proposed scheme assigns some vehicles as edge vehicles by employing a distributed approach, and uses the edge vehicles as FL clients to conduct the training of local models, which learns optimal behaviors based on the interaction with environments. The clients also work as forwarder nodes in information sharing among network entities. The client selection takes into account the vehicle velocity, vehicle distribution, and the wireless link connectivity between vehicles using a fuzzy logic algorithm, resulting in an efficient learning and networking architecture. We use computer simulations to evaluate the proposed scheme in terms of the communication overhead and the information covered in learning.
ISNet: Integrate Image-Level and Semantic-Level Context for Semantic Segmentation

Co-occurrent visual pattern makes aggregating contextual information a common paradigm to enhance the pixel representation for semantic image segmentation. The existing approaches focus on modeling the context from the perspective of the whole image, i.e., aggregating the image-level contextual information. Despite impressive, these methods weaken the significance of the pixel representations of the same category, i.e., the semantic-level contextual information. To address this, this paper proposes to augment the pixel representations by aggregating the image-level and semantic-level contextual information, respectively. First, an image-level context module is designed to capture the contextual information for each pixel in the whole image. Second, we aggregate the representations of the same category for each pixel where the category regions are learned under the supervision of the ground-truth segmentation. Third, we compute the similarities between each pixel representation and the image-level contextual information, the semantic-level contextual information, respectively. At last, a pixel representation is augmented by weighted aggregating both the image-level contextual information and the semantic-level contextual information with the similarities as the weights. Integrating the image-level and semantic-level context allows this paper to report state-of-the-art accuracy on four benchmarks, i.e., ADE20K, LIP, COCOStuff and Cityscapes <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Tertiary lymphoid structures (TLS) identification and density assessment on H&amp;E-stained digital slides of lung cancer

Tertiary lymphoid structures (TLS) are ectopic aggregates of lymphoid cells in inflamed, infected, or tumoral tissues that are easily recognized on an H&amp;E histology slide as discrete entities, distinct from lymphocytes. TLS are associated with improved cancer prognosis but there is no standardised method available to quantify their presence. Previous studies have used immunohistochemistry to determine the presence of specific cells as a marker of the TLS. This has now been proven to be an underestimate of the true number of TLS. Thus, we propose a methodology for the automated identification and quantification of TLS, based on H&amp;E slides. We subsequently determined the mathematical criteria defining a TLS. TLS regions were identified through a deep convolutional neural network and segmentation of lymphocytes was performed through an ellipsoidal model. This methodology had a 92.87% specificity at 95% sensitivity, 88.79% specificity at 98% sensitivity and 84.32% specificity at 99% sensitivity level based on 144 TLS annotated H&amp;E slides implying that the automated approach was able to reproduce the histopathologists’ assessment with great accuracy. We showed that the minimum number of lymphocytes within TLS is 45 and the minimum TLS area is 6,245 μm 2 . Furthermore, we have shown that the density of the lymphocytes is more than 3 times those outside of the TLS. The mean density and standard deviation of lymphocytes within a TLS area are 0.0128/ μm 2 and 0.0026/ μm 2 respectively compared to 0.004/ μm 2 and 0.001/ μm 2 in non-TLS regions. The proposed methodology shows great potential for automated identification and quantification of the TLS density on digital H&amp;E slides.
UFO: Unified Foundational Ontology

The Unified Foundational Ontology (UFO) was developed over the last two decades by consistently putting together theories from areas such as formal ontology in philosophy, cognitive science, linguistics, and philosophical logics. It comprises a number of micro-theories addressing fundamental conceptual modeling notions, including entity types and relationship types. The aim of this paper is to summarize the current state of UFO, presenting a formalization of the ontology, along with the analysis of a number of cases to illustrate the application of UFO and facilitate its comparison with other foundational ontologies in this special issue. (The cases originate from the First FOUST Workshop – the Foundational Stance, an international forum dedicated to Foundational Ontology research.)
DIC Challenge 2.0: Developing Images and Guidelines for Evaluating Accuracy and Resolution of 2D Analyses

The DIC Challenge 2.0 follows on from the work accomplished in the first Digital Image Correlation (DIC) Challenge Reu et al. (Experimental Mechanics 58(7):1067, 1). The second challenge was required to better quantify the spatial resolution of 2D-DIC codes. The goal of this paper is to outline the methods and images for the 2D-DIC community to use to evaluate the performance of their codes and improve the implementation of 2D-DIC. This paper covers the creation of the new challenge images and the analysis and discussion of the results. It proposes a method of unambiguously defining spatial resolution for 2D-DIC and explores the tradeoff between displacement and strain noise (or measurement noise) and spatial resolution for a wide variety of DIC codes by a combination of the images presented here and a performance factor called Metrological Efficiency Indicator (MEI). The performance of the 2D codes generally followed the expected theoretical performance, particularly in the measurement of the displacement. The comparison did however show that even with fairly uniform displacement performance, the calculation of the strain spatial resolution varied widely. This work provides a useful framework for understanding the tradeoff and analyzing the performance of the DIC software using the provided images. It details some of the unique errors associated with the analysis of these images, such as the Pattern Induced Bias (PIB) and imprecision introduced through the strain calculation method. Future authors claiming improvements in 2D accuracy are encouraged to use these images for an unambiguous comparison.
An Adaptive Attention Fusion Mechanism Convolutional Network for Object Detection in Remote Sensing Images

For remote sensing object detection, fusing the optimal feature information automatically and overcoming the sensitivity to adapt multi-scale objects remains a significant challenge for the existing convolutional neural networks. Given this, we develop a convolutional network model with an adaptive attention fusion mechanism (AAFM). The model is proposed based on the backbone network of EfficientDet. Firstly, according to the characteristics of object distribution in datasets, the stitcher is applied to make one image containing objects of various scales. Such a process can effectively balance the proportion of multi-scale objects and handle the scale-variable properties. In addition, inspired by channel attention, a spatial attention model is also introduced in the construction of the adaptive attention fusion mechanism. In this mechanism, the semantic information of the different feature maps is obtained via convolution and different pooling operations. Then, the parallel spatial and channel attention are fused in the optimal proportions by the fusion factors to get the further representative feature information. Finally, the Complete Intersection over Union (CIoU) loss is used to make the bounding box better cover the ground truth. The experimental results of the optical image dataset DIOR demonstrate that, compared with state-of-the-art detectors such as the Single Shot multibox Detector (SSD), You Only Look Once (YOLO) v4, and EfficientDet, the proposed module improves accuracy and has stronger robustness.
A deep learning system for prostate cancer diagnosis and grading in whole slide images of core needle biopsies

Abstract Gleason grading, a risk stratification method for prostate cancer, is subjective and dependent on experience and expertise of the reporting pathologist. Deep Learning (DL) systems have shown promise in enhancing the objectivity and efficiency of Gleason grading. However, DL networks exhibit domain shift and reduced performance on Whole Slide Images (WSI) from a source other than training data. We propose a DL approach for segmenting and grading epithelial tissue using a novel training methodology that learns domain agnostic features. In this retrospective study, we analyzed WSI from three cohorts of prostate cancer patients. 3741 core needle biopsies (CNBs) received from two centers were used for training. The κquad (quadratic-weighted kappa) and AUC were measured for grade group comparison and core-level detection accuracy, respectively. Accuracy of 89.4% and κquad of 0.92 on the internal test set of 425 CNB WSI and accuracy of 85.3% and κquad of 0.96 on an external set of 1201 images, was observed. The system showed an accuracy of 83.1% and κquad of 0.93 on 1303 WSI from the third institution (blind evaluation). Our DL system, used as an assistive tool for CNB review, can potentially improve the consistency and accuracy of grading, resulting in better patient outcomes.
Investigating Explainability of Generative AI for Code through Scenario-based Design

What does it mean for a generative AI model to be explainable? The emergent discipline of explainable AI (XAI) has made great strides in helping people understand discriminative models. Less attention has been paid to generative models that produce artifacts, rather than decisions, as output. Meanwhile, generative AI (GenAI) technologies are maturing and being applied to application domains such as software engineering. Using scenario-based design and question-driven XAI design approaches, we explore users’ explainability needs for GenAI in three software engineering use cases: natural language to code, code translation, and code auto-completion. We conducted 9 workshops with 43 software engineers in which real examples from state-of-the-art generative AI models were used to elicit users’ explainability needs. Drawing from prior work, we also propose 4 types of XAI features for GenAI for code and gathered additional design ideas from participants. Our work explores explainability needs for GenAI for code and demonstrates how human-centered approaches can drive the technical development of XAI in novel domains.