Reviewerly/exHarmony huggingface dataset:
Interpreting machine-learning models in transformed feature space with an application to remote-sensing classification

Model-agnostic tools for interpreting machine-learning models struggle to summarize the joint effects of strongly dependent features in high-dimensional feature spaces, which play an important role in pattern recognition, for example in remote sensing of landcover. This contribution proposes a novel approach that interprets machine-learning models through the lens of feature space transformations. It can be used to enhance unconditional as well as conditional post-hoc diagnostic tools including partial dependence plots, accumulated local effects plots, or permutation feature importance assessments. While the approach can also be applied to nonlinear transformations, we focus on linear ones, including principal component analysis (PCA) and a partial orthogonalization technique. Structured PCA and diagnostics along paths offer opportunities for representing domain knowledge. The new approach is implemented in the R package `wiml`, which can be combined with existing explainable machine-learning packages. A case study on remote-sensing landcover classification with 46 features is used to demonstrate the potential of the proposed approach for model interpretation by domain experts.
Segmentation of thyroid glands and nodules in ultrasound images using the improved U-Net architecture

Identifying thyroid nodules' boundaries is crucial for making an accurate clinical assessment. However, manual segmentation is time-consuming. This paper utilized U-Net and its improved methods to automatically segment thyroid nodules and glands.The 5822 ultrasound images used in the experiment came from two centers, 4658 images were used as the training dataset, and 1164 images were used as the independent mixed test dataset finally. Based on U-Net, deformable-pyramid split-attention residual U-Net (DSRU-Net) by introducing ResNeSt block, atrous spatial pyramid pooling, and deformable convolution v3 was proposed. This method combined context information and extracts features of interest better, and had advantages in segmenting nodules and glands of different shapes and sizes.DSRU-Net obtained 85.8% mean Intersection over Union, 92.5% mean dice coefficient and 94.1% nodule dice coefficient, which were increased by 1.8%, 1.3% and 1.9% compared with U-Net.Our method is more capable of identifying and segmenting glands and nodules than the original method, as shown by the results of correlational studies.
FSA-Net: Rethinking the attention mechanisms in medical image segmentation from releasing global suppressed information

Attention mechanism-based medical image segmentation methods have developed rapidly recently. For the attention mechanisms, it is crucial to accurately capture the distribution weights of the effective features contained in the data. To accomplish this task, most attention mechanisms prefer using the global squeezing approach. However, it will lead to a problem of over-focusing on the global most salient effective features of the region of interest, while suppressing the secondary salient ones. Making partial fine-grained features are abandoned directly. To address this issue, we propose to use a multiple-local perception method to aggregate global effective features, and design a fine-grained medical image segmentation network, named FSA-Net. This network consists of two key components: 1) the novel Separable Attention Mechanisms which replace global squeezing with local squeezing to release the suppressed secondary salient effective features. 2) a Multi-Attention Aggregator (MAA) which can fuse multi-level attention to efficiently aggregate task-relevant semantic information. We conduct extensive experimental evaluations on five publicly available medical image segmentation datasets: MoNuSeg, COVID-19-CT100, GlaS, CVC-ClinicDB, ISIC2018, and DRIVE datasets. Experimental results show that FSA-Net outperforms state-of-the-art methods in medical image segmentation.
StandARone: Infrared-Watermarked Documents as Portable Containers of AR Interaction and Personalization

Hybrid paper interfaces leverage augmented reality (AR) to combine the desired tangibility of paper documents with the affordances of interactive digital media. Typically, the instructions for how the virtual content should be generated are not an intrinsic part of the document but rather accessed through a link to remote resources. To enable hybrid documents to be portable containers of also the AR content, we introduce StandARone documents. Using our system, a document author can define AR content and embed it invisibly on the document using a standard inkjet printer and infrared-absorbing ink. A document consumer can interact with the embedded content using a smartphone with a NIR camera without requiring a network connection. We demonstrate several use cases of StandARone including personalized offline menus, interactive visualizations, and location-aware packaging.
Tor network traffic classification using machine learning based on time-related feature

Application-level defensive security controls like Intrusion Detection and Prevention Systems (IDS/IPS) need to have visibility on the application data portion of the processed network packets to classify network traffic based on protocol and application information and then to make a proper decision against the traffic based on the defined policy. This functionality is required to check the different passing data streams against fingerprints that identify different protocols and applications. Fingerprints can be defined based on signatures, rules, and patterns of the protocols and applications or based on behaviors and deviations from a predefined baseline. The Onion Router (Tor) is one of the most powerful techniques to keep the confidentiality of data and the anonymity of user identities. Tor establishes encrypted tunnels over the distributed network, making it hard for traffic detection appliances to recognize the going Tor sessions. This paper aims to enable traffic detection and analysis appliances to recognize Tor traffic depending on time-related only attributes with high accuracy and low latency by proposing a statistical technique for analyzing and extracting features represented in the network packets. The proposed statistical technique is built based on computing the correlations between the features among only time-related attributes and dropping those with the lowest effectiveness and importance in labeling and classifying data instances and then utilizing Machine Learning to recognize Tor traffic.
Contextual semantic embeddings for ontology subsumption prediction

Automating ontology construction and curation is an important but challenging task in knowledge engineering and artificial intelligence. Prediction by machine learning techniques such as contextual semantic embedding is a promising direction, but the relevant research is still preliminary especially for expressive ontologies in Web Ontology Language (OWL). In this paper, we present a new subsumption prediction method named BERTSubs for classes of OWL ontology. It exploits the pre-trained language model BERT to compute contextual embeddings of a class, where customized templates are proposed to incorporate the class context (e.g., neighbouring classes) and the logical existential restriction. BERTSubs is able to predict multiple kinds of subsumers including named classes from the same ontology or another ontology, and existential restrictions from the same ontology. Extensive evaluation on five real-world ontologies for three different subsumption tasks has shown the effectiveness of the templates and that BERTSubs can dramatically outperform the baselines that use (literal-aware) knowledge graph embeddings, non-contextual word embeddings and the state-of-the-art OWL ontology embeddings.
Application of blockchain in enterprise financing: literature review and knowledge framework

Purpose In recent years, the application of blockchain in enterprise financing has become a hot topic in academic research. This study aims to review the existing literature, construct a knowledge framework for this research topic and propose an agenda for future research. Design/methodology/approach Based on 181 papers published from 2016 to 2020 in core journal databases in China and abroad, this study used bibliometric tools to identify and analyze an overview of literature publications, research hotspot trends and research theme clustering. This study also qualitatively analyzes literature from the dimensions of enabling mechanisms, multitechnology synergy, challenges, theoretical perspectives and research methods. Findings This study presents the research progress of blockchain applications in direct financing, bank credit, supply chain finance and other financing modes and analyzes the similarities and differences between domestic and international literature. This study also reveals enabling mechanisms of blockchain in enterprise financing, reflected as information quality improvement (data elements), trust mechanism innovation (business process) and collaboration structure enhancement (network structure). The study found several challenges (e.g. technological uncertainty, data security and organizational change) and trends (e.g. integrated innovation of multiple digital technologies). Additionally, the authors identified several gaps and opportunities for further research. Research limitations/implications This study adopts a strict strategy of selecting search terms when retrieving the literature, leading to the exclusion of certain papers on this topic. Practical implications This study provides valuable insights into the innovative development of enterprise financing modes enabled by blockchain and emphasizes that managers should clarify the applicable boundaries and necessary conditions of blockchain innovation in different financing scenarios to match technological innovation with industrial expectations. Originality/value This study constructs a knowledge framework on this topic based on a comprehensive review of existing research and proposes several important issues for future research based on the identified research gaps.
CDDO–HS: Child Drawing Development Optimization–Harmony Search Algorithm

Child drawing development optimization (CDDO) is a recent example of a metaheuristic algorithm. The motive for inventing this method is children's learning behavior and cognitive development, with the golden ratio employed to optimize their artwork's aesthetic value. Unfortunately, CDDO suffers from low performance in the exploration phase, and the local best solution stagnates. Harmony search (HS) is a highly competitive algorithm relative to other prevalent metaheuristic algorithms, as its exploration phase performance on unimodal benchmark functions is outstanding. Thus, to avoid these issues, we present CDDOHS, a hybridization of both standards of CDDO and HS. The hybridized model proposed consists of two phases. Initially, the pattern size (PS) is relocated to the algorithm's core and the initial pattern size is set to 80 % of the total population size. Second, the standard harmony search (HS) is added to the pattern size (PS) for the exploration phase to enhance and update the solution after each iteration. Experiments are evaluated using two distinct standard benchmark functions, known as classical test functions, including 23 common functions and 10 CEC-C06 2019 functions. Additionally, the suggested CDDOHS is compared to CDDO, HS, and six other widely used algorithms. Using the Wilcoxon ranksum test, the results indicate that CDDOHS beats alternative algorithms.
Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR.

Electronic Health Record modeling is crucial for digital medicine. However, existing models ignore higher-order interactions among medical codes and their causal relations towards downstream clinical predictions. To address such limitations, we propose a novel framework CACHE, to provide effective and insightful clinical predictions based on hypergraph representation learning and counterfactual and factual reasoning techniques. Experiments on two real EHR datasets show the superior performance of CACHE. Case studies with a domain expert illustrate a preferred capability of CACHE in generating clinically meaningful interpretations towards the correct predictions.
Come and see through my eyes: A systematic review of 360-degree video technology in nursing education

Viewers can watch 360-degree videos from any angle, like a panorama, and experience the virtual environment directly. Recently, there has been a growing interest in immersive and interactive technologies in education, and the use of 360-degree videos has become popular. This systematic review was conducted to present the current use of 360-degree videos in nursing education. Systematic review. In addition to screening the Google Scholar, MEDLINE, SCOPUS, and EBSCO databases, we also performed hand searching. Relevant keywords were used to identify trials published in the above-mentioned databases from inception to March 1, 2023. In the first step, the titles, abstracts, and full texts of the retrieved studies were independently reviewed by two authors according to the inclusion criteria. Studies on which there was disagreement were reviewed by all authors, and a consensus decision was reached. The data from the studies included in the review were analyzed and reported using the PRISMA 2020 checklist. Twelve articles that met the inclusion criteria were reviewed. It was determined that 360-degree video scenarios used in nursing education were mostly related to mental health nursing, and the videos were mostly viewed through head-mounted displays, without any interactive features. Most difficulties encountered in the use of these videos were related to motion sickness. The main implications of the reviewed studies were that 360-degree videos contributed to the development of students' knowledge, skills, and attitudes and that the use of these videos was recommended. As an innovative application the use of 360-degree videos in nursing education was examined from various perspectives in this review. The results showed that the use of such videos was convenient and effective for nursing education.
The impact of interaction on continuous use in online learning platforms: a metaverse perspective

Purpose As online learning is the embryonic form of education in the metaverse, it is extremely important to explore the behavioral preferences of users. The aim is to explore the impact of interactive features on continuous use in online learning and to further explore what kind of interaction mode should be constructed for different types of students to obtain the best educational experience. Design/methodology/approach The study developed an empirical model and used a real-world dataset to test hypotheses. Specifically, the interaction in online learning is analyzed from different dimensions, including the interaction intensity of multiple subjects, the immersion of interactive technology, the timeliness of interactive feedback, and the fun in interaction. Findings The authors found that the intensity of interaction, immersion, timeliness of feedback and fun in the interaction all had significant positive effects on continuous use. Among them, the most important is the interaction between teachers and students. With the growth of user grades, the role of parents in the interaction is getting smaller and smaller, and the fun in the interaction is gradually becoming unnecessary. For high school students, gamified interactions can even have a negative impact. In addition, from the perspective of gender, males prefer immersive interaction, while females pay more attention to themselves and have negative feedback on fees. Originality/value The authors deepened the interaction and summarized the impact of different interactive features on continuous use in online learning platforms. The authors focused on the impact of the immersive experience brought by the application of interactive technology, which can confirm the user behavior preferences of online learning in the context of the metaverse. The research also provides a reference for online learning institutions to set up course interaction modes and targeted marketing programs.
Deep Blind Super-Resolution for Satellite Video

Recent efforts have witnessed remarkable progress in Satellite Video Super-Resolution (SVSR). However, most SVSR methods usually assume the degradation is fixed and known, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g., bicubic</i> downsampling, which makes them vulnerable in real-world scenes with multiple and unknown degradations. To alleviate this issue, blind SR has thus become a research hotspot. Nevertheless, existing approaches are mainly engaged in blur kernel estimation while losing sight of another critical aspect for VSR tasks: temporal compensation, especially compensating for blurry and smooth pixels with vital sharpness from severely degraded satellite videos. Therefore, this paper proposes a practical Blind SVSR algorithm (BSVSR) to explore more sharp cues by considering the pixel-wise blur levels in a coarse-to-fine manner. Specifically, we employed multi-scale deformable convolution to coarsely aggregate the temporal redundancy into adjacent frames by window-slid progressive fusion. Then the adjacent features are finely merged into mid-feature using deformable attention, which measures the blur levels of pixels and assigns more weights to the informative pixels, thus inspiring the representation of sharpness. Moreover, we devise a pyramid spatial transformation module to adjust the solution space of sharp mid-feature, resulting in flexible feature adaptation in multi-level domains. Quantitative and qualitative evaluations on both simulated and real-world satellite videos demonstrate that our BSVSR performs favorably against state-of-the-art non-blind and blind SR models. Code will be available at https://github.com/XY-boy/Blind-Satellite-VSR.
SNCSE: Contrastive Learning for Unsupervised Sentence Embedding with Soft Negative Samples

Unsupervised sentence embedding aims to obtain the most appropriate embedding for a sentence to reflect its semantics. Contrastive learning has been attracting developing attention. For a sentence, current models utilize diverse data augmentation methods to generate positive samples, while consider other independent sentences as negative samples. Then they adopt InfoNCE loss to pull the embeddings of positive pairs gathered, and push those of negative pairs scattered. Although these models have made great progress, we argue that they may suffer from feature suppression, where the models fail to distinguish and decouple textual similarity and semantic similarity. They may overestimate the semantic similarity of any sentence pairs with similar text regardless of the actual semantic difference between them, and vice versa. Herein, we propose contrastive learning for unsupervised sentence embedding with soft negative samples (SNCSE). Soft negative samples share highly similar text but have surely and apparently different semantics with the original samples. Specifically, we take the negation of original sentences as soft negative samples, and propose Bidirectional Margin Loss (BML) to introduce them into traditional contrastive learning framework. Our experimental results on semantic textual similarity (STS) task show that SNCSE can obtain state-of-the-art performance with different encoders, indicating its strength on unsupervised sentence embedding. Our code and models are released at https://github.com/Sense-GVT/SNCSE .
LAVIS: A One-stop Library for Language-Vision Intelligence

We introduce LAVIS, an open-source deep learning library for LAnguage-VISion research and applications.LAVIS aims to serve as a one-stop comprehensive library that brings recent advancements in the language-vision field accessible for researchers and practitioners, as well as fertilizing future research and development.It features a unified interface to easily access state-of-the-art image-language, videolanguage models and common datasets.LAVIS supports training, evaluation and benchmarking on a rich variety of tasks, including multimodal classification, retrieval, captioning, visual question answering, dialogue and pre-training.In the meantime, the library is also highly extensible and configurable, facilitating future development and customization.In this paper, we describe design principles, key components and functionalities of the library, and also present benchmarking results across common language-vision tasks.
SpeechUT: Bridging Speech and Text with Hidden-Unit for Encoder-Decoder Based Speech-Text Pre-training

The rapid development of single-modal pretraining has prompted researchers to pay more attention to cross-modal pre-training methods.In this paper, we propose a unified-modal speech-unit-text pre-training model, SpeechUT, to connect the representations of a speech encoder and a text decoder with a shared unit encoder.Leveraging hidden-unit as an interface to align speech and text, we can decompose the speech-to-text model into a speech-to-unit model and a unit-to-text model, which can be jointly pre-trained with unpaired speech and text data respectively.Our proposed SpeechUT is fine-tuned and evaluated on automatic speech recognition (ASR) and speech translation (ST) tasks.Experimental results show that SpeechUT gets substantial improvements over strong baselines, and achieves state-of-the-art performance on both the Lib-riSpeech ASR and MuST-C ST tasks.To better understand the proposed SpeechUT, detailed analyses are conducted.The code and pretrained models are available at https://aka.ms/SpeechUT.
Unleashing the potential of <scp>AI</scp> for pathology: challenges and recommendations

Abstract Computational pathology is currently witnessing a surge in the development of AI techniques, offering promise for achieving breakthroughs and significantly impacting the practices of pathology and oncology. These AI methods bring with them the potential to revolutionize diagnostic pipelines as well as treatment planning and overall patient care. Numerous peer‐reviewed studies reporting remarkable performance across diverse tasks serve as a testimony to the potential of AI in the field. However, widespread adoption of these methods in clinical and pre‐clinical settings still remains a challenge. In this review article, we present a detailed analysis of the major obstacles encountered during the development of effective models and their deployment in practice. We aim to provide readers with an overview of the latest developments, assist them with insights into identifying some specific challenges that may require resolution, and suggest recommendations and potential future research directions. © 2023 The Authors. The Journal of Pathology published by John Wiley &amp; Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.