Reviewerly/exHarmony huggingface dataset:
Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension

Question answering (QA) is a fundamental means to facilitate assessment and training of narrative comprehension skills for both machines and young children, yet there is scarcity of high-quality QA datasets carefully designed to serve this purpose.In particular, existing datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements.Drawing on the reading education research, we introduce Fairy-taleQA 1 , a dataset focusing on narrative comprehension of kindergarten to eighth-grade students.Generated by educational experts based on an evidence-based theoretical framework, FairytaleQA consists of 10,580 explicit and implicit questions derived from 278 childrenfriendly stories, covering seven types of narrative elements or relations.Our dataset is valuable in two folds: First, we ran existing QA models on our dataset and confirmed that this annotation helps assess models' fine-grained learning skills.Second, the dataset supports question generation (QG) task in the education domain.Through benchmarking with QG models, we show that the QG model trained on FairytaleQA is capable of asking high-quality and more diverse questions.
Application of holography and augmented reality based technology to visualize the internal structure of the dental root – a proof of concept

Abstract Background The Augmented Reality (AR) blends digital information with the real world. Thanks to cameras, sensors, and displays it can supplement the physical world with holographic images. Nowadays, the applications of AR range from navigated surgery to vehicle navigation. Development The purpose of this feasibility study was to develop an AR holographic system implementing Vertucci’s classification of dental root morphology to facilitate the study of tooth anatomy. It was tailored to run on the AR HoloLens 2 (Microsoft) glasses. The 3D tooth models were created in Autodesk Maya and exported to Unity software. The holograms of dental roots can be projected in a natural setting of the dental office. The application allowed to display 3D objects in such a way that they could be rotated, zoomed in/out, and penetrated. The advantage of the proposed approach was that students could learn a 3D internal anatomy of the teeth without environmental visual restrictions. Conclusions It is feasible to visualize internal dental root anatomy with AR holographic system. AR holograms seem to be attractive adjunct for learning of root anatomy.
ACGNet: Action Complement Graph Network for Weakly-Supervised Temporal Action Localization

Weakly-supervised temporal action localization (WTAL) in untrimmed videos has emerged as a practical but challenging task since only video-level labels are available. Existing approaches typically leverage off-the-shelf segment-level features, which suffer from spatial incompleteness and temporal incoherence, thus limiting their performance. In this paper, we tackle this problem from a new perspective by enhancing segment-level representations with a simple yet effective graph convolutional network, namely action complement graph network (ACGNet). It facilitates the current video segment to perceive spatial-temporal dependencies from others that potentially convey complementary clues, implicitly mitigating the negative effects caused by the two issues above. By this means, the segment-level features are more discriminative and robust to spatial-temporal variations, contributing to higher localization accuracies. More importantly, the proposed ACGNet works as a universal module that can be flexibly plugged into different WTAL frameworks, while maintaining the end-to-end training fashion. Extensive experiments are conducted on the THUMOS'14 and ActivityNet1.2 benchmarks, where the state-of-the-art results clearly demonstrate the superiority of the proposed approach.
Controllable atom-photon entanglement via quantum interference near plasmonic nanostructure

Abstract A five-level atomic system is proposed in vicinity of a two-dimensional (2D) plasmonic nanostructure with application in atom-photon entanglement. The behavior of the atom-photon entanglement is discussed with and without a control laser field. The amount of atom-photon entanglement is controlled by the quantum interference created by the plasmonic nanostructure. Thus, the degree of atom-photon entanglement is affected by the atomic distance from the plasmonic nanostructure. In the presence of a control field, maximum entanglement between the atom and its spontaneous emission field is observed.
Creating the European Literary Text Collection (ELTeC): Challenges and Perspectives

The aim of this contribution is to reflect on the process of building the multilingual <em>European Literary Text Collection</em> (ELTeC) that is being created in the framework of the networking project <em>Distant Reading for European Literary History</em> funded by COST (European Cooperation in Science and Technology). To provide some background, we briefly introduce the basic idea of ELTeC with a focus on the overall goals and intended usage scenarios. We then describe the collection composition principles that we have derived from the usage scenarios. In our discussion of the corpus-building process, we focus on collections of novels from four different literary traditions as components of ELTeC: French, Portuguese, Romanian, and Slovenian, selected from the more than twenty collections that are currently in preparation. For each collection, we describe some of the challenges we have encountered and the solutions developed while building ELTeC. In each case, the literary tradition, the history of the language, the current state of digitization of cultural heritage, the resources available locally, and the scholars' training level with regard to digitization and corpus building have been vastly different. How can we, in this context, hope to build comparable collections of novels that can usefully be integrated into a multilingual resource such as ELTeC and used in Distant Reading research? Based on our individual and collective experience with contributing to ELTeC, we end this contribution with some lessons learned regarding collaborative, multilingual corpus building.
Natural Language Processing and Machine Learning Classifier used for Detecting the Author of the Sentence

Detecting the author of the sentence in a collective document can be done by choosing a suitable set of features and implementing using Natural Language Processing in Machine Learning. Training our machine is the basic idea to identify the author name of a specific sentence. This can be done by using 8 different NLP steps like applying stemming algorithm, finding stop-list words, preprocessing the data, and then applying it to a machine learning classifier-Support vector machine (SVM) which classify the dataset into a number of classes specifying the author of the sentence and defines the name of author for each and every sentence with an accuracy of 82%.This paper helps the readers who are interested in knowing the names of the authors who have written some specific words.
An Appraisal on Speech and Emotion Recognition Technologies based on Machine Learning

In earlier days, people used speech as a means of communication or the way a listener is conveyed by voice or expression. But the idea of machine learning and various methods are necessary for the recognition of speech in the matter of interaction with machines. With a voice as a bio-metric through use and significance, speech has become an important part of speech development. In this article, we attempted to explain a variety of speech and emotion recognition techniques and comparisons between several methods based on existing algorithms and mostly speech-based methods. We have listed and distinguished speaking technologies that are focused on specifications, databases, classification, feature extraction, enhancement, segmentation and process of Speech Emotion recognition in this paper.
Image Noise Removal Method Based on Thresholding and Regularization Techniques

In this article, a salt and pepper noise (SPN) removal method is proposed based on thresholding and regularization techniques. The proposed method utilizes the ability to remove noise from an image denoising model based on Total Variation (TV) regularization and characteristics of SPN. First, a technique based on the characteristic of SPN is proposed to detect noisy pixels. Second, a modified TV regularization-based method is applied to restore the above noisy pixels. In addition, numerical implementation of the model based on the Nesterov optimal method is also provided. Five test cases with various noise levels for a large natural image dataset were studied in the experiments. The peak signal-to-noise ratio and structural similarity metrics were employed to assess the image quality after denoising. The experimental results indicated that the proposed method removes SPN remarkably and outperforms state-of-the-art image denoising methods for SPN.
Resource Analysis of Blockchain Consensus Algorithms in Hyperledger Fabric

In the blockchain network, the consensus algorithm is used to tolerate node faults with data consistency and integrity, so it is vital in all blockchain services. Previous studies on the consensus algorithm have the following limitations: 1) no resource consumption analysis was done, 2) performance analysis was not comprehensive in terms of blockchain parameters (e.g., number of orderer nodes, number of fault nodes, batch size, payload size), and 3) practical fault scenarios were not evaluated. In other words, the resource provisioning of consensus algorithms in clouds has not been addressed adequately. As many blockchain services are deployed in the form of blockchain-as-a-service (BaaS), how to provision consensus algorithms becomes a key question to be answered. This study presents a kernel-level analysis for the resource consumption and comprehensive performance evaluations of three major consensus algorithms (i.e., Kafka, Raft, and PBFT). Our experiments reveal that resource consumption differs up to seven times, which demonstrates the importance of proper resource provisioning for BaaS.
A length adaptive algorithm-hardware co-design of transformer on FPGA through sparse attention and dynamic pipelining

Transformers are considered one of the most important deep learning models since 2018, in part because it establishes state-of-the-art (SOTA) records and could potentially replace existing Deep Neural Networks (DNNs). Despite the remarkable triumphs, the prolonged turnaround time of Transformer models is a widely recognized roadblock. The variety of sequence lengths imposes additional computing overhead where inputs need to be zero-padded to the maximum sentence length in the batch to accommodate the parallel computing platforms. This paper targets the field-programmable gate array (FPGA) and proposes a coherent sequence length adaptive algorithm-hardware co-design for Transformer acceleration. Particularly, we develop a hardware-friendly sparse attention operator and a length-aware hardware resource scheduling algorithm. The proposed sparse attention operator brings the complexity of attention-based models down to linear complexity and alleviates the off-chip memory traffic. The proposed length-aware resource hardware scheduling algorithm dynamically allocates the hardware resources to fill up the pipeline slots and eliminates bubbles for NLP tasks. Experiments show that our design has very small accuracy loss and has 80.2 × and 2.6 × speedup compared to CPU and GPU implementation, and 4 × higher energy efficiency than state-of-the-art GPU accelerator optimized via CUBLAS GEMM.
SiamHYPER: Learning a Hyperspectral Object Tracker From an RGB-Based Tracker

Hyperspectral videos can provide the spatial, spectral, and motion information of targets, which makes it possible to track camouflaged targets that are similar to the background. However, hyperspectral object tracking is a challenging task, due to the huge hyperspectral video data volume and the data hungry problem for the model training. Insufficient training data can seriously interfere with the accuracy and generalization of the tracking models. In this paper, a dual deep Siamese network framework for hyperspectral object tracking (SiamHYPER) is proposed for learning a hyperspectral tracker from a pretrained RGB tracker in the case of the data hungry problem. Specifically, in addition to a pretrained RGB-based Siamese tracker, a hyperspectral target-aware module is designed to mine the spectral information during the target prediction, and a spatial-spectral cross-attention module is introduced to further fuse the deep spectral and spatial features extracted from the RGB tracker and the hyperspectral target-aware module. Benefiting from the guidance training of the RGB tracker, a robust hyperspectral object tracker can be trained effectively with only a small number of hyperspectral video samples, to overcome the data hungry problem. In the experiments conducted in this study, the SiamHYPER framework was verified using SiamBAN and SiamRPN++, with 13 000 frames of hyperspectral video for training, and achieved the best performance on the publicly available hyperspectral dataset released as part of the WHISPERS Hyperspectral Object Tracking Challenge <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . The area under the curve (AUC) of SiamHYPER was increased by nearly 8.9% and 7.2%, respectively, when compared with the current state-of-the-art RGB-based and hyperspectral trackers. In addition, the processing speed of SiamHYPER was 19 FPS, which is much higher than that of the current state-of-the-art hyperspectral trackers.
Continual Learning with Lifelong Vision Transformer

Continual learning methods aim at training a neural network from sequential data with streaming labels, relieving catastrophic forgetting. However, existing methods are based on and designed for convolutional neural networks (CNNs), which have not utilized the full potential of newly emerged powerful vision transformers. In this paper, we propose a novel attention-based framework Lifelong Vision Transformer (LVT), to achieve a better stability-plasticity trade-off for continual learning. Specifically, an inter-task attention mechanism is presented in LVT, which implicitly absorbs the previous tasks' information and slows down the drift of important attention between previous tasks and the current task. LVT designs a dual-classifier structure that independently injects new representation to avoid catas-trophic interference and accumulates the new and previous knowledge in a balanced manner to improve the overall performance. Moreover, we develop a confidence-aware memory update strategy to deepen the impression of the previous tasks. The extensive experimental results show that our approach achieves state-of-the-art performance with even fewer parameters on continual learning benchmarks.
HoVer-Trans: Anatomy-Aware HoVer-Transformer for ROI-Free Breast Cancer Diagnosis in Ultrasound Images

Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiation-free and low-cost properties. However, the diagnostic accuracy of breast cancer is still limited due to its inherent limitations. Then, a precise diagnose using breast ultrasound (BUS) image would be significant useful. Many learning-based computer-aided diagnostic methods have been proposed to achieve breast cancer diagnosis/lesion classification. However, most of them require a pre-define region of interest (ROI) and then classify the lesion inside the ROI. Conventional classification backbones, such as VGG16 and ResNet50, can achieve promising classification results with no ROI requirement. But these models lack interpretability, thus restricting their use in clinical practice. In this study, we propose a novel ROI-free model for breast cancer diagnosis in ultrasound images with interpretable feature representations. We leverage the anatomical prior knowledge that malignant and benign tumors have different spatial relationships between different tissue layers, and propose a HoVer-Transformer to formulate this prior knowledge. The proposed HoVer-Trans block extracts the inter- and intra-layer spatial information horizontally and vertically. We conduct and release an open dataset GDPH&SYSUCC for breast cancer diagnosis in BUS. The proposed model is evaluated in three datasets by comparing with four CNN-based models and three vision transformer models via five-fold cross validation. It achieves state-of-the-art classification performance (GDPH&SYSUCC AUC: 0.924, ACC: 0.893, Spec: 0.836, Sens: 0.926) with the best model interpretability. In the meanwhile, our proposed model outperforms two senior sonographers on the breast cancer diagnosis when only one BUS image is given (GDPH&SYSUCC-AUC ours: 0.924 vs. reader1: 0.825 vs. reader2: 0.820).
Improved Gentry–Halevi's fully homomorphic encryption‐based lightweight privacy preserving scheme for securing medical Internet of Things

Abstract Internet of Medical Things (IoMT) solutions have proliferated rapidly in the COVID‐19 pandemic era. The smart medical sensors capture real‐time data from remote patients and communicate it to medical servers in a secure and privacy‐preserving manner. It is a herculean challenge to guarantee security and privacy in Medical IoT applications. Hence, an improved Gentry–Halevi's fully homomorphic encryption‐based (IGHFHE) lightweight privacy preserving user authentication scheme is proposed in this work. The scheme is proposed with an integer matrix computation strategy for securing data computation with privacy protection. It adopts the translation process of Gentry–Halevi's fully homomorphic encryption process for performing homomorphic addition and multiplication, then encrypt an integer matrix modulo that represents a positive integer. Extensive informal investigation and simulation of the proposed IGHFHE scheme shows that it is more resistant to well‐known attacks for preventing authentication breaches. Also, the proposed IGHFHE scheme reduced computational and storage overhead by 4.98% and 5.78% respectively on average in comparison to other prevailing schemes.
DiffusionDet: Diffusion Model for Object Detection

We propose DiffusionDet, a new framework that formulates object detection as a denoising diffusion process from noisy boxes to object boxes. During the training stage, object boxes diffuse from ground-truth boxes to random distribution, and the model learns to reverse this noising process. In inference, the model refines a set of randomly generated boxes to the output results in a progressive way. Our work possesses an appealing property of flexibility, which enables the dynamic number of boxes and iterative evaluation. The extensive experiments on the standard benchmarks show that DiffusionDet achieves favorable performance compared to previous well-established detectors. For example, DiffusionDet achieves 5.3 AP and 4.8 AP gains when evaluated with more boxes and iteration steps, under a zero-shot transfer setting from COCO to CrowdHuman. Our code is available at https://github.com/ShoufaChen/DiffusionDet.
Opinion Target Extraction Using a Shallow Semantic Parsing Framework

In this paper, we present a simplified shallow semantic parsing approach to extracting opinion targets. This is done by formulating opinion target extraction (OTE) as a shallow semantic parsing problem with the opinion expression as the predicate and the corresponding targets as its arguments. In principle, our parsing approach to OTE differs from the state-of-the-art sequence labeling one in two aspects. First, we model OTE from parse tree level, where abundant structured syntactic information is available for use, instead of word sequence level, where only lexical information is available. Second, we focus on determining whether a constituent, rather than a word, is an opinion target or not, via a simplified shallow semantic parsing framework. Evaluation on two datasets shows that structured syntactic information plays a critical role in capturing the domination relationship between an opinion expression and its targets. It also shows that our parsing approach much outperforms the state-of-the-art sequence labeling one.
A first look at unfollowing behavior on GitHub

Many open source software projects rely on contributors to fix bugs and contribute new features. On GitHub, developers often broadcast their activities to followers, which may entice followers to be project contributors. It is important to understand unfollowing behavior, maintain current followers, and attract some followers to become contributors in OSS projects. Our objective in this paper is to provide a comprehensive analysis of unfollowing behavior on GitHub. To the best of our knowledge, we present a first look at unfollowing behavior on GitHub. We collect a dataset containing 701,364 developers and their 4,602,440 following relationships in March 2016. We also crawl their following relationships in May 2013, August 2015 and November 2015. We conduct surveys, define potential impact factors, and analyze the correlation of factors with the likelihood of unfollowing behavior. Our main observations are: (1) Between May 2013 and August 2015, 19.8% of active developers ever unfollowed some users. (2) Developers are more likely to unfollow those who have fewer activities, lower programming language similarity, and asymmetric relationships. Our results give suggestions for developers to reduce the likelihood of being unfollowed by their followers, and attract researchers' attention on relationship dissolution.
Tool-Driven Revolutions in Archaeological Science

There is an argument in philosophy of science that revolutions in science are either idea-driven or tool-driven. We explore this debate in light of recent efforts by many scientific disciplines to embrace methods to improve the reproducibility of their research. One of the most profound changes driven by this concern for reproducibility and transparency is from analysing data using tools dependent on point-and-clicking with a mouse in closed source software, to tools based on writing scripts in open source programming languages and making them openly available. We present bibliometric evidence for this change in ecology and in archaeology to test if the adoption of these new tools is revolutionary or transformational. We identify a positive citation effect for papers that use the open source programming language R. We discuss how computational approaches to improving reproducibility and transparency in archaeology are mediated and transformed by the use of R code.