Reviewerly/exHarmony huggingface dataset:
Facilitating Sustainable Self-Directed Learning Experience with the Use of Mobile-Assisted Language Learning

This study explored the impact of utilizing mobile-assisted language learning to help EFL university students promote their learning performance and support a sustainable self-directed learning experience while developing digital literacy and technology competence. The global pandemic and spread of COVID-19 have accelerated the convergence of technology in the classroom in response to an emergency where the sustainability of education is at stake. With the progress of digital technology, a variety of smart mobile applications have been applied in English language learning and teaching. The use of those technologies can encourage language learners to practice self-directed learning beyond the classroom settings, which is essential for motivated and autonomous learning. Mobile phone applications have great potential as effective pedagogical tools with features such as accessibility, manageable interface, and multifunctional components. However, the use of mobile-assisted language learning for sustainable, self-directed learning outside the classroom has been relatively under-researched. In this study, EFL university students engaged in mobile-assisted language learning that could make their learning more sustainable and independent outside of the classroom. The data of this study show that students’ attitudes toward participating in mobile-assisted English language learning were affirmative. The results show that incorporation of mobile applications into language learning could foster learner motivation and make their learning more sustainable and entertaining than simply using the conventional instructional methods. Participants of this study stated that major benefits of experiencing mobile-assisted learning for their English study were ease of access to learning contents, portability of the learning tools, flexible and self-directed learning environment, better interaction, and improved self-efficacy in English learning performance. The pedagogical implications of the study are discussed, and instructional strategies are suggested to share ideas about a more pertinent learning environment in the era of digital transformation.
TSA-SCC: Text Semantic-Aware Screen Content Coding With Ultra Low Bitrate

Due to the rapid growth of web conferences, remote screen sharing, and online games, screen content has become an important type of internet media information and over 90% of online media interactions are screen based. Meanwhile, as the main component in the screen content, textual information averagely takes up over 40% of the whole image on various commonly used screen content datasets. However, it is difficult to compress the textual information by using the traditional coding schemes as HEVC, which assumes strong spatial and temporal correlations within the image/video. State-of-the-art screen content coding (SCC) standard as HEVC-SCC still adopts a block-based coding framework and does not consider the text semantics for compression, thus inevitably blurring texts at a lower bitrate. In this paper, we propose a general text semantic-aware screen content coding scheme (TSA-SCC) for ultra low bitrate setting. This method detects the abrupt picture in a screen content video (or image), recognizes textual information (including word, position, font type, font size and font color) in the abrupt picture based on neural networks, and encodes texts with text coding tools. The other pictures as well as the background image after removing texts from the abrupt picture via inpainting, are encoded with HEVC-SCC. Compared with HEVC-SCC, the proposed method TSA-SCC reduces bitrate by up to 3× at a similar compression quality. Moreover, TSA-SCC achieves much better visual quality with less bitrate consumption when encoding the screen content video/image at ultra low bitrates.
Intelligent Assistant Diagnosis System of Osteosarcoma MRI Image Based on Transformer and Convolution in Developing Countries

Osteosarcoma is a malignant bone tumor commonly found in adolescents or children, with high incidence and poor prognosis. Magnetic resonance imaging (MRI), which is the more common diagnostic method for osteosarcoma, has a very large number of output images with sparse valid data and may not be easily observed due to brightness and contrast problems, which in turn makes manual diagnosis of osteosarcoma MRI images difficult and increases the rate of misdiagnosis. Current image segmentation models for osteosarcoma mostly focus on convolution, whose segmentation performance is limited due to the neglect of global features. In this paper, we propose an intelligent assisted diagnosis system for osteosarcoma, which can reduce the burden of doctors in diagnosing osteosarcoma from three aspects. First, we construct a classification-image enhancement module consisting of resnet18 and DeepUPE to remove redundant images and improve image clarity, which can facilitate doctors' observation. Then, we experimentally compare the performance of serial, parallel, and hybrid fusion transformer and convolution, and propose a Double U-shaped visual transformer with convolution (DUconViT) for automatic segmentation of osteosarcoma to assist doctors' diagnosis. This experiment utilizes more than 80,000 osteosarcoma MRI images from three hospitals in China. The results show that DUconViT can better segment osteosarcoma with DSC 2.6% and 1.8% higher than Unet and Unet++, respectively. Finally, we propose the pixel point quantification method to calculate the area of osteosarcoma, which provides more reference basis for doctors' diagnosis.
Learning Memory-Augmented Unidirectional Metrics for Cross-modality Person Re-identification

This paper tackles the cross-modality person re-identification (re-ID) problem by suppressing the modality discrepancy. In cross-modality re-ID, the query and gallery images are in different modalities. Given a training identity, the popular deep classification baseline shares the same proxy (i.e., a weight vector in the last classification layer) for two modalities. We find that it has considerable tolerance for the modality gap, because the shared proxy acts as an intermediate relay between two modalities. In response, we propose a Memory-Augmented Unidirectional Metric (MAUM) learning method consisting of two novel designs, i.e., unidirectional metrics, and memory-based augmentation. Specifically, MAUM first learns modality-specific proxies (MS-Proxies) independently under each modality. Afterward, MAUM uses the already-learned MS-Proxies as the static references for pulling close the features in the counterpart modality. These two unidirectional metrics (IR image to RGB proxy and RGB image to IR proxy) jointly alleviate the relay effect and benefit cross-modality association. The cross-modality association is further enhanced by storing the MS-Proxies into memory banks to increase the reference diversity. Importantly, we show that MAUM improves cross-modality re-ID under the modality-balanced setting and gains extra robustness against the modality-imbalance problem. Extensive experiments on SYSU-MMOI and RegDB datasets demonstrate the superiority of MAUM over the state-of-the-art. The code will be available.
High-order tensor flow processing using integrated photonic circuits

Tensor analytics lays mathematical basis for the prosperous promotion of multiway signal processing. To increase computing throughput, mainstream processors transform tensor convolutions to matrix multiplications to enhance parallelism of computing. However, such order-reducing transformation produces data duplicates and consumes additional memory. Here, we demonstrate an integrated photonic tensor flow processor without tensor-matrix transformation, which outputs the convolved tensor as the input tensor 'flows' through the processor. The hybrid manipulation of optical dimensions of wavelength, time, and space enables the direct representation and processing of high-order tensors in optical domain. In the proof-of-concept experiment, processing of multi-channel images and videos is accomplished at the frequency of 20 GHz. A convolutional neural network is demonstrated on the processor, which achieves an accuracy of 97.9 percent on action recognition.
Critical impact of organizational and individual inertia in explaining non-compliant security behavior in the Shadow IT context

Shadow IT refers to the use of information technology (IT) solutions and systems without prior explicit organizational approval. In this research, we have investigated an important role of organizational and individual inertia in explaining non-compliant security behavior in the Shadow IT context. Using the theory of organizational and individual inertia and status quo framework as theoretical lenses, we sought to explain the factors that form an individual's cognitive based inertia. Our study offers important insights into how inertia shapes and drives cognitive based inertia, which drives the behavioral intention to continue using Shadow IT. We suggest several new insights for theory and practitioners on how to better address the Shadow IT phenomenon with the objective that organizations are more agile, productive and efficient but at the same time, more compliant with information security policy requirements.
Deep Convolutional Generative Adversarial Network-Based Food Recognition Using Partially Labeled Data

Traditional machine learning algorithms using hand-crafted feature extraction techniques (such as local binary pattern) have limited accuracy because of high variation in images of the same class (or intraclass variation) for food recognition tasks. In recent works, convolutional neural networks (CNNs) have been applied to this task with better results than all previously reported methods. However, they perform best when trained with large amount of annotated (labeled) food images. This is problematic when obtained in large volume, because they are expensive, laborious, and impractical. This article aims at developing an efficient deep CNN learning-based method for food recognition alleviating these limitations by using partially labeled training data on generative adversarial networks (GANs). We make new enhancements to the unsupervised training architecture introduced by Goodfellow et al., which was originally aimed at generating new data by sampling a dataset. In this article, we make modifications to deep convolutional GANs to make them robust and efficient for classifying food images. Experimental results on benchmarking datasets show the superiority of our proposed method, as compared to the current state-of-the-art methodologies, even when trained with partially labeled training data.
ChronoGraph: Enabling Temporal Graph Traversals for Efficient Information Diffusion Analysis over Time

ChronoGraph is a novel system enabling temporal graph traversals. Compared to snapshot-oriented systems, this traversal-oriented system is suitable for analyzing information diffusion over time without violating a time constraint on temporal paths. The cornerstone of ChronoGraph aims at bridging the chasm between point-based semantics and period-based semantics and the gap between temporal graph traversals and static graph traversals. Therefore, our graph model and traversal language provide the temporal syntax for both semantics, and we present a method converting point-based semantics to period-based ones. Also, ChronoGraph exploits the temporal support and parallelism to handle the temporal degree, which explosively increases compared to static graphs. We demonstrate how three traversal recipes can be implemented on top of our system: temporal breadth-first search (tBFS), temporal depth-first search (tDFS), and temporal single source shortest path (tSSSP). According to our evaluation, our temporal support and parallelism enhance temporal graph traversals in terms of convenience and efficiency. Also, ChronoGraph outperforms existing property graph databases in terms of temporal graph traversals. We prototype ChronoGraph by extending Tinkerpop, a de facto standard for property graphs. Therefore, we expect that our system would be readily accessible to existing property graph users.
Wavelet Denoising Algorithm Based on NDOA Compressed Sensing for Fluorescence Image of Microarray

A microarray can be easily used for quantitatively analyzing the expression levels of DNA genes. Yet, the noises introduced during the application will greatly affect the accuracy of DNA sequence detection. How to reduce the noise constitutes a challenging problem in microarray analysis. Especially, due to the weak fluorescence response, the image of microarray contains difficulties of the low peak-signal-to-noise ratio (PSNR) and luminance contrast. To solve the problem that the wavelet threshold denoising method has poor effective on low PSNR image, a wavelet denoising approach based on compression sensing (CS) optimized by the neural dynamics optimization algorithm (NDOA) is proposed, which preferably solves the denoising difficulties of noise pollution in the microarray image. Under the condition of Gaussian random observation matrix, the effectiveness of NDOA-optimized wavelet denoising based on CS gets better work than the orthogonal matching pursuit and its improved algorithms. The experimental results indicate that the expected wavelet coefficients of the noiseless image have been reconstructed with higher quality. When the compression sampling rate for microarray image is 0.875, the PSNR of the NDOA-optimized wavelet denoising algorithm based on CS is increased about 9 dB, and the root mean squared error is reduced obviously too, in comparison with the wavelet soft-threshold denoising method. It shows that the NDOA-optimized method improves the performance of the classical wavelet threshold denoising.
A Survey of Information Visualization Books

Abstract Information visualization is a rapidly evolving field with a growing volume of scientific literature and texts continually published. To keep abreast of the latest developments in the domain, survey papers and state‐of‐the‐art reviews provide valuable tools for managing the large quantity of scientific literature. Recently, a survey of survey papers was published to keep track of the quantity of refereed survey papers in information visualization conferences and journals. However, no such resources exist to inform readers of the large volume of books being published on the subject, leaving the possibility of valuable knowledge being overlooked. We present the first literature survey of information visualization books that addresses this challenge by surveying the large volume of books on the topic of information visualization and visual analytics. This unique survey addresses some special challenges associated with collections of books (as opposed to research papers) including searching, browsing and cost. This paper features a novel two‐level classification based on both books and chapter topics examined in each book, enabling the reader to quickly identify to what depth a topic of interest is covered within a particular book. Readers can use this survey to identify the most relevant book for their needs amongst a quickly expanding collection. In indexing the landscape of information visualization books, this survey provides a valuable resource to both experienced researchers and newcomers in the data visualization discipline.
Beyond Open vs. Closed

Data too sensitive to be "open" for analysis and re-purposing typically remains "closed" as proprietary information. This dichotomy undermines efforts to make algorithmic systems more fair, transparent, and accountable. Access to proprietary data in particular is needed by government agencies to enforce policy, researchers to evaluate methods, and the public to hold agencies accountable; all of these needs must be met while preserving individual privacy and firm competitiveness. In this paper, we describe an integrated legal-technical approach provided by a third-party public-private data trust designed to balance these competing interests. Basic membership allows firms and agencies to enable low-risk access to data for compliance reporting and core methods research, while modular data sharing agreements support a wide array of projects and use cases. Unless specifically stated otherwise in an agreement, all data access is initially provided to end users through customized synthetic datasets that offer a) strong privacy guarantees, b) removal of signals that could expose competitive advantage, and c) removal of biases that could reinforce discriminatory policies, all while maintaining fidelity to the original data. We find that using synthetic data in conjunction with strong legal protections over raw data strikes a balance between transparency, proprietorship, privacy, and research objectives. This legal-technical framework can form the basis for data trusts in a variety of contexts.
Hierarchical Density-Based Clustering Using MapReduce

Hierarchical density-based clustering is a powerful tool for exploratory data analysis, which can play an important role in the understanding and organization of datasets. However, its applicability to large datasets is limited because the computational complexity of hierarchical clustering methods has a quadratic lower bound in the number of objects to be clustered. MapReduce is a popular programming model to speed up data mining and machine learning algorithms operating on large, possibly distributed datasets. In the literature, there have been attempts to parallelize algorithms such as Single-Linkage, which in principle can also be extended to the broader scope of hierarchical density-based clustering, but hierarchical clustering algorithms are inherently difficult to parallelize with MapReduce. In this paper, we discuss why adapting previous approaches to parallelize Single-Linkage clustering using MapReduce leads to very inefficient solutions when one wants to compute density-based clustering hierarchies. Preliminarily, we discuss one such solution, which is based on an exact, yet very computationally demanding, random blocks parallelization scheme. To be able to efficiently apply hierarchical density-based clustering to large datasets using MapReduce, we then propose a different parallelization scheme that computes an approximate clustering hierarchy based on a much faster, recursive sampling approach. This approach is based on HDBSCAN*, the state-of-the-art hierarchical density-based clustering algorithm, combined with a data summarization technique called data bubbles. The proposed method is evaluated in terms of both runtime and quality of the approximation on a number of datasets, showing its effectiveness and scalability.
Combating Label Noise in Deep Learning using Abstention

We introduce a novel method to combat label noise when training deep neural networks for classification. We propose a loss function that permits abstention during training thereby allowing the DNN to abstain on confusing samples while continuing to learn and improve classification performance on the non-abstained samples. We show how such a deep abstaining classifier (DAC) can be used for robust learning in the presence of different types of label noise. In the case of structured or systematic label noise -- where noisy training labels or confusing examples are correlated with underlying features of the data-- training with abstention enables representation learning for features that are associated with unreliable labels. In the case of unstructured (arbitrary) label noise, abstention during training enables the DAC to be used as an effective data cleaner by identifying samples that are likely to have label noise. We provide analytical results on the loss function behavior that enable dynamic adaption of abstention rates based on learning progress during training. We demonstrate the utility of the deep abstaining classifier for various image classification tasks under different types of label noise; in the case of arbitrary label noise, we show significant improvements over previously published results on multiple image benchmarks. Source code is available at this https URL
Efficient Mitosis Detection in Breast Cancer Histology Images by RCNN

Mitotic cell detection and counting per tissue area is an important aggressiveness indicator for the invasive breast cancer. However, manual mitosis counting by pathologists is extremely labor-intensive. Several automatic mitosis detection methods have been proposed in recent years. Traditional methods using hand-crafted features suffer from large mitotic cell shape variation and the existence of many mimics with similar appearance. Pixel-wise classification working in a sliding window manner is time-consuming which hinders it from clinical application. In this work, we propose an efficient mitosis detection method in breast cancer histology images by applying modified regional convolutional neural network (RCNN). Our method achieves 0.76 in precision, 0.72 recall and 0.736 F1 score on MICCAI TUPAC 2016 datasets, outperforming all the previously published results as far as we know. F1 score of 0.585 is also achieved on ICPR 2014 mitosis dataset. TUPAC 2016 and ICPR 2014 datasets are cross validated without and with color normalization to study the generalization performance. The inference time for a 2000×2000 image is ~0.8 s, making our method a promising tool for clinical deployment.
A novel text mining approach for scholar information extraction from web content in Chinese

Text mining is the process of deriving high-quality information from text so that it can focus on extracting useful information from text or web documents. IoT devices generate massive structured or unstructured data including text data. The opportunity coming behind big data and unstructured data is a great impulse for governments or companies to choose solutions based on text mining approaches to improve strategic business activities and boost decision making. Expert information is an important reference information for decision making. How to collect the expert information from text or web documents is a problem. In this paper, a text mining approach is introduced to crawl and extract expert information from Internet. We build a basic framework and main modules including information extraction, data cleaning and deduplication, expert recommendation model to cope with text data from Web content. We also define several metrics, data structures and propose some algorithms to help text mining. Finally, the experiment is implemented with datasets and the results show that our text mining approach can extract expert attributes accurately.
Designing quantum experiments with a genetic algorithm

We introduce a genetic algorithm that designs quantum optics experiments for engineering quantum states with specific properties. Our algorithm is powerful and flexible, and can easily be modified to find methods of engineering states for a range of applications. Here we focus on quantum metrology. First, we consider the noise-free case, and use the algorithm to find quantum states with a large quantum Fisher information (QFI). We find methods, which only involve experimental elements that are available with current or near-future technology, for engineering quantum states with up to a 100-fold improvement over the best classical state, and a 20-fold improvement over the optimal Gaussian state. Such states are a superposition of the vacuum with a large number of photons (around $80$), and can hence be seen as Schr\"odinger-cat-like states. We then apply the two most dominant noise sources in our setting -- photon loss and imperfect heralding -- and use the algorithm to find quantum states that still improve over the optimal Gaussian state with realistic levels of noise. This will open up experimental and technological work in using exotic non-Gaussian states for quantum-enhanced phase measurements. Finally, we use the Bayesian mean square error to look beyond the regime of validity of the QFI, finding quantum states with precision enhancements over the alternatives even when the experiment operates in the regime of limited data.