Reviewerly/exHarmony huggingface dataset:
Unbalanced data processing using deep sparse learning technique

In view of the limitations of existing unbalanced data modeling technique, a hybrid sampling algorithm encoding the boundary sparse samples is proposed. The boundary samples are recognized by calculating the boundary factors of each sample, based on which the sample space is divided into boundary and non-boundary domain. The negative samples in the non-boundary domain are undersampling. Owing to the sparsity of the samples in the boundary domain, the positive samples are oversampling by leveraging the composite minority oversampling technique using the maximum distance. This is used to retain the information of the positive samples to the maximum extent Taking recall, F1 value, g-mean and AUC values. Next, the evaluation indexes learn the support vector machines (SVM) algorithm in order to verify its effectiveness on five data sets with different balance degrees. Therefore, our method can effectively deal with unbalanced data. The algorithm first introduces the concept of support k-outlier degree to identify the boundary point set and non-boundary point set in the data set. It then leverages the improved smote algorithm to synthesize the new point set by taking the boundary points in a few classes as the target samples. It further calculates the distance by the sampling algorithm for the non-boundary points toward most classes in order to achieve the balance between classes. Compared with other four methods. The experimental results have shown that the proposed algorithm has good performance on different data sets, with an average increase of 3.5%.
Single-Stage Instance Shadow Detection with Bidirectional Relation Learning

Instance shadow detection aims to find shadow instances paired with the objects that cast the shadows. The previous work adopts a two-stage framework to first predict shadow instances, object instances, and shadow-object associations from the region proposals, then leverage a post-processing to match the predictions to form the final shadow-object pairs. In this paper, we present a new single-stage fully-convolutional network architecture with a bidirectional relation learning module to directly learn the relations of shadow and object instances in an end-to-end manner. Compared with the prior work, our method actively explores the internal relationship between shadows and objects to learn a better pairing between them, thus improving the overall performance for instance shadow detection. We evaluate our method on the benchmark dataset for instance shadow detection, both quantitatively and visually. The experimental results demonstrate that our method clearly outperforms the state-of-the-art method.
WDO optimized detection for mammographic masses and its diagnosis: A unified CAD system

For decades, breast cancer is the leading cause of cancer-related deaths among women. Early detection and diagnosis of breast cancer can effectively reduce the mortality rate. Masses are one of the manifestations of imperceptible breast cancer visible in mammograms whose detection and diagnosis is a challenging task due to its subtle nature. In this paper, a unified computer-aided detection/diagnosis scheme is proposed for the automatic detection and diagnosis of mammographic masses. Being simple and effective, multilevel image thresholding based on Otsu's method is considered for localization of suspicious mass lesions, but its performance is compromised while defining the precise threshold value especially when the intensity profile in and around the anomalies does not vary much. To mitigate the issue, a nature-inspired optimization algorithm, wind driven optimization, is proposed which in combination with Otsu's multilevel thresholding identifies the potential candidates of mass lesions. To reduce the false positives and diagnose the malignant masses, a texture-based multi-gradient local quinary pattern (M-GQP) feature is introduced which renders higher consistency in computing gradient information from the uniform and near-uniform areas. Due to eight distinct Sobel masks, it offers a better resolution in edge regions in addition to the micro information at different orientations. Four popular classifiers (support vector machine, Fisher's linear discriminant, K-nearest neighbor, and an ensemble classifier) are also investigated to discriminate malignant tumors from benign lesions and normal tissues. The proposed integrated approach for the detection and characterization of mammographic masses is evaluated on two benchmark databases — mini-MIAS and DDSM comprising of 68 and 500 mammograms, respectively and achieved a sensitivity of 96.9% and 96.2% with 0.09 and 0.17 false positives per image after false positive reduction for the respective datasets. The diagnosis of malignant masses prior to and following false-positive reduction observes an Az value of 0.98 and 0.94 with an accuracy of 99.04% and 92.65%, respectively for the mini-MIAS dataset while the same for the DDSM are 0.99 and 0.92 with an accuracy of 98.33% and 78.50%, respectively. In a three-class classification (normal, benign and malignant), an accuracy of 99.04% and 97.76% with an F1 score of 0.98 and 0.97 for malignancy is obtained for the mini-MIAS and DDSM images, respectively which are promising in nature when compared to other competing schemes in the state-of-the-art.
Introduction to Machine Learning

This chapter introduces the typical machine learning problems, describes the widely used machine learning models, and presents the basic learning algorithms suited for solving various machine learning problems. Note that a machine learning model may be used for accomplishing different machine learning tasks, provided that a proper learning algorithm is chosen, and different models may be adopted for solving the same machine learning problem. It is also interesting to note that all machine learning problems are inherently optimization problems, and more precisely, multi-objective optimization problems, although learning and optimization focus on different types of problems. Finally, we emphasize that it can produce many synergies by integrating optimization and learning, e.g. using machine learning to assist optimization, and using optimization to automate machine learning.
Discrimination-Aware Mechanism for Fine-grained Representation Learning

Recently, with the emergence of retrieval requirements for certain individual in the same superclass, e.g., birds, persons, cars, fine-grained recognition task has attracted a significant amount of attention from academia and industry. In fine-grained recognition scenario, the inter-class differences are quite diverse and subtle, which makes it challenging to extract all the discriminative cues. Traditional training mechanism optimizes the overall discriminativeness of the whole feature. It may stop early when some feature elements has been trained to distinguish training samples well, leaving other elements insufficiently trained for a feature. This would result in a less generalizable feature extractor that only captures major discriminative cues and ignores subtle ones. Therefore, there is a need for a training mechanism that enforces the discriminativeness of all the elements in the feature to capture more the subtle visual cues. In this paper, we propose a Discrimination-Aware Mechanism (DAM) that iteratively identifies insufficiently trained elements and improves them. DAM is able to increase the number of well learned elements, which captures more visual cues by the feature extractor. In this way, a more informative representation is learned, which brings better generalization performance. We show that DAM can be easily applied to both proxy-based and pair-based loss functions, and thus can be used in most existing fine-grained recognition paradigms. Comprehensive experiments on CUB200-2011, Cars196, Market-1501, and MSMT17 datasets demonstrate the advantages of our DAM based loss over the related state-of-the-art approaches.
Low-Frequency Vibration Measurement Based on the Concentric-Circle Grating Projection System

In low-frequency vibration measurement field, optical dynamic 3-D measurement based on camera-projector system is a novel approach. In this system, linear sinusoidal grating is the most common projection mode, which can meet the requirements of some vibration measurement. However, the dynamic measurement range of amplitude using the combination of linear grating and Fourier transform profilometry (FTP) is limited in some vibration measurement environments, because the linear fringe is periodic. To solve the problem, this article proposes a low-frequency vibration measurement method based on the concentric-circle grating camera-projector system and FTP. The center of the circle is traced dynamically and the coordinate system is transformed with the center of the circle as the origin. The phase distribution of concentric-circle grating is obtained by coordinate system transformation and FTP. By exploring the relationship between phase and relative height, the time-varying curve of the object surface height is recovered, which contains the vibration information. The experiments compare the measurement ability of the concentric-circle and linear grating. The results show that the linear grating and concentric-circle grating with the similar amplitude resolution have different amplitude measurement range. In this experiment, the maximum measurement range of linear grating is about 2.4 mm, while that of concentric-circle grating is more than about 3.9 mm. It means that the concentric-circle grating projection method can measure a large dynamic range of amplitude under the condition that the center of the circle can be traced, and solve the problem of amplitude limitation of linear grating in the use of FTP.
Using electrophysiological measures to evaluate the sense of presence in immersive virtual environments: An event‐related potential study

Abstract Sense of presence has been often explored in the context of virtual reality (VR) and immersive visual technologies; however, standardized and objective measures of the sense of presence have been difficult to find. Studies attempting to find physiological correlates of sense presence using electroencephalography (EEG) have reported mixed results. In the present study, we used brain event‐related potentials (ERPs) elicited by auditory stimuli to identify an objective physiological index of sense of presence during VR, attempting to replicate the findings of previous studies and explain the heterogeneity of results reported in the literature. Participants in our experiment were asked to experience an immersive virtual environment using a modern head‐mounted display while passively hearing task‐irrelevant frequent standard and infrequent deviant tones as in a classic auditory oddball paradigm. Subsequently, they were asked to complete a battery of questionnaires aimed to estimate their sense of presence during the VR. EEG and questionnaire data from three‐seventh participants were analyzed. ERP components evoked by the auditory stimuli were then analyzed. Late ERP components (after 450 ms from stimulus onset) registered over central brain areas were associated with the sense of presence as measured with questionnaires, while earlier components were not associated with presence. The use of different questionnaires and the content of the VR environment may both be a plausible explanation for heterogeneous results as reported in previous studies. The present study showed that late ERP components recorded over the central brain may represent good electrophysiological correlates of the subjective sense of presence.
The Innovation of Disruptive Learning Media with Augmented Reality Based 3D Object Concept with Drill Machine Design to Improve Quality of Distance Learning in The Era of Education 4.0

Currently, the world of education is entering a disruptive era. Digital-based educational technology. Especially in the distance education model. The technology used is relatively unsophisticated. This study aims to: (1) develop Disruptive Innovation learning media with Augmented Reality with the concept of 3D objects in the form of a Drill Machine; (2) testing the attractiveness of Disruptive Innovation Learning media with Augmented Reality with a 3D Object Concept in the form of a Drill Machine; and (3) testing the effectiveness level of the product being developed. The method used is research and development (R&amp;amp;D), followed by quasi-experimentation to test its effectiveness. Validation consists of two teams of experts, namely e-learning media experts and vocational education material experts. The findings in this study include augmented reality technology based on 3D objects in the form of a drill machine that was developed has a high level of attractiveness and completeness of vocational material. The object-based 3D augmented reality technology in the form of a drill machine that was developed was proven to be able to improve the understanding skills of vocational students in the education era 4.0.
FridaysForFuture as an Enactive Network: Collective Agency for the Transition Towards Sustainable Development

In this article, we provide a theoretical conceptual analysis of FridaysForFuture (FFF) and of its effort in promoting the governance of socioeconomic transition toward sustainable development. FFF is a social movement that has received outstanding public recognition and visibility across the world in the last 2 years and is of great interest to educational research because it is largely composed of youngsters and appears to play a paideutic role in societal innovation. There is a growing but still limited body of investigation of FFF’s structures, genealogy, and behavior. The same goes for its theoretical and ethical background and principles. Its efforts to promote social change by going beyond individual agency toward collective agency deserve greater attention from educational scientists. We argue that FFF is a complex, self-organizing, informal network, which we define as an enactive network for its ability to retrieve scientific knowledge and transform it into lived meaningful knowledge, and for its capacity to mobilize masses and influence public discourse under a specific ethical umbrella. We provide six macro categories to describe and explain FFF: 1) nested emergent network, 2) collective social agency and leadership, 3) political impact, 4) science-based learning and activism, 5) paideutic function, and 6) ethical (normative) stance. We stress the FFF capacity to recruit high-level scientific knowledge without direct support from schools, and embody strong ethical stances with specific references to the ethics of responsibility and care for the interaction between humanity and the natural world. Finally, we suggest that FFF can be interpreted as an enactive network with the ability to affect collective identity and empower collective agency by encouraging communities into a more scientific, evidence-based, and ethical public discourse.
Cross-Domain Multi-task Learning for Object Detection and Saliency Estimation

Multi-task learning (MTL) is a learning paradigm that aims at joint optimization of multiple tasks using a single neural network for better performance and generalization. In practice, MTL rests on the inherent assumption of availability of common datasets with ground truth labels for each of the downstream tasks. However, collecting such a common annotated dataset is laborious for complex computer vision tasks such as the saliency estimation which would require the eye fixation points as the ground truth data. To this end, we propose a novel MTL framework in the absence of common annotated dataset for joint estimation of important downstream tasks in computer vision - object detection and saliency estimation. Unlike many state-of-the-art methods, that rely on common annotated datasets for training, we consider the annotations from different datasets for jointly training different tasks, calling this setting as cross-domain MTL. We adapt MUTAN [3] framework to fuse features from different datasets to learn domain invariant features capturing the relatedness of different tasks. We demonstrate the improvement in the performance and generalizability of our MTL architecture. We also show that the proposed MTL network offers a 13% reduction in memory footprint due to parameter sharing between the related tasks.
Fingerspelling Detection in American Sign Language

Fingerspelling, in which words are signed letter by letter, is an important component of American Sign Language. Most previous work on automatic fingerspelling recognition has assumed that the boundaries of fingerspelling regions in signing videos are known beforehand. In this paper, we consider the task of fingerspelling detection in raw, untrimmed sign language videos. This is an important step towards building real-world fingerspelling recognition systems. We propose a benchmark and a suite of evaluation metrics, some of which reflect the effect of detection on the downstream fingerspelling recognition task. In addition, we propose a new model that learns to detect fingerspelling via multi-task training, incorporating pose estimation and fingerspelling recognition (transcription) along with detection, and compare this model to several alternatives. The model outperforms all alternative approaches across all metrics, establishing a state of the art on the benchmark.
Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translation

The data scarcity in low-resource languages has become a bottleneck to building robust neural machine translation systems.Finetuning a multilingual pre-trained model (e.g., mBART (Liu et al., 2020a)) on the translation task is a good approach for low-resource languages; however, its performance will be greatly limited when there are unseen languages in the translation pairs.In this paper, we present a continual pre-training (CPT) framework on mBART to effectively adapt it to unseen languages.We first construct noisy mixed-language text from the monolingual corpus of the target language in the translation pair to cover both the source and target languages, and then, we continue pretraining mBART to reconstruct the original monolingual text.Results show that our method can consistently improve the finetuning performance upon the mBART baseline, as well as other strong baselines, across all tested low-resource translation pairs containing unseen languages.Furthermore, our approach also boosts the performance on translation pairs where both languages are seen in the original mBART's pre-training.The code is available at https://github.com/zliucr/cpt-nmt.
Blockchain technology as a means of sustainable development

<h2>Summary</h2> Blockchain technology is a novel computing model and ecosystem for trusted digital services. The technology promotes trust and decentralization, which has inspired new ways to develop smart digital business, engage communities, and automate societies. This makes it a typical multidisciplinary technology enabler for sustainable development with unique properties including trust, automation, decentralization, immutability, and resilience. This Primer aims to emphasize the potential role of blockchain technology in assisting a transition to a sustainable society through shedding light on the rationale behind blockchains, their core properties, and the available variants to get started. The article also conveys the main challenges impeding blockchain technology from attaining its full potential. Therefore, we encourage researchers, professionals, and enthusiasts from different sectors to continue exploring blockchains and investigate their potential for multidisciplinary applications while raising awareness about the considerate and wise use of this technology.
A Novel Loss Function and Training Strategy for Noise-Robust Keyword Spotting

The development of keyword spotting (KWS) systems that are accurate in noisy conditions remains a challenge. Towards this goal, in this paper we propose a novel training strategy relying on multi-condition training for noise-robust KWS. By this strategy, we think of the state-of-the-art KWS models as the composition of a keyword embedding extractor and a linear classifier that are successively trained. To train the keyword embedding extractor, we also propose a new (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N,2</sub> +1)-pair loss function extending the concept behind related loss functions like triplet and N-pair losses to reach larger inter-class and smaller intra-class variation. Experimental results on a noisy version of the Google Speech Commands Dataset show that our proposal achieves around 12% KWS accuracy relative improvement with respect to standard end-to-end multi-condition training when speech is distorted by unseen noises. This performance improvement is achieved without increasing the computational complexity of the KWS model.
Computer-aided automatic approach for denoising of magnetic resonance images

This paper proposes a dual path deep convolution network based on discriminative learning for denoising MR images. The noise in MR images causes problems in identifying the regions of interest. The proposed approach is incorporated using depthwise separable convolution and local response normalisation in one path. Another path of the network is implemented using depthwise separable convolution and group normalisation. The proposed network was used to denoise the images of different noise levels, and it yields better performance as compared with various networks. The network produces clinically relevant results without retraining the network on other datasets as depicted by the evaluation metrics. The evaluation metrics improved remarkably in the results obtained by the proposed model. The additional external clinical validation was performed by the senior radiologists, and the results were found to be satisfactory for medical diagnosis. The statistical analysis of the results proves the suitability of the results for medical analysis. Further, the images were segmented for checking the applicability of the results in the biomedical domain and a remarkable improvement of about (7 ± 0.03) % and (6.5 ± 0.02) was observed in mIoU and BF score.