Reviewerly/exHarmony huggingface dataset:
Random Kernel Forests

Random forests of axis-parallel decision trees still show competitive accuracy in various tasks; however, they have drawbacks that limit their applicability. Namely, they perform poorly for multidimensional sparse data that is frequently input in many text and image processing tasks. A straightforward solution is to create forests of decision trees with oblique splits; however, most training approaches have low performance or are hard to tune. Besides, those ensembles appear unstable and easily overfit, so they should be regularized to find the trade-off between complexity and generalization. This paper proposes an algorithm to train kernel decision trees and random forests. At the top level, this algorithm follows a common greedy procedure to train binary decision trees; however, it produces quasi-optimal linear and kernel splits at the decision stump level. At each stump, the algorithm finds a quasi-optimal distribution of classes to subtrees and trains this stump via optimization of an SVM-like loss function with a margin re-scaling approach, which helps simultaneously optimize the margin between subtree data and arbitrary impurity criteria. We also try to reveal uniform stability-based generalization bounds for those forests and apply them to select the regularization technique. The obtained bounds explicitly consider primary hyperparameters of forests, trees, and decision stumps. The bounds also show a relationship between outliers in decision tree structure, stability and generalization, and how forests smooth these outliers. We performed an experimental evaluation on several tasks, such as studying the reaction of social media users, image recognition, and bank scoring. The experimental results show that the proposed algorithm trains ensembles, which outperform other oblique or kernel forests in many commonly-recognized datasets. Namely, the proposed algorithm shows 99.1% accuracy on MNIST and 58.1% on CIFAR-10. It has been confirmed that the selected regularization technique helps reduce overfitting on several datasets. Therefore, the proposed algorithm may be considered a small step toward customized and regularized ensembles of oblique or kernel trees that keep reasonable training performance on large datasets.We believe that the proposed algorithmcan be utilized as a construction element of approaches to training context-aware models on complex problems with integer inputs and outputs, such as text mining and image recognition.
Zero-Shot Event Detection Based on Ordered Contrastive Learning and Prompt-Based Prediction

Event detection is a classic natural language processing task.However, the constantly emerging new events make supervised methods not applicable to unseen types.Previous zeroshot event detection methods either require predefined event types as heuristic rules or resort to external semantic analyzing tools.To overcome this weakness, we propose an end-to-end framework named Zero-Shot Event Detection Based on Ordered Contrastive Learning and Prompt-Based Prediction (ZEOP).By creatively introducing multiple contrastive samples with ordered similarities, the encoder can learn event representations from both instance-level and class-level, which makes the distinctions between different unseen types more significant.Meanwhile, we utilize the prompt-based prediction to identify trigger words without relying on external resources.Experiments demonstrate that our model detects events more effectively and accurately than state-of-the-art methods.
The Fault in Our Data Stars: Studying Mitigation Techniques against Faulty Training Data in Machine Learning Applications

Machine learning (ML) has been adopted in many safety-critical applications like automated driving and medical diagnosis. Incorrect decisions by ML models can lead to catastrophic consequences, such as vehicle crashes and inappropriate medical procedures, thereby endangering our lives. The correct behaviour of a ML model is contingent upon the availability of well-labelled training data. However, obtaining large and high-quality training datasets for safety-critical applications is difficult, often resulting in the use of faulty training data.We compare the efficacy of five different error mitigation techniques, derived from a survey of more than 200 related articles, which are designed to tolerate noisy/faulty training data. We experimentally find that the error mitigation capabilities of these techniques vary across datasets, ML models, and different kinds of faults. We further find that ensemble learning offers the highest resilience among all the techniques across different configurations, followed by label smoothing.
Challenges and Opportunities of Applying Reinforcement Learning to Autonomous Racing

Simulated motorsports are an exciting environment in which to explore the power and limitations of deep reinforcement learning. Racing requires precise control of a vehicle that is operating at its traction limits while competing wheel-to-wheel with other drivers. We recently demonstrated an agent that can beat the best drivers in the world at the racing game Gran Turismo. In this article, we briefly discuss some of the lessons learned and some of the remaining open research challenges.
New Normal Regulation with Face Recognition Technology Using AttendX for Student Attendance Algorithm

In this new normal era, the community is directed to minimize activities and meet other people or goods. This is aimed at avoiding an increase in the number of the spread of the Covid-19 virus. Following the government directive, a study was conducted to facilitate one of the activities of students and educators, namely online attendance called AttendX. The way this system works is effortless. By just registering, the system can verify faces that have been registered for attendance. This application is designed with modern facial recognition technology that has developed rapidly, with this sophistication can make it easier to detect faces in structured parts such as the forehead, cheekbones, eyes, and even the nose. In compliance with the new normal regulations, the AttendX platform is designed to detect faces if the user is wearing a mask. Because of the convenience of online attendance using faces, this AttendX study resulted in a peak implementation with an accuracy rate of 99&#x0025;. This shows that AttendX can be used to perform student attendance well.
FastRoadSeg: Fast Monocular Road Segmentation Network

Monocular road segmentation is a fundamental component of autonomous driving. High accuracy has always been a focus of the community for this task. However, the exploration of high-speed methods is still lacking. This limits the prevalence of the monocular road segmentation method. In this paper, an efficient encoder-decoder network is proposed for segmenting the road from a single image. Specifically, to achieve high-speed inference, we leverage a shallow encoder for feature extraction and design a lightweight decoder for feature resolution recovery. To avoid the accuracy drop caused by network simplification, we introduce an efficient asymmetric dilated block to improve the ability of features to distinguish drivable/nondrivable roads to maintain the performance without excess computation. Experiments on three datasets verify the effectiveness of our method. On KITTI-Road, our approach ranks second among all monocular methods and even achieves a competitive performance compared to multimodal methods, with an inference speed over 135 FPS on a single TITAN Xp, which is at least 20 times faster than the state-of-the-art monocular method. Furthermore, our approach can be deployed on the embedded platform Jetson TX2 and achieves a speed of over 80 FPS. Codes will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/gongshichina/FastRoadSeg</uri> .
Implementation of virtual reality in construction education: a content-analysis based literature review

In construction education many research initiatives have explored the adoption of innovative and interactive learning experiences for improving the visual, analytical and problem-solving skills of students. Virtual reality (VR), in particular, has been increasingly viewed as a promising way for students to dynamically interact with information, to test concepts in a visual and intuitive manner and, through real-time feedback, to begin to construct their knowledge and spatial skills. However, with the proliferation of consumer market VR devices and headsets, the question of their appropriateness and specific benefits for construction education becomes even more important and educational VR applications still fall short of offering straightforward VR solutions to consistently realize the said benefits. This also reveals a host of conceptual and empirical challenges in how VR for construction education is conceptualized and justified. This study proposes an extensive review of the literature to identify and discuss contextual factors and trends in VR implementation for construction education in relation to three main foundations: (1) educational theoretical foundations, (2) methodological foundations and (3) technological foundations. A content analysis-based approach is adopted to identify and discuss key research themes. Results from the literature review have provided an insight into the current efforts of the implementation of virtual reality into pedagogy. It has provided a timeline of how such implementation has changed throughout the last twenty years. In addition, this study provides a unique perspective of the methodologies deployed in this research topic as well as an overview of the technological configurations adopted for VR use cases in the construction education domain. Based on these achievements, this study aims to pave the way for new research opportunities in the application of VR in construction education.
The Social Psychology of Hate Online: From Cyberbullying to Gaming

The socializing of hate and its saturation on platforms as a resonant and emotional connection online reveal the networked nature of convergent platforms which pump hate as a mechanism of connection and fracture in society in the post-digital age. The violence of hate and negative sentiments online morph to appropriate a multitude of manifestations from cyberbullying and revenge porn to trolling and memes as subversive, denigrative humour. Social media, designed through an architecture for sharing and transaction, distributes hate as a popular sentiment, building connections with disparate communities through the articulation of hate for fellow humans and humanity at large. Trauma induced through hatred and bullying as an active aspect of social media platforms and interactivity distribute sentiments through its excess and disproportionality. This chapter interrogates the sentiment of hate and its workings on social media as a technology of trauma in distributing hate as a form of communion.
Quantum circuit for the direct measurement of the three-tangle of three-qubit states

Abstract Understanding how to decompose quantum computations in the language of the shortest possible sequence of quantum gates is of interest to many researchers due to the importance of the experimental implementation of the desired quantum computations. We contribute to this research by providing a quantum circuit to directly measure the three-tangle of three-qubit quantum states. Direct measurement of outcome probabilities in the computational basis quantifies the three-tangle of the three-qubit quantum states.
A meta-synthesis of studies on the use of augmented reality in mathematics education

Augmented reality (AR) helps three dimensional, virtual objects to be viewed, interactively, in a real-world setting. AR technology is used in many fields such as medicine, advertisement, military, industry, and increasingly in education. AR has an important role in concretizing educational platforms and achieving permanent learning. This study aims to evaluate studies on the use of AR in mathematics education through meta-synthesis. 20 research articles, two master’s theses, and two PhD theses, published between 2010 and 2021, centered on the use of AR technology, utilizing either qualitative or mixed methodology were analyzed in this study. The databases used for selecting the texts including Google Scholar, EBSCO, Education Resources Information Center, Elsevier Scopus, ProQuest Dissertations and Theses Full Text, Springer Link, Taylor & Francis Online, TUBITAK ULAKBIM Dergipark, YOK Thesis Centre. The studies included in the meta-synthesis have been grouped according to their descriptive properties, research methods, data gathering tools, data analysis, the mathematics subjects they focus on, their validity and reliability, and the conclusions and suggestions they present. The similarities and differences between these studies have been compared, and in accordance with the nature of meta-synthesis they have been categorized into themes for synthesis. Parallel to technological progress, it was observed that the use of AR in mathematics education has tended to increase over the years. In conclusion, most of the studies on AR in mathematics education focus on increasing spatial ability. It was seen that the visualization and concretization aspects of AR were often used. This meta-synthesis offers suggestions for future researchers who are interested in the use of AR in mathematics education.
Hostility measure for multi-level study of data complexity

Abstract Complexity measures aim to characterize the underlying complexity of supervised data. These measures tackle factors hindering the performance of Machine Learning ( ML ) classifiers like overlap, density, linearity, etc. The state-of-the-art has mainly focused on the dataset perspective of complexity, i.e., offering an estimation of the complexity of the whole dataset. Recently, the instance perspective has also been addressed. In this paper, the hostility measure , a complexity measure offering a multi-level (instance, class, and dataset) perspective of data complexity is proposed. The proposal is built by estimating the novel notion of hostility : the difficulty of correctly classifying a point, a class, or a whole dataset given their corresponding neighborhoods. The proposed measure is estimated at the instance level by applying the k -means algorithm in a recursive and hierarchical way, which allows to analyze how points from different classes are naturally grouped together across partitions. The instance information is aggregated to provide complexity knowledge at the class and the dataset levels. The validity of the proposal is evaluated through a variety of experiments dealing with the three perspectives and the corresponding comparative with the state-of-the-art measures. Throughout the experiments, the hostility measure has shown promising results and to be competitive, stable, and robust.
An Improved Semi-supervised Variational Autoencoder with Gate Mechanism for Text Classification

In recent years, semi-supervised learning has been investigated to take full advantages of increasing unlabeled data. Although pretrained deep learning models are successfully adopted on a massive amount of unlabeled data, they may not be applicable in specific domains as the data is limited. In this paper, we propose a model, termed Semi-supervised Variational AutoEncoder (SVAE), which consists of Gated Convolutional Neural Networks (GCNN) as both the encoder and the decoder. Since the canonical VAE suffers from Kullback–Leibler (KL) vanishing problem, we attach a layer named Scalar after Batch Normalization (BN) to scale the output of the BN. We conduct experiments on two domain-specific datasets with a small amount of data. The results show that SVAE outperforms other alternative baselines for language modeling and semi-supervised learning studies. Especially, the results in the language modeling validate the effect of combining BN and Scalar for tackling the KL vanishing problem. Moreover, the visualization of the latent representations verifies the performance of SVAE on less data.
Evolving constructions for balanced, highly nonlinear boolean functions

Finding balanced, highly nonlinear Boolean functions is a difficult problem where it is not known what nonlinearity values are possible to be reached in general. At the same time, evolutionary computation is successfully used to evolve specific Boolean function instances, but the approach cannot easily scale for larger Boolean function sizes. Indeed, while evolving smaller Boolean functions is almost trivial, larger sizes become increasingly difficult, and evolutionary algorithms perform suboptimally.
Time-varying dependence of Bitcoin

This paper analyzes Bitcoin investment in terms of portfolio diversification. Over the period July 2011-April 2021, we use the copula-GARCH approach to test the time-varying dependence of Bitcoin in a portfolio composed of six stock markets (CAC40, DJIA, EUROSTOXX50, FTSE100, HANGSENG, and NIKKEI225). Our results reveal that volatility modeling provides better results with the Dynamic Conditional Correlation model. The performance of the portfolio is largely due to the high returns of Bitcoin which allows for better portfolio diversification. As a result, there is a mitigation of the extreme rates of return associated with crypto-currencies. Finally, while Bitcoin's contribution to the portfolio is more attributable to its risk than its return, it does play a role in stabilizing portfolio performance, for varying levels of risk.
Recurrent neural network-induced Gaussian process

In this study, we develop a recurrent neural network-induced Gaussian process (RNNGP) to model sequence data. We derive the equivalence between infinitely wide neural networks and Gaussian processes (GPs) for a relaxed recurrent neural network (RNN) with untied weights. We compute the covariance function of the RNNGP using an analytical iteration formula derived through the RNN procedure with an error-function-based activation function. To simplify our discussion, we use the RNNGP to perform Bayesian inference on vanilla RNNs for various problems, such as Modified National Institute of Standards and Technology digit identification, Mackey–Glass time-series forecasting, and lithium-ion battery state-of-health estimation. The results demonstrate the flexibility of the RNNGP in modeling sequence data. Furthermore, the RNNGP predictions typically outperform those of the original RNNs and GPs, demonstrating the efficiency of the RNNGP as a data-driven model. Moreover, the RNNGP can quantify the uncertainty in the predictions, which implies the significant potential of the RNNGP in uncertainty quantification analyses.
TSPNet: Translation supervised prototype network via residual learning for multimodal social relation extraction

Multimodal social relation extraction requires sufficient features fusion to identify the relation between different targets. Compared with traditional multimodal social relation extraction, there are many semantic gap issues for the few-shot scenario task, such as insufficient across-modality assistance, lacking explicit supervision, and unbalanced relations. To address the above problems, a novel Translation Supervised Prototype Network (TSPNet) is proposed, which extracts all the features of knowledge triples, not just relation features. First, the triple-level unimodal encoder learns textual and visual representation of knowledge triples from the entire information via two-stream encoding. Second, the triple-level multimodal extractor obtains multimodal knowledge triples by employing the residual learner to build the triple-level interaction across modalities. Finally, the intra-triple translation supervised decoder predicts the few-shot relations based on a prototype network supervised with the intra-triple translation as an explicit constraint. Our model achieves SOTA performance on three challenging benchmark datasets for few-shot multimodal social relation extraction, and further analysis shows that our model is effective and owns a strong generalization ability to avoid bias.
Adaptive random down-sampling data augmentation and area attention pooling for low resolution face recognition

State-of-the-art face recognition method have achieved satisfactory performance on various face datasets, but limited to high-resolution (HR) faces. In real scenes, with affects from camera quality or distance etc., low quality face images are often obtained in surveillance scenes, which leads to poor performance of general face recognition approaches. In this work, we explored the reasons why low-resolution (LR) images cannot be coped with the conventional models, and proposed the optimization methods. The main contribution of this work includes two aspects: (1) We proposed an adaptive random down-sampling data augmentation method with the corresponding loss function and training strategy. For the network training, we mapped the face images with different resolutions to the same embedding space, and then explicitly separated different classes of LR and gathered the same class of HR and LR. (2) A pooling module based on attention mechanism, area attention pooling, was proposed. After pooling, the fine-grained information of the identity is retained, and the redundant information of the non-identity is removed. We conducted a series of visualization experiments to prove the effectiveness of our method. We evaluated our methods on the real-world LR face dataset Surveillance Cameras Face Database (SCface) and the public face evaluation dataset Labeled Faces in the Wild (LFW). Compared with state-of-the-art methods, the proposed method achieves satisfactory performance at LR images on SCface and LFW dataset.
Situating ethics

This is a forum for perspectives on designing for marginalized communities worldwide. Articles will discuss design methods, theoretical/conceptual contributions, and participatory interventions with underserved communities. --- Nithya Sambasivan, Editor
Scale adaption-guided human face detection

Anchor-free based object detection has recently seen important progress benefiting from the advances in convolution neural networks. However, the detection performance for human faces is not so satisfactory. First of all, many existing anchor-free methods only focus on a certain scale of the feature map, such a mechanism often fails to perceive the important multi-scale context, resulting in a low recall rate of faces with large scale variations. To solve this problem, we propose to boost the face detection by adaptive learning to perceive the focal scale. To be specific, we design an online scale adaptation strategy to heuristically guide each layer detector to detect faces of different scales in multi-branch structures, which reduces outliers and improves recall rates. In additional, we also argue that the detection head with single convolution layer widely used in anchor-free methods is not robust enough to image context. Therefore, we augment the network by a context-aware detection module. The module dynamically generates different detectors for different input images based on their context to adapt to their image features, reducing the dependence on feature extraction ability of backbone network, and avoiding feature deviations in different scenes. Extensive experiments demonstrate that our method achieves significant performance gains compared to previous anchor-free methods and is comparable to the most advanced anchor-based face detection methods.