Reviewerly/exHarmony huggingface dataset:
Adaptive Compatibility Matrix for Superpixel-CRF

Compatibility Matrix plays a very important role in Fully-connected pairwise Conditional Random Field (full-CRF). However, former studies often fix it on any dataset or make too strong assumptions, which may cause abnormal object co-occurrence in image segmentation. The reason lies on the fixed compatibility matrix will give both normal objects co-occurrence and abnormal objects co-occurrence the same penalty. In this paper, we propose an adaptive compatibility matrix to replace the fixed compatibility matrix in full-CRF. Based on a weaker assumption of local independence, we propose the algorithm for adaptive compatibility matrix to learn from the dataset. In order to decrease the high computational complexity of full-CRF and maintain the accuracy at the same time, we build superpixel-CRF with adaptive compatibility matrix and propose the corresponding method to solve it. Our experiments demonstrate that the adaptive compatibility matrix improves the accuracy of full-CRF. The expansion in superpixel-CRF not only reduces the complexity but also performs well on the results.
BERT-Based Scientific Paper Quality Prediction

In recent years, scholarly databases have made many scientific papers available on the Internet. While these databases facilitate access to excellent papers, they also increase the possibility of encountering inferior papers. However, it is difficult to predict the quality of a paper just from a glance at the paper. In this paper, we propose a machine learning approach to predicting the quality of scientific papers. Specifically, we predict the quality of an article by classifying for the abstract of the paper whether the article is included in a superior journal or not. The proposed model is trained using a BERT-based model widely used in natural language processing. After training, we achieved a test accuracy of 95.1% and 89.6% in medicine and computer science, respectively. In addition, the results of the classification are visualized by evaluating the sentence combinations in the abstract to clarify the details of the classification.
Domain Adaptative Semantic Segmentation by Fine-Grained Alignment

To alleviate the domain gap, we propose an improved domain adaptative network for semantic segmentation. Specifically, we use the method of distinguishing alignment between foreground and background classes for fine-grained adaptating diffrent type of categories. Furthermore, we use a channel and spatial paraller attention module to acquire the rich spatial and channel information from features. However, it will still causes a large inter-domain difference due to the different feature distributions between different domains. We use the self-supervised learning method to generate pseudo labels for better aligning target domain. Finally, we use focal loss in the target domain to alleviate the impact of categories imbalance on the adaptation process. Experiments show that our method achieves better segmentation performance in unsupervised domain adaptative semantic segmentation.
A Differentiable Architecture Search Approach for Few-Shot Image Classification

Few-shot image classification is to learn models to distinguish between unseen categories, even though only a few labeled samples are involved in the training process. To alleviate the over-fitting problem caused by insufficient samples, researchers typically utilize artificially designed simple convolutional neural networks to extract features. However, the feature extraction capability of these networks is not strong enough to extract abstract semantic features, which will affect subsequent feature processing and significantly degrade performance when transferred to other datasets. This paper aims to design a general feature extraction network for few-shot image classification by improving the differentiable architecture search process. We propose a search space regularization method based on DropBlock and an early-stopping strategy based on pooling operation. Through the end-to-end search on the few-shot image dataset CUB, we obtain a light-weighted model FSLNet with excellent generalization ability. In addition, we propose a spatial pyramid self-attention mechanism to optimize the feature expression capability of FSLNet. Experiments show that the FSLNet searched in this paper achieves significant performance. The optimized FSLNet reaches state-of-the-art accuracy on the standard few-shot image classification datasets and in a cross-domain setting.
Chinese Character Style Transfer Model Based on Convolutional Neural Network

With various styles, Chinese characters are one of the most important cultural symbols in China. Designing a set of new fonts with a specific style is a very tedious and massive task. It usually relies on traditional manual methods or computer-aided design for each Chinese character, which is time-consuming and labor-intensive. And the products are often not ideal. Therefore, it is necessary to design a model that can automatically generate new Chinese characters in a specified style. In this paper, a convolutional neural network model is proposed to be applied into the Chinese character style migration. To train the network, we exploit the root mean square optimizer to automatically adjust the deep learning rate and gradually reduce the difference values. Experiments are conducted based on the Chinese character datasets. Ultimately the resulting character style is basically close to the manually designed one, which reaches the target of the font style transfer.
End-to-End Large-Scale Image Retrieval Network with Convolution and Vision Transformers

There has been significant progress in content-based image retrieval with the development of convolutional neural networks and visual transformers. However, there are semantic gaps between high-level semantic information and low-level visual features. To solve this problem, we propose a high-performance image retrieval method based on the convolutional neural network (CNN) and vision transformers, which takes advantage of the local characteristics of the CNN and the long-range dependence characteristics of vision transformers. The proposed convolution and vision transformers network (CVTNet) firstly uses the CNN backbone network to extract the feature representation of the image. Secondly, it uses the vision transformers to enhance the semantic relationship among the feature layer to reduce the semantic gap. Finally, we propose an adaptive weight loss function that fuses triplet loss and second-order similarity loss to capture more image structure information. Extensive experimental results demonstrated that CVTNet achieves significant performance improvement on Revisited Oxford and Paris datasets compared with the baselines.
Reject Options for Incremental Regression Scenarios

Machine learning with a reject option is the empowerment of an algorithm to abstain from prediction when the outcome is likely to be inaccurate. Although the topic has been investigated in the literature already some time ago, it has not lost any of its relevance as machine learning models are increasingly delivered to the market. At present, most work on reject strategies addresses classification tasks. Moreover, the majority of approaches deals with classical batch learning scenarios. In this publication, in contrast, we study the important problem of reject options for incremental online regression tasks. We propose different strategies to model this problem and evaluate different approaches, both in a theoretical and a real world setting from the domain of human motion prediction; from the methods which we evaluate, a clear winner emerges as regards accuracy and efficiency.
Image Super-Resolution Using Deep RCSA Network

The aim of image super-resolution (SR) is to reconstruct high-resolution images from low-resolution images. As a basic image-processing procedure, SR facilitates subsequent tasks. With the boom in deep learning (DL), DL-based image SR approaches have also attracted considerable interest. The main challenge for DL-based image SR is constructing a deep trainable network with powerful representation capability. To meet this challenge, we propose a residual channel-spatial attention (RCSA) network combining the dense module and attention module in the trunk component of the network. For the dense module, we use the sparse residual-dense connection to tackle the gradient-vanishing problem and, thereby, increase the depth of the network. For the attention module, we use the channel-spatial attention mechanism to rescale the importance across the channels and spaces in the feature maps. Both strategies can improve the learning and representational capability of the network. We conducted extensive comparisons with 9 other state-of-the-art methods on 5 standard benchmark datasets. The quantitative and qualitative results show the superior performance of our RCSA method.
Self-Enhancer: A Self-supervised Framework for Low-Supervision, Drifted Data with Significant Missing Values

In this paper, we introduce a self-supervised framework for enhancing regular machine learning methods in scenarios that have: 1. very limited labeled data, 2. drifted data distributions, 3. significant missing values in both training and testing sets. These issues commonly exist in real world data and cause performance degradation to a large extent when directly applying supervised machine learning methods. We propose a multitask self-supervised framework to take care of all those issues by utilising a large amount of unlabeled data, which can bring promising improvements on downstream tasks.
Supervised Learning for Convolutional Neural Network with Barlow Twins

Supervised learning has found many applications as one of the fundamental techniques in machine learning. Recently, Barlow Twins loss has been used in self-supervised learning to train Convolutional Neural Networks to extract invariant features by introducing perturbations to the input data and encouraging the network to learn features that are invariant to these perturbations. It has been shown that the extracted features can achieve good performance for new tasks. However, this kind of ’perturbation-invariance’ is also necessary for the models trained with supervised learning. The main contribution of this research is to explicitly introduce the perturbation-invariance in supervised learning. To do this, we propose to train a pair of identical networks using the standard classification loss and an additional Barlow Twins loss. The different sets of transformations are applied to the two network branches as the perturbations. We show empirically that the proposed method can learn invariant features and results in a higher classification accuracy than the baseline method.
Video Motion Perception for Self-supervised Representation Learning

The motion of a video contains two factors: magnitude and direction, but most of the existing video self-supervised methods ignored the motion direction information. In this paper, we propose a Video Motion Perception (VMP) self-supervised framework, simultaneously taking account of the above two key factors. Specifically, a Motion Direction Perception Module (MDPM) is applied to asking the network to predict the moving direction of the video objects by using two well-designed handcraft strategies. Additionally, we analyze the characteristic of video motion in natural scenes and propose the Motion Change Perception Module (MCPM) accordingly for motion magnitude learning. Experimental results show that VMP achieves competitive performance on different benchmarks, including action recognition, video retrieval, and action similarity labeling.
SVM Ensembles on a Budget

This paper presents a model to train an ensemble of SVMs that achieves better generalization performance at a lower computational training cost than a single SVM. The idea of the proposed model is, instead of training a single SVM on the whole dataset, to train a diverse set of simpler SVMs. Specifically, the proposed algorithm creates B subensembles of T SVMs using a different set of hyper-parameters in each subensemble. Then, in order to gain more diversity, the T SVMs of each of the subsensembles are trained on a different 1/T disjoint fraction of the training set. The paper presents an extensive analysis of the computational training complexity of the algorithm. The experiments show that for any given computational budget, the presented method obtains a better generalization performance than a single SVM.
Ensemble Ranking for Image Retrieval via Deep Hash

In recent years, convolutional neural networks (CNNs) have achieved remarkable success in computer vision applications. Deep hashing combines feature extraction or representation with hash coding jointly; it can extract high-quality image features and generate approximate hash codes containing rich semantic information. Because an image is represented by binary codes instead of a high-dimensional floating-point-number feature matrix, the hashing method can significantly improve the speed of large-scale image retrieval. However, we notice that compared with traditional retrieval methods, image retrieval through binary hash encoding induces performance degradation to a certain extent, and most existing hash retrieval algorithms focus only on the semantic similarity between image pairs, the returned image samples should not only match the ground truth, but also the correct image should be in the front of the result list, they ignore the ranking information of the returned samples, limiting their performance. For this issue, this paper proposes a multimodel ensemble image retrieval framework which can learn compact hash codes containing rich semantic information through hash constraints. The ensemble strategy is introduced, and the weighted voting is applied to integrate the ranking list. Comprehensive experiments on three benchmark datasets show that the proposed method achieves very competitive results.
Two-Dimensional Encoding Method for Neural Synthesis of Tabular Transformation by Example

Programming by example (PBE) is a technology that makes data transformation tasks, especially tabular data transformation, easier for data analysts by automatically generating transformation programs from user-given input–output examples. In recent years, PBE research using machine learning (ML) has emerged because of the recent success of ML in various research fields. We developed an ML-based PBE system for tabular data transformation in previous work. The system is based on the Transformer model that fits sequential data and not two-dimensional structured data like tabular data. Inspired by recent work applying a Transformer model to tasks using two-dimensional data, such as the query answering task for tables or the image computer vision task, we propose a Transformer-based model with positional encoding for two-dimensional tabular data, called tabular positional encoding, to improve the Transformer-based model developed in our previous work. We implemented our proposed model and conducted various experiments. The experimental results show that the Transformer-based model with tabular positional encoding achieves much higher performance than our previous work.
Autonomous Driving Model Defense Study on Hijacking Adversarial Attack

Modern Connected and Autonomous Vehicle (CAV) has several innovative embedded devices aimed at enhancing driving comfort. However, the most advanced embedded equipment did not address security features at the time of development; as a result, it enlarged the attack surfaces of CAVs. Consequently, an attacker can easily compromise the CAV system even with limited hacking capabilities. In case the CAV system is compromised, that can raise significant concerns about safe driving and human safety. We know that the CAV driving model is mainly based on artificial intelligence and deep learning algorithms. Despite deep learning methods being successfully used in Connected and Autonomous Vehicles, slight perturbations of the driving model can be crucial and mislead the driving system. Henceforth, a robust defense mechanism is imperative for safe driving as a countermeasure. This research proposed an effective defense mechanism based on an autoencoder. We study FGSM and AdvGAN for hijacking adversarial attack injection, and simultaneously we use the Nvidia Dave-2 Driving model for our experimentation. Our experiment results demonstrate that the proposed solution outperformed compared with the recent and most typically used defensive strategies; it achieves 93.8% and 91.2% defense success rate in the Whitebox setup and 71.3% and 63.1% defense success rate in the Blackbox setup by FGSM and AdvGAN, respectively.
Feature Fusion Distillation

Most recent efforts made in knowledge distillation (KD) can be credited to filling the representation gap between the cumbersome teacher and its light student. In general, the soft targets, the intermediate feature representation in hidden layers, or a couple of them from the teacher serve as the supervisory signal to educate the student. However, previous works aligned hidden layers one on one and cannot make full use of rich context knowledge. To this end, we propose a Feature Fusion Module (FFM) to concatenate diverse feature maps from different layers to aggregate knowledge as the to-be-distilled dark knowledge. Moreover, to hedge the adverse effects of the fused feature maps, we devise an Asymmetric Switch Function (ASF) to make the transfer process more reliable. The combination of FFM and ASF is termed Feature Fusion Distillation (FFD). Experiments of image classification, object detection, and semantic segmentation on individual benchmarks show FFD jointly assist the student in achieving encouraging performance. It is worth mentioning that when the teacher is ResNet34, the ultimately educated student ResNet18 achieves $$71.40\%$$ top-1 accuracy on ImageNet-1K.
AFS: Attention Using First and Second Order Information to Enrich Features

Convolutional Neural Network (CNN) is the basis of many computer vision tasks. In order to depict complex boundaries in visual tasks, it is essential to fully explore the feature distribution to realize the potential of CNN. However, most of the current research focuses on the design of deeper architectures, and rarely explores high-level feature statistics. To solve this problem, we propose a simple and effective neural network attention insertion module, named Attention Module using First and Second order information fusion (AFS). Our method combines the first-order pooling and the second-order pooling, corresponding to the two independent dimensions of space and channel respectively, and verifies the effectiveness of our AFS through the two connection ways. The feature map outputed by the middle convolutional layer infers the attention map in turn along the channel and space, and then multiplies the attention map with the input feature map for adaptive feature refinement. Our AFS can be integrated with any feedforward CNNs and can be trained end-to-end with negligible overhead. We have conducted a large number of experiments on CIFAR-10 and CIFAR-100, and the experimental results show that our AFS module significantly improves the classification and detection performance on different models.
Feature Selection for Trustworthy Regression Using Higher Moments

Feature Selection is one of the most relevant preprocessing techniques in machine learning. Yet, it is usually only considered in the context of classification tasks. Although many methods designed for classification can be carried over to regression tasks, they usually lack some of the theoretical guarantees, that are provided for classification. In particular, reject-option and certainty measures or, more generally, operations which depend on the posterior distribution rather than its expectation only, are not supported. As machine learning is increasingly used in all areas of the daily life including high risk areas like medicine, such tools are essential. In this work, we focus on the problem how to extend feature selection techniques, such that certainty measures are taken into consideration during the selection process. We show that every method which is applicable in multi-value regression can be extended to take into account the complete distribution by making use of higher moments. We prove that the resulting method can be applied to preserve various certainty measures for regression tasks, including variance and confidence intervals, and we demonstrate this in example applications.
Modeling reanalysis, naturally

We argue that the reanalysis of naturally from manner to speaker-oriented, viz. evidential, adverb is contingent on the principled availability of three merge sites: one in the VP-domain (giving rise to the manner reading), one in the high TP-domain and one in the CP-domain (both giving rise to the evidential reading). Our investigation into so-called bridging contexts for reanalysis, i.e. contexts that invite both the older (manner) and the newer (speaker-oriented) interpretation, suggests that the lexical entry for naturally is syntactically underspecified in such a way that it can be merged in all three positions, with both readings of the adverb being generated by a Content-matching analysis.
Back to the Control Room: Managing Artistic Work

Abstract Control rooms have long been a key domain of investigation in HCI and CSCW as sites for understanding distributed work and fragmented settings, as well as the role and design of digital technologies in that work. Although research has tended to focus mainly on ‘command and control’ configurations, such as rail transport, ambulance dispatch, air traffic and CCTV rooms, centres of coordination shaped by artistic and performative concerns have much to contribute. Our study examines how a professional team of artists and volunteers stage manage and direct the performance of a mixed reality game from a central control room, with remote runners performing live video streaming from the streets nearby to online players. We focus on the work undertaken by team members to bring this about, exploring three key elements that enable it. First, we detail how team members oriented to the work as an artistic performance produced for an audience, how they produced compelling, varied content for online players, and how the quality of the work was ongoingly assessed. Second, we unpack the organisational hierarchy in the control room’s division of labour, and how this was designed to manage the challenges of restricted informational visibility there. Third, we explore the interactional accomplishment of the performance by looking at the role of radio announcements from the event’s director to orchestrate how the performance developed over time. Announcements were used to resolve trouble and provide instructions for avoiding future performative problems; but more centrally, to give artistic direction to runners in order to shape the performance itself. To close we discuss how this study of a performance impacts CSCW’s understandings of control room work, how the problem of ‘diffuse’ tasks like artistic work is co-ordinated, and how orientations towards quality as an artistic concern is manifest in / as control room practices. We also reflect on hierarchical and horizontal control room arrangements, and the role of video as both collaborative resource and product. Graphical abstract