Reviewerly/exHarmony huggingface dataset:
A Model of Diameter Measurement Based on the Machine Vision

If the shaft diameter can be measured in-situ during the finishing process, the closed-loop control of the shaft diameter processing process can be realized and the machining accuracy can be improved. Present work studies the measurement of shaft diameter with the structured light system composed of a laser linear light source and a camera. The shaft is a kind of part with rotationally symmetric structure. When the linear structured light irradiates the surface of the shaft, a light stripe will be formed, and the light stripe is a part of the ellipse. Therefore, the in-situ measurement of the shaft diameter can be realized by the light stripe and the rotational symmetry of the shaft. The measurement model of shaft diameter is established by the ellipse formed by the intersection of the light plane and the measured shaft surface. Firstly, in the camera coordinate system, normal vector of the light plane and the coordinates of the ellipse center are obtained by the calibration; then, the equation of oblique elliptic cone is established by taking the ellipse as the bottom and the optical center of the camera as the top. Next, the measurement model of shaft diameter is obtained by the established oblique elliptic cone equation and theoretical image plane equation. Finally, the accuracy of the measurement model of shaft diameter is tested by the checkerboard calibration plate and a lathe. The test results show that the measurement model of shaft diameter is correct, and when the shaft diameter is 36.162mm, the speed is 1250r/min, the maximum average measurement error is 0.019mm. The measurement accuracy meets the engineering requirement.
On Activism and Academia

In recent years HCI and CSCW work has increasingly begun to address complex social problems and issues of social justice worldwide. Such activist-leaning work is not without problems. Through the experiences and reflections of an activist becoming academic and an academic becoming an activist, we outline these difficulties such as (1) the risk of perpetuating violence, oppression and exploitation when working with marginalised communities, (2) the reception of activist-academic work within our academic communities, and (3) problems of social justice that exist within our academic communities. Building on our own experiences, practices and existing literature from a variety of disciplines we advocate for the possibility of an activist-academic practice, outline possible ways forward and formulate questions we need to answer for HCI to contribute to a more just world.
Semantic Deep Face Models

Face models built from 3D face databases are often used in computer vision and graphics tasks such as face reconstruction, replacement, tracking and manipulation. For such tasks, commonly used multi-linear morphable models, which provide semantic control over facial identity and expression, often lack quality and expressivity due to their linear nature. Deep neural networks offer the possibility of non-linear face modeling, where so far most research has focused on generating realistic facial images with less focus on 3D geometry, and methods that do produce geometry have little or no notion of semantic control, thereby limiting their artistic applicability. We present a method for nonlinear 3D face modeling using neural architectures that provides intuitive semantic control over both identity and expression by disentangling these dimensions from each other, essentially combining the benefits of both multi-linear face models and nonlinear deep face networks. The result is a powerful, semantically controllable, nonlinear, parametric face model. We demonstrate the value of our semantic deep face model with applications of 3D face synthesis, facial performance transfer, performance editing, and 2D landmark-based performance retargeting.
Stock price relevance of voluntary disclosures about blockchain technology and cryptocurrencies

This study examines the value relevance of blockchain and cryptocurrency disclosures in firms’ 10-K filings, which act as proxies for the firms’ involvement in this trending technology and its applications. Using textual analysis to quantify blockchain and cryptocurrency disclosures, the study first shows that these disclosures overall are value relevant. However, when the disclosures are further categorized into cryptocurrency and blockchain disclosures, the result only holds for blockchain disclosures, not for cryptocurrency disclosures. To further identify the topics and themes of these disclosures, we adopt latent Dirichlet allocation (LDA) topic modeling. Among the five topics that are identified from the blockchain and cryptocurrency disclosures through LDA (blockchain technology solutions, risk factors, general business descriptions, payment services, and bitcoin transactions), we find that only the disclosures about blockchain technology solutions and risk factors have positive value relevance, while the disclosures about bitcoin transactions are negative. The results indicate that investors positively value the involvement of blockchain technology applications in business operations or solutions and not cryptocurrency-related issues.
Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples

Deep neural network (DNN) architectures are considered to be robust to random perturbations. Nevertheless, it was shown that they could be severely vulnerable to slight but carefully crafted perturbations of the input, termed as adversarial samples. In recent years, numerous studies have been conducted in this new area called ``Adversarial Machine Learning" to devise new adversarial attacks and to defend against these attacks with more robust DNN architectures. However, most of the current research has concentrated on utilising model loss function to craft adversarial examples or to create robust models. This study explores the usage of quantified epistemic uncertainty obtained from Monte-Carlo Dropout Sampling for adversarial attack purposes by which we perturb the input to the shifted-domain regions where the model has not been trained on. We proposed new attack ideas by exploiting the difficulty of the target model to discriminate between samples drawn from original and shifted versions of the training data distribution by utilizing epistemic uncertainty of the model. Our results show that our proposed hybrid attack approach increases the attack success rates from 82.59% to 85.14%, 82.96% to 90.13% and 89.44% to 91.06% on MNIST Digit, MNIST Fashion and CIFAR-10 datasets, respectively.
MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition With Multidomain Deep Learning Model

Together with the rapid development of the Internet of Things, human activity recognition (HAR) using wearable inertial measurement units (IMUs) becomes a promising technology for many research areas. Recently, deep-learning-based methods pave a new way of understanding and performing analysis of the complex data in the HAR system. However, the performance of these methods is mostly based on the quality and quantity of the collected data. In this article, we innovatively propose to build a large data set based on virtual IMUs and then address technical issues by introducing a multiple-domain deep learning framework consisting of three technical parts. In the first part, we propose to learn the single-frame human activity from the noisy IMU data with hybrid convolutional neural networks in the semisupervised form. For the second part, the extracted data features are fused according to the principle of uncertainty-aware consistency, which reduces the uncertainty by weighting the importance of the features. The transfer learning is performed in the last part based on the newly released archive of motion capture as surface shapes data set, containing abundant synthetic human poses, which enhances the variety and diversity of the training data set and is beneficial for the process of training and feature transfer in the proposed method. The efficiency and effectiveness of the proposed method have been demonstrated in the real deep inertial poser data set. The experimental results show that the proposed methods can surprisingly converge within a few iterations and outperform all competing methods.
FeatureNorm: L2 Feature Normalization for Dynamic Graph Embedding

Dynamic graphs arise in a plethora of practical scenarios such as social networks, communication networks, and financial transaction networks. Given a dynamic graph, it is fundamental and essential to learn a graph representation that is expected not only to preserve structural proximity but also jointly capture the time-evolving patterns. Recently, graph convolutional network (GCN) has been widely explored and used in non-Euclidean application domains. The main success of GCN, especially in handling dependencies and passing messages within nodes, lies in its approximation to Laplacian smoothing. As a matter of fact, this smoothing technique can not only encourage must-link node pairs to get closer but also push cannot-link pairs to shrink together, which potentially cause serious feature shrink or oversmoothing problem, especially when stacking graph convolution in multiple layers or steps. For learning time-evolving patterns, a natural solution is to preserve historical state and combine it with the current interactions to obtain the most recent representation. Then the serious feature shrink or oversmoothing problem could happen when stacking graph convolution explicitly or implicitly according to current prevalent methods, which would make nodes too similar to distinguish each other. To solve this problem in dynamic graph embedding, we analyze the shrinking properties in the node embedding space at first, and then design a simple yet versatile method, which exploits L2 feature normalization constraint to rescale all nodes to hypersphere of a unit ball so that nodes would not shrink together, and yet similar nodes can still get closer. Extensive experiments on four real-world dynamic graph datasets compared with competitive baseline models demonstrate the effectiveness of the proposed method.
Characterizing Automated Data Insights

Many researchers have explored tools that aim to recommend data insights to users. These tools automatically communicate a rich diversity of data insights and offer such insights for many different purposes. However, there is a lack of structured understanding concerning what researchers of these tools mean by "insight" and what tasks in the analysis workflow these tools aim to support. We conducted a systematic review of existing systems that seek to recommend data insights. Grounded in the review, we propose 12 types of automated insights and four purposes of automating insights. We further discuss the design opportunities emerged from our analysis.
Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets

This article presents the data used to evaluate the performance of evolutionary clustering algorithm star (ECA*) compared to five traditional and modern clustering algorithms. Two experimental methods are employed to examine the performance of ECA* against genetic algorithm for clustering++ (GENCLUST++), learning vector quantisation (LVQ), expectation maximisation (EM), K-means++ (KM++) and K-means (KM). These algorithms are applied to 32 heterogenous and multi-featured datasets to determine which one performs well on the three tests. For one, ther paper examines the efficiency of ECA* in contradiction of its corresponding algorithms using clustering evaluation measures. These validation criteria are objective function and cluster quality measures. For another, it suggests a performance rating framework to measurethe the performance sensitivity of these algorithms on varos dataset features (cluster dimensionality, number of clusters, cluster overlap, cluster shape and cluster structure). The contributions of these experiments are two-folds: (i) ECA* exceeds its counterpart aloriths in ability to find out the right cluster number; (ii) ECA* is less sensitive towards dataset features compared to its competitive techniques. Nonetheless, the results of the experiments performed demonstrate some limitations in the ECA*: (i) ECA* is not fully applied based on the premise that no prior knowledge exists; (ii) Adapting and utilising ECA* on several real applications has not been achieved yet.
Addressing Measurement Error in Random Forests Using Quantitative Bias Analysis

Abstract Although variables are often measured with error, the impact of measurement error on machine-learning predictions is seldom quantified. The purpose of this study was to assess the impact of measurement error on the performance of random-forest models and variable importance. First, we assessed the impact of misclassification (i.e., measurement error of categorical variables) of predictors on random-forest model performance (e.g., accuracy, sensitivity) and variable importance (mean decrease in accuracy) using data from the National Comorbidity Survey Replication (2001–2003). Second, we created simulated data sets in which we knew the true model performance and variable importance measures and could verify that quantitative bias analysis was recovering the truth in misclassified versions of the data sets. Our findings showed that measurement error in the data used to construct random forests can distort model performance and variable importance measures and that bias analysis can recover the correct results. This study highlights the utility of applying quantitative bias analysis in machine learning to quantify the impact of measurement error on study results.
Settling the Sharp Reconstruction Thresholds of Random Graph Matching

This paper studies the problem of recovering the hidden vertex correspondence between two edge-correlated random graphs. We focus on the Gaussian model where the two graphs are complete graphs with correlated Gaussian weights and the Erdős-Rényi model where the two graphs are subsampled from a common parent Erdős-Rényi graph <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal {G}}(n,p)$ </tex-math></inline-formula> . For dense Erdős-Rényi graphs with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p=n^{-o(1)}$ </tex-math></inline-formula> , we prove that there exists a sharp threshold, above which one can correctly match all but a vanishing fraction of vertices and below which correctly matching any positive fraction is impossible, a phenomenon known as the “all-or-nothing” phase transition. Even more strikingly, in the Gaussian setting, above the threshold all vertices can be exactly matched with high probability. In contrast, for sparse Erdős-Rényi graphs with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p=n^{-\Theta (1)}$ </tex-math></inline-formula> , we show that the all-or-nothing phenomenon no longer holds and we determine the thresholds up to a constant factor. Along the way, we also derive the sharp threshold for exact recovery, sharpening the existing results in Erdős-Rényi graphs. The proof of the negative results builds upon a tight characterization of the mutual information based on the truncated second-moment computation and an “area theorem” that relates the mutual information to the integral of the reconstruction error. The positive results follows from a tight analysis of the maximum likelihood estimator that takes into account the cycle structure of the induced permutation on the edges.
Review of Research on Technology-Supported Cross-Cultural Learning

Not many review studies have explored the theoretical foundation of cross-cultural learning or the curricula in the research they were reviewing. Furthermore, some review studies only superficially discussed the methodology and findings of the reviewed articles. To address these issues, we reviewed twenty-three studies on technology-supported cross-cultural learning published between 2014 and 2020. We aimed to summarize and analyze previous research in the following areas: (1) theoretical foundation, (2) curricula, (3) technologies, and (4) methodology and findings. Our results showed that the reviewed studies built their research framework based on diverse theoretical foundations; however, the most frequently used models were Byram’s model and the cultural convergence theory. Curricula had the following main focuses: (a) cross-cultural learning, (b) linguistic skills, and (c) pre-service teacher training. The most frequently used technologies were Skype, e-mail, and blogs. We found that most reviewed studies involved the collection of both qualitative and quantitative data. Finally, most of the reviewed studies reported on the role of technologies in facilitating cross-cultural learning, FL/SL learning, and pre-service teacher training. Based on our findings, several implications along with suggestions were prepared. Our findings demonstrated that results from most studies were positive regarding technological support of cross-cultural learning. Therefore, it is suggested that educators and researchers take these results into consideration when designing future studies on cross-cultural learning. Because many scholars did not report some important information, such as what theoretical foundation they built studies on or participants’ demographics, we suggest that such information needs to be included in their research articles as it can be helpful in informing future studies. We also suggest that participants in future studies use variety of technological tools for supporting communication and content creation during cross-cultural learning.
Design Judgment in Data Visualization Practice

Data visualization is becoming an increasingly popular field of design practice. Although many studies have highlighted the knowledge required for effective data visualization design, their focus has largely been on formal knowledge and logical decision-making processes that can be abstracted and codified. Less attention has been paid to the more situated and personal ways of knowing that are prevalent in all design activity. In this study, we conducted semistructured interviews with data visualization practitioners during which they were asked to describe the practical and situated aspects of their design processes. Using a philosophical framework of design judgment from Nelson and Stolterman [23], we analyzed the transcripts to describe the volume and complex layering of design judgments that are used by data visualization practitioners as they describe and interrogate their work. We identify aspects of data visualization practice that require further investigation beyond notions of rational, model- or principle-directed decision-making processes.
A Dataset and Baselines for Visual Question Answering on Art

Answering questions related to art pieces (paintings) is a difficult task, as it implies the understanding of not only the visual information that is shown in the picture, but also the contextual knowledge that is acquired through the study of the history of art. In this work, we introduce our first attempt towards building a new dataset, coined AQUA (Art QUestion Answering). The question-answer (QA) pairs are automatically generated using state-of-the-art question generation methods based on paintings and comments provided in an existing art understanding dataset. The QA pairs are cleansed by crowdsourcing workers with respect to their grammatical correctness, answerability, and answers' correctness. Our dataset inherently consists of visual (painting-based) and knowledge (comment-based) questions. We also present a two-branch model as baseline, where the visual and knowledge questions are handled independently. We extensively compare our baseline model against the state-of-the-art models for question answering, and we provide a comprehensive study about the challenges and potential future directions for visual question answering on art.
Spam Detection in Reviews Using LSTM-Based Multi-Entity Temporal Features

Current works on spam detection in product reviews tend to ignore the temporal relevance among reviews in the user or product entity, resulting in poor detection performance. To address this issue, the present paper proposes ... | Find, read and cite all the research you need on Tech Science Press
Weakly-supervised semantic segmentation with saliency and incremental supervision updating

Weakly-supervised semantic segmentation aims at tackling the dense labeling task using weak supervision so as to reduce human annotation efforts. For weakly-supervised semantic segmentation using only image-level annotation, we propose a novel model of Learning with Saliency and Incremental Supervision Updating (LSISU), in which both the guidances of saliency prior and class information are jointly used and the segmentation supervision is dynamically updated. In the proposed LSISU, we present an image saliency objective complementary to classification loss, by which the trained weakly-supervised deep network can effectively deal with object co-occurrence problem. Meanwhile, we make full use of the class-wise pooling strategy to generate initial mask estimation of high quality. Given an initial annotation, a segmentation network is learned along with incremental supervision updating, which plays a role of region expansion and corrects the falsely estimated supervision for training images. The incremental supervision updating is performed on the fly and involves repeated usage of a fully connected conditional random field algorithm. LSISU achieves superior segmentation performance in terms of mIoU metric on benchmark datasets, which are 62.5% on the PASCAL VOC 2012 test set and 30.1% on the COCO val set.
Adversarial Label-Flipping Attack and Defense for Graph Neural Networks

With the great popularity of Graph Neural Networks (GNNs), the robustness of GNNs to adversarial attacks has received increasing attention. However, existing works neglect adversarial label-flipping attacks, where the attacker can manipulate an unnoticeable fraction of training labels. Exploring the robustness of GNNs to label-flipping attacks is highly critical, especially when labels are collected from external sources and false labels are easy to inject (e.g., recommendation systems). In this work, we introduce the first study of adversarial label-flipping attacks on GNNs. We propose an effective attack model LafAK based on approximated closed form of GNNs and continuous surrogate of non-differentiable objective, efficiently generating attacks via gradient-based optimizers. Furthermore, we show that one key reason for the vulnerability of GNNs to label-flipping attack is overfitting to flipped nodes. Based on this observation, we propose a defense framework which introduces a community-preserving self-supervised task as regularization to avoid overfitting. We demonstrate the effectiveness of our proposed attack model to GNNs on four real-world datasets. The effectiveness of our defense framework is also well validated by the substantial improvements of defense based GNN and its variants under label-flipping attacks.