Reviewerly/exHarmony huggingface dataset:
Technical Validation of Plot Designs by Use of Deep Learning

When does inspecting a certain graphical plot allow for an investigator to reach the right statistical conclusion? Visualizations are commonly used for various tasks in statistics—including model diagnostics and exploratory data analysis—and though attractive due to its intuitive nature, the lack of available methods for validating plots is a major drawback. We propose a new technical validation method for visual reasoning. Our method trains deep neural networks to distinguish between plots simulated under two different data generating mechanisms (null or alternative), and we use the classification accuracy as a technical validation score (TVS). The TVS measures the information content in the plots, and TVS values can be used to compare different plots or different choices of data generating mechanisms, thereby providing a meaningful scale that new visual reasoning procedures can be validated against. We apply the method to three popular diagnostic plots for linear regression, namely scatterplots, quantile-quantile plots and residual plots. We consider various types and degrees of misspecification, as well as different within-plot sample sizes. Our method produces TVSs that increase with increasing sample size and decrease with increasing difficulty, and hence the TVS is a meaningful measure of validity.
Latent Feature Disentanglement for Visual Domain Generalization

Despite remarkable success in a variety of computer vision applications, it is well-known that deep learning can fail catastrophically when presented with out-of-distribution data, where there are usually style differences between the training and test images. Toward addressing this challenge, we consider the domain generalization problem, wherein predictors are trained using data drawn from a family of related training (source) domains and then evaluated on a distinct and unseen test domain. Naively training a model on the aggregate set of data (pooled from all source domains) has been shown to perform suboptimally, since the information learned by that model might be domain-specific and generalizes imperfectly to test domains. Data augmentation has been shown to be an effective approach to overcome this problem. However, its application has been limited to enforcing invariance to simple transformations like rotation, brightness change, etc. Such perturbations do not necessarily cover plausible real-world variations that preserve the semantics of the input (such as a change in the image style). In this paper, taking the advantage of multiple source domains, we propose a novel approach to express and formalize robustness to these kind of real-world image perturbations. The three key ideas underlying our formulation are (1) leveraging disentangled representations of the images to define different factors of variations, (2) generating perturbed images by changing such factors composing the representations of the images, (3) enforcing the learner (classifier) to be invariant to such changes in the images. We use image-to-image translation models to demonstrate the efficacy of this approach. Based on this, we propose a domain-invariant regularization (DIR) loss function that enforces invariant prediction of targets (class labels) across domains which yields improved generalization performance. We demonstrate the effectiveness of our approach on several widely used datasets for the domain generalization problem, on all of which our results are competitive with the state-of-the-art.
Model-Aware Pre-Training for Radial Distortion Rectification

Camera lenses often suffer from optical aberrations, causing radial distortion in the captured images. In those images, there exists a clear and general physical distortion model. However, in existing solutions, such rich geometric prior is under-utilized, and the formulation of an effective prediction target is under-explored. To this end, we introduce Radial Distortion TRansformer (RDTR), a new framework for radial distortion rectification. Our RDTR includes a model-aware pre-training stage for distortion feature extraction and a deformation estimation stage for distortion rectification. Technically, on the one hand, we formulate the general radial distortion (i.e., barrel distortion and pincushion distortion) in camera-captured images with a shared geometric distortion model and perform a unified model-aware pre-training for its learning. With the pre-training, the network is capable of encoding the specific distortion pattern of a radially distorted image. After that, we transfer the learned representations to the learning of distortion rectification. On the other hand, we introduce a new prediction target called backward warping flow for rectifying images with any resolution while avoiding image defects. Extensive experiments are conducted on our synthetic dataset, and the results demonstrate that our method achieves state-of-the-art performance while operating in real-time. Besides, we also validate the generalization of RDTR on real-world images. Our source code and the proposed dataset are publicly available at https://github.com/wwd-ustc/RDTR.
Efficient Pose Estimation via a Lightweight Single-Branch Pose Distillation Network

Accurate lightweight (LW) pose estimation is still a challenging task influenced by different human poses and various complex backgrounds in 2-D human images. To address the above problems, we propose a lightweight single-branch pose distillation network, termed LSPD, which is a lightweight powerful fully convolutional pose network that can be executed quickly with a low computational cost for accurate pose estimation. First, we introduced an efficient end-to-end pose distillation sequence framework, which utilizes a small number of lightweight and strong pose estimation stages to effectively transfer the pose knowledge of our teacher model. Second, we constructed a compact and strong pose estimation stage that uses a type of lightweight multiscale residual block to enhance the image features and the image-dependent spatial features representation ability of the model. At the same time, it reduces the computational cost. Finally, when training is complete, we used the backbone network and the first student stage as the simple architecture to deploy. Extensive experiments demonstrated that the proposed method obtains excellent performance with high accuracy and low model parameters.
Domain-Adaptive Vision Transformers for Generalizing Across Visual Domains

Deep-learning models often struggle to generalize well to unseen domains because of the distribution shift between the training and real-world data. Domain generalization aims to train models that can acquire general features from data across different domains, thereby improving the performance on unseen domains. Inspired by the glance-and-gaze approach, which mimics the way humans perceive the real world, we introduce the domain-adaptive vision transformer (DA-ViT) model, which adopts a human cognitive perspective for domain generalization. We merge glance and gaze blocks to initially capture general information from each block and subsequently acquire more detailed and focused information. Unlike previous methods that predominantly employ convolutional neural networks, we adapted the ViT model to learn features that are robust across different visual domains. DA-ViT is pretrained on the ImageNet 1K dataset and designed to adaptively learn features that are generalizable across various visual domains. We evaluated our adapted model for domain generalization and demonstrated that it outperforms the ResNet50 model based on non-ensemble algorithms by 0.7%p on the VLCS benchmark dataset. Our proposed model introduces a new approach for domain generalization that leverages the capabilities of vision transformers to adapt effectively to diverse visual domains.
Self-Supervised Contrastive Learning for Extracting Radar Word in the Hierarchical Model of Multifunction Radar

The analysis of intercepted multifunction radar (MFR) signals has attracted considerable attention in the field of cognitive electronic reconnaissance. The agility of pulse parameters makes it difficult to recognize their behavior states. Currently, most existing approaches for recognizing MFR behaviors heavily depend on prior information, which can hardly be obtained in a non cooperative way. This study develops a contrastive self supervised method to adaptively segment and cluster MFR pulse sequences into radar words with little prior information. First, a convolutional neural network (CNN) is trained in a self-supervised manner to differentiate between adjacent and nonadjacent pulse group embeddings to learn robust feature representations from sequences of pulse descriptor words (PDWs). Next, based on the learned embeddings, test statistics are calculated for each PDW sequence to detect change points, which are in turn used to segment the sequence into multiple subsegments, each containing only one radar word. Finally, feature vectors are produced for clustering the subsegments into groups of radar words. Simulation results show that without using any labeled data, the proposed method can effectively extract the radar words of a hypothetical MFR under corrupted and overlapped pulse parameters and performs only slightly worse than the state-of-the-art fully supervised method.
Using Continuous Integration Techniques in Open Source Projects—An Exploratory Study

For a growing number of software projects, continuous integration (CI) techniques are becoming an essential part of the process. However, the maturity of their adoption in open source projects varies. In this paper, we present an empirical study on GitHub repositories to explore the use of continuous integration techniques in open source projects. Following the Goal-Question-Metric (GQM) approach, 3 research questions and 7 metrics were defined for such a goal. We mined 10 repositories of open source projects with 101,149 pull requests, 399,671 commits from 20,432 developers. This was followed by exploratory data analysis for each metric. In summary, our results indicate that (RQ1) most failed CI builds required a small change in the pull request to fix the code; (RQ2) CI builds of smaller pull requests are more likely to succeed than larger ones; (RQ3) there was no correlation found between developer experience in committing to the project and the success rate of CI builds. Most of the projects studied have not yet developed a mature approach to using continuous integration techniques. In these cases, developers do not thoroughly test code before submitting pull requests. Furthermore, the results of the study confirmed that developers tend to submit pull requests with small amounts of new or modified code.
Improved Target-Specific Stance Detection on Social Media Platforms by Delving Into Conversation Threads

Target-specific stance detection on social media, which aims at classifying a textual data instance such as a post or a comment into a stance class of a target issue, is an emerging opinion mining paradigm of importance. An example application would be to overcome vaccine hesitancy in combating the coronavirus pandemic. Existing stance detection strategies rely merely on the individual instances which cannot always capture the expressed stance of a given target. We address a new task called conversational stance detection (CSD) which is to infer the stance toward a given target (e.g., COVID-19 vaccination) when given a data instance and its corresponding conversation thread. To carry out the task, we first propose a benchmarking CSD dataset with annotations of stances and the structures of conversation threads among the instances, which is based on six major social media platforms in Hong Kong. To infer the desired stances from both data instances and conversation threads, we propose a model called Branch-bidirectional encoder representations from transformers (BERT) that incorporates contextual information in conversation threads. Extensive experiments on our CSD dataset show that our proposed model outperforms all the baseline models that do not make use of contextual information. Specifically, it improves the F1 score by 10.3% compared with the state-of-the-art method in the SemEval-2016 Task 6 competition. This shows the potential of incorporating rich contextual information on detecting target-specific stances on social media platforms and suggests a more practical way to construct future stance detection tasks.
eBoF: Interactive Temporal Correlation Analysis for Ensemble Data Based on Bag-of-Features

We propose eBoF, a novel time-varying ensemble data visualization approach based on the Bag-of-Features (BoF) model. In the eBoF model, we extract a simple and monotone interval from all target variables of ensemble scalar data as a local feature patch. Each local feature of a semantically simple single interval can be defined as a feature patch within the BoF model, with the duration of each interval (i.e., feature patch) serving as its frequency. Feature clusters in ensemble runs are then identified based on the similarity of temporal correlations. eBoF generates clusters along with their probability distributions across all feature patches while preserving the geo-spatial information, which is often lost in traditional topic modeling or clustering algorithms. The probability distribution across different clusters can help to generate reasonable clustering results, evaluated by domain knowledge. We conduct case studies and performance tests to evaluate the eBoF model and gather feedback from domain experts to further refine it. Evaluation results suggest the proposed eBoF can provide insightful and comprehensive evidence on ensemble simulation data analysis.
Empirical Study for Open Source Libraries in Automotive Software Systems

Open-source software has revolutionized the field of software development, providing a collaborative and transparent approach that encourages knowledge sharing and innovation. However, the adoption of open-source software in automotive systems introduces security concerns that require careful scrutiny and management. While previous studies have investigated the general open-source software ecosystem, this paper focuses specifically on the unique characteristics and challenges of open-source software in automotive vehicles. To achieve these goals, we examine real-world examples and case studies to analyze the impact of opensource software on automotive systems. We extracted and collected 4092 open source components from real-world automotive firmware and compared the differences and similarities to 20010 commonly used components in operating systems such as Linux. The results show that the automotive firmware contains 79.8 open source components on average, which account for 15.84% of all binary files in the firmware. Moreover, 61.15% detected libraries are automotive-specific and are different from the commonly used components. By gaining insights this research provides valuable recommendations to improve automotive security by building the database of automotive-specific open source libraries.
A convergence time of Grover walk on regular graph to stationary state with constant inflow to every vertex

AbstractWe consider a quantum walk model on a finite graph which has an interaction with the outside. Here a quantum walker from the outside penetrates the graph and also a quantum walker in the graph goes out to the outside at every time step. This dynamics of the quantum walk converges to a stationary state. In this paper, we estimate the speed of the convergence to the stationary state on the κ-regular graph with the uniform insertion of the inflow to the graph. We show that larger degree of the regular graph makes the convergence time of this quantum walk model slower under this set-up.Keywords: Quantum walkstationary stateconvergence time AcknowledgmentsWe would like to thank to Norio Konno, Sarato Takahashi, Yusuke Higuchi and Takuya Ohwa for the useful comments.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingA.I. acknowledges financial supports from the Grant-in-Aid for JSPS Research Fellow [grant number JP20J20590]. S.K. acknowledges financial supports from the Grant-in-Aid for JSPS Research Fellow [grant number 20J01175]. E.S. acknowledges financial supports from the Grant-in-Aid of Scientific Research (C) Japan Society for the Promotion of Science [grant number 19K03616] and Research Origin for Dressed Photon.
Financial Causality Extraction Based on Universal Dependencies and Clue Expressions

Abstract This paper proposes a method to extract financial causal knowledge from bi-lingual text data. Domain-specific causal knowledge plays an important role in human intellectual activities, especially expert decision making. Especially, in the financial area, fund managers, financial analysts, etc. need causal knowledge for their works. Natural language processing is highly effective for extracting human-perceived causality; however, there are two major problems with existing methods. First, causality relative to global activities must be extracted from text data in multiple languages; however, multilingual causality extraction has not been established to date. Second, technologies to extract complex causal structures, e.g., nested causalities, are insufficient. We consider that a model using universal dependencies can extract bi-lingual and nested causalities can be established using clues, e.g., “because” and “since.” Thus, to solve these problems, the proposed model extracts nested causalities based on such clues and universal dependencies in multilingual text data. The proposed financial causality extraction method was evaluated on bi-lingual text data from the financial domain, and the results demonstrated that the proposed model outperformed existing models in the experiment.
An intelligent vocabulary size measurement method for second language learner

Abstract This paper presents a new method for accurately measuring the vocabulary size of second language (L2) learners. Traditional vocabulary size tests (VSTs) are limited in capturing a tester’s vocabulary and are often population-specific. To overcome these issues, we propose an intelligent vocabulary size measurement method that utilizes massive robot testers. They are equipped with randomized and word-frequency-based vocabularies to simulate L2 learners’ variant vocabularies. An intelligent vocabulary size test (IVST) is developed to precisely measure vocabulary size for any population. The robot testers “take” the IVST, which dynamically generates quizzes with varying levels of difficulty adapted to the estimated tester’s vocabulary size in real-time using an artificial neural network (ANN) through iterative learning. The effectiveness of the IVST is factually verified by their visible vocabularies. Additionally, we apply a long short-term memory (LSTM) model to further enhance the method’s performance. The proposed method has demonstrated high reliability and effectiveness, achieving accuracies of 98.47% for the IVST and 99.87% for the IVST with LSTM. This novel approach provides a more precise and reliable method for measuring vocabulary size in L2 learners compared to traditional VSTs, offering potential benefits to language learners and educators.
chessboard: An R package for creating network connections based on chess moves

chessboard aims to facilitate the creation of connectivity matrices for sampling networks designed as regular grids.It can handle directed (asymmetric) and undirected (symmetric) spatial (or non-spatial) network connections.chessboard offers various methods to detect neighbors, all based on the chess game, allowing the creation of complex connectivity scenarios.
Why Twitter does not gamify communication

ABSTRACTA central question for understanding social media platforms is whether the design of these systems is itself responsible for the harmful effects they have on society. Do these systems push users toward unhealthy forms of engagement? Is there something inherently toxic about the design that distorts who we are when we use it? In a recent paper, Thi Nguyen argues that the design of Twitter is responsible for many of its most toxic outcomes. Nguyen’s argument is based on an analogy between Twitter and games. He argues that Twitter’s game-like features encourage users to rack up Likes and Retweets rather than engaging in the rich and subtle activity of communication. For Nguyen, this drive for high scores leads to many of the toxic effects of the platform. In this paper, we critique Nguyen’s argument. We contend that, in a crucial respect that matters, Twitter is not game-like. We show that the apparent plausibility of Nguyen’s argument rests upon overlooking this crucial disanalogy. Moreover, drawing out how Nguyen’s analogy breaks down makes clear not just that his account fails to explain Twitter’s toxicity, but also that it actively occludes the design choices that have negative effects on its users.KEYWORDS: Social mediaphilosophy of technologynormativity of artifactsargument by analogy Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Unless noted, all emphases are in original.2 Another objection might arise at this point: what if there are games that do not have constitutive rules that sculpt temporary agency? This is an interesting objection, but it is first-and-foremost an objection to Nguyen’s account of games. If there are games that do not have constitutive rules that sculpt temporary agency, Nguyen would have to draw his proposed analogy between Twitter and games somewhere else.
Digital forensic approaches for metaverse ecosystems

The accelerating pace of digital transformation has given rise to metaverses that can participate freely in contactless environments. More than just game content, metaverses are driving everyday innovation across industries. However, threats are also prevalent, with crimes such as child sexual exploitation and privacy violations occurring in metaverses that mimic reality, making digital forensics for metaverse threats essential. Nevertheless, technical standards for different types of metaverses have yet to be defined, making investigation difficult. Furthermore, even though metaverses are complex forms that combine multiple hardware devices and software applications, existing studies have either focused on a single component or not analyzed the real-world environment. In this study, we derived a metaverse ecosystem with common components that comprise a metaverse and analyzed the hardware and software used throughout the user's metaverse lifecycle from a digital forensics perspective. In particular, we applied real-case-based scenario to the metaverse environment of the most popular Meta's currently in use to identify various artifacts that can be used across the ecosystem and validate the effectiveness of the process. We also developed a metaverse digital forensics tool for the first time in the current situation where open-source and commercial tools do not support metaverse investigations.
GesMessages: Using Mid-air Gestures to Manage Notifications

This paper introduces GesMessages, an innovative mid-air interactive application that uses simple gestures to manage real-time message notifications on laptops and large displays. Leveraging cameras on computers or smart devices, the application offers three distinct gestures: expanding notifications for immediate attention, hiding non-urgent messages, and deleting spam messages. We present the technical setup and system design. Additionally, we explore potential applications in context-awareness systems, contributing to gestural interaction research. Our work fosters a deeper understanding of mid-air interaction’s impact on message management and future interactive systems.