Reviewerly/exHarmony huggingface dataset:
AutoArx: Digital Twins of Living Architectures

Software systems become increasingly interconnected and complex, leading to a heterogeneous system landscape. This entails that architecture information and architecture documentation become more important. Currently, architecture documentation is a mostly manual task, which is costly, tedious and error prone. Even if initial documentation of a system’s architecture is available, it needs to be kept up-to-date as the system evolves, as otherwise its quality will decay to a point where it does not reflect the actual system and is not useful anymore. Therefore, automated support for maintaining and evolving architecture information and documentation of complex systems is highly beneficial to architects and other stakeholders. To achieve this, architecture information must be automatically recovered from heterogeneous data sources at different points in time and consolidated and integrated to provide an up-to-date representation of the system. Subsequently, the recovered architecture information must be automatically updated whenever data sources change over time. In this work, we present an early concept of a co-evolving digital architecture twin to model the system architecture via an architecture information model that combines and relates architecture information recovered from different sources at different points in time. We propose a framework for automated recovery, integration, and co-evolution of architecture information to create and maintain a digital architecture twin that is continuously and automatically updated as the system evolves. We present the general concepts and framework and discuss use cases to motivate benefits.
Differences between antisemitic and non-antisemitic English language tweets

Antisemitism is a global phenomenon on the rise that is negatively affecting Jews and communities more broadly. It has been argued that social media has opened up new opportunities for antisemites to disseminate material and organize. It is, therefore, necessary to get a picture of the scope and nature of antisemitism on social media. However, identifying antisemitic messages in large datasets is not trivial and more work is needed in this area. In this paper, we present and describe an annotated dataset that can be used to train tweet classifiers. We first explain how we created our dataset and approached identifying antisemitic content by experts. We then describe the annotated data, where 11% of conversations about Jews (January 2019-August 2020) and 13% of conversations about Israel (January-August 2020) were labeled antisemitic. Another important finding concerns lexical differences across queries and labels. We find that antisemitic content often relates to conspiracies of Jewish global dominance, the Middle East conflict, and the Holocaust.
Formulas for the numbers of sequences containing a given pattern given number of times

Abstract Explicit recurrent formulas for the numbers of sequences containing a given pattern given number of times are constructed. These formulas depend on the length of the sequence, the length of the pattern and its period only. By means of these results one may find the distribution of statistics of the NIST overlapping matching test for binary sequences and arbitrary pattern parameters.
XLNet-Based Prediction Model for CVSS Metric Values

A plethora of software vulnerabilities are exposed daily, posing a severe threat to the Internet. It is almost impossible for security experts or software developers to deal with all vulnerabilities. Therefore, it is imperative to rapidly assess the severity of the vulnerability to be able to select which one should be given preferential attention. CVSS is now the industry’s de facto evaluation standard, which is calculated with a quantitative formula to measure the severity of a vulnerability. The CVSS formula consists of several metrics related to the vulnerability’s features. Security experts need to determine the values of each metric, which is tedious and time-consuming, therefore hindering the efficiency of severity assessment. To address this problem, in this paper, we propose a method based on a pre-trained model for the prediction of CVSS metric values. More specifically, this method utilizes the XLNet model that is fine-tuned with a self-built corpus to predict the metric values from the vulnerability description text, thus reducing the burden of the assessment procedure. To verify the performance of our method, we compare the XLNet model with other pre-trained models and conventional machine learning techniques. The experimental results show that the method outperforms these models on evaluation metrics, reaching state-of-the-art performance levels.
Object Detection in Large-Scale Remote Sensing Images With a Distributed Deep Learning Framework

With the accumulation and storage of remote sensing images in various satellite data centers, the rapid detection of objects of interest from large-scale remote sensing images is a current research focus and application requirement.Although some cutting-edge object detection algorithms in remote sensing images perform well in terms of accuracy (mAP), their inference speed is slow and requires high hardware requirements that are not suitable for real-time object detection in large-scale remote sensing images.To address this issue, we propose a fast inference framework for object detection in large-scale remote sensing images.On the one hand, we introduce α-IoU Loss on the YWCSL model to implement adaptive weighted loss and gradient, which achieves 64.62% and 79.54% mAP on DIOR-R and DOTA test sets, respectively.More importantly, the inference speed of the YWCSL model reaches 60.74 FPS on a single NVIDIA GeForce RTX 3080 Ti, which is 2.87 times faster than the current state-of-the-art one-stage detector S 2 A-Net.On the other hand, we build a distributed inference framework to enable fast inference on large-scale remote sensing images.Specifically, we save the images on HDFS for distributed storage and deploy the pretrained YWCSL model on the Spark cluster.In addition, we use a custom partitioner RankPartition to repartition the data to further improve the performance of the cluster.When using 5 nodes, the speedup of the cluster reaches 9.54, which is 90.80% higher than the theoretical linear speedup (5.00).Our distributed inference framework for large-scale remote sensing images significantly reduces the dependence of object detection on expensive hardware resources, which has important research significance for the wide application of object detection in remote sensing images.
Computer-Vision-Based Statue Detection with Gaussian Smoothing Filter and EfficientDet

Smart tourism is a developing industry, and numerous nations are planning to establish smart cities in which technology is employed to make life easier and link nearly everything. Many researchers have created object detectors; however, there is a demand for lightweight versions that can fit into smartphones and other edge devices. The goal of this research is to demonstrate the notion of employing a mobile application that can detect statues efficiently on mobile applications, and also improve the performance of the models by employing the Gaussian Smoothing Filter (GSF). In this study, three object detection models, EfficientDet—D0, EfficientDet—D2 and EfficientDet—D4, were trained on original and smoothened images; moreover, their performance was compared to find a model efficient detection score that is easy to run on a mobile phone. EfficientDet—D4, trained on smoothened images, achieves a Mean Average Precision (mAP) of 0.811, an mAP-50 of 1 and an mAP-75 of 0.90.
A Systematic Literature Review of Virtual Reality Locomotion Taxonomies

The change of the user's viewpoint in an immersive virtual environment, called locomotion, is one of the key components in a virtual reality interface. Effects of locomotion, such as simulator sickness or disorientation, depend on the specific design of the locomotion method and can influence the task performance as well as the overall acceptance of the virtual reality system. Thus, it is important that a locomotion method achieves the intended effects. The complexity of this task has increased with the growing number of locomotion methods and design choices in recent years. Locomotion taxonomies are classification schemes that group multiple locomotion methods and can aid in the design and selection of locomotion methods. Like locomotion methods themselves, there exist multiple locomotion taxonomies, each with a different focus and, consequently, a different possible outcome. However, there is little research that focuses on locomotion taxonomies. We performed a systematic literature review to provide an overview of possible locomotion taxonomies and analysis of possible decision criteria such as impact, common elements, and use cases for locomotion taxonomies. We aim to support future research on the design, choice, and evaluation of locomotion taxonomies and thereby support future research on virtual reality locomotion.
RepsNet: Combining Vision with Language for Automated Medical Reports

Writing reports by analyzing medical images is error-prone for inexperienced practitioners and time consuming for experienced ones. In this work, we present RepsNet that adapts pre-trained vision and language models to interpret medical images and generate automated reports in natural language. RepsNet consists of an encoder-decoder model: the encoder aligns the images with natural language descriptions via contrastive learning, while the decoder predicts answers by conditioning on encoded images and prior context of descriptions retrieved by nearest neighbour search. We formulate the problem in a visual question answering setting to handle both categorical and descriptive natural language answers. We perform experiments on two challenging tasks of medical visual question answering (VQA-Rad) and report generation (IU-Xray) on radiology image datasets. Results show that RepsNet outperforms state-of-the-art methods with $$81.08 \%$$ classification accuracy on VQA-Rad 2018 and 0.58 BLEU-1 score on IU-Xray. Supplementary details are available at: https://sites.google.com/view/repsnet .
Algorithms for Pixelwise Shape Deformations Preserving Digital Convexity

In this article, we propose algorithms for pixelwise deformations of digital convex sets preserving their convexity using the combinatorics on words to identify digital convex sets via their boundary words, namely Lyndon and Christoffel words. The notion of removable and insertable points are used with a geometric strategy for choosing one of those pixels for each deformation step. The worst-case time complexity of each deflation and inflation step, which is the atomic deformation, is also analysed.
From Disfluency Detection to Intent Detection and Slot Filling

We present the first empirical study investigating the influence of disfluency detection on downstream tasks of intent detection and slot filling. We perform this study for Vietnamese -- a low-resource language that has no previous study as well as no public dataset available for disfluency detection. First, we extend the fluent Vietnamese intent detection and slot filling dataset PhoATIS by manually adding contextual disfluencies and annotating them. Then, we conduct experiments using strong baselines for disfluency detection and joint intent detection and slot filling, which are based on pre-trained language models. We find that: (i) disfluencies produce negative effects on the performances of the downstream intent detection and slot filling tasks, and (ii) in the disfluency context, the pre-trained multilingual language model XLM-R helps produce better intent detection and slot filling performances than the pre-trained monolingual language model PhoBERT, and this is opposite to what generally found in the fluency context.
DOMINO: Visual Causal Reasoning With Time-Dependent Phenomena

Current work on using visual analytics to determine causal relations among variables has mostly been based on the concept of counterfactuals. As such the derived static causal networks do not take into account the effect of time as an indicator. However, knowing the time delay of a causal relation can be crucial as it instructs how and when actions should be taken. Yet, similar to static causality, deriving causal relations from observational time-series data, as opposed to designed experiments, is not a straightforward process. It can greatly benefit from human insight to break ties and resolve errors. We hence propose a set of visual analytics methods that allow humans to participate in the discovery of causal relations associated with windows of time delay. Specifically, we leverage a well-established method, logic-based causality, to enable analysts to test the significance of potential causes and measure their influences toward a certain effect. Furthermore, since an effect can be a cause of other effects, we allow users to aggregate different temporal cause-effect relations found with our method into a visual flow diagram to enable the discovery of temporal causal networks. To demonstrate the effectiveness of our methods we constructed a prototype system named DOMINO and showcase it via a number of case studies using real-world datasets. Finally, we also used DOMINO to conduct several evaluations with human analysts from different science domains in order to gain feedback on the utility of our system in practical scenarios.
Speech Signal Denoising Algorithm and Simulation Based on Wavelet Threshold

In the era of the gradual development of artificial intelligence, people use sound to control the machine to complete the work. When the machine recognizes the human voice signal, the voice must be a signal with less noise interference, so the voice signal denoising technology is particularly important. Common speech signals are non-stationary random signals, mixed with various noises, and it is difficult to filter out common denoising methods. Aiming at this kind of non-stationary random signal denoising processing, this paper uses the characteristics of wavelet transform multi-resolution analysis to improve the wavelet threshold denoising algorithm and uses the improved wavelet threshold function to remove noise and get the original speech signal. The simulation results show that the improved wavelet threshold denoising algorithm has better performance. When analyzing non-stationary random signals, the denoising effect under different signal-to-noise ratios is very significant.
Decentralised Autonomous Organisations for the AEC and Design Industries

The chapter presents the concept of Decentralised Autonomous Organisation (DAO) and discusses what the current and possible applications are in relation to the AEC, design and design-linked industries. The chapter first introduces theoretical aspects of traditional organisations and then develops the ones behind the creation of automated, computer-based ones. Consensus mechanisms and smart-contracts integration are also presented in conjunction with diffused systems of DAOs’ regulation. Scenarios are presented where DAOs are applied as a coordination tool for competitive and collaborative use within the design field. A comparison table of Ethereum-based DAOs as well as reflections on the pros and cons of DAOs applications are provided to better frame what the current boundaries are of a technology that is also expanding its range of utilisation thanks to the interest of town councils and institutions.
Augmented Reality Based Video Shooting Guidance for Novice Users

Using mobile phones to shoot video is considerably common in our daily life. However, novice users have difficulty in controlling the camera properly due to lack of professional knowledge and skill. In this paper, in order to assist novice users in learning and imitating professional camera movement from watching high quality sample videos, we propose ARCAM, an Augmented Reality (AR) based video shooting guidance method for novice users. Using AR, we visualized the concept of camera movement and embedded it into natural scene to provide real-time guidance. User can follow the guidance while shooting video by matching a calibration frame to the guidance, to achieve the desired camera movement. We conducted a user study comparing the effectiveness of ARCAM to a traditional static arrow guidance. Results showed that ARCAM was more effective in helping users understand the camera work in the sample videos and move the camera with more accuracy. Our work provides insights on designing mobile video shooting application and suggests that AR has great potential in assisting novice video shooters.
A robust edge detection technique based on Matching Pursuit algorithm for natural and medical images

Edge detection has been used widely in image processing, image recognition, and computer vision. Several edge detection techniques have been suggested over time; however, each has a lot of disadvantages. Our method has some steps. At first, the image should be converted into a grayscale image. A 3*3pattern grid is used for producing a discrete vector for any pixel in the present study. Secondly, the length of the pixel vector is increased to linearize and expand. Thirdly, the Matching Pursuit algorithm is applied to the image, and the residual part of the Matching Pursuit algorithm is extracted. Finally, two Hysteresis thresholds have been used for detecting edge pixels. The edge detection problem has been converted into a signal processing problem, so it will be less time-consuming. Moreover, the proposed algorithm is insensitive to noise, and this method can detect weak edge pixels. Also, the performance of the proposed algorithm is compared with three known edge detection algorithms, SOBEL, Laplacian, Canny, and CASENet and the results show that we have a high value of performance parameters such as PR, PSNR, and FOM compared to other algorithms. Furthermore, it is very an effective and reliable method to diagnose and treat diseases in medical images.
DeepWSD: Projecting Degradations in Perceptual Space to Wasserstein Distance in Deep Feature Space

Existing deep learning-based full-reference IQA (FR-IQA) models usually predict the image quality in a deterministic way by explicitly comparing the features, gauging how severely distorted an image is by how far the corresponding feature lies from the space of the reference images. Herein, we look at this problem from a different viewpoint and propose to model the quality degradation in perceptual space from a statistical distribution perspective. As such, the quality is measured based upon the Wasserstein distance in the deep feature domain. More specifically, the 1D Wasserstein distance at each stage of the pre-trained VGG network is measured, based on which the final quality score is performed. The deep Wasserstein distance (DeepWSD) performed on features from neural networks enjoys better interpretability of the quality contamination caused by various types of distortions and presents an advanced quality prediction capability. Extensive experiments and theoretical analysis show the superiority of the proposed DeepWSD in terms of both quality prediction and optimization. The implementation of our method is publicly available at https://github.com/Buka-Xing/DeepWSD.
Revisiting Dropout Regularization for Cross-Modality Person Re-Identification

This paper investigated targeted dropout regularization for improving convolutional neural network classifier performance on cross-modality person re-identification problems. The dropout regularization is carefully applied on specific layers (targeted dropout regularization) or regions of the features map (spatially targeted dropout regularization). The intuition behind the spatially targeted dropout regularization is that the importantness of feature map regions is not equally the same, and the specific object is more likely placed at the center. We experimented heavily with PKU-Sketch-ReID and multi-modality person re-identification datasets with SwinTransformer deep neural network architecture. Three different targeted dropout regularizations are used for the experiments, including block-wise dropout, horizontal block-wise dropout, and vertical-horizontal block-wise dropout. Experiments on three different sketch re-identification datasets show that the proposed spatially targeted dropout regularization can improve the performance of the deep neural network classifiers with the best rank-1 of 73.20% on the PKU-Sketch-ReID, 52.73% on the SYSU-MM01 dataset, and 72.74% on the RegDB dataset.
Detecting anomalies within smart buildings using do-it-yourself internet of things

Detecting anomalies at the time of happening is vital in environments like buildings and homes to identify potential cyber-attacks. This paper discussed the various mechanisms to detect anomalies as soon as they occur. We shed light on crucial considerations when building machine learning models. We constructed and gathered data from multiple self-build (DIY) IoT devices with different in-situ sensors and found effective ways to find the point, contextual and combine anomalies. We also discussed several challenges and potential solutions when dealing with sensing devices that produce data at different sampling rates and how we need to pre-process them in machine learning models. This paper also looks at the pros and cons of extracting sub-datasets based on environmental conditions.
The persuasive role of the past: Policy feedback and citizens' acceptance of information communication technologies during the COVID‐19 pandemic in China

Abstract How can the enforcement of policies in the past influence a society's future adoption of information communication technologies (ICTs)? In this paper, we tackle this question by exploring how past e‐governance policies influence citizens' willingness to use the health QR code, which is a COVID‐19 tracing app widely used in China's pandemic control. Past policies regarding smart‐city development in China involve two aspects: the construction of electronic infrastructure and the applications of specific technologies. Empirical analysis based on a nationwide dataset in China suggests that past policies exhibit persuasive effects and influence citizens' acceptance of the health QR code. Specifically, e‐governance applications in cities significantly enhance citizens' acceptance through the demonstration of their usefulness. However, the construction of e‐governance infrastructure per se does not have the same impact on citizens' acceptance. By connecting citizens' acceptance of new technology with past e‐governance policies, the study illustrates a nuanced policy feedback mechanism through which past policies can substantially reshape public opinion by policy outcomes.