Reviewerly/exHarmony huggingface dataset:
Constructing gene features for robust 3D mesh zero-watermarking

The rapid developments of information technology and network communication have made mesh models convenient for copying and sharing through the Internet. Accordingly, the protection of the intellectual property has become an urgent and crucial task. No doubt that the robust watermarking technology is a clever way to solve this issue. The fault tolerance, however, is proportional to the amount of data. Hence, it is difficult to counteract attacks which deleting or altering a large amount of vertex information. In this article, we aim to propose a robust zero-watermarking technique based on 3D mesh gene features. The model is divided into multiple gene groups. Recessive and dominant features can be extracted from these gene groups to resist the distortion and distortionless attacks, respectively. Simulations have proved that the gene features can be used to effectively resist attacks of noise addition, cropping, smoothing, simplification, and common lossless operations. Thus, the proposed method can firmly protect the intellectual property rights of the mesh model.
Prompting and Tuning: A Two-Stage Unsupervised Domain Adaptive Person Re-identification Method on Vision Transformer Backbone

This paper explores the Vision Transformer (ViT) backbone for Unsupervised Domain Adaptive (UDA) person Re-Identification (Re-ID). While some recent studies have validated ViT for supervised Re-ID, no study has yet to use ViT for UDA Re-ID. We observe that the ViT structure provides a unique advantage for UDA Re-ID, i.e., it has a prompt (the learnable class token) at its bottom layer, that can be used to efficiently condition the deep model for the underlying domain. To utilize this advantage, we propose a novel two-stage UDA pipeline named Prompting And Tuning (PAT) which consists of a prompt learning stage and a subsequent fine-tuning stage. In the first stage, PAT roughly adapts the model from source to target domain by learning the prompts for two domains, while in the second stage, PAT fine-tunes the entire backbone for further adaption to increase the accuracy. Although these two stages both adopt the pseudo labels for training, we show that they have different data preferences. With these two preferences, prompt learning and fine-tuning integrated well with each other and jointly facilitated a competitive PAT method for UDA Re-ID.
Difference-based firefly programming for symbolic regression problems

Automatic programming is a type of programming that has the ability to analyze and solve problems using the principles of symbolic regression analysis. These methods can solve complex problems regardless of whether they have a specific pattern or not. In this work, we are going to introduce the difference-based firefly programming (DFP) method as an improved version of the standard firefly programming method. We have analyzed the performance of this new improved method, which will be described in detail within the scope of this work. In order to evaluate the performance of the newly presented method, the results have been compared to the results of the standard method and the results of other methods that are used to solve the same type of problems. DFP has been used also in forecasting and modeling a real-world time-series problem, where it showed good performance too. In general, the results demonstrated the improved performance of the newly introduced method and showed its ability to efficiently solve complex problems.
Text-based person search via local-relational-global fine grained alignment

The core difficulty of text-based person search is how to achieve fine-grained alignment of visual and linguistic modal data, so as to bridge the gap of modal heterogeneity. Most existing works on this task focus on global and local features extraction and matching, ignoring the importance of relational information. This paper proposes a new text-based person search model, named CM-LRGNet, which extracts Cross-Modal Local-Relational-Global features in an end-to-end manner, and performs fine-grained cross-modal alignment on the above three feature levels. Concretely, we first split the convolutional feature maps to obtain local features of images, and adaptively extract textual local features. Then a relation encoding module is proposed to implicitly learn the relational information implied in the images and texts. Finally, a relation-aware graph attention network is designed to fuse the local and relational features to generate global representations for both images and text queries. Extensive experimental results on benchmark dataset (CUHK-PEDES) show that our approach can achieve state-of-the-art performance (64.18%, 82.97%, 89.85% in terms of Top-1, Top-5, and Top-10 accuracies), by learning and aligning local-relational-global representations from different modalities. Our code has been released in https://github.com/zhangweifeng1218/Text-based-Person-Search.
Spatial-Temporal Feature with Dual-Attention Mechanism for Encrypted Malicious Traffic Detection

While encryption ensures the confidentiality and integrity of user data, more and more attackers try to hide attack behaviours through encryption, which brings new challenges to malicious traffic identification. How to effectively detect encrypted malicious traffic without decrypting traffic and protecting user privacy has become an urgent problem to be solved. Most of the current research only uses a single CNN, RNN, and SAE network to detect encrypted malicious traffic, which does not consider the forward and backward correlation between data packets, so it is difficult to effectively identify malicious features in encrypted traffic. This study proposes an approach that combines spatial-temporal feature with dual-attention mechanism, which is called TLARNN. Specifically, first we use 1D-CNN and BiGRU to extract spatial features in encrypted traffic packets and temporal features between encrypted streams, respectively, which enriches the features of different dimensions, and then, the soft attention mechanism is focused on the encrypted data packets to extract features. Ultimately, the second layer of the soft attention mechanism is used for aggregating malicious features. Several comparative experiments are designed to prove the effectiveness of the proposed scheme. The experimental results demonstrate that the proposed scheme has a significant performance improvement compared to existing ones.
Inviting, Affording and Translating Harm: Understanding the Role of Technological Mediation in Technology-Facilitated Violence

Abstract Technologies not only extend capabilities but also mediate experience and action. To date, however, research on technology-facilitated violence has tended not to focus on the role technological mediation plays in acts of violence facilitated through technology. Building on prior work in the field, this article develops a theoretical framework and typology for understanding the role technological mediation plays in producing technology-facilitated violence. First, drawing on postphenomenological theories of technology, we argue that technology-facilitated violence is best understood as a form of ‘harm translation,’ where a technology’s affordances and other properties ‘invite’ an individual to actualize harmful ends. Then, distinguishing between four modes of harm translation, we construct a typology for analysing the intersections between user intention and technological design that, together, facilitate violence. We argue that by attending to these distinctions our typology may help researchers and designers identify and address the specific causal dynamics involved in producing different kinds of technology-facilitated harm.
Instagram as a learning tool to improve technical vocabulary for sports science students

The present paper shares a learning and teaching experience built on a project-based methodology using Instagram accounts to improve technical vocabulary in the ESP classroom. Participants were 75 undergraduate sports science students who attended our English for Professional and Academic Communication ESP course at the Universidad Politécnica de Madrid (Spain) and participated in the project during the spring semester of 2022. To achieve our objective, the participants were divided into 16 self-arranged groups, each expected to develop a fitness project based on Instagram accounts. At the end of the experience, participants were invited to complete an ad hoc designed questionnaire giving their opinion about the use of Instagram accounts as a learning tool to acquire specific vocabulary in the field of sports science. The learners reported high positive perceptions towards using Instagram to develop technical vocabulary. This paper recommends observing quality standards when integrating Instagram content into English for Specific Purposes (ESP) courses.
Their Mere Existence Is Resistance: Undocu/DACAmented Collegians' Resisting Subordination in Turbulent Times

This critical ethnography examines the resistance efforts of undocu/DACAmented collegians during the Trump campaign and presidency era (2015–2021). Guided by the matrix of resistance, this study sought to highlight the various methods and intentions these collegians use to resist subordination amidst an anti-im/migrant sociopolitical climate. Findings reveal how their efforts are demonstrated via (a) participation in civic engagement in local and national advocacy organizations, (b) protests against border patrol agents' presence on campus, (c) coming out with their liminal legal status, and (d) their professional aspirations. Exigent recommendations are offered for higher education institutional agents at all levels.
Fine-Tuning of Multilingual Models for Sentiment Classification in Code-Mixed Indian Language Texts

We use XLM (Cross-lingual Language Model), a transformer-based model, to perform sentiment analysis on Kannada-English code-mixed texts. The model was fine-tuned for sentiment analysis using the KanCMD dataset. We assessed the model's performance on English-only and Kannada-only scripts. Also, Malayalam and Tamil datasets were used to evaluate the model. Our work shows that transformer-based architectures for sequential classification tasks, at least for sentiment analysis, perform better than traditional machine learning solutions for code-mixed data.
A national risk analysis model (NRAM) for the assessment of COVID‐19 epidemic

COVID-19 has caused a critical health concern and severe economic crisis worldwide. With multiple variants, the epidemic has triggered waves of mass transmission for nearly 3 years. In order to coordinate epidemic control and economic development, it is important to support decision-making on precautions or prevention measures based on the risk analysis for different countries. This study proposes a national risk analysis model (NRAM) combining Bayesian network (BN) with other methods. The model is built and applied through three steps. (1) The key factors affecting the epidemic spreading are identified to form the nodes of BN. Then, each node can be assigned state values after data collection and analysis. (2) The model (NRAM) will be built through the determination of the structure and parameters of the network based on some integrated methods. (3) The model will be applied to scenario deduction and sensitivity analysis to support decision-making in the context of COVID-19. Through the comparison with other models, NRAM shows better performance in the assessment of spreading risk at different countries. Moreover, the model reveals that the higher education level and stricter government measures can achieve better epidemic prevention and control effects. This study provides a new insight into the prevention and control of COVID-19 at the national level.
Representing mental disorders with virtual reality applications: Designing for multimodality and complex participation

In this paper, I present various strategies adopted by creators of artistic interactive virtual reality (VR) experiences to represent mental health problems and their contexts. The strategies can foster dialogues about these problems, as they present their complexities by embedding them into various narrative and non-narrative elements. In discussing the representational complexity of these works, I emphasize design strategies that tackle stereotypes and their ethical representations and which create a specific type of suspense for the experiencer to bypass the representation of suffering that documentary VR works often use. These productions approach mental health issues as dynamic systems and represent them through linearity or non-linearity (see Stepney 2018). This approach offers limited agency to the interactor, in the sense that the user has a sense of embodiment (Kilteni et al., 2012) and is scripted as an interactor in specific cases (Murray 1997). In this paper, I argue that the VR medium’s characteristic of expressing non-linearity via multimodality (see Elleström 2019) and sense of embodiment makes it suitable for such productions.
A Spatial Cross-Scale Attention Network and Global Average Accuracy Loss for SAR Ship Detection

A neural network-based object detection algorithm has the advantages of high accuracy and end-to-end processing, and it has been widely used in synthetic aperture radar (SAR) ship detection. However, the multi-scale variation of ship targets, the complex background of near-shore scenes, and the dense arrangement of some ships make it difficult to improve detection accuracy. To solve the above problem, in this paper, a spatial cross-scale attention network (SCSA-Net) for SAR image ship detection is proposed, which includes a novel spatial cross-scale attention (SCSA) module for eliminating the interference of land background. The SCSA module uses the features at each scale output from the backbone to calculate where the network needs attention in space and enhances the features of the feature pyramid network (FPN) output to eliminate interference from noise, and land complex backgrounds. In addition, this paper analyzes the reasons for the “score shift” problem caused by average precision loss (AP loss) and proposes the global average precision loss (GAP loss) to solve the “score shift” problem. GAP loss enables the network to distinguish positive samples and negative samples faster than focal loss and AP loss, and achieve higher accuracy. Finally, we validate and illustrate the effectiveness of the proposed method by performing it on SAR Ship Detection Dataset (SSDD), SAR-ship-dataset, and High-Resolution SAR Images Dataset (HRSID). The experimental results show that the proposed method can significantly reduce the interference of background noise on the ship detection results, improve the detection accuracy, and achieve superior results to the existing methods.
Configuring augmented reality users: analysing YouTube commercials to understand industry expectations

ABSTRACTABSTRACTCommercial videos are often used to familiarise potential buyers and users with new technologies and their possibilities. In addition, presenting visions of future applications is a way to configure users and define social worlds of technology use. We analyse 30 YouTube videos featuring augmented reality (AR) devices in industrial manufacturing and construction, to explore how these commercial videos situate AR technology and future users by showcasing techno-euphoric promises and imagined use cases. With a video analysis based on Grounded Theory and Situational Analysis, we untangle the promises of AR for manufacturing and construction work; second, we present two prevailing configurations of AR users: 'experts in situ' and 'smart dummies'; and third, we discuss how YouTube videos put forward developmental expectations. In addition, we identify discrepancies between expectations and foreseeable requirements in construction work. Finally, our research could contribute to a more holistic understanding of workplaces and socially robust AR applications.KEYWORDS: augmented realityuser configurationsvideo analysismanufacturingconstruction AcknowledgementThe authors thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting Aimée Sousa Calepso.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are available at the platform YouTube. The links to the respective videos are in Table 1 within this publication. The authors saved the videos for scientific purposes. We offer an Excel sheet including the list of the 30 commercial YouTube videos with information on year/title/duration/initiator/type of initiator/link/keywords in search/field of AR application/short description/notes on each of the respective videos: Ann-Kathrin Wortmeier, Aimée Sousa Calepso, Cordula Kropp, Michael Sedlmair and Daniel Weiskopf. 2022. Replication Data for BauHCI Video Analysis. In DaRUS. https://doi.org/10.18419/darus-2117Additional informationFundingThis work was supported by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy [grant number EXC 2120/1 – 390831618].
Adaptive dewarping of severely warped camera-captured document images based on document map generation

Automated dewarping of camera-captured handwritten documents is a challenging research problem in Computer Vision and Pattern Recognition. Most available systems assume the shape of the camera-captured image boundaries to be anywhere between trapezoidal and octahedral, with linear distortion in areas between the boundaries for dewarping. The majority of the state-of-the-art applications successfully dewarp the simple-to-medium range geometrical distortions with partial selection of control points by a user. The proposed work implements a fully automated technique for control point detection from simple-to-complex geometrical distortions in camera-captured document images. The input image is subject to preprocessing, corner point detection, document map generation, and rendering of the de-warped document image. The proposed algorithm has been tested on five different camera-captured document datasets (one internal and four external publicly available) consisting of 958 images. Both quantitative and qualitative evaluations have been performed to test the efficacy of the proposed system. On the quantitative front, an Intersection Over Union (IoU) score of 0.92, 0.88, and 0.80 for document map generation for low-, medium-, and high-complexity datasets, respectively. Additionally, accuracies of the recognized texts, obtained from a market leading OCR engine, are utilized for quantitative comparative analysis on document images before and after the proposed enhancement. Finally, the qualitative analysis visually establishes the system's reliability by demonstrating improved readability even for severely distorted image samples.
Ultra-robust spatial point-to-point phase unwrapping algorithm for severe interference signal in 3D sensing

Spatial phase unwrapping (SPU) is a thorny ill-posed problem in the field of three-dimensional(3D) sensing, because the influence of interference signal such as noise signal and aliasing signal has always restricted the stable unwrapping of wrapped phase map. Ingeniously, the simplest filtering method has been used to successfully solve the most complex problem of phase unwrapping that has plagued researchers for years, accompanied by an increase in measurement accuracy. And, a point-to-point phase unwrapping algorithm is proposed, which differs from all previous methods of pixel-by-pixel unwrapping of noise-wrapped phase map. Most importantly, a differential iterative model with less than π/2 phase shift is established to solve the long-standing problem of noise-induced local fringe order inaccuracy in SPU. The proposed model is universal to all SPU methods and can achieve ultra-robust anti-noise ability. Without noise correction, the bi-staggered phase unwrapping method (BSPU) is simplified to determine the fringe order of the smooth wrapped phase more efficiently and accurately. Point-to-point inverse calculation of the noise wrapped phase for adaptive correction of errors can be achieved due to the smoothing characteristics of the phase. The phase in the unstable region is effectively estimated by the iterative filtering model to further improve the measuring accuracy. Experimental results show that the proposed method is ultra-robust and universal in 3D sensing.
A Facial Gender Detector on CPU using Multi-dilated Convolution with Attention Modules

Facial gender detectors have evolved into a vital component of an intelligent advertisement display platform. It is helpful to assist a decision of delivering appropriate advertisements to each audience. To reduce system costs, applications deployed in this platform must be able to run on a CPU. This work proposes a facial gender detector (FG-CPU) that can be implemented on a CPU device to support an advertising display platform. The proposed CNN model consists of a multi-dilated convolution with attention modules (MudaNet). The multi-dilated convolution is applied to capture multi-scale features in an efficient manner. The attention module is used to rectify the quality of the feature map. This work’s training and validation process is conducted on the UTKFace, the Labeled Faces in the Wild (LFW), and the Adience Benchmark datasets. As a result, the proposed CNN model is proven to compete with other common and lightweight competitors’ CNN models on these three datasets. Regarding speed, the detector can operate 49.19 frames per second in real-time on a CPU device.
Fractal Dimension of DSSS Frame Preamble: Radiometric Feature for Wireless Device Identification

This paper demonstrates that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fractal dimension of frame preamble</i> serves as a new radiometric feature that can be used together with other known radiometric features to enhance the identification accuracy in wireless device identification. We first propose a fractal dimension estimation scheme for direct-sequence spread spectrum (DSSS) frame preamble, then provide theoretical analysis to reveal how the fractal dimension is primarily determined by the device hardware imperfections, and thus prove that the fractal dimension serves as an intrinsic radiometric feature. We further show simulation results to verify our theoretical modeling of the fractal dimension and also numerically evaluate the effects of device hardware imperfections and wireless channels on the fractal dimension. Finally, by jointly applying the fractal dimension and the five features reported in the literature, we conduct extensive experiments to demonstrate that the fractal dimension can lead to a further improvement of the state-of-the-art result in the radiometric feature-based device identification.