Reviewerly/exHarmony huggingface dataset:
Projection Mapping in the City: Co-creating Public Digital Installations for Climate Awareness with Animation and Interaction Design Undergraduates

We report from a project where students created animated public content to promote the urgency of climate change and encourage positive behavior change amongst a city's citizens and stakeholders. We worked with Newcastle City Council in the UK, whose Net Zero agenda has been formulated to mitigate and adapt to climate change, including by changing public perception and promoting more sustainable lifestyles. 32 final-year Animation and Interaction Design (IxD) undergraduate students worked in a collaborative design process to create seven site-specific public campaigns applying animation and projection mapping technologies. Educational challenges included working with new technology, delivering a professional quality result to an external client, and a short 8-week schedule. The results included an interactive game to engage the public in the concept of plastic bottle deposit recycling scheme; ‘guerilla’ projecting on a major clothing retail store intended to encourage passing shoppers to reflect on their role on the negative aspects of fast fashion; and an installation across some of the underground metro stations to highlight and celebrate the sustainability principles of public transport. Our contribution includes our case study of an agile structured collaborative process, with the dual focus of creating high-quality, engaging and site-specific creative content and promoting a specific aspect of the Council's policy agenda. Additionally, we discuss insights into tailoring projection mapping and animated content for civic communications purposes and encouraging digital animation students – who typically work on their production independently– to engage in collaborative processes and with civic responsibilities.
Self-supervised sub-category exploration for Pseudo label generation

Image segmentation-based applications have been actively investigated. However, it is non-trivial to prepare polygon annotations. Previous studies suggested pseudo label generation methods based on weakly supervised learning to lessen the burden of annotation. Nevertheless, the quality of pseudo labels could not be ideal due to target object characteristics and insufficient data size in the construction domain, as identified in this study. This study proposes a fusion architecture, SESC-CAM, to address the challenge, building upon weakly and self-supervised learning methods. The proposed architecture was validated on the AIM dataset, and the generated pseudo labels recorded a mIoU score of 64.99% and 67.65% after the refinement by using a conditional random field, and outperformed its predecessors by 11.29% and 9.14%. The refined pseudo labels were used to train a segmentation model and recorded a 74% mIoU score in semantic segmentation results. The findings of this study provide insights for automated training data preparation.
Teaching &amp; Learning Positionality in HCI education: reflecting on our identities as educators and facilitating the discussion in the classroom

UX designers and practitioners acknowledge users’ different skills and try to address their needs to design better products and experiences. As HCI educators, we searched for tools to help our students to recognize that they might have different perspectives from the users they will be designing for, generating bias and assumptions in the design process. Positionality refers to the personal and social constructs that define our identity, shaping how we see and interpret the world around us, as well as the way the world sees and interprets us. Practicing positionality in HCI education is a meaningful way to reflect on our teaching approaches, our measures and expectations of students’ performance and engagement. For the students, learning about positionality and reflexivity can have an impact on their design work and research, raising awareness on relationships of power and subjectivity. During this Masterclass, we present tools and methods that can be used for teaching and learning (T&L) activities about positionality. We invite attendees to create their positionality statement and share their experience, facilitating a discussion about the challenges that are common both to T&L and interaction design, such as ethics, privacy, stereotypes and stigmatization.
An Efficient Self-Refinement and Reconstruction Network for Image Denoising

Recent works tend to design effective but deep and complex denoising networks, which usually ignored the industrial requirement of efficiency. In this paper, an effective and efficient self-refinement and reconstruction network (SRRNet) is proposed for image denoising. It is based on the encoder-decoder architecture and three improvements are introduced to solve the problem. Specifically, four novel residual connections of different types are proposed as building blocks to maintain original contextual details. A high-resolution reconstruction module is introduced to connect cross-level encoders and corresponding decoders, so as to boost information flow and result in realistic clear image. And multi-scale dual attention is used for suppressing noise and enhancing beneficial dependency. SRRNet achieves PSNR of 39.83 dB and 39.96 dB on SIDD and DND respectively. Compared with other works, the accuracy is higher and the complexity is lower. Extensive experiments in real-world image denoising and Gaussian noise removal prove that SRRNet balances performance and temporal cost better.
Image Retrieval Method Based on Visual Map Pre-Sampling Construction in Indoor Positioning

In visual indoor positioning systems, the method of constructing a visual map by point-by-point sampling is widely used due to its characteristics of clear static images and simple coordinate calculation. However, too small a sampling interval will cause image redundancy, while too large a sampling interval will lead to the absence of any scene images, which will result in worse positioning efficiency and inferior positioning accuracy. As a result, this paper proposed a visual map construction method based on pre-sampled image features matching, according to the epipolar geometry of adjacent position images, to determine the optimal sampling spacing within the constraints and effectively control the database size while ensuring the integrity of the image information. In addition, in order to realize the rapid retrieval of the visual map and reduce the positioning error caused by the time overhead, an image retrieval method based on deep hashing was also designed in this paper. This method used a convolutional neural network to extract image features to construct the semantic similarity structure to guide the generation of hash code. Based on the log-cosh function, this paper proposed a loss function whose function curve was smooth and not affected by outliers, and then integrated it into the deep network to optimize parameters, for fast and accurate image retrieval. Experiments on the FLICKR25K dataset and the visual map proved that the method proposed in this paper could achieve sub-second image retrieval with guaranteed accuracy, thereby demonstrating its promising performance.
Spectral Super-Resolution for High Dynamic Range Images

The images we commonly use are RGB images that contain three pieces of information: red, green, and blue. On the other hand, hyperspectral (HS) images retain wavelength information. HS images are utilized in various fields due to their rich information content, but acquiring them requires specialized and expensive equipment that is not easily accessible to everyone. Recently, Spectral Super-Resolution (SSR), which generates spectral images from RGB images, has been studied. Conventional SSR methods target Low Dynamic Range (LDR) images. However, some practical applications require High Dynamic Range (HDR) images. In this paper, an SSR method for HDR is proposed. As a practical example, we use the HDR-HS images generated by the proposed method as environment maps and perform spectral image-based lighting. The rendering results by our method are more realistic than conventional renderers and LDR SSR methods, and this is the first attempt to utilize SSR for spectral rendering.
CVMan: A Framework for Clone-Incurred Vulnerability Management

Software clones may cause vulnerability proliferation, which highlights the importance of investigating clone-incurred vulnerabilities. In this paper, we propose a framework for automatically managing clone-incurred vulnerabilities. Two innovations of the framework are the notion of the spatial clone-relation graph, which describes clone-based relationships between software programs, and the temporal clone-relation graph, which describes the evolution of clones in software over time. As a case study, we apply the framework to analyze eight versions of Ubuntu while drawing a number of insights, such as: (i) clones are prevalent with about one-sixth of the codebase being clones; (ii) intra-program clones are often attributed to polymorphisms or functional similarities between procedures, while inter-program clones are often attributed to shared code repositories and the reuse of libraries; (iii) the clone surface of Linux remains stable at around 0.6, meaning that spatial and temporal clones in Linux account for about 60% of the codebase, while the lifetime of 53% clones spans eight versions; and (iv) the clone-incurred vulnerability surface in Linux is small, while vulnerable clones and non-vulnerable clones have similar lifetimes.
BEM-SM: A BERT-Encoder Model with Symmetry Supervision Module for Solving Math Word Problem

In order to find solutions to math word problems, some modules have been designed to check the generated expressions, but they neither take into account the symmetry between math word problems and their corresponding mathematical expressions, nor do they utilize the efficiency of pretrained language models in natural language understanding tasks. Anyway, designing fine-tuning tasks for pretrained language models that encourage cooperation with other modules to improve the performance of math word problem solvers is an unaddressed problem. To solve these problems, in this paper we propose a BERT-based model for solving math word problems with a supervision module. Based on pretrained language models, we present a fine-tuning task to predict the number of different operators in the expressions to learn the potential relationships between the problems and the expressions. Meanwhile, a supervision module is designed to check the incorrect expressions generated and improve the model’s performance by optimizing the encoder. A series of experiments are conducted on three datasets, and the experimental results demonstrate the effectiveness of our model and its component’s designs.
Non-Hermitian ground-state-searching algorithm enhanced by a variational toolbox

Ground-state preparation for a given Hamiltonian is a common quantum-computing task of great importance and has relevant applications in quantum chemistry, computational material modeling, and combinatorial optimization. We consider an approach to simulate dissipative non-Hermitian Hamiltonian quantum dynamics using Hamiltonian simulation techniques to efficiently recover the ground state of a target Hamiltonian. The proposed method facilitates the energy transfer by repeatedly projecting ancilla qubits to the desired state, rendering the effective non-Hermitian Hamiltonian evolution on the system qubits. To make the method more resource friendly in the noisy intermediate-scale quantum (NISQ) and early fault-tolerant era, we combine the non-Hermitian projection algorithm with multiple variational gadgets, including variational module enhancement and variational state recording, to reduce the required circuit depth and avoid the exponentially vanishing success probability for postselections. We compare our method, the non-Hermitian-variational algorithm, with a pure variational method, the quantum approximate optimization algorithm (QAOA), for solving the 3-SAT problem and preparing the ground state for the transverse field Ising model. As demonstrated by numerical evidence, the non-Hermitian-variational algorithm outperforms QAOA in convergence speed with improved quantum resource efficiency.
A Cross-View Image Matching Method with Feature Enhancement

Most cross-view image matching algorithms focus on designing network structures with excellent performance, ignoring the content information of the image. At the same time, there are non-fixed targets such as cars, ships, and pedestrians in ground perspective images and aerial perspective images. Differences in perspective, direction, and scale cause serious interference with the cross-view matching process. This paper proposes a cross-view image matching method with feature enhancement, which first transforms the empty image to generate a transformation image aligned with the ground–aerial image domain to establish a preliminary geometric correspondence between the ground-space image. Then, the rich feature information of the deep network and the edge information of the cross-convolution layer are used to establish the feature correspondence between the ground-space images. The feature fusion module enhances the tolerance of the network model to scale differences, improving the interference problem of transient non-fixed targets on the matching performance in the images. Finally, the maximum pooling and feature aggregation strategies are adopted to aggregate local features with obvious distinguishability into global features to complete the accurate matching between ground images. The experimental results show that the proposed method has good advance and high accuracy on CVUSA, which is commonly used in public datasets, reaching 92.23%, 98.47%, and 99.74% on the top 1, top 5 and top 10 indicators, respectively, outperforming the original method in the dataset with a limited field of view and image center, better completing the cross-perspective image matching task.
ScoringNet: A Neural Network Based Pruning Criteria for Structured Pruning

Convolutional neural networks (CNNs) have shown their great power in multiple computer vision tasks. However, many recent works improve their performance by adding more layers and parameters, which lead to computational redundancy in many application scenarios, making it harder to implement on low-end devices. To solve this problem, model pruning methods are proposed, which aim to lower the computational and memory requirements of CNNs. In this paper, we propose ScoringNet, a neural network (NN) based pruning criteria for structured pruning procedure. ScoringNet generates a set of scores for each output channel in a model, which is used to reconstruct a pruned model later in a structured pruning way. ScoringNet can also use the gradient information to generate better scores, making the pruned model perform better. By using NNs, there are fewer hyperparameters, making it easier to implement. Experiment results demonstrate that the proposed ScoringNet can outperform or achieve competitive results compared to many state-of-the-art methods in both postpruning and pruning-at-initialization setups.
The Multi-User Constrained Pseudorandom Function Security of Generalized GGM Trees for MPC and Hierarchical Wallets

Multi-user (mu) security considers large-scale attackers that, given access to a number of cryptosystem instances, attempt to compromise at least one of them. We initiate the study of mu security of the so-called GGM tree that stems from the pseudorandom generator to pseudorandom function transformation of Goldreich, Goldwasser, and Micali, with a goal to provide references for its recently popularized use in applied cryptography. We propose a generalized model for GGM trees and analyze its mu prefix-constrained pseudorandom function security in the random oracle model. Our model allows to derive concrete bounds and improvements for various protocols, and we showcase on the Bitcoin-Improvement-Proposal standard Bip32 hierarchical wallets and function secret sharing protocols. In both scenarios, we propose improvements with better performance and concrete security bounds at the same time. Compared with the state-of-the-art designs, our SHACAL3 - and Keccak -p-based Bip32 variants reduce the communication cost of MPC-based implementations by 73.3% to 93.8%, whereas our AES -based function secret sharing substantially improves mu security while reducing computations by 50%.
A nexus analysis of the online teaching context in Romanian schools

Abstract The COVID-19 pandemic has forced education institutions to adapt by using online tools to continue teaching when distancing was required. While the pandemic has had a negative effect on education, online learning has proven to be better than learning alone. This new paradigm shift in the way teaching can be delivered has paved the way for online classes to be taken in consideration outside of the pandemic. For countries which have a poor infrastructure in some regions, that makes it hard for all children to arrive to school daily, especially during harsh winters, online teaching could be used to offer continuous learning to all students. It can also continue to be used in some cases where classes cannot be attended due to illnesses. It is relevant to further study the impact of online learning within different schools from different regions, and to recognize the shaping factors for online teaching. The present study was based on a survey, in which 522 teachers from different categories of schools participated. Differences between the teaching processes during the pandemic have been studied based on the school’s funding (public or private), the region the school is based in, and the level of education that teachers provide. Results have highlighted differences based on all the studied categories.
Conditional Knowledge Extraction Using Contextual Information Enhancement

Conditional phrases provide fine-grained domain knowledge in various industries, including medicine, manufacturing, and others. Most existing knowledge extraction research focuses on mining triplets with entities and relations and treats that triplet knowledge as plain facts without considering the conditional modality of such facts. We argue that such approaches are insufficient in building knowledge-based decision support systems in vertical domains, where specific and professional instructions on what facts apply under given circumstances are indispensable. To address this issue, this paper proposes a condition-aware knowledge extraction method using contextual information. In particular, this paper first fine-tunes the pre-training model to leverage a local context enhancement to capture the positional context of conditional phrases; then, a sentence-level context enhancement is used to integrate sentence semantics; finally, the correspondences between conditional phrases and relation triplets are extracted using syntactic attention. Experimental results on public and proprietary datasets show that our model can successfully retrieve conditional phrases with relevant triplets while improving the accuracy of the matching task by 2.68%, compared to the baseline.
Human Activity Recognition by the Image Type Encoding Method of 3-Axial Sensor Data

HAR technology uses computer and machine vision to analyze human activity and gestures by processing sensor data. The 3-axis acceleration and gyro sensor data are particularly effective in measuring human activity as they can calculate movement speed, direction, and angle. Our paper emphasizes the importance of developing a method to expand the recognition range of human activity due to the many types of activities and similar movements that can result in misrecognition. The proposed method uses 3-axis acceleration and gyro sensor data to visually define human activity patterns and improve recognition accuracy, particularly for similar activities. The method involves converting the sensor data into an image format, removing noise using time series features, generating visual patterns of waveforms, and standardizing geometric patterns. The resulting data (1D, 2D, and 3D) can simultaneously process each type by extracting pattern features using parallel convolution layers and performing classification by applying two fully connected layers in parallel to the merged data from the output data of three convolution layers. The proposed neural network model achieved 98.1% accuracy and recognized 18 types of activities, three times more than previous studies, with a shallower layer structure due to the enhanced input data features.
Immersive Free‐Viewpoint Panorama Rendering from Omnidirectional Stereo Video

Abstract In this paper, we tackle the challenging problem of rendering real‐world 360° panorama videos that support full 6 degrees‐of‐freedom (DoF) head motion from a prerecorded omnidirectional stereo (ODS) video. In contrast to recent approaches that create novel views for individual panorama frames, we introduce a video‐specific temporally‐consistent multi‐sphere image (MSI) scene representation. Given a conventional ODS video, we first extract information by estimating framewise descriptive feature maps. Then, we optimize the global MSI model using theory from recent research on neural radiance fields. Instead of a continuous scene function, this multi‐sphere image (MSI) representation depicts colour and density information only for a discrete set of concentric spheres. To further improve the temporal consistency of our results, we apply an ancillary refinement step which optimizes the temporal coherency between successive video frames. Direct comparisons to recent baseline approaches show that our global MSI optimization yields superior performance in terms of visual quality. Our code and data will be made publicly available.
A novel self-boosting dual-branch model for pedestrian attribute recognition

Pedestrian attributes carry broad information of abstract and detailed annotations. Pedestrian attribute recognition (PAR) can generate high-level semantic feature maps and provide auxiliary information in tasks such as person retrieval and re-identification. Since pedestrian attributes have broad categories and appear in complex region combinations in video sequences, enhancing the feature representation of fine-grained attributes is the key to improving pedestrian attribute recognition. This paper proposes an end-to-end feature-enhanced multi-scale dual-branch model for multiple attributes recognition (FEMDAR) with a feature-enhanced block (FEB) and multi-scale modules. The FEB module fuses the contextual features of each level, stacking different rates of dilated convolutions to expand the receptive field of the feature expression effectively. Simultaneously, the feature-enhanced block module utilizes a dual-branch structure to incorporate enhanced feature representation. Moreover, a new residual module is designed with multi-scale modules to extract the inconsistency in various attribute scales. Hence, the proposed model can achieve robust feature representation that can flexibly adapt to multiple pedestrian attributes of different scales. We validate our model on three public large-scale pedestrian attribute datasets. The experimental results show that the FEMDAR model shows prominent advantages in instance-based measurements. Furthermore, the ablation study shows the effectiveness of the proposed FEB and multi-scale modules in feature presentation.