Reviewerly/exHarmony huggingface dataset:
Multiscale Traffic Sign Detection Method in Complex Environment Based on YOLOv4

Traffic sign detection is a challenging problem in the field of unmanned driving, particularly important in complex environments. We propose a method, based on the improved You only look once (YOLO) v4, to detect and recognize multiscale traffic signs in complex environments. This method employs an image preprocessing module that can classify and denoize images of complex environments and then input the images into the improved YOLOv4. We also design an improved feature pyramid structure to replace the original feature pyramid of YOLOv4. This structure uses an adaptive feature fusion module and a multiscale feature transfer mechanism to reduce putative information loss in the feature map generation process and improve the information transfer between deep and shallow features, enhancing the representation ability of feature pyramids. Finally, we use EIOU LOSS and Cluster-NMS to further improve the model performance. The experimental results on the fusion of Tsinghua-Tencent 100 K and our collected dataset show that the proposed method achieves an mAP of 81.78%. Compared to existing methods, our method demonstrates its superiority with regard to traffic sign detection.
Nothing Micro About It: Examining Ableist Microaggressions on Social Media

Ableist microaggressions are subtle forms of discrimination that disabled people experience daily, perpetuating inequalities and maintaining their ongoing marginalization. Despite the importance of understanding such harms, little work has been done to examine how disabled people are discriminated against online. We address this gap by investigating how disabled people experience ableist microaggressions on social media and how they respond to and cope with these experiences. By conducting interviews with 20 participants with various disabilities, we uncover 12 archetypes of ableist microaggressions on social media, reveal participants’ coping mechanisms, and describe the long-term impact on their wellbeing and social media use. Lastly, we present design recommendations, re-evaluating how social media platforms can mitigate and prevent these harmful experiences.
Quantifying the variability mismatch between problem and solution space

A software product line allows to derive individual software products based on a configuration. As the number of configurations is an indicator for the general complexity of a software product line, automatic #SAT analyses have been proposed to provide this information. However, the number of configurations does not need to match the number of derivable products. Due to this mismatch, using the number of configurations to reason about the software complexity (i.e., the number of derivable products) of a software product line can lead to wrong assumptions during implementation and testing. How to compute the actual number of derivable products, however, is unknown. In this paper, we mitigate this problem and present a concept to derive a solution-space feature model which allows to reuse existing #SAT analyses for computing the number of derivable products of a software product line. We apply our concept to a total of 119 subsystems of three industrial software product lines. The results show that the derivation scales for real world software product lines and confirm the mismatch between the number of configurations and the number of products.
Factors Influencing Employees’ Information Security Awareness in the Telework Environment

This study aims to identify and examine factors influencing employees’ information security awareness (ISA) in the telework environment. Specifically, the authors identify and examine the influence factors rooted in the knowledge-attitude-behavior (KAB) model (i.e., knowledge, attitude, and behavior) and knowledge inertia theory (i.e., experience and learning inertia). This study uses online survey data from 305 employees who have telework experience. We apply the structural equation modeling technique to assess the proposed research model. This research is among the pioneering studies that identify and examine the factors influencing employees’ ISA in the telework environment. Our study is also one of the first to investigate antecedents to employees’ ISA rooted in the KAB model and knowledge inertia theory in a telework environment. Results show that employees’ ISA in the telework environment is significantly influenced by their knowledge, behavior toward following security guidelines, and learning inertia, whereas attitude and experience inertia have no significant effect on employees’ ISA.
Autonomous Surveillance for an Indoor Security Robot

Conventional surveillance for a security robot suffers from severe limitations, perceptual aliasing (e.g., different places/objects can appear identical), occlusion (e.g., place/object appearance changes between visits), illumination changes, significant viewpoint changes, etc. This paper proposes an autonomous robotic system based on CNN (convolutional neural network) to perform visual perception and control tasks. The visual perception aims to identify all objects moving in the scene and to verify whether the target is an authorized person. The visual perception system includes a motion detection module, a tracking module, face detection, and a recognition module. The control system includes motion control and navigation (path planning and obstacle avoidance). The empirical validation includes the evaluation metrics, such as model speed, accuracy, precision, recall, ROC (receiver operating characteristic) curve, P-R (precision–recall) curve, F1-score for AlexNet, VggNet, and GoogLeNet, and RMSE (root-mean-square error) value of mapping errors. The experimental results showed that the average accuracy of VggNet under four different illumination changes is 0.95, and it has the best performance under all unstable factors among three CNN architectures. For the accuracy of building maps in real scenes, the mapping error is 0.222 m.
Human Activity Recognition for Assisted Living Based on Scene Understanding

The growing share of the population over the age of 65 is putting pressure on the social health insurance system, especially on institutions that provide long-term care services for the elderly or to people who suffer from chronic diseases or mental disabilities. This pressure can be reduced through the assisted living of the patients, based on an intelligent system for monitoring vital signs and home automation. In this regard, since 2008, the European Commission has financed the development of medical products and services through the ambient assisted living (AAL) program—Ageing Well in the Digital World. The SmartCare Project, which integrates the proposed Computer Vision solution, follows the European strategy on AAL. This paper presents an indoor human activity recognition (HAR) system based on scene understanding. The system consists of a ZED 2 stereo camera and a NVIDIA Jetson AGX processing unit. The recognition of human activity is carried out in two stages: all humans and objects in the frame are detected using a neural network, then the results are fed to a second network for the detection of interactions between humans and objects. The activity score is determined based on the human–object interaction (HOI) detections.
An improved Kalman particle swarm optimization for modeling and optimizing of boiler combustion characteristics

Abstract With the rapid development of the national economy, the demand for electricity is also growing. Thermal power generation accounts for the highest proportion of power generation, and coal is the most commonly used combustion material. The massive combustion of coal has led to serious environmental pollution. It is significant to improve energy conversion efficiency and reduce pollutant emissions effectively. In this paper, an extreme learning machine model based on improved Kalman particle swarm optimization (ELM-IKPSO) is proposed to establish the boiler combustion model. The proposed modeling method is applied to the combustion modeling process of a 300 MWe pulverized coal boiler. The simulation results show that compared with the same type of modeling method, ELM-IKPSO can better predict the boiler thermal efficiency and NOx emission concentration and also show better generalization performance. Finally, multi-objective optimization is carried out on the established model, and a set of mutually non-dominated boiler combustion solutions is obtained.
Adversarial attacks on deep learning networks in image classification based on Smell Bees Optimization Algorithm

Adversarial machine learning is an active trend in artificial intelligence that attempts to fool deep learning models by causing malfunctions during the prediction of decisions. In this work, we are interested in image classification, and propose a black box for adversarial examples generation which is driven by an optimization algorithm. The main ideas of the used approach are firstly inspired from the steganography principles in which hiding information in image pixels with minimal payload capacity to reduce the distortion between the real image and the adversarial image constitutes a constraint to be respected. In fact, this distance must be sufficient to lead to deceive an already trained classifier. Secondly, the selection of relevant pixels for embedding information bits is mainly done by the optimization algorithm Smell Bees Optimization (SBO). Some investigations are done on Convolutional neural network, Softmax classifier and Residual network, providing good results on both MINST and CIFAR datasets.
CMDGAT: Knowledge extraction and retention based continual graph attention network for point cloud registration

Artificial Intelligence-based systems are required to interact with dynamic environments to continuously learn, retain and effectively utilize knowledge. Present AI-based systems scarcely exhibit this ability and mostly follow an isolated learning paradigm. In contrast, a human brain goes through a continual learning process by learning through experiences, retaining the learned knowledge, and using this knowledge on present tasks. Inspired by the learning of a human brain, we propose a continual learning methodology for point cloud registration which is a key component for 3D reconstruction and Augmented Reality (AR) localization. A major challenge that is faced in this context is the accurate correspondence of 3D points. Current learning-based registration techniques perform substantially, but these methods do not take into account the relations among point clouds obtained during sequential scans and perform registrations among point cloud pairs independently. Our proposed methodology increases the expressiveness of point clouds under consideration by using a novel continual graph network architecture with an attention mechanism that utilizes the learning from the association of the previous point cloud pairs to associate and register points in the current pair. The methodology implemented in PYTHON is evaluated on various challenging benchmark datasets including the Oxford, the 3DMatch, the KITTI, and the ETH dataset. The methodology is thoroughly evaluated under various scenarios including correspondence performance, registration performance, and generalization abilities. The results show a remarkable performance improvement as compared to the state-of-the-art methods. More critically, the proposed method offers exemplary globalization performance across unseen datasets and new scenarios.
The Keys to an Open, Interoperable Metaverse

The term ‘Metaverse’ has taken on new interest recently, appearing prominently in the marketing materials of a number of large technology companies. Indeed, many have attempted, or are attempting, to co-opt it for their own purposes, which has resulted in a great deal of confusion among producers and consumers in the marketplace. With this paper, the Web3D Consortium seeks to address this confusion by exploring the history of the ‘Metaverse’, provide a workable definition of the term ‘Metaverse’, and provide a vision for its sustainable, cooperative construction into the future. We believe that all the technologies are in place to fulfill the vision of an open, equitable, and ubiquitous information space. What remains are the key issues that have kept the Metaverse from manifesting the last two decades: poor user experience and poor corporate cooperation.
RealityLens: A User Interface for Blending Customized Physical World View into Virtual Reality

Research has enabled virtual reality (VR) users to interact with the physical world by blending the physical world view into the virtual environment. However, current solutions are designed for specific use cases and hence are not capable of covering users’ varying needs for accessing information about the physical world. This work presents RealityLens, a user interface that allows users to peep into the physical world in VR with the reality lenses they deployed for their needs. For this purpose, we first conducted a preliminary study with experienced VR users to identify users’ needs for interacting with the physical world, which led to a set of features for customizing the scale, placement, and activation method of a reality lens. We evaluated the design in a user study (n=12) and collected the feedback of participants engaged in two VR applications while encountering a range of interventions from the physical world. The results show that users’ VR presence tends to be better preserved when interacting with the physical world with the support of the RealityLens interface.
InterWeave: Presenting Search Suggestions in Context Scaffolds Information Search and Synthesis

Web search is increasingly used to satisfy complex, exploratory information goals. Exploring and synthesizing information into knowledge can be slow and cognitively demanding due to a disconnect between search tools and sense-making workspaces. Our work explores how we might integrate contextual query suggestions within a person's sensemaking environment. We developed InterWeave a prototype that leverages a human wizard to generate contextual search guidance and to place the suggestions within the emergent structure of a searchers' notes. To investigate how weaving suggestions into the sensemaking workspace affects a user's search and sensemaking behavior, we ran a between-subjects study (n=34) where we compare InterWeave's in context placement with a conventional list of query suggestions. InterWeave's approach not only promoted active searching, information gathering and knowledge discovery, but also helped participants keep track of new suggestions and connect newly discovered information to existing knowledge, in comparison to presenting suggestions as a separate list. These results point to directions for future work to interweave contextual and natural search guidance into everyday work.
ARnnotate: An Augmented Reality Interface for Collecting Custom Dataset of 3D Hand-Object Interaction Pose Estimation

Vision-based 3D pose estimation has substantial potential in hand-object interaction applications and requires user-specified datasets to achieve robust performance. We propose ARnnotate, an Augmented Reality (AR) interface enabling end-users to create custom data using a hand-tracking-capable AR device. Unlike other dataset collection strategies, ARnnotate first guides a user to manipulate a virtual bounding box and records its poses and the user's hand joint positions as the labels. By leveraging the spatial awareness of AR, the user manipulates the corresponding physical object while following the in-situ AR animation of the bounding box and hand model, while ARnnotate captures the user's first-person view as the images of the dataset. A 12-participant user study was conducted, and the results proved the system's usability in terms of the spatial accuracy of the labels, the satisfactory performance of the deep neural networks trained with the data collected by ARnnotate, and the users' subjective feedback.
Exploring Virtual Object Translation in Head-Mounted Augmented Reality for Upper Limb Motor Rehabilitation with Motor Performance and Eye Movement Characteristics

Head-mounted augmented reality (AR) technology is currently employed in upper limb motor rehabilitation, and the degrees of freedom (DoF) of virtual object translation modes become critical for rehabilitation tasks in AR settings. Since motor performance is the primary focus of motor rehabilitation, this study assessed it across different translation modes (1DoF and 3DoF) via task efficiency and accuracy analysis. In addition, eye movement characteristics were used to further illustrate motor performance. This research revealed 1DoF and 3DoF modes showing their own benefits in upper limb motor rehabilitation tasks. Finally, this study recommended selecting or integrating these two translation modes for future rehabilitation task design.
Robust bag classification approach for multi-instance learning via subspace fuzzy clustering

Multi-instance learning (MIL) allows predictive algorithms to use complex data representation. The data in MIL is organized in the form of labeled bags of instances, and the labels of instances are not available in the training phase. The processing and classification of complex bag representation result in a complicated hypothesis space. Moreover, identifying essential instances inside is also important, as these instances trigger positive labels for the bag and play a vital role in the model interpretation and bag classification. The recent MIL algorithms are not robust to tackle hypothesis space complication. Additionally, the existing instance selection algorithms are based on explicit assumptions regarding the relationship of instances to the bag label. However, these assumptions may hold for a specific bag in the dataset but may not apply to the whole dataset. To deal with the hypothesis space complication and robust instance selection without any prior assumption, this paper proposes a fuzzy subspace clustering approach for robust instance selection and ensemble-based variant for Fisher vector (FV) encoding to solve MIL problems, named (FCBE-miFV). Specifically, the proposed algorithm uses a subspace fuzzy clustering approach to compute instance selection probabilities, selects essential instances from the bag, transforms the input bag into FV, and classifies the generated bag encodings using a stacking-based ensemble approach to obtain improved bag level classification performance. The proposed FCBE-miFV improves model performance by incorporating essential instances in the bag encoding process. The experimental results show that the FCBE-miFV obtained comparable performance to the state-of-the-art MIL problems.
Efficient object detection method based on aerial optical sensors for remote sensing

Object detection technology for images generated by optical sensors is of great significance in areas such as national defense security, disaster prediction, and smart city construction. Aiming at the problems of small target size, arbitrary target direction, and complex background in aerial image object detection using optical sensors. By using the images captured by a wild range of optical sensors such as CCD(Charge Coupled Device) and SAR(Synthetic Aperture Radar), we propose a more efficient rotating frame object detection algorithm that introduces the MCAB (Multi-branch Convolutional Attention Block) that can process these same types of images taken by the aerial platform using light sensors. First, we construct the module by adding identity, residual branch, and CBAM (Convolutional Block Attention Module) structure to the traditional convolution layer and the activation function to substitute the simple conv module in the network. Secondly, we introduce the Transformer layer at the end of the backbone network to enhance the global perception of the model with low overhead, realize the relationship is modeling between the target and the scene content, and reduce the amount of calculation. We improve the structure of the neck layer by BiFPN (Bidirectional Feature Pyramid Network) to speed up network operation, and CBAM is added after C3 to highlight important characteristic information. Our improved YOLOv5 algorithm is tested on the self-made rotating small target aerial data set. Compared with the original algorithm, our model's mean Average Precision (mAP) is improved by 1.8 percentage points, and the accuracy and recall are also enhanced by 0.46 and 0.34 percentage points.
Real-World Video Deblurring: A Benchmark Dataset and an Efficient Recurrent Neural Network

Real-world video deblurring in real time still remains a challenging task due to the complexity of spatially and temporally varying blur itself and the requirement of low computational cost. To improve the network efficiency, we adopt residual dense blocks into RNN cells, so as to efficiently extract the spatial features of the current frame. Furthermore, a global spatio-temporal attention module is proposed to fuse the effective hierarchical features from past and future frames to help better deblur the current frame. Another issue that needs to be addressed urgently is the lack of a real-world benchmark dataset. Thus, we contribute a novel dataset (BSD) to the community, by collecting paired blurry/sharp video clips using a co-axis beam splitter acquisition system. Experimental results show that the proposed method (ESTRNN) can achieve better deblurring performance both quantitatively and qualitatively with less computational cost against state-of-the-art video deblurring methods. In addition, cross-validation experiments between datasets illustrate the high generality of BSD over the synthetic datasets. The code and dataset are released at https://github.com/zzh-tech/ESTRNN .
Generative Spoken Dialogue Language Modeling

Abstract We introduce dGSLM, the first “textless” model able to generate audio samples of naturalistic spoken dialogues. It uses recent work on unsupervised spoken unit discovery coupled with a dual-tower transformer architecture with cross-attention trained on 2000 hours of two-channel raw conversational audio (Fisher dataset) without any text or labels. We show that our model is able to generate speech, laughter, and other paralinguistic signals in the two channels simultaneously and reproduces more naturalistic and fluid turn taking compared to a text-based cascaded model.1,2
Artificial everyday creativity: creative leaps with AI through critical making

The capabilities of humans and AI systems to be creative and perform alongside one another have given rise to new practices of ‘artificial creativity’. In this article, I argue that artificial creativity demonstrates the potential to empower individuals to interface and critically dialogue with computational systems. Reframed as artificial ‘everyday’ creativity, I focus on the curious, joyful and adjacent modes of everyday creativity by including hybrid materials that embrace alternative pedagogies of code and computation. Through the interdisciplinary design approach of ‘critical making’, I craft two unconventionally-coded artefacts that dialogue with AI systems, namely CryptoCrochet-Key and Internet of Towels. Both artefacts are analysed using a four-pronged creativity framework to understand the material translation processes in the artificial everyday creativity practice. With rising concerns about AI's role in misinformation, bias and discrimination, the discussion explores the generative value and limitations of artificial everyday creativity towards the broader goals of civic data literacy and user empowerment.