Reviewerly/exHarmony huggingface dataset:
Co-gnito: a Participatory Physicalization Game for Urban Mental Mapping

This study introduces Co-gnito, a participatory physicalization game that supports collaborative urban mental mapping through storytelling. Through Co-gnito we investigate gaming as a means to elicit subjective spatial experiences and to steer the synchronous construction of a physicalization that aligns and represents them. Co-gnito was evaluated during seven deployments by analyzing how 28 players mapped their spatial experiences of two university campuses. Our results indicate that storytelling as a gaming mechanic, guided and motivated the gradual addition of personal contributions towards a collective outcome, but its reward system did not nudge the mapping direction as expected. We also demonstrate how the shared construction process of a physicalization is influenced by how the data encoding scheme was negotiated, by the token physical affordances and by the game mechanics. We therefore believe that our core contributions, comprising of: 1) a working research prototype; 2) an augmentation of the physicalization pipeline towards collaborative settings; and 3) a set of reflective considerations, provide actionable knowledge on how to design participatory physicalizations in the future.
Curating Interactive Art for Online Conferences: Artist, Curator and Technologist Experiences in Gather.Town

We present the results of a reflective, practice-based study with creative practitioners who contributed to the Art Track at Creativity & Cognition 2021. We investigate curating an interactive online gallery in the context of the COVID-19 pandemic, focusing on the opportunities afforded and design constraints imposed by the Gather.Town platform and the experiences of the participating practitioners. We present auto-ethnographic reflections from Author 1, who created the online gallery space. We draw on the experience of Author 2 in curatorial research to analyze the experience of emerging practice through interviews with participating artists and curators. Results show that many of the artists were positively surprised by the platform engagement opportunities and conference attendees' engagement with the artworks at the gallery opening, and appreciated the equitable, global reach. Further analysis reveals a desire for future iterations, and an exploration of the platform in a hybrid context, alongside an in-person exhibition.
An asymmetrical-structure auto-encoder for unsupervised representation learning of skeleton sequences

In this paper, we propose a novel framework for unsupervised representation learning using a structure-asymmetrical auto-encoder in which a 2D-CNN-based encoder learns separable spatiotemporal representations in a low-dimensional feature space under the supervision of salient skeleton motion cues. This study addresses the problem of learning action representations of skeleton sequences. The network captures not only correlations of adjacent joints but also long-term motion dependencies by using the proposed unsupervised training, which leads to the advantage that similar movements are gathered around the same cluster, whereas different movements are gathered around distinct clusters. Our method is unsupervised and does not rely on annotations to associate skeleton sequences with actions. Experimental results clearly showed the effectiveness of the proposed representation learning, and improvements compared with skeleton-based generative learning methods. When the proposed network was fine-tuned with partial labeled data, our results still outperformed some fully supervised methods.
Drawing Conversations Mediated by AI

In this pictorial paper, we present a series of drawing conversations held between two humans, mediated by computational GAN models. We consider how this creative collaboration is affected by the hybrid inclusion of more-than-human participants in the form of watercolour and artificial intelligence. Our drawing experiments were an extension of our search for new ways of seeing and telling, which includes a reflection of the extent to which more-than-human elements took part in our creative process. We discuss our tendencies to form strange interpretations and assign meaning to the unpredictable and ambiguous spaces we created with them. We further speculate on the characteristic material agencies they revealed in our interactions with them. Finally, we contend how such collaborations are already and always embedded and embodied in our ways of seeing and knowing in design and creativity research.
Robust video steganography for social media sharing based on principal component analysis

Abstract Most social media channels are lossy where videos are transcoded to reduce transmission bandwidth or storage space, such as social networking sites and video sharing platforms. Video transcoding makes most video steganographic schemes unusable for hidden communication based on social media. This paper proposes robust video steganography against video transcoding to construct reliable hidden communication on social media channels. A new strategy based on principal component analysis is provided to select robust embedding regions. Besides, side information is generated to label these selected regions. Side information compression is designed to reduce the transmission bandwidth cost. Then, one luminance component and one chrominance component are joined to embed secret messages and side information, notifying the receiver of correct extraction positions. Video preprocessing is conducted to improve the applicability of our proposed method to various video transcoding mechanisms. Experimental results have shown that our proposed method provides stronger robustness against video transcoding than other methods and achieves satisfactory security performance against steganalysis. Compared with some existing methods, our proposed method is more robust and reliable to realize hidden communication over social media channels, such as YouTube and Vimeo.
Graph-Community-Enabled Personalized Course-Job Recommendations with Cross-Domain Data Integration

With millions of students/employees browsing course information and job postings every day, the need for accurate, effective, meaningful, and transparent course and job recommender systems is more evident than ever. The current recommendation research has attracted wide attention in the academic and industrial areas. However, existing studies primarily focus on content analysis and user feature extraction of courses or jobs and fail to investigate the problem of cross-domain data integration between career and education. At the same time, it also fails to fully utilize the relations between courses, skills, and jobs, which helps to improve the accuracy of the recommendation. Therefore, this study aims to propose a novel cross-domain recommendation model that can help students/employees search for suitable courses and jobs. Employing a heterogeneous graph and community detection algorithm, this study presents the Graph-Community-Enabled (GCE) model that merges course profiles and recruiting information data. Specifically, to address the skill difference between occupation and curriculum, the skill community calculated by the community detection algorithm is used to connect curriculum and job information. Then, the innovative heterogeneous graph approach and the random walk algorithm enable cross-domain information recommendation. The proposed model is evaluated on real job datasets from recruitment websites and the course datasets from MOOCs and higher education. Experiments show that the model is obviously superior to the classical baselines. The approach described can be replicated in a variety of education/career situations.
Exploiting key points supervision and grouped feature fusion for multiview pedestrian detection

Multiview pedestrian detection detects pedestrians based on the perception of the same environment from multiple perspectives. This task requires feature extraction in a single view with occlusion and aggregation of multiview information. However, existing research is limited by the local occlusion and the multiview feature stitching method, which cannot perform multiview aggregation efficiently. This paper introduces a network that utilizes key points supervision and grouped feature fusion to address these challenges. It uses key points to regress pedestrians in a single view, and augments the pedestrian consistency information in overlapping views by a grouped feature fusion module. Specifically, the proposed key points supervision effectively alleviates false negatives due to occlusion, and the grouped feature fusion module enhances pedestrian location features by computing the similarity and spatial correlation of overlapping views after single view projection to the ground plane, thereby reducing target ambiguity. Quantitative and qualitative results show that the proposed method can reduce false negatives and false positives in multiview pedestrian detection and achieve efficient multiview feature aggregation. Compared to state-of-the-art methods, the proposed model achieves superior performance, achieving the highest MODA of 92.4 and 93.9 on Wildtrack and MultiviewX datasets, respectively. We believe, to the best of our knowledge, that this approach offers a new optimization idea for multiview aggregation.
Anthropomorphization and beyond: conceptualizing humanwashing of AI-enabled machines

Abstract The complex relationships between humans and AI-empowered machines have created and inspired new products and services as well as controversial debates, fiction and entertainment, and last but not least, a striving and vital field of research. The (theoretical) convergence between the two categories of entities has created stimulating concepts and theories in the past, such as the uncanny valley, machinization of humans through datafication, or humanization of machines, known as anthropomorphization. In this article, we identify a new gap in the relational interaction between humans and AI triggered by commercial interests, making use of AI through advertisement, marketing, and corporate communications. Our scope is to broaden the field of AI and society by adding the business-society-nexus. Thus, we build on existing research streams of machinewashing and the analogous phenomenon of greenwashing to theorize about the humanwashing of AI-enabled machines as a specific anthropomorphization notion. In this way, the article offers a contribution to the anthropomorphization literature conceptualizing humanwashing as a deceptive use of AI-enabled machines (AIEMs) aimed at intentionally or unintentionally misleading organizational stakeholders and the broader public about the true capabilities that AIEMs possess.
Minimum Noticeable Difference-Based Adversarial Privacy Preserving Image Generation

Deep learning models are found to be vulnerable to adversarial examples, as wrong predictions can be caused by small perturbation in input for deep learning models. Most of the existing works of adversarial image generation try to achieve attacks for most models, while few of them make efforts on guaranteeing the perceptual quality of the adversarial examples. High quality adversarial examples matter for many applications, especially for the privacy preserving. In this work, we develop a framework based on the Minimum Noticeable Difference (MND) concept to generate adversarial privacy preserving images that have minimum perceptual difference from the clean ones but are able to attack deep learning models. To achieve this, an adversarial loss is firstly proposed to make the deep learning models attacked by the adversarial images successfully. Then, a perceptual quality-preserving loss is developed by taking the magnitude of perturbation and perturbation-caused structural and gradient changes into account, which aims to preserve high perceptual quality for adversarial image generation. To the best of our knowledge, this is the first work on exploring quality-preserving adversarial image generation based on the MND concept for privacy preserving. To evaluate its performance in terms of perceptual quality, the deep models on image classification and face recognition are tested with the proposed method and several anchor methods in this work. Extensive experimental results demonstrate that the proposed MND framework is capable of generating adversarial images with remarkably improved performance metrics (e.g., PSNR, SSIM, and MOS) than that generated with the anchor methods.
Micro-behaviour with Reinforcement Knowledge-aware Reasoning for Explainable Recommendation

Existing practical recommendation scenarios involve multiple micro-behaviour user–item interactions, such as clicks, page views, add-to-favourites, and purchases, which provide fine-grained and a better in-depth understanding of the user's preference. Furthermore, some recommendation methods have incorporated item knowledge into the micro-behaviour of user–item interaction. Although some have proved effective, two insights are often neglected. First, they fail to combine micro-behaviour with the relation of the knowledge graph (KG), and the semantic relationship between micro-behaviour and relation is not captured. Second, they do not provide explicit reasoning for micro-behaviour from user–item interaction data. These insights motivated us to propose a novel model of Micro-behaviour with Reinforcement Knowledge-aware Reasoning for Explainable Recommendation (MBKR), which incorporates micro-behaviour and the KG into reinforcement learning for explainable recommendation. Specifically, the model learns the behaviour by user–item propagation and the relation from the KG and combines the two to calculate the behavioural strength to mine user's interests. In addition, we designed a Shawo-relational path that combines recommendation and interpretability by providing rational paths; these paths capture the semantics of behaviours and relations. Finally, we extensively evaluated our method on several large-scale benchmark datasets, and the results indicate that the proposed method is more effective in providing recommendations than state-of-the-art methods.
How to Debug Inclusivity Bugs? A Debugging Process with Information Architecture

Although some previous research has found ways to find inclusivity bugs (biases in software that introduce inequities), little attention has been paid to how to go about fixing such bugs. Without a process to move from finding to fixing, acting upon such findings is an ad-hoc activity, at the mercy of the skills of each individual developer. To address this gap, we created Why/Where/Fix, a systematic inclusivity debugging process whose inclusivity fault localization harnesses Information Architecture(IA)-the way user-facing information is organized, structured and labeled. We then conducted a multi-stage qualitative empirical evaluation of the effectiveness of Why/Where/Fix, using an Open Source Software (OSS) project's infrastructure as our setting. In our study, the OSS project team used the Why/Where/Fix process to find inclusivity bugs, localize the IA faults behind them, and then fix the IA to remove the inclusivity bugs they had found. Our results showed that using Why/Where/Fix reduced the number of inclusivity bugs that OSS newcomer participants experienced by 90%. Diverse teams have been shown to be more productive as well as more innovative. One form of diversity, cognitive diversity - differences in cognitive styles - helps generate diversity of thoughts. However, cognitive diversity is often not supported in software tools. This means that these tools are not inclusive of individuals with different cognitive styles (e.g., those who like to learn through process vs. those who learn by tinkering), which burdens these individuals with a cognitive “tax” each time they use the tool. In this work, we present an approach that enables software developers to: (1) evaluate their tools, especially those that are information-heavy, to find “inclusivity bugs”- cases where diverse cognitive styles are unsupported, (2) find where in the tool these bugs lurk, and (3) fix these bugs. Our evaluation in an open source project shows that by following this approach developers were able to reduce inclusivity bugs in their projects by 90%.
Semantic Segmentation using Modified U-Net for Autonomous Driving

Scene understanding of urban streets is a crucial component in perception task of autonomous driving application. Semantic segmentation has been extensively used in scene understanding which further provides assistance in subsequent autonomous driving tasks like object detection, path planning and motion control. But, accurate semantic segmentation is a challenging task in computer vision. U-Net is a popular semantic segmentation network used for segmentation task. In this paper, we improve the accuracy of U-Net model by replacing its encoder part with Convolution Neural Network (CNN) architecture. We compared the performance of VGG-16 and ResNet-50 CNNarchitectures. Extensive analysis was performed on Cityscapes dataset and the results demonstrated U-Net with VGG16 encoder shows better performance than ResNet50 encoder. The model is compared with semantic segmentation CNN architectures like Fully Convolutional Network (FCN) and SegNet with mean Intersection over Union (mIoU) improved by 2%.
Online Model Updating with Analog Aggregation in Wireless Edge Learning

We consider federated learning in a wireless edge network, where multiple power-limited mobile devices collaboratively train a global model, using their local data with the assistance of an edge server. Exploiting over-the-air computation, the edge server updates the global model via analog aggregation of the local models over noisy wireless fading channels. Unlike existing works that separately optimize computation and communication at each step of the learning algorithm, in this work, we jointly optimize the training of the global model and the analog aggregation of local models over time. Our objective is to minimize the accumulated training loss at the edge server, subject to individual long-term transmit power constraints at the mobile devices. We propose an efficient algorithm, termed Online Model Updating with Analog Aggregation (OMUAA), to adaptively update the local and global models based on the time-varying communication environment. The trained model of OMUAA is channel- and power-aware, and it is in closed form with low computational complexity. We study the mutual impact between model training and analog aggregation over time, to derive performance bounds on the computation and communication performance metrics. Simulation results based on real-world image classification datasets and typical Long-Term Evolution network settings demonstrate substantial performance gain of OMUAA over the known best alternatives.
TRUST: Real-Time Request Updating with Elastic Resource Provisioning in Clouds

In a commercial cloud, service providers (e.g., video streaming service provider) rent resources from cloud vendors (e.g., Google Cloud Platform) and provide services to cloud users, making a profit from the price gap. Cloud users acquire services by forwarding their requests to corresponding servers. In practice, as a common scenario, traffic dynamics will cause server overload or load-unbalancing. Existing works mainly deal with the problem by two methods: elastic resource provisioning and request updating. Elastic resource provisioning is a fast and agile solution but may cost too much since service providers need to buy extra resources from cloud vendors. Though request updating is a free solution, it will cause a significant delay, resulting in a bad users’ QoS. In this paper, we present a new scheme, called real-time request updating with elastic resource provisioning (TRUST), to help service providers pay less cost with users’ QoS guarantee in clouds. In addition, we propose an efficient algorithm for TRUST with a bounded approximation factor based on randomized rounding. Both small-scale experiment results and large-scale simulation results show the superior performance of our proposed algorithm compared with state-of-the-art benchmarks.
Edge Intelligence-based Privacy Protection Framework for IoT-based Smart Healthcare Systems

Federated Learning (FL) mechanisms determine the implications of sensitive data for constructing on-device Machine Learning (ML) to achieve personalisation in a smart application network, for example, sharing critical information of the smart healthcare industry over the Internet of Things (IoT) systems. The main function of centralised FL can be combined with Edge Intelligence (EI) for processing before final aggregation to reduce data manipulation and privacy hazard. However, executing EI in an Edge Computing (EC) layer also poses privacy risks to clients. Differential Privacy (DP) offers a viable solution by adding artificial noise to a parameter before aggregation. This paper introduces a Federated Edge Aggregator (FEA) framework with DP for safeguarding the high-tech healthcare industry using IoT systems. An iteration-based converged Convolutional Neural Network (CNN) model at Edge Layer (EL) is developed to perform EI to balance $\mathrm{FL}^{\prime}$ s privacy preservation and model performance over an IoT network. The results demonstrated a 90% accuracy performance after specific iterations, better than those of other baseline approaches with accuracy levels of approximately 80% with the same epsilon value of 4. Also, this framework is faster and more successfully meets the privacy preservation paradigm.
Communication Protocol Between Embedded Computers and Fog Computing Environment for Image Processing

In this article a new data transmission protocol between embedded computers and the fog computing environment for image processing is considered. The possibilities of using fog environment for intelligent processing of video are revealed. The problems of transmission protocols are indicated. A comparison of existing video processing systems is given. The place of embedded computers in intelligent video surveillance systems and ways of upgrading these systems are considered. Data from the devices is collected using embedded computers and visualized using IoT technologies. The developed data transmission protocol allows processing images in the fog and/or cloud for intelligent video surveillance systems. It assumes packet data transmission of series of frames with additional information that are processed using machine learning algorithms and neural networks. The effectiveness of the new systems is shown. As an example, the problem of processing video coming from cameras used in the subway to determine damage of the escalator tape and steps is considered. Systems for image processing that implement the proposed protocol can be used not only in the subway, but also in many other areas where Internet of things technologies are supported.