Reviewerly/exHarmony huggingface dataset:
The Aesthetics of Encounter: A Relational-Performative Design Approach to Human-Robot Interaction

This article lays out the framework for relational-performative aesthetics in human-robot interaction, comprising a theoretical lens and design approach for critical practice-based inquiries into embodied meaning-making in human-robot interaction. I explore the centrality of aesthetics as a practice of embodied meaning-making by drawing on my arts-led, performance-based approach to human-robot encounters, as well as other artistic practices. Understanding social agency and meaning as being enacted through the situated dynamics of the interaction, I bring into focus a process of bodying-thinging; entangling and transforming subjects and objects in the encounter and rendering elastic boundaries in-between. Rather than serving to make the strange look more familiar, aesthetics here is about rendering the differences between humans and robots more relational. My notion of a relational-performative design approach— designing with bodying-thinging— proposes that we engage with human-robot encounters from the earliest stages of the robot design. This is where we begin to manifest boundaries that shape meaning-making and the potential for emergence, transformation, and connections arising from intra-bodily resonances ( bodying-thinging ). I argue that this relational-performative approach opens up new possibilities for how we design robots and how they socially participate in the encounter.
Moving Faster than the COVID-19 Pandemic: The Rapid, Digital Transformation of a Public Health System

Abstract Background Queensland, Australia has been successful in containing the COVID-19 pandemic. Underpinning that response has been a highly effective virus containment strategy which relies on identification, isolation, and contact tracing of cases. The dramatic emergence of the COVID-19 pandemic rendered traditional paper-based systems for managing contact tracing no longer fit for purpose. A rapid digital transformation of the public health contact tracing system occurred to support this effort. Objectives The objectives of the digital transformation were to shift legacy systems (paper or standalone electronic systems) to a digitally enabled public health system, where data are centered around the consumer rather than isolated databases. The objective of this paper is to outline this case study and detail the lessons learnt to inform and give confidence to others contemplating digitization of public health systems in response to the COVID-19 pandemic. Methods This case study is set in Queensland, Australia. Universal health care is available. A multidisciplinary team was established consisting of clinical informaticians, developers, data strategists, and health information managers. An agile “pair-programming” approach was undertaken to application development and extensive change efforts were made to maximize adoption of the new digital workflows. Data governance and flows were changed to support rapid management of the pandemic. Results The digital coronavirus application (DCOVA) is a web-based application that securely captures information about people required to quarantine and creates a multiagency secure database to support a successful containment strategy. Conclusion Most of the literature surrounding digital transformation allows time for significant consultation, which was simply not possible under crisis conditions. Our observation is that staff was willing to adopt new digital systems because the reason for change (the COVID-19 pandemic) was clearly pressing. This case study highlights just how critical a unified purpose, is to successful, rapid digital transformation.
Socio-cognitive constraints and opportunities for sustainable intensification in South Asia: insights from fuzzy cognitive mapping in coastal Bangladesh

Abstract Appreciating and dealing with the plurality of farmers’ perceptions and their contextual knowledge and perspectives of the functioning and performance of their agroecosystems—in other words, their ‘mental models’—is central for appropriate and sustainable agricultural development. In this respect, the sustainable development goals (SDGs) aim to eradicate poverty and food insecurity by 2030 by envisioning social inclusivity that incorporates the preferences and knowledge of key stakeholders, including farmers. Agricultural development interventions and policies directed at sustainable intensification (SI), however, do not sufficiently account for farmers’ perceptions, beliefs, priorities, or interests. Considering two contrasting agroecological systems in coastal Bangladesh, we used a fuzzy cognitive mapping (FCM)-based simulation and sensitivity analysis of mental models of respondents of different farm types from 240 farm households. The employed FCM mental models were able to (1) capture farmers’ perception of farming system concepts and relationships for each farm type and (2) assess the impact of external interventions (drivers) on cropping intensification and food security. We decomposed the FCM models’ variance into the first-order sensitivity index (SVI) and total sensitivity index (TSI) using a winding stairs algorithm. Both within and outside polder areas, the highest TSIs (35–68%) were observed for effects of agricultural extension on changes in other concepts in the map, particularly food security and income (SI indicators), indicating the importance of extension programs for SI. Outside polders, drainage and micro-credit were also influential; within polders, the availability of micro-credit appears to affect farmer perceptions of SI indicators more than drainage. This study demonstrated the importance of reflection on the differing perspectives of farmers both within and outside polders to identify entry points for development interventions. In addition, the study underscores the need for micro-farming systems-level research to assess the context-based feasibility of introduced interventions as perceived by farmers of different farm types.
Learning noise-decoupled affine models for extreme low-light image enhancement

How to handle the noise effectively is an important yet challenging problem for low-light image enhancement especially in real-world extreme low-light conditions. Furthermore, contrast enhancement and noise removal are coupled problems, it's hard to trade off well between noise suppression and preservation of details. To this end, this paper proposes an end-to-end network for low-light image enhancement with a particular focus on handling this coupling relationship. The basic idea is to convert low-light image enhancement to local affine color transformations. Instead of image smooth denoising, a special noise processing mechanism is proposed to learn noise-decoupled affine models. Alternatively, to achieve efficient learning, the whole network is trained in bilateral space. Extensive experiments on several benchmark datasets have shown that the proposed method is very competitive to state-of-the-art methods. Especially when processing images captured in extreme low-light conditions, it has a significant advantage over other algorithms in reducing noise while retaining image details.
Not All Features Are Equal: Discovering Essential Features for Preserving Prediction Privacy

When receiving machine learning services from the cloud, the provider does not need to receive all features; in fact, only a subset of the features are necessary for the target prediction task. Discerning this subset is the key problem of this work. We formulate this problem as a gradient-based perturbation maximization method that discovers this subset in the input feature space with respect to the functionality of the prediction model used by the provider. After identifying the subset, our framework, Cloak, suppresses the rest of the features using utility-preserving constant values that are discovered through a separate gradient-based optimization process. We show that Cloak does not necessarily require collaboration from the service provider beyond its normal service, and can be applied in scenarios where we only have black-box access to the service provider’s model. We theoretically guarantee that Cloak’s optimizations reduce the upper bound of the Mutual Information (MI) between the data and the sifted representations that are sent out. Experimental results show that Cloak reduces the mutual information between the input and the sifted representations by 85.01% with only negligible reduction in utility (1.42%). In addition, we show that Cloak greatly diminishes adversaries’ ability to learn and infer non-conducive features.
Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks

A bipartite network is a graph structure where nodes are from two distinct domains and only inter-domain interactions exist as edges. A large number of network embedding methods exist to learn vectorial node representations from general graphs with both homogeneous and heterogeneous node and edge types, including some that can specifically model the distinct properties of bipartite networks. However, these methods are inadequate to model multiplex bipartite networks (e.g., in e-commerce), that have multiple types of interactions (e.g., click, inquiry, and buy) and node attributes. Most real-world multiplex bipartite networks are also sparse and have imbalanced node distributions that are challenging to model. In this paper, we develop an unsupervised Dual HyperGraph Convolutional Network (DualHGCN) model that scalably transforms the multiplex bipartite network into two sets of homogeneous hypergraphs and uses spectral hypergraph convolutional operators, along with intra- and inter-message passing strategies to promote information exchange within and across domains, to learn effective node embeddings. We benchmark DualHGCN using four real-world datasets on link prediction and node classification tasks. Our extensive experiments demonstrate that DualHGCN significantly outperforms state-of-the-art methods, and is robust to varying sparsity levels and imbalanced node distributions.
CLiMP: A Benchmark for Chinese Language Model Evaluation

Linguistically informed analyses of language models (LMs) contribute to the understanding and improvement of these models.Here, we introduce the corpus of Chinese linguistic minimal pairs (CLiMP), which can be used to investigate what knowledge Chinese LMs acquire.CLiMP consists of sets of 1,000 minimal pairs (MPs) for 16 syntactic contrasts in Mandarin, covering 9 major Mandarin linguistic phenomena.The MPs are semiautomatically generated, and human agreement with the labels in CLiMP is 95.8%.We evaluate 11 different LMs on CLiMP, covering n-grams, LSTMs, and Chinese BERT.We find that classifier-noun agreement and verb complement selection are the phenomena that models generally perform best at.However, models struggle the most with the bǎ construction, binding, and filler-gap dependencies.Overall, Chinese BERT achieves an 81.8% average accuracy, while the performances of LSTMs and 5-grams are only moderately above chance level.
CubicleOS: a library OS with software componentisation for practical isolation

Library OSs have been proposed to deploy applications isolated inside containers, VMs, or trusted execution environments. They often follow a highly modular design in which third-party components are combined to offer the OS functionality needed by an application, and they are customised at compilation and deployment time to fit application requirements. Yet their monolithic design lacks isolation across components: when applications and OS components contain security-sensitive data (e.g., cryptographic keys or user data), the lack of isolation renders library OSs open to security breaches via malicious or vulnerable third-party components.
Rapid Determination of Low Heavy Metal Concentrations in Grassland Soils around Mining Using Vis–NIR Spectroscopy: A Case Study of Inner Mongolia, China

Proximal sensing offers a novel means for determination of the heavy metal concentration in soil, facilitating low cost and rapid analysis over large areas. In this respect, spectral data and model variables play an important role. Thus far, no attempts have been made to estimate soil heavy metal content using continuum-removal (CR), different preprocessing and statistical methods, and different modeling variables. Considering the adsorption and retention of heavy metals in spectrally active constituents in soil, this study proposes a method for determining low heavy metal concentrations in soil using spectral bands associated with soil organic matter (SOM) and visible–near-infrared (Vis–NIR). To rapidly determine the concentration of heavy metals using hyperspectral data, partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVMR) statistical methods and 16 preprocessing combinations were developed and explored to determine an optimal combination. The results showed that the multiplicative scatter correction and standard normal variate preprocessing methods evaluated with the second derivative spectral transformation method could accurately determine soil Cr and Ni concentrations. The root-mean-square error (RMSE) values of Vis–NIR model combinations with PLSR, PCR, and SVMR were 0.34, 3.42, and 2.15 for Cr, and 0.07, 1.78, and 1.14 for Ni, respectively. Soil Cr and Ni showed strong spectral responses to the Vis–NIR spectral band. The R2 value of the Vis–NIR-based PLSR model was higher than 0.99, and the RMSE value was 0.07–0.34, suggesting higher stability and accuracy. The results were more accurate for Ni than Cr, and PLSR showed the best performance, followed by SVMR and PCR. This perspective has critical implications for guiding quantitative biogeochemical analysis using proximal sensing data.
Label propagation with structured graph learning for semi-supervised dimension reduction

Graph learning has been demonstrated as one of the most effective methods for semi-supervised dimension reduction, as it can achieve label propagation between labeled and unlabeled samples to improve the feature projection performance. However, most existing methods perform this important label propagation process on the graph with sub-optimal structure, which will reduce the quality of the learned labels and thus affect the subsequent dimension reduction. To alleviate this problem, in this paper, we propose an effective Label Propagation with Structured Graph Learning (LPSGL) method for semi-supervised dimension reduction. In our model, label propagation, semi-supervised structured graph learning and dimension reduction are simultaneously performed in a unified learning framework. We propose a semi-supervised structured graph learning method to characterize the intrinsic semantic relations of samples more accurately. Further, we assign different importance scores for the given and learned labeled samples to differentiate their effects on learning the feature projection matrix. In our method, the semantic information can be propagated more effectively from labeled samples to the unlabeled samples on the learned structured graph. And a more discriminative feature projection matrix can be learned to perform the dimension reduction. An iterative optimization with the proved convergence is proposed to solve the formulated learning framework. Experiments demonstrate the state-of-the-art performance of the proposed method. The source codes and testing datasets are available at https://github.com/FWang-sdnu/LPSGL-code.
Grid-Constrained Data Cleansing Method for Enhanced Bus Load Forecasting

A number of measurement data cleansing methods have been proposed aiming at improving the accuracy of bus load forecasting (BLF). However, almost all these methods only exploit the temporal characteristics of a single measurement time series of bus load data and neglect the information that can be extracted from the power grid. As a matter of fact, the power grid model derived from physical circuit laws can provide an entirely different dimension of information facilitating the cleansing of bus load data. In view of this gap, a comprehensive data cleansing framework is proposed for the sake of enhancing BLF accuracy. Bus load datasets are cleansed based on their consistencies with both the temporal statistics and the physical power grid model. Simulation results on the IEEE 30-bus system verify that the proposed method remarkably improves BLF accuracy under various types of measurement data corruption conditions, including random noise, temporary gross errors, permanent biases, and cyberattacks.
ShowMeAround: Giving Virtual Tours Using Live 360 Video

This demonstration presents ShowMeAround, a video conferencing system designed to allow people to give virtual tours over live 360-video. Using ShowMeAround a host presenter walks through a real space and can live stream a 360-video view to a small group of remote viewers. The ShowMeAround interface has features such as remote pointing and viewpoint awareness to support natural collaboration between the viewers and host presenter. The system also enables sharing of pre-recorded high resolution 360 video and still images to further enhance the virtual tour experience.
The Use of MOOCS in Online Engineering Education

The study demonstrates the possibilities of using massive open online courses in engineering education. In order to study the impact of digital learning formats on the acquisition of key engineering competencies by students, an educational experiment was carried out; it was performed as part of the study of the Fire Safety discipline on the basis of the online educational platform Khan Academy. Conceptually, the educational process was aimed at studying the capabilities of software in the field of fire safety, namely: PyroSim, Pathfinder, FireRisk, FIM, FireCategories, PromRisk, FireDistance, as well as gaining professional competencies in the process of performing practical tasks. The educational experiment involved third-year intramural and extramural students of the Faculty of Engineering (The Saint-Petersburg University of the State Fire Service of the EMERCOM of Russia) and Moscow City Pedagogical University. The online educational course was conducted with an emphasis on consolidation and acquisition of professional engineering knowledge and skills in the development of fire safety projects under simulated risk conditions using modern software and an innovative educational platform. The participants demonstrated a high level of involvement, initiative and professional aspirations in the learning process. In the context of the educational experiment, the key competencies required for 21st century engineers have been identified. The students confirmed the positive impact of digital pedagogical tools on the acquisition and development of key competencies necessary for further professional activity. Having completed an online course on the Khan Academy platform focused on the applied interaction with advanced software in the field of fire safety, students identified skills that should be developed and consolidated, namely: teamwork skills and the ability to work in multidisciplinary teams, the ability to manage projects (management, planning, scheduling, budgeting, etc.); the ability to define, formulate and solve engineering problems; the ability to effectively prioritize; striving for lifelong learning; willingness to take a calculated risk; ability to make professional decisions; self-regulation and self-motivation. However, the development of important engineering competencies such as high ethical standards, honesty and global social, intellectual and technological responsibility; good communication skills, high emotional intelligence and cognitive flexibility; entrepreneurial spirit and customer focus requires educators to search for different pedagogical methods and practices. The assessment of the impact of digital learning formats on the acquisition of key engineering competencies was subjective, but revealed pedagogical approaches to the qualitative development of engineering personnel. An effective combination of traditional classroom and virtual interactions can create a complex paradigm of high-quality engineering and technical education that is competitive in the market environment.
DRNet: A Deep Neural Network With Multi-Layer Residual Blocks Improves Image Denoising

Recently, with the broad applications of deep learning technology in image denoising, many deep neural networks based on the residual block (ResBlock) have been proposed to improve the performance.This paper presents DC-ResBlock, a ResBlock with an extra dilated convolution, to replace standard ResBlock for image denoising.Our study shows that the deep neural network with DC-ResBlocks, named DRNet, can achieve a very competitive result.We experiment with other residual blocks by adding Rectified Linear Unit, batch normalization, and Dilated Convolution to the standard ResBlock in several different ways.Ablation study shows that DC-Resblock has the best denoising performance.To evaluate the robustness of proposed DRNet, we make a statistical measure using many different random seeds instead of just a single seed, as used by many previous studies.DRNet performs well in denoising gray and color images with additive white Gaussian noise.Furthermore, because of its good performance on the Smartphone Image Denoising Dataset, DRNet is hopeful to be applied in practical tasks.The code of DRNet is accessible at https://github.com/JiaHongZ/DRNet.
Selective Attention Based Graph Convolutional Networks for Aspect-Level Sentiment Classification

Recent work on aspect-level sentiment classification has employed Graph Convolutional Networks (GCN) over dependency trees to learn interactions between aspect terms and opinion words.In some cases, the corresponding opinion words for an aspect term cannot be reached within two hops on dependency trees, which requires more GCN layers to model.However, GCNs often achieve the best performance with two layers, and deeper GCNs do not bring any additional gain.Therefore, we design a novel selective attention based GCN model.On one hand, the proposed model enables the direct interaction between aspect terms and context words via the self-attention operation without the distance limitation on dependency trees.On the other hand, a top-k selection procedure is designed to locate opinion words by selecting k context words with the highest attention scores.We conduct experiments on several commonly used benchmark datasets and the results show that our proposed SA-GCN outperforms strong baseline models.
Zero-Shot Text-to-Image Generation

Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training. We describe a simple approach for this task based on a transformer that autoregressively models the text and image tokens as a single stream of data. With sufficient data and scale, our approach is competitive with previous domain-specific models when evaluated in a zero-shot fashion.