Reviewerly/exHarmony huggingface dataset:
Continuous variable quantum teleportation of a thermal state in a thermal environment

The analytical formula for the fidelity of teleportation from Alice to Bob of a thermal state used as an input state is derived using the characteristic function approach. As a resource state it is used a continuous-variable two-mode bosonic state shared between Alice and Bob and the system is placed in a contact with a general environment. The quantum fidelity depends on the average thermal photon number of the input sate and on the parameters of the resource state and environment. In order to evaluate the parameters required for a successful quantum teleportation of a thermal state we estimate the evolution of the entanglement and fidelity of teleportation in the case when Alice and Bob share an initial squeezed thermal state in contact with a thermal bath. We work in the framework of the theory of open quantum systems based on completely positive dynamical semigroups and we use the Gorini–Kossakowski–Lindblad–Sudarshan master equation in order to describe the evolution of the system. The conditions for a successful quantum teleportation are the fidelity of teleportation larger than the threshold value of the classical fidelity of teleportation and entanglement present in the resource state. In order to assure the fulfillment of these conditions it is necessary to select the time of teleportation and to adjust the parameters characterizing the system (squeezing between the modes of the resource state, their average numbers of thermal photons and frequencies), and the temperature of the thermal reservoir. It is shown that in similar physical conditions, the fidelity of teleportation of the thermal states is larger than that one for a coherent state.
User feedback and remote supervision for assisted living with mobile robots: A field study in long-term autonomy

In an ageing society, the at-home use of Socially Assistive Robots (SARs) could provide remote monitoring of their users’ well-being, together with physical and psychological support. However, private home environments are particularly challenging for SARs, due to their unstructured and dynamic nature which often contributes to robots’ failures. For this reason, even though several prototypes of SARs for elderly care have been developed, their commercialisation and wide-spread at-home use are yet to be effective. In this paper, we analyse how including the end users’ feedback impacts the SARs reliability and acceptance. To do so, we introduce a Monitoring and Logging System (MLS) for remote supervision, which increases the explainability of SAR-based systems deployed in older adults’ apartments, while also allowing the exchange of feedback between caregivers, technicians, and older adults. We then present an extensive field study showing how long-term deployment of autonomous SARs can be accomplished by relying on such a feedback loop to address any potential issue. To this end, we provide the results obtained in a 130-week long study where autonomous SARs were deployed in the apartments of 10 older adults, with the aim of possibly serving and assisting future practitioners, with the knowledge collected from this extensive experimental campaign, to fill the gap that currently exists for the widespread adoption of SARs.
Robust image watermarking algorithm using chaotic sequences

This article presents a robust image watermarking scheme that provides high robustness and imperceptibility based on an innovative application of chaotic bits and error-correcting codes. The bits embedded in the image are the parity bits of an error-correcting code whose information sequence is formed by combining the watermark bits with chaotic bits generated from a secret key known to all legitimate users. Therefore, a part of the codeword is perfectly known to these users in the watermark extraction processing regardless of the strength of an attack. Thus, the error-correcting code capability is employed in recovering the watermark bits, improving the robustness of the method. The parity bits are embedded at specific frequencies of the discrete cosine transform and a parameter controls the compromise between robustness and imperceptibility. The proposed method is tested against several signal processing and geometric attacks, and comparison results reveal that it outperforms the existing schemes.
A Co-Design Approach to Explore Health Data Representation for Older Adults in Chile and Ecuador

Displaying the information collected by mobile health technologies remains a challenge, especially when considering representation of health data for older adults - i.e., where and how to display data captured by health devices. We focus on an underrepresented group in HCI research: older adults in the global south, specifically in Chile and Ecuador. We studied the opinions of a group of 18 older adults on health data representation through interviews and remote co-design practices, encouraging them to imagine representations of health data through a presentation of personal objects. They imagined representations of health data in analog formats, where contextual information would be included. Visualization designs can be integrated into objects that older adults use frequently or are close to them. This study contributes design ideas on representations of health data for older adults in the global south and reflections on how to engage this population in remote co-design activities.
Improving Item Cold-start Recommendation via Model-agnostic Conditional Variational Autoencoder

Embedding & MLP has become a paradigm for modern large-scale recommendation system. However, this paradigm suffers from the cold-start problem which will seriously compromise the ecological health of recommendation systems. This paper attempts to tackle the item cold-start problem by generating enhanced warmed-up ID embeddings for cold items with historical data and limited interaction records. From the aspect of industrial practice, we mainly focus on the following three points of item cold-start: 1) How to conduct cold-start without additional data requirements and make strategy easy to be deployed in online recommendation scenarios. 2) How to leverage both historical records and constantly emerging interaction data of new items. 3) How to model the relationship between item ID and side information stably from interaction data. To address these problems, we propose a model-agnostic Conditional Variational Autoencoder based Recommendation(CVAR) framework with some advantages including compatibility on various backbones, no extra requirements for data, utilization of both historical data and recent emerging interactions. CVAR uses latent variables to learn a distribution over item side information and generates desirable item ID embeddings using a conditional decoder. The proposed method is evaluated by extensive offline experiments on public datasets and online A/B tests on Tencent News recommendation platform, which further illustrate the advantages and robustness of CVAR.
Examining the relationship between teachers’ lifelong learning tendencies and digital literacy levels

The purpose of the study is to reveal the relationship between teachers’ lifelong learning tendencies and digital literacy levels. Both single and correlational survey designs from the quantitative research models were employed. To determine the study group, convenience case sampling was used by considering the Covid-19 pandemic and a total of 322 teachers on social media participated in the study. The data were collected in the second semester of the 2020-2021 academic year. In the current study, two scales, namely, “Lifelong Learning Scale” and “Digital Literacy Self-efficacy Scale of Teacher Candidates” were used for data collection tools. As a result of the study, it was determined that lifelong learning tendencies of teachers were at a high level. Besides, teachers find themselves sufficient in terms of digital literacy. It was observed that there was a positive and significant relationship between teachers’ lifelong learning tendencies and digital literacy levels. Ultimately, it was among the obtained results that teachers’ digital literacy levels also predicted their lifelong learning skills.
Few-shot personalized saliency prediction using meta-learning

Personalized saliency maps (PSMs) reflect the gaze patterns of different subjects. Current works of saliency prediction explore the common trend in fixation distribution across all observers as a task. Personalized saliency prediction takes the personal preference of each individual into account. In other words, it considers each subject as a task. Due to the difficulty of obtaining individual labeled data, limited works are focusing on personalized saliency prediction. Our goal is to train a model that can quickly adapt to a new subject by using a few labeled data from the new subject. This paper proposes a meta-learning-based method to solve the few-shot personalized saliency prediction problem and predict better saliency maps of different subjects. Our method learns model parameters to fast adapt to new subjects. In addition, we design a Hard Samples Selection (HSS) strategy to make the training process more effective. Specifically, due to the adaptability of the model to different subjects is reflected in the value of the loss function during training, we regard subjects with high loss function values as hard samples. Then we can select hard samples online and retrain the model based on them to improve the saliency prediction performance. Experimental results show that our proposed method is better than existing methods on the PSM dataset for personalized saliency prediction.
FiGO: Fine-Grained Query Optimization in Video Analytics

Video database management systems (VDBMSs) enable automated analysis of videos at scale using computationally-intensive deep learning models. To reduce the computational overhead of these models, researchers have proposed two techniques: (1) leveraging a specialized, lightweight model to filter out irrelevant frames or to directly answer the query, and (2) using a cascade of models of increasing complexity to answer the query. For both techniques, the query optimizer generates a coarse-grained query plan for the entire video. These techniques suffer from four limitations: (1) lower query accuracy over hard-to-detect predicates, (2) lower filtering efficacy with frequently-occurring objects, (3) lower accuracy due to nontrivial model cascade configuration, and (4) missed optimization opportunities due to coarse-grained planning for the entire video.
Applying and Optimizing NLP Model with CARU

RNN for language models can solve the problem of sparse content and high-dimensional features in traditional N-gram models. However, due to the problems of overfitting and gradient disappearance, the original RNN still lacks long-term content dependence and noise interference. This paper proposes an improved method based on a context word vector for RNN with CARU. In order to alleviate the overfitting problem, a modified DropConnect layer is employed in the proposed model. In addition, the multilayer CARU is used to add contextual word vectors to the model with the feature layer to strengthen the ability to learn long-distance information during the training process. Experimental results show that the proposed method effectively improves the performance of RNN-based language model.
Inferring Native and Non-Native Human Reading Comprehension and Subjective Text Difficulty from Scanpaths in Reading

Eye movements in reading are known to reflect cognitive processes involved in reading comprehension at all linguistic levels, from the sub-lexical to the discourse level. This means that reading comprehension and other properties of the text and/or the reader should be possible to infer from eye movements. Consequently, we develop the first neural sequence architecture for this type of tasks which models scan paths in reading and incorporates lexical, semantic and other linguistic features of the stimulus text. Our proposed model outperforms state-of-the-art models in various tasks. These include inferring reading comprehension or text difficulty, and assessing whether the reader is a native speaker of the text's language. We further conduct an ablation study to investigate the impact of each component of our proposed neural network on its performance.
An Optimal Transport Approach for Selecting a Representative Subsample with Application in Efficient Kernel Density Estimation

Subsampling methods aim to select a subsample as a surrogate for the observed sample. Such methods have been used pervasively in large-scale data analytics, active learning, and privacy-preserving analysis in recent decades. Instead of model-based methods, in this article, we study model-free subsampling methods, which aim to identify a subsample, that is, not confined by model assumptions. Existing model-free subsampling methods are usually built upon clustering techniques or kernel tricks. Most of these methods suffer from either a large computational burden or a theoretical weakness. In particular, the theoretical weakness is that the empirical distribution of the selected subsample may not necessarily converge to the population distribution. Such computational and theoretical limitations hinder the broad applicability of model-free subsampling methods in practice. We propose a novel model-free subsampling method by using optimal transport techniques. Moreover, we develop an efficient subsampling algorithm, that is, adaptive to the unknown probability density function. Theoretically, we show the selected subsample can be used for efficient density estimation by deriving the convergence rate for the proposed subsample kernel density estimator. We also provide the optimal bandwidth for the proposed estimator. Numerical studies on synthetic and real-world datasets demonstrate the performance of the proposed method is superior.
Feminist Care in the Anthropocene: Packing and Unpacking Tensions in Posthumanist HCI

As posthumanist or post-anthropocentric research in HCI and design proliferates and further commits to working with more-than-humans, design research practitioners are left with many open questions and uncertainties with how to productively engage with more-than-humans in their thinking and working. This paper present results from a workshop with 17 researchers working at the intersection of care ethics and posthumanism to highlight tensions in posthumanist engagement aimed at unpacking some of the challenges, obstacles, and questions encountered by researchers interested in more-than-human centered design. In foregrounding tensions with representation, legitimization, unseen labor, and material narratives we contribute to a design research agenda which seeks to explicate and challenge dominant anthropocentric forces from design. We conclude by discussing epistemological care and urge practitioners to take up new ways of imagining through truly messy methods which contribute to a feminist unsettling of HCI's methodological commitments, practices, and praxis.
The impact of social and organisational capital on service innovation capability during COVID-19: the mediating role of strategic environmental scanning

Purpose This purpose of this study was to examine the impact of social and organisational capital on service innovation capability among service firms in the United Arab Emirates (UAE) during the COVID-19 pandemic. Design/methodology/approach To test the proposed research model, data were collected using a cross-sectional questionnaire. The study sample consisted of 188 private and public service sector managers in the UAE. Partial least square-based structural equation modelling (PLS-SEM) was used to examine the research model's validity and reliability and to test the research hypothesis. Findings The empirical evidence indicates that during this pandemic the relationship between social capital and service innovation capability was fully mediated by strategic environmental scanning, while partially mediating the relationship between organisational capital and service innovation capability. Practical implications Managers in service organisations must be proactive during crises such as the COVID-19 pandemic. Specifically, they should emphasise effective environmental scanning and the tracking of customer preferences to provide customised services that are valued and meet the emerging requirements of their customers. Prioritising investment in organisational capital to enhance innovation capacity is also recommended. Originality/value This study is the first to examine strategic environmental scanning as a mediator between social and organisational capital and service innovation capacity during a pandemic. There were significant differences between the findings of our study and previous studies: the authors found that, during crises, management priorities change, and businesses become more reliant on organisational capital to develop service innovation capability.
Development of public cloud manufacturing markets: a mechanism design approach

Online manufacturing markets are new emerging types of markets that are spreading in the shade of public Cloud Manufacturing. This paper introduces a public Cloud Manufacturing market and denotes its differences from a private one. Public Cloud Manufacturing enables service-oriented manufacturing models for everyone in society. The prosperity of such market is tied to appropriate matching between providers to consumers. This paper elaborates on a mechanism to prescribe an appropriate allocation between resources and demands in this public market. The mechanism exploits the VIKOR method to develop preference lists for providers and consumers, the method also is augmented by the Intuitionistic Fuzzy Sets to remove complexities and uncertainties that exist in the process of ranking; and Deferred Acceptance (DA) algorithm is utilised as the matching algorithm of the mechanism. The performance of the mechanism is analysed under influence of the proposer, resource availability and matching algorithm. The influence of proposer study indicates that the situation of the consumer as the proposer is preferred to the providers’ situation as proposer; the influence of resource availability study indicates a balanced situation is preferred to the scarcity and abundancy situations and the influence of matching algorithm indicates that DA outperforms Top Trading Cycle (TTC) algorithm as the matching algorithm of the mechanism.
Weighted Averaging Federated Learning Based on Example Forgetting Events in Label Imbalanced Non-IID

Federated learning, a data privacy-focused distributed learning method, trains a model by aggregating local knowledge from clients. Each client collects and utilizes its own local dataset to train a local model. Local models in the connected federated learning network are uploaded to the server. In the server, local models are aggregated into a global model. During the process, no local data is transmitted in or out of any client. This procedure may protect data privacy; however, federated learning has a worse case of example forgetting problem than centralized learning. The problem manifests in lower performance in testing. We propose federated weighted averaging (FedWAvg). FedWAvg identifies forgettable examples in each client and utilizes that information to rebalance local models via weighting. By weighting clients with more forgettable examples, such clients are better represented and global models can acquire more knowledge from normally neglected clients. FedWAvg diminishes the example forgetting problem and achieve better performance. Our experiments on SVHN and CIFAR-10 datasets demonstrate that our proposed method gets improved performance compared to existing federated learning algorithm in non-IID settings, and that our proposed method can palliate the example forgetting problem.
Feather Hair: Interacting with Sensorized Hair in Public Settings

Human hair opens up new opportunities for embodied interactions that build on its unique physical affordances and location on the body. As hair has high socio-cultural significance, the design of hair interfaces is coupled with social and personal needs. Albeit this makes field investigations indispensable, they are missing from prior work. We present a fabrication approach for gesture-controlled hair interfaces that are robust enough to be deployed in the field. Our approach contributes sensorized feather hair extensions that combine capacitive and piezoresistive sensing. Their tactile properties make the interface blend seamlessly with human hair. We furthermore contribute results from a field experiment where participants gained first-hand experience in various social contexts. These show how hair-based interactions have great potential moving beyond planar touch gestures whilst their social appropriateness is context-sensitive. We synthesize the findings into design implications that ground the future design of usable and socially acceptable hair interfaces.
Hyperscale FPGA-as-a-service architecture for large-scale distributed graph neural network

Graph neural network (GNN) is a promising emerging application for link prediction, recommendation, etc. Existing hardware innovation is limited to single-machine GNN (SM-GNN), however, the enterprises usually adopt huge graph with large-scale distributed GNN (LSD-GNN) that has to be carried out with distributed in-memory storage. The LSD-GNN is very different from SM-GNN in terms of system architecture demand, workflow and operators, and hence characterizations.
CueVR: Studying the Usability of Cue-based Authentication for Virtual Reality

Existing virtual reality (VR) authentication schemes are either slow or prone to observation attacks. We propose CueVR, a cue-based authentication scheme that is resilient against observation attacks by design since vital cues are randomly generated and only visible to the user experiencing the VR environment. We investigate three different input modalities through an in-depth usability study (N=20) and show that while authentication using CueVR is slower than the less secure baseline, it is faster than existing observation resilient cue-based schemes and VR schemes (4.151 s – 7.025 s to enter a 4-digit PIN). Our results also indicate that using the controllers’ trackpad significantly outperforms input using mid-air gestures. We conclude by discussing how visual cues can enhance the security of VR authentication while maintaining high usability. Furthermore, we show how existing real-world authentication schemes combined with VR’s unique characteristics can advance future VR authentication procedures.
A Convex-Programming Approach for Efficient Directed Densest Subgraph Discovery

Given a directed graph G, the directed densest subgraph (DDS) problem refers to finding a subgraph from G, whose density is the highest among all subgraphs of G. The DDS problem is fundamental to a wide range of applications, such as fake follower detection and community mining. Theoretically, the DDS problem closely connects to other essential graph problems, such as network flow and bipartite matching. However, existing DDS solutions suffer from efficiency and scalability issues. In this paper, we develop a convex-programming-based solution by transforming the DDS problem into a set of linear programs. Based on the duality of linear programs, we develop efficient exact and approximation algorithms. Especially, our approximation algorithm can support flexible parameterized approximation guarantees. We have performed an extensive empirical evaluation of our approaches on eight real large datasets. The results show that our proposed algorithms are up to five orders of magnitude faster than the state-of-the-art.