Reviewerly/exHarmony huggingface dataset:
Hugs, Bible Study, and Speakeasies: Designing for Older Adults’ Multimodal Connectedness

Older adults can experience significant changes to their social networks as they age, triggering changes in their social connection practices. In this paper, we extend research on older adults’ connectedness behaviors using a multimodal connectedness framing—that is, how they engage with others across platforms, devices, and modalities. Using the COVID-19 pandemic as a case study, we investigate how older adults navigate a major change or infrastructural breakdown in their social routines. We conducted a survey with 146 U.S.-based older adults (65+), and follow-up interviews with a subset of 23 survey respondents. Findings revealed the resilience and innovation with which older adults adapted their behaviors across multiple modalities to maintain social relationships and playfully connect with others in person and online. Using these findings, we propose that research on designing for aging extend beyond designing for connection in the smart home; we argue for a design agenda that prioritizes designing for smart relationships with the potential to persist across spaces via multimodal connectedness.
Deep Episodic Memory for Verbalization of Robot Experience

The ability to verbalize robot experience in natural language is key for a symbiotic human-robot interaction. While first works approached this problem using template-based verbalization on symbolic episode data only, we explore a novel way in which deep learning methods are used for the creation of an episodic memory from experiences as well as the verbalization of such experience in natural language. To this end, we first collected a complex dataset consisting of more than a thousand multimodal robot episode recordings both from simulation as well as real robot executions, together with representative natural language questions and answers about the robot's past experience. Second, we propose and evaluate an episodic memory verbalization model consisting of a speech encoder and decoder based on the Transformer architecture, combined with an LSTM-based episodic memory auto-encoder, and evaluate the model on simulated and real data from robot execution examples. Our experimental results provide a proof-of-concept for episodic-memory-based verbalization of robot experience.
Research and Application of the Network Learning Platform in Based on Big Data Analysis and Mobile Communication

Abstract With the development of computer information technology, the learning methods of college students have gradually changed, and the Internet-based online learning methods are becoming more and more prosperous. This article uses big data analysis to explore the reasons for the application of the economic management network learning platform in colleges and universities and the impact of these reasons on the learning effect. Finally, some suggestions are put forward for the construction of the network learning platform. The research results not only provide new ideas for the continuous development of online learning platforms, but also have practical guiding significance for the improvement of teaching quality in colleges and universities.
Handling Cross- and Out-of-Domain Samples in Thai Word Segmentation

While word segmentation is a solved problem in many languages, it is still a challenge in continuous-script or low-resource languages.Like other NLP tasks, word segmentation is domain-dependent, which can be a challenge in low-resource languages like Thai and Urdu since there can be domains with insufficient data.This investigation proposes a new solution to adapt an existing domaingeneric model to a target domain, as well as a data augmentation technique to combat the low-resource problems.In addition to domain adaptation, we also propose a framework to handle out-of-domain inputs using an ensemble of domain-specific models called Multi-Domain Ensemble (MDE).To assess the effectiveness of the proposed solutions, we conducted extensive experiments on domain adaptation and out-of-domain scenarios.Moreover, we also proposed a multiple task dataset for Thai text processing, including word segmentation.For domain adaptation, we compared our solution to the state-of-the-art Thai word segmentation (TWS) method and obtained improvements from 93.47% to 98.48% at the character level and 84.03% to 96.75% at the word level.For out-of-domain scenarios, our MDE method significantly outperformed the state-of-the-art TWS and multi-criteria methods.Furthermore, to demonstrate our method's generalizability, we also applied our MDE framework to other languages, namely Chinese, Japanese, and Urdu, and obtained improvements similar to Thai's.
Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive Learning

Context, the embedding of previous collected trajectories, is a powerful construct for Meta-Reinforcement Learning (Meta-RL) algorithms. By conditioning on an effective context, Meta-RL policies can easily generalize to new tasks within a few adaptation steps. We argue that improving the quality of context involves answering two questions: 1. How to train a compact and sufficient encoder that can embed the task-specific information contained in prior trajectories? 2. How to collect informative trajectories of which the corresponding context reflects the specification of tasks? To this end, we propose a novel Meta-RL framework called CCM (Contrastive learning augmented Context-based Meta-RL). We first focus on the contrastive nature behind different tasks and leverage it to train a compact and sufficient context encoder. Further, we train a separate exploration policy and theoretically derive a new information-gain-based objective which aims to collect informative trajectories in a few steps. Empirically, we evaluate our approaches on common benchmarks as well as several complex sparse-reward environments. The experimental results show that CCM outperforms state-of-the-art algorithms by addressing previously mentioned problems respectively.
A probabilistic graph-based method to solve precision-diversity dilemma in recommender systems

Over the last few years, with significant growth of information on the web, users are swamped with a huge amount of information. Recommender system (RS) is an information retrieval technology that aims to provide relevant items to users by considering their preferences. Although several studies in the literature have proposed new RSs to improve the precision of recommendations, some studies have shown that increasing the diversity of recommendations is one way to increase the quality of the user’s experience and to solve the over-fitting problem. While some previous works have tried to make a trade-off between precision and diversity, they could not provide a linear and deterministic trade-off between precision and diversity. As such, these methods are either impractical or infeasible in a real RS. In this paper, we propose a new method to recommend items which are fit to users’ preferences. The method consists of following steps: i) finding users’ preferences using a limited user study, ii) proposing a probabilistic graph-based recommender system (PGBRS) to recommend items with a desirable level of precision and diversity by considering items that are likely to be preferred by the user in the future. PGBRS is a dataset-independent method which is significantly flexible to update with a low implementation complexity.
Transfer and teleportation of system-environment entanglement

We study bidirectional teleportation while explicitly taking into account a mixed environment. This environment initially causes pure dephasing decoherence of the Bell state which assists teleportation. We find that when teleportation is performed in one direction it is accompanied by a transfer of correlations into the teleported qubit state. In the other direction, if no new decoherence process occurs then not only the state of the qubit, but also its correlations with the environment are teleported with unit fidelity. These processes do not depend on the measurement outcome during teleportation and do not differentiate between classical and quantum decoherence. If, on the other hand, the second teleportation step is preceded by decoherence of the Bell state then the situation is much more complicated. Teleportation and transfer of correlations occur simultaneously, yielding different teleported qubit-environment states for different measurement outcomes. These states can differ in the degree of coherence of the teleported qubit, but only for an entangling Bell-state-environment interaction in the first step can they have different amounts of qubit-environment entanglement. In the extreme case, one of the teleported qubit states can be entangled with the environment while the other is separable.
Distant Traffic Light Recognition Using Semantic Segmentation

Traffic light recognition is an important task for automatic driving support systems. Conventional traffic light recognition techniques are categorized into model-based methods, which frequently suffer from environmental changes such as sunlight, and machine-learning-based methods, which have difficulty detecting distant and occluded traffic lights because they fail to represent features efficiently. In this work, we propose a method for recognizing distant traffic lights by utilizing a semantic segmentation for extracting traffic light regions from images and a convolutional neural network (CNN) for classifying the state of the extracted traffic lights. Since semantic segmentation classifies objects pixel by pixel in consideration of the surrounding information, it can successfully detect distant and occluded traffic lights. Experimental results show that the proposed semantic segmentation improves the detection accuracy for distant traffic lights and confirms the accuracy improvement of 12.8 % over the detection accuracy by object detection. In addition, our CNN-based classifier was able to identify the traffic light status more than 30 % more accurately than the color thresholding classification.
The Eighth Dialog System Technology Challenge

This paper introduces the Eighth Dialog System Technology Challenge. In line with recent challenges, the eighth edition focuses on applying end-to-end dialog technologies in a pragmatic way for multi-domain task-completion, noetic response selection, audio visual scene-aware dialog, and schema-guided dialog state tracking tasks. This paper describes the task definition, provided datasets, and evaluation set-up for each track. We also summarize the results of the submitted systems to highlight the overall trends of the state-of-the-art technologies for the tasks.
Dynamic Connectedness and Portfolio Diversification during the Coronavirus Disease 2019 Pandemic: Evidence from the Cryptocurrency Market

This paper examines interlinkages and hedging opportunities between nine major cryptocurrencies from 30 September 2015 to 4 June 2020, a period which notably includes the COVID-19 outbreak lasting from early 2020 to the end of the sample period. Estimated time-varying correlation coefficients that are based on a TVP-VAR show a high degree of interconnectedness among cryptocurrencies throughout the sample period. Notably, the correlations reach their joint minimum during the COVID-19 pandemic indicating that cryptocurrencies acted as a hedge or safe haven during the stressful period of the COVID-19 pandemic. The cryptocurrency weights of the minimum connectedness portfolio were significantly reduced and their hedging effectiveness varied greatly during the pandemic, implying that investors’ preferences changed during the COVID-19 period.
Pyramidal Connected Component Labeling by Irregular Graph Pyramid

This paper presents a new logarithmic-time algorithm which simultaneously assigns labels to all connected components of a binary image in parallel. The irregular graph pyramid of an input binary image is constructed based on the optimized combinatorial structure. The novel small built pyramid has only three levels and is created at worst case with O(log(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> )) complexity for a N×N-sized (2D) binary image. To assign a label to each connected component, instead of the common linear-time raster scan techniques (with complexity O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> )), only two important movements, namely bottom-up and top-down traversing, are needed. First, in the bottom-up traversing, the redundant connections are removed and the contraction kernels are contracted. This results in a simpler graph at top of the pyramid each of its vertices has a unique label identifying corresponding connected component. Such reduced graph preserves all connecting relations including inclusions. Second, in the top-down traversing, each unique label propagates down into each individual corresponding pixel at the base level. The complexity of the labeling propagation procedure in worst cases is O (log(image - size)). The GPU implementation of the algorithm has high performance and the bottleneck is the bandwidth of the memory or equivalently the number of available independent processing elements. Finally, the experimental results show the proposed algorithm outperforms the other state-of-the-art methods.
Lensless optical encryption with speckle-noise suppression and QR codes

The majority of contemporary optical encryption techniques use coherent illumination and suffer from speckle-noise pollution, which severely limits their applicability even when information encoded into special “containers” such as a QR code. Spatially incoherent encryption does not have this drawback, but it suffers from reduced encryption strength due to formation of an unobscured image right on top of the encrypted one by undiffracted light from the encoding diffraction optical element (DOE) in axial configuration. We present a new lensless encryption scheme, experimentally implemented with two liquid crystal spatial light modulators, that does not have this disadvantage because of a special encoding DOE design, which forms desired light distribution in the photosensor plane under spherically diverging illumination without a converging lens. Results of optical experiments on encryption of QR codes and successful information retrieval from decoded images are presented. Conducted analysis of encryption strength demonstrates sufficiently high key sensitivity and large enough key space to resist any brute force attacks.
Readability Research: An Interdisciplinary Approach

Readability is on the cusp of a revolution. Fixed text is becoming fluid as a proliferation of digital reading devices rewrite what a document can do. As past constraints make way for more flexible opportunities, there is great need to understand how reading formats can be tuned to the situation and the individual. We aim to provide a firm foundation for readability research, a comprehensive framework for modern, multi-disciplinary readability research. Readability refers to aspects of visual information design which impact information flow from the page to the reader. Readability can be enhanced by changes to the set of typographical characteristics of a text. These aspects can be modified on-demand, instantly improving the ease with which a reader can process and derive meaning from text. We call on a multi-disciplinary research community to take up these challenges to elevate reading outcomes and provide the tools to do so effectively.
StEP: Style-based Encoder Pre-training for Multi-modal Image Synthesis

We propose a novel approach for multi-modal Image-to-image (I2I) translation. To tackle the one-to-many relationship between input and output domains, previous works use complex training objectives to learn a latent embedding, jointly with the generator, that models the variability of the output domain. In contrast, we directly model the style variability of images, independent of the image synthesis task. Specifically, we pre-train a generic style encoder using a novel proxy task to learn an embedding of images, from arbitrary domains, into a low-dimensional style latent space. The learned latent space introduces several advantages over previous traditional approaches to multi-modal I2I translation. First, it is not dependent on the target dataset, and generalizes well across multiple domains. Second, it learns a more powerful and expressive latent space, which improves the fidelity of style capture and transfer. The proposed style pre-training also simplifies the training objective and speeds up the training significantly. Furthermore, we provide a detailed study of the contribution of different loss terms to the task of multi-modal I2I translation, and propose a simple alternative to VAEs to enable sampling from unconstrained latent spaces. Finally, we achieve state-of-the-art results on six challenging benchmarks with a simple training objective that includes only a GAN loss and a reconstruction loss.
IDIAR: Augmented Reality Dashboards to Supervise Mobile Intervention Studies

Mobile intervention studies employ mobile devices to observe participants' behavior change over several weeks. Researchers regularly monitor high-dimensional data streams to ensure data quality and prevent data loss (e.g., missing engagement or malfunctions). The multitude of problem sources hampers possible automated detection of such irregularities – providing a use case for interactive dashboards. With the advent of untethered head-mounted AR devices, these dashboards can be placed anywhere in the user's physical environment, leveraging the available space and allowing for flexible information arrangement and natural navigation. In this work, we present the user-centered design and the evaluation of IDIAR: Interactive Dashboards in AR, combining a head-mounted display with the familiar interaction of a smartphone. A user study with 15 domain experts for mobile intervention studies shows that participants appreciated the multimodal interaction approach. Based on our findings, we provide implications for research and design of interactive dashboards in AR.
Anomaly Credit Card Fraud Detection Using Deep Learning

Recently, fraudulent credit card transactions is a growing worldwide problem which costs upwards of billions of dollars per year. It becomes a significant security issue for customers, companies, and the financial sector. Innovative prediction techniques can provide the ability to detect fraudulent transactions for financial institutions in an effective manner. In this work, we introduce a deep learning model for anomaly detection for credit card fraud in financial transactions and compare the deep learning model with several existing machine learning algorithms. The proposed model is applied on a dataset gathered in Europe in 2 days in September 2013. The accuracy evaluation metric is used to evaluate the ability of the proposed model to detect credit card fraud.
Modality-aware Style Adaptation for RGB-Infrared Person Re-Identification

RGB-infrared (IR) person re-identification is a challenging task due to the large modality gap between RGB and IR images. Many existing methods bridge the modality gap by style conversion, requiring high-similarity images exchanged by complex CNN structures, like GAN. In this paper, we propose a highly compact modality-aware style adaptation (MSA) framework, which aims to explore more potential relations between RGB and IR modalities by introducing new related modalities. Therefore, the attention is shifted from bridging to filling the modality gap with no requirement on high-quality generated images. To this end, we firstly propose a concise feature-free image generation structure to adapt the original modalities to two new styles that are compatible with both inputs by patch-based pixel redistribution. Secondly, we devise two image style quantification metrics to discriminate styles in image space using luminance and contrast. Thirdly, we design two image-level losses based on the quantified results to guide the style adaptation during an end-to-end four-modality collaborative learning process. Experimental results on two datasets SYSU-MM01 and RegDB show that MSA achieves significant improvements with little extra computation cost and outperforms the state-of-the-art methods.
Small head movements increase and colour noise in data from five video-based P–CR eye trackers

Abstract We empirically investigate the role of small, almost imperceptible balance and breathing movements of the head on the level and colour of noise in data from five commercial video-based P-CR eye trackers. By comparing noise from recordings with completely static artificial eyes to noise from recordings where the artificial eyes are worn by humans, we show that very small head movements increase levels and colouring of the noise in data recorded from all five eye trackers in this study. This increase of noise levels is seen not only in the gaze signal, but also in the P and CR signals of the eye trackers that provide these camera image features. The P and CR signals of the SMI eye trackers correlate strongly during small head movements, but less so or not at all when the head is completely still, indicating that head movements are registered by the P and CR images in the eye camera. By recording with artificial eyes, we can also show that the pupil size artefact has no major role in increasing and colouring noise. Our findings add to and replicate the observation by Niehorster et al., (2021) that lowpass filters in video-based P–CR eye trackers colour the data. Irrespective of source, filters or head movements, coloured noise can be confused for oculomotor drift. We also find that usage of the default head restriction in the EyeLink 1000+, the EyeLink II and the HiSpeed240 result in noisier data compared to less head restriction. Researchers investigating data quality in eye trackers should consider not using the Gen 2 artificial eye from SR Research / EyeLink. Data recorded with this artificial eye are much noisier than data recorded with other artificial eyes, on average 2.2–14.5 times worse for the five eye trackers.
Action recognition of individuals on an airport apron based on tracking bounding boxes of the thermal infrared target

Improvements in the security of airport grounds at night, in fog and in other low-visibility conditions are becoming increasingly important. Existing ground surveillance approaches do not meet the requirements of the rapid development of aviation transportation. There is thus a need to efficiently monitor individuals on airport grounds in all weather and visibility conditions. In this paper, we propose a method of recognizing the actions of individuals on airport grounds based on an infrared image sequence in airport ground surveillance. First, we construct an action dataset having seven action classes according to the action characteristics of individuals moving on airport grounds. Second, taking into account the shortcomings of infrared surveillance images, we adopt the proposed method to extract the target from the background through a tracking algorithm and then compute short-term spatiotemporal feature vectors based on the tracking results. Third, we construct an action classification network that has two long short-term memory layers to extract long-term spatiotemporal features and a fully connected layer for classification. Our experimental results illustrate that the proposed network architecture performs favorably against other network architectures and that the proposed algorithm can achieve state-of-the-art performance of 87.2% classification accuracy on the constructed dataset.