Reviewerly/exHarmony huggingface dataset:
Effect of a Virtual Reality-Based Restorative Environment on the Emotional and Cognitive Recovery of Individuals with Mild-to-Moderate Anxiety and Depression

In this study, restorative environment theory and virtual reality (VR) technology were combined to build different 3D dynamic VR interactive scenes. We discuss the effects of a VR restorative environment on the emotional and cognitive recovery of individuals with mild-to-moderate anxiety and depression. First, we built a VR restorative garden scene, divided into four areas: forest, lawn, horticultural planting, and water features. The scene was verified to have a good recovery effect in 26 participants. Then, 195 participants with mild-to-moderate anxiety and depression were selected as experimental subjects. Through psychological testing and EMG (Electromyography) and EEG (Electroencephalography) data feedback, we further explored the differences in the sense of presence in VR restorative scenes and their effect on individual emotional and cognitive recovery. The results showed that (1) both the restorative environment images and the VR scenes had a healing effect (the reduction in negative emotions and the recovery of positive emotions and cognition), with no difference in the subjective feeling of recovery among the different scenes, but the recovery score of the VR urban environment was higher than that of the natural environment (differing from the results in real environments); (2) a high sense of presence can be experienced in different VR scenes, and interactive activities in VR scenes can provide a great presence experience; (3) the recovery effects of VR restorative environment on emotion and self-efficacy are realized through the presence of VR scenes; (4) a VR restorative environment is helpful for the emotional improvement and cognitive recovery of individuals with mild-to-moderate anxiety and depression. VR urban scenes also have good recovery effects. In terms of cognitive recovery, self-efficacy improved significantly. In addition, from the perspective of EEG indicators, the VR restorative scene experience activated the prefrontal lobe, which is conducive to cognitive recovery in individuals with mild-to-moderate anxiety and depression. In terms of emotional improvement, negative emotions were significantly reduced in the different VR scene groups. In conclusion, we further explored ways to help individuals with mild-to-moderate anxiety and depression, in order to promote the development and application of mental health.
A study of ant-based pheromone spaces for generation constructive hyper-heuristics

Research into the applicability of ant-based optimisation techniques for hyper-heuristics is largely limited. This paper expands upon the existing body of research by presenting a novel ant-based generation constructive hyper-heuristic and then investigates how different pheromone maps affect its performance. Previous work has focused on applying ant-based optimisation techniques that work in the solution space directly to the heuristic space and we hypothesise that this may be problematic for the hyper-heuristic’s efficacy. The focus of this analysis is primarily on how the pheromone map, 2D and 3D, of ant-based methods, can be used for this hyper-heuristic task. 2D pheromone maps are the predominant pheromone map type used by ant-based algorithms. Thus the comparison here is between the existing 2D pheromone map and the newly introduced 3D pheromone map. The analysis consists of multiple experiments with algorithms in the TSP and 1DBPP domain which are assessed in terms of optimality and generality. The results of the experiment demonstrate key differences in performance between the two different pheromone spaces. The 3D pheromone map showed better generality and optimality in the 1DBPP domain whereas the 2D pheromone map showed better generality and only marginally better optimality for the TSP domain. The analysis indicated that the different pheromone maps work most optimally for different types of optimisation problems. The hybrid method showed some improvements in generality but showed little improvements in optimality overall.
Photonic multiplexing techniques for neuromorphic computing

Abstract The simultaneous advances in artificial neural networks and photonic integration technologies have spurred extensive research in optical computing and optical neural networks (ONNs). The potential to simultaneously exploit multiple physical dimensions of time, wavelength and space give ONNs the ability to achieve computing operations with high parallelism and large-data throughput. Different photonic multiplexing techniques based on these multiple degrees of freedom have enabled ONNs with large-scale interconnectivity and linear computing functions. Here, we review the recent advances of ONNs based on different approaches to photonic multiplexing, and present our outlook on key technologies needed to further advance these photonic multiplexing/hybrid-multiplexing techniques of ONNs.
Neural network based reinforcement learning for audio–visual gaze control in human–robot interaction

This paper introduces a novel neural network-based reinforcement learning approach for robot gaze control. Our approach enables a robot to learn and to adapt its gaze control strategy for human–robot interaction neither with the use of external sensors nor with human supervision. The robot learns to focus its attention onto groups of people from its own audio-visual experiences, independently of the number of people, of their positions and of their physical appearances. In particular, we use a recurrent neural network architecture in combination with Q-learning to find an optimal action-selection policy; we pre-train the network using a simulated environment that mimics realistic scenarios that involve speaking/silent participants, thus avoiding the need of tedious sessions of a robot interacting with people. Our experimental evaluation suggests that the proposed method is robust in terms of parameter configuration, i.e. the selection of the parameter values employed by the method do not have a decisive impact on the performance. The best results are obtained when both audio and visual information is jointly used. Experiments with the Nao robot indicate that our framework is a step forward towards the autonomous learning of a socially acceptable gaze behavior.
Improving quantum annealing of the ferromagnetic <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>p</mml:mi></mml:math> -spin model through pausing

The probability of success of quantum annealing can be improved significantly by pausing the annealer during its dynamics, exploiting thermal relaxation in a controlled fashion. In this paper, we investigate the effect of pausing the quantum annealing of the fully connected ferromagnetic $p$-spin model. This analytically solvable model has a searchlike behavior and is often used as a benchmark for the performances of quantum annealing. We numerically show that: (i) the optimal pausing point is 60% longer than the avoided crossing time for the analyzed instance, and (ii) at the optimal pausing point, we register a 45% improvement in the probability of success with respect to a quantum annealing with no pauses of the same duration. These results are in line with those observed experimentally for less connected models with the available quantum annealers. The observed improvement for the $p$-spin model can be up to two orders of magnitude with respect to an isolated quantum dynamics of the same duration.
Balancing Open Science and Data Privacy in the Water Sciences

Abstract Open science practices such as publishing data and code are transforming water science by enabling synthesis and enhancing reproducibility. However, as research increasingly bridges the physical and social science domains (e.g., socio‐hydrology), there is the potential for well‐meaning researchers to unintentionally violate the privacy and security of individuals or communities by sharing sensitive information. Here we identify the contexts in which privacy violations are most likely to occur, such as working with high‐resolution spatial data (e.g., from remote sensing), consumer data (e.g., from smart meters), and/or digital trace data (e.g., from social media). We also suggest practices for identifying and addressing privacy concerns at the individual, institutional, and disciplinary levels. We strongly advocate that the water science community continue moving toward open science and socio‐environmental research and that progress toward these goals be rooted in open and ethical data management.
Virtual machine allocation and migration based on performance-to-power ratio in energy-efficient clouds

The last decade witnessed a dramatic advance in cloud computing research and techniques. One of the key challenges in this field is reducing the massive amount of energy consumption in cloud computing data centers. Many power-aware virtual machine (VM) allocation and consolidation approaches were proposed to reduce energy consumption efficiently. However, most of the existing efficient cloud solutions save energy at the cost of significant performance degradation. In this paper, we propose a strategy to calculate the optimized working utilization levels for host computers. As the performance and power data need to be measured on real platforms, to make our design practical, we propose a strategy named “PPRGear” which is based on the sampling of utilization levels with distinct Performance-to-Power Ratios (PPR) calculated as the number of Server Side Java operations completed during a certain time period divided by the average active power consumption in that period. In addition, we present a framework for virtual machine allocation and migration which leverages the PPR for various host types. By achieving the optimal balance between host utilization and energy consumption, our framework is able to ensure that host computers run at the most power-efficient utilization levels, i.e., the levels with the highest PPR, thus tremendously reducing energy consumption with ignorable sacrifice of performance. Our extensive experiments with real world traces show that compared with three baseline energy-efficient VM allocation and selection algorithms, IqrMc, MadMmt, and ThrRs, our framework is able to reduce the energy consumption up to 69.31% for various host computer types with fewer migration times, shutdown times, and little performance degradation for cloud computing data centers.
What Does BERT Look At? An Analysis of BERT's Attention

Large pre-trained neural networks such as BERT have had great recent success in NLP, motivating a growing body of research investigating what aspects of language they are able to learn from unlabeled data. Most recent analysis has focused on model outputs (e.g., language model surprisal) or internal vector representations (e.g., probing classifiers). Complementary to these works, we propose methods for analyzing the attention mechanisms of pre-trained models and apply them to BERT. BERT's attention heads exhibit patterns such as attending to delimiter tokens, specific positional offsets, or broadly attending over the whole sentence, with heads in the same layer often exhibiting similar behaviors. We further show that certain attention heads correspond well to linguistic notions of syntax and coreference. For example, we find heads that attend to the direct objects of verbs, determiners of nouns, objects of prepositions, and coreferent mentions with remarkably high accuracy. Lastly, we propose an attention-based probing classifier and use it to further demonstrate that substantial syntactic information is captured in BERT's attention.
Distributed multi-sensor multi-view fusion based on generalized covariance intersection

Distributed multi-target tracking (DMTT) is addressed for sensors having different fields of view (FoVs). The proposed approach is based on the idea of fusing the posterior Probability Hypotheses Densities (PHDs) generated by the sensors on the basis of the local measurements. An efficient and robust distributed fusion algorithm combining the Generalized Covariance Intersection (GCI) rule with a suitable Clustering Algorithm (CA) is proposed, named CA-GCI fusion algorithm. The CA is used to decompose each posterior PHD into well-separated components (clusters). For the commonly detected targets, an efficient parallelized GCI fusion strategy is proposed and analyzed in terms of L1 error. For the remaining targets, a suitable compensation strategy is adopted so as to counteract the GCI sensitivity to independent detections while reducing the occurrence of false targets. Detailed implementation steps using a Gaussian Mixture (GM) representation of the PHDs are provided. Numerical experiments clearly confirm the effectiveness of the proposed CA-GCI fusion algorithm.
Evidence Sentence Extraction for Machine Reading Comprehension

Remarkable success has been achieved in the last few years on some limited machine reading comprehension (MRC) tasks.However, it is still difficult to interpret the predictions of existing MRC models.In this paper, we focus on extracting evidence sentences that can explain or support the answers of multiplechoice MRC tasks, where the majority of answer options cannot be directly extracted from reference documents.Due to the lack of ground truth evidence sentence labels in most cases, we apply distant supervision to generate imperfect labels and then use them to train an evidence sentence extractor.To denoise the noisy labels, we apply a recently proposed deep probabilistic logic learning framework to incorporate both sentence-level and cross-sentence linguistic indicators for indirect supervision.We feed the extracted evidence sentences into existing MRC models and evaluate the end-to-end performance on three challenging multiplechoice MRC datasets: MultiRC, RACE, and DREAM, achieving comparable or better performance than the same models that take as input the full reference document.To the best of our knowledge, this is the first work extracting evidence sentences for multiple-choice MRC.
Understanding Attention and Generalization in Graph Neural Networks

We aim to better understand attention over nodes in graph neural networks (GNNs) and identify factors influencing its effectiveness. We particularly focus on the ability of attention GNNs to generalize to larger, more complex or noisy graphs. Motivated by insights from the work on Graph Isomorphism Networks, we design simple graph reasoning tasks that allow us to study attention in a controlled environment. We find that under typical conditions the effect of attention is negligible or even harmful, but under certain conditions it provides an exceptional gain in performance of more than 60% in some of our classification tasks. Satisfying these conditions in practice is challenging and often requires optimal initialization or supervised training of attention. We propose an alternative recipe and train attention in a weakly-supervised fashion that approaches the performance of supervised models, and, compared to unsupervised models, improves results on several synthetic as well as real datasets. Source code and datasets are available at https://github.com/bknyaz/graph_attention_pool.
An investigation on information leakage of DNS over TLS

DNS over TLS (DoT) protects the confidentiality and integrity of DNS communication by encrypting DNS messages transmitted between users and resolvers. In recent years, DoT has been deployed by popular recursive resolvers like Cloudflare and Google. While DoT is supposed to prevent on-path adversaries from learning and tampering with victims' DNS requests and responses, it is unclear how much information can be deduced through traffic analysis on DoT messages. To answer this question, in this work, we develop a DoT fingerprinting method to analyze DoT traffic and determine if a user has visited websites of interest to adversaries. Given that a visit to a website typically introduces a sequence of DNS packets, we can infer the visited websites by modeling the temporal patterns of packet sizes. Our method can identify DoT traffic for websites with a false negative rate of less than 17% and a false positive rate of less than 0.5% when DNS messages are not padded. Moreover, we show that information leakage is still possible even when DoT messages are padded. These findings highlight the challenges of protecting DNS privacy, and indicate the necessity of a thorough analysis of the threats underlying DNS communications for effective defenses.
A Stochastic Treatment of Learning to Rank Scoring Functions

Learning to Rank, a central problem in information retrieval, is a class of machine learning algorithms that formulate ranking as an optimization task. The objective is to learn a function that produces an ordering of a set of documents in such a way that the utility of the entire ordered list is maximized. Learning-to-rank methods do so by learning a function that computes a score for each document in the set. A ranked list is then compiled by sorting documents according to their scores. While such a deterministic mapping of scores to permutations makes sense during inference where stability of ranked lists is required, we argue that its greedy nature during training leads to less robust models. This is particularly problematic when the loss function under optimization---in agreement with ranking metrics---largely penalizes incorrect rankings and does not take into account the distribution of raw scores. In this work, we present a stochastic framework where, instead of a deterministic derivation of permutations from raw scores, permutations are sampled from a distribution defined by raw scores. Our proposed sampling method is differentiable and works well with gradient descent optimizers. We analytically study our proposed method and demonstrate when and why it leads to model robustness. We also show empirically, through experiments on publicly available learning-to-rank datasets, that the application of our proposed method to a class of ranking loss functions leads to significant model quality improvements.
On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models

This study investigates the effects of Markov chain Monte Carlo (MCMC) sampling in unsupervised Maximum Likelihood (ML) learning. Our attention is restricted to the family of unnormalized probability densities for which the negative log density (or energy function) is a ConvNet. We find that many of the techniques used to stabilize training in previous studies are not necessary. ML learning with a ConvNet potential requires only a few hyper-parameters and no regularization. Using this minimal framework, we identify a variety of ML learning outcomes that depend solely on the implementation of MCMC sampling.On one hand, we show that it is easy to train an energy-based model which can sample realistic images with short-run Langevin. ML can be effective and stable even when MCMC samples have much higher energy than true steady-state samples throughout training. Based on this insight, we introduce an ML method with purely noise-initialized MCMC, high-quality short-run synthesis, and the same budget as ML with informative MCMC initialization such as CD or PCD. Unlike previous models, our energy model can obtain realistic high-diversity samples from a noise signal after training.On the other hand, ConvNet potentials learned with non-convergent MCMC do not have a valid steady-state and cannot be considered approximate unnormalized densities of the training data because long-run MCMC samples differ greatly from observed images. We show that it is much harder to train a ConvNet potential to learn a steady-state over realistic images. To our knowledge, long-run MCMC samples of all previous models lose the realism of short-run samples. With correct tuning of Langevin noise, we train the first ConvNet potentials for which long-run and steady-state MCMC samples are realistic images.
DDM: Fast Near-Optimal Multi-Robot Path Planning Using Diversified-Path and Optimal Sub-Problem Solution Database Heuristics

We propose a novel centralized and decoupled algorithm, DDM, for solving multi-robot path planning problems in grid graphs, targeting on-demand and automated warehouse-like settings. Two settings are studied: a traditional one whose objective is to move a set of robots from their respective initial vertices to the goal vertices as quickly as possible, and a dynamic one which requires frequent re-planning to accommodate for goal configuration adjustments. Among other techniques, DDM is mainly enabled through exploiting two innovative heuristics: path diversification and optimal sub-problem solution databases. The two heuristics attack two distinct phases of a decoupling-based planner: while path diversification allows the more effective use of the entire workspace for robot travel, optimal sub-problem solution databases facilitate the fast resolution of local path conflicts. Extensive evaluation demonstrates that DDM achieves high levels of scalability and solution quality close to the optimum.
Gender-Inclusive HCI Research and Design: A Conceptual Review

Recent years have seen a growing number of calls for considering gender in the design or evaluation of software, websites, or other digital technology. Calls like these have arisen from an emerging awareness in HCI of findings from the social sciences that are relevant to the way people use and design technology. However, emerging work on bringing together gender research with software design choices is fragmented across multiple disciplines. This monograph aims to help bring such works together, by synthesizing the current state of affairs and future possibilities on how gender comes together with HCI design. The authors of this monograph consider inclusive design of technology whatever the gender of its users of particular importance. This conceptual review provides an overview of the motivations that have driven research in gender and inclusive HCI design. The authors review the empirical evidence for the impact of gender in thinking and behavior which underlies HCI research and design. They present how HCI design might inadvertently embed and perpetuate gender stereotypes. They also present current HCI design approaches to tackle gender stereotypes and produce gender-inclusive designs. The monograph concludes by discussing possible future directions in this area.
Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension

Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C 3 ), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations. We present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5%) and human readers (96.0%), especiallyon problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C 3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C 3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text. C 3 is available at https://dataset.org/c3/ .