Reviewerly/exHarmony huggingface dataset:
User Interfaces for Browsing the Online Catalogues of Museums and Cultural Heritage Institutions: Preliminary Research on Mapping of Subject Access Points

Browsing through topics in online catalogues is often the first step in the information-seeking process. Therefore, subject analysis and indexing are vitally important and also closely connected to the subject access to information. The aim of this paper is to analyse and compare the subject terms from different knowledge organization systems, used for subject retrieval in online catalogues of museums and cultural heritage institutions, in order to better understand and improve browsing interfaces. Preliminary research and mapping will be conducted on subject terms collected from three sources: (1) the Art UK platform, (2) the Tate's website, and (3) the Art & Architecture Thesaurus. The mapping between the previously mentioned subject vocabularies resulted in the identification of various levels of matching. Based on the analysis and interpretation of mapping results, a few suggestions, aimed at ensuring the functionality and convenience of browsing in the context of online catalogues of collections held in museums and heritage institutions, are offered.
Deep Gaussian Denoising Network Based on Morphological Operators with Low-Precision Arithmetic

This paper presents a deep network based on morphological filters for Gaussian denoising. The morphological filters can be applied with only addition, max, and min functions and require few computational resources. Therefore, the proposed network is suitable for implementation using a small microprocessor. Each layer of the proposed network consists of a top-hat transform, which extracts small peaks and valleys of noise components from the input image. Noise components are iteratively reduced in each layer by subtracting the noise components from the input image. In this paper, the extensions of opening and closing are introduced as linear combinations of the morphological filters for the top-hat transform of this deep network. Multiplications are only required for the linear combination of the morphological filters in the proposed network. Because almost all parameters of the network are structuring elements of the morphological filters, the feature maps and parameters can be represented in short bit-length integer form, which is suitable for implementation with single instructions, multiple data (SIMD) instructions. Denoising examples show that the proposed network obtains denoising results comparable to those of BM3D [1] without linear convolutions and with approximately one tenth the number of parameters of a full-scale deep convolutional neural network [2]. Moreover, the computational time of the proposed method using SIMD instructions of a microprocessor is also presented.
Laplace Redux - Effortless Bayesian Deep Learning

Bayesian formulations of deep learning have been shown to have compelling theoretical properties and offer practical functional benefits, such as improved predictive uncertainty quantification and model selection. The Laplace approximation (LA) is a classic, and arguably the simplest family of approximations for the intractable posteriors of deep neural networks. Yet, despite its simplicity, the LA is not as popular as alternatives like variational Bayes or deep ensembles. This may be due to assumptions that the LA is expensive due to the involved Hessian computation, that it is difficult to implement, or that it yields inferior results. In this work we show that these are misconceptions: we (i) review the range of variants of the LA including versions with minimal cost overhead; (ii) introduce laplace, an easy-to-use software library for PyTorch offering user-friendly access to all major flavors of the LA; and (iii) demonstrate through extensive experiments that the LA is competitive with more popular alternatives in terms of performance, while excelling in terms of computational cost. We hope that this work will serve as a catalyst to a wider adoption of the LA in practical deep learning, including in domains where Bayesian approaches are not typically considered at the moment.
Impact of video content and resolution on the cognitive dynamics of surveillance decision‐making

Abstract Security screeners identify targets from surveillance videos. Their performance can fluctuate as resolution or video content salience varies. Image spatial resolution may be degraded if a camera has low pixel density. Similarly, temporal resolution can be degraded due to transmission interference or low bandwidth. Both types of deteriorated resolution can increase observer uncertainty and reduce target identification accuracy. Further, these outcomes may reflect an interaction between target type and resolution. To help quantify, study, and explain possible interactions, we utilize process‐tracing methodology to understand the cognitive dynamics of surveillance decisions. These insights can improve the development of surveillance augmentation technologies and processes. Mouse tracking is a robust process‐tracing method successfully leveraged to observe continuously unfolding cognitive processes. Lateral vacillating mouse movements reflect decision uncertainty, and trajectory curvature serves measures cognitive conflict to select an unchosen alternative. We utilized mouse tracking to measure decision conflict during a video surveillance task with varying salience due to resolution or target type. Greater cognitive conflict was found when observers classified person‐related targets compared to vehicle‐related targets. Trajectories indicated stronger certainty when classifying events as having occurred, rather than when indicating events did not occur. Greater cognitive conflict was seen under degraded spatial resolution when observers were moderately confident compared to strongly confident or reported themselves as guessing. Under degraded temporal resolution, the greatest cognitive conflict occurred when observers reported they were guessing. Beyond these findings, we have demonstrated the viability of mouse tracking as an unobtrusive process‐tracing method for measuring uncertainty during realistic surveillance.
Gender Heterogeneous Effects of Malaria Risk on Agriculture Productivity: Empirical Evidence from Rural Ethiopia

Debility due to malaria remains a major constraint to economic growth in many Agrarian economies. In the case of Ethiopia, nearly 70% of the population reside in malaria prone areas with focal and multifocal malaria epidemics, presenting a major challenge to subsistence farmers who rely on family labor supply for agricultural production. Various studies have analysed the economic burden of malaria in developing countries, proposing various interventions to reduce its prevalence. These studies have, however, paid little attention to the role of gender in shaping the impact of malaria infections on agricultural labor supply and productivity. There is evidence of existence of complex and diverse gender norms that shape decision making and gender roles in households in many African countries and may influence the risks of malaria infections within households, and access to services for malaria prevention and control. In this study, we examine the direct and indirect effects of malaria risk on agricultural productivity among smallholder farmers. We deviate from previous research in two main aspects: we examine productivity effects of the disease by sex and age of the infected member of the household and estimate the malaria equations using the fractional response model to overcome the limitations of linear and non-linear econometric estimators for bounded data. We find that malaria significantly reduces crop yields and labor productivity. For instance, a 50% increase in malaria prevalence in a household can reduce the per capita land productivity by US$13.4/year, which is about 7.8% reduction in annual per capita income. The estimated loss in labor productivity is about US$1.38/person-day given a 50% rise in malaria prevalence in a household. The results further reveal differential productivity effects of the disease with respect to gender of the household member infected; the effect is greatest when women and children below 14 years in the household fall sick compared to men. The results highlight the need to strengthen women and children’s access to malaria control and prevention services to enhance human health and agricultural productivity. Furthermore, rural development projects may achieve greater health benefits by integrating malaria control interventions with the existing agricultural interventions.
Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little

A possible explanation for the impressive performance of masked language model (MLM) pre-training is that such models have learned to represent the syntactic structures prevalent in classical NLP pipelines. In this paper, we propose a different explanation: MLMs succeed on downstream tasks almost entirely due to their ability to model higher-order word co-occurrence statistics. To demonstrate this, we pre-train MLMs on sentences with randomly shuffled word order, and show that these models still achieve high accuracy after fine-tuning on many downstream tasks—including tasks specifically designed to be challenging for models that ignore word order. Our models perform surprisingly well according to some parametric syntactic probes, indicating possible deficiencies in how we test representations for syntactic information. Overall, our results show that purely distributional information largely explains the success of pre-training, and underscore the importance of curating challenging evaluation datasets that require deeper linguistic knowledge.
Regularization in ResNet with Stochastic Depth

Regularization plays a major role in modern deep learning. From classic techniques such as L1,L2 penalties to other noise-based methods such as Dropout, regularization often yields better generalization properties by avoiding overfitting. Recently, Stochastic Depth (SD) has emerged as an alternative regularization technique for residual neural networks (ResNets) and has proven to boost the performance of ResNet on many tasks [Huang et al., 2016]. Despite the recent success of SD, little is known about this technique from a theoretical perspective. This paper provides a hybrid analysis combining perturbation analysis and signal propagation to shed light on different regularization effects of SD. Our analysis allows us to derive principled guidelines for choosing the survival rates used for training with SD.
Conversational AI Efforts within Facebook AI Applied Research

The goal of the conversational AI team at Facebook AI Applied Research team is to create AI driven dialog capabilities with the augmented/virtual reality product focus. This talk provides an overview of our recent efforts on data collection, multimodal dialog, pipelined model-based policies and end-to-end architectures.
Revisiting Model Stitching to Compare Neural Representations

We revisit and extend model (Lenc & Vedaldi 2015) as a methodology to study the internal representations of neural networks. Given two trained and frozen models $A$ and $B$, we consider a model'' formed by connecting the bottom-layers of $A$ to the top-layers of $B$, with a simple trainable layer between them. We argue that model is a powerful and perhaps under-appreciated tool, which reveals aspects of representations that measures such as centered kernel alignment (CKA) cannot. Through extensive experiments, we use model to obtain quantitative verifications for intuitive statements such as networks learn similar representations'', by demonstrating that good networks of the same architecture, but trained in very different ways (e.g.: supervised vs. self-supervised learning), can be stitched to each other without drop in performance. We also give evidence for the intuition that is better'' by showing that representations learnt with (1) more data, (2) bigger width, or (3) more training time can be plugged in'' to weaker models to improve performance. Finally, our experiments reveal a new structural property of SGD which we call stitching connectivity'', akin to mode-connectivity: typical minima reached by SGD can all be stitched to each other with minimal change in accuracy.
A New Theory of Human Behavior and Motivation

We introduce a novel and comprehensive theory of human behavior and motivation, which incorporates within it both the laws of biology and core aspects of the social sciences. In continuation of the governing postulates of natural sciences we formulate the principle of preservation as the primary law of psychology; provide precise criteria for the definition of basic human needs and uncover their origin; explore the relation between body and mind; and show correlations between individual and social perspectives of life. Subsequently, we integrate these concepts into a unified model of human behavior. We reevaluate the place of psychology within the overall domain of social science and postulate the key role of psychological drives in formation of social processes both in the current and historical perspectives. The paper signifies a step towards establishment of a common paradigm of psychology.
An Improved Anisotropic Diffusion Filtering Approach for noise reduction in MRI

A confined, non-invasive and accurate diagnosis procedure is preferred for investigation of numerous human body disorders. Magnetic Resonance (MR) Imaging is one such crucial and popular diagnostic procedure and therefore these MR images need to be noise free to reduce the chances of error in diagnosis. An improved and novel approach has been suggested to denoise MR images by utilizing unbiased non local means (UNLM) method and anisotropic diffusion method. The proposed method's performance has been compared with other denoising techniques and the results were evaluated on metrices like Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structure Similarity Index (SSIM) values. The proposed method produced successful results and the process of anisotropic diffusion was hence improved. The MR images were well denoised and fine structures were also preserved along with the edges.
Privately Learning Subspaces

Private data analysis suffers a costly curse of dimensionality. However, the data often has an underlying low-dimensional structure. For example, when optimizing via gradient descent, the gradients often lie in or near a low-dimensional subspace. If that low-dimensional structure can be identified, then we can avoid paying (in terms of privacy or accuracy) for the high ambient dimension. We present differentially private algorithms that take input data sampled from a low-dimensional linear subspace (possibly with a small amount of error) and output that subspace (or an approximation to it). These algorithms can serve as a pre-processing step for other procedures.
Improving the user engagement in a fully immersive experience by the means of a conversational non-playable character used as a tourist guide

Virtual reality (VR) is a promising tool for the tourism industry as it allows an amazing replica of locations in the real world offering the opportunity to provide prospective customers with a virtual travel experience. Especially with VR headsets that allow fully immersive travel experiences where the users are essentially placed within a virtual world, which they can move around and, in some cases, interact with. However, even if vision alone is often perceptually dominant, interaction between users and virtual content is a crucial element of any immersive visualization environment in both virtual reality and 360-degree content (photos and videos). In this article we describe a way to improve the interaction between users and virtual content by the means of non-playable (NPC) characters which have the ability to provide a natural language conversation during an immersive experience.
Adversarial Robustness with Non-uniform Perturbations

Robustness of machine learning models is critical for security related applications, where real-world adversaries are uniquely focused on evading neural network based detectors. Prior work mainly focus on crafting adversarial examples (AEs) with small uniform norm-bounded perturbations across features to maintain the requirement of imperceptibility. However, uniform perturbations do not result in realistic AEs in domains such as malware, finance, and social networks. For these types of applications, features typically have some semantically meaningful dependencies. The key idea of our proposed approach is to enable non-uniform perturbations that can adequately represent these feature dependencies during adversarial training. We propose using characteristics of the empirical data distribution, both on correlations between the features and the importance of the features themselves. Using experimental datasets for malware classification, credit risk prediction, and spam detection, we show that our approach is more robust to real-world attacks. Finally, we present robustness certification utilizing non-uniform perturbation bounds, and show that non-uniform bounds achieve better certification.
What Matters for Adversarial Imitation Learning

Adversarial imitation learning has become a popular framework for imitation in continuous control. Over the years, several variations of its components were proposed to enhance the performance of the learned policies as well as the sample complexity of the algorithm. In practice, these choices are rarely tested all together in rigorous empirical studies. It is therefore difficult to discuss and understand what choices, among the high-level algorithmic options as well as low-level implementation details, matter. To tackle this issue, we implement more than 50 of these choices in a generic adversarial imitation learning framework and investigate their impacts in a large-scale study (>500k trained agents) with both synthetic and human-generated demonstrations. While many of our findings confirm common practices, some of them are surprising or even contradict prior work. In particular, our results suggest that artificial demonstrations are not a good proxy for human data and that the very common practice of evaluating imitation algorithms only with synthetic demonstrations may lead to algorithms which perform poorly in the more realistic scenarios with human demonstrations.
Metric Learning in Multilingual Sentence Similarity Measurement for Document Alignment

Document alignment techniques based on multilingual sentence representations have recently shown state of the art results.However, these techniques rely on unsupervised distance measurement techniques, which cannot be fined-tuned to the task at hand.In this paper, instead of these unsupervised distance measurement techniques, we employ Metric Learning to derive task-specific distance measurements.These measurements are supervised, meaning that the distance measurement metric is trained using a parallel dataset.Using a dataset belonging to English, Sinhala, and Tamil, which belong to three different language families, we show that these taskspecific supervised distance learning metrics outperform their unsupervised counterparts, for document alignment.
Universal dynamical scaling laws in three-state quantum walks

We perform a finite-time scaling analysis over the detrapping point of a three-state quantum walk on the line. The coin operator is parametrized by ρ that controls the wave packet spreading velocity. The input state prepared at the origin is set as a symmetric linear combination of two eigenstates of the coin operator with a characteristic mixing angle θ, one of them being the component that results in full spreading occurring at θ_{c}(ρ) for which no fraction of the wave packet remains trapped near the initial position. We show that relevant quantities, such as the survival probability and the participation ratio assume single parameter scaling forms at the vicinity of the detrapping angle θ_{c}. In particular, we show that the participation ratio grows linearly in time with a logarithmic correction, thus, shedding light on previous reports of sublinear behavior.
Comparative Study on Lexicon-based sentiment analysers over Negative sentiment

Sentiment Analysis or Opinion Mining is one of the latest trends of social listening, which is presently reshaping Commercial Organisations. It is a significant task of Natural Language Processing (NLP). The vast availability of product review data within Social media like Twitter, Facebook, and e-commerce site like Amazon, Alibaba. An organisation can get insight into a customer's mind based on a product or what type of opinion the product has generated in the market. Accordingly, an organisation can take some reactive preventive measures. While analysing the above, we have found that negative opinion has a strong effect on customers' minds than the positive one. Also, negative opinions are more viral in terms of diffusion. Our present work is based on a comparison of two available rule-based Sentiment analysers, VADER, and TextBlob on domain-specific product review data from Amazon.co.in. It investigates, which has higher accuracy in terms of classifying negative opinions. Our research has found out that VADER’s negative polarity sentiment classification accuracy is more elevated than TextBlob.
Redundancy techniques for straggler mitigation in distributed optimization and learning

Performance of distributed optimization and learning systems is bottlenecked by straggler nodes and slow communication links, which significantly delay computation. We propose a distributed optim...
Agreeing to Disagree: Annotating Offensive Language Datasets with Annotators' Disagreement.

Since state-of-the-art approaches to offensive language detection rely on supervised learning, it is crucial to quickly adapt them to the continuously evolving scenario of social media. While several approaches have been proposed to tackle the problem from an algorithmic perspective, so to reduce the need for annotated data, less attention has been paid to the quality of these data. Following a trend that has emerged recently, we focus on the level of agreement among annotators while selecting data to create offensive language datasets, a task involving a high level of subjectivity. Our study comprises the creation of three novel datasets of English tweets covering different topics and having five crowd-sourced judgments each. We also present an extensive set of experiments showing that selecting training and test data according to different levels of annotators’ agreement has a strong effect on classifiers performance and robustness. Our findings are further validated in cross-domain experiments and studied using a popular benchmark dataset. We show that such hard cases, where low agreement is present, are not necessarily due to poor-quality annotation and we advocate for a higher presence of ambiguous cases in future datasets, in order to train more robust systems and better account for the different points of view expressed online.
Coherent parallelization of universal classical computation

Abstract Previously, higher-order Hamiltonians (HoH) had been shown to offer an advantage in both metrology and quantum energy storage. Here, we axiomatize a model of computation that allows us to consider such Hamiltonians for the purposes of computation. From this axiomatic model, we formally prove that an HoH-based algorithm can gain up to a quadratic speed-up over classical sequential algorithms—for any possible classical computation. We show how our axiomatic model is grounded in the same physics as that used in HoH-based quantum advantage for metrology and battery charging. Thus we argue that any advance in implementing HoH-based quantum advantage in those scenarios can be co-opted for the purpose of speeding up computation.