Reviewerly/exHarmony huggingface dataset:
Unlocking Fairness: a Trade-off Revisited

The prevailing wisdom is that a model's fairness and its accuracy are in tension with one another. However, there is a pernicious {\em modeling-evaluating dualism} bedeviling fair machine learning in which phenomena such as label bias are appropriately acknowledged as a source of unfairness when designing fair models, only to be tacitly abandoned when evaluating them. We investigate fairness and accuracy, but this time under a variety of controlled conditions in which we vary the amount and type of bias. We find, under reasonable assumptions, that the tension between fairness and accuracy is illusive, and vanishes as soon as we account for these phenomena during evaluation. Moreover, our results are consistent with an opposing conclusion: fairness and accuracy are sometimes in accord. This raises the question, {\em might there be a way to harness fairness to improve accuracy after all?} Since most notions of fairness are with respect to the model's predictions and not the ground truth labels, this provides an opportunity to see if we can improve accuracy by harnessing appropriate notions of fairness over large quantities of {\em unlabeled} data with techniques like posterior regularization and generalized expectation. Indeed, we find that semi-supervision not only improves fairness, but also accuracy and has advantages over existing in-processing methods that succumb to selection bias on the training set.
Enhanced Cycle-Consistent Generative Adversarial Network for Color Normalization of H&amp;E Stained Images

Due to differences in tissue preparations, staining protocols and scanner models, stain colors of digitized histological images are excessively diverse. Color normalization is almost a necessary procedure for quantitative digital pathology analysis. Though several color normalization methods have been proposed, most of them depend on selection of representative templates and may fail in regions not matching the templates. We propose an enhanced cycle-GAN based method with a novel auxiliary input for the generator by computing a stain color matrix for every H&E image in the training set. The matrix guides the translation in the generator, and thus stabilizes the cycle consistency loss. We applied our proposed method as a pre-processing step for a breast metastasis classification task on a dataset from five medical centers and achieved the highest performance compared to other color normalization methods. Furthermore, our method is template-free and may be applied to other datasets without finetuning.
Spatial DIC Errors due to Pattern-Induced Bias and Grey Level Discretization

Digital image correlation (DIC) is an optical metrology method widely used in experimental mechanics for full-field shape, displacement and strain measurements. The required strain resolution for engineering applications of interest mandates DIC to have a high image displacement matching accuracy, on the order of 1/100th of a pixel, which necessitates an understanding of DIC errors. In this paper, we examine two spatial bias terms that have been almost completely overlooked. They cause a persistent offset in the matching of image intensities and thus corrupt DIC results. We name them pattern-induced bias (PIB), and intensity discretization bias (IDB). We show that the PIB error occurs in the presence of an undermatched shape function and is primarily dictated by the underlying intensity pattern for a fixed displacement field and DIC settings. The IDB error is due to the quantization of the gray level intensity values in the digital camera. In this paper we demonstrate these errors and quantify their magnitudes both experimentally and with synthetic images.
Data Parameters: A New Family of Parameters for Learning a Differentiable Curriculum

Recent works have shown that learning from easier instances first can help deep neural networks (DNNs) generalize better. However, knowing which data to present during different stages of training is a challenging problem. In this work, we address this problem by introducing data parameters. More specifically, we equip each sample and class in a dataset with a learnable parameter (data parameters), which governs their importance in the learning process. During training, at each iteration, as we update the model parameters, we also update the data parameters. These updates are done by gradient descent and do not require hand-crafted rules or design. When applied to image classification task on CIFAR10, CIFAR100,WebVision and ImageNet datasets, and object detection task on KITTI dataset, learning a dynamic curriculum via data parameters leads to consistent gains, without any increase in model complexity or training time. When applied to a noisy dataset, the proposed method learns to learn from clean images and improves over the state-of-the-art methods by 14%. To the best of our knowledge, our work is the first curriculum learning method to show gains on large scale image classification and detection tasks.
Imitation Learning via Off-Policy Distribution Matching

When performing imitation learning from expert demonstrations, distribution matching is a popular approach, in which one alternates between estimating distribution ratios and then using these ratios as rewards in a standard reinforcement learning (RL) algorithm. Traditionally, estimation of the distribution ratio requires on-policy data, which has caused previous work to either be exorbitantly data- inefficient or alter the original objective in a manner that can drastically change its optimum. In this work, we show how the original distribution ratio estimation objective may be transformed in a principled manner to yield a completely off-policy objective. In addition to the data-efficiency that this provides, we are able to show that this objective also renders the use of a separate RL optimization unnecessary. Rather, an imitation policy may be learned directly from this objective without the use of explicit rewards. We call the resulting algorithm ValueDICE and evaluate it on a suite of popular imitation learning benchmarks, finding that it can achieve state-of-the-art sample efficiency and performance.
Pairwise Gaussian Loss for Convolutional Neural Networks

Convolutional neural networks (CNNs) have demonstrated great competence in feature representation, and then, achieved a good performance to many classification tasks. Cross-entropy loss, together with softmax, is arguably one of the most commonly used loss functions in CNNs (that is generally called softmax loss). However, the softmax loss can result in a weakly discriminative feature representation since it focuses on the interclass separability rather than the intraclass compactness. This article proposes a pairwise Gaussian loss (PGL) for CNNs that can well address the intraclass compactness through significantly penalizing those similar sample pairs with a relatively large distance. At the same time, PGL can still ensure a good interclass separability. Experiments show that PGL can guarantee that CNNs obtain a better classification performance compared to not only the softmax loss but also others often used in CNNs. Our experiments also show that PGL has a stable convergence for the stochastic gradient descent optimization method in CNNs and a good generalization ability for different structures of CNNs.
Construction and exploitation of an historical knowledge graph to deal with the evolution of ontologies

With the advances of Artificial Intelligence, the need for annotated data increases. However, the quality of these annotations can be impacted by the evolution of domain knowledge since the relations between successive versions of ontologies are rarely described and the history of concepts is not kept at the ontology level. As a consequence, using datasets annotated at different times becomes a real challenge for data- and knowledge-intensive systems. This work presents a way to address this problem. We introduce a Historical Knowledge Graph (HKG), where information from previous versions of an ontology can be found inside a single graph, reducing storage space (no need for versioning) and data treatment time (no need for laborious analysis of each version of the ontology). The HKG proposed in this work represents the evolutionary aspects of the knowledge in a structural way. Examples of the applicability of an HKG for information retrieval and the maintenance of semantic annotations show the capability of our approach for improving the quality of existing techniques.
Hue-Correction Scheme Based on Constant-Hue Plane for Deep-Learning-Based Color-Image Enhancement

In this paper, we propose a novel hue-correction scheme based on the constant-hue plane in the RGB color space for deep-learning-based color-image enhancement. Existing hue-preserving image-enhancement methods cannot be applied to state-of-the-art enhancement methods such as deep-learning-based ones. Our main contributions are a discussion on the enhancement performance and the hue distortion of existing image-enhancement methods as well as the first attempt to make hue correction applicable to any existing image-enhancement methods including deep-learning-based ones. This novel scheme is carried out on the basis of the constant-hue plane in the RGB color space. In simulations, we first evaluated conventional image-enhancement methods in terms of the enhancement performance and hue distortion by using five objective metrics: the maximally saturated color similarity, the hue difference in CIEDE2000, discrete entropy, HIGRADE, and NIQMC. The experimental results show that recent deep-learning-based methods have a higher enhancement performance but cause images to be hue-distorted. In addition, the proposed scheme is demonstrated to be effective for suppressing hue distortion even under the use of deep-learning-based enhancement methods. Furthermore, it allows us not only to correct hue but also to maintain the performance of image-enhancement methods.
Innovative Accounting Interviewing: A Comparison of Real and Virtual Accounting Interviewers

ABSTRACT Recent technological advances make it possible to create automated virtual interviewers, called embodied conversational agents (ECAs). We study how an ECA compares to a human interviewer in three experiments. In experiment 1, we show that two theoretically motivated factors—making the ECA facially and vocally similar to the interviewee—result in the ECA performing similarly to or better than human interviewers for six antecedents of disclosure quality. In two additional experiments, we show that employees are, on average, 21 to 32 percent more likely to disclose violating internal controls to an ECA than to a human, even if the human interviewer has significant interviewing experience. These findings contribute to the ECA design literature by showing that similarity-enhancing features of ECAs increase the antecedents of disclosure. The findings also contribute to the accounting literature by demonstrating that ECA technology can increase the scope of interviewing in accounting without reducing interview quality.
Using Speech Synthesis to Train End-To-End Spoken Language Understanding Models

End-to-end models are an attractive new approach to spoken language understanding (SLU) in which the meaning of an utterance is inferred directly from the raw audio, without employing the standard pipeline composed of a separately trained speech recognizer and natural language understanding module. The downside of end-to-end SLU is that in-domain speech data must be recorded to train the model. We propose a strategy to overcome this requirement in which speech synthesis is used to generate a large synthetic training dataset from several artificial speakers. We confirm the effectiveness of our approach with experiments on two open-source SLU datasets, where synthesized speech is used both as a sole source of training data and as a form of data augmentation.
A Novel U-Shaped Transfer Function for Binary Particle Swarm Optimisation

Particle swarm optimisation (PSO), oneMirjalili, Seyedehzahra ofZhang, Hongyu theMirjalili, Seyedali most elegant algorithms in the field of nature-inspired optimisation, has many variants for solving different types of problems. One of these variantsChalup, Stephan isNoman, Nasimul binary particle swarm optimisation (BPSO), which is suitable for solving combinatorial optimisation problems. A main component of BPSO is the transfer function that maps continuous velocity values to probability values which in turn are used to update particle positions. Transfer function has a significant impact on the performance of BPSO algorithm. This paper proposes a novel transfer function with tunable parameters that allows different U-shaped transfer functions. For evaluating the proposed transfer functions, a set of benchmark functions and 0/1 knapsack problems are employed. The results show that the U-shaped transfer functions can significantly improve the performance of BPSO. It is also shown that the BPSO algorithms equipped with U-shaped transfer functions provide superior results compared to the existing transfer functions in the literature.
A Federated Approach in Training Acoustic Models

In this paper, a novel platform for Acoustic Model training based on Federated Learning (FL) is described. This is the first attempt to introduce Federated Learning techniques in Speech Recognition (SR) tasks. Besides the novelty of the task, the paper describes an easily generalizable FL platform and presents the design decisions used for this task. Amongst the novel algorithms introduced is a hierarchical optimization scheme employing pairs of optimizers and an algorithm for gradient selection, leading to improvements in training time and SR performance. The experimental validation of the proposed system is based on the LibriSpeech task, presenting a speed-up of x1.5 and 6% WERR. The proposed Federated Learning system appears to outperform the golden standard of distributed training in both convergence speed and overall model performance. Further improvements have been experienced in internal tasks.
Context-Aware Group Captioning via Self-Attention and Contrastive Features

While image captioning has progressed rapidly, existing works focus mainly on describing single images. In this paper, we introduce a new task, context-aware group captioning, which aims to describe a group of target images in the context of another group of related reference images. Context-aware group captioning requires not only summarizing information from both the target and reference image group but also contrasting between them. To solve this problem, we propose a framework combining self-attention mechanism with contrastive feature construction to effectively summarize common information from each image group while capturing discriminative information between them. To build the dataset for this task, we propose to group the images and generate the group captions based on single image captions using scene graphs matching. Our datasets are constructed on top of the public Conceptual Captions dataset and our new Stock Captions dataset. Experiments on the two datasets show the effectiveness of our method on this new task.
The Direction of Updatable Encryption Does Not Matter Much

Updatable encryption schemes allow for key rotation on ciphertexts. A client outsourcing storage of encrypted data to a cloud server can change its encryption key. The cloud server can update the stored ciphertexts to the new key using only a token provided by the client. This paper solves two open problems in updatable encryption, that of uni-directional vs. bi-directional updates, and post-quantum security. The main result in this paper is to analyze the security notions based on uni- and bi-directional updates. Surprisingly, we prove that uni- and bi-directional variants of each security notion are equivalent. The second result in this paper is to provide a new and efficient updatable encryption scheme based on the Decisional Learning with Error assumption. This gives us post-quantum security. Our scheme is bi-directional, but because of our main result, this is sufficient.
Using multi-criteria decision analysis to rank European health systems: The Beveridgian financing case

Health is one of the most fundamental human rights. In that sense, the creation of health systems attempted to provide the population with organisations, institutions, and resources to meet their needs. However, health inequalities are prevalent in all countries. Thus, evaluating health systems is vital to understand this issue. Accordingly, the aim of this work is to develop a multi-criteria decision analysis (MCDA) approach to innovatively rank nine of the European health systems with Beveridgian financing that would help determine the shortcomings of the Portuguese National Health Service and discern the operating “best practices”, in a close interaction with the Portuguese Ministry of Health. First, the panel of decision-making actors was guided through the design of a cognitive map to promote their learning process and help them identify eleven fundamental points of view. Second, their operationalisation was facilitated by selecting acceptable descriptors of performance. Finally, an MCDA approach was proposed to evaluate the chosen health systems using an additive model, which included a sensitivity and a robustness analysis. In the end, the model was perceived by the panel as being trustworthy and reliable. This framework can be used for further MCDA modelling in similar applications based on participatory procedures.
Impact of etcd deployment on Kubernetes, Istio, and application performance

Summary This experience article describes lessons learned as we conducted experiments in a Kubernetes‐based environment, the most notable of which was that the performance of both the Kubernetes control plane and the deployed application depends strongly and in unexpected ways on the performance of the etcd database. The article contains (a) detailed descriptions of how networking with and without Istio works in Kubernetes, based on the Flannel Container Networking Interface (CNI) provider in VXLAN mode with IP Virtual Server (IPVS)‐backed Kubernetes Services, (b) a comprehensive discussion about how to conduct load and performance testing using a closed‐loop workload generator, and (c) an open source experiment framework useful for executing experiments in a shared cloud environment and exploring the resulting data. It also shows that statistical analysis may reveal the data resulting from such experiments to be misleading even when careful preparations are made, and that nondeterministic behavior stemming from etcd can affect both the platform as a whole and the deployed application. Finally, it is demonstrated that using high‐performance backing storage for etcd can reduce the occurrence of such nondeterministic behaviors by a statistically significant ( P &lt; .05 ) margin. The implication of this experience article is that systems researchers studying the performance of applications deployed on Kubernetes cannot simply consider their specific application to be under test. Instead, the particularities of the underlying Kubernetes and cloud platform must be taken into account, in particular because their performance can impact that of etcd.
Unbiased Pairwise Learning from Biased Implicit Feedback

Implicit feedback is prevalent in real-world scenarios and is widely used in the construction of recommender systems. However, the application of implicit feedback data is much more complicated than its explicit counterpart because it provides only positive feedback, and we cannot know whether the non-interacted feedback is positive or negative. Furthermore, positive feedback for rare items is observed less frequently than popular items. The relevance of such rare items is often underestimated. Existing solutions to such challenges are subject to bias toward the ideal loss function of interest or accept a simple pointwise approach, which is inappropriate for a ranking task. In this study, we first define an ideal pairwise loss function defined using the ground-truth relevance parameters that should be used to optimize the ranking metrics. Subsequently, we propose a theoretically grounded unbiased estimator for this ideal pairwise loss and a corresponding algorithm, Unbiased Bayesian Personalized Ranking. A pairwise algorithm addressing the two major difficulties in using implicit feedback has yet to be investigated, and the proposed algorithm is the first pairwise method for solving these challenges in a theoretically principal manner. Through theoretical analysis, we provide the critical statistical properties of the proposed unbiased estimator and a practical variance reduction technique. Empirical evaluations using real-world datasets demonstrate the practical strength of our approach.
COVID-19 and the Gendered Use of Emojis on Twitter: Infodemiology Study

Background The online discussion around the COVID-19 pandemic is multifaceted, and it is important to examine the different ways by which online users express themselves. Since emojis are used as effective vehicles to convey ideas and sentiments, they can offer important insight into the public’s gendered discourses about the pandemic. Objective This study aims at exploring how people of different genders (eg, men, women, and sex and gender minorities) are discussed in relation to COVID-19 through the study of Twitter emojis. Methods We collected over 50 million tweets referencing the hashtags #Covid-19 and #Covid19 for a period of more than 2 months in early 2020. Using a mixed method, we extracted three data sets containing tweets that reference men, women, and sexual and gender minorities, and we then analyzed emoji use along each gender category. We identified five major themes in our analysis including morbidity fears, health concerns, employment and financial issues, praise for frontline workers, and unique gendered emoji use. The top 600 emojis were manually classified based on their sentiment, indicating how positive, negative, or neutral each emoji is and studying their use frequencies. Results The findings indicate that the majority of emojis are overwhelmingly positive in nature along the different genders, but sexual and gender minorities, and to a lesser extent women, are discussed more negatively than men. There were also many differences alongside discourses of men, women, and gender minorities when certain topics were discussed, such as death, financial and employment matters, gratitude, and health care, and several unique gendered emojis were used to express specific issues like community support. Conclusions Emoji research can shed light on the gendered impacts of COVID-19, offering researchers an important source of information on health crises as they happen in real time.
Denoising Diffusion Implicit Models

Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a Markovian diffusion process. We construct a class of non-Markovian diffusion processes that lead to the same training objective, but whose reverse process can be much faster to sample from. We empirically demonstrate that DDIMs can produce high quality samples $10 \times$ to $50 \times$ faster in terms of wall-clock time compared to DDPMs, allow us to trade off computation for sample quality, and can perform semantically meaningful image interpolation directly in the latent space.