Reviewerly/exHarmony huggingface dataset:
Fuzzy cognitive mapping and soft models of indigenous knowledge on maternal health in Guerrero, Mexico

Abstract Background Effective health care requires services that are responsive to local needs and contexts. Achieving this in indigenous settings implies communication between traditional and conventional medicine perspectives. Adequate interaction is especially relevant for maternal health because cultural practices have a notable role during pregnancy, childbirth and the postpartum period. Our work with indigenous communities in the Mexican state of Guerrero used fuzzy cognitive mapping to identify actionable factors for maternal health from the perspective of traditional midwives. Methods We worked with twenty-nine indigenous women and men whose communities recognized them as traditional midwives. A group session for each ethnicity explored risks and protective factors for maternal health among the Me’phaa and Nancue ñomndaa midwives. Participants mapped factors associated with maternal health and weighted the influence of each factor on others. Transitive closure summarized the overall influence of each node with all other factors in the map. Using categories set in discussions with the midwives, the authors condensed the relationships with thematic analysis. The composite map combined categories in the Me’phaa and the Nancue ñomndaa maps. Results Traditional midwives in this setting attend to pregnant women’s physical, mental, and spiritual conditions and the corresponding conditions of their offspring and family. The maps described a complex web of cultural interpretations of disease – “frío” (cold or coldness of the womb), “espanto” (fright), and “coraje” (anger) – abandonment of traditional practices of self-care, women’s mental health, and gender violence as influential risk factors. Protective factors included increased male involvement in maternal health (having a caring, working, and loving husband), receiving support from traditional healers, following protective rituals, and better nutrition. Conclusions The maps offer a visual language to present and to discuss indigenous knowledge and to incorporate participant voices into research and decision making. Factors with higher perceived influence in the eyes of the indigenous groups could be a starting point for additional research. Contrasting these maps with other stakeholder views can inform theories of change and support co-design of culturally appropriate interventions.
Asking Easy Questions: A User-Friendly Approach to Active Reward Learning.

Robots can learn the right reward function by querying a human expert. Existing approaches attempt to choose questions where the robot is most uncertain about the human's response; however, they do not consider how easy it will be for the human to answer! In this paper we explore an information gain formulation for optimally selecting questions that naturally account for the human's ability to answer. Our approach identifies questions that optimize the trade-off between robot and human uncertainty, and determines when these questions become redundant or costly. Simulations and a user study show our method not only produces easy questions, but also ultimately results in faster reward learning.
Self-supervised Label Augmentation via Input Transformations

Self-supervised learning, which learns by constructing artificial labels given only the input signals, has recently gained considerable attention for learning representations with unlabeled datasets, i.e., learning without any human-annotated supervision. In this paper, we show that such a technique can be used to significantly improve the model accuracy even under fully-labeled datasets. Our scheme trains the model to learn both original and self-supervised tasks, but is different from conventional multi-task learning frameworks that optimize the summation of their corresponding losses. Our main idea is to learn a single unified task with respect to the joint distribution of the original and self-supervised labels, i.e., we augment original labels via self-supervision of input transformation. This simple, yet effective approach allows to train models easier by relaxing a certain invariant constraint during learning the original and self-supervised tasks simultaneously. It also enables an aggregated inference which combines the predictions from different augmentations to improve the prediction accuracy. Furthermore, we propose a novel knowledge transfer technique, which we refer to as self-distillation, that has the effect of the aggregated inference in a single (faster) inference. We demonstrate the large accuracy improvement and wide applicability of our framework on various fully-supervised settings, e.g., the few-shot and imbalanced classification scenarios.
WEFE: The Word Embeddings Fairness Evaluation Framework

Word embeddings are known to exhibit stereotypical biases towards gender, race, religion, among other criteria. Severa fairness metrics have been proposed in order to automatically quantify these biases. Although all metrics have a similar objective, the relationship between them is by no means clear. Two issues that prevent a clean comparison is that they operate with different inputs, and that their outputs are incompatible with each other. In this paper we propose WEFE, the word embeddings fairness evaluation framework, to encapsulate, evaluate and compare fairness metrics. Our framework needs a list of pre-trained embeddings and a set of fairness criteria, and it is based on checking correlations between fairness rankings induced by these criteria. We conduct a case study showing that rankings produced by existing fairness methods tend to correlate when measuring gender bias. This correlation is considerably less for other biases like race or religion. We also compare the fairness rankings with an embedding benchmark showing that there is no clear correlation between fairness and good performance in downstream tasks.
Invariant Risk Minimization Games

The standard risk minimization paradigm of machine learning is brittle when operating in environments whose test distributions are different from the training distribution due to spurious correlations. Training on data from many environments and finding invariant predictors reduces the effect of spurious features by concentrating models on features that have a causal relationship with the outcome. In this work, we pose such invariant risk minimization as finding the Nash equilibrium of an ensemble game among several environments. By doing so, we develop a simple training algorithm that uses best response dynamics and, in our experiments, yields similar or better empirical accuracy with much lower variance than the challenging bi-level optimization problem of Arjovsky et al. (2019). One key theoretical contribution is showing that the set of Nash equilibria for the proposed game are equivalent to the set of invariant predictors for any finite number of environments, even with nonlinear classifiers and transformations. As a result, our method also retains the generalization guarantees to a large set of environments shown in Arjovsky et al. (2019). The proposed algorithm adds to the collection of successful game-theoretic machine learning algorithms such as generative adversarial networks.
Design of a Robust Radio-Frequency Fingerprint Identification Scheme for Multimode LFM Radar

Radar is an indispensable part of the Internet of Things (IoT). Specific emitter identification is essential to identify the legitimate radars and, more importantly, to reject the malicious radars. Conventional methods rely on pulse parameters that are not capable to identify the specific emitter as two radars may have the same configuration or a malicious radar can perform spoofing attacks. Radio-frequency fingerprint (RFF) is the unique and intrinsic hardware characteristic of devices resulted from hardware imperfection, which can be used as the device identity. This article proposes a robust and reliable radar identification scheme based on the RFF, taking linear frequency modulation (LFM) radar as a case study. This scheme first classifies the operation mode of the pulses, then eliminates the noise effect, and finally identifies the radar emitters based on the transient and modulation-based RFF features. The experimental results verify the effectiveness of our radar identification scheme among three real LFM radars (same model) operating at four modes, each mode with 2000 pulses from each radar. The identification rates of the four modes are all higher than 90% when the signal-to-noise ratio (SNR) is about 5 dB. In addition, mode 3 achieves almost 100% identification accuracy even when the SNR is as low as -10 dB.
Space Debris Detection Using Feature Learning of Candidate Regions in Optical Image Sequences

Space debris detection is important in space situation awareness and space asset protection. In this article, we propose a method to detect space debris using feature learning of candidate regions. The acquired optical image sequences are first processed to remove hot pixels and flicker noise, and the nonuniform background information is removed by the proposed one dimensional mean iteration method. Then, the feature learning of candidate regions (FLCR) method is proposed to extract the candidate regions and to detect space debris. The candidate regions of space debris are precisely extracted, and then classified by a trained deep learning network. The feature learning model is trained using a large number of simulated space debris with different signal to noise ratios (SNRs) and motion parameters, instead of using real space debris, which make it difficult to extract a sufficient number of real space debris with diverse parameters in optical image sequences. Finally, the candidate regions are precisely placed in the optical image sequences. The experiment is performed using the simulated data and acquired image sequences. The results show that the proposed method has good performance when estimating and removing background, and it can detect low SNR space debris with high detection probability.
Crop Disease Classification on Inadequate Low-Resolution Target Images

Currently, various agricultural image classification tasks are carried out on high-resolution images. However, in some cases, we cannot get enough high-resolution images for classification, which significantly affects classification performance. In this paper, we design a crop disease classification network based on Enhanced Super-Resolution Generative adversarial networks (ESRGAN) when only an insufficient number of low-resolution target images are available. First, ESRGAN is used to recover super-resolution crop images from low-resolution images. Transfer learning is applied in model training to compensate for the lack of training samples. Then, we test the performance of the generated super-resolution images in crop disease classification task. Extensive experiments show that using the fine-tuned ESRGAN model can recover realistic crop information and improve the accuracy of crop disease classification, compared with the other four image super-resolution methods.
A Secure Framework for Data Sharing in Private Blockchain-Based WBANs

With the development of sensor devices, wireless sensor networks have been widely used, and Wireless Body Area Networks (WBANs) are relatively common application scenarios in wireless sensor networks. The wearable device is used to collect physiological data of the human body, and the server device is adopted to store physiological data of the human body. The openness of the network environment and network devices’ dynamic nature make WBANs vulnerable to security threats from attackers. The centralized two-hop architecture contains only one hub node, and the data stored in the hub node may be tampered with by attackers. Once attackers occupy the node, the entire network will be paralyzed. To tackle such an issue, it is proposed in this article a model in WBANs architecture based on Blockchain technology, where the authentication protocol and blind signature protocol between nodes are designed in the new WBANs model, making the Blockchain data transmission system in a wireless network environment secure and reliable. Experimental results show that the proposed method is promising and shows higher levels of safety and stability than other methods.
Robust watermarking scheme based LWT and SVD using artificial bee colony optimization

&lt;span&gt;This paper presents a watermarking scheme for grayscale images, in which lifting wavelet transform and singular value decomposition are exploited based on multi-objective artificial bee colony optimization to produce a robust watermarking method. Furthermore, for increasing security encryption of the watermark is done prior to the embedding operation. In the proposed scheme, the actual image is altered to four sub-band over three levels of lifting wavelet transform then the singular value of the watermark image is embedded to the singular value of LH sub-band of the transformed original image. In the embedding operation, multiple scaling factors are utilized on behalf of the single scaling element to get the maximum probable robustness without changing watermark lucidity. Multi-objective artificial bee colony optimization is utilized for the determination of the optimal values for multiple scaling components, which are examined against various types of attacks. For making the proposed scheme more secure, the watermark is encrypted chaotically by logistic chaotic encryption before embedding it to the host (original) image. The experimental results show excellent imperceptibility and good resiliency against a wide range of image processing attacks.&lt;/span&gt;
Single-Shot Neural Relighting and SVBRDF Estimation

We present a novel physically-motivated deep network for joint shape and material estimation, as well as relighting under novel illumination conditions, using a single image captured by a mobile phone camera. Our physically-based modeling leverages a deep cascaded architecture trained on a large-scale synthetic dataset that consists of complex shapes with microfacet SVBRDF. In contrast to prior works that train rendering layers subsequent to inverse rendering, we propose deep feature sharing and joint training that transfer insights across both tasks, to achieve significant improvements in both reconstruction and relighting. We demonstrate in extensive qualitative and quantitative experiments that our network generalizes very well to real images, achieving high-quality shape and material estimation, as well as image-based relighting. Code, models and data will be publicly released.
Evaluation of natural background levels of high mountain karst aquifers in complex hydrogeological settings. A Gaussian mixture model approach in the Port del Comte (SE, Pyrenees) case study

The hydrogeological processes driving the hydrochemical composition of groundwater in the alpine pristine aquifer system of the Port del Comte Massif (PCM) are characterized through the multivariate statistical techniques Principal Component Analysis (PCA) and Gaussian Mixture Models (GMM) in the framework of Compositional Data (CoDa) analysis. Also, the groundwater Natural Background Levels (NBLs) for NO3 and SO4 and Cl are evaluated, which are specially important for indicating the occurrence of groundwater contamination derived from the anthropic activities conducted in the PCM. The different hydrogeochemical facies found in the aquifer system of the PCM comprises low mineralized Ca-HCO3 water for the main Eocene karst aquifer, and Ca-SO4 and highly mineralized NaCl water types in the minor aquifers discharging from the PCM. The NBL values of SO4, Cl and NO3 obtained for the main karst aquifer are 14.33, 4.06 and 6.55 mg/L, respectively. These values are 35, 3 and 1.2 times lower than the respective official NBLs values that were determined by the water administration to be compared with in the case of conducting a pollution assessment characterization in the main karst aquifer. Official overestimation of NBLs can put important groundwater resources in the PCM at risk.
Siamese Regression Tracking With Reinforced Template Updating

Siamese networks are prevalent in visual tracking because of the efficient localization. The networks take both a search patch and a target template as inputs where the target template is usually from the initial frame. Meanwhile, Siamese trackers do not update network parameters online for real-time efficiency. The fixed target template and CNN parameters make Siamese trackers not effective to capture target appearance variations. In this paper, we propose a template updating method via reinforcement learning for Siamese regression trackers. We collect a series of templates and learn to maintain them based on an actor-critic framework. Among this framework, the actor network that is trained by deep reinforcement learning effectively updates the templates based on the tracking result on each frame. Besides the target template, we update the Siamese regression tracker online to adapt to target appearance variations. The experimental results on the standard benchmarks show the effectiveness of both template and network updating. The proposed tracker SiamRTU performs favorably against state-of-the-art approaches.
An Industry Evaluation of Embedding-based Entity Alignment

Embedding-based entity alignment has been widely investigated in recent years, but most proposed methods still rely on an ideal supervised learning setting with a large number of unbiased seed mappings for training and validation, which significantly limits their usage.In this study, we evaluate those state-of-the-art methods in an industrial context, where the impact of seed mappings with different sizes and different biases is explored.Besides the popular benchmarks from DBpedia and Wikidata, we contribute and evaluate a new industrial benchmark that is extracted from two heterogeneous knowledge graphs (KGs) under deployment for medical applications.The experimental results enable the analysis of the advantages and disadvantages of these alignment methods and the further discussion of suitable strategies for their industrial deployment.
Locality-Aware Crowd Counting

Imbalanced data distribution in crowd counting datasets leads to severe under-estimation and over-estimation problems, which has been less investigated in existing works. In this paper, we tackle this challenging problem by proposing a simple but effective locality-based learning paradigm to produce generalizable features by alleviating sample bias. Our proposed method is locality-aware in two aspects. First, we introduce a locality-aware data partition (LADP) approach to group the training data into different bins via locality-sensitive hashing. As a result, a more balanced data batch is then constructed by LADP. To further reduce the training bias and enhance the collaboration with LADP, a new data augmentation method called locality-aware data augmentation (LADA) is proposed where the image patches are adaptively augmented based on the loss. The proposed method is independent of the backbone network architectures, and thus could be smoothly integrated with most existing deep crowd counting approaches in an end-to-end paradigm to boost their performance. We also demonstrate the versatility of the proposed method by applying it for adversarial defense. Extensive experiments verify the superiority of the proposed method over the state of the arts.
OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization

As Deep Neural Networks (DNNs) usually are overparameterized and have millions of weight parameters, it is challenging to deploy these large DNN models on resource-constrained hardware platforms, e.g., smartphones. Numerous network compression methods such as pruning and quantization are proposed to reduce the model size significantly, of which the key is to find suitable compression allocation (e.g., pruning sparsity and quantization codebook) of each layer. Existing solutions obtain the compression allocation in an iterative/manual fashion while finetuning the compressed model, thus suffering from the efficiency issue. Different from the prior art, we propose a novel One-shot Pruning-Quantization (OPQ) in this paper, which analytically solves the compression allocation with pre-trained weight parameters only. During finetuning, the compression module is fixed and only weight parameters are updated. To our knowledge, OPQ is the first work that reveals pre-trained model is sufficient for solving pruning and quantization simultaneously, without any complex iterative/manual optimization at the finetuning stage. Furthermore, we propose a unified channel-wise quantization method that enforces all channels of each layer to share a common codebook, which leads to low bit-rate allocation without introducing extra overhead brought by traditional channel-wise quantization. Comprehensive experiments on ImageNet with AlexNet/MobileNet-V1/ResNet-50 show that our method improves accuracy and training efficiency while obtains significantly higher compression rates compared to the state-of-the-art.
A mechatronic shape display based on auxetic materials

Abstract Shape displays enable people to touch simulated surfaces. A common architecture of such devices uses a mechatronic pin-matrix. Besides their complexity and high cost, these matrix displays suffer from sharp edges due to the discreet representation which reduces their ability to render a large continuous surface when sliding the hand. We propose using an engineered auxetic material actuated by a smaller number of motors. The material bends in multiple directions, feeling smooth and rigid to touch. A prototype implementation uses nine actuators on a 220 mm square section of material. It can display a range of surface curvatures under the palm of a user without aliased edges. In this work we use an auxetic skeleton to provide rigidity on a soft material and demonstrate the potential of this class of surface through user experiments.
SiamRCR: Reciprocal Classification and Regression for Visual Object Tracking

Recently, most siamese network based trackers locate targets via object classification and bounding-box regression. Generally, they select the bounding-box with maximum classification confidence as the final prediction. This strategy may miss the right result due to the accuracy misalignment between classification and regression. In this paper, we propose a novel siamese tracking algorithm called SiamRCR, addressing this problem with a simple, light and effective solution. It builds reciprocal links between classification and regression branches, which can dynamically re-weight their losses for each positive sample. In addition, we add a localization branch to predict the localization accuracy, so that it can work as the replacement of the regression assistance link during inference. This branch makes the training and inference more consistent. Extensive experimental results demonstrate the effectiveness of SiamRCR and its superiority over the state-of-the-art competitors on GOT-10k, LaSOT, TrackingNet, OTB-2015, VOT-2018 and VOT-2019. Moreover, our SiamRCR runs at 65 FPS, far above the real-time requirement.
A multi-perspective view of Internet censorship in Myanmar

In the wake of a military coup in February 2021, Myanmar experienced unprecedented levels of Internet censorship. Beginning with haphazard blocking of social media and intermittent Internet connectivity outages, controls proceeded to stricter blocking of websites, the shutdown of cellular data in several networks, and nearly complete disconnection from the Internet every night. In this study, we use diverse datasets and measurement methods to offer a holistic view into the censorship events in Myanmar that occurred since the coup and show how Internet censorship evolved during this time.