Reviewerly/exHarmony huggingface dataset:
BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning

Model-free Reinforcement Learning (RL) offers an attractive approach to learn control policies for high dimensional systems, but its relatively poor sample complexity often necessitates training in simulated environments. Even in simulation, goal-directed tasks whose natural reward function is sparse remain intractable for state-of-the-art model-free algorithms for continuous control. The bottleneck in these tasks is the prohibitive amount of exploration required to obtain a learning signal from the initial state of the system. In this work, we leverage physical priors in the form of an approximate system dynamics model to design a curriculum for a model-free policy optimization algorithm. Our Backward Reachability Curriculum (BaRC) begins policy training from states that require a small number of actions to accomplish the task, and expands the initial state distribution backwards in a dynamically-consistent manner once the policy optimization algorithm demonstrates sufficient performance. BaRC is general, in that it can accelerate training of any model-free RL algorithm on a broad class of goal-directed continuous control MDPs. Its curriculum strategy is physically intuitive, easy-to-tune, and allows incorporating physical priors to accelerate training without hindering the performance, flexibility, and applicability of the model-free RL algorithm. We evaluate our approach on two representative dynamic robotic learning problems and find substantial performance improvement relative to previous curriculum generation techniques and naive exploration strategies.
The Functional Neural Process

We present a new family of exchangeable stochastic processes, the Functional Neural Processes (FNPs). FNPs model distributions over functions by learning a graph of dependencies on top of latent representations of the points in the given dataset. In doing so, they define a Bayesian model without explicitly positing a prior distribution over latent global parameters; they instead adopt priors over the relational structure of the given dataset, a task that is much simpler. We show how we can learn such models from data, demonstrate that they are scalable to large datasets through mini-batch optimization and describe how we can make predictions for new points via their posterior predictive distribution. We experimentally evaluate FNPs on the tasks of toy regression and image classification and show that, when compared to baselines that employ global latent parameters, they offer both competitive predictions as well as more robust uncertainty estimates.
Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation

Unsupervised text attribute transfer automatically transforms a text to alter a specific attribute (e.g. sentiment) without using any parallel data, while simultaneously preserving its attribute-independent content. The dominant approaches are trying to model the content-independent attribute separately, e.g., learning different attributes' representations or using multiple attribute-specific decoders. However, it may lead to inflexibility from the perspective of controlling the degree of transfer or transferring over multiple aspects at the same time. To address the above problems, we propose a more flexible unsupervised text attribute transfer framework which replaces the process of modeling attribute with minimal editing of latent representations based on an attribute classifier. Specifically, we first propose a Transformer-based autoencoder to learn an entangled latent representation for a discrete text, then we transform the attribute transfer task to an optimization problem and propose the Fast-Gradient-Iterative-Modification algorithm to edit the latent representation until conforming to the target attribute. Extensive experimental results demonstrate that our model achieves very competitive performance on three public data sets. Furthermore, we also show that our model can not only control the degree of transfer freely but also allow to transfer over multiple aspects at the same time.
Concentrated Local Part Discovery With Fine-Grained Part Representation for Person Re-Identification

The attention mechanism for person re-identification has been widely studied with deep convolutional neural networks. This mechanism works as a good complement to the global features extracted from an image of the entire human body. However, existing works mainly focus on discovering local parts with simple feature representations, such as global average pooling. Moreover, these works either require extra supervision, such as labeling of body joints, or pay little attention to the guidance of part learning, resulting in scattered activation of learned parts. Furthermore, existing works usually extract local features from different body parts via global average pooling and then concatenate them together as good global features. We find that local features acquired in this way contribute little to the overall performance. In this paper, we argue the significance of local part description and explore the attention mechanism from both local part discovery and local part representation aspects. For local part discovery, we propose a new constrained attention module to make the activated regions concentrated and meaningful without extra supervision. For local part representation, we propose a statistical-positional-relational descriptor to represent local parts from a fine-grained viewpoint. Extensive experiments are conducted to validate the overall performance, the effectiveness of each component, and the generalization ability. We achieve a rank-1 accuracy of 95.1% on Market1501, 64.7% on CUHK03, 87.1% on DukeMTMC-ReID, and 79.9% on MSMT17, outperforming state-of-the-art methods.
Introduction to Scikit-Learn

We combine the Anaconda distribution with the popular Scikit-Learn library to demonstrate a wide range of supervised machine learning algorithms supplemented with unsupervised learning algorithms where appropriate. With clear examples, all written in Python, we demonstrate how these algorithms work to solve machine learning problems.
Text2FaceGAN: Face Generation from Fine Grained Textual Descriptions

In recent years, powerful generative adversarial networks (GAN) have been developed to automatically synthesize realistic images from text. However, most existing tasks are limited to generating simple images such as flowers from captions. In this paper, we extend this problem to the less addressed domain of face generation from fine-grained textual descriptions of face, e.g., "A person has curly hair, oval face, and mustache". We are motivated by the potential of automated face generation to impact and assist critical tasks such as criminal face reconstruction. Since current datasets for the task are either very small or do not contain captions, we generate captions for images in the CelebA dataset by creating an algorithm to automatically convert a list of attributes to a set of captions. The generated captions are meaningful, versatile and consistent with the general semantics of a face. We then model the highly multi-modal problem of text to face generation as learning the conditional distribution of faces (conditioned on text) in same latent space. We utilize the current state-of-the-art GAN (DC-GAN with GAN-CLS loss) for learning conditional multi-modality. The presence of more fine-grained details and variable length of the captions makes the problem easier for a user but more difficult to handle compared to the other text-to-image tasks. We flipped the labels for real and fake images and added noise in discriminator. Generated images for diverse textual descriptions show promising results. In the end, we show how the widely used inceptions score is not a good metric to evaluate the performance of generative models used for synthesizing faces from text.
Cycle-IR: Deep Cyclic Image Retargeting

Supervised deep learning techniques have achieved great success in various fields due to getting rid of the limitation of handcrafted representations. However, most previous image retargeting algorithms still employ fixed design principles such as using gradient map or handcrafted features to compute saliency map, which inevitably restricts its generality. Deep learning techniques may help to address this issue, but the challenging problem is that we need to build a large-scale image retargeting dataset for the training of deep retargeting models. However, building such a dataset requires huge human efforts. In this paper, we propose a novel deep cyclic image retargeting approach, called Cycle-IR, to firstly implement image retargeting with a single deep model, without relying on any explicit user annotations. Our idea is built on the reverse mapping from the retargeted images to the given images. If the retargeted image has serious distortion or excessive loss of important visual information, the reverse mapping is unlikely to restore the input image well. We constrain this forward-reverse consistency by introducing a cyclic perception coherence loss. In addition, we propose a simple yet effective image retargeting network (IRNet) to implement the image retargeting process. Our IRNet contains a spatial and channel attention layer, which is able to discriminate visually important regions of input images effectively, especially in cluttered images. Given arbitrary sizes of input images and desired aspect ratios, our Cycle-IR can produce visually pleasing target images directly. Extensive experiments on the standard RetargetMe dataset show the superiority of our Cycle-IR.
Attacks on genetic privacy via uploads to genealogical databases

Direct-to-consumer (DTC) genetics services are increasingly popular, with tens of millions of customers. Several DTC genealogy services allow users to upload genetic data to search for relatives, identified as people with genomes that share identical by state (IBS) regions. Here, we describe methods by which an adversary can learn database genotypes by uploading multiple datasets. For example, an adversary who uploads approximately 900 genomes could recover at least one allele at SNP sites across up to 82% of the genome of a median person of European ancestries. In databases that detect IBS segments using unphased genotypes, approximately 100 falsified uploads can reveal enough genetic information to allow genome-wide genetic imputation. We provide a proof-of-concept demonstration in the GEDmatch database, and we suggest countermeasures that will prevent the exploits we describe.
A clustering approach for topic filtering within systematic literature reviews

Within a systematic literature review (SLR), researchers are confronted with vast amounts of articles from scientific databases, which have to be manually evaluated regarding their relevance for a certain field of observation. The evaluation and filtering phase of prevalent SLR methodologies is therefore time consuming and hardly expressible to the intended audience. The proposed method applies natural language processing (NLP) on article meta data and a k-means clustering algorithm to automatically convert large article corpora into a distribution of focal topics. This allows efficient filtering as well as objectifying the process through the discussion of the clustering results. Beyond that, it allows to quickly identify scientific communities and therefore provides an iterative perspective for the so far linear SLR methodology.•NLP and k-means clustering to filter large article corpora during systematic literature reviews.•Automated clustering allows filtering very efficiently as well as effectively compared to manual selection.•Presentation and discussion of the clustering results helps to objectify the nontransparent filtering step in systematic literature reviews.
Introduction to Structural Equation Modelling

Structural equation modelling (SEM) is multivariate quantitative technique employed to describe the relationships among observed variables. The technique helps the researcher to test or validate a theoretical model for theory testing and extension. The multivariate analysis is conducted with an objective to help the researcher for an in-depth explanatory analysis with a required statistical efficiency. A researcher is interested in investigating the constructs emerging out of sets of variables and the relationships among these constructs. This can be explained with an example such as a sales manager might be interested to investigate a phenomenon that behaviour and discipline of salesperson has a direct influence on sales volume. SEM enables the researcher to indulge into a deeper inquiry through a process of scientific hypothesis testing and extend the present body of knowledge by discovering complex relationships among constructs. This book provides a comprehensive understanding on SEM with illustrative applications in two softwares – AMOS, SPSS and R.
Complex ecologies of trust in data practices and data-driven systems

Trust in data practices and data-driven systems is widely seen as both important and elusive. A data trust deficit has been identified, to which proposed solutions are often localised or individualised, focusing either on what institutions can do to increase user trust in their data practices or on data management models that empower the individual user. Scholarship on trust often focuses on typologies of trust. This paper shifts the emphasis to those doing the trusting, by presenting findings from empirical research which explored user perspectives on the data practices of the BBC. These findings challenge the assumption that localised or individualised solutions can be effective. They also suggest that conceptualisations of trust in data practices need to account for the complex range of factors which come into play in relation to trust in data and so move beyond the production of typologies. In this paper, we propose the concept of 'complex ecologies of trust' as a way of addressing all of these issues.
Lite Transformer with Long-Short Range Attention

Transformer has become ubiquitous in natural language processing (e.g., machine translation, question answering); however, it requires enormous amount of computations to achieve high performance, which makes it not suitable for mobile applications that are tightly constrained by the hardware resources and battery. In this paper, we present an efficient mobile NLP architecture, Lite Transformer to facilitate deploying mobile NLP applications on edge devices. The key primitive is the Long-Short Range Attention (LSRA), where one group of heads specializes in the local context modeling (by convolution) while another group specializes in the long-distance relationship modeling (by attention). Such specialization brings consistent improvement over the vanilla transformer on three well-established language tasks: machine translation, abstractive summarization, and language modeling. Under constrained resources (500M/100M MACs), Lite Transformer outperforms transformer on WMT'14 English-French by 1.2/1.7 BLEU, respectively. Lite Transformer reduces the computation of transformer base model by 2.5x with 0.3 BLEU score degradation. Combining with pruning and quantization, we further compressed the model size of Lite Transformer by 18.2x. For language modeling, Lite Transformer achieves 1.8 lower perplexity than the transformer at around 500M MACs. Notably, Lite Transformer outperforms the AutoML-based Evolved Transformer by 0.5 higher BLEU for the mobile NLP setting without the costly architecture search that requires more than 250 GPU years. Code has been made available at this https URL.
Exercise Activity Recognition with Surface Electromyography Sensor using Machine Learning Approach

Discriminating between various human activities has been the traditional focus of research on human activity recognition (HAR). This field of research is currently being applied to numerous daily activities, such as caring for the elderly, healthcare, smart homes, and athletics. A variety of types of sensors that can identify the different physical movements of humans are incorporated into most wearable smart devices, for example, smart armbands, smartphones, and smartwatches. Machine learning algorithms are employed by HAR in order to identify a range of activities in specific areas of normal everyday activities such as doing work and eating meals. Currently, exercise activity recognition (EAR) is regarded as an essential component in ambient assisted living, smart healthcare, and smart rehabilitation. Previous studies involving EAR have emphasized research on the improvement of the accuracy of the machine learning algorithms recognition that uses accelerometers and gyroscopes to provide sensor data. In this study, a framework of EAR using surface electromyography (sEMG) data is proposed. The results of the experiments indicate that the recognition accuracy can be improved by the use of sEMG data obtained from a combination of three sensors.
PAChain: Private, authenticated &amp; auditable consortium blockchain and its implementation

Blockchain provides a distributed ledger recording a globally agreed, immutable transaction history, which may not be suitable for Fintech applications that process sensitive information. This paper aims to solve three important problems for practical blockchain applications: privacy, authentication and auditability. Private transaction means that the transaction can be validated without revealing the transaction details, such as the identity of the transacting parties and the transaction amount. Auditable transaction means that the complete transaction details can be revealed by auditors or law enforcement agencies. Authenticated transaction means that only authorized parties can be involved in the transaction. Auditability prevents a malicious user from abusing the privacy property. Authenticity helps the auditor to trace the identity of the malicious user in the system. In this paper, we present a private, authenticated and auditable consortium blockchain, using a number of cryptographic building blocks. Our scheme can be integrated with the architecture of consortium blockchain. 1
Deep Convolutional Gaussian Processes

We propose deep convolutional Gaussian processes, a deep Gaussian process architecture with convolutional structure. The model is a principled Bayesian framework for detecting hierarchical combinations of local features for image classification. We demonstrate greatly improved image classification performance compared to current convolutional Gaussian process approaches on the MNIST and CIFAR-10 datasets. In particular, we improve state-of-the-art CIFAR-10 accuracy by over 10% points.
Link Prediction Adversarial Attack Via Iterative Gradient Attack

Increasing deep neural networks are applied in solving graph evolved tasks, such as node classification and link prediction. However, the vulnerability of deep models can be revealed using carefully crafted adversarial examples generated by various adversarial attack methods. To explore this security problem, we define the link prediction adversarial attack problem and put forward a novel iterative gradient attack (IGA) strategy using the gradient information in the trained graph autoencoder (GAE) model. Not surprisingly, GAE can be fooled by an adversarial graph with a few links perturbed on the clean one. The results on comprehensive experiments of different real-world graphs indicate that most deep models and even the state-of-the-art link prediction algorithms cannot escape the adversarial attack, such as GAE. We can benefit the attack as an efficient privacy protection tool from the link prediction of unknown violations. On the other hand, the adversarial attack is a robust evaluation metric for current link prediction algorithms of their defensibility.
Facial Expression Retargeting From Human to Avatar Made Easy

Facial expression retargeting from humans to virtual characters is a useful technique in computer graphics and animation. Traditional methods use markers or blendshapes to construct a mapping between the human and avatar faces. However, these approaches require a tedious 3D modeling process, and the performance relies on the modelers' experience. In this article, we propose a brand-new solution to this cross-domain expression transfer problem via nonlinear expression embedding and expression domain translation. We first build low-dimensional latent spaces for the human and avatar facial expressions with variational autoencoder. Then we construct correspondences between the two latent spaces guided by geometric and perceptual constraints. Specifically, we design geometric correspondences to reflect geometric matching and utilize a triplet data structure to express users' perceptual preference of avatar expressions. A user-friendly method is proposed to automatically generate triplets for a system allowing users to easily and efficiently annotate the correspondences. Using both geometric and perceptual correspondences, we trained a network for expression domain translation from human to avatar. Extensive experimental results and user studies demonstrate that even nonprofessional users can apply our method to generate high-quality facial expression retargeting results with less time and effort.
Exploring entropic uncertainty relation and dense coding capacity in a two-qubit X-state

The measurement precision for two incompatible observables in a typical quantum system can be improved by the aid of one particle as a quantum memory. In this work, we study the entropic uncertainty relation in the presence of quantum memory (EUR-QM) and dense coding capacity (DCC) for arbitrary two-qubit X-states, and then we obtain an explicit relationship between the lower bound of the uncertainty and DCC. As an example, we examine the thermal EUR-QM as well as DCC in two kinds of two-qubit spin squeezing models (one-axis twisting model and two-axis counter-twisting model) under an external magnetic field. In the following, we relate EUR-QM to DCC and show analytically that there is an anti-correlated relation between them, and especially in the ground state. Our results show that for both the models, the entropic uncertainty and its bound can be decreased by reinforcing the spin squeezing parameters or decreasing the temperature of the system. Notably, we reveal that the valid dense coding cannot carry out in the one-axis twisting model, nevertheless, if we properly choose the Hamiltonian parameters, the two-axis counter-twisting model not only carries out the valid dense coding but also the optimal dense coding surely can be achieved. Thereby, our observations might offer new insights into quantum measurement precision and the optimal dense coding for the regimes of various solid-state systems.