Reviewerly/exHarmony huggingface dataset:
Using Gans to Augment Data for Cloud Image Segmentation Task

While cloud/sky image segmentation has extensive real-world applications, a large amount of labelled data is needed to train a highly accurate models to perform the task. Scarcity of such volumes of cloud/sky images with corresponding ground-truth binary maps makes it highly difficult to train such complex image segmentation models. In this paper, we demonstrate the effectiveness of using Generative Adversarial Networks (GANs) to generate data to augment the training set in order to increase the prediction accuracy of image segmentation model. We further present a way to estimate ground-truth binary maps for the GAN-generated images to facilitate their effective use as augmented images. Finally, we validate our work with different statistical techniques.
Information Processing Capacity of a Single-Node Reservoir Computer: An Experimental Evaluation

Physical dynamical systems are able to process information in a nontrivial manner. The machine learning paradigm of reservoir computing (RC) provides a suitable framework for information processing in (analog) dynamical systems. The potential of dynamical systems for RC can be quantitatively characterized by the information processing capacity (IPC) measure. Here, we evaluate the IPC measure of a reservoir computer based on a single-analog nonlinear node coupled with delay. We link the extracted IPC measures to the dynamical regime of the reservoir, reporting an experimentally measured nonlinear memory of up to seventh order. In addition, we find a nonhomogeneous distribution of the linear and nonlinear contributions to the IPC as a function of the system operating conditions. Finally, we unveil the role of noise in the IPC of the analog implementation by performing ad hoc numerical simulations. In this manner, we identify the so-called edge of stability as being the most promising operating condition of the experimental implementation for RC purposes in terms of computational power and noise robustness. Similarly, a strong input drive is shown to have beneficial properties, albeit with a reduced memory depth.
A Quantum Convolutional Neural Network for Image Classification

Artificial neural networks have achieved great success in many fields ranging from image recognition to video understanding. However, its high requirements for computing and memory resources have limited further development on processing big data with high dimensions. In recent years, advances in quantum computing show that building neural networks on quantum processors is a potential solution to this problem. In this paper, we propose a novel neural network model named Quantum Convolutional Neural Network (QCNN), aiming at utilizing the computing power of quantum systems to accelerate classical machine learning tasks. The designed QCNN is based on implementable quantum circuits and has a similar structure as classical convolutional neural networks. Numerical simulation results on the MNIST dataset demonstrate the effectiveness of our model.
Trade openness and financial development in <scp>sub‐Saharan</scp> Africa: The role of institutional structures

Abstract This article examines the role of institutional structures in the relationship between trade openness and financial development in sub‐Saharan economies. The study is based on empirical data from sampled sub‐Saharan African countries for a period of 1996–2017. The system generalized method of moment was employed to estimate the models. The findings suggest that, even though trade openness has a positive significant influence on the level of financial development in sub‐Saharan African economies, this relationship is enhanced through the presence of good institutions in these economies. Thus, for these economies to realize the full benefit of the effect of trade openness on financial development, they need to strengthen their institutions.
Semi-supervised Domain Adaptive Retrieval via Discriminative Hashing Learning

Domain adaptive image retrieval (DAR) aims to train the model with well-labeled source domain and target images in order to retrieve source instances given query target samples from the identical category space. However, the practical scenario hinders to manually annotate all retrieved images due to huge labeling cost. Motivated by the realistic demand, we firstly define the semi-supervised domain adaptive retrieval (SDAR) problem, assuming the database includes a small proportion annotated source images and abundant unlabeled ones. To overcome the challenging SDAR, this paper propose a novel method named Discriminative Hashing learning (DHLing) which mainly includes two modules, i.e., domain-specific optimization and domain-invariant memory bank. Specifically, the first component explores the structural knowledge of samples to predict the unlabeled images with pseudo labels to achieve hash coding consistency. While, the second one attempts to construct the domain-invariant memory bank to guide the feature generation and achieve cross-domain alignment. Experimental results on several popular cross-domain retrieval benchmarks illustrate the effectiveness of our proposed DHLing on both conventional DAR and new SDAR scenarios by comparing with the state-of-the-art retrieval methods.
2PGraph: Accelerating GNN Training over Large Graphs on GPU Clusters

Graph neural networks (GNNs) have been emerging as powerful learning tools for unstructured data and successfully applied to many graph-based application domains. Sampling-based graph training is commonly used in existing GNN training frameworks to handle large-scale graphs. However, this type of approach is restricted by the problems of long memory accessing latency, neighborhood explosion during mini-batch sampling, and inefficient loading of vertex features from CPU to GPU. In this paper, we propose 2PGraph, a system that supports high-speed locality-aware mini-batch sampling and GNN layer-aware feature caching. 2PGraph significantly reduces sampling time by vertex-cluster sampling, which improves the locality of vertex access and limits the range of neighborhood expansion. To further reduce the sampling time in a distributed environment, we renumber the vertex numbers in subgraphs after graph partition, which improves the data locality of each partition. 2PGraph also avoids abundant data transfer between CPU and GPU through the feature data caching on available GPU resources with a hit rate of 100%. Furthermore, 2PGraph develops a GNN layer-aware feature caching policy during data parallel training and achieves better cache efficiency and memory utilization. We evaluate 2PGraph against two state-of-the-art industrial GNN frameworks, i.e., PyG and DGL, on a diverse array of benchmarks. Experimental results show that 2PGraph reduces up to 90% mini-batch sampling and 99% data loading time, and achieves up to 8.7 × performance speedup over the state-of-the-art baselines on an 8-GPU cluster.
Deep residual network for face sketch synthesis

Face sketch synthesis plays a crucial role in face recognition for law enforcement applications. However, the current face sketch synthesis approaches generate sketches from photos based on a model trained by a certain database that is usually collected from individuals of the same ethnicity, and therefore such sketches merely inherit distinct facial distributions (shape and texture) of this database. This also makes such models inapplicable for real-world applications which mainly include multiple photo variations such as pose, lighting, skin color, and ethnic origin. In this paper, a unified face sketch synthesis model considering ethnicity issue as well as photo variations is proposed. A new deep learning scheme is designed to handle the generic visual representation and global structure of the face. Towards the final objective, the recent success of deep residual blocks is exploited and incorporated into a plain feedforward network, termed as DResNet, to learn a regression model for face sketch synthesis. A heterogeneous database containing photos with lighting, ethnicity, hair and skin variations is utilized for training the DResNet model. Extensive subjective and objective evaluations showed the superiority of the proposed DResNet method on state-of-the-art face sketch synthesis methods. Experimental results also demonstrated that the proposed DResNet method can be generalized to face sketch synthesis for real-world applications.
Macroeconomic Risk and Political Stability: Perspectives from Emerging and Developing Economies

This study examines the impact of macroeconomic risk on political stability, using data compiled from 38 countries in the sub-region of sub-Saharan Africa (SSA), from 1996 to 2018. Macroeconomic risk index employed in this study is constructed using principal component analysis (PCA) from key economic variables. Empirical estimates verifying the relationship in question are conducted using the two-step system generalized method of moments (TS-SGMM) estimation technique. Presented empirical estimates suggest that macroeconomic risk has an adverse impact on political stability, all things being equal. Reported coefficient estimates further suggest that improvement in rule of law among economies in the sub-region significantly moderates the negative impact exerted on the political environment by macroeconomic risk. Coefficient estimates additionally suggest that improvement in governance and institutional variables (corruption control, government effectiveness, regulatory quality and rule of law) augment efforts at promoting political stability even in an environment characterized by volatile output growth.
HMK: Head-Mounted-Keyboard for Text Input in Virtual or Augmented Reality

Text input is essential in a variety of uses in virtual and augmented reality (VR and AR). We present HMK: an effective text input method that mounts split keyboards on the left and right side of the head mounted display (HMD). Users who can touch-type are able to type using HMK by relying on their familiarity with the normal QWERTY keyboard. We develop custom keycaps to make it easier to find the home position. A study with three participants shows that users retain most of their normal keyboard typing skills. The participants achieved, on average, 34.7 words per minute (WPM) by the end of three days of use, retaining 81 percent of their regular entry speed.
Do You See What I See? Coordinating Multiple Aerial Cameras for Robot Cinematography

Aerial cinematography is significantly expanding the capabilities of film-makers. Recent progress in autonomous unmanned aerial vehicles (UAVs) has further increased the potential impact of aerial cameras, with systems that can safely track actors in unstructured cluttered environments. Professional productions, however, require the use of multiple cameras simultaneously to record different viewpoints of the same scene, which are edited into the final footage either in real time or in post-production. Such extreme motion coordination is particularly hard for unscripted action scenes, which are a common use case of aerial cameras. In this work we develop a real-time multi-UAV coordination system that is capable of recording dynamic targets while maximizing shot diversity and avoiding collisions and mutual visibility between cameras. We validate our approach in multiple cluttered environments of a photo-realistic simulator, and deploy the system using two UAVs in real-world experiments. We show that our coordination scheme has low computational cost and takes only 1.17 ms on average to plan for a team of 3 UAVs over a 10 s time horizon. Supplementary video: https://youtu.be/m2R3anv2ADE
Natural language processing for under-resourced languages: Developing a Welsh natural language toolkit

Language technology is becoming increasingly important across a variety of application domains which have become common place in large, well-resourced languages. However, there is a danger that small, under-resourced languages are being increasingly pushed to the technological margins. Under-resourced languages face significant challenges in delivering the underlying language resources necessary to support such applications. This paper describes the development of a natural language processing toolkit for an under-resourced language, Cymraeg (Welsh). Rather than creating the Welsh Natural Language Toolkit (WNLT) from scratch, the approach involved adapting and enhancing the language processing functionality provided for other languages within an existing framework and making use of external language resources where available. This paper begins by introducing the GATE NLP framework, which was used as the development platform for the WNLT. It then describes each of the core modules of the WNLT in turn, detailing the extensions and adaptations required for Welsh language processing. An evaluation of the WNLT is then reported. Following this, two demonstration applications are presented. The first is a simple text mining application that analyses wedding announcements. The second describes the development of a Twitter NLP application, which extends the core WNLT pipeline. As a relatively small-scale project, the WNLT makes use of existing external language resources where possible, rather than creating new resources. This approach of adaptation and reuse can provide a practical and achievable route to developing language resources for under-resourced languages.
Context-Aware Safe Reinforcement Learning for Non-Stationary Environments

Safety is a critical concern when deploying reinforcement learning agents for realistic tasks. Recently, safe reinforcement learning algorithms have been developed to optimize the agent’s performance while avoiding violations of safety constraints. However, few studies have addressed the nonstationary disturbances in the environments, which may cause catastrophic outcomes. In this paper, we propose the context-aware safe reinforcement learning (CASRL) method, a metal-earning framework to realize safe adaptation in non-stationary environments. We use a probabilistic latent variable model to achieve fast inference of the posterior environment transition distribution given the context data. Safety constraints are then evaluated with uncertainty-aware trajectory sampling. Prior safety constraints are formulated with domain knowledge to improve safety during exploration. The algorithm is evaluated in realistic safety-critical environments with non-stationary disturbances. Results show that the proposed algorithm significantly outperforms existing baselines in terms of safety and robustness.
Supervised multi-view classification via the sparse learning joint the weighted elastic loss

Multi-view data contains the global shared information of all views and the specific information of each view. Usually, the features of all views are concatenated into vectors to be dealt with in the multi-view classification methods, which inevitably ignores the extraction of important views for a specific task and increases the computational complexity. To address the above limitations, we propose a novel supervised classification method via the sparse learning joint the weighted elastic loss. Specifically, for the samples in each class, an elastic sub-model is established by combining the weighted elastic loss and the sparse regularizers. The weighted elastic loss can explore the complementary information of all views and the specific information of each view. The sparse regularizers can select the views that are important to the classification task. Finally, the elastic sub-models of all classes are fused to form the generalized additive classification model. An effective algorithm is designed to optimize the model through solving several small-scale subproblems, which can reduce the computational complexity. The comprehensive evaluations with several advanced methods prove the superiority of the proposed method. Especially on the large-scale Animal dataset, the training time (3.7×103 s) is one time faster than that of the second place (7.5×103 s).
Effective interrelation of Bayesian nonparametric document clustering and embedded-topic modeling

Topic modeling can be synergically interrelated with document clustering. We present an innovative unsupervised approach to the interrelationship of topic modeling with document clustering. The devised approach exploits Bayesian generative modeling and posterior inference, to seamlessly unify and jointly carry out the two tasks, respectively. Specifically, a Bayesian nonparametric model of text collections, formulates an unprecedented interrelationship of word-embedding topics with a Dirichlet process mixture of cluster components. The latter enables countably infinite clusters and permits the automatic inference of their actual number in a statistically principled manner. All latent clusters and topics under the foresaid model are inferred through collapsed Gibbs sampling and parameter estimation. An extensive empirical study of the presented approach is effected on benchmark real-world corpora of text documents. The experimental results demonstrate its higher effectiveness in partitioning text collections and coherently discovering their semantics, compared to state-of-the-art competitors and tailored baselines. Computational efficiency is also looked into under different conditions, in order to provide an insightful analysis of scalability.
Hierarchical Representation Learning for Attributed Networks

Network representation learning, also called network embedding, aiming to learn low dimensional vectors for nodes while preserving essential properties of the network, benefits plenty of practical applications. However, how to do representation learning on the network quickly and effectively is a meaningful and challenging task, especially for the attributed networks. In this paper, we propose HANE, a Hierarchical Attributed Network Embedding framework, which is a fast and effective method by quickly constructing a hierarchical attributed network of different granularities to learn nodes representations. Specifically, for an attributed network, HANE first builds a hierarchy of successively smaller attributed network from fine to coarse by the fast granulation strategy fusing topological structure and node attributes. After using any unsupervised network embedding method to learn nodes representations of the coarsest network, HANE refines the nodes representations of the hierarchical attributed network from coarse to fine. HANE improves the speed of network representation learning while maintaining its performance and the representation learning method of the coarsest network is flexible. We conduct extensive evaluations for the proposed framework HANE on six datasets and two benchmark applications. Experimental results demonstrate that HANE achieves significant improvements over previous state-of-the-art network embedding methods in efficiency and effectiveness.
Critical Co-Design and Agency of the Real

This article explores research that was undertaken in a PhD project that investigated the creation and effectiveness of Critical Co-Design as a methodology. In particular, it explores how this methodology enabled successful outcomes from a workshop that was conducted with young Indigenous Australian women. The methodology and workshop aimed to create a platform for the researcher to work alongside Indigenous peoples in Australia in order to create mutually beneficial ways of working. Here, the authors reflect upon this research and postulate the importance of privileging Indigenous ways of knowing as a way of shifting approaches to design thinking. By examining the interconnectedness between these Indigenous ways of knowing and design thinking epistemology, the authors offer a paradigm for design thinking that engages the politics and the broader promise of (de)coloniality. This article reconfigures how design scholars and creative practitioners ascribe value to Indigeneity and practice by positioning Indigenous ontologies at the forefront of design thinking in general.
UGSC‐GAN: User‐guided sketch colorization with deep convolution generative adversarial networks

Abstract Inspired by the creating process of human paintings, we propose a novel adversarial architecture for multiple sketch colorization which is a scribble‐based, automatic, and exemplar‐based colorization method. The proposed framework has two stages, namely, imitating stage and shading stage. In the imitating stage, to address the challenge of lack of texture in the sketch, we train a grayscale generation network to accomplish a mapping task, namely, generating a grayscale map with textured, grayscale, boundary information from the input sparse sketch. In the shading stage, the model can accurately colorize the objects in the gray image generated in the previous stage, and generate high‐quality colorized images. With the proposed model trained on our database, the experimental results show that our method can generate vivid colorized images and achieve a better performance than previous methods evaluated by FID metric.
Aggressive Approximation of the SoftMax Function for Power-Efficient Hardware Implementations

Neural Network models most often exploit the SoftMax function in the classification stage for computing probabilities through exponentiation and division operations. To reduce the complexity and the energy consumption of such stage, several hardware-friendly approximation strategies have been disclosed in the recent past. This brief evaluates the effects of an aggressive approximation of the SoftMax layer on both classification accuracy and hardware characteristics. Experimental results demonstrate that the proposed circuit, when implemented in a 28 nm FDSOI technology, saves ~65% of silicon area with respect to competitors, dissipating less than 1 pJ. FPGA implementation results confirm a massive energy dissipation reduction with respect to the conventional baseline architecture, without introducing penalties in the Top-1 accuracy.
Regularized Two Granularity Loss Function for Weakly Supervised Video Moment Retrieval

Weakly supervised video moment retrieval or weakly supervised language moment retrieval aims to search the most relevant moment given a language query. In order to guide the model to capture the most matching video segments with the text description, we design a two-granularity loss function that simultaneously considers both video-level and instance-level relationships. Specifically, we first generate coarse video segments and regard each video segment as an instance. For video-level regularized multiple instance loss (MIL), we leverage the latent alignment between all intra-video segments ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ie.</i> , positive bag) and text descriptions. Then, we classify these segments by regarding this procedure as a supervised learning task under noisy labels. With the instance-level regularized loss function, our model can learn to correct noisy instance-level labels so as to locate the more accurate frame boundary from all the positive instances. Comprehensive experimental results on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ActivityNet</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DiDeMo</i> demonstrate that the proposed loss function sets a new state-of-the-art.