Reviewerly/exHarmony huggingface dataset:
SingGAN: Generative Adversarial Network For High-Fidelity Singing Voice Generation

Deep generative models have achieved significant progress in speech synthesis to date, while high-fidelity singing voice synthesis is still an open problem for its long continuous pronunciation, rich high-frequency parts, and strong expressiveness. Existing neural vocoders designed for text-to-speech cannot directly be applied to singing voice synthesis because they result in glitches and poor high-frequency reconstruction. In this work, we propose SingGAN, a generative adversarial network designed for high-fidelity singing voice synthesis. Specifically, 1) to alleviate the glitch problem in the generated samples, we propose source excitation with the adaptive feature learning filters to expand the receptive field patterns and stabilize long continuous signal generation; and 2) SingGAN introduces global and local discriminators at different scales to enrich low-frequency details and promote high-frequency reconstruction; and 3) To improve the training efficiency, SingGAN includes auxiliary spectrogram losses and sub-band feature matching penalty loss. To the best of our knowledge, SingGAN is the first work designed toward high-fidelity singing voice vocoding. Our evaluation of SingGAN demonstrates the state-of-the-art results with higher-quality (MOS 4.05) samples. Also, SingGAN enables a sample speed of 50x faster than real-time on a single NVIDIA 2080Ti GPU. We further show that SingGAN generalizes well to the mel-spectrogram inversion of unseen singers, and the end-to-end singing voice synthesis system SingGAN-SVS enjoys a two-stage pipeline to transform the music scores into expressive singing voices.
Prediction of occurrence of extreme events using machine learning

Machine learning models play a vital role in the prediction task in several fields of study. In this work, we utilize the ability of machine learning algorithms to predict the occurrence of extreme events in a nonlinear mechanical system. Extreme events are rare events that occur ubiquitously in nature. We consider four machine learning models, namely Logistic Regression, Support Vector Machine, Random Forest and Multi-Layer Perceptron in our prediction task. We train these four machine learning models using training set data and compute the performance of each model using the test set data. We show that the Multi-Layer Perceptron model performs better among the four models in the prediction of extreme events in the considered system. The persistent behaviour of the considered machine learning models is cross-checked with randomly shuffled training set and test set data.
Gated recurrent unit with multilingual universal sentence encoder for Arabic aspect-based sentiment analysis

The increasing interactive content in the Internet motivated researchers and data scientists to conduct Aspect-Based Sentiment Analysis (ABSA) research to understand the various sentiments and the different aspects of a product in a single user’s comment. Determining the various aspects along with their polarities (positive, negative, or neutral) from a single comment is a challenging problem. To this end, we have designed and developed a deep learning model based on Gated Recurrent Units (GRU) and features extracted using the Multilingual Universal Sentence Encoder (MUSE). The proposed Pooled-GRU model trained on a Hotels’ Arabic reviews to address two ABSA tasks: (1) aspect extraction, and (2) aspect polarity classification. The proposed model achieved high results with 93.0% F1 score in the former task and 90.86% F1 score in the latter task. Our experimental results show that our proposed model outperforms the baseline model and the related research methods evaluated on the same dataset. More precisely, our proposed model showed 62.1% improvement in the F1 score over the baseline model for the aspect extraction task and 15% improvement in the accuracy over the baseline model for the aspect polarity classification task.
Improving Semantic Segmentation in Aerial Imagery via Graph Reasoning and Disentangled Learning

Semantic segmentation in aerial imagery is still an important, yet challenging task due to the complex characteristics of remote-sensing data. The critical issues consist of: 1) extreme foreground–background imbalance; 2) large intra-class variance; and 3) arbitrary-oriented, dense, and small objects. The above challenges make it unlikely to model the effective global interdependencies of semantic heterogeneous regions. Besides, general semantic segmentation methods suffer from feature ambiguity due to the joint feature learning paradigm, leading to inferior detail information. In this article, we propose an improved semantic segmentation framework to tackle these problems via graph reasoning (GR) and disentangled learning. On the one hand, a simple, yet effective GR unit is introduced to implement coordinate-interaction space mapping and perform relation reasoning over the graph. It can be deployed on the feature pyramid network (FPN) to exploit cross-stage multi-scale information. On the other hand, we propose a so- called disentangled learning paradigm to explicitly model the foreground and boundary objects, instantiated as foreground prior estimation (FPE) and boundary alignment (BA). The indication of the intermediate feature can be effectively emphasized to enhance the discriminative abilities of the network. Extensive experiments over iSAID, ISPRS Vaihingen, and the general Cityscapes datasets demonstrate the effectiveness and efficiency of the proposed framework over other state-of-the-art semantic segmentation methods.
Alternatives to Agrilogistics: Designing for Ecological Thinking

Sustainable HCI (SHCI) researchers have historically looked to small and urban farmers to help situate and extend notions of sustainability within economic, social, and political frameworks. In the face of climate change and the Anthropocene, however, we ask how designing like the alternative farming practices of small and urban farmers might open up new, ecological approaches to agricultural technology. We conducted ethnographic field work with small farmers and their community in Indiana and show how they are challenging "agrilogistics," defined by philosopher Timothy Morton as a strict separation of nature and culture in food production, a separation, he argues, which underlies the substantial agricultural contributions to climate change. Our ethnography led us to suggest new possibilities for design of agricultural technology that support ecological thinking and caring for more-than-human actors through visceral imaginaries, posthuman storytelling, and engaging curiosity, possibilities which may offer ways to disentangle agricultural technology from agrilogistic paradigms.?
Optimized Coverage Planning for UV Surface Disinfection

UV radiation has been used as a disinfection strategy to deactivate a wide range of pathogens, but existing irradiation strategies do not ensure sufficient exposure of all environmental surfaces and/or require long disinfection times. We present a near-optimal coverage planner for mobile UV disinfection robots. The formulation optimizes the irradiation time efficiency, while ensuring that a sufficient dosage of radiation is received by each surface. The trajectory and dosage plan are optimized taking collision and light occlusion constraints into account. We propose a two-stage scheme to approximate the solution of the induced NP-hard optimization, and, for efficiency, perform key irradiance and occlusion calculations on a GPU. Empirical results show that our technique achieves more coverage for the same exposure time as strategies for existing UV robots, can be used to compare UV robot designs, and produces near-optimal plans.
Online evolutionary batch size orchestration for scheduling deep learning workloads in GPU clusters

Efficient GPU resource scheduling is essential to maximize resource utilization and save training costs for the increasing amount of deep learning workloads in shared GPU clusters. Existing GPU schedulers largely rely on static policies to leverage the performance characteristics of deep learning jobs. However, they can hardly reach optimal efficiency due to the lack of elasticity. To address the problem, we propose ONES, an ONline Evolutionary Scheduler for elastic batch size orchestration. ONES automatically manages the elasticity of each job based on the training batch size, so as to maximize GPU utilization and improve scheduling efficiency. It determines the batch size for each job through an online evolutionary search that can continuously optimize the scheduling decisions. We evaluate the effectiveness of ONES with 64 GPUs on TACC's Longhorn supercomputers. The results show that ONES can outperform the prior deep learning schedulers with a significantly shorter average job completion time.
Joint network embedding of network structure and node attributes via deep autoencoder

Network embedding aims to learn a low-dimensional vector for each node in networks, which is effective in a variety of applications such as network reconstruction and community detection. However, the majority of the existing network embedding methods merely exploit the network structure and ignore the rich node attributes, which tend to generate sub-optimal network representation. To learn more desired network representation, diverse information of networks should be exploited. In this paper, we develop a novel deep autoencoder framework to fuse topological structure and node attributes named FSADA. We firstly design a multi-layer autoencoder which consists of multiple non-linear functions to capture and preserve the highly non-linear network structure and node attribute information. Particularly, we adopt a pre-processing procedure to pre-process the original information, which can better facilitate to extract the intrinsic correlations between topological structure and node attributes. In addition, we design an enhancement module that combines topology and node attribute similarity to construct pairwise constraints on nodes, and then a graph regularization is introduced into the framework to enhance the representation in the latent space. Our extensive experimental evaluations demonstrate the superior performance of the proposed method.
Continuous Shortest Path Vector Field Navigation on 3D Triangular Meshes for Mobile Robots

We present a highly efficient approach to compute continuous shortest path vector fields on arbitrarily shaped 3D triangular meshes for robot navigation in complex real-world outdoor environments. The continuity of the vector field allows to query the shortest distance, direction and geodesic path to the goal at any point within the mesh triangles, resulting in accurate paths. In order to avoid impassable areas, our wavefront propagation method runs on a modular extendable multilayer map architecture taking different geometric cost layers into account. We describe the mathematical foundation of the geodesic distances and continuous vector field computation and demonstrate the performance in real-world and multilevel environments on our campus with a tunnel, ramps and stair- cases, and in a difficult, steep forest area with a stone quarry. For reproducibility, we provide a ready-to-use ROS software stack as well as Gazebo simulations.
Discrete embedding for attributed graphs

Attributed graphs refer to graphs where both node links and node attributes are observable for analysis. Attributed graph embedding enables joint representation learning of node links and node attributes. Different from classical graph embedding methods such as Deepwalk and node2vec that first project node links into low-dimensional vectors which are then linearly concatenated with node attribute vectors as node representation, attributed graph embedding fully explores data dependence between node links and attributes by either using node attributes as class labels to supervise structure learning from node links, or reversely using node links to supervise the learning from node attributes. However, existing attributed graph embedding models are designed in continuous Euclidean spaces which often introduce data redundancy and impose challenges to storage and computation costs. In this paper, we study a new problem of discrete embedding for attributed graphs that can learn succinct node representations. Specifically, we present a Binarized Attributed Network Embedding model (BANE for short) to learn binary node representation by factorizing a Weisfeiler-Lehman proximity matrix under the constraint of binary node representation. Furthermore, based on BANE, we propose a new Low-bit Quantization for Attributed Network Representation learning model (LQANR for short) to learn even more compact node representation of bit-width values. Theoretical analysis and empirical studies on real-world datasets show that the new discrete embedding models outperform benchmark methods.
Is there a nonlinear relationship between financial development and income inequality in Africa? Evidence from dynamic panel threshold

Using dynamic panel threshold model, this work examines the nonlinear relationship between financial development and inequality in Africa. The study introduces a robust measure of financial development into the literature. Using 40 African countries selected from high-income, middle-low and low-income countries, the study tests the new financial development indicator. The empirical results validate the existence of threshold levels of financial development that addresses inequality among African countries. Aggregately, financial development tends to promote inequality in high-income countries and reduces the same in low and middle-income countries. Hence, policymakers, in African countries, should be aware of the threshold level of financial development that imparts income inequality. Also, for financial development to have an enormous impact on inequality, all financial development pillars of depth, stability, access and efficiency must be simultaneously accorded a due place in policymaking by practitioners.
Human-Robot Collaborative Multi-Agent Path Planning using Monte Carlo Tree Search and Social Reward Sources

The collaboration between humans and robots in an object search task requires the achievement of shared plans obtained from communicating and negotiating. In this work, we assume that the robot computes, as a first step, a multi-agent plan for both itself and the human. Then, both plans are submitted to human scrutiny, who either agrees or modifies it forcing the robot to adapt its own restrictions or preferences. This process is repeated along the search task as many times as required by the human. Our planner is based on a decentralized variant of Monte Carlo Tree Search (MCTS), with one robot and one human as agents. Moreover, our algorithm allows the robot and the human to optimize their own actions by maintaining a probability distribution over the plans in a joint-action space. The method allows an objective function definition over action sequences, it assumes intermittent communication, it is anytime and suitable for on-line replanning. To test it, we have developed a human-robot communication mobile phone interface. Validation is provided by real-life search experiments of a Parcheesi token in an urban space, including also an acceptability study.
Scene Graph with 3D Information for Change Captioning

Change captioning aims to describe the differences in image pairs with natural language. It is an interesting task under-explored with two main challenges: describing the relative position relationship between objects correctly and overcoming the disturbances from viewpoint changes. To address these issues, we propose a three-dimensional (3D) information aware Scene Graph based Change Captioning (SGCC) model. We extract the semantic attributes of objects and the 3D information of images (i.e., depths of objects, relative two-dimensional image plane distances, and relative angles between objects) to construct the scene graphs for image pairs, then aggregate the nodes representations with a graph convolutional network. Owing to the relative position relationships between objects and the scene graphs, our model thereby is capable of assisting observers to locate the changed objects quickly and being immune to the viewpoint change to some extent. Extensive experiments show that our SGCC model achieves competitive performance with the state-of-the-art models on the CLEVR-Change and Spot-the-Diff datasets, thus verifying the effectiveness of our proposed model. Codes are available at https://github.com/VISLANG-Lab/SGCC.
Coupled Mobile Manipulation via Trajectory Optimization with Free Space Decomposition

This paper presents a real-time method for wholebody trajectory optimization of mobile manipulators in simplified dynamic and unstructured environments.Current trajectory optimization methods typically use decoupling of the mobile base and the robotic arm, which reduces flexibility in motion, does not scale to unstructured environments, and does not consider the future evolution of the environment, which is crucial to avoid dynamic obstacles.Given a goal configuration, such as waypoints generated by a global path planner, we formulate a receding horizon trajectory optimization minimizing the distance-to-target while avoiding collisions with static and dynamic obstacles.The presented method unifies the control of a robotic arm and a non-holonomic base to allow coupled trajectory planning.For collision avoidance, we propose to compute three convex regions englobing the robot's major body parts (i.e., base, shoulder-link and wrist-link) and thus reducing and limiting the number of inequality constraints, regardless of the number of obstacles in the environment.Moreover, our approach incorporates predicted trajectory information to smoothly, and in advance, avoid dynamic obstacles.The presented results show that trajectory optimization for the coupled system can reduce the total execution time by 48% and that applying the convex region generation for individual links allows keeping the computational costs low, even for complex scenarios, enabling onboard implementation.
Automatic tracking of healthy joint kinematics from stereo-radiography sequences

Kinematic tracking of healthy joints in radiography sequences is frequently performed by maximizing similarities between computed perspective projections of 3D computer models and corresponding objects' appearances in radiographic images. Significant human effort associated with manual tracking presents a major bottleneck in biomechanics research methods and limits the scale of target applications. The current work introduces a method for fully-automatic tracking of tibiofemoral and patellofemoral kinematics in stereo-radiography sequences for subjects performing dynamic activities. The proposed method involves the application of convolutional neural networks for annotating radiographs and a multi-stage optimization pipeline for estimating bone pose based on information provided by neural net predictions. Predicted kinematics are evaluated by comparing against manually-tracked trends across 20 distinct trials. Median absolute differences below 1.5 millimeters or degrees for 6 tibiofemoral and 3 patellofemoral degrees of freedom demonstrate the utility of our approach, which improves upon previous semi-automatic methods by enabling end-to-end automation. Implementation of a fully-automatic pipeline for kinematic tracking will benefit evaluation of human movement by enabling large-scale studies of healthy knee kinematics.
Tackling Cold Start of Serverless Applications by Efficient and Adaptive Container Runtime Reusing

During the past few years, serverless computing has changed the paradigm of application development and deployment in the cloud and edge due to its unique advantages, including easy administration, automatic scaling, built-in fault tolerance, etc. Nevertheless, serverless computing is also facing challenges such as long latency due to the cold start. In this paper, we present an in-depth performance analysis of cold start in the serverless framework and propose HotC, a container-based runtime management framework that leverages the lightweight containers to mitigate the cold start and improve the network performance of serverless applications. HotC maintains a live container runtime pool, analyzes the user input or configuration file, and provides available runtime for immediate reuse. To precisely predict the request and efficiently manage the hot containers, we design an adaptive live container control algorithm combining the exponential smoothing model and Markov chain method. Our evaluation results show that HotC introduces negligible overhead and can efficiently improve the performance of various applications with different network traffic patterns in both cloud servers and edge devices.
Perception of earthquake risks and disaster prevention awareness: A comparison of resident surveys in Sendai, Japan and Seattle, WA, USA

Both Sendai City in Japan and Seattle-King County (hereafter referred to as “Seattle”) in Washington State in the United States are in seismically active areas with high earthquake risks. An Earthquake Early Warning (EEW) system has been publicly available in Japan since 2007. The ShakeAlert system on the U.S. West Coast is still being developed and was at the time of this study being rolled out to provide earthquake early warning to institutional and public users. Given the high costs, concomitant need for public funding, and protective behavior aims of EEW systems, a better understanding of how society and individuals perceive the system and how they react to alerts is crucial to maximize the benefits of EEW. In this study, we compared how Sendai and Seattle residents perceived earthquake risk and EEW effectiveness, as well as their willingness to pay (WTP) for an improved (Sendai) or newly adopted (Seattle) EEW system. Similarities in the risks of megaquakes and the differences in recent events and earthquake early warning implementation enabled us to test the extent to which associations between earthquake risk perceptions, risk preparedness, and support for government policies to reduce earthquake risks can be generalized across these contrasting conditions. We found that risk perceptions and perceived effectiveness of EEW in personal protection were significant determinants of WTP for an (improved) EEW system in both regions. However, the association of “fear” with earthquakes was a significant predictor of WTP only in the Seattle survey. One possible explanation for this is that emotional reactions to earthquakes were more varied in the Seattle sample than Sendai, likely due to the much milder earthquakes that have occurred in the Pacific Northwest of the US in recent decades than in Japan. In contrast, most Sendai respondents reported that they felt danger and fear in their recent earthquake experiences.