Reviewerly/exHarmony huggingface dataset:
Parallel graph algorithms in constant adaptive rounds

We study fundamental graph problems such as graph connectivity, minimum spanning forest (MSF), and approximate maximum (weight) matching in a distributed setting. In particular, we focus on the Adaptive Massively Parallel Computation (AMPC) model, which is a theoretical model that captures MapReduce-like computation augmented with a distributed hash table. We show the first AMPC algorithms for all of the studied problems that run in a constant number of rounds and use only O ( n ϵ ) space per machine, where 0 &lt; ϵ &lt; 1. Our results improve both upon the previous results in the AMPC model, as well as the best-known results in the MPC model, which is the theoretical model underpinning many popular distributed computation frameworks, such as MapReduce, Hadoop, Beam, Pregel and Giraph. Finally, we provide an empirical comparison of the algorithms in the MPC and AMPC models in a fault-tolerant distributed computation environment. We empirically evaluate our algorithms on a set of large real-world graphs and show that our AMPC algorithms can achieve improvements in both running time and round-complexity over optimized MPC baselines.
Visually Induced Motion Sickness Susceptibility and Recovery Based on Four Mitigation Techniques

Virtual reality (VR) usage continues to grow, but visually induced motion sickness (VIMS) can decrease VR effectiveness for some users. This study seeks to compare methods of VIMS mitigation and explore sickness among gender and video game experience groups. Participant discomfort and early dropout are problems for studies that involve virtual environment (VE) exposure, but previous research has demonstrated that natural decay and physical, real-world hand-eye coordination tasks can serve as effective mitigation strategies. In this study, 57 participants wore a head-mounted display (HMD) and navigated a maze VE designed to induce cybersickness. Participants then experienced one of four mitigation techniques: real natural decay (HMD off), virtual natural decay (HMD on with idyllic VE and no locomotion), real hand-eye coordination task (HMD off), and virtual hand-eye coordination task (HMD on). Simulator Sickness Questionnaire (SSQ) measures were taken periodically throughout maze and mitigation tasks. Results demonstrated that peak sickness during the maze VE occurred after approximately 10 minutes. Analyses of mitigation techniques showed that real natural decay resulted in significantly more sickness recovery when compared with the virtual hand-eye coordination task for SSQ total score, nausea, and oculomotor constructs, but not disorientation. The real natural decay technique was the most effective at bringing participants' final sickness measure back to their initial baseline measure; however, other mitigation techniques yielded effectiveness, but at a lower rate. This study extends previous research about hand-eye mitigation approaches by demonstrating that natural decay and hand-eye tasks in a virtual and real-world setting were effective in reducing VIMS. Real-world natural decay was the most effective at mitigating VIMS and the virtual hand-eye task was not as effective as the other three tasks. Women experienced more VIMS that men, but also recovered more than men during mitigation. Video gamers experienced less VIMS than non-gamers. These findings bolster extant knowledge about VIMS mitigation techniques and can inform future development of virtual mitigation techniques.
A brief review and challenges of object detection in optical remote sensing imagery

Traditional object detection algorithms and strategies are difficult to meet the requirements of data processing efficiency, performance, speed and intelligence in object detection. Through the study and imitation of the cognitive ability of the brain, deep learning can analyze and process the data features. It has a strong ability of visualization and becomes the mainstream algorithm of current object detection applications. Firstly, we have discussed the developments of traditional object detection methods. Secondly, the frameworks of object detection (e.g. Region-based CNN (R-CNN), Spatial Pyramid Pooling Network (SPP-NET), Fast-RCNN and Faster-RCNN) which combine region proposals and convolutional neural networks (CNNs) are briefly characterized for optical remote sensing applications. You only look once (YOLO) algorithm is the representative of the object detection frameworks (e.g. YOLO and Single Shot MultiBox Detector (SSD)) which transforms the object detection into a regression problem. The limitations of remote sensing images and object detectors have been highlighted and discussed. The feasibility and limitations of these approaches will lead researchers to prudently select appropriate image enhancements. Finally, the problems of object detection algorithms in deep learning are summarized and the future recommendations are also conferred.
Quantum circuit designs of carry lookahead adder optimized for T-count T-depth and qubits

Quantum circuits of arithmetic operations such as addition are needed to implement quantum algorithms in hardware. Quantum circuits based on Clifford+T gates are used as they can be made tolerant to noise. The trade-off of gaining fault tolerance from using Clifford+T gates and error-correcting codes is the high implementation overhead of the T gate. As a result, the T-count and T-depth performance measures have become important in quantum circuit design. Due to noise, the risk for errors in a quantum circuit computation increases as the number of gate layers (or depth) in the circuit increases. As a result, low depth circuits such as quantum carry lookahead adders (QCLA)s have caught the attention of researchers. This work presents two QCLA designs each optimized with emphasis on T-count and T-depth or qubit cost, respectively. In-place and out-of-place versions of each design are shown. The proposed QCLAs are compared against the existing works in terms of T-count and T-depth. The proposed QCLAs for out-of-place addition achieve average T-count savings of 54.34% and 37.21%, respectively. The proposed QCLAs for out-of-place addition achieve up to a 33.33% reduction in T-depth. The proposed QCLAs for in-place addition achieve average T-count savings of 65.31% and 30.63%, respectively. When compared to existing works, the proposed QCLAs for in-place addition achieves T-depth savings ranging from 33.33% to 95.56%.
Perceptual Quality Assessment for Asymmetrically Distorted Stereoscopic Video by Temporal Binocular Rivalry

In this paper, we propose a two-stage weighting based perceptual quality assessment framework for asymmetrically distorted stereoscopic video (SV) sequences by temporal binocular rivalry. Firstly, a traditional 2D image quality assessment (IQA) method is employed to measure spatial distortion, and the temporal distortion is evaluated by the magnitude differences between motion vectors of distorted and reference video frames. Secondly, the structural strength (SS) computed by gradient map and the motion energy (ME) computed by frame difference map are used to estimate the intensity of visual stimulus in spatial and temporal domain respectively. Then, SS and ME are considered as the importance indexes to combine the quality scores of spatial and temporal distortion to estimate perceived distortion of single-view video sequences, which is denoted as the first-stage weighting. Finally, considering that the difference of intensity of visual stimulus between two eyes results in binocular rivalry, a novel temporal binocular rivalry inspired weighting method is designed to integrate the quality scores of left- and right-views for the final visual quality prediction of SV sequences, which is denoted as the second-stage weighting. Experimental results on Waterloo-IVC SV quality databases show that several specific examples of 2D-IQA methods within the proposed framework can obtain highly competitive performance over other existing ones.
A container-based cloud-native architecture for the reproducible execution of multi-population optimization algorithms

Splitting a population into multiple instances is a technique used extensively in recent years to help improve the performance of nature-inspired optimization algorithms. Work on those populations can be done in parallel, and they can interact asynchronously, a fact that can be leveraged to create scalable implementations based on, among other methods, distributed, multi-threaded, parallel, and cloud-native computing. However, the design of these cloud-native, distributed, multi-population algorithms is not a trivial task. Using as a foundation monolithic (single-instance) solutions, adaptations at several levels, from the algorithmic to the functional, must be made to leverage the scalability, elasticity, (limited) fault-tolerance, reproducibility, and cost-effectiveness of cloud systems while, at the same time, conserving the intended functionality. Instead of an evolutive approach, in this paper, we propose a cloud-native optimization framework created from scratch, that can include multiple (population-based) algorithms without increasing the number of parameters that need tuning. This solution goes beyond the current state of the art, since it can support different algorithms at the same time, work asynchronously, and also be readily deployable to any cloud platform. We evaluate this solution's performance and scalability, together with the effect other design parameters had on it, particularly the number and the size of populations with respect to problem size. The implemented platform is an excellent alternative for running locally or in the cloud, thus proving that cloud-native bioinspired algorithms perform better in their "natural" environment than other algorithms, and set a new baseline for scaling and performance of this kind of algorithms in the cloud.
Developing machine-learning regression model with Logical Analysis of Data (LAD)

This paper proposes a regression model based on Logical Analysis of Data (LAD). LAD is known as a combinatorial Boolean supervised data mining technique for pattern generation. It is used mainly for classification problems, and has demonstrated high accuracy compared to other classification techniques. In this paper, we extend the use of LAD to deal with supervised data with continuous responses. We derive a LAD regression model (LADR). Three discretization methods that transform the values of the response into a set of thresholds are tested. At each threshold, LAD analyzes the data as a two-class classification problem and extracts the prescriptive patterns for each class. LADR regression uses the generated patterns from the original data by using cbmLAD software to fit a numerical continuous dependent response. Therefore, a normalized regression model with only binary independent variables is obtained. LADR has been applied for six datasets and obtains better results compared with the linear regression (LR), support vector regression (SVR), Decision Tree Regression (DTR), Random Forest (RF), and Polynomial Regression (PolyR). The performance is evaluated by the Mean Square Error (MSE), Coefficient of Determination (R2), and Mean Absolute Error (MAE) based on a 10-fold cross validation.
ByeGlassesGAN: Identity Preserving Eyeglasses Removal for Face Images

In this paper, we propose a novel image-to-image GAN framework for eyeglasses removal, called ByeGlassesGAN, which is used to automatically detect the position of eyeglasses and then remove them from face images. Our ByeGlassesGAN consists of an encoder, a face decoder, and a segmentation decoder. The encoder is responsible for extracting information from the source face image, and the face decoder utilizes this information to generate glasses-removed images. The segmentation decoder is included to predict the segmentation mask of eyeglasses and completed face region. The feature vectors generated by the segmentation decoder are shared with the face decoder, which facilitates better reconstruction results. Our experiments show that ByeGlassesGAN can provide visually appealing results in the eyeglasses-removed face images even for semi-transparent color eyeglasses or glasses with glare. Furthermore, we demonstrate significant improvement in face recognition accuracy for face images with glasses by applying our method as a pre-processing step in our face recognition experiment.
Native Custom Tokens in the Extended UTXO Model

User-defined tokens—both fungible ERC-20 and non-fungible ERC-721 tokens—are central to the majority of contracts deployed on Ethereum. User-defined tokens are non-native on Ethereum; i.e., they are not directly supported by the ledger, but require custom code. This makes them unnecessarily inefficient, expensive, and complex. The Extended UTXO Model (EUTXO) [5] has been introduced as a generalisation of Bitcoin-style UTXO ledgers, allowing support of more expressive smart contracts, approaching the functionality available to contracts on Ethereum. Specifically, a bisimulation argument established a formal relationship between the EUTXO ledger and a general form of state machines. Nevertheless, transaction outputs in the EUTXO model lock integral quantities of a single native cryptocurrency only, just like Bitcoin. In this paper, we study a generalisation of the EUTXO ledger model with native user-defined tokens. Following the approach proposed in a companion paper [4] for the simpler case of plain Bitcoin-style UTXO ledgers, we generalise transaction outputs to lock not merely coins of a single cryptocurrency, but entire token bundles, including custom tokens whose forging is controlled by forging policy scripts. We show that this leads to a rich ledger model that supports a broad range of interesting use cases. Our main technical contribution is a formalisation of the multi-asset EUTXO ledger in Agda, which we use to establish that the ledger with custom tokens is strictly more expressive than the original EUTXO ledger. In particular, we state and prove a transfer result for inductive and temporal properties from state machines to the multi-asset EUTXO ledger, which was out of scope for the single-currency EUTXO ledger. In practical terms, the resulting system is the basis for the smart contract system of the Cardano blockchain.
A New Long-Term Downward Surface Solar Radiation Dataset over China from 1958 to 2015

Downward surface solar radiation (Rs) plays a dominant role in determining the climate and environment on the Earth. However, the densely distributed ground observations of Rs are usually insufficient to meet the increasing demand of the climate diagnosis and analysis well, so it is essential to build a long-term accurate Rs dataset. The extremely randomized trees (ERT) algorithm was used to generate Rs using routine meteorological observations (2000-2015) from the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA). The estimated Rs values were validated against ground measurements at the national scale with an overall correlation coefficient value of 0.97, a mean bias of 0.04 Wm-2, a root-mean-square-error value of 23.12 Wm-2, and a mean relative error of 9.81%. It indicates that the estimated Rs from the ERT-based model is reasonably accurate. Moreover, the ERT-based model was used to generate a new daily Rs dataset at 756 CDC/CMA stations from 1958 to 2015. The long-term variation trends of Rs at 454 stations covering 46 consecutive years (1970-2015) were also analyzed. The Rs in China showed a significant decline trend (-1.1 Wm-2 per decade) during 1970-2015. A decreasing trend (-2.8 Wm-2 per decade) in Rs during 1970-1992 was observed, followed by a recovery trend (0.23 Wm-2 per decade) during 1992-2015. The recovery trends at individual stations were found at 233 out of 454 stations during 1970-2015, which were mainly located in southern and northern China. The new Rs dataset would substantially provide basic data for the related studies in agriculture, ecology, and meteorology.
Spatial orthogonal attention generative adversarial network for MRI reconstruction

Purpose Recent studies have witnessed that self‐attention modules can better solve the vision understanding problems by capturing long‐range dependencies. However, there are very few works designing a lightweight self‐attention module to improve the quality of MRI reconstruction. Furthermore, it can be observed that several important self‐attention modules (e.g., the non‐local block) cause high computational complexity and need a huge number of GPU memory when the size of the input feature is large. The purpose of this study is to design a lightweight yet effective spatial orthogonal attention module (SOAM) to capture long‐range dependencies, and develop a novel spatial orthogonal attention generative adversarial network, termed as SOGAN, to achieve more accurate MRI reconstruction. Methods We first develop a lightweight SOAM, which can generate two small attention maps to effectively aggregate the long‐range contextual information in vertical and horizontal directions, respectively. Then, we embed the proposed SOAMs into the concatenated convolutional autoencoders to form the generator of the proposed SOGAN. Results The experimental results demonstrate that the proposed SOAMs improve the quality of the reconstructed MR images effectively by capturing long‐range dependencies. Besides, compared with state‐of‐the‐art deep learning‐based CS‐MRI methods, the proposed SOGAN reconstructs MR images more accurately, but with fewer model parameters. Conclusions The proposed SOAM is a lightweight yet effective self‐attention module to capture long‐range dependencies, thus, can improve the quality of MRI reconstruction to a large extent. Besides, with the help of SOAMs, the proposed SOGAN outperforms the state‐of‐the‐art deep learning‐based CS‐MRI methods.
Anomaly Detection of Power Plant Equipment Using Long Short-Term Memory Based Autoencoder Neural Network

Anomaly detection is of great significance in condition-based maintenance of power plant equipment. The conventional fixed threshold detection method is not able to perform early detection of equipment abnormalities. In this study, a general anomaly detection framework based on a long short-term memory-based autoencoder (LSTM-AE) network is proposed. A normal behavior model (NBM) is established to learn the normal behavior patterns of the operating variables of the equipment in space and time. Based on the similarity analysis between the NBM output distribution and the corresponding measurement distribution, the Mahalanobis distance (MD) is used to describe the overall residual (OR) of the model. The reasonable range is obtained using kernel density estimation (KDE) with a 99% confidence interval, and the OR is monitored to detect abnormalities in real-time. An induced draft fan is chosen as a case study. Results show that the established NBM has excellent accuracy and generalizability, with average root mean square errors of 0.026 and 0.035 for the training and test data, respectively, and average mean absolute percentage errors of 0.027%. Moreover, the abnormal operation case shows that the proposed framework can be effectively used for the early detection of abnormalities.
Efficient neural network using pointwise convolution kernels with linear phase constraint

In current efficient convolutional neural networks, 1 × 1 convolution is widely used. However, the amount of computation and the number of parameters of 1 × 1 convolution layers account for a large part of these neural network models. In this paper, we propose to use linear-phase pointwise convolution kernels (LPPC kernels) to reduce the computational complexities and storage costs of these neural networks. We design four types of LPPC kernels based on the parity of the number of input channels and symmetry of the weights of the pointwise convolution kernel. Experimental results show that Type-I LPPC kernels can compress some popular networks better with a small reduction in accuracy than the other types of LPPC kernels. The LPPC kernels can be used as new 1 × 1 convolution kernels to design efficient neural network architectures in the future. Moreover, the LPPC kernels are friendly to low-power hardware accelerator design to achieve lower memory access cost and smaller model size.
Baechi

Machine Learning graphs (or models) can be challenging or impossible to train when either devices have limited memory, or the models are large. Splitting the model graph across multiple devices, today, largely relies on learning-based approaches to generate this placement. While it results in models that train fast on data (i.e., with low step times), learning-based model-parallelism is time-consuming, taking many hours or days to create a placement plan of operators on devices. We present the Baechi system, where we adopt an algorithmic approach to the placement problem for running machine learning training graphs on a small cluster of memory-constrained devices. We implemented Baechi so that it works modularly with TensorFlow. Our experimental results using GPUs show that Baechi generates placement plans in time 654X--206K X faster than today's learning-based approaches, and the placed model's step time is only up to 6.2% higher than expert-based placements.
Automatic Generation of Chatbots for Conversational Web Browsing

In this paper, we describe the foundations for generating a chatbot out of a website equipped with simple, bot-specific HTML annotations. The approach is part of what we call conversational web browsing, i.e., a dialog-based, natural language interaction with websites. The goal is to enable users to use content and functionality accessible through rendered UIs by "talking to websites" instead of by operating the graphical UI using keyboard and mouse. The chatbot mediates between the user and the website, operates its graphical UI on behalf of the user, and informs the user about the state of interaction. We describe the conceptual vocabulary and annotation format, the supporting conversational middleware and techniques, and the implementation of a demo able to deliver conversational web browsing experiences through Amazon Alexa.
The Effects of Full-Body Avatar Movement Predictions in Virtual Reality using Neural Networks

Motion tracking technologies and avatars in virtual reality (VR) showing the movements of the own body enable high levels of presence and a strong illusion of body ownership (IBO) – key features of immersive systems and gaming experiences in virtual environments. Previous work suggests using software-based algorithms that can not only compensate system latency but also predict future movements of the user to increase input performance. However, the effects of movement prediction in VR on input performance are largely unknown. In this paper, we investigate neural network-based predictions of full-body avatar movements in two scenarios: In the first study, we used a standardized 2D Fitts' Law task to examine the information throughput in VR. In the second study, we utilized a full-body VR game to determine the users' performance. We found that both performance and subjective measures in a standardized 2D Fitts' law task could not benefit from the predicted avatar movements. In an immersive gaming scenario, however, the perceived accuracy of the own body location improved. Presence and body assessments remained more stable and were higher than during the Fitts' task. We conclude that machine-learning-based predictions could be used to compensate system-related latency but participants only subjectively benefit under certain conditions.