Reviewerly/exHarmony huggingface dataset:
Improve three-dimensional point localization accuracy in stereo vision systems using a novel camera calibration method

Computer vision systems have demonstrated to be useful in applications of autonomous navigation, especially with the use of stereo vision systems for the three-dimensional mapping of the environment. This article presents a novel camera calibration method to improve the accuracy of stereo vision systems for three-dimensional point localization. The proposed camera calibration method uses the least square method to model the error caused by the image digitalization and the lens distortion. To obtain particular three-dimensional point coordinates, the stereo vision systems use the information of two images taken by two different cameras. Then, the system locates the two-dimensional pixel coordinates of the three-dimensional point in both images and coverts them into angles. With the obtained angles, the system finds the three-dimensional point coordinates through a triangulation process. The proposed camera calibration method is applied in the stereo vision systems, and a comparative analysis between the real and calibrated three-dimensional data points is performed to validate the improvements. Moreover, the developed method is compared with three classical calibration methods to analyze their advantages in terms of accuracy with respect to tested methods.
Survey on visual sentiment analysis

Visual Sentiment Analysis aims to understand how images affect people, in terms of evoked emotions. Although this field is rather new, a broad range of techniques have been developed for various data sources and problems, resulting in a large body of research. This paper reviews pertinent publications and tries to present an exhaustive overview of the field. After a description of the task and the related applications, the subject is tackled under different main headings. The paper also describes principles of design of general Visual Sentiment Analysis systems from three main points of view: emotional models, dataset definition, feature design. A formalization of the problem is discussed, considering different levels of granularity, as well as the components that can affect the sentiment toward an image in different ways. To this aim, this paper considers a structured formalization of the problem which is usually used for the analysis of text, and discusses it's suitability in the context of Visual Sentiment Analysis. The paper also includes a description of new challenges, the evaluation from the viewpoint of progress toward more sophisticated systems and related practical applications, as well as a summary of the insights resulting from this study.
A novel image-dehazing network with a parallel attention block

Image dehazing is a very important pre-processing step to many computer vision tasks such as object recognition and tracking. However, it is a challenging problem because the physical parameters of imaging, e.g. the depth information of scene pixels and the attenuation model, are usually unknown. Based on a physical model, different methods have been proposed to recover these parameters. Existing convolutional neural networks (CNNs) based methods try to solve the image dehazing problem using an end-to-end network to learn a direct mapping between a hazy image and its corresponding clear image. But the representational ability, spatial variant ability and dehazing capability of these network models are hindered by treating all the spatial and channel-wise features indiscriminately. Hence, we propose an end-to-end dehazing network with a parallel spatial/channel-wise attention block for capturing more informative spatial and channel-wise features respectively. Specifically, based on the encoder-decoder framework with a pyramid pooling operation, a novel parallel spatial/channel-wise attention block is proposed and applied to the end of the encoder for guiding the decoder to reconstruct better clear images. In the spatial/channel-wise attention block, the spatial attention module and the channel-wise attention module are connected in parallel, where the spatial attention module highlights important spatial positions of features. Meanwhile, the channel-wise module exploits inter-dependencies among the channel-wise features. Extensive experiments demonstrate that our network with a parallel spatial /channel-wise attention block can achieve better accuracy and visual results over state-of-the-art methods.
Event-triggered distributed state estimation over wireless sensor networks

This paper focuses on the event-triggered distributed state estimation over sensor networks, in which each sensor node selectively transmits the local information to its neighbors for the reduced communication bandwidth and the prolonged network lifetime. Based on an individual stochastic triggering condition, an event-triggered minimum mean square error (MMSE) estimator is proposed in a recursive form, and then an event-triggered distributed state estimation algorithm is developed by repeatedly fusing the local information and the event-triggered information. It is shown that, under network connectivity, collective observability and large enough triggering parameters, the distributed estimator in each sensor node is stable with the uniformly bounded estimation error in mean square. Finally, a target tracking example is provided to illustrate the practical effectiveness of the proposed technique.
A secure and efficient data sharing scheme based on blockchain in industrial Internet of Things

Blockchain possesses potential for enabling secure data sharing of industrial big data generated by the Industrial Internet of Things (IIoT). However, the existing data-sharing schemes based on blockchain secure only the data exchange process between the data-sharing parties; they do not consider the efficiency of data sharing at all. In this study, we propose a data-sharing scheme based on blockchain that comprehensively considers the security and efficiency of data sharing. In this scheme, a secure data-sharing framework is designed based on identity authentication and Hyperledger Fabric to ensure the security of data sharing. We also propose a community detection algorithm to divide the clients into different data-sharing communities according to the similarity of label data. The scope of data sharing is selected according to the results of the community detection evaluated by the sharing degree, which can effectively narrow the scope of query-shared data and improve the efficiency of data sharing. The experimental results show that the proposed scheme is effective for secure and efficient data sharing among different clients.
An FPGA-Based Reconfigurable CNN Accelerator for YOLO

Convolutional neural network (CNN) has been widely used in image processing fields. Object detection models based on CNN, such as YOLO and SSD, have been proved to be the most advanced in many applications. CNN have extremely high requirements on computing power and memory bandwidth, which usually needs to be deployed to a dedicated hardware platform. FPGA has great advantages in reconfigurability and performance power ratio, which is a suitable choice to deploy CNN. In this paper, we propose a reconfigurable CNN accelerator with AXI bus based on ARM + FPGA architecture. The accelerator can receive the configuration signals sent by ARM and complete the calculation during inference of different CNN layers through time-sharing. By combining convolution and pooling operation, the number of data moves of convolutional layer and pooling layer is reduced to reduce the number of off-chip memory accesses. The floating-point number is converted into 16-bit dynamic fixed-point format, which improves the calculation performance. We implemented the proposed architecture on the Xilinx ZCU102 FPGA for YOLOv2 and YOLOv2 Tiny models on COCO and VOC 2007 respectively, with peak performance of 289GOPs at 300MHz clock frequency.
Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Image

In this paper, we propose a deep convolutional recurrent neural network that predicts action sequences for task and motion planning (TAMP) from an initial scene image.Typical TAMP problems are formalized by combining reasoning on a symbolic, discrete level (e.g.first-order logic) with continuous motion planning such as nonlinear trajectory optimization.Due to the great combinatorial complexity of possible discrete action sequences, a large number of optimization/motion planning problems have to be solved to find a solution, which limits the scalability of these approaches.To circumvent this combinatorial complexity, we develop a neural network which, based on an initial image of the scene, directly predicts promising discrete action sequences such that ideally only one motion planning problem has to be solved to find a solution to the overall TAMP problem.A key aspect is that our method generalizes to scenes with many and varying number of objects, although being trained on only two objects at a time.This is possible by encoding the objects of the scene in images as input to the neural network, instead of a fixed feature vector.Results show runtime improvements of several magnitudes.Video: https://youtu.be/i8yyEbbvoEk
Preparing for the quantum revolution: What is the role of higher education?

Quantum sensing, quantum networking and communication, and quantum computing have attracted significant attention recently, as these quantum technologies could offer significant advantages over existing technologies. In order to accelerate the commercialization of these quantum technologies, the workforce must be equipped with the necessary skills. Through a qualitative study of the quantum industry, in a series of interviews with 21 U.S. companies carried out in Fall 2019, we describe the types of activities being carried out in the quantum industry, profile the types of jobs that exist, and describe the skills valued across the quantum industry, as well as in each type of job. The current routes into the quantum industry are detailed, providing a picture of the current role of higher education in training the quantum workforce. Finally, we present the training and hiring challenges the quantum industry is facing and how higher education may optimize the important role it is currently playing.Received 28 May 2020Accepted 24 September 2020DOI:https://doi.org/10.1103/PhysRevPhysEducRes.16.020131Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasEducational policyInstructional materials developmentProfessional TopicsFacultyGraduate studentsLower undergraduate studentsUpper undergraduate studentsQuantum InformationInterdisciplinary PhysicsPhysics Education Research
Contrastive Self-supervised Learning for Graph Classification

Graph classification is a widely studied problem and has broad applications. In many real-world problems, the number of labeled graphs available for training classification models is limited, which renders these models prone to overfitting. To address this problem, we propose two approaches based on contrastive self-supervised learning (CSSL) to alleviate overfitting. In the first approach, we use CSSL to pretrain graph encoders on widely-available unlabeled graphs without relying on human-provided labels, then finetune the pretrained encoders on labeled graphs. In the second approach, we develop a regularizer based on CSSL, and solve the supervised classification task and the unsupervised CSSL task simultaneously. To perform CSSL on graphs, given a collection of original graphs, we perform data augmentation to create augmented graphs out of the original graphs. An augmented graph is created by consecutively applying a sequence of graph alteration operations. A contrastive loss is defined to learn graph encoders by judging whether two augmented graphs are from the same original graph. Experiments on various graph classification datasets demonstrate the effectiveness of our proposed methods. The code is available at https://github.com/UCSD-AI4H/GraphSSL.
Peace engineering: The contribution of blockchain systems to the e-voting process

In recent decades, several countries have faced political tensions due to citizens' perceptions that their elections are fraudulent; some electors have even chosen not to vote because they believe that the results may be falsified. Thus, electoral fraud is a major issue. E-governance and e-voting are now being used in many countries, some of which are investigating blockchain solutions. The aim of this study is to investigate the potential contributions of blockchain technology to peace on a worldwide level by securing voting systems. Unfortunately, this technology is complex and could potentially generate conflict between actors in elections. Taking an exploratory approach, the authors chose a qualitative method to address this specific topic. Election observers and blockchain experts were interviewed to identify the technology's strengths and weaknesses. Our results emphasize the importance of trust and human factors in the voting process.
An Appraisal of Incremental Learning Methods

As a special case of machine learning, incremental learning can acquire useful knowledge from incoming data continuously while it does not need to access the original data. It is expected to have the ability of memorization and it is regarded as one of the ultimate goals of artificial intelligence technology. However, incremental learning remains a long term challenge. Modern deep neural network models achieve outstanding performance on stationary data distributions with batch training. This restriction leads to catastrophic forgetting for incremental learning scenarios since the distribution of incoming data is unknown and has a highly different probability from the old data. Therefore, a model must be both plastic to acquire new knowledge and stable to consolidate existing knowledge. This review aims to draw a systematic review of the state of the art of incremental learning methods. Published reports are selected from Web of Science, IEEEXplore, and DBLP databases up to May 2020. Each paper is reviewed according to the types: architectural strategy, regularization strategy and rehearsal and pseudo-rehearsal strategy. We compare and discuss different methods. Moreover, the development trend and research focus are given. It is concluded that incremental learning is still a hot research area and will be for a long period. More attention should be paid to the exploration of both biological systems and computational models.
Efficient evaluation of quantum observables using entangled measurements

Abstract The advent of cloud quantum computing has led to the rapid development of quantum algorithms. In particular, it is necessary to study variational quantum-classical hybrid algorithms, which are executable on noisy intermediate-scale quantum (NISQ) computers. Evaluations of observables appear frequently in the variational quantum-classical hybrid algorithms for NISQ computers. By speeding up the evaluation of observables, it is possible to realize a faster algorithm and save resources of quantum computers. Grouping of observables with separable measurements has been conventionally used, and the grouping with entangled measurements has also been proposed recently by several teams. In this paper, we show that entangled measurements enhance the efficiency of evaluation of observables, both theoretically and experimentally, by taking into account the covariance effect, which may affect the quality of evaluation of observables. We also propose using a part of entangled measurements for grouping to keep the depth of extra gates constant. Our proposed method is expected to be used in conjunction with other related studies. We hope that entangled measurements would become crucial resources, not only for joint measurements but also for quantum information processing.
Detecting East Asian Prejudice on Social Media

During COVID-19 concerns have heightened about the spread of aggressive and hateful language online, especially hostility directed against East Asia and East Asian people.We report on a new dataset and the creation of a machine learning classifier that categorizes social media posts from Twitter into four classes: Hostility against East Asia, Criticism of East Asia, Meta-discussions of East Asian prejudice, and Non-related.The classifier achieves a macro-F1 score of 0.83.We then conduct an in-depth ground-up error analysis and show that the model struggles with edge cases and ambiguous content.We provide the 20,000 tweet training dataset (annotated by experienced analysts), which also contains several secondary categories and additional flags.We also provide the 40,000 original annotations (before adjudication), the full codebook, annotations for COVID-19 relevance and East Asian relevance and stance for 1,000 hashtags, and the final model.
Machine learning algorithms for predicting the amplitude of chaotic laser pulses

Forecasting the dynamics of chaotic systems from the analysis of their output signals is a challenging problem with applications in most fields of modern science. In this work, we use a laser model to compare the performance of several machine learning algorithms for forecasting the amplitude of upcoming emitted chaotic pulses. We simulate the dynamics of an optically injected semiconductor laser that presents a rich variety of dynamical regimes when changing the parameters. We focus on a particular dynamical regime that can show ultra-high intensity pulses, reminiscent of rogue waves. We compare the goodness of the forecast for several popular methods in machine learning, namely deep learning, support vector machine, nearest neighbors and reservoir computing. Finally, we analyze how their performance for predicting the height of the next optical pulse depends on the amount of noise and the length of the time-series used for training.
Quantum annealing initialization of the quantum approximate optimization algorithm

The quantum approximate optimization algorithm (QAOA) is a prospective near-term quantum algorithm due to its modest circuit depth and promising benchmarks. However, an external parameter optimization required in QAOA could become a performance bottleneck. This motivates studies of the optimization landscape and search for heuristic ways of parameter initialization. In this work we visualize the optimization landscape of the QAOA applied to the MaxCut problem on random graphs, demonstrating that random initialization of the QAOA is prone to converging to local minima with sub-optimal performance. We introduce the initialization of QAOA parameters based on the Trotterized quantum annealing (TQA) protocol, parameterized by the Trotter time step. We find that the TQA initialization allows to circumvent the issue of false minima for a broad range of time steps, yielding the same performance as the best result out of an exponentially scaling number of random initializations. Moreover, we demonstrate that the optimal value of the time step coincides with the point of proliferation of Trotter errors in quantum annealing. Our results suggest practical ways of initializing QAOA protocols on near-term quantum devices and reveals new connections between QAOA and quantum annealing.
ROSF: Leveraging Information Retrieval and Supervised Learning for Recommending Code Snippets

When implementing unfamiliar programming tasks, developers commonly search code examples and learn usage patterns of APIs from the code examples or reuse them by copy-pasting and modifying. For providing high-quality code examples, previous studies present several methods to recommend code snippets mainly based on information retrieval. In this paper, to provide better recommendation results, we propose ROSF, Recommending code Snippets with multi-aspect Features, a novel method combining both information retrieval and supervised learning. In our method, we recommend Top-K code snippets for a given free-form query based on two stages, i.e., coarse-grained searching and fine-grained re-ranking. First, we generate a code snippet candidate set by searching a code snippet corpus using an information retrieval method. Second, we predict probability values of the code snippets for different relevance scores in the candidate set by the learned prediction model from a training set, re-rank these candidate code snippets according to the probability values, and recommend the final results to developers. We conduct several experiments to evaluate our method in a large-scale corpus containing 921,713 real-world code snippets. The results show that ROSF is an effective method for code snippets recommendation and outperforms the-state-of-the-art methods by 20-41percent in Precision and 13-33 percent in NDCG.
Cloud, Fog, or Edge: Where to Compute?

The computing continuum extends the high-performance cloud data centers with energy-efficient and low-latency devices close to the data sources located at the edge of the network. However, the heterogeneity of the computing continuum raises multiple challenges related to application management. These include where to offload an application-from the cloud to the edge-to meet its computation and communication requirements. To support these decisions, we provide, in this article, a detailed performance and carbon footprint analysis of a selection of use case applications with complementary resource requirements across the computing continuum over a real-life evaluation testbed.
Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data

Abstract Across jurisdictions, government and health insurance providers hold a large amount of data from patient interactions with the healthcare system. We aimed to develop a machine learning-based model for predicting adverse outcomes due to diabetes complications using administrative health data from the single-payer health system in Ontario, Canada. A Gradient Boosting Decision Tree model was trained on data from 1,029,366 patients, validated on 272,864 patients, and tested on 265,406 patients. Discrimination was assessed using the AUC statistic and calibration was assessed visually using calibration plots overall and across population subgroups. Our model predicting three-year risk of adverse outcomes due to diabetes complications (hyper/hypoglycemia, tissue infection, retinopathy, cardiovascular events, amputation) included 700 features from multiple diverse data sources and had strong discrimination (average test AUC = 77.7, range 77.7–77.9). Through the design and validation of a high-performance model to predict diabetes complications adverse outcomes at the population level, we demonstrate the potential of machine learning and administrative health data to inform health planning and healthcare resource allocation for diabetes management.
BioELECTRA:Pretrained Biomedical text Encoder using Discriminators

Recent advancements in pretraining strategies in NLP have shown a significant improvement in the performance of models on various text mining tasks.In this paper, we introduce Bio-ELECTRA, a biomedical domain-specific language encoder model that adapts ELECTRA (Clark et al., 2020) for the Biomedical domain.BioELECTRA outperforms the previous models and achieves state of the art (SOTA) on all the 13 datasets in BLURB benchmark and on all the 4 Clinical datasets from BLUE Benchmark across 7 NLP tasks.BioELEC-TRA pretrained on PubMed and PMC full text articles performs very well on Clinical datasets as well.BioELECTRA achieves new SOTA 86.34%(1.39%accuracy improvement) on MedNLI and 64% (2.98% accuracy improvement) on PubMedQA dataset.