Reviewerly/exHarmony huggingface dataset:
Machine Vision Based Traffic Sign Detection Methods: Review, Analyses and Perspectives

Traffic signs recognition (TSR) is an important part of some advanced driver-assistance systems (ADASs) and auto driving systems (ADSs).As the first key step of TSR, traffic sign detection (TSD) is a challenging problem because of different types, small sizes, complex driving scenes, and occlusions.In recent years, there have been a large number of TSD algorithms based on machine vision and pattern recognition.In this paper, a comprehensive review of the literature on TSD is presented.We divide the reviewed detection methods into five main categories: color-based methods, shape-based methods, color-and shape-based methods, machine-learning-based methods, and LIDAR-based methods.The methods in each category are also classified into different subcategories for understanding and summarizing the mechanisms of different methods.For some reviewed methods that lack comparisons on public datasets, we reimplemented part of these methods for comparison.The experimental comparisons and analyses are presented on the reported performance and the performance of our reimplemented methods.Furthermore, future directions and recommendations of the TSD research are given to promote the development of the TSD.
Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation

Existing approaches to dialogue state tracking rely on pre-defined ontologies consisting of a set of all possible slot types and values.Though such approaches exhibit promising performance on single-domain benchmarks, they suffer from computational complexity that increases proportionally to the number of pre-defined slots that need tracking.This issue becomes more severe when it comes to multi-domain dialogues which include larger numbers of slots.In this paper, we investigate how to approach DST using a generation framework without the pre-defined ontology list.Given each turn of user utterance and system response, we directly generate a sequence of belief states by applying a hierarchical encoder-decoder structure.In this way, the computational complexity of our model will be a constant regardless of the number of predefined slots.Experiments on both the multidomain and the single domain dialogue state tracking dataset show that our model not only scales easily with the increasing number of pre-defined domains and slots but also reaches the state-of-the-art performance.
Multi-Component Graph Convolutional Collaborative Filtering

The interactions of users and items in recommender system could be naturally modeled as a user-item bipartite graph. In recent years, we have witnessed an emerging research effort in exploring user-item graph for collaborative filtering methods. Nevertheless, the formation of user-item interactions typically arises from highly complex latent purchasing motivations, such as high cost performance or eye-catching appearance, which are indistinguishably represented by the edges. The existing approaches still remain the differences between various purchasing motivations unexplored, rendering the inability to capture fine-grained user preference. Therefore, in this paper we propose a novel Multi-Component graph convolutional Collaborative Filtering (MCCF) approach to distinguish the latent purchasing motivations underneath the observed explicit user-item interactions. Specifically, there are two elaborately designed modules, decomposer and combiner, inside MCCF. The former first decomposes the edges in user-item graph to identify the latent components that may cause the purchasing relationship; the latter then recombines these latent components automatically to obtain unified embeddings for prediction. Furthermore, the sparse regularizer and weighted random sample strategy are utilized to alleviate the overfitting problem and accelerate the optimization. Empirical results on three real datasets and a synthetic dataset not only show the significant performance gains of MCCF, but also well demonstrate the necessity of considering multiple components.
Deep and reinforcement learning for automated task scheduling in large‐scale cloud computing systems

Summary Cloud computing is undeniably becoming the main computing and storage platform for today's major workloads. From Internet of things and Industry 4.0 workloads to big data analytics and decision‐making jobs, cloud systems daily receive a massive number of tasks that need to be simultaneously and efficiently mapped onto the cloud resources. Therefore, deriving an appropriate task scheduling mechanism that can both minimize tasks' execution delay and cloud resources utilization is of prime importance. Recently, the concept of cloud automation has emerged to reduce the manual intervention and improve the resource management in large‐scale cloud computing workloads. In this article, we capitalize on this concept and propose four deep and reinforcement learning‐based scheduling approaches to automate the process of scheduling large‐scale workloads onto cloud computing resources, while reducing both the resource consumption and task waiting time. These approaches are: reinforcement learning (RL), deep Q networks, recurrent neural network long short‐term memory (RNN‐LSTM), and deep reinforcement learning combined with LSTM (DRL‐LSTM). Experiments conducted using real‐world datasets from Google Cloud Platform revealed that DRL‐LSTM outperforms the other three approaches. The experiments also showed that DRL‐LSTM minimizes the CPU usage cost up to 67 % compared with the shortest job first (SJF), and up to 35 % compared with both the round robin (RR) and improved particle swarm optimization (PSO) approaches. Moreover, our DRL‐LSTM solution decreases the RAM memory usage cost up to 72 % compared with the SJF, up to 65 % compared with the RR, and up to 31.25 % compared with the improved PSO.
Addressing Class Imbalance in Federated Learning

Federated learning (FL) is a promising approach for training decentralized data located on local client devices while improving efficiency and privacy. However, the distribution and quantity of the training data on the clients' side may lead to significant challenges such as class imbalance and non-IID (non-independent and identically distributed) data, which could greatly impact the performance of the common model. While much effort has been devoted to helping FL models converge when encountering non-IID data, the imbalance issue has not been sufficiently addressed. In particular, as FL training is executed by exchanging gradients in an encrypted form, the training data is not completely observable to either clients or server, and previous methods for class imbalance do not perform well for FL. Therefore, it is crucial to design new methods for detecting class imbalance in FL and mitigating its impact. In this work, we propose a monitoring scheme that can infer the composition of training data for each FL round, and design a new loss function -- Ratio Loss to mitigate the impact of the imbalance. Our experiments demonstrate the importance of acknowledging class imbalance and taking measures as early as possible in FL training, and the effectiveness of our method in mitigating the impact. Our method is shown to significantly outperform previous methods, while maintaining client privacy.
Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion

We present Modular interactive VOS (MiVOS) framework which decouples interaction-to-mask and mask propagation, allowing for higher generalizability and better performance. Trained separately, the interaction module converts user interactions to an object mask, which is then temporally propagated by our propagation module using a novel top-k filtering strategy in reading the space-time memory. To effectively take the user’s intent into account, a novel difference-aware module is proposed to learn how to properly fuse the masks before and after each interaction, which are aligned with the target frames by employing the space-time memory. We evaluate our method both qualitatively and quantitatively with different forms of user interactions (e.g., scribbles, clicks) on DAVIS to show that our method outperforms current state-of-the-art algorithms while requiring fewer frame interactions, with the additional advantage in generalizing to different types of user interactions. We contribute a large-scale synthetic VOS dataset with pixel-accurate segmentation of 4.8M frames to accompany our source codes to facilitate future research.
GLM: General Language Model Pretraining with Autoregressive Blank Infilling

There have been various types of pretraining architectures including autoencoding models (e.g., BERT), autoregressive models (e.g., GPT), and encoder-decoder models (e.g., T5).However, none of the pretraining frameworks performs the best for all tasks of three main categories including natural language understanding (NLU), unconditional generation, and conditional generation.We propose a General Language Model (GLM) based on autoregressive blank infilling to address this challenge.GLM improves blank filling pretraining by adding 2D positional encodings and allowing an arbitrary order to predict spans, which results in performance gains over BERT and T5 on NLU tasks.Meanwhile, GLM can be pretrained for different types of tasks by varying the number and lengths of blanks.On a wide range of tasks across NLU, conditional and unconditional generation, GLM outperforms BERT, T5, and GPT given the same model sizes and data, and achieves the best performance from a single pretrained model with 1.25× parameters of BERT Large , demonstrating its generalizability to different downstream tasks. 1
Differential Privacy Preserving of Training Model in Wireless Big Data with Edge Computing

With the popularity of smart devices and the widespread use of machine learning methods, smart edges have become the mainstream of dealing with wireless big data. When smart edges use machine learning models to analyze wireless big data, nevertheless, some models may unintentionally store a small portion of the training data with sensitive records. Thus, intruders can expose sensitive information by careful analysis of this model. To solve this privacy issue, in this paper, we propose and implement a machine learning strategy for smart edges using differential privacy. We focus our attention on privacy protection in training datasets in wireless big data scenario. Moreover, we guarantee privacy protection by adding Laplace mechanisms, and design two different algorithms Output Perturbation (OPP) and Objective Perturbation (OJP), which satisfy differential privacy. In addition, we consider the privacy preserving issues presented in the existing literatures for differential privacy in the correlated datasets, and further provided differential privacy preserving methods for correlated datasets, guaranteeing privacy by theoretical deduction. Finally, we implement the experiments on the TensorFlow, and evaluate our strategy on four datasets, i.e., MNIST, SVHN, CIFAR-10 and STL-10. The experiment results show that our methods can efficiently protect the privacy of training datasets and guarantee the accuracy on benchmark datasets.
A Consensus Novelty Detection Ensemble Approach for Anomaly Detection in Activities of Daily Living

A new approach to creating an ensemble of novelty detection algorithms is proposed in this paper. The novelty detection process identifies new or unknown data by detecting if a test data differs significantly from the data available during training. It is applicable for anomaly detection in a situation where there is sufficiently large training data representing the normal class and little or no training data for the anomalous (abnormal) class. Abnormality in Activities of Daily Living (ADL) is identified as any significant deviation from an individual’s usual behavioural routine. Novelty detection is relevant to ADL anomaly detection since abnormalities in ADL are rare and data representing the anomalous cases are not readily available. The proposed Consensus Novelty Detection Ensemble approach is based on the concept of internal and external consensus. The internal consensus is an internal voting scheme within each model in the ensemble while the external consensus is an external voting scheme among the ensemble models. The weight of each model is estimated based on its performance and a score, called “Normality Score”. Computed score is used in classifying the data as abnormal (anomalous) based on certain threshold crossing, normal otherwise. Experimental evaluation is conducted to detect abnormalities in ADL data obtained from CASAS repository as well as experimental dataset collected for this research. The obtained results show that the proposed approach is able to identify anomalous instances. The proposed approach offers more flexibility compared with the existing approaches by allowing the Normality Score threshold to be adjusted without retraining the models.
NumNet: Machine Reading Comprehension with Numerical Reasoning

Numerical reasoning, such as addition, subtraction, sorting and counting is a critical skill in human's reading comprehension, which has not been well considered in existing machine reading comprehension (MRC) systems.To address this issue, we propose a numerical MRC model named as NumNet, which utilizes a numerically-aware graph neural network to consider the comparing information and performs numerical reasoning over numbers in the question and passage.Our system achieves an EM-score of 64.56% on the DROP dataset, outperforming all existing machine reading comprehension models by considering the numerical relations among numbers.
Tuning Multilingual Transformers for Language-Specific Named Entity Recognition

Our paper addresses the problem of multilingual named entity recognition on the material of 4 languages: Russian, Bulgarian, Czech and Polish.We solve this task using the BERT model.We use a hundred languages multilingual model as base for transfer to the mentioned Slavic languages.Unsupervised pre-training of the BERT model on these 4 languages allows to significantly outperform baseline neural approaches and multilingual BERT.Additional improvement is achieved by extending BERT with a word-level CRF layer.Our system was submitted to BSNLP 2019 Shared Task on Multilingual Named Entity Recognition and took the 1st place in 3 competition metrics out of 4 we participated in.We open-sourced NER models and BERT model pre-trained on the four Slavic languages.
Cross-Lingual Natural Language Generation via Pre-Training

In this work we focus on transferring supervision signals of natural language generation (NLG) tasks between multiple languages. We propose to pretrain the encoder and the decoder of a sequence-to-sequence model under both monolingual and cross-lingual settings. The pre-training objective encourages the model to represent different languages in the shared space, so that we can conduct zero-shot cross-lingual transfer. After the pre-training procedure, we use monolingual data to fine-tune the pre-trained model on downstream NLG tasks. Then the sequence-to-sequence model trained in a single language can be directly evaluated beyond that language (i.e., accepting multi-lingual input and producing multi-lingual output). Experimental results on question generation and abstractive summarization show that our model outperforms the machine-translation-based pipeline methods for zero-shot cross-lingual generation. Moreover, cross-lingual transfer improves NLG performance of low-resource languages by leveraging rich-resource language data. Our implementation and data are available at https://github.com/CZWin32768/xnlg.
Investigating Bias and Fairness in Facial Expression Recognition

Recognition of expressions of emotions and affect from facial images is a well-studied research problem in the fields of affective computing and computer vision with a large number of datasets available containing facial images and corresponding expression labels. However, virtually none of these datasets have been acquired with consideration of fair distribution across the human population. Therefore, in this work, we undertake a systematic investigation of bias and fairness in facial expression recognition by comparing three different approaches, namely a baseline, an attribute-aware and a disentangled approach, on two well-known datasets, RAF-DB and CelebA. Our results indicate that: (i) data augmentation improves the accuracy of the baseline model, but this alone is unable to mitigate the bias effect; (ii) both the attribute-aware and the disentangled approaches equipped with data augmentation perform better than the baseline approach in terms of accuracy and fairness; (iii) the disentangled approach is the best for mitigating demographic bias; and (iv) the bias mitigation strategies are more suitable in the existence of uneven attribute distribution or imbalanced number of subgroup data.
Transfer Learning for Image Segmentation by Combining Image Weighting and Kernel Learning

Many medical image segmentation methods are based on the supervised classification of voxels. Such methods generally perform well when provided with a training set that is representative of the test images to the segment. However, problems may arise when training and test data follow different distributions, for example, due to differences in scanners, scanning protocols, or patient groups. Under such conditions, weighting training images according to distribution similarity have been shown to greatly improve performance. However, this assumes that a part of the training data is representative of the test data; it does not make unrepresentative data more similar. We, therefore, investigate kernel learning as a way to reduce differences between training and test data and explore the added value of kernel learning for image weighting. We also propose a new image weighting method that minimizes maximum mean discrepancy (MMD) between training and test data, which enables the joint optimization of image weights and kernel. Experiments on brain tissue, white matter lesion, and hippocampus segmentation show that both kernel learning and image weighting, when used separately, greatly improve performance on heterogeneous data. Here, MMD weighting obtains similar performance to previously proposed image weighting methods. Combining image weighting and kernel learning, optimized either individually or jointly, can give a small additional improvement in performance.
Diabatic Gates for Frequency-Tunable Superconducting Qubits

We demonstrate diabatic two-qubit gates with Pauli error rates down to 4.3(2) • 10 -3 in as fast as 18 ns using frequency-tunable superconducting qubits.This is achieved by synchronizing the entangling parameters with minima in the leakage channel.The synchronization shows a landscape in gate parameter space that agrees with model predictions and facilitates robust tune-up.We test both iSWAP-like and CPHASE gates with cross-entropy benchmarking.The presented approach can be extended to multibody operations as well.
Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data

Big data analytics (BDA) is a systematic approach for analyzing and identifying different patterns, relations, and trends within a large volume of data. In this paper, we apply BDA to criminal data where exploratory data analysis is conducted for visualization and trends prediction. Several the state-of-the-art data mining and deep learning techniques are used. Following statistical analysis and visualization, some interesting facts and patterns are discovered from criminal data in San Francisco, Chicago, and Philadelphia. The predictive results show that the Prophet model and Keras stateful LSTM perform better than neural network models, where the optimal size of the training data is found to be three years. These promising outcomes will benefit for police departments and law enforcement organizations to better understand crime issues and provide insights that will enable them to track activities, predict the likelihood of incidents, effectively deploy resources and optimize the decision making process.
A New Optimization Algorithm Based on Search and Rescue Operations

In this paper, a new optimization algorithm called the search and rescue optimization algorithm (SAR) is proposed for solving single-objective continuous optimization problems. SAR is inspired by the explorations carried out by humans during search and rescue operations. The performance of SAR was evaluated on fifty-five optimization functions including a set of classic benchmark functions and a set of modern CEC 2013 benchmark functions from the literature. The obtained results were compared with twelve optimization algorithms including well-known optimization algorithms, recent variants of GA, DE, CMA-ES, and PSO, and recent metaheuristic algorithms. The Wilcoxon signed-rank test was used for some of the comparisons, and the convergence behavior of SAR was investigated. The statistical results indicated SAR is highly competitive with the compared algorithms. Also, in order to evaluate the application of SAR on real-world optimization problems, it was applied to three engineering design problems, and the results revealed that SAR is able to find more accurate solutions with fewer function evaluations in comparison with the other existing algorithms. Thus, the proposed algorithm can be considered an efficient optimization method for real-world optimization problems.
Named Entity Recognition for Code-Mixed Indian Corpus using Meta Embedding

In this paper, we utilize the pre-trained embedding, sub-word embedding and closely related languages of languages in the code mixed corpus to create a meta-embedding. We then use the Transformer to encode the code mixed sentence and use Conditional Random Field to predict the Named Entities in the code-mixed text. In contrast to classical Named Entity recognition where the text is monolingual, our approach can predict the Named Entities in code-mixed corpus written both in the native script as well as Roman script. Our method is a novel method to combine the embeddings of closely related languages to identify Named Entity from Code-Mixed Indian text written using native script and Roman script in social media.
Deep Drone Acrobatics

Performing acrobatic maneuvers with quadrotorsis extremely challenging. Acrobatic flight requires high thrustand extreme angular accelerations that push the platform to itsphysical limits. Professional drone pilots often measure their levelof mastery by flying such maneuvers in competitions. In thispaper, we propose to learn a sensorimotor policy that enablesan autonomous quadrotor to fly extreme acrobatic maneuverswith only onboard sensing and computation. We train the policyentirely in simulation by leveraging demonstrations from anoptimal controller that has access to privileged information. Weuse appropriate abstractions of the visual input to enable transferto a real quadrotor. We show that the resulting policy can bedirectly deployed in the physical world without any fine-tuningon real data. Our methodology has several favorable properties:it does not require a human expert to provide demonstrations,it cannot harm the physical system during training, and it canbe used to learn maneuvers that are challenging even for thebest human pilots. Our approach enables a physical quadrotorto fly maneuvers such as the Power Loop, the Barrel Roll, andthe Matty Flip, during which it incurs accelerations of up to 3g.