Reviewerly/exHarmony huggingface dataset:
Deep traffic light detection by overlaying synthetic context on arbitrary natural images

Deep neural networks come as an effective solution to many problems associated with autonomous driving. By providing real image samples with traffic context to the network, the model learns to detect and classify elements of interest, such as pedestrians, traffic signs, and traffic lights. However, acquiring and annotating real data can be extremely costly in terms of time and effort. In this context, we propose a method to generate artificial traffic-related training data for deep traffic light detectors. This data is generated using basic non-realistic computer graphics to blend fake traffic scenes on top of arbitrary image backgrounds that are not related to the traffic domain. Thus, a large amount of training data can be generated without annotation efforts. Furthermore, it also tackles the intrinsic data imbalance problem in traffic light datasets, caused mainly by the low amount of samples of the yellow state. Experiments show that it is possible to achieve results comparable to those obtained with real training data from the problem domain, yielding an average mAP and an average F1-score which are each nearly 4 p.p. higher than the respective metrics obtained with a real-world reference model.
Adversarial Attack against Deep Reinforcement Learning with Static Reward Impact Map

Security problems of deep reinforcement learning draw much attention recently. Previous works on adversary attack mainly focus on preventing the targeted agent from choosing the most desirable action at each step, which may not reduce the cumulative reward effectively. In this paper, we first investigate how changing features affect the cumulative reward achieved by an agent. The static reward impact map is introduced to quantify the influence on the reward of each feature experimentally. By focusing on tasks with the static reward impact map, an adversarial attack method against deep reinforcement learning aiming to minimize the cumulative reward is proposed. Features with the large reward impact are perturbed in crafting an adversarial sample. Deep Q-network is selected to demonstrate the performance of our attack method in the experiments. The results indicate that our proposed method achieves better performance than the existing one-time attack method and the random attack in terms of the cumulative reward and the successful attack rate under both white-box and black-box settings.
Mapping Various Large Virtual Spaces to Small Real Spaces: A Novel Redirected Walking Method for Immersive VR Navigation

Real-walking techniques such as redirected walking can provide users with a highly immersive presence in virtual environments. However, different space sizes or boundary shapes of virtual and real spaces limit the performance of real-walking techniques. This article proposes a novel redirected walking method supporting users continuously navigating in various large virtual spaces by real-walking in small real spaces. Herein, we present a Voronoi-based method to generate paths called the skeleton graph consisting of navigable paths and way-points. To map the skeleton graph of the virtual space to the real space, we propose a static graph mapping method adopting relocation and curvature adjustment. Additionally, a global optimization with boundary constraints is applied to minimize the total curvature of all paths, resulting in the complete convergence of the mapped paths in real space. By applying both virtual and mapped skeleton graphs in the roaming, our method provides users with a continuous walking experience without any interruptions. The experimental results show that unlike the existing methods that are only effective for specific virtual spaces, our method enables effective real-walking roaming in various large virtual spaces, that have polygon-shaped floor plans, and successfully reduces the number of collisions and perceptual distortion in the virtual scenes.
Private and communication-efficient edge learning

With the rise of machine learning (ML) and the proliferation of smart mobile devices, recent years have witnessed a surge of interest in performing ML in wireless edge networks. In this paper, we consider the problem of jointly improving data privacy and communication efficiency of distributed edge learning, both of which are critical performance metrics in wireless edge network computing. Toward this end, we propose a new distributed stochastic gradient method with sparse differential Gaussian-masked stochastic gradients (SDM-DSGD) for non-convex distributed edge learning. Our main contributions are three-fold: i) We theoretically establish the privacy and communication efficiency performance guarantee for our SDM-DSGD method, which outperforms all existing works; ii) We propose a generalized differential-coded DSGD update, which enables a much lower transmit probability for gradient sparsification, and provides an [EQUATION] convergence rate; and iii) We reveal theoretical insights and offer practical design guidelines for the interactions between privacy preservation and communication efficiency - two conflicting performance goals. We conduct extensive experiments with a variety of learning models on MNIST and CIFAR-10 datasets to verify our theoretical findings.
Context-Aware Web Service Clustering and Visualization

With the large number of web services now available via the internet, web service discovery has become a challenging and time-consuming task. Organizing web services into similar clusters is a very efficient approach to reducing the search space. A principal issue for clustering is computing the semantic similarity between services. Current approaches do not consider the domain-specific context in measuring similarity and this has affected their clustering performance. This paper proposes a context-aware similarity (CAS) method that learns domain context by machine learning to produce models of context for terms retrieved from the web. To analyze visually the effect of domain context on the clustering results, the clustering approach applies a spherical associated-keyword-space algorithm. The CAS method analyzes the hidden semantics of services within a particular domain, and the awareness of service context helps to find cluster tensors that characterize the cluster elements. Experimental results show that the clustering approach works efficiently.
The Principle of Predictive Irrelevance or Why Intervals Should Not be Used for Model Comparison Featuring a Point Null Hypothesis

The principle of predictive irrelevance states that when two competing models predict a data set equally well, that data set cannot be used to discriminate the models and – for that specific purpose – the data set is evidentially irrelevant. To highlight the ramifications of the principle, we first show how a single binomial observation can be irrelevant in the sense that it carries no evidential value for discriminating the null hypothesis θ = 1∕2 from a broad class of alternative hypotheses that allow θ to be between 0 and 1. In contrast, the Bayesian credible interval suggests that a single binomial observation does provide some evidence against the null hypothesis. We then generalize this paradoxical result to infinitely long data sequences that are predictively irrelevant throughout. Examples feature a test of a binomial rate and a test of a normal mean. These maximally uninformative data (MUD) sequences yield credible intervals and confidence intervals that are certain to exclude the point of test as the sequence lengthens. The resolution of this paradox requires the insight that interval estimation methods – and, consequently, p-values – may not be used for model comparison involving a point null hypothesis.
Image Retrieval System based on a Binary Auto-Encoder and a Convolutional Neural Network

The amount of image content on the Internet has increased dramatically in recent years; its precise search and retrieval is a challenge at present. The methods that have shown high efficiency are those based on convolution neural networks (CNN) and, particularly, binary coding methods based on hashing functions. This article presents a new image retrieval scheme based on attributes from a CNN, an efficient low-dimensional binary auto-encoder, and, finally, a near-neighbor retrieval stage. The proposed methodology was tested with two image datasets CIFAR-10 and MNIST. The results are compared with existing methods in the literature.
Promising Deep Semantic Nuclei Segmentation Models for Multi-Institutional Histopathology Images of Different Organs

Nuclei segmentation in whole-slide imaging (WSI) plays a crucial role in the field of computational pathology.It is a fundamental task for different applications, such as cancer cell type classification, cancer grading, and cancer subtype classification.However, existing nuclei segmentation methods face many challenges, such as color variation in histopathological images, the overlapping and clumped nuclei, and the ambiguous boundary between different cell nuclei, that limit their performance.In this paper, we present promising deep semantic nuclei segmentation models for multi-institutional WSI images (i.e., collected from different scanners) of different organs.Specifically, we study the performance of pertinent deep learning-based models with nuclei segmentation in WSI images of different stains and various organs.We also propose a feasible deep learning nuclei segmentation model formed by combining robust deep learning architectures.A comprehensive comparative study with existing software and related methods in terms of different evaluation metrics and the number of parameters of each model, emphasizes the efficacy of the proposed nuclei segmentation models.
NPU Speaker Verification System for INTERSPEECH 2020 Far-Field Speaker Verification Challenge

This paper describes the NPU system submitted to Interspeech 2020 Far-Field Speaker Verification Challenge (FFSVC). We particularly focus on far-field text-dependent SV from single (task1) and multiple microphone arrays (task3). The major challenges in such scenarios are short utterance and cross-channel and distance mismatch for enrollment and test. With the belief that better speaker embedding can alleviate the effects from short utterance, we introduce a new speaker embedding architecture - ResNet-BAM, which integrates a bottleneck attention module with ResNet as a simple and efficient way to further improve the representation power of ResNet. This contribution brings up to 1% EER reduction. We further address the mismatch problem in three directions. First, domain adversarial training, which aims to learn domain-invariant features, can yield to 0.8% EER reduction. Second, front-end signal processing, including WPE and beamforming, has no obvious contribution, but together with data selection and domain adversarial training, can further contribute to 0.5% EER reduction. Finally, data augmentation, which works with a specifically-designed data selection strategy, can lead to 2% EER reduction. Together with the above contributions, in the middle challenge results, our single submission system (without multi-system fusion) achieves the first and second place on task 1 and task 3, respectively.
PEMC: Power Efficiency Measurement Calculator to Compute Power Efficiency and CO₂ Emissions in Cloud Data Centers

The power consumption of cloud data centers has a considerable impact on the environment and climate change nowadays. Researchers are seeking to find practical solutions to reduce power consumption in data centers while guaranteeing the desired level of services and service level objectives. With the establishment of the data center industry, the demand for computation and data storage has been continually rising. Energy efficiency is one of the most significant issues faced by these big data centers to meet such high computational requirements. There are many industry acceptable metrics available such as PUE, DCiE, DCP, etc. Power Usage Effectiveness (PUE) metric has proven to be the most popular in measuring energy efficiency; however, it measures the power efficiency alone with no consideration for CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions and the costs involved in total power usage across data centers. In this article, we proposed a novel Power Efficiency Measurement Calculator (PEMC) that combines and calculates the power efficiency, CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions, and the total annual costs incurred. The pseudocode and algorithm to perform these specific PUE, DCiE, and CO2 emission functions are given to explain the working of proposed work. Finally, the proposed PEMC calculator was tested and validated through a case study performed in one of the tier-level data centers in Malaysia and the results demonstrate its effectiveness compared with Power Usage Effectiveness (PUE) and other known calculators.
Embedded decision support system for ultrasound nondestructive evaluation based on extreme learning machines

Decision support systems for nondestructive evaluation of defects are often trained on a personal computer environment, which may slow the assessment task. This work investigates the feasibility of embedding extreme learning machines in microcontrollers, in order to execute in situ training for an ultrasound nondestructive evaluation. Principal component analysis and autoencoders were evaluated as dimensionality reduction methods in a data set acquired from welded SAE 1020 carbon steel plates containing four types of defects. A binary-encoded random weight approach is proposed, whose results were tested over four different probability density functions used to generate the random weights. The obtained experimental results matched the accuracy of a previous work, run in computer environment for higher dimensionality data. In this work a dimensionality reduction of 75% and elapsed time for system training of less than 0.5 ms in microcontroller were achieved, indicating the suitability of such embedded networks for the studied case.
Author Identification Using Chaos Game Representation and Deep Learning

An author unconsciously encodes in the written text a certain style that is often difficult to recognize. Still, there are many computational means developed for this purpose that take into account various features, from lexical and character-based attributes to syntactic or semantic ones. We propose an approach that starts from the character level and uses chaos game representation to illustrate documents like images which are subsequently classified by a deep learning algorithm. The experiments are made on three data sets and the outputs are comparable to the results from the literature. The study also verifies the suitability of the method for small data sets and whether image augmentation can improve the classification efficiency.
Detail Restoration and Tone Mapping Networks for X-Ray Security Inspection

X-ray imaging is one of the most widely used security measures for maintaining airport and transportation security. Conventional X-ray imaging systems typically apply tone-mapping (TM) algorithms to visualize high-dynamic-range (HDR) X-ray images on a standard 8-bit display device. However, X-ray images obtained through traditional TM algorithms often suffer from halo artifacts or detail loss in inter-object overlapping regions, which makes it difficult for an inspector to detect unsafe or hazardous objects. To alleviate these problems, this article proposes a deep learning-based TM method for X-ray inspection. The proposed method consists of two networks called detail-recovery network (DR-Net) and TM network (TM-Net). The goal of DR-Net is to restore the details in the input HDR image, whereas TM-Net aims to compress the dynamic range while preserving the restored details and preventing halo artifacts. Since there are no standard ground-truth images available for the TM of X-ray images, we propose a novel loss function for unsupervised learning of TM-Net. We also introduce a dataset synthesis technique using the Beer-Lambert law for supervised learning of DR-Net. Extensive experiments comparing the performance of our proposed method with state-of-the-art TM methods demonstrate that the proposed method not only achieves visually compelling results but also improves the quantitative performance measures such as FSITM and HDR-VDP-2.2.
BIDIRECTIONAL GATED RECURRENT UNIT FOR SHALLOW PARSING

Shallow parsing is one of the natural language processing tasks, where various syntactic phrases such as noun phrases, verb phrases, and others, are identified.Here shallow parsing is considered as a sequence tagging task, and this paper presents the application of deep learning approach using recurrent neural networks (RNN) for shallow parsing.Specifically, gated recurrent unit (GRU), and bidirectional gated recurrent unit (BiGRU) are applied in parsing Khasi, an Austro-Asiatic language.These variants of the RNN address the vanishing gradient problem and the dependency of the chunk tags on both preceding and subsequent information from the sequential input data.The results have shown an improved performance compared to an existing shallow parser for Khasi.
Improved Whale optimization Algorithm for SVM Model Selection: Application in Medical Diagnosis

Support-Vector Machines (SVM) have gained momentum and have become a widely used classifier for various applications. Indeed, SVM parameters have a significant impact on the accuracy of the prediction model. The present work aims to propose an improved version of the Whale optimization Algorithm (WOA) that can choose the best model for SVM by seeking the optimal parameter values. To surpass the premature convergence problem and outperform the exploitation ability of the original WOA, two improvements are introduced. First, we increase the potential of the best solution, which redirects the search agent toward the global optimum. Second, a new search equation is added to decrease the overflow of diversity. Experimental results proved that the proposed Improved Whale optimization Algorithm (IWOA) is efficient not only in terms of quality of the final solution and convergence rate for optimization benchmarking function but also for reaching the optimal values of the SVM parameters. In this sense, the study has provided a medical diagnosis-oriented problem, classifying medical, i.e., cancer and diabetes, datasets, to demonstrate the effectiveness of the IWOA-optimized SVM.
Fuzzy Parameter Adaptation in Genetic Algorithms for the Optimization of Fuzzy Integrators in Modular Neural Networks for Multimodal Biometry

In this paper, we propose a new method for fuzzy adaptation of the Gap Generation and mutation parameters in Genetic algorithms to optimize Fuzzy Systems used as integration methods in modular neural networks for multimodal biometrics. The Genetic Algorithm is an optimization method inspired on the evolutionary ideas of natural selection and genetics; therefore, we propose an improvement to the convergence of the genetic algorithms using fuzzy logic. Simulation results show that the proposed approach improves the performance of Genetic Algorithms. A comparison of the proposed method using type-1 fuzzy logic for dynamic parameter adaptation with respect to the original Genetic Algorithms approach is presented. Additionally, a statistical test is presented to prove the performance enhancement in the application provided by fuzzy parameter adaptation in the genetic algorithm. The main contribution in this work is the fuzzy adaptation of parameters in the genetic algorithm using type-1 fuzzy logic and with this finding the optimal values of the parameters of the fuzzy integrators, to improve the recognition percentage of the modular neural network for multimodal biometrics.
A New Cluster Validity Index Based on the Adjustment of Within-Cluster Distance

The evaluation on clustering results is an important component of clustering analysis, which can be conducted by the cluster validity index.However, the performances of most existing indices depend on not only the specific clustering algorithms but also the measurements of within-and between-cluster distances and data structures, resulting in limited applications in practice.In this paper, a new within-cluster distance under a general assumption is defined first.After adjusting within-cluster distances of each point according to the adjustment rule, a novel cluster validity index is proposed.Moreover, the notion of chain is introduced to eliminate the effects of sizes, densities, and shapes of clusters.This index does not need any prior information about clustering algorithms and is independent of data structures.Two groups of synthetic datasets with various characteristics and real-world datasets are used to validate this proposed validity index.Experimental results demonstrate that the evaluation accuracy of this index is higher than that of the existing typical indices and performs well on datasets with irregular-shaped clusters.
Investigating Novel Verb Learning in BERT: Selectional Preference Classes and Alternation-Based Syntactic Generalization

Previous studies investigating the syntactic abilities of deep learning models have not targeted the relationship between the strength of the grammatical generalization and the amount of evidence to which the model is exposed during training.We address this issue by deploying a novel word-learning paradigm to test BERT's (Devlin et al., 2018) few-shot learning capabilities for two aspects of English verbs: alternations and classes of selectional preferences.For the former, we fine-tune BERT on a single frame in a verbal-alternation pair and ask whether the model expects the novel verb to occur in its sister frame.For the latter, we fine-tune BERT on an incomplete selectional network of verbal objects and ask whether it expects unattested but plausible verb/object pairs.We find that BERT makes robust grammatical generalizations after just one or two instances of a novel word in fine-tuning.For the verbal alternation tests, we find that the model displays behavior that is consistent with a transitivity bias: verbs seen few times are expected to take direct objects, but verbs seen with direct objects are not expected to occur intransitively.The code for our experiments is available at https://github.com/TristanThrush/few-shot-lm-learning.
The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks

Modern neural networks are often regarded as complex black-box functions whose behavior is difficult to understand owing to their nonlinear dependence on the data and the nonconvexity in their loss landscapes. In this work, we show that these common perceptions can be completely false in the early phase of learning. In particular, we formally prove that, for a class of well-behaved input distributions, the early-time learning dynamics of a two-layer fully-connected neural network can be mimicked by training a simple linear model on the inputs. We additionally argue that this surprising simplicity can persist in networks with more layers and with convolutional architecture, which we verify empirically. Key to our analysis is to bound the spectral norm of the difference between the Neural Tangent Kernel (NTK) at initialization and an affine transform of the data kernel; however, unlike many previous results utilizing the NTK, we do not require the network to have disproportionately large width, and the network is allowed to escape the kernel regime later in training.