OpenRaiser/Intern-Atlas huggingface dataset:
Visual Ground Truth Construction as Faceted Classification
Venue: arXiv cs.CV 2022
Year: 2022

Recent work in Machine Learning and Computer Vision has provided evidence of systematic design flaws in the development of major object recognition benchmark datasets. One such example is ImageNet, wherein, for several categories of images, there are incongruences between the objects they represent and the labels used to annotate them. The consequences of this problem are major, in particular considering the large number of machine learning applications, not least those based on Deep Neural Networks, that have been trained on these datasets. In this paper we posit the problem to be the lack of a knowledge representation (KR) methodology providing the foundations for the construction of these ground truth benchmark datasets. Accordingly, we propose a solution articulated in three main steps: (i) deconstructing the object recognition process in four ordered stages grounded in the philosophical theory of teleosemantics; (ii) based on such stratification, proposing a novel four-phased methodology for organizing objects in classification hierarchies according to their visual properties; and (iii) performing such classification according to the faceted classification paradigm. The key novelty of our approach lies in the fact that we construct the classification hierarchies from visual properties exploiting visual genus-differentiae, and not from linguistically grounded properties. The proposed approach is validated by a set of experiments on the ImageNet hierarchy of musical experiments.
Performance of Hybrid THz and Multi-Antenna RF System with Diversity Combining
Venue: arXiv cs.IT 2022
Year: 2022

Recent studies investigate single-antenna radio frequency (RF) systems mixed with terahertz (THz) wireless communications. This paper considers a two-tier system of THz for backhaul and multiple-antenna assisted RF for the access network. We analyze the system performance by employing both selection combining (SC) and maximal ratio combining (MRC) receivers for the RF link integrated with the THz using the fixed-gain amplify and forward (AF) protocol. We develop the probability density function (PDF) and cumulative distribution function (CDF) of the end-to-end signal-to-noise (SNR) of the dual-hop system over independent and non-identically distributed (i.ni.d.) $α$-$μ$ fading channels with a statistical model for misalignment errors in the THz wireless link. We use the derived statistical results to develop analytical expressions of the outage probability, average bit error rate (BER), and ergodic capacity for the performance assessment of the considered system. We develop diversity order of the system using asymptotic analysis in the high SNR region, demonstrating the scaling of system performance with the number of antennas. We use computer simulations to show the effect of system and channel parameters on the performance of the hybrid THz-RF link with multi-antenna diversity schemes.
Two-dimensional structure functions to characterize convective rolls in the marine atmospheric boundary layer from Sentinel-1 SAR images
Venue: arXiv cs.IT 2022
Year: 2022

We study the shape of convective rolls in the Marine Atmospheric Boundary Layer from Synthetic Aperture Radar images of the ocean. We propose a multiscale analysis with structure functions which allow an easy generalization to analyse high-order statistics and so to finely describe the shape of the rolls. The two main results are : 1) second order structure function characterizes the size and direction of rolls just like correlation or power spectrum do, 2) high order statistics can be studied with skewness and Flatness which characterize the asymmetry and intermittency of rolls respectively. From the best of our knowledge, this is the first time that the asymmetry and intermittency of rolls is shown from radar images of the ocean surface.
Gradients without Backpropagation
Venue: arXiv cs.LG 2022
Year: 2022

Using backpropagation to compute gradients of objective functions for optimization has remained a mainstay of machine learning. Backpropagation, or reverse-mode differentiation, is a special case within the general family of automatic differentiation algorithms that also includes the forward mode. We present a method to compute gradients based solely on the directional derivative that one can compute exactly and efficiently via the forward mode. We call this formulation the forward gradient, an unbiased estimate of the gradient that can be evaluated in a single forward run of the function, entirely eliminating the need for backpropagation in gradient descent. We demonstrate forward gradient descent in a range of problems, showing substantial savings in computation and enabling training up to twice as fast in some cases.
Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines
Venue: arXiv cs.LG 2022
Year: 2022

Preprocessing pipelines in deep learning aim to provide sufficient data throughput to keep the training processes busy. Maximizing resource utilization is becoming more challenging as the throughput of training processes increases with hardware innovations (e.g., faster GPUs, TPUs, and inter-connects) and advanced parallelization techniques that yield better scalability. At the same time, the amount of training data needed in order to train increasingly complex models is growing. As a consequence of this development, data preprocessing and provisioning are becoming a severe bottleneck in end-to-end deep learning pipelines. In this paper, we provide an in-depth analysis of data preprocessing pipelines from four different machine learning domains. We introduce a new perspective on efficiently preparing datasets for end-to-end deep learning pipelines and extract individual trade-offs to optimize throughput, preprocessing time, and storage consumption. Additionally, we provide an open-source profiling library that can automatically decide on a suitable preprocessing strategy to maximize throughput. By applying our generated insights to real-world use-cases, we obtain an increased throughput of 3x to 13x compared to an untuned system while keeping the pipeline functionally identical. These findings show the enormous potential of data pipeline tuning.
A study of deep perceptual metrics for image quality assessment
Venue: arXiv cs.CV 2022
Year: 2022

Several metrics exist to quantify the similarity between images, but they are inefficient when it comes to measure the similarity of highly distorted images. In this work, we propose to empirically investigate perceptual metrics based on deep neural networks for tackling the Image Quality Assessment (IQA) task. We study deep perceptual metrics according to different hyperparameters like the network's architecture or training procedure. Finally, we propose our multi-resolution perceptual metric (MR-Perceptual), that allows us to aggregate perceptual information at different resolutions and outperforms standard perceptual metrics on IQA tasks with varying image deformations. Our code is available at https://github.com/ENSTA-U2IS/MR_perceptual
Evolving Constructions for Balanced, Highly Nonlinear Boolean Functions
Venue: arXiv cs.NE 2022
Year: 2022

Finding balanced, highly nonlinear Boolean functions is a difficult problem where it is not known what nonlinearity values are possible to be reached in general. At the same time, evolutionary computation is successfully used to evolve specific Boolean function instances, but the approach cannot easily scale for larger Boolean function sizes. Indeed, while evolving smaller Boolean functions is almost trivial, larger sizes become increasingly difficult, and evolutionary algorithms perform suboptimally. In this work, we ask whether genetic programming (GP) can evolve constructions resulting in balanced Boolean functions with high nonlinearity. This question is especially interesting as there are only a few known such constructions. Our results show that GP can find constructions that generalize well, i.e., result in the required functions for multiple tested sizes. Further, we show that GP evolves many equivalent constructions under different syntactic representations. Interestingly, the simplest solution found by GP is a particular case of the well-known indirect sum construction.
QuerTCI: A Tool Integrating GitHub Issue Querying with Comment Classification
Venue: arXiv cs.SE 2022
Year: 2022

Empirical Software Engineering (ESE) researchers study (open-source) project issues and the comments and threads within to discover -- among others -- challenges developers face when incorporating new technologies, platforms, and programming language constructs. However, such threads accumulate, becoming unwieldy and hindering any insight researchers may gain. While existing approaches alleviate this burden by classifying issue thread comments, there is a gap between searching popular open-source software repositories (e.g., those on GitHub) for issues containing particular keywords and feeding the results into a classification model. This paper demonstrates a research infrastructure tool called QuerTCI that bridges this gap by integrating the GitHub issue comment search API with the classification models found in existing approaches. Using queries, ESE researchers can retrieve GitHub issues containing particular keywords, e.g., those related to a specific programming language construct, and, subsequently, classify the discussions occurring in those issues. We hope ESE researchers can use our tool to uncover challenges related to particular technologies using specific keywords through popular open-source repositories more seamlessly than previously possible. A tool demonstration video may be found at: https://youtu.be/fADKSxn0QUk.
Spiral Trajectories for Building Inspection with Quadrotors
Venue: arXiv cs.RO 2022
Year: 2022

Inspection of large building is an important task since it can prevent material and human losses. A cheap and fast way to do the inspections is by sensors mounted on quadrotor vehicles. The challenge here is to compute a trajectory so that the building is completely observed while this same trajectory can be followed by the quadrotor in a smooth way. To address the problem, we propose a method that receives a 2.5D model of the target building and computes a smooth trajectory that can be followed by the quadrotor controller. The computed trajectory is a Fourier series that matches the desired behaviour. Our method has been tested in simulation and we have compared it against polynomial trajectories. Our result show that the method is efficient and can be applied to different building shapes.
General Cyclical Training of Neural Networks
Venue: arXiv cs.LG 2022
Year: 2022

This paper describes the principle of "General Cyclical Training" in machine learning, where training starts and ends with "easy training" and the "hard training" happens during the middle epochs. We propose several manifestations for training neural networks, including algorithmic examples (via hyper-parameters and loss functions), data-based examples, and model-based examples. Specifically, we introduce several novel techniques: cyclical weight decay, cyclical batch size, cyclical focal loss, cyclical softmax temperature, cyclical data augmentation, cyclical gradient clipping, and cyclical semi-supervised learning. In addition, we demonstrate that cyclical weight decay, cyclical softmax temperature, and cyclical gradient clipping (as three examples of this principle) are beneficial in the test accuracy performance of a trained model. Furthermore, we discuss model-based examples (such as pretraining and knowledge distillation) from the perspective of general cyclical training and recommend some changes to the typical training methodology. In summary, this paper defines the general cyclical training concept and discusses several specific ways in which this concept can be applied to training neural networks. In the spirit of reproducibility, the code used in our experiments is available at \url{https://github.com/lnsmith54/CFL}.
Improving English to Sinhala Neural Machine Translation using Part-of-Speech Tag
Venue: arXiv cs.CL 2022
Year: 2022

The performance of Neural Machine Translation (NMT) depends significantly on the size of the available parallel corpus. Due to this fact, low resource language pairs demonstrate low translation performance compared to high resource language pairs. The translation quality further degrades when NMT is performed for morphologically rich languages. Even though the web contains a large amount of information, most people in Sri Lanka are unable to read and understand English properly. Therefore, there is a huge requirement of translating English content to local languages to share information among locals. Sinhala language is the primary language in Sri Lanka and building an NMT system that can produce quality English to Sinhala translations is difficult due to the syntactic divergence between these two languages under low resource constraints. Thus, in this research, we explore effective methods of incorporating Part of Speech (POS) tags to the Transformer input embedding and positional encoding to further enhance the performance of the baseline English to Sinhala neural machine translation model.
Dynamic Service Provisioning in the Edge-cloud Continuum with Provable Guarantees
Venue: arXiv cs.NI 2022
Year: 2022

We consider a hierarchical edge-cloud architecture in which services are provided to mobile users as chains of virtual network functions. Each service has specific computation requirements and target delay performance, which require placing the corresponding chain properly and allocating a suitable amount of computing resources. Furthermore, chain migration may be necessary to meet the services' target delay, or convenient to keep the service provisioning cost low. We tackle such issues by formalizing the problem of optimal chain placement and resource allocation in the edge-cloud continuum, taking into account migration, bandwidth, and computation costs. Specifically, we first envision an algorithm that, leveraging resource augmentation, addresses the above problem and provides an upper bound to the amount of resources required to find a feasible solution. We use this algorithm as a building block to devise an efficient approach targeting the minimum-cost solution, while minimizing the required resource augmentation. Our results, obtained through trace-driven, large-scale simulations, show that our solution can provide a feasible solution by using half the amount of resources required by state-of-the-art alternatives.
Combining Varied Learners for Binary Classification using Stacked Generalization
Venue: arXiv cs.LG 2022
Year: 2022

The Machine Learning has various learning algorithms that are better in some or the other aspect when compared with each other but a common error that all algorithms will suffer from is training data with very high dimensional feature set. This usually ends up algorithms into generalization error that deplete the performance. This can be solved using an Ensemble Learning method known as Stacking commonly termed as Stacked Generalization. In this paper we perform binary classification using Stacked Generalization on high dimensional Polycystic Ovary Syndrome dataset and prove the point that model becomes generalized and metrics improve significantly. The various metrics are given in this paper that also point out a subtle transgression found with Receiver Operating Characteristic Curve that was proved to be incorrect.
Machine learning models and facial regions videos for estimating heart rate: a review on Patents, Datasets and Literature
Venue: arXiv cs.LG 2022
Year: 2022

Estimating heart rate is important for monitoring users in various situations. Estimates based on facial videos are increasingly being researched because it makes it possible to monitor cardiac information in a non-invasive way and because the devices are simpler, requiring only cameras that capture the user's face. From these videos of the user's face, machine learning is able to estimate heart rate. This study investigates the benefits and challenges of using machine learning models to estimate heart rate from facial videos, through patents, datasets, and articles review. We searched Derwent Innovation, IEEE Xplore, Scopus, and Web of Science knowledge bases and identified 7 patent filings, 11 datasets, and 20 articles on heart rate, photoplethysmography, or electrocardiogram data. In terms of patents, we note the advantages of inventions related to heart rate estimation, as described by the authors. In terms of datasets, we discovered that most of them are for academic purposes and with different signs and annotations that allow coverage for subjects other than heartbeat estimation. In terms of articles, we discovered techniques, such as extracting regions of interest for heart rate reading and using Video Magnification for small motion extraction, and models such as EVM-CNN and VGG-16, that extract the observed individual's heart rate, the best regions of interest for signal extraction and ways to process them.
Enhanced DeepONet for Modeling Partial Differential Operators Considering Multiple Input Functions
Venue: arXiv cs.LG 2022
Year: 2022

Machine learning, especially deep learning is gaining much attention due to the breakthrough performance in various cognitive applications. Recently, neural networks (NN) have been intensively explored to model partial differential equations as NN can be viewed as universal approximators for nonlinear functions. A deep network operator (DeepONet) architecture was proposed to model the general non-linear continuous operators for partial differential equations (PDE) due to its better generalization capabilities than existing mainstream deep neural network architectures. However, existing DeepONet can only accept one input function, which limits its application. In this work, we explore the DeepONet architecture to extend it to accept two or more input functions. We propose new Enhanced DeepONet or EDeepONet high-level neural network structure, in which two input functions are represented by two branch DNN sub-networks, which are then connected with output truck network via inner product to generate the output of the whole neural network. The proposed EDeepONet structure can be easily extended to deal with multiple input functions. Our numerical results on modeling two partial differential equation examples shows that the proposed enhanced DeepONet is about 7X-17X or about one order of magnitude more accurate than the fully connected neural network and is about 2X-3X more accurate than a simple extended DeepONet for both training and test.
Symphony: Composing Interactive Interfaces for Machine Learning
Venue: arXiv cs.HC 2022
Year: 2022

Interfaces for machine learning (ML), information and visualizations about models or data, can help practitioners build robust and responsible ML systems. Despite their benefits, recent studies of ML teams and our interviews with practitioners (n=9) showed that ML interfaces have limited adoption in practice. While existing ML interfaces are effective for specific tasks, they are not designed to be reused, explored, and shared by multiple stakeholders in cross-functional teams. To enable analysis and communication between different ML practitioners, we designed and implemented Symphony, a framework for composing interactive ML interfaces with task-specific, data-driven components that can be used across platforms such as computational notebooks and web dashboards. We developed Symphony through participatory design sessions with 10 teams (n=31), and discuss our findings from deploying Symphony to 3 production ML projects at Apple. Symphony helped ML practitioners discover previously unknown issues like data duplicates and blind spots in models while enabling them to share insights with other stakeholders.
Simulating User-Level Twitter Activity with XGBoost and Probabilistic Hybrid Models
Venue: arXiv cs.LG 2022
Year: 2022

The Volume-Audience-Match simulator, or VAM was applied to predict future activity on Twitter related to international economic affairs. VAM was applied to do timeseries forecasting to predict the: (1) number of total activities, (2) number of active old users, and (3) number of newly active users over the span of 24 hours from the start time of prediction. VAM then used these volume predictions to perform user link predictions. A user-user edge was assigned to each of the activities in the 24 future timesteps. VAM considerably outperformed a set of baseline models in both the time series and user-assignment tasks
Cyclical Focal Loss
Venue: arXiv cs.CV 2022
Year: 2022

The cross-entropy softmax loss is the primary loss function used to train deep neural networks. On the other hand, the focal loss function has been demonstrated to provide improved performance when there is an imbalance in the number of training samples in each class, such as in long-tailed datasets. In this paper, we introduce a novel cyclical focal loss and demonstrate that it is a more universal loss function than cross-entropy softmax loss or focal loss. We describe the intuition behind the cyclical focal loss and our experiments provide evidence that cyclical focal loss provides superior performance for balanced, imbalanced, or long-tailed datasets. We provide numerous experimental results for CIFAR-10/CIFAR-100, ImageNet, balanced and imbalanced 4,000 training sample versions of CIFAR-10/CIFAR-100, and ImageNet-LT and Places-LT from the Open Long-Tailed Recognition (OLTR) challenge. Implementing the cyclical focal loss function requires only a few lines of code and does not increase training time. In the spirit of reproducibility, our code is available at \url{https://github.com/lnsmith54/CFL}.
End-to-end contextual asr based on posterior distribution adaptation for hybrid ctc/attention system
Venue: arXiv cs.CL 2022
Year: 2022

End-to-end (E2E) speech recognition architectures assemble all components of traditional speech recognition system into a single model. Although it simplifies ASR system, it introduces contextual ASR drawback: the E2E model has worse performance on utterances containing infrequent proper nouns. In this work, we propose to add a contextual bias attention (CBA) module to attention based encoder decoder (AED) model to improve its ability of recognizing the contextual phrases. Specifically, CBA utilizes the context vector of source attention in decoder to attend to a specific bias embedding. Jointly learned with the basic AED parameters, CBA can tell the model when and where to bias its output probability distribution. At inference stage, a list of bias phrases is preloaded and we adapt the posterior distributions of both CTC and attention decoder according to the attended bias phrase of CBA. We evaluate the proposed method on GigaSpeech and achieve a consistent relative improvement on recall rate of bias phrases ranging from 15% to 28% compared to the baseline model. Meanwhile, our method shows a strong anti-bias ability as the performance on general tests only degrades 1.7% even 2,000 bias phrases are present.