OpenRaiser/Intern-Atlas huggingface dataset:
Mitigating severe over-parameterization in deep convolutional neural networks through forced feature abstraction and compression with an entropy-based heuristic
Venue: arXiv cs.CV 2021
Year: 2021

Convolutional Neural Networks (CNNs) such as ResNet-50, DenseNet-40 and ResNeXt-56 are severely over-parameterized, necessitating a consequent increase in the computational resources required for model training which scales exponentially for increments in model depth. In this paper, we propose an Entropy-Based Convolutional Layer Estimation (EBCLE) heuristic which is robust and simple, yet effective in resolving the problem of over-parameterization with regards to network depth of CNN model. The EBCLE heuristic employs a priori knowledge of the entropic data distribution of input datasets to determine an upper bound for convolutional network depth, beyond which identity transformations are prevalent offering insignificant contributions for enhancing model performance. Restricting depth redundancies by forcing feature compression and abstraction restricts over-parameterization while decreasing training time by 24.99% - 78.59% without degradation in model performance. We present empirical evidence to emphasize the relative effectiveness of broader, yet shallower models trained using the EBCLE heuristic, which maintains or outperforms baseline classification accuracies of narrower yet deeper models. The EBCLE heuristic is architecturally agnostic and EBCLE based CNN models restrict depth redundancies resulting in enhanced utilization of the available computational resources. The proposed EBCLE heuristic is a compelling technique for researchers to analytically justify their HyperParameter (HP) choices for CNNs. Empirical validation of the EBCLE heuristic in training CNN models was established on five benchmarking datasets (ImageNet32, CIFAR-10/100, STL-10, MNIST) and four network architectures (DenseNet, ResNet, ResNeXt and EfficientNet B0-B2) with appropriate statistical tests employed to infer any conclusive claims presented in this paper.
Over-the-Air Federated Multi-Task Learning
Venue: arXiv cs.LG 2021
Year: 2021

In this letter, we introduce over-the-air computation into the communication design of federated multi-task learning (FMTL), and propose an over-the-air federated multi-task learning (OA-FMTL) framework, where multiple learning tasks deployed on edge devices share a non-orthogonal fading channel under the coordination of an edge server (ES). Specifically, the model updates for all the tasks are transmitted and superimposed concurrently over a non-orthogonal uplink fading channel, and the model aggregations of all the tasks are reconstructed at the ES through a modified version of the turbo compressed sensing algorithm (Turbo-CS) that overcomes inter-task interference. Both convergence analysis and numerical results show that the OA-FMTL framework can significantly improve the system efficiency in terms of reducing the number of channel uses without causing substantial learning performance degradation.
Pairing Conceptual Modeling with Machine Learning
Venue: arXiv cs.SE 2021
Year: 2021

Both conceptual modeling and machine learning have long been recognized as important areas of research. With the increasing emphasis on digitizing and processing large amounts of data for business and other applications, it would be helpful to consider how these areas of research can complement each other. To understand how they can be paired, we provide an overview of machine learning foundations and development cycle. We then examine how conceptual modeling can be applied to machine learning and propose a framework for incorporating conceptual modeling into data science projects. The framework is illustrated by applying it to a healthcare application. For the inverse pairing, machine learning can impact conceptual modeling through text and rule mining, as well as knowledge graphs. The pairing of conceptual modeling and machine learning in this this way should help lay the foundations for future research.
Edge-Unfolding Prismatoids: Tall or Rectangular Base
Venue: arXiv cs.CG 2021
Year: 2021

We show how to edge-unfold a new class of convex polyhedra, specifically a new class of prismatoids (the convex hull of two parallel convex polygons, called the top and base), by constructing a nonoverlapping "petal unfolding" in two new cases: (1) when the top and base are sufficiently far from each other; and (2) when the base is a rectangle and all other faces are nonobtuse triangles. The latter result extends a previous result by O'Rourke that the petal unfolding of a prismatoid avoids overlap when the base is a triangle (possibly obtuse) and all other faces are nonobtuse triangles. We also illustrate the difficulty of extending this result to a general quadrilateral base by giving a counterexample to our technique.
A Reinforcement Learning Approach for Sequential Spatial Transformer Networks
Venue: arXiv cs.LG 2021
Year: 2021

Spatial Transformer Networks (STN) can generate geometric transformations which modify input images to improve the classifier's performance. In this work, we combine the idea of STN with Reinforcement Learning (RL). To this end, we break the affine transformation down into a sequence of simple and discrete transformations. We formulate the task as a Markovian Decision Process (MDP) and use RL to solve this sequential decision-making problem. STN architectures learn the transformation parameters by minimizing the classification error and backpropagating the gradients through a sub-differentiable sampling module. In our method, we are not bound to the differentiability of the sampling modules. Moreover, we have freedom in designing the objective rather than only minimizing the error; e.g., we can directly set the target as maximizing the accuracy. We design multiple experiments to verify the effectiveness of our method using cluttered MNIST and Fashion-MNIST datasets and show that our method outperforms STN with a proper definition of MDP components.
Geometric Processing for Image-based 3D Object Modeling
Venue: arXiv cs.CV 2021
Year: 2021

Image-based 3D object modeling refers to the process of converting raw optical images to 3D digital representations of the objects. Very often, such models are desired to be dimensionally true, semantically labeled with photorealistic appearance (reality-based modeling). Laser scanning was deemed as the standard (and direct) way to obtaining highly accurate 3D measurements of objects, while one would have to abide the high acquisition cost and its unavailability on some of the platforms. Nowadays the image-based methods backboned by the recently developed advanced dense image matching algorithms and geo-referencing paradigms, are becoming the dominant approaches, due to its high flexibility, availability and low cost. The largely automated geometric processing of images in a 3D object reconstruction workflow, from ordered/unordered raw imagery to textured meshes, is becoming a critical part of the reality-based 3D modeling. This article summarizes the overall geometric processing workflow, with focuses on introducing the state-of-the-art methods of three major components of geometric processing: 1) geo-referencing; 2) Image dense matching 3) texture mapping. Finally, we will draw conclusions and share our outlooks of the topics discussed in this article.
Point to Point Ethernet TransmissionWireless Backhaul Links Clustering
Venue: arXiv cs.NI 2021
Year: 2021

Arxiv is acting weird and throwing error: "Bad character(s) in field Abstract." for no reason. Please refer to the manuscript.
Collaborative Edge Learning in MIMO-NOMA Uplink Transmission Environment
Venue: arXiv cs.IT 2021
Year: 2021

Multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) cellular network is promising for supporting massive connectivity. This paper exploits low-latency machine learning in the MIMO-NOMA uplink transmission environment, where a substantial amount of data must be uploaded from multiple data sources to a one-hop away edge server for machine learning. A delay-aware edge learning framework with the collaboration of data sources, the edge server, and the base station, referred to as DACEL, is proposed. Based on the delay analysis of DACEL, a NOMA channel allocation algorithm is further designed to minimize the learning delay. The simulation results show that the proposed algorithm outperforms the baseline schemes in terms of learning delay reduction.
Integrating topic modeling and word embedding to characterize violent deaths
Venue: arXiv cs.CL 2021
Year: 2021

There is an escalating need for methods to identify latent patterns in text data from many domains. We introduce a new method to identify topics in a corpus and represent documents as topic sequences. Discourse Atom Topic Modeling draws on advances in theoretical machine learning to integrate topic modeling and word embedding, capitalizing on the distinct capabilities of each. We first identify a set of vectors ("discourse atoms") that provide a sparse representation of an embedding space. Atom vectors can be interpreted as latent topics: Through a generative model, atoms map onto distributions over words; one can also infer the topic that generated a sequence of words. We illustrate our method with a prominent example of underutilized text: the U.S. National Violent Death Reporting System (NVDRS). The NVDRS summarizes violent death incidents with structured variables and unstructured narratives. We identify 225 latent topics in the narratives (e.g., preparation for death and physical aggression); many of these topics are not captured by existing structured variables. Motivated by known patterns in suicide and homicide by gender, and recent research on gender biases in semantic space, we identify the gender bias of our topics (e.g., a topic about pain medication is feminine). We then compare the gender bias of topics to their prevalence in narratives of female versus male victims. Results provide a detailed quantitative picture of reporting about lethal violence and its gendered nature. Our method offers a flexible and broadly applicable approach to model topics in text data.
Current Landscape of the Russian Sentiment Corpora
Venue: arXiv cs.CL 2021
Year: 2021

Currently, there are more than a dozen Russian-language corpora for sentiment analysis, differing in the source of the texts, domain, size, number and ratio of sentiment classes, and annotation method. This work examines publicly available Russian-language corpora, presents their qualitative and quantitative characteristics, which make it possible to get an idea of the current landscape of the corpora for sentiment analysis. The ranking of corpora by annotation quality is proposed, which can be useful when choosing corpora for training and testing. The influence of the training dataset on the performance of sentiment analysis is investigated based on the use of the deep neural network model BERT. The experiments with review corpora allow us to conclude that on average the quality of models increases with an increase in the number of training corpora. For the first time, quality scores were obtained for the corpus of reviews of ROMIP seminars based on the BERT model. Also, the study proposes the task of the building a universal model for sentiment analysis.
Dynamic Schnyder Woods
Venue: arXiv cs.CG 2021
Year: 2021

A realizer, commonly known as Schnyder woods, of a triangulation is a partition of its interior edges into three oriented rooted trees. A flip in a realizer is a local operation that transforms one realizer into another. Two types of flips in a realizer have been introduced: colored flips and cycle flips. A corresponding flip graph is defined for each of these two types of flips. The vertex sets are the realizers, and two realizers are adjacent if they can be transformed into each other by one flip. In this paper we study the relation between these two types of flips and their corresponding flip graphs. We show that a cycle flip can be obtained from linearly many colored flips. We also prove an upper bound of $O(n^2)$ on the diameter of the flip graph of realizers defined by colored flips. In addition, a data structure is given to dynamically maintain a realizer over a sequence of colored flips which supports queries, including getting a node's barycentric coordinates, in $O(\log n)$ time per flip or query.
Dizygotic Conditional Variational AutoEncoder for Multi-Modal and Partial Modality Absent Few-Shot Learning
Venue: arXiv cs.CV 2021
Year: 2021

Data augmentation is a powerful technique for improving the performance of the few-shot classification task. It generates more samples as supplements, and then this task can be transformed into a common supervised learning issue for solution. However, most mainstream data augmentation based approaches only consider the single modality information, which leads to the low diversity and quality of generated features. In this paper, we present a novel multi-modal data augmentation approach named Dizygotic Conditional Variational AutoEncoder (DCVAE) for addressing the aforementioned issue. DCVAE conducts feature synthesis via pairing two Conditional Variational AutoEncoders (CVAEs) with the same seed but different modality conditions in a dizygotic symbiosis manner. Subsequently, the generated features of two CVAEs are adaptively combined to yield the final feature, which can be converted back into its paired conditions while ensuring these conditions are consistent with the original conditions not only in representation but also in function. DCVAE essentially provides a new idea of data augmentation in various multi-modal scenarios by exploiting the complement of different modality prior information. Extensive experimental results demonstrate our work achieves state-of-the-art performances on miniImageNet, CIFAR-FS and CUB datasets, and is able to work well in the partial modality absence case.
False Negative Reduction in Video Instance Segmentation using Uncertainty Estimates
Venue: arXiv cs.CV 2021
Year: 2021

Instance segmentation of images is an important tool for automated scene understanding. Neural networks are usually trained to optimize their overall performance in terms of accuracy. Meanwhile, in applications such as automated driving, an overlooked pedestrian seems more harmful than a falsely detected one. In this work, we present a false negative detection method for image sequences based on inconsistencies in time series of tracked instances given the availability of image sequences in online applications. As the number of instances can be greatly increased by this algorithm, we apply a false positive pruning using uncertainty estimates aggregated over instances. To this end, instance-wise metrics are constructed which characterize uncertainty and geometry of a given instance or are predicated on depth estimation. The proposed method serves as a post-processing step applicable to any neural network that can also be trained on single frames only. In our tests, we obtain an improved trade-off between false negative and false positive instances by our fused detection approach in comparison to the use of an ordinary score value provided by the instance segmentation network during inference.
A Meta-Heuristic Search Algorithm based on Infrasonic Mating Displays in Peafowls
Venue: arXiv cs.NE 2021
Year: 2021

Meta-heuristic techniques are important as they are used to find solutions to computationally intractable problems. Simplistic methods such as exhaustive search become computationally expensive and unreliable as the solution space for search algorithms increase. As no method is guaranteed to perform better than all others in all classes of optimization search problems, there is a need to constantly find new and/or adapt old search algorithms. This research proposes an Infrasonic Search Algorithm, inspired from the Gravitational Search Algorithm and the mating behaviour in peafowls. The Infrasonic Search Algorithm identified competitive solutions to 23 benchmark unimodal and multimodal test functions compared to the Genetic Algorithm, Particle Swarm Optimization Algorithm and the Gravitational Search Algorithm.
R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network
Venue: arXiv cs.CV 2021
Year: 2021

Images captured in weak illumination conditions could seriously degrade the image quality. Solving a series of degradation of low-light images can effectively improve the visual quality of images and the performance of high-level visual tasks. In this study, a novel Retinex-based Real-low to Real-normal Network (R2RNet) is proposed for low-light image enhancement, which includes three subnets: a Decom-Net, a Denoise-Net, and a Relight-Net. These three subnets are used for decomposing, denoising, contrast enhancement and detail preservation, respectively. Our R2RNet not only uses the spatial information of the image to improve the contrast but also uses the frequency information to preserve the details. Therefore, our model acheived more robust results for all degraded images. Unlike most previous methods that were trained on synthetic images, we collected the first Large-Scale Real-World paired low/normal-light images dataset (LSRW dataset) to satisfy the training requirements and make our model have better generalization performance in real-world scenes. Extensive experiments on publicly available datasets demonstrated that our method outperforms the existing state-of-the-art methods both quantitatively and visually. In addition, our results showed that the performance of the high-level visual task (i.e. face detection) can be effectively improved by using the enhanced results obtained by our method in low-light conditions. Our codes and the LSRW dataset are available at: https://github.com/abcdef2000/R2RNet.
Weight Divergence Driven Divide-and-Conquer Approach for Optimal Federated Learning from non-IID Data
Venue: arXiv cs.LG 2021
Year: 2021

Federated Learning allows training of data stored in distributed devices without the need for centralizing training data, thereby maintaining data privacy. Addressing the ability to handle data heterogeneity (non-identical and independent distribution or non-IID) is a key enabler for the wider deployment of Federated Learning. In this paper, we propose a novel Divide-and-Conquer training methodology that enables the use of the popular FedAvg aggregation algorithm by overcoming the acknowledged FedAvg limitations in non-IID environments. We propose a novel use of Cosine-distance based Weight Divergence metric to determine the exact point where a Deep Learning network can be divided into class agnostic initial layers and class-specific deep layers for performing a Divide and Conquer training. We show that the methodology achieves trained model accuracy at par (and in certain cases exceeding) with numbers achieved by state-of-the-art Aggregation algorithms like FedProx, FedMA, etc. Also, we show that this methodology leads to compute and bandwidth optimizations under certain documented conditions.
Software quality: A Historical and Synthetic Content Analysis
Venue: arXiv cs.SE 2021
Year: 2021

Interconnected computers and software systems have become an indispensable part of people's lives, therefore software quality research is becoming more and more important. There have been multiple attempts to synthesize knowledge gained in software quality research, however, they were focused mainly on single aspects of software quality and not to structure the knowledge in a holistic way. The aim of our study was to close this gap. The software quality publications were harvested from the Scopus bibliographic database. The metadata was exported first to CRexlporer, which was employed to identify historical roots, and next to VOSViewer, which was used as a part of the synthetic content analysis. In our study we defined synthetic context analysis as a triangulation of bibliometrics and content analysis. Our search resulted in 14451 publications. The performance bibliometric study showed that the production of research publications relating to software quality is currently following an exponential growth trend and that the software quality research community is growing. The most productive country was the United States and the most productive Institution The Florida Atlantic University. The synthetic content analysis revealed that the published knowledge can be structured into 10 themes, the most important being the themes regarding software quality improvement with enhancing software engineering, advanced software testing, and improved defect and fault prediction with machine learning and data mining. According to the analysis of the hot topics, it seems that future research will be directed into developing and using a full specter of new artificial intelligence tools (not just machine learning and data mining) and focusing on how to assure software quality in agile development paradigms.
ICDAR 2021 Competition on Scientific Literature Parsing
Venue: arXiv cs.IR 2021
Year: 2021

Scientific literature contain important information related to cutting-edge innovations in diverse domains. Advances in natural language processing have been driving the fast development in automated information extraction from scientific literature. However, scientific literature is often available in unstructured PDF format. While PDF is great for preserving basic visual elements, such as characters, lines, shapes, etc., on a canvas for presentation to humans, automatic processing of the PDF format by machines presents many challenges. With over 2.5 trillion PDF documents in existence, these issues are prevalent in many other important application domains as well. Our ICDAR 2021 Scientific Literature Parsing Competition (ICDAR2021-SLP) aims to drive the advances specifically in document understanding. ICDAR2021-SLP leverages the PubLayNet and PubTabNet datasets, which provide hundreds of thousands of training and evaluation examples. In Task A, Document Layout Recognition, submissions with the highest performance combine object detection and specialised solutions for the different categories. In Task B, Table Recognition, top submissions rely on methods to identify table components and post-processing methods to generate the table structure and content. Results from both tasks show an impressive performance and opens the possibility for high performance practical applications.
Rate and Power Adaptation for Multihop Regenerative Relaying Systems
Venue: arXiv cs.IT 2021
Year: 2021

In this work, we provide a global framework analysis of a multi-hop relaying systems wherein the transmitter (TX) communicates with the receiver (RX) through a set of intermediary relays deployed either in series or in parallel. Regenerative based relaying scheme is assumed such as the repetition-coded decoded-and-forward (DF) wherein the decoding is threshold-based. To reflect a wide range of fading, we introduce the generalized $H$-function (also termed as Fox-$H$ function) distribution model which enables the modeling of radio-frequency (RF) fading like Weibull and Gamma, as well as the free-space optic (FSO) such as the Double Generalized Gamma and Málaga fading. In this context, we introduce various power and rate adaptation policies based on the channel state information (CSI) availability at TX and RX. Finally, we address the effects of relaying topology, number of relays and fading model, etc, on the performance reliability of each link adaptation policy.