OpenRaiser/Intern-Atlas huggingface dataset:
DARDet: A Dense Anchor-free Rotated Object Detector in Aerial Images
Venue: arXiv cs.CV 2021
Year: 2021

Rotated object detection in aerial images has received increasing attention for a wide range of applications. However, it is also a challenging task due to the huge variations of scale, rotation, aspect ratio, and densely arranged targets. Most existing methods heavily rely on a large number of pre-defined anchors with different scales, angles, and aspect ratios, and are optimized with a distance loss. Therefore, these methods are sensitive to anchor hyper-parameters and easily suffer from performance degradation caused by boundary discontinuity. To handle this problem, in this paper, we propose a dense anchor-free rotated object detector (DARDet) for rotated object detection in aerial images. Our DARDet directly predicts five parameters of rotated boxes at each foreground pixel of feature maps. We design a new alignment convolution module to extracts aligned features and introduce a PIoU loss for precise and stable regression. Our method achieves state-of-the-art performance on three commonly used aerial objects datasets (i.e., DOTA, HRSC2016, and UCAS-AOD) while keeping high efficiency. Code is available at https://github.com/zf020114/DARDet.
Fieldable Cross-Layer Optimized Embedded Software Defined Radio is Finally Here!
Venue: arXiv cs.NI 2021
Year: 2021

The concept of cross-layer optimization has been around for several years now. The primary goal of the cross-layer approach was to liberate the strict boundary between the layers of the traditional OSI protocol stack. This is to enable information flow between layers which then can be leveraged to optimize the network's performance across the layers. This concept has been of keen interest for tactical application as there is an overwhelming requirement to operate in a challenging and dynamic environment. The advent of software defined radios (SDR) accelerated the growth of this domain due to the added flexibility provided by SDRs. Even with the immense interest and progress in this area of research, there has been a gaping abyss between solutions designed in theory and ones deployed in practice. To the best of our knowledge, this is the first time in literature, an embedded SDR has been leveraged to successfully design a cross-layer optimized transceiver that provides high throughput and high reliability in a ruggedized, weatherized, and fieldable form-factor. The design ethos focuses on efficiency and flexibility such that optimization objectives, cross-layer interactions can be reconfigured rapidly. To demonstrate our claims, we provide results from extensive outdoor over-the-air evaluation in various settings with up to 10-node network typologies. The results demonstrate high reliability, throughput, and dynamic routing capability achieving high technology readiness level (TRL) for tactical applications.
Adversarial Examples Generation for Reducing Implicit Gender Bias in Pre-trained Models
Venue: arXiv cs.CL 2021
Year: 2021

Over the last few years, Contextualized Pre-trained Neural Language Models, such as BERT, GPT, have shown significant gains in various NLP tasks. To enhance the robustness of existing pre-trained models, one way is adversarial examples generation and evaluation for conducting data augmentation or adversarial learning. In the meanwhile, gender bias embedded in the models seems to be a serious problem in practical applications. Many researches have covered the gender bias produced by word-level information(e.g. gender-stereotypical occupations), while few researchers have investigated the sentence-level cases and implicit cases. In this paper, we proposed a method to automatically generate implicit gender bias samples at sentence-level and a metric to measure gender bias. Samples generated by our method will be evaluated in terms of accuracy. The metric will be used to guide the generation of examples from Pre-trained models. Therefore, those examples could be used to impose attacks on Pre-trained Models. Finally, we discussed the evaluation efficacy of our generated examples on reducing gender bias for future research.
Lifetime Maximization for UAV-Enabled Cognitive-NOMA IoT Networks: Joint Location, Power, and Decoding Order Optimization
Venue: arXiv cs.IT 2021
Year: 2021

This paper investigates a cognitive unmanned aerial vehicle (UAV) enabled Internet of Things (IoT) network, where secondary/cognitive IoT devices upload their data to the UAV hub following a non-orthogonal multiple access (NOMA) protocol in the spectrum of the primary network. We aim to maximize the minimum lifetime of IoT devices by jointly optimizing the UAV location, transmit power, and decoding order subject to interference-power constraints in presence of the imperfect channel state information (CSI). To solve the formulated non-convex mixed-integer programming problem, we first jointly optimize the UAV location and transmit power for a given decoding order and obtain the globally optimal solution with the assistance of Lagrange duality and then obtain the best decoding order by exhaustive search, which is applicable to relatively small-scale scenarios. For large-scale scenarios, we propose a low-complexity sub-optimal algorithm by transforming the original problem into a more tractable equivalent form and applying the successive convex approximation (SCA) technique and penalty function method. Numerical results demonstrate that the proposed design significantly outperforms the benchmark schemes.
From cold resistor to secure key exchanger
Venue: arXiv cs.CR 2021
Year: 2021

Utilizing a formerly published cold resistor circuitry, a secure key exchange system is conceived and explored. A circuit realization of the system is constructed and simulated. Similarly to the Pao-Lo key exchanger, this system is secure in the steady-state limit but crackable in the transient situations.
Enhance Images as You Like with Unpaired Learning
Venue: arXiv cs.CV 2021
Year: 2021

Low-light image enhancement exhibits an ill-posed nature, as a given image may have many enhanced versions, yet recent studies focus on building a deterministic mapping from input to an enhanced version. In contrast, we propose a lightweight one-path conditional generative adversarial network (cGAN) to learn a one-to-many relation from low-light to normal-light image space, given only sets of low- and normal-light training images without any correspondence. By formulating this ill-posed problem as a modulation code learning task, our network learns to generate a collection of enhanced images from a given input conditioned on various reference images. Therefore our inference model easily adapts to various user preferences, provided with a few favorable photos from each user. Our model achieves competitive visual and quantitative results on par with fully supervised methods on both noisy and clean datasets, while being 6 to 10 times lighter than state-of-the-art generative adversarial networks (GANs) approaches.
Deep Fraud Detection on Non-attributed Graph
Venue: arXiv cs.LG 2021
Year: 2021

Fraud detection problems are usually formulated as a machine learning problem on a graph. Recently, Graph Neural Networks (GNNs) have shown solid performance on fraud detection. The successes of most previous methods heavily rely on rich node features and high-fidelity labels. However, labeled data is scarce in large-scale industrial problems, especially for fraud detection where new patterns emerge from time to time. Meanwhile, node features are also limited due to privacy and other constraints. In this paper, two improvements are proposed: 1) We design a graph transformation method capturing the structural information to facilitate GNNs on non-attributed fraud graphs. 2) We propose a novel graph pre-training strategy to leverage more unlabeled data via contrastive learning. Experiments on a large-scale industrial dataset demonstrate the effectiveness of the proposed framework for fraud detection.
Bipartite 3-Regular Counting Problems with Mixed Signs
Venue: arXiv cs.CC 2021
Year: 2021

We prove a complexity dichotomy for a class of counting problems expressible as bipartite 3-regular Holant problems. For every problem of the form $\operatorname{Holant}\left(f\mid =_3 \right)$, where $f$ is any integer-valued ternary symmetric constraint function on Boolean variables, we prove that it is either P-time computable or #P-hard, depending on an explicit criterion of $f$. The constraint function can take both positive and negative values, allowing for cancellations. The dichotomy extends easily to rational valued functions of the same type. In addition, we discover a new phenomenon: there is a set $\mathcal{F}$ with the property that for every $f \in \mathcal{F}$ the problem $\operatorname{Holant}\left(f\mid =_3 \right)$ is planar P-time computable but #P-hard in general, yet its planar tractability is by a combination of a holographic transformation by $\left[\begin{smallmatrix} 1 & 1 \\ 1 & -1 \end{smallmatrix}\right]$ to FKT together with an independent global argument.
Internal Data Imputation in Data Warehouse Dimensions
Venue: arXiv cs.DB 2021
Year: 2021

Missing values occur commonly in the multidimensional data warehouses. They may generate problems of usefulness of data since the analysis performed on a multidimensional data warehouse is through different dimensions with hierarchies where we can roll up or drill down to the different parameters of analysis. Therefore, it's essential to complete these missing values in order to carry out a better analysis. There are existing data imputation methods which are suitable for numeric data, so they can be applied for fact tables but not for dimension tables. Some other data imputation methods need extra time and effort costs. As consequence, we propose in this article an internal data imputation method for multidimensional data warehouse based on the existing data and considering the intra-dimension and inter-dimension relationships.
A free lunch from ViT:Adaptive Attention Multi-scale Fusion Transformer for Fine-grained Visual Recognition
Venue: arXiv cs.CV 2021
Year: 2021

Learning subtle representation about object parts plays a vital role in fine-grained visual recognition (FGVR) field. The vision transformer (ViT) achieves promising results on computer vision due to its attention mechanism. Nonetheless, with the fixed size of patches in ViT, the class token in deep layer focuses on the global receptive field and cannot generate multi-granularity features for FGVR. To capture region attention without box annotations and compensate for ViT shortcomings in FGVR, we propose a novel method named Adaptive attention multi-scale Fusion Transformer (AFTrans). The Selective Attention Collection Module (SACM) in our approach leverages attention weights in ViT and filters them adaptively to correspond with the relative importance of input patches. The multiple scales (global and local) pipeline is supervised by our weights sharing encoder and can be easily trained end-to-end. Comprehensive experiments demonstrate that AFTrans can achieve SOTA performance on three published fine-grained benchmarks: CUB-200-2011, Stanford Dogs and iNat2017.
Sequences of linear codes where the rate times distance grows rapidly
Venue: arXiv cs.IT 2021
Year: 2021

For a linear code $C$ of length $n$ with dimension $k$ and minimum distance $d$, it is desirable that the quantity $kd/n$ is large. Given an arbitrary field $\mathbb{F}$, we introduce a novel, but elementary, construction that produces a recursively defined sequence of $\mathbb{F}$-linear codes $C_1,C_2, C_3, \dots$ with parameters $[n_i, k_i, d_i]$ such that $k_id_i/n_i$ grows quickly in the sense that $k_id_i/n_i>\sqrt{k_i}-1>2i-1$. Another example of quick growth comes from a certain subsequence of Reed-Muller codes. Here the field is $\mathbb{F}=\mathbb{F}_2$ and $k_i d_i/n_i$ is asymptotic to $3n_i^{c}/\sqrt{π\log_2(n_i)}$ where $c=\log_2(3/2)\approx 0.585$.
SPaR.txt, a cheap Shallow Parsing approach for Regulatory texts
Venue: arXiv cs.CL 2021
Year: 2021

Automated Compliance Checking (ACC) systems aim to semantically parse building regulations to a set of rules. However, semantic parsing is known to be hard and requires large amounts of training data. The complexity of creating such training data has led to research that focuses on small sub-tasks, such as shallow parsing or the extraction of a limited subset of rules. This study introduces a shallow parsing task for which training data is relatively cheap to create, with the aim of learning a lexicon for ACC. We annotate a small domain-specific dataset of 200 sentences, SPaR.txt, and train a sequence tagger that achieves 79,93 F1-score on the test set. We then show through manual evaluation that the model identifies most (89,84%) defined terms in a set of building regulation documents, and that both contiguous and discontiguous Multi-Word Expressions (MWE) are discovered with reasonable accuracy (70,3%).
Label it be! A large-scale study of issue labeling in modern open-source repositories
Venue: arXiv cs.SE 2021
Year: 2021

In a wave of growth, open-source projects need to modernize and change how they deal with processes, methods, and communication with their contributors. We could observe that open-source projects are constantly evolving to improve their management of the entire community. Starting with community communication, software development, managing open-source projects faces crucial challenges. One of the enabling environments that open-source communities found to achieve community communications objectives was code repositories with integration with issue trackers. Using issue trackers in their projects should encompass an infrastructure capable of hosting the project source code and community participation. Some issue trackers use a structure in which the issue's title and description are the key information. However, we have observed a slight change in this strategy over the years, as more and more data are fundamental to solving the issue. For example, labeling the issues could enable users to provide the issue with more contextual information. By understanding how modern issue trackers handle issue labeling, this study analyzes the impact, engagement, and influence that labels have on the Github repositories, based on a database of 10,673,459 issues mined from 13,280 repositories in 180 Github featured topics. We found that 78.75\% of the repositories label their issues, with more adherence from those repositories that contain big numbers of issues. The labeling practice is essential and prioritized as a first step in the issue resolution process in 65.91\% of the first events. Issues with labels draw more attention and impact by collecting more subscribers, assigns, and comments, helping to engage contributors to the resolution.
The influence of deflections on the static and dynamic behaviour of masonry columns
Venue: arXiv cs.CE 2021
Year: 2021

This paper studies the influence of bending deflections on the structural behaviour of masonry columns. Some explicit solutions are presented, and the combined effects of the constitutive and geometric nonlinearities are investigated through an iterative numerical procedure. The results show that considering second-order effects affects both the collapse load and the dynamical properties of masonry beams significantly.
A Survey of Selected Algorithms Used in Military Applications from the Viewpoints of Dataflow and GaAs
Venue: arXiv cs.DC 2021
Year: 2021

This is a short survey of ten algorithms that are often used for military purposes, followed by analysis of their potential suitability for dataflow and GaAs, which are a specific architecture and technology for supercomputers on a chip, respectively. Whenever an algorithm or a device is used in military settings, it is natural to assume strict requirements related to speed, reliability, scale, energy, size, and accuracy. The two aforementioned paradigms seem to be promising in fulfilling most of these requirements.
A Lightweight, Anonymous and Confidential Genomic Computing for Industrial Scale Deployment
Venue: arXiv cs.CR 2021
Year: 2021

This paper studies anonymous and confidential genomic case and control computing within the federated framework leveraging SPDZ. Our contribution mainly comprises the following three-fold: \begin{itemize} \item In the first fold, an efficient construction of Beaver triple generators (BTGs) formalized in the 3-party computation leveraging multiplicatively homomorphic key management protocols (mHKMs) is presented and analysed. Interestingly, we are able to show the equivalence between BTGs and mHKMs. We then propose a lightweight construction of BTGs, and show that our construction is secure against semi-honest adversary if the underlying multiplicatively homomorphic encryption is semantically secure. \item In the second fold, a decoupling model for SPDZ with explicit separation of BTGs from MPC servers (MPCs) is introduced and formalized, where BTGs aim to generate the Beaver triples while MPCs to process the input data. A new notion, which we call blind triple dispensation protocol, is then introduced for securely dispensing the generated Beaver triples, and constructed from mHKMs. We demonstrate the power of mHKMs by showing that it is a useful notion not only for generating Beaver triples but also for securely dispensing triples as well. \item In the third-fold, a lightweight genomic case and control computing model is proposed, which reaches the anonymity and confidentiality simultaneously. An efficient truncation algorithm leveraging the depicted BTGs above is then proposed by eliminating computational cost heavy PRandBitL() and PRandInt() protocols involved in the state-of-the-art solutions and thus largely benefits us computing residual vectors for industrial scale deployment.
Label Propagation Through Optimal Transport
Venue: arXiv cs.LG 2021
Year: 2021

In this paper, we tackle the transductive semi-supervised learning problem that aims to obtain label predictions for the given unlabeled data points according to Vapnik's principle. Our proposed approach is based on optimal transport, a mathematical theory that has been successfully used to address various machine learning problems, and is starting to attract renewed interest in semi-supervised learning community. The proposed approach, Optimal Transport Propagation (OTP), performs in an incremental process, label propagation through the edges of a complete bipartite edge-weighted graph, whose affinity matrix is constructed from the optimal transport plan between empirical measures defined on labeled and unlabeled data. OTP ensures a high degree of predictions certitude by controlling the propagation process using a certainty score based on Shannon's entropy. We also provide a convergence analysis of our algorithm. Experiments task show the superiority of the proposed approach over the state-of-the-art. We make our code publicly available.
A Modified Q-Learning Algorithm for Rate-Profiling of Polarization Adjusted Convolutional (PAC) Codes
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we propose a reinforcement learning based algorithm for rate-profile construction of Arikan's Polarization Assisted Convolutional (PAC) codes. This method can be used for any blocklength, rate, list size under successive cancellation list (SCL) decoding and convolutional precoding polynomial. To the best of our knowledge, we present, for the first time, a set of new reward and update strategies which help the reinforcement learning agent discover much better rate-profiles than those present in existing literature. Simulation results show that PAC codes constructed with the proposed algorithm perform better in terms of frame erasure rate (FER) compared to the PAC codes constructed with contemporary rate profiling designs for various list lengths. Further, by using a (64, 32) PAC code as an example, it is shown that the choice of convolutional precoding polynomial can have a significant impact on rate-profile construction of PAC codes.
CertainNet: Sampling-free Uncertainty Estimation for Object Detection
Venue: arXiv cs.CV 2021
Year: 2021

Estimating the uncertainty of a neural network plays a fundamental role in safety-critical settings. In perception for autonomous driving, measuring the uncertainty means providing additional calibrated information to downstream tasks, such as path planning, that can use it towards safe navigation. In this work, we propose a novel sampling-free uncertainty estimation method for object detection. We call it CertainNet, and it is the first to provide separate uncertainties for each output signal: objectness, class, location and size. To achieve this, we propose an uncertainty-aware heatmap, and exploit the neighboring bounding boxes provided by the detector at inference time. We evaluate the detection performance and the quality of the different uncertainty estimates separately, also with challenging out-of-domain samples: BDD100K and nuImages with models trained on KITTI. Additionally, we propose a new metric to evaluate location and size uncertainties. When transferring to unseen datasets, CertainNet generalizes substantially better than previous methods and an ensemble, while being real-time and providing high quality and comprehensive uncertainty estimates.
An energy-based model for neuro-symbolic reasoning on knowledge graphs
Venue: arXiv cs.LG 2021
Year: 2021

Machine learning on graph-structured data has recently become a major topic in industry and research, finding many exciting applications such as recommender systems and automated theorem proving. We propose an energy-based graph embedding algorithm to characterize industrial automation systems, integrating knowledge from different domains like industrial automation, communications and cybersecurity. By combining knowledge from multiple domains, the learned model is capable of making context-aware predictions regarding novel system events and can be used to evaluate the severity of anomalies that might be indicative of, e.g., cybersecurity breaches. The presented model is mappable to a biologically-inspired neural architecture, serving as a first bridge between graph embedding methods and neuromorphic computing - uncovering a promising edge application for this upcoming technology.