Reviewerly/exHarmony huggingface dataset:
Aerial Single-View Depth Completion With Image-Guided Uncertainty Estimation

On the pursuit of autonomous flying robots, the scientific community has been developing onboard real-time algorithms for localisation, mapping and planning. Despite recent progress, the available solutions still lack accuracy and robustness in many aspects. While mapping for autonomous cars had a substantive boost using deep-learning techniques to enhance LIDAR measurements using image-based depth completion, the large viewpoint variations experienced by aerial vehicles are still posing major challenges for learning-based mapping approaches. In this letter, we propose a depth completion and uncertainty estimation approach that better handles the challenges of aerial platforms, such as large viewpoint and depth variations, and limited computing resources. The core of our method is a novel compact network that performs both depth completion and confidence estimation using an image-guided approach. Real-time performance onboard a GPU suitable for small flying robots is achieved by sharing deep features between both tasks. Experiments demonstrate that our network outperforms the state-of-the-art in depth completion and uncertainty estimation for single-view methods on mobile GPUs. We further present a new photorealistic aerial depth completion dataset that exhibits more challenging depth completion scenarios than the established indoor and car driving datasets. The dataset includes an open-source, visual-inertial UAV simulator for photo-realistic data generation. Our results show that our network trained on this dataset can be directly deployed on real-world outdoor aerial public datasets without fine-tuning or style transfer.
Optimal Document Exchange and New Codes for Insertions and Deletions

We give the first communication-optimal document exchange protocol. For any n and k <; n our randomized scheme takes any n-bit file F and computes a Θ(k log n/k) -bit summary from which one can reconstruct F, with high probability, given a related file F' with edit distance ED(F,F') ≤ k. The size of our summary is information-theoretically order optimal for all values of k, giving a randomized solution to a longstanding open question of [Orlitsky; FOCS'91]. It also is the first non-trivial solution for the interesting setting where a small constant fraction of symbols have been edited, producing an optimal summary of size O(H(δ)n) for k=δ n. This concludes a long series of better-and-better protocols which produce larger summaries for sub-linear values of k and sub-polynomial failure probabilities. In particular, the recent break-through of [Belazzougui, Zhang; FOCS'16] assumes that k <; n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ε</sup> , produces a summary of size O(klog <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> k + k log n), and succeeds with probability 1-(k log n) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O(1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">)</sup> . We also give an efficient derandomized document exchange protocol with summary size O(k log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> n/k). This improves, for any k, over a deterministic document exchange protocol by Belazzougui with summary size O(k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> + k log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> n). Our deterministic document exchange directly provides new efficient systematic error correcting codes for insertions and deletions. These (binary) codes correct any δ fraction of adversarial insertions/deletions while having a rate of 1 - O(δ log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 1/δ) and improve over the codes of Guruswami and Li and Haeupler, Shahrasbi and Vitercik which have rate 1 - Θ (√δ log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O(1)</sup> 1/ε).
A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis

Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, which are often challenging for machine learning due to a lack of human-annotated learning samples. With the advent of deep neural networks, a promising way to cope with the lack of training data is to pre-train models on images from a different domain and then fine-tune them on historical documents. In the current research, a typical example of such cross-domain transfer learning is the use of neural networks that have been pre-trained on the ImageNet database for object recognition. It remains a mostly open question whether or not this pre-training helps to analyse historical documents, which have fundamentally different image properties when compared with ImageNet. In this paper, we present a comprehensive empirical survey on the effect of ImageNet pre-training for diverse historical document analysis tasks, including character recognition, style classification, manuscript dating, semantic segmentation, and content-based retrieval. While we obtain mixed results for semantic segmentation at pixel-level, we observe a clear trend across different network architectures that ImageNet pre-training has a positive effect on classification as well as content-based retrieval.
Ensemble learning by means of a multi-objective optimization design approach for dealing with imbalanced data sets

Ensemble learning methods have already shown to be powerful techniques for creating classifiers. However, when dealing with real-world engineering problems, class imbalance is usually found. In such scenario, canonical machine learning algorithms may not present desirable solutions, and techniques for overcoming this problem must be used. In addition to using learning algorithms that alleviate the imbalance between classes, multi-objective optimization design (MOOD) approaches can be used to improve the prediction performance of ensembles of classifiers. This paper proposes a study of different MOOD approaches for ensemble learning. First, a taxonomy on multi-objective ensemble learning (MOEL) is proposed. In it, four types of existing approaches are defined: multi-objective ensemble member generation, multi-objective ensemble member selection, multi-objective ensemble member combination, and multi-objective ensemble member selection and combination. Additionally, new approaches can be derived by combining the previous ones, such as multi-objective ensemble member generation and selection, multi-objective ensemble member generation and combination and multi-objective ensemble member generation, selection and combination. With the given taxonomy, two experiments are conducted for comparing (1) the performance of the MOEL techniques for generating and aggregating base models on several imbalanced benchmark problems and (2) the performance of MOEL techniques against other machine learning techniques in a real-world imbalanced drinking-water quality anomaly detection problem. Finally, results indicate that MOOD is able to improve the predictive performance of existing ensemble learning techniques.
Graphical Object Detection in Document Images

Graphical elements: particularly tables and figures contain a visual summary of the most valuable information contained in a document. Therefore, localization of such graphical objects in the document images is the initial step to understand the content of such graphical objects or document images. In this paper, we present a novel end-to-end trainable deep learning based framework to localize graphical objects in the document images called as Graphical Object Detection ( GOD ). Our framework is data-driven and does not require any heuristics or meta-data to locate graphical objects in the document images. The GOD explores the concept of transfer learning and domain adaptation to handle scarcity of labeled training images for graphical object detection task in the document images. Performance analysis carried out on the various public benchmark data sets: ICDAR -2013, ICDAR - POD2017 and UNLV shows that our model yields promising results as compared to state-of-the-art techniques.
A Performance Evaluation of Containers Running on Managed Kubernetes Services

Container-based virtualisation technologies are gaining more and more traction in recent years across Cloud platforms and this will likely continue in the coming years. As such, containers orchestration technologies are becoming indispensable. Kubernetes has become the de facto standard because of its robustness, maturity and rich features. To free users of the burden of having to configure and maintain complex Kubernetes infrastructures, but still make use of its functionalities, all major Cloud providers are now offering cloud-native managed Kubernetes alternatives. The goal of this paper is to investigate the performance of containers running in such hosted services. For this purpose, we conduct a series of experimental evaluations of containers to monitor the behaviour of system resources including CPU, memory, disk and network. A baseline consisting of a manually deployed Kubernetes cluster was built for comparison. In particular, we consider the Amazon Elastic Container Service for Kubernetes (EKS), Microsoft Azure Kubernetes Service (AKS) and Google Kubernetes Engine (GKE). The Australia-wide NeCTAR Research Cloud was used for the baseline.
Understanding token-based ecosystems – a taxonomy of blockchain-based business models of start-ups

Abstract Start-ups in the blockchain context generate millions by means of initial coin offerings (ICOs). Many of these crowdfunding endeavours are very successful, others are not. However, despite the increasing investments in ICOs, there is still neither sufficient theoretical knowledge nor a comprehensive understanding of the different types of business models and the implications for these token-based ecosystems. Scientific research equally lacks a thorough understanding of the different business model forms and their influence on collaboration in token-based economies. We bridge this gap by presenting a taxonomy of real-world blockchain-based start-ups. For this taxonomy, we used 195 start-ups and performed a cluster-analysis in order to identify three different archetypes and thus gain a deeper understanding. Our taxonomy and the archetypes can equally be seen as strategic guidance for practitioners as well as a starting point for future research concerning the token-based business models.
Generative Counterfactual Introspection for Explainable Deep Learning

In this work, we propose an introspection technique for deep neural networks that relies on a generative model to instigate salient editing of the input image for model interpretation. Such modification provides the fundamental interventional operation that allows us to obtain answers to counterfactual inquiries, i.e., what meaningful change can be made to the input image in order to alter the prediction. We demonstrate how to reveal interesting properties of the given classifiers by utilizing the proposed introspection approach on both the MNIST and the CelebA dataset.
<i>C</i>-Differentials, Multiplicative Uniformity, and (Almost) Perfect <i>c</i>-Nonlinearity

In this paper we define a new (output) multiplicative differential, and the corresponding c-differential uniformity. With this new concept, even for characteristic 2, there are perfect c-nonlinear (PcN) functions. We first characterize the c-differential uniformity of a function in terms of its Walsh transform. We further look at some of the known perfect nonlinear (PN) functions and show that only one remains a PcN function, under a different condition on the parameters. In fact, the p-ary Gold PN function increases its c-differential uniformity significantly, under some conditions on the parameters. We then precisely characterize the c-differential uniformity of the inverse function (in any dimension and characteristic), relevant for the Rijndael (and Advanced Encryption Standard) block cipher.
ReMemNN: A novel memory neural network for powerful interaction in aspect-based sentiment analysis

Deep neural networks have been employed to analyze the sentiment of text sequences and achieved significant effect. However, these models still face the issues of weakness of pre-trained word embeddings and weak interaction between the specific aspect and the context in attention mechanism. The pre-trained word embeddings lack the specific semantic information from the context. The weak interaction results in poor attention weights and produces limited aspect dependent sentiment representation in aspect-based sentiment analysis (ABSA). In this paper, we propose a novel end-to-end memory neural network, termed Recurrent Memory Neural Network (ReMemNN), to mitigate the above-mentioned problems. In ReMemNN, to tackle weakness of pre-trained word embeddings, a specially module named embedding adjustment learning module is designed to transfer the pre-trained word embeddings into adjustment word embeddings. To tackle weak interaction in attention mechanism, a multielement attention mechanism is designed to generate powerful attention weights and more precise aspect dependent sentiment representation. Besides, an explicit memory module is designed to store these different representations and generate hidden states and representations. Extensive experimental results on all datasets show that ReMemNN outperforms typical baselines and achieve the state-of-the-art performance. Besides, these experimental results also demonstrate that ReMemNN is language-independent and dataset type-independent.
Aspect-Level Drug Reviews Sentiment Analysis Based on Double BiGRU and Knowledge Transfer

Aspect-level sentiment analysis is a fine-grained sentiment analysis task designed to identify the sentiment polarity of specific target in a sentence. However, this task is rarely used in drug reviews. Some models for this task ignore the impact of target semantics, and others do not perform well because the datasets are relatively smaller. Therefore, we propose a Pretraining and Multi-task learning model based on Double BiGRU (PM-DBiGRU). In PM-DBiGRU, we first use the pretrained weight learned from short text-level drug review sentiment classification task to initialize related weight of our model. Then two BiGRU networks are applied to generate the bidirectional semantic representations of the target and drug review, and attention mechanism is used to obtain the target-specific representation for aspect-level drug review. The multi-task learning is further utilized to transfer the helpful domain knowledge from the short text-level drug review corpus. We also propose a dataset SentiDrugs for aspect-level drug review sentiment classification, in which each review may contain one or more targets. Experimental results on SentiDrugs demonstrate that our approach can improve the performance of aspect-level drug reviews sentiment classification compared with other state-of-the-art architectures.
Robust Texture Description Using Local Grouped Order Pattern and Non-Local Binary Pattern

Local binary pattern (LBP) and its many variants have shown effectiveness for texture classification. However, most of these LBP methods focus on encoding local intensity differences between a central pixel and its neighboring sampling points and consequently have two major problems: 1) they are unable to describe the intensity order relationships among neighboring sampling points, and 2) they fail to capture long-range pixel interactions that take place outside a compact neighborhood. In view of these problems, in this paper we propose two novel operators, called local grouped order pattern (LGOP) and non-local binary pattern (NLBP), for texture description. For the first problem, LGOP groups the neighboring sampling points by referring to a dominant direction and encodes the groupwise intensity order relationships. For the second problem, NLBP computes several anchors based on global image statistics and progressively encodes non-local intensity differences between the neighboring sampling points and anchors. Finally, we combine LGOP and NLBP via central pixel encoding to construct discriminative histogram features as texture descriptor LGONBP. Experiments on four texture benchmark databases (i.e., Outex, CUReT, UMD and KTH-TIPS) demonstrate the superiority of LGONBP over state-of-the-art LBP variants for texture classification under both noise-free and noisy conditions. The code is available at <uri>https://github.com/stc-cqupt/LGONBP</uri>.
Chinese Street View Text: Large-Scale Chinese Text Reading With Partially Supervised Learning

Most existing text reading benchmarks make it difficult to evaluate the performance of more advanced deep learning models in large vocabularies due to the limited amount of training data. To address this issue, we introduce a new large-scale text reading benchmark dataset named Chinese Street View Text (C-SVT) with 430,000 street view images, which is at least 14 times as large as the existing Chinese text reading benchmarks. To recognize Chinese text in the wild while keeping large-scale datasets labeling cost-effective, we propose to annotate one part of the C-SVT dataset (30,000 images) in locations and text labels as full annotations and add 400,000 more images, where only the corresponding text-of-interest in the regions is given as weak annotations. To exploit the rich information from the weakly annotated data, we design a text reading network in a partially supervised learning framework, which enables to localize and recognize text, learn from fully and weakly annotated data simultaneously. To localize the best matched text proposals from weakly labeled images, we propose an online proposal matching module incorporated in the whole model, spotting the keyword regions by sharing parameters for end-to-end training. Compared with fully supervised training algorithms, this model can improve the end-to-end recognition performance remarkably by 4.03% in F-score at the same labeling cost. The proposed model can also achieve state-of-the-art results on the ICDAR 2017-RCTW dataset, which demonstrates the effectiveness of the proposed partially supervised learning framework.
Groundwater contamination source identification based on a hybrid particle swarm optimization-extreme learning machine

When the simulation-optimization method is applied to groundwater contamination source identification (GCSI), the numerical simulation model is usually embedded in the optimization model as a constraint condition. Hundreds and thousands of simulation model calls, while solving the optimization model, often lead to computational disaster. Establishing a surrogate model for the simulation model can effectively alleviate this drawback. When using extreme learning machine (ELM) to establish a surrogate model for a simulation model, the output weights are calculated using randomly given input weights and hidden-layer deviations, which may lead to inadequate accuracy of the surrogate model and insufficient generalization ability to samples not appearing in the training set. However, by combining the ELM with particle swarm optimization (PSO), the PSO can be used to optimize the selection of the input weights and hidden-layer deviations of the ELM; thus, calculating the output weights and establishing a surrogate model based on the PSO-ELM instead of the simulation model embedded in the optimization model to complete the GCSI. The uncertainty of the parameters is rarely considered when the simulation-optimization method is applied to GCSI. Thus, the uncertainty of the parameters is considered in GCSI based on the PSO-ELM. The results show that compared with the ELM, the PSO-ELM can establish the surrogate model with higher accuracy. The surrogate model based on PSO-ELM can be embedded in the optimization model to effectively solve GCSI problems. The predictive model based on PSO-ELM can predict the release histories of contamination sources corresponding to different random parameters.
Lightweight Deep Learning Based Intelligent Edge Surveillance Techniques

Decentralized edge computing techniques have been attracted strongly attentions in many applications of intelligent Internet of Things (IIoT). Among these applications, intelligent edge surveillance (INES) methods play a very important role to recognize object feature information automatically from surveillance video by virtue of edge computing together with image processing and computer vision. Traditional centralized surveillance techniques recognize objects at the cost of high latency, high cost and also require high occupied storage. In this paper, we propose a deep learning-based INES technique for a specific IIoT application. First, a depthwise separable convolutional strategy is introduced to build a lightweight deep neural network to reduce its computational cost. Second, we combine edge computing with cloud computing to reduce network traffic. Third, our proposed INES method is applied into the practical construction site for the validation of a specific IIoT application. The detection speed of the proposed INES reaches 16 frames per second in the edge device. After the joint computing of edge and cloud, the detection precision can reach as high as 89%. In addition, the operating cost at the edge device is only one-tenth of that of the centralized server. Experiment results are given to confirm the proposed INES method in terms of both computational cost and detection accuracy.
Ship Type Recognition via a Coarse-to-Fine Cascaded Convolution Neural Network

Most previous research has handled the task of ship type recognition by exploring hand-craft ship features, which may fail to distinguish ships with similar visual appearances. This situation motivates us to propose a novel deep learning based ship type recognition framework which we have named coarse-to-fine cascaded convolution neural network (CFCCNN). First, the proposed CFCCNN framework formats the input training ship images and data, and provides trainable input data for the hidden layers of the CFCCNN. Second, the coarse and fine steps are run in a nesting manner to explore discriminative features for different ship types. More specifically, the coarse step is trained in a similar manner to the traditional convolution neural network, while the fine step introduces regularisation mechanisms to extract more intrinsic ship features, and fine tunes parameter settings to obtain better recognition performance. Finally, we evaluate the performance of the CFCCNN model for recognising the most common types of merchant ship (oil tanker, container, LNG tanker, chemical carrier, general cargo, bulk carrier, etc.). The experimental results show that the proposed framework obtains better recognition performance than the conventional methods of ship type recognition.