Reviewerly/exHarmony huggingface dataset:
Mixed Multi-channel Graph Convolution Network on Complex Relation Graph

Graph Convolutional Networks (GCNs) have attracted increasing attention on network representation learning because they can generate node embeddings by aggregating and transforming information within node neighborhoods. However, the classification effect of GCNs is far from optimal on complex relationship graphs. The main reason is that classification rules on relationship graphs rely on node features rather than structure. most of the existing GCNs do not preserve the feature similarity of the node pair, which may lead to the loss of critical information. To address these issues, this paper proposes a node classification network based on semi-supervised learning: Mif-GCN. Specifically, we propose a mixed graph convolution module to adaptively integrate the adjacency matrices of the initial graph and the feature graph in order to explore their hidden information. In addition, we use an attention mechanism to adaptively extract embeddings from the initial graph, feature graph, and mixed graph. The ultimate goal of the model is to extract relevant information for improving classification accuracy. We validate the effectiveness of Mif-GCN on multiple datasets, including paper citation networks and relational networks. The experimental results outperform the existing methods, which further explore the classification rules.
Improving Meta-Learning Classification with Prototype Enhancement

Meta-learning classification is about training a classifier by remembering some common knowledge from limited labeled examples in a support set. It has attracted more and more attention in machine learning community. Despite the success, existing Meta-learning algorithms usually ignore the relationship between the samples of the support set and the samples of the query set in a task after feature extraction and thus fail to obtain the most discriminative features, which leads to an unreliable class prototype and a low distinguishability of feature representations. In this paper, we propose a novel metric-based meta-learning algorithm called Prototype Enhancement Network (PEN) to tackle the above problems. The proposed algorithm uses: (1) a Task Context-Related (TCR) Module to obtain a more discriminative and context-related feature representation of samples within a task; and (2) a Prototype Reset (PR) module to expands the support set with partially unlabeled query set samples to mitigate the low-data problem and obtain a more representative class prototype. The cooperation of the TCR module and the PR module effectively enhance the reliability and the accuracy of the class prototype. Experiments are conducted on two classification benchmarks (mini-ImageNet and tiered-ImageN et) and the results confirm the effectiveness of PEN compared with other state-of-the-art methods.
Sequence Squeezing: A Defense Method Against Adversarial Examples for API Call-Based RNN Variants

Adversarial examples are known to mislead deep learning models so that the models will classify them incorrectly, even in domains where such models have achieved state-of-the-art performance. Until recently, research on both adversarial attack and defense methods focused on computer vision, primarily using convolutional neural networks (CNNs). In recent years, adversarial example generation methods for recurrent neural networks (RNNs) have been published, demonstrating that RNN classifiers are also vulnerable to such attacks. In this paper, we present a novel defense method, referred to as sequence squeezing, aimed at making RNN variant (e.g., LSTM) classifiers more robust against such attacks. Our method differs from existing defense methods, which were designed only for non-sequence based models. We also implement three additional defense methods inspired by recently published CNN defense methods as baselines for our method. Using sequence squeezing, we were able to decrease the effectiveness of such adversarial attacks from 99.9% to 15%, outperforming all of the baseline defense methods.
Automated detection of illegal nonmetallic minerals mining places according to Sentinel-2 data

Currently, the problem of illegal mining is still acute. Such illegal use of natural resources harms the environment and leads to irrational use of mineral resources. Modern methods with the use of remote sensing technologies will effectively detect such law violations. In the current study, a method for automatically detection of non-metallic mineral extraction sites based on remote sensing data analysis has been developed. The study uses Sentinel-2 satellite images with spatial resolution 10 m and 20 m and considers four types of minerals: sand, clay, carbonate rocks and sand gravel mix. The spectral indices help to determine the specific quantitative characteristics of the mineral resources. The result is probability maps with mineral resourses characteristics in each pixel. In order to determine to which of known classes relates the point, you need to find the covariance matrices for all classes and take the class with the smallest Mahalanobis distance to the point. Based on the obtained probability maps, an analysis of the applicability of the selected spectral indices was performed, as well as a visual assessment of the quality of interpretation. For each spectral channel and index, two frequency histograms were created to determine how different the channels values and spectral indices on the entire scene and at the reference objects. Each object found by the program was checked for it presence on the studied territory. The developed system is a modern, secure, non-contact method for the rational land use monitoring and natural resources extracted by open-pit mining study.
Synchronous Weight Quantization-Compression for Low-Bit Quantized Neural Network

Deep neural networks (DNNs) usually have multiple layers and thousands of trainable parameters to ensure high accuracy. Due to the requirement of large amounts of computation and memory, these networks are not suitable for real-time and resource-constrained mobile or embedded systems. Various techniques such as network pruning, weight sharing, network quantization, and weight encoding have been proposed to improve computational and memory efficiency. This paper presents a synchronous weight quantization-compression (SWQC) technique to compress the weights of low-bit quantized neural network (QNN). Specifically, it quantizes the weights not strictly according to their values but based on compression efficiency and their probabilities of being different quantized results. In the process of weight quantization, the compression efficiency of weights is considered as an important factor. With the help of retraining, a high compression rate and accuracy can be achieved. Verification is performed on 4-bit QNNs using the MNIST and CIFAR10 datasets. Results show that no classification accuracy is lost when the compression rate approaches 5.4X and 4.4X for the two datasets, respectively. The compression rate of the MNIST experiment is increased to 12.1X with a 1% accuracy drop, while the CIFAR10 experiment achieves a compression rate of 5.6X with the accuracy drop of about 0.6%.
Predicting Appearance of Vehicles From Blind Spots Based on Pedestrian Behaviors at Crossroads

Conventional prediction approaches for traffic scenes primarily predict the future states of visible objects ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., not in blind spots) based on their current observations. This study focused on the prediction of future states of objects in blind spots ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., those outside the filed-of-view or occluded regions) based on the current observations of other visible objects. We proposed a method that predicts the appearance of vehicles from a blind spot based on the behaviors of visible pedestrians who observe vehicles in the blind spot. Our proposed method utilizes a spatiotemporal 3D convolutional neural network and learns pedestrian behaviors for predictions. The method explicitly represents subtle motions and the surrounding environments of pedestrians using pose estimation and semantic segmentation. To conduct evaluation experiments, we built two datasets of videos capturing real traffic scenes. The datasets are collected by cameras with and without ego-motions. Using the datasets, we conducted experiments not only on simpler configurations but also on realistic traffic environments. Based on the experimental results, the following conclusions could be obtained: (i) our proposed method achieved a high performance at a level similar to that of humans in our prediction task, and predicted the appearance of vehicles from blind spots more than 1.5 s before they actually appeared. (ii) Explicit representations of pose and semantic masks captured information complementary to RGB videos, and ensembling the representations improved the prediction performance. (iii) Fine-tuning the models using videos with ego-motions is important to achieve good prediction in the videos captured by driving cars.
Diverse Generation from a Single Video Made Possible

GANs are able to perform generation and manipulation tasks, trained on a single video. However, these single video GANs require unreasonable amount of time to train on a single video, rendering them almost impractical. In this paper we question the necessity of a GAN for generation from a single video, and introduce a non-parametric baseline for a variety of generation and manipulation tasks. We revive classical space-time patches-nearest-neighbors approaches and adapt them to a scalable unconditional generative model, without any learning. This simple baseline surprisingly outperforms single-video GANs in visual quality and realism (confirmed by quantitative and qualitative evaluations), and is disproportionately faster (runtime reduced from several days to seconds). Other than diverse video generation, we demonstrate other applications using the same framework, including video analogies and spatio-temporal retargeting. Our proposed approach is easily scaled to Full-HD videos. These observations show that the classical approaches, if adapted correctly, significantly outperform heavy deep learning machinery for these tasks. This sets a new baseline for single-video generation and manipulation tasks, and no less important – makes diverse generation from a single video practically possible for the first time.
Inside the Binary Reflected Gray Code: Flip-Swap Languages in 2-Gray Code Order

A flip-swap language is a set \(\mathbf{S}\) of binary strings of length n such that \(\mathbf{S} \cup \{0^n\}\) is closed under two operations (when applicable): (1) Flip the leftmost 1; and (2) Swap the leftmost 1 with the bit to its right. Flip-swap languages model many combinatorial objects including necklaces, Lyndon words, prefix normal words, left factors of k-ary Dyck words, and feasible solutions to 0-1 knapsack problems. We prove that any flip-swap language forms a cyclic 2-Gray code when listed in binary reflected Gray code (BRGC) order. Furthermore, a generic successor rule computes the next string when provided with a membership tester. The rule generates each string in the aforementioned flip-swap languages in O(n)-amortized per string, except for prefix normal words of length n which require \(O(n^{1.864})\)-amortized per string. Our work generalizes results on necklaces and Lyndon words by Vajnovski [Inf. Process. Lett. 106(3):96−99, 2008].
User Identification across Social Networking Sites using User Profiles and Posting Patterns

With the prevalence of online social networking sites (OSNs) and mobile devices, people are increasingly reliant on a variety of OSNs for keeping in touch with family and friends, and using it as a source of information. For example, a user might utilise multiple OSNs for different purposes, such as using Flickr to share holiday pictures with family and friends, and Twitter to post short messages about their thoughts. Identifying the same user across multiple OSNs is an important task as this allows us to understand the usage patterns of users among different OSNs, make recommendations when a user registers for a new OSN, and various other useful applications. To address this problem, we proposed an algorithm based on the multilayer perceptron using various types of features, namely: (i) user profile, such as name, location, description; (ii) temporal distribution of user generated content; and (iii) embedding based on user name, real name and description. Using a Twitter and Flickr dataset of users and their posting activities, we perform an empirical study on how these features affect the performance of user identification across the two OSN s and discuss our main findings based on the different features.
Deep learning-based anomalous object detection system for panoramic cameras managed by a Jetson TX2 board

Social conflicts appearing in the media are increasing public awareness about security issues, resulting in a higher demand of more exhaustive environment monitoring methods. Automatic video surveillance systems are a powerful assistance to public and private security agents. Since the arrival of deep learning, object detection and classification systems have experienced a large improvement in both accuracy and versatility. However, deep learning-based object detection and classification systems often require expensive GPU-based hardware to work properly. This paper presents a novel deep learning-based foreground anomalous object detection system for video streams supplied by panoramic cameras, specially designed to build power efficient video surveillance systems. The system optimises the process of searching for anomalous objects through a new potential detection generator managed by three different multivariant homoscedastic distributions. Experimental results obtained after its deployment in a Jetson TX2 board attest the good performance of the system, postulating it as a solvent approach to power saving video surveillance systems.
Ten best practices to strengthen stewardship and sharing of water science data in Canada

Abstract Water science data are a valuable asset that both underpins the original research project and bolsters new research questions, particularly in view of the increasingly complex water issues facing Canada and the world. Whilst there is general support for making data more broadly accessible, and a number of water science journals and funding agencies have adopted policies that require researchers to share data in accordance with the findable, accessible, interoperable, reusable (FAIR) principles, there are still questions about effective management of data to protect their usefulness over time. Incorporating data management practices and standards at the outset of a water science research project will enable researchers to efficiently locate, analyse and use data throughout the project lifecycle, and will ensure the data maintain their value after the project has ended. Here, some common misconceptions about data management are highlighted, along with insights and practical advice to assist established and early career water science researchers as they integrate data management best practices and tools into their research. Freely available tools and training opportunities made available in Canada through Global Water Futures, The Gordon Foundation DataStream, the Digital Research Alliance of Canada Portage Network, Compute Canada, and university libraries, among others are compiled. These include webinars, training videos, and individual support for the water science community that together enable researchers to protect their data assets and meet the expectations of journals and funders. The perspectives shared here have been developed as part of the Global Water Futures programme's efforts to improve data management and promote the use of common data practices and standards in the context of water science in Canada. Ten best practices are proposed that may be broadly applicable to other disciplines in the natural sciences and can be adopted and adapted globally.
Exploring Label Correlations for Partitioning the Label Space in Multi-label Classification

Recent works on Multi-Label Classification (MLC) present multiple strategies to explore label correlations in a way to improve classifiers performances. However, these works focus only in the traditional local and global approaches, i.e., transforming the original problem into a set of binary local problems, or dealing globally with all classes simultaneously. Very few works have investigated strategies to use label correlations in order to partition the label space in a different ways. While in local partitions several binary classifiers are used (one per label), global partitions use only one classifier to deal with all labels. On the contrary, here we propose a strategy that explores the correlations between labels to partition the label space aiming to find partitions in-between (hybrid) the local and global ones. We believe in-between local and global partitions better cluster similar labels, improving the multi-label classifiers ability to explore label correlations. We compared the hybrid partitions with global, local and random generated partitions. Our experimental results showed that the hybrid partitions lead to competitive results and, in general, were slightly better than global and local partitions. The random partitions were also competitive with the global and local partitions, showing that the current local and global approaches still need improvements in order to consider label correlations.
Protection of Personal Data Using Anonymization

Data quality and security issues are very closely related. To ensure a high level of reliability in distributed systems and resilience from external attacks, the process of consolidating distributed data is critical. For consolidated systems, the access process relies heavily on data preprocessing, which, in turn, allows them to be anonymized. The analysis of closely related processes of consolidation and anonymization allows us to offer a secure access platform for distributed data, which makes it possible to implement secure access systems that depend only on the type and format of the data. It turns out that in the program stack for working with data, optimization can be done only with the entire framework, but not with its components. In this paper we perform analysis of data security as a complex problem related to both data quality and system architectures used to protect personal data.
Impact of Labeling Schemes on Dense Crowd Counting Using Convolutional Neural Networks with Multiscale Upsampling

Gatherings of thousands to millions of people frequently occur for an enormous variety of educational, social, sporting, and political events, and automated counting of these high-density crowds is useful for safety, management, and measuring significance of an event. In this work, we show that the regularly accepted labeling scheme of crowd density maps for training deep neural networks may not be the most effective one. We propose an alternative inverse k-nearest neighbor (i[Formula: see text]NN) map mechanism that, even when used directly in existing state-of-the-art network structures, shows superior performance. We also provide new network architecture mechanisms that we demonstrate in our own MUD-i[Formula: see text]NN network architecture, which uses multi-scale drop-in replacement upsampling via transposed convolutions to take full advantage of the provided i[Formula: see text]NN labeling. This upsampling combined with the i[Formula: see text]NN maps further improves crowd counting accuracy. We further analyze several variations of the i[Formula: see text]NN labeling mechanism, which apply transformations on the [Formula: see text]NN measure before generating the map, in order to consider the impact of camera perspective views, image resolutions, and the changing rates of the mapping functions. To alleviate the effects of crowd density changes in each image, we also introduce an attenuation mechanism in the i[Formula: see text]NN mapping. Experimentally, we show that inverse square root [Formula: see text]NN map variation (iR[Formula: see text]NN) provides the best performance. Discussions are provided on computational complexity, label resolutions, the gains in mapping and upsampling, and details of critical cases such as various crowd counts, uneven crowd densities, and crowd occlusions.
Salience-CAM: Visual Explanations from Convolutional Neural Networks via Salience Score

In recent years, Convolutional Neural Networks (CNN s) have been widely applied in various applications due to its powerful learning capability. However, its lack of explainability hinders its further usage in tasks requiring high reliability. Therefore, interpretability technique is the key to the application and deployment of CNN models. As a typical interpretability technique for CNN, Class Activation Map (CAM) utilizing the gradient based weights and activation map is widely applied to traditional CNN models for offering visual interpretability. However, the activation map adopted by CAM cannot loyally quantify the relevance between input samples and activation values. Hence, in this paper, we propose a new interpretability approach called Salience-CAM employing salience scores to accurately measure the relevance between input samples and activation values. To evaluate the effectiveness of Salience-CAM, comprehensive experiments are conducted on 6 selected time series datasets. By leveraging an evaluation algorithm proposed in this paper, the experimental results show that our proposed Salience-CAM outperforms the baseline by discovering more discriminative features.
Transferability of graph neural networks: An extended graphon approach

We study spectral graph convolutional neural networks (GCNNs), where filters are defined as continuous functions of the graph shift operator (GSO) through functional calculus. A spectral GCNN is not tailored to one specific graph and can be transferred between different graphs. It is hence important to study the GCNN transferability: the capacity of the network to have approximately the same repercussion on different graphs that represent the same phenomenon. Transferability ensures that GCNNs trained on certain graphs generalize if the graphs in the test set represent the same phenomena as the graphs in the training set. In this paper, we consider a model of transferability based on graphon analysis. Graphons are limit objects of graphs, and, in the graph paradigm, two graphs represent the same phenomenon if both approximate the same graphon. Our main contributions can be summarized as follows: 1) we prove that any fixed GCNN with continuous filters is transferable under graphs that approximate the same graphon, 2) we prove transferability for graphs that approximate unbounded graphon shift operators, which are defined in this paper, and 3) we obtain non-asymptotic approximation results, proving linear stability of GCNNs. This extends current state-of-the-art results which show asymptotic transferability for polynomial filters under graphs that approximate bounded graphons.