Reviewerly/exHarmony huggingface dataset:
Explainable prediction of Qcodes for NOTAMs using column generation

AbstractAbstractA NOtice To AirMen (NOTAM) contains important flight route related information. To search and filter them, NOTAMs are grouped into categories called QCodes. In this paper, we develop a tool to predict, with some explanations, a Qcode for a NOTAM. We present a way to extend the interpretable binary classification using column generation proposed in Dash, Gunluk, and Wei (2018 Dash, S., Gunluk, O., & Wei, D. (2018). Boolean decision rules via column generation. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, & R. Garnett (Eds.), Advances in neural information processing systems. (Vol. 31) Curran Associates, Inc. Retrieved from https://proceedings.neurips.cc/paper/2018/file/743 394beff4b1282ba735e5e3723ed74-Paper.pdf [Google Scholar]) to a multiclass text classification method. We describe the techniques used to tackle the issues related to one-vs-rest classification, such as multiple outputs and class imbalances. Furthermore, we introduce some heuristics, including the use of a CP-SAT solver for the subproblems, to reduce the training time. Finally, we show that our approach compares favorably with state-of-the-art machine learning algorithms like Linear SVM and small neural networks while adding the needed interpretability component.Keywords: Artificial intelligencecombinatorial optimizationpractice of OR AcknowledgementsThe first author is supported by a research grant from the project DEpendable and Explainable Learning (DEEL). We would like to thank Giuliano Antoniol for coordinating between DEEL and Polytechnique Montreal. We thank two anonymous reviewers for their detailed reading and useful comments.Disclosure statementIn accordance with Taylor & Francis policy and his ethical obligation as a researcher, the fourth author (F. Lecue) is reporting that he has a financial and/or business interests in a company that may be affected by the research reported in the enclosed paper. He has disclosed those interests fully to Taylor & Francis, and he has in place an approved plan for managing any potential conflicts arising from that involvement. The other authors declare that they have no competing interest.Availability statementAll data used for our experiments are available at https://github.com/krooonal/NOTAM_explainable_prediction_data.
Classification of Spatial Objects with the Use of Graph Neural Networks

Classification is one of the most-common machine learning tasks. In the field of GIS, deep-neural-network-based classification algorithms are mainly used in the field of remote sensing, for example for image classification. In the case of spatial data in the form of polygons or lines, the representation of the data in the form of a graph enables the use of graph neural networks (GNNs) to classify spatial objects, taking into account their topology. In this article, a method for multi-class classification of spatial objects using GNNs is proposed. The method was compared to two others that are based solely on text classification or text classification and an adjacency matrix. The use case for the developed method was the classification of planning zones in local spatial development plans. The experiments indicated that information about the topology of objects has a significant impact on improving the classification results using GNNs. It is also important to take into account different input parameters, such as the document length, the form of the training data representation, or the network architecture used, in order to optimize the model.
Underwater Optical Image Restoration Method for Natural/Artificial Light

This paper investigates the underwater optical image restoration method under the background of underwater target detection based on optical vision in AUVs. The light source used for AUV detection is different when the AUV operates in different depths. The natural light source is used in shallow water and the artificial light source is used in deep water. This paper investigates underwater optical image restoration in these two light conditions. Aiming at the problem of image blurring in underwater optical images, the traditional underwater image restoration method based on scattering model can obtain satisfactory image restoration performance in natural light conditions. However, it cannot obtain the same image restoration result in artificial light conditions. To solve this problem, this paper presents an improved underwater optical image restoration method based on the scattering model. The scattering model and power spectrum are used to solve the initial parameters of the filter, and the parameters are optimized based on an evaluation index. The index of image definition is introduced to evaluate the restoration performance and to achieve the satisfactory image restoration result in both natural light and artificial light conditions. The effectiveness of the presented method is verified by experiments.
Leveraging Vision Attention Transformers for Detection of Artificially Synthesized Dermoscopic Lesion Deepfakes Using Derm-CGAN

Synthesized multimedia is an open concern that has received much too little attention in the scientific community. In recent years, generative models have been utilized in maneuvering deepfakes in medical imaging modalities. We investigate the synthesized generation and detection of dermoscopic skin lesion images by leveraging the conceptual aspects of Conditional Generative Adversarial Networks and state-of-the-art Vision Transformers (ViT). The Derm-CGAN is architectured for the realistic generation of six different dermoscopic skin lesions. Analysis of the similarity between real and synthesized fakes revealed a high correlation. Further, several ViT variations were investigated to distinguish between actual and fake lesions. The best-performing model achieved an accuracy of 97.18% which has over 7% marginal gain over the second best-performing network. The trade-off of the proposed model compared to other networks, as well as a benchmark face dataset, was critically analyzed in terms of computational complexity. This technology is capable of harming laymen through medical misdiagnosis or insurance scams. Further research in this domain would be able to assist physicians and the general public in countering and resisting deepfake threats.
Designing Digital COVID-19 Screening: Insights and Deliberations

Due to the global COVID-19 pandemic, public health control and screening measures have been introduced at healthcare facilities, including those housing our most vulnerable populations. These warning measures situated at hospital entrances are presently labour-intensive, requiring additional staff to conduct manual temperature checks and risk-assessment questionnaires of every individual entering the premises. To make this process more efficient, we present eGate, a digital COVID-19 health-screening smart Internet of Things system deployed at multiple entry points around a children's hospital. This paper reports on design insights based on the experiences of concierge screening staff stationed alongside the eGate system. Our work contributes towards social-technical deliberations on how to improve design and deploy of digital health-screening systems in hospitals. It specifically outlines a series of design recommendations for future health screening interventions, key considerations relevant to digital screening control systems and their implementation, and the plausible effects on the staff who work alongside them.
Detection of DoH Traffic Tunnels Using Deep Learning for Encrypted Traffic Classification

Currently, the primary concerns on the Internet are security and privacy, particularly in encrypted communications to prevent snooping and modification of Domain Name System (DNS) data by hackers who may attack using the HTTP protocol to gain illegal access to the information. DNS over HTTPS (DoH) is the new protocol that has made remarkable progress in encrypting Domain Name System traffic to prevent modifying DNS traffic and spying. To alleviate these challenges, this study explored the detection of DoH traffic tunnels of encrypted traffic, with the aim to determine the gained information through the use of HTTP. To implement the proposed work, state-of-the-art machine learning algorithms were used including Random Forest (RF), Gaussian Naive Bayes (GNB), Logistic Regression (LR), k-Nearest Neighbor (KNN), the Support Vector Classifier (SVC), Linear Discriminant Analysis (LDA), Decision Tree (DT), Adaboost, Gradient Boost (SGD), and LSTM neural networks. Moreover, ensemble models consisting of multiple base classifiers were utilized to carry out a series of experiments and conduct a comparative study. The CIRA-CIC-DoHBrw2020 dataset was used for experimentation. The experimental findings showed that the detection accuracy of the stacking model for binary classification was 99.99%. In the multiclass classification, the gradient boosting model scored maximum values of 90.71%, 90.71%, 90.87%, and 91.18% in Accuracy, Recall, Precision, and AUC. Moreover, the micro average ROC curve for the LSTM model scored 98%.
Deep causal learning for robotic intelligence

This invited Review discusses causal learning in the context of robotic intelligence. The Review introduces the psychological findings on causal learning in human cognition, as well as the traditional statistical solutions for causal discovery and causal inference. Additionally, we examine recent deep causal learning algorithms, with a focus on their architectures and the benefits of using deep nets, and discuss the gap between deep causal learning and the needs of robotic intelligence.
Handwritten Digits Recognition Based on a Parallel Optoelectronic Time-Delay Reservoir Computing System

In this work, the performance of an optoelectronic time-delay reservoir computing system for performing a handwritten digit recognition task is numerically investigated, and a scheme to improve the recognition speed using multiple parallel reservoirs is proposed. By comparing four image injection methods based on a single time-delay reservoir, we find that when injecting the histograms of oriented gradient (HOG) features of the digit image, the accuracy rate (AR) is relatively high and is less affected by the offset phase. To improve the recognition speed, we construct a parallel time-delay reservoir system including multi-reservoirs, where each reservoir processes part of the HOG features of one image. Based on 6 parallel reservoirs with each reservoir possessing 100 virtual nodes, the AR can reach about 97.8%, and the reservoir processing speed can reach about 1 × 106 digits per second. Meanwhile, the parallel reservoir system shows strong robustness to the parameter mismatch between multi-reservoirs.
Mapping the Landscape of Blockchain Technology Knowledge: A Patent Co-Citation and Semantic Similarity Approach

The potential applications of blockchain technology across various business functions and industries have generated significant interest. However, its underlying knowledge structure remains unclear. This study aimed to gain a deeper understanding of the technological domain and knowledge structure of blockchain technology by analyzing 4753 USPTO patent data from 2008 to 2019. We used multiple approaches, such as analyzing patent filing volumes, constructing co-citation networks, and examining text (patent abstract) data with a variant of bidirectional encoder representations from transformers (BERT). The results demonstrate the advantages of using an NLP-based BERT text analysis approach for examining technological knowledge and relationships within the blockchain technology field. Our findings reveal that the field of blockchain technology is expanding and diversifying, with increasing patent filings in both cryptocurrency and distributed ledger technologies and growing knowledge similarity between these two subdomains. We also found that patent assignees (companies) engage differently in innovative activities within the blockchain technology domain based on their prior experience in the field. These results hold potential for informing future research in emerging technology studies and guiding industry and policy decisions related to blockchain technology.
A network of allostructions: quantified subject constructions in Russian

Abstract This article contributes to Construction Grammar, historical linguistics, and Russian linguistics through an in-depth corpus study of predicate agreement in constructions with quantified subjects. Statistical analysis of approximately 39,000 corpus examples indicates that these constructions constitute a network of constructions (“allostructions”) with various preferences for singular or plural agreement. Factors pull in different directions, and we observe a relatively stable situation in the face of variation. We present an analysis of a multidimensional network of allostructions in Russian, thus contributing to our understanding of allostructional relationships in Construction Grammar. With regard to historical linguistics, language stability is an understudied field. We illustrate an interplay of divergent factors that apparently resists language change. The syntax of numerals and other quantifiers represents a notoriously complex phenomenon of the Russian language. Our study sheds new light on the contributions of factors that favor singular or plural agreement in sentences with quantified subjects.
A chaos opposition‐based dwarf mongoose approach for workflow scheduling in cloud

Abstract Cloud computing (CC) systems are a form of public infrastructure that has been in use since its beginnings. In such technologies, clients can access current services according to their requirements without knowing where the service is hosted or how it is supplied, and only pay for the services that they really consume. The CC system faces several challenges. Due to the large range of clients and services offered by this platform, it can be concluded that the scheduling problem and resource usage are its primary issues. In order to successfully balance the three main qualities of service (QoS) criteria: time, cost, and resource usage, researchers were inspired to improve the workflow scheduling (WFS) methods in cloud. Since the randomness of the initial population and the inadequate exploration/exploitation capacity, which has an impact on the quality of the solutions, traditional intelligent optimization algorithms have a poor convergence rate when it comes to resource scheduling. In this research, we suggested a hybridization method combining the Dwarf Mongoose algorithm (DWO) and Chaos opposition based learning algorithm called CO‐DWO in an attempt to develop a more effective optimization algorithm for workflow scheduling issue. The CO‐DWO can be much more advantageous in optimization with a better convergence rate in comparison with other literature techniques.
mHealth Systems Need a Privacy-by-Design Approach: Commentary on “Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review”

Brauneck and colleagues have combined technical and legal perspectives in their timely and valuable paper "Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review." Researchers who design mobile health (mHealth) systems must adopt the same privacy-by-design approach that privacy regulations (eg, General Data Protection Regulation) do. In order to do this successfully, we will have to overcome implementation challenges in privacy-enhancing technologies such as differential privacy. We will also have to pay close attention to emerging technologies such as private synthetic data generation.
Intelligent identification technology for high‐order digital modulation signals under low signal‐to‐noise ratio conditions

Based on the successful application of generative adversarial network (GAN) models in the field of image generation, this article introduces GANs into the field of deep learning for communication systems and surveys its application in modulation classification. To solve the difficulties in feature extraction, to address the low recognition accuracy of existing radio signal modulation-type recognition methods, and to adapt to complex electromagnetic environments with high noise interference intensity, this article presents a modulation recognition model for high-order digital signals. This model uses the Morlet wavelet transform to analyse time-frequency signals, uses the excellent image generation performance of a GAN model to extract and reconstruct the features of noise-contaminated time-frequency images, and designs an integrated classification network architecture to classify and predict reconstructed images. The experimental results show that the algorithm model proposed in this article can significantly improve the recognition accuracy of high-order digital modulated signals under low signal-to-noise ratio conditions and can achieve 90% recognition accuracy at a signal-to-noise ratio of 1 dB.
Robust Human Matting via Semantic Guidance

Automatic human matting is highly desired for many real applications. We investigate recent human matting methods and show that common bad cases happen when semantic human segmentation fails. This indicates that semantic understanding is crucial for robust human matting. From this, we develop a fast yet accurate human matting framework, named Semantic Guided Human Matting (SGHM). It builds on a semantic human segmentation network and introduces a light-weight matting module with only marginal computational cost. Unlike previous works, our framework is data efficient, which requires a small amount of matting ground-truth to learn to estimate high quality object mattes. Our experiments show that trained with merely 200 matting images, our method can generalize well to real-world datasets, and outperform recent methods on multiple benchmarks, while remaining efficient. Considering the unbearable labeling cost of matting data and widely available segmentation data, our method becomes a practical and effective solution for the task of human matting. Source code is available at https://github.com/cxgincsu/SemanticGuidedHumanMatting .
What Role Does Data Augmentation Play in Knowledge Distillation?

Knowledge distillation is an effective way to transfer knowledge from a large model to a small model, which can significantly improve the performance of the small model. In recent years, some contrastive learning-based knowledge distillation methods (i.e., SSKD and HSAKD) have achieved excellent performance by utilizing data augmentation. However, the worth of data augmentation has always been overlooked by researchers in knowledge distillation, and no work analyzes its role in particular detail. To fix this gap, we analyze the effect of data augmentation on knowledge distillation from a multi-sided perspective. In particular, we demonstrate the following properties of data augmentation: (a) data augmentation can effectively help knowledge distillation work even if the teacher model does not have the information about augmented samples, and our proposed diverse and rich Joint Data Augmentation (JDA) is more valid than single rotating in knowledge distillation; (b) using diverse and rich augmented samples to assist the teacher model in training can improve its performance, but not the performance of the student model; (c) the student model can achieve excellent performance when the proportion of augmented samples is within a suitable range; (d) data augmentation enables knowledge distillation to work better in a few-shot scenario; (e) data augmentation is seamlessly compatible with some knowledge distillation methods and can potentially further improve their performance. Enlightened by the above analysis, we propose a method named Cosine Confidence Distillation (CCD) to transfer the augmented samples' knowledge more reasonably. And CCD achieves better performance than the latest SOTA HSAKD with fewer storage requirements on CIFAR-100 and ImageNet-1k. Our code is released at https://github.com/liwei-group/CCD .
Optimizing Multi-variable Time Series Forecasting Using Metaheuristics

Multi-variable time series forecasting is one of several applications of machine learning. Creating an artificial environment capable of replicating real-world behavior is useful for understanding the intrinsic relationship between variables. However, selecting a predictor that ensures good performance for variables of different natures is not always a simple process. An algorithmic approach based on metaheuristics could be a good alternative to find the best predictive model for variables. Each predictor is optimized for forecasting a particular variable in a multi-agent artificial environment to improve the overall performance. The resulting environment is compared with other solutions that use only the same type of predictor for each variable. Finally, we can assert that using a multi-agent environment can improve the performance, accuracy, and generalization of our model.
RDRN: Recursively Defined Residual Network for Image Super-Resolution

Deep convolutional neural networks (CNNs) have obtained remarkable performance in single image super-resolution (SISR). However, very deep networks can suffer from training difficulty and hardly achieve further performance gain. There are two main trends to solve that problem: improving the network architecture for better propagation of features through large number of layers and designing an attention mechanism for selecting most informative features. Recent SISR solutions propose advanced attention and self-attention mechanisms. However, constructing a network to use an attention block in the most efficient way is a challenging problem. To address this issue, we propose a general recursively defined residual block (RDRB) for better feature extraction and propagation through network layers. Based on RDRB we designed recursively defined residual network (RDRN), a novel network architecture which utilizes attention blocks efficiently. Extensive experiments show that the proposed model achieves state-of-the-art results on several popular super-resolution benchmarks and outperforms previous methods by up to 0.43 dB.
Depth Estimation via Sparse Radar Prior and Driving Scene Semantics

Depth estimation is an essential module for the perception system of autonomous driving. The state-of-the-art methods introduce LiDAR to improve the performance of monocular depth estimation, but it faces the challenges of weather durability and high hardware cost. Unlike existing LiDAR and image-based methods, a two-stage network is proposed to integrate highly sparse radar data in this paper, in which sparse pre-mapping module and feature fusion module are proposed for radar feature extraction and feature fusion respectively. Considering the highly structured driving scenario, we introduce semantic information of the scenario to further improve the loss function, thus making the network more focused on the target region. Finally, we propose a novel depth dataset construction strategy by integrating binary mask-based filtering and interpolation methods based on the nuScenes dataset. And the effectiveness of our proposed method has been demonstrated through extensive experiments, which outperform existing methods in all metrics.