Reviewerly/exHarmony huggingface dataset:
Optimizing and Evaluating Swin Transformer for Aircraft Classification: Analysis and Generalizability of the MTARSI Dataset

Aircraft classification via remote sensing images has many commercial and military applications.The Swin-Transformer has shown great promise, recently dominating general purpose image classification benchmarks such as ImageNet.In this manuscript, we test whether the performance of Swin-Transformer on general purpose image classification translate to domain specific aircraft classification using the Multi-Type Aircraft from Remote Sensing Images dataset.We also investigate the effect of training procedure vs. model selection on the validation score.Our carefully trained Swin-Transformer model achieved an impressive 99.4 % validation set accuracy without super-resolution, and 99.5 % with super-resolution.Moreover, the generalization of models trained on the MTARSI dataset to real-world and synthetic aircraft classification is evaluated with some out-of-distribution samples.Our results demonstrated that the lack of complexity and heterogeneity of the MTARSI dataset, and the labelling errors resulted in models which struggle to achieve high accuracy on the adopted test samples despite near perfect validation scores.
Category-Oriented Adversarial Data Augmentation via Statistic Similarity for Satellite Images

Deep learning is one of the essential technologies for remote sensing tasks, which heavily depends on the quantity of training data. However, it is difficult to obtain or label the remotely sensed images in their non-cooperative imaging mode. Data augmentation is a viable solution to this issue, but most of the current data augmentation methods are task specific or dataset specific, which are not as applicable as a generalized solution for the remotely sensed images. In this paper, we propose a category-oriented adversarial data augmentation method using statistic similarity cross categories, which formulates the common appearance-based statistic factors in the object detection into a combination index, to depict the statistic similarity between different categories and to generate new adversarial samples between similar categories with more reliable physical significance. Experimental results demonstrated that, taking the most advanced RT method as a baseline, the total mAP can be increased by 2.0% on the DOTA dataset for the object detection task by using our proposed method.
Hybrid extreme learning machine optimized bat algorithm based on ensemble empirical mode decomposition for modeling dissolved oxygen in river

In the present chapter, we use the empirical mode decomposition (EMD), the ensemble EMD (EEMD), and the complete ensemble EMD with adaptive noise (CEEMDAN) for dissolved oxygen (DO) prediction. First, based on water temperature (Tw), DO was modeled using three machines learning models, namely, extreme learning machine (ELM), the ELM optimized Bat algorithm (Bat-ELM), and relevance vector machine (RVM). Second, river Tw was decomposed using EMD, EEMD, and CEEMDAN into several intrinsic mode functions (IMF), which were used as input to the ELM, Bat-ELM, and RVM. The performances of the models were evaluated using the root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), and the Nash-Sutcliffe efficiency (NSE). From the obtained results, the models based on EMD, EEMD, and CEEMDAN estimated DO highly more accurate than the single models, with mean RMSE, MAE, R, and NSE of 0.835°C, 0.571°C, 0.965, and 0.930 against the values of 2.788°C, 2.232°C, 0.511, and 0.250, respectively.
Promoting online interactions of foreign language learners in a computer-mediated learning environment

Practical mastering of a foreign language by university students in the distance learning environment is associated with the search for ways and means of adequate teaching and learning interaction patterns. The present study concentrates on the problem of teaching foreign languages using information and communication technologies. The focus is made on the possibilities of using various e-learning resources and services to develop students’ communicative competence. Based on the survey conducted among teachers and students, the authors explore how to organize effective online interactions of foreign language learners and outline the following components of those interactions: lingvo-cognitive, information, communicative, perceptual, and intercultural ones. The potential of e-learning resources is evaluated to promote the cognitive and social involvement of foreign language learners in meaningful oral and written online interactions. The paper presents the extended and diversified range of distance learning tools that have proven effective for online classes.
Community Smell Detection and Refactoring in SLACK: The CADOCS Project

Software engineering is a human-centered activity involving various stakeholders with different backgrounds that have to communicate and collaborate to reach shared objectives. The emergence of conflicts among stakeholders may lead to undesired effects on software maintainability, yet it is often unavoidable in the long run. Community smells, i.e., sub-optimal communication and collaboration practices, have been defined to map recurrent conflicts among developers. While some community smell detection tools have been proposed in the recent past, these can be mainly used for research purposes because of their limited level of usability and user engagement. To facilitate a wider use of community smell-related information by practitioners, we present CADOCS, a client-server conversational agent that builds on top of a previous community smell detection tool proposed by Almarini et al. to (1) make it usable within a well-established communication channel like Slack and (2) augment it by providing initial support to software analytics instruments useful to diagnose and refactor community smells. We describe the features of the tool and the preliminary evaluation conducted to assess and improve robustness and usability.
Learning High Dimensional Multi-response Linear Models with Non-oracular Quantum Search

This paper studies linear regression models for high dimensional multi-response data with a hybrid quantum computing algorithm. We propose an intuitively appealing estimation method based on identifying the linearly independent columns in the coefficient matrix. Our method relaxes the low rank constraint in the existing literature and allows the rank to diverge with dimensions. The linearly independent columns are selected by a novel non-oracular quantum search (NQS) algorithm which is significantly faster than classical search methods implemented on electronic computers. Besides, NQS achieves a near optimal computational complexity as existing quantum search algorithms but does not require any oracle information of the solution state. We prove the proposed estimation procedure enjoys desirable theoretical properties. Intensive numerical experiments are also conducted to demonstrate the finite sample performance of the proposed method, and a comparison is made with some popular competitors. The results show that our method outperforms all of the alternative methods under various circumstances.
Automatic State Matching Gaussian Process Ensemble for Wood Planer Control

Wood planers are high speed sophisticated lumber finishing machines that are difficult to operate and for which the available data shows complex, non-linear patterns. We present a machine learning approach to build a control loop for an industrial wood planer. In order to predict the thickness of the outgoing boards with better accuracy than the industry standard whilst allowing dynamic planer adjustments, we use an ensemble of Gaussian Processes with a specialized weighting scheme we call Automatic State Matching. It reduces the prediction error by 39% compared to current industrial practice.
Improving 3D Markerless Pose Estimation of Animals in the Wild using Low-Cost Cameras

Tracking the 3D motion of agile animals in the wild will enable new insight into the design of robotic controllers. However, in-field 3D pose estimation of high-speed wildlife such as cheetahs is still a challenge [1]. In this work, we aim to solve two of these challenges: unnatural pose estimates during highly occluded sequences and synchronization error between multi-view data. We expand on our previous Full Trajectory Estimation (FTE) method with two significant additions: Pairwise FTE (PW-FTE) and Shutter-delay FTE (SD-FTE). The PW-FTE expands on image-dependent pairwise terms, produced by a convolutional neural network (CNN), to infer occluded 2D keypoints, while SD-FTE uses shutter delay estimation to correct the synchronization error. Lastly, we combine both methods into PW-SD-FTE and perform a quantitative and qualitative analysis on a subset of AcinoSet, the video dataset of rapid and agile motions of cheetahs. We found that SD-FTE has significant benefits in tracking the position of the cheetah in the world frame, while PW-FTE provided a more robust 3D pose estimate during events of high occlusion. The PW-SD-FTE was found to retain both advantages, resulting in an improved baseline for AcinoSet. Code and data can be found at https://github.com/African-Robotics-Unit/AcinoSet/tree/pw_sd_fte.
E-LSTM: An extension to the LSTM architecture for incorporating long lag dependencies

The Long Short-Term Memory (LSTM) architecture is one of the most successful types of Recurrent Neural Networks (RNNs). However, the number of parameters that LSTMs need to achieve acceptable performance might be larger than desired for standard devices. In this work, an Extended LSTM (E-LSTM) architecture is proposed to reduce the number of parameters needed to achieve similar performance to LSTMs. The architecture of the proposed E-LSTM is characterized by higher and explicit connectivity between distant past and current cell states' values, increasing the likelihood of a higher information transmission between data points separated by long lags. Analysis of possible nonlinear relations in the data sets aids in the process of determining an appropriate placement of increased connectivity, performed using the Distance Correlation (DC) method. The proposed E-LSTM reduces the number of parameters needed, by an order of magnitude for some cases, with acceptable increases in both CPU time and memory needed for the training process.
A Fuzzy OWL Ontologies Embedding for Complex Ontology Alignments

The semantic heterogeneity concern in the information integration can be handled by applying ontology alignment. The purpose of the ontology alignment procedure is to locate concepts that are semantically identical in two ontologies. But, one of these alignments’ downsides is the lack of expressiveness and uncertainties, which can be accounted by using fuzzy complex alignments. To address this issue, the use of an effective strategy, consisting of two parts, is applied. We proceeded by establishing of a fuzzification approach that enables a semantic representation of both crisp and fuzzy data. The next step was to model fuzzy OWL 2 ontologies in vector space by a semantic embedding-based ontology matching technique and compute their similarity scores to determine the correlation levels. Then, it is reinforced by a stable marriage-based alignment extraction algorithm to establish a high-quality matching. Our proposed alignment scheme has been validated and reviewed on the benchmark tracks supplied by the Ontology Alignment Evaluation Initiative (OAEI). Experimental findings demonstrated the effectiveness of our matching method.
Multiple-Perspective Caption Generation with Initial Attention Weights

Caption generation is one of multimodal learning tasks and deep learning contributes to the improvement of the caption generation. However, usual caption generation systems receive images and generate their captions automatically. So, it depends on the system whether the captions are generated based on where parts of the image are focused. Human captioners can generate multiple captions based on different focusing points of the image but the system generates only a single caption from the image. To overcome this problem, we pay attention to the initial attention weights, which denote the focusing points of the image. We propose a multiple-perspective caption generation system with the initial attention weights to control captions via the attention mechanism. Moreover, we try to generate the initial attention weights automatically to use the general corpus for object detection and caption generation. First, we determine focusing points from detected object positions and connect the detected object with captions by comparing the object tag with the subject of the caption. Second, we develop the multiple-perspective caption generation system with attention mechanism and control the subject of a caption using the initial attention weights. In experiments, we generated the initial attention weights from MS COCO dataset and the accuracy of the initial attention achieved 94.3% with human judgment. Moreover, we trained the proposed system with MS COCO dataset including the initial attention weights and confirmed the proposed system was able to generate multiple-perspective captions with different subject words based on the initial attention weights.
Cannot See the Forest for the Trees: Aggregating Multiple Viewpoints to Better Classify Objects in Videos

Recently, both long-tailed recognition and object tracking have made great advances individually. TAO benchmark presented a mixture of the two, long-tailed object tracking, in order to further reflect the aspect of the real-world. To date, existing solutions have adopted detectors showing robustness in long-tailed distributions, which derive per-frame results. Then, they used tracking algorithms that combine the temporally independent detections to finalize tracklets. However, as the approaches did not take temporal changes in scenes into account, inconsistent classification results in videos led to low overall performance. In this paper, we present a set classifier that improves accuracy of classifying tracklets by aggregating information from multiple viewpoints contained in a tracklet. To cope with sparse annotations in videos, we further propose augmentation of tracklets that can maximize data efficiency. The set classifier is plug-and-playable to existing object trackers, and highly improves the performance of long-tailed object tracking. By simply attaching our method to QDTrack on top of ResNet-101, we achieve the new state-of-the-art, 19.9% and 15.7% <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$TrackAP_{50}$</tex> on TAO validation and test sets, respectively. Our code is available at this link <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/sukjunhwang/setclassifier.
Label Pair of Instances-Based Safe Screening for Multilabel Rank Support Vector Machine

Rank support vector machine (RSVM) is widely used in multilabel classification problems. However, as the number of labels and instances soars, the training efficiency of the model will be greatly reduced. Unfortunately, few effective methods can solve this problem. In this article, we propose a safe screening rule (SSR) for RSVM to improve its training speed. This is the first attempt to construct SSR for multilabel learning problems. SSR for RSVM can screen and delete most of the instances based on their relevant–irrelevant label pairs, which is the biggest difference in the existing SSR. After this process, the scale of RSVM can be substantially reduced before solving it. The sequential version of SSR for RSVM is further introduced to accelerate the whole parameter tuning process. One important advantage of SSR is that it is safe, which means we can obtain the same optimal solution as the original problem by utilizing it. Extensive experiments with five benchmark datasets, three large-scale datasets, and one type 2 diabetes dataset show that our approach is efficient and safe.
Profiler: Distributed Model to Detect Phishing

Many Machine Learning (ML) based phishing detection algorithms are not adept to recognise "concept drift"; attackers introduce small changes in the statistical characteristics of their phishing attempts to successfully bypass detection. This leads to the classification problem of frequent false positives and false negatives, and a reliance on manual reporting of phishing by users. Profiler is a distributed phishing risk assessment tool that combines three email profiling dimensions: (1) threat level, (2) cognitive manipulation, and (3) email content type to detect email phishing. Unlike pure ML-based approaches, Profiler does not require large data sets to be effective and evaluations on real-world data sets show that it can be useful in conjunction with ML algorithms to mitigate the impact of concept drift.
Vehicle Detection Based on YOLOv3 in Adverse Weather Conditions

The target detection algorithm is mainly affected by multiple factors such as illumination, clarity, overlap, target size, detection accuracy, and speed decrease greatly in adverse weather conditions. In order to solve these problems, we test the detection performance of YOLOv3 for pedestrians and vehicles with fogged photos. The fogging method is based on the airlight model. The results are shown that the YOLOv3 algorithm can be used for pedestrian and vehicle detection in haze environments, which may be a useful guideline for developing and improving related traffic safety detection systems.
HD-Net: Hierarchical Distillation Network for High-Efficiency Single Image Deraining

Rain streaks usually result in severe image visual degradation and foreground occlusion, affecting the quality of computer tasks in outdoor scenes. Currently, the mainstream methods in single-image deraining are based on data-driven. However, the deep learning network could be imperfect, with limited power for learning the global information from rain streaks all over the map. In order to solve this problem, we proposed a novel Hierarchical Distillation Network (HD-Net). In this network, Hierarchical Feature Extraction Block (HFEB) can fully utilize the Transformer's learning ability in high-level features, integrate local detail extraction and global structure representation, and compensate for the weakness of the Convolutional Neural Network (CNN), which is overattentive to the underlying image features. Furthermore, the Distillation-Calibration Block (DCB) are adopted to avoid feature redundancy during model training and calibrate the channel and spatial information through the feature transmission, which could significantly improve the learning efficiency. Finally, the experiment results show that our model performs better than traditional CNN models and state-of-the-art methods.
Deepsatdata: Building Large Scale Datasets of Satellite Images for Training Machine Learning Models

This paper presents DeepSatData a free and open source pipeline for automatically generating satellite imagery datasets for training machine learning models. The implementation presented can be used to query, download and process freely available Sentinel-2 data for the generation of large scale datasets required for training deep neural networks (DNN). We discuss design considerations faced from the point of view of DNN training and evaluation such as checking the quality of ground truth data and assessing the scalability of the approach. Accompanying code is made publicly available in https://github.com/michaeltrs/DeepSatData.
Defed: An Edge-Feature-Enhanced Image Denoised Network Against Adversarial Attacks for Secure Internet of Things

With the prosperous development of Internet of Things (IoT), IoT devices have been deployed in various applications, which generates large volume of image data to trace and record the users’ behaviors, resulting in better IoT services. To accurately analyze these huge data to further improve users’ experience on IoT services, deep neural networks (DNNs) are gaining more attention and have become increasingly popular. However, recent studies have shown that DNN models are vulnerable to adversarial attacks, which leads to the risk of applications in practice. Previous works are devoted to extract invariant features from the content circled by edges in images, while such features cannot efficiently deal with the adversarial effect. In this work, we first study this problem from a new angle by exploring the edge feature information, which is intractable to be influenced by adversarial attacks demonstrated by our empirical analysis. Based on this, we propose a novel edge feature-enhanced defense approach called Defed which incorporates edge feature information into denoised network to defend against various adversarial attacks in image area. For the training phase, we only add benign images as the input and exert Gaussian noise to substitute the adversarial attacks to mitigate the dependency of models on specific adversarial attacks. For inference, we design a combination of multiple Defeds trained by different Gaussian noise levels and deploy confidence intervals to judge whether an image is adversarial or not. Experiments over real-world data sets on image classification demonstrate the efficacy and superiority compared to the state-of-the-art defense approaches.
Transition Recovery Attack on Embedded State Machines Using Power Analysis

Modern embedded systems are ever present within our daily lives. Such devices remain vulnerable to Differential Power Analysis via side-channel attacks, which provide a powerful and flexible framework for exfiltrating valuable information. This work applies these principles with respect to determining properties of the underlying finite state machine dictating code execution on a STM32F030 microcontroller. A K-nearest neighbors classification model is used to determine the order of state transitions within the finite state machine with a maximum accuracy of 81% over 256 different classes. A second experiment was performed to evaluate the effect of dimensionality on the classification accuracy and found an ability to classify with an accuracy over 95% in classes with 32 elements and perfectly classify between only two classes. Results validate the need for protections against power-based side-channel attacks when sensitive information is handled by FSM-controlled components.