Reviewerly/exHarmony huggingface dataset:
Contrastive Multiview Coding With Electro-Optics for SAR Semantic Segmentation

In the training of deep learning models, how the model parameters are initialized greatly affects the model performance, sample efficiency, and convergence speed. Recently, representation learning for model initialization has been actively studied in the remote sensing field. In particular, the appearance characteristics of the imagery obtained using the synthetic aperture radar (SAR) sensor are quite different from those of general electro-optical (EO) images, and thus, representation learning is even more important in remote sensing domain. Motivated from contrastive multiview coding, we propose multimodal representation learning for SAR semantic segmentation. Unlike previous studies, our method jointly uses EO imagery, SAR imagery, and a label mask. Several experiments show that our approach is superior to the existing methods in model performance, sample efficiency, and convergence speed.
CNN-Based Continuous Authentication on Smartphones With Conditional Wasserstein Generative Adversarial Network

With the widespread usage of mobile devices, the authentication mechanisms are urgently needed to identify users for information leakage prevention. In this article, we present CAGANet, a convolutional neural network (CNN)-based continuous authentication on smartphones using a conditional Wasserstein generative adversarial network (CWGAN) for data augmentation, which utilizes smartphone sensors of the accelerometer, gyroscope, and magnetometer to sense phone movements incurred by user operation behaviors. Specifically, based on the preprocessed real data, CAGANet employs CWGAN to generate additional sensor data for data augmentation that are used to train the designed CNN. With the augmented data, CAGANet utilizes the trained CNN to extract deep features and then performs principal component analysis (PCA) to select appropriate representative features for different classifiers. With the CNN-extracted features, CAGANet trains four one-class classifiers of OC-SVM, LOF, isolation forest (IF), and EE in the enrollment phase and authenticates the current user as a legitimate user or an impostor based on the trained classifiers in the authentication phase. To evaluate the performance of CAGANet, we conduct extensive experiments in terms of the efficiency of CWGAN, the effectiveness of CWGAN augmentation and the designed CNN, the accuracy on unseen users, and comparison with traditional augmentation approaches and with representative authentication methods, respectively. The experimental results show that CAGANet with the IF classifier can achieve the lowest equal error rate (EER) of 3.64% on 2-s sampling data.
Remote Sensing Image Super-Resolution via Dual-Resolution Network Based on Connected Attention Mechanism

Limited by hardware conditions and complex degradation processes, the obtained remote sensing images (RSIs) are often low-resolution (LR) data with insufficient high-frequency information. Image super-resolution (SR) aims to improve the spatial resolution of images and add reasonable detailed information. Although existing convolutional neural network (CNN)-based methods achieve good performance by adding residual structure and attention mechanism to the network, simply stacking the residual structure and embedding the attention module directly on the residual branch lead to localized use of features and information loss. To address the above problems, we propose a dual-resolution connected attention network (DRCAN). Specifically, a high-resolution (HR) learning branch is constructed to complement the mapping learning between LR images and HR images, and a connected attention module with residual learning is introduced to make full use of the different levels of intermediate layer features. Besides, we collect data at different resolutions from Google Earth to form a dataset named XD IPIU for RSIs SR. Extensive experiments demonstrate the effectiveness of the proposed model and DRCAN shows the state-of-the-art performance in terms of quantitative evaluation and visual quality.
Frame-based Multi-level Semantics Representation for text matching

Text matching is a fundamental and critical problem in natural language understanding (NLU), where multi-level semantics matching is the most challenging task. Human beings can always leverage their semantic knowledge, while neural computer systems first learn sentence semantic representations and then perform text matching based on learned representation. However, without sufficient semantic information, computer systems will not perform very well. To bridge the gap, we propose a novel Frame-based Multi-level Semantics Representation (FMSR) model, which utilizes frame knowledge to extract multi-level semantic information within sentences explicitly for the text matching task. Specifically, different from existing methods that only rely on the sophisticated architectures, FMSR model, which leverages both frame and frame elements in FrameNet, is designed to integrate multi-level semantic information with attention mechanisms to learn better sentence representations. Our extensive experimental results show that FMSR model performs better than the state-of-the-art technologies on two text matching tasks.
Data augmentation for ML-driven data preparation and integration

In recent years, we have witnessed the development of novel data augmentation (DA) techniques for creating additional training data needed by machine learning based solutions. In this tutorial, we will provide a comprehensive overview of techniques developed by the data management community for data preparation and data integration. In addition to surveying task-specific DA operators that leverage rules, transformations, and external knowledge for creating additional training data, we also explore the advanced DA techniques such as interpolation, conditional generation, and DA policy learning. Finally, we describe the connection between DA and other machine learning paradigms such as active learning, pre-training, and weakly-supervised learning. We hope that this discussion can shed light on future research directions for a holistic data augmentation framework for high-quality dataset creation.
A success model for cyber threat intelligence management platforms

The increasingly persistent and sophisticated threat actors, along with the sheer speed at which cyber attacks unfold, have made timely decision making imperative for ensuring the continued security of most organisations. Consequently, threat intelligence management platforms have been widely adopted to assist organisations in creating and participating in inter-organisational sharing efforts to protect against cyber attacks. Measuring the effectiveness and success of these platforms is critical to understanding how such products and services should be designed, implemented and used. Nevertheless, it is a fact that research and practice lack a common understanding of factors influencing the success of threat intelligence management platforms. To investigate these issues, we adopted the DeLone and McLean information system success model to threat intelligence sharing and employed a survey-based approach to collect data from 152 security professionals for its empirical assessment. Subsequently, we identified several factors that are decisive for the organisational success of a threat intelligence management platform.
Hybrid genetic model for clustering ensemble

Clustering ensemble has received considerable research interest and led to a proliferation of studies, since it has great capabilities to combine multiple base clusters to generate a more robust and stable consensus result. Genetic algorithms are optimization methods which can search heuristically by simulating the natural evolution process with highly parallel and adaptive characteristics. However, to our knowledge, there are very few existing methods using a genetic model to solve clustering ensemble problems. In this paper, a novel hybrid genetic model for clustering ensemble (HGMCE) is proposed innovatively, and the corresponding objective function is designed. Each base clustering is regarded as a new attribute of data, and the result of clustering ensemble can be evaluated by the objective function. Then the proposed model can be inferred with the optimization, combination, and transcendence of base clustering results step by step, which makes it possible to maintain the diversity of the population and provides more possibilities to avoid falling into the local optimal solution. Furthermore, an algorithm named HGCEA corresponding to the proposed model is designed to solve the clustering ensemble problem. To evaluate the potential of HGCEA, extensive experiments are carried out on ten real datasets, including comparison with clustering groups and clustering ensemble groups. The results of accuracy and normalized mutual information demonstrate the superiority of the proposed algorithm in integrating effective clustering over the state-of-the-art.
Human–Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot

People with severe disabilities require assistance to perform their routine activities; a Human–Machine Interface (HMI) will allow them to activate devices that respond according to their needs. In this work, an HMI based on electrooculography (EOG) is presented, the instrumentation is placed on portable glasses that have the task of acquiring both horizontal and vertical EOG signals. The registration of each eye movement is identified by a class and categorized using the one hot encoding technique to test precision and sensitivity of different machine learning classification algorithms capable of identifying new data from the eye registration; the algorithm allows to discriminate blinks in order not to disturb the acquisition of the eyeball position commands. The implementation of the classifier consists of the control of a three-wheeled omnidirectional robot to validate the response of the interface. This work proposes the classification of signals in real time and the customization of the interface, minimizing the user’s learning curve. Preliminary results showed that it is possible to generate trajectories to control an omnidirectional robot to implement in the future assistance system to control position through gaze orientation.
How does terms of trade volatility affect macroeconomic volatility? The roles of financial development and institutions

This paper examines the effect of terms-of-trade volatility on growth volatility. It also investigates whether financial development and institutional quality mitigate the negative effect of terms-of-trade volatility on growth volatility. The model is empirically tested for 45 African countries over the period 1997–2017. The empirical strategy is based on the generalized method of moments, and the following results are established. Strong evidence is provided to support that terms of trade volatility increases growth volatility in African countries. The results further indicate that institutional quality mitigates the negative effect of terms-of-trade volatility. When financial development is considered, we provide evidence that financial development is a shock absorber, with financial institutions having a stronger effect than financial markets. These results are robust to the use of alternative measures of growth volatility, terms of trade volatility, and different specifications.
An instance-oriented performance measure for classification

Performance evaluation is significant in data classification. The existing evaluation methods ignore the characteristics (such as classification difficulty) of each instance. In practice, it is necessary to measure classification performance from the perspective of instances. In this paper, an instance-oriented classification performance metric is proposed based on the classification difficulty of each instance, named degree of credibility (Cr ). Cr conforms to the natural cognition that the lower the probability of misclassifying relatively easy instances, the more credible the classifier. It focuses on the credibility of each instance’s prediction, which opens up a new way for classifier evaluation. Moreover, several important properties of Cr are identified, laying solid theoretical foundation for classifier evaluation. Also, the concept of acceptable classifier is proposed to judge whether the trained model and its parameter set reach excellent ranks at the current technology level instead of relying entirely on human experience. The experimental results of twelve classifiers on twelve datasets indicate the physical significance and good statistical consistency and discriminatory ability of Cr, as well as the feasibility of acceptable classifiers for model selection and training. Furthermore, the proposal of approximate difficulty greatly improves the computation efficiency of instance difficulty.
Adoption of Robotics Technology in Healthcare Sector

Healthcare professionals are facing a challenging time because of the COVID-19 pandemic. They are expected to perform their duties at the same time they need to take care of their own health. In such a disastrous time, robotics technology is emerging as the best support tool for healthcare professionals. In this paper, the researchers will discuss the role of robotics during the COVID-19 pandemic and propose certain adoption factors for robotics in health care. Robotics adoption is in the early stage in the healthcare industry, and there is further scope of primary research for the same. In this study, factors of adoption are taken from the Technology Acceptance Model and Diffusion of Innovation theory.
Evolutionary-Mean shift algorithm for dynamic multimodal function optimization

Recently, many dynamic optimization algorithms based on metaheuristic methods have been proposed. Although these schemes are highly efficient in determining a single global optimum, they fail in locating multiple optimal solutions. The central goal of dynamic multimodal optimization is to detect multiple optimal solutions for an optimization problem where its objective function is modified over time. Locating many optimal solutions (global and local) in a dynamic multimodal optimization problem is particularly crucial for several applications since the best solution could not always be the best implementable alternative due to various practical limitations. In spite of its importance, the problem of dynamic multimodal optimization through evolutionary principles has been scarcely considered in the literature. On the other hand, mean shift is a non-parametric and iterative process for detecting local maxima in a density function represented by a set of samples. Mean shift maintains interesting adaptive characteristics that allow it to find local maxima under dynamic environments. In this paper, the mean shift scheme is proposed to detect global and local optima in dynamic optimization problems. In the proposed method, the search strategy of the mean shift is modified to consider not only the density but also the fitness value of the candidate solutions. A competitive memory, along with a dynamic strategy, has also been added to accelerate the convergence process by using important information from previous environments. As a result, the proposed approach can effectively identify most of the global and local optima in dynamic environments. To demonstrate the performance of the proposed algorithm, a set of comparisons with other well-known dynamic optimization methods has been conducted. The study considers the benchmark generator of the CEC competition for dynamic optimization. The experimental results suggest a very competitive performance of the proposed scheme in terms of accuracy and robustness.
Single Image Super-Resolution Using Asynchronous Multi-Scale Network

An existing multi-scale residual network (MSRN) has demonstrated its success on conducting the single image super-resolution (SISR) task. The MSRN consists of a number of multi-scale residual blocks (MSRBs), and each MSRB performs convolutions by exploiting two different sizes of windows for conducting multi-scale feature extraction. The smaller window is used to extract image features at a low scale, while the larger one is used for a high scale. To significantly reduce the number of parameters involved in the MSRB, a new feature extraction module, called the asynchronous multi-scale block (AMB), is proposed in this paper. It is based on the fact that the larger window used in the MSRB can be replaced by two smaller windows without affecting the original MSRB's function. Consequently, by replacing each MSRB with our AMB, an asynchronous multi-scale network (AMNet) is then constructed, which can yield a significant reduction on computational complexity. This means that more AMBs can be used in our AMNet to deliver superior SISR performance, while maintaining the same or comparable computational complexity to that of the MSRN. To consolidate all image features generated from all scales, a new fusion scheme, called the adaptive feature fusion block (AFFB), is proposed that weights the extracted features according to their importance for further increasing SISR's performance. Extensive experimental results have clearly shown the superiority of our proposed AMNet when compared with multiple state-of-the-arts.
A new variational method for selective segmentation of medical images

Selective segmentation aims to separate a subset of target objects or regions of interests in an image. It is widely used in medical image analysis for some specific tasks such as extracting anatomic organs or lesions. However, selective segmentation of medical images is usually challenged by their limited imaging quality. In this paper, we propose a two-phase selective segmentation method. The first phase is a preprocessing step, which aims to reduce influence of noise or cluttered background on segmentation. The second phase performs selective segmentation on the preprocessed image. For the first phase, we propose a new image smoothing model which can effectively reduce noise or intensity inhomogeneity inside objects while retain edges of the original image. Moreover, the proposed model has attractive mathematical and physical properties, in that it has one single optimal solution. For the second phase, we propose a modified Gout's active contour method, which can obtain targeted objects more efficiently and accurately. Our main contribution is the new image smoothing model, which can effectively attenuate complicated background but preserve edges of targeted object. Extensive experiments on real medical images show that, our smoothing model can greatly facilitate the second phase, and our method can significantly improve some existing related methods in terms of either visual assessment or quantitative evaluation.
Moving Car Recognition and Removal for 3D Urban Modelling Using Oblique Images

With the progress of photogrammetry and computer vision technology, three-dimensional (3D) reconstruction using aerial oblique images has been widely applied in urban modelling and smart city applications. However, state-of-the-art image-based automatic 3D reconstruction methods cannot effectively handle the unavoidable geometric deformation and incorrect texture mapping problems caused by moving cars in a city. This paper proposes a method to address this situation and prevent the influence of moving cars on 3D modelling by recognizing moving cars and combining the recognition results with a photogrammetric 3D modelling procedure. Through car detection using a deep learning method and multiview geometry constraints, we can analyse the state of a car’s movement and apply a proper preprocessing method to the geometrically model generation and texture mapping steps of 3D reconstruction pipelines. First, we apply the traditional Mask R-CNN object detection method to detect cars from oblique images. Then, a detected car and its corresponding image patch calculated by the geometry constraints in the other view images are used to identify the moving state of the car. Finally, the geometry and texture information corresponding to the moving car will be processed according to its moving state. Experiments on three different urban datasets demonstrate that the proposed method is effective in recognizing and removing moving cars and can repair the geometric deformation and error texture mapping problems caused by moving cars. In addition, the methods proposed in this paper can be applied to eliminate other moving objects in 3D modelling applications.
Cryptocurrencies and Price Prediction: A Survey

Cryptocurrencies are fungible digital assets whose market capitalization has not stopped growing since the appearance of their first use case in 2009, Bitcoin. However, one of the biggest problems facing cryptocurrencies is the enormous fluctuation of their value in the market. To help understand different patterns in cryptocurrency ecosystems, several machine learning-based solutions have been proposed in the literature. This paper aims to study in detail the solutions proposed in the literature for the detection of patterns and anomalies in cryptocurrency ecosystems. The aim is to bring together different proposals and studies to help users of this market to understand how it works.
Feedback Capacity of MIMO Gaussian Channels

Finding a computable expression for the feedback capacity of channels with non-white Gaussian, additive noise is a long standing open problem. In this paper, we solve this problem in the scenario where the channel has multiple inputs and multiple outputs (MIMO) and the noise process is generated as the output of a state-space model (a hidden Markov model). The main result is a computable characterization of the feedback capacity as a finite-dimensional convex optimization problem. Our solution subsumes all previous solutions to the feedback capacity including the auto-regressive moving-average (ARMA) noise process of first order, even if it is a non-stationary process. The capacity problem can be viewed as the problem of maximizing the measurements' entropy rate of a controlled (policy-dependent) state-space subject to a power constraint. We formulate the finite-block version of this problem as a sequential convex optimization problem, which in turn leads to a single-letter and computable upper bound. By optimizing over a family of time-invariant policies that correspond to the channel inputs distribution, a tight lower bound is realized. We show that one of the optimization constraints in the capacity characterization boils down to a Riccati equation, revealing an interesting relation between explicit capacity formulae and Riccati equations.
Fine-grained few shot learning with foreground object transformation

Traditional fine-grained image classification generally requires abundant labeled samples to deal with the low inter-class variance but high intra-class variance problem. However, in many scenarios we may have limited samples for some novel sub-categories, leading to the fine-grained few shot learning (FG-FSL) setting. To address this challenging task, we propose a novel method named foreground object transformation (FOT), which is composed of a foreground object extractor and a posture transformation generator. The former aims to remove image background, which tends to increase the difficulty of fine-grained image classification as it amplifies the intra-class variance while reduces inter-class variance. The latter transforms the posture of the foreground object to generate additional samples for the novel sub-category. As a data augmentation method, FOT can be conveniently applied to any existing few shot learning algorithm and greatly improve its performance on FG-FSL tasks. In particular, in combination with FOT, simple fine-tuning baseline methods can be competitive with the state-of-the-art methods both in inductive setting and transductive setting. Moreover, FOT can further boost the performances of latest excellent methods and bring them up to the new state-of-the-art. In addition, we also show the effectiveness of FOT on general FSL tasks.