Reviewerly/exHarmony huggingface dataset:
Object Detection Using Adaptive Mask RCNN in Optical Remote Sensing Images

Fast and automatic object detection in remote sensing images is a critical and challenging task for civilian and military applications.Recently, deep learning approaches were introduced to overcome the limitation of traditional object detection methods.In this paper, adaptive mask Region-based Convolutional Network (mask-RCNN) is utilized for multi-class object detection in remote sensing images.Transfer learning, data augmentation, and fine-tuning were adopted to overcome objects scale variability, small size, the density of objects, and the scarcity of annotated remote sensing image.Also, five optimization methods were investigated namely: Adaptive Moment Estimation (Adam), stochastic gradient decent (SGD), adaptive learning rate method (Adelta), Root Mean Square Propagation (RMSprop) and hybrid optimization.In hybrid optimization, the training process begins Adam then switches to SGD when appropriate and vice versa.Also, the behaviour of adaptive mask RCNN was compared to baseline deep object detection methods.Several experiments were conducted on the challenging NWPU-VHR-10 dataset.The hybrid method Adam_SGD acheived the highest Accuracy precision, with 95%.Experimental results showed detection performance in terms of accuracy and intersection over union (IOU) boost of performance up to 6%.
Dual Relation Semi-Supervised Multi-Label Learning

Multi-label learning (MLL) solves the problem that one single sample corresponds to multiple labels. It is a challenging task due to the long-tail label distribution and the sophisticated label relations. Semi-supervised MLL methods utilize a small-scale labeled samples and large-scale unlabeled samples to enhance the performance. However, these approaches mainly focus on exploring the data distribution in feature space while ignoring mining the label relation inside of each instance. To this end, we proposed a Dual Relation Semi-supervised Multi-label Learning (DRML) approach which jointly explores the feature distribution and the label relation simultaneously. A dual-classifier domain adaptation strategy is proposed to align features while generating pseudo labels to improve learning performance. A relation network is proposed to explore the relation knowledge. As a result, DRML effectively explores the feature-label and label-label relations in both labeled and unlabeled samples. It is an end-to-end model without any extra knowledge. Extensive experiments illustrate the effectiveness and efficiency of our method1.
Sparsity-Inducing Binarized Neural Networks

Binarization of feature representation is critical for Binarized Neural Networks (BNNs). Currently, sign function is the commonly used method for feature binarization. Although it works well on small datasets, the performance on ImageNet remains unsatisfied. Previous methods mainly focus on minimizing quantization error, improving the training strategies and decomposing each convolution layer into several binary convolution modules. However, whether sign is the only option for binarization has been largely overlooked. In this work, we propose the Sparsity-inducing Binarized Neural Network (Si-BNN), to quantize the activations to be either 0 or +1, which introduces sparsity into binary representation. We further introduce trainable thresholds into the backward function of binarization to guide the gradient propagation. Our method dramatically outperforms current state-of-the-arts, lowering the performance gap between full-precision networks and BNNs on mainstream architectures, achieving the new state-of-the-art on binarized AlexNet (Top-1 50.5%), ResNet-18 (Top-1 59.7%), and VGG-Net (Top-1 63.2%). At inference time, Si-BNN still enjoys the high efficiency of exclusive-not-or (xnor) operations.
Using Information on Class Interrelations to Improve Classification of Multiclass Imbalanced Data: A New Resampling Algorithm

Abstract The relations between multiple imbalanced classes can be handled with a specialized approach which evaluates types of examples’ difficulty based on an analysis of the class distribution in the examples’ neighborhood, additionally exploiting information about the similarity of neighboring classes. In this paper, we demonstrate that such an approach can be implemented as a data preprocessing technique and that it can improve the performance of various classifiers on multiclass imbalanced datasets. It has led us to the introduction of a new resampling algorithm, called Similarity Oversampling and Undersampling Preprocessing (SOUP), which resamples examples according to their difficulty. Its experimental evaluation on real and artificial datasets has shown that it is competitive with the most popular decomposition ensembles and better than specialized preprocessing techniques for multi-imbalanced problems.
High-fidelity geometric gate for silicon-based spin qubits

High-fidelity manipulation is the key for the physical realization of fault-tolerant quantum computation. Here, we present a protocol to realize universal nonadiabatic geometric gates for silicon-based spin qubits. We find that the advantage of geometric gates over dynamical gates depends crucially on the evolution loop for the construction of the geometric phase. Under appropriate evolution loops, both the geometric single-qubit gates and the CNOT gate can outperform their dynamical counterparts for both systematic and detuning noises. We also perform randomized benchmarking using noise amplitudes consistent with experiments in silicon. For the static noise model, the averaged fidelities of geometric gates are around 99.90\% or above, while for the time-dependent $1/f$-type noise, the fidelities are around 99.98\% when only the detuning noise is present. We also show that the improvement in fidelities of the geometric gates over dynamical ones typically increases with the exponent $\alpha$ of the $1/f$ noise, and the ratio can be as high as 4 when $\alpha\approx 3$. Our results suggest that geometric gates with judiciously chosen evolution loops can be a powerful way to realize high-fidelity quantum gates.
Automatic Modulation Classification Based on Novel Feature Extraction Algorithms

In order to solve the problems that the accuracy of modulation recognition algorithms of MSK and MQAM signals is not ideal under the condition of low signal-to-noise ratio (SNR) in Additional White Gaussian Noise (AWGN) environment, two novel features, the differential nonlinear phase Peak Factor (PF) and the reciprocal of amplitude envelope variance of cyclic spectrum at zero frequency cross section after difference and forth power processing, are constructed, which can complete the recognition of MSK signal and the classification of MPSK and MQAM signals respectively. This paper proposes a mixed recognition algorithm based on the two new features and other classical features, and design a tree shaped multi-layer smooth support vector machine classifier based on feature selection algorithm (FS_DT-SSVM) to recognize eleven kinds of digital modulation signals. The simulation results illustrate that the algorithm can achieve the classification of the modulation signals {2FSK, MSK, 2ASK, 4ASK, 8ASK, 2PSK, 4PSK, 8PSK, 16QAM, 32QAM, 64QAM} with small SNR. When the SNR is not less than -1dB, the recognition rate of the classifier for all signals exceed 97%, which validates the effectiveness of the proposed modulation recognition algorithm.
A multi-cascaded model with data augmentation for enhanced paraphrase detection in short texts

Paraphrase detection is an important task in text analytics with numerous applications such as plagiarism detection, duplicate question identification, and enhanced customer support helpdesks. Deep models have been proposed for representing and classifying paraphrases. These models, however, require large quantities of human-labeled data, which is expensive to obtain. In this work, we present a data augmentation strategy and a multi-cascaded model for improved paraphrase detection in short texts. Our data augmentation strategy considers the notions of paraphrases and non-paraphrases as binary relations over the set of texts. Subsequently, it uses graph theoretic concepts to efficiently generate additional paraphrase and non-paraphrase pairs in a sound manner. Our multi-cascaded model employs three supervised feature learners (cascades) based on CNN and LSTM networks with and without soft-attention. The learned features, together with hand-crafted linguistic features, are then forwarded to a discriminator network for final classification. Our model is both wide and deep and provides greater robustness across clean and noisy short texts. We evaluate our approach on three benchmark datasets and show that it produces a comparable or state-of-the-art performance on all three.
Learning Representations From Skeletal Self-Similarities for Cross-View Action Recognition

Existing research attention in vision-based action recognition is generally paid on recognizing actions from the same views seen in the training data. One of the big challenges in action recognition lies in the large variations of action representations as actions are captured from totally different viewpoints. This paper addresses this problem by learning view-invariant representations from skeletal self-similarities of varying scales with a very light multi-stream neural network (MSNN). As human skeletons have been proved to be an effective feature modality used for action recognition and are easy to obtain, we first create a view-invariant action description by formulating skeletal self-similarities at each frame as an image (SSI), which can show a high structural stability under view changes. Accordingly, a MSNN is designed based on 3D CNN and LSTM units to learn representations from SSIs of multiple scales, where the scheme of multiple scales provides our method with a good robustness to view changes. In addition, we integrate the computation of SSIs into the MSNN by wrapping it as a custom learnable layer thanks to its simplicity, instead of normalizing and transforming skeletons using a hand-crafted preprocessing. Extensive experimental evaluations on three challenging cross-view datasets demonstrate the effectiveness of our proposed method, which achieves superior performance to the state-of-the-art algorithms on cross-view recognition. The source code of this work will be released shortly to facilitate future studies in this field.
Combining Network Visualization and Data Mining for Tax Risk Assessment

This paper presents a novel approach, called MALDIVE, to support tax administrations in the tax risk assessment for discovering tax evasion and tax avoidance. MALDIVE relies on a network model describing several kinds of relationships among taxpayers. Our approach suitably combines various data mining and visual analytics methods to support public officers in identifying risky taxpayers. MALDIVE consists of a 4-step pipeline: (i) A social network is built from the taxpayers data and several features of this network are extracted by computing both classical social network indexes and domain-specific indexes; (ii) an initial set of risky taxpayers is identified by applying machine learning algorithms; (iii) the set of risky taxpayers is possibly enlarged by means of an information diffusion strategy and the output is shown to the analyst through a network visualization system; (iv) a visual inspection of the network is performed by the analyst in order to validate and refine the set of risky taxpayers. We discuss the effectiveness of the MALDIVE approach through both quantitative analyses and case studies performed on real data in collaboration with the Italian Revenue Agency.
A Novel Bio-Inspired Deep Learning Approach for Liver Cancer Diagnosis

Current research on computer-aided diagnosis (CAD) of liver cancer is based on traditional feature engineering methods, which have several drawbacks including redundant features and high computational cost. Recent deep learning models overcome these problems by implicitly capturing intricate structures from large-scale medical image data. However, they are still affected by network hyperparameters and topology. Hence, the state of the art in this area can be further optimized by integrating bio-inspired concepts into deep learning models. This work proposes a novel bio-inspired deep learning approach for optimizing predictive results of liver cancer. This approach contributes to the literature in two ways. Firstly, a novel hybrid segmentation algorithm is proposed to extract liver lesions from computed tomography (CT) images using SegNet network, UNet network, and artificial bee colony optimization (ABC), namely, SegNet-UNet-ABC. This algorithm uses the SegNet for separating liver from the abdominal CT scan, then the UNet is used to extract lesions from the liver. In parallel, the ABC algorithm is hybridized with each network to tune its hyperparameters, as they highly affect the segmentation performance. Secondly, a hybrid algorithm of the LeNet-5 model and ABC algorithm, namely, LeNet-5/ABC, is proposed as feature extractor and classifier of liver lesions. The LeNet-5/ABC algorithm uses the ABC to select the optimal topology for constructing the LeNet-5 network, as network structure affects learning time and classification accuracy. For assessing performance of the two proposed algorithms, comparisons have been made to the state-of-the-art algorithms on liver lesion segmentation and classification. The results reveal that the SegNet-UNet-ABC is superior to other compared algorithms regarding Jaccard index, Dice index, correlation coefficient, and convergence time. Moreover, the LeNet-5/ABC algorithm outperforms other algorithms regarding specificity, F1-score, accuracy, and computational time.
Intelligent Real-Time Earthquake Detection by Recurrent Neural Networks

Taiwan that is located at the junction of the Eurasian Plate and the Philippine Sea Plate is one of the most active seismic zones in the world. Devastating earthquakes have occurred around the island and have caused severe damages from time to time. To avoid the severe loss, earthquake early warning (EEW) is of great importance, and one of the most critical issues of EEW is fast and reliable detection for the presence of earthquakes. Traditional methods for earthquake detection usually use criterion-based algorithms to detect the onset of the earthquake waves. Currently, the thresholds for those criteria are usually decided empirically and may result in excessive false alarms. Obviously, false alarms can cause undue panics and diminish the credibility of the system. In this article, the recurrent neural network (RNN) models are adopted to develop a real-time EEW system. The developed system is designed to identify the occurrence of an earthquake event, and the duration of the P-wave and the S-wave. It was trained and tested using the seismograms recorded in Taiwan from 2016 to 2017. From the simulation results, the proposed scheme outperforms the traditional criterion-based schemes in terms of detection accuracy and processing time.
Distributed ledger technology for securities clearing and settlement: benefits, risks, and regulatory implications

Abstract This article outlines the benefits and risks of the distributed ledger technology (DLT) for the clearing and settlement of exchange-traded and OTC securities, followed by a description of the technology’s potential role for central counterparties and central securities depositories. Although the industry and scholars are attempting to solve the technological and operational issues that DLT systems still face, outstanding legal risks are such that the financial industry is asking for more regulatory guidance and intervention. This article wants to contribute to the public policy debate by presenting potential regulatory barriers that may have to be removed for DLT to be fully adopted. In addition, it identifies areas requiring an update of the legal framework in order to address certain prudential and conduct risks that this technology could introduce.
A Light Field Image Quality Assessment Model Based on Symmetry and Depth Features

This paper presents a new full-reference image quality assessment (IQA) method for conducting the perceptual quality evaluation of the light field (LF) images, called the symmetry and depth feature-based model (SDFM). Specifically, the radial symmetry transform is first employed on the luminance components of the reference and distorted LF images to extract their symmetry features for capturing the spatial quality of each view of an LF image. Second, the depth feature extraction scheme is designed to explore the geometry information inherited in an LF image for modeling its LF structural consistency across views. The similarity measurements are subsequently conducted on the comparison of their symmetry and depth features separately, which are further combined to achieve the quality score for the distorted LF image. Note that the proposed SDFM that explores the symmetry and depth features is conformable to the human vision system, which identifies the objects by sensing their structures and geometries. Extensive simulation results on the dense light fields dataset have clearly shown that the proposed SDFM outperforms multiple classical and recently developed IQA algorithms on quality evaluation of the LF images.
Single-Path Mobile AutoML: Efficient ConvNet Design and NAS Hyperparameter Optimization

Can we reduce the search cost of Neural Architecture Search (NAS) from days down to only a few hours? NAS methods automate the design of Convolutional Networks (ConvNets) under hardware constraints and they have emerged as key components of AutoML frameworks. However, the NAS problem remains challenging due to the combinatorially large design space and the significant search time (at least 200 GPU-hours). In this article, we alleviate the NAS search cost down to less than 3 hours, while achieving state-of-the-art image classification results under mobile latency constraints. We propose a novel differentiable NAS formulation, namely Single-Path NAS, that uses one single-path over-parameterized ConvNet to encode all architectural decisions based on shared convolutional kernel parameters, hence drastically decreasing the search overhead. Single-Path NAS achieves state-of-the-art top-1 ImageNet accuracy (75.62%), hence outperforming existing mobile NAS methods in similar latency settings (~80 ms). In particular, we enhance the accuracy-runtime trade-off in differentiable NAS by treating the Squeeze-and-Excitation path as a fully searchable operation with our novel single-path encoding. Our method has an overall cost of only 8 epochs (24 TPU-hours), which is up to 5,000× faster compared to prior work. Moreover, we study how different NAS formulation choices affect the performance of the designed ConvNets. Furthermore, we exploit the efficiency of our method to answer an interesting question: instead of empirically tuning the hyperparameters of the NAS solver (as in prior work), can we automatically find the hyperparameter values that yield the desired accuracy-runtime trade-off (e.g., target runtime for different platforms)? We view our extensive experimental results as a valuable exploration for NAS-based cloud AutoML services, and we open-source our entire codebase at: https://github.com/dstamoulis/single-path-nas.
End-to-End Optimization of Deep Learning Applications

The irregularity of recent Convolutional Neural Network (CNN) models such as less data reuse and parallelism due to the extensive network pruning and simplification creates new challenges for FPGA acceleration. Furthermore, without proper optimization, there could be significant overheads when integrating FPGAs into existing machine learning frameworks like TensorFlow. Such a problem is mostly overlooked by previous studies. However, our study shows that a naive FPGA integration into TensorFlow could lead to up to 8.45x performance degradation. To address the challenges mentioned above, we propose several SW/HW co-design approaches to perform the end-to-end optimization of deep learning applications. We present a flexible and composable architecture called FlexCNN. It can deliver high computation efficiency for different types of convolution layers using techniques including dynamic tiling and data layout optimization. FlexCNN is further integrated into the TensorFlow framework with a fully-pipelined software-hardware integration flow. This alleviates the high overheads of TensorFlow-FPGA handshake and other non-CNN processing stages. We use OpenPose, a popular CNN-based application for human pose recognition, as a case study. Experimental results show that with the FlexCNN architecture optimizations, we can achieve 2.3x performance improvement. The pipelined integration stack leads to a further 5x speedup. Overall, the SW/HW co-optimization produces a speedup of 11.5x and results in an end-to-end performance of 23.8FPS for OpenPose with floating-point precision, which is the highest performance reported for this application on FPGA in the literature.
Readiness, Roles, and Responsibilities of Stakeholders for Sustainable Mobile Learning Adoption in Higher Education

The extant literature on mobile learning in higher education has mainly focused on students and academics. This study conducted in a Nigerian university considered a wider group of stakeholders (students, academics, IT personnel, and administrative leaders) in its investigation of stakeholders’ awareness, willingness, and readiness for mobile learning adoption, using a mixed methods approach. The findings revealed that while the awareness of mobile learning amongst students was low, they were keen on adoption. Academics were more tentative, for while they were willing, it was clear that a shift in their teaching philosophy and pedagogy would be required for mobile learning to work effectively. For those in IT and administrative leadership roles, their perceptions were more resource-focused. While this study only considered the views of internal stakeholders, it also uncovered the need for the involvement of external stakeholders. Building on these findings, a strategy table which outlines the roles and responsibilities of each stakeholder group was created. The table provides a blueprint which otherwise has been missing, to guide sustainable mobile learning adoption and implementation not only in higher education in developing countries, but which may have more global appeal.
Stochastic Selection of Activation Layers for Convolutional Neural Networks

In recent years, the field of deep learning has achieved considerable success in pattern recognition, image segmentation, and many other classification fields. There are many studies and practical applications of deep learning on images, video, or text classification. Activation functions play a crucial role in discriminative capabilities of the deep neural networks and the design of new “static” or “dynamic” activation functions is an active area of research. The main difference between “static” and “dynamic” functions is that the first class of activations considers all the neurons and layers as identical, while the second class learns parameters of the activation function independently for each layer or even each neuron. Although the “dynamic” activation functions perform better in some applications, the increased number of trainable parameters requires more computational time and can lead to overfitting. In this work, we propose a mixture of “static” and “dynamic” activation functions, which are stochastically selected at each layer. Our idea for model design is based on a method for changing some layers along the lines of different functional blocks of the best performing CNN models, with the aim of designing new models to be used as stand-alone networks or as a component of an ensemble. We propose to replace each activation layer of a CNN (usually a ReLU layer) by a different activation function stochastically drawn from a set of activation functions: in this way, the resulting CNN has a different set of activation function layers.