Reviewerly/exHarmony huggingface dataset:
A novel ensemble pruning approach based on information exchange glowworm swarm optimization and complementarity measure

Ensemble pruning has been widely used for enhancing classification ability employing a smaller number of classifiers. Ensemble pruning extracts a part of classifiers with good overall performance to form the final ensemble. Diversity and accuracy of classifiers are of vital importance for a successful ensemble. It is hard for the members in one ensemble to achieve both good diversity and high accuracy, simultaneously, because there is a tradeoff between them. Existing works usually search for the tradeoff in terms of diversity measures, or find it utilizing heuristic algorithms, which cannot gain the exact solution without exhaustive search. To address the above issue, a novel ensemble pruning method based on information exchange glowworm swarm optimization and complementarity measure, abbreviated EPIECM, is proposed using the combination of information exchange glowworm swarm optimization (IEGSO) and complementarity measure (COM). Firstly, multiple generated classifiers are utilized to construct a pool of learners who perform diversely. Secondly, COM is employed to pre-prune the classifiers with poor comprehensive performance, and the pre-pruned ensemble is formed utilizing the retaining classifiers. Finally, the optimal subset of classifiers is combined from the remaining constituents after pre-pruning with IEGSO. Empirical results on 27 UCI datasets indicate that EPIECM significantly outperforms other techniques.
Deficient regimes of anti-money laundering and countering the financing of terrorism: agenda of digital banking and financial inclusion

Purpose The purpose of this paper is to discuss the dilemma of digital banking and the financial inclusion agenda of countries with the level of strength of the anti-money laundering and countering the financing of terrorism (AML/CFT) regime. Design/methodology/approach This study develops an AML/CFT compliance index using the assessment data of FATF to measure the level compliance strength of countries to measure the impact of the strength of the AML/CFT regime on the financial inclusion. Financial literacy, literacy, number of bank branches and income level of countries are used as other control variables in regression analysis, which is used to test the developed model. Findings The results suggest that the AML/CFT compliance level of a country is a significant factor in determining the level of financial inclusion. Besides, the number of bank branches for 100,000 people, literacy and financial literacy are significant factors in financial inclusion. However, the results reveal that financial literacy is significant over literacy in determining financial inclusion. Therefore, having considered the importance of the AML/CFT regime for financial inclusion, regulators are required to strengthen the AML/CFT regime and make clarity on the AML/CFT regulations. This clarity will promote the digitalization and financial inclusion over time. Practical implications Most of the studies related to financial inclusion and AML/CFT aspects are qualitative. Therefore, this is only the start of measuring the strength of an AML/CFT regime. More appropriate measures will be developed in the future based on this foundation. Originality/value This paper is an original work done by the author, which discusses the issues of digital banking and financial inclusion agenda of countries with the compliance strength of the AML/CFT regime. The AML/CFT compliance index is the original idea of the author, which can be used as a quantitative measure to capture the strength of the AML/CFT regimes in future studies.
Feature Matching for Remote Sensing Image Registration via Manifold Regularization

Feature matching is critical in analyzing remote sensing images, aiming to find the optimal mapping between correspondences. Regularization technology is essential to ensure the well-posedness of feature matching. However, current regularization-based methods scarcely consider the geometry structure of the image, which is beneficial for estimating the mapping, especially when the image pairs have a large view or scale change and local distortion. In this article, we introduce manifold regularization to overcome this limit and formulate feature matching as a unified semisupervised latent variable mixture model for both rigid and nonrigid transformations. Especially, we apply a Bayesian model with latent variables indicating whether matches in the putative correspondences are outliers or inliers. Moreover, we employ all the feature points, only part of which have correct matches, to express the intrinsic structure, which is preserved by manifold regularization. Finally, we combine manifold regularization with three different transformation models (e.g., rigid, affine, and thin-plate spline) to estimate the corresponding mappings. Experimental results on four remote sensing image datasets demonstrate that our method can significantly outperform the state of the art.
Exploring Multi-scale Deep Encoder-Decoder and PatchGAN for Perceptual Ultrasound Image Super-Resolution

Ultrasound visual imaging is currently one of three mainstream image diagnosis technologies in the medical industry, but due to the limitations of sensors, transmission media and ultrasound characteristics, the quality of ultrasound imaging may be poor, especially its low spatial resolution. We incorporate, in this paper, a new multi-scale deep encoder-decoder structure into a PatchGAN (patch generative adversarial network) based framework for fast perceptual ultrasound image super-resolution (SR). Specifically, the entire algorithm is carried out in two stages: ultrasound SR image generation and image refinement. In the first stage, a multi-scale deep encoder-decoder generator is employed to accurately super-resolve the LR ultrasound images. In the second stage, we advocate the confrontational characteristics of the discriminator to impel the generator such that more realistic high-resolution (HR) ultrasound images can be produced. The assessments in terms of PSNR/IFC/SSIM, inference efficiency and visual effects demonstrate its effectiveness and superiority, when compared to the most state-of-the-art methods.
Violent activity recognition by E-textile sensors based on machine learning methods

In the new era of technology with the development of wearable sensors, it is possible to collect data and analyze the same for recognition of different human activities. Activity recognition is used to monitor humans’ activity in various applications like assistance for an elderly and disabled person, Health care, physical activity monitoring, and also to identify a physical attack on a person etc. This paper presents the techniques of classifying the data from normal activity and violent attack on a victim. To solve this problem, the paper emphasis on classifying different activities using machine learning (supervised) techniques. Various experiments have been conducted using wearable inertial fabric sensors for different activities. These wearable e-textile sensors were woven onto the jacket worn by a healthy subject. The main steps which outline the process of activity recognition: location of sensors, pre-processing of the statistical data and activity. Three supervised algorithmic techniques were used namely Decision tree, k-NN classifier and Support Vector Machine (SVM). Based on the experimental work, the results obtained show that the SVM algorithm offers an overall good performance matched in terms of accuracy i.e. 97.6% and computation time of 0.85 seconds for k-NN and Decision Tree for all activities.
Distance and Visual Angle of Line-of-Sight of a Small Drone

Determining the distance of the line-of-sight (LOS) of a small drone is essential in both regulating drone operation and drone operator training considering public safety. A field experiment was conducted to determine the LOS distance and visual angle of a small drone. Human participants were requested to observe a drone in one of the predetermined locations in the air. They responded whether they could see and hear the drone using a five-point scale. It was found that auditory signals were insignificant in drone detection because most of the participants could not hear the drone while they could still see the drone in most of the test locations. Logistic regression analyses were conducted to predict the probability of catching the drone visually. Two models were built considering the “definitely yes” and “definitely or probably yes” criteria of visual detection. These models may be used to estimate the LOS distance and visual angle. Assuming a 50% probability of visual catching and the “definitely or probably yes” criterion, the distance and visual angle of the LOS for the Mavic Air drone without a protector were approximately 307 m and 0.065°, respectively.
WAFFLe: Weight Anonymized Factorization for Federated Learning

In domains where data are sensitive or private, there is great value in methods that can learn in a distributed manner without the data ever leaving the local devices. In light of this need, federated learning has emerged as a popular training paradigm. However, many federated learning approaches trade transmitting data for communicating updated weight parameters for each local device. Therefore, a successful breach that would have otherwise directly compromised the data instead grants whitebox access to the local model, which opens the door to a number of attacks, including exposing the very data federated learning seeks to protect. Additionally, in distributed scenarios, individual client devices commonly exhibit high statistical heterogeneity. Many common federated approaches learn a single global model; while this may do well on average, performance degrades when the i.i.d. assumption is violated, underfitting individuals further from the mean and raising questions of fairness. To address these issues, we propose Weight Anonymized Factorization for Federated Learning (WAFFLe), an approach that combines the Indian Buffet Process with a shared dictionary of weight factors for neural networks. Experiments on MNIST, FashionMNIST, and CIFAR-10 demonstrate WAFFLe’s significant improvement to local test performance and fairness while simultaneously providing an extra layer of security.
Leveraging Contextual Information for Monocular Depth Estimation

Humans strongly rely on visual cues to understand scenes such as segmenting, detecting objects, or measuring the distance from nearby objects. Recent studies suggest that deep neural networks can take advantage of contextual representation for the estimation of a depth map for a given image. Therefore, focusing on the scene context can be beneficial for successful depth estimation. In this study, a novel network architecture is proposed to improve the performance by leveraging the contextual information for monocular depth estimation. We introduce a depth prediction network with the proposed attentive skip connection and a global context module, to obtain meaningful semantic features and enhance the performance of the model. Furthermore, our model is validated through several experiments on the KITTI and NYU Depth V2 datasets. The experimental results demonstrate the effectiveness of the proposed network, which achieves a state-of-the-art monocular depth estimation performance while maintaining a high running speed.
Text-Based Image Retrieval Using Deep Learning

This chapter is mainly an advanced version of the previous version of the chapter named “An Insight to Deep Learning Architectures” in the encyclopedia. This chapter mainly focusses on giving the insights of information retrieval after the year 2014, as the earlier part has been discussed in the previous version. Deep learning plays an important role in today's era, and this chapter makes use of such deep learning architectures which have evolved over time and have proved to be efficient in image search/retrieval nowadays. In this chapter, various techniques to solve the problem of natural language processing to process text query are mentioned. Recurrent neural nets, deep restricted Boltzmann machines, general adversarial nets have been discussed seeing how they revolutionize the field of information retrieval.
Hyperspectral image denoising based on low-rank coefficients and orthonormal dictionary

Hyperspectral images (HSIs) are unavoidably contaminated by noise during data acquisition and transmission. A variety of noise reduction approaches have been developed for HSIs, in which the low-rank based methods have emerged as a powerful tool. However, most of low-rank based HSI denoising methods are designed to explore the low-rankness in the pixel space under a fixed dictionary, without seeking the optimal dictionary and exploiting the low-rankness of representation coefficients. In this work, a novel HSI denoising model based on Low-Rank Coefficients and Orthonormal Dictionary (LRCOD) is proposed. The novelty of this work lies in LRCOD's exploiting the low-rankness of representation coefficients and learning an orthonormal dictionary for HSI denoising. To solve the proposed model, an efficient alternating minimization algorithm is developed, since one sub-problem has a closed-form solution and the other can be efficiently solved by the curvilinear search algorithm. Compared with gradient-type algorithms, our algorithm is easy to implement as there is no need to tune optimization parameters like step sizes. The experimental results over both simulated and real datasets verified the superiority of the proposed method. Specifically, under various levels of noise in different scenes, LRCOD yields the best MPSNR in 30 of 35 cases and runs faster than other methods except one non-iterative algorithm.
Context reinforced neural topic modeling over short texts

As one of the prevalent topic mining methods, neural topic modeling has attracted a lot of interests due to the advantages of low training costs and strong generalisation abilities. However, the existing neural topic models may suffer from the feature sparsity problem when applied to short texts, due to the lack of context in each message. To alleviate this issue, we propose a Context Reinforced Neural Topic Model (CRNTM), whose characteristics can be summarized as follows. First, by assuming that each short text covers only a few salient topics, the proposed CRNTM infers the topic for each word in a narrow range. Second, our model exploits pre-trained word embeddings by treating topics as multivariate Gaussian distributions or Gaussian mixture distributions in the embedding space. Extensive experiments on two benchmark short corpora validate the effectiveness of the proposed model on both topic discovery and text classification.
Transcoding for Live Streaming-based on Vehicular Fog Computing: An Actor-Critic DRL Approach

As one of the most frequently launched applications in vehicular networks, live-streaming service is gaining momentum from the rapid development of automotive industry and telecommunication technologies. However, due to the dynamic characteristics of wireless links and topology of vehicular networks, providing high quality, low latency, and low variance video streaming for vehicles is a big challenge. This study aims to maximize video quality while decreasing time-delays and bitrate switches in vehicular fog computing (VFC) systems, by jointly optimizing vehicle scheduling, bitrate selection, computational and spectrum resource allocation. Considering the dynamic characteristics of vehicular networks and available computational/spectrum resource, the above problem is modeled as a Markov decision process (MDP). Since the action space of the MDP is multi-dimensional continuous variables mixed with discrete variables, the study utilizes an actor-critic deep reinforcement learning (DRL) algorithm to resolve the MDP problem. Extensive simulation results based on the dataset of the real world show the effectiveness of the proposed algorithm.
Visual short-term memory and attention: An investigation of familiarity and stroke count in Chinese characters.

Using Chinese characters, we investigated how stroke count and frequency of use influence attention and short-term memory (STM) encoding in Mainland Chinese speakers. To isolate specific components of attention we employed the Theory of Visual Attention (TVA), which allowed estimates of STM capacity, processing speed, and the threshold of visual perception. An analysis of TVA parameters revealed that familiarity affects both the memory capacity and processing speed of objects, whereas the threshold for visual perception remained unaffected. Interestingly, our results also indicate that modulation of attention is driven solely by familiarity with the characters, independent of the actual physical aspect of Chinese characters. We propose that mental categories and prior knowledge play a vital role in the processing of information in attention, as well as in how this information is stored and represented in visual STM. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Effect of source statistics on utilizing photon entanglement in quantum key distribution

A workflow for evaluation of entanglement source quality is proposed. Based on quantum state density matrices obtained from theoretical models and experimental data, we make an estimate of a potential performance of a quantum entanglement source in quantum key distribution protocols. This workflow is showcased for continuously pumped spontaneous parametric down-conversion (SPDC) source, where it highlights the trade-off between entangled pair generation rate and entanglement quality caused by multiphoton nature of the generated quantum states. We employ this characterization technique to show that secure key rate of down-converted photon pairs is limited to 0.029 bits per detection window due to intrinsic multiphoton contributions. We also report that there exists one optimum gain for continuous-wave down-conversion sources. We find a bound for secure key rate extracted from SPDC sources and make a comparison with perfectly single-pair quantum states, such as those produced by quantum dots.
An end-to-end learning of driving strategies based on DDPG and imitation learning

The Deep Deterministic Policy Gradient Algorithm (DDPG) has great advantages in continuous control problems and plays a very important role in the field of autonomous driving. However, the performance of the traditional DDPG algorithm depends on the initialization parameter settings, and it is difficult to achieve satisfactory results in the actual environment. At the same time, traditional DDPG also needs a lot of exploration to converge to a suitable control strategy. This paper proposes a DDPG algorithm framework based on imitation learning (DDPG-IL). The framework first obtains demonstration data (via IL) and stores it in the expert pool, meanwhile the DDPG algorithm is pre-trained. Then the algorithm makes reasonable use of the demonstration data and its own exploration data for learning. Finally, when the algorithm reaches an approximate expert level, it gradually becomes an ordinary reinforcement learning and continues training by self-learning until the algorithm converges to a stable state. The experimental comparison on the racing simulator TORCS proves that our proposed DDPG-IL algorithm has more advantages and can obtain better performance than the traditional DDPG algorithm.
Coded Secure Multi-Party Computation for Massive Matrices with Adversarial Nodes

In this work1, we consider the problem of secure multi-party computation (MPC), consisting of Γ sources, each has access to a large private matrix, N processing nodes or workers, and one master. The master is interested in the result of a polynomial function of the input matrices. Each source sends a randomized functions of its matrix, called as its share, to each server. The workers process their shares in interaction with each other, and send some results to the master such that it can derive the final results. There are several constraints: (1) each worker has a constraint on its storage, such that it can store equivalent of 1 m fraction of size of each input matrices from each source, (2) up to t of the workers are adversary and may collude to gain information about the private inputs or can do malicious actions to make the final result incorrect. The objective is to design an MPC scheme with the minimum number of the workers, called recovery threshold, such that the final result is correct, servers learn no information about the input matrices, and the master learns nothing beyond the final result. In this paper, we propose an MPC scheme that achieves the recovery threshold of 3t + 2m - 1 workers, which is order-wise less than the recovery threshold of the conventional methods. The main challenge is to manage the errors propagated through the network by the adversarial nodes when the workers interact with each other in each round.
Path following identification of unmanned aerial vehicles for navigation spoofing and its application

As unmanned aerial vehicles (UAVs) use global navigation satellite systems for positioning, the openness and weak security of satellite navigation signals make these systems easily falsifiable. By leveraging these characteristics, we propose a path following identification algorithm to gain control of UAVs using navigation spoofing as a countermeasure against illegal activities. Our contribution is threefold. First, we redefine the UAV kinematics model by treating the spoofing signal as wind. Subsequently, the relation between the spoofing signal and UAV heading angle is identified based on a path following algorithm, and the relation accuracy is experimentally verified. Finally, we propose the spoofing sparse A* search (SSAS) algorithm, whose effectiveness is verified by simulations of a UAV directed to a designated area while avoiding obstacles and forbidden areas.
Observability Metrics for Single-Target Tracking With Bearings-Only Measurements

This work considers the observability problem in bearings-only tracking (BOT). Although the rank criterion has always been used for observability analysis, the evident drawback that it can only give a qualitative result prevents us from exploiting more information from the observability matrix. In reality, we want to know not only if the system is observable but also the degree of the observability. In order to quantitatively describe the system observability, we propose a series of metrics for BOT. Being derived based on the condition number, the novel metrics can reflect the observability degree as well as evaluate the tracking performance. Besides, the metrics can be extended to be used for sensor trajectory optimization and sensor configuration, which are crucial to enhancing the tracking performance.