Reviewerly/exHarmony huggingface dataset:
Shape-driven multiple extended target tracking and classification based on B-Spline and PHD filter

Gaussian inverse Wishart probability hypothesis density (GIW-PHD) filter has been considered a promising algorithm for tracking an unknown number of multiple extended targets (MET) with ellipsoidal shapes. However, when the MET are close to one another with irregularly varying shapes, the tracking accuracy will degrade seriously due to the incorrect measurement partition. To address the problem, we propose a new multiple extended target tracking and classification algorithm based on the shape driven strategy under the framework of PHD. First, the B-spline curve technique is employed to estimate the irregular MET shapes, and then the shape features are extracted for improving the measurement partition and state update for the closely spaced MET. Finally, the MET are classified according to the estimated shape information and the Gaussian mixture implementation of the proposed algorithm is derived and presented in this work. Experimental results show that the proposed technique has a better tracking performance than the existing GIW-PHD for the closely spaced MET with irregular shapes.
Variable structure multiple model fixed-interval smoothing

This paper focuses on fixed-interval smoothing for stochastic hybrid systems. When the truth-mode mismatch is encountered, existing smoothing methods based on fixed structure of model-set have significant performance degradation and are inapplicable. We develop a fixed-interval smoothing method based on forward- and backward-filtering in the Variable Structure Multiple Model (VSMM) framework in this paper. We propose to use the Simplified Equivalent model Interacting Multiple Model (SEIMM) in the forward and the backward filters to handle the difficulty of different mode-sets used in both filters, and design a re-filtering procedure in the model-switching stage to enhance the estimation performance. To improve the computational efficiency, we make the basic model-set adaptive by the Likely-Model Set (LMS) algorithm. It turns out that the smoothing performance is further improved by the LMS due to less competition among models. Simulation results are provided to demonstrate the better performance and the computational efficiency of our proposed smoothing algorithms.
Node Slicing Broad Learning System for Text Classification

Text classification is playing an increasingly important role in natural language processing (NLP). Most research adopts deep structure neural networks to achieve text classification tasks. However, deep structure networks often suffer from time-consuming trainning process and hardware dependence. In this paper, a flat network called broad learning system (BLS) is employed to derive a novel learning method — node slicing broad learning system (NSBLS). Firstly, one-to-one correspondence between the words and the feature node groups is established to obtain a feature layer with rich words, on the basic of which the enhancement layer is generated representing the global information. Then we activate some nodes in the feature node groups, compact them with the enhancement layer and use ridge regression to obtain multiple outputs. Finally, an intergration BLS layer is used to correct and combine the multiple outputs to get the final output. The experiment shows that NSBLS has good performance on several datasets.
Exploring Dementia Detection from Speech: Cross Corpus Analysis

In this work, we present a qualitative and quantitative analysis of speech and language features derived from two different corpora with the aim to predict early signs of dementia. One corpus consists of the Interdisciplinary Longitudinal Study on Adult Development and Aging (ILSE) designed to investigate satisfying and healthy aging. It consists of more than 6500 hours of biographic interviews from 1000 participants recorded over the course of 20 years. The other corpus is a cross sectional data set created for the ADReSS challenge 2020. In an experimental study we describe a large variety of acoustic and linguistic features that are automatically extracted from speech and corresponding transcriptions. We compare different traditional classifiers, i.e. Gaussian Mixture Models, Linear Discriminant Analysis, and Support Vector Machines. Our final performance results surpass the ADReSS benchmarks.
GOS: A Large-Scale Annotated Outdoor Scene Synthetic Dataset

Scene editing has attracted increasing research interests owing to its valuable applications in the field of photography and entertainment. With style-based GAN being proposed, images can be reasonably edited on specific semantics by manipulating in latent space of the generator. However, existing datasets cannot satisfy the demands of large amounts of diverse data and rich semantic annotations at the same time, which makes the existing method difficult to edit on the content of outdoor scene images. To address these problems, we propose a large-scale, diverse synthetic dataset called "GOS dataset" generated based on a video game, which contains fine-grained semantic annotations. Extensive experiments show that utilizing the features obtained from the annotations of our dataset achieves better performance in outdoor scene editing, especially for distance and viewpoint of scenes, which indicates the extracted features have a certain generalization capability.
AdverFacial: Privacy-Preserving Universal Adversarial Perturbation Against Facial Micro-Expression Leakages

Privacy safeguards are crucial, notably now with increased virtual conferencing usage during the Covid pandemic. In contrast to conventional facial expressions that are visually obvious to humans, micro-expressions are involuntary and transient facial expressions, commonly manifested involuntarily when we aim to withhold our emotions. Advanced micro-expression recognition techniques exist that can reveal the genuine emotions that people attempt to conceal, thus threatening individual emotional privacy, as fundamental human rights would dictate that one should have a choice of what emotion is being shown or not shown. We propose the novel universal adversarial perturbation-based approach - AdverFacial - for privacy concealment against automated micro-expression analysis via deep learning techniques. We derive the optimal strategy to achieve micro-expression misclassification with a high success rate, low perceptibility and cross neural network transferability. We perform experiments on two popular datasets with state-of-the-art microexpression spotting and recognition models and demonstrate our approach’s effectiveness in emotional concealment.
Derivative feature and residual spatial attention for low-light image enhancement

Images taken in low-light conditions often have the problem of poor visibility. Besides inadequate lightings, different types of image quality degradation, such as a large amount of noise and color loss due to the limited quality of cameras and camera ISO setting, cause low quality of the captured image. However, directly amplifying the darkness of the lowlight image will inescapably bring into the pollution of the image. Therefore, the task of low-light image enhancement needs to kindle the dark regions and remove image degradation. To achieve this task, our work builds a Retinex theorybased neural network, which decomposes the input images into an illumination map and a reflectance map. Illumination map, representing the light information, is used for brightness adjustment, while reflectance map, representing the color information, is responsible for reconstructing low-light image into enhanced image with adjusted illumination map. However, there are few studies that notice the derivative of the image is used to solve the noise problem in Retinex decomposition and use spatial attention-based residual structures to increase the effect of light enhancement. For Decomposition sub-Network (Decom-Net), we purpose derivative features to alleviate the occurrence of noise in the reflectance map in the process of low-light image decomposition. For Illumination Enhancement sub-Network (Relight- Net), we use the Gaussian blur for reducing the problem of brightness enhancement degradation and build the Residual Spatial Attention Block (RSAB) to enlarge the volume and increase the capability of pixel-to-pixel mapping. Experiments are implemented to shows the effectiveness of our network, which improves the performance of previous methods on a large scale.
Combating False Sense of Security: Breaking the Defense of Adversarial Training Via Non-Gradient Adversarial Attack

Adversarial training is believed to be the most robust and effective defense method against adversarial attacks. Gradient-based adversarial attack methods are generally adopted to evaluate the effectiveness of adversarial training. However, in this paper, by diving into the existing adversarial attack literature, we find that adversarial examples generated by these attack methods tend to be less imperceptible, which may lead to an inaccurate estimation for the effectiveness of the adversarial training. The existing adversarial attacks mostly adopt gradient-based optimization methods and such optimization methods have difficulties in searching the most effective adversarial examples (i.e., the global extreme points). On the contrast, in this work, we propose a novel Non-Gradient Attack (NGA) to overcome the above-mentioned problem. Extensive experiments show that NGA significantly outperforms the state-of-the-art adversarial attacks on Attack Success Rate (ASR) by 2% ∼ 7%.
Exploring the Effect of ℓ<sub>0</sub>/ℓ<sub>2</sub> Regularization in Neural Network Pruning using the LC Toolkit

The LC Toolkit is an open-source library written in Python and PyTorch that allows to compress any neural network using several compressions including quantization, pruning, and low-rank. The versatility of the framework is rooted in the principled mathematical formulation of the underlying network compression problems with subsequent optimization by learning-compression (LC) algorithm. In this paper, we utilize the LC toolkit’s common algorithmic base to take a deeper look into ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> -constrained pruning problems defined as follows: given a budget of κ non-zero weights, which weights should be pruned in the final network? We observe that ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> -pruned networks have a different connectivity structure compared to pruning results using ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> norm. We propose a change to the formulation of the problem involving a small amount of ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> weight decay which has a favorable effect on connectivity structure. We study the properties of the proposed ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> + ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> formulation using the LC toolkit and empirically demonstrate that such a scheme achieves a competitive sparsity-error tradeoff while having better structural sparsity.
Heterogeneous Graph Node Classification With Multi-Hops Relation Features

In recent years, knowledge graph (KG) has obtained many achievements in both research and industrial fields. However, most KG algorithms consider node embedding with only structure and node features, but not relation features. In this paper, we propose a novel Heterogeneous Attention (HAT) algorithm and use both node-based and path-based attention mechanisms to learn various types of nodes and edges on the KG. To better capture representations, multi-hop relation features are involved to generate edge embeddings and help the model obtain more semantic information. To capture a more complex representation, we design different encoder parameters for different types of nodes and edges in HAT. Extensive experiments validate that our HAT significantly outperforms the state-of-the-art methods on both the public datasets and a large-scale real-world fintech dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Research on task allocation of multi-UAVs based on improved Particle Swarm Optimization algorithm

Task allocation of multiple unmanned aerial vehicles (multi-UAVs) is a typical NP-hard problem. In this paper, according to practical battlefield needs, mathematical model is constructed based on complex constrains of task allocation, and objective function is constructed based on multi-UAVs’ global voyage and task time. An improved strategy of particle position based on basic Particle Swarm Optimization (PSO) algorithm is applied to the problem, and reasonable allocation schemes are obtained. The allocation schemes meet the complex constrains including task sequence, time window, UAVs’ capacities and flight path, and can be chosen and adjusted flexibly by the decision maker according to the practical battlefield needs. A large number of simulation experiments show that improved PSO algorithm is effective and provides a reference for multi-UAVs’ task allocation problem with complex constrains and multi-objectives.
DAOs vs. PBCs for Public Administration and Social Policy Entities

The law in most countries has long recognized entities other than individual humans a matter of social policy. Entities recognized by the law are subject to the benefits of legal enforcement of applicable rights (e.g., property ownership rights) and burdens (e.g., taxation). Legal identities are thus a fundamental characteristic of modern society. Corporations are traditional, non-human legal entities. Public benefit corporations have recently emerged as a new type of corporation. Technology advancements have also created robots and decentralized autonomous organizations (DAOs) that are also gaining legal recognition. This chapter reviews and contrasts recent traditional and cyber evolutions of corporate legal entities. The benefits and challenges of the cyber approaches for corporate entities are developed and positioned within broader digital transformation trends impacting public and private sectors and accelerated by the recent pandemic.
Speaker Identity Preservation in Dysarthric Speech Reconstruction by Adversarial Speaker Adaptation

Dysarthric speech reconstruction (DSR), which aims to improve the quality of dysarthric speech, remains a challenge, not only because we need to restore the speech to be normal, but also must preserve the speaker’s identity. The speaker representation extracted by the speaker encoder (SE) optimized for speaker verification has been explored to control the speaker identity. However, the SE may not be able to fully capture the characteristics of dysarthric speakers that are previously unseen. To address this research problem, we propose a novel multi-task learning strategy, i.e., adversarial speaker adaptation (ASA). The primary task of ASA fine-tunes the SE with the speech of the target dysarthric speaker to effectively capture identityrelated information, and the secondary task applies adversarial training to avoid the incorporation of abnormal speaking patterns into the reconstructed speech, by regularizing the distribution of reconstructed speech to be close to that of reference speech with high quality. Experiments show that the proposed approach can achieve enhanced speaker similarity and comparable speech naturalness with a strong baseline approach. Compared with dysarthric speech, the reconstructed speech achieves 22.3% and 31.5% absolute word error rate reduction for speakers with moderate and moderate-severe dysarthria respectively. Our demo page is released here <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Low-light image enhancement based on variational Retinex model

The low-light image enhancement plays a crucial role in computer vision and multimedia applications. However, it is still a challenging task, as the degraded images reduce the visual naturalness and visibility. To address this problem, we build a novel variational Retinex model to accurately estimate the illumination and reflectance components. The illumination and reflectance are jointly updated by alternating optimization algorithm. Experimental results on several public datasets demonstrate that the proposed method outperforms the state-of-the-art methods in Retinex decomposition and illumination adjustment.
Domain Adaptation via Mutual Information Maximization for Handwriting Recognition

Deep learning models for handwriting recognition have been developed in recent years. To improve the model’s generalization ability for sequence modeling task, this paper proposes to use domain adaptation with statistical distribution alignment and entropy regularization. For statistical distribution alignment, a domain adaptation loss function is proposed by using both the first and second order statistical information of deep feature representations, which is equivalent to maximizing the mutual information in feature spaces of the source domain and target domain. For entropy regularization, the entropy of the predicted text symbols of unlabeled samples in the target domain is also utilized as an additional loss function, which maximizes the mutual information between the feature space and pattern space in the target domain. Experimental results on the IAM handwriting dataset have demonstrated the effectiveness of the proposed domain adaptation method for sequence modeling task.
Interfacing SYCL and Python for XPU Programming

This paper introduces a new framework to help build and use SYCL-based Python native extensions. We present the core design and implementation detail of the framework that includes an overview of the API, a technique to support asynchronous SYCL kernel execution via Python, and discussion around the usage of Python extension generator tools to build SYCL-based extensions. Details of ongoing work are presented and we demonstrate the development of a performance portable Python native extension that relies on the SYCL-based oneMKL specification.
Fuzzy cognitive model of agricultural economic growth

Agrarian growth is becoming increasingly important to many countries as the global demand for food rises, natural resources become scarcer, and environmental problems deepen. Herein, I propose a mechanism for designing agricultural growth management strategies that is based on fuzzy cognitive logic. The research presented is built on three main findings. First, it integrates established theories of economic growth, economic cyclicality, and sectoral market theories into a model of agricultural growth management. This enables the identification of main growth factors and the determination of the nature of their effects on agricultural dynamics. Second, I develop an algorithm for cognitive analysis of agricultural growth management and justify both this mathematical apparatus and the tools it uses. And third, I conduct a computational experiment that applies cognitive technologies to generate what I believe is the best agricultural economic growth strategy for Russia.
A Closer Look at Autoencoders for Unsupervised Anomaly Detection

Unsupervised anomaly detection is a challenging problem, where the aim is to detect irregular data instances. Interestingly, generative models can learn data distribution, and thus have been proposed for anomaly detection. In this direction, the variational autoencoder (VAE) is popular, as it enforces an explicit probabilistic interpretation of the latent space. We note that there are other generative autoencoders (AEs) such as the denoising AE (DAE) and contractive AE (CAE), which also model data generation process without enforcing an explicit probabilistic latent space interpretation. While it is intuitively straightforward to see the benefit of a latent space with explicit probabilistic interpretation for generative tasks, it is unclear how this can be crucial for anomaly detection problems. Consequently, our exposition in this paper is to investigate the extent to which different latent space attributes of AEs impact their performances for anomaly detection tasks. We take the conventional and deterministic AE that we refer to as plain AE (PAE) as the baseline for performance comparison. Our results obtained using five different datasets reveal that an explicit probabilistic latent space is not necessary for good performance. The best results on most of the datasets are obtained using CAE, which enjoys stable latent representations.
Median filter for denoising MRI: Literature review

In this paper, we study Alzheimer&#x2019;s disease, noise in MRI, the Median filter and state of the art in this domain. The disease of the century, Alzheimer&#x2019;s disease, which characterized the loss of various abilities such: cognitive, thinking, remembering, reasoning and behavioral. One of the tools to recognize an early detection of Alzheimer&#x2019;s disease is medical imaging and the most utilized is the Magnetic Resonance Imaging (MRI). It is a grey color image with intensity in the range 0-255. Medical image segmentation helps us to pull out precious knowledge from a large quantity of medical image data. For better image segmentation, further phases must be processed. Like, reducing noise from MRI. Gaussian noise and Salt and pepper noise are examples of noises present in images. There are many denoising techniques, like the filtering domain and especially the median filter that proves its effectiveness in reducing the Salt and pepper noise.
Adversarial Linear Quadratic Regulator under Falsified Actions

Reinforcement learning (RL) has been widely employed in communications, in the areas of interference management, resource allocation, signal detection, and power control, etc. Nevertheless, RL is vulnerable under various malicious attacks, such as adversarial examples and privacy intrusions. In this paper, a falsification attack on the agent actions in a scalar linear quadratic regulator (LQR) system is studied. This adversarial problem is formulated as a novel dynamic game by introducing an adversarial belief, and subgame perfect equilibria (SPEs) are characterized under different adversarial constraints. Numerical experiments show the impact of strategic interactions and justify the theoretic results.
Adoption Of Blockchain In Trade Finance And Its Impact On Financial Decision Making

Blockchain has been able to create a significant impact in the fields of banking, investing and cryptocurrency in the last few years. It&#x2019;s recent deployment in the area of trade finance has also shaped up the area of banking and finance. Blockchain has been able to overcome the drawbacks that were eminent with the traditional methods of trade financing like cost, time and errors involved in it. Big and established companies, especially operating in the trading and shipping industry have been able to deploy blockchain methods in their financial transactions and have observed significant impacts due to it. By incorporating Blockchain, automated financial decision making is also made possible as the technology is able to confirm the authenticity of the transactions on its own. However, a comprehensive study of the technology in the area of trade finance is minimal. Thus, the aim of this paper is to study the adoption of blockchain technologies in trade finance and their impact on financial decision making, while also providing future implications for the same. The approach followed is of a case study approach that would make use of a few major use cases of established organizations in the sectors of trading &amp; investment and technology &amp; IT consulting with respect to the method of blockchain that they adopted. The study would be useful for academicians, the subject matter experts, and the organizations operating in the sectors mentioned as they would be able to take decisions to deploy the technology to their benefit.