Reviewerly/exHarmony huggingface dataset:
Research on Path Planning Method of Mobile Robot Based on Improved Spider Swarm Algorithm

Path planning is one of the important fields of mobile robot research. Its advantages and disadvantages directly affect the interaction ability between the robot and the surrounding environment. The commonly used global path planning methods are prone to problems such as decision-making speed decline and deadlock when expanding space. During iterative optimization, we simulate the motion law of spider population, that is, according to female Male spiders cooperate, attract each other and interact with each other in the process of marriage, and finally get the optimal solution. Experiments show that the improved spider colony algorithm can realize the path planning of mobile robot, improve its search ability, and avoid falling into local optimization in the process of path planning. Compared with spider colony algorithm and particle swarm optimization algorithm, the optimized shortest path and actual path are better.
CLARC

The increasing ageing of the European population is an unstoppable process causing more and more demand for health and social assistance resources. The Comprehensive Geriatric Assessment (CGA) is an indispensable tool to identify the psychosocial and functional limitations of geriatric patients. The problem is that the CGA process is very long and demands a lot of time from health professionals. It is necessary to develop a robotic platform that helps in CGA. In this sense, Clarc is a mobile robot developed to help the physician to capture and manage their data during CGA procedures. The robotic platform was tested in some healthcare centers and results show elderly participants accepted the technology. Clarc is a tool that facilitates the work of health professionals given the ease of access to patient data and their evaluations, freeing these professionals to focus their activity on purely clinical tasks that require their knowledge.
Towards Face Representation Learning Conditioned on the Soft Biometrics

Abstract: In this paper, we present a method to leverage soft biometric as a means of conditioning biometrics for better face representation learning. By conditioning, we meant the soft biometric trait (age, gender, etc.) is used as an auxiliary biometric for training along with face modality while it is absent during the inference stage. We propose a two-stream deep neural network consisting of a multilayer perceptron network (MLP) and a convolutional neural network (CNN), which can learn a feature representation from soft biometric vectors and face images, respectively. The two-stream network can be optimized simultaneously and the information can be exploited from both biometrics. The learned conditioning soft biometric representation from the MLP serves as a center prototype of the feature learned from the face network, which is beneficial to contract the intra-class variation of the face feature representation. Due to the lacking of the face dataset that comes along with soft biometrics, we construct a database for evaluation purposes. Extensive experiments are performed on two face datasets that equip with soft biometrics and the results show the superiority of our method compared to the face modality alone.
The elliptic function in graph filters

This paper applies the elliptic function to design graph filters, which can obtain arbitrary precision for step graph spectral responses. This method takes the mathematical form of the traditional elliptic analog filter, and its zero-pole points are recalculated. The approach obtains the coefficients of graph filters at a low computation cost by the polynomial multiplication rather than solving a nonlinear problem. Elliptic graph filters can control the ripples in the pass- and stop-band and width of transition band. Numerical experiments show the proposed approach outperformances the compared methods in designing the desired graph frequency responses.
A training method to improve the effect of small target detection

Deep learning target detection has always been a major research direction in the field of artificial intelligence. Its research results are widely used in the fields of automatic driving, security system and medical treatment. This paper proposes a method to improve the detection effect of small targets, which realizes the detection of objects of different scales in the input image, especially to improve the detection effect of small-scale targets. Before the collected data set is sent to the neural network for training, it is first divided into three different scales according to the size of the target to be detected in the image of the data set. Then one or several images in the large target data set are stitched, the images in the small target data set are enlarged, and the above two types of images are used to form a new data set. Finally, the new data set and the original data set are sent to the neural network for training. In this paper, the YOLOX target detection network is used for verification. The results show that the detection effect of the network obtained by this method on small targets has been improved, and the missed detection rate has been reduced from 31.2% to 27.5%. At the same time, the detection effect of large and medium-sized targets has not been sacrificed.
Research on loss function for adaptive label thresholding algorithm

The loss function plays a key role in the performance of online multi-label classification algorithms. Based on the design idea of the multi-label classification hinge loss function in the adaptive label thresholding algorithm, this paper expands several binary classification loss functions to new multi-label classification loss functions, proposes several adaptive label thresholding algorithms based on these new loss functions, and investigate the impact of different loss functions on multilabel classification performance. The experimental results show that the adaptive label thresholding algorithm based on the logistic loss function achieves the best performance, and the adaptive label thresholding algorithms using different loss functions are all better than several advanced comparison algorithms.
Multi-resolution 2D-3D registration of vascular intervention based on normalized mutual information and gradient difference fusion

The registration between intraoperative 2D digital subtraction angiography and preoperative 3D computed tomography angiography can improve the visual perception of doctors during vascular interventional surgery and provide threedimensional information of blood vessels. Therefore, improving the accuracy and robustness of 2D-3D vascular registration is the key to vascular interventional surgery. In this paper, we propose a similarity measure that fuses normalized mutual information with gradient difference and adds a multi-resolution strategy to the registration framework. Experiments show that the mTRE of the proposed method is 2.1mm, and the time of each registration iteration is 174.6s. Compared with normalized mutual information and gradient difference, the proposed method has higher accuracy and faster efficiency, and achieves better results in a shorter time.
Ship detection based on improved faster R-CNN in SAR images

Ship detection in synthetic aperture radar (SAR) images is receiving more and more attention. At the same time, the need for high-precision and intelligent ship detection is becoming more and more urgent. To further improve the detection performance in SAR images, this paper proposes an improved Faster R-CNN based on feature pyramid network (FPN) and cascade network for SAR ship detection. First, Faster R-CNN is used as a baseline network to realize ship detection. Then, FPN was used to deal with the multi-scale problem of ships. Finally, the cascade network is used to further improve the ship detection performance. Experimental results on the public SAR ship detection dataset (SSDD) show that this method has better detection performance than YOLOv2, Faster R-CNN, Cascade R-CNN and RetinaNet.
Conformer-Based Speech Recognition with Linear Nyström Attention and Rotary Position Embedding

Self-attention has become an important component for end-to-end (E2E) automatic speech recognition (ASR). Recently, Convolution-augmented Transformer (Conformer) with relative positional encoding (RPE) achieved state-of-the-art performance. However, the computational and memory complexity of self-attention grows quadratically with the input sequence length. Effect of this can be significant for the Conformer encoder when processing longer sequences. In this work, we propose to replace self-attention with a linear complexity Nyström attention which is a low-rank approximation of the attention scores based on the Nyström method. In addition, we propose to use Rotary Position Embedding (RoPE) with Nyström attention since RPE is of quadratic complexity. Moreover, we show that models can be made even lighter by removing self-attention sub-layers from top encoder layers without any drop in the performance. Furthermore, we demonstrate that Convolutional sub-layers in Conformer can effectively recover the information lost due to the Nyström approximation.
Joint Learning for Addressee Selection and Response Generation in Multi-Party Conversation

A large number of multi-party conversation scenarios exist in social networks, which have been seldom studied in the field of human-machine conversation. In this paper, we study a novel task of joint learning for addressee selection and response generation in multi-party conversations. Systems are expected to select whom they address and generate the corresponding response. To solve it, we propose an end-to-end addressee selection and response generation (ASRG) model, containing an addressee selection module and a response generation module. In the selection module, we develop an addressee prediction attention scheme to obtain a unique context vector for each candidate, thereby calculating the probability of the candidate more accurately. In the generation module, we propose a Focus Transformer to generate responses. These two modules are jointly learnt to fully explore the correlations between addressee and response. Experimental results show ASRG remarkably outperforms baselines and generates relevant content for different addressees.
Encrypted Image Visual Security Index via Non-Local Recognizable Degree Evaluation

With the development of perceptual image encryption techniques, visual security evaluations of perceptually encrypted images have gained much attention. Current visual security indices isolate neither global nor local visual security evaluations. They ignore the fact that humans can reorganize partial local information to infer global information and local information can possibly be leaked in any position of the encrypted image. To address this problem, we propose a block-based non-local recognizable degree measure with a global structure similarity measure as a visual security index. In our index, the non-local searching strategy is utilized to capture leaked local information in any position of an encrypted image. The recognizable degree is evaluated by appearance recognizability and spatial structural recognizability involving human visual properties. In addition, weighted Minkowski pooling is adopted to evaluate the overall recognizable degree of all blocks depending on highly recognizable blocks. Furthermore, this overall recognizable degree is adjusted by global structure similarity, which is used to alleviate the global region structure distortion problem induced by the block partition. Experimental results demonstrate the sharpness and good robustness of our proposed index on different databases and various encryption types.
A Global to Local Guiding Network for Missing Data Imputation

Missing data imputation aims to accurately impute the unobserved regions with complete data in real world. Although many recent methods have made remarkable advances, the local homogenous regions especially in boundary and the reasonable of the imputed data are still two most challenging issues. To address these issues, we propose a novel Global to Local Guiding Network (G2LGN) based on generative adversarial network for missing data imputation, which is composed of a Global-Impute-Net (GIN), a Local-Impute-Net (LIN) and an Impute Guider Model (IGM). The GIN looks at the entire missing regions to generate and impute data as a whole. Considering the reasonable of the GIN results, IGM is assigned to capture coherent information between global and local and guide the LIN to look only at a small area centered at the missing focused regions. After the processing of these three modules, local imputed results are concatenated to global imputed results, which impute reasonable values and refine local details from rough to accurate. The comprehensive experiments on both numeric datasets and image dataset demonstrate our method is significantly superior to other 3 state-of-the-art approaches and 7 traditional methods. Besides, the extensive ablation study validates the superior performance for dealing with missing data imputation.
Few-Shot Generation By Modeling Stereoscopic Priors

Few-shot image generation, which aims to generate images from only a few images for a new category, has attracted some research interest in recent years. However, existing few-shot generation methods only focus on 2D images, ignoring 3D information. In this work, we propose a few-shot generative network which leverages 3D priors to improve the diversity and quality of generated images. Inspired by classic graphics rendering pipelines, we unravel the image generation process into three factors: shape, viewpoint and texture. This disentangled representation enables us to make the most of both 3D and 2D information in few-shot generation. To be specific, by changing the viewpoint and extracting textures from different real images, we can generate various new images even in data-scarce settings. Extensive experiments show the effectiveness of our method.
Multi Layered Deep Neural Network for Feature Extraction in Cross Domain Crowd Counting

Automated crowd density monitoring is an emerging area of research. It is a vital technology that assists during recent disease outbreaks in preserving social distancing, crowd management and other widespread applications in public security and traffic control. Modern methods to count people in crowded scenes mainly rely on Convolutional Neural Network (CNN) based models. But the model&#x2019;s ability to adapt for different domains which is referred to as cross domain crowd counting is a challenging task. To remedy this difficulty, many researchers used Spatial Fully Convolutional Network (SFCN) based crowd counting models with synthetic crowd scene data. They covered many image domains with few-shot learning to reduce the domain adaptation gap between source and target image domains. In this paper, we propose a new multi-layered model architecture instead of SFCN single-layered model architecture. The proposed model extracts more meaningful features in image scenes along with large scale variations to increase the accuracy in cross domain crowd counting. Furthermore, with extensive experiments using four real-world datasets and analysis, we show that the proposed multi-layered architecture performs well with synthetic image data and few-shot learning in reducing domain shifts.
Exploring Dual Stream Global Information For Image Captioning

In recent years, image caption methods based on the encoder-decoder framework have made promising achievements, but most of them lack the exploitation of global information. In general, visual global information can provide more fine-grain details for recognizing small objects. On the other hand, the textual global information provides a coarse understanding of the visual scene. In this paper, we propose Dual Global Enhanced Transformer (DGET) to explicitly utilize both visual and textual global information. In encoding stages, we complement two visual features with different properties to obtain a global enhanced visual representation by a novel Global Enhanced Encoder (GEE). During decoding, we proposed Global Enhanced Decoder (GED) to utilize the textual global information explicitly. To validate our model, we conduct extensive experiments on the COCO image captioning dataset and achieve superior performance over many state-of-the-art methods.
Graph Learning Information Criterion

In this paper, we propose a parameter selection method for graph learning. Graph learning, a technique of learning graphs from observations, is required in many applications, e.g., classification, prediction, and clustering. However, there is no established method to determine hyperparameters that control the strength of the regularization reflecting prior knowledge. To resolve the problem, we consider a model selection criterion for the graph learning problem based on Laplacian constrained Gaussian Markov random field. The proposed criterion is the value based on model evidence, which is used for model selection in Bayesian statistics. It can be estimated by averaging the negative log-likelihood over the posterior distribution of a graph learning model. To compute this criterion, we present an efficient sampler of the posterior distribution. In the experiment with random graphs, we demonstrate that the proposed method can select hyperparameters having a good trade-off between F-measure and relative error.
A Bert Based Joint Learning Model with Feature Gated Mechanism for Spoken Language Understanding

Intent detection (ID) and slot filling (SF) are two major tasks for spoken language understanding (SLU). Recent joint learning approaches consider the relationship between intent detection and slot filling, which leverage the shared knowledge across two tasks to benefit each other. However, most existing methods do not make full use of the BERT model and gate mechanisms to improve the semantic correlation between slot filling and intent detection tasks. In this paper, we propose a joint learning model based on BERT, which introduce dual encoder structure and utilizes semantic information by performing feature gate mechanisms in predicting intents and slots. Experimental results demonstrate that our proposed method provides very competitive results on CAIS and DDoST datasets.
On-Demand Generation of Multi-Mode Classful Objects by Feeding Fixed Random Vector to Trained Transposed Convolutional Network

On-demand generation of an object that belongs to a specific class with multiple distinctive mode is of great importance to various advanced Artificial Intelligence (AI) applications. We propose a method to generate, on-demand, complex high-dimensional classful objects that are rich in mode by feeding a low-dimensional input vector to a pre-trained Transposed Convolutional Network (TCN). The low-dimensional input vector is made up of two fix-sized segments. The first segment employs the one-hot scheme to specify the class attribute of a object, while the second segment consists of a vector of real numbers that are generated using an algorithm called Fixed Random Encoding (FRE) to encode the mode information that is specific to that particular object. Additionally, new modes can be proposed by slightly tweaking the values in the second segment. As a proof of concept, two generator networks are built and trained to produce objects with increasing level of complexity. The first one is used to generate images of any hand-written digit with various mode/style on demand; while the second one is used to generate human face images in color of different individuals that belong to a pre-specified gender.
Image generation of train wheel tread damage image based on improved generative adversarial network

Aiming at the problem of insufficient data in the detection of train wheel tread damage, a tread image generation method based on generative adversarial network is proposed. In order to ensure the authenticity of the generated tread image, a symmetric skip connection network is used in the model to build a generative network, and the Wasserstein distance is introduced into the loss function of the network. This method effectively solves the problem of the shortage of tread damage image datasets, provides a data basis for the later detection of train wheel tread damage, and also provides technical support for the construction of train wheel image datasets.
The need for a financial sector legal standard to support the NIST Cybersecurity Framework

The National Institute of Standards and Technology (NIST) published the NIST Framework for Improving Critical Infrastructure Cybersecurity of 2014, followed by an updated version in 2017. The Framework, which was developed as a joint effort between the U.S. Federal Government and the private sector, serves only as a guideline and is not mandated by any legal authority. Currently, adoption of the Framework is voluntary. The Financial Sector, one of sixteen Department of Homeland Security critical infrastructure sectors, should be incentivized to adopt the framework, based on inconsistency and accountability of best practices implementation across the sector. Global cyber attack opportunists used the 2020 COVID-19 pandemic to exploit cybersecurity vulnerabilities and gaps in the U.S. Financial Sector. The NIST Cybersecurity Framework provides guidelines for strengthening cybersecurity and identifies areas of potential cyber attack impacts. This paper is a summary of the author's published 2021 doctoral dissertation, which includes research and analysis of reported Financial Sector risks, failures and impacts due to weak or lack of cybersecurity controls. The study also provides analysis of success stories of Financial Sector and other entities which have adopted the NIST Cybersecurity Framework. Dr. Goodwin is a Senior Member of IEEE.
Multi-view images from camera array super-resolution based on deep learning

With the development of technology, we have increasing demand for high-quality images in many area. But existing imaging systems are limited by hardware limitations such as the density of their inherent sensor arrays, the image resolution sometimes fails to meet the requirements. Super-resolution technology provides a solution to improve the quality of image from the software aspect without changing the hardware device. In terms of the number of images processed in superresolution area, we generally divided super-resolution into single-image super-resolution reconstruction and multi-image super-resolution reconstruction. Limited by the details contained in the input image itself, single-image super-resolution reconstruction is often difficult to achieve the desired effect.Compared with the single-image super-resolution algorithm, the multi-image super-resolution algorithm has the advantage of making full use of the complementary information between the image sequences. Therefore, multi-image super-resolution algorithm tends to have a stronger super-resolution capability. However, it is very difficult to capture the image sequence of the same scene with complementary information. Camera array system can capture the same scene from multiple perspectives, which can better solve the problem of image sequence acquisition with complementary information. Because most of the current methods about camera array are based on traditional methods , we propose a multi-view images from camera array super-resolution with deep learning method. In this paper , we use a set of multi-view images as input. Then, feature of each image is refined with redundant information(i.e., angular information) from that of its adjacent images by Local Feature Comparison (LFC) module. Feature improved will be aggregated by Global Feature Fusion (GFF) module we proposed. Finally, we can get only one high-resolution image via high-resolution reconstruction module. Benefiting from way, the proposed method achieves better visual quality with more realistic and natural textures.