Reviewerly/exHarmony huggingface dataset:
Median Filter Aided CNN Model for Removal of Gaussian Noise from Images

Image denoising, is a research problem where we aim to recover noise-free images from those that are contaminated with noise. It is also a very challenging problem for all researchers in the field of computer vision. There are various types of noise which differ on the basis of distribution and behaviour, e.g., salt-and-pepper noise, Poisson noise, Gaussian noise, etc. In this paper, we focus on eliminating Gaussian noise from contaminated images. Historically, many methods have been proposed for image denoising but recently, it has been observed that in most of the cases, Convolutional Neural Network (CNN) is used which outperforms the conventional methods. Here, we propose a CNN as the base model, to which we add other modules to improve the performance of the image denoising method. Our model uses an attention-guided CNN, called (ADNet), where we add median filter layers for restoring images contaminated by Gaussian noise. We apply median filters on all the feature channels of an image as well as increase the dilation rate of the dilated convolutions in the ADNet and so we see, in case of the convolutional layers, that the receptive field size is increased, which in turn aids image denoising. By quantitative analysis, we are able to show that our model performs significantly well when tested on the BSD500 and Set12 datasets.
An EfficientNet-like Feature Extractor and Focal CTC Loss for Image-base Sequence Recognition

Image-based sequence recognition is an interesting topic in computer vision, which has various potential applications in real life. This paper proposes a novel convolutional-recurrent neural network (CRNN) for image-based sequence recognition. Particularly, we introduce a new convolutional backbone network for feature extraction based on the EfficientNet architecture and use focal CTC loss to train the network. Our method beats several existing state-of-the-art methods on the ICDAR 2019 Robust Reading Challenge on Scanned Receipts OCR and Information Extraction (SROIE) challenge and the IAM handwriting dataset. The experimental results show that our method yields an F1 score equivalent to the top 2 on the ICDAR 2019 SROIE challenge.
Quantum and Approximation Algorithms for Maximum Witnesses of Boolean Matrix Products

The problem of finding maximum (or minimum) witnesses of the Boolean product of two Boolean matrices (MW for short) has a number of important applications, in particular the all-pairs lowest common ancestor (LCA) problem in directed acyclic graphs (dags). The best known upper time-bound on the MW problem for $$n\times n$$ Boolean matrices of the form $$O(n^{2.575})$$ has not been substantially improved since 2006. In order to obtain faster algorithms for this problem, we study quantum algorithms for MW and approximation algorithms for MW (in the standard computational model). Some of our quantum algorithms are input or output sensitive. Our fastest quantum algorithm for the MW problem, and consequently for the related problems, runs in time $$\tilde{O}(n^{2+\lambda /2})=\tilde{O}(n^{2.434})$$ , where $$\lambda $$ satisfies the equation $$\omega (1, \lambda , 1) = 1 + 1.5 \, \lambda $$ and $$\omega (1, \lambda , 1)$$ is the exponent of the multiplication of an $$n \times n^{\lambda }$$ matrix by an $$n^{\lambda } \times n$$ matrix. Next, we consider a relaxed version of the MW problem (in the standard model) asking for reporting a witness of bounded rank (the maximum witness has rank 1) for each non-zero entry of the matrix product. First, by adapting the fastest known algorithm for maximum witnesses, we obtain an algorithm for the relaxed problem that reports for each non-zero entry of the product matrix a witness of rank at most $$\ell $$ in time $$\tilde{O}((n/\ell )n^{\omega (1,\log _n \ell ,1)}).$$ Then, by reducing the relaxed problem to the so called k-witness problem, we provide an algorithm that reports for each non-zero entry C[i, j] of the product matrix C a witness of rank $$O(\lceil W_C(i,j)/k\rceil )$$ , where $$W_C(i,j)$$ is the number of witnesses for C[i, j], with high probability. The algorithm runs in $$\tilde{O}(n^{\omega }k^{0.4653} +n^{2+o(1)}k)$$ time, where $$\omega =\omega (1,1,1)$$ .
Organizational and pedagogical conditions for network training of teachers of engineering education for pupils

Abstract Nowadays the problems of vocational guidance of pupils for engineering professions corresponding to the modern technological paradigm are particularly significant. The problem of preparing teachers for the design and implementation of engineering education programs for schoolchildren is one of these issues. The purpose of the article is to define the organizational and pedagogical conditions for network training of teachers of engineering education for pupils. The research involved 900 students of the 10th grade and 80 headmasters of secondary schools. In order to identify the students’ attitude to their engineering training and the shortage of teachers of engineering education of pupils, the testing has been carried out. The analysis of the test results by means of descriptive statistics and content analysis has confirmed the feasibility of implementing a network approach in the practice of training teachers of engineering education for pupils. According to the results of the study, it has been found out that these conditions identified by the authors allow organizations forming a networked educational space to purposefully integrate their resources in providing opportunities for students to master both pedagogical and engineering competencies necessary for a teacher of engineering education for pupils.
Hierarchical quantization for billion-scale similarity retrieval on GPUs

Existing billion-scale approximate nearest neighbor (ANN) search methods are usually based on GPU implementations. However, the search accuracy of these methods are limited by their indexing structures and encoding algorithms which are not adaptable to GPUs. We introduce an ANN search method called Vector and Product Quantization Tree (V-PQT) based on GPUs, based on a novel two-level tree indexing structure that increase indexing efficiency and a novel encoding algorithm that improved encoding quality of previous quantization methods by a large margin. Comparing to other state-of-the-art methods based on GPUs, V-PQT system can provide a significantly better recall with the same level of runtime. The experimental result shows that the proposed method has 27% and 20% improved to the conventional methods in the term of search accuracy on two public billion-scale datasets.
News2Image: Automated System of Image Recommendation to News Articles

This paper presents an image recommendation system called “News2Image”, which takes as input a news text and outputs a small set of images selected from the database such that the selected images present contexts similar to the keywords of the input text. The proposed framework starts by preprocessing and summarizing the news for the selection of its keywords, which are then converted to vectors using word2vec. The framework associates each database image with a set of sentences describing its content using image captioning. After preprocessing the sentences and translating them into the word vectors as performed for input news texts, the word subspaces are compared using the Mutual Subspace Method (MSM). Experimental evaluation of the proposed framework including a comparison with the previous approach demonstrates its overall effectiveness.
Asymmetrically distorted 3D video quality assessment: From the motion variation to perceived quality

The two-stage framework has been widely used in objective image/video quality assessment (IQA/VQA) methods, which includes local quality measurement and global quality pooling. Most of the existing IQA/VQA methods dedicate to local quality degradation modeling. Nevertheless, the significant pooling strategies are often neglected, especially in the quality assessment methods for asymmetrically distorted 3D video sequences. In this paper, motivated by the fact that the perceptual visual information might be affected by the visual masking due to motion suppression, a novel pooling strategy is proposed for visual quality assessment of asymmetrically distorted 3D video sequences by considering the motion variation-based perceptual visual information (MVPVI). In specific, to extract valuable visual information of video sequences, the proposed method computes the motion energy information (MEI) and edge energy information (EEI) by quantifying the frame difference map and the gradient map, respectively. Furthermore, in order to simulate the phenomenon of motion suppression in local motion scenes, where the perceptual information of low-attentional regions might be neglected by the human visual system (HVS), the proposed method first employs the coefficient of variation (CV) to determine the type of motion (local or global) in consecutive frames. Then, the perceptual visual information of the two cases is quantified. Finally, to simulate the role of HVS as an optimal information extractor, the estimated perceptual visual information of each single-view video sequence is used as the weighting factor for the quality pooling of left and right-view video sequences. Extensive comparison experiments conducted on Waterloo-IVC 3D video quality databases demonstrate that the proposed pooling strategy outperforms other relevant pooling strategies, and it achieves better performance than state-of-the-art 3D-IQA/VQA methods when combined with 2D-IQA/VQA methods.
An Improved YOLOv3 Model Based on ResNet50

In computer vision, object detection is one of the most challenging problems. In the one-stage object detection, YOLOv3 is one of the most popular object detection models. Because of its excellent detection speed and the same accuracy, it has a wide range of practical applications, and many kinds of derivative versions have been derived. In this paper, we used four tricks to upgrade the original yolov3 network which are resnest50, image mixup, label smoothing and CIoU loss. By adding these tricks, the average accuracy and speed of the improved model on coco2017 datasets are better than those of traditional YOLOv3.
End-to-End Speech Synthesis for Tibetan Multidialect

The research on Tibetan speech synthesis technology has been mainly focusing on single dialect, and thus there is a lack of research on Tibetan multidialect speech synthesis technology. This paper presents an end-to-end Tibetan multidialect speech synthesis model to realize a speech synthesis system which can be used to synthesize different Tibetan dialects. Firstly, Wylie transliteration scheme is used to convert the Tibetan text into the corresponding Latin letters, which effectively reduces the size of training corpus and the workload of front-end text processing. Secondly, a shared feature prediction network with a cyclic sequence-to-sequence structure is built, which maps the Latin transliteration vector of Tibetan character to Mel spectrograms and learns the relevant features of multidialect speech data. Thirdly, two dialect-specific WaveNet vocoders are combined with the feature prediction network, which synthesizes the Mel spectrum of Lhasa-Ü-Tsang and Amdo pastoral dialect into time-domain waveform, respectively. The model avoids using a large number of Tibetan dialect expertise for processing some time-consuming tasks, such as phonetic analysis and phonological annotation. Additionally, it can directly synthesize Lhasa-Ü-Tsang and Amdo pastoral speech on the existing text annotation. The experimental results show that the synthesized speech of Lhasa-Ü-Tsang and Amdo pastoral dialect based on our proposed method has better clarity and naturalness than the Tibetan monolingual model.
UAV Path Planning Based on The Fusion Algorithm of Genetic and Improved Ant Colony

A track planning method for UAV based on the fusion algorithm(GIACO) of genetic algorithm(GA) and improved ant colony algorithm (IACO) is introduced. The optimal solution that obtained by GA initializes the pheromone matrix of ant colony to improve the convergence speed. To save from a trouble of local extremum, the state transition rule of ant colony algorithm is changed and the feasible potential number of grids is considered. The method of smoothing the path uses the gradient descent. And the step size can be adjusted for evading sudden threats. Simulation experiment analysis results show that the fusion algorithm can not only evade threat safely, but also plan the trajectory quickly, safely and effectively.
A Multi-Site Quad-Band Radio Frequency Interference Monitoring Alerting and Reporting System

This paper reviews the motivation behind and development of a deployable Radio Frequency Interference (RFI) detection, alerting and reporting system which simultaneously monitors all Global Navigation Satellite System (GNSS) L-band signal transmission for disruption, captures interference events, characterizes them, notifies stakeholders of event occurrence and lastly marshals the captured data to cloud storage. Results of a multi-site international deployment program are presented and discussed.
Defenses Against Multi-sticker Physical Domain Attacks on Classifiers

Recently, physical domain adversarial attacks have drawn significant attention from the machine learning community. One important attack proposed by Eykholt et al. can fool a classifier by placing black and white stickers on an object such as a road sign. While this attack may pose a significant threat to visual classifiers, there are currently no defenses designed to protect against this attack. In this paper, we propose new defenses that can protect against multi-sticker attacks. We present defensive strategies capable of operating when the defender has full, partial, and no prior information about the attack. By conducting extensive experiments, we show that our proposed defenses can outperform existing defenses against physical attacks when presented with a multi-sticker attack.
Estimating Maximum Likelihood using the Combined Linear and Nonlinear Function in NMS for Object Detection

In object detection, Non-Maximum Suppression helps to eliminate the bounding boxes that fall below the minimum confidence threshold among N number of predicted bounding boxes. There are instances where the returned bounding boxes might be a false positive one or it may return multiple bounding boxes. To identify one accurate bounding box prediction, an algorithm that includes truer positive and reject false positives should be designed for object detection. To address this issue, many IOU (Intersection over Union) guided NMS algorithm has been suggested. The proposed research work combines linear and nonlinear functions to implement a novel fuzzy IOU-guided NMS to estimate the maximum likelihood (i.e., bounding box in our case) to fine-tune the task of object detection. The proposed work reveals that the predicted bounding boxes are very close to the ground truth and is efficient in eliminating false positives among all predicted bounding boxes.
Machine learning-based PV power forecasting methods for electrical grid management and energy trading

In this chapter, we show how it is possible to predict the day-ahead photovoltaic (PV) generation using machine learning (ML) techniques both at the level of an individual photovoltaic system and at the national level for the Italian case study. We address some critical issues related to the implementation of ML forecasting models/methods. We show two possible applications of the day-ahead PV generation forecast for PV producers/traders and for the Italian Transmission System Operator (TSO). The first deals with the day-ahead scheduling of the PV generation of utility-scale PV farms that PV producers/traders should deliver to the Italian TSO. The second is related to the imbalance between supply and demand at national level, hence to the prediction of the Italian net load made by the Italian TSO to plan the reserves needed to resolve the day-ahead production uncertainty. For both cases, we provide the economic value of solar forecasting under the current imbalance regulatory framework.
Regional Style Transfer Based on Partial Convolution Generative Adversarial Network

Nowadays style transfer has become one of the major fields of image processing. Recently, many awesome methods have been proposed. Traditional research of style transfer forces on transformation of whole painting. However, research of more flexible regional style transfer, which transforms different parts of a picture into different styles, remain open. In this paper, we present a novel Generative Adversarial Network (GAN) based regional style transfer method, by which the style features of two style datasets could be automatically learned. By given a mask, our method can transfer part of the input content image to one style and rest to the other. Specifically, we preprocess two style datasets to extract style features by two training partial convolution-based discriminators. The experimental results show that our method achieves significant performance efficiently.
A Heuristic-Based Decision Tree for Connected Components Labeling of 3D Volumes

Connected Components Labeling represents a fundamental step for many Computer Vision and Image Processing pipelines. Since the first appearance of the task in the sixties, many algorithmic solutions to optimize the computational load needed to label an image have been proposed. Among them, block-based scan approaches and decision trees revealed to be some of the most valuable strategies. However, due to the cost of the manual construction of optimal decision trees and the computational limitations of automatic strategies employed in the past, the application of blocks and decision trees has been restricted to small masks, and thus to 2D algorithms. With this paper we present a novel heuristic algorithm based on decision tree learning methodology, called Entropy Partitioning Decision Tree (EPDT). It allows to compute near-optimal decision trees for large scan masks. Experimental results demonstrate that algorithms based on the generated decision trees outperform state-of-the-art competitors.
Recommendation Model based on Mobile Commerce in Cloud Computing

To address the traditional collaborative filtering of data sparsity and cold start problem, a cloud user situational interest recommendation model under different trust information environment was proposed. A mobile commerce situational interest and rich trust information recommendation model trust relationship was introduced to solve the data sparseness problem existing in collaborative filtering algorithm, and processing methods to solve complex social network recommended by MapReduce data was proposed. Mobile commerce recommendation model of situational interest and sparse trust information was mainly devoted to solve trust less information available the reality circumstances lead to inaccurate problem based on the recommendations, specifically the situational interest similarity matrix and potential trust degree matrix were combined into a composite matrix. Then, we use the Resnick recommended formula and MapReduce data processing method to implement the recommender model.
Locally-Connected Interrelated Network: A Forward Propagation Primitive

End-to-end learning for planning is a promising approach for finding good robot strategies in situations where the state transition, observation, and reward functions are initially unknown. Many neural network architectures for this approach have shown positive results. Across these networks, seemingly small components have been used repeatedly in different architectures, which means improving the efficiency of these components has great potential to improve the overall performance of the network. This paper aims to improve one such component: The forward propagation module. In particular, we propose Locally-Connected Interrelated Network (LCI-Net)—a novel type of locally connected layer with unshared but interrelated weights—to improve the efficiency of information propagation and learning stochastic transition models for planning. LCI-Net is a small differentiable neural network module that can be plugged into various existing architectures. For evaluation purposes, we apply LCI-Net to QMDP-Net; QMDP-Net is a neural network for solving POMDP problems whose transition, observation, and reward functions are learned. Simulation tests on benchmark problems involving 2D and 3D navigation and grasping indicate promising results: Changing only the forward propagation module alone with LCI-Net improves QMDP-Net generalization capability by a factor of up to 10.
Learning Control Sets for Lattice Planners from User Preferences

This work investigates the design of a motion planner that can capture user preferences. In detail, we generate a sparse control set for a lattice planner which closely follows the preferences of a user. Given a set of demonstrated trajectories from a single user, we estimate user preferences based on a weighted sum of trajectory features. We then optimize a set of connections in the lattice of given size for the user cost function. The restricted number of connections limits the branching factor, ensuring strong performance during subsequent motion planning. Further, every trajectory in the control set reflects the learned user preference while the sub-optimality due to the size restriction is minimized. We show that this problem is optimally solved by applying a separation principle: First, we find the best estimate of the user cost function given the data, then an optimal control set is computed given that estimate. We evaluate our work in a simulation for an autonomous robot in a four-dimensional spatiotemporal lattice and show that the proposed approach is suitable to replicate the demonstrated behaviour while enjoying substantially increased performance.
Enchodroma Tumor Detection From MRI Images Using SVM Classifier

Magnetic resonance imaging is a standard modality used in medicine for bone diagnosis and treatment. It offers the advantage to be a non-invasive technique that enables the analysis of bone tissues. The early detection of tumor in the bone leads on saving the patients' life through proper care. The accurate detection of tumor in the MRI scans are very easy to perform. Furthermore, the tumor detection in an image is useful not only for medical experts, but also for other purposes like segmentation and 3D reconstruction. The manual delineation and visual inspection will be limited to avoid time consumption by medical doctors. The bone tumor tissue detection allows localizing a mass of abnormal cells in a slice of magnetic resonance (MR).
Research on pruning algorithm of target detection model with YOLOv4

With the development of deep convolutional networks in target detection, deep convolutional networks are developing in the direction of deeper parameters with greater amounts. In order to reduce the parameters and inference time of the target detection network model, a pruning algorithm combining channels and layers is proposed and used for pedestrian detection. Through L1 regularization of the channel scale factor, the channels of the convolutional layer become sparse. The channels and shortcut layer with less information are pruned, so that the model is compressed. Pruning the YOLOv4 model based on this principle. Experiments prove that sparse training is important for model pruning. Through comparative experiments, the pruned YOLOv4 model can better detect targets on the INRIA dataset. Compared with the original YOLOv4, although there is a small loss of accuracy, the volume, parameter amount and reasoning time of the pruned model are greatly reduced.