Reviewerly/exHarmony huggingface dataset:
Automatic detection of low surface brightness galaxies from Sloan Digital Sky Survey images

Low surface brightness (LSB) galaxies are galaxies with central surface brightness fainter than the night sky. Due to the faint nature of LSB galaxies and the comparable sky background, it is difficult to search LSB galaxies automatically and efficiently from large sky survey. In this study, we established the Low Surface Brightness Galaxies Auto Detect model (LSBG-AD), which is a data-driven model for end-to-end detection of LSB galaxies from Sloan Digital Sky Survey (SDSS) images. Object detection techniques based on deep learning are applied to the SDSS field images to identify LSB galaxies and estimate their coordinates at the same time. Applying LSBG-AD to 1120 SDSS images, we detected 1197 LSB galaxy candidates, of which 1081 samples are already known and 116 samples are newly found candidates. The B-band central surface brightness of the candidates searched by the model ranges from 22 mag arcsec $^ {- 2} $ to 24 mag arcsec $^ {- 2} $, quite consistent with the surface brightness distribution of the standard sample. 96.46\% of LSB galaxy candidates have an axis ratio ($b/a$) greater than 0.3, and 92.04\% of them have $fracDev\_r$\textless 0.4, which is also consistent with the standard sample. The results show that the LSBG-AD model learns the features of LSB galaxies of the training samples well, and can be used to search LSB galaxies without using photometric parameters. Next, this method will be used to develop efficient algorithms to detect LSB galaxies from massive images of the next generation observatories.
BUSnet: A Deep Learning Model of Breast Tumor Lesion Detection for Ultrasound Images

Ultrasound (US) imaging is a main modality for breast disease screening. Automatically detecting the lesions in US images is essential for developing the artificial-intelligence-based diagnostic support technologies. However, the intrinsic characteristics of ultrasound imaging, like speckle noise and acoustic shadow, always degenerate the detection accuracy. In this study, we developed a deep learning model called BUSnet to detect the breast tumor lesions in US images with high accuracy. We first developed a two-stage method including the unsupervised region proposal and bounding-box regression algorithms. Then, we proposed a post-processing method to enhance the detecting accuracy further. The proposed method was used to a benchmark dataset, which includes 487 benign samples and 210 malignant samples. The results proved the effectiveness and accuracy of the proposed method.
ThreadStates: State-based Visual Analysis of Disease Progression

A growing number of longitudinal cohort studies are generating data with extensive patient observations across multiple timepoints. Such data offers promising opportunities to better understand the progression of diseases. However, these observations are usually treated as general events in existing visual analysis tools. As a result, their capabilities in modeling disease progression are not fully utilized. To fill this gap, we designed and implemented ThreadStates, an interactive visual analytics tool for the exploration of longitudinal patient cohort data. The focus of ThreadStates is to identify the states of disease progression by learning from observation data in a human-in-the-loop manner. We propose a novel Glyph Matrix design and combine it with a scatter plot to enable seamless identification, observation, and refinement of states. The disease progression patterns are then revealed in terms of state transitions using Sankey-based visualizations. We employ sequence clustering techniques to find patient groups with distinctive progression patterns, and to reveal the association between disease progression and patient-level features. The design and development were driven by a requirement analysis and iteratively refined based on feedback from domain experts over the course of a 10-month design study. Case studies and expert interviews demonstrate that ThreadStates can successively summarize disease states, reveal disease progression, and compare patient groups.
An Efficient Multi-Task Network for Pedestrian Intrusion Detection

In the growth of intelligent surveillance and computer vision, pedestrian intrusion detection (PID) has been widely applied in security, automatic driving and many other fields. Vision-based PID aims at detecting whether pedestrians invade an area-of-interest (AoI). For dynamic PID task, we need to identify the invading individuals from a large amount of pedestrian objects in a varying AoI scene, which is very challenging. We take the first attempt to solve this issue and propose a network named PIDNet to address it. In the PIDNet, we design an efficient dual-branch network, which combines region segmentation and pedestrian object detection. The final intrusion judgement is made by analyzing the position relation between a segmented AoI and a detected pedestrian. With the lightweight network design, PIDNet can detect pedestrian objects and segment AoI simultaneously, and make intrusion judgment efficiently. In order to achieve better intrusion detection performance, we further propose a netowrk with a cross-association strategy (cross-PIDNet) to improve the feature extraction capability of dual-branch network. In this strategy, we build the connection between detection branch and segmentation branch, avoiding the redundancy of non-critical intrusion features and enhancing the expression of critical intrusion features. Meanwhile, we design a multi-scale intrusion judgment module, which makes the judgment of intrusion state more robust. Since neither public datasets nor a benchmark is available in this direction, we set up a benchmark dataset and corresponding evaluation metrics. Experimental results indicate that our cross-PIDNet can reach 74.7% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$PID\_{A}cc$</tex-math></inline-formula> and 50.8% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$PID\_{A}P$</tex-math></inline-formula> .
Branch-and-cut-and-price for multi-agent path finding

The Multi-Agent Path Finding problem aims to find a set of collision-free paths that minimizes the total cost of all paths. The problem is extensively studied in artificial intelligence due to its relevance to robotics, video games and logistics applications, but is seldom considered in the mathematical optimization community. This paper tackles the problem using a branch-and-cut-and-price algorithm that incorporates a shortest path pricing problem for finding paths for every agent independently and thirteen classes of constraints for resolving different types of conflicts. Experimental results show that this mathematical approach solves 2402 of 4430 instances compared to 2039 and 1939 by the state-of-the-art solvers Lazy CBS and CBSH2-RTC published in artificial intelligence venues. • The MAPF problem finds minimal-cost collision-free paths for cooperating agents. • This paper presents BCP, an exact algorithm for MAPF. • BCP uses branch-and-cut-and-price to decompose MAPF into easier subproblems. • It finds paths independently for each agent using a novel shortest path problem. • It then resolves thirteen classes of conflicts between agents using constraints. • BCP outperforms the two state-of-the-art solvers CBSH2-RTC and Lazy CBS.
ET-YOLOv5s: Toward Deep Identification of Students’ in-Class Behaviors

Recognizing students' behaviors in classes based on videos of their activity plays an important role in improving teaching quality and paying attention to the healthy growth of students.The existing student behavior recognition methods mainly focus on the behavior of the single student and have low performance and efficiency.To recognize the behaviors of multiple students in the classroom at the same time, we propose a fast and effective solution, called ET-YOLOv5s, which is an improved YOLOv5s with ESRGAN (Enhanced Super-Resolution Generative Adversarial Network) and a tiny object detection module in this paper.First, the ESRGAN is adopted to generate high-definition images from original images of real classroom environments in colleges to improve the recognition accuracy.Then a tiny object detection module is added to the YOLOv5s to detect the smaller objects on high-definition images, such as phones, books, and students sitting at the back of the classroom.Finally, the experimental results show that the proposed solution can detect 11 kinds of students' behaviors in classrooms, including playing mobile phones, having class, sitting with hands on face, sitting turn right, sitting turn left, bowing, sleeping, and so on.Compared with YOLOv4, YOLOv5s, and other tiny target detection algorithms, it has better detection performance and can effectively deal with the behavior recognition of multiple students.
Relevance of Green Manufacturing and IoT in Industrial Transformation and Marketing Management

In the current scenario technology has impacted all the sectors where materials and manufacturing sectors are also included. Green Manufacturing (GM) and Internet of Things (IoT) are two important applications applied in major business domains for positive results. This study shows the role of IoT as an example of emerging technology in industrial transformation (ITN) in general and in three areas of Marketing Management (M.Mgt) explained in five research proposals. This extended research article shows the relevance of IoT for achieving optimum growth, development and safe working for ITN and M.M.gt. This research paper is an instance-based qualitative analysis that explains how IoT contributed in the important fields of M.Mgt. These fields are Customer Relationship Management (CRM), Building advanced Business Process Models (BPM) and Product Life Cycle (PLC). The paper also covers a general impact and advantages of Green Manufacturing in BPM and PLC. The results show that GM and IoT are well integrated for ITN and M.Mgnt. Also the working of ITN and M.Mgnt became effective and contributed to the social benefits with the applications of GM and IoT.
Identifying self-admitted technical debt in issue tracking systems using machine learning

Abstract Technical debt is a metaphor indicating sub-optimal solutions implemented for short-term benefits by sacrificing the long-term maintainability and evolvability of software. A special type of technical debt is explicitly admitted by software engineers (e.g. using a TODO comment); this is called Self-Admitted Technical Debt or SATD. Most work on automatically identifying SATD focuses on source code comments. In addition to source code comments, issue tracking systems have shown to be another rich source of SATD, but there are no approaches specifically for automatically identifying SATD in issues. In this paper, we first create a training dataset by collecting and manually analyzing 4,200 issues (that break down to 23,180 sections of issues) from seven open-source projects (i.e., Camel, Chromium, Gerrit, Hadoop, HBase, Impala, and Thrift) using two popular issue tracking systems (i.e., Jira and Google Monorail). We then propose and optimize an approach for automatically identifying SATD in issue tracking systems using machine learning. Our findings indicate that: 1) our approach outperforms baseline approaches by a wide margin with regard to the F1-score; 2) transferring knowledge from suitable datasets can improve the predictive performance of our approach; 3) extracted SATD keywords are intuitive and potentially indicating types and indicators of SATD; 4) projects using different issue tracking systems have less common SATD keywords compared to projects using the same issue tracking system; 5) a small amount of training data is needed to achieve good accuracy.
Local Semantic Correlation Modeling Over Graph Neural Networks for Deep Feature Embedding and Image Retrieval

Deep feature embedding aims to learn discriminative features or feature embeddings for image samples which can minimize their intra-class distance while maximizing their inter-class distance. Recent state-of-the-art methods have been focusing on learning deep neural networks with carefully designed loss functions. In this work, we propose to explore a new approach to deep feature embedding. We learn a graph neural network to characterize and predict the local correlation structure of images in the feature space. Based on this correlation structure, neighboring images collaborate with each other to generate and refine their embedded features based on local linear combination. Graph edges learn a correlation prediction network to predict the correlation scores between neighboring images. Graph nodes learn a feature embedding network to generate the embedded feature for a given image based on a weighted summation of neighboring image features with the correlation scores as weights. Our extensive experimental results under the image retrieval settings demonstrate that our proposed method outperforms the state-of-the-art methods by a large margin, especially for top-1 recalls.
Robust Event-Based Vision Model Estimation by Dispersion Minimisation

We propose a novel Dispersion Minimisation framework for event-based vision model estimation, with applications to optical flow and high-speed motion estimation. The framework extends previous event-based motion compensation algorithms by avoiding computing an optimisation score based on an explicit image-based representation, which provides three main benefits: i) The framework can be extended to perform incremental estimation, i.e., on an event-by-event basis. ii) Besides purely visual transformations in 2D, the framework can readily use additional information, e.g., by augmenting the events with depth, to estimate the parameters of motion models in higher dimensional spaces. iii) The optimisation complexity only depends on the number of events. We achieve this by modelling the event alignment according to candidate parameters and minimising the resultant dispersion, which is computed by a family of suitable entropy-based measures. Data whitening is also proposed as a simple and effective pre-processing step to make the framework's accuracy performance more robust, as well as other event-based motion-compensation methods. The framework is evaluated on several challenging motion estimation problems, including 6-DOF transformation, rotational motion, and optical flow estimation, achieving state-of-the-art performance.
An improved method for text detection using Adam optimization algorithm

Optical Character Recognition (OCR) is an automatic identification technique which is applied in different application areas to translate documents or images into analysable and editable data. Printed or typed characters are easy to recognize as they have well defined shape and size, but this is not true in case of handwritten text. Handwriting of every individual is different so OCR face difficulty to recognize the characters. In past, researchers have been used different Machine Learning and Artificial Intelligence tools and techniques to analyse handwritten and printed documents and also worked to create an electronic format file from them. It is difficult to reuse this information as it is very difficult to search the content from these documents by lines or words. To solve this problem, OpenCV technique is used in this research work which focuses on training and testing of neural network model to conduct Document Image Analysis. The proposed model is named as J&M model for Text Detection from Hand written images. Implementation of research work is done in Python on MNIST database of handwritten digits. From this research work, 99.5% of training accuracy and 99% of testing accuracy was achieved along with training loss of 1.5%.
Microscopic nuclei classification, segmentation, and detection with improved deep convolutional neural networks (DCNN)

Nuclei classification, segmentation, and detection from pathological images are challenging tasks due to cellular heterogeneity in the Whole Slide Images (WSI).In this work, we propose advanced DCNN models for nuclei classification, segmentation, and detection tasks. The Densely Connected Neural Network (DCNN) and Densely Connected Recurrent Convolutional Network (DCRN) models are applied for the nuclei classification tasks. The Recurrent Residual U-Net (R2U-Net) and the R2UNet-based regression model named the University of Dayton Net (UD-Net) are applied for nuclei segmentation and detection tasks respectively. The experiments are conducted on publicly available datasets, including Routine Colon Cancer (RCC) classification and detection and the Nuclei Segmentation Challenge 2018 datasets for segmentation tasks. The experimental results were evaluated with a five-fold cross-validation method, and the average testing results are compared against the existing approaches in terms of precision, recall, Dice Coefficient (DC), Mean Squared Error (MSE), F1-score, and overall testing accuracy by calculating pixels and cell-level analysis.The results demonstrate around 2.6% and 1.7% higher performance in terms of F1-score for nuclei classification and detection tasks when compared to the recently published DCNN based method. Also, for nuclei segmentation, the R2U-Net shows around 91.90% average testing accuracy in terms of DC, which is around 1.54% higher than the U-Net model.The proposed methods demonstrate robustness with better quantitative and qualitative results in three different tasks for analyzing the WSI.
SecureDL: A privacy preserving deep learning model for image recognition over cloud

The key benefits of cloud services such as low cost, access flexibility, and mobility have attracted worldwide users to utilize deep learning algorithms for computer vision. These cloud servers are maintained by third parties, where users are always concerned about sharing their confidential data with them. In this paper, we addressed these concerns for by developing SecureDL, a privacy-preserving image recognition model for encrypted data over cloud. The proposed block-based image encryption scheme is well designed to protect image’s visual information. The scheme constitutes an order-preserving permutation ordered binary number system and pseudo-random matrices. The proposed method is proved to be secure in a probabilistic viewpoint, and using various cryptographic attacks. Experiments are conducted over several image recognition datasets, and the trade-off analytics between the achieved recognition accuracy and data encryption is well described. SecureDL overcomes the storage and computational overheads that occur with fully-homomorphic and multi-party computation based secure recognition schemes.
Redy

Redy is a cloud service that provides high performance caches using RDMA-accessible remote memory. An application can customize the performance of each cache with a service level objective (SLO) for latency and throughput. By using remote memory, it can leverage stranded memory and spot VM instances to reduce the cost of its caches and improve data center resource utilization. Redy automatically customizes the resource configuration for the given SLO, handles the dynamics of remote memory regions, and recovers from failures. The experimental evaluation shows that Redy can deliver its promised performance and robustness under remote memory dynamics in the cloud. We augment a production key-value store, FASTER, with a Redy cache. When the working set exceeds local memory, using Redy is significantly faster than spilling to SSDs.
Regression random machines: An ensemble support vector regression model with free kernel choice

Machine learning techniques have one of their main objectives to reduce the generalized prediction error. Support vector models have as a main challenge the choice of an appropriate kernel function, as well as the estimation of its hyperparameters. Such procedures are usually performed through some tests and tuning processes which require a high computational performance. In contrast, ensemble methods present a good approach to combine several models which result in a greater predictive capacity. In this paper, we propose a new ensemble method to support vector regression, namely regression random machines. The proposed method eliminates the need to choose the best kernel function during the tuning process using a random mixture of kernel functions combined with a properly bagging ensemble which considers the strength and agreement of the single models. The results demonstrated a good predictive performance through lower generalization error which overlaps the single and bagged versions of support vector models with different kernels. The usefulness of the proposed method is illustrated by simulation studies that were realized over eight artificial scenarios and twenty-seven real-world applications.
An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering

Graph neural networks (GNNs) have been widely applied in the recommendation tasks and have achieved very appealing performance. However, most GNN-based recommendation methods suffer from the problem of data sparsity in practice. Meanwhile, pre-training techniques have achieved great success in mitigating data sparsity in various domains such as natural language processing (NLP) and computer vision (CV) . Thus, graph pre-training has the great potential to alleviate data sparsity in GNN-based recommendations. However, pre-training GNNs for recommendations faces unique challenges. For example, user-item interaction graphs in different recommendation tasks have distinct sets of users and items, and they often present different properties. Therefore, the successful mechanisms commonly used in NLP and CV to transfer knowledge from pre-training tasks to downstream tasks such as sharing learned embeddings or feature extractors are not directly applicable to existing GNN-based recommendations models. To tackle these challenges, we delicately design an adaptive graph pre-training framework for localized collaborative filtering (ADAPT) . It does not require transferring user/item embeddings, and is able to capture both the common knowledge across different graphs and the uniqueness for each graph simultaneously. Extensive experimental results have demonstrated the effectiveness and superiority of ADAPT.
Do Neural Network Weights Account for Classes Centers?

The exploitation of deep neural networks (DNNs) as descriptors in feature learning challenges enjoys apparent popularity over the past few years. The above tendency focuses on the development of effective loss functions that ensure both high feature discrimination among different classes, as well as low geodesic distance between the feature vectors of a given class. The vast majority of the contemporary works rely their formulation on an empirical assumption about the feature space of a network's last hidden layer, claiming that the weight vector of a class accounts for its geometrical center in the studied space. This article at hand follows a theoretical approach and indicates that the aforementioned hypothesis is not exclusively met. This fact raises stability issues regarding the training procedure of a DNN, as shown in our experimental study. Consequently, a specific symmetry is proposed and studied both analytically and empirically that satisfies the above assumption, addressing the established convergence issues. More specifically, the aforementioned symmetry suggests that all weight vectors are unit, coplanar, and their vector summation equals zero. Such a layout is proven to ensure a more stable learning curve compared against the corresponding ones succeeded by popular models in the field of feature learning.