Reviewerly/exHarmony huggingface dataset:
A rapid segmentation method of cell boundary for developing embryos using machine learning with a personal computer

Cell segmentation is crucial in the study of morphogenesis in developing embryos, but it had been limited in its accuracy until machine learning methods for image segmentation like U-Net. However, these methods take too much time. In this study, we provide a rapid method for cell segmentation using machine learning with a personal computer, termed Cell Segmentator using Machine Learning (CSML). CSML took four seconds per image with a personal computer for segmentation on average, much less than time to obtain an image. We observed that F-value of segmentation by CSML was around 0.97, showing better performance than state-of-the-art methods like RACE and watershed in assessing the segmentation of Xenopus ectodermal cells. CSML also showed slightly better performance and faster than other machine learning-based methods such as U-Net. CSML required only one whole embryo image for training a Fully Convolutional Network classifier and only two parameters. To validate its accuracy, we compared CSML to other methods in assessing several indicators of cell shape. We also examined the generality of this approach by measuring its performance of segmentation of independent images. Our data demonstrate the superiority of CSML, and we expect this application to improve efficiency in cell shape studies.
Plugins to Detect Vulnerable Plugins: An Empirical Assessment of the Security Scanner Plugins for WordPress

WordPress, possibly world's the most popular Content Management System (CMS), which supports around 455 million websites and claims 60.3% of all content management systems in use. The WordPress core is known to be relatively secure, but its plugin ecosystem is not. 92% of vulnerabilities found in WordPress powered websites are attributed to third-party plugins that those websites depend on.This paper presents an empirical study, where we examine the efficacy of 11 WordPress security scanner plugins in the detection of known vulnerabilities in another set of 51 insecure plugins. The results are mixed, with some security scanner plugins failing entirely and even the most effective plugins failing to identify significant vulnerabilities. The findings are derived based on both a quantitative analysis and a deeper qualitative analysis.
Mini Pool: Pooling hardware architecture using minimized local memory for CNN accelerators

Research to solve the problem of memory bandwidth shortage of convolutional neural network accelerators is continuously being studied with the development of deep neural network technology. We drastically eliminate the memory bandwidth of the pooling process through parallelization and propose a hardware structure that minimizes the amount of internal memory used for pooling. The proposed pooling hardware has identified that some constraints exist to minimize internal memory, but is applicable to most existing networks, and uses less than 60% of memory resources than conventional pooling hardware.
SCV-Stereo: Learning Stereo Matching From a Sparse Cost Volume

Convolutional neural network (CNN)-based stereo matching approaches generally require a dense cost volume (DCV) for disparity estimation. However, generating such cost volumes is computationally-intensive and memory-consuming, hindering CNN training and inference efficiency. To address this problem, we propose SCV-Stereo, a novel CNN architecture, capable of learning dense stereo matching from sparse cost volume (SCV) representations. Our inspiration is derived from the fact that DCV representations are somewhat redundant and can be replaced with SCV representations. Benefiting from these SCV representations, our SCV-Stereo can update disparity estimations in an iterative fashion for accurate and efficient stereo matching. Extensive experiments carried out on the KITTI Stereo benchmarks demonstrate that our SCV-Stereo can significantly minimize the trade-off between accuracy and efficiency for stereo matching. Our project page is https://sites.google.com/view/scv-stereo.
RETRACTED: The wheel of science: A model for managing scientific activities in higher education as a factor in developing flexible skills of the youth in the region

• A structural and functional model for managing scientific activities has been developed. • Tools for managing scientific activities have been proposed. • The main stages of scientific activity management have been structured. The purpose of the study was to create and introduce a model for the management of scientific activities that reflects the current situation, contributes to the implementation of general management functions, describes the capabilities of science, and can be further used to develop students’ flexible skills. The experiment was conducted in the South Ural State Humanitarian Pedagogical University (Ural Federal District, Russia) in 2019–2021. The theoretical framework of this study was the theory of management functions by Henri Fayol. The cross-functional teams included individuals of different ages (teachers and students) who had equal rights and had not previously collaborated with each other in the field of scientific research. The functional roles of study participants necessary for creating a research product were “manager”, “idea giver”, “test conductor”, “analyst”, and “editor”. In total, seven teams of 49 students were formed. The main theoretical results of this study are represented by defined concepts of general management functions, established principles for the management of scientific activities, offered tools for managing scientific activities, and the developed structural and functional model for managing research activities. The practical research results reside in the experimental implementation of the scientific activity management model in higher education. As a result of the experiment, intellectual product-related (scientific publications, presentations delivered at conferences) and educational (improvement of students’ flexible skills) results were obtained. Furthermore, the influence of research activities on the formation of flexible skills of students and the dynamics of their skills formation in accordance with the function performed in the team (positive, neutral, and negative) was revealed. In general, the students involved in research activity management showed an improvement in all flexible skills. The scientific value of the developed model is in the implementation of the DBR methodology, supplemented by a mix of philosophical, theoretical, and methodological approaches. It allowed forming and studying the social mechanism of research activity management to improve students’ flexible skills.
Learning decomposed hierarchical feature for better transferability of deep models

Deep models have achieved prominent results in pattern recognition tasks, especially computer vision and natural language processing. However, the dataset bias caused by the distribution discrepancy between the training and testing data hinders the generalization ability of deep models. Though many domain adaptation approaches have been proposed to mitigate such negative effect, most of them improve the transferability of features by aligning global distributions of deep models. Few researchers pay attention to the versatility of deep features which can play a vital role in cross-domain recognition. In this paper, we propose to enrich the classic deep learning models by capturing high-low-frequency information and multi-scale features, which deal with the domain shift that cannot be easily addressed by merely feature-level alignment. The Hierarchical Transfer Network (HTN) leverages octave convolution, pyramid features, and self-attention mechanism for revamping the classic models, which can be further integrated with any domain alignment approaches by replacing the feature extractor with the proposed HTN. Extensive experiments have been conducted on three public domain adaptation benchmarks. The results show that the proposed HTN can effectively improve adversarial-based, statistics-based, and norm-based domain adaptation approaches, achieving competitive performance without involving model complexity.
Cascade Attention Blend Residual Network For Single Image Super-Resolution

Nowadays, deep convolutional neural networks are playing an increasingly important role in single-image super-resolution vision applications. Yet, most of the existing deep convolution-based methodologies are insufficiently intelligent to capture targeted information when the distribution of spatial and channel information is uneven for low-resolution images. To address this research issue, we propose a cascade attention blend residual network, with the non-local channel and multi-scale attention being considered for channel-wise dependencies and multi-scale receptive fields, respectively. Cascading both attentions in a potent blend residual block aims to learn more spatial and channel correlations between low- and super-resolution images. Experimental results demonstrate that the proposed method achieves promising performance for super-resolution image reconstruction, as well as gains an average reduction of 50.9% network parameters, compared to some state-of-the-art methods.
Morphological Feature Aware Multi-CNN Model for Multilingual Text Recognition

Text recognition is a crucial and challenging task, which aims at translating a cropped text instance image into a target string sequence. Recently, Convolutional neural networks (CNN) have been widely used in text recognition tasks as it can effectively capture semantic and structural information in text. However, most existing methods are usually based on contextual clues. If only recognize a single character, the accuracy of these approaches can be reduced. For example, it is difficult to distinguish 0 and O in the traditional CNN network because they are very similar in composition and structure. To solve this problem, we propose a novel neural network model called Morphological Feature Aware Multi-CNN Model for Multilingual Text Recognition (MFAM-CNN) in this article. We introduce a contour extraction model to enrich the representation ability, which can distinguish characters with similar shapes. Self-adaptive text density classification module is designed to recognize characters of different densities, improving the accuracy of character recognition. In general, the model is more sensitive to the overall size of the text, which improves the recognition rate of similar text. To evaluate the effectiveness of our approach, we make a dataset containing Chinese, numbers, and letters called SC dataset. Extensive experiments are conducted on the above SC dataset and ARTI-TEXT dataset, the results demonstrate that our model significantly improves the performance, achieving 98.03% and 97.77% respectively.
Reinforcement Learning From Hierarchical Critics

In this study, we investigate the use of global information to speed up the learning process and increase the cumulative rewards of reinforcement learning (RL) in competition tasks. Within the framework of actor-critic RL, we introduce multiple cooperative critics from two levels of a hierarchy and propose an RL from the hierarchical critics (RLHC) algorithm. In our approach, each agent receives value information from local and global critics regarding a competition task and accesses multiple cooperative critics in a top-down hierarchy. Thus, each agent not only receives low-level details, but also considers coordination from higher levels, thereby obtaining global information to improve the training performance. Then, we test the proposed RLHC algorithm against a benchmark algorithm, that is, proximal policy optimization (PPO), under four experimental scenarios consisting of tennis, soccer, banana collection, and crawler competitions within the Unity environment. The results show that RLHC outperforms the benchmark on these four competitive tasks.
Representation learning of graphs using graph convolutional multilayer networks based on motifs

The graph structure is a commonly used data storage mode, and it turns out that the low-dimensional embedded representation of nodes in the graph is extremely useful in various typical tasks, such as node classification, link prediction, etc. However, most of the existing approaches start from the binary relationship (i.e., edges) in the graph and have not leveraged higher order local structure (i.e., motifs) of the graph. Here, we propose mGCMN – a novel framework which utilizes node feature information and the higher order local structure of the graph to effectively generate node embeddings for previously unseen data. Through research we have found that different types of networks have different key motifs. And the advantages of our method over the baseline methods have been demonstrated in a large number of experiments on citation network and social network datasets. At the same time, a positive correlation between increase of the classification accuracy and the clustering coefficient is revealed. It is believed that using high order structural information can truly manifest the potential of the network, which will greatly improve the learning efficiency of the graph neural network and promote a brand-new learning mode establishment.
Afdn: Attention-Based Feedback Dehazing Network For Uav Remote Sensing Image Haze Removal

To efficiently remove haze in unmanned aerial vehicle (UAV) remote sensing images, a novel attention-based feedback dehazing network (AFDN) is proposed, which is constructed by feedback connections and attention-based feedback blocks (AFBs). It has three major advantages compared with other dehazing algorithms: 1) The feedback connections, which allow network to use previous state to improve current performance, can effectively help the proposed AFDN generate clear remote sensing scenes progressively. 2) The AFBs are specially designed to extract global residual features, in which the dual attention block can usefully reduce redundant information and improve the fitting ability of network. 3) To obtain abundant texture information from UAV remote sensing images and restore real ground surfaces, an energy loss is employed for texture features learning. Experiments on synthetic datasets and real UAV remote sensing images verify the superiority of AFDN over several state-of-the-art methods in terms of qualitative and quantitative analysis.
Real-Time Face Mask Detection in Deep Learning using Convolution Neural Network

COVID -19 Pandemic has affected human being without distinguishing the region and religion around the world. Even though vaccine is available now, we should still be use precautionary steps to avoid being affected by this virus. Use of face mask will help to stop the spread of the virus. COVID - 19 face mask detector will help in detecting whether a person is wearing mask or not by the use of its on face mask net with deep learning techniques. With suggested technique of deep learning get an accuracy of 98 percent. Our suggested technique also works with still pictures and give effective results with live video stream captured using the CCTV cameras and able to identify whether person is using face mask or not. It is very useful at the place of the mass gathering and screening like shopping mall and railway station and shopping streets and colleges. By using this tool for the face mask will help in regulating the use of it and if person found not using it will be easily warned by the regulator.
Detecting C&amp;W Adversarial Images Based on Noise Addition-Then-Denoising

In this paper, we focus on detecting adversarial images generated by the white-box adversarial attack proposed by Carlini and Wagner (C&W for short). The C&W attack is one of the most powerful attacks which has achieved nearly 100% attack success rates for fooling deep neural network (DNN) yet keeping the visual quality of adversarial image. Considering that the C&W attack optimizes a loss function based on the logit layer of DNN to find adversarial perturbations, we first add Gaussian noise to destroy the perturbations. For the high-confidence adversarial image, a strong Gaussian noise is employed. In order to reduce the impact of such strong noise on a legitimate image, a FFDNet filter is utilized to execute denoising. By comparing the prediction on a test image with that on its noise added-then-denoised version, the proposed method detects the test image as adversarial when the predictions are different. The experiments on ImageNet show that the proposed method can effectively detect targeted and un-targeted C&W adversarial images generated on famous models: Resnet-50, Inception v2, and Inception v3, achieving higher F1 scores than the-state-of-art.
Reinforced Curriculum Learning For Autonomous Driving In Carla

Autonomous Vehicles promise to transport people in a safer, accessible, and even efficient way. Nowadays, real-world autonomous vehicles are build by large teams from big companies with a tremendous amount of engineering effort. Deep Reinforcement Learning can be used instead, without domain experts, to learn end-to-end driving policies. Here, we combine Curriculum Learning with deep reinforcement learning, in order to learn without any prior domain knowledge, an end-to-end competitive driving policy for the CARLA autonomous driving simulator. To our knowledge, this is the first work which provides consistent results of our driving policy on all the town scenarios provided by CARLA. Moreover, we point out two important issues in reinforcement learning: the former is about learning the value function in a stable way, whereas the latter is related to normalizing the learned advantage function. A proposal of a solution to these problems is provided.
Wavelet invariants for statistically robust multi-reference alignment

Abstract We propose a nonlinear, wavelet-based signal representation that is translation invariant and robust to both additive noise and random dilations. Motivated by the multi-reference alignment problem and generalizations thereof, we analyze the statistical properties of this representation given a large number of independent corruptions of a target signal. We prove the nonlinear wavelet-based representation uniquely defines the power spectrum but allows for an unbiasing procedure that cannot be directly applied to the power spectrum. After unbiasing the representation to remove the effects of the additive noise and random dilations, we recover an approximation of the power spectrum by solving a convex optimization problem, and thus reduce to a phase retrieval problem. Extensive numerical experiments demonstrate the statistical robustness of this approximation procedure.
Explainable link prediction based on multi-granularity relation-embedded representation

Existing link prediction methods focus on mining relations of nodes in terms of network structure, ignoring rich attributes of nodes. In the micro-blog social networks, text contents describe users’ diverse behaviors, which depicts ones’ multi-dimensional and multi-granularity preferences. Thus, based on users’ multi-granularity interest, we can predict and explain social relations among users. In this work, we develop an explainable social relation extraction method based on hierarchical semantic specificity matrices of user interest. First, according to users’ micro-blog contents, we model user interest subjects and design their semantic specificity matrices. Then, on the basis of interest subjects, we learn multi-dimensional and multi-granularity semantic specificity matrices with translation mechanism, which reflects user pairs’ follow relation causes from multiple aspects. Finally, by leveraging multi-granularity semantic specificity matrices, we predict and explain users’ social follow relation. To justify our proposal, we conduct extensive experiments on Sina Weibo and Tencent Weibo datasets. The experimental results demonstrate that the proposed model outperforms state-of-the-art methods in Hits, MeanRank and MRR metrics. The proposal can effectively predict users’ follow relation from the aspects of multi-granularity interest subjects, and improve the accuracy of link prediction in social networks.
An Adaptive BP Neural Network Model for Teaching Quality Evaluation in Colleges and Universities

There is currently no fair, rational, or scientific approach for evaluating college teachers’ teaching abilities. Mathematical methods are frequently used to measure the teaching capacity of college instructors in order to make it more scientific. Traditional statistical analysis evaluation models, fuzzy evaluation methods, grey decision methods, and the analytic hierarchy process (AHP) are only a few examples. Because teacher assessment is a nonlinear problem, even though the preceding methods have produced some positive results, they are vulnerable to some subjectivity. In this paper, the neural network model is incorporated into the adaptive vector and momentum of the modified BP neural network of a gradient descent method to boost the model’s convergence speed, and the model is thoroughly researched to evaluate university teaching quality, and the network structure is omitted to address the complex nonlinear problem of college and university teaching quality assessment. The model’s comprehensive evaluation of teaching activities is then bolstered by the addition of new evaluation indexes to the existing ones.
Emotion Analysis of Covid Tweets using FastText Supervised Classifier model

The world has come to a standstill due to the Covid-19 Pandemic. Managing mental and psychological health remains as important as physical health in such situations. With work from home becoming the new normal, social media has been an integral part of sharing user sentiments through tweets during these tough times which can later be used to interpret the emotions behind those tweets. Our system of study aims to perform emotion analysis by fetching user data from twitter, and analysing those tweets to understand user sentiments over a period of time. This is achieved by using Facebook's Fasttext for text classification which classifies tweets into emotions namely Anger, Relief, Boredom, Happiness, Hate, Fun, Love, Surprise, Worry, Enthusiasm, Sadness and Empty in the most fastest and efficient way as compared to other algorithms. FastText is an NLP Library generally used for text representations and classifications. The classified sentiments over a period of time gives better understanding of people's mental health and how the sentiments have changed overtime.
Usable comprehensive-factor authentication for a secure time attendance system

In information security, it is widely accepted that the more authentication factors are used, the higher the security level. However, more factors cannot guarantee usability in real usage because human and other non-technical factors are involved. This paper proposes the use of all possible authentication factors, called comprehensive-factor authentication, which can maintain the required security level and usability in real-world implementation. A case study of an implementation of a secure time attendance system that applies this approach is presented. The contribution of this paper is therefore to provide a security scheme seamlessly integrating all classical authentication factors plus a location factor into one single system in a real environment with a security and usability focus. Usability factors emerging from the study are related to a seamless process including the least number of actions required, the lowest amount of time taken, health safety during the pandemic, and data privacy compliance.