Reviewerly/exHarmony huggingface dataset:
Learning to Estimate Without Bias

The Gauss-Markov theorem states that the weighted least squares estimator is a linear minimum variance unbiased estimation (MVUE) in linear models. In this paper, we take a first step towards extending this result to non-linear settings via deep learning with bias constraints. The classical approach to designing non-linear MVUEs is through maximum likelihood estimation (MLE) which often involves computationally challenging optimizations. On the other hand, deep learning methods allow for non-linear estimators with fixed computational complexity. Learning based estimators perform optimally on average with respect to their training set but may suffer from significant bias in other parameters. To avoid this, we propose to add a simple bias constraint to the loss function, resulting in an estimator we refer to as Bias Constrained Estimator (BCE). We prove that this yields asymptotic MVUEs that behave similarly to the classical MLEs and asymptotically attain the Cramer Rao bound. We demonstrate the advantages of our approach in the context of signal to noise ratio estimation as well as covariance estimation. A second motivation to BCE is in applications where multiple estimates of the same unknown are averaged for improved performance. Examples include distributed sensor networks and data augmentation in test-time. In such applications, we show that BCE leads to asymptotically consistent estimators.
An Improved Particle Filter Based on Robustness Factor and Weight Optimization

Aiming at the problems of poor robustness and limited precision of UPF (Unscented Particle Filter) in processing nonlinear and non-Gaussian systems, this paper proposes an improved unscented particle filter algorithm based on robustness factor and weight optimization (IUPF). Combining the latest observation information, IUPF uses the more computationally efficient edge unscented Kalman filter to generate the recommended distribution, and increases the robustness factor when gross errors occur. The relatively unscented particle filter effectively avoids the problem of excessive particle weight variance.; At the same time, IUPF introduces a re-sampling method with optimized weights in the re-sampling process, which effectively solves the problem of particle depletion and improves the diversity of particles. Through theoretical derivation and simulation analysis, it can be known that the estimation accuracy of the IUPF algorithm is improved compared with the UPF algorithm, and the robustness is enhanced.
Significant Itemset Mining with Support-Attractiveness Framework

Support based popular itemset mining often misguides the users in proper knowledge discovery in connection with many real world applications such as social network analysis, source identification of epidemic diseases etc. Support only expresses the frequency of the itemsets in the database. In reality, support-based pruning technique does not bother about the significance of the itemsets in the context of correlation or associability. Therefore, support-based pruning technique often ignores valuable itemsets. In addition, support-based pruning technique sometimes generates commonsense itemsets that has lack of unexpectedness. Furthermore, it promotes isolated items that have no utilization in association rule mining. To fill up the shortcomings of support-based pruning technique, this paper first introduces the concept of attractiveness of the itemsets and thereafter, develops an efficient support-attractiveness framework (SAF) for itemset pruning. An efficient algorithm called SAFApriori (Support-Attractiveness Framework based Apriori) is proposed for significant itemset mining based on the SAF. The proposed algorithm has been tested with real and synthetic datasets and the obtained results show the effectiveness of the proposed algorithm.
New constructions of equality test scheme for cloud-assisted wireless sensor networks

Public key encryption with equality test enables the user to determine whether two ciphertexts contain the same information without decryption. Therefore, it may serve as promising cryptographic technique for cloud-assisted wireless sensor networks (CWSNs) to maintain data privacy. In this paper, an efficient RSA with equality test algorithm is proposed. The presented scheme also handles the attackers based on their authorization ability. Precisely, the proposed scheme is proved to be one-way against chosen-ciphertext attack security and indistinguishable against chosen ciphertext attacks. Moreover, the experimental evaluations depict that the underlying scheme is efficient in terms of encryption, decryption, and equality testing. Thus, this scheme may be used as a practical solution in context of CWSNs, where the users may compare two ciphertexts without decryption.
On Injecting Entropy-Like Features into Deep Neural Networks for Content Relevance Assessment

This paper describes in details an innovative technique of injection of a global (or generally large-scale) quality measure into a deep neural network (DNN) in order to compensate for the tendency of DNNs to found the resulting classification virtually from a superposition of local neighbourhood transformations and projections. We used a state probability-like feature as the global quality measure and injected it into a DNN-based classifier deployed in a specific task of determining which parts of a web page are of certain interest for further processing by NLP techniques. Our goal was to decompose web sites of various internet discussion forums to useful content, i.e. the posts of users, and useless content, i.e. forum graphics, menus, banners, advertisements, etc.
Active Learning for Imbalanced Domains: the ALOD and ALOD-RE Algorithms

Active learning strategies are used to acquire an enlarged labelled training set that allows the learner to achieve a better performance. To this end, unlabelled instances are carefully selected for labelling by an human expert in order to achieve the best performance with the smallest number of questions to this oracle. Several techniques exist to select the most informative samples within active learning strategies. However, the effectiveness of these methods when applied to problems with imbalanced classes was not studied before. In this paper, we focus on the improvement of instance selection strategies for active learning techniques in the presence of class imbalance. In an imbalanced setting, learning algorithms have difficulties to focus on the minority class due to its under-representation. Still, this is typically the class of interest for the end-user. This mismatch between the classes distribution and the goals of the end user is known as the class imbalance problem. In an active learning setting for class imbalance problems, this becomes an even more challenging issue. We propose two novel active learning algorithms, ALOD and ALOD-RE centered around selecting the most informative samples to be labelled, while considering the selection of possible minority class cases and the generation of synthetic minority class examples to improve the learner performance. To this end, our two proposed solutions combine: active learning procedures, resampling strategies, and anomaly detection methods. Through an extensive set of experiments we show that the incorporation of outlier detection and resampling techniques in the active learning procedure benefits the learners performance on imbalanced domains. The performance advantages of our proposed ALOD and ALOD-RE algorithms are clearly supported by our experimental results.
Few-Shot Segmentation via Complementary Prototype Learning and Cascaded Refinement

Prototype learning has been widely explored for few-shot segmentation. Existing methods typically learn the prototype from the foreground features of all support images, which rarely consider the background similarities between the query images and the support images. This unbalanced prototype learning strategy limits its capability to mutually correct the segmentation errors between the foreground and background. In this paper, we propose a Complementary Prototype Learning and Cascaded Refinement (CPLCR) network for few-shot segmentation. Firstly, both the foreground and background features of the support images are used to learn our complementary prototypes. Then, the foreground and background similarity maps are jointly derived between the query image feature and our complementary prototypes, which capture more comprehensive prior information. Finally, we fuse the query image feature, foreground prototype and the foreground/background similarity maps together, and feed them to a cascaded refinement module, which recursively reuses the output of previous iteration to refine the segmentation result. Extensive experimental results show that the proposed CPLCR model outperforms many state-of-the-art methods for 1-shot and 5-shot segmentation.
Domain Balanced Sampling and Iterative Search for Product Identification

This paper introduces our solution to the 1st Workshop on Multimodal Product Identification in Livestreaming and Watch and Buy Challenge, a real-world task in a live stream scene and is challenging due to factors such as lighting, occlusion, and cross-domain. We model this task as the object detection and image retrieval problem. In the whole pipeline, we mainly focus on the problem of cross-domain retrieval and propose a domain-balanced sampling method, which enhances the robustness of multi-domain retrieval. Besides, to eliminate the influence of irrelevant clothing, we propose an iterative cross-search strategy, which greatly improves the accuracy of matching. In addition, we also experiment with the exploitation of text information, including multimodal product classification and multimodal intent recognition. With the aforementioned method, we achieved an F1 score of 69.2% and finally achieve first place in the competition.
Pose Graph Fusion for Robot Follow Control

Intelligent transportation systems are often required to estimate their ego state globally while matching their trajectory to the motion of an object of interest (OoI). For example, industrial robots need to synchronize their motion to the trajectory of an OoI to perform visual inspection task in motion. For that, the ego state has to be estimated both globally and relatively to the OoI. In this work, a pose graph method is proposed, which holistically solves the global and relative state estimation. We demonstrate through closed-loop software-in-the-loop simulation that under ideal noise parameter settings, the proposed approach provides comparable performance to that of a Kalman filter, which is proven to be optimal under such conditions. Furthermore, it is shown that the proposed approach is more robust to inaccurate noise parameters than a Kalman filter. Finally, it is shown by experimental validation that the overall procedure performs effectively on a real robot.
뷰티전공 성인학습자의 온라인 강의 품질이 학습몰입도와 학습만족도에 미치는 영향

This study was conducted on 223 adult college learners majoring in beauty in Seoul and Kyeonggi-do, and SPSS v. 21.0 was used for data analysis. The purpose of the study is to identify how quality of online classes affect learning flow and learning satisfaction. The results are as follows. First, the results of hypothesis testing of the positive (+) effect of on-line lecture quality on learning immersion are as follows. It was found that learning content and learning performance, which are sub-factors of on-line lecture quality, had an important positive(+) effect on academic achievement and interest convenience. Second, the results of testing the hypothesis that on-line lecture quality has a significant positive(+) effect on learning satisfaction are as follows. It was found that learning contents and learning outcomes, which are sub-factors of learning satisfaction, have an important positive(+) effect on the learning environment and learning motivation. Third, the results of examining whether learning immersion has a significant positive(+) effect on learning satisfaction are as follows. It was found that academic achievement and interest convenience, which are sub-factors of learning immersion, had an important positive(+) effect on the learning environment and learning motivation. The results of the study are as follows. Adult learners who both work and study take traditional lectures with restrictions of time and space. However, in case of online classes, they are able to concentrate on their studies by themselves without any restrictions of time, space and other people. In addition, they could repeat lectures and take control of how to take classes, subsequently leading to natural learning flow and high learning satisfaction.
The Formation of Analytic Abilities for Coaching Rhythmic Gymnastics

Currently gymnast’s success depends on the masterly possession of all groups of movements with and without an object. Each competitive composition consists of various technical elements with an apparatus that carries both quantitative and qualitative value. At the same time, in the process of analyzing the content of competitive compositions, it was found that over the past 3 years, the proportion of the evaluated movements by the apparatus has increased almost 2 times. As a result of a consistent change in the requirements of the rules of competitions in rhythmic gymnastics, coaches are forced to devote most of the time of the training process to learning new elements, ligaments, connections with gymnasts and drawing up new compositions of competitive programs. Which requires coaches to constantly search for scientifically grounded creative approaches to the training process and analytical thinking, necessary for the implementation of all types of training of athletes. The professional training of a sports coach is a complex and dynamic pedagogical system, the effectiveness of which depends on many factors that manifest themselves on the basis of the general laws of the pedagogical process and its management. A systematic approach to the formation and assessment of the competence of students allows you to fully reveal the potential of a future coach. At the same time, the basic analytical skills of the future coach serve as the basis for the formation of interdisciplinary knowledge. The article discusses the issues of the effectiveness of the use of analytical activities and the implementation of the thesis in the aspect of the professional competence of future coaches in rhythmic gymnastics.
Visual Vehicle Counting and Classification System

Motion capturing involves the Identification of objects in motion and plotting their activity by analyzing them. When considered concerning a video sequence, motion capture can be defined as a procedure of unmasking things in action in lines of frames using specific and proficient digital image processing techniques. Methodology: To ingress need contrasting time for trial and execution shows the difference in pace and memory requisites.   Results: The result obtained shows the performance of the algorithms and models in the given conditions. It also reflects the best suitable environment for the techniques. The work was accomplished by testing the algorithms for numerous sequences of the input video. The identification rate analysis gives the identification rate of foreground pixels for various colors divergent from the background model. Conclusions: The objective of computer vision is to imitate the human eye utilizing advanced digital images through three principle handling parts that are executed consistently, i.e., acquisition of pictures, picture processing, and picture investigation and comprehension. It has gained a lot of attention from researchers in an enormous field. The basic purpose of this study was to prospecting tracking and computer-aided object detection techniques. For this purpose, several methods were observed, studied critically, and used. Originality: In the study, an improved frame differencing method for tracking an object is briefly reviewed, and an enhanced version of the algorithm was also implemented using MATLAB. The proposed algorithm was tested over distinct video sequences. It was observed that the objects in motion were identified with minimal error rate compared to a traditional frame differencing method. Limitations: One of the major challenges in tracking objects in action is to design algorithms or techniques for monitoring the things present in disrupted or random videos, like videos attained from broadcast news networks or home videos. These videos contain noise, and some of them may be unstructured, compressed, and denominationally having edited pieces obtained in different ways by moving cameras.
Improved One-Stage Algorithm with Attention Fusion for Human Sperm Detection Based on Deep Learning

The popular methods of human sperm detection are mostly based on machine learning. Although most deep learning methods have got state-of-art performance in object detection with the advancement of deep learning, only a few of them were used successfully in human sperm detection. With the deep learning method, the detection task is divided into different scales in feature maps of different level. The sperm target is so small under the phase-contrast microscope that the detection task is hard even with deep learning technology. Feature Pyramid Network (FPN) was designed to mitigate this problem in one-stage detector. In this paper, we proposed a new FPN based method in which the feature map was fused with attention module to get the correlation information of low-level feature maps. Eventually, we got an AP of 83.77 which is higher than YOLO v4 and some other detectors.
Rock classification model based on transfer learning and convolutional neural network

research-article Rock classification model based on transfer learning and convolutional neural network Share on Authors: Huaian Yi Guilin University of Technology, China Guilin University of Technology, ChinaView Profile , Jinzhao Su Guilin University of Technology, China Guilin University of Technology, ChinaView Profile , Runji Fang Guilin University of Technology, China Guilin University of Technology, ChinaView Profile Authors Info & Claims ICIIP 2021: 2021 6th International Conference on Intelligent Information ProcessingJuly 2021 Pages 142–146https://doi.org/10.1145/3480571.3480595Online:29 October 2021Publication History 0citation29DownloadsMetricsTotal Citations0Total Downloads29Last 12 Months29Last 6 weeks8 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Deep Neural Networks for Biomedical Image Segmentation: Trends and Best Practices

Biomedical image segmentation is an important process in computer-aided diagnostic systems. Segmentation allows an image to be divided and tagged into anatomical sub-regions such as bones, muscles, blood vessels, and various pathological structures, facilitating image analysis and detecting various diseases. However, it is challenging due to the different features that images present, including noise and contrast. This article provides an overview of deep neural networks for biomedical image segmentation specifically computed tomography, dermoscopy, MRI, ultrasound, and X-ray. Additionally, best practices are discussed to improve the accuracy and sensitivity of results, including in unbalanced datasets.
A robust framework for mathematical formula detection

Mathematical formulas identification is a crucial step in the pipeline of many tasks such as mathematical information retrieval, storing digital science documents, etc. For basic mathematical formulas recognition, all these tasks need to detect the bounding boxes of mathematical expression as a prerequisite step. Currently, deep learning-based object detection methods work well for mathematical formula detection (MFD). These methods are divided into two categories: anchor self-study and anchor not self-study. The anchor self-study method is efficient with large quantity labels but not so well with small quantities, whereas the second type of method works better with small quantities. Therefore, we proposed an algorithm that keeps the good prediction of each type and then merges both into final results. To demonstrate the hypothesis, we select two typical object detection methods: YOLOv5 and Faster RCNN as the representation of two kind approaches to building an MFD framework. Our experiment results on ICDAR2021-MFD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> achieved the F1 score of the whole system is 89.3 while the single detector just reached 74.2, 88.9 (Faster RCNN and YOLOv5 respectively) that proving the effectiveness of the proposal.
SketchyDepth: from Scene Sketches to RGB-D Images

Sketch-based content generation is a creative and fun activity suited to casual and professional users that has many different applications. Today it is possible to generate the geometry and appearance of a single object by sketching it. Yet, only the appearance can be synthesized from a sketch of a whole scene. In this paper we propose the first method to generate both the depth map and image of a whole scene from a sketch. We demonstrate how generating geometrical information as a depth map is beneficial from a twofold perspective. On one hand, it improves the quality of the image synthesized from the sketch. On the other, it unlocks depth-enabled creative effects like Bokeh, fog, light variation, 3D photos and many others, which help enhancing the final output in a controlled way. We validate our method showing how generating depth maps directly from sketches produces better qualitative results with respect to alternative methods, i.e. running MiDaS after image generation. Finally we introduce depth sketching, a depth manipulation technique to further condition image generation without the need of additional annotation or training.
Evaluating deep transfer learning for whole-brain cognitive decoding

Research in many fields has shown that transfer learning (TL) is well-suited to improve the performance of deep learning (DL) models in datasets with small numbers of samples. This empirical success has triggered interest in the application of TL to cognitive decoding analyses with functional neuroimaging data. Here, we systematically evaluate TL for the application of DL models to the decoding of cognitive states (e.g., viewing images of faces or houses) from whole-brain functional Magnetic Resonance Imaging (fMRI) data. We first pre-train two DL architectures on a large, public fMRI dataset and subsequently evaluate their performance in an independent experimental task and a fully independent dataset. The pre-trained DL models consistently achieve higher decoding accuracies and generally require less training time and data than model variants that were not pre-trained, while also outperforming linear baseline models trained from scratch, clearly underlining the benefits of pre-training. We demonstrate that these benefits arise from the ability of the pre-trained models to reuse many of their learned features when training with new data, providing deeper insights into the mechanisms giving rise to the benefits of pre-training. Yet, we also surface nuanced challenges for whole-brain cognitive decoding with DL models when interpreting the decoding decisions of the pre-trained models, as these have learned to utilize the fMRI data in unforeseen and counterintuitive ways to identify individual cognitive states.