Reviewerly/exHarmony huggingface dataset:
Mixpatch: A New Method for Training Histopathology Image Classifiers

Background and Objective: CNN-based image processing has been applied to histopathological analysis to detect and classify cancerous tumors automatically. However, CNN-based classifiers generally predict a label with overconfidence, which becomes a serious problem when used in the medical domain. The objective of this study is to propose a new training method, called MixPatch, designed to improve a CNN-based classifier by specifically addressing the prediction uncertainty problem and examine its effectiveness in improving diagnosis performance in the context of histopathology image analysis.Methods: MixPatch generates and uses a new sub-training dataset, which consists of mixed-patches and their new ground-truth labels, for every single mini-batch. Mixed-patches are generated using small size clean patches that have been confirmed by pathologists and their ground-truth labels are defined using a proportion-based soft labeling method.Results: Our results obtained using a large histopathology image dataset shows that the proposed method alleviates overconfidence more effectively, and performs better, than other state-of-the-art competing methods, not only at the patch-level but also at the slide level.Conclusions: By specifically considering the mixed-region variation characteristics of histopathology images, MixPatch augments the extant mixed image methods for medical image analysis in which prediction uncertainty is a crucial issue. The proposed method provides a new way to systematically alleviate overconfidence problems and improve prediction accuracy, contributing toward more reliable histopathology image analysis.
Application of distance learning in professional development

The article examines the essence of distance learning, its state at the present stage, both in Uzbekistan and abroad. The advantages and disadvantages of this knowledge acquisition system are discussed, and the results of its application are analyzed.
Research on the Design of Active Learning Algorithm based on Query-by-Committee for Intelligent Fetal Monitoring

The realization of intelligent fetal monitoring is helpful to timely detect fetal abnormality and save medical costs. However, the probability of misjudgment tends to be high when modeling the imbalanced clinical data directly. Moreover, reliable interpretation of cardiotocography (CTG) requires multiple obstetricians to annotate, which consumes excessive time and labor cost. In this paper, a K-means method based on adaptive Genetic Algorithm-Fl balanced diversity Weighted Query-by-Committee (QBC) algorithm (KGA-WQBC) was proposed to solve the above problems. The proposed active learning algorithm was improved from the following two aspects. Firstly, an adaptive KGA operator was designed to conduct a preliminary selection of a large number of unlabeled samples, which increased the accuracy of the committee. Then, the WQBC operator was designed to improve the measurement of the committee's disagreement degree after data exploration. Compared with other initial sample selection strategies and disagreement degree weighting means, the results showed that only 41% of the labeled instances were used to make the evaluation index as high as 98.02%, which had the best overall performance. In conclusion, the proposed KGA-WQBC algorithm is reasonable and feasible, and effectively solves the problem of imbalanced data and reduces the cost of medical personnel labeling for intelligent fetal monitoring.
A Progressive Multi-View Learning Approach for Multi-Loss Optimization in 3D Object Recognition

3D object recognition is a well studied 2D multi-view object classification task that achieves high accuracy if the object textures are distinctive. However, if objects are texture-less and are only differentiable by their shapes but at certain viewpoints. Thus, the problem is still very challenging. Furthermore, the existing methods are mostly based on supervised learning with lots of images per object which are difficult to collect and label them for training. In this letter, we introduced a multi-loss view invariant stochastic prototype embedding to minimize and improve the recognition accuracy of novel objects at different viewpoints by using a progressive multi-view learning approach. An extensive experimental results show that the proposed method outperforms the state-of-the-art methods on different types datasets and also on different backbones.
A Syntax-Aware Encoder for Authorship Attribution

Authorship Attribution (AA) refers to the task of predicting the author of a given text by learning unique writing styles of different authors. It often involves the extraction of social language characteristics of texts such as writing styles to identify authors. Among the many facets of text styles, we postulate that authors have unique and signature syntactic information entangled in their texts. This paper investigates this hypothesis and proposes a Syntax-Aware Encoder (SAE), a novel graph convolutional network based model augmented with the dependency tree to learn the syntax representation for short texts. Subsequently, we leverage the learned latent representations to perform AA. Extensive experiments are conducted on two social medial short-text datasets. The results show that SAE outperforms the state-of-the-art baselines on the AA task.
Learning with limited supervision

The recent successes in computer vision, and machine learning in general, have been mostly around using a huge corpus of intricately labeled data for training models. But, in real-world cases, acquiring such large datasets will require a lot of manual annotation, which may be strenuous, out of budget, or even prone to errors. Thus, it is necessary to develop methods where the amount of labeled data is minimized or can be obtained at low to no annotation cost. This chapter is a brief summary of methods that can be used to learn recognition models from such data which involve limited manual supervision. We will look into two dimensions of learning with limited supervision – first, reducing the number of manually labeled data required to learn recognition models, and second, reducing the level of supervision from strong to weak which can be mined from the web, easily queried from an oracle, or imposed as rule-based labels derived from domain knowledge. We will provide an overview of the theoretical frameworks and some experimental results.
Sentiment Analysis of Movie Reviews based on Pretraining and Dual Branch Coding

Due to the high dimensionality and sparsity of text and the complex semantics of the natural language, sentiment analysis of movie reviews presents difficult challenges. To solve these problems, a novel architecture that contains a robustly optimized bidirectional encoder representations from transformers pretraining approach (RoBERTa), a bidirectional long short-term memory (BiLSTM), a temporal convolutional network (TCN) and a convolutional layer is proposed in this paper. The proposed architecture is called dual branch feature coding network based on RoBERTa (DBN-Ro). In DBN-Ro, the RoBERTa pretraining model is used to obtain the word embedding and the dual branch network is used to extract the contextual semantic and multi-level representations. The convolutional layer is used to perform dimensionality compression on the stitched vectors. The DBN-Ro is experimentally validated on two movie reviews, the results show that it achieves better prediction results in sentiment analysis of movie reviews tasks.
Entropy-based Deep Product Quantization for Visual Search and Deep Feature Compression

With the emergence of various machine-to-machine and machine-to-human tasks with deep learning, the amount of deep feature data is increasing. Deep product quantization is widely applied in deep feature retrieval tasks and has achieved good accuracy. However, it does not focus on the compression target primarily, and its output is a fixed-length quantization index, which is not suitable for subsequent compression. In this paper, we propose an entropy-based deep product quantization algorithm for deep feature compression. Firstly, it introduces entropy into hard and soft quantization strategies, which can adapt to the codebook optimization and codeword determination operations in the training and testing processes respectively. Secondly, the loss functions related to entropy are designed to adjust the distribution of quantization index, so that it can accommodate to the subsequent entropy coding module. Experimental results carried on retrieval tasks show that the proposed method can be generally combined with deep product quantization and its extended schemes, and can achieve a better compression performance under near lossless condition.
End-to-End Generative Adversarial Face Hallucination Through Residual In Internal Dense Network

Face hallucination has been a highly attractive computer vision research topic in recent years. It is still a particularly challenging task since the human face has a complex and delicate structure. In this paper, we propose a novel network structure, namely end-to-end Generative Adversarial Face Hallucination through Residual in Internal Dense Network (GAFH-RIDN), to hallucinate an unaligned tiny (32×32 pixels) low-resolution face image to its 8× (256×256 pixels) high-resolution counterpart. We propose a new architecture called Residual in Internal Dense Block (RIDB) for the generator and exploit an improved discriminator, Relativistic average Discriminator (RaD). In GAFH-RIDN, the generator is used to generate visually pleasant hallucinated face images, while the improved discriminator aims to evaluate how much input images are realistic. With continual adversarial learning, GAFH-RIDN is able to hallucinate perceptually plausible face images. Extensive experiments on large face datasets demonstrate that the proposed method significantly outperforms other state-of-the-art methods.
Real-time Yawning Detection Based on Machine Learning Algorithm and Time Series Classification using Facial Feature Points

Yawning detection is one of the facial action recognition problems, which has a wide range of applications. However, traditional approaches in relevant fields have many shortcomings. This paper first discusses how to utilize OpenPose to capture facial feature points, and then puts forward a new yawning detection method LR+BOSS by integrating machine learning algorithm logistics regression and time series classification algorithm BOSS, based on the YawDD dataset. In the experiments, we demonstrate that such a strategy makes obvious improvements compared with the traditional method and has huge potential to be applied in the real-time yawn detection system.
Underwater Image Enhancement Using Bi- Histogram Equalization with Fuzzy Plateau Limit

The images captured in hazy underwater conditions suffer from having poor contrast and low visibility. Hence there is a need for the enhancement of imaging data from underwater exploration and inspection activity so that it can be analysed to extract the desired information. Also, the image enhancement technique must be computationally efficient for it to be used in real time environment. In this paper a novel contrast enhancement technique is proposed called ‘Bi- Histogram Equalization with Fuzzy Plateau Limit’ (BHEFPL) which is an extension to bi-histogram equalization with clipping to be used for underwater image enhancement. The proposed method employs the use of fuzzy logic so as to perform optimal enhancement with respect to image performance metrics which also results in improved visual quality. The proposed technique trumps other contrast enhancement methods in terms of objective and subjective analysis.
UAV Aerial Survey and Communication

Over the past several decades, Unmanned Aerial Vehicles (UAVs) have been used in a variety of applications with 2 basic classifications of UAVs’ a scivilian and military drones. Drones capture a variety of multimedia data. Among the multimedia data, images with overlapping regions need to be stitched to generate a panorama which would provide image data of ‘n’ number of images captured by a drone. The data captured by drones should be effectively communicated to a Ground Control Station (GCS). Hence in the research, 4 drones capture both text data and images. Each drone generates a corresponding panorama for the set of images captured by it and communicates both its text data and panorama to the GCS. 2 desktops are used for performing the experiments using client-server communication. Client desktop is used for performing simulations using AirSim simulator (which consists of 4 drones) on the Unreal Engine 4.25 platform, and generate panoramas for the set of images captured by each drone. Server desktop acting as GCS is used to accumulate text data and image data from 4 drones. Image stitching analysis has been done using 2 Python versions and Open CV versions, and 2 AirSim environments. Image stitching results were more effective with the use of Python version 3.7.1 and Open CV version 3.4.2 pair (image stitching success rate, and image stitching accuracy = 100%) when compared to that with Python version 3.9.1 and Open CV version 4.5.2 pair (image stitching success rate = 75%, image stitching accuracy = 33.33%). Both the text data and panoramas from 4 drones were successfully transmitted to the GCS.
A Meta-analysis of the Effectiveness of Spelling-related Interventions for Children with Spelling Difficulties

A Meta-analysis of the Effectiveness of Spelling-related Interventions for Children with Spelling Difficulties 의 이용 수, 등재여부, 발행기관, 저자, 초록, 목차, 참고문헌 등 논문에 관한 다양한 정보 및 관련논문 목록과 논문의 분야별 BEST, NEW 논문 목록을 확인 하실 수 있습니다.
Framework Design and Application of Enterprise Intranet Automation Operation and Maintenance

Based on the automatic operation and maintenance technology to solve the enterprise intranet operation and maintenance in security, efficiency, specification and other aspects of some shortcomings. The automatic operation and maintenance technology framework proposes a development framework to support knowledge precipitation, decomposes complex knowledge into atomic operation script tools, and then construct the automated APPs suitable for different scenarios flexibly through the process arrangement. The security scheme carries out multiple security protection design for the security risks of automatic operation and maintenance, it includes security for user access layer, network access layer, platform layer and tool execution layer. In the aspect of operation and maintenance privilege control, abstract IT resources into CMDB resource model, then through tools and CMDB resources are authorized separately, to achieve operational tools control and operational IT resource control. The preliminary application results show that the application of automatic technology can make enterprise intranet operation and maintenance more standardized, more secure, more reliable and more efficient.
Model Pembelajaran Kooperatif Jigsaw Berbantuan Media Video Meningkatkan Hasil Belajar IPS Siswa Kelas IV Sekolah Dasar

Lack of teacher ability to create fun learning and be accompanied by interesting learning media. This study aims to analyze the Jigsaw cooperative learning model assisted by video media to improve the learning outcomes of fourth-grade elementary school students. This type of research is classroom action research which is carried out in two cycles. Each cycle consists of stages of planning, implementing actions, observing and evaluating, and reflecting. The implementation of the action in each cycle is three meetings. The subjects of this study were students of class IVA, totaling 26 people. Data collection in this study was carried out by the test method. Data were analyzed by descriptive statistical analysis method. The results showed that in the first cycle the average social studies learning outcomes were 80,77 in the high category. Furthermore, in the second cycle, the average social studies learning outcomes were obtained at 86,37 in the high category. Based on the calculation of the gain score, the quality of improving social studies learning outcomes in the first cycle was 0,16 with a low predicate and the second cycle was 0,46 with a moderate predicate. Based on the results of this study, it can be concluded that the application of the Jigsaw-type cooperative learning model assisted by video media can improve social studies learning outcomes for fourth-grade elementary school students.
Stroke Based Painterly Rendering with Mass Data through Auto Warping Generation

Painting is done according to the artist's style.The most representative of the style is the texture and shape of the brush stroke.Computer simulations allow the artist's painting to be produced by taking this stroke and pasting it onto the image.This is called stroke-based rendering.The quality of the result depends on the number or quality of this stroke, since the stroke is taken to create the image.It is not easy to render using a large amount of information, as there is a limit to having a stroke scanned.In this work, we intend to produce rendering results using mass data that produces large amounts of strokes by expanding existing strokes through warping.Through this, we have produced results that have higher quality than conventional studies.Finally, we also compare the correlation between the amount of data and the results.
Location-Based Services Protection via Multi-Layered System

increasing of using new technologies and devices are crucial for the last twenty years. Nowadays, GPS and WLAN are wide use technologies. Moreover, one of the most important services becomes the Location-Based Services (LBS). These services help to find place or a person using GPS or Wi-Fi triangulation. Nevertheless, there are a lot of problems with protection of user’s personal information. The new solution that is presented in this paper has great advantages among others. First of all, we presented a multi-layered system. The key principles of its work are the follows. It takes third party servers and cache queries in order to protect LBS user’s privacy. In this case LBS servers do not receive user’s data in the way that can be danger for his/her data’s protection. This technology has a lot of advantages. For instance, it is a hybrid system. It means that user can store his information separately on the server and mobile device. The relationship between advertisers, analytics and users are also discussed in this paper. Moreover, all key points of this protection method are taken with examples. As this is the concept of the multi-layered protection system, we stated that we will continue our work on it in order to create an algorithm and a working prototype of it.
An Agent-Based Market Simulator for Back-Testing Deep Reinforcement Learning Based Trade Execution Strategies

Researchers have reported success in developing autonomous trade execution systems based on Deep Reinforcement Learning (DRL) techniques aiming to minimize the execution costs. However, they all back-test the trade execution policies on historical datasets. One of the biggest drawbacks of back-testing on historical datasets is that it cannot account for the permanent market impacts caused by interactions among various trading agents and real-world factors such as network latency and computational delays.
Fall Detection Algorithm using Body Angle for Accurate Classification of Falls and ADLs

Falls in elderly are an important issue for the aging populations around the world. They cause severe physical and emotional injuries and surge healthcare and hospitalization costs. Detection of falls in time help reduce the consequences of the fall by providing a faster rescue to the patient which prevents more serious injuries. The present research describes a threshold-based algorithm to distinguish between falls and Activities of Daily Living (ADL). This algorithm was implemented on a wearable device attached to the waist. Three thresholds are used in order to distinguish a fall from any other activity. Falls are simulated by 10 young volunteers under supervised conditions and ADLs are performed by 10 elderly subjects. Experimental results show that the system detects falls compared to ADL with a sensitivity and specificity of 93.3% and 100% respectively.
A Fast Two’s Complement Generator

The two&#x2019;s complement of a number is defined as the one&#x2019;s complement of that number plus 1, while the addition operation tends to consume more resources and power. This paper proposes a novel two&#x2019;s complement generator without involving addition, which can solve this problem. The results show the proposed solution can simplify design, improve performance and reduce power compared with traditional adder-based solutions. In addition, the new design is based on regular cells and fully modular, which leads to good scalability and straightforward assembly into large systems. Furthermore, the proposed design supports multi-functions, and it is also suited for use in arithmetic units, including adders, multipliers to speed up two&#x2019;s complement operations.
A Method to Correct Perspective Distortion of Ground Area without Camera Parameters

This study proposes a method for geometric transformation of a ground area in mobile camera images without cameras' internal parameters for registering them to Geographic Information System (GIS) database. If mobile camera images can be registered in GIS, it will be possible to share the latest geographic information by combining it with crowdsourcing in times of disaster. To achieve it, it is necessary to map the mobile camera images to the GIS image database. However, it is difficult to estimate the direct correspondence because most of the mobile camera images are landscape images, and the perspective of the ground area differs significantly. In contrast, GIS image data contain directly downward images. If the cameras' internal parameters and the posture at the shooting are known, it is possible to convert the image to top-view. However, considering the premise of crowdsourcing image collection, these parameters are not necessarily known. Therefore, we develop a method to correct the perspective distortion of the ground area in the image and convert it to a top-eye view even when the camera parameters are unknown.
Learn to Look Around: Deep Reinforcement Learning Agent for Video Saliency Prediction

In the video saliency prediction task, one of the key issues is the utilization of temporal contextual information of keyframes. In this paper, a deep reinforcement learning agent for video saliency prediction is proposed, designed to look around adjacent frames and adaptively generate a salient contextual window that contains the most correlated information of keyframe for saliency prediction. More specifically, an action set step by step decides whether to expand the window, meanwhile a state set and reward function evaluate the effectiveness of the current window. The deep Q-learning algorithm is followed to train the agent to learn a policy to achieve its goal. The proposed agent can be regarded as plug-and-play which is compatible with generic video saliency prediction models. Experimental results on various datasets demonstrate that our method can achieve an advanced performance.
ABSG : a Socially Inspired Coalition Formation Agent Model

In the context of the circular economy, we conceived a new decision support system. Its goal is to design new products from highly variable post-used components. This system is open, allowing components to be added or removed at anytime. Components are avatarized as virtual agents and seen as a multiagent system. A user can require the system to design products. Therefore agents of the system must form coalitions that represent a product. We propose a new socially inspired agent model adapted to this system to tackle a coalition structure generation problem. This agent-centered model allows agents to find out with which agent to form a coalition based on the Humanities and Social Sciences principles of attraction.